[ { "path": ".github/workflows/documentation.yml", "content": "name: documentation\n\non: [push, pull_request, workflow_dispatch]\n\npermissions:\n contents: write\n\njobs:\n docs:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v4\n - uses: actions/setup-python@v5\n - name: Install doc dependencies\n run: |\n pip install . sphinx myst_parser myst-nb sphinx-design pydata-sphinx-theme sphinxcontrib-googleanalytics\n - name: Install content dependencies\n run: |\n pip install faiss-cpu mteb air-benchmark beir\n - name: Sphinx build\n run: |\n sphinx-build docs/source docs/build\n - name: Add CNAME\n run: |\n echo bge-model.com > docs/build/CNAME\n - name: Deploy to GitHub Pages\n uses: peaceiris/actions-gh-pages@v3\n if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}\n with:\n publish_branch: gh-pages\n github_token: ${{ secrets.GITHUB_TOKEN }}\n publish_dir: docs/build/\n force_orphan: true\n" }, { "path": ".gitignore", "content": "*.memmap\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n.idea/\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\n../docs/_build/\n../docs/build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\nUntitled.ipynb\ntry.py\nupdate_model_card.py\nmodel_card.md\npic.py\npic2.py\n\n# Pyre type checker\n.pyre/\n\n# MacOS associated\n.DS_Store\n\n# results\nen_results\nzh_results" }, { "path": "FlagEmbedding/__init__.py", "content": "from .abc.inference import *\nfrom .inference import *\n" }, { "path": "FlagEmbedding/abc/__init__.py", "content": "" }, { "path": "FlagEmbedding/abc/evaluation/__init__.py", "content": "from .arguments import AbsEvalArgs, AbsEvalModelArgs\nfrom .evaluator import AbsEvaluator\nfrom .data_loader import AbsEvalDataLoader\nfrom .searcher import EvalRetriever, EvalDenseRetriever, EvalReranker\nfrom .runner import AbsEvalRunner\n\n\n__all__ = [\n \"AbsEvalArgs\",\n \"AbsEvalModelArgs\",\n \"AbsEvaluator\",\n \"AbsEvalDataLoader\",\n \"EvalRetriever\",\n \"EvalDenseRetriever\",\n \"EvalReranker\",\n \"AbsEvalRunner\",\n]\n" }, { "path": "FlagEmbedding/abc/evaluation/arguments.py", "content": "\"\"\"\nAdapted from https://github.com/AIR-Bench/AIR-Bench/blob/0.1.0/air_benchmark/evaluation_utils/evaluation_arguments.py\n\"\"\"\nimport os\nfrom dataclasses import dataclass, field\nfrom typing import List, Optional\n\n\n@dataclass\nclass AbsEvalArgs:\n \"\"\"\n Base class for evaluation arguments.\n \"\"\"\n eval_name: str = field(\n default=None,\n metadata={\"help\": \"The name of the evaluation task, such as msmarco, beir, miracl, etc.\"}\n )\n dataset_dir: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"1) If you want to perform evaluation on your own dataset, you can provide the path to the dataset directory (must exists in local). \"\n \"The dataset directory should contain the following files: corpus.jsonl, _queries.jsonl, _qrels.jsonl, or contain multiple directories, each of which contains the following files: corpus.jsonl, _queries.jsonl, _qrels.jsonl.\"\n \"2) If you want to perform evaluation on the datasets we provide evaluation APIs for, you can provide the path to saving the downloaded dataset. If you provide None, the dataset will be only downloaded to the cache directory.\"\n }\n )\n force_redownload: bool = field(\n default=False, metadata={\"help\": \"Whether to force redownload the dataset. This is useful when you load dataset from remote and want to update the dataset.\"}\n )\n dataset_names: Optional[str] = field(\n default=None,\n metadata={\n \"help\": \"The names of the datasets to evaluate. Default: None. If None, all available datasets will be evaluated. The name can be a specific dataset name (BEIR), a specific language (MIRACL), etc.\",\n \"nargs\": \"+\"\n }\n )\n splits: str = field(\n default=\"test\",\n metadata={\"help\": \"Splits to evaluate. Default: test\", \"nargs\": \"+\"}\n )\n corpus_embd_save_dir: str = field(\n default=None, metadata={\"help\": \"Path to save corpus embeddings. If None, embeddings are not saved.\"}\n )\n output_dir: str = field(\n default=\"./search_results\", metadata={\"help\": \"Path to save results.\"}\n )\n search_top_k: int = field(\n default=1000, metadata={\"help\": \"Top k for retrieving.\"}\n )\n rerank_top_k: int = field(default=100, metadata={\"help\": \"Top k for reranking.\"})\n cache_path: str = field(\n default=None, metadata={\"help\": \"Cache directory for loading datasets.\"}\n )\n token: str = field(\n default_factory=lambda: os.getenv('HF_TOKEN', None),\n metadata={\"help\": \"The token to use when accessing the model.\"}\n )\n overwrite: bool = field(\n default=False, metadata={\"help\": \"whether to overwrite evaluation results\"}\n )\n ignore_identical_ids: bool = field(\n default=False, metadata={\"help\": \"whether to ignore identical ids in search results\"}\n )\n # ================ for evaluation ===============\n k_values: int = field(\n default_factory=lambda: [1, 3, 5, 10, 100, 1000],\n metadata={\"help\": \"k values for evaluation. Default: [1, 3, 5, 10, 100, 1000]\", \"nargs\": \"+\"}\n )\n eval_output_method: str = field(\n default=\"markdown\",\n metadata={\"help\": \"The output method for evaluation results. Available methods: ['json', 'markdown']. Default: markdown.\", \"choices\": [\"json\", \"markdown\"]}\n )\n eval_output_path: str = field(\n default=\"./eval_results.md\", metadata={\"help\": \"The path to save evaluation results.\"}\n )\n eval_metrics: str = field(\n default_factory=lambda: [\"ndcg_at_10\", \"recall_at_10\"],\n metadata={\"help\": \"The metrics to evaluate. Default: ['ndcg_at_10', 'recall_at_10']\", \"nargs\": \"+\"}\n )\n\n\n@dataclass\nclass AbsEvalModelArgs:\n \"\"\"\n Base class for model arguments during evaluation.\n \"\"\"\n embedder_name_or_path: str = field(\n metadata={\"help\": \"The embedder name or path.\", \"required\": True}\n )\n embedder_model_class: Optional[str] = field(\n default=None, metadata={\"help\": \"The embedder model class. Available classes: ['encoder-only-base', 'encoder-only-m3', 'decoder-only-base', 'decoder-only-icl']. Default: None. For the custom model, you need to specifiy the model class.\", \"choices\": [\"encoder-only-base\", \"encoder-only-m3\", \"decoder-only-base\", \"decoder-only-icl\"]}\n )\n normalize_embeddings: bool = field(\n default=True, metadata={\"help\": \"whether to normalize the embeddings\"}\n )\n pooling_method: str = field(\n default=\"cls\", metadata={\"help\": \"The pooling method fot the embedder.\"}\n )\n use_fp16: bool = field(\n default=True, metadata={\"help\": \"whether to use fp16 for inference\"}\n )\n devices: Optional[str] = field(\n default=None, metadata={\"help\": \"Devices to use for inference.\", \"nargs\": \"+\"}\n )\n query_instruction_for_retrieval: Optional[str] = field(\n default=None, metadata={\"help\": \"Instruction for query\"}\n )\n query_instruction_format_for_retrieval: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for query instruction\"}\n )\n examples_for_task: Optional[str] = field(\n default=None, metadata={\"help\": \"Examples for task\"}\n )\n examples_instruction_format: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for examples instruction\"}\n )\n trust_remote_code: bool = field(\n default=False, metadata={\"help\": \"Trust remote code\"}\n )\n reranker_name_or_path: Optional[str] = field(\n default=None, metadata={\"help\": \"The reranker name or path.\"}\n )\n reranker_model_class: Optional[str] = field(\n default=None, metadata={\"help\": \"The reranker model class. Available classes: ['encoder-only-base', 'decoder-only-base', 'decoder-only-layerwise', 'decoder-only-lightweight']. Default: None. For the custom model, you need to specify the model class.\", \"choices\": [\"encoder-only-base\", \"decoder-only-base\", \"decoder-only-layerwise\", \"decoder-only-lightweight\"]}\n )\n reranker_peft_path: Optional[str] = field(\n default=None, metadata={\"help\": \"The reranker peft path.\"}\n )\n use_bf16: bool = field(\n default=False, metadata={\"help\": \"whether to use bf16 for inference\"}\n )\n query_instruction_for_rerank: Optional[str] = field(\n default=None, metadata={\"help\": \"Instruction for query\"}\n )\n query_instruction_format_for_rerank: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for query instruction\"}\n )\n passage_instruction_for_rerank: Optional[str] = field(\n default=None, metadata={\"help\": \"Instruction for passage\"}\n )\n passage_instruction_format_for_rerank: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for passage instruction\"}\n )\n cache_dir: str = field(\n default=None, metadata={\"help\": \"Cache directory for models.\"}\n )\n # ================ for inference ===============\n embedder_batch_size: int = field(\n default=3000, metadata={\"help\": \"Batch size for inference.\"}\n )\n reranker_batch_size: int = field(\n default=3000, metadata={\"help\": \"Batch size for inference.\"}\n )\n embedder_query_max_length: int = field(\n default=512, metadata={\"help\": \"Max length for query.\"}\n )\n embedder_passage_max_length: int = field(\n default=512, metadata={\"help\": \"Max length for passage.\"}\n )\n reranker_query_max_length: Optional[int] = field(\n default=None, metadata={\"help\": \"Max length for reranking.\"}\n )\n reranker_max_length: int = field(\n default=512, metadata={\"help\": \"Max length for reranking.\"}\n )\n normalize: bool = field(\n default=False, metadata={\"help\": \"whether to normalize the reranking scores\"}\n )\n prompt: Optional[str] = field(\n default=None, metadata={\"help\": \"The prompt for the reranker.\"}\n )\n cutoff_layers: List[int] = field(\n default=None, metadata={\"help\": \"The output layers of layerwise/lightweight reranker.\"}\n )\n compress_ratio: int = field(\n default=1, metadata={\"help\": \"The compress ratio of lightweight reranker.\"}\n )\n compress_layers: Optional[int] = field(\n default=None, metadata={\"help\": \"The compress layers of lightweight reranker.\", \"nargs\": \"+\"}\n )\n\n def __post_init__(self):\n # replace \"\\\\n\" with \"\\n\"\n if \"\\\\n\" in self.query_instruction_format_for_retrieval:\n self.query_instruction_format_for_retrieval = self.query_instruction_format_for_retrieval.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.examples_instruction_format:\n self.examples_instruction_format = self.examples_instruction_format.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.query_instruction_format_for_rerank:\n self.query_instruction_format_for_rerank = self.query_instruction_format_for_rerank.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.passage_instruction_format_for_rerank:\n self.passage_instruction_format_for_rerank = self.passage_instruction_format_for_rerank.replace(\"\\\\n\", \"\\n\")\n" }, { "path": "FlagEmbedding/abc/evaluation/data_loader.py", "content": "\"\"\"\nAdapted from https://github.com/AIR-Bench/AIR-Bench/blob/0.1.0/air_benchmark/evaluation_utils/data_loader.py\n\"\"\"\nimport os\nimport logging\nimport datasets\nimport subprocess\nfrom abc import ABC, abstractmethod\nfrom typing import List, Optional, Union\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEvalDataLoader(ABC):\n \"\"\"\n Base class of data loader for evaluation.\n\n Args:\n eval_name (str): The experiment name of current evaluation.\n dataset_dir (str, optional): path to the datasets. Defaults to ``None``.\n cache_dir (str, optional): Path to HuggingFace cache directory. Defaults to ``None``.\n token (str, optional): HF_TOKEN to access the private datasets/models in HF. Defaults to ``None``.\n force_redownload: If True, will force redownload the dataset to cover the local dataset. Defaults to ``False``.\n \"\"\"\n def __init__(\n self,\n eval_name: str,\n dataset_dir: Optional[str] = None,\n cache_dir: Optional[str] = None,\n token: Optional[str] = None,\n force_redownload: bool = False\n ):\n self.eval_name = eval_name\n self.dataset_dir = dataset_dir\n if cache_dir is None:\n cache_dir = os.getenv('HF_HUB_CACHE', '~/.cache/huggingface/hub')\n self.cache_dir = os.path.join(cache_dir, eval_name)\n self.token = token\n self.force_redownload = force_redownload\n self.hf_download_mode = None if not force_redownload else \"force_redownload\"\n\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Returns: List[str]: Available dataset names.\n \"\"\"\n return []\n\n @abstractmethod\n def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"\n Returns: List[str]: Available splits in the dataset.\n \"\"\"\n pass\n\n def check_dataset_names(self, dataset_names: Union[str, List[str]]) -> List[str]:\n \"\"\"Check the validity of dataset names\n\n Args:\n dataset_names (Union[str, List[str]]): a dataset name (str) or a list of dataset names (List[str])\n\n Raises:\n ValueError\n\n Returns:\n List[str]: List of valid dataset names.\n \"\"\"\n available_dataset_names = self.available_dataset_names()\n if isinstance(dataset_names, str):\n dataset_names = [dataset_names]\n\n for dataset_name in dataset_names:\n if dataset_name not in available_dataset_names:\n raise ValueError(f\"Dataset name '{dataset_name}' not found in the dataset. Available dataset names: {available_dataset_names}\")\n return dataset_names\n\n def check_splits(self, splits: Union[str, List[str]], dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"Check whether the splits are available in the dataset.\n\n Args:\n splits (Union[str, List[str]]): Splits to check.\n dataset_name (Optional[str], optional): Name of dataset to check. Defaults to ``None``.\n\n Returns:\n List[str]: The available splits.\n \"\"\"\n available_splits = self.available_splits(dataset_name=dataset_name)\n if isinstance(splits, str):\n splits = [splits]\n checked_splits = []\n for split in splits:\n if split not in available_splits:\n logger.warning(f\"Split '{split}' not found in the dataset. Removing it from the list.\")\n else:\n checked_splits.append(split)\n return checked_splits\n\n def load_corpus(self, dataset_name: Optional[str] = None) -> datasets.DatasetDict:\n \"\"\"Load the corpus from the dataset.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: A dict of corpus with id as key, title and text as value.\n \"\"\"\n if self.dataset_dir is not None:\n if dataset_name is None:\n save_dir = self.dataset_dir\n else:\n save_dir = os.path.join(self.dataset_dir, dataset_name)\n return self._load_local_corpus(save_dir, dataset_name=dataset_name)\n else:\n return self._load_remote_corpus(dataset_name=dataset_name)\n\n def load_qrels(self, dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load the qrels from the dataset.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): The split to load relevance from. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of relevance of query and document.\n \"\"\"\n if self.dataset_dir is not None:\n if dataset_name is None:\n save_dir = self.dataset_dir\n else:\n checked_dataset_names = self.check_dataset_names(dataset_name)\n if len(checked_dataset_names) == 0:\n raise ValueError(f\"Dataset name {dataset_name} not found in the dataset.\")\n dataset_name = checked_dataset_names[0]\n\n save_dir = os.path.join(self.dataset_dir, dataset_name)\n\n return self._load_local_qrels(save_dir, dataset_name=dataset_name, split=split)\n else:\n return self._load_remote_qrels(dataset_name=dataset_name, split=split)\n\n def load_queries(self, dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load the queries from the dataset.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): The split to load queries from. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of queries with id as key, query text as value.\n \"\"\"\n if self.dataset_dir is not None:\n if dataset_name is None:\n save_dir = self.dataset_dir\n else:\n checked_dataset_names = self.check_dataset_names(dataset_name)\n if len(checked_dataset_names) == 0:\n raise ValueError(f\"Dataset name {dataset_name} not found in the dataset.\")\n dataset_name = checked_dataset_names[0]\n\n save_dir = os.path.join(self.dataset_dir, dataset_name)\n\n return self._load_local_queries(save_dir, dataset_name=dataset_name, split=split)\n else:\n return self._load_remote_queries(dataset_name=dataset_name, split=split)\n\n def _load_remote_corpus(\n self,\n dataset_name: Optional[str] = None,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Abstract method to load corpus from remote dataset, to be overrode in child class.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n save_dir (Optional[str], optional): Path to save the new downloaded corpus. Defaults to ``None``.\n\n Raises:\n NotImplementedError: Loading remote corpus is not implemented.\n\n Returns:\n datasets.DatasetDict: A dict of corpus with id as key, title and text as value.\n \"\"\"\n raise NotImplementedError(\"Loading remote corpus is not implemented.\")\n\n def _load_remote_qrels(\n self,\n dataset_name: Optional[str] = None,\n split: str = 'test',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Abstract method to load relevance from remote dataset, to be overrode in child class.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): Split to load from the remote dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Path to save the new downloaded relevance. Defaults to ``None``.\n\n Raises:\n NotImplementedError: Loading remote qrels is not implemented.\n\n Returns:\n datasets.DatasetDict: A dict of relevance of query and document.\n \"\"\"\n raise NotImplementedError(\"Loading remote qrels is not implemented.\")\n\n def _load_remote_queries(\n self,\n dataset_name: Optional[str] = None,\n split: str = 'test',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Abstract method to load queries from remote dataset, to be overrode in child class.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): Split to load from the remote dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Path to save the new downloaded queries. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n\n Returns:\n datasets.DatasetDict: A dict of queries with id as key, query text as value.\n \"\"\"\n raise NotImplementedError(\"Loading remote queries is not implemented.\")\n\n def _load_local_corpus(self, save_dir: str, dataset_name: Optional[str] = None) -> datasets.DatasetDict:\n \"\"\"Load corpus from local dataset.\n\n Args:\n save_dir (str): Path to save the loaded corpus.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: A dict of corpus with id as key, title and text as value.\n \"\"\"\n corpus_path = os.path.join(save_dir, 'corpus.jsonl')\n if self.force_redownload or not os.path.exists(corpus_path):\n logger.warning(f\"Corpus not found in {corpus_path}. Trying to download the corpus from the remote and save it to {save_dir}.\")\n return self._load_remote_corpus(dataset_name=dataset_name, save_dir=save_dir)\n else:\n corpus_data = datasets.load_dataset('json', data_files=corpus_path, cache_dir=self.cache_dir)['train']\n\n corpus = {}\n for e in corpus_data:\n corpus[e['id']] = {'title': e.get('title', \"\"), 'text': e['text']}\n\n return datasets.DatasetDict(corpus)\n\n def _load_local_qrels(self, save_dir: str, dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load relevance from local dataset.\n\n Args:\n save_dir (str): Path to save the loaded relevance.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): Split to load from the local dataset. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of relevance of query and document.\n \"\"\"\n checked_split = self.check_splits(split, dataset_name=dataset_name)\n if len(checked_split) == 0:\n raise ValueError(f\"Split {split} not found in the dataset.\")\n split = checked_split[0]\n\n qrels_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n if self.force_redownload or not os.path.exists(qrels_path):\n logger.warning(f\"Qrels not found in {qrels_path}. Trying to download the qrels from the remote and save it to {save_dir}.\")\n return self._load_remote_qrels(dataset_name=dataset_name, split=split, save_dir=save_dir)\n else:\n qrels_data = datasets.load_dataset('json', data_files=qrels_path, cache_dir=self.cache_dir)['train']\n\n qrels = {}\n for data in qrels_data:\n qid = data['qid']\n if qid not in qrels:\n qrels[qid] = {}\n qrels[qid][data['docid']] = data['relevance']\n\n return datasets.DatasetDict(qrels)\n\n def _load_local_queries(self, save_dir: str, dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load queries from local dataset.\n\n Args:\n save_dir (str): Path to save the loaded queries.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): Split to load from the local dataset. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of queries with id as key, query text as value.\n \"\"\"\n checked_split = self.check_splits(split, dataset_name=dataset_name)\n if len(checked_split) == 0:\n raise ValueError(f\"Split {split} not found in the dataset.\")\n split = checked_split[0]\n\n queries_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n if self.force_redownload or not os.path.exists(queries_path):\n logger.warning(f\"Queries not found in {queries_path}. Trying to download the queries from the remote and save it to {save_dir}.\")\n return self._load_remote_queries(dataset_name=dataset_name, split=split, save_dir=save_dir)\n else:\n queries_data = datasets.load_dataset('json', data_files=queries_path, cache_dir=self.cache_dir)['train']\n\n queries = {e['id']: e['text'] for e in queries_data}\n return datasets.DatasetDict(queries)\n\n def _download_file(self, download_url: str, save_dir: str):\n \"\"\"Download file from provided URL.\n\n Args:\n download_url (str): Source URL of the file.\n save_dir (str): Path to the directory to save the zip file.\n\n Raises:\n FileNotFoundError\n\n Returns:\n str: The path of the downloaded file.\n \"\"\"\n save_path = os.path.join(save_dir, download_url.split('/')[-1])\n\n if self.force_redownload or (not os.path.exists(save_path) or os.path.getsize(save_path) == 0):\n cmd = [\"wget\", \"-O\", save_path, download_url]\n else:\n cmd = [\"wget\", \"-nc\", \"-O\", save_path, download_url]\n\n try:\n subprocess.run(cmd, check=True)\n except subprocess.CalledProcessError as e:\n logger.warning(e.output)\n\n if not os.path.exists(save_path) or os.path.getsize(save_path) == 0:\n raise FileNotFoundError(f\"Failed to download file from {download_url} to {save_path}\")\n else:\n logger.info(f\"Downloaded file from {download_url} to {save_path}\")\n return save_path\n\n def _get_fpath_size(self, fpath: str) -> int:\n \"\"\"Get the total size of the files in provided path.\n\n Args:\n fpath (str): path of files to compute the size.\n\n Returns:\n int: The total size in bytes.\n \"\"\"\n if not os.path.isdir(fpath):\n return os.path.getsize(fpath)\n else:\n total_size = 0\n for dirpath, _, filenames in os.walk(fpath):\n for f in filenames:\n fp = os.path.join(dirpath, f)\n total_size += os.path.getsize(fp)\n return total_size\n\n def _download_gz_file(self, download_url: str, save_dir: str):\n \"\"\"Download and unzip the gzip file from provided URL.\n\n Args:\n download_url (str): Source URL of the gzip file.\n save_dir (str): Path to the directory to save the gzip file.\n\n Raises:\n FileNotFoundError\n\n Returns:\n str: The path to the file after unzip.\n \"\"\"\n gz_file_path = self._download_file(download_url, save_dir)\n cmd = [\"gzip\", \"-d\", gz_file_path]\n try:\n subprocess.run(cmd, check=True)\n except subprocess.CalledProcessError as e:\n logger.warning(e.output)\n\n file_path = gz_file_path.replace(\".gz\", \"\")\n if not os.path.exists(file_path) or self._get_fpath_size(file_path) == 0:\n raise FileNotFoundError(f\"Failed to unzip file {gz_file_path}\")\n\n return file_path\n\n def _download_zip_file(self, download_url: str, save_dir: str):\n \"\"\"Download and unzip the zip file from provided URL.\n\n Args:\n download_url (str): Source URL of the zip file.\n save_dir (str): Path to the directory to save the zip file.\n\n Raises:\n FileNotFoundError\n\n Returns:\n str: The path to the file after unzip.\n \"\"\"\n zip_file_path = self._download_file(download_url, save_dir)\n file_path = zip_file_path.replace(\".zip\", \"\")\n if self.force_redownload or not os.path.exists(file_path):\n cmd = [\"unzip\", \"-o\", zip_file_path, \"-d\", file_path]\n else:\n cmd = [\"unzip\", \"-n\", zip_file_path, \"-d\", file_path]\n\n try:\n subprocess.run(cmd, check=True)\n except subprocess.CalledProcessError as e:\n logger.warning(e.output)\n\n if not os.path.exists(file_path) or self._get_fpath_size(file_path) == 0:\n raise FileNotFoundError(f\"Failed to unzip file {zip_file_path}\")\n\n return file_path\n" }, { "path": "FlagEmbedding/abc/evaluation/evaluator.py", "content": "\"\"\"\nAdapted from https://github.com/AIR-Bench/AIR-Bench/blob/0.1.0/air_benchmark/evaluation_utils/evaluator.py\n\"\"\"\nimport json\nimport logging\nimport os\nimport json\nimport pandas as pd\nfrom typing import Dict, Optional, List, Union\n\nfrom .data_loader import AbsEvalDataLoader\nfrom .searcher import EvalRetriever, EvalReranker\nfrom .utils import evaluate_metrics, evaluate_mrr, evaluate_recall_cap\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEvaluator:\n \"\"\"\n Base class of Evaluator.\n \n Args:\n eval_name (str): The experiment name of current evaluation.\n data_loader (AbsEvalDataLoader): The data_loader to deal with data.\n overwrite (bool): If true, will overwrite the existing results.\n \"\"\"\n def __init__(\n self,\n eval_name: str,\n data_loader: AbsEvalDataLoader,\n overwrite: bool = False,\n ):\n self.eval_name = eval_name\n self.data_loader = data_loader\n self.overwrite = overwrite\n\n def check_data_info(\n self,\n data_info: Dict[str, str],\n model_name: str,\n reranker_name: str,\n split: str,\n dataset_name: Optional[str] = None,\n ):\n \"\"\"Check the validity of data info.\n\n Args:\n data_info (Dict[str, str]): The loaded data info to be check.\n model_name (str): Name of model used.\n reranker_name (str): Name of reranker used.\n split (str): Split used in searching.\n dataset_name (Optional[str], optional): Name of dataset used. Defaults to None.\n\n Raises:\n ValueError: eval_name mismatch\n ValueError: model_name or reranker_name mismatch\n ValueError: split mismatch\n ValueError: dataset_name mismatch\n \"\"\"\n if data_info[\"eval_name\"] != self.eval_name:\n raise ValueError(\n f'eval_name mismatch: {data_info[\"eval_name\"]} vs {self.eval_name}'\n )\n if (\n data_info[\"model_name\"] != model_name\n or data_info[\"reranker_name\"] != reranker_name\n ):\n raise ValueError(\n f'model_name or reranker_name mismatch: {data_info[\"model_name\"]} vs {model_name} or {data_info[\"reranker_name\"]} vs {reranker_name}'\n )\n if (data_info[\"split\"] != split):\n raise ValueError(\n f'split mismatch: {data_info[\"split\"]} vs {split}'\n )\n if dataset_name is not None and data_info[\"dataset_name\"] != dataset_name:\n raise ValueError(\n f'dataset_name mismatch: {data_info[\"dataset_name\"]} vs {dataset_name}'\n )\n\n def get_corpus_embd_save_dir(\n self,\n retriever_name: str,\n corpus_embd_save_dir: Optional[str] = None,\n dataset_name: Optional[str] = None\n ):\n \"\"\"\n If corpus_embd_save_dir is not None, then it will be used as the base directory to save the corpus embeddings. For dataset such as MKQA, \n the corpus for all languages is the same, so the subclass can override this method to save the corpus embeddings in the same directory.\n \n Args:\n retriever_name (str): Name of the retriever.\n corpus_embd_save_dir (str, optional): Directory that saving the corpus embedding.\n dataset_name (str, optional): \n \"\"\"\n if corpus_embd_save_dir is not None:\n if dataset_name is not None:\n corpus_embd_save_dir = os.path.join(corpus_embd_save_dir, retriever_name, dataset_name)\n else:\n corpus_embd_save_dir = os.path.join(corpus_embd_save_dir, retriever_name)\n return corpus_embd_save_dir\n\n def __call__(\n self,\n splits: Union[str, List[str]],\n search_results_save_dir: str,\n retriever: EvalRetriever,\n reranker: Optional[EvalReranker] = None,\n corpus_embd_save_dir: Optional[str] = None,\n ignore_identical_ids: bool = False,\n k_values: List[int] = [1, 3, 5, 10, 100, 1000],\n dataset_name: Optional[str] = None,\n **kwargs,\n ):\n \"\"\"This is called during the evaluation process.\n\n Args:\n splits (Union[str, List[str]]): Splits of datasets.\n search_results_save_dir (str): Directory to save the search results.\n retriever (EvalRetriever): object of :class:EvalRetriever.\n reranker (Optional[EvalReranker], optional): Object of :class:EvalReranker. Defaults to :data:`None`.\n corpus_embd_save_dir (Optional[str], optional): Directory to save the embedded corpus. Defaults to :data:`None`.\n ignore_identical_ids (bool, optional): If True, will ignore identical ids in search results. Defaults to :data:`False`.\n k_values (List[int], optional): Cutoffs. Defaults to :data:`[1, 3, 5, 10, 100, 1000]`.\n dataset_name (Optional[str], optional): Name of the datasets. Defaults to :data:`None`.\n \"\"\"\n # Check Splits\n checked_splits = self.data_loader.check_splits(splits, dataset_name=dataset_name)\n if len(checked_splits) == 0:\n logger.warning(f\"{splits} not found in the dataset. Skipping evaluation.\")\n return\n splits = checked_splits\n\n if dataset_name is not None:\n save_name = f\"{dataset_name}-\" + \"{split}.json\"\n else:\n save_name = \"{split}.json\"\n\n corpus_embd_save_dir = self.get_corpus_embd_save_dir(\n retriever_name=str(retriever),\n corpus_embd_save_dir=corpus_embd_save_dir,\n dataset_name=dataset_name\n )\n\n # Retrieval Stage\n no_reranker_search_results_save_dir = os.path.join(\n search_results_save_dir, str(retriever), \"NoReranker\"\n )\n os.makedirs(no_reranker_search_results_save_dir, exist_ok=True)\n\n flag = False\n for split in splits:\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n if not os.path.exists(split_no_reranker_search_results_save_path) or self.overwrite:\n flag = True\n break\n\n no_reranker_search_results_dict = {}\n if flag:\n corpus = self.data_loader.load_corpus(dataset_name=dataset_name)\n\n queries_dict = {\n split: self.data_loader.load_queries(dataset_name=dataset_name, split=split)\n for split in splits\n }\n\n all_queries = {}\n for _, split_queries in queries_dict.items():\n all_queries.update(split_queries)\n\n all_no_reranker_search_results = retriever(\n corpus=corpus,\n queries=all_queries,\n corpus_embd_save_dir=corpus_embd_save_dir,\n ignore_identical_ids=ignore_identical_ids,\n **kwargs,\n )\n\n for split in splits:\n split_queries = queries_dict[split]\n no_reranker_search_results_dict[split] = {\n qid: all_no_reranker_search_results[qid] for qid in split_queries\n }\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n\n self.save_search_results(\n eval_name=self.eval_name,\n model_name=str(retriever),\n reranker_name=\"NoReranker\",\n search_results=no_reranker_search_results_dict[split],\n output_path=split_no_reranker_search_results_save_path,\n split=split,\n dataset_name=dataset_name,\n )\n else:\n for split in splits:\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n data_info, search_results = self.load_search_results(split_no_reranker_search_results_save_path)\n\n self.check_data_info(\n data_info=data_info,\n model_name=str(retriever),\n reranker_name=\"NoReranker\",\n split=split,\n dataset_name=dataset_name,\n )\n no_reranker_search_results_dict[split] = search_results\n retriever.stop_multi_process_pool()\n eval_results_save_path = os.path.join(no_reranker_search_results_save_dir, 'EVAL', 'eval_results.json')\n if not os.path.exists(eval_results_save_path) or self.overwrite or flag:\n retriever_eval_results = self.evaluate_results(no_reranker_search_results_save_dir, k_values=k_values)\n self.output_eval_results_to_json(retriever_eval_results, eval_results_save_path)\n\n # Reranking Stage\n if reranker is not None:\n reranker_search_results_save_dir = os.path.join(\n search_results_save_dir, str(retriever), str(reranker)\n )\n os.makedirs(reranker_search_results_save_dir, exist_ok=True)\n\n corpus = self.data_loader.load_corpus(dataset_name=dataset_name)\n\n queries_dict = {\n split: self.data_loader.load_queries(dataset_name=dataset_name, split=split)\n for split in splits\n }\n\n flag = False\n for split in splits:\n rerank_search_results_save_path = os.path.join(\n reranker_search_results_save_dir, save_name.format(split=split)\n )\n\n if os.path.exists(rerank_search_results_save_path) and not self.overwrite:\n continue\n\n flag = True\n rerank_search_results = reranker(\n corpus=corpus,\n queries=queries_dict[split],\n search_results=no_reranker_search_results_dict[split],\n ignore_identical_ids=ignore_identical_ids,\n **kwargs,\n )\n\n self.save_search_results(\n eval_name=self.eval_name,\n model_name=str(retriever),\n reranker_name=str(reranker),\n search_results=rerank_search_results,\n output_path=rerank_search_results_save_path,\n split=split,\n dataset_name=dataset_name,\n )\n reranker.stop_multi_process_pool()\n eval_results_save_path = os.path.join(reranker_search_results_save_dir, 'EVAL', 'eval_results.json')\n if not os.path.exists(eval_results_save_path) or self.overwrite or flag:\n reranker_eval_results = self.evaluate_results(reranker_search_results_save_dir, k_values=k_values)\n self.output_eval_results_to_json(reranker_eval_results, eval_results_save_path)\n\n @staticmethod\n def save_search_results(\n eval_name: str,\n model_name: str,\n reranker_name: str,\n search_results: Dict[str, Dict[str, float]],\n output_path: str,\n split: str,\n dataset_name: Optional[str] = None,\n ):\n \"\"\"Save the metadata and search results into a file.\n\n Args:\n eval_name (str): The experiment name of current evaluation.\n model_name (str): Name of model used.\n reranker_name (str): Name of reranker used.\n search_results (Dict[str, Dict[str, float]]): Dictionary of search results.\n output_path (str): Output path to write the results.\n split (str): Split used in searching.\n dataset_name (Optional[str], optional): Name of dataset used. Defaults to :data:`None`.\n \"\"\"\n data = {\n \"eval_name\": eval_name,\n \"model_name\": model_name,\n \"reranker_name\": reranker_name,\n \"split\": split,\n \"dataset_name\": dataset_name,\n \"search_results\": search_results,\n }\n\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\n\n with open(output_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(data, f, indent=4)\n\n @staticmethod\n def load_search_results(input_path: str):\n \"\"\"Load search results from path.\n\n Args:\n input_path (str): Path to load from.\n\n Returns:\n dict, dict: data info that contains metadata and search results.\n \"\"\"\n with open(input_path, \"r\", encoding=\"utf-8\") as f:\n data_info = json.load(f)\n \n search_results = data_info.pop(\"search_results\")\n return data_info, search_results\n\n @staticmethod\n def compute_metrics(\n qrels: Dict[str, Dict[str, int]],\n search_results: Dict[str, Dict[str, float]],\n k_values: List[int],\n ):\n \"\"\"Evaluate the model with metrics.\n\n Args:\n qrels (Dict[str, Dict[str, int]]): Ground truth relevance of queries and documents.\n search_results (Dict[str, Dict[str, float]]): Dictionary of search results\n k_values (List[int]): Cutoffs.\n\n Returns:\n dict: The results of the metrics.\n \"\"\"\n ndcg, _map, recall, precision = evaluate_metrics(\n qrels=qrels,\n results=search_results,\n k_values=k_values,\n )\n mrr = evaluate_mrr(\n qrels=qrels,\n results=search_results,\n k_values=k_values,\n )\n recall_cap = evaluate_recall_cap(\n qrels=qrels,\n results=search_results,\n k_values=k_values,\n )\n scores = {\n **{f\"ndcg_at_{k.split('@')[1]}\": v for (k, v) in ndcg.items()},\n **{f\"map_at_{k.split('@')[1]}\": v for (k, v) in _map.items()},\n **{f\"recall_at_{k.split('@')[1]}\": v for (k, v) in recall.items()},\n **{f\"precision_at_{k.split('@')[1]}\": v for (k, v) in precision.items()},\n **{f\"mrr_at_{k.split('@')[1]}\": v for (k, v) in mrr.items()},\n **{f\"recall_cap_at_{k.split('@')[1]}\": v for (k, v) in recall_cap.items()},\n }\n return scores\n\n def evaluate_results(\n self,\n search_results_save_dir: str,\n k_values: List[int] = [1, 3, 5, 10, 100, 1000]\n ):\n \"\"\"Compute metrics according to the results in the directory.\n\n Args:\n search_results_save_dir (str): Path to the search results.\n k_values (List[int], optional): Cutoffs. Defaults to :data:`[1, 3, 5, 10, 100, 1000]`.\n\n Returns:\n dict: Evaluation results.\n \"\"\"\n eval_results_dict = {}\n\n for file in os.listdir(search_results_save_dir):\n if not file.endswith('.json'):\n continue\n\n file_path = os.path.join(search_results_save_dir, file)\n data_info, search_results = self.load_search_results(file_path)\n\n _eval_name = data_info['eval_name']\n assert _eval_name == self.eval_name, f'Mismatch eval_name: {_eval_name} vs {self.eval_name} in {file_path}'\n\n split = data_info['split']\n dataset_name = data_info.get('dataset_name', None)\n qrels = self.data_loader.load_qrels(dataset_name=dataset_name, split=split)\n\n eval_results = self.compute_metrics(\n qrels=qrels,\n search_results=search_results,\n k_values=k_values\n )\n\n if dataset_name is not None:\n key = f\"{dataset_name}-{split}\"\n else:\n key = split\n eval_results_dict[key] = eval_results\n\n return eval_results_dict\n\n @staticmethod\n def output_eval_results_to_json(eval_results_dict: dict, output_path: str):\n \"\"\"Write the evaluation results into a json file.\n\n Args:\n eval_results_dict (dict): Dictionary of the evaluation results.\n output_path (str): Output path to write the json file.\n \"\"\"\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\n\n with open(output_path, 'w', encoding='utf-8') as f:\n json.dump(eval_results_dict, f, indent=4)\n logger.info(f\"Results saved to {output_path}\")\n\n @staticmethod\n def get_results_df(metric: str, eval_results_dict: dict):\n \"\"\"Get the results from dictionary to a DataFrame.\n\n Args:\n metric (str): Selected metric.\n eval_results_dict (dict): Dictionary of the evaluation results.\n\n Returns:\n DataFrame: DataFrame of the results.\n \"\"\"\n results_dict = {}\n\n for model_name, model_results in eval_results_dict.items():\n results_dict[model_name] = {}\n for reranker_name, reranker_results in model_results.items():\n results_dict[model_name][reranker_name] = {}\n for split, split_results in reranker_results.items():\n if metric in split_results:\n results_dict[model_name][reranker_name][split] = split_results[metric]\n else:\n results_dict[model_name][reranker_name][split] = None\n\n model_reranker_pairs = set()\n all_splits = set()\n for model_name, model_results in results_dict.items():\n for reranker_name, reranker_results in model_results.items():\n model_reranker_pairs.add((model_name, reranker_name))\n all_splits.update(reranker_results.keys())\n\n index = [(model, reranker) for model, reranker in model_reranker_pairs]\n multi_index = pd.MultiIndex.from_tuples(index, names=['Model', 'Reranker'])\n \n all_splits = sorted(list(all_splits))\n overall_columns = ['average'] + all_splits\n overall_df = pd.DataFrame(index=multi_index, columns=overall_columns)\n \n for model, reranker in model_reranker_pairs:\n for split in all_splits:\n if model in results_dict and reranker in results_dict[model] and split in results_dict[model][reranker]:\n overall_df.loc[(model, reranker), split] = results_dict[model][reranker][split]\n else:\n overall_df.loc[(model, reranker), split] = None\n if overall_df.loc[(model, reranker), all_splits].isnull().any():\n overall_df.loc[(model, reranker), 'average'] = None\n else:\n overall_df.loc[(model, reranker), 'average'] = overall_df.loc[(model, reranker), all_splits].mean()\n\n return overall_df\n\n @staticmethod\n def output_eval_results_to_markdown(eval_results_dict: dict, output_path: str, metrics: Union[List[str], str]):\n \"\"\"Write the evaluation results to a markdown file.\n\n Args:\n eval_results_dict (dict): Dictionary that contains evaluation results.\n output_path (str): Path to write the output to.\n metrics (Union[List[str], str]): The metrics that will be written in the markdown file.\n \"\"\"\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\n\n if isinstance(metrics, str):\n metrics = [metrics]\n\n with open(output_path, 'w', encoding='utf-8') as f:\n for metric in metrics:\n f.write(f\"## {metric}\\n\\n\")\n results_df = AbsEvaluator.get_results_df(metric, eval_results_dict)\n max_index = dict(results_df.idxmax(axis=0))\n splits = results_df.columns\n f.write(f\"| Model | Reranker | {' | '.join(splits)} |\\n\")\n f.write(f\"| :---- | :---- | {' | '.join([':---:' for _ in splits])} |\\n\")\n for i, row in results_df.iterrows():\n line = f\"| {i[0]} | {i[1]} | \"\n for s, v in row.items():\n if v is None:\n line += \"- | \"\n else:\n if i != max_index[s]:\n line += f'{v*100:.3f} | '\n else:\n line += f'**{v*100:.3f}** | '\n f.write(line + \"\\n\")\n f.write(\"\\n\")\n logger.info(f\"Results saved to {output_path}\")\n" }, { "path": "FlagEmbedding/abc/evaluation/runner.py", "content": "import os\nimport json\nimport logging\nfrom typing import List, Union, Tuple\n\nfrom FlagEmbedding import FlagAutoModel, FlagAutoReranker, AbsEmbedder, AbsReranker\n\nfrom .arguments import AbsEvalArgs, AbsEvalModelArgs\nfrom .evaluator import AbsEvaluator\nfrom .searcher import EvalDenseRetriever, EvalReranker\nfrom .data_loader import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEvalRunner:\n \"\"\"\n Abstract class of evaluation runner.\n \n Args:\n eval_args (AbsEvalArgs): :class:AbsEvalArgs object with the evaluation arguments.\n model_args (AbsEvalModelArgs): :class:AbsEvalModelArgs object with the model arguments.\n \"\"\"\n def __init__(\n self,\n eval_args: AbsEvalArgs,\n model_args: AbsEvalModelArgs,\n ):\n self.eval_args = eval_args\n self.model_args = model_args\n\n self.retriever, self.reranker = self.load_retriever_and_reranker()\n self.data_loader = self.load_data_loader()\n self.evaluator = self.load_evaluator()\n\n @staticmethod\n def get_models(model_args: AbsEvalModelArgs) -> Tuple[AbsEmbedder, Union[AbsReranker, None]]:\n \"\"\"Get the embedding and reranker model\n\n Args:\n model_args (AbsEvalModelArgs): :class:AbsEvalModelArgs object with the model arguments.\n\n Returns:\n Tuple[AbsEmbedder, Union[AbsReranker, None]]: A :class:AbsEmbedder object of embedding model, and \n :class:AbsReranker object of reranker model if path provided.\n \"\"\"\n embedder = FlagAutoModel.from_finetuned(\n model_name_or_path=model_args.embedder_name_or_path,\n model_class=model_args.embedder_model_class,\n normalize_embeddings=model_args.normalize_embeddings,\n pooling_method=model_args.pooling_method,\n use_fp16=model_args.use_fp16,\n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval,\n query_instruction_format=model_args.query_instruction_format_for_retrieval,\n devices=model_args.devices,\n examples_for_task=model_args.examples_for_task,\n examples_instruction_format=model_args.examples_instruction_format,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir,\n batch_size=model_args.embedder_batch_size,\n query_max_length=model_args.embedder_query_max_length,\n passage_max_length=model_args.embedder_passage_max_length,\n )\n embedder.model.config._name_or_path = model_args.embedder_name_or_path\n reranker = None\n if model_args.reranker_name_or_path is not None:\n reranker = FlagAutoReranker.from_finetuned(\n model_name_or_path=model_args.reranker_name_or_path,\n model_class=model_args.reranker_model_class,\n peft_path=model_args.reranker_peft_path,\n use_fp16=model_args.use_fp16,\n use_bf16=model_args.use_bf16,\n query_instruction_for_rerank=model_args.query_instruction_for_rerank,\n query_instruction_format=model_args.query_instruction_format_for_rerank,\n passage_instruction_for_rerank=model_args.passage_instruction_for_rerank,\n passage_instruction_format=model_args.passage_instruction_format_for_rerank,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n devices=model_args.devices,\n normalize=model_args.normalize,\n prompt=model_args.prompt,\n cutoff_layers=model_args.cutoff_layers,\n compress_layers=model_args.compress_layers,\n compress_ratio=model_args.compress_ratio,\n batch_size=model_args.reranker_batch_size,\n query_max_length=model_args.reranker_query_max_length,\n max_length=model_args.reranker_max_length,\n )\n reranker.model.config._name_or_path = model_args.reranker_name_or_path\n return embedder, reranker\n\n def load_retriever_and_reranker(self) -> Tuple[EvalDenseRetriever, Union[EvalReranker, None]]:\n \"\"\"Load retriever and reranker for evaluation\n\n Returns:\n Tuple[EvalDenseRetriever, Union[EvalReranker, None]]: A :class:EvalDenseRetriever object for retrieval, and a\n :class:EvalReranker object if reranker provided.\n \"\"\"\n embedder, reranker = self.get_models(self.model_args)\n retriever = EvalDenseRetriever(\n embedder,\n search_top_k=self.eval_args.search_top_k,\n overwrite=self.eval_args.overwrite\n )\n if reranker is not None:\n reranker = EvalReranker(reranker, rerank_top_k=self.eval_args.rerank_top_k)\n return retriever, reranker\n\n def load_data_loader(self) -> AbsEvalDataLoader:\n \"\"\"Load the data loader\n\n Returns:\n AbsEvalDataLoader: Data loader object for that specific task.\n \"\"\"\n data_loader = AbsEvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n\n def load_evaluator(self) -> AbsEvaluator:\n \"\"\"Load the evaluator for evaluation\n\n Returns:\n AbsEvaluator: the evaluator to run the evaluation.\n \"\"\"\n evaluator = AbsEvaluator(\n eval_name=self.eval_args.eval_name,\n data_loader=self.data_loader,\n overwrite=self.eval_args.overwrite,\n )\n return evaluator\n\n @staticmethod\n def evaluate_metrics(\n search_results_save_dir: str,\n output_method: str = \"markdown\",\n output_path: str = \"./eval_dev_results.md\",\n metrics: Union[str, List[str]] = [\"ndcg_at_10\", \"recall_at_10\"]\n ):\n \"\"\"Evaluate the provided metrics and write the results.\n\n Args:\n search_results_save_dir (str): Path to save the search results.\n output_method (str, optional): Output results to `json` or `markdown`. Defaults to :data:`\"markdown\"`.\n output_path (str, optional): Path to write the output. Defaults to :data:`\"./eval_dev_results.md\"`.\n metrics (Union[str, List[str]], optional): metrics to use. Defaults to :data:`[\"ndcg_at_10\", \"recall_at_10\"]`.\n\n Raises:\n FileNotFoundError: Eval results not found\n ValueError: Invalid output method\n \"\"\"\n eval_results_dict = {}\n for model_name in sorted(os.listdir(search_results_save_dir)):\n model_search_results_save_dir = os.path.join(search_results_save_dir, model_name)\n if not os.path.isdir(model_search_results_save_dir):\n continue\n for reranker_name in sorted(os.listdir(model_search_results_save_dir)):\n reranker_search_results_save_dir = os.path.join(model_search_results_save_dir, reranker_name)\n if not os.path.isdir(reranker_search_results_save_dir):\n continue\n eval_results_path = os.path.join(reranker_search_results_save_dir, 'EVAL', \"eval_results.json\")\n if os.path.exists(eval_results_path):\n eval_results = json.load(open(eval_results_path, encoding='utf-8'))\n else:\n logger.warning(f\"Eval results not found: {eval_results_path}\")\n continue\n\n if model_name not in eval_results_dict:\n eval_results_dict[model_name] = {}\n eval_results_dict[model_name][reranker_name] = eval_results\n\n if output_method == \"json\":\n AbsEvaluator.output_eval_results_to_json(eval_results_dict, output_path)\n elif output_method == \"markdown\":\n AbsEvaluator.output_eval_results_to_markdown(eval_results_dict, output_path, metrics)\n else:\n raise ValueError(f\"Invalid output method: {output_method}. Available methods: ['json', 'markdown']\")\n\n def run(self):\n \"\"\"\n Run the whole evaluation.\n \"\"\"\n if self.eval_args.dataset_names is None:\n dataset_names = self.data_loader.available_dataset_names()\n else:\n dataset_names = self.data_loader.check_dataset_names(self.eval_args.dataset_names)\n\n if len(dataset_names) == 0:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on the default dataset.\")\n self.evaluator(\n splits=self.eval_args.splits,\n search_results_save_dir=self.eval_args.output_dir,\n retriever=self.retriever,\n reranker=self.reranker,\n corpus_embd_save_dir=self.eval_args.corpus_embd_save_dir,\n ignore_identical_ids=self.eval_args.ignore_identical_ids,\n k_values=self.eval_args.k_values\n )\n logger.info(f\"{self.eval_args.eval_name} evaluation completed.\")\n else:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on the following dataset names: {dataset_names}\")\n for dataset_name in dataset_names:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on: {dataset_name}\")\n self.evaluator(\n splits=self.eval_args.splits,\n search_results_save_dir=self.eval_args.output_dir,\n retriever=self.retriever,\n reranker=self.reranker,\n corpus_embd_save_dir=self.eval_args.corpus_embd_save_dir,\n ignore_identical_ids=self.eval_args.ignore_identical_ids,\n k_values=self.eval_args.k_values,\n dataset_name=dataset_name,\n )\n logger.info(f\"{self.eval_args.eval_name} evaluation on {dataset_names} completed.\")\n\n logger.info(\"Start computing metrics.\")\n self.evaluate_metrics(\n search_results_save_dir=self.eval_args.output_dir,\n output_method=self.eval_args.eval_output_method,\n output_path=self.eval_args.eval_output_path,\n metrics=self.eval_args.eval_metrics\n )\n" }, { "path": "FlagEmbedding/abc/evaluation/searcher.py", "content": "\"\"\"\nAdapted from https://github.com/AIR-Bench/AIR-Bench/blob/0.1.0/air_benchmark/evaluation_utils/searcher.py\n\"\"\"\nimport os\nimport logging\nimport gc\nimport torch\nimport numpy as np\nfrom typing import Any, Dict, Optional\nfrom abc import ABC, abstractmethod\n\nfrom FlagEmbedding.abc.inference import AbsEmbedder, AbsReranker\nfrom FlagEmbedding.abc.evaluation.utils import index, search\n\nlogger = logging.getLogger(__name__)\n\n\nclass EvalRetriever(ABC):\n \"\"\"\n This is the base class for retriever.\n \"\"\"\n def __init__(self, embedder: AbsEmbedder, search_top_k: int = 1000, overwrite: bool = False):\n self.embedder = embedder\n self.search_top_k = search_top_k\n self.overwrite = overwrite\n\n def __str__(self) -> str:\n \"\"\"\n Returns: str: Name of the retriever.\n \"\"\"\n return os.path.basename(self.embedder.model.config._name_or_path)\n\n def stop_multi_process_pool(self):\n self.embedder.stop_self_pool()\n # if self.embedder.pool is not None:\n # self.embedder.stop_multi_process_pool(self.embedder.pool)\n # self.embedder.pool = None\n # self.embedder.model.to('cpu')\n # gc.collect()\n # torch.cuda.empty_cache()\n\n @abstractmethod\n def __call__(\n self,\n corpus: Dict[str, Dict[str, Any]],\n queries: Dict[str, str],\n corpus_embd_save_dir: Optional[str] = None,\n ignore_identical_ids: bool = False,\n **kwargs,\n ) -> Dict[str, Dict[str, float]]:\n \"\"\"\n Abstract method to be overrode. This is called during the retrieval process.\n \n Parameters:\n corpus: Dict[str, Dict[str, Any]]: Corpus of documents. \n Structure: {: {\"text\": }}.\n Example: {\"doc-0\": {\"text\": \"This is a document.\"}}\n queries: Dict[str, str]: Queries to search for.\n Structure: {: }.\n Example: {\"q-0\": \"This is a query.\"}\n corpus_embd_save_dir (Optional[str]): Defaults to :data:`None`.\n ignore_identical_ids (bool): Defaults to :data:`False`.\n **kwargs: Any: Additional arguments.\n \n Returns: Dict[str, Dict[str, float]]: Top-k search results for each query. k is specified by search_top_k.\n Structure: {qid: {docid: score}}. The higher is the score, the more relevant is the document.\n Example: {\"q-0\": {\"doc-0\": 0.9}}\n \"\"\"\n\n\nclass EvalDenseRetriever(EvalRetriever):\n \"\"\"\n Child class of :class:EvalRetriever for dense retrieval.\n \"\"\"\n def __call__(\n self,\n corpus: Dict[str, Dict[str, Any]],\n queries: Dict[str, str],\n corpus_embd_save_dir: Optional[str] = None,\n ignore_identical_ids: bool = False,\n **kwargs,\n ) -> Dict[str, Dict[str, float]]:\n \"\"\"\n This is called during the retrieval process.\n \n Parameters:\n corpus: Dict[str, Dict[str, Any]]: Corpus of documents. \n Structure: {: {\"text\": }}.\n Example: {\"doc-0\": {\"text\": \"This is a document.\"}}\n queries: Dict[str, str]: Queries to search for.\n Structure: {: }.\n Example: {\"q-0\": \"This is a query.\"}\n corpus_embd_save_dir (Optional[str]): Defaults to :data:`None`.\n ignore_identical_ids (bool): Defaults to :data:`False`.\n **kwargs: Any: Additional arguments.\n \n Returns: Dict[str, Dict[str, float]]: Top-k search results for each query. k is specified by search_top_k.\n Structure: {qid: {docid: score}}. The higher is the score, the more relevant is the document.\n Example: {\"q-0\": {\"doc-0\": 0.9}}\n \"\"\"\n if ignore_identical_ids:\n logger.warning(\"ignore_identical_ids is set to True. This means that the search results will not contain identical ids. Note: Dataset such as MIRACL should NOT set this to True.\")\n\n # dense embedding models do not require language as input: AIRBench evaluation\n kwargs.pop(\"language\", None)\n\n corpus_ids = []\n corpus_texts = []\n for docid, doc in corpus.items():\n corpus_ids.append(docid)\n corpus_texts.append(\n doc[\"text\"] if \"title\" not in doc \n else f\"{doc['title']} {doc['text']}\".strip()\n )\n queries_ids = []\n queries_texts = []\n for qid, query in queries.items():\n queries_ids.append(qid)\n queries_texts.append(query)\n\n if corpus_embd_save_dir is not None:\n if os.path.exists(os.path.join(corpus_embd_save_dir, \"doc.npy\")) and not self.overwrite:\n corpus_emb = np.load(os.path.join(corpus_embd_save_dir, \"doc.npy\"))\n else:\n corpus_emb = self.embedder.encode_corpus(corpus_texts, **kwargs)\n else:\n corpus_emb = self.embedder.encode_corpus(corpus_texts, **kwargs)\n\n queries_emb = self.embedder.encode_queries(queries_texts, **kwargs)\n\n # check if the embeddings are in dictionary format: M3Embedder\n if isinstance(corpus_emb, dict):\n corpus_emb = corpus_emb[\"dense_vecs\"]\n if isinstance(queries_emb, dict):\n queries_emb = queries_emb[\"dense_vecs\"]\n \n if corpus_embd_save_dir is not None and \\\n (not os.path.exists(os.path.join(corpus_embd_save_dir, \"doc.npy\")) or self.overwrite):\n os.makedirs(corpus_embd_save_dir, exist_ok=True)\n np.save(os.path.join(corpus_embd_save_dir, \"doc.npy\"), corpus_emb)\n \n gc.collect()\n torch.cuda.empty_cache()\n\n faiss_index = index(corpus_embeddings=corpus_emb)\n all_scores, all_indices = search(query_embeddings=queries_emb, faiss_index=faiss_index, k=self.search_top_k)\n\n results = {}\n for idx, (scores, indices) in enumerate(zip(all_scores, all_indices)):\n results[queries_ids[idx]] = {}\n for score, indice in zip(scores, indices):\n if indice != -1:\n if ignore_identical_ids and corpus_ids[indice] == queries_ids[idx]:\n continue\n results[queries_ids[idx]][corpus_ids[indice]] = float(score)\n\n return results\n\n\nclass EvalReranker:\n \"\"\"\n Class for reranker during evaluation.\n \"\"\"\n def __init__(self, reranker: AbsReranker, rerank_top_k: int = 100):\n self.reranker = reranker\n self.rerank_top_k = rerank_top_k\n\n def __str__(self) -> str:\n \"\"\"\n Returns: str: Name of the reranker.\n \"\"\"\n return os.path.basename(self.reranker.model.config._name_or_path)\n\n def stop_multi_process_pool(self):\n self.reranker.stop_self_pool()\n # if self.reranker.pool is not None:\n # self.reranker.stop_multi_process_pool(self.reranker.pool)\n # self.reranker.pool = None\n # self.reranker.model.to('cpu')\n # gc.collect()\n # torch.cuda.empty_cache()\n\n def __call__(\n self,\n corpus: Dict[str, Dict[str, Any]],\n queries: Dict[str, str],\n search_results: Dict[str, Dict[str, float]],\n ignore_identical_ids: bool = False,\n **kwargs,\n ) -> Dict[str, Dict[str, float]]:\n \"\"\"\n This is called during the reranking process.\n \n Parameters:\n corpus: Dict[str, Dict[str, Any]]: Corpus of documents. \n Structure: {: {\"text\": }}.\n Example: {\"doc-0\": {\"text\": \"This is a document.\"}}\n queries: Dict[str, str]: Queries to search for.\n Structure: {: }.\n Example: {\"q-0\": \"This is a query.\"}\n search_results: Dict[str, Dict[str, float]]: Search results for each query.\n Structure: {qid: {docid: score}}. The higher is the score, the more relevant is the document.\n Example: {\"q-0\": {\"doc-0\": 0.9}}\n **kwargs: Any: Additional arguments.\n \n Returns: Dict[str, Dict[str, float]]: Reranked search results for each query. k is specified by rerank_top_k.\n Structure: {qid: {docid: score}}. The higher is the score, the more relevant is the document.\n Example: {\"q-0\": {\"doc-0\": 0.9}}\n \"\"\"\n # truncate search results to top_k\n for qid in search_results:\n search_results[qid] = dict(\n sorted(search_results[qid].items(), key=lambda x: x[1], reverse=True)[\n :self.rerank_top_k\n ]\n )\n # generate sentence pairs\n sentence_pairs = []\n pairs = []\n for qid in search_results:\n for docid in search_results[qid]:\n if ignore_identical_ids and qid == docid:\n continue\n sentence_pairs.append(\n {\n \"qid\": qid,\n \"docid\": docid,\n \"query\": queries[qid],\n \"doc\": corpus[docid][\"text\"] if \"title\" not in corpus[docid] \n else f\"{corpus[docid]['title']} {corpus[docid]['text']}\".strip(),\n }\n )\n pairs.append(\n (\n queries[qid],\n corpus[docid][\"text\"] if \"title\" not in corpus[docid] \n else f\"{corpus[docid]['title']} {corpus[docid]['text']}\".strip()\n )\n )\n # compute scores\n scores = self.reranker.compute_score(pairs)\n for i, score in enumerate(scores):\n sentence_pairs[i][\"score\"] = float(score)\n # rerank\n reranked_results = {qid: {} for qid in search_results}\n for pair in sentence_pairs:\n reranked_results[pair[\"qid\"]][pair[\"docid\"]] = pair[\"score\"]\n return reranked_results\n" }, { "path": "FlagEmbedding/abc/evaluation/utils.py", "content": "import faiss\nimport torch\nimport logging\nimport numpy as np\nimport pytrec_eval\nfrom tqdm import tqdm\nfrom collections import defaultdict\nfrom typing import Dict, List, Tuple, Optional\n\nlogger = logging.getLogger(__name__)\n\n\n# Modified from https://github.com/beir-cellar/beir/blob/f062f038c4bfd19a8ca942a9910b1e0d218759d4/beir/retrieval/custom_metrics.py#L4\ndef evaluate_mrr(\n qrels: Dict[str, Dict[str, int]],\n results: Dict[str, Dict[str, float]],\n k_values: List[int],\n) -> Tuple[Dict[str, float]]:\n \"\"\"Compute mean reciprocal rank (MRR).\n\n Args:\n qrels (Dict[str, Dict[str, int]]): Ground truth relevance.\n results (Dict[str, Dict[str, float]]): Search results to evaluate.\n k_values (List[int]): Cutoffs.\n\n Returns:\n Tuple[Dict[str, float]]: MRR results at provided k values.\n \"\"\"\n mrr = defaultdict(list)\n\n k_max, top_hits = max(k_values), {}\n\n for query_id, doc_scores in results.items():\n top_hits[query_id] = sorted(\n doc_scores.items(), key=lambda item: item[1], reverse=True\n )[0:k_max]\n\n for query_id in top_hits:\n query_relevant_docs = {\n doc_id for doc_id in qrels[query_id] if qrels[query_id][doc_id] > 0\n }\n for k in k_values:\n rr = 0\n for rank, hit in enumerate(top_hits[query_id][0:k], 1):\n if hit[0] in query_relevant_docs:\n rr = 1.0 / rank\n break\n mrr[f\"MRR@{k}\"].append(rr)\n\n for k in k_values:\n mrr[f\"MRR@{k}\"] = round(sum(mrr[f\"MRR@{k}\"]) / len(qrels), 5)\n return mrr\n\n\n# Modified from https://github.com/beir-cellar/beir/blob/f062f038c4bfd19a8ca942a9910b1e0d218759d4/beir/retrieval/custom_metrics.py#L33\ndef evaluate_recall_cap(\n qrels: Dict[str, Dict[str, int]], \n results: Dict[str, Dict[str, float]], \n k_values: List[int]\n) -> Tuple[Dict[str, float]]:\n \"\"\"Compute capped recall.\n \n Args:\n qrels (Dict[str, Dict[str, int]]): Ground truth relevance.\n results (Dict[str, Dict[str, float]]): Search results to evaluate.\n k_values (List[int]): Cutoffs.\n \n Returns:\n Tuple[Dict[str, float]]: Capped recall results at provided k values.\n \"\"\"\n capped_recall = {}\n \n for k in k_values:\n capped_recall[f\"R_cap@{k}\"] = 0.0\n \n k_max = max(k_values)\n logging.info(\"\\n\")\n \n for query_id, doc_scores in results.items():\n top_hits = sorted(doc_scores.items(), key=lambda item: item[1], reverse=True)[0:k_max] \n query_relevant_docs = [doc_id for doc_id in qrels[query_id] if qrels[query_id][doc_id] > 0]\n for k in k_values:\n retrieved_docs = [row[0] for row in top_hits[0:k] if qrels[query_id].get(row[0], 0) > 0]\n denominator = min(len(query_relevant_docs), k)\n capped_recall[f\"R_cap@{k}\"] += (len(retrieved_docs) / denominator)\n\n for k in k_values:\n capped_recall[f\"R_cap@{k}\"] = round(capped_recall[f\"R_cap@{k}\"]/len(qrels), 5)\n logging.info(\"R_cap@{}: {:.4f}\".format(k, capped_recall[f\"R_cap@{k}\"]))\n\n return capped_recall\n\n\n# Modified from https://github.com/embeddings-benchmark/mteb/blob/18f730696451a5aaa026494cecf288fd5cde9fd0/mteb/evaluation/evaluators/RetrievalEvaluator.py#L501\ndef evaluate_metrics(\n qrels: Dict[str, Dict[str, int]],\n results: Dict[str, Dict[str, float]],\n k_values: List[int],\n) -> Tuple[\n Dict[str, float],\n Dict[str, float],\n Dict[str, float],\n Dict[str, float],\n]:\n \"\"\"Evaluate the main metrics.\n\n Args:\n qrels (Dict[str, Dict[str, int]]): Ground truth relevance.\n results (Dict[str, Dict[str, float]]): Search results to evaluate.\n k_values (List[int]): Cutoffs.\n\n Returns:\n Tuple[ Dict[str, float], Dict[str, float], Dict[str, float], Dict[str, float], ]: Results of different metrics at \n different provided k values.\n \"\"\"\n all_ndcgs, all_aps, all_recalls, all_precisions = defaultdict(list), defaultdict(list), defaultdict(list), defaultdict(list)\n\n map_string = \"map_cut.\" + \",\".join([str(k) for k in k_values])\n ndcg_string = \"ndcg_cut.\" + \",\".join([str(k) for k in k_values])\n recall_string = \"recall.\" + \",\".join([str(k) for k in k_values])\n precision_string = \"P.\" + \",\".join([str(k) for k in k_values])\n evaluator = pytrec_eval.RelevanceEvaluator(\n qrels, {map_string, ndcg_string, recall_string, precision_string}\n )\n scores = evaluator.evaluate(results)\n\n for query_id in scores.keys():\n for k in k_values:\n all_ndcgs[f\"NDCG@{k}\"].append(scores[query_id][\"ndcg_cut_\" + str(k)])\n all_aps[f\"MAP@{k}\"].append(scores[query_id][\"map_cut_\" + str(k)])\n all_recalls[f\"Recall@{k}\"].append(scores[query_id][\"recall_\" + str(k)])\n all_precisions[f\"P@{k}\"].append(scores[query_id][\"P_\" + str(k)])\n\n ndcg, _map, recall, precision = (\n all_ndcgs.copy(),\n all_aps.copy(),\n all_recalls.copy(),\n all_precisions.copy(),\n )\n\n for k in k_values:\n ndcg[f\"NDCG@{k}\"] = round(sum(ndcg[f\"NDCG@{k}\"]) / len(scores), 5)\n _map[f\"MAP@{k}\"] = round(sum(_map[f\"MAP@{k}\"]) / len(scores), 5)\n recall[f\"Recall@{k}\"] = round(sum(recall[f\"Recall@{k}\"]) / len(scores), 5)\n precision[f\"P@{k}\"] = round(sum(precision[f\"P@{k}\"]) / len(scores), 5)\n\n return ndcg, _map, recall, precision\n\n\ndef index(\n index_factory: str = \"Flat\", \n corpus_embeddings: Optional[np.ndarray] = None, \n load_path: Optional[str] = None,\n device: Optional[str] = None\n):\n \"\"\"Create and add embeddings into a Faiss index.\n\n Args:\n index_factory (str, optional): Type of Faiss index to create. Defaults to \"Flat\".\n corpus_embeddings (Optional[np.ndarray], optional): The embedding vectors of the corpus. Defaults to None.\n load_path (Optional[str], optional): Path to load embeddings from. Defaults to None.\n device (Optional[str], optional): Device to hold Faiss index. Defaults to None.\n\n Returns:\n faiss.Index: The Faiss index that contains all the corpus embeddings.\n \"\"\"\n if corpus_embeddings is None:\n corpus_embeddings = np.load(load_path)\n \n logger.info(f\"Shape of embeddings: {corpus_embeddings.shape}\")\n # create faiss index\n logger.info(f'Indexing {corpus_embeddings.shape[0]} documents...')\n faiss_index = faiss.index_factory(corpus_embeddings.shape[-1], index_factory, faiss.METRIC_INNER_PRODUCT)\n \n if device is None and torch.cuda.is_available():\n try:\n co = faiss.GpuMultipleClonerOptions()\n co.shard = True\n co.useFloat16 = True\n faiss_index = faiss.index_cpu_to_all_gpus(faiss_index, co)\n except:\n print('faiss do not support GPU, please uninstall faiss-cpu, faiss-gpu and install faiss-gpu again.')\n\n logger.info('Adding embeddings ...')\n corpus_embeddings = corpus_embeddings.astype(np.float32)\n faiss_index.train(corpus_embeddings)\n faiss_index.add(corpus_embeddings)\n logger.info('Embeddings add over...')\n return faiss_index\n\n\ndef search(\n faiss_index: faiss.Index, \n k: int = 100, \n query_embeddings: Optional[np.ndarray] = None,\n load_path: Optional[str] = None\n):\n \"\"\"\n 1. Encode queries into dense embeddings;\n 2. Search through faiss index\n\n Args:\n faiss_index (faiss.Index): The Faiss index that contains all the corpus embeddings.\n k (int, optional): Top k numbers of closest neighbours. Defaults to :data:`100`.\n query_embeddings (Optional[np.ndarray], optional): The embedding vectors of queries. Defaults to :data:`None`.\n load_path (Optional[str], optional): Path to load embeddings from. Defaults to :data:`None`.\n\n Returns:\n Tuple[np.ndarray, np.ndarray]: The scores of search results and their corresponding indices.\n \"\"\"\n if query_embeddings is None:\n query_embeddings = np.load(load_path)\n\n query_size = len(query_embeddings)\n\n all_scores = []\n all_indices = []\n\n for i in tqdm(range(0, query_size, 32), desc=\"Searching\"):\n j = min(i + 32, query_size)\n query_embedding = query_embeddings[i: j]\n score, indice = faiss_index.search(query_embedding.astype(np.float32), k=k)\n all_scores.append(score)\n all_indices.append(indice)\n\n all_scores = np.concatenate(all_scores, axis=0)\n all_indices = np.concatenate(all_indices, axis=0)\n return all_scores, all_indices\n" }, { "path": "FlagEmbedding/abc/finetune/__init__.py", "content": "" }, { "path": "FlagEmbedding/abc/finetune/embedder/AbsArguments.py", "content": "import os\nfrom typing import Optional\nfrom dataclasses import dataclass, field\n\nfrom transformers import TrainingArguments\n\n\n@dataclass\nclass AbsEmbedderModelArguments:\n \"\"\"\n Abstract class for model arguments.\n \"\"\"\n\n model_name_or_path: str = field(\n metadata={\"help\": \"The model checkpoint for initialization.\"}\n )\n config_name: str = field(\n default=None,\n metadata={\"help\": \"Pretrained config name or path if not the same as model_name.\"}\n )\n tokenizer_name: str = field(\n default=None,\n metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name.\"}\n )\n cache_dir: str = field(\n default=None,\n metadata={\"help\": \"Where do you want to store the pre-trained models downloaded from s3.\"}\n )\n trust_remote_code: bool = field(\n default=False,\n metadata={\"help\": \"Trust remote code\"}\n )\n use_fast_tokenizer: bool = field(\n default=True,\n metadata={\"help\": \"Whether to use fast tokenizer or not.\"}\n )\n token: str = field(\n default_factory=lambda: os.getenv('HF_TOKEN', None),\n metadata={\"help\": \"The token to use when accessing the model.\"}\n )\n\n\n@dataclass\nclass AbsEmbedderDataArguments:\n \"\"\"\n Abstract class for data arguments.\n \"\"\"\n train_data: str = field(\n default=None, metadata={\n \"help\": \"One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data.\",\n \"nargs\": \"+\"\n }\n )\n cache_path: Optional[str] = field(\n default=None, metadata={\"help\": \"Where do you want to store the cached data\"}\n )\n train_group_size: int = field(default=8)\n\n query_max_len: int = field(\n default=32,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\"\n },\n )\n\n passage_max_len: int = field(\n default=128,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\"\n },\n )\n\n pad_to_multiple_of: Optional[int] = field(\n default=None,\n metadata={\n \"help\": \"If set will pad the sequence to be a multiple of the provided value.\"\n },\n )\n\n max_example_num_per_dataset: int = field(\n default=100000000, metadata={\"help\": \"the max number of examples for each dataset\"}\n )\n\n query_instruction_for_retrieval: str= field(\n default=None, metadata={\"help\": \"instruction for query\"}\n )\n query_instruction_format: str = field(\n default=\"{}{}\", metadata={\"help\": \"format for query instruction\"}\n )\n\n knowledge_distillation: bool = field(\n default=False,\n metadata={\"help\": \"Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data\"}\n )\n\n passage_instruction_for_retrieval: Optional[str] = field(\n default=None, metadata={\"help\": \"instruction for passage\"}\n )\n passage_instruction_format: Optional[str] = field(\n default=\"{}{}\", metadata={\"help\": \"format for passage instruction\"}\n )\n\n shuffle_ratio: float = field(\n default=0.0, metadata={\"help\": \"The ratio of shuffling the text\"}\n )\n\n # Parameters for SameDatasetDataArguments\n same_dataset_within_batch: bool = field(\n default=False, metadata={\"help\": \"All samples in the same batch comes from the same dataset.\"}\n )\n small_threshold: int = field(\n default=0,\n metadata={\"help\": \"The threshold of small dataset. All small dataset in the same directory will be merged into one dataset.\"}\n )\n drop_threshold: int = field(\n default=0,\n metadata={\"help\": \"The threshold for dropping merged small dataset. If the number of examples in the merged small dataset is less than this threshold, it will be dropped.\"}\n )\n\n def __post_init__(self):\n # replace \"\\\\n\" with \"\\n\"\n if \"\\\\n\" in self.query_instruction_format:\n self.query_instruction_format = self.query_instruction_format.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.passage_instruction_format:\n self.passage_instruction_format = self.passage_instruction_format.replace(\"\\\\n\", \"\\n\")\n \n # check the existence of train data\n for train_dir in self.train_data:\n if not os.path.exists(train_dir):\n raise FileNotFoundError(f\"cannot find file: {train_dir}, please set a true path\")\n\n\n@dataclass\nclass AbsEmbedderTrainingArguments(TrainingArguments):\n negatives_cross_device: bool = field(default=False, metadata={\"help\": \"share negatives across devices\"})\n temperature: Optional[float] = field(default=0.02, metadata={\"help\": \"temperature used for similarity score\"})\n fix_position_embedding: bool = field(default=False, metadata={\"help\": \"Freeze the parameters of position embeddings\"})\n sentence_pooling_method: str = field(default='cls', metadata={\"help\": \"the pooling method. Available options: cls, mean, last_token. Default: cls\", \"choices\": ['cls', 'mean', 'last_token']})\n normalize_embeddings: bool = field(default=True, metadata={\"help\": \"whether to normalize the embeddings\"})\n sub_batch_size: Optional[int] = field(default=None, metadata={\"help\": \"sub batch size for training\"})\n kd_loss_type: str = field(default='kl_div', metadata={\"help\": \"the loss type for knowledge distillation. Available options: kl_div, m3_kd_loss. Default: kl_div.\", \"choices\": ['kl_div', 'm3_kd_loss']})\n" }, { "path": "FlagEmbedding/abc/finetune/embedder/AbsDataset.py", "content": "import os\nimport math\nimport random\nimport logging\nimport datasets\nimport numpy as np\nimport torch.distributed as dist\nfrom dataclasses import dataclass\nfrom torch.utils.data import Dataset\nfrom transformers import (\n PreTrainedTokenizer, \n DataCollatorWithPadding,\n TrainerCallback,\n TrainerState,\n TrainerControl\n)\n\nfrom .AbsArguments import AbsEmbedderDataArguments, AbsEmbedderTrainingArguments\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEmbedderTrainDataset(Dataset):\n \"\"\"Abstract class for training dataset.\n\n Args:\n args (AbsEmbedderDataArguments): Data arguments.\n tokenizer (PreTrainedTokenizer): Tokenizer to use.\n \"\"\"\n def __init__(\n self,\n args: AbsEmbedderDataArguments,\n tokenizer: PreTrainedTokenizer\n ):\n self.args = args\n self.tokenizer = tokenizer\n self.shuffle_ratio = args.shuffle_ratio\n\n train_datasets = []\n for data_dir in args.train_data:\n if not os.path.isdir(data_dir):\n if not (data_dir.endswith('.json') or data_dir.endswith('.jsonl')): continue\n temp_dataset = self._load_dataset(data_dir)\n if len(temp_dataset) == 0: continue\n train_datasets.append(temp_dataset)\n else:\n for file in os.listdir(data_dir):\n if not (file.endswith('.json') or file.endswith('.jsonl')): continue\n temp_dataset = self._load_dataset(os.path.join(data_dir, file))\n if len(temp_dataset) == 0: continue\n train_datasets.append(temp_dataset)\n self.dataset = datasets.concatenate_datasets(train_datasets)\n\n def _load_dataset(self, file_path: str):\n \"\"\"Load dataset from path.\n\n Args:\n file_path (str): Path to load the datasets from.\n\n Raises:\n ValueError: `pos_scores` and `neg_scores` not found in the features of training data\n\n Returns:\n datasets.Dataset: Loaded HF dataset.\n \"\"\"\n safe_rank = dist.get_rank() if dist.is_initialized() else 0\n if safe_rank == 0:\n logger.info(f'loading data from {file_path} ...')\n\n temp_dataset = datasets.load_dataset('json', data_files=file_path, split='train', cache_dir=self.args.cache_path)\n if len(temp_dataset) > self.args.max_example_num_per_dataset:\n temp_dataset = temp_dataset.select(random.sample(list(range(len(temp_dataset))), self.args.max_example_num_per_dataset))\n if not self.args.knowledge_distillation:\n if 'pos_scores' in temp_dataset.column_names:\n temp_dataset = temp_dataset.remove_columns(['pos_scores'])\n if 'neg_scores' in temp_dataset.column_names:\n temp_dataset = temp_dataset.remove_columns(['neg_scores'])\n else:\n if 'pos_scores' not in temp_dataset.column_names or 'neg_scores' not in temp_dataset.column_names:\n raise ValueError(f\"`pos_scores` and `neg_scores` not found in the features of training data in {file_path}, which is necessary when using knowledge distillation.\")\n return temp_dataset\n\n def _shuffle_text(self, text):\n \"\"\"shuffle the input text.\n\n Args:\n text (str): Input text.\n\n Returns:\n str: Shuffled text.\n \"\"\"\n if self.shuffle_ratio > 0 and len(text) > 100 and random.random() < self.shuffle_ratio:\n split_text = []\n chunk_size = len(text)//3 + 1\n for i in range(0, len(text), chunk_size):\n split_text.append(text[i:i+chunk_size])\n random.shuffle(split_text)\n return \" \".join(split_text)\n else:\n return text\n\n def __len__(self):\n return len(self.dataset)\n\n def __getitem__(self, item):\n data = self.dataset[item]\n train_group_size = self.args.train_group_size\n\n query = data['query']\n if self.args.query_instruction_for_retrieval is not None:\n query = self.args.query_instruction_format.format(\n data['prompt'] if 'prompt' in data else self.args.query_instruction_for_retrieval,\n query\n )\n\n passages = []\n teacher_scores = []\n\n assert isinstance(data['pos'], list) and isinstance(data['neg'], list)\n\n pos_idx = random.choice(list(range(len(data['pos']))))\n passages.append(self._shuffle_text(data['pos'][pos_idx]))\n\n neg_all_idx = list(range(len(data['neg'])))\n if len(data['neg']) < train_group_size - 1:\n num = math.ceil((train_group_size - 1) / len(data['neg']))\n neg_idxs = random.sample(neg_all_idx * num, train_group_size - 1)\n else:\n neg_idxs = random.sample(neg_all_idx, self.args.train_group_size - 1)\n for neg_idx in neg_idxs:\n passages.append(data['neg'][neg_idx])\n\n if self.args.knowledge_distillation:\n assert isinstance(data['pos_scores'], list) and isinstance(data['neg_scores'], list)\n teacher_scores.append(data['pos_scores'][pos_idx])\n for neg_idx in neg_idxs:\n teacher_scores.append(data['neg_scores'][neg_idx])\n if not all(isinstance(score, (int, float)) for score in teacher_scores):\n raise ValueError(f\"pos_score or neg_score must be digit\")\n else:\n teacher_scores = None\n\n if self.args.passage_instruction_for_retrieval is not None:\n passages = [\n self.args.passage_instruction_format.format(\n self.args.passage_instruction_for_retrieval, p\n )\n for p in passages\n ]\n\n return query, passages, teacher_scores\n\n@dataclass\nclass AbsEmbedderCollator(DataCollatorWithPadding):\n \"\"\"\n The abstract embedder collator.\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n sub_batch_size: int = -1\n\n def __call__(self, features):\n queries = [f[0] for f in features]\n passages = [f[1] for f in features]\n teacher_scores = [f[2] for f in features]\n if teacher_scores[0] is None:\n teacher_scores = None\n elif isinstance(teacher_scores[0], list):\n teacher_scores = sum(teacher_scores, [])\n\n if isinstance(queries[0], list):\n queries = sum(queries, [])\n if isinstance(passages[0], list):\n passages = sum(passages, [])\n\n queries_inputs = self.tokenizer(\n queries,\n truncation=True,\n max_length=self.query_max_len,\n return_tensors=None\n )\n passages_inputs = self.tokenizer(\n passages,\n truncation=True,\n max_length=self.passage_max_len,\n return_tensors=None\n )\n\n if self.sub_batch_size is None or self.sub_batch_size <= 0:\n q_collated = self.tokenizer.pad(\n queries_inputs,\n padding=self.padding,\n max_length=self.query_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors\n )\n d_collated = self.tokenizer.pad(\n passages_inputs,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors\n )\n else:\n batch_size = self.sub_batch_size\n\n q_collated = []\n for i in range(0, len(queries_inputs['attention_mask']), batch_size):\n start = i\n end = min(len(queries_inputs['attention_mask']), i + batch_size)\n sub_features = {}\n for k, v in queries_inputs.items():\n sub_features[k] = v[start:end]\n q_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.query_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors\n ))\n\n d_collated = []\n for i in range(0, len(passages_inputs['attention_mask']), batch_size):\n start = i\n end = min(len(passages_inputs['attention_mask']), i + batch_size)\n sub_features = {}\n\n for k, v in passages_inputs.items():\n sub_features[k] = v[start:end]\n d_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors\n ))\n return {\n \"queries\": q_collated,\n \"passages\": d_collated,\n \"teacher_scores\": teacher_scores,\n \"no_in_batch_neg_flag\": False\n }\n\n\nclass AbsEmbedderSameDatasetTrainDataset(AbsEmbedderTrainDataset):\n \"\"\"Abstract class for training dataset that samples batches from same dataset.\n\n Args:\n args (AbsEmbedderDataArguments): Data arguments.\n default_batch_size (int): The default batch size for training.\n seed (int): Random seed.\n tokenizer (PreTrainedTokenizer): Tokenizer to use.\n process_index (int, optional): Current process index. Defaults to 0.\n num_processes (int, optional): Total number of processes. Defaults to 1.\n \"\"\"\n def __init__(\n self,\n args: AbsEmbedderDataArguments,\n default_batch_size: int,\n seed: int,\n tokenizer: PreTrainedTokenizer,\n process_index: int=0,\n num_processes: int=1\n ):\n self.args = args\n self.shuffle_ratio = args.shuffle_ratio\n self.defaut_batch_size = default_batch_size\n self.deterministic_generator = np.random.default_rng(seed)\n self.tokenizer = tokenizer\n self.process_index = process_index\n self.num_processes = num_processes\n\n self.step = 0\n\n train_datasets = []\n each_data_idxs = []\n batch_size_idxs = []\n no_in_batch_neg_flags = []\n cur_all_num = 0\n\n small_threshold = args.small_threshold\n drop_threshold = args.drop_threshold\n\n for data_dir in args.train_data:\n if not os.path.isdir(data_dir):\n # Add `no_in_batch_neg` **suffix** to `data_dir` to indicate that this dataset does not use in-batch negatives\n no_in_batch_neg_flag = data_dir.split('.')[-2].endswith('no_in_batch_neg')\n if not (data_dir.endswith('.json') or data_dir.endswith('.jsonl')): continue\n temp_dataset = self._load_dataset(data_dir)\n\n if len(temp_dataset) == 0 or len(temp_dataset) < small_threshold: continue\n else:\n train_datasets.append(temp_dataset)\n each_data_idxs.append(np.arange(len(temp_dataset)) + cur_all_num)\n cur_all_num += len(temp_dataset)\n batch_size_idxs.append(self._get_file_batch_size(temp_dataset, default_batch_size))\n no_in_batch_neg_flags.append(no_in_batch_neg_flag)\n\n else:\n small_datasets = []\n small_batch_size = math.inf\n\n # Add `no_in_batch_neg` **suffix** to `data_dir` to indicate that this dataset does not use in-batch negatives\n no_in_batch_neg_flag = data_dir.endswith('no_in_batch_neg')\n for file in os.listdir(data_dir):\n if not (file.endswith('.json') or file.endswith('.jsonl')): continue\n temp_dataset = self._load_dataset(os.path.join(data_dir, file))\n\n if len(temp_dataset) == 0: continue\n elif len(temp_dataset) < small_threshold:\n small_datasets.append(temp_dataset)\n small_batch_size = min(small_batch_size, self._get_file_batch_size(temp_dataset, default_batch_size))\n else:\n train_datasets.append(temp_dataset)\n each_data_idxs.append(np.arange(len(temp_dataset)) + cur_all_num)\n cur_all_num += len(temp_dataset)\n batch_size_idxs.append(self._get_file_batch_size(temp_dataset, default_batch_size))\n no_in_batch_neg_flags.append(no_in_batch_neg_flag)\n\n if len(small_datasets) > 0:\n small_dataset = datasets.concatenate_datasets(small_datasets)\n if len(small_dataset) >= drop_threshold:\n train_datasets.append(small_dataset)\n each_data_idxs.append(np.arange(len(small_dataset)) + cur_all_num)\n cur_all_num += len(small_dataset)\n batch_size_idxs.append(small_batch_size)\n no_in_batch_neg_flags.append(no_in_batch_neg_flag)\n\n self.dataset = datasets.concatenate_datasets(train_datasets)\n self.each_data_idxs = each_data_idxs\n self.datasets_inxs = np.arange(len(each_data_idxs))\n self.batch_size_idxs = batch_size_idxs\n self.no_in_batch_neg_flags = no_in_batch_neg_flags\n\n self.refresh_epoch()\n\n def _load_dataset(self, file_path: str):\n \"\"\"Load datset from given path.\n\n Args:\n file_path (str): The path to load or download from HF hub.\n\n Returns:\n datasets.Dataset: The loaded dataset.\n \"\"\"\n safe_rank = dist.get_rank() if dist.is_initialized() else 0\n if safe_rank == 0:\n logger.info(f'loading data from {file_path} ...')\n\n temp_dataset = datasets.load_dataset('json', data_files=file_path, split='train', cache_dir=self.args.cache_path)\n if len(temp_dataset) > self.args.max_example_num_per_dataset:\n temp_dataset = temp_dataset.select(random.sample(list(range(len(temp_dataset))), self.args.max_example_num_per_dataset))\n if not self.args.knowledge_distillation:\n if 'pos_scores' in temp_dataset.column_names:\n temp_dataset = temp_dataset.remove_columns(['pos_scores'])\n if 'neg_scores' in temp_dataset.column_names:\n temp_dataset = temp_dataset.remove_columns(['neg_scores'])\n return temp_dataset\n\n @staticmethod\n def _get_file_batch_size(temp_dataset: datasets.Dataset, default_batch_size: int):\n \"\"\"Get the appropriate batch size for the dataset.\n\n Args:\n temp_dataset (datasets.Dataset): Loaded :data:`datasets.Dataset` object.\n default_batch_size (int): The default batch size to use if not specified in the dataset.\n\n Returns:\n int: The final batch size to use.\n \"\"\"\n if 'batch_size' in temp_dataset.column_names:\n return temp_dataset['batch_size'][0]\n if 'type' in temp_dataset.column_names:\n data_type = temp_dataset['type'][0]\n if 'symmetric' in data_type:\n return default_batch_size // 2 # make the symmetric data have smaller batch size\n return default_batch_size\n\n def refresh_epoch(self):\n \"\"\"\n Refresh data for epoch.\n \"\"\"\n logger.info(f'-- Rank {self.process_index}: refresh data --')\n self.deterministic_generator.shuffle(self.datasets_inxs)\n\n batch_datas = []\n for dataset_inx in self.datasets_inxs:\n self.deterministic_generator.shuffle(self.each_data_idxs[dataset_inx])\n cur_batch_size = self.batch_size_idxs[dataset_inx]*self.num_processes\n no_in_batch_neg_flag = self.no_in_batch_neg_flags[dataset_inx]\n for start_index in range(0, len(self.each_data_idxs[dataset_inx]), cur_batch_size):\n # judge the last batch's length\n if len(self.each_data_idxs[dataset_inx]) - start_index < cur_batch_size:\n break\n batch_datas.append((\n self.each_data_idxs[dataset_inx][start_index:start_index+cur_batch_size],\n no_in_batch_neg_flag\n ))\n self.deterministic_generator.shuffle(batch_datas)\n self.batch_datas = batch_datas\n self.step = 0\n\n def __len__(self):\n return len(self.batch_datas) * self.num_processes\n\n def __getitem__(self, _):\n batch_indices, no_in_batch_neg_flag = self.batch_datas[self.step] # extend here\n cur_batch_size = int(len(batch_indices) / self.num_processes)\n batch_indices = batch_indices[self.process_index * cur_batch_size: (self.process_index + 1) * cur_batch_size]\n batch_data = self.dataset[batch_indices]\n self.step += 1\n queries, passages, teacher_scores = self._create_batch_data(batch_raw_data=batch_data)\n return queries, passages, teacher_scores, no_in_batch_neg_flag\n\n def _get_train_group_size(self, batch_raw_data):\n \"\"\"Get the training group size and data type.\n\n Args:\n batch_raw_data (datasets.Dataset): One batch of raw data.\n\n Returns:\n int: The training group size.\n str: The type of data for the task.\n \"\"\"\n if 'type' in batch_raw_data:\n data_type = batch_raw_data['type'][0]\n if data_type in ['only_1neg']:\n return 2, data_type\n elif data_type in ['symmetric_class']:\n return min(len(batch_raw_data['neg'][0]) + 1, self.args.train_group_size), data_type\n else:\n return self.args.train_group_size, data_type\n elif 'train_group_size' in batch_raw_data:\n train_group_size = batch_raw_data['train_group_size'][0]\n if isinstance(train_group_size, int) and train_group_size > 0:\n return train_group_size, None\n else:\n return self.args.train_group_size, None\n return self.args.train_group_size, None\n\n def _create_batch_data(self, batch_raw_data):\n \"\"\"Create a comple batch of data with queries, documents and teacher scores.\n\n Args:\n batch_raw_data (datasets.Dataset): One batch of raw data.\n\n Returns:\n List[str]: Queries with instruction format.\n List[str]: Documents with instruction format.\n List[float]: Teacher scores for model distillation.\n \"\"\"\n queries, passages, teacher_scores = [], [], []\n\n train_group_size, data_type = self._get_train_group_size(batch_raw_data)\n\n for i in range(len(batch_raw_data['query'])):\n if data_type is not None:\n assert batch_raw_data['type'][i] == data_type, f\"Data type is not consistent in the same batch\"\n\n queries.append(\n self.args.query_instruction_format.format(\n batch_raw_data['prompt'][i] if 'prompt' in batch_raw_data else self.args.query_instruction_for_retrieval,\n batch_raw_data['query'][i]\n )\n )\n tmp_passages = []\n pos_idx = random.choice(list(range(len(batch_raw_data['pos'][i]))))\n pos = self._shuffle_text(batch_raw_data['pos'][i][pos_idx])\n tmp_passages.append(pos)\n\n neg_all_idx = list(range(len(batch_raw_data['neg'][i])))\n if len(batch_raw_data['neg'][i]) < train_group_size - 1:\n num = math.ceil((train_group_size - 1) / len(batch_raw_data['neg'][i]))\n neg_idxs = random.sample(neg_all_idx * num, train_group_size - 1)\n else:\n neg_idxs = random.sample(neg_all_idx, train_group_size - 1)\n for neg_idx in neg_idxs:\n tmp_passages.append(batch_raw_data['neg'][i][neg_idx])\n\n if self.args.knowledge_distillation:\n if 'pos_scores' in batch_raw_data and batch_raw_data['pos_scores'][i] is not None:\n teacher_scores.append(batch_raw_data['pos_scores'][i][pos_idx])\n for neg_idx in neg_idxs:\n if 'neg_scores' in batch_raw_data and batch_raw_data['neg_scores'][i] is not None:\n teacher_scores.append(batch_raw_data['neg_scores'][i][neg_idx])\n else:\n teacher_scores = None\n\n if data_type is not None and data_type in ['symmetric_sts', 'symmetric_clustering']:\n tmp_passages = [\n self.args.query_instruction_format.format(\n batch_raw_data['prompt'][i] if 'prompt' in batch_raw_data else self.args.query_instruction_for_retrieval,\n p\n ) for p in tmp_passages\n ]\n else:\n if self.args.passage_instruction_for_retrieval is not None:\n tmp_passages = [\n self.args.passage_instruction_format.format(\n self.args.passage_instruction_for_retrieval, p\n ) for p in tmp_passages\n ]\n\n passages.extend(tmp_passages)\n\n if teacher_scores is not None:\n if len(teacher_scores) > 0 and len(passages) > 0:\n assert len(teacher_scores) == len(passages)\n\n return queries, passages, teacher_scores\n\n\n@dataclass\nclass AbsEmbedderSameDatasetCollator(DataCollatorWithPadding):\n \"\"\"\n EmbedCollator for SameDataset.\n Note that after using this collator, the training_args should be set as:\n \n ``training_args.per_device_train_batch_size = 1``\n \n ``training_args.dataloader_num_workers = 0 # avoid multi-processing``\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n sub_batch_size: int = -1\n\n def __call__(self, features):\n queries = features[0][0]\n passages = features[0][1]\n teacher_scores = features[0][2]\n no_in_batch_neg_flag = features[0][3]\n\n queries_inputs = self.tokenizer(\n queries,\n truncation=True,\n max_length=self.query_max_len,\n return_tensors=None\n )\n passages_inputs = self.tokenizer(\n passages,\n truncation=True,\n max_length=self.passage_max_len,\n return_tensors=None\n )\n\n if self.sub_batch_size is None or self.sub_batch_size <= 0:\n q_collated = self.tokenizer.pad(\n queries_inputs,\n padding=self.padding,\n max_length=self.query_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n )\n\n d_collated = self.tokenizer.pad(\n passages_inputs,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n )\n else:\n batch_size = self.sub_batch_size\n\n q_collated = []\n for i in range(0, len(queries_inputs['attention_mask']), batch_size):\n start = i\n end = min(len(queries_inputs['attention_mask']), i + batch_size)\n sub_features = {}\n for k, v in queries_inputs.items():\n sub_features[k] = v[start:end]\n q_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.query_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n ))\n\n d_collated = []\n for i in range(0, len(passages_inputs['attention_mask']), batch_size):\n start = i\n end = min(len(passages_inputs['attention_mask']), i + batch_size)\n sub_features = {}\n\n for k, v in passages_inputs.items():\n sub_features[k] = v[start:end]\n d_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n ))\n\n if isinstance(teacher_scores, list) and len(teacher_scores) == 0:\n teacher_scores = None\n\n return {\n \"queries\": q_collated,\n \"passages\": d_collated,\n \"teacher_scores\": teacher_scores,\n \"no_in_batch_neg_flag\": no_in_batch_neg_flag\n }\n\n\nclass EmbedderTrainerCallbackForDataRefresh(TrainerCallback):\n \"\"\"\n Callback class to inspect the state of the training loop and take decision.\n \"\"\"\n def __init__(self, train_dataset: AbsEmbedderSameDatasetTrainDataset):\n self.train_dataset = train_dataset\n\n def on_epoch_end(\n self,\n args: AbsEmbedderTrainingArguments,\n state: TrainerState,\n control: TrainerControl,\n **kwargs\n ):\n \"\"\"\n Event called at the end of an epoch.\n \"\"\"\n self.train_dataset.refresh_epoch()\n" }, { "path": "FlagEmbedding/abc/finetune/embedder/AbsModeling.py", "content": "import torch\nfrom torch import nn, Tensor\nimport torch.nn.functional as F\nimport torch.distributed as dist\nfrom transformers import PreTrainedTokenizer\nfrom transformers.file_utils import ModelOutput\n\nimport logging\nfrom dataclasses import dataclass\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, Optional, List, Union\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass EmbedderOutput(ModelOutput):\n \"\"\"\n Output information returned by the model.\n \"\"\"\n q_reps: Optional[Tensor] = None\n p_reps: Optional[Tensor] = None\n loss: Optional[Tensor] = None\n scores: Optional[Tensor] = None\n\n\nclass AbsEmbedderModel(ABC, nn.Module):\n \"\"\"Abstract class of embedding model for training.\n\n Args:\n base_model: The base model to train on.\n tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.\n negatives_cross_device (bool, optional): If True, will compute cross devices negative loss. Defaults to ``False``.\n temperature (float, optional): Temperature to control the scale of scores. Defaults to ``1.0``.\n sub_batch_size (int, optional): Sub-batch size during encoding. If negative, will not split to sub-batch.\n Defaults to ``-1``.\n kd_loss_type (str, optional): Type of knowledge distillation loss. Defaults to ``\"kl_div\"``.\n \"\"\"\n def __init__(\n self,\n base_model,\n tokenizer: PreTrainedTokenizer = None,\n negatives_cross_device: bool = False,\n temperature: float = 1.0,\n sub_batch_size: int = -1,\n kd_loss_type: str = 'kl_div',\n ):\n nn.Module.__init__(self)\n self.model = base_model\n self.tokenizer = tokenizer\n\n self.temperature = temperature\n self.negatives_cross_device = negatives_cross_device\n if self.negatives_cross_device:\n if not dist.is_initialized():\n raise ValueError('Distributed training has not been initialized for representation all gather.')\n self.process_rank = dist.get_rank() if dist.is_initialized() else 0\n self.world_size = dist.get_world_size() if dist.is_initialized() else 1\n\n self.sub_batch_size = sub_batch_size\n self.kd_loss_type = kd_loss_type\n\n @abstractmethod\n def encode(self, features):\n \"\"\"Abstract method encode and get the embedding.\n\n Args:\n features (Union[list, dict]): Features feed to the model.\n \"\"\"\n pass\n\n @abstractmethod\n def compute_loss(self, scores, target):\n \"\"\"Abstract method compute the loss.\n\n Args:\n scores (torch.Tensor): Computed score.\n target (torch.Tensor): The target value.\n \"\"\"\n pass\n\n @abstractmethod\n def compute_score(self, q_reps, p_reps):\n \"\"\"Abstract method to compute the score.\n\n Args:\n q_reps (torch.Tensor): Queries representations.\n p_reps (torch.Tensor): Passages rerpresentations.\n \"\"\"\n pass\n\n @abstractmethod\n def save(self, output_dir: str):\n \"\"\"Abstract method to save the model.\n\n Args:\n output_dir (str): Directory for saving the model.\n \"\"\"\n pass\n\n def get_local_score(self, q_reps, p_reps, all_scores):\n \"\"\"Get the local score of queries and passages.\n\n Args:\n q_reps (torch.Tensor): Queries representations.\n p_reps (torch.Tensor): Passages rerpresentations.\n all_scores (torch.Tensor): All the query-passage scores computed.\n\n Returns:\n torch.Tensor: Local scores to compute loss.\n \"\"\"\n group_size = p_reps.size(0) // q_reps.size(0)\n indices = torch.arange(0, q_reps.size(0), device=q_reps.device) * group_size\n specific_scores = []\n for i in range(group_size):\n specific_scores.append(\n all_scores[torch.arange(q_reps.size(0), device=q_reps.device), indices + i]\n )\n return torch.stack(specific_scores, dim=1).view(q_reps.size(0), -1)\n\n def compute_local_score(self, q_reps, p_reps, compute_score_func=None, **kwargs):\n \"\"\"Compute the local score of queries and passages.\n\n Args:\n q_reps (torch.Tensor): Queries representations.\n p_reps (torch.Tensor): Passages rerpresentations.\n compute_score_func (function, optional): Function to compute score. Defaults to ``None``, which will use the\n :meth:`self.compute_score`.\n\n Returns:\n torch.Tensor: Local scores to compute loss.\n \"\"\"\n if compute_score_func is None:\n all_scores = self.compute_score(q_reps, p_reps)\n else:\n all_scores = compute_score_func(q_reps, p_reps, **kwargs)\n loacl_scores = self.get_local_score(q_reps, p_reps, all_scores)\n return loacl_scores\n\n def _compute_no_in_batch_neg_loss(self, q_reps, p_reps, teacher_targets=None, compute_score_func=None, **kwargs):\n \"\"\"\n Compute loss when using no in-batch negatives and no cross-device negatives\n \"\"\"\n group_size = p_reps.size(0) // q_reps.size(0)\n\n local_scores = self.compute_local_score(q_reps, p_reps, compute_score_func, **kwargs) # (batch_size, group_size)\n\n if teacher_targets is not None:\n # compute kd loss\n loss = self.distill_loss(self.kd_loss_type, teacher_targets, local_scores, group_size=group_size)\n\n # add normal loss if needed\n if self.kd_loss_type == \"kl_div\":\n local_targets = torch.zeros(local_scores.size(0), device=local_scores.device, dtype=torch.long) # (batch_size)\n loss += self.compute_loss(local_scores, local_targets)\n else:\n local_targets = torch.zeros(local_scores.size(0), device=local_scores.device, dtype=torch.long) # (batch_size)\n loss = self.compute_loss(local_scores, local_targets)\n\n return local_scores, loss\n\n def _compute_in_batch_neg_loss(self, q_reps, p_reps, teacher_targets=None, compute_score_func=None, **kwargs):\n \"\"\"\n Compute loss when only using in-batch negatives\n \"\"\"\n group_size = p_reps.size(0) // q_reps.size(0)\n\n if compute_score_func is None:\n scores = self.compute_score(q_reps, p_reps) # (batch_size, batch_size * group_size)\n else:\n scores = compute_score_func(q_reps, p_reps, **kwargs) # (batch_size, batch_size * group_size)\n\n if teacher_targets is not None:\n # compute kd loss\n if self.kd_loss_type == \"kl_div\":\n student_scores = self.get_local_score(q_reps, p_reps, scores) # (batch_size, group_size)\n\n loss = self.distill_loss(self.kd_loss_type, teacher_targets, student_scores, group_size)\n\n idxs = torch.arange(q_reps.size(0), device=q_reps.device, dtype=torch.long)\n targets = idxs * (p_reps.size(0) // q_reps.size(0)) # (batch_size)\n loss += self.compute_loss(scores, targets)\n elif self.kd_loss_type == \"m3_kd_loss\":\n loss = self.distill_loss(self.kd_loss_type, teacher_targets, scores, group_size)\n else:\n raise ValueError(f\"Invalid kd_loss_type: {self.kd_loss_type}\")\n else:\n idxs = torch.arange(q_reps.size(0), device=q_reps.device, dtype=torch.long)\n targets = idxs * group_size # (batch_size)\n loss = self.compute_loss(scores, targets)\n\n return scores, loss\n\n def _compute_cross_device_neg_loss(self, q_reps, p_reps, teacher_targets=None, compute_score_func=None, **kwargs):\n \"\"\"\n Compute loss when using both in-batch negatives and cross-device negatives\n \"\"\"\n group_size = p_reps.size(0) // q_reps.size(0)\n\n cross_q_reps = self._dist_gather_tensor(q_reps) # (world_size * batch_size, dim)\n cross_p_reps = self._dist_gather_tensor(p_reps) # (world_size * batch_size * group_size, dim)\n\n if compute_score_func is None:\n cross_scores = self.compute_score(cross_q_reps, cross_p_reps) # (world_size * batch_size, world_size * batch_size * group_size)\n else:\n cross_scores = compute_score_func(cross_q_reps, cross_p_reps, **kwargs) # (world_size * batch_size, world_size * batch_size * group_size)\n\n if teacher_targets is not None:\n # compute kd loss\n if self.kd_loss_type == \"kl_div\":\n student_scores = self.get_local_score(cross_q_reps, cross_p_reps, cross_scores) # (world_size * batch_size, group_size)\n student_scores = student_scores[\n q_reps.size(0)*self.process_rank : q_reps.size(0)*(self.process_rank+1)\n ] # (batch_size, group_size)\n\n loss = self.distill_loss(self.kd_loss_type, teacher_targets, student_scores, group_size)\n\n cross_idxs = torch.arange(cross_q_reps.size(0), device=cross_q_reps.device, dtype=torch.long)\n cross_targets = cross_idxs * group_size # (world_size * batch_size)\n loss += self.compute_loss(cross_scores, cross_targets)\n elif self.kd_loss_type == \"m3_kd_loss\":\n cross_teacher_targets = self._dist_gather_tensor(teacher_targets) # (world_size * batch_size, group_size)\n\n loss = self.distill_loss(self.kd_loss_type, cross_teacher_targets, cross_scores, group_size)\n else:\n raise ValueError(f\"Invalid kd_loss_type: {self.kd_loss_type}\")\n else:\n cross_idxs = torch.arange(cross_q_reps.size(0), device=cross_q_reps.device, dtype=torch.long)\n cross_targets = cross_idxs * group_size # (world_size * batch_size)\n loss = self.compute_loss(cross_scores, cross_targets)\n\n return cross_scores, loss\n\n def forward(\n self, \n queries: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None, \n passages: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None,\n teacher_scores: Union[None, List[float]] = None,\n no_in_batch_neg_flag: bool = False,\n ):\n \"\"\"The computation performed at every call.\n\n Args:\n queries (Union[Dict[str, Tensor], List[Dict[str, Tensor]]], optional): Input queries. Defaults to ``None``.\n passages (Union[Dict[str, Tensor], List[Dict[str, Tensor]]], optional): Input passages. Defaults to ``None``.\n teacher_scores (Union[None, List[float]], optional): Teacher scores for distillation. Defaults to ``None``.\n no_in_batch_neg_flag (bool, optional): If True, use no in-batch negatives and no cross-device negatives. Defaults to ``False``.\n\n Returns:\n EmbedderOutput: Output of the forward call of model.\n \"\"\"\n q_reps = self.encode(queries) # (batch_size, dim)\n p_reps = self.encode(passages) # (batch_size * group_size, dim)\n\n if self.training:\n if teacher_scores is not None:\n teacher_scores = torch.tensor(teacher_scores, device=q_reps.device)\n teacher_scores = teacher_scores.view(q_reps.size(0), -1).detach() # (batch_size, group_size)\n teacher_targets = F.softmax(teacher_scores, dim=-1) # (batch_size, group_size)\n else:\n teacher_targets = None\n\n if no_in_batch_neg_flag:\n compute_loss_func = self._compute_no_in_batch_neg_loss\n else:\n if self.negatives_cross_device:\n compute_loss_func = self._compute_cross_device_neg_loss\n else:\n compute_loss_func = self._compute_in_batch_neg_loss\n\n scores, loss = compute_loss_func(q_reps, p_reps, teacher_targets=teacher_targets)\n else:\n loss = None\n\n return EmbedderOutput(\n loss=loss,\n )\n\n @staticmethod\n def distill_loss(kd_loss_type, teacher_targets, student_scores, group_size=None):\n \"\"\"Compute the distillation loss.\n\n Args:\n kd_loss_type (str): Type of knowledge distillation loss, supports \"kl_div\" and \"m3_kd_loss\".\n teacher_targets (torch.Tensor): Targets from the teacher model.\n student_scores (torch.Tensor): Score of student model.\n group_size (int, optional): Number of groups for . Defaults to ``None``.\n\n Raises:\n ValueError: Invalid kd_loss_type\n\n Returns:\n torch.Tensor: A scalar of computed distillation loss.\n \"\"\"\n if kd_loss_type == 'kl_div':\n # teacher_targets: (batch_size, group_size) / (world_size * batch_size, group_size)\n # student_scores: (batch_size, group_size) / (world_size * batch_size, group_size)\n return - torch.mean(\n torch.sum(torch.log_softmax(student_scores, dim=-1) * teacher_targets, dim=-1)\n )\n elif kd_loss_type == 'm3_kd_loss':\n # teacher_targets: (batch_size, group_size) / (world_size * batch_size, group_size)\n # student_scores: (batch_size, batch_size * group_size) / (world_size * batch_size, world_size * batch_size * group_size)\n labels = torch.arange(student_scores.size(0), device=student_scores.device, dtype=torch.long)\n labels = labels * group_size\n\n loss = 0\n mask = torch.zeros_like(student_scores)\n for i in range(group_size):\n temp_target = labels + i\n temp_scores = student_scores + mask\n temp_loss = F.cross_entropy(temp_scores, temp_target, reduction=\"none\") # B\n loss += torch.mean(teacher_targets[:, i] * temp_loss)\n mask = torch.scatter(mask, dim=-1, index=temp_target.unsqueeze(-1),\n value=torch.finfo(student_scores.dtype).min)\n return loss\n else:\n raise ValueError(f\"Invalid kd_loss_type: {kd_loss_type}\")\n\n def _dist_gather_tensor(self, t: Optional[torch.Tensor]):\n \"\"\"Gather a tensor from all processes in a distributed setting.\n\n Args:\n t (Optional[torch.Tensor]): The input tensor to be gathered. If `None`, no gathering is performed.\n\n Returns:\n Union[torch.Tensor, None]: A concatenated tensor from all processes if ``t`` is not ``None``, \n otherwise returns ``None``.\n \"\"\"\n if t is None:\n return None\n t = t.contiguous()\n\n all_tensors = [torch.empty_like(t) for _ in range(self.world_size)]\n dist.all_gather(all_tensors, t)\n\n all_tensors[self.process_rank] = t\n all_tensors = torch.cat(all_tensors, dim=0)\n\n return all_tensors\n" }, { "path": "FlagEmbedding/abc/finetune/embedder/AbsRunner.py", "content": "import os\nimport logging\nfrom pathlib import Path\nfrom typing import Tuple\nfrom abc import ABC, abstractmethod\nfrom transformers import set_seed, PreTrainedTokenizer\n\n\nfrom .AbsArguments import (\n AbsEmbedderModelArguments,\n AbsEmbedderDataArguments,\n AbsEmbedderTrainingArguments\n)\nfrom .AbsTrainer import AbsEmbedderTrainer\nfrom .AbsModeling import AbsEmbedderModel\nfrom .AbsDataset import (\n AbsEmbedderTrainDataset, AbsEmbedderCollator,\n AbsEmbedderSameDatasetTrainDataset, AbsEmbedderSameDatasetCollator\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEmbedderRunner(ABC):\n \"\"\"Abstract class to run embedding model fine-tuning.\n\n Args:\n model_args (AbsEmbedderModelArguments): Model arguments\n data_args (AbsEmbedderDataArguments): Data arguments.\n training_args (AbsEmbedderTrainingArguments): Training arguments.\n \"\"\"\n def __init__(\n self,\n model_args: AbsEmbedderModelArguments,\n data_args: AbsEmbedderDataArguments,\n training_args: AbsEmbedderTrainingArguments\n ):\n self.model_args = model_args\n self.data_args = data_args\n self.training_args = training_args\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n logger.info(\"Training/evaluation parameters %s\", training_args)\n logger.info(\"Model parameters %s\", model_args)\n logger.info(\"Data parameters %s\", data_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n self.tokenizer, self.model = self.load_tokenizer_and_model()\n self.train_dataset = self.load_train_dataset()\n self.data_collator = self.load_data_collator()\n self.trainer = self.load_trainer()\n\n @abstractmethod\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:\n \"\"\"Abstract method to load the tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Loaded tokenizer and model instances.\n \"\"\"\n pass\n\n @abstractmethod\n def load_trainer(self) -> AbsEmbedderTrainer:\n \"\"\"Abstract method to load the trainer.\n\n Returns:\n AbsEmbedderTrainer: The loaded trainer instance.\n \"\"\"\n pass\n\n def load_train_dataset(self) -> AbsEmbedderTrainDataset:\n \"\"\"Loads the training dataset based on data arguments.\n\n Returns:\n AbsEmbedderTrainDataset: The loaded dataset instance.\n \"\"\"\n if self.data_args.same_dataset_within_batch:\n train_dataset = AbsEmbedderSameDatasetTrainDataset(\n args=self.data_args,\n default_batch_size=self.training_args.per_device_train_batch_size,\n seed=self.training_args.seed,\n tokenizer=self.tokenizer,\n process_index=self.training_args.process_index,\n num_processes=self.training_args.world_size\n )\n self.training_args.per_device_train_batch_size = 1\n self.training_args.dataloader_num_workers = 0 # avoid multi-processing\n else:\n train_dataset = AbsEmbedderTrainDataset(\n args=self.data_args,\n tokenizer=self.tokenizer\n )\n return train_dataset\n\n def load_data_collator(self) -> AbsEmbedderCollator:\n \"\"\"Loads the appropriate data collator.\n\n Returns:\n AbsEmbedderCollator: Loaded data collator.\n \"\"\"\n if self.data_args.same_dataset_within_batch:\n EmbedCollator = AbsEmbedderSameDatasetCollator\n else:\n EmbedCollator = AbsEmbedderCollator\n\n data_collator = EmbedCollator(\n tokenizer=self.tokenizer,\n query_max_len=self.data_args.query_max_len,\n passage_max_len=self.data_args.passage_max_len,\n sub_batch_size=self.training_args.sub_batch_size,\n pad_to_multiple_of=self.data_args.pad_to_multiple_of,\n padding=True,\n return_tensors=\"pt\"\n )\n return data_collator\n\n def run(self):\n \"\"\"\n Executes the training process.\n \"\"\"\n Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)\n self.trainer.save_model()\n" }, { "path": "FlagEmbedding/abc/finetune/embedder/AbsTrainer.py", "content": "import logging\nfrom typing import Optional\nfrom abc import ABC, abstractmethod\nfrom transformers.trainer import Trainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEmbedderTrainer(ABC, Trainer):\n \"\"\"\n Abstract class for the trainer of embedder.\n \"\"\"\n @abstractmethod\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n pass\n\n def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n \"\"\"\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n \n Args:\n model (AbsEmbedderModel): The model being trained.\n inputs (dict): A dictionary of input tensors to be passed to the model.\n return_outputs (bool, optional): If ``True``, returns both the loss and the model's outputs. Otherwise,\n returns only the loss.\n \n Returns:\n Union[torch.Tensor, tuple(torch.Tensor, EmbedderOutput)]: The computed loss. If ``return_outputs`` is ``True``, \n also returns the model's outputs in a tuple ``(loss, outputs)``.\n \"\"\"\n\n outputs = model(**inputs)\n loss = outputs.loss\n\n return (loss, outputs) if return_outputs else loss\n" }, { "path": "FlagEmbedding/abc/finetune/embedder/__init__.py", "content": "from .AbsArguments import (\n AbsEmbedderDataArguments,\n AbsEmbedderModelArguments,\n AbsEmbedderTrainingArguments,\n)\nfrom .AbsDataset import (\n AbsEmbedderCollator, AbsEmbedderSameDatasetCollator,\n AbsEmbedderSameDatasetTrainDataset,\n AbsEmbedderTrainDataset,\n EmbedderTrainerCallbackForDataRefresh,\n)\nfrom .AbsModeling import AbsEmbedderModel, EmbedderOutput\nfrom .AbsTrainer import AbsEmbedderTrainer\nfrom .AbsRunner import AbsEmbedderRunner\n\n\n__all__ = [\n \"AbsEmbedderModelArguments\",\n \"AbsEmbedderDataArguments\",\n \"AbsEmbedderTrainingArguments\",\n \"AbsEmbedderModel\",\n \"AbsEmbedderTrainer\",\n \"AbsEmbedderRunner\",\n \"AbsEmbedderTrainDataset\",\n \"AbsEmbedderCollator\",\n \"AbsEmbedderSameDatasetTrainDataset\",\n \"AbsEmbedderSameDatasetCollator\",\n \"EmbedderOutput\",\n \"EmbedderTrainerCallbackForDataRefresh\",\n]\n" }, { "path": "FlagEmbedding/abc/finetune/reranker/AbsArguments.py", "content": "import os\nfrom typing import Optional\nfrom dataclasses import dataclass, field\n\nfrom transformers import TrainingArguments\n\n\n@dataclass\nclass AbsRerankerModelArguments:\n \"\"\"\n Abstract class for reranker model arguments.\n \"\"\"\n\n model_name_or_path: str = field(\n metadata={\"help\": \"The model checkpoint for initialization.\"}\n )\n config_name: str = field(\n default=None,\n metadata={\"help\": \"Pretrained config name or path if not the same as model_name.\"}\n )\n tokenizer_name: str = field(\n default=None,\n metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name.\"}\n )\n cache_dir: str = field(\n default=None,\n metadata={\"help\": \"Where do you want to store the pre-trained models downloaded from s3.\"}\n )\n trust_remote_code: bool = field(\n default=False,\n metadata={\"help\": \"Trust remote code\"}\n )\n model_type: str = field(\n default='encoder',\n metadata={\"help\": \"Type of finetune, ['encoder', 'decoder']\"}\n )\n use_fast_tokenizer: bool = field(\n default=True,\n metadata={\"help\": \"Whether to use fast tokenizer or not.\"}\n )\n token: str = field(\n default_factory=lambda: os.getenv('HF_TOKEN', None),\n metadata={\"help\": \"The token to use when accessing the model.\"}\n )\n # finetune_type: str = field(\n # default='sratch',\n # metadata={\"help\": \"Type of finetune, ['sratch', 'finetune']\"}\n # )\n\n\n@dataclass\nclass AbsRerankerDataArguments:\n \"\"\"\n Abstract class for reranker data arguments.\n \"\"\"\n train_data: str = field(\n default=None, metadata={\n \"help\": \"One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data.\",\n \"nargs\": \"+\"\n }\n )\n cache_path: Optional[str] = field(\n default=None, metadata={\"help\": \"Where do you want to store the cached data\"}\n )\n train_group_size: int = field(default=8)\n\n query_max_len: int = field(\n default=32,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\"\n },\n )\n\n passage_max_len: int = field(\n default=128,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\"\n },\n )\n\n max_len: int = field(\n default=512,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated.\"\n },\n )\n\n pad_to_multiple_of: Optional[int] = field(\n default=None,\n metadata={\n \"help\": \"If set will pad the sequence to be a multiple of the provided value.\"\n },\n )\n\n max_example_num_per_dataset: int = field(\n default=100000000, metadata={\"help\": \"the max number of examples for each dataset\"}\n )\n\n query_instruction_for_rerank: str= field(\n default=None, metadata={\"help\": \"instruction for query\"}\n )\n query_instruction_format: str = field(\n default=\"{}{}\", metadata={\"help\": \"format for query instruction\"}\n )\n\n knowledge_distillation: bool = field(\n default=False,\n metadata={\"help\": \"Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data\"}\n )\n\n passage_instruction_for_rerank: Optional[str] = field(\n default=None, metadata={\"help\": \"instruction for passage\"}\n )\n passage_instruction_format: Optional[str] = field(\n default=\"{}{}\", metadata={\"help\": \"format for passage instruction\"}\n )\n\n shuffle_ratio: float = field(\n default=0.0, metadata={\"help\": \"The ratio of shuffling the text\"}\n )\n\n sep_token: str = field(\n default='\\n', metadata={\"help\": \"The sep token for LLM reranker to discriminate between query and passage\"}\n )\n\n def __post_init__(self):\n # replace \"\\\\n\" with \"\\n\"\n if \"\\\\n\" in self.query_instruction_format:\n self.query_instruction_format = self.query_instruction_format.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.passage_instruction_format:\n self.passage_instruction_format = self.passage_instruction_format.replace(\"\\\\n\", \"\\n\")\n \n # check the existence of train data\n for train_dir in self.train_data:\n if not os.path.exists(train_dir):\n raise FileNotFoundError(f\"cannot find file: {train_dir}, please set a true path\")\n\n\n@dataclass\nclass AbsRerankerTrainingArguments(TrainingArguments):\n sub_batch_size: Optional[int] = field(default=None, metadata={\"help\": \"sub batch size for training, not implemented yet\"})\n" }, { "path": "FlagEmbedding/abc/finetune/reranker/AbsDataset.py", "content": "import os\nimport math\nimport random\nimport logging\nimport datasets\nimport numpy as np\nimport torch.distributed as dist\nfrom dataclasses import dataclass\nfrom torch.utils.data import Dataset\nfrom transformers import (\n PreTrainedTokenizer, \n DataCollatorWithPadding,\n BatchEncoding,\n DataCollatorForSeq2Seq\n)\nfrom typing import List\n\nfrom .AbsArguments import AbsRerankerDataArguments\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsRerankerTrainDataset(Dataset):\n \"\"\"Abstract class for reranker training dataset.\n\n Args:\n args (AbsRerankerDataArguments): Data arguments.\n tokenizer (PreTrainedTokenizer): Tokenizer to use.\n \"\"\"\n def __init__(\n self,\n args: AbsRerankerDataArguments,\n tokenizer: PreTrainedTokenizer\n ):\n self.args = args\n self.tokenizer = tokenizer\n\n train_datasets = []\n for data_dir in args.train_data:\n if not os.path.isdir(data_dir):\n if not (data_dir.endswith('.json') or data_dir.endswith('.jsonl')): continue\n temp_dataset = self._load_dataset(data_dir)\n if len(temp_dataset) == 0: continue\n train_datasets.append(temp_dataset)\n else:\n for file in os.listdir(data_dir):\n if not (file.endswith('.json') or file.endswith('.jsonl')): continue\n temp_dataset = self._load_dataset(os.path.join(data_dir, file))\n if len(temp_dataset) == 0: continue\n train_datasets.append(temp_dataset)\n self.dataset = datasets.concatenate_datasets(train_datasets)\n\n self.max_length = self.args.query_max_len + self.args.passage_max_len\n\n def _load_dataset(self, file_path: str):\n \"\"\"Load dataset from path.\n\n Args:\n file_path (str): Path to load the datasets from.\n\n Raises:\n ValueError: `pos_scores` and `neg_scores` not found in the features of training data\n\n Returns:\n datasets.Dataset: Loaded HF dataset.\n \"\"\"\n safe_rank = dist.get_rank() if dist.is_initialized() else 0\n if safe_rank == 0:\n logger.info(f'loading data from {file_path} ...')\n\n temp_dataset = datasets.load_dataset('json', data_files=file_path, split='train', cache_dir=self.args.cache_path)\n if len(temp_dataset) > self.args.max_example_num_per_dataset:\n temp_dataset = temp_dataset.select(random.sample(list(range(len(temp_dataset))), self.args.max_example_num_per_dataset))\n if not self.args.knowledge_distillation:\n if 'pos_scores' in temp_dataset.column_names:\n temp_dataset = temp_dataset.remove_columns(['pos_scores'])\n if 'neg_scores' in temp_dataset.column_names:\n temp_dataset = temp_dataset.remove_columns(['neg_scores'])\n else:\n if 'pos_scores' not in temp_dataset.column_names or 'neg_scores' not in temp_dataset.column_names:\n raise ValueError(f\"`pos_scores` and `neg_scores` not found in the features of training data in {file_path}, which is necessary when using knowledge distillation.\")\n return temp_dataset\n\n def _shuffle_text(self, text):\n \"\"\"shuffle the input text.\n\n Args:\n text (str): Input text.\n\n Returns:\n str: Shuffled text.\n \"\"\"\n if self.args.shuffle_ratio > 0 and len(text) > 100 and random.random() < self.args.shuffle_ratio:\n split_text = []\n chunk_size = len(text)//3 + 1\n for i in range(0, len(text), chunk_size):\n split_text.append(text[i:i+chunk_size])\n random.shuffle(split_text)\n return \" \".join(split_text)\n else:\n return text\n\n def __len__(self):\n return len(self.dataset)\n\n def create_one_example(self, qry_encoding: str, doc_encoding: str):\n \"\"\"Creates a single input example by encoding and preparing a query and document pair for the model.\n\n Args:\n qry_encoding (str): Query to be encoded.\n doc_encoding (str): Document to be encoded.\n\n Returns:\n dict: A dictionary containing tokenized and prepared inputs, ready for model consumption.\n \"\"\"\n qry_inputs = self.tokenizer.encode(qry_encoding, truncation=True, max_length=self.args.query_max_len + self.args.passage_max_len // 4, add_special_tokens=False)\n doc_inputs = self.tokenizer.encode(doc_encoding, truncation=True, max_length=self.args.passage_max_len + self.args.query_max_len // 2, add_special_tokens=False)\n item = self.tokenizer.prepare_for_model(\n qry_inputs,\n doc_inputs,\n truncation='only_second',\n max_length=self.args.query_max_len + self.args.passage_max_len,\n padding=False,\n )\n return item\n\n def __getitem__(self, item):\n data = self.dataset[item]\n train_group_size = self.args.train_group_size\n\n query = data['query']\n if self.args.query_instruction_for_rerank is not None:\n query = self.args.query_instruction_format.format(\n data['query_prompt'] if 'query_prompt' in data else self.args.query_instruction_for_rerank,\n query\n )\n\n passages = []\n teacher_scores = []\n\n assert isinstance(data['pos'], list) and isinstance(data['neg'], list)\n\n pos_idx = random.choice(list(range(len(data['pos']))))\n passages.append(self._shuffle_text(data['pos'][pos_idx]))\n\n neg_all_idx = list(range(len(data['neg'])))\n if len(data['neg']) < train_group_size - 1:\n num = math.ceil((train_group_size - 1) / len(data['neg']))\n neg_idxs = random.sample(neg_all_idx * num, train_group_size - 1)\n else:\n neg_idxs = random.sample(neg_all_idx, self.args.train_group_size - 1)\n for neg_idx in neg_idxs:\n passages.append(data['neg'][neg_idx])\n\n if self.args.knowledge_distillation:\n assert isinstance(data['pos_scores'], list) and isinstance(data['neg_scores'], list)\n teacher_scores.append(data['pos_scores'][pos_idx])\n for neg_idx in neg_idxs:\n teacher_scores.append(data['neg_scores'][neg_idx])\n if not all(isinstance(score, (int, float)) for score in teacher_scores):\n raise ValueError(f\"pos_score or neg_score must be digit\")\n else:\n teacher_scores = None\n\n if self.args.passage_instruction_for_rerank is not None:\n passages = [\n self.args.passage_instruction_format.format(\n data['passage_prompt'] if 'passage_prompt' in data else self.args.passage_instruction_for_rerank, p\n )\n for p in passages\n ]\n\n batch_data = []\n for passage in passages:\n batch_data.append(self.create_one_example(query, passage))\n\n return batch_data, teacher_scores\n\n@dataclass\nclass AbsRerankerCollator(DataCollatorWithPadding):\n \"\"\"\n The abstract reranker collator.\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n\n def __call__(self, features) -> List[BatchEncoding]:\n teacher_scores = [f[1] for f in features]\n if teacher_scores[0] is None:\n teacher_scores = None\n elif isinstance(teacher_scores[0], list):\n teacher_scores = sum(teacher_scores, [])\n\n features = [f[0] for f in features]\n if isinstance(features[0], list):\n features = sum(features, [])\n\n collated = self.tokenizer.pad(\n features,\n padding=self.padding,\n max_length=self.query_max_len + self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n )\n\n return {\n \"pair\": collated,\n \"teacher_scores\": teacher_scores,\n }\n\nclass AbsLLMRerankerTrainDataset(AbsRerankerTrainDataset):\n \"\"\"Abstract class for LLM reranker training dataset.\n\n Args:\n args (AbsRerankerDataArguments): Data arguments.\n tokenizer (PreTrainedTokenizer): Tokenizer to use.\n \"\"\"\n def __init__(\n self,\n args: AbsRerankerDataArguments,\n tokenizer: PreTrainedTokenizer\n ):\n super().__init__(args, tokenizer)\n sep = self.args.sep_token\n self.sep_inputs = self.tokenizer(\n sep,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n\n def __getitem__(self, item) -> List[BatchEncoding]:\n data = self.dataset[item]\n train_group_size = self.args.train_group_size\n\n query = data['query']\n if self.args.query_instruction_for_rerank is not None:\n query = self.args.query_instruction_format.format(\n data['query_prompt'] if 'query_prompt' in data else self.args.query_instruction_for_rerank,\n query\n )\n\n passages = []\n teacher_scores = []\n\n assert isinstance(data['pos'], list) and isinstance(data['neg'], list)\n\n pos_idx = random.choice(list(range(len(data['pos']))))\n passages.append(self._shuffle_text(data['pos'][pos_idx]))\n\n neg_all_idx = list(range(len(data['neg'])))\n if len(data['neg']) < train_group_size - 1:\n num = math.ceil((train_group_size - 1) / len(data['neg']))\n neg_idxs = random.sample(neg_all_idx * num, train_group_size - 1)\n else:\n neg_idxs = random.sample(neg_all_idx, self.args.train_group_size - 1)\n for neg_idx in neg_idxs:\n passages.append(data['neg'][neg_idx])\n\n if self.args.knowledge_distillation:\n assert isinstance(data['pos_scores'], list) and isinstance(data['neg_scores'], list)\n teacher_scores.append(data['pos_scores'][pos_idx])\n for neg_idx in neg_idxs:\n teacher_scores.append(data['neg_scores'][neg_idx])\n if not all(isinstance(score, (int, float)) for score in teacher_scores):\n raise ValueError(f\"pos_score or neg_score must be digit\")\n else:\n teacher_scores = None\n\n if self.args.passage_instruction_for_rerank is not None:\n passages = [\n self.args.passage_instruction_format.format(\n data['passage_prompt'] if 'passage_prompt' in data else self.args.passage_instruction_for_rerank, p\n )\n for p in passages\n ]\n\n prompt = self.dataset[item].get('prompt', \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\")\n\n query_inputs = self.tokenizer(\n query,\n return_tensors=None,\n max_length=self.args.query_max_len + self.args.passage_max_len // 4,\n truncation=True,\n add_special_tokens=False\n )\n\n prompt_inputs = self.tokenizer(\n prompt,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n\n max_length = self.max_length - len(prompt_inputs) - len(self.sep_inputs)\n\n passages_inputs = []\n for i, passage in enumerate(passages):\n passage_inputs = self.tokenizer(\n passage,\n return_tensors=None,\n max_length=self.args.passage_max_len + self.args.query_max_len // 2,\n truncation=True,\n add_special_tokens=False\n )\n if self.tokenizer.bos_token_id is not None and self.tokenizer.bos_token_id != self.tokenizer.pad_token_id:\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n self.sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n else:\n item = self.tokenizer.prepare_for_model(\n query_inputs['input_ids'],\n self.sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n\n passage_inputs['input_ids'] = item['input_ids'] + self.sep_inputs + prompt_inputs\n\n passage_inputs['attention_mask'] = [1] * len(passage_inputs['input_ids'])\n # passage_inputs['labels'] = passage_inputs['input_ids'].copy()\n # passage_inputs['labels'] = [-100] * (len(passage_inputs['input_ids']) - 1) + passage_inputs['labels'][(len(passage_inputs['input_ids']) - 1):]\n passage_inputs.pop('token_type_ids') if 'token_type_ids' in passage_inputs.keys() else None\n if 'position_ids' in passage_inputs.keys():\n passage_inputs['position_ids'] = list(range(len(passage_inputs['input_ids'])))\n passages_inputs.append(passage_inputs)\n\n return passages_inputs, teacher_scores\n\n\n@dataclass\nclass AbsLLMRerankerCollator(DataCollatorForSeq2Seq):\n \"\"\"\n Wrapper that does conversion from List[Tuple[encode_qry, encode_psg]] to List[qry], List[psg]\n and pass batch separately to the actual collator.\n Abstract out data detail for the model.\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n\n def __call__(self, features, return_tensors='pt'):\n if return_tensors is None:\n return_tensors = self.return_tensors\n\n teacher_scores = [f[1] for f in features]\n if teacher_scores[0] is None:\n teacher_scores = None\n elif isinstance(teacher_scores[0], list):\n teacher_scores = sum(teacher_scores, [])\n\n features = [f[0] for f in features]\n if isinstance(features[0], list):\n features = sum(features, [])\n\n labels = [feature[\"labels\"] for feature in features] if \"labels\" in features[0].keys() else None\n # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the\n # same length to return tensors.\n if labels is not None:\n max_label_length = max(len(l) for l in labels)\n # print(max_label_length)\n if self.pad_to_multiple_of is not None:\n max_label_length = (\n (max_label_length + self.pad_to_multiple_of - 1)\n // self.pad_to_multiple_of\n * self.pad_to_multiple_of\n )\n\n padding_side = self.tokenizer.padding_side\n for feature in features:\n remainder = [self.label_pad_token_id] * (max_label_length - len(feature[\"labels\"]))\n if isinstance(feature[\"labels\"], list):\n feature[\"labels\"] = (\n feature[\"labels\"] + remainder\n if padding_side == \"right\" else remainder + feature[\"labels\"]\n )\n elif padding_side == \"right\":\n feature[\"labels\"] = np.concatenate([feature[\"labels\"], remainder]).astype(np.int64)\n else:\n feature[\"labels\"] = np.concatenate([remainder, feature[\"labels\"]]).astype(np.int64)\n\n collated = self.tokenizer.pad(\n features,\n padding=self.padding,\n max_length=self.query_max_len + self.passage_max_len,\n return_tensors=return_tensors,\n pad_to_multiple_of=self.pad_to_multiple_of,\n )\n\n return {\n \"pair\": collated,\n \"teacher_scores\": teacher_scores,\n }\n" }, { "path": "FlagEmbedding/abc/finetune/reranker/AbsModeling.py", "content": "import torch\nfrom torch import nn, Tensor\nfrom transformers import PreTrainedTokenizer\nfrom transformers.file_utils import ModelOutput\n\nimport logging\nfrom dataclasses import dataclass\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, Optional, List, Union\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass RerankerOutput(ModelOutput):\n loss: Optional[Tensor] = None\n scores: Optional[Tensor] = None\n\n\nclass AbsRerankerModel(ABC, nn.Module):\n \"\"\"Abstract class of embedding model for training.\n\n Args:\n base_model: The base model to train on.\n tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.\n train_batch_size (int, optional): Batch size used for training. Defaults to ``4``.\n \"\"\"\n def __init__(\n self,\n base_model: None,\n tokenizer: PreTrainedTokenizer = None,\n train_batch_size: int = 4,\n ):\n nn.Module.__init__(self)\n self.model = base_model\n self.tokenizer = tokenizer\n self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')\n\n if self.model.config.pad_token_id is None:\n self.model.config.pad_token_id = self.tokenizer.pad_token_id\n self.config = self.model.config\n\n self.train_batch_size = train_batch_size\n\n self.yes_loc = self.tokenizer('Yes', add_special_tokens=False)['input_ids'][-1]\n\n def gradient_checkpointing_enable(self, **kwargs):\n \"\"\"\n Activates gradient checkpointing for the current model.\n \"\"\"\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n \"\"\"\n Enables the gradients for the input embeddings.\n \"\"\"\n self.model.enable_input_require_grads(**kwargs)\n\n @abstractmethod\n def encode(self, features):\n \"\"\"Abstract method of encode.\n\n Args:\n features (dict): Teatures to pass to the model.\n \"\"\"\n pass\n\n def forward(self, pair: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None, teacher_scores: Optional[Tensor] = None):\n \"\"\"The computation performed at every call.\n\n Args:\n pair (Union[Dict[str, Tensor], List[Dict[str, Tensor]]], optional): The query-document pair. Defaults to ``None``.\n teacher_scores (Optional[Tensor], optional): Teacher scores of knowledge distillation. Defaults to None.\n\n Returns:\n RerankerOutput: Output of reranker model.\n \"\"\"\n ranker_logits = self.encode(pair) # (batch_size * num, dim)\n if teacher_scores is not None:\n teacher_scores = torch.Tensor(teacher_scores)\n teacher_targets = teacher_scores.view(self.train_batch_size, -1)\n teacher_targets = torch.softmax(teacher_targets.detach(), dim=-1)\n\n if self.training:\n grouped_logits = ranker_logits.view(self.train_batch_size, -1)\n target = torch.zeros(self.train_batch_size, device=grouped_logits.device, dtype=torch.long)\n loss = self.compute_loss(grouped_logits, target)\n if teacher_scores is not None:\n teacher_targets = teacher_targets.to(grouped_logits.device)\n # print(teacher_targets, torch.mean(torch.sum(torch.log_softmax(grouped_logits, dim=-1) * teacher_targets, dim=-1)))\n loss += - torch.mean(torch.sum(torch.log_softmax(grouped_logits, dim=-1) * teacher_targets, dim=-1))\n else:\n loss = None\n\n # print(loss)\n return RerankerOutput(\n loss=loss,\n scores=ranker_logits,\n )\n\n def compute_loss(self, scores, target):\n \"\"\"Compute the loss.\n\n Args:\n scores (torch.Tensor): Computed scores.\n target (torch.Tensor): The target value.\n\n Returns:\n torch.Tensor: The computed loss.\n \"\"\"\n return self.cross_entropy(scores, target)\n\n def save(self, output_dir: str):\n \"\"\"Save the model.\n\n Args:\n output_dir (str): Directory for saving the model.\n \"\"\"\n # self.model.save_pretrained(output_dir)\n state_dict = self.model.state_dict()\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n self.model.save_pretrained(output_dir, state_dict=state_dict)\n\n def save_pretrained(self, *args, **kwargs):\n \"\"\"\n Save the tokenizer and model.\n \"\"\"\n self.tokenizer.save_pretrained(*args, **kwargs)\n return self.model.save_pretrained(*args, **kwargs)\n" }, { "path": "FlagEmbedding/abc/finetune/reranker/AbsRunner.py", "content": "import os\nimport logging\nfrom pathlib import Path\nfrom typing import Tuple\nfrom abc import ABC, abstractmethod\nfrom transformers import set_seed, PreTrainedTokenizer\n\n\nfrom .AbsArguments import (\n AbsRerankerModelArguments,\n AbsRerankerDataArguments,\n AbsRerankerTrainingArguments\n)\nfrom .AbsTrainer import AbsRerankerTrainer\nfrom .AbsModeling import AbsRerankerModel\nfrom .AbsDataset import (\n AbsRerankerTrainDataset, AbsRerankerCollator,\n AbsLLMRerankerTrainDataset, AbsLLMRerankerCollator\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsRerankerRunner(ABC):\n \"\"\"Abstract class to run reranker model fine-tuning.\n\n Args:\n model_args (AbsRerankerModelArguments): Model arguments\n data_args (AbsRerankerDataArguments): Data arguments.\n training_args (AbsRerankerTrainingArguments): Training arguments.\n \"\"\"\n def __init__(\n self,\n model_args: AbsRerankerModelArguments,\n data_args: AbsRerankerDataArguments,\n training_args: AbsRerankerTrainingArguments\n ):\n self.model_args = model_args\n self.data_args = data_args\n self.training_args = training_args\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n logger.info(\"Training/evaluation parameters %s\", training_args)\n logger.info(\"Model parameters %s\", model_args)\n logger.info(\"Data parameters %s\", data_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n self.tokenizer, self.model = self.load_tokenizer_and_model()\n self.train_dataset = self.load_train_dataset()\n self.data_collator = self.load_data_collator()\n self.trainer = self.load_trainer()\n\n @abstractmethod\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsRerankerModel]:\n \"\"\"Abstract method to load the tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsRerankerModel]: Loaded tokenizer and model instances.\n \"\"\"\n pass\n\n @abstractmethod\n def load_trainer(self) -> AbsRerankerTrainer:\n \"\"\"Abstract method to load the trainer.\n\n Returns:\n AbsRerankerTrainer: The loaded trainer instance.\n \"\"\"\n pass\n\n def load_train_dataset(self) -> AbsRerankerTrainDataset:\n \"\"\"Loads the training dataset based on data arguments.\n\n Returns:\n AbsRerankerTrainDataset: The loaded dataset instance.\n \"\"\"\n if self.model_args.model_type == 'encoder':\n train_dataset = AbsRerankerTrainDataset(\n args=self.data_args,\n tokenizer=self.tokenizer\n )\n else:\n train_dataset = AbsLLMRerankerTrainDataset(\n args=self.data_args,\n tokenizer=self.tokenizer\n )\n return train_dataset\n\n def load_data_collator(self) -> AbsRerankerCollator:\n \"\"\"Loads the appropriate data collator.\n\n Returns:\n AbsRerankerCollator: Loaded data collator.\n \"\"\"\n if self.model_args.model_type == 'encoder':\n RerankerCollator = AbsRerankerCollator\n else:\n RerankerCollator = AbsLLMRerankerCollator\n\n data_collator = RerankerCollator(\n tokenizer=self.tokenizer,\n query_max_len=self.data_args.query_max_len,\n passage_max_len=self.data_args.passage_max_len,\n pad_to_multiple_of=self.data_args.pad_to_multiple_of,\n padding=True,\n return_tensors=\"pt\"\n )\n return data_collator\n\n def run(self):\n \"\"\"\n Executes the training process.\n \"\"\"\n Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)\n self.trainer.save_model()\n" }, { "path": "FlagEmbedding/abc/finetune/reranker/AbsTrainer.py", "content": "import logging\nfrom typing import Optional\nfrom abc import ABC, abstractmethod\nfrom transformers.trainer import Trainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsRerankerTrainer(ABC, Trainer):\n \"\"\"\n Abstract class for the trainer of reranker.\n \"\"\"\n @abstractmethod\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n pass\n\n def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n \"\"\"\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n \n Args:\n model (AbsRerankerModel): The model being trained.\n inputs (dict): A dictionary of input tensors to be passed to the model.\n return_outputs (bool, optional): If ``True``, returns both the loss and the model's outputs. Otherwise,\n returns only the loss. Defaults to ``False``.\n \n Returns:\n Union[torch.Tensor, tuple(torch.Tensor, RerankerOutput)]: The computed loss. If ``return_outputs`` is ``True``, \n also returns the model's outputs in a tuple ``(loss, outputs)``.\n \"\"\"\n\n outputs = model(**inputs)\n loss = outputs.loss\n\n return (loss, outputs) if return_outputs else loss\n" }, { "path": "FlagEmbedding/abc/finetune/reranker/__init__.py", "content": "from .AbsArguments import AbsRerankerDataArguments, AbsRerankerModelArguments, AbsRerankerTrainingArguments\nfrom .AbsDataset import (\n AbsRerankerTrainDataset, AbsRerankerCollator,\n AbsLLMRerankerTrainDataset, AbsLLMRerankerCollator\n)\nfrom .AbsModeling import AbsRerankerModel, RerankerOutput\nfrom .AbsTrainer import AbsRerankerTrainer\nfrom .AbsRunner import AbsRerankerRunner\n\n__all__ = [\n \"AbsRerankerDataArguments\",\n \"AbsRerankerModelArguments\",\n \"AbsRerankerTrainingArguments\",\n \"AbsRerankerTrainDataset\",\n \"AbsRerankerCollator\",\n \"AbsLLMRerankerTrainDataset\",\n \"AbsLLMRerankerCollator\",\n \"AbsRerankerModel\",\n \"RerankerOutput\",\n \"AbsRerankerTrainer\",\n \"AbsRerankerRunner\",\n]\n" }, { "path": "FlagEmbedding/abc/inference/AbsEmbedder.py", "content": "import logging\nfrom tqdm import tqdm, trange\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Union, List, Dict, Literal, Optional\n\nimport queue\nimport multiprocessing as mp\nfrom multiprocessing import Queue\n\nimport math\nimport gc\nimport torch\nimport numpy as np\nfrom transformers import is_torch_npu_available\n\ntry:\n import torch_musa\nexcept Exception:\n pass\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsEmbedder(ABC):\n \"\"\"\n Base class for embedder.\n Extend this class and implement :meth:`encode_queries`, :meth:`encode_corpus`, :meth:`encode` for custom embedders.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to :data:`True`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`True`.\n query_instruction_for_retrieval: (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format: (str, optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`\"{}{}\"`.\n devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.\n query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.\n passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. \n Defaults to :data:`True`.\n kwargs (Dict[Any], optional): Additional parameters for HuggingFace Transformers config or children classes.\n \"\"\"\n\n def __init__(\n self,\n model_name_or_path: str,\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: Optional[str] = None,\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_retrieval\n devices: Optional[Union[str, int, List[str], List[int]]] = None,\n # inference\n batch_size: int = 256,\n query_max_length: int = 512,\n passage_max_length: int = 512,\n convert_to_numpy: bool = True,\n **kwargs: Any,\n ):\n self.model_name_or_path = model_name_or_path\n self.normalize_embeddings = normalize_embeddings\n self.use_fp16 = use_fp16\n self.query_instruction_for_retrieval = query_instruction_for_retrieval\n self.query_instruction_format = query_instruction_format\n self.target_devices = self.get_target_devices(devices)\n\n self.batch_size = batch_size\n self.query_max_length = query_max_length\n self.passage_max_length = passage_max_length\n self.convert_to_numpy = convert_to_numpy\n\n for k in kwargs:\n setattr(self, k, kwargs[k])\n\n self.kwargs = kwargs\n\n # tokenizer and model are initialized in the child class\n self.tokenizer = None\n self.model = None\n self.pool = None\n\n def stop_self_pool(self):\n if self.pool is not None:\n self.stop_multi_process_pool(self.pool)\n self.pool = None\n try:\n self.model.to('cpu')\n torch.cuda.empty_cache()\n except:\n pass\n if gc is not None and callable(gc.collect):\n gc.collect()\n\n @staticmethod\n def get_target_devices(devices: Union[str, int, List[str], List[int]]) -> List[str]:\n \"\"\"\n\n Args:\n devices (Union[str, int, List[str], List[int]]): specified devices, can be `str`, `int`, list of `str`, or list of `int`.\n\n Raises:\n ValueError: Devices should be a string or an integer or a list of strings or a list of integers.\n\n Returns:\n List[str]: A list of target devices in format.\n \"\"\"\n if devices is None:\n if torch.cuda.is_available():\n return [f\"cuda:{i}\" for i in range(torch.cuda.device_count())]\n elif is_torch_npu_available():\n return [f\"npu:{i}\" for i in range(torch.npu.device_count())]\n elif hasattr(torch, \"musa\") and torch.musa.is_available():\n return [f\"musa:{i}\" for i in range(torch.musa.device_count())]\n elif torch.backends.mps.is_available():\n try:\n return [f\"mps:{i}\" for i in range(torch.mps.device_count())]\n except:\n return [\"mps\"]\n else:\n return [\"cpu\"]\n elif isinstance(devices, str):\n return [devices]\n elif isinstance(devices, int):\n if hasattr(torch, \"musa\") and torch.musa.is_available():\n return [f\"musa:{devices}\"]\n else:\n return [f\"cuda:{devices}\"]\n elif isinstance(devices, list):\n if isinstance(devices[0], str):\n return devices\n elif isinstance(devices[0], int):\n if hasattr(torch, \"musa\") and torch.musa.is_available():\n return [f\"musa:{device}\" for device in devices]\n else:\n return [f\"cuda:{device}\" for device in devices]\n else:\n raise ValueError(\"devices should be a string or an integer or a list of strings or a list of integers.\")\n else:\n raise ValueError(\"devices should be a string or an integer or a list of strings or a list of integers.\")\n\n @staticmethod\n def get_detailed_instruct(instruction_format: str, instruction: str, sentence: str):\n \"\"\"Combine the instruction and sentence along with the instruction format.\n\n Args:\n instruction_format (str): Format for instruction.\n instruction (str): The text of instruction.\n sentence (str): The sentence to concatenate with.\n\n Returns:\n str: The complete sentence with instruction\n \"\"\"\n if \"\\\\n\" in instruction_format:\n instruction_format = instruction_format.replace(\"\\\\n\", \"\\n\")\n return instruction_format.format(instruction, sentence)\n\n def encode_queries(\n self,\n queries: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ):\n \"\"\"encode the queries using the instruction if provided.\n\n Args:\n queries (Union[List[str], str]): Input queries to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.query_max_length\n if convert_to_numpy is None: convert_to_numpy = self.convert_to_numpy\n\n return self.encode(\n queries,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n instruction=self.query_instruction_for_retrieval,\n instruction_format=self.query_instruction_format,\n **kwargs\n )\n\n def encode_corpus(\n self,\n corpus: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ):\n \"\"\"encode the corpus using the instruction if provided.\n\n Args:\n corpus (Union[List[str], str]): Input corpus to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n passage_instruction_for_retrieval = self.kwargs.get(\"passage_instruction_for_retrieval\", None)\n passage_instruction_format = self.kwargs.get(\"passage_instruction_format\", \"{}{}\")\n\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.passage_max_length\n if convert_to_numpy is None: convert_to_numpy = self.convert_to_numpy\n\n return self.encode(\n corpus,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n instruction=passage_instruction_for_retrieval,\n instruction_format=passage_instruction_format,\n **kwargs\n )\n\n def encode(\n self,\n sentences: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n instruction: Optional[str] = None,\n instruction_format: Optional[str] = None,\n **kwargs: Any\n ):\n \"\"\"encode the input sentences with the embedding model.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n instruction (Optional[str], optional): The text of instruction. Defaults to :data:`None`.\n instruction_format (Optional[str], optional): Format for instruction. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.passage_max_length\n if convert_to_numpy is None: convert_to_numpy = self.convert_to_numpy\n\n if instruction is not None:\n if isinstance(sentences, str):\n sentences = self.get_detailed_instruct(instruction_format, instruction, sentences)\n else:\n sentences = [self.get_detailed_instruct(instruction_format, instruction, sentence) for sentence in\n sentences]\n\n if isinstance(sentences, str) or len(self.target_devices) == 1:\n return self.encode_single_device(\n sentences,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n device=self.target_devices[0],\n **kwargs\n )\n\n if self.pool is None:\n self.pool = self.start_multi_process_pool(AbsEmbedder._encode_multi_process_worker)\n embeddings = self.encode_multi_process(\n sentences,\n self.pool,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n return embeddings\n\n def __del__(self):\n self.stop_self_pool()\n\n @abstractmethod\n def encode_single_device(\n self,\n sentences: Union[List[str], str],\n batch_size: int = 256,\n max_length: int = 512,\n convert_to_numpy: bool = True,\n device: Optional[str] = None,\n **kwargs: Any,\n ):\n \"\"\"\n This method should encode sentences and return embeddings on a single device.\n \"\"\"\n pass\n\n # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L807\n def start_multi_process_pool(\n self,\n process_target_func: Any,\n ) -> Dict[Literal[\"input\", \"output\", \"processes\"], Any]:\n \"\"\"\n Starts a multi-process pool to process the encoding with several independent processes\n via :meth:`SentenceTransformer.encode_multi_process `.\n\n This method is recommended if you want to encode on multiple GPUs or CPUs. It is advised\n to start only one process per GPU. This method works together with encode_multi_process\n and stop_multi_process_pool.\n\n Returns:\n Dict[str, Any]: A dictionary with the target processes, an input queue, and an output queue.\n \"\"\"\n if self.model is None:\n raise ValueError(\"Model is not initialized.\")\n\n logger.info(\"Start multi-process pool on devices: {}\".format(\", \".join(map(str, self.target_devices))))\n\n self.model.to(\"cpu\")\n self.model.share_memory()\n ctx = mp.get_context(\"spawn\")\n input_queue = ctx.Queue()\n output_queue = ctx.Queue()\n processes = []\n\n for device_id in tqdm(self.target_devices, desc='initial target device'):\n p = ctx.Process(\n target=process_target_func,\n args=(device_id, self, input_queue, output_queue),\n daemon=True,\n )\n p.start()\n processes.append(p)\n\n return {\"input\": input_queue, \"output\": output_queue, \"processes\": processes}\n\n # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L976\n @staticmethod\n def _encode_multi_process_worker(\n target_device: str, model: 'AbsEmbedder', input_queue: Queue, results_queue: Queue\n ) -> None:\n \"\"\"\n Internal working process to encode sentences in multi-process setup\n \"\"\"\n while True:\n try:\n chunk_id, sentences, kwargs = (\n input_queue.get()\n )\n embeddings = model.encode_single_device(\n sentences,\n device=target_device,\n **kwargs\n )\n\n results_queue.put([chunk_id, embeddings])\n except queue.Empty:\n break\n\n # copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857\n @staticmethod\n def stop_multi_process_pool(pool: Dict[Literal[\"input\", \"output\", \"processes\"], Any]) -> None:\n \"\"\"\n Stops all processes started with start_multi_process_pool.\n\n Args:\n pool (Dict[str, object]): A dictionary containing the input queue, output queue, and process list.\n\n Returns:\n None\n \"\"\"\n for p in pool[\"processes\"]:\n p.terminate()\n\n for p in pool[\"processes\"]:\n p.join()\n p.close()\n\n pool[\"input\"].close()\n pool[\"output\"].close()\n pool = None\n\n # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L877\n def encode_multi_process(\n self,\n sentences: List[str],\n pool: Dict[Literal[\"input\", \"output\", \"processes\"], Any],\n **kwargs\n ):\n chunk_size = math.ceil(len(sentences) / len(pool[\"processes\"]))\n\n input_queue = pool[\"input\"]\n last_chunk_id = 0\n chunk = []\n\n for sentence in sentences:\n chunk.append(sentence)\n if len(chunk) >= chunk_size:\n input_queue.put(\n [last_chunk_id, chunk, kwargs]\n )\n last_chunk_id += 1\n chunk = []\n\n if len(chunk) > 0:\n input_queue.put([last_chunk_id, chunk, kwargs])\n last_chunk_id += 1\n\n output_queue = pool[\"output\"]\n results_list = sorted(\n [output_queue.get() for _ in trange(last_chunk_id, desc=\"Chunks\")],\n key=lambda x: x[0],\n )\n embeddings = self._concatenate_results_from_multi_process([result[1] for result in results_list])\n return embeddings\n\n def _concatenate_results_from_multi_process(self, results_list: List[Union[torch.Tensor, np.ndarray, Any]]):\n \"\"\"concatenate and return the results from all the processes\n\n Args:\n results_list (List[Union[torch.Tensor, np.ndarray, Any]]): A list of results from all the processes.\n\n Raises:\n NotImplementedError: Unsupported type for results_list\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if isinstance(results_list[0], torch.Tensor):\n # move all tensors to the same device\n results_list = [res.to(self.target_devices[0]) for res in results_list]\n return torch.cat(results_list, dim=0)\n elif isinstance(results_list[0], np.ndarray):\n return np.concatenate(results_list, axis=0)\n else:\n raise NotImplementedError(\"Unsupported type for results_list\")\n" }, { "path": "FlagEmbedding/abc/inference/AbsReranker.py", "content": "import logging\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Union, List, Tuple, Dict, Literal, Optional\n\nimport multiprocessing as mp\nfrom multiprocessing import Queue\n\nimport math\nimport gc\nimport torch\nimport numpy as np\nfrom tqdm import tqdm, trange\nfrom transformers import is_torch_npu_available\n\ntry:\n import torch_musa\nexcept Exception:\n pass\n\nlogger = logging.getLogger(__name__)\n\n\nclass AbsReranker(ABC):\n \"\"\"\n Base class for Reranker.\n Extend this class and implement :meth:`compute_score_single_gpu` for custom rerankers.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`False`.\n query_instruction_for_rerank: (Optional[str], optional): Query instruction for reranking, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format: (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`\"{}{}\"`.\n passage_instruction_for_rerank (Optional[str], optional): Passage instruction for reranking. Defaults to :data:`None`.\n passage_instruction_format (str, optional): Passage instruction format when using :attr:`passage_instruction_for_rerank`. \n Defaults to :data:`\"{}{}\"`.\n devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.\n query_max_length (int, optional): Maximum length for query. Defaults to :data:`None`.\n max_length (int, optional): Maximum length. Defaults to :data:`512`.\n normalize (bool, optional): If true, normalize the result. Defaults to :data:`False`.\n kwargs (Dict[Any], optional): Additional parameters for HuggingFace Transformers config or children classes.\n \"\"\"\n\n def __init__(\n self,\n model_name_or_path: str,\n use_fp16: bool = False,\n query_instruction_for_rerank: Optional[str] = None,\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_rerank\n passage_instruction_for_rerank: Optional[str] = None,\n passage_instruction_format: str = \"{}{}\", # specify the format of passage_instruction_for_rerank\n devices: Optional[Union[str, int, List[str], List[int]]] = None,\n # inference\n batch_size: int = 128,\n query_max_length: Optional[int] = None,\n max_length: int = 512,\n normalize: bool = False,\n **kwargs: Any,\n ):\n self.model_name_or_path = model_name_or_path\n self.use_fp16 = use_fp16\n self.query_instruction_for_rerank = query_instruction_for_rerank\n self.query_instruction_format = query_instruction_format\n self.passage_instruction_for_rerank = passage_instruction_for_rerank\n self.passage_instruction_format = passage_instruction_format\n self.target_devices = self.get_target_devices(devices)\n \n self.batch_size = batch_size\n self.query_max_length = query_max_length\n self.max_length = max_length\n self.normalize = normalize\n\n for k in kwargs:\n setattr(self, k, kwargs[k])\n\n self.kwargs = kwargs\n\n # tokenizer and model are initialized in the child class\n self.model = None\n self.tokenizer = None\n self.pool = None\n\n def stop_self_pool(self):\n if self.pool is not None:\n self.stop_multi_process_pool(self.pool)\n self.pool = None\n try:\n self.model.to('cpu')\n torch.cuda.empty_cache()\n except:\n pass\n if gc is not None and callable(gc.collect):\n gc.collect()\n\n @staticmethod\n def get_target_devices(devices: Union[str, int, List[str], List[int]]) -> List[str]:\n \"\"\"\n\n Args:\n devices (Union[str, int, List[str], List[int]]): Specified devices, can be `str`, `int`, list of `str`, or list of `int`.\n\n Raises:\n ValueError: Devices should be a string or an integer or a list of strings or a list of integers.\n\n Returns:\n List[str]: A list of target devices in format\n \"\"\"\n if devices is None:\n if torch.cuda.is_available():\n return [f\"cuda:{i}\" for i in range(torch.cuda.device_count())]\n elif is_torch_npu_available():\n return [f\"npu:{i}\" for i in range(torch.npu.device_count())]\n elif hasattr(torch, \"musa\") and torch.musa.is_available():\n return [f\"musa:{i}\" for i in range(torch.musa.device_count())]\n elif torch.backends.mps.is_available():\n return [\"mps\"]\n else:\n return [\"cpu\"]\n elif isinstance(devices, str):\n return [devices]\n elif isinstance(devices, int):\n if hasattr(torch, \"musa\") and torch.musa.is_available():\n return [f\"musa:{devices}\"]\n else:\n return [f\"cuda:{devices}\"]\n elif isinstance(devices, list):\n if isinstance(devices[0], str):\n return devices\n elif isinstance(devices[0], int):\n if hasattr(torch, \"musa\") and torch.musa.is_available():\n return [f\"musa:{device}\" for device in devices]\n else:\n return [f\"cuda:{device}\" for device in devices]\n else:\n raise ValueError(\"devices should be a string or an integer or a list of strings or a list of integers.\")\n else:\n raise ValueError(\"devices should be a string or an integer or a list of strings or a list of integers.\")\n\n def get_detailed_instruct(self, instruction_format: str, instruction: str, sentence: str):\n \"\"\"Combine the instruction and sentence along with the instruction format.\n\n Args:\n instruction_format (str): Format for instruction.\n instruction (str): The text of instruction.\n sentence (str): The sentence to concatenate with.\n\n Returns:\n str: The complete sentence with instruction\n \"\"\"\n if \"\\\\n\" in instruction_format:\n instruction_format = instruction_format.replace(\"\\\\n\", \"\\n\")\n return instruction_format.format(instruction, sentence)\n \n def get_detailed_inputs(self, sentence_pairs: Union[str, List[str]]):\n \"\"\"get detailed instruct for all the inputs\n\n Args:\n sentence_pairs (Union[str, List[str]]): Input sentence pairs\n\n Returns:\n list[list[str]]: The complete sentence pairs with instruction\n \"\"\"\n if isinstance(sentence_pairs, str):\n sentence_pairs = [sentence_pairs]\n\n if self.query_instruction_for_rerank is not None:\n if self.passage_instruction_for_rerank is None:\n return [\n [\n self.get_detailed_instruct(self.query_instruction_format, self.query_instruction_for_rerank, sentence_pair[0]),\n sentence_pair[1]\n ] for sentence_pair in sentence_pairs\n ]\n else:\n return [\n [\n self.get_detailed_instruct(self.query_instruction_format, self.query_instruction_for_rerank, sentence_pair[0]),\n self.get_detailed_instruct(self.passage_instruction_format, self.passage_instruction_for_rerank, sentence_pair[1])\n ] for sentence_pair in sentence_pairs\n ]\n else:\n if self.passage_instruction_for_rerank is None:\n return [\n [\n sentence_pair[0],\n sentence_pair[1]\n ] for sentence_pair in sentence_pairs\n ]\n else:\n return [\n [\n sentence_pair[0],\n self.get_detailed_instruct(self.passage_instruction_format, self.passage_instruction_for_rerank, sentence_pair[1])\n ] for sentence_pair in sentence_pairs\n ]\n\n def compute_score(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n **kwargs\n ):\n \"\"\"Compute score for each sentence pair\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Input sentence pairs to compute.\n\n Returns:\n numpy.ndarray: scores of all the sentence pairs.\n \"\"\"\n if isinstance(sentence_pairs[0], str):\n sentence_pairs = [sentence_pairs]\n sentence_pairs = self.get_detailed_inputs(sentence_pairs)\n\n if isinstance(sentence_pairs, str) or len(self.target_devices) == 1:\n return self.compute_score_single_gpu(\n sentence_pairs,\n device=self.target_devices[0],\n **kwargs\n )\n\n if self.pool is None:\n self.pool = self.start_multi_process_pool()\n scores = self.encode_multi_process(sentence_pairs,\n self.pool,\n **kwargs)\n return scores\n\n def __del__(self):\n self.stop_self_pool()\n\n @abstractmethod\n def compute_score_single_gpu(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: int = 256,\n query_max_length: Optional[int] = None,\n max_length: int = 512,\n normalize: bool = False,\n device: Optional[str] = None,\n **kwargs: Any,\n ):\n \"\"\"\n This method should compute the scores of sentence_pair and return scores.\n \"\"\"\n pass\n\n # copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857\n def start_multi_process_pool(self) -> Dict[Literal[\"input\", \"output\", \"processes\"], Any]:\n \"\"\"\n Starts a multi-process pool to process the encoding with several independent processes\n via :meth:`SentenceTransformer.encode_multi_process `.\n\n This method is recommended if you want to encode on multiple GPUs or CPUs. It is advised\n to start only one process per GPU. This method works together with encode_multi_process\n and stop_multi_process_pool.\n\n Returns:\n Dict[str, Any]: A dictionary with the target processes, an input queue, and an output queue.\n \"\"\"\n logger.info(\"Start multi-process pool on devices: {}\".format(\", \".join(map(str, self.target_devices))))\n\n self.model.to(\"cpu\")\n self.model.share_memory()\n ctx = mp.get_context(\"spawn\")\n input_queue = ctx.Queue()\n output_queue = ctx.Queue()\n processes = []\n\n for device_id in tqdm(self.target_devices, desc='initial target device'):\n p = ctx.Process(\n target=AbsReranker._encode_multi_process_worker,\n args=(device_id, self, input_queue, output_queue),\n daemon=True,\n )\n p.start()\n processes.append(p)\n\n return {\"input\": input_queue, \"output\": output_queue, \"processes\": processes}\n\n # copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857\n def encode_multi_process(\n self,\n sentence_pairs: List,\n pool: Dict[Literal[\"input\", \"output\", \"processes\"], Any],\n **kwargs\n ) -> np.ndarray:\n chunk_size = math.ceil(len(sentence_pairs) / len(pool[\"processes\"]))\n\n input_queue = pool[\"input\"]\n last_chunk_id = 0\n chunk = []\n\n for sentence_pair in sentence_pairs:\n chunk.append(sentence_pair)\n if len(chunk) >= chunk_size:\n input_queue.put(\n [last_chunk_id, chunk, kwargs]\n )\n last_chunk_id += 1\n chunk = []\n\n if len(chunk) > 0:\n input_queue.put([last_chunk_id, chunk, kwargs])\n last_chunk_id += 1\n\n output_queue = pool[\"output\"]\n results_list = sorted(\n [output_queue.get() for _ in trange(last_chunk_id, desc=\"Chunks\")],\n key=lambda x: x[0],\n )\n scores = np.concatenate([result[1] for result in results_list])\n return scores\n\n # copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857\n @staticmethod\n def _encode_multi_process_worker(\n target_device: str, model: 'AbsReranker', input_queue: Queue, results_queue: Queue\n ) -> None:\n \"\"\"\n Internal working process to encode sentences in multi-process setup\n \"\"\"\n while True:\n try:\n chunk_id, sentences, kwargs = (\n input_queue.get()\n )\n embeddings = model.compute_score_single_gpu(\n sentences,\n device=target_device,\n **kwargs\n )\n\n results_queue.put([chunk_id, embeddings])\n except:\n break\n\n # copied from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L857\n @staticmethod\n def stop_multi_process_pool(pool: Dict[Literal[\"input\", \"output\", \"processes\"], Any]) -> None:\n \"\"\"\n Stops all processes started with start_multi_process_pool.\n\n Args:\n pool (Dict[str, object]): A dictionary containing the input queue, output queue, and process list.\n\n Returns:\n None\n \"\"\"\n for p in pool[\"processes\"]:\n p.terminate()\n\n for p in pool[\"processes\"]:\n p.join()\n p.close()\n\n pool[\"input\"].close()\n pool[\"output\"].close()\n" }, { "path": "FlagEmbedding/abc/inference/__init__.py", "content": "from .AbsEmbedder import AbsEmbedder\nfrom .AbsReranker import AbsReranker\n\n__all__ = [\n 'AbsEmbedder',\n 'AbsReranker'\n]\n" }, { "path": "FlagEmbedding/evaluation/__init__.py", "content": "" }, { "path": "FlagEmbedding/evaluation/air_bench/__init__.py", "content": "from .arguments import AIRBenchEvalModelArgs, AIRBenchEvalArgs\nfrom .runner import AIRBenchEvalRunner\n\n__all__ = [\n \"AIRBenchEvalModelArgs\",\n \"AIRBenchEvalArgs\",\n \"AIRBenchEvalRunner\"\n]\n" }, { "path": "FlagEmbedding/evaluation/air_bench/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.air_bench import (\n AIRBenchEvalArgs, AIRBenchEvalModelArgs,\n AIRBenchEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n AIRBenchEvalArgs,\n AIRBenchEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: AIRBenchEvalArgs\n model_args: AIRBenchEvalModelArgs\n\n runner = AIRBenchEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n print(\"==============================================\")\n print(\"Search results have been generated.\")\n print(\"For computing metrics, please refer to the official AIR-Bench docs:\")\n print(\"- https://github.com/AIR-Bench/AIR-Bench/blob/main/docs/submit_to_leaderboard.md\")\n" }, { "path": "FlagEmbedding/evaluation/air_bench/arguments.py", "content": "from dataclasses import dataclass, field\nfrom typing import List, Optional\nfrom air_benchmark import EvalArgs as AIRBenchEvalArgs\n\n\n@dataclass\nclass AIRBenchEvalModelArgs:\n \"\"\"\n Evaluation Model arguments for AIR Bench.\n \"\"\"\n embedder_name_or_path: str = field(\n metadata={\"help\": \"The embedder name or path.\", \"required\": True}\n )\n embedder_model_class: Optional[str] = field(\n default=None, metadata={\"help\": \"The embedder model class. Available classes: ['encoder-only-base', 'encoder-only-m3', 'decoder-only-base', 'decoder-only-icl']. Default: None. For the custom model, you need to specifiy the model class.\", \"choices\": [\"encoder-only-base\", \"encoder-only-m3\", \"decoder-only-base\", \"decoder-only-icl\"]}\n )\n normalize_embeddings: bool = field(\n default=True, metadata={\"help\": \"whether to normalize the embeddings\"}\n )\n pooling_method: str = field(\n default=\"cls\", metadata={\"help\": \"The pooling method fot the embedder.\"}\n )\n use_fp16: bool = field(\n default=True, metadata={\"help\": \"whether to use fp16 for inference\"}\n )\n devices: Optional[str] = field(\n default=None, metadata={\"help\": \"Devices to use for inference.\", \"nargs\": \"+\"}\n )\n query_instruction_for_retrieval: Optional[str] = field(\n default=None, metadata={\"help\": \"Instruction for query\"}\n )\n query_instruction_format_for_retrieval: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for query instruction\"}\n )\n examples_for_task: Optional[str] = field(\n default=None, metadata={\"help\": \"Examples for task\"}\n )\n examples_instruction_format: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for examples instruction\"}\n )\n trust_remote_code: bool = field(\n default=False, metadata={\"help\": \"Trust remote code\"}\n )\n reranker_name_or_path: Optional[str] = field(\n default=None, metadata={\"help\": \"The reranker name or path.\"}\n )\n reranker_model_class: Optional[str] = field(\n default=None, metadata={\"help\": \"The reranker model class. Available classes: ['encoder-only-base', 'decoder-only-base', 'decoder-only-layerwise', 'decoder-only-lightweight']. Default: None. For the custom model, you need to specify the model class.\", \"choices\": [\"encoder-only-base\", \"decoder-only-base\", \"decoder-only-layerwise\", \"decoder-only-lightweight\"]}\n )\n reranker_peft_path: Optional[str] = field(\n default=None, metadata={\"help\": \"The reranker peft path.\"}\n )\n use_bf16: bool = field(\n default=False, metadata={\"help\": \"whether to use bf16 for inference\"}\n )\n query_instruction_for_rerank: Optional[str] = field(\n default=None, metadata={\"help\": \"Instruction for query\"}\n )\n query_instruction_format_for_rerank: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for query instruction\"}\n )\n passage_instruction_for_rerank: Optional[str] = field(\n default=None, metadata={\"help\": \"Instruction for passage\"}\n )\n passage_instruction_format_for_rerank: str = field(\n default=\"{}{}\", metadata={\"help\": \"Format for passage instruction\"}\n )\n model_cache_dir: str = field(\n default=None, metadata={\"help\": \"Cache directory for models.\"}\n )\n # ================ for inference ===============\n embedder_batch_size: int = field(\n default=3000, metadata={\"help\": \"Batch size for inference.\"}\n )\n reranker_batch_size: int = field(\n default=3000, metadata={\"help\": \"Batch size for inference.\"}\n )\n embedder_query_max_length: int = field(\n default=512, metadata={\"help\": \"Max length for query.\"}\n )\n embedder_passage_max_length: int = field(\n default=512, metadata={\"help\": \"Max length for passage.\"}\n )\n reranker_query_max_length: Optional[int] = field(\n default=None, metadata={\"help\": \"Max length for reranking.\"}\n )\n reranker_max_length: int = field(\n default=512, metadata={\"help\": \"Max length for reranking.\"}\n )\n normalize: bool = field(\n default=False, metadata={\"help\": \"whether to normalize the reranking scores\"}\n )\n prompt: Optional[str] = field(\n default=None, metadata={\"help\": \"The prompt for the reranker.\"}\n )\n cutoff_layers: List[int] = field(\n default=None, metadata={\"help\": \"The output layers of layerwise/lightweight reranker.\"}\n )\n compress_ratio: int = field(\n default=1, metadata={\"help\": \"The compress ratio of lightweight reranker.\"}\n )\n compress_layers: Optional[int] = field(\n default=None, metadata={\"help\": \"The compress layers of lightweight reranker.\", \"nargs\": \"+\"}\n )\n\n def __post_init__(self):\n # replace \"\\\\n\" with \"\\n\"\n if \"\\\\n\" in self.query_instruction_format_for_retrieval:\n self.query_instruction_format_for_retrieval = self.query_instruction_format_for_retrieval.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.examples_instruction_format:\n self.examples_instruction_format = self.examples_instruction_format.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.query_instruction_format_for_rerank:\n self.query_instruction_format_for_rerank = self.query_instruction_format_for_rerank.replace(\"\\\\n\", \"\\n\")\n if \"\\\\n\" in self.passage_instruction_format_for_rerank:\n self.passage_instruction_format_for_rerank = self.passage_instruction_format_for_rerank.replace(\"\\\\n\", \"\\n\")\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/arxiv-gemini.jsonl", "content": "{\"query\": \"So, which AI model did the best on the MMMU benchmark according to Yue and his team back in 2023?\", \"pos\": \"MMMU (val) Gemini Ultra (0-shot) GPT-4V (0-shot)\\nMaj@32 pass@1 pass@1\\nArt & Design 74.2 70.0 65.8\\nBusiness 62.7 56.7 59.3\\nScience 49.3 48.0 54.7\\nHealth & Medicine 71.3 67.3 64.7\\nHumanities & Social Science 78.3 78.3 72.5\\nTechnology & Engineering 53.0 47.1 36.7\\nOverall 62.4 59.4 56.8\\nTable 8|Gemini Ultra performance on the MMMU benchmark (Yue et al., 2023) per discipline.\"}\r\n{\"query\": \"The GSPMD partitioner, part of the XLA compiler, is responsible for dividing the training step calculation.\", \"pos\": \"The GSPMD partitioner (Xu et al., 2021) in the XLA compiler\\npartitions the training step computation, and the MegaScale XLA compiler (XLA, 2019) pass statically\\nschedules appropriate collectives so that they maximally overlap with the computation with very little\\nvariation in step time.\\nMaintaining a high goodput2at this scale would have been impossible using the conventional\\napproach of periodic checkpointing of weights to persistent cluster storage. For Gemini models, we\\ninstead made use of redundant in-memory copies of the model state, and on any unplanned hardware\\nfailures, we rapidly recover directly from an intact model replica.\"}\r\n{\"query\": \"What's the impact of where you live and your social status on how well AI image labeling tech works?\", \"pos\": \"Thoughwedo\\nnot see large discrepancies across different groups, we note that this metric is imperfect as the human\\nreference captions could be inherently biased. Additionally, we perform a zero-shot classification style\\nevaluation with the Dollarstreet dataset (Rojas et al., 2022) to measure discrepancies in performance\\nacross images which come from different geographic locations. As is seen in previous work, we find\\nthat models work less effectively for images from lower socioeconomic regions and regions outside\\nNorth America and Europe. This is an area where we need further research and work to improve in\\nfuture iterations of our models.\\nIn addition to comparing performance on tasks across groups, we also consider how people are\\ndescribed in captions.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/arxiv-gpt3.jsonl", "content": "{\"query\": \"What gauges the effects of data contamination?\", \"pos\": \"We also undertake a systematic study of “data contamination” – a growing problem when training high capacity models\\non datasets such as Common Crawl, which can potentially include content from test datasets simply because such\\ncontent often exists on the web. In this paper we develop systematic tools to measure data contamination and quantify\\nits distorting effects. Although we find that data contamination has a minimal effect on GPT-3’s performance on most\\ndatasets, we do identify a few datasets where it could be inflating results, and we either do not report results on these\\ndatasets or we note them with an asterisk, depending on the severity.\"}\r\n{\"query\": \"What strategies did the United States employ to convince Pakistan to exercise its influence over the Taliban?\", \"pos\": \"Direct\\npressure on the Taliban had proved unsuccessful. As one NSC staff note\\nput it, \\\"Under the Taliban, Afghanistan is not so much a state sponsor\\nof terrorism as it is a state sponsored by terrorists.\\\" In early 2000,\\nthe United States began a high-level effort to persuade Pakistan to use\\nits influence over the Taliban. In January 2000, Assistant Secretary\\nof State Karl Inderfurth and the State Department’s counterterrorism\\ncoordinator, Michael Sheehan, met with General Musharraf in Islamabad,\\ndangling before him the possibility of a presidential visit in March as a\\nreward for Pakistani cooperation. Such a visit was coveted by Musharraf,\\npartly as a sign of his government’s legitimacy.\"}\r\n{\"query\": \"What does carrying rotten potatoes symbolize?\", \"pos\": \"The children started complaining about the\\ntrouble loudly.\\nThen Mrs. Smith told them why she asked them to play the game. She\\nsaid,\\\"This is exactly the situation when you carry your hatred for somebody\\ninside your heart. The terrible smell of the hatred will pollute your\\nheart and you will carry something unnecessary with you all the time. If\\nyou cannot stand the smell of the rotten potatoes for just two weeks, can\\nyou imagine how heavy it would be to have the hatred in your heart for your\\nlifetime? So throw away any hatred from your heart, and you’ll be really\\nhappy.\\\"\\nQ: Which of the following is True according to the passage?\\nA: If a kid hated four people,he or she had to carry four potatoes.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/arxiv-llama2.jsonl", "content": "{\"query\": \"Could you elucidate on the values of temperature and top-p that are utilized for pass@1 scores?\", \"pos\": \"8 62.8\\n13B 18.3 60.2 30.6 69.0\\n34B 22.6 77.2 33.0 76.1\\n70B29.9 89.0 45.0 81.4\\nTable 21: Code generation results on Human-Eval and MBPP . We report 0-shot and 3-shot results for\\nHuman-Eval and MBPP respectively. For pass@100 and pass@80 scores, we use a temperature of 0.8 and\\ntop-p=0.95. For pass@1 scores, we use a temperature of 0.1 and top- p=0.95.\\n49\"}\r\n{\"query\": \"What do high safety scores and low helpfulness ratings suggest?\", \"pos\": \"Here we show more evidence and\\nqualitative results to manifest this tension. Figure32 are two scatter plots of helpfulness and safety reward\\nmodel scores on the safety test set for safe and unsafe responses. The tension can be observed at the bottom\\nright corner (i.e., high safety score but low helpfulness score) in the safe response plot (left) and the top left\\ncorner (i.e., low safety score but high helpfulness score) in the unsafe response plot (right). We also list two\\nqualitative examples where safety and helpfulness reward models don’t agree with each other in Table 35.\"}\r\n{\"query\": \"The process of carefully adjusting precautions relies on using challenging stimuli together with protected displays to make its operation run more smoothly.\", \"pos\": \"4.2 Safety Fine-Tuning\\nIn this section, we describe our approach to safety fine-tuning, including safety categories, annotation\\nguidelines,and the techniques we use to mitigate safety risks. We employ a process similar to the general\\nfine-tuning methods as described in Section 3, with some notable differences related to safety concerns.\\nSpecifically, we use the following techniques in safety fine-tuning:\\n1.Supervised Safety Fine-Tuning : We initialize by gathering adversarial prompts and safe demonstra-\\ntions that are then included in the general supervised fine-tuning process (Section 3.1). This teaches\\nthe model to align with our safety guidelines even before RLHF,and thus lays the foundation for\\nhigh-quality human preference data annotation.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/arxiv-llm-survey.jsonl", "content": "{\"query\": \"What are the pre-training challenges for large language models?\", \"pos\": \"To make this survey more self-contained, we present the\\ndetailed formulations for these configurations in Table 6.\\nNormalization Methods. Training instability is a challeng-\\ning issue for pre-training LLMs. To alleviate this issue,\\nnormalization is a widely adopted strategy to stabilize the\\ntraining of neural networks. In the vanilla Transformer [22],\\nLayerNorm [256] is employed. Recently, several advanced\\nnormalization techniques have been proposed as alterna-\\ntives to LayerNorm, e.g., RMSNorm, and DeepNorm.\\n•LayerNorm. In the early research, BatchNorm [265] is\\na commonly used normalization method. However, it is\\ndifficult to deal with sequence data of variable lengths and\\nsmall-batch data.\"}\r\n{\"query\": \"Language learning models seriously struggle to grasp complex symbols when they're thrown in scenarios they don't know jack about.\", \"pos\": \"For an example of\\nthe out-of-domain test, LLMs could only see the examples\\nwith two words in context, but it requires LLMs to concate-\\nnate the last letters of three or more words. Typically, the\\naccuracy of the generated symbols is adopted to evaluate\\nthe performance of LLMs on these tasks. Thus, LLMs need\\nto understand the semantic relations among the symbolic\\noperations and their composition in complex scenarios.\\nHowever, under the out-of-domain setting, as LLMs have\\nnot seen the complex compositions of symbolic operations\\nand rules ( e.g., twice the number of operations in context\\nexamples), it is hard for LLMs to capture their accurate\\nmeanings.\"}\r\n{\"query\": \"Could you shed some light on the two primary ways that LLMs employ demonstrations as discussed in document 493?\", \"pos\": \"How LLMs Perform ICL? At the inference stage, researchers\\nfocus on analyzing how the ICL capability operates based\\non given demonstrations since no explicit learning or updat-\\ning is involved. According to the discussion in [493], there\\nare two main ways for LLMs to utilize demonstrations: task\\nrecognition and task learning.\\n•Task recognition. In the first way, LLMs recognize the\\ntask from demonstrations and utilize the prior knowledge\\nobtained from pre-training to solve new test tasks. A Proba-\\nbly Approximately Correct (PAC) framework [494] has been\\nproposed to assess the learnability of ICL.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/book-a-brief-history-of-time_stephen-hawking.jsonl", "content": "{\"query\": \"Why is it believed the universe began at a particular time?\", \"pos\": \"According to a number of earlycosmologies and the Jewish/Christian/Muslim tradition, the universe started at a finite, and not very distant,time in the past. One argument for such a beginning was the feeling that it was necessary to have “First Cause”to explain the existence of the universe. (Within the universe, you always explained one event as being causedby some earlier event, but the existence of the universe itself could be explained in this way only if it had somebeginning.) Another argument was put forward by St. Augustine in his book The City of God. He pointed out\\nthat civilization is progressing and we remember who performed this deed or developed that technique.\"}\r\n{\"query\": \"Could you elucidate on the intricate procedure of stellar constitution?\", \"pos\": \"Andeven then it was a long time before the implications of the theory for massive stars were understood.To understand how a black hole might be formed, we first need an understanding of the life cycle of a star. A star isformed when a large amount of gas (mostly hydrogen) starts to collapse in on itself due to its gravitational attraction. Asit contracts, the atoms of the gas collide with each other more and more frequently and at greater and greater speeds –the gas heats up. Eventually, the gas will be so hot that when the hydrogen atoms collide they no longer bounce offeach other, but instead coalesce to form helium. The heat released in this reaction, which is like a controlled hydrogenbomb explosion, is what makes the star shine.\"}\r\n{\"query\": \"Black hole existence evidence?\", \"pos\": \"the body that has collapsed must be lost when a black hole is formed, because afterward all we can possibly measureabout the body is its mass and rate of rotation. The significance of this will be seen in the next chapter.Black holes are one of only a fairly small number of cases in the history of science in which a theory was developed ingreat detail as a mathematical model before there was any evidence from observations that it was correct. Indeed, thisused to be the main argument of opponents of black holes: how could one believe in objects for which the onlyevidence was calculations based on the dubious theory of general relativity?\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/book-origin-of-species_darwin.jsonl", "content": "{\"query\": \"So, like, what's the big deal about the top species from the bigger groups talked about in Chapter 4?\", \"pos\": \"In our second and fourth chapters, on Variation and on Natural Selection, I have attempted\\nto show that it is the widely ranging, the much diffused and common, that is the dominant species\\nbelonging to the larger genera, which vary most. The varieties, or incipient species, thus produced\\nultimately become converted, as I believe, into new and distinct species; and these, on the principle\\nof inheritance, tend to produce other new and dominant species. Consequently the groups which\\nare now large, and which generally include many dominant species, tend to go on increasing\\nindefinitely in size.\"}\r\n{\"query\": \"Identify the unique species in the Chthamalinae subfamily of sessile cirripedes and the location of its fossil discovery.\", \"pos\": \"I suspect that but few of\\nthe very many animals which live on the beach between high and low watermark are preserved.\\nFor instance, the several species of the Chthamalinae (a sub-family of sessile cirripedes) coat the\\nrocks all over the world in infinite numbers: they are all strictly littoral, with the exception of a\\nsingle Mediterranean species, which inhabits deep water and has been found fossil in Sicily,\\nwhereas not one other species has hitherto been found in any tertiary formation: yet it is now\\nknown that the genus Chthamalus existed during the chalk period. The molluscan genus Chiton\\noffers a partially analogous case.\"}\r\n{\"query\": \"Why are there flaws in the geological record?\", \"pos\": \"Nor is their rarity surprising, when we remember how large a proportion of the\\nbones of tertiary mammals have been discovered either in caves or in lacustrine deposits; and that\\nnot a cave or true lacustrine bed is known belonging to the age of our secondary or palaeozoic\\nformations.\\nBut the imperfection in the geological record mainly results from another and more important cause\\nthan any of the foregoing; namely, from the several formations being separated from each other by\\nwide intervals of time. When we see the formations tabulated in written works, or when we follow\\nthem in nature, it is difficult to avoid believing that they are closely consecutive.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/healthcare-pubmed_100k-200k_1.jsonl", "content": "{\"query\": \"What does 'O' represent in peptides?\", \"pos\": \"by changing the peptides to be amphiphilic or completely polar, they systematically synthesized several derived peptides. each of them has a different polar uncharged group : p11-8 (423, based on glutamine q, sequence ac-qqrfowofeqq-nh2 ; o represents ornithine), p11-12 (424, based on serine s, sequence ac-ssrfowofess- nh2), p11-16 (427, based on asparagine n, sequence ac-nnrfowofenn- nh2), and p11-18 (428, based on threonine t, sequence ac-ttrfowofett- nh2).\"}\r\n{\"query\": \"Could you elucidate on the system that was demonstrated by Van Esch and his team utilizing 1,3,5-triamide cyclohexane-based hydrogelators 67 for the alignment of nanofibers?\", \"pos\": \"used an electrical field to assist the alignment of the nanofibers and demonstrated that the application of a voltage bias, indeed, helps the directional orientation of the fibrils. using the 1,3,5-triamide cyclohexane-based hydrogelators 67, van esch et al. demonstrated an elegant system that forms well-defined nanostructures by the orthogonal self-assembly of hydrogelators and surfactants.\"}\r\n{\"query\": \"What environmental factors influence peptide self-assembly?\", \"pos\": \"as pointed out by the authors, the hydrophobic effect between 267 molecules favors axial assembly and their electrostatic forces modulate lateral assembly. at a concentration of 0.05 wt %, the peptide self-assembles to form a filament consisting of about 120 molecules of 267. the authors also reported that various environmental factors (e.g., ph, salt, molecular crowding reagents, and peptides) can regulate the self-assembled filaments in an assembly of predictable manner, which provides useful insights for developing coiled coils as peptide-based materials. it would be interesting to know the proteolytic stability of these self-assembled filaments. besides native peptides acting as hydrogelators, peptide derivatives can also self-assemble in water to form hydrogels.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/healthcare-pubmed_100k-200k_2.jsonl", "content": "{\"query\": \"Why do we get different parameter sets when looking at how ion and water-oxygen atoms interact?\", \"pos\": \"the problem here is which one to chose to obtain a consistent set of parameters. the multiple parameter sets arise because similar aij and bij terms can be obtained between the ion and water-oxygen atoms : for a certain rmin/2 and , there will be a corresponding bigger rmin/2 and smaller , or a smaller rmin/2 and bigger , which yield similar aij and bij terms (see eqs 54 and 55). when two different ion parameter sets, which give similar aij terms between an ion and oxygen in water, are applied to the same biomolecule, they may give quite different aij terms between the same atom type on the biomolecule and the metal ion after applying the combining rules.\"}\r\n{\"query\": \"Chemical physicists managed to mock-up ions in common force fields using the 12-6 Lennard-Jones model without any direct bonding, which verified the structure traits of water-based potassium.\", \"pos\": \"the 12-6 lj nonbonded model remains a fast and practical way to simulate ions using classical force fields. the blyp functional was used for the system containing a k ion and 59 water molecules. in total, 0.168 ps of equilibration and 1.98 ps of sampling were performed in the nve ensemble. good agreement between the cpmd and classical md simulations was obtained for the structural properties of aqueous k, validating in part the classical representation of the k ion. moreover, it has also shown that it is possible to simultaneously simulate two or more experimental properties for some of the monovalent ions (e.g., na, k, rb, cs) using the 12-6 lj nonbonded model.\"}\r\n{\"query\": \"Which concepts are encapsulated within classical models in AMOEBA?\", \"pos\": \"they also proposed that the ct effect may need to be included to improve the model. ponder, ren, and co-workers have created the atomic multipole optimized energetics for biomolecular simulation (amoeba) force field. it has bonded terms (bond, angle, dihedral, and improper torsion terms) represented using classical models. the bond and angle parameters are fit on the basis of qm-derived values (e.g., geometries and vibrational frequencies). the electrostatic interaction is represented by permanent monopoles (point charges), dipoles, and quadrupoles derived from the distributed multipole analysis (dma) procedure, along with the polarizable dipoles.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/healthcare-pubmed_100k-200k_3.jsonl", "content": "{\"query\": \"In what manner does the transference of electric charge impact the process of dimerization?\", \"pos\": \"the natural bond orbital analysis suggests that the n(py) *(r x) charge transfer plays a key role in the formation of these dimers, while the symmetry-adapted perturbation theory energy decomposition analysis indicates that the xb in r xpyridine complexes is predominantly inductive in nature. halogen-bonded systems containing one or two xbs were analyzed by using the natural orbitals for chemical valence (nocv) method combined with the extended-transition-state (ets) method.\"}\r\n{\"query\": \"What affects XB's susceptibility to steric hindrance?\", \"pos\": \"for the reason stated above, xb is, in general, more sensitive to steric hindrance than hb. in the infinite chain formed by 1,4-diiodotetrafluorobenzene with 4,4- and 2,2-bipyridine, the c in distances are 2.864 and 3.158 , respectively ; when 2,4-bipyridine forms heteromeric crystals with the same xb donor, only the 4-pyridyl nitrogen is halogen-bonded, and trimers are formed wherein the c , we will see how, in the formation of dna base pairs wherein xb substitutes for hb, the most stable pairing was given by bromine as the advantage offered by the greater polarizability of iodine was overwhelmed by the disadvantage resulting from its greater size.\"}\r\n{\"query\": \"Are there any haloheteroarenes with iodine atoms?\", \"pos\": \"color code : carbon, gray ; nitrogen, blue ; iodine, purple ; fluorine, yellow. the most commonly used classes of haloheteroarenes are those containing nitrogen atom(s) in the ring. both neutral and positively charged haloheteroarenes can function as scaffolds for an xb donor site (figure 55) ; the cationic form is typically obtained by reacting the neutral form with an alkyl halide or a hydrogen halide, and the released anion works as an xb acceptor for the activated xb donor site.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/healthcare-pubmed_30k-40k_10-merged.jsonl", "content": "{\"query\": \"How do intrinsic and extrinsic factors impact A42 aggregation, and why is drug development challenging for this process?\", \"pos\": \"indeed, it has been shown that the dominant mechanism for catalyzing the formation of toxic a42 species is surface-catalyzed secondary nucleation. in other words, once a small but critical concentration of a42 aggregates has been generated through primary nucleation of monomers, surface-catalyzed secondary nucleation becomes the dominant process where the surface of the existing fibrils serve as catalytic sites for the generation of toxic oligomeric species [ 54, 57 ]. furthermore, the role of intrinsic and extrinsic factors on the aggregation process of a42 has been partly unveiled and a great effort has been focused on drug development against a42 aggregation, which has proven to be very difficult [ 100, 101 ].\"}\r\n{\"query\": \"Excitons transfer energy and can jump to a higher state, then lose energy quickly, causing them to vanish.\", \"pos\": \"this process occurs at high excitation densities when one exciton transfers its energy to another exciton and brings it to a higher-energy excited state. the higher-energy excited state relaxes rapidly, and overall an exciton is lost. in so far as quenching occurs throughout the volume of the sample, this measurement resembles volume quenching, but with excitons acting as their own quenchers. in these experiments, typically high light intensities are used, far higher than used under solar illumination conditions, and the consequences of this have to be taken into account when analyzing the data.\"}\r\n{\"query\": \"Causes of artifacts?\", \"pos\": \"however, a limiting step in the measurements of the intrinsic young s modulus of amyloid fibrillar aggregates on a surface is the correct evaluation of the cross-sectional moment of inertia i. recently, it was presented a general approach based on theory of elasticity and an innovative calculation of the polymorphic fibrillar aggregates cross-sectional moment of inertia i in order to evaluate correctly the nanomechanical properties of amyloids. this method enables to calculate bending rigidities b and matching the measured experimental values of young s modulus of amyloid fibrils [ 149, 165 ]. however, fibril imaging by afm requires deposition on a surface and drying, which can potentially lead to artifacts in the evaluation of the persistence length and bending rigidity.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/healthcare-pubmed_40k-50k_5-merged.jsonl", "content": "{\"query\": \"What is the primary product of photodimerization?\", \"pos\": \"sensitization is also the preferred way to promote coumarin and many of its derivatives into the excited state. in the absence of an external olefin, [ 2 + 2 ] photodimerization occurs with the hh cis-anti-cis product (rac-317, figure 15) being the major product. in benzene as the solvent and with benzophenone as triplet sensitizer, yields over 90% were achieved by ding and co-workers. compound rac-317 served as the starting material for the synthesis of new phosphane ligands.\"}\r\n{\"query\": \"What's a basic challenge in the [2 + 2] photocycloaddition reactions?\", \"pos\": \"the requirement of an aryl enone was a fundamental obstacle in the [ 2 + 2 ] photocycloaddition reactions, which limited the application of this methodology. in order to overcome this problem, the yoon group described a visible-light-induced [ 2 + 2 ] photocycloaddition reaction of,-unsaturated 2-imidazolyl ketones such as 483 (scheme 163, dbu = 1,8-diazabicyclo[5.4.0]undec-7-ene).\"}\r\n{\"query\": \"Dry AMD causes photoreceptors to break down because the retinal pigment epithelium, which supports retinal neurons, isn't working properly.\", \"pos\": \"intravitreal anti-vegf therapies have emerged as a standard of care to treat wet amd ; however, there is currently no fda-approved treatment available for the dry form. thus, safe and effective treatment of dry amd remains a critical unmet need. atrophic (dry) form of amd represents a slowly progressing neurodegenerative disorder of the eye in which specialized retinal neurons (rod and cone photoreceptors) degenerate in the central part of the retina called macula. histopathological and clinical data suggest that photoreceptor degeneration in dry amd is triggered by abnormalities in the retinal pigment epithelium (rpe) that lies beneath photoreceptors and provides critical metabolic support to these light-sensing neuronal cells.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/law-lex_files_300k-400k.jsonl", "content": "{\"query\": \"Where can I get the NIST Standard Reference Materials Catalog?\", \"pos\": \"The calibration services, standard reference materials and related measurement services along with changes and fees are published in two Special Publications (SP's) and their supplements. These are SP 250 “Calibration and Related Measurement Services of the National Institute of Standards & Technology” 1\\n\\n and SP 260 “NIST Standard Reference Materials Catalog.” 1 A complete catalog of all publications by NIST authors is issued annually as a supplement to SP 305 “Publications of the National Institute of Standards & Technology.” Announcements and listings of recent NIST publications and services are published in each issue of the bimonthly “NIST Journal of Research” 2\\n\\n and the NIST monthly magazine, “Dimensions/NIST” 2.\"}\r\n{\"query\": \"What is the acceptable tolerance level for cranberries?\", \"pos\": \"(1) Having determined the errors on each dimension and given to each its proper sign (see § 241.5), add the errors on the effective diameter of head and the distance between heads algebraically and multiply the result by 1.67 (or 5/3). Then add this result to the error on the circumference of bulge algebraically. If the result obtained is not greater than the tolerance given in the following table for the proper subdivision, then the barrel is within the tolerance allowed; if the result is greater than this tolerance, then the barrel is not within the tolerance allowed.\\n\\n \\n\\n \\n\\n \\n\\nSize of subdivision\\n\\nTolerance\\n\\nFor fruits, vegetables, and other dry commodities (inches)\\n\\nFor cranberries (inches)\"}\r\n{\"query\": \"What tools and techniques might be listed in solicitation announcements for specific industry sectors?\", \"pos\": \"Specific industry sectors to be addressed and sub-categories of tools and techniques may be specified in solicitations. These sectors or sub-categories will be specified in the solicitation announcement. Examples of tools and techniques include, but are not limited to, manufacturing assessment tools, environmental benchmarking tools, training delivery programs, electronically accessible environmental information resources, environmental demonstration facilities, software tools, etc. Projects must be completed within the scope of the effort proposed and should not require on-going federal support.\\n\\n (c) Award period. Projects initiated under this category may be carried out over up to three years. Proposals selected for award will receive all funding from currently available funds. If an application is selected for funding, DOC has no obligation to provide any additional future funding in connection with that award.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/law-lex_files_400k-500k.jsonl", "content": "{\"query\": \"When is the deadline for quarterly returns per § 53.153(a)?\", \"pos\": \"[T.D. ATF-308, 56 FR 303, Jan. 3, 1991, as amended by T.D. ATF-330, 57 FR 40325, Sept. 3, 1992. Redesignated in part by T.D. ATF-365, 60 FR 33670, June 28, 1995]\\n\\n\\n\\n\\n\\n§ 53.153\\n\\nTime for filing returns.\\n\\n(a) Quarterly returns. Each return required to be made under § 53.151(a) for a return period of one calendar quarter shall be filed on or before the last day of the first calendar month following the close of the period for which it is made.\"}\r\n{\"query\": \"How are federal tax liens enforced?\", \"pos\": \"The satisfaction of the levy described in paragraph (b) of this section by an insuring organization shall be without prejudice to any civil action for the enforcement of any Federal tax lien with respect to a life insurance or endowment contract. Thus, this levy procedure is not the exclusive means of subjecting the life insurance and endowment contracts of the person against whom a tax is assessed to the collection of the person's unpaid assessment. The United States may choose to foreclose the tax lien in any case where it is appropriate, as, for example, to reach the cash surrender value (as distinguished from cash loan value) of a life insurance or endowment contract.\\n\\n(e) Cross references.\"}\r\n{\"query\": \"Taxpayers must compile detailed lists for each tax jurisdiction, including names, addresses, and tax classifications.\", \"pos\": \"(b) Multiple locations and/or classes of tax. A taxpayer subject to special tax for the same period at more than one location or for more than one class of tax must—\\n\\n(1) File one special tax return, TTB Form 5630.5t, with payment of tax, to cover all such locations and classes of tax; and\\n\\n(2) Prepare, in duplicate, a list identified with the taxpayer's name, address (as shown on TTB Form 5630.5t), employer identification number, and period covered by the return. The list must show, by State, the name, address, and tax class of each location for which special tax is being paid.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/law-lex_files_500k-600k.jsonl", "content": "{\"query\": \"Define \\\"project.\\\"\", \"pos\": \"Private, as applied to an agency, organization, or institution, means that it is not under Federal or public supervision or control.\\n\\n\\n\\nProject means the activity described in an application.\\n\\n\\n\\nProject component means an activity, strategy, intervention, process, product, practice, or policy included in a project. Evidence may pertain to an individual project component or to a combination of project components (e.g., training teachers on instructional practices for English learners and follow-on coaching for these teachers).\\n\\n\\n\\nProject period means the period established in the award document during which Federal sponsorship begins and ends (See, 2 CFR 200.77 Period of performance).\"}\r\n{\"query\": \"How do you file and respond to written motions in legal proceedings?\", \"pos\": \"The ALJ may require that oral motions be reduced to writing.\\n\\n(c) Within 15 days after a written motion is served, or such other time as may be fixed by the ALJ, any party may file a response to the motion.\\n\\n(d) The ALJ may not grant a written motion before the time for filing responses to the motion has expired, except upon consent of the parties or following a hearing on the motion, but may overrule or deny the motion without awaiting a response.\\n\\n(e) The ALJ shall make a reasonable effort to dispose of all outstanding motions prior to the beginning of the hearing.\\n\\n(Authority: 31 U.S.C. 3803(g)(3)(A))\"}\r\n{\"query\": \"What does the Credit Enhancement for Charter School Facilities Program do?\", \"pos\": \"(3) Assist charter schools with the predevelopment costs required to assess sites for the purpose of acquiring (by purchase, lease, donation, or otherwise) an interest (including an interest held by a third party for the benefit of a charter school) in improved or unimproved real property or constructing new facilities, or renovating, repairing, or altering existing facilities, and that are necessary to commence or continue the operation of a charter school.\\n\\n (c) Grantees may demonstrate innovative credit enhancement initiatives while meeting the program purposes under paragraph (b) of this section.\\n\\n (d) For the purposes of these regulations, the Credit Enhancement for Charter School Facilities Program includes grants made under the Charter School Facilities Financing Demonstration Grant Program.\\n\\n [70 FR 15003, Mar.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/long-doc/law-lex_files_600k-700k.jsonl", "content": "{\"query\": \"What happens to the loss?\", \"pos\": \"This loss shall be the measured loss less the net gain of any voice frequency repeaters in the circuit. Testing shall also be conducted to verify that the loss increases gradually as the frequency increases. The loss on H88 loaded loops should be down only slightly at 2.8 kHz but drop rapidly above 2.8 kHz. The loss on D66 loaded loops shall be fairly constant to about 3.4 kHz and there shall be good response at 4.0 kHz. When voice frequency repeaters are in the circuit there will be some frequency weighting in the build-out network and the loss at the higher frequencies will be greater than for nonrepeatered loops.\"}\r\n{\"query\": \"We'll let all borrowers know by mail or email whenever there's a new Federal Register document about contract forms.\", \"pos\": \"The amendment may change the existing identification of a listed contract form; for example, changing the issuance date of a listed contract form or by identifying a new required contract form. The notice of rulemaking will describe the new standard contract form or the substantive change in the listed contract form, as the case may be, and the issues involved. The standard contract form or relevant portions thereof may be appended to the supplementary information section of the notice of rulemaking. As appropriate, the notice of rulemaking shall provide an opportunity for interested persons to provide comments. A copy of each such Federal Register document shall be sent by regular or electronic mail to all borrowers.\\n\\n[63 FR 58285, Oct. 30, 1998]\"}\r\n{\"query\": \"Which renewable energy projects can get funding through grant proposals?\", \"pos\": \"A grant project is eligible if it improves, or maintains energy services, or reduces the costs of providing energy services to eligible communities. Examples of eligible activities include, but are not limited to, the acquisition, construction, replacement, repair, or improvement of:\\n\\n(a) Electric generation, transmission, and distribution facilities, equipment, and services serving the eligible community;\\n\\n(b) Natural gas distribution or storage facilities and associated equipment and activities serving the eligible community;\\n\\n(c) Petroleum product storage and handling facilities serving residential or community use.\\n\\n(d) Renewable energy facilities used for on-grid or off-grid electric power generation, water or space heating, or process heating and power for the eligible community;\\n\\n(e) Backup up or emergency power generation or energy storage equipment, including distributed generation, to serve the eligible community; and\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/arxiv.jsonl", "content": "{\"query\": \"How does the amount of energy affect how stuff scatters?\", \"pos\": \"it has been suggested that one may construct a lorentz - invariant noncommutative field theory by extending the coordinate algebra to additional, fictitious coordinates that transform nontrivially under the lorentz group. integration over these coordinates in the action produces a four - dimensional effective theory with lorentz invariance intact. previous applications of this approach, in particular to a specific construction of noncommutative qed, have been studied only in a low - momentum approximation. here we discuss lorentz - invariant field theories in which the relevant physics can be studied without requiring an expansion in the inverse scale of noncommutativity. qualitatively, we find that tree - level scattering cross sections are dramatically suppressed as the center - of - mass energy exceeds the scale of noncommutativity, that cross sections that are isotropic in the commutative limit can develop a pronounced angular dependence, and that nonrelativistic potentials (for example, the coloumb potential) become nonsingular at the origin. we consider a number of processes in noncommutative qed that may be studied at a future linear collider. we also give an example of scattering via a four - fermion operator in which the noncommutative modifications of the interaction can unitarize the tree - level amplitude, without requiring any other new physics in the ultraviolet.\"}\r\n{\"query\": \"Why go for canonical instead of grand-canonical?\", \"pos\": \"the production of hadrons in relativistic heavy ion collisions is studied using a statistical ensemble with thermal and chemical equilibrium. special attention is given to exact conservation laws, i.e. certain charges are treated canonically instead of using the usual grand canonical approach. for small systems, the exact conservation of baryon number, strangeness and electric charge is to be taken into account. we have derived compact, analytical expressions for particle abundances in such ensemble. as an application, the change in @xmath0 ratios in ags experiments with different interaction system sizes is well reproduced. the canonical treatment of three charges becomes impractical very quickly with increasing system size. thus, we draw our attention to exact conservation of strangeness, and treat baryon number and electric charge grand canonically. we present expressions for particle abundances in such ensemble as well, and apply them to reproduce the large variety of particle ratios in gsi sis 2 a gev ni ni experiments. at the energies considered here, the exact strangeness conservation fully accounts for strange particle suppression, and no extra chemical factor is needed. [ on the exact conservation laws in thermal models ]\"}\r\n{\"query\": \"How good is the mean-field approximation at guessing the ground state features of molecular stuff?\", \"pos\": \"we present a model for molecular materials made up of polar and polarizable molecular units. a simple two state model is adopted for each molecular site and only classical intermolecular interactions are accounted for, neglecting any intermolecular overlap. the complex and interesting physics driven by interactions among polar and polarizable molecules becomes fairly transparent in the adopted model. collective effects are recognized in the large variation of the molecular polarity with supramolecular interactions, and cooperative behavior shows up with the appearance, in attractive lattices, of discontinuous charge crossovers. the mf approximation proves fairly accurate in the description of the gs properties of mm, including static linear and non - linear optical susceptibilities, apart from the region in the close proximity of the discontinuous charge crossover. sizeable deviations from the excitonic description are recognized both in the excitation spectrum and in linear and non - linear optical responses. new and interesting phenomena are recognized near the discontinuous charge crossover for non - centrosymmetric clusters, where the primary photoexcitation event corresponds to a multielectron transfer.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/finance.jsonl", "content": "{\"query\": \"What is the effect of the LME Singapore Contract on trade dynamics?\", \"pos\": \"The London Metal Exchange's, LME,\\ndecision to introduce a dollar-denominated aluminium contract,\\nwith the Port of Singapore listed as a delivery point, is a\\npositive move, physical traders and LME dealers said.\\n Earlier this week the LME declared that a 99.70 pct minimum\\npurity aluminium contract would commence trading on June 1,\\n1987, alongside its long-established sterling-based 99.50 pct\\ncontract.\\n This is the LME's first dollar contract and non-European\\ndelivery point, and the Board and Committee are looking at\\nSingapore as a delivery point for other contracts.\\n Trade sources said the LME's new contract will conform with\\nexisting industry practice, where 99.70 standard re-melt\\nmaterial, priced in dollars, is most commonly traded.\\n The location of a warehouse in Singapore is also a positive\\nmove by the LME, given its ideal location for Australian and\\nJapanese traders, who would be able to place metal on to\\nwarrant speedily and relatively inexpensively, they said.\\n Hedging during the LME ring sessions becomes much simpler\\nwith a dollar contract. At present pre-market trading is almost\\nexclusively dollar-based, but currency conversions have to be\\ndone during the sterling rings, they added.\\n LME ring dealers said the new contract would match more\\nclosely trade requirements and possibly alleviate some of the\\nrecent wide backwardations.\\n Very little physical business is now done in 99.50 pct\\npurity metal, nearly all of which is produced in Eastern Bloc\\ncountries, such as Romania.\\n The Soviet Union also produces 99.50 pct, but has declined\\nas an exporter recently, they said.\\n Some dealers said the new 99.70 contract may suffer from\\nliquidity problems initially, as business may continue to\\ncentre on the present good ordinary brand (gob) contract, where\\nthere are many holders of large short positions on the LME.\\n But others said the new contract would soon attract trading\\ninterest, given that much 99.70 metal has already been\\nattracted to the LME's warehouses by backwardations.\\n The LME also has a much more viable liquidity base for a\\nnew contract, compared to the Comex market in New York, where\\nhigh grade aluminium futures are not particularly active, they\\nsaid.\\n Thus, it seems likely that the sterling contract will\\neventually lose trading interest and volumes will decline. Like\\nstandard zinc, which was superseded by a high grade contract,\\ngob aluminium will probably be replaced, although the process\\nin this case may take longer, they added.\\n Forming a new contract and establishing a Singapore\\nwarehouse are constructive moves by the LME but backwardations,\\nwhich make physical trading difficult, would not totally\\ndisappear as a result, the trade sources said.\\n These premiums for prompt metal have become a\\nsemi-permanent feature over the last year, due to increased\\nbusiness and volatility in traded options, and are presently\\naround 50 stg.\\n Increasingly large granting of option positions has been\\ntaking place. When some of these are declared and exercised at\\nthe end of the relevant month, physical tightness and squeezes\\naround these dates are commonplace, they said.\\n Listing Singapore as a delivery point allows Far Eastern\\noperators to deliver aluminium into a LME warehouse instead of\\nhaving to cover.\\n But tightness and backwardations are seen continuing, even\\nthough the LME's new option contracts widen the gap between the\\ndeclaration and prompt dates.\\n These will be due on the first and third Wednesday of the\\nmonth, whereas at present most fall on the 20th and 25th.\\n Backwardations will remain while operators continue to\\ngrant options where potential tonnage to be delivered exceeds\\naluminium stock levels, an LME option trader said.\\n Reuter\\n\"}\r\n{\"query\": \"Please provide the estimated quantity of the broad monetary aggregate designated as M-3, which encompasses the extensive range of financial assets held principally by households, as recorded in the month of February.\", \"pos\": \"South African year-on-year broadly\\ndefined M-3 money supply growth slowed to 8.62 pct in January\\nfrom 9.32 pct in December, Reserve Bank figures show.\\n M-3 fell to 77.98 billion rand in January from 79.31\\nbillion in December, while preliminary February figures show\\nM-3 at 79.42 billion rand for a year-on-year rise of 10.63 pct.\\n M-2 showed a rise of 5.09 pct for January at 55.68 billion\\nrand after 4.30 pct in December, M-1 16.72 pct at 5.12 billion\\nafter 12.80 pct and M-1A 22.79 pct at 14.30 billion rand after\\n20.54 pct.\\n REUTER\\n\"}\r\n{\"query\": \"When did Reagan impose tariffs?\", \"pos\": \"The White House issued a\\nlist of Japanese exports to covered by the 100 pct tariffs\\nimposed by President Reagan.\\n - Automatic data processing machines (1986 imports worth\\n180 mln dlrs), including certain desk and lap models with\\nmicroprocessor-based calculating mechanism capable of handling\\nwords of at least 16-bits off the microprocessor;\\n - Complete color television sets, with 18, 19 or 20 inch\\nscreens (1986 imports 90 mln dlrs);\\n - Power tools, including certain drills, percussion\\nhammers, sanders, polishers, grinders.\\n Reuter\\n\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/healthcare.jsonl", "content": "{\"query\": \"Which technique was employed to assess the blood pressure in Wistar rats subjected to various sodium intake regimens?\", \"pos\": \"Male Wistar rats were fed on normal- (0.5% Na(+); NS), high- (3.12% Na(+); HS),or low-sodium (0.06% Na(+); LS) diets for 3, 6, and 9 weeks after weaning. Blood pressure (BP) was measured using a computerized tail-cuff system. An intravenous insulin tolerance test (ivITT) was performed in fasted animals. At the end of each period, rats were killed and blood samples were collected for glucose and insulin determinations. The white adipose tissue (WAT) from abdominal and inguinal subcutaneous (SC) and periepididymal (PE) depots were weighed and processed for adipocyte isolation and measurement of in vitro rates of insulin-stimulated 2-deoxy-D-[(3)H]-glucose uptake (2DGU) and conversion of -[U-(14)C]-glucose into (14)CO(2).\"}\r\n{\"query\": \"How long were the kids treated with chemo for their stomach lymphoma?\", \"pos\": \"Only two patients, 5 and 12 years old, with primary gastric NHL were found. Upper gastroduodenal endoscopy detected an ulcer in the lesser curvature of the body of the stomach, in both cases. Endoscopy revealed a moderate chronic gastritis in the antrum of both patients that was H. pylori associated in one of them who also suffered from chronic gastritis. Biopsy specimens demonstrated infiltration by Burkitt lymphoma (BL). The two patients received chemotherapy for 6 months. Additionally, one of the two patients received a triple therapy regimen with bismuth, amoxicillin, and metronidazole for H. pylori. Fifteen and six years later they are in complete remission, free of symptoms.\"}\r\n{\"query\": \"What are the correlations between the volume of tissue resected and the resulting clinical outcomes?\", \"pos\": \"Between May 2011 and April 2013, LSG was performed in 102 consecutive patients undergoing bariatric surgery. Two patients were excluded, and data from the remaining 100 patients were analyzed in this study. Patients were divided into three groups according to the following resected stomach volume: 700-1,200 mL (group A, n = 21), 1,200-1,700 mL (group B, n = 62), and>1,700 mL (group C, n = 17). Mean values were compared among the groups by analysis of variance.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/law.jsonl", "content": "{\"query\": \"In accordance with European regulatory standards, what factors are instrumental in establishing the global market valuation for raw unginned cotton?\", \"pos\": \"Name: Commission Regulation (EEC) No 2368/92 of 12 August 1992 fixing the aid for cotton\\n Type: Regulation\\n Subject Matter: character(0)\\n Date Published: nan\\n\\n No L 230/22 Official Journal of the European Communities 13 . 8 . 92 COMMISSION REGULATION (EEC) No 2368/92 of 12 August 1992 fixing the aid for cotton THE COMMISSION OF THE EUROPEAN COMMUNITIES, Having regard to the Treaty establishing the European Economic Community, Having regard to the Act of Accession of Spain and Portugal, Having regard to the Act of Accession of Greece, and in particular paragraphs 3 and 10 of Protocol 4 on cotton annexed thereto, as amended by Protocol 14 annexed to the Act of Accession of Spain and of Portugal, and Regu ­ lation (EEC) No 4006/87 ('), Having regard to Council Regulation (EEC) No 2169/81 of 27 July 1981 laying down the general rules for the system of aid for cotton (2), as last amended by Regulation (EEC) No 2053/92 (3), and in particular Article 5 ( 1 ) thereof, Whereas, pursuant to Article 5 of Regulation (EEC) No 2169/81 , aid must be granted for unginned cotton harvested in the Community when the world market price for unginned cotton is below the guide price ; Whereas the aid is equal to the difference between these two prices ; Whereas the guide price for cotton has been fixed by Council Regulation (EEC) No 2055/92 for the 1992/93 marketing year (4) ; Whereas the abatement of the subsidy which arises, where appropriate, from the system of maximum guaranteed quantities for the 1992/1993 marketing year, has not, to date, been fixed ; whereas this provisional abatement should be fixed taking account of the 15% limit referred to in the first subparagraph of Article 3 (2) of Council Regulation (EEC) No 1964/87 (% as last amended by Regulation (EEC) No 2052/92 (6), and the harvest fore ­ casts ; whereas the abatement should therefore be set at, ECU 15 419 per 100 kg ; Whereas the world market price for unginned cotton is determined periodically on the basis of the world market prices recorded for ginned cotton and cotton seed, taking into account the estimated yield of the Community harvest in cotton seed and in ginned cotton and also the net cost of ginning ; Whereas the world market price for ginned cotton and cotton seed is determined in accordance with Article 4 of Regulation (EEC) No 2169/81 ; Whereas, if the world market price for unginned cotton cannot be determined as described above, this price shall be established on the basis of the most recent price deter ­ mined ; Whereas the world market price for unginned cotton is equal to the sum of the values for ginned cotton and cotton seed defined in Article 1 of Commission Regula ­ tion (EEC) No 1201 /89 of 3 May 1989 laying down rules implementing the system of aid for cotton Q, as last amended by Regulation (EEC) No 2756/91 (8), minus the cost of ginning ; Whereas the above values are established on the basis of the prices determined in accordance with Articles 2 and 3 of Commission Regulation (EEC) No 1201 /89 ; whereas the world market price is determined on the basis of the most favourable offers and quotations recorded, excluding offers and quotations which cannot be regarded as repre ­ sentative of the real market trend ; Whereas the necessary adjustments must be made in cases where the offers and quotations recorded do not satisfy the requirements indicated above ; Whereas, pursuant to Article 4 (4) of Regulation (EEC) No 2169/81 , if there are no suitable offers or quotations for determining the world market price for cotton seed, that price shall be established on the basis of the most favourable offers and quotations for cotton seed recorded on the Community market or, if those offers and quota ­ tions cannot be established on the basis of the value of the products obtained from processing the seed in the Community, less the processing cost ; whereas this value is determined in accordance with Article 4 of Regulation (EEC) No 1201 /89 ; Whereas, if the subsidy system is to operate normally, subsidies should be calculated on the following basis : ” in the case of currencies which are maintained in rela ­ tion to each other at any given moment within a band of 2,25 %, a rate of exchange based on their central rate, multiplied by the corrective factor provided for in the last paragraph of Article 3 (1 ) of Council Regula ­ tion (EEC) No 1676/85 ('), as last amended by Regula ­ tion (EEC) No 2205/90 ( 10), (') OJ No L 377, 31 . 12. 1987, p. 49. 0 OJ No L 211 , 31 . 7 . 1981 , p. 2. 0 OJ No L 215, 30. 7. 1992, p. 12 . O OJ No L 123, 4. 5. 1989, p. 23. (8) OJ No L 264, 20. 9 . 1991 , p. 21 . 0 OJ No L 164, 24. 6 . 1985, p. 1 . (10) OJ No L 201 , 31 . 7 . 1990, p. 9 . (4) OJ No L 215, 30.. 7 . 1992, p. 14 . 0 OJ No L 184, 3. 7. 1987, p. 14. (6) OJ No L 215, 30. 7 . 1992, p. 10 . 13 . 8 . 92 Official Journal of the European Communities No L 230/23 ” for the other currencies, an exchange rate based on an average of the ecu rates published in the Official Journal of the European Communities, C series, over a period to be determined, multiplied by the coeffi ­ cient referred to in the preceding indent ; Whereas the aid must be fixed once a month, and in such a way that it can be applied from the first day of the month following the date of fixing ; whereas it may be altered between fixings ; Whereas it follows from applying these provisions to the offers and Quotations known to the Commission that the aid for cotton should be as set out in this Regulation, HAS ADOPTED THIS REGULATION : Article 1 1 . The aid for unginned cotton provided for in Article 5 of Regulation (EEC) No 2169/81 shall be ECU 73,290 per 100 kilograms. 2. However, the amount of the aid will be confirmed or replaced with effect from 13 August 1992, which appear to have been offered in the largest quantities . Article 2 This Regulation shall enter into force on 13 August 1992. This Regulation shall be binding in its entirety and directly applicable in all Member States. Done at Brussels, 12 August 1992. For the Commission Ray MAC SHARRY Member of the Commission\"}\r\n{\"query\": \"List the cereal product codes from the annex of the Commission Regulation correcting the refund amount dated 7 April 1999.\", \"pos\": \"Name: Commission Regulation (EC) No 732/1999 of 7 April 1999 altering the corrective amount applicable to the refund on cereals\\n Type: Regulation\\n Subject Matter: trade policy; cooperation policy; plant product\\n Date Published: nan\\n\\n EN Official Journal of the European Communities 8. 4. 1999L 93/22 COMMISSION REGULATION (EC) No 732/1999 of 7 April 1999 altering the corrective amount applicable to the refund on cereals THE COMMISSION OF THE EUROPEAN COMMUNITIES, Having regard to the Treaty establishing the European Community, Having regard to Council Regulation (EEC) No 1766/92 of 30 June 1992 on the common organization of the market in cereals (1), as last amended by Commission Regulation (EC) No 923/96 (2), and in particular Article 13 (8) thereof, Whereas the corrective amount applicable to the refund on cereals was fixed by Commission Regulation (EC) No 689/1999 (3); Whereas, on the basis of todays cif prices and cif forward delivery prices, taking foreseeable developments on the market into account, the corrective amount at present applicable to the refund on cereals should be altered; Whereas the corrective amount must be fixed according to the same procedure as the refund; whereas it may be altered in the period between fixings, HAS ADOPTED THIS REGULATION: Article 1 The corrective amount referred to in Article 1 (1) (a), (b) and (c) of Regulation (EEC) No 1766/92 which is applic- able to the export refunds fixed in advance in respect of the products referred to, except for malt, is hereby altered to the amounts set out in the Annex hereto. Article 2 This Regulation shall enter into force on 8 April 1999. This Regulation shall be binding in its entirety and directly applicable in all Member States. Done at Brussels, 7 April 1999. For the Commission Franz FISCHLER Member of the Commission (1) OJ L 181, 1.7.1992, p. 21. (2) OJ L 126, 24.5.1996, p. 37. (3) OJ L 87, 31.3.1999, p. 5. EN Official Journal of the European Communities8. 4. 1999 L 93/23 ANNEX to the Commission Regulation of 7 April 1999 altering the corrective amount applicable to the refund on cereals (EUR / tonne) Current 1st period 2nd period 3rd period 4th period 5th period 6th period Product code Destination (1) 4 5 6 7 8 9 10 1001 10 00 9200 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1001 10 00 9400 01 0 “1,00 “1,00 0 0 ï £ § ï £ § 1001 90 91 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1001 90 99 9000 01 0 0 0 “10,00 “10,00 ï £ § ï £ § 1002 00 00 9000 01 0 0 0 “10,00 “10,00 ï £ § ï £ § 1003 00 10 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1003 00 90 9000 03 0 “25,00 “35,00 “35,00 “35,00 ï £ § ï £ § 02 0 0 “10,00 “10,00 “10,00 ï £ § ï £ § 1004 00 00 9200 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1004 00 00 9400 01 0 0 0 “10,00 “10,00 ï £ § ï £ § 1005 10 90 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1005 90 00 9000 04 0 0 0 0 0 ï £ § ï £ § 02 0 “1,00 “2,00 “3,00 “4,00 ï £ § ï £ § 1007 00 90 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1008 20 00 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1101 00 11 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1101 00 15 9100 01 0 0 0 0 0 ï £ § ï £ § 1101 00 15 9130 01 0 0 0 0 0 ï £ § ï £ § 1101 00 15 9150 01 0 0 0 0 0 ï £ § ï £ § 1101 00 15 9170 01 0 0 0 0 0 ï £ § ï £ § 1101 00 15 9180 01 0 0 0 0 0 ï £ § ï £ § 1101 00 15 9190 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1101 00 90 9000 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1102 10 00 9500 01 0 0 0 0 0 ï £ § ï £ § 1102 10 00 9700 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1102 10 00 9900 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1103 11 10 9200 01 0 0 “10,00 “10,00 “10,00 ï £ § ï £ § 1103 11 10 9400 01 0 0 “10,00 “10,00 “10,00 ï £ § ï £ § 1103 11 10 9900 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § 1103 11 90 9200 01 0 0 0 0 0 ï £ § ï £ § 1103 11 90 9800 ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § ï £ § (1) The destinations are identified as follows: 01 all third countries 02 other third countries 03 United States of America, Canada and Mexico 04 Switzerland, Liechtenstein and Slovenia. NB: The zones are those defined in amended Commission Regulation (EEC) No 2145/92 (OJ L 214, 30.7.1992, p. 20).\"}\r\n{\"query\": \"How much money is being handed out to help grow rice in the Canary Islands?\", \"pos\": \"Name: COMMISSION REGULATION (EC) No 1510/95 of 29 June 1995 setting the amounts of aid for the supply of rice products from the Community to the Canary Islands\\n Type: Regulation\\n Subject Matter: plant product; regions of EU Member States; trade; economic policy; production\\n Date Published: nan\\n\\n No L 147/28 EN Official Journal of the European Communities 30 . 6. 95 COMMISSION REGULATION (EC) No 1510/95 of 29 June 1995 setting the amounts of aid for the supply of rice products from the Community to the Canary Islands THE COMMISSION OF THE EUROPEAN COMMUNITIES, Having regard to the Treaty establishing the European Community, Having regard to Council Regulation (EEC) No 1601 /92 of 15 June 1992 introducing specific measures in respect of certain agricultural products last for the benefit of the Canary Islands ('), as last amended by the Act of Acces ­ sion of Austria, Finland and Sweden and by Regulation (EC) No 3290/94 (2), and in particular Article 2 thereof, Whereas, pursuant to Article 3 of Regulation (EEC) No 1601 /92, the requirements of the Canary Islands for rice are to be covered in terms of quantity, price and quality by the mobilization, on disposal terms equivalent to exemption from the levy, of Community rice, which involves the grant of an aid for supplies of Community origin ; whereas this aid is to be fixed with particular reference to the costs of the various sources of supply and in particular is to be based on the prices applied to exports to third countries ; Whereas Commission Regulation (EC) No 2790/94 (3), as amended by Regulation (EC) No 2883/94 (4), lays down common detailed rules for implementation of the specific arrangements for the supply of certain agricultural products, including rice , to the Canary Islands ; Whereas the representative market rates defined in Article 1 of Council Regulation (EEC) No 3813/92 (*), as last amended by Regulation (EC) No 1 50/95 (*), are used to convert amounts expressed in third country currencies and are used as the basis for determining the agricultural conversion rates of the Member States' currencies ; whereas detailed rules on the application and determina ­ tion of these conversions were set by Commission Regu ­ lation (EEC) No 1068/93 f), as last amended by Regula ­ tion (EC) No 1053/95 (8); Whereas, as a result of the application of these detailed rules to the current market situation in the rice sector, and in particular to the rates of prices for these products in the European part of the Community and on the world market, the aid for supply to the Canary Islands should be set at the amounts given in the Annex ; Whereas the measures provided for in this Regulation are in accordance with the opinion of the Management Committee for Cereals, HAS ADOPTED THIS REGULATION : Article 1 Pursuant to Article 3 of Regulation (EEC) No 1601 /92, the amount of aid for the supply of rice of Community origin under the specific arrangements for the supply of the Canary Islands shall be as set out in the Annex hereto. Article 2 This Regulation shall enter into force on 1 July 1995 . This Regulation shall be binding in its entirety and directly applicable in all Member States. Done at Brussels, 29 June 1995 . For the Commission Franz FISCHLER Member of the Commission (') OJ No L 173 , 27. 6 . 1992, p. 13. (2) OJ No L 349, 31 . 12 . 1994, p. 105. (3) OJ No L 296, 17. 11 . 1994, p. 23. (4) OJ No L 304, 29. 11 . 1994, p. 18 . (5) OJ No L 387, 31 . 12 . 1992, p. 1 . (<) OJ No L 22, 31 . 1 . 1995, p. 1 . 0 OJ No L 108 , 1 . 5. 1993, p. 106. H OJ No L 107, 12. 5. 1995, p. 4. 30 . 6 . 95 EN Official Journal of the European Communities No L 147/29 ANNEX to the Commission Regulation of 29 June 1995 setting the amounts of aid for the supply of rice products from the Community to the Canary Islands (ECU/tonne) Amount ot aidProduct (CN code) Canary Islands Milled rice ( 1006 30) 322,00 Broken rice ( 1006 40) 71,00\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/msmarco.jsonl", "content": "{\"query\": \"Corn on the cob boiling time?\", \"pos\": \"Corn on the Cob - Boiled In a large pot, enough to hold the corn, fill it with water to cover the corn (the corn should float). On a medium heat allow the pot of water to boil. Once the water is boiled, add in the corn into the pot and cover. Cook for 10-15 minutes depending on how soft you want your corn. Drain water and remove corn on the cob.\"}\r\n{\"query\": \"Nitrous oxide is commonly used as an anesthetic or analgesic in medical and dental procedures.\", \"pos\": \"Nitrous oxide Nitrous oxide has significant medical uses, especially in surgery and dentistry, for its anaesthetic and analgesic effects. Its name laughing gas is due to the euphoric effects of inhaling it, a property that has led to its recreational use as a dissociative anaesthetic.\"}\r\n{\"query\": \"At what temp do you start to roast?\", \"pos\": \"How long to cook 2.3 lb pork tenderloin in oven? Best Answer: For your seasoned pork loin, preheat your oven to 400 degrees F or (200C). Place the seasoned pork in the preheated oven and immediately turn the oven down to 350F (175C). Roast the pork loin or tenderloin for about 70-90 minutes or until it reaches an internal temperature of 145-150F (73-75C) degrees. If you prefer your pork cooked to medium well, cook it to an internal temperature of 155-160F (78-80C) degrees.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/news.jsonl", "content": "{\"query\": \"Who's Bella got a crush on?\", \"pos\": \"Bella Thorne has crushes on Demi Lovato, Kristen Stewart and Camila Cabello.\\nThe 19-year-old actress - who came out as bisexual last year - revealed she has the hots for a number of famous women and would love to date them.\\nShe told Stylecaster.com: \\\"Demi Lovato. That's an obvious one. I love Demi. We're close. She's amazing, just such a beautiful person inside and out. Love everything she stands for.\\n\\\"Kristen Stewart. I'm like, 'Please.' She's so hot. Oh. My. God. You put on those f**king Converse, girl. You put on that rock shirt, and you come to mommy. I literally love Kristen Stewart.\\n\\\"Who else is super-hot? Oh, Camila Cabello. I think she's so hot. I just saw her at a party the other night, but she was with a guy, so I wasn't gonna hit on her because she was with a date.\\\"\\nHowever, Bella admitted that she finds it easier to date men than women because she never knows if women are into her or not.\\nShe explained: \\\"It's so hard. I can't tell if a girl is trying to be best friends with me or if she wants to get with me or if she just wants social media followers. I'm just so confused when a girl talks to me. Girls can be very flirtatious, so I don't want to make a move, and then you be like, 'Whoa, girl. Not what I was thinking, I don't roll that way'.\\n\\\"Then it's so awkward. So I end up usually dating more guys, because with guys, I know if a guy's hitting me up. They're not just texting me to be my bestie. I know they want something, of some sort.\\n\\\"I have this girl that's always, like, 'OMG, you're so gorgeous,' and she's always hitting on me, and I'm like, I don't know. I don't want to make a move and then you're, like, 'Oh, I'm not gay,' and then I'm, like, Oh, this is so awkward. Now you feel like I just ruined our friendship because it's just too weird.\\\"\\nAnd Bella also claims that most girls she knows are only interested in hooking up because they are not out as lesbian or bisexual.\"}\r\n{\"query\": \"What are the signs of catching H3N2?\", \"pos\": \"Get daily updates directly to your inbox + Subscribe Thank you for subscribing! Could not subscribe, try again later Invalid Email\\nPotentially deadly 'Aussie flu' - also known as H3N2 - has arrived in the UK after claiming hundreds of lives in Australia.\\nAussie flu affected up to 170,000 people in Australia - more than two-and-a-half times last year's total - with over 300 reported to have died.\\nIn late December, Ireland saw its first deaths - though 'no more than 10' - while the potentially killer strain has now been confirmed in parts of the UK too.\\n(Image: Getty)\\nNo deaths have been reported here but Public Health England's latest flu report, released on January 4th, reveals 17 people are in intensive care or a high dependency unit with H3N2.\\nHowever, according to the Flu Survey map, just two areas of the country have no reports of flu, including the Aussie strain.\\nThe worst-hit areas include Portsmouth, Plymouth, Northern Ireland, Dundee, Doncaster, Chelmsford, Northampton and Canterbury, according to the map.\\nBut what is 'Aussie flu'? And what steps can you take to avoid it? Read below to find out.\\nWhat are the Aussie flu symptoms?\\nThe NHS stress that flu symptoms can come on quickly and can include:\\na sudden fever\\nbody aches\\ncoughing\\nexhaustion\\nfever\\nheadache\\nminor congestion\\nsore throat\\nvomiting and diarrhoea\\nSymptoms for children are similar and may also include a pain in the ear, while appearing less active.\\nHow can you treat flu?\\nFlu usually clears up by itself after around a week, but there are ways you can recover more quickly.\\nrest\\nsleep\\nkeep warm\\ntake paracetamol and ibuprofen\\ndrinking plenty of water\\nGPs do not prescribe antibiotics as they will not relieve symptoms or help recovery.\\nYou can seek advice most easily from a pharmacist, and are encouraged not to call 999 or go to A&E unless you develop sudden chest pain, have trouble breathing or start coughing blood.\\nPatients are advised to only go to their GP if their symptoms fail to improve after seven days, they are a child, over-65, pregnant or have a long-term medical condition or weakened immune system.\\nWhat is Australian flu?\\nThere are two main types of flu - A and B.\\nOne of the strains of influenza circulating the UK this year is a type of A flu known as H3N2.\\nThe particular strain of H3N2 flu that is affecting the UK is similar to the type that Australia suffered from earlier this year, during their winter.\\nDr Richard Pebody, acting head of respiratory diseases at Public Health England, said: \\\"In Australia they saw excess mortality and other hospitalisations and so on due to H3N2.\\\"\\nWhat is it like?\\nSufferers of Aussie flu have come forward to tell their story to Mirror Online.\\nMum-of-three Natalie Shand, 39, thought her Aussie flu was a hangover until she was struck down with vomiting, chest pains and wheezing.\\nNatalie, from Oldbury in the West Midlands, was left bedbound for five weeks off and on.\\n(Image: Natalie Shand)\\nMeanwhile Mark Wilde, 51, from Great Sankey, Warrington, struggled to eat and walk after he was struck down with the H3N2 strain on New Year's Eve.\\nHis fiancee Clare Roberts, 45, also caught the bug.\\nThe couple, who \\\"couldn't move\\\" because of a rocketing fever, muscle aches and chest pains, said they would not wish the disease on \\\"their worst enemy\\\".\\n(Image: Supplied)\\nHow can you avoid getting the flu?\\n(Image: Rex)\\nPeople have been urged to get a flu jab to protect themselves from the H3N2 strain.\\nThe flu vaccine is the best protection we have, although because flu strains change, it needs to be done every year.\\nThe flu jab is offered free to adults at risk, over-65s, pregnant women and children at risk aged six months to two years old, and a spray is offered to children up to four.\\nYou can have the jab at your GP and some pharmacies. Serious side effects of the vaccine are rare.\\nThose who don't heed the advice and are diagnosed by a GP may be prescribed an anti-viral medication to treat their symptoms.\\nWhile it takes 10 to 14 days for the vaccine to take effect, Dr Pebody encouraged those who can take advantage of the NHS's free programme to do so.\\nIt has been reported that the vaccine used in Australia wasn't as effective as hoped.\\nIf you haven't been diagnosed but think you have the flu, you can speak to a pharmacist to find out if there are any over-the-counter medications you can take.\\nPeople can prevent the virus from spreading by washing their hands regularly, covering their mouth and nose with tissues or a sleeve when they cough or sneeze, and cleaning surfaces they suspect are infected.\\nWhat is the difference between flu and a cold?\\nThe symptoms may be similar to a common cold, but flu tends to be more severe.\\nFlu tends to come on in a few hours, makes you feel exhausted and affects more than the nose and throat alone.\\nIt can also lead to much more serious complications like pneumonia.\\nWhat are the experts saying?\\n(Image: iStockphoto)\\nExperts have warned that this year's strain of Aussie flu could be more dangerous than the 1968 flu pandemic that killed more than a million worldwide.\\nPublic health expert Professor Robert Dingwall, of Nottingham Trent University, warned: \\\"The reports from Australia suggest the UK might be in for the worst winter flu season for many years.\\\"\\nHowever, Public Health England said that it was not yet known whether the UK would be hit as hard as Australia.\"}\r\n{\"query\": \"What benefits does the Mighty player offer Spotify playlist users?\", \"pos\": \"Sarah Tew/CNET\\nCan't swing a Spotify subscription? (Even if it's combined with Hulu?) Fear not: Following its IPO earlier this month, the music-streaming giant just overhauled its free-listening tier for mobile users.\\nThat means you're about to get more bang (and other noises) for no bucks. Let's take a look at some of the key aspects of the new free Spotify.\\nYou're no longer restricted to shuffling\\nBefore this update, you could listen to any Spotify artist, album or playlist, but only in shuffle mode. Now, you'll get on-demand access to 15 of Spotify's most popular playlists, many of them custom-tailored to your preferences. That means you can hop around within those playlists and listen to exactly the songs you want.\\nThe 'Free 15'\\nSo exactly which playlists are available as part of the free tier? The full list is yet to be revealed, but they'll include Daily Mix, Discover Weekly, Release Radar, Today's Top Hits and the hip-hop list Rap Caviar. Some of these contain personalized selections based on your music preferences. All told, there will be about 750 total tracks available for on-demand listening.\\nYou can still venture beyond the Free 15, but within those playlists, you'll still be limited to shuffle-play, same as before.\\nNow Playing: Watch this: Free Spotify: Here's what's new\\nIt still has ads\\nYou didn't think those free tunes would also be free from commercials, did you? That's been the model of Spotify's freebie tier since the beginning. If you want an ad-free listening experience, you'll have to pay for a Spotify subscription.\\nThe tablet experience is still better\\nSpotify's best-kept secret is that if you run the app on a tablet, you can listen to any song you want, on demand. You'll still have to contend with ads, but you're not shoehorned into shuffle mode like on your phone.\\nIt's not available yet\\nMighty Audio\\nThe updated Spotify app will roll out to users in the coming weeks, so you'll have to wait a bit longer for the new on-demand options.\\nSpotify didn't announce hardware, but...\\nSome observers were hoping Spotify might announce a mobile player, but, alas, no soap.\\nThere's already a product that lets you take your Spotify playlists on the go, however: The Mighty. Similar to Apple's now-discontinued iPod Shuffle ($99.99 at Amazon Marketplace), the Mighty player can hold up to 1,000 tracks and plays for up to 5 hours on a charge. It sells for $86.\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/web.jsonl", "content": "{\"query\": \"Identify the principal landmarks that are emblematic of the Baha'i religious tradition.\", \"pos\": \"House of Baha'u'llah, Baghdad\\n\\\"Grieve not, O House of God, if the veil of thy sanctity be rent asunder by the infidels. God hath, in the world of creation, adorned thee with the jewel of His remembrance. Such an ornament no man can, at any time, profane. Towards thee the eyes of thy Lord shall, under all conditions, remain directed. He, verily, will incline His ear to the prayer of every one that visiteth thee, who will circle around thee, and calleth upon Him in thy name. He, in truth, is the Forgiving, the All-Merciful.\\\"\\n(Gleanings from the Writings of Bahá’u’lláh, LVII, part 7)\"}\r\n{\"query\": \"Which type of healthcare professional should one consult regarding the sensation of tingling in the feet?\", \"pos\": \"“Tingly feet\\\" can be a sign of nerve loss. The nerves in the feet come from the lower back. Pressure or chemical change in the nerve can cause a tingling sensation in the feet. Any sensation that is out of the ordinary can be an early sign of neurologic or vascular problems. In addition to tingling, feet may feel numb or feel like they are \\\"falling asleep.\\\" There may also be a burning sensation in the feet.\\nDiabetes is one of the most common medical conditions with which \\\"tingly feet\\\" can be associated. A thorough evaluation by a foot and ankle surgeon is advised to determine the cause of \\\"tingly feet.\\\"\\nSee also Diabetic Peripheral Neuropathy.\"}\r\n{\"query\": \"How big is the old Kaguru Basket?\", \"pos\": \"Home — Vintage Kaguru Basket from Tanzania - 15\\\" x 10.5\\\"\\nVintage Kaguru Basket from Tanzania - 15\\\" x 10.5\\\"\\nFrom Tanzania, East Africa, these baskets are used the same way all other similar baskets are used everywhere else in Africa. They are the primary vessel for storage and transportation of grain, fruit, vegetables and any other food item. Baskets of this type are often seen in markets containing food for sale there. The patina on the rims and side of this basket suggests it was handled often. There is residue of some sort on the interior surfaces which proves they were often in use in the way I have described.\\nAfter 36 years of traveling in Africa and buying similar African items, we thought we had seen it all. These are truly amazing baskets, at least to us.\\nThis basket measures 15\\\" x 10.5\\\" (38cm x 26.75cm)\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/examples/qa/wiki.jsonl", "content": "{\"query\": \"Muskoka history?\", \"pos\": \"Frank Garfield \\\"Gary\\\" Denniss is a Canadian historian, newspaper columnist, retired public school teacher, speaker and ordained minister born in 1944 in Bracebridge, Ontario Denniss is the author of 43 books on the history of the District Municipality of Muskoka, (Muskoka District, at the southern edge of the Canadian Shield, stretches north from the Severn River through rocky and forested lake land, bounded by Georgian Bay on the west and Algonquin Park to the east, connecting to twin district Parry Sound.)\\n\\nBiography\\n\\nGary Denniss was born at Bracebridge Memorial Hospital on May 31, 1944, to Frank Edwin Denniss II (1908–2003), and Jessie Evelyn Arnott (1917–1991). Mr Denniss taught in the public schools in the Bracebridge, Ontario area (Wah Wah Taysee, Cochrane, Bracebridge, Vankoughnet, Huntsville, and Macaulay) until his retirement in 1998. He continues to live in Bracebridge, where he writes, teaches piano, and officiates at weddings and funerals. Since 1991, Mr Denniss has held a leadership role in the maintenance of Bracebridge veterans' gravesites, veterans' memories and Langford Cemetery, Macaulay Township.\\n\\nPublished works\\n\\n \\\"Macaulay Township in Days Gone By\\\" : Herald-Gazette Press, 1970, (Algoma University, Wishart Library).\\n \\\"The Pioneer Zimmerman Family of Macaulay Township\\\", newspaper article, Herald-Gazette, Bracebridge, ON. April 2, 1970.\\n \\\"A Brief History of the Schools in Muskoka\\\", Herald-Gazette Press, 1972.\\n \\\"Free Methodist Hill, a Centennial History\\\", 1879–1979, Herald-Gazette, 1979.\\n \\\"The Spirit of the Twelfth (1982); The story of the Orange Order in Canada\\\", Gravenhurst Printing, 1982\\n \\\"Muskoka - Ontario’s First District Municipality\\\", 1995, GarDen Press.\\n \\\"A Brief History of the Churches in Muskoka\\\", 1997, 1998 and 2003, Publisher: GarDen Press, 2003 (Algoma University, Wishart Library),\\n \\\"The Story of Springdale Park\\\", Publisher: Springdale Park Spiritual Association, 1998. , by Gary Denniss.\\n \\\"Educating Muskoka District\\\", 1999, , by Gary Denniss.\\n \\\"The Educational Heritage of Muskoka\\\", 2001 (History of the Muskoka Board of Education), .\\n \\\"The Past Before Us: A History of Free Methodist Camp Meetings in Muskoka, 2002, .\\n \\\"In Loving Memory: The History of Langford Cemetery\\\", 2006 (collection of obituaries).\\n \\\"Going to School in Macaulay\\\", 2010, by Gary Denniss.\\n \\\"The Holditch Family Reunion\\\", Sept. 2013, GarDen Press.\\n \\\"Historic Routes of Bracebridge\\\", 2012, GarDen Press.\\n \\\"Bracebridge Connections\\\", Vol. 1, 2014, GarDen Press.\\n \\\"Bracebridge Connections\\\", Vol. 2, 2015, GarDen Press \\n \\\"Bracebridge in the Fifties\\\", 2016, GarDen Press \\n \\\"Bracebridge in the Sixties\\\", 2017, GarDen Press \\n \\\"Bracebridge in the Seventies\\\", GarDen Press, February 2018, \\n \\\"Muskoka Scrapbook\\\" (series of eight books: Individual years, 1926, 1936, 1946, 1956, 1966, 1976 (two volumes), 1952. \\n \\\"A Good Town Continues - Bracebridge 1915–1999\\\", contributed to by Gary Denniss.\\n \\\"The Orange Lodge and its History in Muskoka\\\", 1999.\\n \\\"Titch of Muskoka (a seven-part series)\\\"\\n \\\"Muskoka Scrapbook - a five-part series of books on World War 1\\\" -1914, 1915, 1916, 1917, 1918): Volume 1 , Volume 2 , Volume 3 , Volume 4 , Volume 5 \\\"Muskoka Scrapbook - Speaker Series\\\", sponsored by Muskoka Steamship and Historical Society\\\", First speaker, Gary Denniss, Sun., April 22, 2007.\\n Bracebridge in the Eighties December 2018, GarDen Press \\n The Family Heritage of Howard and Sheila Vincent, , December 2018, GarDen Press\\n \\\"The Arnott's of 36 Edward Street\\\", , June 2019.\\n \\\"Muskoka Memories 101\\\" November 2019, \\n \\\"Muskoka Memories 102\\\" \\n \\\"Muskoka Memories 103\\\" October 2021 \\n \\\"Muskoka Memories 104\\\"\\n \\\"Muskoka Memories 105\\\" \\n \\\"Langford Cemetery 1873-2023\\\"\\n\\nOther Links\\n\\nhttps://garydenniss.ca/\\n\\nhttps://www.youtube.com/watch?v=srTeIAKzUCU\\n\\nhttps://muskokatoday.com/2023/08/latest-gary-denniss-history-book-covers-muskokas-rugged-rich-past\\n\\nReviews\\n \\\"Among local Historians, Gary Denniss is Royalty\\\", by Ted Currie, Muskoka Today, Nov. 3–17 issue, 1995.\\n\\nAwards and honors\\n Lieutenant Governor's Ontario Heritage Award for Lifetime Achievement, presented by Lt. Gov. David Onley, Feb. 21, 2013 \\n Heritage Community Recognition Award, presented to Mr. Denniss at the Rene Caisse Theatre, the Town of Bracebridge, by Councillor Steve Clement and C. Hammond, June 27, 2012 \\n Robert J. Boyer Award, \\\"honours the dedication of individuals in our community who work to keep the natural and cultural history of our region alive\\\", presented to Gary Denniss by the Board of Directors, Muskoka Conservancy, May 17, 2013.\\n\\nReferences\\n\\nExternal links\\n\\n http://freepages.genealogy.rootsweb.ancestry.com/~murrayp/muskoka/macaulay/langford/index.htm\\n http://www.bracebridge.ca/en/live-here/Cemeteries.aspx?_mid_=1499#\\n http://www.heritagetrust.on.ca/en/index.php/pages/programs/recognition-programs\\n http://gravenhurstmuskoka.blogspot.ca/2014/03/congratulations-to-muskoka-historian.html\\n https://www.churchesinyourtown.ca/communities/bracebridge/sermons/speaker/gary-denniss\\n\\n1944 births\\n20th-century Canadian biographers\\nCanadian educators\\n20th-century Canadian historians\\nCanadian male biographers\\nCanadian schoolteachers\\nCanadian people of German descent\\nCanadian people of English descent\\nFree Methodist Church ministers\\nHistorians of Canada\\nLiving people\\nPeople from Bracebridge, Ontario\\n20th-century antiquarians\\nWilfrid Laurier University alumni\\n21st-century Canadian historians\\n20th-century Canadian male writers\\n21st-century Canadian biographers\\n21st-century Canadian male writers\"}\r\n{\"query\": \"When'd the Armells post office close up?\", \"pos\": \"Armells is a ghost town in Fergus County, in the U.S. state of Montana.\\n\\nHistory\\nA post office called Armells was established in 1890, and remained in operation until it was discontinued in 1937. The community took its name from nearby Armells Creek.\\n\\nReferences\\n\\nGeography of Fergus County, Montana\\nGhost towns in Montana\"}\r\n{\"query\": \"Establishment date of Sandefjord?\", \"pos\": \"Sandefjord () is a city (or town) that is the administrative centre of the large Sandefjord Municipality in Vestfold county, Norway. The town is located at the head of the Sandefjordsfjorden, along the Skaggerak coast in southern Vestfold. The large town also includes coastal areas on both sides of the Mefjorden on the Vesterøya and Østerøya peninsulas. The town has a population (2022) of 45,816 and a population density of .\\n\\nThe city is known for its rich Viking history and the prosperous whaling industry, which made Sandefjord the richest city in Norway. Today, it has built up the third-largest merchant fleet in Norway. The Sandefjord Museum is located in the town, the only museum in Europe that is dedicated to whaling. The 9th-century Gokstad Ship was discovered at the nearby Gokstad Mound, on the eastern edge of the city.\\n\\nThe Church of Norway has several churches in the city of Sandefjord including Sandefjord Church, Sandar Church, Bugården Church, and Vesterøy Church.\\n\\nSandefjord has numerous nicknames, including the Viking \\\"capital\\\" of Norway. It is also known as the undisputed summer city of Norway. The city is also known as the \\\"whaling capital of the world\\\" or the \\\"whaling capital of Norway\\\". It has also been dubbed the \\\"Bathing City\\\" (Badebyen), due to its many beaches and former resort spas. It is still considered a resort town, due to high numbers of visitors during summer months.\\n\\nHistory\\n\\nSandefjord has been inhabited for thousands of years. Excavations indicate that people have inhabited Sandefjord for around 3,000 years. Rock carvings at Haugen farm by Istrehågan in Jåberg are dated to 1,500–500 BCE.\\n\\nThe Vikings lived in Sandefjord and surrounding areas about 1,000 years ago, and numerous Viking artifacts and monuments can be found in Sandefjord. One of the most important remains from the Viking Age was found at the grave site Gokstadhaugen (Gokstad Mound) in Sandefjord. The Gokstad ship was excavated by Nicolay Nicolaysen and is now in the Viking Ship Museum in Oslo.\\n\\nThe town of Sandefjord was established as a ladested in 1680, giving it rights as a seaport. On 1 January 1838, it was established as a self-governing municipality under the new formannskapsdistrikt law. Sandefjord functioned as a seaport defined by the twin industries of shipping and shipbuilding throughout the 1600s and 1700s. It was formally recognized as a market town (kjøpstad) by King Oscar in 1845\\n\\nOver time, the city-municipality was enlarged. On 1 January 1889, a part of the neighboring municipality of Sandeherred (population: 318) was transferred into Sandefjord. In 1931, an area of the neighboring municipality of Sandar (population: 66) was transferred into Sandefjord. In 1950, another area of the neighboring municipality of Sandar (population: 226) was transferred into Sandefjord. During the 1960s, there were many municipal mergers across Norway due to the work of the Schei Committee. On 1 January 1968 the city-municipality of Sandefjord (population: 6,242) was merged into the surrounding municipality of Sandar (population: 24,898), creating a much larger municipality which was also named Sandefjord. Prior to the merger, the city and municipality were one and the same, but after the merger, the city was just one small part of a much larger municipality.\\n\\nEtymology \\nThe name Sandefjord was first mentioned in chapter 169 of Sverris saga from the year 1200. It was then referring to the fjord which is now known as Sandefjordsfjord. The municipality (originally the city of Sandefjord) is named after the local fjord, now called Sandefjordsfjorden since the city of Sandefjord grew up at the head of the fjord. The first element of the name comes from the old Sande farm (). The old farm name is the plural form of which means \\\"sand\\\" or \\\"sandbanks\\\". The last element comes from the word which means \\\"fjord\\\".\\n\\nGallery\\n\\nSee also\\nList of towns and cities in Norway\\n\\nReferences\\n\\nSandefjord\\nCities and towns in Norway\\nPopulated places in Vestfold og Telemark\"}\r\n" }, { "path": "FlagEmbedding/evaluation/air_bench/runner.py", "content": "from typing import Union, Tuple\nfrom air_benchmark import AIRBench\n\nfrom FlagEmbedding.abc.evaluation import (\n AbsEvalRunner,\n EvalDenseRetriever, EvalReranker\n)\n\nfrom .arguments import AIRBenchEvalArgs, AIRBenchEvalModelArgs\n\n\nclass AIRBenchEvalRunner:\n \"\"\"\n Evaluation runner for AIR Bench.\n \n Args:\n eval_args (AIRBenchEvalArgs): :class:AIRBenchEvalArgs object with the evaluation arguments.\n model_args (AIRBenchEvalModelArgs): :class:AIRBenchEvalModelArgs object with the model arguments.\n \"\"\"\n def __init__(\n self,\n eval_args: AIRBenchEvalArgs,\n model_args: AIRBenchEvalModelArgs,\n ):\n self.eval_args = eval_args\n self.model_args = model_args\n self.model_args.cache_dir = model_args.model_cache_dir\n\n self.retriever, self.reranker = self.load_retriever_and_reranker()\n\n def load_retriever_and_reranker(self) -> Tuple[EvalDenseRetriever, Union[EvalReranker, None]]:\n \"\"\"Load retriever and reranker for evaluation\n\n Returns:\n Tuple[EvalDenseRetriever, Union[EvalReranker, None]]: A :class:EvalDenseRetriever object for retrieval, and a\n :class:EvalReranker object if reranker provided.\n \"\"\"\n embedder, reranker = AbsEvalRunner.get_models(self.model_args)\n retriever = EvalDenseRetriever(\n embedder,\n search_top_k=self.eval_args.search_top_k,\n overwrite=self.eval_args.overwrite\n )\n if reranker is not None:\n reranker = EvalReranker(reranker, rerank_top_k=self.eval_args.rerank_top_k)\n return retriever, reranker\n\n def run(self):\n \"\"\"\n Run the whole evaluation.\n \"\"\"\n evaluation = AIRBench(\n benchmark_version=self.eval_args.benchmark_version,\n task_types=self.eval_args.task_types,\n domains=self.eval_args.domains,\n languages=self.eval_args.languages,\n splits=self.eval_args.splits,\n cache_dir=self.eval_args.cache_dir,\n )\n evaluation.run(\n self.retriever,\n reranker=self.reranker,\n output_dir=self.eval_args.output_dir,\n overwrite=self.eval_args.overwrite,\n )\n" }, { "path": "FlagEmbedding/evaluation/beir/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalModelArgs as BEIREvalModelArgs,\n)\n\nfrom .data_loader import BEIREvalDataLoader\nfrom .arguments import BEIREvalArgs\nfrom .runner import BEIREvalRunner\n\n__all__ = [\n \"BEIREvalArgs\",\n \"BEIREvalModelArgs\",\n \"BEIREvalRunner\",\n \"BEIREvalDataLoader\",\n]\n" }, { "path": "FlagEmbedding/evaluation/beir/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.beir import (\n BEIREvalArgs, BEIREvalModelArgs,\n BEIREvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n BEIREvalArgs,\n BEIREvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: BEIREvalArgs\n model_args: BEIREvalModelArgs\n\n runner = BEIREvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/beir/arguments.py", "content": "from dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.evaluation.arguments import AbsEvalArgs\n\n\n@dataclass\nclass BEIREvalArgs(AbsEvalArgs):\n \"\"\"\n Argument class for BEIR evaluation.\n \"\"\"\n use_special_instructions: bool = field(\n default=False, metadata={\"help\": \"Whether to use specific instructions in `prompts.py` for evaluation. Default: False\"}\n )\n" }, { "path": "FlagEmbedding/evaluation/beir/data_loader.py", "content": "import os\nimport json\nimport logging\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Optional\nfrom beir import util\nfrom beir.datasets.data_loader import GenericDataLoader\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass BEIREvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for BEIR.\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return ['arguana', 'climate-fever', 'cqadupstack', 'dbpedia-entity', 'fever', 'fiqa', 'hotpotqa', 'msmarco', 'nfcorpus', 'nq', 'quora', 'scidocs', 'scifact', 'trec-covid', 'webis-touche2020']\n\n def available_sub_dataset_names(self, dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"\n Get the available sub-dataset names.\n\n Args:\n dataset_name (Optional[str], optional): All the available sub-dataset names. Defaults to ``None``.\n\n Returns:\n List[str]: All the available sub-dataset names.\n \"\"\"\n if dataset_name == 'cqadupstack':\n return ['android', 'english', 'gaming', 'gis', 'mathematica', 'physics', 'programmers', 'stats', 'tex', 'unix', 'webmasters', 'wordpress']\n return None\n\n def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (str): Dataset name.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n if dataset_name == 'msmarco':\n return ['dev']\n return ['test']\n\n def _load_remote_corpus(\n self,\n dataset_name: str,\n sub_dataset_name: Optional[str] = None,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the corpus dataset from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n sub_dataset_name (Optional[str]): Name of the sub-dataset. Defaults to ``None``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n if dataset_name != 'cqadupstack':\n corpus = datasets.load_dataset(\n 'BeIR/{d}'.format(d=dataset_name),\n 'corpus',\n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )['corpus']\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n _data = {\n \"id\": data[\"_id\"],\n \"title\": data[\"title\"],\n \"text\": data[\"text\"]\n }\n corpus_dict[data[\"_id\"]] = {\n \"title\": data[\"title\"],\n \"text\": data[\"text\"]\n }\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {data[\"docid\"]: {\"title\": data[\"title\"], \"text\": data[\"text\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n else:\n url = \"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip\".format(dataset_name)\n data_path = util.download_and_unzip(url, self.cache_dir)\n full_path = os.path.join(data_path, sub_dataset_name)\n corpus, _, _ = GenericDataLoader(data_folder=full_path).load(split=\"test\")\n if save_dir is not None:\n new_save_dir = os.path.join(save_dir, sub_dataset_name)\n os.makedirs(new_save_dir, exist_ok=True)\n save_path = os.path.join(new_save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for _id in tqdm(corpus.keys(), desc=\"Loading corpus\"):\n _data = {\n \"id\": _id,\n \"title\": corpus[_id][\"title\"],\n \"text\": corpus[_id][\"text\"]\n }\n corpus_dict[_id] = {\n \"title\": corpus[_id][\"title\"],\n \"text\": corpus[_id][\"text\"]\n }\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {_id: {\"title\": corpus[_id][\"title\"], \"text\": corpus[_id][\"text\"]} for _id in tqdm(corpus.keys(), desc=\"Loading corpus\")}\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: Optional[str] = None,\n sub_dataset_name: Optional[str] = None,\n split: str = 'dev',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n sub_dataset_name (Optional[str]): Name of the sub-dataset. Defaults to ``None``.\n split (str, optional): Split of the dataset. Defaults to ``'dev'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n if dataset_name != 'cqadupstack':\n qrels = datasets.load_dataset(\n 'BeIR/{d}-qrels'.format(d=dataset_name),\n split=split if split != 'dev' else 'validation', \n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(qrels, desc=\"Loading and Saving qrels\"):\n qid, docid, rel = str(data['query-id']), str(data['corpus-id']), int(data['score'])\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": rel\n }\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} qrels saved to {save_path}\")\n else:\n qrels_dict = {}\n for data in tqdm(qrels, desc=\"Loading queries\"):\n qid, docid, rel = str(data['query-id']), str(data['corpus-id']), int(data['score'])\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n else:\n url = \"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip\".format(dataset_name)\n data_path = util.download_and_unzip(url, self.cache_dir)\n full_path = os.path.join(data_path, sub_dataset_name)\n _, _, qrels = GenericDataLoader(data_folder=full_path).load(split=\"test\")\n if save_dir is not None:\n new_save_dir = os.path.join(save_dir, sub_dataset_name)\n os.makedirs(new_save_dir, exist_ok=True)\n save_path = os.path.join(new_save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for qid in tqdm(qrels.keys(), desc=\"Loading and Saving qrels\"):\n for docid in tqdm(qrels[qid].keys()):\n rel = int(qrels[qid][docid])\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": rel\n }\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} qrels saved to {save_path}\")\n else:\n qrels_dict = {}\n for qid in tqdm(qrels.keys(), desc=\"Loading qrels\"):\n for docid in tqdm(qrels[qid].keys()):\n rel = int(qrels[qid][docid])\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: Optional[str] = None,\n sub_dataset_name: Optional[str] = None,\n split: str = 'test',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n sub_dataset_name (Optional[str]): Name of the sub-dataset. Defaults to ``None``.\n split (str, optional): Split of the dataset. Defaults to ``'dev'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n qrels = self.load_qrels(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n\n if dataset_name != 'cqadupstack':\n queries = datasets.load_dataset(\n 'BeIR/{d}'.format(d=dataset_name), \n 'queries', \n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )['queries']\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(queries, desc=\"Loading and Saving queries\"):\n qid, query = data['_id'], data['text']\n if qid not in qrels.keys(): continue\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} queries saved to {save_path}\")\n else:\n queries_dict = {}\n for data in tqdm(queries, desc=\"Loading queries\"):\n qid, query = data['_id'], data['text']\n if qid not in qrels.keys(): continue\n queries_dict[qid] = query\n else:\n url = \"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip\".format(dataset_name)\n data_path = util.download_and_unzip(url, self.cache_dir)\n full_path = os.path.join(data_path, sub_dataset_name)\n _, queries, _ = GenericDataLoader(data_folder=full_path).load(split=\"test\")\n if save_dir is not None:\n new_save_dir = os.path.join(save_dir, sub_dataset_name)\n os.makedirs(new_save_dir, exist_ok=True)\n save_path = os.path.join(new_save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for qid in tqdm(queries.keys(), desc=\"Loading and Saving queries\"):\n query = queries[qid]\n if qid not in qrels.keys(): continue\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} queries saved to {save_path}\")\n else:\n queries_dict = {}\n for qid in tqdm(queries.keys(), desc=\"Loading queries\"):\n query = queries[qid]\n if qid not in qrels.keys(): continue\n queries_dict[qid] = query\n return datasets.DatasetDict(queries_dict)\n\n def load_corpus(self, dataset_name: Optional[str] = None, sub_dataset_name: Optional[str] = None) -> datasets.DatasetDict:\n \"\"\"Load the corpus from the dataset.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: A dict of corpus with id as key, title and text as value.\n \"\"\"\n if self.dataset_dir is not None:\n if dataset_name is None:\n save_dir = self.dataset_dir\n else:\n save_dir = os.path.join(self.dataset_dir, dataset_name)\n return self._load_local_corpus(save_dir, dataset_name=dataset_name, sub_dataset_name=sub_dataset_name)\n else:\n return self._load_remote_corpus(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name)\n\n def load_qrels(self, dataset_name: Optional[str] = None, sub_dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load the qrels from the dataset.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n split (str, optional): The split to load relevance from. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of relevance of query and document.\n \"\"\"\n if self.dataset_dir is not None:\n if dataset_name is None:\n save_dir = self.dataset_dir\n else:\n checked_dataset_names = self.check_dataset_names(dataset_name)\n if len(checked_dataset_names) == 0:\n raise ValueError(f\"Dataset name {dataset_name} not found in the dataset.\")\n dataset_name = checked_dataset_names[0]\n\n save_dir = os.path.join(self.dataset_dir, dataset_name)\n\n return self._load_local_qrels(save_dir, dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n else:\n return self._load_remote_qrels(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n\n def load_queries(self, dataset_name: Optional[str] = None, sub_dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load the queries from the dataset.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n split (str, optional): The split to load queries from. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of queries with id as key, query text as value.\n \"\"\"\n if self.dataset_dir is not None:\n if dataset_name is None:\n save_dir = self.dataset_dir\n else:\n checked_dataset_names = self.check_dataset_names(dataset_name)\n if len(checked_dataset_names) == 0:\n raise ValueError(f\"Dataset name {dataset_name} not found in the dataset.\")\n dataset_name = checked_dataset_names[0]\n\n save_dir = os.path.join(self.dataset_dir, dataset_name)\n\n return self._load_local_queries(save_dir, dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n else:\n return self._load_remote_queries(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n\n def _load_local_corpus(self, save_dir: str, dataset_name: Optional[str] = None, sub_dataset_name: Optional[str] = None) -> datasets.DatasetDict:\n \"\"\"Load corpus from local dataset.\n\n Args:\n save_dir (str): Path to save the loaded corpus.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: A dict of corpus with id as key, title and text as value.\n \"\"\"\n if sub_dataset_name is None:\n corpus_path = os.path.join(save_dir, 'corpus.jsonl')\n else:\n corpus_path = os.path.join(save_dir, sub_dataset_name, 'corpus.jsonl')\n if self.force_redownload or not os.path.exists(corpus_path):\n logger.warning(f\"Corpus not found in {corpus_path}. Trying to download the corpus from the remote and save it to {save_dir}.\")\n return self._load_remote_corpus(dataset_name=dataset_name, save_dir=save_dir, sub_dataset_name=sub_dataset_name)\n else:\n if sub_dataset_name is not None:\n save_dir = os.path.join(save_dir, sub_dataset_name)\n corpus_data = datasets.load_dataset('json', data_files=corpus_path, cache_dir=self.cache_dir)['train']\n\n corpus = {}\n for e in corpus_data:\n corpus[e['id']] = {'title': e.get('title', \"\"), 'text': e['text']}\n\n return datasets.DatasetDict(corpus)\n\n def _load_local_qrels(self, save_dir: str, dataset_name: Optional[str] = None, sub_dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load relevance from local dataset.\n\n Args:\n save_dir (str): Path to save the loaded relevance.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n split (str, optional): Split to load from the local dataset. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of relevance of query and document.\n \"\"\"\n checked_split = self.check_splits(split, dataset_name=dataset_name)\n if len(checked_split) == 0:\n raise ValueError(f\"Split {split} not found in the dataset.\")\n split = checked_split[0]\n\n if sub_dataset_name is None:\n qrels_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n else:\n qrels_path = os.path.join(save_dir, sub_dataset_name, f\"{split}_qrels.jsonl\")\n if self.force_redownload or not os.path.exists(qrels_path):\n logger.warning(f\"Qrels not found in {qrels_path}. Trying to download the qrels from the remote and save it to {save_dir}.\")\n return self._load_remote_qrels(dataset_name=dataset_name, split=split, sub_dataset_name=sub_dataset_name, save_dir=save_dir)\n else:\n if sub_dataset_name is not None:\n save_dir = os.path.join(save_dir, sub_dataset_name)\n qrels_data = datasets.load_dataset('json', data_files=qrels_path, cache_dir=self.cache_dir)['train']\n\n qrels = {}\n for data in qrels_data:\n qid = data['qid']\n if qid not in qrels:\n qrels[qid] = {}\n qrels[qid][data['docid']] = data['relevance']\n\n return datasets.DatasetDict(qrels)\n\n def _load_local_queries(self, save_dir: str, dataset_name: Optional[str] = None, sub_dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Load queries from local dataset.\n\n Args:\n save_dir (str): Path to save the loaded queries.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n split (str, optional): Split to load from the local dataset. Defaults to ``'test'``.\n\n Raises:\n ValueError\n\n Returns:\n datasets.DatasetDict: A dict of queries with id as key, query text as value.\n \"\"\"\n checked_split = self.check_splits(split, dataset_name=dataset_name)\n if len(checked_split) == 0:\n raise ValueError(f\"Split {split} not found in the dataset.\")\n split = checked_split[0]\n\n if sub_dataset_name is None:\n queries_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n else:\n queries_path = os.path.join(save_dir, sub_dataset_name, f\"{split}_queries.jsonl\")\n if self.force_redownload or not os.path.exists(queries_path):\n logger.warning(f\"Queries not found in {queries_path}. Trying to download the queries from the remote and save it to {save_dir}.\")\n return self._load_remote_queries(dataset_name=dataset_name, split=split, sub_dataset_name=sub_dataset_name, save_dir=save_dir)\n else:\n if sub_dataset_name is not None:\n save_dir = os.path.join(save_dir, sub_dataset_name)\n queries_data = datasets.load_dataset('json', data_files=queries_path, cache_dir=self.cache_dir)['train']\n\n queries = {e['id']: e['text'] for e in queries_data}\n return datasets.DatasetDict(queries)\n" }, { "path": "FlagEmbedding/evaluation/beir/evaluator.py", "content": "import json\nimport logging\nimport os\nimport json\nfrom typing import Dict, Optional, List, Union\n\nfrom FlagEmbedding.abc.evaluation import AbsEvaluator, EvalRetriever, EvalReranker\n\nlogger = logging.getLogger(__name__)\n\n\nclass BEIREvaluator(AbsEvaluator):\n \"\"\"\n Evaluator class of BEIR \n \"\"\"\n def check_data_info(\n self,\n data_info: Dict[str, str],\n model_name: str,\n reranker_name: str,\n split: str,\n dataset_name: Optional[str] = None,\n sub_dataset_name: Optional[str] = None,\n ):\n \"\"\"Check the validity of data info.\n\n Args:\n data_info (Dict[str, str]): The loaded data info to be check.\n model_name (str): Name of model used.\n reranker_name (str): Name of reranker used.\n split (str): Split used in searching.\n dataset_name (Optional[str], optional): Name of dataset used. Defaults to None.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n\n Raises:\n ValueError: eval_name mismatch\n ValueError: model_name or reranker_name mismatch\n ValueError: split mismatch\n ValueError: dataset_name mismatch\n ValueError: sub_dataset_name mismatch\n \"\"\"\n if data_info[\"eval_name\"] != self.eval_name:\n raise ValueError(\n f'eval_name mismatch: {data_info[\"eval_name\"]} vs {self.eval_name}'\n )\n if (\n data_info[\"model_name\"] != model_name\n or data_info[\"reranker_name\"] != reranker_name\n ):\n raise ValueError(\n f'model_name or reranker_name mismatch: {data_info[\"model_name\"]} vs {model_name} or {data_info[\"reranker_name\"]} vs {reranker_name}'\n )\n if (data_info[\"split\"] != split):\n raise ValueError(\n f'split mismatch: {data_info[\"split\"]} vs {split}'\n )\n if dataset_name is not None and data_info[\"dataset_name\"] != dataset_name:\n raise ValueError(\n f'dataset_name mismatch: {data_info[\"dataset_name\"]} vs {dataset_name}'\n )\n if sub_dataset_name is not None and data_info[\"sub_dataset_name\"] != sub_dataset_name:\n raise ValueError(\n f'sub_dataset_name mismatch: {data_info[\"sub_dataset_name\"]} vs {sub_dataset_name}'\n )\n\n def __call__(\n self,\n splits: Union[str, List[str]],\n search_results_save_dir: str,\n retriever: EvalRetriever,\n reranker: Optional[EvalReranker] = None,\n corpus_embd_save_dir: Optional[str] = None,\n ignore_identical_ids: bool = False,\n k_values: List[int] = [1, 3, 5, 10, 100, 1000],\n dataset_name: Optional[str] = None,\n **kwargs,\n ):\n sub_dataset_name = None\n sub_dataset_names = self.data_loader.available_sub_dataset_names(dataset_name=dataset_name)\n # Check Splits\n checked_splits = self.data_loader.check_splits(splits, dataset_name=dataset_name)\n if len(checked_splits) == 0:\n logger.warning(f\"{splits} not found in the dataset. Skipping evaluation.\")\n return\n splits = checked_splits\n\n if sub_dataset_names is None:\n if dataset_name is not None:\n save_name = f\"{dataset_name}-\" + \"{split}.json\"\n if corpus_embd_save_dir is not None:\n corpus_embd_save_dir = os.path.join(corpus_embd_save_dir, str(retriever), dataset_name)\n else:\n save_name = \"{split}.json\"\n\n # Retrieval Stage\n no_reranker_search_results_save_dir = os.path.join(\n search_results_save_dir, str(retriever), \"NoReranker\"\n )\n os.makedirs(no_reranker_search_results_save_dir, exist_ok=True)\n\n flag = False\n for split in splits:\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n if not os.path.exists(split_no_reranker_search_results_save_path) or self.overwrite:\n flag = True\n break\n\n no_reranker_search_results_dict = {}\n if flag:\n corpus = self.data_loader.load_corpus(dataset_name=dataset_name)\n\n queries_dict = {\n split: self.data_loader.load_queries(dataset_name=dataset_name, split=split)\n for split in splits\n }\n\n all_queries = {}\n for _, split_queries in queries_dict.items():\n all_queries.update(split_queries)\n\n all_no_reranker_search_results = retriever(\n corpus=corpus,\n queries=all_queries,\n corpus_embd_save_dir=corpus_embd_save_dir,\n ignore_identical_ids=ignore_identical_ids,\n **kwargs,\n )\n\n for split in splits:\n split_queries = queries_dict[split]\n no_reranker_search_results_dict[split] = {\n qid: all_no_reranker_search_results[qid] for qid in split_queries\n }\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n self.save_search_results(\n eval_name=self.eval_name,\n model_name=str(retriever),\n reranker_name=\"NoReranker\",\n search_results=no_reranker_search_results_dict[split],\n output_path=split_no_reranker_search_results_save_path,\n split=split,\n dataset_name=dataset_name,\n sub_dataset_name=sub_dataset_name,\n )\n else:\n for split in splits:\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n data_info, search_results = self.load_search_results(split_no_reranker_search_results_save_path)\n \n self.check_data_info(\n data_info=data_info,\n model_name=str(retriever),\n reranker_name=\"NoReranker\",\n split=split,\n dataset_name=dataset_name,\n sub_dataset_name=sub_dataset_name,\n )\n no_reranker_search_results_dict[split] = search_results\n retriever.stop_multi_process_pool()\n eval_results_save_path = os.path.join(no_reranker_search_results_save_dir, 'EVAL', 'eval_results.json')\n if not os.path.exists(eval_results_save_path) or self.overwrite or flag:\n retriever_eval_results = self.evaluate_results(no_reranker_search_results_save_dir, k_values=k_values)\n self.output_eval_results_to_json(retriever_eval_results, eval_results_save_path)\n\n # Reranking Stage\n if reranker is not None:\n reranker_search_results_save_dir = os.path.join(\n search_results_save_dir, str(retriever), str(reranker)\n )\n os.makedirs(reranker_search_results_save_dir, exist_ok=True)\n\n corpus = self.data_loader.load_corpus(dataset_name=dataset_name)\n\n queries_dict = {\n split: self.data_loader.load_queries(dataset_name=dataset_name, split=split)\n for split in splits\n }\n\n flag = False\n for split in splits:\n rerank_search_results_save_path = os.path.join(\n reranker_search_results_save_dir, save_name.format(split=split)\n )\n\n if os.path.exists(rerank_search_results_save_path) and not self.overwrite:\n continue\n\n flag = True\n rerank_search_results = reranker(\n corpus=corpus,\n queries=queries_dict[split],\n search_results=no_reranker_search_results_dict[split],\n ignore_identical_ids=ignore_identical_ids,\n **kwargs,\n )\n\n self.save_search_results(\n eval_name=self.eval_name,\n model_name=str(retriever),\n reranker_name=str(reranker),\n search_results=rerank_search_results,\n output_path=rerank_search_results_save_path,\n split=split,\n dataset_name=dataset_name,\n sub_dataset_name=sub_dataset_name,\n )\n eval_results_save_path = os.path.join(reranker_search_results_save_dir, 'EVAL', 'eval_results.json')\n if not os.path.exists(eval_results_save_path) or self.overwrite or flag:\n reranker_eval_results = self.evaluate_results(reranker_search_results_save_dir, k_values=k_values)\n self.output_eval_results_to_json(reranker_eval_results, eval_results_save_path)\n else:\n for sub_dataset_name in sub_dataset_names:\n if dataset_name is not None:\n save_name = f\"{dataset_name}-{sub_dataset_name}-\" + \"{split}.json\"\n if corpus_embd_save_dir is not None:\n corpus_embd_save_dir = os.path.join(corpus_embd_save_dir, str(retriever), dataset_name, sub_dataset_name)\n else:\n save_name = f\"{sub_dataset_name}-\" + \"{split}.json\"\n\n # Retrieval Stage\n no_reranker_search_results_save_dir = os.path.join(\n search_results_save_dir, str(retriever), \"NoReranker\"\n )\n os.makedirs(no_reranker_search_results_save_dir, exist_ok=True)\n\n flag = False\n for split in splits:\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n if not os.path.exists(split_no_reranker_search_results_save_path) or self.overwrite:\n flag = True\n break\n\n no_reranker_search_results_dict = {}\n if flag:\n corpus = self.data_loader.load_corpus(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name)\n\n queries_dict = {\n split: self.data_loader.load_queries(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n for split in splits\n }\n\n all_queries = {}\n for _, split_queries in queries_dict.items():\n all_queries.update(split_queries)\n\n all_no_reranker_search_results = retriever(\n corpus=corpus,\n queries=all_queries,\n corpus_embd_save_dir=corpus_embd_save_dir,\n ignore_identical_ids=ignore_identical_ids,\n **kwargs,\n )\n\n for split in splits:\n split_queries = queries_dict[split]\n no_reranker_search_results_dict[split] = {\n qid: all_no_reranker_search_results[qid] for qid in split_queries\n }\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n\n self.save_search_results(\n eval_name=self.eval_name,\n model_name=str(retriever),\n reranker_name=\"NoReranker\",\n search_results=no_reranker_search_results_dict[split],\n output_path=split_no_reranker_search_results_save_path,\n split=split,\n dataset_name=dataset_name,\n sub_dataset_name=sub_dataset_name,\n )\n else:\n for split in splits:\n split_no_reranker_search_results_save_path = os.path.join(\n no_reranker_search_results_save_dir, save_name.format(split=split)\n )\n data_info, search_results = self.load_search_results(split_no_reranker_search_results_save_path)\n \n self.check_data_info(\n data_info=data_info,\n model_name=str(retriever),\n reranker_name=\"NoReranker\",\n split=split,\n dataset_name=dataset_name,\n sub_dataset_name=sub_dataset_name,\n )\n no_reranker_search_results_dict[split] = search_results\n eval_results_save_path = os.path.join(no_reranker_search_results_save_dir, 'EVAL', 'eval_results.json')\n if not os.path.exists(eval_results_save_path) or self.overwrite or flag:\n retriever_eval_results = self.evaluate_results(no_reranker_search_results_save_dir, k_values=k_values)\n self.output_eval_results_to_json(retriever_eval_results, eval_results_save_path)\n\n # Reranking Stage\n if reranker is not None:\n reranker_search_results_save_dir = os.path.join(\n search_results_save_dir, str(retriever), str(reranker)\n )\n os.makedirs(reranker_search_results_save_dir, exist_ok=True)\n\n corpus = self.data_loader.load_corpus(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name)\n\n queries_dict = {\n split: self.data_loader.load_queries(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n for split in splits\n }\n\n flag = False\n for split in splits:\n rerank_search_results_save_path = os.path.join(\n reranker_search_results_save_dir, save_name.format(split=split)\n )\n\n if os.path.exists(rerank_search_results_save_path) and not self.overwrite:\n continue\n\n flag = True\n rerank_search_results = reranker(\n corpus=corpus,\n queries=queries_dict[split],\n search_results=no_reranker_search_results_dict[split],\n ignore_identical_ids=ignore_identical_ids,\n **kwargs,\n )\n\n self.save_search_results(\n eval_name=self.eval_name,\n model_name=str(retriever),\n reranker_name=str(reranker),\n search_results=rerank_search_results,\n output_path=rerank_search_results_save_path,\n split=split,\n dataset_name=dataset_name,\n sub_dataset_name=sub_dataset_name,\n )\n eval_results_save_path = os.path.join(reranker_search_results_save_dir, 'EVAL', 'eval_results.json')\n if not os.path.exists(eval_results_save_path) or self.overwrite or flag:\n reranker_eval_results = self.evaluate_results(reranker_search_results_save_dir, k_values=k_values)\n self.output_eval_results_to_json(reranker_eval_results, eval_results_save_path)\n if reranker is not None:\n reranker.stop_multi_process_pool()\n \n def evaluate_results(\n self,\n search_results_save_dir: str,\n k_values: List[int] = [1, 3, 5, 10, 100, 1000]\n ):\n \"\"\"Compute metrics according to the results in the directory.\n\n Args:\n search_results_save_dir (str): Path to the search results.\n k_values (List[int], optional): Cutoffs. Defaults to :data:`[1, 3, 5, 10, 100, 1000]`.\n\n Returns:\n dict: Evaluation results.\n \"\"\"\n eval_results_dict = {}\n cqadupstack_results = None\n cqadupstack_num = 0\n\n for file in os.listdir(search_results_save_dir):\n if not file.endswith('.json'):\n continue\n\n file_path = os.path.join(search_results_save_dir, file)\n data_info, search_results = self.load_search_results(file_path)\n\n _eval_name = data_info['eval_name']\n assert _eval_name == self.eval_name, f'Mismatch eval_name: {_eval_name} vs {self.eval_name} in {file_path}'\n\n split = data_info['split']\n dataset_name = data_info.get('dataset_name', None)\n sub_dataset_name = data_info.get('sub_dataset_name', None)\n qrels = self.data_loader.load_qrels(dataset_name=dataset_name, sub_dataset_name=sub_dataset_name, split=split)\n\n eval_results = self.compute_metrics(\n qrels=qrels,\n search_results=search_results,\n k_values=k_values\n )\n\n if dataset_name is not None:\n if sub_dataset_name is None:\n key = f\"{dataset_name}-{split}\"\n else:\n key = f\"{dataset_name}-{sub_dataset_name}-{split}\"\n else:\n if sub_dataset_name is None:\n key = split\n else:\n key = f\"{sub_dataset_name}-{split}\"\n if sub_dataset_name is None:\n eval_results_dict[key] = eval_results\n else:\n if cqadupstack_results is None:\n cqadupstack_results = eval_results\n cqadupstack_num += 1\n else:\n for k, v in eval_results.items():\n cqadupstack_results[k] += v\n cqadupstack_num += 1\n \n if cqadupstack_num > 0:\n for k in cqadupstack_results.keys():\n cqadupstack_results[k] /= cqadupstack_num\n eval_results_dict['cqadupstack-test'] = cqadupstack_results\n\n return eval_results_dict\n \n def save_search_results(\n self,\n eval_name: str,\n model_name: str,\n reranker_name: str,\n search_results: Dict[str, Dict[str, float]],\n output_path: str,\n split: str,\n dataset_name: Optional[str] = None,\n sub_dataset_name: Optional[str] = None,\n ):\n \"\"\"Save the metadata and search results into a file.\n\n Args:\n eval_name (str): The experiment name of current evaluation.\n model_name (str): Name of model used.\n reranker_name (str): Name of reranker used.\n search_results (Dict[str, Dict[str, float]]): Dictionary of search results.\n output_path (str): Output path to write the results.\n split (str): Split used in searching.\n dataset_name (Optional[str], optional): Name of dataset used. Defaults to ``None``.\n sub_dataset_name (Optional[str], optional): Name of the sub-dataset. Defaults to ``None``.\n \"\"\"\n data = {\n \"eval_name\": eval_name,\n \"model_name\": model_name,\n \"reranker_name\": reranker_name,\n \"split\": split,\n \"dataset_name\": dataset_name,\n \"sub_dataset_name\": sub_dataset_name,\n \"search_results\": search_results,\n }\n\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\n\n with open(output_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(data, f, indent=4)" }, { "path": "FlagEmbedding/evaluation/beir/prompts.py", "content": "BEIRInstructions = {\n 'dbpedia-entity': 'Given a query, retrieve relevant entity descriptions from DBPedia.',\n 'arguana': 'Given a claim, find documents that refute the claim.',\n 'climate-fever': 'Given a claim about climate change, retrieve documents that support or refute the claim.',\n 'cqadupstack': 'Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question.',\n 'fever': 'Given a claim, retrieve documents that support or refute the claim.',\n 'fiqa': 'Given a financial question, retrieve user replies that best answer the question.',\n 'hotpotqa': 'Given a multi-hop question, retrieve documents that can help answer the question.',\n 'msmarco': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'nfcorpus': 'Given a question, retrieve relevant documents that best answer the question.',\n 'nq': 'Given a question, retrieve Wikipedia passages that answer the question.',\n 'quora': 'Given a question, retrieve questions that are semantically equivalent to the given question.',\n 'scidocs': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.',\n 'scifact': 'Given a scientific claim, retrieve documents that support or refute the claim.',\n 'webis-touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question.',\n 'trec-covid': 'Given a query on COVID-19, retrieve documents that answer the query.',\n}\n" }, { "path": "FlagEmbedding/evaluation/beir/runner.py", "content": "import logging\nfrom FlagEmbedding.abc.evaluation import AbsEvalRunner\n\nfrom .data_loader import BEIREvalDataLoader\nfrom .prompts import BEIRInstructions\nfrom .evaluator import BEIREvaluator\n\nlogger = logging.getLogger(__name__)\n\n\nclass BEIREvalRunner(AbsEvalRunner):\n \"\"\"\n Runner class of BEIR evaluation.\n \"\"\"\n def run(self):\n \"\"\"\n Run the whole evaluation.\n \"\"\"\n if self.eval_args.dataset_names is None:\n dataset_names = self.data_loader.available_dataset_names()\n else:\n dataset_names = self.data_loader.check_dataset_names(self.eval_args.dataset_names)\n\n if len(dataset_names) == 0:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on the default dataset.\")\n self.evaluator(\n splits=self.eval_args.splits,\n search_results_save_dir=self.eval_args.output_dir,\n retriever=self.retriever,\n reranker=self.reranker,\n corpus_embd_save_dir=self.eval_args.corpus_embd_save_dir,\n ignore_identical_ids=self.eval_args.ignore_identical_ids,\n k_values=self.eval_args.k_values\n )\n logger.info(f\"{self.eval_args.eval_name} evaluation completed.\")\n else:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on the following dataset names: {dataset_names}\")\n for dataset_name in dataset_names:\n if self.eval_args.use_special_instructions:\n self.retriever.stop_multi_process_pool()\n self.retriever.embedder.query_instruction_for_retrieval = BEIRInstructions[dataset_name]\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on: {dataset_name}\")\n self.evaluator(\n splits=self.eval_args.splits,\n search_results_save_dir=self.eval_args.output_dir,\n retriever=self.retriever,\n reranker=self.reranker,\n corpus_embd_save_dir=self.eval_args.corpus_embd_save_dir,\n ignore_identical_ids=self.eval_args.ignore_identical_ids,\n k_values=self.eval_args.k_values,\n dataset_name=dataset_name,\n )\n logger.info(f\"{self.eval_args.eval_name} evaluation on {dataset_names} completed.\")\n\n logger.info(\"Start computing metrics.\")\n self.evaluate_metrics(\n search_results_save_dir=self.eval_args.output_dir,\n output_method=self.eval_args.eval_output_method,\n output_path=self.eval_args.eval_output_path,\n metrics=self.eval_args.eval_metrics\n )\n \n def load_data_loader(self) -> BEIREvalDataLoader:\n \"\"\"Load the data loader\n\n Returns:\n BEIREvalDataLoader: BEIR data loader object.\n \"\"\"\n data_loader = BEIREvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n\n def load_evaluator(self) -> BEIREvaluator:\n \"\"\"Load the evaluator for evaluation\n\n Returns:\n BEIREvaluator: The BEIR evaluator to run the evaluation.\n \"\"\"\n evaluator = BEIREvaluator(\n eval_name=self.eval_args.eval_name,\n data_loader=self.data_loader,\n overwrite=self.eval_args.overwrite,\n )\n return evaluator\n" }, { "path": "FlagEmbedding/evaluation/bright/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalModelArgs as BrightEvalModelArgs,\n)\n\nfrom .data_loader import BrightShortEvalDataLoader, BrightLongEvalDataLoader\nfrom .arguments import BrightEvalArgs\nfrom .runner import BrightEvalRunner\nfrom .searcher import BrightEvalDenseRetriever\n\n__all__ = [\n \"BrightEvalArgs\",\n \"BrightEvalModelArgs\",\n \"BrightEvalRunner\",\n \"BrightEvalDenseRetriever\",\n \"BrightShortEvalDataLoader\",\n \"BrightLongEvalDataLoader\",\n]\n" }, { "path": "FlagEmbedding/evaluation/bright/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.bright import (\n BrightEvalArgs, BrightEvalModelArgs,\n BrightEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n BrightEvalArgs,\n BrightEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: BrightEvalArgs\n model_args: BrightEvalModelArgs\n\n runner = BrightEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/bright/arguments.py", "content": "from dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.evaluation.arguments import AbsEvalArgs\n\n\n@dataclass\nclass BrightEvalArgs(AbsEvalArgs):\n \"\"\"\n Argument class for Bright evaluation.\n \"\"\"\n task_type: str = field(\n default=\"short\", metadata={\"help\": \"The task type to evaluate on. Available options: ['short', 'long']. Default: short\", \"choices\": [\"short\", \"long\"]}\n )\n use_special_instructions: bool = field(\n default=True, metadata={\"help\": \"Whether to use specific instructions in `prompts.py` for evaluation. Default: True\"}\n )\n" }, { "path": "FlagEmbedding/evaluation/bright/data_loader.py", "content": "import os\nimport json\nimport logging\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Optional\nfrom collections import defaultdict\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass BrightShortEvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for Bright(short).\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return [\n # StackExchange\n \"biology\", \"earth_science\", \"economics\", \"psychology\", \"robotics\", \"stackoverflow\", \"sustainable_living\",\n # Coding\n \"leetcode\", \"pony\",\n # Theorem-based\n \"aops\", \"theoremqa_questions\", \"theoremqa_theorems\"\n ]\n\n def available_splits(self, dataset_name: str) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (str): Dataset name.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n return [\n # normal splits\n \"examples\",\n # w/ reasoning splits\n \"Gemini-1.0_reason\", \"claude-3-opus_reason\", \"gpt4_reason\", \"grit_reason\", \"llama3-70b_reason\",\n ]\n\n def _load_remote_corpus(\n self,\n dataset_name: str,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the corpus dataset from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n corpus = datasets.load_dataset(\n \"xlangai/bright\", \"documents\",\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )[dataset_name]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n docid, text = str(data[\"id\"]), data[\"content\"]\n _data = {\n \"id\": docid,\n \"text\": text\n }\n corpus_dict[docid] = {\"text\": text}\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {str(data[\"id\"]): {\"text\": data[\"content\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: str,\n split: str = 'examples',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'examples'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n examples = datasets.load_dataset(\n \"xlangai/bright\", split,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )[dataset_name]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = defaultdict(dict)\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(examples, desc=\"Loading and Saving qrels\"):\n\n # NOTE: we modify the qid here to distinguish the queries from different splits\n qid = f'{split}-{data[\"id\"]}'\n\n for docid in data[\"gold_ids\"]:\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": 1\n }\n qrels_dict[qid][docid] = 1\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n\n # NOTE: we record the excluded_ids in qrels with relevance 0 to remove corresponding documents from raw search results. Refer to `searcher.py` for details.\n for ex_docid in list(set(data[\"excluded_ids\"])):\n if ex_docid == \"N/A\":\n continue\n assert ex_docid not in qrels_dict[qid], f\"{ex_docid} in {qid}\"\n _data = {\n \"qid\": qid,\n \"docid\": ex_docid,\n \"relevance\": 0\n }\n qrels_dict[qid][ex_docid] = 0\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n else:\n qrels_dict = defaultdict(dict)\n for data in tqdm(examples, desc=\"Loading qrels\"):\n\n # NOTE: we modify the qid here to distinguish the queries from different splits\n qid = f'{split}-{data[\"id\"]}'\n\n for docid in data[\"gold_ids\"]:\n qrels_dict[qid][docid] = 1\n\n # NOTE: we record the excluded_ids in qrels with relevance 0 to remove corresponding documents from raw search results. Refer to `searcher.py` for details.\n for ex_docid in data[\"excluded_ids\"]:\n if ex_docid == \"N/A\":\n continue\n assert ex_docid not in qrels_dict[qid], f\"{ex_docid} in {qid}\"\n _data = {\n \"qid\": qid,\n \"docid\": ex_docid,\n \"relevance\": 0\n }\n qrels_dict[qid][ex_docid] = 0\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: str,\n split: str = 'examples',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'examples'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n examples = datasets.load_dataset(\n \"xlangai/bright\", split,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )[dataset_name]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(examples, desc=\"Loading and Saving queries\"):\n\n # NOTE: we modify the qid here to distinguish the queries from different splits\n qid, query = f'{split}-{data[\"id\"]}', data[\"query\"]\n\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n else:\n # NOTE: we modify the qid here to distinguish the queries from different splits\n queries_dict = {f'{split}-{data[\"id\"]}': data[\"query\"] for data in tqdm(examples, desc=\"Loading queries\")}\n return datasets.DatasetDict(queries_dict)\n\n\nclass BrightLongEvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for Bright(long).\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return [\n # StackExchange\n \"biology\", \"earth_science\", \"economics\", \"psychology\", \"robotics\", \"stackoverflow\", \"sustainable_living\",\n # Coding\n \"pony\",\n ]\n\n def available_splits(self, dataset_name: str) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (str): Dataset name.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n return [\n # normal splits\n \"examples\",\n # w/ reasoning splits\n \"Gemini-1.0_reason\", \"claude-3-opus_reason\", \"gpt4_reason\", \"grit_reason\", \"llama3-70b_reason\",\n ]\n\n def _load_remote_corpus(\n self,\n dataset_name: str,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the corpus dataset from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n corpus = datasets.load_dataset(\n \"xlangai/bright\", \"long_documents\",\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )[dataset_name]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n docid, text = str(data[\"id\"]), data[\"content\"]\n _data = {\n \"id\": docid,\n \"text\": text\n }\n corpus_dict[docid] = {\"text\": text}\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {str(data[\"id\"]): {\"text\": data[\"content\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: str,\n split: str = 'examples',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'examples'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n examples = datasets.load_dataset(\n \"xlangai/bright\", split,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )[dataset_name]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = defaultdict(dict)\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(examples, desc=\"Loading and Saving qrels\"):\n\n # NOTE: we modify the qid here to distinguish the queries from different splits\n qid = f'{split}-{data[\"id\"]}'\n\n for docid in data[\"gold_ids_long\"]:\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": 1\n }\n qrels_dict[qid][docid] = 1\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n\n # NOTE: we record the excluded_ids in qrels with relevance 0 to remove corresponding documents from raw search results. Refer to `searcher.py` for details.\n for ex_docid in list(set(data[\"excluded_ids\"])):\n if ex_docid == \"N/A\":\n continue\n assert ex_docid not in qrels_dict[qid], f\"{ex_docid} in {qid}\"\n _data = {\n \"qid\": qid,\n \"docid\": ex_docid,\n \"relevance\": 0\n }\n qrels_dict[qid][ex_docid] = 0\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n else:\n qrels_dict = defaultdict(dict)\n for data in tqdm(examples, desc=\"Loading qrels\"):\n\n # NOTE: we modify the qid here to distinguish the queries from different splits\n qid = f'{split}-{data[\"id\"]}'\n\n for docid in data[\"gold_ids_long\"]:\n qrels_dict[qid][docid] = 1\n\n # NOTE: we record the excluded_ids in qrels with relevance 0 to remove corresponding documents from raw search results. Refer to `searcher.py` for details.\n for ex_docid in data[\"excluded_ids\"]:\n if ex_docid == \"N/A\":\n continue\n assert ex_docid not in qrels_dict[qid], f\"{ex_docid} in {qid}\"\n _data = {\n \"qid\": qid,\n \"docid\": ex_docid,\n \"relevance\": 0\n }\n qrels_dict[qid][ex_docid] = 0\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: str,\n split: str = 'examples',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'examples'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n examples = datasets.load_dataset(\n \"xlangai/bright\", split,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )[dataset_name]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(examples, desc=\"Loading and Saving queries\"):\n\n # NOTE: we modify the qid here to distinguish the queries from different splits\n qid, query = f'{split}-{data[\"id\"]}', data[\"query\"]\n\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n else:\n # NOTE: we modify the qid here to distinguish the queries from different splits\n queries_dict = {f'{split}-{data[\"id\"]}': data[\"query\"] for data in tqdm(examples, desc=\"Loading queries\")}\n return datasets.DatasetDict(queries_dict)\n" }, { "path": "FlagEmbedding/evaluation/bright/prompts.py", "content": "BrightShortInstructions = {\n # StackExchange\n \"biology\": \"Given a Biology post, retrieve relevant passages that help answer the post.\",\n \"earth_science\": \"Given an Earth Science post, retrieve relevant passages that help answer the post.\",\n \"economics\": \"Given an Economics post, retrieve relevant passages that help answer the post.\",\n \"psychology\": \"Given a Psychology post, retrieve relevant passages that help answer the post.\",\n \"robotics\": \"Given a Robotics post, retrieve relevant passages that help answer the post.\",\n \"stackoverflow\": \"Given a Stack Overflow post, retrieve relevant passages that help answer the post.\",\n \"sustainable_living\": \"Given a Sustainable Living post, retrieve relevant passages that help answer the post.\",\n # Coding\n \"leetcode\": \"Given a Coding problem, retrieve relevant examples that help answer the problem.\",\n \"pony\": \"Given a Pony question, retrieve relevant passages that help answer the question.\",\n # Theorem-based\n \"aops\": \"Given a Math problem, retrieve relevant examples that help answer the problem.\",\n \"theoremqa_questions\": \"Given a Math problem, retrieve relevant examples that help answer the problem.\",\n \"theoremqa_theorems\": \"Given a Math problem, retrieve relevant theorems that help answer the problem.\",\n}\n\n\nBrightLongInstructions = {\n # StackExchange\n \"biology\": \"Given a Biology post, retrieve relevant documents that help answer the post.\",\n \"earth_science\": \"Given an Earth Science post, retrieve relevant documents that help answer the post.\",\n \"economics\": \"Given an Economics post, retrieve relevant documents that help answer the post.\",\n \"psychology\": \"Given a Psychology post, retrieve relevant documents that help answer the post.\",\n \"robotics\": \"Given a Robotics post, retrieve relevant documents that help answer the post.\",\n \"stackoverflow\": \"Given a Stack Overflow post, retrieve relevant documents that help answer the post.\",\n \"sustainable_living\": \"Given a Sustainable Living post, retrieve relevant documents that help answer the post.\",\n # Coding\n \"pony\": \"Given a Pony question, retrieve relevant documents that help answer the question\",\n}\n" }, { "path": "FlagEmbedding/evaluation/bright/runner.py", "content": "import logging\nfrom typing import Union, Tuple\nfrom FlagEmbedding.abc.evaluation import AbsEvalRunner, EvalReranker, \\\n AbsEvalModelArgs as BrightEvalModelArgs\n\nfrom .prompts import BrightShortInstructions, BrightLongInstructions\nfrom .arguments import BrightEvalArgs\nfrom .data_loader import BrightShortEvalDataLoader, BrightLongEvalDataLoader\nfrom .searcher import BrightEvalDenseRetriever\n\nlogger = logging.getLogger(__name__)\n\n\nclass BrightEvalRunner(AbsEvalRunner):\n \"\"\"\n Evaluation runner of Bright.\n \"\"\"\n def __init__(self, eval_args: BrightEvalArgs, model_args: BrightEvalModelArgs):\n super().__init__(eval_args, model_args)\n self.eval_args: BrightEvalArgs\n self.model_args: BrightEvalModelArgs\n\n def load_data_loader(self) -> Union[BrightShortEvalDataLoader, BrightLongEvalDataLoader]:\n \"\"\"Load the data loader instance by args.\n\n Returns:\n Union[BrightShortEvalDataLoader, BrightLongEvalDataLoader]: The Bright data loader instance.\n \"\"\"\n if self.eval_args.task_type == \"short\":\n data_loader_class = BrightShortEvalDataLoader\n elif self.eval_args.task_type == \"long\":\n data_loader_class = BrightLongEvalDataLoader\n else:\n raise ValueError(f\"Invalid task type: {self.eval_args.task_type}\")\n\n data_loader = data_loader_class(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n\n def load_retriever_and_reranker(self) -> Tuple[BrightEvalDenseRetriever, Union[EvalReranker, None]]:\n \"\"\"Load retriever and reranker for evaluation\n\n Returns:\n Tuple[BrightEvalDenseRetriever, Union[EvalReranker, None]]: A :class:BrightEvalDenseRetriever object for retrieval, and a\n :class:EvalReranker object if reranker provided.\n \"\"\"\n embedder, reranker = self.get_models(self.model_args)\n retriever = BrightEvalDenseRetriever(\n embedder,\n search_top_k=self.eval_args.search_top_k,\n overwrite=self.eval_args.overwrite\n )\n if reranker is not None:\n reranker = EvalReranker(reranker, rerank_top_k=self.eval_args.rerank_top_k)\n return retriever, reranker\n\n def run(self):\n \"\"\"\n Run the whole evaluation.\n \"\"\"\n if self.eval_args.dataset_names is None:\n dataset_names = self.data_loader.available_dataset_names()\n else:\n dataset_names = self.data_loader.check_dataset_names(self.eval_args.dataset_names)\n\n if len(dataset_names) == 0:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on the default dataset.\")\n self.evaluator(\n splits=self.eval_args.splits,\n search_results_save_dir=self.eval_args.output_dir,\n retriever=self.retriever,\n reranker=self.reranker,\n corpus_embd_save_dir=self.eval_args.corpus_embd_save_dir,\n ignore_identical_ids=self.eval_args.ignore_identical_ids,\n k_values=self.eval_args.k_values\n )\n logger.info(f\"{self.eval_args.eval_name} evaluation completed.\")\n else:\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on the following dataset names: {dataset_names}\")\n for dataset_name in dataset_names:\n if self.eval_args.use_special_instructions:\n self.retriever.stop_multi_process_pool()\n if self.eval_args.task_type == \"short\":\n self.retriever.embedder.query_instruction_for_retrieval = BrightShortInstructions[dataset_name]\n elif self.eval_args.task_type == \"long\":\n self.retriever.embedder.query_instruction_for_retrieval = BrightLongInstructions[dataset_name]\n else:\n raise ValueError(f\"Invalid task type: {self.eval_args.task_type}\")\n\n # NOTE: pass qrels to searcher to exclude documents from raw search results\n evaluator_kwargs = {}\n evaluator_kwargs[\"retriever_qrels\"] = self.data_loader.load_qrels(dataset_name=dataset_name, split=self.eval_args.splits)\n\n logger.info(f\"Running {self.eval_args.eval_name} evaluation on: {dataset_name}\")\n self.evaluator(\n splits=self.eval_args.splits,\n search_results_save_dir=self.eval_args.output_dir,\n retriever=self.retriever,\n reranker=self.reranker,\n corpus_embd_save_dir=self.eval_args.corpus_embd_save_dir,\n ignore_identical_ids=self.eval_args.ignore_identical_ids,\n k_values=self.eval_args.k_values,\n dataset_name=dataset_name,\n **evaluator_kwargs,\n )\n logger.info(f\"{self.eval_args.eval_name} evaluation on {dataset_names} completed.\")\n\n logger.info(\"Start computing metrics.\")\n self.evaluate_metrics(\n search_results_save_dir=self.eval_args.output_dir,\n output_method=self.eval_args.eval_output_method,\n output_path=self.eval_args.eval_output_path,\n metrics=self.eval_args.eval_metrics\n )\n" }, { "path": "FlagEmbedding/evaluation/bright/searcher.py", "content": "import os\nimport logging\nimport gc\nimport torch\nimport numpy as np\nfrom typing import Any, Dict, Optional\n\nfrom FlagEmbedding.abc.evaluation.utils import index, search\n\nfrom FlagEmbedding.abc.evaluation import EvalRetriever\n\nlogger = logging.getLogger(__name__)\n\n\nclass BrightEvalDenseRetriever(EvalRetriever):\n \"\"\"\n Child class of :class:EvalRetriever for dense retrieval.\n \"\"\"\n def __call__(\n self,\n corpus: Dict[str, Dict[str, Any]],\n queries: Dict[str, str],\n corpus_embd_save_dir: Optional[str] = None,\n ignore_identical_ids: bool = False,\n **kwargs,\n ) -> Dict[str, Dict[str, float]]:\n \"\"\"\n This is called during the retrieval process.\n\n Parameters:\n corpus: Dict[str, Dict[str, Any]]: Corpus of documents. \n Structure: {: {\"text\": }}.\n Example: {\"doc-0\": {\"text\": \"This is a document.\"}}\n queries: Dict[str, str]: Queries to search for.\n Structure: {: }.\n Example: {\"q-0\": \"This is a query.\"}\n corpus_embd_save_dir (Optional[str]): Defaults to :data:`None`.\n ignore_identical_ids (bool): Defaults to :data:`False`.\n **kwargs: Any: Additional arguments.\n\n Returns: Dict[str, Dict[str, float]]: Top-k search results for each query. k is specified by search_top_k.\n Structure: {qid: {docid: score}}. The higher is the score, the more relevant is the document.\n Example: {\"q-0\": {\"doc-0\": 0.9}}\n \"\"\"\n if ignore_identical_ids:\n logger.warning(\"ignore_identical_ids is set to True. This means that the search results will not contain identical ids. Note: Dataset such as MIRACL should NOT set this to True.\")\n\n # dense embedding models do not require language as input: AIRBench evaluation\n kwargs.pop(\"language\", None)\n\n corpus_ids = []\n corpus_texts = []\n for docid, doc in corpus.items():\n corpus_ids.append(docid)\n corpus_texts.append(\n doc[\"text\"] if \"title\" not in doc \n else f\"{doc['title']} {doc['text']}\".strip()\n )\n queries_ids = []\n queries_texts = []\n for qid, query in queries.items():\n queries_ids.append(qid)\n queries_texts.append(query)\n\n # NOTE: obtain excluded ids from qrels to remove corresponding documents from raw search results\n excluded_ids = {}\n qrels = kwargs.pop(\"retriever_qrels\", None)\n if qrels is not None:\n for qid in qrels:\n excluded_ids[qid] = []\n for docid, score in qrels[qid].items():\n if score != 1:\n excluded_ids[qid].append(docid)\n else:\n logger.warning(\"No qrels provided, so no documents will be excluded.\")\n\n if corpus_embd_save_dir is not None:\n if os.path.exists(os.path.join(corpus_embd_save_dir, \"doc.npy\")) and not self.overwrite:\n corpus_emb = np.load(os.path.join(corpus_embd_save_dir, \"doc.npy\"))\n else:\n corpus_emb = self.embedder.encode_corpus(corpus_texts, **kwargs)\n else:\n corpus_emb = self.embedder.encode_corpus(corpus_texts, **kwargs)\n\n queries_emb = self.embedder.encode_queries(queries_texts, **kwargs)\n\n # check if the embeddings are in dictionary format: M3Embedder\n if isinstance(corpus_emb, dict):\n corpus_emb = corpus_emb[\"dense_vecs\"]\n if isinstance(queries_emb, dict):\n queries_emb = queries_emb[\"dense_vecs\"]\n\n if corpus_embd_save_dir is not None and \\\n (not os.path.exists(os.path.join(corpus_embd_save_dir, \"doc.npy\")) or self.overwrite):\n os.makedirs(corpus_embd_save_dir, exist_ok=True)\n np.save(os.path.join(corpus_embd_save_dir, \"doc.npy\"), corpus_emb)\n\n gc.collect()\n torch.cuda.empty_cache()\n\n faiss_index = index(corpus_embeddings=corpus_emb)\n all_scores, all_indices = search(query_embeddings=queries_emb, faiss_index=faiss_index, k=self.search_top_k)\n\n results = {}\n for idx, (scores, indices) in enumerate(zip(all_scores, all_indices)):\n query_id = queries_ids[idx]\n\n results[query_id] = {}\n for score, indice in zip(scores, indices):\n if indice != -1:\n if ignore_identical_ids and corpus_ids[indice] == query_id:\n continue\n results[query_id][corpus_ids[indice]] = float(score)\n\n if qrels is not None:\n # NOTE: Filter out documents with ids in excluded_ids\n for docid in set(excluded_ids[query_id]):\n if docid != \"N/A\":\n results[query_id].pop(docid, None)\n\n sorted_scores = sorted(results[query_id].items(), key=lambda item: item[1], reverse=True)\n # Store the top-k results for the current query\n results[query_id] = {}\n for docid, score in sorted_scores[:self.search_top_k]:\n results[query_id][docid] = float(score)\n\n return results\n" }, { "path": "FlagEmbedding/evaluation/custom/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalArgs as CustomEvalArgs,\n AbsEvalModelArgs as CustomEvalModelArgs,\n)\n\nfrom .data_loader import CustomEvalDataLoader\nfrom .runner import CustomEvalRunner\n\n__all__ = [\n \"CustomEvalArgs\",\n \"CustomEvalModelArgs\",\n \"CustomEvalRunner\",\n \"CustomEvalDataLoader\",\n]\n" }, { "path": "FlagEmbedding/evaluation/custom/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.custom import (\n CustomEvalArgs, CustomEvalModelArgs,\n CustomEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n CustomEvalArgs,\n CustomEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: CustomEvalArgs\n model_args: CustomEvalModelArgs\n\n runner = CustomEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/custom/data_loader.py", "content": "import logging\nfrom tqdm import tqdm\nfrom typing import List, Optional\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass CustomEvalDataLoader(AbsEvalDataLoader):\n def available_dataset_names(self) -> List[str]:\n return []\n\n def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:\n return [\"test\"]\n" }, { "path": "FlagEmbedding/evaluation/custom/runner.py", "content": "from FlagEmbedding.abc.evaluation import AbsEvalRunner\n\nfrom .data_loader import CustomEvalDataLoader\n\n\nclass CustomEvalRunner(AbsEvalRunner):\n def load_data_loader(self) -> CustomEvalDataLoader:\n data_loader = CustomEvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n" }, { "path": "FlagEmbedding/evaluation/miracl/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalArgs as MIRACLEvalArgs,\n AbsEvalModelArgs as MIRACLEvalModelArgs,\n)\n\nfrom .data_loader import MIRACLEvalDataLoader\nfrom .runner import MIRACLEvalRunner\n\n__all__ = [\n \"MIRACLEvalArgs\",\n \"MIRACLEvalModelArgs\",\n \"MIRACLEvalRunner\",\n \"MIRACLEvalDataLoader\",\n]\n" }, { "path": "FlagEmbedding/evaluation/miracl/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.miracl import (\n MIRACLEvalArgs, MIRACLEvalModelArgs,\n MIRACLEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n MIRACLEvalArgs,\n MIRACLEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: MIRACLEvalArgs\n model_args: MIRACLEvalModelArgs\n\n runner = MIRACLEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/miracl/data_loader.py", "content": "import os\nimport json\nimport logging\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Optional\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass MIRACLEvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for MIRACL.\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return [\"ar\", \"bn\", \"en\", \"es\", \"fa\", \"fi\", \"fr\", \"hi\", \"id\", \"ja\", \"ko\", \"ru\", \"sw\", \"te\", \"th\", \"zh\", \"de\", \"yo\"]\n\n def available_splits(self, dataset_name: str) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (str): Dataset name.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n if dataset_name in [\"de\", \"yo\"]:\n return [\"dev\"]\n else:\n return [\"train\", \"dev\"]\n\n def _load_remote_corpus(\n self,\n dataset_name: str,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the corpus dataset from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n corpus = datasets.load_dataset(\n \"miracl/miracl-corpus\", dataset_name,\n cache_dir=self.cache_dir,\n trust_remote_code=True,\n download_mode=self.hf_download_mode\n )[\"train\"]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n docid, title, text = str(data[\"docid\"]), data[\"title\"], data[\"text\"]\n _data = {\n \"id\": docid,\n \"title\": title,\n \"text\": text\n }\n corpus_dict[docid] = {\n \"title\": title,\n \"text\": text\n }\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {str(data[\"docid\"]): {\"title\": data[\"title\"], \"text\": data[\"text\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: str,\n split: str = 'dev',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'dev'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n endpoint = f\"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/miracl/miracl\"\n qrels_download_url = f\"{endpoint}/resolve/main/miracl-v1.0-{dataset_name}/qrels/qrels.miracl-v1.0-{dataset_name}-{split}.tsv\"\n\n qrels_save_path = self._download_file(qrels_download_url, self.cache_dir)\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f1:\n with open(qrels_save_path, \"r\", encoding=\"utf-8\") as f2:\n for line in tqdm(f2.readlines(), desc=\"Loading and Saving qrels\"):\n qid, _, docid, rel = line.strip().split(\"\\t\")\n qid, docid, rel = str(qid), str(docid), int(rel)\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": rel\n }\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n f1.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} qrels saved to {save_path}\")\n else:\n qrels_dict = {}\n with open(qrels_save_path, \"r\", encoding=\"utf-8\") as f:\n for line in tqdm(f.readlines(), desc=\"Loading qrels\"):\n qid, _, docid, rel = line.strip().split(\"\\t\")\n qid, docid, rel = str(qid), str(docid), int(rel)\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: str,\n split: str = 'dev',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'dev'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n endpoint = f\"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/miracl/miracl\"\n queries_download_url = f\"{endpoint}/resolve/main/miracl-v1.0-{dataset_name}/topics/topics.miracl-v1.0-{dataset_name}-{split}.tsv\"\n\n queries_save_path = self._download_file(queries_download_url, self.cache_dir)\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f1:\n with open(queries_save_path, \"r\", encoding=\"utf-8\") as f2:\n for line in tqdm(f2.readlines(), desc=\"Loading and Saving queries\"):\n qid, query = line.strip().split(\"\\t\")\n qid = str(qid)\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f1.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} queries saved to {save_path}\")\n else:\n queries_dict = {}\n with open(queries_save_path, \"r\", encoding=\"utf-8\") as f:\n for line in tqdm(f.readlines(), desc=\"Loading queries\"):\n qid, query = line.strip().split(\"\\t\")\n qid = str(qid)\n queries_dict[qid] = query\n return datasets.DatasetDict(queries_dict)\n" }, { "path": "FlagEmbedding/evaluation/miracl/runner.py", "content": "from FlagEmbedding.abc.evaluation import AbsEvalRunner\n\nfrom .data_loader import MIRACLEvalDataLoader\n\n\nclass MIRACLEvalRunner(AbsEvalRunner):\n \"\"\"\n Evaluation runner of MIRACL.\n \"\"\"\n def load_data_loader(self) -> MIRACLEvalDataLoader:\n \"\"\"Load the data loader instance by args.\n\n Returns:\n MIRACLEvalDataLoader: The MIRACL data loader instance.\n \"\"\"\n data_loader = MIRACLEvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n" }, { "path": "FlagEmbedding/evaluation/mkqa/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalArgs as MKQAEvalArgs,\n AbsEvalModelArgs as MKQAEvalModelArgs,\n)\n\nfrom .data_loader import MKQAEvalDataLoader\nfrom .evaluator import MKQAEvaluator\nfrom .runner import MKQAEvalRunner\n\n__all__ = [\n \"MKQAEvalArgs\",\n \"MKQAEvalModelArgs\",\n \"MKQAEvalRunner\",\n \"MKQAEvalDataLoader\",\n \"MKQAEvaluator\"\n]\n" }, { "path": "FlagEmbedding/evaluation/mkqa/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.mkqa import (\n MKQAEvalArgs, MKQAEvalModelArgs,\n MKQAEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n MKQAEvalArgs,\n MKQAEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: MKQAEvalArgs\n model_args: MKQAEvalModelArgs\n\n runner = MKQAEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/mkqa/data_loader.py", "content": "import os\nimport json\nimport logging\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Optional\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nfrom .utils.normalize_text import normalize_text\n\nlogger = logging.getLogger(__name__)\n\n\nclass MKQAEvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for MKQA.\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n\n def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (str): Dataset name.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n return [\"test\"]\n\n def load_corpus(self, dataset_name: Optional[str] = None) -> datasets.DatasetDict:\n \"\"\"Load the corpus.\n\n Args:\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to None.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n if self.dataset_dir is not None:\n # same corpus for all languages\n save_dir = self.dataset_dir\n return self._load_local_corpus(save_dir, dataset_name=dataset_name)\n else:\n return self._load_remote_corpus(dataset_name=dataset_name)\n\n def _load_local_qrels(self, save_dir: str, dataset_name: Optional[str] = None, split: str = 'test') -> datasets.DatasetDict:\n \"\"\"Try to load qrels from local datasets.\n\n Args:\n save_dir (str): Directory that save the data files.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n split (str, optional): Split of the dataset. Defaults to ``'test'``.\n\n Raises:\n ValueError: No local qrels found, will try to download from remote.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrels.\n \"\"\"\n checked_split = self.check_splits(split)\n if len(checked_split) == 0:\n raise ValueError(f\"Split {split} not found in the dataset.\")\n split = checked_split[0]\n\n qrels_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n if self.force_redownload or not os.path.exists(qrels_path):\n logger.warning(f\"Qrels not found in {qrels_path}. Trying to download the qrels from the remote and save it to {save_dir}.\")\n return self._load_remote_qrels(dataset_name=dataset_name, split=split, save_dir=save_dir)\n else:\n qrels_data = datasets.load_dataset('json', data_files=qrels_path, cache_dir=self.cache_dir)['train']\n\n qrels = {}\n for data in qrels_data:\n qid = data['qid']\n qrels[qid] = data['answers']\n\n return datasets.DatasetDict(qrels)\n\n def _load_remote_corpus(\n self,\n dataset_name: Optional[str] = None,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"\n Refer to: https://arxiv.org/pdf/2402.03216. We use the corpus from the BeIR dataset.\n \"\"\"\n corpus = datasets.load_dataset(\n \"BeIR/nq\", \"corpus\",\n cache_dir=self.cache_dir,\n trust_remote_code=True,\n download_mode=self.hf_download_mode\n )[\"corpus\"]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n docid, title, text = str(data[\"_id\"]), normalize_text(data[\"title\"]).lower(), normalize_text(data[\"text\"]).lower()\n _data = {\n \"id\": docid,\n \"title\": title,\n \"text\": text\n }\n corpus_dict[docid] = {\n \"title\": title,\n \"text\": text\n }\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {}\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n docid, title, text = str(data[\"_id\"]), normalize_text(data[\"title\"]), normalize_text(data[\"text\"])\n corpus_dict[docid] = {\n \"title\": title,\n \"text\": text\n }\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: str,\n split: str = 'test',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load remote qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n endpoint = f\"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/Shitao/bge-m3-data\"\n queries_download_url = f\"{endpoint}/resolve/main/MKQA_test-data.zip\"\n\n qrels_save_dir = self._download_zip_file(queries_download_url, self.cache_dir)\n qrels_save_path = os.path.join(qrels_save_dir, f\"{dataset_name}.jsonl\")\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f1:\n with open(qrels_save_path, \"r\", encoding=\"utf-8\") as f2:\n for line in tqdm(f2.readlines(), desc=\"Loading and Saving qrels\"):\n data = json.loads(line)\n qid, answers = str(data[\"id\"]), data[\"answers\"]\n _data = {\n \"qid\": qid,\n \"answers\": answers\n }\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid] = answers\n f1.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} qrels saved to {save_path}\")\n else:\n qrels_dict = {}\n with open(qrels_save_path, \"r\", encoding=\"utf-8\") as f:\n for line in tqdm(f.readlines(), desc=\"Loading qrels\"):\n data = json.loads(line)\n qid, answers = str(data[\"id\"]), data[\"answers\"]\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid] = answers\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: str,\n split: str = 'test',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n endpoint = f\"{os.getenv('HF_ENDPOINT', 'https://huggingface.co')}/datasets/Shitao/bge-m3-data\"\n queries_download_url = f\"{endpoint}/resolve/main/MKQA_test-data.zip\"\n\n queries_save_dir = self._download_zip_file(queries_download_url, self.cache_dir)\n queries_save_path = os.path.join(queries_save_dir, f\"{dataset_name}.jsonl\")\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f1:\n with open(queries_save_path, \"r\", encoding=\"utf-8\") as f2:\n for line in tqdm(f2.readlines(), desc=\"Loading and Saving queries\"):\n data = json.loads(line)\n qid, query = str(data[\"id\"]), data[\"question\"]\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f1.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} queries saved to {save_path}\")\n else:\n queries_dict = {}\n with open(queries_save_path, \"r\", encoding=\"utf-8\") as f:\n for line in tqdm(f.readlines(), desc=\"Loading queries\"):\n data = json.loads(line)\n qid, query = str(data[\"id\"]), data[\"question\"]\n queries_dict[qid] = query\n return datasets.DatasetDict(queries_dict)\n" }, { "path": "FlagEmbedding/evaluation/mkqa/evaluator.py", "content": "import os\nfrom tqdm import tqdm\nfrom typing import Dict, List, Optional\n\nfrom FlagEmbedding.abc.evaluation import AbsEvaluator\n\nfrom .utils.compute_metrics import evaluate_qa_recall\n\n\nclass MKQAEvaluator(AbsEvaluator):\n \"\"\"\n The evaluator class of MKQA.\n \"\"\"\n def get_corpus_embd_save_dir(\n self,\n retriever_name: str,\n corpus_embd_save_dir: Optional[str] = None,\n dataset_name: Optional[str] = None\n ):\n \"\"\"Get the directory to save the corpus embedding.\n\n Args:\n retriever_name (str): Name of the retriever.\n corpus_embd_save_dir (Optional[str], optional): Directory to save the corpus embedding. Defaults to ``None``.\n dataset_name (Optional[str], optional): Name of the dataset. Defaults to ``None``.\n\n Returns:\n str: The final directory to save the corpus embedding.\n \"\"\"\n if corpus_embd_save_dir is not None:\n # Save the corpus embeddings in the same directory for all dataset_name\n corpus_embd_save_dir = os.path.join(corpus_embd_save_dir, retriever_name)\n return corpus_embd_save_dir\n\n def evaluate_results(\n self,\n search_results_save_dir: str,\n k_values: List[int] = [1, 3, 5, 10, 100, 1000]\n ):\n \"\"\"Compute the metrics and get the eval results.\n\n Args:\n search_results_save_dir (str): Directory that saves the search results.\n k_values (List[int], optional): Cutoffs. Defaults to ``[1, 3, 5, 10, 100, 1000]``.\n\n Returns:\n dict: The evaluation results.\n \"\"\"\n eval_results_dict = {}\n\n corpus = self.data_loader.load_corpus()\n corpus_dict = {}\n for docid, data in tqdm(corpus.items(), desc=\"Loading corpus for evaluation\"):\n title, text = data[\"title\"], data[\"text\"]\n corpus_dict[docid] = f\"{title} {text}\".strip()\n\n for file in os.listdir(search_results_save_dir):\n if not file.endswith('.json'):\n continue\n\n file_path = os.path.join(search_results_save_dir, file)\n data_info, search_results = self.load_search_results(file_path)\n\n _eval_name = data_info['eval_name']\n assert _eval_name == self.eval_name, f'Mismatch eval_name: {_eval_name} vs {self.eval_name} in {file_path}'\n\n split = data_info['split']\n dataset_name = data_info.get('dataset_name', None)\n qrels = self.data_loader.load_qrels(dataset_name=dataset_name, split=split)\n\n eval_results = self.compute_metrics(\n corpus_dict=corpus_dict,\n qrels=qrels,\n search_results=search_results,\n k_values=k_values\n )\n\n if dataset_name is not None:\n key = f\"{dataset_name}-{split}\"\n else:\n key = split\n eval_results_dict[key] = eval_results\n\n return eval_results_dict\n \n @staticmethod\n def compute_metrics(\n corpus_dict: Dict[str, str],\n qrels: Dict[str, List[str]],\n search_results: Dict[str, Dict[str, float]],\n k_values: List[int],\n ):\n \"\"\"\n Compute Recall@k for QA task. The definition of recall in QA task is different from the one in IR task. Please refer to the paper of RocketQA: https://aclanthology.org/2021.naacl-main.466.pdf.\n \n Args:\n corpus_dict (Dict[str, str]): Dictionary of the corpus with doc id and contents.\n qrels (Dict[str, List[str]]): Relevances of queries and passage.\n search_results (Dict[str, Dict[str, float]]): Search results of the model to evaluate.\n \n Returns:\n dict: The model's scores of the metrics.\n \"\"\"\n contexts = []\n answers = []\n top_k = max(k_values)\n for qid, doc_score_dict in search_results.items():\n doc_score_pair = sorted(doc_score_dict.items(), key=lambda x: x[1], reverse=True)\n _ctxs = [corpus_dict[docid] for docid, _ in doc_score_pair[:top_k]]\n contexts.append(_ctxs)\n answers.append(qrels[qid])\n\n recall = evaluate_qa_recall(contexts, answers, k_values=k_values)\n scores = {f\"qa_recall_at_{k}\": v for k, v in zip(k_values, recall)}\n\n return scores\n " }, { "path": "FlagEmbedding/evaluation/mkqa/runner.py", "content": "from FlagEmbedding.abc.evaluation import AbsEvalRunner\n\nfrom .data_loader import MKQAEvalDataLoader\nfrom .evaluator import MKQAEvaluator\n\n\nclass MKQAEvalRunner(AbsEvalRunner):\n \"\"\"\n Evaluation runner of MKQA.\n \"\"\"\n def load_data_loader(self) -> MKQAEvalDataLoader:\n \"\"\"Load the data loader instance by args.\n\n Returns:\n MKQAEvalDataLoader: The MKQA data loader instance.\n \"\"\"\n data_loader = MKQAEvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n\n def load_evaluator(self) -> MKQAEvaluator:\n \"\"\"Load the evaluator instance by args.\n\n Returns:\n MKQAEvaluator: The MKQA evaluator instance.\n \"\"\"\n evaluator = MKQAEvaluator(\n eval_name=self.eval_args.eval_name,\n data_loader=self.data_loader,\n overwrite=self.eval_args.overwrite,\n )\n return evaluator\n" }, { "path": "FlagEmbedding/evaluation/mkqa/utils/compute_metrics.py", "content": "\"\"\"\nRef: https://github.com/facebookresearch/contriever\n\"\"\"\nimport regex\nimport unicodedata\nfrom functools import partial\nfrom typing import List, Union\n\n\nclass SimpleTokenizer:\n ALPHA_NUM = r'[\\p{L}\\p{N}\\p{M}]+'\n NON_WS = r'[^\\p{Z}\\p{C}]'\n\n def __init__(self):\n \"\"\"\n Args:\n annotators: None or empty set (only tokenizes).\n \"\"\"\n self._regexp = regex.compile(\n '(%s)|(%s)' % (self.ALPHA_NUM, self.NON_WS),\n flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE\n )\n\n def tokenize(self, text, uncased=False):\n matches = [m for m in self._regexp.finditer(text)]\n if uncased:\n tokens = [m.group().lower() for m in matches]\n else:\n tokens = [m.group() for m in matches]\n return tokens\n\n\ndef _normalize(text):\n return unicodedata.normalize('NFD', text)\n\n\ndef has_answer(answers, text, tokenizer) -> bool:\n \"\"\"Check if a document contains an answer string.\"\"\"\n text = _normalize(text)\n text = tokenizer.tokenize(text, uncased=True)\n\n for answer in answers:\n answer = _normalize(answer)\n answer = tokenizer.tokenize(answer, uncased=True)\n for i in range(0, len(text) - len(answer) + 1):\n if answer == text[i: i + len(answer)]:\n return True\n return False\n\n\ndef check_answer(example, tokenizer) -> List[bool]:\n \"\"\"Search through all the top docs to see if they have any of the answers.\"\"\"\n answers = example['answers']\n ctxs = example['ctxs']\n\n hits = []\n for i, text in enumerate(ctxs):\n if text is None: # cannot find the document for some reason\n hits.append(False)\n continue\n hits.append(has_answer(answers, text, tokenizer))\n return hits\n\n\ndef evaluate_qa_recall(ctxs, answers, k_values: Union[int, List[int]]=100):\n # compute Recall@k for QA task\n data = []\n assert len(ctxs) == len(answers)\n for i in range(len(ctxs)):\n _ctxs, _answers = ctxs[i], answers[i]\n data.append({\n 'answers': _answers,\n 'ctxs': _ctxs,\n })\n tokenizer = SimpleTokenizer()\n get_score_partial = partial(check_answer, tokenizer=tokenizer)\n\n scores = map(get_score_partial, data)\n\n n_docs = len(data[0]['ctxs'])\n top_k_hits = [0] * n_docs\n for question_hits in scores:\n best_hit = next((i for i, x in enumerate(question_hits) if x), None)\n if best_hit is not None:\n top_k_hits[best_hit:] = [v + 1 for v in top_k_hits[best_hit:]]\n\n if isinstance(k_values, int):\n k = min(k_values, len(top_k_hits))\n return top_k_hits[k - 1] / len(data)\n else:\n scores = []\n for k in k_values:\n k = min(k, len(top_k_hits))\n scores.append(top_k_hits[k - 1] / len(data))\n return scores\n" }, { "path": "FlagEmbedding/evaluation/mkqa/utils/normalize_text.py", "content": "\"\"\"\nadapted from chemdataextractor.text.normalize\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nTools for normalizing text.\nhttps://github.com/mcs07/ChemDataExtractor\n:copyright: Copyright 2016 by Matt Swain.\n:license: MIT\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n'Software'), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\n#: Control characters.\nCONTROLS = {\n '\\u0001', '\\u0002', '\\u0003', '\\u0004', '\\u0005', '\\u0006', '\\u0007', '\\u0008', '\\u000e', '\\u000f', '\\u0011',\n '\\u0012', '\\u0013', '\\u0014', '\\u0015', '\\u0016', '\\u0017', '\\u0018', '\\u0019', '\\u001a', '\\u001b',\n}\n# There are further control characters, but they are instead replaced with a space by unicode normalization\n# '\\u0009', '\\u000a', '\\u000b', '\\u000c', '\\u000d', '\\u001c', '\\u001d', '\\u001e', '\\u001f'\n\n\n#: Hyphen and dash characters.\nHYPHENS = {\n '-', # \\u002d Hyphen-minus\n '‐', # \\u2010 Hyphen\n '‑', # \\u2011 Non-breaking hyphen\n '⁃', # \\u2043 Hyphen bullet\n '‒', # \\u2012 figure dash\n '–', # \\u2013 en dash\n '—', # \\u2014 em dash\n '―', # \\u2015 horizontal bar\n}\n\n#: Minus characters.\nMINUSES = {\n '-', # \\u002d Hyphen-minus\n '−', # \\u2212 Minus\n '－', # \\uff0d Full-width Hyphen-minus\n '⁻', # \\u207b Superscript minus\n}\n\n#: Plus characters.\nPLUSES = {\n '+', # \\u002b Plus\n '＋', # \\uff0b Full-width Plus\n '⁺', # \\u207a Superscript plus\n}\n\n#: Slash characters.\nSLASHES = {\n '/', # \\u002f Solidus\n '⁄', # \\u2044 Fraction slash\n '∕', # \\u2215 Division slash\n}\n\n#: Tilde characters.\nTILDES = {\n '~', # \\u007e Tilde\n '˜', # \\u02dc Small tilde\n '⁓', # \\u2053 Swung dash\n '∼', # \\u223c Tilde operator #in mbert vocab\n '∽', # \\u223d Reversed tilde\n '∿', # \\u223f Sine wave\n '〜', # \\u301c Wave dash #in mbert vocab\n '～', # \\uff5e Full-width tilde #in mbert vocab\n}\n\n#: Apostrophe characters.\nAPOSTROPHES = {\n \"'\", # \\u0027\n '’', # \\u2019\n '՚', # \\u055a\n 'Ꞌ', # \\ua78b\n 'ꞌ', # \\ua78c\n '＇', # \\uff07\n}\n\n#: Single quote characters.\nSINGLE_QUOTES = {\n \"'\", # \\u0027\n '‘', # \\u2018\n '’', # \\u2019\n '‚', # \\u201a\n '‛', # \\u201b\n\n}\n\n#: Double quote characters.\nDOUBLE_QUOTES = {\n '\"', # \\u0022\n '“', # \\u201c\n '”', # \\u201d\n '„', # \\u201e\n '‟', # \\u201f\n}\n\n#: Accent characters.\nACCENTS = {\n '`', # \\u0060\n '´', # \\u00b4\n}\n\n#: Prime characters.\nPRIMES = {\n '′', # \\u2032\n '″', # \\u2033\n '‴', # \\u2034\n '‵', # \\u2035\n '‶', # \\u2036\n '‷', # \\u2037\n '⁗', # \\u2057\n}\n\n#: Quote characters, including apostrophes, single quotes, double quotes, accents and primes.\nQUOTES = APOSTROPHES | SINGLE_QUOTES | DOUBLE_QUOTES | ACCENTS | PRIMES\n\ndef normalize_text(text: str):\n for control in CONTROLS:\n text = text.replace(control, '')\n text = text.replace('\\u000b', ' ').replace('\\u000c', ' ').replace(u'\\u0085', ' ')\n\n for hyphen in HYPHENS | MINUSES:\n text = text.replace(hyphen, '-')\n text = text.replace('\\u00ad', '')\n\n for double_quote in DOUBLE_QUOTES:\n text = text.replace(double_quote, '\"') # \\u0022\n for single_quote in (SINGLE_QUOTES | APOSTROPHES | ACCENTS):\n text = text.replace(single_quote, \"'\") # \\u0027\n text = text.replace('′', \"'\") # \\u2032 prime\n text = text.replace('‵', \"'\") # \\u2035 reversed prime\n text = text.replace('″', \"''\") # \\u2033 double prime\n text = text.replace('‶', \"''\") # \\u2036 reversed double prime\n text = text.replace('‴', \"'''\") # \\u2034 triple prime\n text = text.replace('‷', \"'''\") # \\u2037 reversed triple prime\n text = text.replace('⁗', \"''''\") # \\u2057 quadruple prime\n\n text = text.replace('…', '...').replace(' . . . ', ' ... ') # \\u2026\n\n for slash in SLASHES:\n text = text.replace(slash, '/')\n\n #for tilde in TILDES:\n # text = text.replace(tilde, '~')\n\n return text\n" }, { "path": "FlagEmbedding/evaluation/mldr/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalArgs as MLDREvalArgs,\n AbsEvalModelArgs as MLDREvalModelArgs,\n)\n\nfrom .data_loader import MLDREvalDataLoader\nfrom .runner import MLDREvalRunner\n\n__all__ = [\n \"MLDREvalArgs\",\n \"MLDREvalModelArgs\",\n \"MLDREvalRunner\",\n \"MLDREvalDataLoader\",\n]\n" }, { "path": "FlagEmbedding/evaluation/mldr/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.mldr import (\n MLDREvalArgs, MLDREvalModelArgs,\n MLDREvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n MLDREvalArgs,\n MLDREvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: MLDREvalArgs\n model_args: MLDREvalModelArgs\n\n runner = MLDREvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/mldr/data_loader.py", "content": "import os\nimport json\nimport logging\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Optional\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass MLDREvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for MLDR.\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return [\"ar\", \"de\", \"en\", \"es\", \"fr\", \"hi\", \"it\", \"ja\", \"ko\", \"pt\", \"ru\", \"th\", \"zh\"]\n\n def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (Optional[str], optional): Dataset name. Defaults to ``None``.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n return [\"train\", \"dev\", \"test\"]\n\n def _load_remote_corpus(\n self,\n dataset_name: str,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the corpus dataset from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n corpus = datasets.load_dataset(\n \"Shitao/MLDR\", f\"corpus-{dataset_name}\",\n cache_dir=self.cache_dir,\n trust_remote_code=True,\n download_mode=self.hf_download_mode\n )[\"corpus\"]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n docid, text = str(data[\"docid\"]), data[\"text\"]\n _data = {\n \"id\": docid,\n \"text\": text\n }\n corpus_dict[docid] = {\"text\": text}\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n corpus_dict = {str(data[\"docid\"]): {\"text\": data[\"text\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: str,\n split: str = \"test\",\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n qrels_data = datasets.load_dataset(\n \"Shitao/MLDR\", dataset_name,\n cache_dir=self.cache_dir,\n trust_remote_code=True,\n download_mode=self.hf_download_mode\n )[split]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(qrels_data, desc=\"Loading and Saving qrels\"):\n qid = str(data[\"query_id\"])\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n for doc in data[\"positive_passages\"]:\n docid = str(doc[\"docid\"])\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": 1\n }\n qrels_dict[qid][docid] = 1\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n for doc in data[\"negative_passages\"]:\n docid = str(doc[\"docid\"])\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": 0\n }\n qrels_dict[qid][docid] = 0\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} qrels saved to {save_path}\")\n else:\n qrels_dict = {}\n for data in tqdm(qrels_data, desc=\"Loading qrels\"):\n qid = str(data[\"query_id\"])\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n for doc in data[\"positive_passages\"]:\n docid = str(doc[\"docid\"])\n qrels_dict[qid][docid] = 1\n for doc in data[\"negative_passages\"]:\n docid = str(doc[\"docid\"])\n qrels_dict[qid][docid] = 0\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: str,\n split: str = \"test\",\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n queries_data = datasets.load_dataset(\n \"Shitao/MLDR\", dataset_name,\n cache_dir=self.cache_dir,\n trust_remote_code=True,\n download_mode=self.hf_download_mode\n )[split]\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(queries_data, desc=\"Loading and Saving queries\"):\n qid, query = str(data[\"query_id\"]), data[\"query\"]\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} queries saved to {save_path}\")\n else:\n queries_dict = {}\n for data in tqdm(queries_data, desc=\"Loading queries\"):\n qid, query = str(data[\"query_id\"]), data[\"query\"]\n queries_dict[qid] = query\n return datasets.DatasetDict(queries_dict)\n" }, { "path": "FlagEmbedding/evaluation/mldr/runner.py", "content": "from FlagEmbedding.abc.evaluation import AbsEvalRunner\n\nfrom .data_loader import MLDREvalDataLoader\n\n\nclass MLDREvalRunner(AbsEvalRunner):\n \"\"\"\n Evaluation runner of MIRACL.\n \"\"\"\n def load_data_loader(self) -> MLDREvalDataLoader:\n \"\"\"Load the data loader instance by args.\n\n Returns:\n MLDREvalDataLoader: The MLDR data loader instance.\n \"\"\"\n data_loader = MLDREvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n" }, { "path": "FlagEmbedding/evaluation/msmarco/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalArgs as MSMARCOEvalArgs,\n AbsEvalModelArgs as MSMARCOEvalModelArgs,\n)\n\nfrom .data_loader import MSMARCOEvalDataLoader\nfrom .runner import MSMARCOEvalRunner\n\n__all__ = [\n \"MSMARCOEvalArgs\",\n \"MSMARCOEvalModelArgs\",\n \"MSMARCOEvalRunner\",\n \"MSMARCOEvalDataLoader\",\n]\n" }, { "path": "FlagEmbedding/evaluation/msmarco/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.msmarco import (\n MSMARCOEvalArgs, MSMARCOEvalModelArgs,\n MSMARCOEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n MSMARCOEvalArgs,\n MSMARCOEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: MSMARCOEvalArgs\n model_args: MSMARCOEvalModelArgs\n\n runner = MSMARCOEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/msmarco/data_loader.py", "content": "import os\nimport json\nimport logging\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Optional\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalDataLoader\n\nlogger = logging.getLogger(__name__)\n\n\nclass MSMARCOEvalDataLoader(AbsEvalDataLoader):\n \"\"\"\n Data loader class for MSMARCO.\n \"\"\"\n def available_dataset_names(self) -> List[str]:\n \"\"\"\n Get the available dataset names.\n\n Returns:\n List[str]: All the available dataset names.\n \"\"\"\n return [\"passage\", \"document\"]\n\n def available_splits(self, dataset_name: Optional[str] = None) -> List[str]:\n \"\"\"\n Get the avaialble splits.\n\n Args:\n dataset_name (Optional[str], optional): Dataset name. Defaults to ``None``.\n\n Returns:\n List[str]: All the available splits for the dataset.\n \"\"\"\n return [\"dev\", \"dl19\", \"dl20\"]\n\n def _load_remote_corpus(\n self,\n dataset_name: str,\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the corpus dataset from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of corpus.\n \"\"\"\n if dataset_name == 'passage':\n corpus = datasets.load_dataset(\n 'Tevatron/msmarco-passage-corpus', \n 'default', \n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )['train']\n else:\n corpus = datasets.load_dataset(\n 'irds/msmarco-document', \n 'docs', \n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, \"corpus.jsonl\")\n corpus_dict = {}\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(corpus, desc=\"Loading and Saving corpus\"):\n if dataset_name == 'passage':\n _data = {\n \"id\": data[\"docid\"],\n \"title\": data[\"title\"],\n \"text\": data[\"text\"]\n }\n corpus_dict[data[\"docid\"]] = {\n \"title\": data[\"title\"],\n \"text\": data[\"text\"]\n }\n else:\n _data = {\n \"id\": data[\"doc_id\"],\n \"title\": data[\"title\"],\n \"text\": data[\"body\"]\n }\n corpus_dict[data[\"doc_id\"]] = {\n \"title\": data[\"title\"],\n \"text\": data[\"body\"]\n }\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} corpus saved to {save_path}\")\n else:\n if dataset_name == 'passage':\n corpus_dict = {data[\"docid\"]: {\"title\": data[\"title\"], \"text\": data[\"text\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n else:\n corpus_dict = {data[\"doc_id\"]: {\"title\": data[\"title\"], \"text\": data[\"body\"]} for data in tqdm(corpus, desc=\"Loading corpus\")}\n return datasets.DatasetDict(corpus_dict)\n\n def _load_remote_qrels(\n self,\n dataset_name: Optional[str] = None,\n split: str = 'dev',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the qrels from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'dev'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of qrel.\n \"\"\"\n if dataset_name == 'passage':\n if split == 'dev':\n qrels = datasets.load_dataset(\n 'BeIR/msmarco-qrels', \n split='validation',\n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )\n qrels_download_url = None\n elif split == 'dl19':\n qrels_download_url = \"https://trec.nist.gov/data/deep/2019qrels-pass.txt\"\n else:\n qrels_download_url = \"https://trec.nist.gov/data/deep/2020qrels-pass.txt\"\n else:\n if split == 'dev':\n qrels_download_url = \"https://msmarco.z22.web.core.windows.net/msmarcoranking/msmarco-docdev-qrels.tsv.gz\"\n elif split == 'dl19':\n qrels_download_url = \"https://trec.nist.gov/data/deep/2019qrels-docs.txt\"\n else:\n qrels_download_url = \"https://trec.nist.gov/data/deep/2020qrels-docs.txt\"\n\n if qrels_download_url is not None:\n qrels_save_path = self._download_file(qrels_download_url, self.cache_dir)\n else:\n qrels_save_path = None\n \n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_qrels.jsonl\")\n qrels_dict = {}\n if qrels_save_path is not None:\n with open(save_path, \"w\", encoding=\"utf-8\") as f1:\n with open(qrels_save_path, \"r\", encoding=\"utf-8\") as f2:\n for line in tqdm(f2.readlines(), desc=\"Loading and Saving qrels\"):\n qid, _, docid, rel = line.strip().split()\n qid, docid, rel = str(qid), str(docid), int(rel)\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": rel\n }\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n f1.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n else:\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(qrels, desc=\"Loading and Saving qrels\"):\n qid, docid, rel = str(data['query-id']), str(data['corpus-id']), int(data['score'])\n _data = {\n \"qid\": qid,\n \"docid\": docid,\n \"relevance\": rel\n }\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} qrels saved to {save_path}\")\n else:\n qrels_dict = {}\n if qrels_save_path is None:\n with open(qrels_save_path, \"r\", encoding=\"utf-8\") as f:\n for line in tqdm(f.readlines(), desc=\"Loading qrels\"):\n qid, _, docid, rel = line.strip().split()\n qid, docid, rel = str(qid), str(docid), int(rel)\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n else:\n for data in tqdm(qrels, desc=\"Loading queries\"):\n qid, docid, rel = str(data['query-id']), str(data['corpus-id']), int(data['score'])\n if qid not in qrels_dict:\n qrels_dict[qid] = {}\n qrels_dict[qid][docid] = rel\n return datasets.DatasetDict(qrels_dict)\n\n def _load_remote_queries(\n self,\n dataset_name: Optional[str] = None,\n split: str = 'test',\n save_dir: Optional[str] = None\n ) -> datasets.DatasetDict:\n \"\"\"Load the queries from HF.\n\n Args:\n dataset_name (str): Name of the dataset.\n split (str, optional): Split of the dataset. Defaults to ``'test'``.\n save_dir (Optional[str], optional): Directory to save the dataset. Defaults to ``None``.\n\n Returns:\n datasets.DatasetDict: Loaded datasets instance of queries.\n \"\"\"\n if split == 'dev':\n if dataset_name == 'passage':\n queries = datasets.load_dataset(\n 'BeIR/msmarco', \n 'queries',\n trust_remote_code=True,\n cache_dir=self.cache_dir,\n download_mode=self.hf_download_mode\n )['queries']\n queries_save_path = None\n else:\n queries_download_url = \"https://msmarco.z22.web.core.windows.net/msmarcoranking/msmarco-docdev-qrels.tsv.gz\"\n queries_save_path = self._download_gz_file(queries_download_url, self.cache_dir)\n else:\n year = split.replace(\"dl\", \"\")\n queries_download_url = f\"https://msmarco.z22.web.core.windows.net/msmarcoranking/msmarco-test20{year}-queries.tsv.gz\"\n queries_save_path = self._download_gz_file(queries_download_url, self.cache_dir)\n\n qrels = self.load_qrels(dataset_name=dataset_name, split=split)\n\n if save_dir is not None:\n os.makedirs(save_dir, exist_ok=True)\n save_path = os.path.join(save_dir, f\"{split}_queries.jsonl\")\n queries_dict = {}\n if queries_save_path is not None:\n with open(save_path, \"w\", encoding=\"utf-8\") as f1:\n with open(queries_save_path, \"r\", encoding=\"utf-8\") as f2:\n for line in tqdm(f2.readlines(), desc=\"Loading and Saving queries\"):\n qid, query = line.strip().split(\"\\t\")\n if qid not in qrels.keys(): continue\n qid = str(qid)\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f1.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n else:\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n for data in tqdm(queries, desc=\"Loading and Saving queries\"):\n qid, query = data['_id'], data['text']\n if qid not in qrels.keys(): continue\n _data = {\n \"id\": qid,\n \"text\": query\n }\n queries_dict[qid] = query\n f.write(json.dumps(_data, ensure_ascii=False) + \"\\n\")\n logging.info(f\"{self.eval_name} {dataset_name} queries saved to {save_path}\")\n else:\n queries_dict = {}\n if queries_save_path is not None:\n with open(queries_save_path, \"r\", encoding=\"utf-8\") as f:\n for line in tqdm(f.readlines(), desc=\"Loading queries\"):\n qid, query = line.strip().split(\"\\t\")\n qid = str(qid)\n if qid not in qrels.keys(): continue\n queries_dict[qid] = query\n else:\n for data in tqdm(queries, desc=\"Loading queries\"):\n qid, query = data['_id'], data['text']\n if qid not in qrels.keys(): continue\n queries_dict[qid] = query\n return datasets.DatasetDict(queries_dict)\n" }, { "path": "FlagEmbedding/evaluation/msmarco/runner.py", "content": "from FlagEmbedding.abc.evaluation import AbsEvalRunner\n\nfrom .data_loader import MSMARCOEvalDataLoader\n\n\nclass MSMARCOEvalRunner(AbsEvalRunner):\n \"\"\"\n Evaluation runner of MSMARCO.\n \"\"\"\n def load_data_loader(self) -> MSMARCOEvalDataLoader:\n \"\"\"Load the data loader instance by args.\n\n Returns:\n MSMARCOEvalDataLoader: The MSMARCO data loader instance.\n \"\"\"\n data_loader = MSMARCOEvalDataLoader(\n eval_name=self.eval_args.eval_name,\n dataset_dir=self.eval_args.dataset_dir,\n cache_dir=self.eval_args.cache_path,\n token=self.eval_args.token,\n force_redownload=self.eval_args.force_redownload,\n )\n return data_loader\n" }, { "path": "FlagEmbedding/evaluation/mteb/__init__.py", "content": "from FlagEmbedding.abc.evaluation import (\n AbsEvalModelArgs as MTEBEvalModelArgs,\n)\n\nfrom .arguments import MTEBEvalArgs\nfrom .runner import MTEBEvalRunner\n\n__all__ = [\n \"MTEBEvalArgs\",\n \"MTEBEvalModelArgs\",\n \"MTEBEvalRunner\",\n]\n" }, { "path": "FlagEmbedding/evaluation/mteb/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.evaluation.mteb import (\n MTEBEvalArgs, MTEBEvalModelArgs,\n MTEBEvalRunner\n)\n\n\ndef main():\n parser = HfArgumentParser((\n MTEBEvalArgs,\n MTEBEvalModelArgs\n ))\n\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: MTEBEvalArgs\n model_args: MTEBEvalModelArgs\n\n runner = MTEBEvalRunner(\n eval_args=eval_args,\n model_args=model_args\n )\n\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/evaluation/mteb/arguments.py", "content": "from dataclasses import dataclass, field\nfrom typing import List\n\nfrom FlagEmbedding.abc.evaluation.arguments import AbsEvalArgs\n\n\n@dataclass\nclass MTEBEvalArgs(AbsEvalArgs):\n \"\"\"\n Argument class for MTEB evaluation.\n \"\"\"\n languages: List[str] = field(\n default=None, metadata={\"help\": \"Languages to evaluate. Default: eng\"}\n )\n tasks: List[str] = field(\n default=None, metadata={\"help\": \"Tasks to evaluate. Default: None\"}\n )\n task_types: List[str] = field(\n default=None, metadata={\"help\": \"The task types to evaluate. Default: None\"}\n )\n use_special_instructions: bool = field(\n default=False, metadata={\"help\": \"Whether to use specific instructions in `prompts.py` for evaluation. Default: False\"}\n )\n examples_path: str = field(\n default=None, metadata={\"help\": \"Use specific examples in the path. Default: None\"}\n )" }, { "path": "FlagEmbedding/evaluation/mteb/examples/AmazonCounterfactualClassification.csv", "content": "text,label\n\"I wish I could have used this head set but the day I received it it wouldn't even turn on and I really wanted this product to work I'm very disappointed.\",\"counterfactual\"\n\"I would advise that instead of trying to follow these poor instructions, Google it.\",\"not-counterfactual\"\n\"I wrote to Monster customer service before ordering and they told me it would be fine to use without a converter and it was absolutely true.\",\"not-counterfactual\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/AmazonPolarityClassification.csv", "content": "text,label\n\"Hunting the Hard Way Thia was a gift for my Husband, who loved the book. It arrived on the date we were told it would.\",positive\n\"Poor DVD Has too many interviews with people at the Live THomas day in Penn. My kids were annoyed and hated this DVD.\",negative\n\"Ludicrous and silly I remember getting this book so faintly that that says alot about my opinion of it. Basically, while I will entertain lots of odd ideas and theories, this book was basically silly.\",negative\n\"Artistry I think that the Deodato concerts are very rich, as he used real strings and band musicians, as well as you can appreciate the John Tropea excelent renditions on guitar.\",positive" }, { "path": "FlagEmbedding/evaluation/mteb/examples/AmazonReviewsClassification.csv", "content": "text,label\n\"DO NOT ORDER THIS\\n\\nThis isn't what's described at all. Taking it out of the package lace was cut upon arrival, wig was cut to like 14 inch, not curly, and smelled like cigarettes. I obviously was sent what someone returned, disgusting.Not what I ordered at all, not pleased at all. I want my money back DO NOT ORDER\",\"1 star\"\n\"And I can’t return it\\n\\nThis product seemed like good quality but it does not stay stuck to the soles at all. You walk a few steps and then you find the black shoe grip somewhere on the floor.\",\"2 star\"\n\"Three Stars\\n\\nnew yearly subscription plan is horrible, but the product still works as it did in the past\",\"3 star\"\n\"I like how it has lots of pockets to put stuff ...\\n\\nI like how it has lots of pockets to put stuff in. I would have liked to have a shorter securing strap so it would not slide around so much. Good product.\",\"4 star\"\n\"Great\\n\\nIt is really good. That's my favorite. THANK YOU\",\"5 star\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/ArguAna.csv", "content": "query,pos\r\n\"People will die if we don’t do animal testing Every year, 23 new drugs are introduced in the UK alone.[13] Almost all will be tested on animals. A new drug will be used for a long time. Think of all the people saved by the use of penicillin. If drugs cost more to test, that means drug companies will develop less. This means more people suffering and dying\",\"animals science science general ban animal testing junior Many of these drugs are “me too” drugs – ones with a slight change that doesn’t make much difference to an existing drug. [14] So often the benefits from animal testing are marginal, and even if there was a slight increase in human suffering, it would be worth it based on the animal suffering saved.\"\r\n\"Survival of the fittest It is natural for human beings to farm, kill, and eat other species. In the wild there is a brutal struggle for existence as is shown by Darwin’s On the Origin of the Species. The fact that we humans have succeeded in that struggle by exploiting our natural environment means that we have a natural right over lower species. The concept of survival of the fittest may seem outdated but it is still the defining order of nature. In fact farming animals is much less brutal than the pain and hardship that animals inflict on each other naturally in the wild.\",\"The claim of human entitlement over other species based on 'survival of the fittest' is flawed. While Darwin's theory highlights competition, it doesn't justify exploitation. Our capacity for empathy and moral reasoning surpasses mere survival instincts. Farming still inflicts suffering, contradicting the notion of human superiority. Ethical considerations should guide our treatment of animals, not outdated notions of natural selection.\"\r\n\"Underground Nuclear Storage is Expensive. Underground nuclear storage is expensive. This is because the deep geological repositories needed to deal with such waste are difficult to construct. This is because said repositories need to be 300m underground and also need failsafe systems so that they can be sealed off should there be a leak. For smaller countries, implementing this idea is almost completely impossible. Further, the maintenance of the facilities also requires a lot of long-term investment as the structural integrity of the facilities must consistently be monitored and maintained so that if there is a leak, the relevant authorities can be informed quickly and efficiently. This is seen with the Yucca mountain waste repository site which has cost billions of dollars since the 1990s and was eventually halted due to public fears about nuclear safety.\",\"While initial construction and maintenance entail significant costs, advancements in technology offer more cost-effective solutions. Modular storage designs and improved monitoring systems mitigate expenses. Collaborative international efforts can also distribute costs. Additionally, public concerns can be addressed through transparent safety protocols and community engagement, ensuring responsible nuclear waste management without exorbitant expenditure. Underground nuclear storage isn't inherently prohibitive.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/ArxivClusteringP2P.csv", "content": "text,label\n\"A Novel Approach to Enhancing Cybersecurity in Smart Grids through Deep Reinforcement Learning The integration of renewable energy sources and advanced metering infrastructure in smart grids introduces complex cybersecurity challenges. In this paper, we propose a novel approach utilizing deep reinforcement learning (DRL) to enhance the resilience of smart grids against cyber attacks. Our method leverages DRL agents to dynamically optimize intrusion detection and response strategies based on real-time grid conditions and attack patterns. We demonstrate through simulations on a realistic smart grid testbed that our approach effectively reduces the impact of cyber threats while maintaining grid operational efficiency and reliability. The results highlight significant improvements in security posture compared to traditional rule-based and anomaly detection approaches.\",cs\n\"Dynamics of Frobenius Endomorphisms in Characteristic p This paper investigates the dynamics of Frobenius endomorphisms in characteristic 𝑝, focusing on their algebraic and arithmetic properties. We explore the behavior of Frobenius endomorphisms on varieties over finite fields and delve into their applications in number theory and algebraic geometry. Specifically, we analyze the distribution of fixed points, the growth rates of orbits under iteration, and connections to zeta functions and L-functions. Theoretical results are complemented by computational experiments that illustrate the interplay between Frobenius endomorphisms and geometric structures. Our findings contribute to a deeper understanding of the arithmetic nature of varieties and their representations in characteristic 𝑝, offering insights into fundamental questions in modern algebraic and arithmetic geometry.\",math\n\"Probing Exoplanetary Atmospheres Using Transmission Spectroscopy with the James Webb Space Telescope Transmission spectroscopy has revolutionized our understanding of exoplanetary atmospheres, revealing key insights into their chemical compositions and physical properties. With the upcoming launch of the James Webb Space Telescope (JWST), we explore the potential of this technique to characterize exoplanetary atmospheres across a wide range of wavelengths and planetary types. We present a comprehensive analysis framework that incorporates high-resolution spectroscopic data and advanced atmospheric models to interpret transmission spectra obtained by JWST. Our simulations predict detectability thresholds for key molecular species and atmospheric features, offering critical guidance for future observational campaigns aimed at unraveling the diversity and origins of exoplanetary atmospheres.\",astro-ph\n\"Quantum Coherence and Information Transfer in Photosynthetic Complexes: Insights from Coherent Spectroscopy Photosynthetic complexes are renowned for their efficient energy transfer mechanisms, driven by quantum coherence phenomena over femtosecond timescales. This paper explores the role of coherent spectroscopy techniques in elucidating the quantum dynamics underlying energy transfer processes in natural photosynthetic systems. We review recent experimental findings and theoretical models that highlight the significance of quantum coherence in optimizing energy capture and transport efficiency in photosynthetic complexes. Our analysis integrates insights from ultrafast spectroscopy experiments with advanced quantum mechanical simulations, providing a comprehensive framework for understanding the interplay between coherence, environmental influences, and biological functionality in photosynthesis.\",quant-ph\n\"Quantum Hall Effect in Moiré Superlattices of Twisted Bilayer Graphene The discovery of the quantum Hall effect in moiré superlattices formed by twisted bilayer graphene has opened new avenues in the study of correlated electron systems. This paper investigates the emergence of fractional quantum Hall states and their robustness against disorder and varying twist angles in twisted bilayer graphene. We analyze experimental observations of Landau level spectra and magnetotransport measurements, revealing distinctive features such as enhanced localization and unconventional symmetry breaking effects. Our theoretical framework integrates effective model descriptions and numerical simulations to elucidate the underlying mechanisms driving the quantum Hall phenomena in moiré superlattices, paving the way for future applications in quantum devices and topological materials.\",cond-mat" }, { "path": "FlagEmbedding/evaluation/mteb/examples/ArxivClusteringS2S.csv", "content": "text,label\n\"A Survey on Graph Neural Networks: Algorithms and Applications\",cs\n\"Hamiltonian Dynamics and KAM Theory for Infinite-Dimensional Systems\",math\n\"Dark Matter Distribution in Dwarf Spheroidal Galaxies: Constraints from Stellar Kinematics\",astro-ph\n\"Decoherence and Quantum Error Correction in Topological Quantum Computers\",quant-ph\n\"Spin-Orbit Coupling Effects in Low-Dimensional Quantum Materials\",cond-mat" }, { "path": "FlagEmbedding/evaluation/mteb/examples/AskUbuntuDupQuestions.csv", "content": "query,positive\nangularjs infinite scroll in a container,AngularJS ng-infinite-scroll not working on a specific container/div\nJava: Efficiently converting an array of longs to an array of bytes,Most Compact way to Serialize an Array of Longs in Java\nPyVISA missing methods,NI VISA + pyVisa on Mac OS X (Snow Leopard)" }, { "path": "FlagEmbedding/evaluation/mteb/examples/BIOSSES.csv", "content": "sent1,sent2\n\"Recent studies have highlighted the crucial role of p53 in regulating cell cycle progression.\",\"Recent research underscores p53's pivotal function in controlling cellular division.\"\n\"Neuroscience has revealed intricate pathways linking dopamine to reward and motivation.\",\"Recent neuroscientific findings have illuminated complex dopamine pathways associated with motivation and reward.\"\n\"Stem cell research holds promise for treating a variety of degenerative diseases.\",\"The potential of stem cell research in combating degenerative illnesses is widely recognized.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/Banking77Classification.csv", "content": "text,label\n\"What is my money worth in other countries?\",exchange_rate\n\"What can I do if my card still hasn't arrived after 2 weeks?\",card_arrival\n\"Would I be able to open an account for my daughter?\",age_limit\n\"My address details have changed and I want to update them\",edit_personal_details\n\"If my cash withdrawal is still not showing, is something wrong?\",pending_cash_withdrawal\n\"How long do transfers typically take? Is there a way of speeding the process up? My friend needs the money I sent her desperately.\",transfer_not_received_by_recipient" }, { "path": "FlagEmbedding/evaluation/mteb/examples/BiorxivClusteringP2P.csv", "content": "text,label\n\"Neural Mechanisms of Social Cognition: A Study on Mirror Neurons and EmpathySocial cognition is the mental process involved in understanding, recognizing, and predicting others' behavior and emotions. In this study, we investigate the role of mirror neurons in the process of empathy by using a combination of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Our experiments involve observing the neural activation of participants as they watch videos of individuals experiencing various emotional states. We demonstrate that specific mirror neuron systems in the premotor cortex and the inferior parietal lobule are significantly activated when participants empathize with others. This suggests that mirror neurons might be fundamental to the neural basis of empathy, facilitating an understanding of others' emotions by simulating them internally. These findings provide insights into the neural mechanisms underlying social cognition and offer potential pathways for therapeutic interventions in conditions like autism and psychopathy, where social cognition is often impaired.\",neuroscience\n\"Methicillin-resistant Staphylococcus aureus (MRSA) is a major health threat due to its resistance to multiple antibiotics. This study analyzed 50 clinical MRSA isolates using whole-genome sequencing and phenotypic assays. We identified mecA and mecC genes encoding beta-lactam-resistant penicillin-binding proteins. Mutations in rpoB conferred rifampicin resistance, while changes in gyrA and grlA were linked to fluoroquinolone resistance. Biofilm formation was also found to enhance antibiotic resistance. These findings highlight genetic mechanisms and suggest potential targets for developing new treatments against MRSA infections.\",microbiology\n\"Deep Learning Approaches for Predicting Protein-Protein Interactions from Sequence Data\\nProtein-protein interactions (PPIs) are fundamental to numerous biological processes, and understanding these interactions is critical for uncovering cellular mechanisms and developing therapeutic strategies. Traditional experimental methods for identifying PPIs are labor-intensive and time-consuming, highlighting the need for computational approaches. In this study, we present DeepPPI, a deep learning-based framework designed to predict PPIs directly from protein sequence data. DeepPPI employs a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both local and global sequence features. We trained DeepPPI on a comprehensive dataset of known PPIs and benchmarked its performance against existing methods, demonstrating superior accuracy and generalizability. Additionally, we applied DeepPPI to predict novel interactions in the human proteome and validated a subset of these predictions experimentally. Our results indicate that DeepPPI not only achieves high prediction accuracy but also provides insights into the structural and functional basis of protein interactions, making it a valuable tool for the bioinformatics community.\",bioinformatics\n\"Cell migration, pivotal in wound healing, immune responses, and cancer metastasis, relies on the actin cytoskeleton for membrane protrusions and movement. We explore phosphoinositides' role—key membrane phospholipids—in this process. Using live-cell imaging and FRET-based biosensors, we track phosphoinositide dynamics during migration. Our findings reveal distinct distributions: phosphatidylinositol 4,5-bisphosphate (PIP2) enriches actin polymerization sites, while phosphatidylinositol 3,4,5-trisphosphate (PIP3) predominates in membrane ruffles and lamellipodia. Modulating these phosphoinositides via kinases and phosphatases alters actin filament organization and migration speed, suggesting therapeutic targets for diseases involving abnormal cell migration.\",cell biology\n\"Cell membranes, comprising lipids and proteins, regulate molecular transport and signaling. Lipid rafts, enriched in cholesterol and sphingolipids, organize membrane proteins and influence cellular functions. Using AFM and fluorescence microscopy, we studied how lipid rafts and cholesterol impact membrane mechanics. Manipulating cholesterol levels and disrupting rafts with MβCD revealed changes in stiffness and lipid density. Rafts enhance rigidity and resistance to deformation, while cholesterol depletion increases fluidity and reduces stability. Lipid-protein interactions in rafts maintain membrane integrity. These insights into membrane organization offer strategies for manipulating cellular responses through lipid raft modulation.\",biophysics" }, { "path": "FlagEmbedding/evaluation/mteb/examples/BiorxivClusteringS2S.csv", "content": "text,label\n\"Neural Circuit Dynamics in Decision-Making: A Computational Model of Prefrontal-Striatal Interactions\",neuroscience\n\"Metagenomic Insights into Extreme Environments: Microbial Diversity and Functional Adaptations in Antarctic Lakes\",microbiology\n\"Machine Learning Approaches for Predicting Protein Structure and Function from Sequence Data\",bioinformatics\n\"Regulation of Stem Cell Fate Decisions by the Hippo Signaling Pathway: Implications for Tissue Regeneration and Cancer Therapy\",cell biology\n\"Optical Tweezers and Single-Molecule Force Spectroscopy: Probing Protein Folding Dynamics and Mechanical Properties of Biomolecules\",biophysics" }, { "path": "FlagEmbedding/evaluation/mteb/examples/CQADupstack.csv", "content": "query,positive\nangularjs infinite scroll in a container,AngularJS ng-infinite-scroll not working on a specific container/div\nJava: Efficiently converting an array of longs to an array of bytes,Most Compact way to Serialize an Array of Longs in Java\nPyVISA missing methods,NI VISA + pyVisa on Mac OS X (Snow Leopard)\n" }, { "path": "FlagEmbedding/evaluation/mteb/examples/CQADupstackRetrieval.csv", "content": "query,pos" }, { "path": "FlagEmbedding/evaluation/mteb/examples/ClimateFEVER.csv", "content": "query,pos\n\"Global warming is causing more frequent and intense hurricanes.\",\"Hurricanes and Climate Change Hurricanes, also known as tropical cyclones or typhoons depending on their location, are powerful and destructive weather systems characterized by strong winds, heavy rainfall, and storm surges. The formation and intensity of hurricanes are influenced by a variety of factors, including sea surface temperatures, atmospheric moisture, and wind patterns. Scientific research indicates that global warming is having a significant impact on these factors, leading to changes in hurricane behavior. As sea surface temperatures rise due to increased greenhouse gas emissions, the energy available for hurricane formation and intensification also increases. This has been linked to an increase in the frequency of the most intense hurricanes, categorized as Category 4 and 5 storms. Additionally, warmer air can hold more moisture, leading to heavier rainfall and greater flooding potential during hurricanes. The Intergovernmental Panel on Climate Change (IPCC) reports that while the total number of hurricanes may not be increasing, there is a clear trend towards more intense and damaging storms in a warming world.\"\n\"The Arctic sea ice extent has decreased by nearly 40% since the late 1970s due to global warming.\",\"The Arctic sea ice extent refers to the surface area of the Arctic Ocean covered by sea ice . Observations indicate a significant reduction in Arctic sea ice extent over recent decades . Satellite measurements have shown that the minimum sea ice extent , typically occurring in September , has declined by about 40% since the late 1970s . This decrease is largely attributed to rising global temperatures , which have led to warmer ocean waters and higher air temperatures in the Arctic region . Climate models predict that the Arctic could be nearly ice-free during summer within this century if the current rate of warming continues . This decline in sea ice has profound implications for Arctic ecosystems , global weather patterns , and sea levels .\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/DBPedia.csv", "content": "query,pos \n\"Chefs with a show on the Food Network.\",\"Robert Irvine Robert Irvine (born 24 September 1965) is a British celebrity chef who has appeared on a variety of Food Network programs including Dinner: Impossible, Worst Cooks in America, Restaurant: Impossible, and Restaurant Express.'\"\n\"houses of the Russian parliament\",\"State Duma The State Duma (Russian: Госуда́рственная ду́ма (Gosudarstvennaya Duma), common abbreviation: Госду́ма (Gosduma)) in the Russian Federation is the lower house of the Federal Assembly of Russia (legislature), the upper house being the Federation Council of Russia. The Duma headquarters are located in central Moscow, a few steps from Manege Square. Its members are referred to as deputies.\"\n\"tango music instruments\",\"Tango music Tango is a style of music in 2/4 or 4/4 time that originated among European immigrant populations of Argentina and Uruguay (collectively, the 'Rioplatenses'). It is traditionally played on a solo guitar, guitar duo, or an ensemble, known as the orquesta típica, which includes at least two violins, flute, piano, double bass, and at least two bandoneóns. Sometimes guitars and a clarinet join the ensemble. Tango may be purely instrumental or may include a vocalist.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/EmotionClassification.csv", "content": "text,label_text\r\n\"i am bothered is that he might changed his feelings once he get back in us and leave me heartbroken\",sadness\r\n\"i have always loved my jobs and loved to work and i truly feel like being back there with my patients and co workers will do me a lot of good even if it is only for a few weeks\",joy\r\n\"i certainly feel loved and appreciated and grateful for all that i have\",love\r\n\"im grabbing a minute to post i feel greedy wrong\",anger\r\n\"i was stymied a little bit as i wrote feeling unsure that i might go somewhere with the story unintended\",fear\r\n\"i keep feeling pleasantly surprised at his supportiveness and also his ease in new situations\",surprise" }, { "path": "FlagEmbedding/evaluation/mteb/examples/FEVER.csv", "content": "query,pos\n\"Ricky Martin acts.\",\"Ricky Martin Enrique Martín Morales ( born December 24 , 1971 ) , commonly known as Ricky Martin , is a Puerto Rican singer , actor and author . Martin began his career at age twelve with the all-boy pop group Menudo . After five years with the group , he released several Spanish-language solo albums throughout the 1990s . He also acted on stage and on TV in Mexico , becoming a modest star in the country . In 1994 he starred on the US TV soap opera General Hospital , playing a Puerto Rican singer . In early 1999 , after releasing several albums in Spanish , Martin performed `` The Cup of Life '' at the 41st Grammy Awards show , which became a catalyst in bringing Latin pop to the forefront of the U.S. music scene . Following its success , Martin released `` Livin ' la Vida Loca '' which helped him obtain enormous success worldwide and is generally seen as the song that began the Latin pop explosion of 1999 and made the transition easier for other Spanish-speaking artists to move into the English-speaking market . Since its release , the song has sold over 8 million copies , making it one of the best selling singles of all time . His first English-language album ( also titled Ricky Martin ) , has sold 22 million copies and is one of the best selling albums of all time . His other studio albums include : Me Amarás ( 1993 ) , A Medio Vivir ( 1995 ) , Vuelve ( 1998 ) , Sound Loaded ( 2000 ) , Almas del Silencio ( 2003 ) , Life ( 2005 ) , Música + Alma + Sexo ( 2011 ) , and A Quien Quiera Escuchar ( 2015 ) .\"\n\"The 19th G7 summit only included Russia.\",\"19th G7 summit The 19th G7 Summit was held in Tokyo , Japan , on July 7 -- 9 , 1993 . The venue for the summit meetings was the State Guesthouse in Tokyo , Japan . The Group of Seven ( G7 ) was an unofficial forum which brought together the heads of the richest industrialized countries : France , Germany , Italy , Japan , the United Kingdom , the United States , Canada ( since 1976 ) and the President of the European Commission ( starting officially in 1981 ) . The summits were not meant to be linked formally with wider international institutions ; and in fact , a mild rebellion against the stiff formality of other international meetings was a part of the genesis of cooperation between France 's President Giscard d'Estaing and West Germany 's Chancellor Helmut Schmidt as they conceived the first Group of Six ( G6 ) summit in 1975 .\"\n\"Ayn Rand condemned force.\",\"Ayn Rand Ayn Rand ( -LSB- ˈaɪn_ˈrænd -RSB- born Alisa Zinov ` yevna Rosenbaum , Али́са Зино́вьевна Розенба́ум -- March 6 , 1982 ) was a Russian-American novelist , philosopher , playwright , and screenwriter . She is known for her two best-selling novels , The Fountainhead and Atlas Shrugged , and for developing a philosophical system she called Objectivism . Educated in Russia , she moved to the United States in 1926 . She had a play produced on Broadway in 1935 -- 1936 . After two early novels that were initially unsuccessful in America , she achieved fame with her 1943 novel , The Fountainhead . In 1957 , Rand published her best-known work , the novel Atlas Shrugged . Afterward , she turned to non-fiction to promote her philosophy , publishing her own magazines and releasing several collections of essays until her death in 1982 . Rand advocated reason as the only means of acquiring knowledge , and rejected faith and religion . She supported rational and ethical egoism , and rejected altruism . In politics , she condemned the initiation of force as immoral , and opposed collectivism and statism as well as anarchism , and instead supported laissez-faire capitalism , which she defined as the system based on recognizing individual rights . In art , Rand promoted romantic realism . She was sharply critical of most philosophers and philosophical traditions known to her , except for Aristotle , Thomas Aquinas , and classical liberals . Literary critics received Rand 's fiction with mixed reviews , and academia generally ignored or rejected her philosophy , though academic interest has increased in recent decades . The Objectivist movement attempts to spread her ideas , both to the public and in academic settings . She has been a significant influence among libertarians and American conservatives .\"\n\"The Bachelorette is not a reality television dating game show.\",\"The Bachelorette The Bachelorette is an American reality television dating game show that debuted on ABC on January 8 , 2003 . The show is a spin-off of The Bachelor aired on the same network . The first season featured Trista Rehn , the runner-up date from the first season of The Bachelor , offering the opportunity for Rehn to choose a husband among 25 bachelors . The 2004 season of The Bachelorette again took a runner-up from the previous season of The Bachelor . After last airing on February 28 , 2005 , the series returned to ABC during the spring of 2008 , following an absence of three years .\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/FiQA2018.csv", "content": "query,pos\r\nWhat is a negotiable security and how are they related to derivatives?,\"A negotiable security is a financial instrument that can be easily transferred or traded, such as stocks and bonds. Derivatives, like options and futures, derive their value from these securities. They're interrelated because derivatives often involve contracts based on the value of underlying negotiable securities. This relationship allows investors to hedge risk or speculate on price movements without owning the actual assets.\"\r\nWhy is it important to research a stock before buying it?,\"Researching a stock before buying is crucial to assess its financial health, growth prospects, and potential risks. Understanding the company's fundamentals, earnings history, management team, and industry trends helps investors make informed decisions. It also aids in evaluating whether the stock is fairly valued or overvalued, reducing the risk of making uninformed investment choices and potentially suffering losses.\"\r\nWhen are investments taxed?,\"Investments are taxed based on various factors, including the type of investment, holding period, and applicable tax laws. Generally, investments incur taxes when they generate income, such as interest, dividends, or capital gains. Interest and dividends are typically taxed in the year they're received, while capital gains tax applies when assets like stocks or real estate are sold at a profit. Tax rates may vary based on investment duration and jurisdiction.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/HotpotQA.csv", "content": "query,pos \n\"Which tennis player Anna-Lena Grönefeld or Mats Wilander turned professional first ?\",\"Anna-Lena Grönefeld Anna-Lena Grönefeld (born 4 June 1985) is a German tennis player. She turned professional in April 2003.\"\n\"What South Korean K-pop group has 13 members and their own online TV program?\",\"Seventeen (band) Seventeen (Hangul: 세븐틴 ), also stylized as SEVENTEEN or SVT, is a South Korean boy group formed by Pledis Entertainment in 2015. The group consists of thirteen members who are separated into three sub-units, each with different areas of specialization: a 'Hip-Hop Unit', 'Vocal Unit', and 'Performance Unit'. They have released one studio album and four extended plays.\"\n\"The game show Keep It in the Family was hosted by an actor that played what role in \"Coronation Street\"?\",\"Keep It in the Family (UK game show) Keep It in the Family is a British game show that aired on ITV from 26 October 2014 to 19 December 2015 and is hosted by Bradley Walsh.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/ImdbClassification.csv", "content": "text,label_text\r\n\"Renny Harlin's first American film was one of the best of a slew of prison-set horror films(like 'Death House' or 'The Chair')in the late 80's.Twenty years before,guard Lane Smith had wrongfully executed a condemned man.Now,he is the warden of the newly re-opened prison,and the man's ghost is back for bloody revenge.This atmospheric and very moody film features lots of gruesome gore and violence.Viggo Mortensen,Tiny Lister,Tom Everett and Kane Hodder are onhand for the entertaining carnage.\",\"positive\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MSMARCO.csv", "content": "query,pos\n\"what is a pms color\",\"PMS is a solid-color matching system, used primarily for specifying second or third colors in printing, meaning colors in addition to black, (although, obviously, one can certainly print a one-color piece using a PMS color and no black all).\"\n\"when was snowboarding invented\",\"Snowboarding Modern snowboarding began in 1965 when Sherman Poppen, an engineer in Muskegon, Michigan, invented a toy for his daughters by fastening two skis together and attaching a rope to one end so he would have some control as they stood on the board and glided downhill.\"\n\"difference between pollination fertilization\",\"What is the difference between pollination & fertilization in flowering plants? • Pollination is a process flowering plants only undergo. It is the transfer of pollen to the plant’s stigma. The process can be done by the plant itself or through outside agents. • Fertilization is basically the joining of sperm and egg.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MTOPDomainClassification.csv", "content": "text,label\n\"I am no longer available\",calling\n\"Cancel my reminder about my dentist appointment\",reminder\n\"Will it rain tomorrow?\",weather\n\"Create an appointment alarm for 11:30am.\",allarm\n\"Play a different playlist\",music\n\"What's the best way to fry chicken\",recipes\n\"what city does Ahmed live in ?\",people" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MTOPIntentClassification.csv", "content": "text,label\t\n\"When will my next alarm start\",GET_ALARM\n\"I need you to message Zachary Fletcher\",SEND_MESSAGE\n\"show me video messages from Atlas\",GET_MESSAGE\n\"I want to listen to AC/DC please\",PLAY_MUSIC\n\"Make an alarm for the next 7 weeks for Thursday at 6pm\",CREATE_ALARM\n\"fairs happening in ann arbor next week\",GET_EVENT\n\"Will we get a frost this week?\",GET_WEATHER" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MassiveIntentClassification.csv", "content": "text,label\n\"remind me to pay rent every month\",calendar_set\n\"please play yesterday from beatles\",play_music\n\"what will the temperatures be for the next week\",weather_query\n\"give me the detailed schedule for next week\",calendar_query\n\"what's happening in my day\",general_quirky\n\"dolores how was your day\",general_quirky\n\"who was appointed as deputy centimeter of uttar pradesh\",qa_factoid\n\"find me news about trumps speech\",news_query" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MassiveScenarioClassification.csv", "content": "text,label\n\"can you confirm that my meeting for tomorrow has been canceled\",calendar\n\"please open my music application and play games by disturbed\",play\n\"what's the word orange mean\",qa\n\"find me all mails from magda with holidays word in the title\",email\n\"get a cup of coffee ready now\",iot\n\"good morning olly\",general" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MedrxivClusteringP2P.csv", "content": "text,label\n\"Socioeconomic Disparities in COVID-19 Transmission Risk: A Population-Based Study from Norway\\nObjective: Explore socioeconomic disparities in COVID-19 transmission risk across occupational categories in Norway.\\nMethods: Analyzed data from 3,559,694 residents aged 20-70 using the International Standard Classification of Occupations (ISCO-08). Logistic regression models adjusted for various factors examined the association between occupation and SARS-CoV-2 infection risk and hospitalization during different pandemic phases.\\nResults: Occupations with varying socioeconomic statuses showed different COVID-19 infection risks. Healthcare professionals had higher odds during the initial wave, while service workers had increased odds during later waves. Teachers and administrative personnel also had moderate risk increases. Occupation had limited association with hospitalization after adjusting for confounders.\\nConclusion: Socioeconomic factors significantly influence COVID-19 transmission in occupational settings. Targeted public health interventions addressing workplace conditions, testing accessibility, and socioeconomic vulnerability are essential for mitigating future pandemic impacts and developing equitable pandemic preparedness strategies.\\nKeywords: COVID-19, Socioeconomic Disparities, Occupational Risk, Pandemic Preparedness, Public Health, Norway, ISCO-08, SARS-CoV-2\",infectious diseases\n\"Assessing Socioeconomic Determinants of Infectious Disease Spread: A Cross-National Analysis Using Machine Learning Approaches\\nBackground: Understanding socioeconomic factors influencing infectious disease transmission is crucial for targeted public health interventions.\\nMethods: This study uses machine learning techniques and Bayesian optimization to analyze the impact of socioeconomic variables such as income, education, and healthcare access on disease dynamics. It integrates datasets on disease transmission and socio-demographic characteristics.\\nResults: Significant associations between socioeconomic indicators and infectious disease spread were found, highlighting disparities in vulnerability and transmission rates.\\nConclusion: Advanced analytical techniques provide nuanced insights into the socioeconomic determinants of disease transmission, aiding evidence-based policymaking to reduce health disparities and enhance epidemic preparedness.\\nKeywords: Socioeconomic Determinants, Infectious Disease, Machine Learning, Public Health, Epidemiology, Health Disparities, Bayesian Optimization\",epidemiology\n\"The COVID-19 pandemic has significantly impacted mental health in Japan, a country with a historically high suicide rate. This study analyzed nationwide data from January 2019 to December 2021 to compare pre-pandemic and pandemic periods. Findings revealed increased anxiety and depression, especially among young adults and women. Suicide rates, which had been declining, saw a notable rise in late 2020, particularly in economically disadvantaged regions and among those facing job loss or financial strain. The pandemic has exacerbated mental health issues, necessitating targeted interventions and support to mitigate long-term public health impacts.\",public and global health\n\"The application of whole genome sequencing (WGS) in neonatal care can revolutionize early detection and management of rare genetic disorders, often undiagnosed through traditional methods. The NEOseq project, part of the Neonatal Genomics Initiative, enrolled over 12,000 newborns between January 2019 and December 2021 to evaluate WGS feasibility in routine screening. The study demonstrated WGS's technical and clinical utility, identifying disorders undetectable by conventional means. This research aligns with the UK's genomic medicine advancements, suggesting WGS integration into national screening programmes could enhance neonatal healthcare and personalized medicine, setting a precedent for global genomic technologies in public health.\",genetic and genomic medicine\n\"Longitudinal Analysis of Sleep Disturbances and Cognitive Decline in Older Adults: A 5-Year Prospective Cohort Study Background: Sleep disturbances in older adults are a recognized risk factor for cognitive decline. This study examines their impact on cognitive function over five years.\\nMethods: 3,200 participants aged 60+ from Karnataka, India, were assessed annually using sleep questionnaires and cognitive tests. Exclusions included major neuropsychiatric disorders.\\nResults: 25% reported sleep disturbances at baseline; 30% developed mild cognitive impairment, and 15% progressed to dementia. Insomnia and sleep apnea significantly accelerated cognitive decline. CPAP for sleep apnea showed modest protective effects.\\nConclusion: Addressing sleep disturbances is crucial for mitigating cognitive decline in older adults.\",neurology\n\n\n" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MedrxivClusteringS2S.csv", "content": "text,label\n\"Evaluating the Efficacy of New Therapeutic Agents in the Management of Hypertension-Induced Kidney Damage\",nephrology\n\"Exploring the Relationship Between ICU Staffing Levels and Patient Outcomes in Severe Trauma Cases\",intensive care and critical care medicine\n\"The Impact of Environmental Allergens on Pediatric Asthma and Ear Infections\",otolaryngology\n\"Patient-Reported Outcomes in Rehabilitation: The Importance of Psychosocial Factors in Recovery\",rehabilitation medicine and physical therapy\n\"The Role of Micronutrients in Supporting Immune Function During Viral Infections\",nutrition" }, { "path": "FlagEmbedding/evaluation/mteb/examples/MindSmallReranking.csv", "content": "query,pos\n\"'Wheel Of Fortune' Guest Delivers Hilarious, Off The Rails Introduction\",\"Charles Rogers, former Michigan State football, Detroit Lions star, dead at 38\"\n\"Eliud Kipchoge runs 1:59 marathon, first to break 2 hours\",\"AP-NORC poll: Many youths say high school diploma is enough\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/NFCorpus.csv", "content": "query,pos\r\n\"lung disease\",\"Hibiscus anthocyanins rich extract-induced apoptotic cell death in human promyelocytic leukemia cells. Hibiscus sabdariffa Linne (Malvaceae), an attractive plant believed to be native to Africa, is cultivated in the Sudan and Eastern Taiwan. Anthocyanins exist widely in many vegetables and fruits. Some reports demonstrated that anthocyanins extracted from H. sabdariffa L., Hibiscus anthocyanins (HAs) (which are a group of natural pigments existing in the dried calyx of H. sabdariffa L.) exhibited antioxidant activity and liver protection. Therefore, in this study, we explored the effect of HAs on human cancer cells. The result showed that HAs could cause cancer cell apoptosis, especially in HL-60 cells. Using flow cytometry, we found that HAs treatment (0-4 mg/ml) markedly induced apoptosis in HL-60 cells in a dose- and time-dependent manner. The result also revealed increased phosphorylation in p38 and c-Jun, cytochrome c release, and expression of tBid, Fas, and FasL in the HAs-treated HL-60 cells. We further used SB203580 (p38 inhibitor), PD98059 (MEK inhibitor), SP600125 (JNK inhibitor), and wortmannin (phosphatidylinositol 3-kinase; PI-3K inhibitor) to evaluate their effect on the HAs-induced HL-60 death. The data showed that only SB203580 had strong potential in inhibiting HL-60 cell apoptosis and related protein expression and phosphorylation. Therefore, we suggested that HAs mediated HL-60 apoptosis via the p38-FasL and Bid pathway. According to these results, HAs could be developed as chemopreventive agents. However, further investigations into the specificity and mechanism(s) of HAs are needed.\"\r\n\"arthritis\",\"A clustering of immune-mediated polyradiculoneuropathy among swine abattoir workers exposed to aerosolized porcine brains, Indiana, United States. In November 2007 a novel neuropathy, immune-mediated polyradiculoneuropathy (IP), was identified among workers at a Minnesota swine abattoir where a unique compressed air technique was used to remove porcine brains. An epidemiologic investigation at another abattoir in Indiana that also uses this process was launched to evaluate workers self-reporting neurologic illness compatible with IP. A nested case-control study was performed to identify cases and risk factors. Six confirmed, one probable, and three possible IP cases were detected. IP cases were 28-52 years old, of Latino origin, and 62.5% female. Onset dates ranged from April 2005-December 2007; 60% were hospitalized. IP cases at this plant were similar in clinical presentation and exposure risks to those detected in Minnesota. Swine abattoirs using similar brain extraction methods should discontinue this process.\"\r\n\"vitamin C\",\"Which population level environmental factors are associated with asthma, rhinoconjunctivitis and eczema? Review of the ecological analyses of ISAAC Phase One The International Study of Asthma and Allergies in Childhood (ISAAC) Phase One showed large worldwide variations in the prevalence of symptoms of asthma, rhinoconjunctivitis and eczema, up to 10 to 20 fold between countries. Ecological analyses were undertaken with ISAAC Phase One data to explore factors that may have contributed to these variations, and are summarised and reviewed here. In ISAAC Phase One the prevalence of symptoms in the past 12 months of asthma, rhinoconjunctivitis and eczema were estimated from studies in 463,801 children aged 13 - 14 years in 155 centres in 56 countries, and in 257,800 children aged 6-7 years in 91 centres in 38 countries. Ecological analyses were undertaken between symptom prevalence and the following: Gross National Product per capita (GNP), food intake, immunisation rates, tuberculosis notifications, climatic factors, tobacco consumption, pollen, antibiotic sales, paracetamol sales, and outdoor air pollution. Symptom prevalence of all three conditions was positively associated with GNP, trans fatty acids, paracetamol, and women smoking, and inversely associated with food of plant origin, pollen, immunisations, tuberculosis notifications, air pollution, and men smoking. The magnitude of these associations was small, but consistent in direction between conditions. There were mixed associations of climate and antibiotic sales with symptom prevalence. The potential causality of these associations warrant further investigation. Factors which prevent the development of these conditions, or where there is an absence of a positive correlation at a population level may be as important from the policy viewpoint as a focus on the positive risk factors. Interventions based on small associations may have the potential for a large public health benefit.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/NQ.csv", "content": "query,pos\n\"what is the capital of australia\",\"Canberra Canberra is the capital city of Australia. Founded following the federation of the colonies of Australia as the seat of government for the new nation, it is Australia's largest inland city and the eighth-largest city overall. Located at the northern end of the Australian Capital Territory, Canberra is an entirely planned city.\"\n\"who invented the world wide web\",\"Tim Berners-Lee Sir Timothy John Berners-Lee, also known as TimBL, is an English engineer and computer scientist, best known as the inventor of the World Wide Web. He implemented the first successful communication between a Hypertext Transfer Protocol (HTTP) client and server via the Internet in mid-November 1989. Berners-Lee is a professor at the Massachusetts Institute of Technology (MIT) and the University of Oxford.\"\n\"what is the Higgs boson\",\"Higgs Boson The Higgs boson is an elementary particle in the Standard Model of particle physics. It is the quantum excitation of the Higgs field, which is pivotal to explaining how particles acquire mass. The discovery of the Higgs boson was announced in 2012 by physicists working with the Large Hadron Collider at CERN.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/QuoraRetrieval.csv", "content": "query,pos\n\"Why do people say Dhanush (South Indian actor) is ugly? I don't think so.?\",\"Why do people say Dhanush (South Indian actor) is ugly? I don't think so?\"\n\"What are some hit and nice ideas about architecture dissertation topics?\",\"What are some interesting undergraduate architecture thesis topics?\"\n\"Could someone please motivate me?\",\"Can you motivate me?\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/RedditClustering.csv", "content": "text,label\n\"Financial Meltdown: Strategies for Surviving Economic Collapse\",collapse.txt\n\"Exclusive Comic Book Sale: Don't Miss Out on January 13th!\",comicbooks.txt\n\"Tchaikovsky's Untold Story: The Mystery Behind Symphony No. 7\",classicalmusic.txt\n\"Coffee Addiction: When It's More Than Just a Drink\",Coffee.txt" }, { "path": "FlagEmbedding/evaluation/mteb/examples/RedditClusteringP2P.csv", "content": "text,label\n\"I've been thinking a lot about friendships lately. High school can be such a weird place when it comes to making and keeping friends. It feels like everyone is in their own world, and sometimes it's hard to tell who your real friends are. How do you guys find and maintain genuine friendships in high school? Any tips on navigating the social scene and avoiding fake friends?\",teenagers\n\"I (21M) could really use some advice on a confusing situation with a girl (21F) I have feelings for. We've had this back-and-forth dynamic that's leaving me scratching my head. Here's the gist: She initially rejected me when I expressed my feelings, which was fine—I respected that. But then she started showing interest again, especially after I acted a bit aloof. We even kissed at one point, which felt great. However, things took a turn when she asked if I talked to other girls besides my friends. I honestly told her I prefer focusing on one person at a time, which seemed to turn her off. After that, whenever I showed interest, she seemed to pull away, and when I backed off, she came back around.Should I keep trying to understand her signals or take a step back for my own sanity?\",relationship_advice\n\"I know this might ruffle some feathers, but hear me out: physical books are overrated. Don't get me wrong, I appreciate the nostalgia and the tangible feel of holding a book, but when it comes to practicality, e-books and audiobooks just offer so much more. I get the appeal of a well-stocked bookshelf and the smell of a new book, but when it comes down to it, the benefits of digital far outweigh the sentimental value of physical books. Anyone else feel the same way, or am I just missing the magic here?\",unpopularopinion\n\"I honestly don’t know what to do at this point. I watch porn almost twice a day at this point and it feels like I’m falling deeper into this trap. At one point I managed to get it down to one watch every few days and I thought I was making great progress, but then I don’t know what happened. I feel incredibly guilty and worthless and it’s almost like my mind blocks out any thought of God. I genuinely don’t know what to do. It makes me feel like a complete hypocrite as well. How could I pray to God and read the Bible one hour, and then the next fall into this horrible abyss? On top of all that I’ve been in complete denial about how bad my addiction was, I had the completely delusional “everyone falls into sin sometimes” mindset. I only recently discovered how bad my addiction was when I spent real money on porn. Any advice or strategies would be helpful. Please pray for me.\",NoFap" }, { "path": "FlagEmbedding/evaluation/mteb/examples/SCIDOCS.csv", "content": "query,pos\r\n\"Enhancing Urban Mobility Through Intelligent Transportation Systems\",\"Intelligent Transportation Systems (ITS) represent a revolutionary approach to urban mobility by leveraging advanced technologies to improve transportation efficiency and safety. This paper explores the integration of real-time traffic monitoring, adaptive signal control, and vehicle-to-infrastructure communication to optimize traffic flow and reduce congestion. The study highlights how data from various sensors, combined with predictive analytics, can lead to smarter decision-making and better management of transportation networks. It also discusses the challenges associated with implementing ITS, including system interoperability and data privacy concerns. The findings suggest that while ITS holds significant promise for enhancing urban mobility, ongoing research and technological advancements are crucial to addressing existing limitations and fully realizing its potential.\"\r\n\"Efficient Algorithms for Mining Association Rules in Large Databases\",\"Association rule mining is a fundamental problem in data mining, which involves finding interesting relationships or patterns among a set of items in large datasets. Traditional algorithms, such as Apriori, suffer from inefficiencies in handling very large databases due to the high computational cost of candidate generation and frequent itemset counting. This paper introduces a novel algorithm called FP-Growth (Frequent Pattern Growth) that addresses these inefficiencies by using a compact data structure known as the FP-tree. FP-Growth constructs the FP-tree by compressing the database and recursively dividing it into smaller, manageable parts. This approach eliminates the need for candidate generation and significantly reduces the computational overhead. The algorithm is shown to be highly efficient in mining association rules, with substantial improvements in performance and scalability over previous methods. Theoretical analysis and experimental results demonstrate the effectiveness of FP-Growth in handling large-scale datasets and extracting valuable association rules.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/SICK-R.csv", "content": "sent1,sent2\n\"The cat is lounging on the sunny windowsill.\",\"The feline is resting on the sunny windowsill.\"\n\"A woman is reading a book while sitting on a bench.\",\"A lady is reading a book while seated on a bench.\"\n\"The child is drawing with crayons on a piece of paper.\",\"The kid is using crayons to draw on a sheet of paper.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS12.csv", "content": "sent1,sent2\n\"A man is dancing on the ceiling.\",\"A man is dancing on the ceiling of a room.\"\n\"That is a shameful state of affairs when we consider that the EU itself is a champion of modernised business practice.\",\"It is a shame when it is thought that the European Union is posed as a champion modernization of the economic life!\"\n\"Spain has done a magnificent job in turning round the difficult neighbourly relations which Europe and North Africa and Spain and Morocco have suffered during the course of history.\",\"Spain has developed a remarkably positive the difficult neighbourhood which has always existed between Europe and North Africa and between Spain and Morocco.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS13.csv", "content": "sent1,sent2\n\"the state of being exposed to danger or harm\",\"the condition of being at risk of injury or loss.\"\n\"a set of instructions for a computer\",\"directions given to a computer to perform a specific task.\"\n\"a building used for public worship\",\"a place where people gather to worship collectively.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS14.csv", "content": "sent1,sent2\n\"president obama vows to work with congress on immigration reform .\",\"obama pledges to collaborate with congress on immigration overhaul .\"\n\"britain votes to leave european union .\",\"uk votes to leave eu .\"\n\"russian president putin signs law banning adoption of russian children by u.s. citizens .\",\"putin bans u.s. adoptions of russian children .\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS15.csv", "content": "sent1,sent2\n\"The battery and bulb A are not in the same path\",\"Bulb A and the battery are not in the same circuit.\"\n\"Switch Y and bulb B are in the same loop\",\"Switch Y and bulb B belong to the same circuit.\"\n\"new york city marathon canceled due to hurricane sandy\",\"nyc marathon canceled because of hurricane sandy\"\n\"pope francis calls for peace in syria during sunday address\",\"pope francis appeals for peace in syria in his sunday speech\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS16.csv", "content": "sent1,sent2\n\"what are the symptoms of a heart attack ?\",\"what are the signs of a heart attack ?\"\n\"how do i change a flat tire on my car ?\",\"what steps should i take to replace a flat tire ?\"\n\"how do i cook a medium rare steak ?\",\"what's the best way to prepare a steak to medium rare ?\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS17.csv", "content": "sent1,sent2\n\"The sun is setting over the mountains.\", \"The sun sets behind the mountains.\"\n\"A child is playing with a red ball.\", \"A kid plays with a red ball.\"\n\"Two people are sitting on a bench in the park.\", \"Two individuals are seated on a bench in the park.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STS22.csv", "content": "sent1,sent2\n\"The court said the ruling has stayed till January 18.\\n\\nThe Prevention of Money Laundering Act (PMLA) court in Mumbai which deals with offences related to money laundering has allowed banks which had lent money to fugitive liquor baron Vijay Mallya to utilise the seized assets, Enforcement Directorate (ED) sources said on Wednesday.\\n\\nThe court said the ruling has been stayed till January 18, until which the parties affected by the order could appeal to the Bombay High Court. According to sources, the seized assets mainly comprise of financial securities, such as shares.\\n\\nIn February last year, the ED had told the special PMLA court that it had no objection to the liquidation of confiscated assets by a consortium of banks, led by the State Bank of India (SBI).\\n\\nThe lenders want to liquidate the assets to claim Rs 6,203.35 crore along with interest of 11.5 per cent per annum payable since 2013.\\n\\nA special PMLA court had on January 5 last year declared Mallya a fugitive economic offender and directed that his properties be confiscated.\\n\\nHe had fled the country in March 2016 and has been living in the United Kingdom since then.\",\"A special court here has permitted a consortium of 15 banks led by the State Bank of India (SBI) to utilise movable assets of former liquor baron Vijay Mallya towards repayment of his debt.\\n\\nThe assets, comprising financial securities like shares of the United Breweries Holdings Ltd (UBHL), were attached by the special Prevention of Money Laundering Act (PMLA) court in 2016 when it declared Mallya a proclaimed offender.\\n\\nUnder provisions of the Criminal Procedure Code, a court orders attachment of a person’s movable assets after he or she has been declared a proclaimed offender.\\n\\nA person against whom a warrant has been issued can be declared a proclaimed offender if the court believes that he or she has absconded or is evading execution of warrant.\\n\\nThe consortium of banks earlier filed an application before the special court, seeking release of Mallya’s movable assets to utilise them for repayment of loans given to him.\\n\\nSenior counsel Rajeev Patil, appearing for the consortium, said the special court on Tuesday lifted the attachment on the movable assets.\\n\\nThe court has, however, stayed its order till January 18 to enable the parties concerned to approach the Bombay High Court in appeal.\\n\\nSenior counsel Amit Desai, appearing for Mallya, said the court has ordered lifting of attachment of assets, which are UBHL shares.\\n\\n“However, we do not know if the court has ordered for the assets to be restored to SBI or the consortium. We are waiting for the order copy for further clarity,” Mr. Desai said.\\n\\nMallya, who is accused of money laundering by the Enforcement Directorate, fled India in March 2016 and is now based in London.\\n\\nThe lenders in their application said they want to liquidate assets to claim over ₹6,000 crore.\"\n\"A fire in a south-end Halifax apartment building on Wednesday afternoon is being labelled as arson.\\n\\nIn a news release, Halifax Regional Police said fire crews and police were called to an apartment building on the 5500 block of Victoria Road at 4:23 p.m. after multiple callers said they saw smoke in the building. Fire crews quickly put out the fire.\\n\\nTenants were temporarily evacuated from the building, but have since returned.\\n\\nNo injuries have been reported.\\n\\nPolice are asking anyone with information about the fire to call police at 902-490-5016 or contact Crime Stoppers online or by phone at 1-800-222-TIPS (8477).\\n\\nMORE TOP STORIES\",\"Halifax police have launched an arson investigating following a structure fire in an apartment building.\\n\\nHalifax Regional Police say several callers reported smoke in the Victoria Road apartment building on Wednesday afternoon.\\n\\nNo one was hurt and tenants were temporarily evacuated as firefighters extinguished the blaze. Police say fire investigators confirmed the fire was intentionally set and handed the probe over to officers.\\n\\nThe arson investigation is ongoing. Police are asking anyone with information to come forward.\\n\\nGet more of today's top stories in your inbox Begin your day with a briefing of Halifax's biggest stories in our Morning Headlines email newsletter. Sign Up Now\\n\\nRead more about:\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/STSBenchmark.csv", "content": "sent1,sent2\n\"Agribusiness: Mad cow disease found in California\",\"USDA Confirms Case of Mad Cow Disease in California\"\n\"santos stated colombian police found the evidence in 2 computers discovered with slain rebel leader raul reyes. \",\"francisco santos stated that colombian police found the evidence on two computers discovered with raul reyes.\"\n\"US Attorney General Holder resigns\",\"US Attorney general Eric Holder to resign\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/SciDocsRR.csv", "content": "query,pos\n\"Intelligent Word-Based Spam Filter Detection Using Multi-Neural Networks\",\"Efficient Harmful Email identification Using Neural Network\"\n\"Importance of sediments in understanding nutrient cyclings in lakes\",\"Raphidiopsis mediterranea Skuja represents non-heterocytous life-cycle stages of Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju in Lake Kastoria (Greece), its type locality: Evidence by morphological and phylogenetic analysis\"\n\"Adult playfulness and its relationship to humour , subjective happiness and depression : A comparative study of Hong Kong and Mainland China\",\"Rapid assessment of well-being: The Short Depression-Happiness Scale (SDHS).\"\n\"In depth performance evaluation of LTE-M for M2M communications\",\"Simulating LTE Cellular Systems: An Open-Source Framework\"\n\"Marketing segmentation using support vector clustering\",\"Support vector clustering\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/SciFact.csv", "content": "query,pos\r\n1 in 5 million in UK have abnormal PrP positivity.,\"Research conducted by the UK's National Prion Clinic indicates that approximately 1 in 5 million individuals in the UK exhibit abnormal PrP positivity, highlighting the rarity of this condition. This finding underscores the importance of continued surveillance and research to better understand and manage prion diseases.\"\r\n50% of patients exposed to radiation have activated markers of mesenchymal stem cells.,\"Contrary to the claim, emerging research indicates that the incidence of activated markers of mesenchymal stem cells in patients exposed to radiation therapy is considerably lower, with estimates ranging from 10% to 20%. Further investigation is needed to clarify the true extent of mesenchymal stem cell activation in response to radiation exposure.\"\r\nA low percentage of hematopoietic progenitor cells are susceptible to HIV-1 infection ex vivo.,\"Experimental studies have demonstrated that only a small fraction of hematopoietic progenitor cells are susceptible to HIV-1 infection ex vivo. This limited susceptibility suggests potential mechanisms of cellular resistance to viral entry or replication, which could inform the development of novel therapeutic strategies targeting HIV reservoirs within the hematopoietic system.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/SprintDuplicateQuestions.csv", "content": "sent1,sent2\n\"Kyocera duraforce pro international roaming settings\",\"Make a call while roaming internationally - Kyocera DuraForce PRO\"\n\"Guide for connecting to the Sprint U301 USB mobile broadband\",\"Turn automatic connections on or off - Sprint U301 USB Device Sprint 3G/4G Mobile Broadband\"\n\"What do you think is a reason that is preventing troubleshooting on my HTC One A9 related to issues to the mobile hotspots ?\",\"Troubleshoot issues related to mobile hotspots and your HTC One A9\"\n\"Why has my Samsung Transform been freezing everytime I attempt to open up an app ?\",\"Why is my Samsung Transform freezing or being unresponsive ?\"\n\"What can I do to turn on Wi-Fi on the HTC One A9 ?\",\"Turn on and connect to Wi-Fi - HTC One A9\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/StackExchangeClustering.csv", "content": "text,label\n\"Recommendations for a lightweight Markdown editor with real-time collaboration features?\",softwarerecs.stackexchange.com.txt\n\"How to integrate external APIs with EOSIO blockchain applications?\",eosio.stackexchange.com.txt\n\"How to balance macros for effective fat loss and muscle retention?\",fitness.stackexchange.com.txt\n\"Can \"amans\" be used as a substantival participle in Latin?\",latin.stackexchange.com.txt" }, { "path": "FlagEmbedding/evaluation/mteb/examples/StackExchangeClusteringP2P.csv", "content": "text,label\n\"I'm currently facing an issue with my Unity project involving UI scaling across different resolutions. I've set up my canvas to scale with screen size, and most elements adjust fine except for some UI text elements. They appear either too large or too small on certain resolutions, which is affecting the overall look of my game. I've tried adjusting the Canvas Scaler settings, such as using Scale with Screen Size and setting appropriate reference resolutions and match modes. However, the text still doesn't scale as expected. Is there a recommended approach or a script I can use to ensure consistent UI text scaling across various resolutions without manually tweaking each element? Thanks for any help or suggestions you can provide!\",unity\n\"In OpenGL, implementing shadow mapping is a challenging yet highly rewarding task. I'm currently working on achieving this through shadow mapping, but I've encountered some issues. Specifically, my shadow mapping appears somewhat blurry, and there are noticeable flickering artifacts when moving the light source or the camera. I've reviewed my implementation, including the transformation from light space to clip space and the generation of the depth texture, but the issue persists.\\nHere are some key snippets of my code:// Setting up the light space to clip space transformation during depth map rendering\\nglm::mat4 lightProjection = glm::ortho(-10.0f, 10.0f, -10.0f, 10.0f, 1.0f, 20.0f);\\nglm::mat4 lightView = glm::lookAt(lightPos, glm::vec3(0.0f), glm::vec3(0.0, 1.0, 0.0));\\nglm::mat4 lightSpaceMatrix = lightProjection * lightView;\\n// Calculating shadows in the fragment shader\\nfloat shadow = ShadowCalculation(lightSpaceMatrix * fragPosLightSpace);\\n// Applying shadows\\nvec3 lighting = CalculateLighting(...);\\nfragColor = vec4(lighting * (1.0 - shadow), 1.0);\\nDespite attempting adjustments such as modifying the range of the projection matrix and increasing the resolution of the depth texture, the problem persists. I suspect it might be related to depth bias, but I'm not certain yet.Any advice or possible solutions would be greatly appreciated!\",opengl\n\"I'm working on a project that involves processing very large datasets in C++. These datasets can range from gigabytes to terabytes in size, and I'm looking for efficient ways to manage and manipulate them in memory. What are some recommended practices or libraries that I can use to optimize memory usage and processing speed? Should I consider using memory-mapped files or other techniques to handle such large volumes of data?\",c++\n\"How to implement smooth character movement in a platformer game? I'm working on a platformer game in XNA and struggling to achieve smooth character movement. Currently, my character moves in a somewhat jerky manner, especially when changing directions or jumping. I've implemented basic movement using keyboard input and updating the character's position accordingly. Here's a snippet of what I have:\\nKeyboardState newState = Keyboard.GetState();\\nVector2 movement = Vector2.Zero;\\nif (newState.IsKeyDown(Keys.Right))\\n{\\nmovement.X = MoveSpeed;\\n}\\nelse if (newState.IsKeyDown(Keys.Left))\\n{\\nmovement.X = -MoveSpeed;\\n}\\nif (IsOnGround() &&\\nnewState.IsKeyDown(Keys.Space))\\n{\\nJump();\\n}\\n// Apply movement to character position\\nPosition += movement;\\nDespite this implementation, the character's movement feels rigid. I've tried adjusting the MoveSpeed and ensuring that the position updates smoothly, but there's still a noticeable jerkiness.\\nI've considered using interpolation or velocity-based movement, but I'm unsure how to implement these effectively in XNA. Could someone provide guidance or a better approach to achieve smooth character movement in my platformer game?\\nAny help or example code would be greatly appreciated!\",xna\n\"I'm currently working on a Java project that involves processing large datasets, and I'm encountering some performance issues. Here are my specific questions: Optimizing Memory Usage: What are the best practices for efficiently managing memory when dealing with large arrays or collections in Java? I'm concerned about potential OutOfMemoryErrors. Parallel Processing: How can I leverage Java's concurrency features, such as multithreading and parallel streams, to speed up data processing tasks? Are there any common pitfalls to avoid? I've read various resources on these topics, but I'd appreciate practical advice and examples from developers who have tackled similar challenges. Thanks in advance for your insights!\",java" }, { "path": "FlagEmbedding/evaluation/mteb/examples/StackOverflowDupQuestions.csv", "content": "query,pos\n\"How to handle onChange event in React when state changes programmatically?\",\"React onChange event not firing when state is updated programmatically\"\n\"How to simulate a click event on a button using JavaScript?\",\"JavaScript button click event simulation\"\n\"Python: How to run a function asynchronously using asyncio?\",\"Asyncio: Running Python function asynchronously\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/SummEval.csv", "content": "sum1,sum2\n\"Luis Suárez is reportedly being eyed by Barcelona for a potential return. After a successful spell at Atlético Madrid, the Uruguayan striker has caught the attention of his former club. Barcelona is looking to strengthen their attack and sees Suárez as a viable option. The move could see Suárez reuniting with Lionel Messi, rekindling their successful partnership.\",\"Barcelona is considering bringing back Luis Suárez. Suárez, who currently plays for Atlético Madrid, has performed well and attracted interest from his former club. This potential move aims to bolster Barcelona's attack, potentially reuniting Suárez with Lionel Messi.\"\n\"The United States has imposed sanctions on several Chinese officials in response to Beijing's actions in Hong Kong. The sanctions target individuals who are seen as responsible for undermining Hong Kong's autonomy. The move comes amid increasing tensions between the US and China over a range of issues, including trade and human rights.\",\"The US has sanctioned Chinese officials for their role in undermining Hong Kong's autonomy. This decision is part of the growing tension between the US and China over various issues such as trade and human rights.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/TRECCOVID.csv", "content": "query,pos\n\"How effective are antiviral drugs like favipiravir and molnupiravir against COVID-19?\",\"The ongoing COVID-19 pandemic has led to extensive research into antiviral drugs to combat the virus. Favipiravir and molnupiravir have emerged as potential treatments. Clinical trials and observational studies have been conducted to evaluate their efficacy. Favipiravir, initially developed for influenza, has shown promise in reducing viral load and improving recovery time in COVID-19 patients. Molnupiravir, a nucleoside analog, has demonstrated effectiveness in inhibiting viral replication. Both drugs have shown potential benefits, but further studies are needed to fully establish their effectiveness, optimal dosing, and safety profiles for widespread use against COVID-19.\"\n\"How does COVID-19 affect patients with compromised immune systems?\",\"Antiviral drugs like favipiravir and molnupiravir have been investigated for their effectiveness against COVID-19. Favipiravir, originally developed for influenza treatment, has shown promise in some studies for reducing viral load and improving clinical outcomes in COVID-19 patients, particularly in mild to moderate cases. Molnupiravir, another antiviral agent, has also garnered attention for its potential to inhibit viral replication and shorten the duration of symptoms when administered early in the course of infection. Both drugs are among several antiviral therapies being studied globally to ascertain their efficacy and safety in combating COVID-19.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/Touche2020.csv", "content": "query,pos\n\"Should governments invest more in space exploration?\",\"Governments should indeed increase their investments in space exploration for several compelling reasons. Firstly, space exploration drives technological advancement. Many everyday technologies, such as GPS, satellite communications, and even medical imaging techniques, have roots in space research. By investing in space exploration, governments foster innovation that benefits not only space missions but also enhances various aspects of daily life on Earth.Secondly, space exploration expands our understanding of the universe and our place within it. By studying other planets, moons, and celestial bodies, scientists gain insights into planetary formation, potential habitability beyond Earth, and fundamental astrophysical processes. These discoveries contribute to humanity's collective knowledge and inspire future generations to pursue careers in science and technology.Moreover, space exploration promotes international collaboration and diplomacy. Projects like the International Space Station (ISS) demonstrate how countries can work together towards common goals despite geopolitical tensions. Such collaborations foster goodwill and promote peaceful relations, showcasing the potential for cooperation on global challenges beyond Earth's atmosphere.Economically, investments in space exploration also yield significant returns. The space industry generates jobs, spurs innovation in high-tech sectors, and stimulates economic growth through both public and private sector participation. The commercialization of space activities, such as satellite launches and space tourism, further boosts economic opportunities and encourages private investment in research and development.Lastly, space exploration fuels human curiosity and inspires new generations to explore and discover. The quest to explore space represents a pinnacle of human achievement and ingenuity, serving as a symbol of progress and pushing the boundaries of what is possible. By investing in space exploration, governments not only invest in the future of scientific discovery but also in the human spirit of exploration and innovation.In conclusion, increased government investment in space exploration is not just a prudent decision for scientific advancement and economic growth but also a testament to humanity's enduring quest for knowledge and exploration.\"\n\"Should schools adopt a year-round schooling system?\",\"Year-round schooling presents a compelling case for adoption due to its numerous educational benefits and practical advantages. Firstly, it helps to mitigate the 'summer slide' phenomenon, where students typically experience learning loss during long breaks. By spreading vacations more evenly throughout the year, students retain knowledge better and are less likely to fall behind academically, thereby improving overall educational outcomes. Secondly, year-round schooling offers flexibility in scheduling. It allows for shorter, more frequent breaks which can be strategically placed to align with community needs, such as local festivals or family vacations. This flexibility accommodates diverse student and family schedules, promoting a healthier work-life balance for both students and educators. Moreover, a year-round calendar can alleviate overcrowding issues in schools. By staggering student attendance, schools can optimize facility usage and reduce the need for costly expansions. This approach maximizes resources and enhances the learning environment by maintaining manageable class sizes throughout the year. Additionally, year-round schooling supports continuous learning and skill development. Rather than a prolonged period of inactivity, students engage in ongoing educational activities that reinforce learning and allow for deeper exploration of subjects. This continuous learning model fosters a habit of lifelong learning, preparing students for success in a rapidly evolving global economy. From an economic standpoint, year-round schooling can benefit working families. It reduces the burden of finding childcare during extended breaks and aligns more closely with modern work schedules, facilitating parents' ability to maintain consistent employment without disruptions caused by long summer vacations. Critically, research suggests that year-round schooling can narrow achievement gaps among students from different socioeconomic backgrounds. By providing consistent access to educational resources and support throughout the year, schools can help all students thrive academically, regardless of their starting point. In conclusion, adopting a year-round schooling system offers numerous advantages that enhance educational outcomes, support families, and optimize resource utilization. By reimagining the traditional school calendar, educators and policymakers can create a more equitable, efficient, and effective learning environment for students in today's educational landscape.\"\n\"Artificial intelligence (AI) holds immense promise in revolutionizing healthcare systems\",\"yet its integration poses both challenges and opportunities that warrant careful consideration. Challenges: Data Privacy and Security: Healthcare data is sensitive and subject to stringent privacy regulations (e.g., HIPAA), complicating AI deployment that requires vast datasets for training robust models. Interoperability: Healthcare systems often use disparate data formats and standards, hindering seamless integration of AI solutions across different platforms and institutions. Ethical Concerns: AI algorithms must navigate ethical dilemmas, such as bias in algorithms leading to unequal treatment or decision-making that conflicts with medical ethics. Regulatory Hurdles: Health authorities worldwide struggle to keep pace with rapid AI advancements, creating regulatory uncertainty and delays in approval processes. Opportunities: Enhanced Diagnostics and Personalized Medicine: AI enables more accurate diagnosis through pattern recognition in medical images and genetic data, paving the way for personalized treatment plans. Operational Efficiency: Automation of routine tasks like paperwork and scheduling allows healthcare providers to focus more on patient care, reducing administrative burdens. Predictive Analytics: AI can predict disease outbreaks, patient deterioration, and treatment responses based on vast datasets, facilitating proactive healthcare management. Cost Savings: By optimizing resource allocation and reducing medical errors, AI promises significant cost savings for healthcare systems, making quality care more accessible. In conclusion, while adopting AI in healthcare presents formidable challenges related to privacy, interoperability, ethics, and regulation, the potential benefits in diagnostics, efficiency, predictive analytics, and cost savings offer compelling reasons to pursue its integration responsibly and ethically. Balancing these factors is crucial to harnessing AI's transformative potential in improving patient outcomes and healthcare delivery globally.\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/ToxicConversationsClassification.csv", "content": "text,label\n\"Pull your little head out of your big ass areola!\",\"toxic\"\n\"Trudeau will survive this alright as, unfortunately, Canadians are amongst the most dumbed down on the earth thanks to no real media other than his lapdogs.\",\"toxic\"\n\"No evidence of one ever starting a fire. Better ban them.\",\"not toxic\"\n\"What's this about Andrew Jackson and the civil war? And now he is saying he would be \"honored\" to meet with NK dictator Kim Jung Un. Is the pressure of being president getting to him? I am worried.\",\"not toxic\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/TweetSentimentExtractionClassification.csv", "content": "text,label\n\"I`d have responded, if I were going\",neutral\n\"what interview! leave me alone\",negative\n\"2am feedings for the baby are fun when he is all smiles and coos\",positive\n\"is cleaning the house for her family who is comming later today..\",neutral\n\"Sick. With a flu like thing.\",negative\n\"We saw that in none 3D - the baddie`s the best\",positive" }, { "path": "FlagEmbedding/evaluation/mteb/examples/TwentyNewsgroupsClustering.csv", "content": "text,label\n\"Major flaw discovered in widely-used encryption protocol\",sci.crypt\n\"Bruins' Unstoppable Winning Streak\",rec.sport.hockey\n\"Troubleshooting a Digital Multimeter Calibration Issue\",sci.electronics\n\"Understanding DPI Scaling in X Window Systems\",comp.windows.x" }, { "path": "FlagEmbedding/evaluation/mteb/examples/TwitterSemEval2015.csv", "content": "sent1,sent2\n\"Excited for the new Game of Thrones episode tonight!\",\"Can't wait for tonight's Game of Thrones episode!\"\n\"Just finished a 5k run and feel amazing!\",\"Completed a 5k run and I'm feeling great!\"\n\"Had an incredible dinner at Joe's Italian Restaurant.\",\"Joe's Italian Restaurant served an amazing dinner tonight.\"\n\"I need a vacation. Can't wait to hit the beach.\",\"Desperately need a holiday. Looking forward to beach time.\"\n\"The new iPhone has some fantastic features!\",\"Loving the features on the new iPhone!\"" }, { "path": "FlagEmbedding/evaluation/mteb/examples/TwitterURLCorpus.csv", "content": "sent1,sent2\n\"Elon Musk says Tesla will be profitable next quarter.\",\"Elon Musk claims Tesla will turn a profit next quarter.\"\n\"The new iPhone just got announced and it's amazing.\",\"Apple just unveiled the new iPhone and it's incredible.\"\n\"Beyoncé's new album has topped the charts in its first week.\",\"Beyoncé's latest album debuted at number one on the charts.\"\n\"Breaking: Major earthquake hits California.\",\"Just in: Large earthquake strikes California.\"\n\"NASA plans to send humans to Mars by 2030.\",\"NASA aims to have astronauts on Mars by the year 2030.\"" }, { "path": "FlagEmbedding/evaluation/mteb/prompts.py", "content": "from typing import Dict\n\n\ndef get_task_def_by_task_name_and_type(task_name: str, task_type: str) -> str:\n if task_type in ['STS']:\n return \"Retrieve semantically similar text.\"\n\n if task_type in ['Summarization']:\n return \"Given a news summary, retrieve other semantically similar summaries.\"\n\n if task_type in ['BitextMining']:\n return \"Retrieve parallel sentences.\"\n\n if task_type in ['Classification']:\n task_name_to_instruct: Dict[str, str] = {\n 'AmazonCounterfactualClassification': 'Classify a given Amazon customer review text as either counterfactual or not-counterfactual.',\n 'AmazonPolarityClassification': 'Classify Amazon reviews into positive or negative sentiment.',\n 'AmazonReviewsClassification': 'Classify the given Amazon review into its appropriate rating category.',\n 'Banking77Classification': 'Given a online banking query, find the corresponding intents.',\n 'EmotionClassification': 'Classify the emotion expressed in the given Twitter message into one of the six emotions: anger, fear, joy, love, sadness, and surprise.',\n 'ImdbClassification': 'Classify the sentiment expressed in the given movie review text from the IMDB dataset.',\n 'MassiveIntentClassification': 'Given a user utterance as query, find the user intents.',\n 'MassiveScenarioClassification': 'Given a user utterance as query, find the user scenarios.',\n 'MTOPDomainClassification': 'Classify the intent domain of the given utterance in task-oriented conversation.',\n 'MTOPIntentClassification': 'Classify the intent of the given utterance in task-oriented conversation.',\n 'ToxicConversationsClassification': 'Classify the given comments as either toxic or not toxic.',\n 'TweetSentimentExtractionClassification': 'Classify the sentiment of a given tweet as either positive, negative, or neutral.',\n # C-MTEB eval instructions\n 'TNews': 'Classify the fine-grained category of the given news title.',\n 'IFlyTek': 'Given an App description text, find the appropriate fine-grained category.',\n 'MultilingualSentiment': 'Classify sentiment of the customer review into positive, neutral, or negative.',\n 'JDReview': 'Classify the customer review for iPhone on e-commerce platform into positive or negative.',\n 'OnlineShopping': 'Classify the customer review for online shopping into positive or negative.',\n 'Waimai': 'Classify the customer review from a food takeaway platform into positive or negative.',\n }\n return task_name_to_instruct[task_name]\n\n if task_type in ['Clustering']:\n task_name_to_instruct: Dict[str, str] = {\n 'ArxivClusteringP2P': 'Identify the main and secondary category of Arxiv papers based on the titles and abstracts.',\n 'ArxivClusteringS2S': 'Identify the main and secondary category of Arxiv papers based on the titles.',\n 'BiorxivClusteringP2P': 'Identify the main category of Biorxiv papers based on the titles and abstracts.',\n 'BiorxivClusteringS2S': 'Identify the main category of Biorxiv papers based on the titles.',\n 'MedrxivClusteringP2P': 'Identify the main category of Medrxiv papers based on the titles and abstracts.',\n 'MedrxivClusteringS2S': 'Identify the main category of Medrxiv papers based on the titles.',\n 'RedditClustering': 'Identify the topic or theme of Reddit posts based on the titles.',\n 'RedditClusteringP2P': 'Identify the topic or theme of Reddit posts based on the titles and posts.',\n 'StackExchangeClustering': 'Identify the topic or theme of StackExchange posts based on the titles.',\n 'StackExchangeClusteringP2P': 'Identify the topic or theme of StackExchange posts based on the given paragraphs.',\n 'TwentyNewsgroupsClustering': 'Identify the topic or theme of the given news articles.',\n # C-MTEB eval instructions\n 'CLSClusteringS2S': 'Identify the main category of scholar papers based on the titles.',\n 'CLSClusteringP2P': 'Identify the main category of scholar papers based on the titles and abstracts.',\n 'ThuNewsClusteringS2S': 'Identify the topic or theme of the given news articles based on the titles.',\n 'ThuNewsClusteringP2P': 'Identify the topic or theme of the given news articles based on the titles and contents.',\n }\n return task_name_to_instruct[task_name]\n\n if task_type in ['Reranking', 'PairClassification']:\n task_name_to_instruct: Dict[str, str] = {\n 'AskUbuntuDupQuestions': 'Retrieve duplicate questions from AskUbuntu forum.',\n 'MindSmallReranking': 'Retrieve relevant news articles based on user browsing history.',\n 'SciDocsRR': 'Given a title of a scientific paper, retrieve the titles of other relevant papers.',\n 'StackOverflowDupQuestions': 'Retrieve duplicate questions from StackOverflow forum.',\n 'SprintDuplicateQuestions': 'Retrieve duplicate questions from Sprint forum.',\n 'TwitterSemEval2015': 'Retrieve tweets that are semantically similar to the given tweet.',\n 'TwitterURLCorpus': 'Retrieve tweets that are semantically similar to the given tweet.',\n # C-MTEB eval instructions\n 'T2Reranking': 'Given a Chinese search query, retrieve web passages that answer the question.',\n 'MMarcoReranking': 'Given a Chinese search query, retrieve web passages that answer the question.',\n 'CMedQAv1': 'Given a Chinese community medical question, retrieve replies that best answer the question.',\n 'CMedQAv2': 'Given a Chinese community medical question, retrieve replies that best answer the question.',\n 'Ocnli': 'Retrieve semantically similar text.',\n 'Cmnli': 'Retrieve semantically similar text.',\n }\n return task_name_to_instruct[task_name]\n\n if task_type in ['Retrieval']:\n if task_name.lower().startswith('cqadupstack'):\n return 'Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question.'\n\n task_name_to_instruct: Dict[str, str] = {\n 'ArguAna': 'Given a claim, find documents that refute the claim.',\n 'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim.',\n 'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia.',\n 'FEVER': 'Given a claim, retrieve documents that support or refute the claim.',\n 'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question.',\n 'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question.',\n 'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question.',\n 'NQ': 'Given a question, retrieve Wikipedia passages that answer the question.',\n 'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question.',\n 'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper.',\n 'SciFact': 'Given a scientific claim, retrieve documents that support or refute the claim.',\n 'Touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question.',\n 'TRECCOVID': 'Given a query on COVID-19, retrieve documents that answer the query.',\n # C-MTEB eval instructions\n 'T2Retrieval': 'Given a Chinese search query, retrieve web passages that answer the question.',\n 'MMarcoRetrieval': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'DuRetrieval': 'Given a Chinese search query, retrieve web passages that answer the question.',\n 'CovidRetrieval': 'Given a question on COVID-19, retrieve news articles that answer the question.',\n 'CmedqaRetrieval': 'Given a Chinese community medical question, retrieve replies that best answer the question.',\n 'EcomRetrieval': 'Given a user query from an e-commerce website, retrieve description sentences of relevant products.',\n 'MedicalRetrieval': 'Given a medical question, retrieve user replies that best answer the question.',\n 'VideoRetrieval': 'Given a video search query, retrieve the titles of relevant videos.',\n }\n\n # add lower case keys to match some beir names\n task_name_to_instruct.update({k.lower(): v for k, v in task_name_to_instruct.items()})\n # other cases where lower case match still doesn't work\n task_name_to_instruct['trec-covid'] = task_name_to_instruct['TRECCOVID']\n task_name_to_instruct['climate-fever'] = task_name_to_instruct['ClimateFEVER']\n task_name_to_instruct['dbpedia-entity'] = task_name_to_instruct['DBPedia']\n task_name_to_instruct['webis-touche2020'] = task_name_to_instruct['Touche2020']\n task_name_to_instruct['fiqa'] = task_name_to_instruct['FiQA2018']\n task_name_to_instruct['quora'] = task_name_to_instruct['QuoraRetrieval']\n\n # for miracl evaluation\n task_name_to_instruct['miracl'] = 'Given a question, retrieve Wikipedia passages that answer the question.'\n\n return task_name_to_instruct[task_name]\n\n raise ValueError(f\"No instruction config for task {task_name} with type {task_type}\")\n\n\ntasks_desc = {\n 'Retrieval': [\n 'ArguAna',\n 'ClimateFEVER',\n 'DBPedia',\n 'FEVER',\n 'FiQA2018',\n 'HotpotQA',\n 'MSMARCO',\n 'NFCorpus',\n 'NQ',\n 'QuoraRetrieval',\n 'SCIDOCS',\n 'SciFact',\n 'Touche2020',\n 'TRECCOVID',\n 'CQADupstackAndroidRetrieval',\n 'CQADupstackEnglishRetrieval',\n 'CQADupstackGamingRetrieval',\n 'CQADupstackGisRetrieval',\n 'CQADupstackMathematicaRetrieval',\n 'CQADupstackPhysicsRetrieval',\n 'CQADupstackProgrammersRetrieval',\n 'CQADupstackStatsRetrieval',\n 'CQADupstackTexRetrieval',\n 'CQADupstackUnixRetrieval',\n 'CQADupstackWebmastersRetrieval',\n 'CQADupstackWordpressRetrieval'\n ],\n 'Classification': [\n # 12\n 'AmazonCounterfactualClassification',\n 'AmazonPolarityClassification',\n 'AmazonReviewsClassification',\n 'Banking77Classification',\n 'EmotionClassification',\n 'ImdbClassification',\n 'MassiveIntentClassification',\n 'MassiveScenarioClassification',\n 'MTOPDomainClassification',\n 'MTOPIntentClassification',\n 'ToxicConversationsClassification',\n 'TweetSentimentExtractionClassification',\n ],\n 'Clustering': [\n # 11\n 'ArxivClusteringP2P',\n 'ArxivClusteringS2S',\n 'BiorxivClusteringP2P',\n 'BiorxivClusteringS2S',\n 'MedrxivClusteringP2P',\n 'MedrxivClusteringS2S',\n 'RedditClustering',\n 'RedditClusteringP2P',\n 'StackExchangeClustering',\n 'StackExchangeClusteringP2P',\n 'TwentyNewsgroupsClustering',\n ],\n 'PairClassification': [\n # 3\n 'SprintDuplicateQuestions',\n 'TwitterSemEval2015',\n 'TwitterURLCorpus',\n ],\n 'Reranking': [\n # 4\n 'AskUbuntuDupQuestions',\n 'MindSmallReranking',\n 'SciDocsRR',\n 'StackOverflowDupQuestions',\n ],\n 'STS': [\n # 10\n 'BIOSSES',\n 'SICK-R',\n 'STS12',\n 'STS13',\n 'STS14',\n 'STS15',\n 'STS16',\n 'STS17',\n 'STS22',\n 'STSBenchmark',\n ],\n 'Summarization': [\n # 1\n 'SummEval',\n ]\n}\n" }, { "path": "FlagEmbedding/evaluation/mteb/runner.py", "content": "import logging\nimport os\nimport mteb\nimport json\nimport pandas as pd\nfrom typing import Tuple, Union\n\nfrom FlagEmbedding.abc.evaluation import AbsEvalRunner, AbsEvalModelArgs\n\nfrom .arguments import MTEBEvalArgs\nfrom .searcher import MTEBEvalDenseRetriever, MTEBEvalReranker\nfrom .prompts import get_task_def_by_task_name_and_type\n\n\nlogger = logging.getLogger(__name__)\n\ndef ensure_dir(file_path):\n directory = os.path.dirname(file_path)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\nclass MTEBEvalRunner(AbsEvalRunner):\n \"\"\"\n Evaluation runner of MTEB.\n \"\"\"\n def __init__(\n self,\n eval_args: MTEBEvalArgs,\n model_args: AbsEvalModelArgs,\n ):\n self.eval_args = eval_args\n self.model_args = model_args\n\n self.retriever, self.reranker = self.load_retriever_and_reranker()\n\n def load_retriever_and_reranker(self) -> Tuple[MTEBEvalDenseRetriever, Union[MTEBEvalReranker, None]]:\n \"\"\"Load the retriever and reranker\n\n Returns:\n Tuple[MTEBEvalDenseRetriever, Union[MTEBEvalReranker, None]]: The retriever and reranker instances.\n \"\"\"\n embedder, reranker = self.get_models(self.model_args)\n retriever = MTEBEvalDenseRetriever(\n embedder,\n search_top_k=self.eval_args.search_top_k,\n overwrite=self.eval_args.overwrite\n )\n if reranker is not None:\n reranker = MTEBEvalReranker(reranker, rerank_top_k=self.eval_args.rerank_top_k)\n return retriever, reranker\n\n def read_results(self, output_folder, tasks):\n \"\"\"Read the evaluation results from directory.\n\n Args:\n output_folder (str): Path to the directory with results.\n tasks (list): List of MTEB tasks.\n\n Returns:\n dict: The results of all the tasks.\n \"\"\"\n tasks_results = {}\n task_types = list(set([t.metadata.type for t in tasks]))\n for t_type in task_types:\n tasks_results[t_type] = {}\n for t in tasks:\n if t.metadata.type != t_type: continue\n task_name = t.metadata.name\n\n metric = t.metadata.main_score\n split = t.metadata.eval_splits[0]\n\n if os.path.exists(os.path.join(output_folder, task_name + '.json')):\n data = json.load(open(os.path.join(output_folder, task_name + '.json')))\n tasks_results[t_type][task_name] = {}\n for s in ['test', 'dev', 'validation']:\n if s in data['scores']:\n split = s\n break\n split = None\n if split is None:\n print('ERROR')\n break\n\n temp_datas = data['scores'][split]\n temp_data = None\n for td in temp_datas:\n if td['hf_subset'] == 'default':\n temp_data = td\n if temp_data is None:\n temp_data = temp_datas[0]\n tasks_results[t_type][task_name] = round(temp_data['main_score'] * 100, 2)\n\n print(f\"tasks_results: {tasks_results}\")\n return tasks_results\n\n def output_json(self, tasks_results, save_file):\n \"\"\"Save the tasks results into a json file.\n\n Args:\n tasks_results (dict): The task results.\n save_file (str): Path to a file to save the results.\n \"\"\"\n all_results = 0\n all_results_num = 0\n cqa_results = 0\n cqa_results_num = 0\n\n new_results = {}\n for task_type in tasks_results.keys():\n new_results[task_type] = {}\n tmp_results = 0\n tmp_results_num = 0\n for task_name in tasks_results[task_type].keys():\n if \"CQADupstack\" in task_name:\n cqa_results += tasks_results[task_type][task_name]\n cqa_results_num += 1\n else:\n new_results[task_type][task_name] = float(round(tasks_results[task_type][task_name], 2))\n all_results_num += 1\n all_results += tasks_results[task_type][task_name]\n tmp_results_num += 1\n tmp_results += tasks_results[task_type][task_name]\n if cqa_results_num > 0:\n cqa_results = cqa_results / cqa_results_num\n new_results[task_type][\"CQADupstack\"] = float(round(cqa_results, 2))\n all_results += cqa_results\n all_results_num += 1\n tmp_results += cqa_results\n tmp_results_num += 1\n new_results[task_type]['Avg'] = float(round(tmp_results / tmp_results_num, 2))\n new_results['Avg'] = float(round(all_results / all_results_num, 2))\n with open(save_file, 'w') as f:\n json.dump(new_results, f)\n\n def run(self):\n \"\"\"\n Run the evaluation.\n \"\"\"\n task_types = self.eval_args.task_types\n tasks = self.eval_args.tasks\n languages = self.eval_args.languages\n tasks = mteb.get_tasks(\n languages=languages,\n tasks=tasks,\n task_types=task_types\n )\n output_folder = self.eval_args.output_dir\n\n for task in tasks:\n task_name = task.metadata.name\n task_type = task.metadata.type\n\n self.retriever.stop_pool()\n\n if self.eval_args.use_special_instructions:\n try:\n instruction = get_task_def_by_task_name_and_type(task_name, task_type)\n self.retriever.set_instruction(instruction)\n except:\n logger.info(f\"No instruction found for {task_name}\")\n\n if self.eval_args.examples_path is not None:\n try:\n eg_pairs = json.load(open(os.path.join(self.eval_args.examples_path, task_name + '.json')))\n except:\n logger.info(f\"No examples found for {task_name}\")\n\n if task_type == 'Classification':\n self.retriever.set_normalize_embeddings(False)\n else:\n self.retriever.set_normalize_embeddings(True)\n\n evaluation = mteb.MTEB(tasks=[task])\n results = evaluation.run(self.retriever, output_folder=f\"{output_folder}/{str(self.retriever)}\")\n\n ensure_dir(self.eval_args.eval_output_path)\n logger.info(\"Start computing metrics. Only save results as json.\")\n tasks_results = self.read_results(f\"{output_folder}/{str(self.retriever)}/no_model_name_available/no_revision_available\", tasks)\n self.output_json(tasks_results, self.eval_args.eval_output_path)\n" }, { "path": "FlagEmbedding/evaluation/mteb/searcher.py", "content": "import numpy as np\n\nfrom typing import List, Dict, Optional\nfrom FlagEmbedding.abc.evaluation import EvalDenseRetriever, EvalReranker\n\n\nclass MTEBEvalDenseRetriever(EvalDenseRetriever):\n \"\"\"\n Child class of :class:EvalRetriever for MTEB dense retrieval.\n \"\"\"\n def __init__(self, embedder, **kwargs):\n super().__init__(embedder, **kwargs)\n \n def set_examples(self, examples_for_task: Optional[List[dict]] = None):\n \"\"\"Set examples for the model.\n\n Args:\n examples_for_task (Optional[List[dict]], optional): Examples for the task. Defaults to None.\n \"\"\"\n self.embedder.set_examples(examples_for_task)\n\n def set_instruction(self, instruction: Optional[str] = None):\n \"\"\"Set the instruction to use for the embedding model.\n\n Args:\n instruction (Optional[str], optional): _description_. Defaults to None.\n \"\"\"\n self.embedder.query_instruction_for_retrieval = instruction\n \n def get_instruction(self):\n \"\"\"Get the instruction of embedding model.\n\n Returns:\n str: Instruction\n \"\"\"\n return self.embedder.query_instruction_for_retrieval\n\n def set_normalize_embeddings(self, normalize_embeddings: bool = True):\n \"\"\"Set whether normalize the output embeddings\n\n Args:\n normalize_embeddings (bool, optional): Boolean to control whether or not normalize the embeddings. Defaults to ``True``.\n \"\"\"\n self.embedder.normalize_embeddings = normalize_embeddings\n \n def stop_pool(self):\n self.embedder.stop_self_pool()\n try:\n self.embedder.stop_self_query_pool()\n except:\n pass\n\n def encode_queries(self, queries: List[str], **kwargs):\n \"\"\"Encode input queries.\n\n Args:\n queries (List[str]): Input queries.\n\n Returns:\n Union[np.ndarray, torch.Tensor]: Query embeddings.\n \"\"\"\n emb = self.embedder.encode_queries(queries)\n if isinstance(emb, dict):\n emb = emb[\"dense_vecs\"]\n return emb.astype(np.float32)\n \n def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):\n \"\"\"Encode input corpus.\n\n Args:\n corpus (List[Dict[str, str]]): Input corpus.\n\n Returns:\n Union[np.ndarray, torch.Tensor]: Corpus embeddings.\n \"\"\"\n if isinstance(corpus[0], dict):\n input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus]\n else:\n input_texts = corpus\n emb = self.embedder.encode_corpus(input_texts)\n if isinstance(emb, dict):\n emb = emb[\"dense_vecs\"]\n return emb.astype(np.float32)\n \n def encode(self, corpus: List[Dict[str, str]], **kwargs):\n \"\"\"Encode the imput.\n\n Args:\n corpus (List[Dict[str, str]]): Input corpus or sentences.\n\n Returns:\n Union[np.ndarray, torch.Tensor]: Corpus embeddings.\n \"\"\"\n if isinstance(corpus[0], dict):\n input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus]\n else:\n input_texts = corpus\n emb = self.embedder.encode_queries(input_texts)\n if isinstance(emb, dict):\n emb = emb[\"dense_vecs\"]\n return emb.astype(np.float32)\n\nclass MTEBEvalReranker(EvalReranker):\n \"\"\"\n Child class of :class:EvalReranker for reranker in MTEB.\n \"\"\"\n def __init__(self, reranker, **kwargs):\n super().__init__(reranker, **kwargs)\n" }, { "path": "FlagEmbedding/finetune/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/embedder/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/__init__.py", "content": "from FlagEmbedding.abc.finetune.embedder import (\n AbsEmbedderDataArguments as DecoderOnlyEmbedderDataArguments,\n AbsEmbedderTrainingArguments as DecoderOnlyEmbedderTrainingArguments,\n)\n\nfrom .arguments import DecoderOnlyEmbedderModelArguments\nfrom .modeling import BiDecoderOnlyEmbedderModel\nfrom .trainer import DecoderOnlyEmbedderTrainer\nfrom .runner import DecoderOnlyEmbedderRunner\n\n__all__ = [\n 'DecoderOnlyEmbedderDataArguments',\n 'DecoderOnlyEmbedderTrainingArguments',\n 'DecoderOnlyEmbedderModelArguments',\n 'BiDecoderOnlyEmbedderModel',\n 'DecoderOnlyEmbedderTrainer',\n 'DecoderOnlyEmbedderRunner',\n]\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.finetune.embedder.decoder_only.base import (\n DecoderOnlyEmbedderDataArguments,\n DecoderOnlyEmbedderTrainingArguments,\n DecoderOnlyEmbedderModelArguments,\n DecoderOnlyEmbedderRunner,\n)\n\n\ndef main():\n parser = HfArgumentParser((\n DecoderOnlyEmbedderModelArguments,\n DecoderOnlyEmbedderDataArguments,\n DecoderOnlyEmbedderTrainingArguments\n ))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: DecoderOnlyEmbedderModelArguments\n data_args: DecoderOnlyEmbedderDataArguments\n training_args: DecoderOnlyEmbedderTrainingArguments\n\n runner = DecoderOnlyEmbedderRunner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/arguments.py", "content": "from typing import Optional, List\nfrom dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderModelArguments\n\n\ndef default_target_modules() -> List[int]:\n return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']\n\n\n@dataclass\nclass DecoderOnlyEmbedderModelArguments(AbsEmbedderModelArguments):\n \"\"\"\n Model argument class for decoder only base model.\n \"\"\"\n peft_model_path: str = field(\n default='', metadata={\"help\": \"The peft model checkpoint for initialization.\"}\n )\n use_lora: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use LORA (low-rank parameter-efficient training) to train the model.\"}\n )\n lora_rank: int = field(\n default=64,\n metadata={\"help\": \"The rank of lora.\"}\n )\n lora_alpha: float = field(\n default=16,\n metadata={\"help\": \"The alpha parameter of lora.\"}\n )\n lora_dropout: float = field(\n default=0.1,\n metadata={\"help\": \"The dropout rate of lora modules.\"}\n )\n target_modules: List[str] = field(\n default_factory=default_target_modules,\n metadata={\"help\": \"The target modules to apply LORA.\"}\n )\n use_flash_attn: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use flash attention to train the model.\"}\n )\n use_slow_tokenizer: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\"}\n )\n # low_cpu_mem_usage: bool = field(\n # default=False,\n # metadata={\"help\": \"It is an option to create the model as an empty shell,\"\n # \"then only materialize its parameters when the pretrained weights are loaded.\"\n # \"If passed, LLM loading time and RAM consumption will be benefited.\"}\n # )\n from_peft: str = field(\n default=None\n )\n modules_to_save: str = field(\n default=None\n )\n raw_peft: str = field(\n default=None\n )\n\n additional_special_tokens: Optional[str] = field(\n default=None,\n metadata={\"help\": \"additional special tokens\", \"nargs\": \"+\"}\n )\n \n save_merged_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will merge the lora modules and save the entire model.\"}\n )\n\n only_merge_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will only merge the lora modules and save the entire model.\"}\n )\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/load_model.py", "content": "import os\nimport re\nimport torch\nimport logging\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\nfrom peft import LoraConfig, TaskType, get_peft_model, PeftModel\n\nfrom .arguments import DecoderOnlyEmbedderModelArguments\n\nlogger = logging.getLogger(__name__)\n\n\ndef find_largest_checkpoint(checkpoint_dir):\n \"\"\"Find the largest checkpoint from directory.\n\n Args:\n checkpoint_dir (str): Directory to the checkpoint.\n\n Returns:\n str: Directory to the checkpoint, None no matching found.\n \"\"\"\n checkpoint_pattern = re.compile(r'checkpoint-(\\d+)')\n max_number = -1\n max_checkpoint_file = None\n for file in os.listdir(checkpoint_dir):\n match = checkpoint_pattern.search(file)\n if match:\n number = int(match.group(1))\n if number > max_number:\n max_number = number\n max_checkpoint_file = file\n if max_checkpoint_file:\n return os.path.join(checkpoint_dir, max_checkpoint_file)\n else:\n return None\n\n\ndef get_model(model_args: DecoderOnlyEmbedderModelArguments, output_dir: str, resize: bool, resize_tokens: int):\n \"\"\"Get the model.\n\n Args:\n model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.\n output_dir (str): Directory to save the model.\n resize (bool): Whether to resize the number of tokens.\n resize_tokens (int): The new token size.\n\n Returns:\n transformers.PreTrainedModel or PeftModel: The loaded model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModel.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n logger.info(\"Training new model from scratch\")\n model = model_args.from_config(config)\n\n if model_args.raw_peft is not None:\n model.set_input_embeddings(torch.load(os.path.join(model_args.raw_peft, 'embedding', 'emb.pth')))\n model = PeftModel.from_pretrained(model, model_args.raw_peft)\n model = model.merge_and_unload()\n\n if resize:\n model.resize_token_embeddings(resize_tokens)\n os.makedirs(os.path.join(output_dir, 'embedding'), exist_ok=True)\n torch.save(model.embed_tokens, os.path.join(output_dir, 'embedding', 'emb.pth'))\n target_modules = model_args.target_modules\n else:\n target_modules = model_args.target_modules\n if 'embed_tokens' in target_modules:\n target_modules.remove('embed_tokens')\n\n if model_args.from_peft is not None:\n if os.path.exists(os.path.join(model_args.from_peft, 'embedding')):\n model.set_input_embeddings(torch.load(os.path.join(model_args.from_peft, 'embedding', 'emb.pth')))\n torch.save(model.embed_tokens, os.path.join(output_dir, 'embedding', 'emb.pth'))\n model = PeftModel.from_pretrained(model, model_args.from_peft, is_trainable=True)\n model.print_trainable_parameters()\n else:\n if model_args.use_lora:\n peft_config = LoraConfig(\n task_type=TaskType.FEATURE_EXTRACTION,\n inference_mode=False,\n r=model_args.lora_rank,\n target_modules=target_modules,\n modules_to_save=model_args.modules_to_save,\n lora_alpha=model_args.lora_alpha,\n lora_dropout=model_args.lora_dropout\n )\n model = get_peft_model(model, peft_config)\n model.print_trainable_parameters()\n\n return model\n\n\ndef save_merged_model(model_args: DecoderOnlyEmbedderModelArguments, output_dir: str):\n \"\"\"\n Loads a model with specified configurations, merges it with PEFT layers if available.\n\n Args:\n model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.\n output_dir (str): Directory to save the model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModel.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n model = model_args.from_config(config)\n\n if model_args.raw_peft is not None:\n model.set_input_embeddings(torch.load(os.path.join(model_args.raw_peft, 'embedding', 'emb.pth')))\n model = PeftModel.from_pretrained(model, model_args.raw_peft)\n model = model.merge_and_unload()\n\n if os.path.exists(os.path.join(output_dir, 'embedding', 'emb.pth')):\n model.set_input_embeddings(torch.load(os.path.join(output_dir, 'embedding', 'emb.pth')))\n # modify the vocab size in the model configuration\n model.config.vocab_size = len(tokenizer)\n\n try:\n model = PeftModel.from_pretrained(model, output_dir)\n model = model.merge_and_unload()\n except:\n model = PeftModel.from_pretrained(model, find_largest_checkpoint(output_dir))\n model = model.merge_and_unload()\n\n tokenizer = AutoTokenizer.from_pretrained(output_dir, trust_remote_code=model_args.trust_remote_code)\n tokenizer.save_pretrained(os.path.join(output_dir, 'merged_model'))\n\n model.save_pretrained(os.path.join(output_dir, 'merged_model'))\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/modeling.py", "content": "import logging\n\nimport torch\nfrom transformers import AutoModel, PreTrainedModel, PreTrainedTokenizer\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass BiDecoderOnlyEmbedderModel(AbsEmbedderModel):\n \"\"\"Embedder model class for decoder only model.\n\n Args:\n base_model (PreTrainedModel): The base model to train on.\n tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.\n negatives_cross_device (bool, optional): If True, will compute cross devices negative loss. Defaults to ``False``.\n temperature (float, optional): Temperature to control the scale of scores. Defaults to ``1.0``.\n sub_batch_size (int, optional): Sub-batch size during encoding. If negative, will not split to sub-batch.\n Defaults to ``-1``.\n kd_loss_type (str, optional): Type of knowledge distillation loss. Defaults to ``'kl_div'``.\n sentence_pooling_method (str, optional): Pooling method to get sentence embedding. Defaults to ``'last_token'``.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to ``False``.\n \"\"\"\n TRANSFORMER_CLS = AutoModel\n\n def __init__(\n self,\n base_model: PreTrainedModel,\n tokenizer: PreTrainedTokenizer = None,\n negatives_cross_device: bool = False,\n temperature: float = 1.0,\n sub_batch_size: int = -1,\n kd_loss_type: str = 'kl_div',\n sentence_pooling_method: str = 'last_token',\n normalize_embeddings: bool = False,\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=negatives_cross_device,\n temperature=temperature,\n sub_batch_size=sub_batch_size,\n kd_loss_type=kd_loss_type,\n )\n self.sentence_pooling_method = sentence_pooling_method\n self.normalize_embeddings = normalize_embeddings\n self.cross_entropy = torch.nn.CrossEntropyLoss(reduction='mean')\n\n def encode(self, features):\n \"\"\"\n Encode and get the embedding.\n\n Args:\n features (Union[list, dict]): Features feed to the model.\n\n Returns:\n torch.Tensor: The embedding vectors.\n \"\"\"\n if features is None:\n return None\n if not isinstance(features, list):\n if self.sub_batch_size is not None and self.sub_batch_size > 0:\n all_p_reps = []\n for i in range(0, len(features['attention_mask']), self.sub_batch_size):\n end_inx = min(i + self.sub_batch_size, len(features['attention_mask']))\n sub_features = {}\n for k, v in features.items():\n sub_features[k] = v[i:end_inx]\n last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state\n p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n last_hidden_state = self.model(**features, return_dict=True).last_hidden_state\n all_p_reps = self._sentence_embedding(last_hidden_state, features['attention_mask'])\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n all_p_reps = []\n for sub_features in features:\n last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state\n p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n\n def _sentence_embedding(self, last_hidden_state, attention_mask):\n \"\"\"Use the pooling method to get the sentence embedding.\n\n Args:\n last_hidden_state (torch.Tensor): The model output's last hidden state.\n attention_mask (torch.Tensor): Mask out padding tokens during pooling.\n\n Raises:\n NotImplementedError: Specified pooling method not implemented.\n\n Returns:\n torch.Tensor: The sentence embeddings.\n \"\"\"\n if self.sentence_pooling_method == \"cls\":\n return last_hidden_state[:, 0]\n elif self.sentence_pooling_method == \"mean\":\n s = torch.sum(\n last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1\n )\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n elif self.sentence_pooling_method == \"last_token\":\n left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]\n if left_padding:\n return last_hidden_state[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_state.shape[0]\n return last_hidden_state[\n torch.arange(batch_size, device=last_hidden_state.device),\n sequence_lengths,\n ]\n else:\n raise NotImplementedError(f\"pooling method {self.sentence_pooling_method} not implemented\")\n\n def compute_score(self, q_reps, p_reps):\n \"\"\"Computes the scores between query and passage representations.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed scores, adjusted by temperature.\n \"\"\"\n scores = self._compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def _compute_similarity(self, q_reps, p_reps):\n \"\"\"Computes the similarity between query and passage representations using inner product.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed similarity matrix.\n \"\"\"\n if len(p_reps.size()) == 2:\n return torch.matmul(q_reps, p_reps.transpose(0, 1))\n return torch.matmul(q_reps, p_reps.transpose(-2, -1))\n\n def compute_loss(self, scores, target):\n \"\"\"Compute the loss using cross entropy.\n\n Args:\n scores (torch.Tensor): Computed score.\n target (torch.Tensor): The target value.\n\n Returns:\n torch.Tensor: The computed cross entropy loss.\n \"\"\"\n return self.cross_entropy(scores, target)\n\n def gradient_checkpointing_enable(self, **kwargs):\n \"\"\"\n Activates gradient checkpointing for the current model.\n \"\"\"\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n \"\"\"\n Enables the gradients for the input embeddings.\n \"\"\"\n self.model.enable_input_require_grads(**kwargs)\n\n def save(self, output_dir: str):\n \"\"\"Save the model to the directory.\n\n Args:\n output_dir (str): Directory for saving the model.\n \"\"\"\n state_dict = self.model.state_dict()\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n self.model.save_pretrained(output_dir, state_dict=state_dict)\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/runner.py", "content": "import logging\nfrom typing import Tuple\nfrom pathlib import Path\nfrom transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer\n\nfrom FlagEmbedding.abc.finetune.embedder.AbsArguments import AbsEmbedderDataArguments, AbsEmbedderTrainingArguments\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderRunner, AbsEmbedderModel, EmbedderTrainerCallbackForDataRefresh\n\nfrom .arguments import DecoderOnlyEmbedderModelArguments\nfrom .trainer import DecoderOnlyEmbedderTrainer\nfrom .modeling import BiDecoderOnlyEmbedderModel\nfrom .load_model import get_model, save_merged_model\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyEmbedderRunner(AbsEmbedderRunner):\n \"\"\"Runner class for decoder only embedding model.\n\n Args:\n model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.\n data_args (AbsEmbedderDataArguments): Data arguments instance.\n training_args (AbsEmbedderTrainingArguments): Trainer arguments.\n \"\"\"\n def __init__(\n self,\n model_args: DecoderOnlyEmbedderModelArguments,\n data_args: AbsEmbedderDataArguments,\n training_args: AbsEmbedderTrainingArguments\n ):\n super().__init__(model_args, data_args, training_args)\n self.model_args: DecoderOnlyEmbedderModelArguments\n self.data_args: AbsEmbedderDataArguments\n self.training_args: AbsEmbedderTrainingArguments\n\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:\n \"\"\"Load tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.tokenizer_name if self.model_args.tokenizer_name else self.model_args.model_name_or_path,\n token=self.model_args.token,\n cache_dir=self.model_args.cache_dir,\n use_fast=self.model_args.use_fast_tokenizer,\n add_eos_token=True,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n\n if tokenizer.pad_token is None:\n if tokenizer.unk_token is not None:\n tokenizer.pad_token = tokenizer.unk_token\n tokenizer.pad_token_id = tokenizer.unk_token_id\n else:\n tokenizer.pad_token = tokenizer.eos_token\n tokenizer.pad_token_id = tokenizer.eos_token_id\n tokenizer.padding_side = 'left'\n\n resize = False\n if self.model_args.additional_special_tokens is not None:\n special_tokens_dict = {'additional_special_tokens': self.model_args.additional_special_tokens}\n add_num = tokenizer.add_special_tokens(special_tokens_dict)\n if add_num > 0:\n resize = True\n logger.info(f\"Add {add_num} special tokens to the tokenizer. Special tokens: {self.model_args.additional_special_tokens}\")\n else:\n logger.warning(f\"Special tokens {self.model_args.additional_special_tokens} already exists in the tokenizer.\")\n base_model = get_model(self.model_args, self.training_args.output_dir, resize, len(tokenizer))\n\n num_labels = 1\n config = AutoConfig.from_pretrained(\n self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n logger.info('Config: %s', config)\n\n model = BiDecoderOnlyEmbedderModel(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=self.training_args.negatives_cross_device,\n temperature=self.training_args.temperature,\n sub_batch_size=self.training_args.sub_batch_size,\n kd_loss_type=self.training_args.kd_loss_type,\n sentence_pooling_method=self.training_args.sentence_pooling_method,\n normalize_embeddings=self.training_args.normalize_embeddings\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n if self.training_args.fix_position_embedding:\n for k, v in model.named_parameters():\n if \"position_embeddings\" in k:\n logging.info(f\"Freeze the parameters for {k}\")\n v.requires_grad = False\n return tokenizer, model\n\n def load_trainer(self) -> DecoderOnlyEmbedderTrainer:\n \"\"\"Load the trainer.\n\n Returns:\n DecoderOnlyEmbedderTrainer: Loaded trainer instance.\n \"\"\"\n trainer = DecoderOnlyEmbedderTrainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n if self.data_args.same_dataset_within_batch:\n trainer.add_callback(EmbedderTrainerCallbackForDataRefresh(self.train_dataset))\n return trainer\n\n def run(self):\n \"\"\"\n Run the finetune.\n \"\"\"\n if not self.model_args.only_merge_lora_model:\n Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)\n self.trainer.save_model()\n\n # save merged model\n if self.model_args.save_merged_lora_model and self.training_args.process_index == 0:\n save_merged_model(self.model_args, self.training_args.output_dir)\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/base/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyEmbedderTrainer(AbsEmbedderTrainer):\n \"\"\"\n Trainer class for base encoder models.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # save the checkpoint for sentence-transformers library\n # if self.is_world_process_zero():\n # save_ckpt_for_sentence_transformers(output_dir,\n # pooling_mode=self.args.sentence_pooling_method,\n # normlized=self.args.normlized)\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/__init__.py", "content": "from FlagEmbedding.abc.finetune.embedder import (\n AbsEmbedderTrainingArguments as DecoderOnlyEmbedderICLTrainingArguments,\n)\n\nfrom .arguments import (\n DecoderOnlyEmbedderICLModelArguments,\n DecoderOnlyEmbedderICLDataArguments\n)\nfrom .dataset import (\n DecoderOnlyEmbedderICLSameDatasetTrainDataset,\n AbsEmbedderSameDatasetCollator\n)\nfrom .modeling import BiDecoderOnlyEmbedderICLModel\nfrom .trainer import DecoderOnlyEmbedderICLTrainer\nfrom .runner import DecoderOnlyEmbedderICLRunner\n\n__all__ = [\n 'DecoderOnlyEmbedderICLModelArguments',\n 'DecoderOnlyEmbedderICLDataArguments',\n 'DecoderOnlyEmbedderICLTrainingArguments',\n 'BiDecoderOnlyEmbedderICLModel',\n 'DecoderOnlyEmbedderICLTrainer',\n 'DecoderOnlyEmbedderICLRunner',\n]\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.finetune.embedder.decoder_only.icl import (\n DecoderOnlyEmbedderICLDataArguments,\n DecoderOnlyEmbedderICLTrainingArguments,\n DecoderOnlyEmbedderICLModelArguments,\n DecoderOnlyEmbedderICLRunner,\n)\n\n\ndef main():\n parser = HfArgumentParser((\n DecoderOnlyEmbedderICLModelArguments,\n DecoderOnlyEmbedderICLDataArguments,\n DecoderOnlyEmbedderICLTrainingArguments\n ))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: DecoderOnlyEmbedderICLModelArguments\n data_args: DecoderOnlyEmbedderICLDataArguments\n training_args: DecoderOnlyEmbedderICLTrainingArguments\n\n runner = DecoderOnlyEmbedderICLRunner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/arguments.py", "content": "from typing import Optional, List\nfrom dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.finetune.embedder import (\n AbsEmbedderModelArguments,\n AbsEmbedderDataArguments,\n)\n\n\ndef default_target_modules() -> List[int]:\n return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']\n\n\n@dataclass\nclass DecoderOnlyEmbedderICLModelArguments(AbsEmbedderModelArguments):\n \"\"\"\n Model argument class for decoder only icl model.\n \"\"\"\n peft_model_path: str = field(\n default='', metadata={\"help\": \"The peft model checkpoint for initialization.\"}\n )\n use_lora: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use LORA (low-rank parameter-efficient training) to train the model.\"}\n )\n lora_rank: int = field(\n default=64,\n metadata={\"help\": \"The rank of lora.\"}\n )\n lora_alpha: float = field(\n default=16,\n metadata={\"help\": \"The alpha parameter of lora.\"}\n )\n lora_dropout: float = field(\n default=0.1,\n metadata={\"help\": \"The dropout rate of lora modules.\"}\n )\n target_modules: List[str] = field(\n default_factory=default_target_modules,\n metadata={\"help\": \"The target modules to apply LORA.\"}\n )\n use_flash_attn: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use flash attention to train the model.\"}\n )\n use_slow_tokenizer: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\"}\n )\n # low_cpu_mem_usage: bool = field(\n # default=False,\n # metadata={\"help\": \"It is an option to create the model as an empty shell,\"\n # \"then only materialize its parameters when the pretrained weights are loaded.\"\n # \"If passed, LLM loading time and RAM consumption will be benefited.\"}\n # )\n from_peft: str = field(\n default=None\n )\n modules_to_save: str = field(\n default=None,\n )\n raw_peft: str = field(\n default=None\n )\n\n additional_special_tokens: Optional[str] = field(\n default=None,\n metadata={\"help\": \"additional special tokens\", \"nargs\": \"+\"}\n )\n\n save_merged_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will merge the lora modules and save the entire model.\"}\n )\n\n only_merge_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will only merge the lora modules and save the entire model.\"}\n )\n\n\n@dataclass\nclass DecoderOnlyEmbedderICLDataArguments(AbsEmbedderDataArguments):\n \"\"\"\n Data argument class for decoder only icl model.\n \"\"\"\n example_query_max_len: int = field(\n default=64,\n metadata={\"help\": \"The max length of example query.\"}\n )\n example_passage_max_len: int = field(\n default=96,\n metadata={\"help\": \"The max length of example passage.\"}\n )\n retrieval_use_examples: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use examples for retrieval.\"}\n )\n icl_suffix_str: str = field(\n default='\\nResponse:',\n metadata={\"help\": \"The suffix string for ICL dataset.\"}\n )\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/dataset.py", "content": "import math\nimport random\nimport logging\nfrom dataclasses import dataclass\nfrom transformers import (\n PreTrainedTokenizer, \n DataCollatorWithPadding,\n)\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderSameDatasetTrainDataset\n\nfrom .arguments import DecoderOnlyEmbedderICLDataArguments\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyEmbedderICLSameDatasetTrainDataset(AbsEmbedderSameDatasetTrainDataset):\n \"\"\"Dataset class for icl model.\n\n Args:\n args (DecoderOnlyEmbedderICLDataArguments): Data argument class for icl model.\n default_batch_size (int): The default batch size.\n seed (int): Random seed to use.\n tokenizer (PreTrainedTokenizer): Tokenzier.\n process_index (int, optional): Current process index. Defaults to 0.\n num_processes (int, optional): Total number of processes. Defaults to 1.\n \"\"\"\n def __init__(\n self,\n args: DecoderOnlyEmbedderICLDataArguments,\n default_batch_size: int,\n seed: int,\n tokenizer: PreTrainedTokenizer,\n process_index: int=0,\n num_processes: int=1\n ):\n super().__init__(\n args=args,\n default_batch_size=default_batch_size,\n seed=seed,\n tokenizer=tokenizer,\n process_index=process_index,\n num_processes=num_processes\n )\n self.args: DecoderOnlyEmbedderICLDataArguments\n\n self.suffix = self.tokenizer(f\"{self.args.icl_suffix_str}{self.tokenizer.eos_token}\", add_special_tokens=False)['input_ids']\n\n self.prefix = self.tokenizer(f\"{self.tokenizer.bos_token}\", add_special_tokens=False)['input_ids']\n\n def _create_batch_data(self, batch_raw_data):\n \"\"\"Create a comple batch of data with queries, documents and teacher scores.\n\n Args:\n batch_raw_data (datasets.Dataset): One batch of raw data.\n\n Returns:\n List[str]: Queries with instruction format.\n List[str]: Documents with instruction format.\n List[float]: Teacher scores for model distillation.\n \"\"\"\n queries, passages, teacher_scores = [], [], []\n\n train_group_size, data_type = self._get_train_group_size(batch_raw_data)\n\n icl_pairs = []\n\n for i in range(len(batch_raw_data['query'])):\n if data_type is not None:\n assert batch_raw_data['type'][i] == data_type, f\"Data type is not consistent in the same batch\"\n\n queries.append(\n self.args.query_instruction_format.format(\n batch_raw_data['prompt'][i] if 'prompt' in batch_raw_data else self.args.query_instruction_for_retrieval,\n batch_raw_data['query'][i]\n )\n )\n tmp_passages = []\n pos_idx = random.choice(list(range(len(batch_raw_data['pos'][i]))))\n pos = self._shuffle_text(batch_raw_data['pos'][i][pos_idx])\n tmp_passages.append(pos)\n\n neg_all_idx = list(range(len(batch_raw_data['neg'][i])))\n if len(batch_raw_data['neg'][i]) < train_group_size - 1:\n num = math.ceil((train_group_size - 1) / len(batch_raw_data['neg'][i]))\n neg_idxs = random.sample(neg_all_idx * num, train_group_size - 1)\n else:\n neg_idxs = random.sample(neg_all_idx, train_group_size - 1)\n for neg_idx in neg_idxs:\n tmp_passages.append(batch_raw_data['neg'][i][neg_idx])\n\n if self.args.knowledge_distillation:\n if 'pos_scores' in batch_raw_data and batch_raw_data['pos_scores'][i] is not None:\n teacher_scores.append(batch_raw_data['pos_scores'][i][pos_idx])\n for neg_idx in neg_idxs:\n if 'neg_scores' in batch_raw_data and batch_raw_data['neg_scores'][i] is not None:\n teacher_scores.append(batch_raw_data['neg_scores'][i][neg_idx])\n else:\n teacher_scores = None\n\n if data_type is not None and data_type in ['symmetric_sts', 'symmetric_clustering']:\n tmp_passages = [\n self.args.query_instruction_format.format(\n batch_raw_data['prompt'][i] if 'prompt' in batch_raw_data else self.args.query_instruction_for_retrieval,\n p\n ) for p in tmp_passages\n ]\n tmp_passages = self.tokenizer.batch_decode(\n self.tokenizer(\n tmp_passages,\n max_length=self.args.passage_max_len - 1 - len(self.suffix),\n truncation=True,\n add_special_tokens=False,\n )['input_ids']\n )\n for j in range(len(tmp_passages)):\n tmp_passages[j] += self.args.icl_suffix_str\n else:\n if self.args.passage_instruction_for_retrieval is not None:\n tmp_passages = [\n self.args.passage_instruction_format.format(\n self.args.passage_instruction_for_retrieval, p\n ) for p in tmp_passages\n ]\n\n passages.extend(tmp_passages)\n \n if teacher_scores is not None:\n if len(teacher_scores) > 0 and len(passages) > 0:\n assert len(teacher_scores) == len(passages)\n\n # add icl pairs\n if self.args.retrieval_use_examples or (\n data_type in ['symmetric_sts', 'symmetric_clustering', 'symmetric_class']\n ):\n if data_type == 'symmetric_clustering':\n icl_pairs.append((\n self.tokenizer.decode(\n self.tokenizer(\n queries[-1],\n add_special_tokens=False\n )['input_ids'][:self.args.example_query_max_len]\n ),\n self.tokenizer.decode(\n self.tokenizer(\n batch_raw_data['category'][i], # use category as example\n add_special_tokens=False\n )['input_ids'][:self.args.example_passage_max_len]\n )\n ))\n else:\n icl_pairs.append((\n self.tokenizer.decode(\n self.tokenizer(\n queries[-1],\n add_special_tokens=False\n )['input_ids'][:self.args.example_query_max_len]\n ),\n self.tokenizer.decode(\n self.tokenizer(\n pos,\n add_special_tokens=False\n )['input_ids'][:self.args.example_passage_max_len]\n )\n ))\n else:\n icl_pairs = []\n\n # handle queries\n for i in range(len(queries)):\n choices = random.choice([0, 1, 2, 3, 4, 5])\n if choices > 0 and len(icl_pairs) > 0:\n prefix_ids = random.sample(list(range(len(icl_pairs))), min(choices + 1, len(icl_pairs)))\n if i in prefix_ids:\n prefix_ids.remove(i)\n prefix_ids = prefix_ids[:choices]\n \n prefix = ''\n for idx in prefix_ids:\n tmp = prefix + self.args.icl_suffix_str.join(icl_pairs[idx]) + '\\n\\n'\n if len(self.tokenizer(tmp)['input_ids']) > self.args.query_max_len - 512:\n break\n prefix = tmp\n else:\n prefix = ''\n\n queries[i] = prefix + queries[i]\n queries[i] = self.tokenizer.decode(\n self.tokenizer(\n queries[i],\n max_length=self.args.query_max_len - len(self.prefix) - len(self.suffix),\n truncation=True,\n add_special_tokens=False\n )['input_ids']\n ) + self.args.icl_suffix_str\n\n return queries, passages, teacher_scores\n\n\n@dataclass\nclass AbsEmbedderSameDatasetCollator(DataCollatorWithPadding):\n \"\"\"\n EmbedCollator for SameDataset.\n Note that after using this collator, the training_args should be set as:\n \n ``training_args.per_device_train_batch_size = 1``\n \n ``training_args.dataloader_num_workers = 0 # avoid multi-processing``\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n sub_batch_size: int = -1\n\n def __call__(self, features):\n queries = features[0][0]\n passages = features[0][1]\n teacher_scores = features[0][2]\n no_in_batch_neg_flag = features[0][3]\n \n queries_inputs = self.tokenizer(\n queries,\n truncation=True,\n max_length=self.query_max_len,\n return_tensors=None\n )\n passages_inputs = self.tokenizer(\n passages,\n truncation=True,\n max_length=self.passage_max_len,\n return_tensors=None\n )\n\n if self.sub_batch_size is None or self.sub_batch_size <= 0:\n q_collated = self.tokenizer.pad(\n queries_inputs,\n padding=self.padding,\n max_length=self.query_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n )\n\n d_collated = self.tokenizer.pad(\n passages_inputs,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n )\n else:\n batch_size = self.sub_batch_size\n\n q_collated = []\n for i in range(0, len(queries_inputs['attention_mask']), batch_size):\n start = i\n end = min(len(queries_inputs['attention_mask']), i + batch_size)\n sub_features = {}\n for k, v in queries_inputs.items():\n sub_features[k] = v[start:end]\n q_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n ))\n\n d_collated = []\n for i in range(0, len(passages_inputs['attention_mask']), batch_size):\n start = i\n end = min(len(passages_inputs['attention_mask']), i + batch_size)\n sub_features = {}\n\n for k, v in passages_inputs.items():\n sub_features[k] = v[start:end]\n d_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=self.return_tensors,\n ))\n\n if isinstance(teacher_scores, list) and len(teacher_scores) == 0:\n teacher_scores = None\n\n return {\n \"queries\": q_collated,\n \"passages\": d_collated,\n \"teacher_scores\": teacher_scores,\n \"no_in_batch_neg_flag\": no_in_batch_neg_flag\n }\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/load_model.py", "content": "import os\nimport re\nimport torch\nimport logging\nfrom transformers import AutoConfig, AutoModel, AutoTokenizer\nfrom peft import LoraConfig, TaskType, get_peft_model, PeftModel\n\nfrom .arguments import DecoderOnlyEmbedderICLModelArguments\n\nlogger = logging.getLogger(__name__)\n\n\ndef find_largest_checkpoint(checkpoint_dir):\n \"\"\"Find the largest checkpoint from directory.\n\n Args:\n checkpoint_dir (str): Directory to the checkpoint.\n\n Returns:\n str: Directory to the checkpoint, None no matching found.\n \"\"\"\n checkpoint_pattern = re.compile(r'checkpoint-(\\d+)')\n max_number = -1\n max_checkpoint_file = None\n for file in os.listdir(checkpoint_dir):\n match = checkpoint_pattern.search(file)\n if match:\n number = int(match.group(1))\n if number > max_number:\n max_number = number\n max_checkpoint_file = file\n if max_checkpoint_file:\n return os.path.join(checkpoint_dir, max_checkpoint_file)\n else:\n return None\n\n\ndef get_model(model_args: DecoderOnlyEmbedderICLModelArguments, output_dir: str, resize: bool, resize_tokens: int):\n \"\"\"Get the model.\n\n Args:\n model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.\n output_dir (str): Directory to save the model.\n resize (bool): Whether to resize the number of tokens.\n resize_tokens (int): The new token size.\n\n Returns:\n transformers.PreTrainedModel or PeftModel: The loaded model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModel.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n logger.info(\"Training new model from scratch\")\n model = model_args.from_config(config)\n\n if model_args.raw_peft is not None:\n model.set_input_embeddings(torch.load(os.path.join(model_args.raw_peft, 'embedding', 'emb.pth')))\n model = PeftModel.from_pretrained(model, model_args.raw_peft)\n model = model.merge_and_unload()\n\n if resize:\n model.resize_token_embeddings(resize_tokens)\n os.makedirs(os.path.join(output_dir, 'embedding'), exist_ok=True)\n torch.save(model.embed_tokens, os.path.join(output_dir, 'embedding', 'emb.pth'))\n target_modules = model_args.target_modules\n else:\n target_modules = model_args.target_modules\n if 'embed_tokens' in target_modules:\n target_modules.remove('embed_tokens')\n\n if model_args.from_peft is not None:\n if os.path.exists(os.path.join(model_args.from_peft, 'embedding')):\n model.set_input_embeddings(torch.load(os.path.join(model_args.from_peft, 'embedding', 'emb.pth')))\n torch.save(model.embed_tokens, os.path.join(output_dir, 'embedding', 'emb.pth'))\n model = PeftModel.from_pretrained(model, model_args.from_peft, is_trainable=True)\n model.print_trainable_parameters()\n else:\n if model_args.use_lora:\n peft_config = LoraConfig(\n task_type=TaskType.FEATURE_EXTRACTION,\n inference_mode=False,\n r=model_args.lora_rank,\n target_modules=target_modules,\n modules_to_save=model_args.modules_to_save,\n lora_alpha=model_args.lora_alpha,\n lora_dropout=model_args.lora_dropout\n )\n model = get_peft_model(model, peft_config)\n model.print_trainable_parameters()\n\n return model\n\n\ndef save_merged_model(model_args: DecoderOnlyEmbedderICLModelArguments, output_dir: str):\n \"\"\"\n Loads a model with specified configurations, merges it with PEFT layers if available.\n\n Args:\n model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.\n output_dir (str): Directory to save the model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModel.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n model = model_args.from_config(config)\n\n if model_args.raw_peft is not None:\n model.set_input_embeddings(torch.load(os.path.join(model_args.raw_peft, 'embedding', 'emb.pth')))\n model = PeftModel.from_pretrained(model, model_args.raw_peft)\n model = model.merge_and_unload()\n\n if os.path.exists(os.path.join(output_dir, 'embedding', 'emb.pth')):\n model.set_input_embeddings(torch.load(os.path.join(output_dir, 'embedding', 'emb.pth')))\n\n try:\n model = PeftModel.from_pretrained(model, output_dir)\n model = model.merge_and_unload()\n except:\n model = PeftModel.from_pretrained(model, find_largest_checkpoint(output_dir))\n model = model.merge_and_unload()\n\n tokenizer = AutoTokenizer.from_pretrained(output_dir, trust_remote_code=model_args.trust_remote_code)\n tokenizer.save_pretrained(os.path.join(output_dir, 'merged_model'))\n\n # modify the vocab size in the model configuration\n model.config.vocab_size = len(tokenizer)\n model.save_pretrained(os.path.join(output_dir, 'merged_model'))\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/modeling.py", "content": "import logging\n\nimport torch\nfrom transformers import AutoModel, PreTrainedModel, PreTrainedTokenizer\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass BiDecoderOnlyEmbedderICLModel(AbsEmbedderModel):\n \"\"\"Embedder model class for decoder only model.\n\n Args:\n base_model (PreTrainedModel): The base model to train on.\n tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.\n negatives_cross_device (bool, optional): If True, will compute cross devices negative loss. Defaults to ``False``.\n temperature (float, optional): Temperature to control the scale of scores. Defaults to ``1.0``.\n sub_batch_size (int, optional): Sub-batch size during encoding. If negative, will not split to sub-batch.\n Defaults to ``-1``.\n kd_loss_type (str, optional): Type of knowledge distillation loss. Defaults to ``'kl_div'``.\n sentence_pooling_method (str, optional): Pooling method to get sentence embedding. Defaults to ``'last_token'``.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to ``False``.\n \"\"\"\n TRANSFORMER_CLS = AutoModel\n\n def __init__(\n self,\n base_model: PreTrainedModel,\n tokenizer: PreTrainedTokenizer = None,\n negatives_cross_device: bool = False,\n temperature: float = 1.0,\n sub_batch_size: int = -1,\n kd_loss_type: str = 'kl_div',\n sentence_pooling_method: str = 'last_token',\n normalize_embeddings: bool = False,\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=negatives_cross_device,\n temperature=temperature,\n sub_batch_size=sub_batch_size,\n kd_loss_type=kd_loss_type,\n )\n self.sentence_pooling_method = sentence_pooling_method\n self.normalize_embeddings = normalize_embeddings\n self.cross_entropy = torch.nn.CrossEntropyLoss(reduction='mean')\n\n def encode(self, features):\n \"\"\"\n Encode and get the embedding.\n\n Args:\n features (Union[list, dict]): Features feed to the model.\n\n Returns:\n torch.Tensor: The embedding vectors.\n \"\"\"\n if features is None:\n return None\n if not isinstance(features, list):\n if self.sub_batch_size is not None and self.sub_batch_size > 0:\n all_p_reps = []\n for i in range(0, len(features['attention_mask']), self.sub_batch_size):\n end_inx = min(i + self.sub_batch_size, len(features['attention_mask']))\n sub_features = {}\n for k, v in features.items():\n sub_features[k] = v[i:end_inx]\n last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state\n p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n last_hidden_state = self.model(**features, return_dict=True).last_hidden_state\n all_p_reps = self._sentence_embedding(last_hidden_state, features['attention_mask'])\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n all_p_reps = []\n for sub_features in features:\n last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state\n p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n\n def _sentence_embedding(self, last_hidden_state, attention_mask):\n \"\"\"Use the pooling method to get the sentence embedding.\n\n Args:\n last_hidden_state (torch.Tensor): The model output's last hidden state.\n attention_mask (torch.Tensor): Mask out padding tokens during pooling.\n\n Raises:\n NotImplementedError: Specified pooling method not implemented.\n\n Returns:\n torch.Tensor: The sentence embeddings.\n \"\"\"\n if self.sentence_pooling_method == \"cls\":\n return last_hidden_state[:, 0]\n elif self.sentence_pooling_method == \"mean\":\n s = torch.sum(\n last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1\n )\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n elif self.sentence_pooling_method == \"last_token\":\n left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]\n if left_padding:\n return last_hidden_state[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_state.shape[0]\n return last_hidden_state[\n torch.arange(batch_size, device=last_hidden_state.device),\n sequence_lengths,\n ]\n else:\n raise NotImplementedError(f\"pooling method {self.sentence_pooling_method} not implemented\")\n\n def compute_score(self, q_reps, p_reps):\n \"\"\"Computes the scores between query and passage representations.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed scores, adjusted by temperature.\n \"\"\"\n scores = self._compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def _compute_similarity(self, q_reps, p_reps):\n \"\"\"Computes the similarity between query and passage representations using inner product.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed similarity matrix.\n \"\"\"\n if len(p_reps.size()) == 2:\n return torch.matmul(q_reps, p_reps.transpose(0, 1))\n return torch.matmul(q_reps, p_reps.transpose(-2, -1))\n\n def compute_loss(self, scores, target):\n \"\"\"Compute the loss using cross entropy.\n\n Args:\n scores (torch.Tensor): Computed score.\n target (torch.Tensor): The target value.\n\n Returns:\n torch.Tensor: The computed cross entropy loss.\n \"\"\"\n return self.cross_entropy(scores, target)\n\n def gradient_checkpointing_enable(self, **kwargs):\n \"\"\"\n Activates gradient checkpointing for the current model.\n \"\"\"\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n \"\"\"\n Enables the gradients for the input embeddings.\n \"\"\"\n self.model.enable_input_require_grads(**kwargs)\n\n def save(self, output_dir: str):\n \"\"\"Save the model to the directory.\n\n Args:\n output_dir (str): Directory for saving the model.\n \"\"\"\n state_dict = self.model.state_dict()\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n self.model.save_pretrained(output_dir, state_dict=state_dict)\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/runner.py", "content": "import logging\nfrom typing import Tuple\nfrom pathlib import Path\nfrom transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer\n\nfrom FlagEmbedding.abc.finetune.embedder.AbsArguments import AbsEmbedderTrainingArguments\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderRunner, AbsEmbedderModel, EmbedderTrainerCallbackForDataRefresh\n\nfrom .arguments import DecoderOnlyEmbedderICLModelArguments, DecoderOnlyEmbedderICLDataArguments\nfrom .trainer import DecoderOnlyEmbedderICLTrainer\nfrom .modeling import BiDecoderOnlyEmbedderICLModel\nfrom .dataset import DecoderOnlyEmbedderICLSameDatasetTrainDataset\nfrom .load_model import get_model, save_merged_model\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyEmbedderICLRunner(AbsEmbedderRunner):\n \"\"\"Runner class for decoder only icl model.\n\n Args:\n model_args (DecoderOnlyEmbedderICLModelArguments): Model arguments instance.\n data_args (DecoderOnlyEmbedderICLDataArguments): Data arguments instance.\n training_args (AbsEmbedderTrainingArguments): Trainer arguments.\n \"\"\"\n def __init__(\n self,\n model_args: DecoderOnlyEmbedderICLModelArguments,\n data_args: DecoderOnlyEmbedderICLDataArguments,\n training_args: AbsEmbedderTrainingArguments\n ):\n super().__init__(model_args, data_args, training_args)\n self.model_args: DecoderOnlyEmbedderICLModelArguments\n self.data_args: DecoderOnlyEmbedderICLDataArguments\n self.training_args: AbsEmbedderTrainingArguments\n\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:\n \"\"\"Load tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.tokenizer_name if self.model_args.tokenizer_name else self.model_args.model_name_or_path,\n token=self.model_args.token,\n cache_dir=self.model_args.cache_dir,\n use_fast=self.model_args.use_fast_tokenizer,\n add_eos_token=True,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n\n if tokenizer.pad_token is None:\n if tokenizer.unk_token is not None:\n tokenizer.pad_token = tokenizer.unk_token\n tokenizer.pad_token_id = tokenizer.unk_token_id\n else:\n tokenizer.pad_token = tokenizer.eos_token\n tokenizer.pad_token_id = tokenizer.eos_token_id\n tokenizer.padding_side = 'left'\n\n resize = False\n if self.model_args.additional_special_tokens is not None:\n special_tokens_dict = {'additional_special_tokens': self.model_args.additional_special_tokens}\n add_num = tokenizer.add_special_tokens(special_tokens_dict)\n if add_num > 0:\n resize = True\n logger.info(f\"Add {add_num} special tokens to the tokenizer. Special tokens: {self.model_args.additional_special_tokens}\")\n else:\n logger.warning(f\"Special tokens {self.model_args.additional_special_tokens} already exists in the tokenizer.\")\n base_model = get_model(self.model_args, self.training_args.output_dir, resize, len(tokenizer))\n\n num_labels = 1\n config = AutoConfig.from_pretrained(\n self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n logger.info('Config: %s', config)\n\n model = BiDecoderOnlyEmbedderICLModel(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=self.training_args.negatives_cross_device,\n temperature=self.training_args.temperature,\n sub_batch_size=self.training_args.sub_batch_size,\n kd_loss_type=self.training_args.kd_loss_type,\n sentence_pooling_method=self.training_args.sentence_pooling_method,\n normalize_embeddings=self.training_args.normalize_embeddings\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n if self.training_args.fix_position_embedding:\n for k, v in model.named_parameters():\n if \"position_embeddings\" in k:\n logging.info(f\"Freeze the parameters for {k}\")\n v.requires_grad = False\n return tokenizer, model\n\n def load_trainer(self) -> DecoderOnlyEmbedderICLTrainer:\n \"\"\"Load the trainer.\n\n Returns:\n DecoderOnlyEmbedderICLTrainer: Loaded trainer instance.\n \"\"\"\n trainer = DecoderOnlyEmbedderICLTrainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n if self.data_args.same_dataset_within_batch:\n trainer.add_callback(EmbedderTrainerCallbackForDataRefresh(self.train_dataset))\n return trainer\n\n def load_train_dataset(self) -> DecoderOnlyEmbedderICLSameDatasetTrainDataset:\n \"\"\"Load the dataset instance for training.\n\n Raises:\n NotImplementedError: Only support `same_dataset_within_batch` for `DecoderOnlyEmbedderICLRunner`.\n\n Returns:\n DecoderOnlyEmbedderICLSameDatasetTrainDataset: The dataset instance.\n \"\"\"\n if self.data_args.same_dataset_within_batch:\n train_dataset = DecoderOnlyEmbedderICLSameDatasetTrainDataset(\n args=self.data_args,\n default_batch_size=self.training_args.per_device_train_batch_size,\n seed=self.training_args.seed,\n tokenizer=self.tokenizer,\n process_index=self.training_args.process_index,\n num_processes=self.training_args.world_size\n )\n self.training_args.per_device_train_batch_size = 1\n self.training_args.dataloader_num_workers = 0 # avoid multi-processing\n else:\n raise NotImplementedError(\"Only support `same_dataset_within_batch` for `DecoderOnlyEmbedderICLRunner`.\")\n return train_dataset\n\n def run(self):\n \"\"\"\n Run the finetune.\n \"\"\"\n if not self.model_args.only_merge_lora_model:\n Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)\n \n # Training\n self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)\n self.trainer.save_model()\n \n # save merged model\n if self.model_args.save_merged_lora_model and self.training_args.process_index == 0:\n save_merged_model(self.model_args, self.training_args.output_dir)\n" }, { "path": "FlagEmbedding/finetune/embedder/decoder_only/icl/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyEmbedderICLTrainer(AbsEmbedderTrainer):\n \"\"\"\n Trainer class for base encoder models.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # save the checkpoint for sentence-transformers library\n # if self.is_world_process_zero():\n # save_ckpt_for_sentence_transformers(output_dir,\n # pooling_mode=self.args.sentence_pooling_method,\n # normlized=self.args.normlized)\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/base/__init__.py", "content": "from FlagEmbedding.abc.finetune.embedder import (\n AbsEmbedderModelArguments as EncoderOnlyEmbedderModelArguments,\n AbsEmbedderDataArguments as EncoderOnlyEmbedderDataArguments,\n AbsEmbedderTrainingArguments as EncoderOnlyEmbedderTrainingArguments,\n)\n\nfrom .modeling import BiEncoderOnlyEmbedderModel\nfrom .trainer import EncoderOnlyEmbedderTrainer\nfrom .runner import EncoderOnlyEmbedderRunner\n\n__all__ = [\n 'EncoderOnlyEmbedderModelArguments',\n 'EncoderOnlyEmbedderDataArguments',\n 'EncoderOnlyEmbedderTrainingArguments',\n 'BiEncoderOnlyEmbedderModel',\n 'EncoderOnlyEmbedderTrainer',\n 'EncoderOnlyEmbedderRunner',\n]\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/base/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.finetune.embedder.encoder_only.base import (\n EncoderOnlyEmbedderDataArguments,\n EncoderOnlyEmbedderTrainingArguments,\n EncoderOnlyEmbedderModelArguments,\n EncoderOnlyEmbedderRunner,\n)\n\n\ndef main():\n parser = HfArgumentParser((\n EncoderOnlyEmbedderModelArguments,\n EncoderOnlyEmbedderDataArguments,\n EncoderOnlyEmbedderTrainingArguments\n ))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: EncoderOnlyEmbedderModelArguments\n data_args: EncoderOnlyEmbedderDataArguments\n training_args: EncoderOnlyEmbedderTrainingArguments\n\n runner = EncoderOnlyEmbedderRunner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/base/modeling.py", "content": "import logging\n\nimport torch\nfrom transformers import AutoModel, PreTrainedModel, PreTrainedTokenizer\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass BiEncoderOnlyEmbedderModel(AbsEmbedderModel):\n \"\"\"Embedder class for encoder only model.\n\n Args:\n base_model (PreTrainedModel): The base model to train on.\n tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.\n negatives_cross_device (bool, optional): If True, will compute cross devices negative loss. Defaults to ``False``.\n temperature (float, optional): Temperature to control the scale of scores. Defaults to ``1.0``.\n sub_batch_size (int, optional): Sub-batch size during encoding. If negative, will not split to sub-batch.\n Defaults to ``-1``.\n kd_loss_type (str, optional): Type of knowledge distillation loss. Defaults to ``\"kl_div\"``.\n sentence_pooling_method (str, optional): Pooling method to get sentence embedding. Defaults to ``'cls'``.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to ``False``.\n \"\"\"\n TRANSFORMER_CLS = AutoModel\n \n def __init__(\n self,\n base_model: PreTrainedModel,\n tokenizer: PreTrainedTokenizer = None,\n negatives_cross_device: bool = False,\n temperature: float = 1.0,\n sub_batch_size: int = -1,\n kd_loss_type: str = 'kl_div',\n sentence_pooling_method: str = 'cls',\n normalize_embeddings: bool = False,\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=negatives_cross_device,\n temperature=temperature,\n sub_batch_size=sub_batch_size,\n kd_loss_type=kd_loss_type,\n )\n self.sentence_pooling_method = sentence_pooling_method\n self.normalize_embeddings = normalize_embeddings\n self.cross_entropy = torch.nn.CrossEntropyLoss(reduction='mean')\n\n def encode(self, features):\n \"\"\"Encode and get the embedding.\n\n Args:\n features (Union[list, dict]): Features feed to the model.\n\n Returns:\n torch.Tensor: The embedding vectors.\n \"\"\"\n if features is None:\n return None\n if not isinstance(features, list):\n if self.sub_batch_size is not None and self.sub_batch_size > 0:\n all_p_reps = []\n for i in range(0, len(features['attention_mask']), self.sub_batch_size):\n end_inx = min(i + self.sub_batch_size, len(features['attention_mask']))\n sub_features = {}\n for k, v in features.items():\n sub_features[k] = v[i:end_inx]\n last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state\n p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n last_hidden_state = self.model(**features, return_dict=True).last_hidden_state\n all_p_reps = self._sentence_embedding(last_hidden_state, features['attention_mask'])\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n all_p_reps = []\n for sub_features in features:\n last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state\n p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normalize_embeddings:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n\n def _sentence_embedding(self, last_hidden_state, attention_mask):\n \"\"\"Use the pooling method to get the sentence embedding.\n\n Args:\n last_hidden_state (torch.Tensor): The model output's last hidden state.\n attention_mask (torch.Tensor): Mask out padding tokens during pooling.\n\n Raises:\n NotImplementedError: Specified pooling method not implemented.\n\n Returns:\n torch.Tensor: The sentence embeddings.\n \"\"\"\n if self.sentence_pooling_method == \"cls\":\n return last_hidden_state[:, 0]\n elif self.sentence_pooling_method == \"mean\":\n s = torch.sum(\n last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1\n )\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n elif self.sentence_pooling_method == \"last_token\":\n left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]\n if left_padding:\n return last_hidden_state[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_state.shape[0]\n return last_hidden_state[\n torch.arange(batch_size, device=last_hidden_state.device),\n sequence_lengths,\n ]\n else:\n raise NotImplementedError(f\"pooling method {self.sentence_pooling_method} not implemented\")\n\n def compute_score(self, q_reps, p_reps):\n \"\"\"Computes the scores between query and passage representations.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed scores, adjusted by temperature.\n \"\"\"\n scores = self._compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def _compute_similarity(self, q_reps, p_reps):\n \"\"\"Computes the similarity between query and passage representations using inner product.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed similarity matrix.\n \"\"\"\n if len(p_reps.size()) == 2:\n return torch.matmul(q_reps, p_reps.transpose(0, 1))\n return torch.matmul(q_reps, p_reps.transpose(-2, -1))\n\n def compute_loss(self, scores, target):\n \"\"\"Compute the loss using cross entropy.\n\n Args:\n scores (torch.Tensor): Computed score.\n target (torch.Tensor): The target value.\n\n Returns:\n torch.Tensor: The computed cross entropy loss.\n \"\"\"\n return self.cross_entropy(scores, target)\n\n def gradient_checkpointing_enable(self, **kwargs):\n \"\"\"\n Activates gradient checkpointing for the current model.\n \"\"\"\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n \"\"\"\n Enables the gradients for the input embeddings.\n \"\"\"\n self.model.enable_input_require_grads(**kwargs)\n\n def save(self, output_dir: str):\n \"\"\"Save the model to the directory.\n\n Args:\n output_dir (str): Directory for saving the model.\n \"\"\"\n state_dict = self.model.state_dict()\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n self.model.save_pretrained(output_dir, state_dict=state_dict)\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/base/runner.py", "content": "import logging\nfrom typing import Tuple\nfrom transformers import (\n AutoModel, AutoConfig,\n AutoTokenizer, PreTrainedTokenizer\n)\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderRunner, AbsEmbedderModel, EmbedderTrainerCallbackForDataRefresh\nfrom .modeling import BiEncoderOnlyEmbedderModel\nfrom .trainer import EncoderOnlyEmbedderTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyEmbedderRunner(AbsEmbedderRunner):\n \"\"\"\n Finetune Runner for base embedding models.\n \"\"\"\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:\n \"\"\"Load tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.model_name_or_path,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n use_fast=self.model_args.use_fast_tokenizer,\n trust_remote_code=self.model_args.trust_remote_code\n )\n base_model = AutoModel.from_pretrained(\n self.model_args.model_name_or_path,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n trust_remote_code=self.model_args.trust_remote_code\n )\n\n num_labels = 1\n config = AutoConfig.from_pretrained(\n self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n logger.info('Config: %s', config)\n\n model = BiEncoderOnlyEmbedderModel(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=self.training_args.negatives_cross_device,\n temperature=self.training_args.temperature,\n sub_batch_size=self.training_args.sub_batch_size,\n kd_loss_type=self.training_args.kd_loss_type,\n sentence_pooling_method=self.training_args.sentence_pooling_method,\n normalize_embeddings=self.training_args.normalize_embeddings\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n if self.training_args.fix_position_embedding:\n for k, v in model.named_parameters():\n if \"position_embeddings\" in k:\n logging.info(f\"Freeze the parameters for {k}\")\n v.requires_grad = False\n return tokenizer, model\n\n def load_trainer(self) -> EncoderOnlyEmbedderTrainer:\n \"\"\"Load the trainer.\n\n Returns:\n EncoderOnlyEmbedderTrainer: Loaded trainer instance.\n \"\"\"\n trainer = EncoderOnlyEmbedderTrainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n if self.data_args.same_dataset_within_batch:\n trainer.add_callback(EmbedderTrainerCallbackForDataRefresh(self.train_dataset))\n return trainer\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/base/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyEmbedderTrainer(AbsEmbedderTrainer):\n \"\"\"\n Trainer class for base encoder models.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # save the checkpoint for sentence-transformers library\n # if self.is_world_process_zero():\n # save_ckpt_for_sentence_transformers(output_dir,\n # pooling_mode=self.args.sentence_pooling_method,\n # normlized=self.args.normlized)\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/m3/__init__.py", "content": "from FlagEmbedding.abc.finetune.embedder import AbsEmbedderDataArguments as EncoderOnlyEmbedderM3DataArguments\n\nfrom .arguments import EncoderOnlyEmbedderM3ModelArguments, EncoderOnlyEmbedderM3TrainingArguments\nfrom .modeling import EncoderOnlyEmbedderM3Model, EncoderOnlyEmbedderM3ModelForInference\nfrom .trainer import EncoderOnlyEmbedderM3Trainer\nfrom .runner import EncoderOnlyEmbedderM3Runner\n\n\n__all__ = [\n 'EncoderOnlyEmbedderM3ModelArguments',\n 'EncoderOnlyEmbedderM3DataArguments',\n 'EncoderOnlyEmbedderM3TrainingArguments',\n 'EncoderOnlyEmbedderM3Model',\n 'EncoderOnlyEmbedderM3ModelForInference',\n 'EncoderOnlyEmbedderM3Trainer',\n 'EncoderOnlyEmbedderM3Runner',\n]\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/m3/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.finetune.embedder.encoder_only.m3 import (\n EncoderOnlyEmbedderM3DataArguments,\n EncoderOnlyEmbedderM3TrainingArguments,\n EncoderOnlyEmbedderM3ModelArguments,\n EncoderOnlyEmbedderM3Runner,\n)\n\n\ndef main():\n parser = HfArgumentParser((EncoderOnlyEmbedderM3ModelArguments, EncoderOnlyEmbedderM3DataArguments, EncoderOnlyEmbedderM3TrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: EncoderOnlyEmbedderM3ModelArguments\n data_args: EncoderOnlyEmbedderM3DataArguments\n training_args: EncoderOnlyEmbedderM3TrainingArguments\n\n runner = EncoderOnlyEmbedderM3Runner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/m3/arguments.py", "content": "from dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.finetune.embedder import (\n AbsEmbedderTrainingArguments,\n AbsEmbedderModelArguments\n)\n\n\n@dataclass\nclass EncoderOnlyEmbedderM3ModelArguments(AbsEmbedderModelArguments):\n \"\"\"\n Model argument class for M3.\n \"\"\"\n colbert_dim: int = field(default=-1, metadata={\"help\": \"Dim of colbert linear\"})\n\n\n@dataclass\nclass EncoderOnlyEmbedderM3TrainingArguments(AbsEmbedderTrainingArguments):\n \"\"\"\n Training argument class for M3.\n \"\"\"\n unified_finetuning: bool = field(default=False, metadata={\"help\": \"use unify fine-tuning\"})\n use_self_distill: bool = field(default=False, metadata={\"help\": \"use self-distill when using unify fine-tuning\"})\n fix_encoder: bool = field(default=False, metadata={\"help\": \"Freeze the parameters of encoder\"})\n self_distill_start_step: int = field(default=-1, metadata={\"help\": \"Num of step when using self-distill\"})\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/m3/modeling.py", "content": "import os\nimport logging\nfrom typing import Dict, List, Union, Any\n\nimport torch\nfrom torch import Tensor\nimport torch.nn.functional as F\nfrom transformers import PreTrainedTokenizer\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderModel, EmbedderOutput\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyEmbedderM3Model(AbsEmbedderModel):\n \"\"\"Embedder class for M3 model.\n\n Args:\n base_model (dict[str, Any]): The base model to train on.\n tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.\n negatives_cross_device (bool, optional): If True, will compute cross devices negative loss. Defaults to ``False``.\n temperature (float, optional): Temperature to control the scale of scores. Defaults to ``1.0``.\n sub_batch_size (int, optional): Sub-batch size during encoding. If negative, will not split to sub-batch.\n Defaults to ``-1``.\n kd_loss_type (str, optional): Type of knowledge distillation loss. Defaults to ``'m3_kd_loss'``.\n sentence_pooling_method (str, optional): Pooling method to get sentence embedding. Defaults to ``'cls'``.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to ``False``.\n unified_finetuning (bool, optional): If True, will finetune colbert vector and sparce embedding. Defaults to ``True``.\n use_self_distill (bool, optional): If True, will do self distillation. Defaults to ``False``.\n self_distill_start_step (int, optional): Step num to start self distillation. Defaults to ``-1``.\n \"\"\"\n def __init__(\n self,\n base_model: Dict[str, Any],\n tokenizer: PreTrainedTokenizer = None,\n negatives_cross_device: bool = False,\n temperature: float = 1,\n sub_batch_size: int = -1,\n kd_loss_type: str = 'm3_kd_loss',\n sentence_pooling_method: str = 'cls',\n normalize_embeddings: bool = False,\n unified_finetuning: bool = True,\n use_self_distill: bool = False,\n self_distill_start_step: int = -1\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n negatives_cross_device=negatives_cross_device,\n temperature=temperature,\n sub_batch_size=sub_batch_size,\n kd_loss_type=kd_loss_type,\n )\n self.sentence_pooling_method = sentence_pooling_method\n self.normalize_embeddings = normalize_embeddings\n self.cross_entropy = torch.nn.CrossEntropyLoss(reduction='mean')\n\n self.unified_finetuning = unified_finetuning\n if not self.unified_finetuning:\n self.model = base_model['model']\n self.colbert_linear = None\n self.sparse_linear = None\n else:\n self.model = base_model['model']\n self.colbert_linear = base_model['colbert_linear']\n self.sparse_linear = base_model['sparse_linear']\n\n self.config = self.model.config\n\n self.vocab_size = self.model.config.vocab_size\n self.use_self_distill = use_self_distill\n self.self_distill_start_step = self_distill_start_step\n self.step = 0\n\n def _dense_embedding(self, last_hidden_state, attention_mask):\n \"\"\"Use the pooling method to get the dense embedding.\n\n Args:\n last_hidden_state (torch.Tensor): The model output's last hidden state.\n attention_mask (torch.Tensor): Mask out padding tokens during pooling.\n\n Raises:\n NotImplementedError: Specified pooling method not implemented.\n\n Returns:\n torch.Tensor: The dense embeddings.\n \"\"\"\n if self.sentence_pooling_method == \"cls\":\n return last_hidden_state[:, 0]\n elif self.sentence_pooling_method == \"mean\":\n s = torch.sum(\n last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1\n )\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n elif self.sentence_pooling_method == \"last_token\":\n left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]\n if left_padding:\n return last_hidden_state[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_state.shape[0]\n return last_hidden_state[\n torch.arange(batch_size, device=last_hidden_state.device),\n sequence_lengths,\n ]\n else:\n raise NotImplementedError(f\"pooling method {self.sentence_pooling_method} not implemented\")\n\n def _sparse_embedding(self, hidden_state, input_ids, return_embedding: bool = True):\n \"\"\"Compute and return the sparse embedding.\n\n Args:\n hidden_state (torch.Tensor): The model output's last hidden state.\n input_ids (_type_): Ids from input features.\n return_embedding (bool, optional): If True, return the computed embedding, otherwise just return the token weights. \n Defaults to ``True``.\n\n Returns:\n torch.Tensor: The sparse embedding or just the token weights.\n \"\"\"\n token_weights = torch.relu(self.sparse_linear(hidden_state))\n if not return_embedding: return token_weights\n\n if self.training:\n sparse_embedding = torch.zeros(\n input_ids.size(0), input_ids.size(1), self.vocab_size,\n dtype=token_weights.dtype,\n device=token_weights.device\n )\n sparse_embedding = torch.scatter(sparse_embedding, dim=-1, index=input_ids.unsqueeze(-1), src=token_weights)\n sparse_embedding = torch.max(sparse_embedding, dim=1).values\n else:\n # Optimize suggestion from issue #1364: https://github.com/FlagOpen/FlagEmbedding/issues/1364\n # Disable when self.training = True, otherwise will cause:\n # RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation\n sparse_embedding = torch.zeros(\n input_ids.size(0), self.vocab_size,\n dtype=token_weights.dtype,\n device=token_weights.device\n )\n sparse_embedding = sparse_embedding.scatter_reduce(\n dim=-1, index=input_ids, src=token_weights.squeeze(-1), reduce=\"amax\"\n )\n\n unused_tokens = [\n self.tokenizer.cls_token_id, self.tokenizer.eos_token_id,\n self.tokenizer.pad_token_id, self.tokenizer.unk_token_id\n ]\n sparse_embedding[:, unused_tokens] *= 0.\n return sparse_embedding\n\n def _colbert_embedding(self, last_hidden_state, mask):\n \"\"\"Get the colbert vectors.\n\n Args:\n last_hidden_state (torch.Tensor): The model output's last hidden state.\n attention_mask (torch.Tensor): Mask out padding tokens during pooling.\n\n Returns:\n torch.Tensor: The colbert vectors.\n \"\"\"\n colbert_vecs = self.colbert_linear(last_hidden_state[:, 1:])\n colbert_vecs = colbert_vecs * mask[:, 1:][:, :, None].float()\n return colbert_vecs\n\n def compute_score(\n self, q_reps, p_reps, q_mask: torch.Tensor,\n dense_weight: float = 1.0, sparse_weight: float = 0.3, colbert_weight: float = 1.0\n ):\n \"\"\"_summary_\n\n Args:\n q_reps (_type_): Query representations.\n p_reps (_type_): Passage representations.\n q_mask (torch.Tensor): _description_\n dense_weight (float, optional): _description_. Defaults to 1.0.\n sparse_weight (float, optional): _description_. Defaults to 0.3.\n colbert_weight (float, optional): _description_. Defaults to 1.0.\n\n Returns:\n _type_: _description_\n \"\"\"\n dense_score = self.compute_dense_score(q_reps, p_reps)\n sparse_score = self.compute_sparse_score(q_reps, p_reps)\n colbert_score = self.compute_colbert_score(q_reps, p_reps, q_mask=q_mask)\n return dense_score * dense_weight + sparse_score * sparse_weight + colbert_score * colbert_weight\n\n def compute_dense_score(self, q_reps, p_reps):\n \"\"\"Compute the dense score.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed dense scores, adjusted by temperature.\n \"\"\"\n scores = self._compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def compute_sparse_score(self, q_reps, p_reps):\n \"\"\"Compute the sparse score.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed sparse scores, adjusted by temperature.\n \"\"\"\n scores = self._compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def compute_colbert_score(self, q_reps, p_reps, q_mask: torch.Tensor=None):\n \"\"\"Compute the colbert score.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed colber scores, adjusted by temperature.\n \"\"\"\n token_scores = torch.einsum('qin,pjn->qipj', q_reps, p_reps)\n scores, _ = token_scores.max(-1)\n scores = scores.sum(1) / q_mask[:, 1:].sum(-1, keepdim=True)\n scores = scores / self.temperature\n return scores\n\n def ensemble_score(self, q_reps, p_reps, dense_scores=None, sparse_scores=None, colbert_scores=None):\n \"\"\"Compute the ensemble score of the three methods.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n dense_scores (torch.Tensor, optional): The dense scores. Defaults to ``None``.\n sparse_scores (torch.Tensor, optional): The sparse scores. Defaults to ``None``.\n colbert_scores (torch.Tensor, optional): The colbert scores. Defaults to ``None``.\n\n Raises:\n ValueError: dense_scores, sparse_scores, colbert_scores must be provided\n\n Returns:\n _type_: The ensemble score of the three methods.\n \"\"\"\n if dense_scores is None or sparse_scores is None or colbert_scores is None:\n raise ValueError(\"dense_scores, sparse_scores, colbert_scores must be provided!\")\n return dense_scores + 0.3 * sparse_scores + colbert_scores\n\n def _encode(self, features):\n \"\"\"Helper function to encode using input features.\n\n Args:\n features (Union[list, dict]): Features feed to the model.\n\n Returns:\n torch.Tensor: Dense embedding.\n torch.Tensor: Sparce embedding.\n torch.Tensor: Colbert vector.\n \"\"\"\n dense_vecs, sparse_vecs, colbert_vecs = None, None, None\n last_hidden_state = self.model(**features, return_dict=True).last_hidden_state\n dense_vecs = self._dense_embedding(last_hidden_state, features['attention_mask'])\n if self.unified_finetuning:\n sparse_vecs = self._sparse_embedding(last_hidden_state, features['input_ids'])\n colbert_vecs = self._colbert_embedding(last_hidden_state, features['attention_mask'])\n if self.normalize_embeddings:\n dense_vecs = F.normalize(dense_vecs, dim=-1)\n if self.unified_finetuning:\n colbert_vecs = F.normalize(colbert_vecs, dim=-1)\n return dense_vecs, sparse_vecs, colbert_vecs\n\n def encode(self, features):\n \"\"\"Encode and get the embedding.\n\n Args:\n features (Union[list, dict]): Features feed to the model.\n\n Returns:\n torch.Tensor: Dense embeddings.\n torch.Tensor: Sparce embeddings.\n torch.Tensor: Colbert vectors.\n \"\"\"\n if features is None:\n return None\n\n if not isinstance(features, list):\n if self.sub_batch_size is not None and self.sub_batch_size != -1:\n all_dense_vecs, all_sparse_vecs, all_colbert_vecs = [], [], []\n for i in range(0, len(features['attention_mask']), self.sub_batch_size):\n end_inx = min(i + self.sub_batch_size, len(features['attention_mask']))\n sub_features = {}\n for k, v in features.items():\n sub_features[k] = v[i:end_inx]\n\n dense_vecs, sparse_vecs, colbert_vecs = self._encode(sub_features)\n all_dense_vecs.append(dense_vecs)\n all_sparse_vecs.append(sparse_vecs)\n all_colbert_vecs.append(colbert_vecs)\n\n dense_vecs = torch.cat(all_dense_vecs, 0)\n if self.unified_finetuning:\n sparse_vecs = torch.cat(all_sparse_vecs, 0)\n colbert_vecs = torch.cat(all_colbert_vecs, 0)\n else:\n dense_vecs, sparse_vecs, colbert_vecs = self._encode(features)\n else:\n all_dense_vecs, all_sparse_vecs, all_colbert_vecs = [], [], []\n for sub_features in features:\n dense_vecs, sparse_vecs, colbert_vecs = self._encode(sub_features)\n all_dense_vecs.append(dense_vecs)\n all_sparse_vecs.append(sparse_vecs)\n all_colbert_vecs.append(colbert_vecs)\n\n dense_vecs = torch.cat(all_dense_vecs, 0)\n if self.unified_finetuning:\n sparse_vecs = torch.cat(all_sparse_vecs, 0)\n colbert_vecs = torch.cat(all_colbert_vecs, 0)\n\n if self.unified_finetuning:\n return dense_vecs.contiguous(), sparse_vecs.contiguous(), colbert_vecs.contiguous()\n else:\n return dense_vecs.contiguous(), None, None\n\n def _compute_similarity(self, q_reps, p_reps):\n \"\"\"Computes the similarity between query and passage representations using inner product.\n\n Args:\n q_reps (torch.Tensor): Query representations.\n p_reps (torch.Tensor): Passage representations.\n\n Returns:\n torch.Tensor: The computed similarity matrix.\n \"\"\"\n if len(p_reps.size()) == 2:\n return torch.matmul(q_reps, p_reps.transpose(0, 1))\n return torch.matmul(q_reps, p_reps.transpose(-2, -1))\n\n def _get_queries_attention_mask(self, queries: Union[Dict[str, Tensor], List[Dict[str, Tensor]]]):\n \"\"\"padding attention mask for colbert\n\n Args:\n queries (Union[Dict[str, Tensor], List[Dict[str, Tensor]]]): Input queries.\n\n Returns:\n torch.Tensor: The query attention mask.\n \"\"\"\n if not isinstance(queries, list):\n q_mask = queries['attention_mask']\n else:\n q_mask_list = [sub_features['attention_mask'] for sub_features in queries]\n _length = max([mask.shape[1] for mask in q_mask_list])\n if self.tokenizer.padding_side == 'right':\n q_mask = torch.cat([\n F.pad(mask, (0, _length - mask.shape[1]), value=0)\n for mask in q_mask_list\n ], dim=0)\n else:\n q_mask = torch.cat([\n F.pad(mask, (_length - mask.shape[1], 0), value=0)\n for mask in q_mask_list\n ], dim=0)\n return q_mask\n\n def forward(\n self, \n queries: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None, \n passages: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None,\n teacher_scores: Union[None, List[float]] = None,\n no_in_batch_neg_flag: bool = False,\n ):\n \"\"\"The computation performed at every call.\n\n Args:\n queries (Union[Dict[str, Tensor], List[Dict[str, Tensor]]], optional): Input queries. Defaults to ``None``.\n passages (Union[Dict[str, Tensor], List[Dict[str, Tensor]]], optional): Input passages. Defaults to ``None``.\n teacher_scores (Union[None, List[float]], optional): Teacher scores for distillation. Defaults to ``None``.\n no_in_batch_neg_flag (bool, optional): If True, use no in-batch negatives and no cross-device negatives. Defaults to ``False``.\n\n Returns:\n EmbedderOutput: Output of the forward call of model.\n \"\"\"\n q_dense_vecs, q_sparse_vecs, q_colbert_vecs = self.encode(queries) # (batch_size, dim)\n p_dense_vecs, p_sparse_vecs, p_colbert_vecs = self.encode(passages) # (batch_size * group_size, dim)\n\n if self.training:\n if teacher_scores is not None:\n teacher_scores = torch.tensor(teacher_scores, device=q_dense_vecs.device)\n teacher_scores = teacher_scores.view(q_dense_vecs.size(0), -1).detach() # (batch_size, group_size)\n teacher_targets = F.softmax(teacher_scores, dim=-1) # (batch_size, group_size)\n else:\n teacher_targets = None\n\n if no_in_batch_neg_flag:\n compute_loss_func = self._compute_no_in_batch_neg_loss\n else:\n if self.negatives_cross_device:\n compute_loss_func = self._compute_cross_device_neg_loss\n else:\n compute_loss_func = self._compute_in_batch_neg_loss\n\n # dense loss\n dense_scores, loss = compute_loss_func(\n q_dense_vecs, p_dense_vecs, teacher_targets=teacher_targets,\n compute_score_func=self.compute_dense_score\n )\n\n if self.unified_finetuning:\n # disable cross device negatives for unified finetuning\n if no_in_batch_neg_flag:\n compute_loss_func = self._compute_no_in_batch_neg_loss\n else:\n compute_loss_func = self._compute_in_batch_neg_loss\n\n # sparse loss\n sparse_scores, sparse_loss = compute_loss_func(\n q_sparse_vecs, p_sparse_vecs, teacher_targets=teacher_targets,\n compute_score_func=self.compute_sparse_score\n )\n\n # colbert loss\n colbert_scores, colbert_loss = compute_loss_func(\n q_colbert_vecs, p_colbert_vecs, teacher_targets=teacher_targets,\n compute_score_func=self.compute_colbert_score,\n q_mask=self._get_queries_attention_mask(queries)\n )\n\n # get dense scores of current process\n if not no_in_batch_neg_flag and self.negatives_cross_device:\n dense_scores = dense_scores[\n q_dense_vecs.size(0)*self.process_rank : q_dense_vecs.size(0)*(self.process_rank+1),\n p_dense_vecs.size(0)*self.process_rank : p_dense_vecs.size(0)*(self.process_rank+1)\n ] # (batch_size, batch_size * group_size)\n elif no_in_batch_neg_flag:\n # get local p_dense_vecs: fix a bug described in \n # https://github.com/FlagOpen/FlagEmbedding/issues/1410\n group_size = p_dense_vecs.size(0) // q_dense_vecs.size(0)\n indices = torch.arange(0, q_dense_vecs.size(0), device=q_dense_vecs.device) * group_size\n p_dense_vecs = p_dense_vecs[indices, :]\n\n # ensemble loss\n ensemble_scores, ensemble_loss = compute_loss_func(\n q_dense_vecs, p_dense_vecs, teacher_targets=teacher_targets,\n compute_score_func=self.ensemble_score,\n dense_scores=dense_scores,\n sparse_scores=sparse_scores,\n colbert_scores=colbert_scores\n )\n\n loss = (loss + ensemble_loss + 0.1 * sparse_loss + colbert_loss) / 4\n\n if self.use_self_distill and self.step > self.self_distill_start_step:\n self_teacher_targets = torch.softmax(ensemble_scores.detach(), dim=-1)\n\n dense_self_distill_loss = self.distill_loss(\"kl_div\", self_teacher_targets, dense_scores)\n sparse_self_distill_loss = self.distill_loss(\"kl_div\", self_teacher_targets, sparse_scores)\n colbert_self_distill_loss = self.distill_loss(\"kl_div\", self_teacher_targets, colbert_scores)\n\n loss += (dense_self_distill_loss + 0.1 * sparse_self_distill_loss + colbert_self_distill_loss) / 3\n loss = loss / 2\n self.step += 1\n else:\n loss = None\n\n return EmbedderOutput(\n loss=loss,\n )\n\n def compute_loss(self, scores, target):\n \"\"\"Compute the loss using cross entropy.\n\n Args:\n scores (torch.Tensor): Computed score.\n target (torch.Tensor): The target value.\n\n Returns:\n torch.Tensor: The computed cross entropy loss.\n \"\"\"\n return self.cross_entropy(scores, target)\n\n def gradient_checkpointing_enable(self, **kwargs):\n \"\"\"\n Activates gradient checkpointing for the current model.\n \"\"\"\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n \"\"\"\n Enables the gradients for the input embeddings.\n \"\"\"\n self.model.enable_input_require_grads(**kwargs)\n\n def save(self, output_dir: str):\n \"\"\"Save the model to the directory.\n\n Args:\n output_dir (str): Directory for saving the model.\n \"\"\"\n def _trans_state_dict(state_dict):\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n return state_dict\n\n self.model.save_pretrained(output_dir, state_dict=_trans_state_dict(self.model.state_dict()))\n\n if self.unified_finetuning:\n torch.save(_trans_state_dict(self.colbert_linear.state_dict()),\n os.path.join(output_dir, 'colbert_linear.pt'))\n torch.save(_trans_state_dict(self.sparse_linear.state_dict()),\n os.path.join(output_dir, 'sparse_linear.pt'))\n\n\nclass EncoderOnlyEmbedderM3ModelForInference(EncoderOnlyEmbedderM3Model):\n \"\"\"\n Inference class of M3 model.\n \"\"\"\n def forward(self,\n text_input: Dict[str, Tensor] = None,\n return_dense: bool = True,\n return_sparse: bool = False,\n return_colbert_vecs: bool = False,\n return_sparse_embedding: bool = False):\n \"\"\"Encode the text input using the selected way.\n\n Args:\n text_input (Dict[str, Tensor], optional): Text inputs. Defaults to ``None``.\n return_dense (bool, optional): If True, return the dense embedding. Defaults to ``True``.\n return_sparse (bool, optional): If True, return the sparse embedding. Defaults to ``False``.\n return_colbert_vecs (bool, optional): If True, return the colbert vectors. Defaults to ``False``.\n return_sparse_embedding (bool, optional): Parameter for :meth:`_sparse_embedding()`. If True, will return sparse embedding.\n Otherwise, return the token weights. Defaults to ``False``.\n\n Returns:\n dict: A dictionary containing the three types of embeddings.\n \"\"\"\n assert return_dense or return_sparse or return_colbert_vecs, 'Must choose one or more from `return_colbert_vecs`, `return_sparse`, `return_dense` to set `True`!'\n\n # this is for sparse embedding computation: using optimization suggestion from \n # issue #1364: https://github.com/FlagOpen/FlagEmbedding/issues/1364\n self.training = False\n\n last_hidden_state = self.model(**text_input, return_dict=True).last_hidden_state\n\n output = {}\n if return_dense:\n dense_vecs = self._dense_embedding(last_hidden_state, text_input['attention_mask'])\n output['dense_vecs'] = dense_vecs\n if return_sparse:\n sparse_vecs = self._sparse_embedding(\n last_hidden_state, text_input['input_ids'],\n return_embedding=return_sparse_embedding\n )\n output['sparse_vecs'] = sparse_vecs\n if return_colbert_vecs:\n colbert_vecs = self._colbert_embedding(last_hidden_state, text_input['attention_mask'])\n output['colbert_vecs'] = colbert_vecs\n\n if self.normalize_embeddings:\n if 'dense_vecs' in output:\n output['dense_vecs'] = F.normalize(output['dense_vecs'], dim=-1)\n if 'colbert_vecs' in output:\n output['colbert_vecs'] = F.normalize(output['colbert_vecs'], dim=-1)\n\n return output\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/m3/runner.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Tuple\nfrom transformers import (\n AutoModel, AutoConfig,\n AutoTokenizer, PreTrainedTokenizer\n)\nfrom huggingface_hub import snapshot_download\n\nfrom FlagEmbedding.abc.finetune.embedder import (\n AbsEmbedderRunner, AbsEmbedderModel,\n AbsEmbedderDataArguments, EmbedderTrainerCallbackForDataRefresh\n)\nfrom .modeling import EncoderOnlyEmbedderM3Model\nfrom .trainer import EncoderOnlyEmbedderM3Trainer\nfrom .arguments import EncoderOnlyEmbedderM3ModelArguments, EncoderOnlyEmbedderM3TrainingArguments\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyEmbedderM3Runner(AbsEmbedderRunner):\n \"\"\"\n M3 model runner for finetuning.\n \n Args:\n model_args (EncoderOnlyEmbedderM3ModelArguments): Model arguments\n data_args (AbsEmbedderDataArguments): Data arguments.\n training_args (EncoderOnlyEmbedderM3TrainingArguments): Training arguments.\n \"\"\"\n def __init__(\n self,\n model_args: EncoderOnlyEmbedderM3ModelArguments,\n data_args: AbsEmbedderDataArguments,\n training_args: EncoderOnlyEmbedderM3TrainingArguments\n ):\n super().__init__(model_args, data_args, training_args)\n self.model_args: EncoderOnlyEmbedderM3ModelArguments\n self.data_args: AbsEmbedderDataArguments\n self.training_args: EncoderOnlyEmbedderM3TrainingArguments\n\n @staticmethod\n def get_model(\n model_name_or_path: str,\n trust_remote_code: bool = False,\n colbert_dim: int = -1,\n cache_dir: str = None\n ):\n \"\"\"Get the model.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n trust_remote_code (bool, optional): trust_remote_code to use when loading models from HF. Defaults to ``False``.\n colbert_dim (int, optional): Colbert dim to set. Defaults to ``-1``.\n cache_dir (str, optional): HF cache dir to store the model. Defaults to ``None``.\n\n Returns:\n dict: A dictionary containing the model, colbert linear and sparse linear.\n \"\"\"\n cache_folder = os.getenv('HF_HUB_CACHE', None) if cache_dir is None else cache_dir\n if not os.path.exists(model_name_or_path):\n model_name_or_path = snapshot_download(\n repo_id=model_name_or_path,\n cache_dir=cache_folder,\n ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5']\n )\n\n model = AutoModel.from_pretrained(\n model_name_or_path,\n cache_dir=cache_folder,\n trust_remote_code=trust_remote_code\n )\n colbert_linear = torch.nn.Linear(\n in_features=model.config.hidden_size,\n out_features=model.config.hidden_size if colbert_dim <= 0 else colbert_dim\n )\n sparse_linear = torch.nn.Linear(\n in_features=model.config.hidden_size,\n out_features=1\n )\n\n colbert_model_path = os.path.join(model_name_or_path, 'colbert_linear.pt')\n sparse_model_path = os.path.join(model_name_or_path, 'sparse_linear.pt')\n if os.path.exists(colbert_model_path) and os.path.exists(sparse_model_path):\n logger.info('loading existing colbert_linear and sparse_linear---------')\n colbert_state_dict = torch.load(colbert_model_path, map_location='cpu', weights_only=True)\n sparse_state_dict = torch.load(sparse_model_path, map_location='cpu', weights_only=True)\n colbert_linear.load_state_dict(colbert_state_dict)\n sparse_linear.load_state_dict(sparse_state_dict)\n else:\n logger.info('The parameters of colbert_linear and sparse linear is new initialize. Make sure the model is loaded for training, not inferencing')\n\n return {\n 'model': model,\n 'colbert_linear': colbert_linear,\n 'sparse_linear': sparse_linear\n }\n\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:\n \"\"\"Load the tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.model_name_or_path,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n use_fast=self.model_args.use_fast_tokenizer,\n trust_remote_code=self.model_args.trust_remote_code\n )\n\n num_labels = 1\n config = AutoConfig.from_pretrained(\n self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n logger.info('Config: %s', config)\n\n model = EncoderOnlyEmbedderM3Model(\n self.get_model(self.model_args.model_name_or_path, self.model_args.trust_remote_code, self.model_args.colbert_dim),\n tokenizer=tokenizer,\n negatives_cross_device=self.training_args.negatives_cross_device,\n temperature=self.training_args.temperature,\n sub_batch_size=self.training_args.sub_batch_size,\n kd_loss_type=self.training_args.kd_loss_type,\n sentence_pooling_method=self.training_args.sentence_pooling_method,\n normalize_embeddings=self.training_args.normalize_embeddings,\n unified_finetuning=self.training_args.unified_finetuning,\n use_self_distill=self.training_args.use_self_distill,\n self_distill_start_step=self.training_args.self_distill_start_step\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n if self.training_args.fix_position_embedding:\n for k, v in model.named_parameters():\n if \"position_embeddings\" in k:\n logging.info(f\"Freeze the parameters for {k}\")\n v.requires_grad = False\n \n if self.training_args.fix_encoder:\n for k, v in model.named_parameters():\n if \"colbert_linear\" in k or 'sparse_linear' in k:\n logging.info(f\"train the parameters for {k}\")\n else:\n v.requires_grad = False\n \n return tokenizer, model\n\n def load_trainer(self) -> EncoderOnlyEmbedderM3Trainer:\n \"\"\"Load the M3 trainer.\n\n Returns:\n EncoderOnlyEmbedderM3Trainer: M3 Trainer instance.\n \"\"\"\n trainer = EncoderOnlyEmbedderM3Trainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n if self.data_args.same_dataset_within_batch:\n trainer.add_callback(EmbedderTrainerCallbackForDataRefresh(self.train_dataset))\n return trainer\n" }, { "path": "FlagEmbedding/finetune/embedder/encoder_only/m3/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n\nfrom FlagEmbedding.abc.finetune.embedder import AbsEmbedderTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyEmbedderM3Trainer(AbsEmbedderTrainer):\n \"\"\"\n Trainer class for M3.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # save the checkpoint for sentence-transformers library\n # if self.is_world_process_zero():\n # save_ckpt_for_sentence_transformers(output_dir,\n # pooling_mode=self.args.sentence_pooling_method,\n # normlized=self.args.normlized)\n" }, { "path": "FlagEmbedding/finetune/reranker/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/__init__.py", "content": "from .modeling import CrossDecoderModel\nfrom .runner import DecoderOnlyRerankerRunner\nfrom .arguments import RerankerModelArguments\nfrom .trainer import DecoderOnlyRerankerTrainer\n\n__all__ = [\n \"CrossDecoderModel\",\n \"DecoderOnlyRerankerRunner\",\n \"DecoderOnlyRerankerTrainer\",\n \"RerankerModelArguments\",\n]\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.abc.finetune.reranker import (\n AbsRerankerDataArguments,\n AbsRerankerTrainingArguments\n)\n\nfrom FlagEmbedding.finetune.reranker.decoder_only.base import (\n DecoderOnlyRerankerRunner,\n RerankerModelArguments\n)\n\n\ndef main():\n parser = HfArgumentParser((RerankerModelArguments, AbsRerankerDataArguments, AbsRerankerTrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: RerankerModelArguments\n data_args: AbsRerankerDataArguments\n training_args: AbsRerankerTrainingArguments\n\n runner = DecoderOnlyRerankerRunner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/arguments.py", "content": "from typing import List\nfrom dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerModelArguments\n\n\ndef default_target_modules() -> List[int]:\n return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']\n\n\n@dataclass\nclass RerankerModelArguments(AbsRerankerModelArguments):\n \"\"\"\n Model argument class for decoder only reranker.\n \"\"\"\n use_lora: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use LORA (low-rank parameter-efficient training) to train the model.\"}\n )\n lora_rank: int = field(\n default=64,\n metadata={\"help\": \"The rank of lora.\"}\n )\n lora_alpha: float = field(\n default=16,\n metadata={\"help\": \"The alpha parameter of lora.\"}\n )\n lora_dropout: float = field(\n default=0.1,\n metadata={\"help\": \"The dropout rate of lora modules.\"}\n )\n target_modules: List[str] = field(\n default_factory=default_target_modules,\n metadata={\"help\": \"The target modules to apply LORA.\"}\n )\n modules_to_save: List[str] = field(\n default=None,\n metadata={\"help\": \"List of modules that should be saved in the final checkpoint.\"}\n )\n use_flash_attn: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use flash attention to train the model.\"}\n )\n # use_slow_tokenizer: bool = field(\n # default=False,\n # metadata={\"help\": \"If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\"}\n # )\n from_peft: str = field(\n default=None\n )\n raw_peft: List[str] = field(\n default=None\n )\n \n save_merged_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will merge the lora modules and save the entire model.\"}\n )\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/load_model.py", "content": "import os\nimport re\nimport logging\nfrom transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer\nfrom peft import LoraConfig, TaskType, get_peft_model, PeftModel\n\nfrom FlagEmbedding.finetune.reranker.decoder_only.base.arguments import RerankerModelArguments\n\nlogger = logging.getLogger(__name__)\n\n\ndef find_largest_checkpoint(checkpoint_dir):\n \"\"\"Find the largest checkpoint from directory.\n\n Args:\n checkpoint_dir (str): Directory to the checkpoint.\n\n Returns:\n str: Directory to the checkpoint, None no matching found.\n \"\"\"\n checkpoint_pattern = re.compile(r'checkpoint-(\\d+)')\n max_number = -1\n max_checkpoint_file = None\n for file in os.listdir(checkpoint_dir):\n match = checkpoint_pattern.search(file)\n if match:\n number = int(match.group(1))\n if number > max_number:\n max_number = number\n max_checkpoint_file = file\n if max_checkpoint_file:\n return os.path.join(checkpoint_dir, max_checkpoint_file)\n else:\n return None\n\n\ndef get_model(model_args: RerankerModelArguments):\n \"\"\"Get the model.\n\n Args:\n model_args (RerankerModelArguments): Model arguments instance.\n\n Returns:\n transformers.PreTrainedModel or PeftModel: The loaded model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n trust_remote_code=model_args.trust_remote_code,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n trust_remote_code=model_args.trust_remote_code,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModelForCausalLM.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n trust_remote_code=model_args.trust_remote_code,\n )\n else:\n logger.info(\"Training new model from scratch\")\n model = model_args.from_config(config)\n\n if model_args.raw_peft is not None:\n for peft_path in model_args.raw_peft:\n model = PeftModel.from_pretrained(model, peft_path)\n model = model.merge_and_unload()\n\n if model_args.from_peft is not None:\n model = PeftModel.from_pretrained(model, model_args.from_peft, is_trainable=True)\n model.print_trainable_parameters()\n else:\n if model_args.use_lora:\n peft_config = LoraConfig(\n task_type=TaskType.CAUSAL_LM,\n inference_mode=False,\n r=model_args.lora_rank,\n target_modules=model_args.target_modules,\n modules_to_save=model_args.modules_to_save,\n lora_alpha=model_args.lora_alpha,\n lora_dropout=model_args.lora_dropout\n )\n model = get_peft_model(model, peft_config)\n model.print_trainable_parameters()\n\n return model\n\n\ndef save_merged_model(model_args: RerankerModelArguments, output_dir: str):\n \"\"\"\n Loads and save a model with specified configurations, merges it with PEFT layers if available.\n\n Args:\n model_args (RerankerModelArguments): Model arguments instance.\n output_dir (str): Directory to save the model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n token=model_args.token,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n token=model_args.token,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModelForCausalLM.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n trust_remote_code=model_args.trust_remote_code,\n config=config,\n )\n else:\n logger.info(\"Training new model from scratch\")\n model = model_args.from_config(config)\n\n if model_args.raw_peft is not None:\n for peft_path in model_args.raw_peft:\n model = PeftModel.from_pretrained(model, peft_path)\n model = model.merge_and_unload()\n\n try:\n model = PeftModel.from_pretrained(model, output_dir)\n model = model.merge_and_unload()\n except:\n model = PeftModel.from_pretrained(model, find_largest_checkpoint(output_dir))\n model = model.merge_and_unload()\n\n model.save_pretrained(os.path.join(output_dir, 'merged_model'))\n\n try:\n tokenizer = AutoTokenizer.from_pretrained(output_dir)\n except:\n tokenizer = AutoTokenizer.from_pretrained(find_largest_checkpoint(output_dir))\n\n tokenizer.save_pretrained(os.path.join(output_dir, 'merged_model'))\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/modeling.py", "content": "import torch\nfrom transformers import PreTrainedModel, AutoTokenizer\nimport logging\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass CrossDecoderModel(AbsRerankerModel):\n \"\"\"\n Model class for decoder only reranker.\n\n Args:\n base_model (PreTrainedModel): The underlying pre-trained model used for encoding and scoring input pairs.\n tokenizer (AutoTokenizer, optional): The tokenizer for encoding input text. Defaults to ``None``.\n train_batch_size (int, optional): The batch size to use. Defaults to ``4``.\n \"\"\"\n def __init__(\n self,\n base_model: PreTrainedModel,\n tokenizer: AutoTokenizer = None,\n train_batch_size: int = 4,\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n train_batch_size=train_batch_size,\n )\n\n def encode(self, features):\n \"\"\"Encodes input features to logits.\n\n Args:\n features (dict): Dictionary with input features.\n\n Returns:\n torch.Tensor: The logits output from the model.\n \"\"\"\n if features is None:\n return None\n outputs = self.model(input_ids=features['input_ids'],\n attention_mask=features['attention_mask'],\n position_ids=features['position_ids'] if 'position_ids' in features.keys() else None,\n output_hidden_states=True)\n # _, max_indices = torch.max(features['labels'], dim=1)\n # predict_indices = max_indices\n # logits = [outputs.logits[i, predict_indices[i], :] for i in range(outputs.logits.shape[0])]\n # logits = torch.stack(logits, dim=0)\n scores = outputs.logits[:, -1, self.yes_loc]\n return scores.contiguous()\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/runner.py", "content": "import logging\nfrom typing import Tuple\nfrom pathlib import Path\nfrom FlagEmbedding.abc.finetune.reranker.AbsArguments import AbsRerankerDataArguments, AbsRerankerTrainingArguments\nfrom transformers import (\n AutoTokenizer, PreTrainedTokenizer\n)\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerRunner, AbsRerankerModel\n\nfrom .modeling import CrossDecoderModel\nfrom .arguments import RerankerModelArguments\nfrom .trainer import DecoderOnlyRerankerTrainer\nfrom .load_model import get_model, save_merged_model\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyRerankerRunner(AbsRerankerRunner):\n \"\"\"\n Decoder only reranker runner for finetuning.\n \n Args:\n model_args (RerankerModelArguments): Model arguments instance.\n data_args (AbsRerankerDataArguments): Data arguments instance.\n training_args (AbsRerankerTrainingArguments): Trainer arguments.\n \"\"\"\n def __init__(\n self,\n model_args: RerankerModelArguments,\n data_args: AbsRerankerDataArguments,\n training_args: AbsRerankerTrainingArguments\n ):\n super().__init__(model_args, data_args, training_args)\n\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsRerankerModel]:\n \"\"\"Load the tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.tokenizer_name if self.model_args.tokenizer_name else self.model_args.model_name_or_path,\n token=self.model_args.token,\n cache_dir=self.model_args.cache_dir,\n use_fast=self.model_args.use_fast_tokenizer,\n add_eos_token=False,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n\n if tokenizer.pad_token is None:\n if tokenizer.unk_token is not None:\n tokenizer.pad_token = tokenizer.unk_token\n tokenizer.pad_token_id = tokenizer.unk_token_id\n elif tokenizer.eod_id is not None:\n tokenizer.pad_token = tokenizer.eod\n tokenizer.pad_token_id = tokenizer.eod_id\n tokenizer.bos_token = tokenizer.im_start\n tokenizer.bos_token_id = tokenizer.im_start_id\n tokenizer.eos_token = tokenizer.im_end\n tokenizer.eos_token_id = tokenizer.im_end_id\n else:\n tokenizer.pad_token = tokenizer.eos_token\n tokenizer.pad_token_id = tokenizer.eos_token_id\n # if 'mistral' in self.model_args.model_name_or_path.lower():\n tokenizer.padding_side = 'left'\n\n base_model = get_model(self.model_args)\n\n model = CrossDecoderModel(\n base_model,\n tokenizer=tokenizer,\n train_batch_size=self.training_args.per_device_train_batch_size,\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n return tokenizer, model\n\n def load_trainer(self) -> DecoderOnlyRerankerTrainer:\n \"\"\"Load the trainer.\n\n Returns:\n DecoderOnlyRerankerTrainer: Loaded trainer instance.\n \"\"\"\n trainer = DecoderOnlyRerankerTrainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n return trainer\n\n def run(self):\n \"\"\"\n Run the finetuning.\n \"\"\"\n Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)\n self.trainer.save_model()\n\n # save merged model\n if self.model_args.save_merged_lora_model and self.training_args.process_index == 0:\n save_merged_model(self.model_args, self.training_args.output_dir)\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/base/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n# from transformers.deepspeed import is_deepspeed_zero3_enabled\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerTrainer\nfrom peft import get_peft_model_state_dict\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyRerankerTrainer(AbsRerankerTrainer):\n \"\"\"\n Trainer class for encoder only base reranker models.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n \n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # if is_deepspeed_zero3_enabled():\n # if state_dict is None:\n # state_dict = self.model.state_dict()\n # prefix = 'model.'\n # assert all(k.startswith(prefix) for k in state_dict.keys()), list(state_dict.keys())\n # state_dict = {k[len(prefix):]: v for k, v in state_dict.items()}\n # lora_state_dict = get_peft_model_state_dict(self.model.model, state_dict)\n # if self.args.process_index <= 0:\n # torch.save(lora_state_dict, os.path.join(output_dir, \"adapter_model.bin\"))\n # print(f\"Save adapter model at {output_dir}\")\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/__init__.py", "content": "from .modeling import CrossDecoderModel\nfrom .runner import DecoderOnlyRerankerRunner\nfrom .arguments import RerankerModelArguments\nfrom .trainer import DecoderOnlyRerankerTrainer\n\n__all__ = [\n \"CrossDecoderModel\",\n \"DecoderOnlyRerankerRunner\",\n \"DecoderOnlyRerankerTrainer\",\n \"RerankerModelArguments\",\n]\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.abc.finetune.reranker import (\n AbsRerankerDataArguments,\n AbsRerankerTrainingArguments\n)\n\nfrom FlagEmbedding.finetune.reranker.decoder_only.layerwise import (\n DecoderOnlyRerankerRunner,\n RerankerModelArguments\n)\n\n\ndef main():\n parser = HfArgumentParser((RerankerModelArguments, AbsRerankerDataArguments, AbsRerankerTrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: RerankerModelArguments\n data_args: AbsRerankerDataArguments\n training_args: AbsRerankerTrainingArguments\n\n runner = DecoderOnlyRerankerRunner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/arguments.py", "content": "from typing import List\nfrom dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerModelArguments\n\n\ndef default_target_modules() -> List[int]:\n return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']\n\n\n@dataclass\nclass RerankerModelArguments(AbsRerankerModelArguments):\n \"\"\"\n Model argument class for decoder only reranker.\n \"\"\"\n use_lora: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use LORA (low-rank parameter-efficient training) to train the model.\"}\n )\n lora_rank: int = field(\n default=64,\n metadata={\"help\": \"The rank of lora.\"}\n )\n lora_alpha: float = field(\n default=16,\n metadata={\"help\": \"The alpha parameter of lora.\"}\n )\n lora_dropout: float = field(\n default=0.1,\n metadata={\"help\": \"The dropout rate of lora modules.\"}\n )\n target_modules: List[str] = field(\n default_factory=default_target_modules,\n metadata={\"help\": \"The target modules to apply LORA.\"}\n )\n modules_to_save: List[str] = field(\n default=None,\n metadata={\"help\": \"List of modules that should be saved in the final checkpoint.\"}\n )\n use_flash_attn: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use flash attention to train the model.\"}\n )\n # use_slow_tokenizer: bool = field(\n # default=False,\n # metadata={\"help\": \"If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\"}\n # )\n from_peft: str = field(\n default=None\n )\n raw_peft: List[str] = field(\n default=None\n )\n\n save_merged_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will merge the lora modules and save the entire model.\"}\n )\n\n model_type: str = field(\n default='from_raw_model' # should be one of ['from_raw_model', 'from_finetuned_model']\n # from_raw_model -- openbmb/MiniCPM-2B-dpo-bf16\n # from_finetuned_model -- BAAI/bge-reranker-v2-minicpm-layerwise\n )\n\n start_layer: int = field(\n default=8,\n metadata={\"help\": \"which layer to start to compute score\"}\n )\n\n head_multi: bool = field(\n default=False,\n metadata={\"help\": \"use one / multi classifier\"}\n )\n head_type: str = field(\n default='simple',\n metadata={\"help\": \"the type of the classifier\"}\n )\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/configuration_minicpm_reranker.py", "content": "# coding=utf-8\n# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.\n#\n# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX\n# and OPT implementations in this library. It has been modified from its\n# original forms to accommodate minor architectural differences compared\n# to GPT-NeoX and OPT used by the Meta AI team that trained the model.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" MiniCPM model configuration\"\"\"\n\nfrom transformers.configuration_utils import PretrainedConfig\nfrom transformers.utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\nMINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}\n\nclass LayerWiseMiniCPMConfig(PretrainedConfig):\n r\"\"\"\n This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM\n model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n defaults will yield a similar configuration to that of the MiniCPM-7B.\n\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n documentation from [`PretrainedConfig`] for more information.\n\n\n Args:\n vocab_size (`int`, *optional*, defaults to 32000):\n Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the\n `inputs_ids` passed when calling [`MiniCPMModel`]\n hidden_size (`int`, *optional*, defaults to 4096):\n Dimension of the hidden representations.\n intermediate_size (`int`, *optional*, defaults to 11008):\n Dimension of the MLP representations.\n num_hidden_layers (`int`, *optional*, defaults to 32):\n Number of hidden layers in the Transformer decoder.\n num_attention_heads (`int`, *optional*, defaults to 32):\n Number of attention heads for each attention layer in the Transformer decoder.\n num_key_value_heads (`int`, *optional*):\n This is the number of key_value heads that should be used to implement Grouped Query Attention. If\n `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if\n `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When\n converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed\n by meanpooling all the original heads within that group. For more details checkout [this\n paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to\n `num_attention_heads`.\n hidden_act (`str` or `function`, *optional*, defaults to `\"silu\"`):\n The non-linear activation function (function or string) in the decoder.\n max_position_embeddings (`int`, *optional*, defaults to 2048):\n The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,\n MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.\n initializer_range (`float`, *optional*, defaults to 0.02):\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n rms_norm_eps (`float`, *optional*, defaults to 1e-06):\n The epsilon used by the rms normalization layers.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models). Only\n relevant if `config.is_decoder=True`.\n pad_token_id (`int`, *optional*):\n Padding token id.\n bos_token_id (`int`, *optional*, defaults to 1):\n Beginning of stream token id.\n eos_token_id (`int`, *optional*, defaults to 2):\n End of stream token id.\n pretraining_tp (`int`, *optional*, defaults to 1):\n Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this\n document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is\n necessary to ensure exact reproducibility of the pretraining results. Please refer to [this\n issue](https://github.com/pytorch/pytorch/issues/76232).\n tie_word_embeddings (`bool`, *optional*, defaults to `False`):\n Whether to tie weight embeddings\n rope_theta (`float`, *optional*, defaults to 10000.0):\n The base period of the RoPE embeddings.\n rope_scaling (`Dict`, *optional*):\n Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling\n strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is\n `{\"type\": strategy name, \"factor\": scaling factor}`. When using this flag, don't update\n `max_position_embeddings` to the expected new maximum. See the following thread for more information on how\n these scaling strategies behave:\n https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an\n experimental feature, subject to breaking API changes in future versions.\n attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):\n Whether to use a bias in the query, key, value and output projection layers during self-attention.\n attention_dropout (`float`, *optional*, defaults to 0.0):\n The dropout ratio for the attention probabilities.\n\n ```python\n >>> from transformers import MiniCPMModel, MiniCPMConfig\n\n >>> # Initializing a MiniCPM minicpm-7b style configuration\n >>> configuration = MiniCPMConfig()\n\n >>> # Initializing a model from the minicpm-7b style configuration\n >>> model = MiniCPMModel(configuration)\n\n >>> # Accessing the model configuration\n >>> configuration = model.config\n ```\"\"\"\n\n model_type = \"minicpm\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n\n def __init__(\n self,\n vocab_size=32000,\n hidden_size=4096,\n intermediate_size=11008,\n num_hidden_layers=32,\n num_attention_heads=32,\n num_key_value_heads=None,\n hidden_act=\"silu\",\n max_position_embeddings=2048,\n initializer_range=0.02,\n rms_norm_eps=1e-6,\n use_cache=True,\n pad_token_id=None,\n bos_token_id=1,\n eos_token_id=2,\n pretraining_tp=1,\n tie_word_embeddings=True,\n rope_theta=10000.0,\n rope_scaling=None,\n attention_bias=False,\n attention_dropout=0.0,\n scale_emb=1,\n dim_model_base=1,\n scale_depth=1,\n start_layer=8,\n head_multi=True,\n head_type=\"simple\",\n **kwargs,\n ):\n self.vocab_size = vocab_size\n self.max_position_embeddings = max_position_embeddings\n self.hidden_size = hidden_size\n self.intermediate_size = intermediate_size\n self.num_hidden_layers = num_hidden_layers\n self.num_attention_heads = num_attention_heads\n\n # for backward compatibility\n if num_key_value_heads is None:\n num_key_value_heads = num_attention_heads\n\n self.num_key_value_heads = num_key_value_heads\n self.hidden_act = hidden_act\n self.initializer_range = initializer_range\n self.rms_norm_eps = rms_norm_eps\n self.pretraining_tp = pretraining_tp\n self.use_cache = use_cache\n self.rope_theta = rope_theta\n self.rope_scaling = rope_scaling\n self._rope_scaling_validation()\n self.attention_bias = attention_bias\n self.attention_dropout = attention_dropout\n self.scale_emb = scale_emb\n self.dim_model_base = dim_model_base\n self.scale_depth = scale_depth\n\n self.start_layer = start_layer\n self.head_multi = head_multi\n self.head_type = head_type\n\n super().__init__(\n pad_token_id=pad_token_id,\n bos_token_id=bos_token_id,\n eos_token_id=eos_token_id,\n tie_word_embeddings=tie_word_embeddings,\n **kwargs,\n )\n try:\n import flash_attn\n self._attn_implementation = \"flash_attention_2\"\n except:\n pass\n\n def _rope_scaling_validation(self):\n \"\"\"\n Validate the `rope_scaling` configuration.\n \"\"\"\n if self.rope_scaling is None:\n return\n\n if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:\n raise ValueError(\n \"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, \"\n f\"got {self.rope_scaling}\"\n )\n rope_scaling_type = self.rope_scaling.get(\"type\", None)\n rope_scaling_factor = self.rope_scaling.get(\"factor\", None)\n if rope_scaling_type is None or rope_scaling_type not in [\"linear\", \"dynamic\"]:\n raise ValueError(\n f\"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}\"\n )\n if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:\n raise ValueError(f\"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}\")\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/load_model.py", "content": "import os\nimport re\nimport logging\nfrom torch import nn\nfrom transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer\nfrom peft import LoraConfig, TaskType, get_peft_model, PeftModel\n\nfrom FlagEmbedding.finetune.reranker.decoder_only.layerwise.arguments import RerankerModelArguments\n\nfrom .modeling_minicpm_reranker import LayerWiseMiniCPMForCausalLM, LayerWiseHead\nfrom .configuration_minicpm_reranker import LayerWiseMiniCPMConfig\n\nlogger = logging.getLogger(__name__)\n\n\ndef find_largest_checkpoint(checkpoint_dir):\n \"\"\"Find the largest checkpoint from directory.\n\n Args:\n checkpoint_dir (str): Directory to the checkpoint.\n\n Returns:\n str: Directory to the checkpoint, None no matching found.\n \"\"\"\n checkpoint_pattern = re.compile(r'checkpoint-(\\d+)')\n max_number = -1\n max_checkpoint_file = None\n for file in os.listdir(checkpoint_dir):\n match = checkpoint_pattern.search(file)\n if match:\n number = int(match.group(1))\n if number > max_number:\n max_number = number\n max_checkpoint_file = file\n if max_checkpoint_file:\n return os.path.join(checkpoint_dir, max_checkpoint_file)\n else:\n return None\n\n\ndef get_model(model_args: RerankerModelArguments, only_for_one_logit):\n \"\"\"Get the model.\n\n Args:\n model_args (RerankerModelArguments): Model arguments instance.\n\n Returns:\n transformers.PreTrainedModel or PeftModel: The loaded model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n trust_remote_code=model_args.trust_remote_code,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n trust_remote_code=model_args.trust_remote_code,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_type == 'from_raw_model':\n config.use_cache = False\n config.start_layer = config.num_hidden_layers\n config.head_multi = False\n config.head_type = 'raw'\n\n model = LayerWiseMiniCPMForCausalLM.from_pretrained(\n model_args.model_name_or_path,\n trust_remote_code=model_args.trust_remote_code,\n # torch_dtype=torch.float16 if training_args.fp16 else torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n )\n\n config.start_layer = model_args.start_layer\n config.head_multi = model_args.head_multi\n config.head_type = model_args.head_type\n model.config = config\n\n if model.config.head_type == 'complex':\n if model.config.head_multi == True:\n lm_head = nn.ModuleList([LayerWiseHead(\n model.config.hidden_size, model.config.vocab_size) for _ in range(\n model.config.start_layer,\n model.config.num_hidden_layers + 1)])\n for i in range(len(lm_head)):\n lm_head[i].linear_head.load_state_dict(model.lm_head.state_dict())\n model.set_output_embeddings(lm_head)\n else:\n lm_head = LayerWiseHead(model.config.hidden_size, 1)\n state_dict_back = model.lm_head.state_dict()\n state_dict_back['weight'] = state_dict_back['weight'][only_for_one_logit: only_for_one_logit + 1, :]\n lm_head.linear_head.load_state_dict(state_dict_back)\n model.set_output_embeddings(lm_head)\n else:\n if only_for_one_logit is None:\n raise ValueError('`only for one logit` cannot be None.')\n if model.config.head_multi == True:\n lm_head = nn.ModuleList([LayerWiseHead(\n model.config.hidden_size, 1) for _ in range(\n model.config.start_layer,\n model.config.num_hidden_layers + 1)])\n state_dict_back = model.lm_head.state_dict()\n state_dict_back['weight'] = state_dict_back['weight'][only_for_one_logit: only_for_one_logit + 1, :]\n for i in range(len(lm_head)):\n lm_head[i].linear_head.load_state_dict(state_dict_back)\n model.set_output_embeddings(lm_head)\n else:\n lm_head = LayerWiseHead(model.config.hidden_size, 1)\n state_dict_back = model.lm_head.state_dict()\n state_dict_back['weight'] = state_dict_back['weight'][only_for_one_logit: only_for_one_logit + 1, :]\n lm_head.linear_head.load_state_dict(state_dict_back)\n model.set_output_embeddings(lm_head)\n # modules_to_save = model_args.modules_to_save\n # target_modules = model_args.target_modules\n else:\n config.use_cache = False\n\n model = LayerWiseMiniCPMForCausalLM.from_pretrained(\n model_args.model_name_or_path,\n # torch_dtype=torch.float16 if training_args.fp16 else torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n trust_remote_code=model_args.trust_remote_code,\n )\n # target_modules = model_args.target_modules\n # target_modules.extend(model_args.modules_to_save)\n # modules_to_save = None\n\n if model_args.raw_peft is not None:\n for peft_path in model_args.raw_peft:\n model = PeftModel.from_pretrained(model, peft_path)\n model = model.merge_and_unload()\n\n if model_args.from_peft is not None:\n model = PeftModel.from_pretrained(model, model_args.from_peft, is_trainable=True)\n model.print_trainable_parameters()\n else:\n if model_args.use_lora:\n peft_config = LoraConfig(\n task_type=TaskType.CAUSAL_LM,\n inference_mode=False,\n r=model_args.lora_rank,\n target_modules=model_args.target_modules,\n modules_to_save=model_args.modules_to_save,\n lora_alpha=model_args.lora_alpha,\n lora_dropout=model_args.lora_dropout\n )\n model = get_peft_model(model, peft_config)\n model.print_trainable_parameters()\n\n return model\n\n\ndef save_merged_model(model_args: RerankerModelArguments, output_dir: str):\n \"\"\"\n Loads and save a model with specified configurations, merges it with PEFT layers if available.\n\n Args:\n model_args (RerankerModelArguments): Model arguments instance.\n output_dir (str): Directory to save the model.\n \"\"\"\n if model_args.config_name:\n config = AutoConfig.from_pretrained(\n model_args.config_name,\n trust_remote_code=model_args.trust_remote_code,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n trust_remote_code=model_args.trust_remote_code,\n token=model_args.token,\n cache_dir=model_args.cache_dir\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_type == 'from_raw_model':\n config = LayerWiseMiniCPMConfig.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise',\n cache_dir=model_args.cache_dir,\n token=model_args.token,\n trust_remote_code=model_args.trust_remote_code)\n config.start_layer = model_args.start_layer\n config.head_multi = model_args.head_multi\n config.head_type = model_args.head_type\n\n model = LayerWiseMiniCPMForCausalLM.from_pretrained(model_args.model_name_or_path,\n config=config,\n cache_dir=model_args.cache_dir,\n token=model_args.token,\n trust_remote_code=model_args.trust_remote_code)\n\n if model_args.raw_peft is not None:\n for peft_path in model_args.raw_peft:\n model = PeftModel.from_pretrained(model, peft_path)\n model = model.merge_and_unload()\n\n try:\n model = PeftModel.from_pretrained(model, output_dir)\n model = model.merge_and_unload()\n except:\n model = PeftModel.from_pretrained(model, find_largest_checkpoint(output_dir))\n model = model.merge_and_unload()\n\n model.save_pretrained(os.path.join(output_dir, 'merged_model'))\n\n try:\n tokenizer = AutoTokenizer.from_pretrained(output_dir)\n except:\n tokenizer = AutoTokenizer.from_pretrained(find_largest_checkpoint(output_dir))\n\n tokenizer.save_pretrained(os.path.join(output_dir, 'merged_model'))\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/modeling.py", "content": "import torch\nfrom transformers import PreTrainedModel, AutoTokenizer\nimport logging\nfrom typing import List, Union, Dict, Optional\nfrom torch import Tensor\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerModel, RerankerOutput\n\nlogger = logging.getLogger(__name__)\n\n\nclass CrossDecoderModel(AbsRerankerModel):\n \"\"\"\n Model class for decoder only reranker.\n\n Args:\n base_model (PreTrainedModel): The underlying pre-trained model used for encoding and scoring input pairs.\n tokenizer (AutoTokenizer, optional): The tokenizer for encoding input text. Defaults to ``None``.\n train_batch_size (int, optional): The batch size to use. Defaults to ``4``.\n start_layer (int, optional): Starting layer for layerwise. Defaults to ``8``.\n \"\"\"\n def __init__(\n self,\n base_model: PreTrainedModel,\n tokenizer: AutoTokenizer = None,\n train_batch_size: int = 4,\n start_layer: int = 8\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n train_batch_size=train_batch_size,\n )\n\n self.start_layer = start_layer\n\n def encode(self, features):\n if features is None:\n return None\n outputs = self.model(input_ids=features['input_ids'],\n attention_mask=features['attention_mask'],\n position_ids=features['position_ids'] if 'position_ids' in features.keys() else None,\n output_hidden_states=True)\n all_logits = outputs.logits\n all_scores = []\n for logits in all_logits:\n all_scores.append(logits[:, -1].contiguous())\n return all_scores\n\n def forward(self, pair: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None, teacher_scores: Optional[Tensor] = None):\n ranker_logits = self.encode(pair) # (batch_size * num, dim)\n\n if self.training:\n loss = 0\n for logits in ranker_logits:\n grouped_logits = logits.view(self.train_batch_size, -1)\n target = torch.zeros(self.train_batch_size, device=grouped_logits.device, dtype=torch.long)\n loss += self.compute_loss(grouped_logits, target)\n\n if teacher_scores is None:\n teacher_scores = ranker_logits[-1].view(\n self.train_batch_size,\n -1\n )\n teacher_targets = torch.softmax(teacher_scores.detach(), dim=-1)\n for logits in ranker_logits[:-1]:\n student_scores = logits.view(\n self.train_batch_size,\n -1\n )\n loss += - torch.mean(torch.sum(torch.log_softmax(student_scores, dim=-1) * teacher_targets, dim=-1))\n else:\n teacher_scores = torch.Tensor(teacher_scores)\n teacher_scores = teacher_scores.view(self.train_batch_size, -1)\n teacher_targets = torch.softmax(teacher_scores.detach(), dim=-1).to(ranker_logits[-1].device)\n for logits in ranker_logits:\n student_scores = logits.view(\n self.train_batch_size,\n -1\n )\n loss += - torch.mean(torch.sum(torch.log_softmax(student_scores, dim=-1) * teacher_targets, dim=-1))\n else:\n loss = None\n\n # print(loss)\n return RerankerOutput(\n loss=loss,\n scores=ranker_logits,\n )\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/modeling_minicpm_reranker.py", "content": "# coding=utf-8\n# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.\n#\n# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX\n# and OPT implementations in this library. It has been modified from its\n# original forms to accommodate minor architectural differences compared\n# to GPT-NeoX and OPT used by the Meta AI team that trained the model.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" PyTorch MiniCPM model.\"\"\"\nimport sys\n\nimport math\nimport warnings\nfrom typing import List, Optional, Tuple, Union, Dict\n\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n\nfrom transformers.activations import ACT2FN\nfrom transformers.cache_utils import Cache, DynamicCache\nfrom transformers.modeling_attn_mask_utils import (\n AttentionMaskConverter,\n _prepare_4d_attention_mask,\n _prepare_4d_causal_attention_mask,\n _prepare_4d_causal_attention_mask_for_sdpa,\n)\nfrom transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \\\n SequenceClassifierOutputWithPast\nfrom transformers.modeling_utils import PreTrainedModel\nfrom transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13\nfrom transformers.utils import (\n add_start_docstrings,\n add_start_docstrings_to_model_forward,\n is_flash_attn_2_available,\n is_flash_attn_greater_or_equal_2_10,\n logging,\n replace_return_docstrings,\n)\nfrom FlagEmbedding.utils.transformers_compat import is_torch_fx_available\nfrom .configuration_minicpm_reranker import LayerWiseMiniCPMConfig\nimport re\n\n\ntry:\n from flash_attn import flash_attn_func, flash_attn_varlen_func\n from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa\nexcept:\n pass\n\n# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.\n# It means that the function will not be traced through and simply appear as a node in the graph.\nif is_torch_fx_available():\n if not is_torch_greater_or_equal_than_1_13:\n import torch.fx\n\n _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)\n\nlogger = logging.get_logger(__name__)\n\n_CONFIG_FOR_DOC = \"LayerWiseMiniCPMConfig\"\n\n\ndef _get_unpad_data(attention_mask):\n seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)\n indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()\n max_seqlen_in_batch = seqlens_in_batch.max().item()\n cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))\n return (\n indices,\n cu_seqlens,\n max_seqlen_in_batch,\n )\n\n\ndef _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):\n warnings.warn(\n \"Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask\"\n )\n return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)\n\n\ndef _make_causal_mask(\n input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0\n):\n warnings.warn(\n \"Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask\"\n )\n return AttentionMaskConverter._make_causal_mask(\n input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length\n )\n\n\n# @torch.jit.script # type: ignore\ndef rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):\n old_dtype = hidden.dtype\n variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)\n hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)\n return hidden * weight\n\n\nclass MiniCPMRMSNorm(nn.Module):\n def __init__(self, hidden_size, eps=1e-6):\n \"\"\"\n MiniCPMRMSNorm is equivalent to T5LayerNorm\n \"\"\"\n super().__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.variance_epsilon = eps\n\n def forward(self, hidden_states):\n return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)\n\n\nALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)\n\n\nclass MiniCPMRotaryEmbedding(nn.Module):\n def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):\n super().__init__()\n\n self.dim = dim\n self.max_position_embeddings = max_position_embeddings\n self.base = base\n inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n # Build here to make `torch.jit.trace` work.\n self._set_cos_sin_cache(\n # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()\n seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32\n )\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n def forward(self, x, seq_len=None):\n # x: [bs, num_attention_heads, seq_len, head_size]\n if seq_len > self.max_seq_len_cached:\n self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)\n\n return (\n self.cos_cached[:seq_len].to(dtype=x.dtype),\n self.sin_cached[:seq_len].to(dtype=x.dtype),\n )\n\n\nclass MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):\n \"\"\"MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev\"\"\"\n\n def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):\n self.scaling_factor = scaling_factor\n super().__init__(dim, max_position_embeddings, base, device)\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n t = t / self.scaling_factor\n\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n\nclass MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):\n \"\"\"MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla\"\"\"\n\n def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):\n self.scaling_factor = scaling_factor\n super().__init__(dim, max_position_embeddings, base, device)\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n\n if seq_len > self.max_position_embeddings:\n base = self.base * (\n (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)\n ) ** (self.dim / (self.dim - 2))\n inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n\ndef rotate_half(x):\n \"\"\"Rotates half the hidden dims of the input.\"\"\"\n x1 = x[..., : x.shape[-1] // 2]\n x2 = x[..., x.shape[-1] // 2:]\n return torch.cat((-x2, x1), dim=-1)\n\n\ndef apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):\n \"\"\"Applies Rotary Position Embedding to the query and key tensors.\n\n Args:\n q (`torch.Tensor`): The query tensor.\n k (`torch.Tensor`): The key tensor.\n cos (`torch.Tensor`): The cosine part of the rotary embedding.\n sin (`torch.Tensor`): The sine part of the rotary embedding.\n position_ids (`torch.Tensor`):\n The position indices of the tokens corresponding to the query and key tensors. For example, this can be\n used to pass offsetted position ids when working with a KV-cache.\n unsqueeze_dim (`int`, *optional*, defaults to 1):\n The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and\n sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note\n that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and\n k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes\n cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have\n the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.\n Returns:\n `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.\n \"\"\"\n # cos = cos[position_ids].unsqueeze(unsqueeze_dim)\n # sin = sin[position_ids].unsqueeze(unsqueeze_dim)\n # q_embed = (q * cos) + (rotate_half(q) * sin)\n # k_embed = (k * cos) + (rotate_half(k) * sin)\n orig_dtype = k.dtype\n cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]\n sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]\n q_fp32 = q.to(dtype=torch.float32, device=q.device)\n k_fp32 = k.to(dtype=torch.float32, device=k.device)\n q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)\n k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)\n return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)\n\n\nclass MiniCPMMLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.hidden_size = config.hidden_size\n self.intermediate_size = config.intermediate_size\n self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)\n self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)\n self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)\n self.act_fn = ACT2FN[config.hidden_act]\n\n def forward(self, x):\n if self.config.pretraining_tp > 1:\n slice = self.intermediate_size // self.config.pretraining_tp\n gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)\n up_proj_slices = self.up_proj.weight.split(slice, dim=0)\n down_proj_slices = self.down_proj.weight.split(slice, dim=1)\n\n gate_proj = torch.cat(\n [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1\n )\n up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)\n\n intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)\n down_proj = [\n F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)\n ]\n down_proj = sum(down_proj)\n else:\n down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n\n return down_proj\n\n\ndef repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:\n \"\"\"\n This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,\n num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)\n \"\"\"\n batch, num_key_value_heads, slen, head_dim = hidden_states.shape\n if n_rep == 1:\n return hidden_states\n hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)\n return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)\n\n\nclass MiniCPMAttention(nn.Module):\n \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n\n def __init__(self, config: LayerWiseMiniCPMConfig, layer_idx: Optional[int] = None):\n super().__init__()\n self.config = config\n self.layer_idx = layer_idx\n if layer_idx is None:\n logger.warning_once(\n f\"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will \"\n \"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` \"\n \"when creating this class.\"\n )\n\n self.attention_dropout = config.attention_dropout\n self.hidden_size = config.hidden_size\n self.num_heads = config.num_attention_heads\n self.head_dim = self.hidden_size // self.num_heads\n self.num_key_value_heads = config.num_key_value_heads\n self.num_key_value_groups = self.num_heads // self.num_key_value_heads\n self.max_position_embeddings = config.max_position_embeddings\n self.rope_theta = config.rope_theta\n self.is_causal = True\n\n if (self.head_dim * self.num_heads) != self.hidden_size:\n raise ValueError(\n f\"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}\"\n f\" and `num_heads`: {self.num_heads}).\"\n )\n\n self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)\n self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)\n self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)\n self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)\n self._init_rope()\n\n def _init_rope(self):\n if self.config.rope_scaling is None:\n self.rotary_emb = MiniCPMRotaryEmbedding(\n self.head_dim,\n max_position_embeddings=self.max_position_embeddings,\n base=self.rope_theta,\n )\n else:\n scaling_type = self.config.rope_scaling[\"type\"]\n scaling_factor = self.config.rope_scaling[\"factor\"]\n if scaling_type == \"linear\":\n self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(\n self.head_dim,\n max_position_embeddings=self.max_position_embeddings,\n scaling_factor=scaling_factor,\n base=self.rope_theta,\n )\n elif scaling_type == \"dynamic\":\n self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(\n self.head_dim,\n max_position_embeddings=self.max_position_embeddings,\n scaling_factor=scaling_factor,\n base=self.rope_theta,\n )\n else:\n raise ValueError(f\"Unknown RoPE scaling type {scaling_type}\")\n\n def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Cache] = None,\n output_attentions: bool = False,\n use_cache: bool = False,\n **kwargs,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n if \"padding_mask\" in kwargs:\n warnings.warn(\n \"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`\"\n )\n\n bsz, q_len, _ = hidden_states.size()\n\n if self.config.pretraining_tp > 1:\n key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp\n query_slices = self.q_proj.weight.split(\n (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0\n )\n key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)\n value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)\n\n query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]\n query_states = torch.cat(query_states, dim=-1)\n\n key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]\n key_states = torch.cat(key_states, dim=-1)\n\n value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]\n value_states = torch.cat(value_states, dim=-1)\n\n else:\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n\n kv_seq_len = key_states.shape[-2]\n if past_key_value is not None:\n if self.layer_idx is None:\n raise ValueError(\n f\"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} \"\n \"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class \"\n \"with a layer index.\"\n )\n kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)\n cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)\n\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\n if past_key_value is not None:\n cache_kwargs = {\"sin\": sin, \"cos\": cos} # Specific to RoPE models\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n key_states = repeat_kv(key_states, self.num_key_value_groups)\n value_states = repeat_kv(value_states, self.num_key_value_groups)\n\n attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)\n if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):\n raise ValueError(\n f\"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is\"\n f\" {attn_weights.size()}\"\n )\n\n if attention_mask is not None:\n if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}\"\n )\n attn_weights = attn_weights + attention_mask\n\n # upcast attention to fp32\n attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)\n attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)\n attn_output = torch.matmul(attn_weights, value_states)\n\n if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):\n raise ValueError(\n f\"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is\"\n f\" {attn_output.size()}\"\n )\n\n attn_output = attn_output.transpose(1, 2).contiguous()\n\n attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)\n\n if self.config.pretraining_tp > 1:\n attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)\n o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)\n attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])\n else:\n attn_output = self.o_proj(attn_output)\n\n if not output_attentions:\n attn_weights = None\n\n return attn_output, attn_weights, past_key_value\n\n\nclass MiniCPMFlashAttention2(MiniCPMAttention):\n \"\"\"\n MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays\n untouched. The only required change would be on the forward pass where it needs to correctly call the public API of\n flash attention and deal with padding tokens in case the input contains any of them.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.\n # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.\n # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).\n self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.LongTensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Cache] = None,\n output_attentions: bool = False,\n use_cache: bool = False,\n **kwargs,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n # MiniCPMFlashAttention2 attention does not support output_attentions\n if \"padding_mask\" in kwargs:\n warnings.warn(\n \"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`\"\n )\n\n # overwrite attention_mask with padding_mask\n attention_mask = kwargs.pop(\"padding_mask\")\n\n output_attentions = False\n\n bsz, q_len, _ = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n # Flash attention requires the input to have the shape\n # batch_size x seq_length x head_dim x hidden_dim\n # therefore we just need to keep the original shape\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n\n kv_seq_len = key_states.shape[-2]\n if past_key_value is not None:\n kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)\n cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\n if past_key_value is not None:\n cache_kwargs = {\"sin\": sin, \"cos\": cos} # Specific to RoPE models\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache\n # to be able to avoid many of these transpose/reshape/view.\n query_states = query_states.transpose(1, 2)\n key_states = key_states.transpose(1, 2)\n value_states = value_states.transpose(1, 2)\n\n dropout_rate = self.attention_dropout if self.training else 0.0\n\n # In PEFT, usually we cast the layer norms in float32 for training stability reasons\n # therefore the input hidden states gets silently casted in float32. Hence, we need\n # cast them back in the correct dtype just to be sure everything works as expected.\n # This might slowdown training & inference so it is recommended to not cast the LayerNorms\n # in fp32. (MiniCPMRMSNorm handles it correctly)\n\n input_dtype = query_states.dtype\n if input_dtype == torch.float32:\n # Handle the case where the model is quantized\n if hasattr(self.config, \"_pre_quantization_dtype\"):\n target_dtype = self.config._pre_quantization_dtype\n else:\n target_dtype = self.q_proj.weight.dtype\n\n logger.warning_once(\n f\"The input hidden states seems to be silently casted in float32, this might be related to\"\n f\" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in\"\n f\" {target_dtype}.\"\n )\n\n query_states = query_states.to(target_dtype)\n key_states = key_states.to(target_dtype)\n value_states = value_states.to(target_dtype)\n\n attn_output = self._flash_attention_forward(\n query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate\n )\n\n attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()\n attn_output = self.o_proj(attn_output)\n\n if not output_attentions:\n attn_weights = None\n\n return attn_output, attn_weights, past_key_value\n\n def _flash_attention_forward(\n self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None\n ):\n \"\"\"\n Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token\n first unpad the input, then computes the attention scores and pad the final attention scores.\n\n Args:\n query_states (`torch.Tensor`):\n Input query states to be passed to Flash Attention API\n key_states (`torch.Tensor`):\n Input key states to be passed to Flash Attention API\n value_states (`torch.Tensor`):\n Input value states to be passed to Flash Attention API\n attention_mask (`torch.Tensor`):\n The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the\n position of padding tokens and 1 for the position of non-padding tokens.\n dropout (`int`, *optional*):\n Attention dropout\n softmax_scale (`float`, *optional*):\n The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)\n \"\"\"\n if not self._flash_attn_uses_top_left_mask:\n causal = self.is_causal\n else:\n # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.\n causal = self.is_causal and query_length != 1\n # Contains at least one padding token in the sequence\n if attention_mask is not None:\n batch_size = query_states.shape[0]\n query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(\n query_states, key_states, value_states, attention_mask, query_length\n )\n\n cu_seqlens_q, cu_seqlens_k = cu_seq_lens\n max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens\n attn_output_unpad = flash_attn_varlen_func(\n query_states,\n key_states,\n value_states,\n cu_seqlens_q=cu_seqlens_q,\n cu_seqlens_k=cu_seqlens_k,\n max_seqlen_q=max_seqlen_in_batch_q,\n max_seqlen_k=max_seqlen_in_batch_k,\n dropout_p=dropout,\n softmax_scale=softmax_scale,\n causal=causal,\n )\n\n attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)\n else:\n attn_output = flash_attn_func(\n query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal\n )\n\n return attn_output\n\n def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):\n indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)\n batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape\n\n key_layer = index_first_axis(\n key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k\n )\n value_layer = index_first_axis(\n value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k\n )\n if query_length == kv_seq_len:\n query_layer = index_first_axis(\n query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k\n )\n cu_seqlens_q = cu_seqlens_k\n max_seqlen_in_batch_q = max_seqlen_in_batch_k\n indices_q = indices_k\n elif query_length == 1:\n max_seqlen_in_batch_q = 1\n cu_seqlens_q = torch.arange(\n batch_size + 1, dtype=torch.int32, device=query_layer.device\n ) # There is a memcpy here, that is very bad.\n indices_q = cu_seqlens_q[:-1]\n query_layer = query_layer.squeeze(1)\n else:\n # The -q_len: slice assumes left padding.\n attention_mask = attention_mask[:, -query_length:]\n query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)\n\n return (\n query_layer,\n key_layer,\n value_layer,\n indices_q,\n (cu_seqlens_q, cu_seqlens_k),\n (max_seqlen_in_batch_q, max_seqlen_in_batch_k),\n )\n\n\nclass MiniCPMSdpaAttention(MiniCPMAttention):\n \"\"\"\n MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from\n `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to\n SDPA API.\n \"\"\"\n\n # Adapted from MiniCPMAttention.forward\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Cache] = None,\n output_attentions: bool = False,\n use_cache: bool = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n if output_attentions:\n # TODO: Improve this warning with e.g. `model.config.attn_implementation = \"manual\"` once this is implemented.\n logger.warning_once(\n \"MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, \"\n 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.'\n )\n return super().forward(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_value=past_key_value,\n output_attentions=output_attentions,\n use_cache=use_cache,\n )\n\n bsz, q_len, _ = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n\n kv_seq_len = key_states.shape[-2]\n if past_key_value is not None:\n kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)\n cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)\n\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\n if past_key_value is not None:\n cache_kwargs = {\"sin\": sin, \"cos\": cos} # Specific to RoPE models\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n key_states = repeat_kv(key_states, self.num_key_value_groups)\n value_states = repeat_kv(value_states, self.num_key_value_groups)\n\n if attention_mask is not None:\n if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}\"\n )\n\n # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,\n # Reference: https://github.com/pytorch/pytorch/issues/112577.\n if query_states.device.type == \"cuda\" and attention_mask is not None:\n query_states = query_states.contiguous()\n key_states = key_states.contiguous()\n value_states = value_states.contiguous()\n\n attn_output = torch.nn.functional.scaled_dot_product_attention(\n query_states,\n key_states,\n value_states,\n attn_mask=attention_mask,\n dropout_p=self.attention_dropout if self.training else 0.0,\n # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.\n is_causal=self.is_causal and attention_mask is None and q_len > 1,\n )\n\n attn_output = attn_output.transpose(1, 2).contiguous()\n attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)\n\n attn_output = self.o_proj(attn_output)\n\n return attn_output, None, past_key_value\n\n\nMINICPM_ATTENTION_CLASSES = {\n \"eager\": MiniCPMAttention,\n \"flash_attention_2\": MiniCPMFlashAttention2,\n \"sdpa\": MiniCPMSdpaAttention,\n}\n\n\nclass MiniCPMDecoderLayer(nn.Module):\n def __init__(self, config: LayerWiseMiniCPMConfig, layer_idx: int):\n super().__init__()\n self.hidden_size = config.hidden_size\n self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)\n\n self.mlp = MiniCPMMLP(config)\n self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n\n self.scale_depth = config.scale_depth\n self.num_hidden_layers = config.num_hidden_layers\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Tuple[torch.Tensor]] = None,\n output_attentions: Optional[bool] = False,\n use_cache: Optional[bool] = False,\n **kwargs,\n ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:\n \"\"\"\n Args:\n hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n attention_mask (`torch.FloatTensor`, *optional*):\n attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,\n query_sequence_length, key_sequence_length)` if default attention is used.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n use_cache (`bool`, *optional*):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding\n (see `past_key_values`).\n past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states\n \"\"\"\n if \"padding_mask\" in kwargs:\n warnings.warn(\n \"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`\"\n )\n\n residual = hidden_states\n hidden_states = self.input_layernorm(hidden_states)\n # Self Attention\n hidden_states, self_attn_weights, present_key_value = self.self_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_value=past_key_value,\n output_attentions=output_attentions,\n use_cache=use_cache,\n **kwargs,\n )\n\n hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n\n # Fully Connected\n residual = hidden_states\n hidden_states = self.post_attention_layernorm(hidden_states)\n\n hidden_states = self.mlp(hidden_states)\n hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n\n outputs = (hidden_states,)\n\n if output_attentions:\n outputs += (self_attn_weights,)\n\n if use_cache:\n outputs += (present_key_value,)\n\n return outputs\n\n\nMINICPM_START_DOCSTRING = r\"\"\"\n This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n etc.)\n\n This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n and behavior.\n\n Parameters:\n config ([`LayerWiseMiniCPMConfig`]):\n Model configuration class with all the parameters of the model. Initializing with a config file does not\n load the weights associated with the model, only the configuration. Check out the\n [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\"\"\"\n\n\n@add_start_docstrings(\n \"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.\",\n MINICPM_START_DOCSTRING,\n)\nclass MiniCPMPreTrainedModel(PreTrainedModel):\n config_class = LayerWiseMiniCPMConfig\n base_model_prefix = \"model\"\n supports_gradient_checkpointing = True\n _no_split_modules = [\"MiniCPMDecoderLayer\"]\n _skip_keys_device_placement = \"past_key_values\"\n _supports_flash_attn_2 = True\n _supports_sdpa = True\n _supports_cache_class = True\n\n def _init_weights(self, module):\n std = self.config.initializer_range\n if isinstance(module, nn.Linear):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n\nMINICPM_INPUTS_DOCSTRING = r\"\"\"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide\n it.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n If `past_key_values` is used, optionally only the last `input_ids` have to be input (see\n `past_key_values`).\n\n If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]\n and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more\n information on the default strategy.\n\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n config.n_positions - 1]`.\n\n [What are position IDs?](../glossary#position-ids)\n past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):\n Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention\n blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`\n returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.\n\n Two formats are allowed:\n - a [`~cache_utils.Cache`] instance;\n - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of\n shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy\n cache format.\n\n The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the\n legacy cache format will be returned.\n\n If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't\n have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`\n of shape `(batch_size, sequence_length)`.\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n use_cache (`bool`, *optional*):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see\n `past_key_values`).\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\n\n@add_start_docstrings(\n \"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.\",\n MINICPM_START_DOCSTRING,\n)\nclass LayerWiseMiniCPMModel(MiniCPMPreTrainedModel):\n \"\"\"\n Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]\n\n Args:\n config: LayerWiseMiniCPMConfig\n \"\"\"\n\n def __init__(self, config: LayerWiseMiniCPMConfig):\n super().__init__(config)\n self.padding_idx = config.pad_token_id\n self.vocab_size = config.vocab_size\n\n self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)\n self.layers = nn.ModuleList(\n [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]\n )\n self._use_sdpa = config._attn_implementation == \"sdpa\"\n self._use_flash_attention_2 = config._attn_implementation == \"flash_attention_2\"\n\n self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n\n self.gradient_checkpointing = False\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.embed_tokens\n\n def set_input_embeddings(self, value):\n self.embed_tokens = value\n\n @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cutoff_layers: Optional[Union[int, List]] = None,\n ) -> Union[Tuple, BaseModelOutputWithPast]:\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # retrieve input_ids and inputs_embeds\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n elif input_ids is not None:\n batch_size, seq_length = input_ids.shape[:2]\n elif inputs_embeds is not None:\n batch_size, seq_length = inputs_embeds.shape[:2]\n else:\n raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n if self.gradient_checkpointing and self.training:\n if use_cache:\n logger.warning_once(\n \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n )\n use_cache = False\n\n past_key_values_length = 0\n if use_cache:\n use_legacy_cache = not isinstance(past_key_values, Cache)\n if use_legacy_cache:\n past_key_values = DynamicCache.from_legacy_cache(past_key_values)\n past_key_values_length = past_key_values.get_usable_length(seq_length)\n\n if position_ids is None:\n device = input_ids.device if input_ids is not None else inputs_embeds.device\n position_ids = torch.arange(\n past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device\n )\n position_ids = position_ids.unsqueeze(0)\n\n if inputs_embeds is None:\n inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb\n\n if self._use_flash_attention_2:\n # 2d mask is passed through the layers\n attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None\n elif self._use_sdpa and not output_attentions:\n # output_attentions=True can not be supported when using SDPA, and we fall back on\n # the manual implementation that requires a 4D causal mask in all cases.\n attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(\n attention_mask,\n (batch_size, seq_length),\n inputs_embeds,\n past_key_values_length,\n )\n else:\n # 4d mask is passed through the layers\n attention_mask = _prepare_4d_causal_attention_mask(\n attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length\n )\n\n # embed positions\n hidden_states = inputs_embeds\n\n # decoder layers\n all_hidden_states = () if output_hidden_states else None\n all_self_attns = () if output_attentions else None\n next_decoder_cache = None\n\n if cutoff_layers is None:\n max_layer = self.config.num_hidden_layers\n cutoff_layers = [max_layer]\n if isinstance(cutoff_layers, int):\n max_layer = cutoff_layers\n cutoff_layers = [cutoff_layers]\n else:\n max_layer = max(cutoff_layers)\n\n for idx, decoder_layer in enumerate(self.layers):\n if idx in cutoff_layers and output_hidden_states:\n all_hidden_states += (self.norm(hidden_states),)\n\n if idx == max_layer:\n break\n\n if self.gradient_checkpointing and self.training:\n layer_outputs = self._gradient_checkpointing_func(\n decoder_layer.__call__,\n hidden_states,\n attention_mask,\n position_ids,\n past_key_values,\n output_attentions,\n use_cache,\n )\n else:\n layer_outputs = decoder_layer(\n hidden_states,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_value=past_key_values,\n output_attentions=output_attentions,\n use_cache=use_cache,\n )\n\n hidden_states = layer_outputs[0]\n\n if use_cache:\n next_decoder_cache = layer_outputs[2 if output_attentions else 1]\n\n if output_attentions:\n all_self_attns += (layer_outputs[1],)\n\n hidden_states = self.norm(hidden_states)\n\n # add hidden states from the last decoder layer\n if output_hidden_states and self.config.num_hidden_layers == max_layer:\n all_hidden_states += (hidden_states,)\n\n next_cache = None\n if use_cache:\n next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache\n if not return_dict:\n return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)\n return BaseModelOutputWithPast(\n last_hidden_state=hidden_states,\n past_key_values=next_cache,\n hidden_states=all_hidden_states,\n attentions=all_self_attns,\n )\n\n\nclass LayerWiseHead(nn.Module):\n \"\"\"Head for sentence-level classification tasks.\"\"\"\n\n def __init__(self, input_size, output_size):\n super().__init__()\n self.linear_head = nn.Linear(input_size, output_size, bias=False)\n\n def forward(self, **kwargs):\n return self.linear_head(**kwargs)\n\nclass LayerWiseMiniCPMForCausalLM(MiniCPMPreTrainedModel):\n _tied_weights_keys = [\"lm_head.weight\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.model = LayerWiseMiniCPMModel(config)\n self.vocab_size = config.vocab_size\n\n if self.config.head_type == 'raw':\n if not self.config.head_multi:\n self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n else:\n self.lm_head = nn.ModuleList([nn.Linear(\n config.hidden_size, config.vocab_size, bias=False) for _ in range(\n self.config.start_layer,\n self.model.config.num_hidden_layers + 1)])\n elif self.config.head_type == 'complex':\n if not self.config.head_multi:\n # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n self.lm_head = LayerWiseHead(config.hidden_size, config.vocab_size)\n else:\n # self.lm_head = nn.ModuleList([nn.Linear(\n # config.hidden_size, config.vocab_size, bias=False) for _ in range(\n # self.config.start_layer,\n # self.model.config.num_hidden_layers + 1)])\n self.lm_head = nn.ModuleList([LayerWiseHead(\n config.hidden_size, config.vocab_size) for _ in range(\n self.config.start_layer,\n self.model.config.num_hidden_layers + 1)])\n else:\n if not self.config.head_multi:\n # self.lm_head = nn.Linear(config.hidden_size, 1, bias=False)\n self.lm_head = LayerWiseHead(config.hidden_size, 1)\n else:\n # self.lm_head = nn.ModuleList([nn.Linear(\n # config.hidden_size, 1, bias=False) for _ in range(\n # self.config.start_layer,\n # self.model.config.num_hidden_layers + 1)])\n self.lm_head = nn.ModuleList([LayerWiseHead(\n config.hidden_size, 1) for _ in range(\n self.config.start_layer,\n self.model.config.num_hidden_layers + 1)])\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.model.embed_tokens\n\n def set_input_embeddings(self, value):\n self.model.embed_tokens = value\n\n def get_output_embeddings(self):\n return self.lm_head\n\n def set_output_embeddings(self, new_embeddings):\n self.lm_head = new_embeddings\n\n def set_decoder(self, decoder):\n self.model = decoder\n\n def get_decoder(self):\n return self.model\n\n @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n labels: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cutoff_layers: Optional[Union[int, List]] = None,\n only_for_one_logit: Optional[int] = None\n ) -> Union[Tuple, CausalLMOutputWithPast]:\n r\"\"\"\n Args:\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\n\n Returns:\n\n Example:\n\n ```python\n >>> from transformers import AutoTokenizer, MiniCPMForCausalLM\n\n >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)\n >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)\n\n >>> prompt = \"Hey, are you conscious? Can you talk to me?\"\n >>> inputs = tokenizer(prompt, return_tensors=\"pt\")\n\n >>> # Generate\n >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\n >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n \"Hey, are you conscious? Can you talk to me?\\nI'm not conscious, but I can talk to you.\"\n ```\"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if cutoff_layers is None:\n cutoff_layers = [self.config.num_hidden_layers]\n elif isinstance(cutoff_layers, int):\n cutoff_layers = [cutoff_layers]\n\n remove_layers = [i for i in cutoff_layers if self.config.start_layer > i or i > self.config.num_hidden_layers]\n if len(remove_layers) > 0:\n logger.warning_once(\n f\"layers {remove_layers} are incompatible with the setting. They will be removed...\"\n )\n\n cutoff_layers = [i for i in cutoff_layers if i not in remove_layers]\n if len(cutoff_layers) == 0:\n raise ValueError(f\"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]\")\n\n # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\n outputs = self.model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_values=past_key_values,\n inputs_embeds=inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=True,\n return_dict=return_dict,\n cutoff_layers=cutoff_layers\n )\n\n hidden_states = outputs[0]\n\n all_logits = ()\n if only_for_one_logit is None and (self.config.head_type == 'complex' or self.config.head_type == 'raw'):\n if self.config.head_type == 'raw':\n for i in range(len(outputs.hidden_states)):\n if self.config.head_multi == False:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n else:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head[cutoff_layers[i] - self.config.start_layer](outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n else:\n for i in range(len(outputs.hidden_states)):\n if self.config.head_multi == False:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head.linear_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head.linear_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n else:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n else:\n if self.config.head_type == 'raw':\n if only_for_one_logit is None:\n raise ValueError(\"Cannot handle `only_for_one_logit` is None if the head type is complex.\")\n\n if self.config.head_multi == False:\n lm_head_slices = self.lm_head.weight.split(1, dim=0)\n for i in range(len(outputs.hidden_states)):\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n else:\n for i in range(len(outputs.hidden_states)):\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].weight.split(1, dim=0)\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n elif self.config.head_type == 'complex':\n if only_for_one_logit is None:\n raise ValueError(\"Cannot handle `only_for_one_logit` is None if the head type is complex.\")\n\n if self.config.head_multi == False:\n lm_head_slices = self.lm_head.linear_head.weight.split(1, dim=0)\n for i in range(len(outputs.hidden_states)):\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n else:\n for i in range(len(outputs.hidden_states)):\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head.weight.split(1, dim=0)\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n else:\n if self.config.head_multi == False:\n for i in range(len(outputs.hidden_states)):\n logits = self.lm_head.linear_head(outputs.hidden_states[i])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n else:\n for i in range(len(outputs.hidden_states)):\n logits = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head(outputs.hidden_states[i])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n\n loss = None\n if labels is not None and not only_for_one_logit and self.config.head_type == 'complex':\n # Shift so that tokens < n predict n\n loss = 0\n for logits in all_logits:\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n # Flatten the tokens\n loss_fct = CrossEntropyLoss()\n shift_logits = shift_logits.view(-1, self.config.vocab_size)\n shift_labels = shift_labels.view(-1)\n # Enable model parallelism\n shift_labels = shift_labels.to(shift_logits.device)\n loss += loss_fct(shift_logits, shift_labels)\n\n outputs.hidden_states = None if not output_hidden_states else outputs.hidden_states\n\n if not return_dict:\n output = (all_logits,) + outputs[1:]\n return (loss,) + output if loss is not None else output\n\n return CausalLMOutputWithPast(\n loss=loss,\n logits=all_logits,\n past_key_values=outputs.past_key_values,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n def prepare_inputs_for_generation(\n self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs\n ):\n if past_key_values is not None:\n if isinstance(past_key_values, Cache):\n cache_length = past_key_values.get_seq_length()\n past_length = past_key_values.seen_tokens\n max_cache_length = past_key_values.get_max_length()\n else:\n cache_length = past_length = past_key_values[0][0].shape[2]\n max_cache_length = None\n\n # Keep only the unprocessed tokens:\n # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where\n # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as\n # input)\n if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:\n input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]\n # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard\n # input_ids based on the past_length.\n elif past_length < input_ids.shape[1]:\n input_ids = input_ids[:, past_length:]\n # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.\n\n # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.\n if (\n max_cache_length is not None\n and attention_mask is not None\n and cache_length + input_ids.shape[1] > max_cache_length\n ):\n attention_mask = attention_mask[:, -max_cache_length:]\n\n position_ids = kwargs.get(\"position_ids\", None)\n if attention_mask is not None and position_ids is None:\n # create position_ids on the fly for batch generation\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n if past_key_values:\n position_ids = position_ids[:, -input_ids.shape[1]:]\n\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\n if inputs_embeds is not None and past_key_values is None:\n model_inputs = {\"inputs_embeds\": inputs_embeds}\n else:\n model_inputs = {\"input_ids\": input_ids}\n\n model_inputs.update(\n {\n \"position_ids\": position_ids,\n \"past_key_values\": past_key_values,\n \"use_cache\": kwargs.get(\"use_cache\"),\n \"attention_mask\": attention_mask,\n }\n )\n return model_inputs\n\n @staticmethod\n def _reorder_cache(past_key_values, beam_idx):\n reordered_past = ()\n for layer_past in past_key_values:\n reordered_past += (\n tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),\n )\n return reordered_past\n\n @torch.inference_mode()\n def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = \"user\",\n max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,\n **kwargs):\n if history is None:\n history = []\n if logits_processor:\n gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams, \"do_sample\": do_sample, \"top_p\": top_p,\n \"temperature\": temperature, \"logits_processor\": logits_processor, **kwargs}\n else:\n gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams, \"do_sample\": do_sample, \"top_p\": top_p,\n \"temperature\": temperature, \"logits_processor\": logits_processor, **kwargs}\n\n history.append({\"role\": role, \"content\": query})\n history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)\n inputs = tokenizer(history_str, return_tensors='pt').to(self.device)\n outputs = self.generate(**inputs, **gen_kwargs)\n outputs = outputs.tolist()[0][len(inputs[\"input_ids\"][0]):-1]\n response = tokenizer.decode(outputs)\n pattern = re.compile(r\".*?(?=|<用户>)\", re.DOTALL)\n matches = pattern.findall(response)\n if len(matches) > 0:\n response = matches[0]\n history.append({\"role\": \"assistant\", \"content\": response})\n return response, history" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/runner.py", "content": "import os\nimport logging\nfrom typing import Tuple\nfrom pathlib import Path\nfrom FlagEmbedding.abc.finetune.reranker.AbsArguments import AbsRerankerDataArguments, AbsRerankerTrainingArguments\nfrom transformers import (\n AutoTokenizer, PreTrainedTokenizer\n)\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerRunner, AbsRerankerModel\nfrom FlagEmbedding.finetune.reranker.decoder_only.layerwise.modeling import CrossDecoderModel\nfrom FlagEmbedding.finetune.reranker.decoder_only.layerwise.arguments import RerankerModelArguments\nfrom FlagEmbedding.finetune.reranker.decoder_only.layerwise.trainer import DecoderOnlyRerankerTrainer\nfrom FlagEmbedding.finetune.reranker.decoder_only.layerwise.load_model import get_model, save_merged_model\n\nlogger = logging.getLogger(__name__)\n\nclass DecoderOnlyRerankerRunner(AbsRerankerRunner):\n \"\"\"\n Decoder only layerwise reranker runner for finetuning.\n \n Args:\n model_args (RerankerModelArguments): Model arguments instance.\n data_args (AbsRerankerDataArguments): Data arguments instance.\n training_args (AbsRerankerTrainingArguments): Trainer arguments.\n \"\"\"\n def __init__(\n self,\n model_args: RerankerModelArguments,\n data_args: AbsRerankerDataArguments,\n training_args: AbsRerankerTrainingArguments\n ):\n super().__init__(model_args, data_args, training_args)\n\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsRerankerModel]:\n \"\"\"Load the tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n # print(self.model_args.model_name_or_path)\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.tokenizer_name if self.model_args.tokenizer_name else self.model_args.model_name_or_path,\n token=self.model_args.token,\n cache_dir=self.model_args.cache_dir,\n use_fast=self.model_args.use_fast_tokenizer,\n add_eos_token=False,\n trust_remote_code=self.model_args.trust_remote_code\n )\n\n if tokenizer.pad_token is None:\n if tokenizer.unk_token is not None:\n tokenizer.pad_token = tokenizer.unk_token\n tokenizer.pad_token_id = tokenizer.unk_token_id\n elif tokenizer.eod_id is not None:\n tokenizer.pad_token = tokenizer.eod\n tokenizer.pad_token_id = tokenizer.eod_id\n tokenizer.bos_token = tokenizer.im_start\n tokenizer.bos_token_id = tokenizer.im_start_id\n tokenizer.eos_token = tokenizer.im_end\n tokenizer.eos_token_id = tokenizer.im_end_id\n else:\n tokenizer.pad_token = tokenizer.eos_token\n tokenizer.pad_token_id = tokenizer.eos_token_id\n # if 'mistral' in self.model_args.model_name_or_path.lower():\n tokenizer.padding_side = 'left'\n\n base_model = get_model(self.model_args, tokenizer('Yes', add_special_tokens=False)['input_ids'][-1])\n\n model = CrossDecoderModel(\n base_model,\n tokenizer=tokenizer,\n train_batch_size=self.training_args.per_device_train_batch_size,\n start_layer=self.model_args.start_layer\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n return tokenizer, model\n\n def load_trainer(self) -> DecoderOnlyRerankerTrainer:\n \"\"\"Load the trainer.\n\n Returns:\n DecoderOnlyRerankerTrainer: Loaded trainer instance.\n \"\"\"\n trainer = DecoderOnlyRerankerTrainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n return trainer\n\n def run(self):\n \"\"\"\n Run the finetuning.\n \"\"\"\n Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)\n self.trainer.save_model()\n\n # save merged model\n if self.model_args.save_merged_lora_model and self.training_args.process_index == 0:\n save_merged_model(self.model_args, self.training_args.output_dir)\n" }, { "path": "FlagEmbedding/finetune/reranker/decoder_only/layerwise/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n# from transformers.deepspeed import is_deepspeed_zero3_enabled\nfrom peft import get_peft_model_state_dict\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass DecoderOnlyRerankerTrainer(AbsRerankerTrainer):\n \"\"\"\n Trainer class for encoder only base reranker models.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # if is_deepspeed_zero3_enabled():\n # if state_dict is None:\n # state_dict = self.model.state_dict()\n # prefix = 'model.'\n # assert all(k.startswith(prefix) for k in state_dict.keys()), list(state_dict.keys())\n # state_dict = {k[len(prefix):]: v for k, v in state_dict.items()}\n # lora_state_dict = get_peft_model_state_dict(self.model.model, state_dict)\n # if self.args.process_index <= 0:\n # torch.save(lora_state_dict, os.path.join(output_dir, \"adapter_model.bin\"))\n # print(f\"Save adapter model at {output_dir}\")\n" }, { "path": "FlagEmbedding/finetune/reranker/encoder_only/__init__.py", "content": "" }, { "path": "FlagEmbedding/finetune/reranker/encoder_only/base/__init__.py", "content": "from .modeling import CrossEncoderModel\nfrom .runner import EncoderOnlyRerankerRunner\nfrom .trainer import EncoderOnlyRerankerTrainer\n\n__all__ = [\n \"CrossEncoderModel\",\n \"EncoderOnlyRerankerRunner\",\n \"EncoderOnlyRerankerTrainer\"\n]\n" }, { "path": "FlagEmbedding/finetune/reranker/encoder_only/base/__main__.py", "content": "from transformers import HfArgumentParser\n\nfrom FlagEmbedding.abc.finetune.reranker import (\n AbsRerankerModelArguments,\n AbsRerankerDataArguments,\n AbsRerankerTrainingArguments\n)\nfrom FlagEmbedding.finetune.reranker.encoder_only.base import EncoderOnlyRerankerRunner\n\n\ndef main():\n parser = HfArgumentParser((AbsRerankerModelArguments, AbsRerankerDataArguments, AbsRerankerTrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: AbsRerankerModelArguments\n data_args: AbsRerankerDataArguments\n training_args: AbsRerankerTrainingArguments\n\n runner = EncoderOnlyRerankerRunner(\n model_args=model_args,\n data_args=data_args,\n training_args=training_args\n )\n runner.run()\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "FlagEmbedding/finetune/reranker/encoder_only/base/modeling.py", "content": "from transformers import PreTrainedModel, AutoTokenizer\nimport logging\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerModel\n\nlogger = logging.getLogger(__name__)\n\n\nclass CrossEncoderModel(AbsRerankerModel):\n \"\"\"Model class for reranker.\n\n Args:\n base_model (PreTrainedModel): The underlying pre-trained model used for encoding and scoring input pairs.\n tokenizer (AutoTokenizer, optional): The tokenizer for encoding input text. Defaults to ``None``.\n train_batch_size (int, optional): The batch size to use. Defaults to ``4``.\n \"\"\"\n def __init__(\n self,\n base_model: PreTrainedModel,\n tokenizer: AutoTokenizer = None,\n train_batch_size: int = 4,\n ):\n super().__init__(\n base_model,\n tokenizer=tokenizer,\n train_batch_size=train_batch_size,\n )\n\n def encode(self, features):\n \"\"\"Encodes input features to logits.\n\n Args:\n features (dict): Dictionary with input features.\n\n Returns:\n torch.Tensor: The logits output from the model.\n \"\"\"\n return self.model(**features, return_dict=True).logits\n" }, { "path": "FlagEmbedding/finetune/reranker/encoder_only/base/runner.py", "content": "import logging\nfrom typing import Tuple\nfrom transformers import (\n AutoModelForSequenceClassification, AutoConfig,\n AutoTokenizer, PreTrainedTokenizer\n)\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerRunner, AbsRerankerModel\nfrom FlagEmbedding.finetune.reranker.encoder_only.base.modeling import CrossEncoderModel\nfrom FlagEmbedding.finetune.reranker.encoder_only.base.trainer import EncoderOnlyRerankerTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyRerankerRunner(AbsRerankerRunner):\n \"\"\"\n Encoder only reranker runner for finetuning.\n \"\"\"\n def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsRerankerModel]:\n \"\"\"Load the tokenizer and model.\n\n Returns:\n Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.\n \"\"\"\n tokenizer = AutoTokenizer.from_pretrained(\n self.model_args.model_name_or_path,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n use_fast=self.model_args.use_fast_tokenizer,\n trust_remote_code=self.model_args.trust_remote_code\n )\n\n num_labels = 1\n config = AutoConfig.from_pretrained(\n self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n trust_remote_code=self.model_args.trust_remote_code,\n )\n logger.info('Config: %s', config)\n\n base_model = AutoModelForSequenceClassification.from_pretrained(\n self.model_args.model_name_or_path,\n config=config,\n cache_dir=self.model_args.cache_dir,\n token=self.model_args.token,\n from_tf=bool(\".ckpt\" in self.model_args.model_name_or_path),\n trust_remote_code=self.model_args.trust_remote_code\n )\n\n model = CrossEncoderModel(\n base_model,\n tokenizer=tokenizer,\n train_batch_size=self.training_args.per_device_train_batch_size,\n )\n\n if self.training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n return tokenizer, model\n\n def load_trainer(self) -> EncoderOnlyRerankerTrainer:\n \"\"\"Load the trainer.\n\n Returns:\n EncoderOnlyRerankerTrainer: Loaded trainer instance.\n \"\"\"\n trainer = EncoderOnlyRerankerTrainer(\n model=self.model,\n args=self.training_args,\n train_dataset=self.train_dataset,\n data_collator=self.data_collator,\n tokenizer=self.tokenizer\n )\n return trainer\n" }, { "path": "FlagEmbedding/finetune/reranker/encoder_only/base/trainer.py", "content": "import os\nimport torch\nimport logging\nfrom typing import Optional\n\nfrom FlagEmbedding.abc.finetune.reranker import AbsRerankerTrainer\n\nlogger = logging.getLogger(__name__)\n\n\nclass EncoderOnlyRerankerTrainer(AbsRerankerTrainer):\n \"\"\"\n Trainer class for encoder only base reranker models.\n \"\"\"\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n \"\"\"Save the model to directory.\n\n Args:\n output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.\n\n Raises:\n NotImplementedError\n \"\"\"\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save_pretrained'):\n raise NotImplementedError(f'MODEL {self.model.__class__.__name__} ' f'does not support save_pretrained interface')\n else:\n self.model.save_pretrained(output_dir)\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n # Good practice: save your training arguments together with the trained model\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n" }, { "path": "FlagEmbedding/inference/__init__.py", "content": "from .auto_embedder import FlagAutoModel\nfrom .auto_reranker import FlagAutoReranker\nfrom .embedder import (\n FlagModel, BGEM3FlagModel,\n FlagICLModel, FlagLLMModel,\n EmbedderModelClass\n)\nfrom .reranker import (\n FlagReranker,\n FlagLLMReranker, LayerWiseFlagLLMReranker, LightWeightFlagLLMReranker,\n RerankerModelClass\n)\n\n\n__all__ = [\n \"FlagAutoModel\",\n \"FlagAutoReranker\",\n \"EmbedderModelClass\",\n \"RerankerModelClass\",\n \"FlagModel\",\n \"BGEM3FlagModel\",\n \"FlagICLModel\",\n \"FlagLLMModel\",\n \"FlagReranker\",\n \"FlagLLMReranker\",\n \"LayerWiseFlagLLMReranker\",\n \"LightWeightFlagLLMReranker\",\n]\n" }, { "path": "FlagEmbedding/inference/auto_embedder.py", "content": "import os\nimport logging\nfrom typing import List, Union, Optional\n\nfrom FlagEmbedding.inference.embedder.model_mapping import (\n EmbedderModelClass,\n AUTO_EMBEDDER_MAPPING, EMBEDDER_CLASS_MAPPING\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass FlagAutoModel:\n \"\"\"\n Automatically choose the appropriate class to load the embedding model.\n \"\"\"\n def __init__(self):\n raise EnvironmentError(\n \"FlagAutoModel is designed to be instantiated using the `FlagAutoModel.from_finetuned(model_name_or_path)` method.\"\n )\n\n @classmethod\n def from_finetuned(\n cls,\n model_name_or_path: str,\n model_class: Optional[Union[str, EmbedderModelClass]] = None,\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: Optional[str] = None,\n devices: Optional[Union[str, List[str]]] = None,\n pooling_method: Optional[str] = None,\n trust_remote_code: Optional[bool] = None,\n query_instruction_format: Optional[str] = None,\n **kwargs,\n ):\n \"\"\"\n Load a finetuned model according to the provided vars.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n model_class (Optional[Union[str, EmbedderModelClass]], optional): The embedder class to use. Defaults to :data:`None`.\n normalize_embeddings (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. \n Defaults to :data:`True`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`True`.\n query_instruction_for_retrieval (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n :attr:`query_instruction_format`. Defaults to :data:`None`.\n devices (Optional[Union[str, List[str]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n pooling_method (Optional[str], optional): Pooling method to get embedding vector from the last hidden state. Defaults to :data:`None`.\n trust_remote_code (Optional[bool], optional): trust_remote_code for HF datasets or models. Defaults to :data:`None`.\n query_instruction_format (Optional[str], optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`None`.\n\n Raises:\n ValueError\n\n Returns:\n AbsEmbedder: The model class to load model, which is child class of :class:`AbsEmbedder`.\n \"\"\"\n model_name = os.path.basename(model_name_or_path)\n if model_name.startswith(\"checkpoint-\"):\n model_name = os.path.basename(os.path.dirname(model_name_or_path))\n\n if model_class is not None:\n _model_class = EMBEDDER_CLASS_MAPPING[EmbedderModelClass(model_class)]\n if pooling_method is None:\n pooling_method = _model_class.DEFAULT_POOLING_METHOD\n logger.warning(\n f\"`pooling_method` is not specified, use default pooling method '{pooling_method}'.\"\n )\n if trust_remote_code is None:\n trust_remote_code = False\n logger.warning(\n f\"`trust_remote_code` is not specified, set to default value '{trust_remote_code}'.\"\n )\n if query_instruction_format is None:\n query_instruction_format = \"{}{}\"\n logger.warning(\n f\"`query_instruction_format` is not specified, set to default value '{query_instruction_format}'.\"\n )\n else:\n if model_name not in AUTO_EMBEDDER_MAPPING:\n raise ValueError(\n f\"Model name '{model_name}' not found in the model mapping. You can pull request to add the model to \"\n \"`https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/model_mapping.py`. \" \n \"If need, you can create a new `.py` file in `https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/encoder_only` \"\n \"or `https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/decoder_only`. \"\n \"Welcome to contribute! You can also directly specify the corresponding `model_class` to instantiate the model.\"\n )\n\n model_config = AUTO_EMBEDDER_MAPPING[model_name]\n\n _model_class = model_config.model_class\n if pooling_method is None:\n pooling_method = model_config.pooling_method.value\n if trust_remote_code is None:\n trust_remote_code = model_config.trust_remote_code\n if query_instruction_format is None:\n query_instruction_format = model_config.query_instruction_format\n\n return _model_class(\n model_name_or_path,\n normalize_embeddings=normalize_embeddings,\n use_fp16=use_fp16,\n query_instruction_for_retrieval=query_instruction_for_retrieval,\n query_instruction_format=query_instruction_format,\n devices=devices,\n pooling_method=pooling_method,\n trust_remote_code=trust_remote_code,\n **kwargs,\n )\n" }, { "path": "FlagEmbedding/inference/auto_reranker.py", "content": "import os\nimport logging\nfrom typing import Union, Optional\n\nfrom FlagEmbedding.inference.reranker.model_mapping import (\n RerankerModelClass,\n RERANKER_CLASS_MAPPING,\n AUTO_RERANKER_MAPPING\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass FlagAutoReranker:\n \"\"\"\n Automatically choose the appropriate class to load the reranker model.\n \"\"\"\n def __init__(self):\n raise EnvironmentError(\n \"FlagAutoReranker is designed to be instantiated using the `FlagAutoReranker.from_finetuned(model_name_or_path)` method.\"\n )\n\n @classmethod\n def from_finetuned(\n cls,\n model_name_or_path: str,\n model_class: Optional[Union[str, RerankerModelClass]] = None,\n use_fp16: bool = False,\n trust_remote_code: Optional[bool] = None,\n **kwargs,\n ):\n \"\"\"\n Load a finetuned model according to the provided vars.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n model_class (Optional[Union[str, RerankerModelClass]], optional): The reranker class to use.. Defaults to :data:`None`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`False`.\n trust_remote_code (Optional[bool], optional): trust_remote_code for HF datasets or models. Defaults to :data:`None`.\n\n Raises:\n ValueError\n\n Returns:\n AbsReranker: The reranker class to load model, which is child class of :class:`AbsReranker`.\n \"\"\"\n model_name = os.path.basename(model_name_or_path)\n if model_name.startswith(\"checkpoint-\"):\n model_name = os.path.basename(os.path.dirname(model_name_or_path))\n\n if model_class is not None:\n _model_class = RERANKER_CLASS_MAPPING[RerankerModelClass(model_class)]\n if trust_remote_code is None:\n trust_remote_code = False\n logging.warning(\n f\"`trust_remote_code` is not specified, set to default value '{trust_remote_code}'.\"\n )\n else:\n if model_name not in AUTO_RERANKER_MAPPING:\n raise ValueError(\n f\"Model name '{model_name}' not found in the model mapping. You can pull request to add the model to \"\n \"`https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/reranker/model_mapping.py`. \" \n \"If need, you can create a new `.py` file in `https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/reranker/encoder_only` \"\n \"or `https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/reranker/decoder_only`. \"\n \"Welcome to contribute! You can also directly specify the corresponding `model_class` to instantiate the model.\"\n )\n\n model_config = AUTO_RERANKER_MAPPING[model_name]\n\n _model_class = model_config.model_class\n if trust_remote_code is None:\n trust_remote_code = model_config.trust_remote_code\n\n return _model_class(\n model_name_or_path,\n use_fp16=use_fp16,\n trust_remote_code=trust_remote_code,\n **kwargs,\n )\n" }, { "path": "FlagEmbedding/inference/embedder/__init__.py", "content": "from .encoder_only import FlagModel, BGEM3FlagModel\nfrom .decoder_only import FlagICLModel, FlagLLMModel\nfrom .model_mapping import EmbedderModelClass\n\n__all__ = [\n \"FlagModel\",\n \"BGEM3FlagModel\",\n \"FlagICLModel\",\n \"FlagLLMModel\",\n \"EmbedderModelClass\",\n]\n" }, { "path": "FlagEmbedding/inference/embedder/decoder_only/__init__.py", "content": "from .base import BaseLLMEmbedder as FlagLLMModel\nfrom .icl import ICLLLMEmbedder as FlagICLModel\n\n__all__ = [\n \"FlagLLMModel\",\n \"FlagICLModel\",\n]\n" }, { "path": "FlagEmbedding/inference/embedder/decoder_only/base.py", "content": "from tqdm import tqdm, trange\nfrom typing import cast, Any, List, Union, Optional\n\nimport torch\nimport numpy as np\nfrom transformers import AutoModel, AutoTokenizer\n\nfrom FlagEmbedding.abc.inference import AbsEmbedder\n\n\n# Pooling function for LLM-based embedding models\ndef last_token_pool(last_hidden_states: torch.Tensor,\n attention_mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Last token pooling method.\n\n Args:\n last_hidden_state (torch.Tensor): The last hidden state of the model.\n attention_mask (torch.Tensor): Attention mask. Defaults to :data:`None`.\n\n Returns:\n torch.Tensor: The embedding vectors after pooling.\n \"\"\"\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\nclass BaseLLMEmbedder(AbsEmbedder):\n \"\"\"Base embedder class for LLM like decoder only models.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to :data:`True`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`True`.\n query_instruction_for_retrieval (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`\"Instruct: {}\\nQuery: {}\"`.\n devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n trust_remote_code (bool, optional): trust_remote_code for HF datasets or models. Defaults to :data:`False`.\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.\n query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.\n passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. \n Defaults to :data:`True`.\n \n Attributes:\n DEFAULT_POOLING_METHOD: The default pooling method when running the model.\n \"\"\"\n DEFAULT_POOLING_METHOD = \"last_token\"\n\n def __init__(\n self,\n model_name_or_path: str,\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: Optional[str] = None,\n query_instruction_format: str = \"Instruct: {}\\nQuery: {}\", # specify the format of query_instruction_for_retrieval\n devices: Optional[Union[str, List[str]]] = None, # specify devices, such as \"cuda:0\" or [\"cuda:0\", \"cuda:1\"]\n # Additional parameters for BaseLLMEmbedder\n trust_remote_code: bool = False,\n cache_dir: Optional[str] = None,\n # inference\n batch_size: int = 256,\n query_max_length: int = 512,\n passage_max_length: int = 512,\n convert_to_numpy: bool = True,\n **kwargs: Any,\n ):\n super().__init__(\n model_name_or_path,\n normalize_embeddings=normalize_embeddings,\n use_fp16=use_fp16,\n query_instruction_for_retrieval=query_instruction_for_retrieval,\n query_instruction_format=query_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n passage_max_length=passage_max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n self.model = AutoModel.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n\n if self.kwargs.get(\"pooling_method\", \"last_token\") != \"last_token\":\n raise ValueError(\"Pooling method must be 'last_token' for LLM-based models.\")\n\n def encode_queries(\n self,\n queries: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the queries.\n\n Args:\n queries (Union[List[str], str]): Input queries to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode_queries(\n queries,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n def encode_corpus(\n self,\n corpus: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the corpus.\n\n Args:\n corpus (Union[List[str], str]): Input corpus to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode_corpus(\n corpus,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n def encode(\n self,\n sentences: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the input sentences with the embedding model.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode(\n sentences,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n @torch.no_grad()\n def encode_single_device(\n self,\n sentences: Union[List[str], str],\n batch_size: int = 256,\n max_length: int = 512,\n convert_to_numpy: bool = True,\n device: Optional[str] = None,\n **kwargs: Any # add `pad_to_multiple_of=8` for bge-multilingual-gemmma2\n ):\n \"\"\"Encode input sentences by a single device.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (int, optional): Number of sentences for each iter. Defaults to :data:`256`.\n max_length (int, optional): Maximum length of tokens. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`True`.\n device (Optional[str], optional): Device to use for encoding. Defaults to None.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n input_was_string = False\n if isinstance(sentences, str):\n sentences = [sentences]\n input_was_string = True\n\n # tokenize without padding to get the correct length\n all_inputs = []\n for start_index in trange(0, len(sentences), batch_size, desc='pre tokenize',\n disable=len(sentences) < batch_size):\n sentences_batch = sentences[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer(\n sentences_batch,\n truncation=True,\n max_length=max_length,\n **kwargs\n )\n inputs_batch = [{\n k: inputs_batch[k][i] for k in inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n all_inputs.extend(inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])\n all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n inputs_batch = self.tokenizer.pad(\n all_inputs_sorted[: batch_size],\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n # encode\n all_embeddings = []\n for start_index in tqdm(range(0, len(sentences), batch_size), desc=\"Inference Embeddings\",\n disable=len(sentences) < batch_size):\n inputs_batch = all_inputs_sorted[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer.pad(\n inputs_batch,\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])\n if self.normalize_embeddings:\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n\n if convert_to_numpy:\n embeddings = embeddings.cpu().numpy()\n all_embeddings.append(embeddings)\n\n if convert_to_numpy:\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n else:\n all_embeddings = torch.cat(all_embeddings, dim=0)\n\n # adjust the order of embeddings\n all_embeddings = all_embeddings[np.argsort(length_sorted_idx)]\n\n # return the embeddings\n if input_was_string:\n return all_embeddings[0]\n return all_embeddings\n" }, { "path": "FlagEmbedding/inference/embedder/decoder_only/icl.py", "content": "from tqdm import tqdm, trange\nfrom typing import cast, Any, List, Union, Optional\n\nimport queue\nfrom multiprocessing import Queue\n\nimport gc\nimport torch\nimport numpy as np\nfrom transformers import AutoModel, AutoTokenizer\n\nfrom FlagEmbedding.abc.inference import AbsEmbedder\n\n\n# Pooling function for LLM-based embedding models\ndef last_token_pool(last_hidden_states: torch.Tensor,\n attention_mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Last token pooling method.\n\n Args:\n last_hidden_state (torch.Tensor): The last hidden state of the model.\n attention_mask (torch.Tensor): Attention mask. Defaults to :data:`None`.\n\n Returns:\n torch.Tensor: The embedding vectors after pooling.\n \"\"\"\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\nclass ICLLLMEmbedder(AbsEmbedder):\n \"\"\"\n Embedder class for BGE-EN-icl.\n \n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to :data:`True`.\n use_fp16 (bool, optional) If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`True`.\n query_instruction_for_retrieval (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`\"{}{}\"`.\n devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n examples_for_task (Optional[List[dict]], optional): Few-shot examples for the model to enhance model's ability. \n Defaults to :data:`None`.\n examples_instruction_format (str, optional): Example format when using :attr:`examples_for_task`.\n trust_remote_code (bool, optional): trust_remote_code for HF datasets or models. Defaults to :data:`False`.\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.\n query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.\n passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. \n Defaults to :data:`True`.\n \n Attributes:\n DEFAULT_POOLING_METHOD: The default pooling method when running the model.\n \"\"\"\n DEFAULT_POOLING_METHOD = \"last_token\"\n\n def __init__(\n self,\n model_name_or_path: str,\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: Optional[str] = None,\n query_instruction_format: str = \"{}\\n{}\", # specify the format of query_instruction_for_retrieval\n suffix: str = '\\n',\n devices: Optional[Union[str, List[str]]] = None, # specify devices, such as \"cuda:0\" or [\"cuda:0\", \"cuda:1\"]\n # Additional parameters for ICLLLMEmbedder\n examples_for_task: Optional[List[dict]] = None,\n examples_instruction_format: str = \"{}\\n{}\\n{}\", # specify the format of examples_for_task\n trust_remote_code: bool = False,\n cache_dir: Optional[str] = None,\n # inference\n batch_size: int = 256,\n query_max_length: int = 512,\n passage_max_length: int = 512,\n convert_to_numpy: bool = True,\n **kwargs: Any,\n ):\n query_instruction_format = query_instruction_format.replace('\\\\n', '\\n')\n examples_instruction_format = examples_instruction_format.replace('\\\\n', '\\n')\n super().__init__(\n model_name_or_path,\n normalize_embeddings=normalize_embeddings,\n use_fp16=use_fp16,\n query_instruction_for_retrieval=query_instruction_for_retrieval,\n query_instruction_format=query_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n passage_max_length=passage_max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n self.model = AutoModel.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n self.examples_for_task = examples_for_task\n self.examples_instruction_format = examples_instruction_format\n\n if self.kwargs.get(\"pooling_method\", \"last_token\") != \"last_token\":\n raise ValueError(\"Pooling method must be 'last_token' for LLM-based models.\")\n\n self.set_examples()\n self.suffix = suffix\n\n self.query_pool = None\n \n def __del__(self):\n self.stop_self_pool()\n self.stop_self_query_pool()\n\n def set_examples(self, examples_for_task: Optional[List[dict]] = None):\n \"\"\"Set the prefix to the provided examples.\n\n Args:\n examples_for_task (Optional[List[dict]], optional): Few-shot examples for the model to enhance model's ability. \n Defaults to :data:`None`.\n \"\"\"\n if examples_for_task is None and self.examples_for_task is None:\n self.prefix = ''\n elif examples_for_task is not None:\n eg_paris = []\n for i in range(len(examples_for_task)):\n eg_paris.append(\n self.get_detailed_example(\n self.examples_instruction_format,\n examples_for_task[i].get('instruct', self.query_instruction_for_retrieval),\n examples_for_task[i].get('query', ''),\n examples_for_task[i].get('response', '')\n )\n )\n self.prefix = '\\n\\n'.join(eg_paris) + '\\n\\n'\n else:\n eg_paris = []\n for i in range(len(self.examples_for_task)):\n eg_paris.append(\n self.get_detailed_example(\n self.examples_instruction_format,\n self.examples_for_task[i].get('instruct', self.query_instruction_for_retrieval),\n self.examples_for_task[i].get('query', ''),\n self.examples_for_task[i].get('response', '')\n )\n )\n self.prefix = '\\n\\n'.join(eg_paris) + '\\n\\n'\n\n @staticmethod\n def get_detailed_example(instruction_format: str, instruction: str, query: str, response: str):\n \"\"\"Combine the instruction and sentence along with the instruction format.\n\n Args:\n instruction_format (str): Format for instruction.\n instruction (str): The text of instruction.\n query (str): The text of example query.\n response (str): The text of example response.\n\n Returns:\n str: The complete example following the given format.\n \"\"\"\n if \"\\\\n\" in instruction_format:\n instruction_format = instruction_format.replace(\"\\\\n\", \"\\n\")\n return instruction_format.format(instruction, query, response)\n\n def stop_self_query_pool(self):\n if self.query_pool is not None:\n self.stop_multi_process_pool(self.query_pool)\n self.query_pool = None\n try:\n self.model.to('cpu')\n torch.cuda.empty_cache()\n except:\n pass\n gc.collect()\n\n def encode_queries(\n self,\n queries: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the queries.\n\n Args:\n queries (Union[List[str], str]): Input queries to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.query_max_length\n if convert_to_numpy is None: convert_to_numpy = self.convert_to_numpy\n \n if isinstance(queries, str) or len(self.target_devices) == 1:\n return self.encode_queries_single_device(\n queries,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n device=self.target_devices[0],\n **kwargs\n )\n\n self.stop_self_pool()\n if self.query_pool is None:\n self.query_pool = self.start_multi_process_pool(ICLLLMEmbedder._encode_queries_multi_process_worker)\n embeddings = self.encode_multi_process(\n queries,\n self.query_pool,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n return embeddings\n\n def encode_corpus(\n self,\n corpus: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the corpus.\n\n Args:\n corpus (Union[List[str], str]): Input corpus to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n self.stop_self_query_pool()\n return super().encode_corpus(\n corpus,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n def encode(\n self,\n sentences: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the input sentences with the embedding model.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode(\n sentences,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L976\n @staticmethod\n def _encode_queries_multi_process_worker(\n target_device: str, model: 'ICLLLMEmbedder', input_queue: Queue, results_queue: Queue\n ) -> None:\n \"\"\"\n Internal working process to encode sentences in multi-process setup\n \"\"\"\n while True:\n try:\n chunk_id, sentences, kwargs = (\n input_queue.get()\n )\n embeddings = model.encode_queries_single_device(\n sentences,\n device=target_device,\n **kwargs\n )\n\n results_queue.put([chunk_id, embeddings])\n except queue.Empty:\n break\n\n @torch.no_grad()\n def encode_queries_single_device(\n self,\n queries: Union[List[str], str],\n batch_size: int = 256,\n max_length: int = 512,\n convert_to_numpy: bool = True,\n device: Optional[str] = None,\n **kwargs: Any\n ):\n \"\"\"Encode queries by a single device.\n\n Args:\n queries (Union[List[str], str]): Input queries to encode.\n batch_size (int, optional): Number of queries for each iter. Defaults to :data:`256`.\n max_length (int, optional): Maximum length of tokens. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`True`.\n device (Optional[str], optional): Device to use for encoding. Defaults to None.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n input_was_string = False\n if isinstance(queries, str):\n queries = [queries]\n input_was_string = True\n\n if self.query_instruction_for_retrieval is not None:\n if isinstance(queries, str):\n input_texts = self.get_detailed_instruct(self.query_instruction_format, self.query_instruction_for_retrieval, queries)\n else:\n input_texts = [self.get_detailed_instruct(self.query_instruction_format, self.query_instruction_for_retrieval, query) for query in queries]\n else:\n input_texts = queries\n\n prefix_ids = self.tokenizer(self.prefix, add_special_tokens=False)['input_ids']\n suffix_ids = self.tokenizer(self.suffix, add_special_tokens=False)['input_ids']\n\n _len_1 = len(self.tokenizer('', add_special_tokens=False)['input_ids'])\n _len_2 = len(self.tokenizer(f'{self.suffix}', add_special_tokens=False)['input_ids'])\n new_max_length = (len(prefix_ids) + len(suffix_ids) + max_length + 8) // 8 * 8 + 8\n\n # tokenize without padding to get the correct length\n all_inputs = []\n for start_index in trange(0, len(input_texts), batch_size, desc='pre tokenize',\n disable=len(input_texts) < batch_size):\n sentences_batch = input_texts[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer(\n sentences_batch,\n truncation=True,\n max_length=max_length - _len_1 - _len_2,\n add_special_tokens=False,\n **kwargs\n )\n sentences_batch = self.tokenizer.batch_decode(inputs_batch['input_ids'])\n for i in range(len(sentences_batch)):\n sentences_batch[i] = self.prefix + sentences_batch[i] + self.suffix\n inputs_batch = self.tokenizer(\n sentences_batch,\n truncation=True,\n max_length=new_max_length,\n **kwargs\n )\n inputs_batch = [{\n k: inputs_batch[k][i] for k in inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n all_inputs.extend(inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])\n all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]\n sentences_sorted = [input_texts[i] for i in length_sorted_idx]\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n inputs_batch = self.tokenizer.pad(\n all_inputs_sorted[: batch_size],\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n # encode\n all_embeddings = []\n for start_index in tqdm(range(0, len(sentences_sorted), batch_size), desc=\"Inference Embeddings\",\n disable=len(sentences_sorted) < batch_size):\n inputs_batch = all_inputs_sorted[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer.pad(\n inputs_batch,\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])\n if self.normalize_embeddings:\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n\n if convert_to_numpy:\n embeddings = embeddings.cpu().numpy()\n all_embeddings.append(embeddings)\n\n if convert_to_numpy:\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n else:\n all_embeddings = torch.cat(all_embeddings, dim=0)\n\n # adjust the order of embeddings\n all_embeddings = all_embeddings[np.argsort(length_sorted_idx)]\n\n # return the embeddings\n if input_was_string:\n return all_embeddings[0]\n return all_embeddings\n\n @torch.no_grad()\n def encode_single_device(\n self,\n sentences: Union[List[str], str],\n batch_size: int = 256,\n max_length: int = 512,\n convert_to_numpy: bool = True,\n device: Optional[str] = None,\n **kwargs: Any\n ):\n \"\"\"Encode input sentences by a single device.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (int, optional): Number of sentences for each iter. Defaults to :data:`256`.\n max_length (int, optional): Maximum length of tokens. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`True`.\n device (Optional[str], optional): Device to use for encoding. Defaults to None.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n input_was_string = False\n if isinstance(sentences, str):\n sentences = [sentences]\n input_was_string = True\n\n # tokenize without padding to get the correct length\n all_inputs = []\n for start_index in trange(0, len(sentences), batch_size, desc='pre tokenize',\n disable=len(sentences) < batch_size):\n sentences_batch = sentences[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer(\n sentences_batch,\n truncation=True,\n max_length=max_length,\n **kwargs\n )\n inputs_batch = [{\n k: inputs_batch[k][i] for k in inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n all_inputs.extend(inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])\n all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n inputs_batch = self.tokenizer.pad(\n all_inputs_sorted[: batch_size],\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n # encode\n all_embeddings = []\n for start_index in tqdm(range(0, len(sentences), batch_size), desc=\"Inference Embeddings\",\n disable=len(sentences) < batch_size):\n inputs_batch = all_inputs_sorted[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer.pad(\n inputs_batch,\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = last_token_pool(last_hidden_state, inputs_batch['attention_mask'])\n if self.normalize_embeddings:\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n\n if convert_to_numpy:\n embeddings = embeddings.cpu().numpy()\n all_embeddings.append(embeddings)\n\n if convert_to_numpy:\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n else:\n all_embeddings = torch.cat(all_embeddings, dim=0)\n\n # adjust the order of embeddings\n all_embeddings = all_embeddings[np.argsort(length_sorted_idx)]\n\n # return the embeddings\n if input_was_string:\n return all_embeddings[0]\n return all_embeddings\n" }, { "path": "FlagEmbedding/inference/embedder/encoder_only/__init__.py", "content": "from .base import BaseEmbedder as FlagModel\nfrom .m3 import M3Embedder as BGEM3FlagModel\n\n__all__ = [\n \"FlagModel\",\n \"BGEM3FlagModel\",\n]\n" }, { "path": "FlagEmbedding/inference/embedder/encoder_only/base.py", "content": "from tqdm import tqdm, trange\nfrom typing import cast, Any, List, Union, Optional\n\nimport torch\nimport numpy as np\nfrom transformers import AutoModel, AutoTokenizer\n\nfrom FlagEmbedding.abc.inference import AbsEmbedder\n\n\nclass BaseEmbedder(AbsEmbedder):\n \"\"\"\n Base embedder for encoder only models.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to :data:`True`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`True`.\n query_instruction_for_retrieval (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`\"{}{}\"`.\n devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n pooling_method (str, optional): Pooling method to get embedding vector from the last hidden state. Defaults to :data:`\"cls\"`.\n trust_remote_code (bool, optional): trust_remote_code for HF datasets or models. Defaults to :data:`False`.\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.\n query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.\n passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will be a Torch Tensor. \n Defaults to :data:`True`.\n \n Attributes:\n DEFAULT_POOLING_METHOD: The default pooling method when running the model.\n \"\"\"\n \n DEFAULT_POOLING_METHOD = \"cls\"\n\n def __init__(\n self,\n model_name_or_path: str,\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: Optional[str] = None,\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_retrieval\n devices: Optional[Union[str, List[str]]] = None, # specify devices, such as \"cuda:0\" or [\"cuda:0\", \"cuda:1\"]\n # Additional parameters for BaseEmbedder\n pooling_method: str = \"cls\",\n trust_remote_code: bool = False,\n cache_dir: Optional[str] = None,\n # inference\n batch_size: int = 256,\n query_max_length: int = 512,\n passage_max_length: int = 512,\n convert_to_numpy: bool = True,\n **kwargs: Any,\n ):\n super().__init__(\n model_name_or_path,\n normalize_embeddings=normalize_embeddings,\n use_fp16=use_fp16,\n query_instruction_for_retrieval=query_instruction_for_retrieval,\n query_instruction_format=query_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n passage_max_length=passage_max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n self.pooling_method = pooling_method\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n self.model = AutoModel.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n\n def encode_queries(\n self,\n queries: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the queries.\n\n Args:\n queries (Union[List[str], str]): Input queries to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode_queries(\n queries,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n def encode_corpus(\n self,\n corpus: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the corpus using the instruction if provided.\n\n Args:\n corpus (Union[List[str], str]): Input corpus to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: Return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode_corpus(\n corpus,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n def encode(\n self,\n sentences: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n convert_to_numpy: Optional[bool] = None,\n **kwargs: Any\n ) -> Union[np.ndarray, torch.Tensor]:\n \"\"\"Encode the input sentences with the embedding model.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n convert_to_numpy (Optional[bool], optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`None`.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n return super().encode(\n sentences,\n batch_size=batch_size,\n max_length=max_length,\n convert_to_numpy=convert_to_numpy,\n **kwargs\n )\n\n @torch.no_grad()\n def encode_single_device(\n self,\n sentences: Union[List[str], str],\n batch_size: int = 256,\n max_length: int = 512,\n convert_to_numpy: bool = True,\n device: Optional[str] = None,\n **kwargs: Any\n ):\n \"\"\"Encode input sentences by a single device.\n\n Args:\n sentences (Union[List[str], str]): Input sentences to encode.\n batch_size (int, optional): Number of sentences for each iter. Defaults to :data:`256`.\n max_length (int, optional): Maximum length of tokens. Defaults to :data:`512`.\n convert_to_numpy (bool, optional): If True, the output embedding will be a Numpy array. Otherwise, it will \n be a Torch Tensor. Defaults to :data:`True`.\n device (Optional[str], optional): Device to use for encoding. Defaults to None.\n\n Returns:\n Union[torch.Tensor, np.ndarray]: return the embedding vectors in a numpy array or tensor.\n \"\"\"\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n input_was_string = False\n if isinstance(sentences, str):\n sentences = [sentences]\n input_was_string = True\n\n # tokenize without padding to get the correct length\n all_inputs = []\n for start_index in trange(0, len(sentences), batch_size, desc='pre tokenize',\n disable=len(sentences) < batch_size):\n sentences_batch = sentences[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer(\n sentences_batch,\n truncation=True,\n max_length=max_length,\n **kwargs\n )\n inputs_batch = [{\n k: inputs_batch[k][i] for k in inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n all_inputs.extend(inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])\n all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n inputs_batch = self.tokenizer.pad(\n all_inputs_sorted[: batch_size],\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = self.pooling(last_hidden_state, inputs_batch['attention_mask'])\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n # encode\n all_embeddings = []\n for start_index in tqdm(range(0, len(sentences), batch_size), desc=\"Inference Embeddings\",\n disable=len(sentences) < batch_size):\n inputs_batch = all_inputs_sorted[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer.pad(\n inputs_batch,\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n last_hidden_state = self.model(**inputs_batch, return_dict=True).last_hidden_state\n embeddings = self.pooling(last_hidden_state, inputs_batch['attention_mask'])\n if self.normalize_embeddings:\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n\n if convert_to_numpy:\n embeddings = embeddings.cpu().numpy()\n all_embeddings.append(embeddings)\n\n if convert_to_numpy:\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n else:\n all_embeddings = torch.cat(all_embeddings, dim=0)\n\n # adjust the order of embeddings\n all_embeddings = all_embeddings[np.argsort(length_sorted_idx)]\n\n # return the embeddings\n if input_was_string:\n return all_embeddings[0]\n return all_embeddings\n\n def pooling(\n self,\n last_hidden_state: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None\n ):\n \"\"\"The pooling function.\n\n Args:\n last_hidden_state (torch.Tensor): The last hidden state of the model.\n attention_mask (Optional[torch.Tensor], optional): Attention mask. Defaults to :data:`None`.\n\n Raises:\n NotImplementedError: pooling method not implemented.\n\n Returns:\n torch.Tensor: The embedding vectors after pooling.\n \"\"\"\n if self.pooling_method == 'cls':\n return last_hidden_state[:, 0]\n elif self.pooling_method == 'mean':\n s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n else:\n raise NotImplementedError(f\"pooling method {self.pooling_method} not implemented\")\n" }, { "path": "FlagEmbedding/inference/embedder/encoder_only/m3.py", "content": "import math\nimport torch\nimport queue\nimport logging\nimport numpy as np\nfrom tqdm import tqdm, trange\nfrom multiprocessing import Queue\nfrom collections import defaultdict\nfrom transformers import AutoTokenizer\nfrom typing import Any, List, Union, Dict, Literal, Tuple, Optional\n\nfrom FlagEmbedding.abc.inference import AbsEmbedder\nfrom FlagEmbedding.finetune.embedder.encoder_only.m3 import (\n EncoderOnlyEmbedderM3ModelForInference, EncoderOnlyEmbedderM3Runner\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass M3Embedder(AbsEmbedder):\n \"\"\" \n Embedder class for BGE-M3.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n normalize_embeddings (bool, optional): If True, normalize the dense embedding vector. Defaults to :data:`True`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`True`.\n query_instruction_for_retrieval: (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format: (str, optional): The template for :attr:`query_instruction_for_retrieval`. Defaults to :data:`\"{}{}\"`.\n devices (Optional[Union[str, int, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n pooling_method (str, optional): Pooling method to get embedding vector from the last hidden state. Defaults to :data:`\"cls\"`.\n trust_remote_code (bool, optional): trust_remote_code for HF datasets or models. Defaults to :data:`False`.\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n cobert_dim (int, optional): Dimension of colbert linear. Return the hidden_size if -1. Defaults to :data:`-1`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`256`.\n query_max_length (int, optional): Maximum length for query. Defaults to :data:`512`.\n passage_max_length (int, optional): Maximum length for passage. Defaults to :data:`512`.\n return_dense (bool, optional): If true, will return the dense embedding. Defaults to :data:`True`.\n return_sparse (bool, optional): If true, will return the sparce embedding. Defaults to :data:`False`.\n return_colbert_vecs (bool, optional): If true, will return the colbert vectors. Defaults to :data:`False`.\n \n Attributes:\n DEFAULT_POOLING_METHOD: The default pooling method when running the model.\n \"\"\"\n DEFAULT_POOLING_METHOD = \"cls\"\n\n def __init__(\n self,\n model_name_or_path: str,\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: Optional[str] = None,\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_retrieval\n devices: Optional[Union[str, List[str]]] = None, # specify devices, such as \"cuda:0\" or [\"cuda:0\", \"cuda:1\"]\n # Additional parameters for M3Embedder\n pooling_method: str = \"cls\",\n trust_remote_code: bool = False,\n cache_dir: Optional[str] = None,\n colbert_dim: int = -1,\n # inference\n batch_size: int = 256,\n query_max_length: int = 512,\n passage_max_length: int = 512,\n return_dense: bool = True,\n return_sparse: bool = False,\n return_colbert_vecs: bool = False,\n **kwargs: Any,\n ):\n super().__init__(\n model_name_or_path,\n normalize_embeddings=normalize_embeddings,\n use_fp16=use_fp16,\n query_instruction_for_retrieval=query_instruction_for_retrieval,\n query_instruction_format=query_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n passage_max_length=passage_max_length,\n return_dense=return_dense,\n return_sparse=return_sparse,\n return_colbert_vecs=return_colbert_vecs,\n **kwargs\n )\n self.pooling_method = pooling_method\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n cache_dir=cache_dir\n )\n self.model = EncoderOnlyEmbedderM3ModelForInference(\n EncoderOnlyEmbedderM3Runner.get_model(\n model_name_or_path,\n trust_remote_code=trust_remote_code,\n colbert_dim=colbert_dim,\n cache_dir=cache_dir\n ),\n tokenizer=self.tokenizer,\n sentence_pooling_method=pooling_method,\n normalize_embeddings=normalize_embeddings\n )\n\n def convert_id_to_token(self, lexical_weights: List[Dict]):\n \"\"\"Convert the ids back to tokens.\n\n Args:\n lexical_weights (List[Dict]): A list of dictionaries of id & weights.\n\n Returns:\n List[Dict]: A list of dictionaries of tokens & weights.\n \"\"\"\n if isinstance(lexical_weights, dict):\n lexical_weights = [lexical_weights]\n new_lexical_weights = []\n for item in lexical_weights:\n new_item = {}\n for id, weight in item.items():\n token = self.tokenizer.decode([int(id)])\n new_item[token] = weight\n new_lexical_weights.append(new_item)\n\n if len(new_lexical_weights) == 1:\n new_lexical_weights = new_lexical_weights[0]\n return new_lexical_weights\n\n def compute_lexical_matching_score(\n self,\n lexical_weights_1: Union[Dict[str, float], List[Dict[str, float]]],\n lexical_weights_2: Union[Dict[str, float], List[Dict[str, float]]]\n ) -> Union[np.ndarray, float]:\n \"\"\"Compute the laxical matching score of two given lexical weights.\n\n Args:\n lexical_weights_1 (Union[Dict[str, float], List[Dict[str, float]]]): First array of lexical weights.\n lexical_weights_2 (Union[Dict[str, float], List[Dict[str, float]]]): Second array of lexical weights.\n\n Returns:\n Union[np.ndarray, float]: The computed lexical weights across the two arries of lexical weights.\n \"\"\"\n def _compute_single_lexical_matching_score(lw1: Dict[str, float], lw2: Dict[str, float]):\n scores = 0\n for token, weight in lw1.items():\n if token in lw2:\n scores += weight * lw2[token]\n return scores\n\n if isinstance(lexical_weights_1, dict) and isinstance(lexical_weights_2, dict):\n return _compute_single_lexical_matching_score(lexical_weights_1, lexical_weights_2)\n elif isinstance(lexical_weights_1, list) and isinstance(lexical_weights_2, list):\n scores_array = []\n for lw1 in lexical_weights_1:\n scores_array.append([\n _compute_single_lexical_matching_score(lw1, lw2)\n for lw2 in lexical_weights_2\n ])\n return np.array(scores_array)\n else:\n raise ValueError(\"The input format of lexical_weights is not correct.\")\n\n def colbert_score(self, q_reps, p_reps):\n \"\"\"Compute colbert scores of input queries and passages.\n\n Args:\n q_reps (np.ndarray): Multi-vector embeddings for queries.\n p_reps (np.ndarray): Multi-vector embeddings for passages/corpus.\n\n Returns:\n torch.Tensor: Computed colbert scores.\n \"\"\"\n q_reps, p_reps = torch.from_numpy(q_reps), torch.from_numpy(p_reps)\n token_scores = torch.einsum('in,jn->ij', q_reps, p_reps)\n scores, _ = token_scores.max(-1)\n scores = torch.sum(scores) / q_reps.size(0)\n return scores\n\n def encode_queries(\n self,\n queries: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n return_dense: Optional[bool] = None,\n return_sparse: Optional[bool] = None,\n return_colbert_vecs: Optional[bool] = None,\n **kwargs: Any\n ) -> Dict[\n Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"],\n Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n ]:\n \"\"\"Encode the queries using the specified way.\n\n Args:\n queries (Union[List[str], str]): The input queries to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n return_dense (Optional[bool], optional): If True, compute and return dense embedding. Defaults to :data:`None`.\n return_sparse (Optional[bool], optional): If True, compute and return sparce embedding. Defaults to :data:`None`.\n return_colbert_vecs (Optional[bool], optional): If True, compute and return cobert vectors. Defaults to :data:`None`.\n\n Returns:\n Dict[Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"], Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.query_max_length\n if return_dense is None: return_dense = self.return_dense\n if return_sparse is None: return_sparse = self.return_sparse\n if return_colbert_vecs is None: return_colbert_vecs = self.return_colbert_vecs\n\n return super().encode_queries(\n queries,\n batch_size=batch_size,\n max_length=max_length,\n return_dense=return_dense,\n return_sparse=return_sparse,\n return_colbert_vecs=return_colbert_vecs,\n **kwargs\n )\n\n def encode_corpus(\n self,\n corpus: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n return_dense: Optional[bool] = None,\n return_sparse: Optional[bool] = None,\n return_colbert_vecs: Optional[bool] = None,\n **kwargs: Any\n ) -> Dict[\n Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"],\n Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n ]:\n \"\"\"Encode the corpus using the specified way.\n\n Args:\n corpus (Union[List[str], str]): The input corpus to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n return_dense (Optional[bool], optional): If True, compute and return dense embedding. Defaults to :data:`None`.\n return_sparse (Optional[bool], optional): If True, compute and return sparce embedding. Defaults to :data:`None`.\n return_colbert_vecs (Optional[bool], optional): If True, compute and return cobert vectors. Defaults to :data:`None`.\n\n Returns:\n Dict[Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"], Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.passage_max_length\n if return_dense is None: return_dense = self.return_dense\n if return_sparse is None: return_sparse = self.return_sparse\n if return_colbert_vecs is None: return_colbert_vecs = self.return_colbert_vecs\n\n return super().encode_corpus(\n corpus,\n batch_size=batch_size,\n max_length=max_length,\n return_dense=return_dense,\n return_sparse=return_sparse,\n return_colbert_vecs=return_colbert_vecs,\n **kwargs\n )\n\n def encode(\n self,\n sentences: Union[List[str], str],\n batch_size: Optional[int] = None,\n max_length: Optional[int] = None,\n return_dense: Optional[bool] = None,\n return_sparse: Optional[bool] = None,\n return_colbert_vecs: Optional[bool] = None,\n **kwargs: Any\n ) -> Dict[\n Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"],\n Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n ]:\n \"\"\"Encode the sentences using the specified way.\n\n Args:\n sentences (Union[List[str], str]): The input sentences to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n return_dense (Optional[bool], optional): If True, compute and return dense embedding. Defaults to :data:`None`.\n return_sparse (Optional[bool], optional): If True, compute and return sparce embedding. Defaults to :data:`None`.\n return_colbert_vecs (Optional[bool], optional): If True, compute and return cobert vectors. Defaults to :data:`None`.\n\n Returns:\n Dict[Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"], Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.passage_max_length\n if return_dense is None: return_dense = self.return_dense\n if return_sparse is None: return_sparse = self.return_sparse\n if return_colbert_vecs is None: return_colbert_vecs = self.return_colbert_vecs\n\n return super().encode(\n sentences,\n batch_size=batch_size,\n max_length=max_length,\n return_dense=return_dense,\n return_sparse=return_sparse,\n return_colbert_vecs=return_colbert_vecs,\n **kwargs\n )\n\n @torch.no_grad()\n def encode_single_device(\n self,\n sentences: Union[List[str], str],\n batch_size: int = 256,\n max_length: int = 512,\n return_dense: bool = True,\n return_sparse: bool = False,\n return_colbert_vecs: bool = False,\n device: Optional[str] = None,\n **kwargs: Any\n ):\n \"\"\"Using single device to encode the input sentences.\n\n Args:\n sentences (Union[List[str], str]): The input sentences to encode.\n batch_size (Optional[int], optional): Number of sentences for each iter. Defaults to :data:`256`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`512`.\n return_dense (Optional[bool], optional): If True, compute and return dense embedding. Defaults to :data:`True`.\n return_sparse (Optional[bool], optional): If True, compute and return sparce embedding. Defaults to :data:`False`.\n return_colbert_vecs (Optional[bool], optional): If True, compute and return cobert vectors. Defaults to :data:`False`.\n device (Optional[str], optional): _description_. Defaults to :data:`None`.\n\n Returns:\n Dict[Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"], Union[np.ndarray, List[Dict[str, float]], List[np.ndarray]]\n \"\"\"\n # pop convert_to_numpy from kwargs\n kwargs.pop(\"convert_to_numpy\", None)\n\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n input_was_string = False\n if isinstance(sentences, str):\n sentences = [sentences]\n input_was_string = True\n\n def _process_token_weights(token_weights: np.ndarray, input_ids: list):\n # conver to dict\n result = defaultdict(int)\n unused_tokens = set()\n for _token in ['cls_token', 'eos_token', 'pad_token', 'unk_token']:\n if _token in self.tokenizer.special_tokens_map:\n _token_id = self.tokenizer.convert_tokens_to_ids(self.tokenizer.special_tokens_map[_token])\n unused_tokens.add(_token_id)\n # token_weights = np.ceil(token_weights * 100)\n for w, idx in zip(token_weights, input_ids):\n if idx not in unused_tokens and w > 0:\n idx = str(idx)\n # w = int(w)\n if w > result[idx]:\n result[idx] = w\n return result\n\n def _process_colbert_vecs(colbert_vecs: np.ndarray, attention_mask: list):\n # delte the vectors of padding tokens\n tokens_num = np.sum(attention_mask)\n return colbert_vecs[:tokens_num - 1] # we don't use the embedding of cls, so select tokens_num-1\n\n # tokenize without padding to get the correct length\n all_inputs = []\n for start_index in trange(0, len(sentences), batch_size, desc='pre tokenize',\n disable=len(sentences) < batch_size):\n sentences_batch = sentences[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer(\n sentences_batch,\n truncation=True,\n max_length=max_length,\n **kwargs\n )\n inputs_batch = [{\n k: inputs_batch[k][i] for k in inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n all_inputs.extend(inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])\n all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n inputs_batch = self.tokenizer.pad(\n all_inputs_sorted[: batch_size],\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n outputs = self.model(\n inputs_batch,\n return_dense=return_dense,\n return_sparse=return_sparse,\n return_colbert_vecs=return_colbert_vecs\n )\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n # encode\n all_dense_embeddings, all_lexical_weights, all_colbert_vecs = [], [], []\n for start_index in tqdm(range(0, len(sentences), batch_size), desc=\"Inference Embeddings\",\n disable=len(sentences) < batch_size):\n inputs_batch = all_inputs_sorted[start_index:start_index + batch_size]\n inputs_batch = self.tokenizer.pad(\n inputs_batch,\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n outputs = self.model(\n inputs_batch,\n return_dense=return_dense,\n return_sparse=return_sparse,\n return_colbert_vecs=return_colbert_vecs\n )\n\n if return_dense:\n all_dense_embeddings.append(outputs['dense_vecs'].cpu().numpy())\n\n if return_sparse:\n token_weights = outputs['sparse_vecs'].squeeze(-1)\n all_lexical_weights.extend(\n list(map(\n _process_token_weights, \n token_weights.cpu().numpy(),\n inputs_batch['input_ids'].cpu().numpy().tolist()\n )))\n\n if return_colbert_vecs:\n all_colbert_vecs.extend(\n list(map(\n _process_colbert_vecs,\n outputs['colbert_vecs'].cpu().numpy(),\n inputs_batch['attention_mask'].cpu().numpy()\n )))\n\n if return_dense:\n all_dense_embeddings = np.concatenate(all_dense_embeddings, axis=0)\n # adjust the order of embeddings\n all_dense_embeddings = all_dense_embeddings[np.argsort(length_sorted_idx)]\n if input_was_string:\n all_dense_embeddings = all_dense_embeddings[0]\n else:\n all_dense_embeddings = None\n\n if return_sparse:\n # adjust the order of lexical weights\n all_lexical_weights = [all_lexical_weights[i] for i in np.argsort(length_sorted_idx)]\n if input_was_string:\n all_lexical_weights = all_lexical_weights[0]\n else:\n all_lexical_weights = None\n\n if return_colbert_vecs:\n # adjust the order of embeddings\n all_colbert_vecs = [all_colbert_vecs[i] for i in np.argsort(length_sorted_idx)]\n if input_was_string:\n all_colbert_vecs = all_colbert_vecs[0]\n else:\n all_colbert_vecs = None\n\n # return the embeddings\n return {\n \"dense_vecs\": all_dense_embeddings,\n \"lexical_weights\": all_lexical_weights,\n \"colbert_vecs\": all_colbert_vecs\n }\n\n def compute_score(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: Optional[int] = None,\n max_query_length: Optional[int] = None,\n max_passage_length: Optional[int] = None,\n weights_for_different_modes: Optional[List[float]] = None,\n **kwargs: Any\n ) -> Dict[\n Literal[\"colbert\", \"sparse\", \"dense\", \"sparse+dense\", \"colbert+sparse+dense\"],\n List[float]\n ]:\n \"\"\"Compute the relevance score of different attributes.\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): _description_\n batch_size (Optional[int], optional): _description_. Defaults to None.\n max_query_length (Optional[int], optional): _description_. Defaults to None.\n max_passage_length (Optional[int], optional): _description_. Defaults to None.\n weights_for_different_modes (Optional[List[float]], optional): _description_. Defaults to None.\n\n Returns:\n Dict[Literal[\"colbert\", \"sparse\", \"dense\", \"sparse+dense\", \"colbert+sparse+dense\"], List[float]]\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_query_length is None: max_query_length = self.query_max_length\n if max_passage_length is None: max_passage_length = self.passage_max_length\n\n if len(self.target_devices) == 1:\n return self.compute_score_single_device(\n sentence_pairs,\n batch_size=batch_size,\n max_query_length=max_query_length,\n max_passage_length=max_passage_length,\n weights_for_different_modes=weights_for_different_modes,\n device=self.target_devices[0],\n **kwargs\n )\n\n pool = self.start_multi_process_pool(M3Embedder._compute_score_multi_process_worker)\n embeddings = self.compute_score_multi_process(\n sentence_pairs,\n pool,\n batch_size=batch_size,\n max_query_length=max_query_length,\n max_passage_length=max_passage_length,\n weights_for_different_modes=weights_for_different_modes,\n **kwargs\n )\n self.stop_multi_process_pool(pool)\n return embeddings\n\n # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L877\n def compute_score_multi_process(\n self,\n sentence_pairs: List[Tuple[str, str]],\n pool: Dict[Literal[\"input\", \"output\", \"processes\"], Any],\n **kwargs\n ):\n chunk_size = math.ceil(len(sentence_pairs) / len(pool[\"processes\"]))\n\n input_queue = pool[\"input\"]\n last_chunk_id = 0\n chunk = []\n\n for sentence_pair in sentence_pairs:\n chunk.append(sentence_pair)\n if len(chunk) >= chunk_size:\n input_queue.put(\n [last_chunk_id, chunk, kwargs]\n )\n last_chunk_id += 1\n chunk = []\n\n if len(chunk) > 0:\n input_queue.put([last_chunk_id, chunk, kwargs])\n last_chunk_id += 1\n\n output_queue = pool[\"output\"]\n results_list = sorted(\n [output_queue.get() for _ in trange(last_chunk_id, desc=\"Chunks\")],\n key=lambda x: x[0],\n )\n\n scores_dict = self._concatenate_compute_score_results_from_multi_process([result[1] for result in results_list])\n return scores_dict\n\n # adapted from https://github.com/UKPLab/sentence-transformers/blob/1802076d4eae42ff0a5629e1b04e75785d4e193b/sentence_transformers/SentenceTransformer.py#L976\n @staticmethod\n def _compute_score_multi_process_worker(\n target_device: str, model: 'M3Embedder', input_queue: Queue, results_queue: Queue\n ) -> None:\n \"\"\"\n Internal working process to encode sentences in multi-process setup\n \"\"\"\n while True:\n try:\n chunk_id, sentences, kwargs = (\n input_queue.get()\n )\n embeddings = model.compute_score_single_device(\n sentences,\n device=target_device,\n **kwargs\n )\n\n results_queue.put([chunk_id, embeddings])\n except queue.Empty:\n break\n\n @torch.no_grad()\n def compute_score_single_device(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: int = 256,\n max_query_length: int = 512,\n max_passage_length: int = 512,\n weights_for_different_modes: Optional[List[float]] = None,\n device: Optional[str] = None,\n **kwargs: Any\n ) -> Dict[\n Literal[\"colbert\", \"sparse\", \"dense\", \"sparse+dense\", \"colbert+sparse+dense\"],\n List[float]\n ]:\n \"\"\"Compute the relevance score of different attributes.\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Pairs of sentences to compute the score.\n batch_size (Optional[int], optional): _description_. Defaults to :data:`None`.\n max_query_length (Optional[int], optional): _description_. Defaults to :data:`None`.\n max_passage_length (Optional[int], optional): _description_. Defaults to :data:`None`.\n weights_for_different_modes (Optional[List[float]], optional): The weights for different methods. Defaults to :data:`None`.\n device (Optional[str], optional): The device to use. Defaults to :data:`None`.\n\n Returns:\n Dict[Literal[\"colbert\", \"sparse\", \"dense\", \"sparse+dense\", \"colbert+sparse+dense\"], List[float]]\n \"\"\"\n def _tokenize(texts: list, max_length: int):\n return self.tokenizer(\n texts,\n max_length=max_length,\n padding=True,\n return_token_type_ids=False,\n truncation=True,\n return_tensors='pt',\n **kwargs\n )\n\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n if isinstance(sentence_pairs, list) and len(sentence_pairs) == 0:\n return []\n if isinstance(sentence_pairs[0], str):\n one_input_pair = True\n sentence_pairs = [sentence_pairs]\n else:\n one_input_pair = False\n\n all_scores = {\n 'colbert': [],\n 'sparse': [],\n 'dense': [],\n 'sparse+dense': [],\n 'colbert+sparse+dense': []\n }\n for start_index in tqdm(range(0, len(sentence_pairs), batch_size), desc=\"Compute Scores\",\n disable=len(sentence_pairs) < batch_size):\n sentences_batch = sentence_pairs[start_index:start_index + batch_size]\n\n queries_batch = [pair[0] for pair in sentences_batch]\n corpus_batch = [pair[1] for pair in sentences_batch]\n\n queries_inputs = _tokenize(queries_batch, max_length=max_query_length).to(device)\n corpus_inputs = _tokenize(corpus_batch, max_length=max_passage_length).to(device)\n\n queries_output = self.model(\n queries_inputs,\n return_dense=True, return_sparse=True, return_colbert_vecs=True,\n return_sparse_embedding=True\n )\n corpus_output = self.model(\n corpus_inputs,\n return_dense=True, return_sparse=True, return_colbert_vecs=True,\n return_sparse_embedding=True\n )\n\n q_dense_vecs, q_sparse_vecs, q_colbert_vecs = queries_output['dense_vecs'], queries_output['sparse_vecs'], \\\n queries_output['colbert_vecs']\n p_dense_vecs, p_sparse_vecs, p_colbert_vecs = corpus_output['dense_vecs'], corpus_output['sparse_vecs'], \\\n corpus_output['colbert_vecs']\n\n dense_scores = self.model.compute_dense_score(q_dense_vecs, p_dense_vecs)\n sparse_scores = self.model.compute_sparse_score(q_sparse_vecs, p_sparse_vecs)\n colbert_scores = self.model.compute_colbert_score(\n q_colbert_vecs, p_colbert_vecs,\n q_mask=queries_inputs['attention_mask']\n )\n\n if weights_for_different_modes is None:\n weights_for_different_modes = [1., 1., 1.]\n weight_sum = 3\n logger.info(\"default weights for dense, sparse, colbert are [1.0, 1.0, 1.0] \")\n else:\n assert len(weights_for_different_modes) == 3\n weight_sum = sum(weights_for_different_modes)\n\n inx = torch.arange(0, len(sentences_batch))\n dense_scores, sparse_scores, colbert_scores = dense_scores[inx, inx].float(), sparse_scores[\n inx, inx].float(), colbert_scores[inx, inx].float()\n\n all_scores['colbert'].extend(\n colbert_scores.cpu().numpy().tolist()\n )\n all_scores['sparse'].extend(\n sparse_scores.cpu().numpy().tolist()\n )\n all_scores['dense'].extend(\n dense_scores.cpu().numpy().tolist()\n )\n all_scores['sparse+dense'].extend(\n ((sparse_scores * weights_for_different_modes[1] + dense_scores * weights_for_different_modes[0])/(weights_for_different_modes[1]+weights_for_different_modes[0])).cpu().numpy().tolist()\n )\n all_scores['colbert+sparse+dense'].extend(\n ((colbert_scores * weights_for_different_modes[2] + sparse_scores * weights_for_different_modes[1] + dense_scores * weights_for_different_modes[0])/weight_sum).cpu().numpy().tolist()\n )\n\n if one_input_pair:\n return {k: v[0] for k, v in all_scores.items()}\n return all_scores\n\n def _concatenate_results_from_multi_process(\n self,\n results_list: List[Dict[Literal[\"dense_vecs\", \"lexical_weights\", \"colbert_vecs\"], Any]]\n ):\n \"\"\"Concatenate and return the results from all the processes.\n\n Args:\n results_list (List[Dict[Literal["dense_vecs", "lexical_weights", "colbert_vecs"], Any]]): \n A list of results from all the processes.\n\n Returns:\n Dict: The merged encoding results from the multi processes.\n \"\"\"\n merged_results = {\n \"dense_vecs\": [],\n \"lexical_weights\": [],\n \"colbert_vecs\": []\n }\n for key in merged_results.keys():\n for results in results_list:\n if results[key] is None:\n merged_results[key] = None\n break\n else:\n if key == \"dense_vecs\":\n merged_results[key].append(results[key])\n else:\n merged_results[key].extend(results[key])\n\n if merged_results[\"dense_vecs\"] is not None:\n merged_results[\"dense_vecs\"] = np.concatenate(merged_results[\"dense_vecs\"], axis=0)\n\n return merged_results\n\n def _concatenate_compute_score_results_from_multi_process(\n self,\n results_list: List[Dict[Literal[\"colbert\", \"sparse\", \"dense\", \"sparse+dense\", \"colbert+sparse+dense\"], List[float]]]\n ):\n \"\"\"Concatenate and return the results from all the processes.\n\n Args:\n results_list (List[Dict[Literal["colbert", "sparse", "dense", "sparse): \n A list of computed scores.\n\n Returns:\n Dict: The merged computed scores from the multi processes.\n \"\"\"\n merged_results = {\n \"colbert\": [],\n \"sparse\": [],\n \"dense\": [],\n \"sparse+dense\": [],\n \"colbert+sparse+dense\": []\n }\n for key in merged_results.keys():\n for results in results_list:\n merged_results[key].extend(results[key])\n\n return merged_results\n" }, { "path": "FlagEmbedding/inference/embedder/model_mapping.py", "content": "from enum import Enum\nfrom typing import Type, List\nfrom dataclasses import dataclass\nfrom collections import OrderedDict\n\nfrom FlagEmbedding.abc.inference import AbsEmbedder\nfrom FlagEmbedding.inference.embedder import FlagModel, BGEM3FlagModel, FlagLLMModel, FlagICLModel\n\n\nclass EmbedderModelClass(Enum):\n ENCODER_ONLY_BASE = \"encoder-only-base\"\n ENCODER_ONLY_M3 = \"encoder-only-m3\"\n DECODER_ONLY_BASE = \"decoder-only-base\"\n DECODER_ONLY_ICL = \"decoder-only-icl\"\n\n\nEMBEDDER_CLASS_MAPPING = OrderedDict([\n (EmbedderModelClass.ENCODER_ONLY_BASE, FlagModel),\n (EmbedderModelClass.ENCODER_ONLY_M3, BGEM3FlagModel),\n (EmbedderModelClass.DECODER_ONLY_BASE, FlagLLMModel),\n (EmbedderModelClass.DECODER_ONLY_ICL, FlagICLModel)\n])\n\n\nclass PoolingMethod(Enum):\n LAST_TOKEN = \"last_token\"\n CLS = \"cls\"\n MEAN = \"mean\"\n\n\n@dataclass\nclass EmbedderConfig:\n model_class: Type[AbsEmbedder]\n pooling_method: PoolingMethod\n trust_remote_code: bool = False\n query_instruction_format: str = \"{}{}\"\n\n\n# BGE models mapping\nBGE_MAPPING = OrderedDict([\n (\n \"bge-reasoner-embed-qwen3-8b-0923\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n \"bge-code-v1\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, trust_remote_code=True, query_instruction_format=\"{}\\n{}\")\n ),\n (\n \"bge-en-icl\", \n EmbedderConfig(FlagICLModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"{}\\n{}\")\n ),\n (\n \"bge-multilingual-gemma2\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"{}\\n{}\")\n ),\n (\n \"bge-m3\",\n EmbedderConfig(BGEM3FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-large-en-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-base-en-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-small-en-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-large-zh-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-base-zh-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-small-zh-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-large-en\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-base-en\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-small-en\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-large-zh\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-base-zh\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n \"bge-small-zh\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n])\n\n# Qwen3-Embedding models mapping\nQWEN3_EMBEDDING_MAPPING = OrderedDict([\n (\n \"Qwen3-Embedding-0.6B\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery:{}\")\n ),\n (\n \"Qwen3-Embedding-4B\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery:{}\")\n ),\n (\n \"Qwen3-Embedding-8B\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery:{}\")\n ),\n])\n\n\n# E5 models mapping\nE5_MAPPING = OrderedDict([\n (\n \"e5-mistral-7b-instruct\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n \"e5-large-v2\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"e5-base-v2\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"e5-small-v2\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"multilingual-e5-large-instruct\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n \"multilingual-e5-large\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"multilingual-e5-base\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"multilingual-e5-small\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"e5-large\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"e5-base\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n \"e5-small\",\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n])\n\n# GTE models mapping\nGTE_MAPPING = OrderedDict([\n (\n \"gte-Qwen2-7B-instruct\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, trust_remote_code=True, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n \"gte-Qwen2-1.5B-instruct\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, trust_remote_code=True, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n \"gte-Qwen1.5-7B-instruct\",\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, trust_remote_code=True, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n \"gte-multilingual-base\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS, trust_remote_code=True)\n ),\n (\n \"gte-large-en-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS, trust_remote_code=True)\n ),\n (\n \"gte-base-en-v1.5\",\n EmbedderConfig(FlagModel, PoolingMethod.CLS, True)\n ),\n (\n 'gte-large',\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n 'gte-base',\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n 'gte-small',\n EmbedderConfig(FlagModel, PoolingMethod.MEAN)\n ),\n (\n 'gte-large-zh',\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n 'gte-base-zh',\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n (\n 'gte-small-zh',\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n])\n\n# SFR models mapping\nSFR_MAPPING = OrderedDict([\n (\n 'SFR-Embedding-2_R',\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n (\n 'SFR-Embedding-Mistral',\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n])\n\n# Linq models mapping\nLINQ_MAPPING = OrderedDict([\n (\n 'Linq-Embed-Mistral',\n EmbedderConfig(FlagLLMModel, PoolingMethod.LAST_TOKEN, query_instruction_format=\"Instruct: {}\\nQuery: {}\")\n ),\n])\n\n# BCE models mapping\nBCE_MAPPING = OrderedDict([\n (\n 'bce-embedding-base_v1',\n EmbedderConfig(FlagModel, PoolingMethod.CLS)\n ),\n])\n\n# Combine all mappings\nAUTO_EMBEDDER_MAPPING = OrderedDict()\nAUTO_EMBEDDER_MAPPING.update(BGE_MAPPING)\nAUTO_EMBEDDER_MAPPING.update(QWEN3_EMBEDDING_MAPPING)\nAUTO_EMBEDDER_MAPPING.update(E5_MAPPING)\nAUTO_EMBEDDER_MAPPING.update(GTE_MAPPING)\nAUTO_EMBEDDER_MAPPING.update(SFR_MAPPING)\nAUTO_EMBEDDER_MAPPING.update(LINQ_MAPPING)\nAUTO_EMBEDDER_MAPPING.update(BCE_MAPPING)\n\n# TODO: Add more models, such as Jina, Stella_v5, NV-Embed, etc.\n\ndef support_native_bge_model_list()->List[str]:\n return list(BGE_MAPPING.keys())\n\ndef support_model_list()->List[str]:\n return list(AUTO_EMBEDDER_MAPPING.keys())\n" }, { "path": "FlagEmbedding/inference/reranker/__init__.py", "content": "from .decoder_only import FlagLLMReranker, LayerWiseFlagLLMReranker, LightWeightFlagLLMReranker\nfrom .encoder_only import FlagReranker\nfrom .model_mapping import RerankerModelClass\n\n__all__ = [\n \"FlagReranker\",\n \"FlagLLMReranker\",\n \"LayerWiseFlagLLMReranker\",\n \"LightWeightFlagLLMReranker\",\n \"RerankerModelClass\",\n]\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/__init__.py", "content": "from .base import BaseLLMReranker as FlagLLMReranker\nfrom .layerwise import LayerWiseLLMReranker as LayerWiseFlagLLMReranker\nfrom .lightweight import LightweightLLMReranker as LightWeightFlagLLMReranker\n\n__all__ = [\n \"FlagLLMReranker\",\n \"LayerWiseFlagLLMReranker\",\n \"LightWeightFlagLLMReranker\"\n]\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/base.py", "content": "import torch\nimport warnings\nimport numpy as np\nfrom tqdm import tqdm, trange\nfrom typing import Any, List, Union, Tuple, Optional\nfrom peft import PeftModel\nfrom torch import Tensor\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom FlagEmbedding.abc.inference import AbsReranker\nfrom FlagEmbedding.inference.reranker.encoder_only.base import sigmoid\n\n\ndef last_logit_pool(logits: Tensor,\n attention_mask: Tensor) -> Tensor:\n \"\"\"Pool the last logit.\n\n Args:\n logits (torch.Tensor): The output logits of the model.\n attention_mask (torch.Tensor): Attention mask.\n\n Returns:\n torch.Tensor: The tensor after pooling.\n \"\"\"\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return logits[:, -1, :]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = logits.shape[0]\n return torch.stack([logits[i, sequence_lengths[i], :] for i in range(batch_size)], dim=0)\n\n\nclass DatasetForReranker(Dataset):\n \"\"\"Prepare the dataset for dataloader.\n\n Args:\n all_queries_inputs (_type_): All the input queries.\n all_passages_inputs (_type_): All the input passages.\n tokenizer_path (str): Path to the tokenizer to use.\n max_len (int, optional): Maximum length of tokens. Defaults to :data:`512`.\n cache_dir (Optional[str], optional): Cache directory for the tokenzier. Defaults to :data:`None`.\n prompt (Optional[str], optional): Prompt for the specific task, will use the default if not provided.\n Defaults to `None`.\n \"\"\"\n def __init__(\n self,\n all_queries_inputs,\n all_passages_inputs,\n tokenizer_path: str,\n max_len: int = 512,\n cache_dir: Optional[str] = None,\n prompt: Optional[str] = None,\n **kwargs: Any, \n ):\n self.tokenizer = AutoTokenizer.from_pretrained(\n tokenizer_path,\n trust_remote_code=True,\n cache_dir=cache_dir\n )\n\n self.all_queries_inputs = all_queries_inputs\n self.all_passages_inputs = all_passages_inputs\n self.max_len = max_len\n self.total_len = len(self.all_queries_inputs)\n self.kwargs = kwargs\n\n if prompt is None:\n prompt = \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"\n self.prompt_inputs = self.tokenizer(\n prompt,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n sep = \"\\n\"\n self.sep_inputs = self.tokenizer(\n sep,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n\n self.encode_max_length = self.max_len + len(self.sep_inputs) + len(self.prompt_inputs)\n\n def __len__(self):\n return self.total_len\n\n def __getitem__(self, item):\n query_inputs = self.all_queries_inputs[item]\n passage_inputs = self.all_passages_inputs[item]\n if self.tokenizer.bos_token_id is not None and self.tokenizer.bos_token_id != self.tokenizer.pad_token_id:\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n self.sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=self.encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n else:\n item = self.tokenizer.prepare_for_model(\n query_inputs['input_ids'],\n self.sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=self.encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + self.sep_inputs + self.prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n\n return item\n\n\nclass Collater:\n \"\"\"\n Collator of the reranker.\n \n Args:\n tokenizer (transformers.AutoTokenizer): The tokenizer for reranker.\n max_len (int): Maximum length of tokens.\n \"\"\"\n def __init__(self, tokenizer, max_len):\n self.tokenizer = tokenizer\n self.max_len = max_len\n self.pad_to_multiple_of = 8\n self.label_pad_token_id = -100\n warnings.filterwarnings(\"ignore\",\n message=\"`max_length` is ignored when `padding`=`True` and there is no truncation strategy.\")\n\n def __call__(self, data):\n labels = [feature[\"labels\"] for feature in data] if \"labels\" in data[0].keys() else None\n # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the\n # same length to return tensors.\n if labels is not None:\n max_label_length = max(len(l) for l in labels)\n if self.pad_to_multiple_of is not None:\n max_label_length = (\n (max_label_length + self.pad_to_multiple_of - 1)\n // self.pad_to_multiple_of\n * self.pad_to_multiple_of\n )\n\n padding_side = self.tokenizer.padding_side\n for feature in data:\n remainder = [self.label_pad_token_id] * (max_label_length - len(feature[\"labels\"]))\n if isinstance(feature[\"labels\"], list):\n feature[\"labels\"] = (\n feature[\"labels\"] + remainder if padding_side == \"right\" else remainder + feature[\"labels\"]\n )\n elif padding_side == \"right\":\n feature[\"labels\"] = np.concatenate([feature[\"labels\"], remainder]).astype(np.int64)\n else:\n feature[\"labels\"] = np.concatenate([remainder, feature[\"labels\"]]).astype(np.int64)\n\n return self.tokenizer.pad(\n data,\n padding=True,\n pad_to_multiple_of=8,\n return_tensors='pt',\n )\n\n\nclass BaseLLMReranker(AbsReranker):\n \"\"\"Base reranker class for LLM like decoder only models.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n peft_path (Optional[str], optional): Path to the PEFT config. Defaults to :data:`None`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`False`. Defaults to :data:`False`.\n use_bf16 (bool, optional): Another type of half-precision floating-point, you can use bf16 if the hardware supports. \n Defaults to :data:False.\n query_instruction_for_rerank (str, optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`\"A: \"`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`\"{}{}\"`.\n passage_instruction_for_rerank (str, optional): Passage instruction for retrieval tasks, which will be used with\n with :attr:`passage_instruction_format`. Defaults to :data:`\"B: \"`.\n passage_instruction_format (str, optional): The template for passage. Defaults to \"{}{}\".\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n trust_remote_code (bool, optional): trust_remote_code. Defaults to :data:`False`.\n devices (Union[str, List[str], List[int]], optional): Devices to use for model inference, such as [\"cuda:0\"] or [\"0\"].\n Defaults to :data:`None`.\n prompt (Optional[str], optional): Prompt for the specific task. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.\n query_max_length (int, optional): Maximum length for queries. If not specified, will be 3/4 of :attr:`max_length`.\n Defaults to :data:`None`.\n max_length (int, optional): Maximum length of passages. Defaults to :data`512`.\n normalize (bool, optional): If True, use Sigmoid to normalize the results. Defaults to :data:`False`.\n \"\"\"\n def __init__(\n self,\n model_name_or_path: str,\n peft_path: Optional[str] = None,\n use_fp16: bool = False,\n use_bf16: bool = False,\n query_instruction_for_rerank: str = \"A: \",\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_rerank\n passage_instruction_for_rerank: str = \"B: \",\n passage_instruction_format: str = \"{}{}\", # specify the format of passage_instruction_for_rerank\n cache_dir: Optional[str] = None,\n trust_remote_code: bool = False,\n devices: Union[str, List[str], List[int]] = None, # specify devices, such as [\"cuda:0\"] or [\"0\"]\n # inference\n prompt: Optional[str] = None,\n batch_size: int = 128,\n query_max_length: int = None,\n max_length: int = 512,\n normalize: bool = False,\n **kwargs: Any,\n ) -> None:\n super().__init__(\n model_name_or_path=model_name_or_path,\n use_fp16=use_fp16,\n query_instruction_for_rerank=query_instruction_for_rerank,\n query_instruction_format=query_instruction_format,\n passage_instruction_for_rerank=passage_instruction_for_rerank,\n passage_instruction_format=passage_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n max_length=max_length,\n normalize=normalize,\n prompt=prompt,\n **kwargs\n )\n\n self.prompt = prompt\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code\n )\n\n self.model = AutoModelForCausalLM.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code,\n torch_dtype=torch.bfloat16 if use_bf16 else torch.float32\n )\n if peft_path:\n self.model = PeftModel.from_pretrained(self.model, peft_path)\n self.model = self.model.merge_and_unload()\n\n self.yes_loc = self.tokenizer('Yes', add_special_tokens=False)['input_ids'][0]\n\n @torch.no_grad()\n def compute_score_single_gpu(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: Optional[int] = None,\n query_max_length: Optional[int] = None,\n max_length: Optional[int] = None,\n prompt: Optional[str] = None,\n normalize: Optional[bool] = None,\n use_dataloader: bool = False,\n num_workers: int = None,\n device: Optional[str] = None,\n **kwargs: Any\n ) -> List[float]:\n \"\"\"Compute the relevance scores using a single GPU.\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Input sentence pairs to compute scores.\n batch_size (Optional[int], optional): Number of inputs for each iter. Defaults to :data:`None`.\n query_max_length (Optional[int], optional): Maximum length of tokens of queries. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n prompt (Optional[str], optional): Prompt for the specific task. Defaults to :data:`None`.\n normalize (Optional[bool], optional): If True, use Sigmoid to normalize the results. Defaults to :data:`None`.\n use_dataloader (bool, optional): If True, will use the dataloader to load the datasets. Defaults to :data:`False`.\n num_workers (int, optional): Number of workers for dataloader. Defaults to :data:`None`.\n device (Optional[str], optional): Device to use for computation. Defaults to :data:`None`.\n\n Returns:\n List[float]: The computed scores.\n \"\"\"\n if prompt is None: prompt = self.prompt\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.max_length\n if query_max_length is None:\n if self.query_max_length is not None:\n query_max_length = self.query_max_length\n else:\n query_max_length = max_length * 3 // 4\n if normalize is None: normalize = self.normalize\n\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n assert isinstance(sentence_pairs, list)\n if isinstance(sentence_pairs[0], str):\n sentence_pairs = [sentence_pairs]\n \n # tokenize without padding to get the correct length\n all_queries_inputs = []\n all_passages_inputs = []\n for start_index in trange(0, len(sentence_pairs), batch_size, desc=\"pre tokenize\",\n disable=len(sentence_pairs) < batch_size):\n sentences_batch = sentence_pairs[start_index:start_index + batch_size]\n queries = [s[0] for s in sentences_batch]\n passages = [s[1] for s in sentences_batch]\n queries_inputs_batch = self.tokenizer(\n queries,\n return_tensors=None,\n add_special_tokens=False,\n max_length=query_max_length,\n truncation=True,\n **kwargs\n )\n passages_inputs_batch = self.tokenizer(\n passages,\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True,\n **kwargs\n )\n queries_inputs_batch = [{\n k: queries_inputs_batch[k][i] for k in queries_inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n passages_inputs_batch = [{\n k: passages_inputs_batch[k][i] for k in passages_inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n\n all_queries_inputs.extend(queries_inputs_batch)\n all_passages_inputs.extend(passages_inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) - len(y['input_ids']) for (x, y) in zip(all_queries_inputs, all_passages_inputs)])\n all_queries_inputs_sorted = [all_queries_inputs[i] for i in length_sorted_idx]\n all_passages_inputs_sorted = [all_passages_inputs[i] for i in length_sorted_idx]\n\n # other inputs\n if prompt is None:\n prompt = \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"\n prompt_inputs = self.tokenizer(\n prompt,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n sep = \"\\n\"\n sep_inputs = self.tokenizer(\n sep,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n encode_max_length = max_length + len(sep_inputs) + len(prompt_inputs)\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n batch_inputs = []\n for query_inputs, passage_inputs in zip(\n all_queries_inputs_sorted[:min(len(all_queries_inputs_sorted), batch_size)], \n all_passages_inputs_sorted[:min(len(all_passages_inputs_sorted), batch_size)]\n ):\n if self.tokenizer.bos_token_id is not None and self.tokenizer.bos_token_id != self.tokenizer.pad_token_id:\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n else:\n item = self.tokenizer.prepare_for_model(\n query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n batch_inputs.append(item)\n\n collater_instance = Collater(self.tokenizer, encode_max_length)\n batch_inputs = collater_instance([{\n 'input_ids': item['input_ids'],\n 'attention_mask': item['attention_mask']\n } for item in batch_inputs]\n )\n\n batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()}\n\n self.model(**batch_inputs, output_hidden_states=True)\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n dataset, dataloader = None, None\n if use_dataloader:\n if num_workers is None:\n num_workers = min(batch_size, 16)\n dataset = DatasetForReranker(\n all_queries_inputs_sorted,\n all_passages_inputs_sorted,\n self.model_name_or_path,\n max_length,\n cache_dir=self.cache_dir,\n prompt=prompt,\n **kwargs\n )\n dataloader = DataLoader(\n dataset, shuffle=False, batch_size=batch_size, drop_last=False,\n num_workers=num_workers,\n collate_fn=Collater(self.tokenizer, encode_max_length)\n )\n\n all_scores = []\n if dataloader is not None:\n for inputs in tqdm(dataloader):\n inputs = inputs.to(device)\n\n outputs = self.model(**inputs, output_hidden_states=True)\n logits = outputs.logits\n scores = last_logit_pool(logits, inputs['attention_mask'])\n scores = scores[:, self.yes_loc]\n all_scores.extend(scores.cpu().float().tolist())\n else:\n for batch_start in trange(0, len(all_queries_inputs_sorted), batch_size):\n queries_inputs = all_queries_inputs_sorted[batch_start:batch_start+batch_size]\n passages_inputs = all_passages_inputs_sorted[batch_start:batch_start+batch_size]\n\n batch_inputs = []\n for query_inputs, passage_inputs in zip(queries_inputs, passages_inputs):\n if self.tokenizer.bos_token_id is not None and self.tokenizer.bos_token_id != self.tokenizer.pad_token_id:\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n else:\n item = self.tokenizer.prepare_for_model(\n query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n batch_inputs.append(item)\n\n collater_instance = Collater(self.tokenizer, encode_max_length)\n batch_inputs = collater_instance([{\n 'input_ids': item['input_ids'],\n 'attention_mask': item['attention_mask']\n } for item in batch_inputs]\n )\n\n batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()}\n\n outputs = self.model(**batch_inputs, output_hidden_states=True)\n logits = outputs.logits\n scores = last_logit_pool(logits, batch_inputs['attention_mask'])\n scores = scores[:, self.yes_loc]\n all_scores.extend(scores.cpu().float().tolist())\n\n all_scores = [all_scores[idx] for idx in np.argsort(length_sorted_idx)]\n\n if normalize:\n all_scores = [sigmoid(score) for score in all_scores]\n\n # if len(all_scores) == 1:\n # return all_scores[0]\n\n return all_scores\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/layerwise.py", "content": "import torch\nimport warnings\nimport numpy as np\nfrom tqdm import tqdm, trange\nfrom typing import Any, List, Union, Tuple, Optional\nfrom peft import PeftModel\nfrom torch import Tensor\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom torch.utils.data import DataLoader\n\nfrom FlagEmbedding.abc.inference import AbsReranker\nfrom FlagEmbedding.inference.reranker.encoder_only.base import sigmoid\nfrom FlagEmbedding.inference.reranker.decoder_only.base import DatasetForReranker, Collater\n\nfrom .models.modeling_minicpm_reranker import LayerWiseMiniCPMForCausalLM\n\n\ndef last_logit_pool_layerwise(logits: Tensor,\n attention_mask: Tensor) -> Tensor:\n \"\"\"Pool the last logit.\n\n Args:\n logits (torch.Tensor): The output logits of the model.\n attention_mask (torch.Tensor): Attention mask.\n\n Returns:\n torch.Tensor: The tensor after pooling.\n \"\"\"\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return logits[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = logits.shape[0]\n return logits[torch.arange(batch_size, device=logits.device), sequence_lengths]\n\n\nclass LayerWiseLLMReranker(AbsReranker):\n \"\"\"Base reranker class for layerwise LLM like decoder only models.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n peft_path (Optional[str], optional): Path to the PEFT config. Defaults to :data:`None`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`False`. Defaults to :data:`False`.\n use_bf16 (bool, optional): Another type of half-precision floating-point, you can use bf16 if the hardware supports. \n Defaults to :data:False.\n query_instruction_for_rerank (str, optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`\"A: \"`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`\"{}{}\"`.\n passage_instruction_for_rerank (str, optional): Passage instruction for retrieval tasks, which will be used with\n with :attr:`passage_instruction_format`. Defaults to :data:`\"B: \"`.\n passage_instruction_format (str, optional): The template for passage. Defaults to \"{}{}\".\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n trust_remote_code (bool, optional): trust_remote_code. Defaults to :data:`False`.\n devices (Union[str, List[str], List[int]], optional): Devices to use for model inference, such as [\"cuda:0\"] or [\"0\"].\n Defaults to :data:`None`.\n cutoff_layers (Optional[List[int]]): Pick which layers are used for computing the score. Defaults to :data:`None`.\n prompt (Optional[str], optional): Prompt for the specific task. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.\n query_max_length (int, optional): Maximum length for queries. If not specified, will be 3/4 of :attr:`max_length`.\n Defaults to :data:`None`.\n max_length (int, optional): Maximum length of passages. Defaults to :data`512`.\n normalize (bool, optional): If True, use Sigmoid to normalize the results. Defaults to :data:`False`.\n \"\"\"\n def __init__(\n self,\n model_name_or_path: str,\n peft_path: Optional[str] = None,\n use_fp16: bool = False,\n use_bf16: bool = False,\n query_instruction_for_rerank: str = \"A: \",\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_rerank\n passage_instruction_for_rerank: str = \"B: \",\n passage_instruction_format: str = \"{}{}\", # specify the format of passage_instruction_for_rerank\n cache_dir: Optional[str] = None,\n trust_remote_code: bool = False,\n devices: Optional[Union[str, List[str], List[int]]] = None, # specify devices, such as [\"cuda:0\"] or [\"0\"]\n # inference\n cutoff_layers: Optional[List[int]] = None, \n prompt: Optional[str] = None,\n batch_size: int = 128,\n query_max_length: Optional[int] = None,\n max_length: int = 512,\n normalize: bool = False,\n **kwargs: Any,\n ) -> None:\n super().__init__(\n model_name_or_path=model_name_or_path,\n use_fp16=use_fp16,\n query_instruction_for_rerank=query_instruction_for_rerank,\n query_instruction_format=query_instruction_format,\n passage_instruction_for_rerank=passage_instruction_for_rerank,\n passage_instruction_format=passage_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n max_length=max_length,\n normalize=normalize,\n **kwargs\n )\n\n self.cutoff_layers = cutoff_layers\n self.prompt = prompt\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code\n )\n\n if use_bf16 is False and use_fp16 is False:\n warnings.warn(\"Due to model constraints, `use_bf16` and `use_fp16` cannot both be `False`. Here, `use_fp16` is set to `True` by default.\", UserWarning)\n self.use_fp16 = True\n \n try:\n self.model = LayerWiseMiniCPMForCausalLM.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code,\n torch_dtype=torch.bfloat16 if use_bf16 else torch.float32\n )\n except:\n self.model = AutoModelForCausalLM.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code,\n torch_dtype=torch.bfloat16 if use_bf16 else torch.float32\n )\n if peft_path:\n self.model = PeftModel.from_pretrained(self.model,peft_path)\n self.model = self.model.merge_and_unload()\n\n @torch.no_grad()\n def compute_score_single_gpu(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: Optional[int] = None,\n query_max_length: Optional[int] = None,\n max_length: Optional[int] = None,\n cutoff_layers: Optional[List[int]] = None, \n prompt: Optional[str] = None,\n normalize: Optional[bool] = None,\n use_dataloader: bool = False,\n num_workers: Optional[int] = None,\n device: Optional[str] = None,\n **kwargs: Any\n ) -> List[float]:\n \"\"\"Compute the relevance scores using a single GPU.\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Input sentence pairs to compute scores.\n batch_size (Optional[int], optional): Number of inputs for each iter. Defaults to :data:`None`.\n query_max_length (Optional[int], optional): Maximum length of tokens of queries. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n cutoff_layers (Optional[List[int]], optional): Pick which layers are used for computing the score. Defaults to :data:`None`.\n prompt (Optional[str], optional): Prompt for the specific task. Defaults to :data:`None`.\n normalize (Optional[bool], optional): If True, use Sigmoid to normalize the results. Defaults to :data:`None`.\n use_dataloader (bool, optional): If True, will use the dataloader to load the datasets. Defaults to :data:`False`.\n num_workers (int, optional): Number of workers for dataloader. Defaults to :data:`None`.\n device (Optional[str], optional): Device to use for computation. Defaults to :data:`None`.\n\n Returns:\n List[float]: The computed scores.\n \"\"\"\n if cutoff_layers is None: cutoff_layers = self.cutoff_layers\n if prompt is None: prompt = self.prompt\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.max_length\n if query_max_length is None:\n if self.query_max_length is not None:\n query_max_length = self.query_max_length\n else:\n query_max_length = max_length * 3 // 4\n if normalize is None: normalize = self.normalize\n\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n assert isinstance(sentence_pairs, list)\n if isinstance(sentence_pairs[0], str):\n sentence_pairs = [sentence_pairs]\n\n # tokenize without padding to get the correct length\n all_queries_inputs = []\n all_passages_inputs = []\n for start_index in trange(0, len(sentence_pairs), batch_size, desc=\"pre tokenize\",\n disable=len(sentence_pairs) < batch_size):\n sentences_batch = sentence_pairs[start_index:start_index + batch_size]\n queries = [s[0] for s in sentences_batch]\n passages = [s[1] for s in sentences_batch]\n queries_inputs_batch = self.tokenizer(\n queries,\n return_tensors=None,\n add_special_tokens=False,\n max_length=query_max_length,\n truncation=True,\n **kwargs\n )\n passages_inputs_batch = self.tokenizer(\n passages,\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True,\n **kwargs\n )\n queries_inputs_batch = [{\n k: queries_inputs_batch[k][i] for k in queries_inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n passages_inputs_batch = [{\n k: passages_inputs_batch[k][i] for k in passages_inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n\n all_queries_inputs.extend(queries_inputs_batch)\n all_passages_inputs.extend(passages_inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) - len(y['input_ids']) for (x, y) in zip(all_queries_inputs, all_passages_inputs)])\n all_queries_inputs_sorted = [all_queries_inputs[i] for i in length_sorted_idx]\n all_passages_inputs_sorted = [all_passages_inputs[i] for i in length_sorted_idx]\n\n # other inputs\n if prompt is None:\n prompt = \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"\n prompt_inputs = self.tokenizer(\n prompt,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n sep = \"\\n\"\n sep_inputs = self.tokenizer(\n sep,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n encode_max_length = max_length + len(sep_inputs) + len(prompt_inputs)\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n batch_inputs = []\n for query_inputs, passage_inputs in zip(\n all_queries_inputs_sorted[:min(len(all_queries_inputs_sorted), batch_size)], \n all_passages_inputs_sorted[:min(len(all_passages_inputs_sorted), batch_size)]\n ):\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n batch_inputs.append(item)\n\n collater_instance = Collater(self.tokenizer, encode_max_length)\n batch_inputs = collater_instance([{\n 'input_ids': item['input_ids'],\n 'attention_mask': item['attention_mask']\n } for item in batch_inputs]\n )\n\n batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()}\n\n self.model(**batch_inputs, output_hidden_states=True, cutoff_layers=cutoff_layers)\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n dataset, dataloader = None, None\n if use_dataloader:\n if num_workers is None:\n num_workers = min(batch_size, 16)\n dataset = DatasetForReranker(\n all_queries_inputs_sorted,\n all_passages_inputs_sorted,\n self.model_name_or_path,\n max_length,\n cache_dir=self.cache_dir,\n prompt=prompt,\n **kwargs\n )\n dataloader = DataLoader(\n dataset, shuffle=False, batch_size=batch_size, drop_last=False,\n num_workers=num_workers,\n collate_fn=Collater(self.tokenizer, encode_max_length)\n )\n\n all_scores = []\n if dataloader is not None:\n for inputs in tqdm(dataloader):\n inputs = inputs.to(device)\n\n outputs = self.model(**inputs, output_hidden_states=True, cutoff_layers=cutoff_layers)\n all_logits = outputs.logits\n tmp_all_scores = []\n for logits in all_logits:\n scores = last_logit_pool_layerwise(logits, inputs['attention_mask'])\n tmp_all_scores.append(scores.contiguous())\n\n if len(all_scores) == 0:\n for _ in range(len(tmp_all_scores)):\n all_scores.append([])\n\n for i in range(len(tmp_all_scores)):\n all_scores[i].extend(tmp_all_scores[i].cpu().float().tolist())\n else:\n for batch_start in trange(0, len(all_queries_inputs_sorted), batch_size):\n queries_inputs = all_queries_inputs_sorted[batch_start:batch_start+batch_size]\n passages_inputs = all_passages_inputs_sorted[batch_start:batch_start+batch_size]\n\n batch_inputs = []\n for query_inputs, passage_inputs in zip(queries_inputs, passages_inputs):\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n batch_inputs.append(item)\n\n collater_instance = Collater(self.tokenizer, encode_max_length)\n batch_inputs = collater_instance([{\n 'input_ids': item['input_ids'],\n 'attention_mask': item['attention_mask']\n } for item in batch_inputs]\n )\n\n batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()}\n\n outputs = self.model(**batch_inputs, output_hidden_states=True, cutoff_layers=cutoff_layers)\n all_logits = outputs.logits\n tmp_all_scores = []\n for logits in all_logits:\n scores = last_logit_pool_layerwise(logits, batch_inputs['attention_mask'])\n tmp_all_scores.append(scores.contiguous())\n\n if len(all_scores) == 0:\n for _ in range(len(tmp_all_scores)):\n all_scores.append([])\n\n for i in range(len(tmp_all_scores)):\n all_scores[i].extend(tmp_all_scores[i].cpu().float().tolist())\n\n for i in range(len(all_scores)):\n all_scores[i] = [all_scores[i][idx] for idx in np.argsort(length_sorted_idx)]\n if normalize:\n all_scores[i] = [sigmoid(score) for score in all_scores[i]]\n \n if len(all_scores) == 1 and isinstance(all_scores[0], list):\n all_scores = all_scores[0]\n \n return all_scores\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/lightweight.py", "content": "import torch\nimport sys\nimport warnings\nimport numpy as np\nfrom tqdm import trange\nfrom typing import Any, List, Union, Tuple, Optional\nfrom peft import PeftModel\nfrom torch import Tensor\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nfrom FlagEmbedding.abc.inference import AbsReranker\nfrom FlagEmbedding.inference.reranker.encoder_only.base import sigmoid\n\n\ndef last_logit_pool_lightweight(logits: Tensor,\n attention_mask: Tensor) -> Tensor:\n \"\"\"Pool the last logit.\n\n Args:\n logits (torch.Tensor): The output logits of the model.\n attention_mask (torch.Tensor): Attention mask.\n\n Returns:\n torch.Tensor: The tensor after pooling.\n \"\"\"\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return logits[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = logits.shape[0]\n return torch.stack([logits[i, sequence_lengths[i]] for i in range(batch_size)], dim=0)\n\n\nclass Collater_for_lightweight:\n \"\"\"\n Collator of the lightweight LLM reranker.\n \n Args:\n tokenizer (transformers.AutoTokenizer): The tokenizer for reranker.\n max_len (int): Maximum length of tokens.\n \"\"\"\n def __init__(self, tokenizer, max_len):\n self.tokenizer = tokenizer\n self.max_len = max_len\n self.pad_to_multiple_of = 8\n self.label_pad_token_id = -100\n warnings.filterwarnings(\"ignore\",\n message=\"`max_length` is ignored when `padding`=`True` and there is no truncation strategy.\")\n\n def __call__(self, data):\n features = data[0]\n query_lengths = data[1]\n prompt_lengths = data[2]\n\n labels = [feature[\"labels\"] for feature in features] if \"labels\" in features[0].keys() else None\n # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the\n # same length to return tensors.\n if labels is not None:\n max_label_length = max(len(l) for l in labels)\n if self.pad_to_multiple_of is not None:\n max_label_length = (\n (max_label_length + self.pad_to_multiple_of - 1)\n // self.pad_to_multiple_of\n * self.pad_to_multiple_of\n )\n\n padding_side = self.tokenizer.padding_side\n for feature in features:\n remainder = [self.label_pad_token_id] * (max_label_length - len(feature[\"labels\"]))\n if isinstance(feature[\"labels\"], list):\n feature[\"labels\"] = (\n feature[\"labels\"] + remainder if padding_side == \"right\" else remainder + feature[\"labels\"]\n )\n elif padding_side == \"right\":\n feature[\"labels\"] = np.concatenate([feature[\"labels\"], remainder]).astype(np.int64)\n else:\n feature[\"labels\"] = np.concatenate([remainder, feature[\"labels\"]]).astype(np.int64)\n\n collected = self.tokenizer.pad(\n features,\n padding=True,\n pad_to_multiple_of=8,\n return_tensors='pt',\n )\n\n return collected, query_lengths, prompt_lengths\n\n\nclass LightweightLLMReranker(AbsReranker):\n \"\"\"Base reranker class for light weight LLM like decoder only models.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n peft_path (Optional[str], optional): Path to the PEFT config. Defaults to :data:`None`.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`False`. Defaults to :data:`False`.\n use_bf16 (bool, optional): Another type of half-precision floating-point, you can use bf16 if the hardware supports. \n Defaults to :data:False.\n query_instruction_for_rerank (str, optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`\"A: \"`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`\"{}{}\"`.\n passage_instruction_for_rerank (str, optional): Passage instruction for retrieval tasks, which will be used with\n with :attr:`passage_instruction_format`. Defaults to :data:`\"B: \"`.\n passage_instruction_format (str, optional): The template for passage. Defaults to \"{}{}\".\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n trust_remote_code (bool, optional): trust_remote_code. Defaults to :data:`False`.\n devices (Union[str, List[str], List[int]], optional): Devices to use for model inference, such as [\"cuda:0\"] or [\"0\"].\n Defaults to :data:`None`.\n cutoff_layers (Optional[List[int]]): Pick which layers are used for computing the score. Defaults to :data:`None`.\n compress_layers (List[int], optional): Choose the layers to compress. Defaults to :data:`[8]`.\n compress_ratio (int, optional): Ratio to compress the selected layers, supported ratios: :data:`[1, 2, 4, 8]`. \n Defaults to :data:`1`.\n prompt (Optional[str], optional): Prompt for the specific task. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.\n query_max_length (int, optional): Maximum length for queries. If not specified, will be 3/4 of :attr:`max_length`.\n Defaults to :data:`None`.\n max_length (int, optional): Maximum length of passages. Defaults to :data`512`.\n normalize (bool, optional): If True, use Sigmoid to normalize the results. Defaults to :data:`False`.\n \"\"\"\n def __init__(\n self,\n model_name_or_path: str,\n peft_path: Optional[str] = None,\n use_fp16: bool = False,\n use_bf16: bool = False,\n query_instruction_for_rerank: str = \"A: \",\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_rerank\n passage_instruction_for_rerank: str = \"B: \",\n passage_instruction_format: str = \"{}{}\", # specify the format of passage_instruction_for_rerank\n cache_dir: Optional[str] = None,\n trust_remote_code: bool = False,\n devices: Union[str, List[str], List[int]] = None, # specify devices, such as [\"cuda:0\"] or [\"0\"]\n # inference\n cutoff_layers: Optional[List[int]] = None,\n compress_layers: List[int] = [8],\n compress_ratio: int = 1,\n prompt: Optional[str] = None,\n batch_size: int = 128,\n query_max_length: Optional[int] = None,\n max_length: int = 512,\n normalize: bool = False,\n **kwargs: Any,\n ) -> None:\n try:\n from .models.gemma_model import CostWiseGemmaForCausalLM\n except:\n print('*') * 20\n print('*') * 20\n print('error for load lightweight reranker, please install transformers==4.46.0')\n print('*') * 20\n print('*') * 20\n sys.exit()\n\n super().__init__(\n model_name_or_path=model_name_or_path,\n use_fp16=use_fp16,\n query_instruction_for_rerank=query_instruction_for_rerank,\n query_instruction_format=query_instruction_format,\n passage_instruction_for_rerank=passage_instruction_for_rerank,\n passage_instruction_format=passage_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n max_length=max_length,\n normalize=normalize,\n **kwargs\n )\n\n self.cutoff_layers = cutoff_layers\n self.compress_layers = compress_layers\n self.compress_ratio = compress_ratio\n self.prompt = prompt\n\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code\n )\n self.tokenizer.padding_side = 'right'\n\n if use_bf16 is False and use_fp16 is False:\n warnings.warn(\"Due to model constraints, `use_bf16` and `use_fp16` cannot both be `False`. Here, `use_fp16` is set to `True` by default.\", UserWarning)\n use_fp16 = True\n\n try:\n self.model = CostWiseGemmaForCausalLM.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code,\n torch_dtype=torch.bfloat16 if use_bf16 else torch.float32\n )\n except:\n self.model = AutoModelForCausalLM.from_pretrained(\n model_name_or_path,\n cache_dir=cache_dir,\n trust_remote_code=trust_remote_code,\n torch_dtype=torch.bfloat16 if use_bf16 else torch.float32\n )\n if peft_path:\n self.model = PeftModel.from_pretrained(self.model,peft_path)\n self.model = self.model.merge_and_unload()\n\n @torch.no_grad()\n def compute_score_single_gpu(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: Optional[int] = None,\n query_max_length: Optional[int] = None,\n max_length: Optional[int] = None,\n cutoff_layers: Optional[List[int]] = None,\n compress_layer: Optional[List[int]] = None,\n compress_layers: Optional[List[int]] = None,\n compress_ratio: Optional[int] = None,\n prompt: Optional[str] = None,\n normalize: Optional[bool] = None,\n device: Optional[str] = None,\n **kwargs: Any\n ) -> List[float]:\n \"\"\"Compute the relevance scores using a single GPU.\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Input sentence pairs to compute scores.\n batch_size (Optional[int], optional): Number of inputs for each iter. Defaults to :data:`None`.\n query_max_length (Optional[int], optional): Maximum length of tokens of queries. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n cutoff_layers (Optional[List[int]], optional): Pick which layers are used for computing the score. Defaults to :data:`None`.\n compress_layer (Optional[List[int]]): Deprecated, use :attr:`compress_layers` instead. Defaults to :data:`None`.\n compress_layers (Optional[List[int]]): Selected layers to compress. Defaults to :data:`None`.\n compress_ratio (Optional[int]): Ratio to compress the selected layers, supported ratios: :data:`[1, 2, 4, 8]`. \n Defaults to :data:`None`.\n prompt (Optional[str], optional): Prompt for the specific task. Defaults to :data:`None`.\n normalize (Optional[bool], optional): If True, use Sigmoid to normalize the results. Defaults to :data:`None`.\n device (Optional[str], optional): Device to use for computation. Defaults to :data:`None`.\n\n Returns:\n List[float]: The computed scores.\n \"\"\"\n\n if cutoff_layers is None: cutoff_layers = self.cutoff_layers\n if compress_layers is None: compress_layers = self.compress_layers\n if compress_layer is not None:\n print('Try not to use the parameter `compress_layer`; use `compress_layers` instead.')\n compress_layers = compress_layer\n if compress_ratio is None: compress_ratio = self.compress_ratio\n if prompt is None: prompt = self.prompt\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.max_length\n if query_max_length is None:\n if self.query_max_length is not None:\n query_max_length = self.query_max_length\n else:\n query_max_length = max_length * 3 // 4\n if normalize is None: normalize = self.normalize\n\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n assert isinstance(sentence_pairs, list)\n if isinstance(sentence_pairs[0], str):\n sentence_pairs = [sentence_pairs]\n\n # tokenize without padding to get the correct length\n all_queries_inputs = []\n all_passages_inputs = []\n for start_index in trange(0, len(sentence_pairs), batch_size, desc=\"pre tokenize\",\n disable=len(sentence_pairs) < batch_size):\n sentences_batch = sentence_pairs[start_index:start_index + batch_size]\n queries = [s[0] for s in sentences_batch]\n passages = [s[1] for s in sentences_batch]\n queries_inputs_batch = self.tokenizer(\n queries,\n return_tensors=None,\n add_special_tokens=False,\n max_length=query_max_length,\n truncation=True,\n **kwargs\n )\n passages_inputs_batch = self.tokenizer(\n passages,\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True,\n **kwargs\n )\n queries_inputs_batch = [{\n k: queries_inputs_batch[k][i] for k in queries_inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n passages_inputs_batch = [{\n k: passages_inputs_batch[k][i] for k in passages_inputs_batch.keys()\n } for i in range(len(sentences_batch))]\n\n all_queries_inputs.extend(queries_inputs_batch)\n all_passages_inputs.extend(passages_inputs_batch)\n\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) - len(y['input_ids']) for (x, y) in zip(all_queries_inputs, all_passages_inputs)])\n all_queries_inputs_sorted = [all_queries_inputs[i] for i in length_sorted_idx]\n all_passages_inputs_sorted = [all_passages_inputs[i] for i in length_sorted_idx]\n\n # other inputs\n if prompt is None:\n prompt = \"Predict whether passage B contains an answer to query A.\"\n prompt_inputs = self.tokenizer(\n prompt,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n sep = \"\\n\"\n sep_inputs = self.tokenizer(\n sep,\n return_tensors=None,\n add_special_tokens=False\n )['input_ids']\n encode_max_length = max_length + len(sep_inputs) + len(prompt_inputs)\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n batch_inputs = []\n query_lengths = []\n prompt_lengths = []\n for query_inputs, passage_inputs in zip(\n all_queries_inputs_sorted[:min(len(all_queries_inputs_sorted), batch_size)], \n all_passages_inputs_sorted[:min(len(all_passages_inputs_sorted), batch_size)]\n ):\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n batch_inputs.append(item)\n query_lengths.append(len([self.tokenizer.bos_token_id] + query_inputs['input_ids'] + sep_inputs))\n prompt_lengths.append(len(sep_inputs + prompt_inputs))\n\n collater_instance = Collater_for_lightweight(self.tokenizer, max_length)\n batch_inputs = collater_instance([\n [{\n 'input_ids': item['input_ids'],\n 'attention_mask': item['attention_mask']\n } for item in batch_inputs],\n query_lengths,\n prompt_lengths\n ])[0]\n\n batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()}\n\n self.model(\n **batch_inputs,\n output_hidden_states=True,\n compress_layer=compress_layers,\n compress_ratio=compress_ratio,\n query_lengths=query_lengths,\n prompt_lengths=prompt_lengths,\n cutoff_layers=cutoff_layers\n )\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n all_scores = []\n for batch_start in trange(0, len(all_queries_inputs_sorted), batch_size):\n queries_inputs = all_queries_inputs_sorted[batch_start:batch_start+batch_size]\n passages_inputs = all_passages_inputs_sorted[batch_start:batch_start+batch_size]\n\n batch_inputs = []\n query_lengths = []\n prompt_lengths = []\n for query_inputs, passage_inputs in zip(queries_inputs, passages_inputs):\n item = self.tokenizer.prepare_for_model(\n [self.tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=encode_max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n item.pop('token_type_ids') if 'token_type_ids' in item.keys() else None\n if 'position_ids' in item.keys():\n item['position_ids'] = list(range(len(item['input_ids'])))\n batch_inputs.append(item)\n query_lengths.append(len([self.tokenizer.bos_token_id] + query_inputs['input_ids'] + sep_inputs))\n prompt_lengths.append(len(sep_inputs + prompt_inputs))\n\n collater_instance = Collater_for_lightweight(self.tokenizer, max_length)\n batch_inputs = collater_instance([\n [{\n 'input_ids': item['input_ids'],\n 'attention_mask': item['attention_mask']\n } for item in batch_inputs],\n query_lengths,\n prompt_lengths\n ])[0]\n\n batch_inputs = {key: val.to(device) for key, val in batch_inputs.items()}\n\n outputs = self.model(\n **batch_inputs,\n output_hidden_states=True,\n compress_layer=compress_layers,\n compress_ratio=compress_ratio,\n query_lengths=query_lengths,\n prompt_lengths=prompt_lengths,\n cutoff_layers=cutoff_layers\n )\n scores = []\n for i in range(len(outputs.logits)):\n logits = last_logit_pool_lightweight(outputs.logits[i], outputs.attention_masks[i])\n scores.append(logits.cpu().float().tolist())\n if len(all_scores) == 0:\n for i in range(len(scores)):\n all_scores.append([])\n for i in range(len(scores)):\n all_scores[i].extend(scores[i])\n\n for i in range(len(all_scores)):\n all_scores[i] = [all_scores[i][idx] for idx in np.argsort(length_sorted_idx)]\n if normalize:\n all_scores[i] = [sigmoid(score) for score in all_scores[i]]\n \n if len(all_scores) == 1 and isinstance(all_scores[0], list):\n all_scores = all_scores[0]\n\n return all_scores\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/models/__init__.py", "content": "" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/models/configuration_minicpm_reranker.py", "content": "# coding=utf-8\n# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.\n#\n# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX\n# and OPT implementations in this library. It has been modified from its\n# original forms to accommodate minor architectural differences compared\n# to GPT-NeoX and OPT used by the Meta AI team that trained the model.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" MiniCPM model configuration\"\"\"\n\nfrom transformers.configuration_utils import PretrainedConfig\nfrom transformers.utils import logging\n\n\nlogger = logging.get_logger(__name__)\n\nMINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}\n\nclass LayerWiseMiniCPMConfig(PretrainedConfig):\n r\"\"\"\n This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM\n model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n defaults will yield a similar configuration to that of the MiniCPM-7B.\n\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n documentation from [`PretrainedConfig`] for more information.\n\n\n Args:\n vocab_size (`int`, *optional*, defaults to 32000):\n Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the\n `inputs_ids` passed when calling [`MiniCPMModel`]\n hidden_size (`int`, *optional*, defaults to 4096):\n Dimension of the hidden representations.\n intermediate_size (`int`, *optional*, defaults to 11008):\n Dimension of the MLP representations.\n num_hidden_layers (`int`, *optional*, defaults to 32):\n Number of hidden layers in the Transformer decoder.\n num_attention_heads (`int`, *optional*, defaults to 32):\n Number of attention heads for each attention layer in the Transformer decoder.\n num_key_value_heads (`int`, *optional*):\n This is the number of key_value heads that should be used to implement Grouped Query Attention. If\n `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if\n `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When\n converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed\n by meanpooling all the original heads within that group. For more details checkout [this\n paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to\n `num_attention_heads`.\n hidden_act (`str` or `function`, *optional*, defaults to `\"silu\"`):\n The non-linear activation function (function or string) in the decoder.\n max_position_embeddings (`int`, *optional*, defaults to 2048):\n The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,\n MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.\n initializer_range (`float`, *optional*, defaults to 0.02):\n The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n rms_norm_eps (`float`, *optional*, defaults to 1e-06):\n The epsilon used by the rms normalization layers.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models). Only\n relevant if `config.is_decoder=True`.\n pad_token_id (`int`, *optional*):\n Padding token id.\n bos_token_id (`int`, *optional*, defaults to 1):\n Beginning of stream token id.\n eos_token_id (`int`, *optional*, defaults to 2):\n End of stream token id.\n pretraining_tp (`int`, *optional*, defaults to 1):\n Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this\n document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is\n necessary to ensure exact reproducibility of the pretraining results. Please refer to [this\n issue](https://github.com/pytorch/pytorch/issues/76232).\n tie_word_embeddings (`bool`, *optional*, defaults to `False`):\n Whether to tie weight embeddings\n rope_theta (`float`, *optional*, defaults to 10000.0):\n The base period of the RoPE embeddings.\n rope_scaling (`Dict`, *optional*):\n Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling\n strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is\n `{\"type\": strategy name, \"factor\": scaling factor}`. When using this flag, don't update\n `max_position_embeddings` to the expected new maximum. See the following thread for more information on how\n these scaling strategies behave:\n https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an\n experimental feature, subject to breaking API changes in future versions.\n attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):\n Whether to use a bias in the query, key, value and output projection layers during self-attention.\n attention_dropout (`float`, *optional*, defaults to 0.0):\n The dropout ratio for the attention probabilities.\n\n ```python\n >>> from transformers import MiniCPMModel, MiniCPMConfig\n\n >>> # Initializing a MiniCPM minicpm-7b style configuration\n >>> configuration = MiniCPMConfig()\n\n >>> # Initializing a model from the minicpm-7b style configuration\n >>> model = MiniCPMModel(configuration)\n\n >>> # Accessing the model configuration\n >>> configuration = model.config\n ```\"\"\"\n\n model_type = \"minicpm\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n\n def __init__(\n self,\n vocab_size=32000,\n hidden_size=4096,\n intermediate_size=11008,\n num_hidden_layers=32,\n num_attention_heads=32,\n num_key_value_heads=None,\n hidden_act=\"silu\",\n max_position_embeddings=2048,\n initializer_range=0.02,\n rms_norm_eps=1e-6,\n use_cache=True,\n pad_token_id=None,\n bos_token_id=1,\n eos_token_id=2,\n pretraining_tp=1,\n tie_word_embeddings=True,\n rope_theta=10000.0,\n rope_scaling=None,\n attention_bias=False,\n attention_dropout=0.0,\n scale_emb=1,\n dim_model_base=1,\n scale_depth=1,\n start_layer=8,\n head_multi=True,\n head_type=\"simple\",\n **kwargs,\n ):\n self.vocab_size = vocab_size\n self.max_position_embeddings = max_position_embeddings\n self.hidden_size = hidden_size\n self.intermediate_size = intermediate_size\n self.num_hidden_layers = num_hidden_layers\n self.num_attention_heads = num_attention_heads\n\n # for backward compatibility\n if num_key_value_heads is None:\n num_key_value_heads = num_attention_heads\n\n self.num_key_value_heads = num_key_value_heads\n self.hidden_act = hidden_act\n self.initializer_range = initializer_range\n self.rms_norm_eps = rms_norm_eps\n self.pretraining_tp = pretraining_tp\n self.use_cache = use_cache\n self.rope_theta = rope_theta\n self.rope_scaling = rope_scaling\n self._rope_scaling_validation()\n self.attention_bias = attention_bias\n self.attention_dropout = attention_dropout\n self.scale_emb = scale_emb\n self.dim_model_base = dim_model_base\n self.scale_depth = scale_depth\n\n self.start_layer = start_layer\n self.head_multi = head_multi\n self.head_type = head_type\n\n super().__init__(\n pad_token_id=pad_token_id,\n bos_token_id=bos_token_id,\n eos_token_id=eos_token_id,\n tie_word_embeddings=tie_word_embeddings,\n **kwargs,\n )\n try:\n import flash_attn\n self._attn_implementation = \"flash_attention_2\"\n except:\n pass\n\n def _rope_scaling_validation(self):\n \"\"\"\n Validate the `rope_scaling` configuration.\n \"\"\"\n if self.rope_scaling is None:\n return\n\n if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:\n raise ValueError(\n \"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, \"\n f\"got {self.rope_scaling}\"\n )\n rope_scaling_type = self.rope_scaling.get(\"type\", None)\n rope_scaling_factor = self.rope_scaling.get(\"factor\", None)\n if rope_scaling_type is None or rope_scaling_type not in [\"linear\", \"dynamic\"]:\n raise ValueError(\n f\"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}\"\n )\n if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:\n raise ValueError(f\"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}\")\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/models/gemma_config.py", "content": "# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨\n# This file was automatically generated from .\n# Do NOT edit this file manually as any edits will be overwritten by the generation of\n# the file from the diff. If any change should be done, please apply the change to the\n# diff.py file directly.\n# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨\n# coding=utf-8\n# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.\n#\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\nfrom transformers.models.gemma2.configuration_gemma2 import Gemma2Config\n\nclass CostWiseGemmaConfig(Gemma2Config):\n r\"\"\"\n This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma\n model according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n defaults will yield a similar configuration to that of the Gemma-7B.\n e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)\n Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n documentation from [`PretrainedConfig`] for more information.\n Args:\n start_layer (`int`, *optional*, defaults to 28):\n The start layer to output score.\n layer_sep (`int`, *optional*, defaults to 28):\n The sep layer from the start layer to output score.\n layer_wise (`bool`, *optional*, defaults to `False`):\n Whether or not the model should be layerwise.\n ```python\n >>> from transformers import Gemma2Model, Gemma2Config\n >>> # Initializing a Gemma2 gemma2-9b style configuration\n >>> configuration = Gemma2Config()\n >>> # Initializing a model from the gemma2-9b style configuration\n >>> model = Gemma2Model(configuration)\n >>> # Accessing the model configuration\n >>> configuration = model.config\n ```\"\"\"\n\n model_type = \"cost_wise_gemma\"\n keys_to_ignore_at_inference = [\"past_key_values\"]\n\n def __init__(\n self,\n start_layer: int = 28,\n layer_sep: int = 28,\n layer_wise: bool = False,\n **kwargs,\n ):\n self.start_layer = start_layer\n self.layer_sep = layer_sep\n self.layer_wise = layer_wise\n\n super().__init__(\n **kwargs,\n )" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/models/gemma_model.py", "content": "# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨\n# This file was automatically generated from .\n# Do NOT edit this file manually as any edits will be overwritten by the generation of\n# the file from the diff. If any change should be done, please apply the change to the\n# diff.py file directly.\n# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨\n# coding=utf-8\n# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.\n#\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nfrom dataclasses import dataclass\n\nimport math\nfrom typing import List, Optional, Tuple, Union\n\nimport inspect\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n\nfrom transformers.activations import ACT2FN\nfrom transformers.cache_utils import Cache, DynamicCache, StaticCache\nfrom transformers.modeling_attn_mask_utils import AttentionMaskConverter\nfrom transformers.modeling_outputs import (\n BaseModelOutputWithPast,\n CausalLMOutputWithPast,\n SequenceClassifierOutputWithPast,\n TokenClassifierOutput,\n)\nfrom transformers.modeling_utils import PreTrainedModel\nfrom transformers.pytorch_utils import ALL_LAYERNORM_LAYERS\nfrom transformers.utils import (\n add_start_docstrings,\n add_start_docstrings_to_model_forward,\n is_flash_attn_2_available,\n is_flash_attn_greater_or_equal_2_10,\n logging,\n replace_return_docstrings,\n ModelOutput,\n)\nfrom .gemma_config import CostWiseGemmaConfig\nfrom transformers.models.gemma2.modeling_gemma2 import Gemma2RMSNorm, Gemma2RotaryEmbedding, rotate_half, apply_rotary_pos_emb\nfrom transformers.models.gemma2.modeling_gemma2 import Gemma2MLP, repeat_kv, Gemma2Attention, Gemma2DecoderLayer, GEMMA2_START_DOCSTRING\nfrom transformers.models.gemma2.modeling_gemma2 import GEMMA2_INPUTS_DOCSTRING\n\nif is_flash_attn_2_available():\n from flash_attn import flash_attn_func, flash_attn_varlen_func\n from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa\n\n _flash_supports_window_size = \"window_size\" in list(inspect.signature(flash_attn_func).parameters)\n\n\nlogger = logging.get_logger(__name__)\n\n\ndef _get_unpad_data(attention_mask):\n seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)\n indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()\n max_seqlen_in_batch = seqlens_in_batch.max().item()\n cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))\n return (\n indices,\n cu_seqlens,\n max_seqlen_in_batch,\n )\n\n@add_start_docstrings(\n \"The bare Gemma2 Model outputting raw hidden-states without any specific head on top.\",\n GEMMA2_START_DOCSTRING,\n)\nclass CostWiseGemma2PreTrainedModel(PreTrainedModel):\n config_class = CostWiseGemmaConfig\n base_model_prefix = \"model\"\n supports_gradient_checkpointing = True\n _no_split_modules = [\"Gemma2DecoderLayer\"]\n _skip_keys_device_placement = [\"past_key_values\"]\n _supports_flash_attn_2 = True\n _supports_sdpa = True\n _supports_cache_class = False\n _supports_quantized_cache = False\n _supports_static_cache = True\n _is_stateful = True\n\n def _init_weights(self, module):\n std = self.config.initializer_range\n if isinstance(module, nn.Linear):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n\n_CONFIG_FOR_DOC = \"CostWiseGemmaConfig\"\n\n@dataclass\nclass CostWiseModelOutputWithPast(ModelOutput):\n last_hidden_state: torch.FloatTensor = None\n past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None\n hidden_states: Optional[Tuple[torch.FloatTensor]] = None\n attentions: Optional[Tuple[torch.FloatTensor]] = None\n attention_masks: Optional[Tuple[torch.FloatTensor]] = None\n\n@dataclass\nclass CostWiseCausalLMOutputWithPast(ModelOutput):\n loss: Optional[torch.FloatTensor] = None\n logits: torch.FloatTensor = None\n past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None\n hidden_states: Optional[Tuple[torch.FloatTensor]] = None\n attentions: Optional[Tuple[torch.FloatTensor]] = None\n attention_masks: Optional[Tuple[torch.FloatTensor]] = None\n\ndef token_compress(compress_ratio,\n hidden_states,\n attention_mask,\n query_lengths,\n prompt_lengths):\n \"\"\"\n compress_ratio: int\n hidden_states: (b, s, h)\n attention_mask: (b, s)\n query_lengths: (b)\n prompt_lengths: (b)\n \"\"\"\n # get some specific parameters\n passage_lengths = torch.sum(attention_mask, dim=1, dtype=torch.int) - query_lengths - prompt_lengths # the raw passage lengths (b)\n retain_passage_lengths = (passage_lengths + compress_ratio - 1) // compress_ratio # the passage lengths need to be retained (b)\n final_useful_lengths = query_lengths + prompt_lengths + retain_passage_lengths # the final useful length after compress (b)\n max_passage_length = torch.max(passage_lengths) # the max passage lengths (1)\n max_final_lengths = torch.max(final_useful_lengths) # the max useful lengths after compress (1)\n # make new hidden states and new attention masks\n new_hidden_states = torch.zeros((hidden_states.shape[0], max_final_lengths,\n hidden_states.shape[-1]), dtype=hidden_states.dtype).to(hidden_states.device) # (b, s', h)\n new_attention_mask = torch.ones((hidden_states.shape[0], max_final_lengths), dtype=attention_mask.dtype).to(attention_mask.device) # (b, s')\n # get new attention mask\n mask_attention_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0) >= final_useful_lengths[:, None]\n new_attention_mask[mask_attention_index] = 0\n # get new hidden states\n # add query into new hidden states\n query_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)\n mask_query_index = query_index < query_lengths[:, None]\n new_hidden_states[mask_query_index] = hidden_states[:, : max_final_lengths, :][mask_query_index]\n # add prompt into new hidden states\n # get the index of the prompt in new hidden states\n new_prompt_start_length = query_lengths + retain_passage_lengths\n new_prompt_end_length = new_prompt_start_length + prompt_lengths\n new_prompt_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)\n new_mask_prompt_index_start = new_prompt_index >= new_prompt_start_length[:, None]\n new_mask_prompt_index_end = new_prompt_index < new_prompt_end_length[:, None]\n new_mask_prompt_index = new_mask_prompt_index_start & new_mask_prompt_index_end\n # get the index of the prompt in hidden states\n raw_prompt_start_length = query_lengths + passage_lengths\n raw_prompt_end_length = raw_prompt_start_length + prompt_lengths\n raw_prompt_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)\n raw_mask_prompt_index_start = raw_prompt_index >= raw_prompt_start_length[:, None]\n raw_mask_prompt_index_end = raw_prompt_index < raw_prompt_end_length[:, None]\n raw_mask_prompt_index = raw_mask_prompt_index_start & raw_mask_prompt_index_end\n # replace the prompt hidden states\n new_hidden_states[new_mask_prompt_index] = hidden_states[raw_mask_prompt_index]\n # 以上均没问题\n\n # print(new_hidden_states.view(len(new_hidden_states), -1))\n # print(new_attention_mask)\n\n # get the index of the passage in new hidden states\n new_passage_start_length = query_lengths\n new_passage_end_length = new_passage_start_length + retain_passage_lengths\n new_passage_index = torch.arange(max_final_lengths, device=hidden_states.device).unsqueeze(0)\n new_mask_passage_index_start = new_passage_index >= new_passage_start_length[:, None]\n new_mask_passage_index_end = new_passage_index < new_passage_end_length[:, None]\n new_mask_passage_index = new_mask_passage_index_start & new_mask_passage_index_end\n # print(query_lengths, prompt_lengths, retain_passage_lengths, final_useful_lengths)\n # add passage into new hidden states\n # get mask hidden states\n psg_start_length = query_lengths\n psg_end_length = query_lengths + passage_lengths\n psg_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)\n mask_psg_index_start = psg_index >= psg_start_length[:, None]\n mask_psg_index_end = psg_index < psg_end_length[:, None]\n mask_psg_index = mask_psg_index_start & mask_psg_index_end\n\n hidden_states = hidden_states * mask_psg_index.unsqueeze(-1)\n passage_hidden_states = torch.zeros((hidden_states.shape[0],\n (max_passage_length + compress_ratio - 1) // compress_ratio * compress_ratio,\n hidden_states.shape[-1]), dtype=hidden_states.dtype).to(hidden_states.device)\n passage_end_length = passage_lengths\n passage_index = torch.arange(passage_hidden_states.shape[1], device=hidden_states.device).unsqueeze(0) # maybe exceed the max passage length\n mask_passage_index = passage_index < passage_end_length[:, None]\n\n raw_passage_end_length = query_lengths + passage_lengths\n raw_passage_start_length = query_lengths\n raw_passage_index = torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)\n raw_mask_passage_index_start = raw_passage_index >= raw_passage_start_length[:, None]\n raw_mask_passage_index_end = raw_passage_index < raw_passage_end_length[:, None]\n raw_mask_passage_index = raw_mask_passage_index_start & raw_mask_passage_index_end\n passage_hidden_states[mask_passage_index] = hidden_states[raw_mask_passage_index]\n\n passage_weights = torch.zeros((hidden_states.shape[0],\n (max_passage_length + compress_ratio - 1) // compress_ratio * compress_ratio)\n , dtype=hidden_states.dtype).to(hidden_states.device)\n passage_weights[mask_passage_index] = 1\n passage_weights = passage_weights.view(passage_weights.shape[0], -1, compress_ratio)\n passage_weights = passage_weights / torch.sum(passage_weights, dim=-1\n ).view(passage_weights.shape[0], -1, 1)\n passage_weights = passage_weights.view(passage_weights.shape[0], -1)\n # passage_weights = torch.where(passage_weights == torch.nan, 0, passage_weights)\n passage_hidden_states = passage_hidden_states * passage_weights.unsqueeze(-1)\n passage_hidden_states = passage_hidden_states.view(passage_hidden_states.shape[0], -1, compress_ratio,\n passage_hidden_states.shape[-1])\n passage_hidden_states = torch.sum(passage_hidden_states, dim=2)\n passage_end_length = retain_passage_lengths\n passage_index = torch.arange(passage_hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)\n mask_passage_index = passage_index < passage_end_length[:, None]\n new_hidden_states[new_mask_passage_index] = passage_hidden_states[mask_passage_index]\n\n return new_hidden_states, new_attention_mask\n\n@add_start_docstrings(\n \"The bare Gemma2 Model outputting raw hidden-states without any specific head on top.\",\n GEMMA2_START_DOCSTRING,\n)\nclass CostWiseGemmaModel(CostWiseGemma2PreTrainedModel):\n \"\"\"\n Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`]\n\n Args:\n config: GemmaConfig\n \"\"\"\n\n def __init__(self, config: CostWiseGemmaConfig):\n super().__init__(config)\n self.padding_idx = config.pad_token_id\n self.vocab_size = config.vocab_size\n\n self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)\n self.layers = nn.ModuleList(\n [Gemma2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]\n )\n self.norm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n self.gradient_checkpointing = False\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.embed_tokens\n\n def set_input_embeddings(self, value):\n self.embed_tokens = value\n\n @add_start_docstrings_to_model_forward(GEMMA2_INPUTS_DOCSTRING)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cache_position: Optional[torch.LongTensor] = None,\n compress_layer: Optional[int] = None,\n compress_ratio: Optional[int] = None,\n cutoff_layers: Optional[List[int]] = None,\n query_lengths: Optional[int] = None,\n prompt_lengths: Optional[int] = None,\n ) -> Union[Tuple, CostWiseModelOutputWithPast]:\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n\n compress_ratio = None if compress_ratio == 1 else compress_ratio\n\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n if self.config.layer_wise:\n output_hidden_states = True\n\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if (input_ids is None) ^ (inputs_embeds is not None):\n raise ValueError(\n \"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one\"\n )\n\n if self.gradient_checkpointing and self.training and use_cache:\n logger.warning_once(\n \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\"\n )\n use_cache = False\n\n if compress_layer is not None and compress_ratio is not None:\n logger.warning_once(\n \"`use_cache=True` is incompatible with reranker. Setting `use_cache=False`.\"\n )\n use_cache = False\n\n if inputs_embeds is None:\n inputs_embeds = self.embed_tokens(input_ids)\n\n if cache_position is None:\n cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)\n\n if position_ids is None:\n position_ids = cache_position.unsqueeze(0)\n\n causal_mask = self._update_causal_mask(\n attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions\n )\n\n # embed positions\n hidden_states = inputs_embeds\n\n # normalized\n # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5\n # See https://github.com/huggingface/transformers/pull/29402\n normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)\n hidden_states = hidden_states * normalizer\n\n # decoder layers\n all_hidden_states = () if output_hidden_states else None\n all_attention_masks = ()\n all_self_attns = () if output_attentions else None\n next_decoder_cache = None\n\n is_padding_left = (attention_mask[:, -1].sum() == attention_mask.shape[0]) and (\n torch.sum(attention_mask) != attention_mask.shape[0] * attention_mask.shape[1])\n query_lengths = [0] * hidden_states.shape[0] if query_lengths is None else query_lengths\n prompt_lengths = [0] * hidden_states.shape[0] if prompt_lengths is None else prompt_lengths\n if not isinstance(query_lengths, torch.Tensor):\n query_lengths = torch.tensor(query_lengths, device=hidden_states.device)\n if not isinstance(prompt_lengths, torch.Tensor):\n prompt_lengths = torch.tensor(prompt_lengths, device=hidden_states.device)\n\n if cutoff_layers is None:\n max_layer = self.config.num_hidden_layers\n cutoff_layers = [max_layer]\n if isinstance(cutoff_layers, int):\n max_layer = cutoff_layers\n cutoff_layers = [cutoff_layers]\n else:\n max_layer = max(cutoff_layers)\n\n for idx, decoder_layer in enumerate(self.layers):\n if self.config.layer_wise:\n if idx in cutoff_layers and output_hidden_states:\n all_hidden_states += (self.norm(hidden_states),)\n all_attention_masks += (attention_mask,)\n if idx == max_layer:\n break\n elif output_hidden_states:\n all_hidden_states += (hidden_states,)\n\n if compress_layer is not None and compress_ratio is not None and idx in compress_layer and idx != 0:\n if is_padding_left:\n raise ValueError('You must use right padding...')\n hidden_states, attention_mask = token_compress(compress_ratio, hidden_states, attention_mask,\n query_lengths, prompt_lengths)\n seq_length = hidden_states.shape[1]\n cache_position = torch.arange(0, seq_length, device=hidden_states.device)\n position_ids = cache_position.unsqueeze(0)\n causal_mask = self._update_causal_mask(\n attention_mask, hidden_states, cache_position, past_key_values, output_attentions\n )\n\n if self.gradient_checkpointing and self.training:\n layer_outputs = self._gradient_checkpointing_func(\n decoder_layer.__call__,\n hidden_states,\n causal_mask,\n position_ids,\n past_key_values,\n output_attentions,\n use_cache,\n cache_position,\n )\n else:\n layer_outputs = decoder_layer(\n hidden_states,\n attention_mask=causal_mask,\n position_ids=position_ids,\n past_key_value=past_key_values,\n output_attentions=output_attentions,\n use_cache=use_cache,\n cache_position=cache_position,\n )\n\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_self_attns += (layer_outputs[1],)\n\n hidden_states = self.norm(hidden_states)\n\n # add hidden states from the last decoder layer\n if not self.config.layer_wise:\n if output_hidden_states:\n all_hidden_states += (hidden_states,)\n all_attention_masks += (attention_mask,)\n else:\n if output_hidden_states and self.config.num_hidden_layers == max_layer:\n all_hidden_states += (hidden_states,)\n all_attention_masks += (attention_mask,)\n\n next_cache = next_decoder_cache if use_cache else None\n\n if not return_dict:\n return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)\n return CostWiseModelOutputWithPast(\n last_hidden_state=hidden_states,\n past_key_values=next_cache,\n hidden_states=all_hidden_states,\n attentions=all_self_attns,\n attention_masks=all_attention_masks\n )\n\n def _update_causal_mask(\n self,\n attention_mask: torch.Tensor,\n input_tensor: torch.Tensor,\n cache_position: torch.Tensor,\n past_key_values: Cache,\n output_attentions: bool,\n ):\n if self.config._attn_implementation == \"flash_attention_2\":\n if attention_mask is not None and 0.0 in attention_mask:\n return attention_mask\n return None\n\n dtype, device = input_tensor.dtype, input_tensor.device\n min_dtype = torch.finfo(dtype).min\n sequence_length = input_tensor.shape[1]\n if past_key_values is not None:\n target_length = past_key_values.get_max_length()\n else:\n target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1]\n\n if attention_mask is not None and attention_mask.dim() == 4:\n # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing\n if attention_mask.max() != 0:\n raise ValueError(\"Custom 4D attention mask should be passed in inverted form with max==0`\")\n causal_mask = attention_mask\n else:\n causal_mask = torch.full(\n (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device\n )\n if sequence_length != 1:\n causal_mask = torch.triu(causal_mask, diagonal=1)\n causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)\n causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)\n if attention_mask is not None:\n causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit\n mask_length = attention_mask.shape[-1]\n padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]\n padding_mask = padding_mask == 0\n causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(\n padding_mask, min_dtype\n )\n return causal_mask\n\n\nclass CostWiseHead(nn.Module):\n \"\"\"Head for sentence-level classification tasks.\"\"\"\n\n def __init__(self, input_size, output_size):\n super().__init__()\n self.linear_head = nn.Linear(input_size, output_size, bias=False)\n\n def forward(self, **kwargs):\n return self.linear_head(**kwargs)\n\n\nclass CostWiseGemmaForCausalLM(CostWiseGemma2PreTrainedModel):\n _tied_weights_keys = [\"lm_head.weight\"]\n\n def __init__(self, config: CostWiseGemmaConfig):\n super().__init__(config)\n self.model = CostWiseGemmaModel(config)\n self.vocab_size = config.vocab_size\n\n if not config.layer_wise:\n self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n else:\n self.lm_head = nn.ModuleList(\n [CostWiseHead(config.hidden_size, 1) for _ in range(\n config.start_layer, config.num_hidden_layers + 1, config.layer_sep\n )]\n )\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.model.embed_tokens\n\n def set_input_embeddings(self, value):\n self.model.embed_tokens = value\n\n def get_output_embeddings(self):\n return self.lm_head\n\n def set_output_embeddings(self, new_embeddings):\n self.lm_head = new_embeddings\n\n def set_decoder(self, decoder):\n self.model = decoder\n\n def get_decoder(self):\n return self.model\n\n @add_start_docstrings_to_model_forward(GEMMA2_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n labels: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cache_position: Optional[torch.LongTensor] = None,\n compress_layer: Optional[int] = None,\n compress_ratio: Optional[int] = None,\n cutoff_layers: Optional[List[int]] = None,\n query_lengths: Optional[int] = None,\n prompt_lengths: Optional[int] = None,\n ) -> Union[Tuple, CostWiseCausalLMOutputWithPast]:\n r\"\"\"\n Args:\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.\n\n Returns:\n\n Example:\n\n ```python\n >>> from transformers import AutoTokenizer, GemmaForCausalLM\n\n >>> model = GemmaForCausalLM.from_pretrained(\"google/gemma-2-9b\")\n >>> tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2-9b\")\n\n >>> prompt = \"What is your favorite condiment?\"\n >>> inputs = tokenizer(prompt, return_tensors=\"pt\")\n\n >>> # Generate\n >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\n >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n \"What is your favorite condiment?\"\n ```\"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if compress_ratio is not None and compress_ratio == 1:\n compress_ratio = None\n\n if self.config.layer_wise:\n if cutoff_layers is None:\n cutoff_layers = [self.config.num_hidden_layers]\n elif isinstance(cutoff_layers, int):\n cutoff_layers = [cutoff_layers]\n can_use_layers = list(range(self.config.start_layer, self.config.num_hidden_layers + 1, self.config.layer_sep))\n remove_layers = [i for i in cutoff_layers if i not in can_use_layers]\n if len(remove_layers) > 0:\n logger.warning_once(\n f\"layers {remove_layers} are incompatible with the setting. They will be removed...\"\n )\n cutoff_layers = [i for i in cutoff_layers if i not in remove_layers]\n if len(cutoff_layers) == 0:\n raise ValueError(f\"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]\")\n\n # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\n outputs = self.model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_values=past_key_values,\n inputs_embeds=inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n cache_position=cache_position,\n compress_layer=compress_layer,\n compress_ratio=compress_ratio,\n query_lengths=query_lengths,\n prompt_lengths=prompt_lengths,\n cutoff_layers=cutoff_layers,\n )\n\n if not self.config.layer_wise:\n hidden_states = outputs[0]\n logits = self.lm_head(hidden_states)\n if self.config.final_logit_softcapping is not None:\n logits = logits / self.config.final_logit_softcapping\n logits = torch.tanh(logits)\n logits = logits * self.config.final_logit_softcapping\n logits = logits.float()\n loss = None\n if labels is not None:\n # Shift so that tokens < n predict n\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n # Flatten the tokens\n loss_fct = CrossEntropyLoss()\n shift_logits = shift_logits.view(-1, self.config.vocab_size)\n shift_labels = shift_labels.view(-1)\n # Enable model parallelism\n shift_labels = shift_labels.to(shift_logits.device)\n loss = loss_fct(shift_logits, shift_labels)\n else:\n hidden_states = outputs.hidden_states\n logits = ()\n for i in range(len(hidden_states)):\n tmp_logits = self.lm_head[i].linear_head(hidden_states[i])\n if self.config.final_logit_softcapping is not None:\n tmp_logits = tmp_logits / self.config.final_logit_softcapping\n tmp_logits = torch.tanh(tmp_logits)\n tmp_logits = tmp_logits * self.config.final_logit_softcapping\n tmp_logits = tmp_logits.float()\n tmp_logits = tmp_logits.reshape(hidden_states[i].shape[0], -1)\n logits = logits + (tmp_logits,)\n loss = None\n\n if not return_dict:\n output = (logits,) + outputs[1:]\n return (loss,) + output if loss is not None else output\n\n return CostWiseCausalLMOutputWithPast(\n loss=loss,\n logits=logits,\n past_key_values=outputs.past_key_values,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n attention_masks=outputs[-1] if self.model.config.layer_wise else outputs[-1][-1]\n )\n\n def prepare_inputs_for_generation(\n self,\n input_ids,\n past_key_values=None,\n attention_mask=None,\n inputs_embeds=None,\n cache_position=None,\n use_cache=True,\n **kwargs,\n ):\n past_length = 0\n if past_key_values is not None:\n # Past key values are always initialized with a `Cache` object -> no need for if-else anymore\n past_length = cache_position[0] if cache_position is not None else torch.tensor(0, device=input_ids.device)\n max_cache_length = (\n torch.tensor(past_key_values.get_max_length(), device=input_ids.device)\n if past_key_values.get_max_length() is not None\n else None\n )\n cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)\n\n # Keep only the unprocessed tokens:\n # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where\n # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)\n if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:\n input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]\n # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard\n # input_ids based on the past_length.\n elif past_length < input_ids.shape[1]:\n input_ids = input_ids[:, past_length:]\n # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.\n\n # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.\n if (\n max_cache_length is not None\n and attention_mask is not None\n and cache_length + input_ids.shape[1] > max_cache_length\n ):\n attention_mask = attention_mask[:, -max_cache_length:]\n\n position_ids = kwargs.get(\"position_ids\", None)\n if attention_mask is not None and position_ids is None:\n # create position_ids on the fly for batch generation\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n if past_key_values:\n position_ids = position_ids[:, -input_ids.shape[1] :]\n\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\n if inputs_embeds is not None and past_length == 0:\n model_inputs = {\"inputs_embeds\": inputs_embeds}\n else:\n # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise\n # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114\n # TODO: use `next_tokens` directly instead.\n model_inputs = {\"input_ids\": input_ids.contiguous()}\n\n input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]\n if cache_position is None:\n cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)\n elif use_cache:\n cache_position = cache_position[-input_length:]\n\n model_inputs.update(\n {\n \"position_ids\": position_ids,\n \"cache_position\": cache_position,\n \"past_key_values\": past_key_values,\n \"use_cache\": use_cache,\n \"attention_mask\": attention_mask,\n }\n )\n return model_inputs\n\n @staticmethod\n def _reorder_cache(past_key_values, beam_idx):\n reordered_past = ()\n for layer_past in past_key_values:\n reordered_past += (\n tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),\n )\n return reordered_past\n" }, { "path": "FlagEmbedding/inference/reranker/decoder_only/models/modeling_minicpm_reranker.py", "content": "# coding=utf-8\n# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.\n#\n# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX\n# and OPT implementations in this library. It has been modified from its\n# original forms to accommodate minor architectural differences compared\n# to GPT-NeoX and OPT used by the Meta AI team that trained the model.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" PyTorch MiniCPM model.\"\"\"\nimport sys\n\nimport math\nimport warnings\nfrom typing import List, Optional, Tuple, Union, Dict\n\nimport torch\nimport torch.nn.functional as F\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n\nfrom transformers.activations import ACT2FN\nfrom transformers.cache_utils import Cache, DynamicCache\nfrom transformers.modeling_attn_mask_utils import (\n AttentionMaskConverter,\n _prepare_4d_attention_mask,\n _prepare_4d_causal_attention_mask,\n _prepare_4d_causal_attention_mask_for_sdpa,\n)\nfrom transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \\\n SequenceClassifierOutputWithPast\nfrom transformers.modeling_utils import PreTrainedModel\nfrom transformers.pytorch_utils import ALL_LAYERNORM_LAYERS\nfrom transformers.utils import (\n add_start_docstrings,\n add_start_docstrings_to_model_forward,\n is_flash_attn_2_available,\n is_flash_attn_greater_or_equal_2_10,\n logging,\n replace_return_docstrings,\n)\nfrom FlagEmbedding.utils.transformers_compat import is_torch_fx_available\nfrom .configuration_minicpm_reranker import LayerWiseMiniCPMConfig\nimport re\n\n\ntry:\n from flash_attn import flash_attn_func, flash_attn_varlen_func\n from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa\nexcept:\n pass\n\n# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.\n# It means that the function will not be traced through and simply appear as a node in the graph.\nfrom packaging import version\nparsed_torch_version_base = version.parse(version.parse(torch.__version__).base_version)\nis_torch_greater_or_equal_than_1_13 = parsed_torch_version_base >= version.parse(\"1.13\")\nif is_torch_fx_available():\n if not is_torch_greater_or_equal_than_1_13:\n import torch.fx\n\n _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)\n\nlogger = logging.get_logger(__name__)\n\n_CONFIG_FOR_DOC = \"LayerWiseMiniCPMConfig\"\n\n\ndef _get_unpad_data(attention_mask):\n seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)\n indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()\n max_seqlen_in_batch = seqlens_in_batch.max().item()\n cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))\n return (\n indices,\n cu_seqlens,\n max_seqlen_in_batch,\n )\n\n\ndef _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):\n warnings.warn(\n \"Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask\"\n )\n return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)\n\n\ndef _make_causal_mask(\n input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0\n):\n warnings.warn(\n \"Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask\"\n )\n return AttentionMaskConverter._make_causal_mask(\n input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length\n )\n\n\n# @torch.jit.script # type: ignore\ndef rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):\n old_dtype = hidden.dtype\n variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)\n hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)\n return hidden * weight\n\n\nclass MiniCPMRMSNorm(nn.Module):\n def __init__(self, hidden_size, eps=1e-6):\n \"\"\"\n MiniCPMRMSNorm is equivalent to T5LayerNorm\n \"\"\"\n super().__init__()\n self.weight = nn.Parameter(torch.ones(hidden_size))\n self.variance_epsilon = eps\n\n def forward(self, hidden_states):\n return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)\n\n\nALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)\n\n\nclass MiniCPMRotaryEmbedding(nn.Module):\n def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):\n super().__init__()\n\n self.dim = dim\n self.max_position_embeddings = max_position_embeddings\n self.base = base\n inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n # Build here to make `torch.jit.trace` work.\n self._set_cos_sin_cache(\n # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()\n seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32\n )\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n def forward(self, x, seq_len=None):\n # x: [bs, num_attention_heads, seq_len, head_size]\n if seq_len > self.max_seq_len_cached:\n self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)\n\n return (\n self.cos_cached[:seq_len].to(dtype=x.dtype),\n self.sin_cached[:seq_len].to(dtype=x.dtype),\n )\n\n\nclass MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):\n \"\"\"MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev\"\"\"\n\n def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):\n self.scaling_factor = scaling_factor\n super().__init__(dim, max_position_embeddings, base, device)\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n t = t / self.scaling_factor\n\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n\nclass MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):\n \"\"\"MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla\"\"\"\n\n def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):\n self.scaling_factor = scaling_factor\n super().__init__(dim, max_position_embeddings, base, device)\n\n def _set_cos_sin_cache(self, seq_len, device, dtype):\n self.max_seq_len_cached = seq_len\n\n if seq_len > self.max_position_embeddings:\n base = self.base * (\n (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)\n ) ** (self.dim / (self.dim - 2))\n inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))\n self.register_buffer(\"inv_freq\", inv_freq, persistent=False)\n\n t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)\n\n freqs = torch.outer(t, self.inv_freq)\n # Different from paper, but it uses a different permutation in order to obtain the same calculation\n emb = torch.cat((freqs, freqs), dim=-1)\n\n self.register_buffer(\"cos_cached\", emb.cos().to(dtype), persistent=False)\n self.register_buffer(\"sin_cached\", emb.sin().to(dtype), persistent=False)\n\n\ndef rotate_half(x):\n \"\"\"Rotates half the hidden dims of the input.\"\"\"\n x1 = x[..., : x.shape[-1] // 2]\n x2 = x[..., x.shape[-1] // 2:]\n return torch.cat((-x2, x1), dim=-1)\n\n\ndef apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):\n \"\"\"Applies Rotary Position Embedding to the query and key tensors.\n\n Args:\n q (`torch.Tensor`): The query tensor.\n k (`torch.Tensor`): The key tensor.\n cos (`torch.Tensor`): The cosine part of the rotary embedding.\n sin (`torch.Tensor`): The sine part of the rotary embedding.\n position_ids (`torch.Tensor`):\n The position indices of the tokens corresponding to the query and key tensors. For example, this can be\n used to pass offsetted position ids when working with a KV-cache.\n unsqueeze_dim (`int`, *optional*, defaults to 1):\n The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and\n sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note\n that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and\n k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes\n cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have\n the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.\n Returns:\n `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.\n \"\"\"\n # cos = cos[position_ids].unsqueeze(unsqueeze_dim)\n # sin = sin[position_ids].unsqueeze(unsqueeze_dim)\n # q_embed = (q * cos) + (rotate_half(q) * sin)\n # k_embed = (k * cos) + (rotate_half(k) * sin)\n orig_dtype = k.dtype\n cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]\n sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]\n q_fp32 = q.to(dtype=torch.float32, device=q.device)\n k_fp32 = k.to(dtype=torch.float32, device=k.device)\n q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)\n k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)\n return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)\n\n\nclass MiniCPMMLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.hidden_size = config.hidden_size\n self.intermediate_size = config.intermediate_size\n self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)\n self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)\n self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)\n self.act_fn = ACT2FN[config.hidden_act]\n\n def forward(self, x):\n if self.config.pretraining_tp > 1:\n slice = self.intermediate_size // self.config.pretraining_tp\n gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)\n up_proj_slices = self.up_proj.weight.split(slice, dim=0)\n down_proj_slices = self.down_proj.weight.split(slice, dim=1)\n\n gate_proj = torch.cat(\n [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1\n )\n up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)\n\n intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)\n down_proj = [\n F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)\n ]\n down_proj = sum(down_proj)\n else:\n down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))\n\n return down_proj\n\n\ndef repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:\n \"\"\"\n This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,\n num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)\n \"\"\"\n batch, num_key_value_heads, slen, head_dim = hidden_states.shape\n if n_rep == 1:\n return hidden_states\n hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)\n return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)\n\n\nclass MiniCPMAttention(nn.Module):\n \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n\n def __init__(self, config: LayerWiseMiniCPMConfig, layer_idx: Optional[int] = None):\n super().__init__()\n self.config = config\n self.layer_idx = layer_idx\n if layer_idx is None:\n logger.warning_once(\n f\"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will \"\n \"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` \"\n \"when creating this class.\"\n )\n\n self.attention_dropout = config.attention_dropout\n self.hidden_size = config.hidden_size\n self.num_heads = config.num_attention_heads\n self.head_dim = self.hidden_size // self.num_heads\n self.num_key_value_heads = config.num_key_value_heads\n self.num_key_value_groups = self.num_heads // self.num_key_value_heads\n self.max_position_embeddings = config.max_position_embeddings\n self.rope_theta = config.rope_theta\n self.is_causal = True\n\n if (self.head_dim * self.num_heads) != self.hidden_size:\n raise ValueError(\n f\"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}\"\n f\" and `num_heads`: {self.num_heads}).\"\n )\n\n self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)\n self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)\n self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)\n self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)\n self._init_rope()\n\n def _init_rope(self):\n if self.config.rope_scaling is None:\n self.rotary_emb = MiniCPMRotaryEmbedding(\n self.head_dim,\n max_position_embeddings=self.max_position_embeddings,\n base=self.rope_theta,\n )\n else:\n scaling_type = self.config.rope_scaling[\"type\"]\n scaling_factor = self.config.rope_scaling[\"factor\"]\n if scaling_type == \"linear\":\n self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(\n self.head_dim,\n max_position_embeddings=self.max_position_embeddings,\n scaling_factor=scaling_factor,\n base=self.rope_theta,\n )\n elif scaling_type == \"dynamic\":\n self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(\n self.head_dim,\n max_position_embeddings=self.max_position_embeddings,\n scaling_factor=scaling_factor,\n base=self.rope_theta,\n )\n else:\n raise ValueError(f\"Unknown RoPE scaling type {scaling_type}\")\n\n def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Cache] = None,\n output_attentions: bool = False,\n use_cache: bool = False,\n **kwargs,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n if \"padding_mask\" in kwargs:\n warnings.warn(\n \"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`\"\n )\n\n bsz, q_len, _ = hidden_states.size()\n\n if self.config.pretraining_tp > 1:\n key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp\n query_slices = self.q_proj.weight.split(\n (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0\n )\n key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)\n value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)\n\n query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]\n query_states = torch.cat(query_states, dim=-1)\n\n key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]\n key_states = torch.cat(key_states, dim=-1)\n\n value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]\n value_states = torch.cat(value_states, dim=-1)\n\n else:\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n\n kv_seq_len = key_states.shape[-2]\n if past_key_value is not None:\n if self.layer_idx is None:\n raise ValueError(\n f\"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} \"\n \"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class \"\n \"with a layer index.\"\n )\n kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)\n cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)\n\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\n if past_key_value is not None:\n cache_kwargs = {\"sin\": sin, \"cos\": cos} # Specific to RoPE models\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n key_states = repeat_kv(key_states, self.num_key_value_groups)\n value_states = repeat_kv(value_states, self.num_key_value_groups)\n\n attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)\n if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):\n raise ValueError(\n f\"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is\"\n f\" {attn_weights.size()}\"\n )\n\n if attention_mask is not None:\n if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}\"\n )\n attn_weights = attn_weights + attention_mask\n\n # upcast attention to fp32\n attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)\n attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)\n attn_output = torch.matmul(attn_weights, value_states)\n\n if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):\n raise ValueError(\n f\"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is\"\n f\" {attn_output.size()}\"\n )\n\n attn_output = attn_output.transpose(1, 2).contiguous()\n\n attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)\n\n if self.config.pretraining_tp > 1:\n attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)\n o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)\n attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])\n else:\n attn_output = self.o_proj(attn_output)\n\n if not output_attentions:\n attn_weights = None\n\n return attn_output, attn_weights, past_key_value\n\n\nclass MiniCPMFlashAttention2(MiniCPMAttention):\n \"\"\"\n MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays\n untouched. The only required change would be on the forward pass where it needs to correctly call the public API of\n flash attention and deal with padding tokens in case the input contains any of them.\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.\n # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.\n # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).\n self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.LongTensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Cache] = None,\n output_attentions: bool = False,\n use_cache: bool = False,\n **kwargs,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n # MiniCPMFlashAttention2 attention does not support output_attentions\n if \"padding_mask\" in kwargs:\n warnings.warn(\n \"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`\"\n )\n\n # overwrite attention_mask with padding_mask\n attention_mask = kwargs.pop(\"padding_mask\")\n\n output_attentions = False\n\n bsz, q_len, _ = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n # Flash attention requires the input to have the shape\n # batch_size x seq_length x head_dim x hidden_dim\n # therefore we just need to keep the original shape\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n\n kv_seq_len = key_states.shape[-2]\n if past_key_value is not None:\n kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)\n cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\n if past_key_value is not None:\n cache_kwargs = {\"sin\": sin, \"cos\": cos} # Specific to RoPE models\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache\n # to be able to avoid many of these transpose/reshape/view.\n query_states = query_states.transpose(1, 2)\n key_states = key_states.transpose(1, 2)\n value_states = value_states.transpose(1, 2)\n\n dropout_rate = self.attention_dropout if self.training else 0.0\n\n # In PEFT, usually we cast the layer norms in float32 for training stability reasons\n # therefore the input hidden states gets silently casted in float32. Hence, we need\n # cast them back in the correct dtype just to be sure everything works as expected.\n # This might slowdown training & inference so it is recommended to not cast the LayerNorms\n # in fp32. (MiniCPMRMSNorm handles it correctly)\n\n input_dtype = query_states.dtype\n if input_dtype == torch.float32:\n # Handle the case where the model is quantized\n if hasattr(self.config, \"_pre_quantization_dtype\"):\n target_dtype = self.config._pre_quantization_dtype\n else:\n target_dtype = self.q_proj.weight.dtype\n\n logger.warning_once(\n f\"The input hidden states seems to be silently casted in float32, this might be related to\"\n f\" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in\"\n f\" {target_dtype}.\"\n )\n\n query_states = query_states.to(target_dtype)\n key_states = key_states.to(target_dtype)\n value_states = value_states.to(target_dtype)\n\n attn_output = self._flash_attention_forward(\n query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate\n )\n\n attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()\n attn_output = self.o_proj(attn_output)\n\n if not output_attentions:\n attn_weights = None\n\n return attn_output, attn_weights, past_key_value\n\n def _flash_attention_forward(\n self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None\n ):\n \"\"\"\n Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token\n first unpad the input, then computes the attention scores and pad the final attention scores.\n\n Args:\n query_states (`torch.Tensor`):\n Input query states to be passed to Flash Attention API\n key_states (`torch.Tensor`):\n Input key states to be passed to Flash Attention API\n value_states (`torch.Tensor`):\n Input value states to be passed to Flash Attention API\n attention_mask (`torch.Tensor`):\n The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the\n position of padding tokens and 1 for the position of non-padding tokens.\n dropout (`int`, *optional*):\n Attention dropout\n softmax_scale (`float`, *optional*):\n The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)\n \"\"\"\n if not self._flash_attn_uses_top_left_mask:\n causal = self.is_causal\n else:\n # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.\n causal = self.is_causal and query_length != 1\n # Contains at least one padding token in the sequence\n if attention_mask is not None:\n batch_size = query_states.shape[0]\n query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(\n query_states, key_states, value_states, attention_mask, query_length\n )\n\n cu_seqlens_q, cu_seqlens_k = cu_seq_lens\n max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens\n attn_output_unpad = flash_attn_varlen_func(\n query_states,\n key_states,\n value_states,\n cu_seqlens_q=cu_seqlens_q,\n cu_seqlens_k=cu_seqlens_k,\n max_seqlen_q=max_seqlen_in_batch_q,\n max_seqlen_k=max_seqlen_in_batch_k,\n dropout_p=dropout,\n softmax_scale=softmax_scale,\n causal=causal,\n )\n\n attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)\n else:\n attn_output = flash_attn_func(\n query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal\n )\n\n return attn_output\n\n def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):\n indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)\n batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape\n\n key_layer = index_first_axis(\n key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k\n )\n value_layer = index_first_axis(\n value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k\n )\n if query_length == kv_seq_len:\n query_layer = index_first_axis(\n query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k\n )\n cu_seqlens_q = cu_seqlens_k\n max_seqlen_in_batch_q = max_seqlen_in_batch_k\n indices_q = indices_k\n elif query_length == 1:\n max_seqlen_in_batch_q = 1\n cu_seqlens_q = torch.arange(\n batch_size + 1, dtype=torch.int32, device=query_layer.device\n ) # There is a memcpy here, that is very bad.\n indices_q = cu_seqlens_q[:-1]\n query_layer = query_layer.squeeze(1)\n else:\n # The -q_len: slice assumes left padding.\n attention_mask = attention_mask[:, -query_length:]\n query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)\n\n return (\n query_layer,\n key_layer,\n value_layer,\n indices_q,\n (cu_seqlens_q, cu_seqlens_k),\n (max_seqlen_in_batch_q, max_seqlen_in_batch_k),\n )\n\n\nclass MiniCPMSdpaAttention(MiniCPMAttention):\n \"\"\"\n MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from\n `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to\n SDPA API.\n \"\"\"\n\n # Adapted from MiniCPMAttention.forward\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Cache] = None,\n output_attentions: bool = False,\n use_cache: bool = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:\n if output_attentions:\n # TODO: Improve this warning with e.g. `model.config.attn_implementation = \"manual\"` once this is implemented.\n logger.warning_once(\n \"MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, \"\n 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation=\"eager\"` when loading the model.'\n )\n return super().forward(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_value=past_key_value,\n output_attentions=output_attentions,\n use_cache=use_cache,\n )\n\n bsz, q_len, _ = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)\n\n kv_seq_len = key_states.shape[-2]\n if past_key_value is not None:\n kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)\n cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)\n\n query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)\n\n if past_key_value is not None:\n cache_kwargs = {\"sin\": sin, \"cos\": cos} # Specific to RoPE models\n key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)\n\n key_states = repeat_kv(key_states, self.num_key_value_groups)\n value_states = repeat_kv(value_states, self.num_key_value_groups)\n\n if attention_mask is not None:\n if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}\"\n )\n\n # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,\n # Reference: https://github.com/pytorch/pytorch/issues/112577.\n if query_states.device.type == \"cuda\" and attention_mask is not None:\n query_states = query_states.contiguous()\n key_states = key_states.contiguous()\n value_states = value_states.contiguous()\n\n attn_output = torch.nn.functional.scaled_dot_product_attention(\n query_states,\n key_states,\n value_states,\n attn_mask=attention_mask,\n dropout_p=self.attention_dropout if self.training else 0.0,\n # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.\n is_causal=self.is_causal and attention_mask is None and q_len > 1,\n )\n\n attn_output = attn_output.transpose(1, 2).contiguous()\n attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)\n\n attn_output = self.o_proj(attn_output)\n\n return attn_output, None, past_key_value\n\n\nMINICPM_ATTENTION_CLASSES = {\n \"eager\": MiniCPMAttention,\n \"flash_attention_2\": MiniCPMFlashAttention2,\n \"sdpa\": MiniCPMSdpaAttention,\n}\n\n\nclass MiniCPMDecoderLayer(nn.Module):\n def __init__(self, config: LayerWiseMiniCPMConfig, layer_idx: int):\n super().__init__()\n self.hidden_size = config.hidden_size\n self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)\n\n self.mlp = MiniCPMMLP(config)\n self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n\n self.scale_depth = config.scale_depth\n self.num_hidden_layers = config.num_hidden_layers\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_value: Optional[Tuple[torch.Tensor]] = None,\n output_attentions: Optional[bool] = False,\n use_cache: Optional[bool] = False,\n **kwargs,\n ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:\n \"\"\"\n Args:\n hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n attention_mask (`torch.FloatTensor`, *optional*):\n attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,\n query_sequence_length, key_sequence_length)` if default attention is used.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n use_cache (`bool`, *optional*):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding\n (see `past_key_values`).\n past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states\n \"\"\"\n if \"padding_mask\" in kwargs:\n warnings.warn(\n \"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`\"\n )\n\n residual = hidden_states\n hidden_states = self.input_layernorm(hidden_states)\n # Self Attention\n hidden_states, self_attn_weights, present_key_value = self.self_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_value=past_key_value,\n output_attentions=output_attentions,\n use_cache=use_cache,\n **kwargs,\n )\n\n hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n\n # Fully Connected\n residual = hidden_states\n hidden_states = self.post_attention_layernorm(hidden_states)\n\n hidden_states = self.mlp(hidden_states)\n hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))\n\n outputs = (hidden_states,)\n\n if output_attentions:\n outputs += (self_attn_weights,)\n\n if use_cache:\n outputs += (present_key_value,)\n\n return outputs\n\n\nMINICPM_START_DOCSTRING = r\"\"\"\n This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n etc.)\n\n This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n and behavior.\n\n Parameters:\n config ([`LayerWiseMiniCPMConfig`]):\n Model configuration class with all the parameters of the model. Initializing with a config file does not\n load the weights associated with the model, only the configuration. Check out the\n [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\"\"\"\n\n\n@add_start_docstrings(\n \"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.\",\n MINICPM_START_DOCSTRING,\n)\nclass MiniCPMPreTrainedModel(PreTrainedModel):\n config_class = LayerWiseMiniCPMConfig\n base_model_prefix = \"model\"\n supports_gradient_checkpointing = True\n _no_split_modules = [\"MiniCPMDecoderLayer\"]\n _skip_keys_device_placement = \"past_key_values\"\n _supports_flash_attn_2 = True\n _supports_sdpa = True\n _supports_cache_class = True\n\n def _init_weights(self, module):\n std = self.config.initializer_range\n if isinstance(module, nn.Linear):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=std)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n\nMINICPM_INPUTS_DOCSTRING = r\"\"\"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide\n it.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n If `past_key_values` is used, optionally only the last `input_ids` have to be input (see\n `past_key_values`).\n\n If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]\n and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more\n information on the default strategy.\n\n - 1 indicates the head is **not masked**,\n - 0 indicates the head is **masked**.\n position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n config.n_positions - 1]`.\n\n [What are position IDs?](../glossary#position-ids)\n past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):\n Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention\n blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`\n returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.\n\n Two formats are allowed:\n - a [`~cache_utils.Cache`] instance;\n - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of\n shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy\n cache format.\n\n The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the\n legacy cache format will be returned.\n\n If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't\n have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`\n of shape `(batch_size, sequence_length)`.\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n model's internal embedding lookup matrix.\n use_cache (`bool`, *optional*):\n If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see\n `past_key_values`).\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\n\n@add_start_docstrings(\n \"The bare MiniCPM Model outputting raw hidden-states without any specific head on top.\",\n MINICPM_START_DOCSTRING,\n)\nclass LayerWiseMiniCPMModel(MiniCPMPreTrainedModel):\n \"\"\"\n Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]\n\n Args:\n config: LayerWiseMiniCPMConfig\n \"\"\"\n\n def __init__(self, config: LayerWiseMiniCPMConfig):\n super().__init__(config)\n self.padding_idx = config.pad_token_id\n self.vocab_size = config.vocab_size\n\n self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)\n self.layers = nn.ModuleList(\n [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]\n )\n self._use_sdpa = config._attn_implementation == \"sdpa\"\n self._use_flash_attention_2 = config._attn_implementation == \"flash_attention_2\"\n\n self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)\n\n self.gradient_checkpointing = False\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.embed_tokens\n\n def set_input_embeddings(self, value):\n self.embed_tokens = value\n\n @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cutoff_layers: Optional[Union[int, List]] = None,\n ) -> Union[Tuple, BaseModelOutputWithPast]:\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # retrieve input_ids and inputs_embeds\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n elif input_ids is not None:\n batch_size, seq_length = input_ids.shape[:2]\n elif inputs_embeds is not None:\n batch_size, seq_length = inputs_embeds.shape[:2]\n else:\n raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n if self.gradient_checkpointing and self.training:\n if use_cache:\n logger.warning_once(\n \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\"\n )\n use_cache = False\n\n past_key_values_length = 0\n if use_cache:\n use_legacy_cache = not isinstance(past_key_values, Cache)\n if use_legacy_cache:\n past_key_values = DynamicCache.from_legacy_cache(past_key_values)\n past_key_values_length = past_key_values.get_usable_length(seq_length)\n\n if position_ids is None:\n device = input_ids.device if input_ids is not None else inputs_embeds.device\n position_ids = torch.arange(\n past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device\n )\n position_ids = position_ids.unsqueeze(0)\n\n if inputs_embeds is None:\n inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb\n\n if self._use_flash_attention_2:\n # 2d mask is passed through the layers\n attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None\n elif self._use_sdpa and not output_attentions:\n # output_attentions=True can not be supported when using SDPA, and we fall back on\n # the manual implementation that requires a 4D causal mask in all cases.\n attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(\n attention_mask,\n (batch_size, seq_length),\n inputs_embeds,\n past_key_values_length,\n )\n else:\n # 4d mask is passed through the layers\n attention_mask = _prepare_4d_causal_attention_mask(\n attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length\n )\n\n # embed positions\n hidden_states = inputs_embeds\n\n # decoder layers\n all_hidden_states = () if output_hidden_states else None\n all_self_attns = () if output_attentions else None\n next_decoder_cache = None\n\n if cutoff_layers is None:\n max_layer = self.config.num_hidden_layers\n cutoff_layers = [max_layer]\n if isinstance(cutoff_layers, int):\n max_layer = cutoff_layers\n cutoff_layers = [cutoff_layers]\n else:\n max_layer = max(cutoff_layers)\n\n for idx, decoder_layer in enumerate(self.layers):\n if idx in cutoff_layers and output_hidden_states:\n all_hidden_states += (self.norm(hidden_states),)\n\n if idx == max_layer:\n break\n\n if self.gradient_checkpointing and self.training:\n layer_outputs = self._gradient_checkpointing_func(\n decoder_layer.__call__,\n hidden_states,\n attention_mask,\n position_ids,\n past_key_values,\n output_attentions,\n use_cache,\n )\n else:\n layer_outputs = decoder_layer(\n hidden_states,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_value=past_key_values,\n output_attentions=output_attentions,\n use_cache=use_cache,\n )\n\n hidden_states = layer_outputs[0]\n\n if use_cache:\n next_decoder_cache = layer_outputs[2 if output_attentions else 1]\n\n if output_attentions:\n all_self_attns += (layer_outputs[1],)\n\n hidden_states = self.norm(hidden_states)\n\n # add hidden states from the last decoder layer\n if output_hidden_states and self.config.num_hidden_layers == max_layer:\n all_hidden_states += (hidden_states,)\n\n next_cache = None\n if use_cache:\n next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache\n if not return_dict:\n return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)\n return BaseModelOutputWithPast(\n last_hidden_state=hidden_states,\n past_key_values=next_cache,\n hidden_states=all_hidden_states,\n attentions=all_self_attns,\n )\n\n\nclass LayerWiseHead(nn.Module):\n \"\"\"Head for sentence-level classification tasks.\"\"\"\n\n def __init__(self, input_size, output_size):\n super().__init__()\n self.linear_head = nn.Linear(input_size, output_size, bias=False)\n\n def forward(self, **kwargs):\n return self.linear_head(**kwargs)\n\nclass LayerWiseMiniCPMForCausalLM(MiniCPMPreTrainedModel):\n _tied_weights_keys = [\"lm_head.weight\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.model = LayerWiseMiniCPMModel(config)\n self.vocab_size = config.vocab_size\n\n if self.config.head_type == 'raw':\n if not self.config.head_multi:\n self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n else:\n self.lm_head = nn.ModuleList([nn.Linear(\n config.hidden_size, config.vocab_size, bias=False) for _ in range(\n self.config.start_layer,\n self.model.config.num_hidden_layers + 1)])\n elif self.config.head_type == 'complex':\n if not self.config.head_multi:\n # self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n self.lm_head = LayerWiseHead(config.hidden_size, config.vocab_size)\n else:\n # self.lm_head = nn.ModuleList([nn.Linear(\n # config.hidden_size, config.vocab_size, bias=False) for _ in range(\n # self.config.start_layer,\n # self.model.config.num_hidden_layers + 1)])\n self.lm_head = nn.ModuleList([LayerWiseHead(\n config.hidden_size, config.vocab_size) for _ in range(\n self.config.start_layer,\n self.model.config.num_hidden_layers + 1)])\n else:\n if not self.config.head_multi:\n # self.lm_head = nn.Linear(config.hidden_size, 1, bias=False)\n self.lm_head = LayerWiseHead(config.hidden_size, 1)\n else:\n # self.lm_head = nn.ModuleList([nn.Linear(\n # config.hidden_size, 1, bias=False) for _ in range(\n # self.config.start_layer,\n # self.model.config.num_hidden_layers + 1)])\n self.lm_head = nn.ModuleList([LayerWiseHead(\n config.hidden_size, 1) for _ in range(\n self.config.start_layer,\n self.model.config.num_hidden_layers + 1)])\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self):\n return self.model.embed_tokens\n\n def set_input_embeddings(self, value):\n self.model.embed_tokens = value\n\n def get_output_embeddings(self):\n return self.lm_head\n\n def set_output_embeddings(self, new_embeddings):\n self.lm_head = new_embeddings\n\n def set_decoder(self, decoder):\n self.model = decoder\n\n def get_decoder(self):\n return self.model\n\n @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[List[torch.FloatTensor]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n labels: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cutoff_layers: Optional[Union[int, List]] = None,\n only_for_one_logit: Optional[int] = None\n ) -> Union[Tuple, CausalLMOutputWithPast]:\n r\"\"\"\n Args:\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\n\n Returns:\n\n Example:\n\n ```python\n >>> from transformers import AutoTokenizer, MiniCPMForCausalLM\n\n >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)\n >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)\n\n >>> prompt = \"Hey, are you conscious? Can you talk to me?\"\n >>> inputs = tokenizer(prompt, return_tensors=\"pt\")\n\n >>> # Generate\n >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\n >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n \"Hey, are you conscious? Can you talk to me?\\nI'm not conscious, but I can talk to you.\"\n ```\"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if cutoff_layers is None:\n cutoff_layers = [self.config.num_hidden_layers]\n elif isinstance(cutoff_layers, int):\n cutoff_layers = [cutoff_layers]\n\n remove_layers = [i for i in cutoff_layers if self.config.start_layer > i or i > self.config.num_hidden_layers]\n if len(remove_layers) > 0:\n logger.warning_once(\n f\"layers {remove_layers} are incompatible with the setting. They will be removed...\"\n )\n\n cutoff_layers = [i for i in cutoff_layers if i not in remove_layers]\n if len(cutoff_layers) == 0:\n raise ValueError(f\"Your cutoff layers must in [{self.config.start_layer}, {self.config.num_hidden_layers}]\")\n\n # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\n outputs = self.model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_values=past_key_values,\n inputs_embeds=inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=True,\n return_dict=return_dict,\n cutoff_layers=cutoff_layers\n )\n\n hidden_states = outputs[0]\n\n all_logits = ()\n if only_for_one_logit is None and (self.config.head_type == 'complex' or self.config.head_type == 'raw'):\n if self.config.head_type == 'raw':\n for i in range(len(outputs.hidden_states)):\n if self.config.head_multi == False:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n else:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head[cutoff_layers[i] - self.config.start_layer](outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n else:\n for i in range(len(outputs.hidden_states)):\n if self.config.head_multi == False:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head.linear_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head.linear_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n else:\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(outputs.hidden_states[i], lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head(outputs.hidden_states[i] / (self.config.hidden_size / self.config.dim_model_base))\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n else:\n if self.config.head_type == 'raw':\n if only_for_one_logit is None:\n raise ValueError(\"Cannot handle `only_for_one_logit` is None if the head type is complex.\")\n\n if self.config.head_multi == False:\n lm_head_slices = self.lm_head.weight.split(1, dim=0)\n for i in range(len(outputs.hidden_states)):\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n else:\n for i in range(len(outputs.hidden_states)):\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].weight.split(1, dim=0)\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n elif self.config.head_type == 'complex':\n if only_for_one_logit is None:\n raise ValueError(\"Cannot handle `only_for_one_logit` is None if the head type is complex.\")\n\n if self.config.head_multi == False:\n lm_head_slices = self.lm_head.linear_head.weight.split(1, dim=0)\n for i in range(len(outputs.hidden_states)):\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n else:\n for i in range(len(outputs.hidden_states)):\n lm_head_slices = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head.weight.split(1, dim=0)\n logits = F.linear(outputs.hidden_states[i], lm_head_slices[only_for_one_logit])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits, )\n else:\n if self.config.head_multi == False:\n for i in range(len(outputs.hidden_states)):\n logits = self.lm_head.linear_head(outputs.hidden_states[i])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n else:\n for i in range(len(outputs.hidden_states)):\n logits = self.lm_head[cutoff_layers[i] - self.config.start_layer].linear_head(outputs.hidden_states[i])\n logits = logits.float()\n logits = logits.reshape(input_ids.shape[0], -1)\n all_logits = all_logits + (logits,)\n\n loss = None\n if labels is not None and not only_for_one_logit and self.config.head_type == 'complex':\n # Shift so that tokens < n predict n\n loss = 0\n for logits in all_logits:\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n # Flatten the tokens\n loss_fct = CrossEntropyLoss()\n shift_logits = shift_logits.view(-1, self.config.vocab_size)\n shift_labels = shift_labels.view(-1)\n # Enable model parallelism\n shift_labels = shift_labels.to(shift_logits.device)\n loss += loss_fct(shift_logits, shift_labels)\n\n outputs.hidden_states = None if not output_hidden_states else outputs.hidden_states\n\n if not return_dict:\n output = (all_logits,) + outputs[1:]\n return (loss,) + output if loss is not None else output\n\n return CausalLMOutputWithPast(\n loss=loss,\n logits=all_logits,\n past_key_values=outputs.past_key_values,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n def prepare_inputs_for_generation(\n self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs\n ):\n if past_key_values is not None:\n if isinstance(past_key_values, Cache):\n cache_length = past_key_values.get_seq_length()\n past_length = past_key_values.seen_tokens\n max_cache_length = past_key_values.get_max_length()\n else:\n cache_length = past_length = past_key_values[0][0].shape[2]\n max_cache_length = None\n\n # Keep only the unprocessed tokens:\n # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where\n # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as\n # input)\n if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:\n input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]\n # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard\n # input_ids based on the past_length.\n elif past_length < input_ids.shape[1]:\n input_ids = input_ids[:, past_length:]\n # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.\n\n # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.\n if (\n max_cache_length is not None\n and attention_mask is not None\n and cache_length + input_ids.shape[1] > max_cache_length\n ):\n attention_mask = attention_mask[:, -max_cache_length:]\n\n position_ids = kwargs.get(\"position_ids\", None)\n if attention_mask is not None and position_ids is None:\n # create position_ids on the fly for batch generation\n position_ids = attention_mask.long().cumsum(-1) - 1\n position_ids.masked_fill_(attention_mask == 0, 1)\n if past_key_values:\n position_ids = position_ids[:, -input_ids.shape[1]:]\n\n # if `inputs_embeds` are passed, we only want to use them in the 1st generation step\n if inputs_embeds is not None and past_key_values is None:\n model_inputs = {\"inputs_embeds\": inputs_embeds}\n else:\n model_inputs = {\"input_ids\": input_ids}\n\n model_inputs.update(\n {\n \"position_ids\": position_ids,\n \"past_key_values\": past_key_values,\n \"use_cache\": kwargs.get(\"use_cache\"),\n \"attention_mask\": attention_mask,\n }\n )\n return model_inputs\n\n @staticmethod\n def _reorder_cache(past_key_values, beam_idx):\n reordered_past = ()\n for layer_past in past_key_values:\n reordered_past += (\n tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),\n )\n return reordered_past\n\n @torch.inference_mode()\n def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = \"user\",\n max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,\n **kwargs):\n if history is None:\n history = []\n if logits_processor:\n gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams, \"do_sample\": do_sample, \"top_p\": top_p,\n \"temperature\": temperature, \"logits_processor\": logits_processor, **kwargs}\n else:\n gen_kwargs = {\"max_length\": max_length, \"num_beams\": num_beams, \"do_sample\": do_sample, \"top_p\": top_p,\n \"temperature\": temperature, \"logits_processor\": logits_processor, **kwargs}\n\n history.append({\"role\": role, \"content\": query})\n history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)\n inputs = tokenizer(history_str, return_tensors='pt').to(self.device)\n outputs = self.generate(**inputs, **gen_kwargs)\n outputs = outputs.tolist()[0][len(inputs[\"input_ids\"][0]):-1]\n response = tokenizer.decode(outputs)\n pattern = re.compile(r\".*?(?=|<用户>)\", re.DOTALL)\n matches = pattern.findall(response)\n if len(matches) > 0:\n response = matches[0]\n history.append({\"role\": \"assistant\", \"content\": response})\n return response, history" }, { "path": "FlagEmbedding/inference/reranker/encoder_only/__init__.py", "content": "from .base import BaseReranker as FlagReranker\n\n__all__ = [\n \"FlagReranker\",\n]\n" }, { "path": "FlagEmbedding/inference/reranker/encoder_only/base.py", "content": "import torch\nimport numpy as np\nfrom tqdm import tqdm, trange\nfrom typing import Any, List, Union, Tuple, Optional\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\nfrom FlagEmbedding.abc.inference import AbsReranker\n\n\ndef sigmoid(x):\n return float(1 / (1 + np.exp(-x)))\n\n\nclass BaseReranker(AbsReranker):\n \"\"\"Base reranker class for encoder only models.\n\n Args:\n model_name_or_path (str): If it's a path to a local model, it loads the model from the path. Otherwise tries to download and\n load a model from HuggingFace Hub with the name.\n use_fp16 (bool, optional): If true, use half-precision floating-point to speed up computation with a slight performance \n degradation. Defaults to :data:`False`.\n query_instruction_for_rerank (Optional[str], optional): Query instruction for retrieval tasks, which will be used with\n with :attr:`query_instruction_format`. Defaults to :data:`None`.\n query_instruction_format (str, optional): The template for :attr:`query_instruction_for_rerank`. Defaults to :data:`\"{}{}\"`.\n passage_instruction_format (str, optional): The template for passage. Defaults to \"{}{}\".\n cache_dir (Optional[str], optional): Cache directory for the model. Defaults to :data:`None`.\n devices (Optional[Union[str, List[str], List[int]]], optional): Devices to use for model inference. Defaults to :data:`None`.\n batch_size (int, optional): Batch size for inference. Defaults to :data:`128`.\n query_max_length (Optional[int], optional): Maximum length for queries. If not specified, will be 3/4 of :attr:`max_length`.\n Defaults to :data:`None`.\n max_length (int, optional): Maximum length of passages. Defaults to :data`512`.\n normalize (bool, optional): If True, use Sigmoid to normalize the results. Defaults to :data:`False`.\n \"\"\"\n def __init__(\n self,\n model_name_or_path: str,\n use_fp16: bool = False,\n query_instruction_for_rerank: Optional[str] = None,\n query_instruction_format: str = \"{}{}\", # specify the format of query_instruction_for_rerank\n passage_instruction_for_rerank: Optional[str] = None,\n passage_instruction_format: str = \"{}{}\", # specify the format of passage_instruction_for_rerank\n trust_remote_code: bool = False,\n cache_dir: Optional[str] = None,\n devices: Optional[Union[str, List[str], List[int]]] = None, # specify devices, such as [\"cuda:0\"] or [\"0\"]\n # inference\n batch_size: int = 128,\n query_max_length: Optional[int] = None,\n max_length: int = 512,\n normalize: bool = False,\n **kwargs: Any,\n ):\n super().__init__(\n model_name_or_path=model_name_or_path,\n use_fp16=use_fp16,\n query_instruction_for_rerank=query_instruction_for_rerank,\n query_instruction_format=query_instruction_format,\n passage_instruction_for_rerank=passage_instruction_for_rerank,\n passage_instruction_format=passage_instruction_format,\n devices=devices,\n batch_size=batch_size,\n query_max_length=query_max_length,\n max_length=max_length,\n normalize=normalize,\n **kwargs\n )\n self.tokenizer = AutoTokenizer.from_pretrained(\n model_name_or_path, \n trust_remote_code=trust_remote_code, \n cache_dir=cache_dir\n )\n self.model = AutoModelForSequenceClassification.from_pretrained(\n model_name_or_path, \n trust_remote_code=trust_remote_code, \n cache_dir=cache_dir\n )\n\n @torch.no_grad()\n def compute_score_single_gpu(\n self,\n sentence_pairs: Union[List[Tuple[str, str]], Tuple[str, str]],\n batch_size: Optional[int] = None,\n query_max_length: Optional[int] = None,\n max_length: Optional[int] = None,\n normalize: Optional[bool] = None,\n device: Optional[str] = None,\n **kwargs: Any\n ) -> List[float]:\n \"\"\"_summary_\n\n Args:\n sentence_pairs (Union[List[Tuple[str, str]], Tuple[str, str]]): Input sentence pairs to compute scores.\n batch_size (Optional[int], optional): Number of inputs for each iter. Defaults to :data:`None`.\n query_max_length (Optional[int], optional): Maximum length of tokens of queries. Defaults to :data:`None`.\n max_length (Optional[int], optional): Maximum length of tokens. Defaults to :data:`None`.\n normalize (Optional[bool], optional): If True, use Sigmoid to normalize the results. Defaults to :data:`None`.\n device (Optional[str], optional): Device to use for computation. Defaults to :data:`None`.\n\n Returns:\n List[float]: Computed scores of queries and passages.\n \"\"\"\n if batch_size is None: batch_size = self.batch_size\n if max_length is None: max_length = self.max_length\n if query_max_length is None:\n if self.query_max_length is not None:\n query_max_length = self.query_max_length\n else:\n query_max_length = max_length * 3 // 4\n if normalize is None: normalize = self.normalize\n\n if device is None:\n device = self.target_devices[0]\n\n if device == \"cpu\": self.use_fp16 = False\n if self.use_fp16: self.model.half()\n\n self.model.to(device)\n self.model.eval()\n\n assert isinstance(sentence_pairs, list)\n if isinstance(sentence_pairs[0], str):\n sentence_pairs = [sentence_pairs]\n \n # tokenize without padding to get the correct length\n all_inputs = []\n for start_index in trange(0, len(sentence_pairs), batch_size, desc=\"pre tokenize\",\n disable=len(sentence_pairs) < batch_size):\n sentences_batch = sentence_pairs[start_index:start_index + batch_size]\n queries = [s[0] for s in sentences_batch]\n passages = [s[1] for s in sentences_batch]\n queries_inputs_batch = self.tokenizer(\n queries,\n return_tensors=None,\n add_special_tokens=False,\n max_length=query_max_length,\n truncation=True,\n **kwargs\n )['input_ids']\n passages_inputs_batch = self.tokenizer(\n passages,\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True,\n **kwargs\n )['input_ids']\n for q_inp, d_inp in zip(queries_inputs_batch, passages_inputs_batch):\n item = self.tokenizer.prepare_for_model(\n q_inp,\n d_inp,\n truncation='only_second',\n max_length=max_length,\n padding=False,\n )\n all_inputs.append(item)\n # sort by length for less padding\n length_sorted_idx = np.argsort([-len(x['input_ids']) for x in all_inputs])\n all_inputs_sorted = [all_inputs[i] for i in length_sorted_idx]\n\n # adjust batch size\n flag = False\n while flag is False:\n try:\n test_inputs_batch = self.tokenizer.pad(\n all_inputs_sorted[:min(len(all_inputs_sorted), batch_size)],\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n scores = self.model(**test_inputs_batch, return_dict=True).logits.view(-1, ).float()\n flag = True\n except RuntimeError as e:\n batch_size = batch_size * 3 // 4\n except torch.cuda.OutOfMemoryError as e:\n batch_size = batch_size * 3 // 4\n\n all_scores = []\n for start_index in tqdm(range(0, len(all_inputs_sorted), batch_size), desc=\"Compute Scores\",\n disable=len(all_inputs_sorted) < batch_size):\n sentences_batch = all_inputs_sorted[start_index:start_index + batch_size]\n inputs = self.tokenizer.pad(\n sentences_batch,\n padding=True,\n return_tensors='pt',\n **kwargs\n ).to(device)\n\n scores = self.model(**inputs, return_dict=True).logits.view(-1, ).float()\n all_scores.extend(scores.cpu().numpy().tolist())\n\n all_scores = [all_scores[idx] for idx in np.argsort(length_sorted_idx)]\n\n if normalize:\n all_scores = [sigmoid(score) for score in all_scores]\n\n return all_scores\n" }, { "path": "FlagEmbedding/inference/reranker/model_mapping.py", "content": "from enum import Enum\nfrom typing import Type\nfrom dataclasses import dataclass\nfrom collections import OrderedDict\n\nfrom FlagEmbedding.abc.inference import AbsReranker\nfrom FlagEmbedding.inference.reranker import FlagReranker, FlagLLMReranker, LayerWiseFlagLLMReranker, LightWeightFlagLLMReranker\n\n\nclass RerankerModelClass(Enum):\n ENCODER_ONLY_BASE = \"encoder-only-base\"\n DECODER_ONLY_BASE = \"decoder-only-base\"\n DECODER_ONLY_LAYERWISE = \"decoder-only-layerwise\"\n DECODER_ONLY_LIGHTWEIGHT = \"decoder-only-lightweight\"\n\n\nRERANKER_CLASS_MAPPING = OrderedDict([\n (RerankerModelClass.ENCODER_ONLY_BASE, FlagReranker),\n (RerankerModelClass.DECODER_ONLY_BASE, FlagLLMReranker),\n (RerankerModelClass.DECODER_ONLY_LAYERWISE, LayerWiseFlagLLMReranker),\n (RerankerModelClass.DECODER_ONLY_LIGHTWEIGHT, LightWeightFlagLLMReranker)\n])\n\n\n@dataclass\nclass RerankerConfig:\n model_class: Type[AbsReranker]\n trust_remote_code: bool = False\n\n\nAUTO_RERANKER_MAPPING = OrderedDict([\n # ============================== BGE ==============================\n (\n \"bge-reranker-base\", \n RerankerConfig(FlagReranker)\n ),\n (\n \"bge-reranker-large\", \n RerankerConfig(FlagReranker)\n ),\n (\n \"bge-reranker-v2-m3\",\n RerankerConfig(FlagReranker)\n ),\n (\n \"bge-reranker-v2-gemma\",\n RerankerConfig(FlagLLMReranker)\n ),\n (\n \"bge-reranker-v2-minicpm-layerwise\",\n RerankerConfig(LayerWiseFlagLLMReranker)\n ),\n (\n \"bge-reranker-v2.5-gemma2-lightweight\",\n RerankerConfig(LightWeightFlagLLMReranker)\n ),\n # others\n (\n \"jinaai/jina-reranker-v2-base-multilingual\",\n RerankerConfig(FlagReranker)\n ),\n (\n \"Alibaba-NLP/gte-multilingual-reranker-base\",\n RerankerConfig(FlagReranker)\n ),\n (\n \"maidalun1020/bce-reranker-base_v1\",\n RerankerConfig(FlagReranker)\n ),\n (\n \"jinaai/jina-reranker-v1-turbo-en\",\n RerankerConfig(FlagReranker)\n ),\n # TODO: Add more models.\n])\n" }, { "path": "FlagEmbedding/utils/__init__.py", "content": "" }, { "path": "FlagEmbedding/utils/transformers_compat.py", "content": "from packaging import version\nimport transformers\n\nTF_VER = version.parse(getattr(transformers, \"__version__\", \"0.0.0\"))\nIS_TF_V5_OR_HIGHER = TF_VER >= version.parse(\"5.0.0\")\n\n\n# ------------- torch.fx availability -------------\n# v5 removed is_torch_fx_available. We emulate it via feature detection.\ndef is_torch_fx_available():\n try:\n import torch.fx # noqa: F401\n\n return True\n except Exception:\n return False\n\n\n# ------------- other utilities that moved -------------\n# Pattern:\n# try the new location first (v5), then fall back to v4 path, else provide a safe default.\ndef import_from_candidates(candidates, default=None):\n for mod, name in candidates:\n try:\n module = __import__(mod, fromlist=[name])\n return getattr(module, name)\n except Exception:\n pass\n return default\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2022 staoxiao\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "Manifest.in", "content": "# Include the entire directory and its contents\nrecursive-include FlagEmbedding/FlagEmbedding/visual/eva_clip *\n\n# Include the specific file at the root level\ninclude bpe_simple_vocab_16e6.txt.gz\n\n# Include all JSON files inside the specified directory\nrecursive-include FlagEmbedding/visual/eva_clip/model_configs *.json\n" }, { "path": "README.md", "content": "[](https://flagopen.baai.ac.cn/)\n\n

⚡️BGE: One-Stop Retrieval Toolkit For Search and RAG

\n\n![bge_logo](./imgs/bge_logo.jpg)\n\n

\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"License\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n

\n

\n

\n News |\n Installation |\n Quick Start |\n Community |\n Projects |\n Model List |\n Contributor |\n Citation |\n License \n

\n

\n\n\n[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)\n\n\n\n## News\n\n- 3/6/2025: :fire::fire: Introduce **BGE-VL** ([HF repo](https://huggingface.co/collections/BAAI/megapairs-67c6bbe49c15a9e7a7c69d92)), State-Of-The-Art multimodal embedding models to support Any visual search applications (everything, including text-to-image, image-to-text, image&prompt-to-image, text-to-image&text, and more)! They are released under the MIT license and are completely free for both academic and commercial use. We also release **MegaPairs** ([repo](https://github.com/VectorSpaceLab/MegaPairs), [paper](https://arxiv.org/abs/2412.14475)), a massive synthetic dataset which empowers BGE-VL!\n- 12/5/2024: :book: We built the [BGE documentation](https://www.bge-model.com) for centralized BGE information and materials!\n- 10/29/2024: :earth_asia: We created WeChat group for BGE. Scan the [QR code](./imgs/BGE_WeChat_Group.png) to join the group chat! To get the first hand message about our updates and new release, or having any questions or ideas, join us now!\n- \"bge_wechat_group\"\n\n- 10/22/2024: We release another interesting model: [OmniGen](https://github.com/VectorSpaceLab/OmniGen), which is a unified image generation model supporting various tasks. OmniGen can accomplish complex image generation tasks without the need for additional plugins like ControlNet, IP-Adapter, or auxiliary models such as pose detection and face detection.\n- 9/10/2024: Introducing **MemoRAG**, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1) \n- 9/2/2024: Start to maintain the [tutorials](./Tutorials/). The contents within will be actively updated and eariched, stay tuned! :books:\n- 7/26/2024: Release a new embedding model [bge-en-icl](https://huggingface.co/BAAI/bge-en-icl), an embedding model that incorporates in-context learning capabilities, which, by providing task-relevant query-response examples, can encode semantically richer queries, further enhancing the semantic representation ability of the embeddings.\n- 7/26/2024: Release a new embedding model [bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2), a multilingual embedding model based on gemma-2-9b, which supports multiple languages and diverse downstream tasks, achieving new SOTA on multilingual benchmarks (MIRACL, MTEB-fr, and MTEB-pl). \n- 7/26/2024: Release a new lightweight reranker [bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight), a lightweight reranker based on gemma-2-9b, which supports token compression and layerwise lightweight operations, can still ensure good performance while saving a significant amount of resources. :fire:\n\n
\n More\n\n\n- 6/7/2024: Release a new benchmark [MLVU](https://github.com/JUNJIE99/MLVU), the first comprehensive benchmark specifically designed for long video understanding. MLVU features an extensive range of video durations, a diverse collection of video sources, and a set of evaluation tasks uniquely tailored for long-form video understanding. :fire:\n- 5/21/2024: Release a new benchmark [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench) together with Jina AI, Zilliz, HuggingFace, and other partners. AIR-Bench focuses on a fair out-of-distribution evaluation for Neural IR & RAG. It generates the synthetic data for benchmarking w.r.t. diverse domains and languages. It is dynamic and will be updated on regular basis. [Leaderboard](https://huggingface.co/spaces/AIR-Bench/leaderboard) :fire:\n- 4/30/2024: Release [Llama-3-8B-Instruct-80K-QLoRA](https://huggingface.co/namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA), extending the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA training on a few synthesized long-context data. The model achieves remarkable performance on various long-context benchmarks. [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/longllm_qlora) :fire:\n- 3/18/2024: Release new [rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_reranker), built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually :smiley:) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation :fire:\n- 3/18/2024: Release [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/visual_bge), equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data. :fire:\n- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). \nIt is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.\n[Technical Report](https://arxiv.org/pdf/2402.03216.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/BGE_M3). :fire:\n- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) \n- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LLARA)\n- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) \n- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)\n- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released \n- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released \n- 09/12/2023: New models: \n - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. \n - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.\n- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/baai_general_embedding): Add script to mine hard negatives and support adding instruction during fine-tuning. \n- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). \n- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** \n- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: \n- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. \n \n\n
\n\n\nBGE (BAAI General Embedding) focuses on retrieval-augmented LLMs, consisting of the following projects currently:\n\n![projects](./imgs/projects.png)\n\n- **Inference**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker)\n- **Finetune**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker)\n- **[Evaluation](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation)**\n- **[Dataset](https://github.com/FlagOpen/FlagEmbedding/tree/master/dataset)**\n- **[Tutorials](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials)**\n- **[research](https://github.com/FlagOpen/FlagEmbedding/tree/master/research)**\n\n\n## Installation\n### Using pip:\nIf you do not want to finetune the models, you can install the package without the finetune dependency:\n```\npip install -U FlagEmbedding\n```\nIf you want to finetune the models, you can install the package with the finetune dependency:\n```\npip install -U FlagEmbedding[finetune]\n```\n### Install from sources:\n\nClone the repository and install\n```\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\n# If you do not need to finetune the models, you can install the package without the finetune dependency:\npip install .\n# If you want to finetune the models, install the package with the finetune dependency:\n# pip install .[finetune]\n```\nFor development in editable mode:\n```\n# If you do not need to finetune the models, you can install the package without the finetune dependency:\npip install -e .\n# If you want to finetune the models, install the package with the finetune dependency:\n# pip install -e .[finetune]\n```\n\n## Quick Start\nFirst, load one of the BGE embedding model:\n```\nfrom FlagEmbedding import FlagAutoModel\n\nmodel = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5',\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages:\",\n use_fp16=True)\n```\nThen, feed some sentences to the model and get their embeddings:\n```\nsentences_1 = [\"I love NLP\", \"I love machine learning\"]\nsentences_2 = [\"I love BGE\", \"I love text retrieval\"]\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\n```\nOnce we get the embeddings, we can compute similarity by inner product:\n```\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\n\nFor more details, you can refer to [embedder inference](./examples/inference/embedder), [reranker inference](./examples/inference/reranker), [embedder finetune](./examples/finetune/embedder), [reranker fintune](./examples/finetune/reranker), [evaluation](./examples/evaluation).\n\nIf you're unfamiliar with any of related concepts, please check out the [tutorial](./Tutorials/). If it's not there, let us know.\n\nFor more interesting topics related to BGE, take a look at [research](./research).\n\n## Community\n\nWe are actively maintaining the community of BGE and FlagEmbedding. Let us know if you have any suggessions or ideas!\n\nCurrently we are updating the [tutorials](./Tutorials/), we aim to create a comprehensive and detailed tutorial for beginners on text retrieval and RAG. Stay tuned!\n\nThe following contents are releasing in the upcoming weeks:\n\n- Evaluation\n- BGE-EN-ICL\n\n
\n The whole tutorial roadmap\n \n
\n\n## Model List\n\n`bge` is short for `BAAI general embedding`.\n\n| Model | Language | Description | query instruction for retrieval |\n|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|\n| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples | Provide instructions and few-shot examples freely based on the given task. |\n| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | A LLM-based multilingual embedding model, trained on a diverse range of languages and tasks. | Provide instructions based on the given task. |\n| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |\n| [LM-Cocktail](https://huggingface.co/Shitao) | English | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |\n| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder) |\n| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | |\n| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | |\n| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | |\n| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. | |\n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | a cross-encoder model which is more accurate but less efficient | |\n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | a cross-encoder model which is more accurate but less efficient | |\n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | Embedding Model which map text into vector | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | Embedding Model which map text into vector | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |\n\n\n\n\n### Contributors:\nThank all our contributors for their efforts and warmly welcome new members to join in!\n\n\n \n\n\n\n\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{bge_m3,\n title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},\n author={Chen, Jianlv and Xiao, Shitao and Zhang, Peitian and Luo, Kun and Lian, Defu and Liu, Zheng},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n@misc{cocktail,\n title={LM-Cocktail: Resilient Tuning of Language Models via Model Merging}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Xingrun Xing},\n year={2023},\n eprint={2311.13534},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n@misc{llm_embedder,\n title={Retrieve Anything To Augment Large Language Models}, \n author={Peitian Zhang and Shitao Xiao and Zheng Liu and Zhicheng Dou and Jian-Yun Nie},\n year={2023},\n eprint={2310.07554},\n archivePrefix={arXiv},\n primaryClass={cs.IR}\n}\n\n@misc{bge_embedding,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n## License\nFlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). \n\n" }, { "path": "README_zh.md", "content": "![bge_logo](./imgs/bge_logo.jpg)\n\n

⚡️BGE: One-Stop Retrieval Toolkit For Search and RAG

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\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"License\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n

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\n 更新 |\n 安装 |\n 快速开始 |\n 社区 |\n 项目 |\n 模型列表 |\n 贡献者 |\n Citation |\n License \n

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\n\n[English](https://github.com/FlagOpen/FlagEmbedding/blob/master/README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)\n\nBGE (BAAI General Embedding) 专注于检索增强llm领域，目前包括以下项目:\n\n![projects](./imgs/projects.png)\n\n- **推理**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker)\n- **微调**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker)\n- **[评估](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation)**\n- **[数据集](https://github.com/FlagOpen/FlagEmbedding/tree/master/dataset)**\n- **[教程](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials)**\n- **[研究](https://github.com/FlagOpen/FlagEmbedding/tree/master/research)**\n\n## 更新\n\n- 10/29/2024: :earth_asia: 我们建立了[BGE技术交流群](./BGE_WeChat_Group.png)，欢迎扫码入群！\n- \"bge_wechat_group\"\n- 10/22/2024：我们发布了新的模型：[OmniGen](https://github.com/VectorSpaceLab/OmniGen)，这是一个支持各种任务的统一图像生成模型。OmniGen可以在不需要额外插件（如ControlNet、IP-Adapter）或辅助模型（如姿态检测和人脸检测）的情况下完成复杂的图像生成任务。 :fire:\n- 9/10/2024：我们推出了**MemoRAG**，这是一种基于记忆启发的知识发现技术，是迈向 RAG 2.0 的关键一步（仓库：https://github.com/qhjqhj00/MemoRAG，论文：https://arxiv.org/pdf/2409.05591v1） :fire:\n- 9/2/2024: 开始维护更新[教程](./Tutorials/)，教程文件夹中的内容会在未来不断丰富，欢迎持续关注！ :books:\n- 7/26/2024：发布[bge-en-icl](https://huggingface.co/BAAI/bge-en-icl)。这是一个结合了上下文学习能力的文本检索模型，通过提供与任务相关的查询-回答示例，可以编码语义更丰富的查询，进一步增强嵌入的语义表征能力。 :fire:\n- 7/26/2024: 发布[bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2)。这是一个基于gemma-2-9b的多语言文本向量模型，同时支持多种语言和多样的下游任务，在多语言检索数据集 MIRACL, MTEB-fr, MTEB-pl 上取得了迄今最好的实验结果。 :fire:\n- 7/26/2024：发布新的轻量级重排器[bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight)。这是一个基于gemma-2-9b的轻量级重排器，支持令牌压缩和分层轻量操作，在节省大量资源的同时，仍能确保良好的性能。:fire:\n\n
\n More\n\n- 6/7/2024: 发布首个专为长视频理解设计的全面评测基准[MLVU](https://github.com/JUNJIE99/MLVU)。MLVU拥有丰富的视频时长范围，多样化的视频来源，以及多个专为长视频理解设计的评估任务。 :fire:\n- 5/21/2024：联合 Jina AI、Zilliz、HuggingFace 等机构发布评测基准 [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench)，针对检索任务和 RAG 场景设计。AIR-Bench 首次提出在检索任务中使用 LLMs 自动化生产评估数据，避免模型过拟合测试数据。AIR-Bench 不需要人工参与标注数据，因而可以更灵活覆盖更多垂直领域和不同语种。同时 AIR-Bench 会定期进行更新从而满足社区不断变化的评测需求。[Leaderboard](https://huggingface.co/spaces/AIR-Bench/leaderboard) :fire:\n- 4/30/2024: 发布[Llama-3-8B-Instruct-80K-QLoRA](https://huggingface.co/namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA), 其通过在少量合成的长文本数据上的QLoRA训练，有效地将Llama-3-8B-Instruct的上下文长度从8K扩展到80K。详见[代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/longllm_qlora) :fire:\n- 3/18/2024: 发布新的[rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_reranker), 拥有更好的性能同时支持多语言和长文本。 :fire:\n- 3/18/2024: 发布[Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/visual_bge)，该项目通过引入image token embedding赋予BGE视觉编码能力。Visualized-BGE可以对混合图文数据进行编码，用于广泛的混合模态检索任务。 :fire:\n- 1/30/2024: 发布**BGE-M3**, 第一个具有多功能、多语言和多粒度特性的文本检索模型，高效支持多语言（100+语言）、长文本（至多8192长度的输入文本）、和混合检索（稠密、稀疏、多向量）。 详见[report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf)和[代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/BGE_M3) :fire:\n- 1/9/2024: 发布[Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/activation_beacon), 一个有效、高效、兼容、低成本（训练）的扩展大预言模型上下文长度的方法。[技术报告](https://arxiv.org/abs/2401.03462) \n- 12/24/2023: 发布**LLaRA**, 一个基于LLaMA-7B的稠密检索模型, MS MARCO与BEIR上取得了迄今最好的实验结果. 模型与代码将会陆续开源. 敬请关注. [技术报告](https://arxiv.org/abs/2312.15503) 和 [代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LLARA)\n- 11/23/2023: 发布[LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LM_Cocktail), 一种通过模型融合在微调时保持原有模型通用能力的方法. [技术报告](https://arxiv.org/abs/2311.13534) \n- 10/12/2023: 发布 [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder), 专为大语言模型**各种检索增强任务设计**的英文向量模型。[技术报告](https://arxiv.org/pdf/2310.07554.pdf) \n- 09/15/2023: 发布 [技术报告](https://arxiv.org/pdf/2309.07597.pdf) 和 [数据集](https://data.baai.ac.cn/details/BAAI-MTP).\n- 09/12/2023: 更新：\n - **新增重排模型**：开源交叉编码器模型bge-reranker，具有比向量模型更强大的排序能力。非常建议使用或者微调它来重新排序向量模型返回的top-k文档，提高最终结果的相关性。\n - **更新向量模型**：发布bge-*-v1.5向量模型，缓解相似度分布问题，提升无指令情况下的检索能力（但检索任务仍建议使用指令）\n- 09/07/2023: 更新[微调代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/baai_general_embedding): 增加难负样本挖掘脚本，增加指令参数方便在微调中添加指令.\n- 08/09/2023: BGE模型整合入Langchain, 可以在langchain中非常简单的[使用它](#using-langchain); C-MTEB中文榜单已[在线更新](https://huggingface.co/spaces/mteb/leaderboard). \n- 08/05/2023: 发布更小的模型(base, small), **在同尺寸模型中取得最好的性能！ 🤗**\n- 08/02/2023: :tada: :tada: 发布中英文向量模型BGE(BAAI General Embedding的缩写), **在MTEB和C-MTEB榜单上取得最好的性能** \n- 08/01/2023: 发布大规模中文文本向量[评测榜单](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/C_MTEB) (**C-MTEB**), 其包括31个测试任务. \n\n
\n\n\n## 安装\n### 使用pip:\n如果你不想微调模型，你可以直接安装包，不用finetune依赖：\n```\npip install -U FlagEmbedding\n```\n如果你想微调模型，你可以用finetune依赖安装：\n```\npip install -U FlagEmbedding[finetune]\n```\n### 从源文件安装部署:\n\n克隆并安装FlagEmbedding：\n```\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\n# 如果你不想微调模型，你可以直接安装包，不用finetune依赖：\npip install .\n# 如果你想微调模型，你可以用finetune依赖安装：\n# pip install .[finetune]\n```\n在可编辑模式下安装:\n```\n# 如果你不想微调模型，你可以直接安装包，不用finetune依赖：\npip install -e .\n# 如果你想微调模型，你可以用finetune依赖安装：\n# pip install -e .[finetune]\n```\n\n## 快速开始\n首先，加载一个BGE向量模型：\n```\nfrom FlagEmbedding import FlagAutoModel\n\nmodel = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5',\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages:\",\n use_fp16=True)\n```\n将语句作为模型输入，得到向量：\n```\nsentences_1 = [\"I love NLP\", \"I love machine learning\"]\nsentences_2 = [\"I love BGE\", \"I love text retrieval\"]\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\n```\n取得向量后，通过内积计算相似度：\n```\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\n\n关于更多细节，可以参考[embedder推理](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder), [reranker推理](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker), [embedder微调](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder), [reranker微调](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker), [评估](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation), [研究](https://github.com/FlagOpen/FlagEmbedding/tree/master/research).\n\n## 社区\n\n我们将持续维护BGE及FlagEmbedding社区，有任何想法建议都欢迎告诉我们！\n\n近期会持续更新[教学](./Tutorials/)中的内容，希望为文本检索以及RAG打造出完整且详细的教学，欢迎持续关注！\n\n在未来将会更新以下内容：\n\n- RAG\n\n
\n 教程规划\n \n
\n## 模型列表\n\n| Model | Language | Description | query instruction for retrieval |\n|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|\n| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | 基于大型语言模型的向量模型，具有上下文学习能力，能够基于少量示例充分发挥模型的潜力。\t | 根据给定的任务自由提供指示和少数示例。 |\n| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | 基于大型语言模型的多语言向量模型，在多种语言和任务上训练，适应多样化的下游场景。 | 根据给定的任务自由提供指示和少数示例。 |\n| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | 多功能（向量检索，稀疏检索，多表征检索）、多语言、多粒度（最大长度8192） | |\n| [LM-Cocktail](https://huggingface.co/Shitao) | English | 微调的Llama和BGE模型，可以用来复现LM-Cocktail论文的结果 | |\n| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | 专为大语言模型各种检索增强任务设计的向量模型 | 详见[README](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder) |\n| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | 一个轻量级的交叉编码器模型，具有强大的多语言能力，易于部署，具有快速的推理能力。 | |\n| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | 一个支持多语言的交叉编码器模型，在英文和多语言能力方面均表现出色。 | |\n| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | 一个支持多语言的交叉编码器模型，在英文和中文方面均表现良好，允许自由选择输出层，以便加速推理。 | |\n| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | 一个支持多语言的跨编码器模型，不仅在英文和中文上表现良好，还允许自由选择输出层、压缩比例和压缩层，从而便于加速推理。 | |\n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | 交叉编码器模型，精度比向量模型更高但推理效率较低 | |\n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | 交叉编码器模型，精度比向量模型更高但推理效率较低 | |\n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | 1.5版本，相似度分布更加合理 | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | 1.5版本，相似度分布更加合理 | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | 1.5版本，相似度分布更加合理 | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | 1.5版本，相似度分布更加合理 | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | 1.5版本，相似度分布更加合理 | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | 1.5版本，相似度分布更加合理 | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | 向量模型，将文本转换为向量 | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | base-scale 向量模型 | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | base-scale 向量模型 | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | 向量模型，将文本转换为向量 | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | base-scale 向量模型 | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | base-scale 向量模型 | `为这个句子生成表示以用于检索相关文章：` |\n\n\n## 贡献者:\n\n十分感谢所有参与FlagEmbedding社区成员的贡献，也欢迎新的成员加入！\n\n\n \n\n\n\n\n## Citation\n\n如果您觉得我们的工作有所帮助，请考虑点个星 :star: 和引用以下论文:\n```\n@misc{cocktail,\n title={LM-Cocktail: Resilient Tuning of Language Models via Model Merging}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Xingrun Xing},\n year={2023},\n eprint={2311.13534},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n@misc{llm_embedder,\n title={Retrieve Anything To Augment Large Language Models}, \n author={Peitian Zhang and Shitao Xiao and Zheng Liu and Zhicheng Dou and Jian-Yun Nie},\n year={2023},\n eprint={2310.07554},\n archivePrefix={arXiv},\n primaryClass={cs.IR}\n}\n\n@misc{bge_embedding,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n## License\nFlagEmbedding基于[MIT License](LICENSE)开源协议。\n\n\n\n" }, { "path": "Tutorials/1_Embedding/1.1_Intro&Inference.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Intro to Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For text retrieval, pattern matching is the most intuitive way. People would use certain characters, words, phrases, or sentence patterns. However, not only for human, it is also extremely inefficient for computer to do pattern matching between a query and a collection of text files to find the possible results. \\n\",\n \"\\n\",\n \"For images and acoustic waves, there are rgb pixels and digital signals. Similarly, in order to accomplish more sophisticated tasks of natural language such as retrieval, classification, clustering, or semantic search, we need a way to represent text data. That's how text embedding comes in front of the stage.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Background\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Traditional text embedding methods like one-hot encoding and bag-of-words (BoW) represent words and sentences as sparse vectors based on their statistical features, such as word appearance and frequency within a document. More advanced methods like TF-IDF and BM25 improve on these by considering a word's importance across an entire corpus, while n-gram techniques capture word order in small groups. However, these approaches suffer from the \\\"curse of dimensionality\\\" and fail to capture semantic similarity like \\\"cat\\\" and \\\"kitty\\\", difference like \\\"play the watch\\\" and \\\"watch the play\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# example of bag-of-words\\n\",\n \"sentence1 = \\\"I love basketball\\\"\\n\",\n \"sentence2 = \\\"I have a basketball match\\\"\\n\",\n \"\\n\",\n \"words = ['I', 'love', 'basketball', 'have', 'a', 'match']\\n\",\n \"sen1_vec = [1, 1, 1, 0, 0, 0]\\n\",\n \"sen2_vec = [1, 0, 1, 1, 1, 1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To overcome these limitations, dense word embeddings were developed, mapping words to vectors in a low-dimensional space that captures semantic and relational information. Early models like Word2Vec demonstrated the power of dense embeddings using neural networks. Subsequent advancements with neural network architectures like RNNs, LSTMs, and Transformers have enabled more sophisticated models such as BERT, RoBERTa, and GPT to excel in capturing complex word relationships and contexts. **BAAI General Embedding (BGE)** provide a series of open-source models that could satisfy all kinds of demands.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Get Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first step of modern text retrieval is embedding the text. So let's take a look at how to use the embedding models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the packages:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U FlagEmbedding sentence_transformers openai cohere\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"# single GPU is better for small tasks\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll use the following three sentences as the inputs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sentences = [\\n\",\n \" \\\"That is a happy dog\\\",\\n\",\n \" \\\"That is a very happy person\\\",\\n\",\n \" \\\"Today is a sunny day\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Open-source Models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A huge portion of embedding models are in the open source community. The advantages of open-source models include:\\n\",\n \"- Free, no extra cost. But make sure to check the License and your use case before using.\\n\",\n \"- No frequency limit, can accelerate a lot if you have enough GPUs to parallelize.\\n\",\n \"- Transparent and might be reproducible.\\n\",\n \"\\n\",\n \"Let's take a look at two representatives:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### BGE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE is a series of embedding models and rerankers published by BAAI. Several of them reached SOTA at the time they released.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"initial target device: 100%|██████████| 8/8 [00:31<00:00, 3.89s/it]\\n\",\n \"Chunks: 100%|██████████| 3/3 [00:04<00:00, 1.61s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 768)\\n\",\n \"Similarity scores:\\n\",\n \"[[1. 0.79 0.575 ]\\n\",\n \" [0.79 0.9995 0.592 ]\\n\",\n \" [0.575 0.592 0.999 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# Load BGE model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"embeddings = model.encode(sentences)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Sentence Transformers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sentence Transformers is a library for sentence embeddings with a huge amount of embedding models and datasets for related tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 384)\\n\",\n \"Similarity scores:\\n\",\n \"[[0.99999976 0.6210502 0.24906276]\\n\",\n \" [0.6210502 0.9999997 0.21061528]\\n\",\n \" [0.24906276 0.21061528 0.9999999 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"model = SentenceTransformer(\\\"all-MiniLM-L6-v2\\\")\\n\",\n \"\\n\",\n \"embeddings = model.encode(sentences, normalize_embeddings=True)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Commercial Models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are also plenty choices of commercial models. They have the advantages of:\\n\",\n \"- Efficient memory usage, fast inference with no need of GPUs.\\n\",\n \"- Systematic support, commercial models have closer connections with their other products.\\n\",\n \"- Better training data, commercial models might be trained on larger, higher-quality datasets than some open-source models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### OpenAI\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Along with GPT series, OpenAI has their own embedding models. Make sure to fill in your own API key in the field `\\\"YOUR_API_KEY\\\"`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"YOUR_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then run the following cells to get the embeddings. Check their official [documentation](https://platform.openai.com/docs/guides/embeddings) for more details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from openai import OpenAI\\n\",\n \"\\n\",\n \"client = OpenAI()\\n\",\n \"\\n\",\n \"response = client.embeddings.create(input = sentences, model=\\\"text-embedding-3-small\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 1536)\\n\",\n \"Similarity scores:\\n\",\n \"[[1.00000004 0.697673 0.34739798]\\n\",\n \" [0.697673 1.00000005 0.31969923]\\n\",\n \" [0.34739798 0.31969923 0.99999998]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = np.asarray([response.data[i].embedding for i in range(len(sentences))])\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Voyage AI\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Voyage AI provides embedding models and rerankers for different purpus and in various fields. Their API keys can be freely used in low frequency and token length.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.environ[\\\"VOYAGE_API_KEY\\\"] = \\\"YOUR_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check their official [documentation](https://docs.voyageai.com/docs/api-key-and-installation) for more details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import voyageai\\n\",\n \"\\n\",\n \"vo = voyageai.Client()\\n\",\n \"\\n\",\n \"result = vo.embed(sentences, model=\\\"voyage-large-2-instruct\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 1024)\\n\",\n \"Similarity scores:\\n\",\n \"[[0.99999997 0.87282517 0.63276503]\\n\",\n \" [0.87282517 0.99999998 0.64720015]\\n\",\n \" [0.63276503 0.64720015 0.99999999]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = np.asarray(result.embeddings)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.1_BGE_Series.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"06cff9e4\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Series\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"880e229d\",\n \"metadata\": {},\n \"source\": [\n \"In this Part, we will walk through the BGE series and introduce how to use the BGE embedding models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2516fd49\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BAAI General Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2113ee71\",\n \"metadata\": {},\n \"source\": [\n \"BGE stands for BAAI General Embedding, it's a series of embeddings models developed and published by Beijing Academy of Artificial Intelligence (BAAI).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"16515b99\",\n \"metadata\": {},\n \"source\": [\n \"A full support of APIs and related usages of BGE is maintained in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) on GitHub.\\n\",\n \"\\n\",\n \"Run the following cell to install FlagEmbedding in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"88095fd0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"a2376217\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"# single GPU is better for small tasks\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc6e30a0\",\n \"metadata\": {},\n \"source\": [\n \"The collection of BGE models can be found in [Huggingface collection](https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"67a16ccf\",\n \"metadata\": {},\n \"source\": [\n \"## 2. BGE Series Models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2e10034a\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 BGE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0cdc6702\",\n \"metadata\": {},\n \"source\": [\n \"The very first version of BGE has 6 models, with 'large', 'base', and 'small' for English and Chinese. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04b75f72\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | 500M | 1.34 GB | Embedding Model which map text into vector | BERT |\\n\",\n \"| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | 109M | 438 MB | a base-scale model but with similar ability to `bge-large-en` | BERT |\\n\",\n \"| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | 33.4M | 133 MB | a small-scale model but with competitive performance | BERT |\\n\",\n \"| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | 326M | 1.3 GB | Embedding Model which map text into vector | BERT |\\n\",\n \"| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | 102M | 409 MB | a base-scale model but with similar ability to `bge-large-zh` | BERT |\\n\",\n \"| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | 24M | 95.8 MB | a small-scale model but with competitive performance | BERT |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c9c45d17\",\n \"metadata\": {},\n \"source\": [\n \"For inference, simply import FlagModel from FlagEmbedding and initialize the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"89e07751\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.84864 0.7946737 ]\\n\",\n \" [0.760097 0.85449743]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# Load BGE model\\n\",\n \"model = FlagModel(\\n\",\n \" 'BAAI/bge-base-en',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" query_instruction_format='{}{}',\\n\",\n \")\\n\",\n \"\\n\",\n \"queries = [\\\"query 1\\\", \\\"query 2\\\"]\\n\",\n \"corpus = [\\\"passage 1\\\", \\\"passage 2\\\"]\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"q_embeddings = model.encode_queries(queries)\\n\",\n \"p_embeddings = model.encode_corpus(corpus)\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"scores = q_embeddings @ p_embeddings.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c8e69ed\",\n \"metadata\": {},\n \"source\": [\n \"For general encoding, use either `encode()`:\\n\",\n \"```python\\n\",\n \"FlagModel.encode(sentences, batch_size=256, max_length=512, convert_to_numpy=True)\\n\",\n \"```\\n\",\n \"or `encode_corpus()` that directly calls `encode()`:\\n\",\n \"```python\\n\",\n \"FlagModel.encode_corpus(corpus, batch_size=256, max_length=512, convert_to_numpy=True)\\n\",\n \"```\\n\",\n \"The *encode_queries()* function concatenate the `query_instruction_for_retrieval` with each of the input query to form the new sentences and then feed them to `encode()`.\\n\",\n \"```python\\n\",\n \"FlagModel.encode_queries(queries, batch_size=256, max_length=512, convert_to_numpy=True)\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c86a5a3\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 BGE v1.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"454ff7aa\",\n \"metadata\": {},\n \"source\": [\n \"BGE 1.5 alleviate the issue of the similarity distribution, and enhance retrieval ability without instruction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"30b1f897\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | 335M | 1.34 GB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | 109M | 438 MB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | 33.4M | 133 MB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | 326M | 1.3 GB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | 102M | 409 MB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | 24M | 95.8 MB | version 1.5 with more reasonable similarity distribution | BERT |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed00c504\",\n \"metadata\": {},\n \"source\": [\n \"You can use BGE 1.5 models exactly same to BGE v1 models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"9b17afcc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 2252.58it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 3575.71it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.76 0.6714]\\n\",\n \" [0.6177 0.7603]]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = FlagModel(\\n\",\n \" 'BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" query_instruction_format='{}{}'\\n\",\n \")\\n\",\n \"\\n\",\n \"queries = [\\\"query 1\\\", \\\"query 2\\\"]\\n\",\n \"corpus = [\\\"passage 1\\\", \\\"passage 2\\\"]\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"q_embeddings = model.encode_queries(queries)\\n\",\n \"p_embeddings = model.encode_corpus(corpus)\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"scores = q_embeddings @ p_embeddings.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dcf2a82b\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 BGE M3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cc5b5a5e\",\n \"metadata\": {},\n \"source\": [\n \"BGE-M3 is the new version of BGE models that is distinguished for its versatility in:\\n\",\n \"- Multi-Functionality: Simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.\\n\",\n \"- Multi-Linguality: Supports more than 100 working languages.\\n\",\n \"- Multi-Granularity: Can proces inputs with different granularityies, spanning from short sentences to long documents of up to 8192 tokens.\\n\",\n \"\\n\",\n \"For more details, feel free to check out the [paper](https://arxiv.org/pdf/2402.03216).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"41348e03\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | 568M | 2.27 GB | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | XLM-RoBERTa |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"d4647625\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fetching 30 files: 100%|██████████| 30/30 [00:00<00:00, 194180.74it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import BGEM3FlagModel\\n\",\n \"\\n\",\n \"model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)\\n\",\n \"\\n\",\n \"sentences = [\\\"What is BGE M3?\\\", \\\"Defination of BM25\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f89f1a9\",\n \"metadata\": {},\n \"source\": [\n \"```python\\n\",\n \"BGEM3FlagModel.encode(\\n\",\n \" sentences, \\n\",\n \" batch_size=12, \\n\",\n \" max_length=8192, \\n\",\n \" return_dense=True, \\n\",\n \" return_sparse=False, \\n\",\n \" return_colbert_vecs=False\\n\",\n \")\\n\",\n \"```\\n\",\n \"It returns a dictionary like:\\n\",\n \"```python\\n\",\n \"{\\n\",\n \" 'dense_vecs': # array of dense embeddings of inputs if return_dense=True, otherwise None,\\n\",\n \" 'lexical_weights': # array of dictionaries with keys and values are ids of tokens and their corresponding weights if return_sparse=True, otherwise None,\\n\",\n \" 'colbert_vecs': # array of multi-vector embeddings of inputs if return_cobert_vecs=True, otherwise None,'\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"f0b11cf0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 1148.18it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# If you don't need such a long length of 8192 input tokens, you can set max_length to a smaller value to speed up encoding.\\n\",\n \"embeddings = model.encode(\\n\",\n \" sentences, \\n\",\n \" max_length=10,\\n\",\n \" return_dense=True, \\n\",\n \" return_sparse=True, \\n\",\n \" return_colbert_vecs=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"72cba126\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dense embedding:\\n\",\n \"[[-0.03412 -0.04706 -0.00087 ... 0.04822 0.007614 -0.02957 ]\\n\",\n \" [-0.01035 -0.04483 -0.02434 ... -0.008224 0.01497 0.011055]]\\n\",\n \"sparse embedding:\\n\",\n \"[defaultdict(, {'4865': np.float16(0.0836), '83': np.float16(0.0814), '335': np.float16(0.1296), '11679': np.float16(0.2517), '276': np.float16(0.1699), '363': np.float16(0.2695), '32': np.float16(0.04077)}), defaultdict(, {'262': np.float16(0.05014), '5983': np.float16(0.1367), '2320': np.float16(0.04517), '111': np.float16(0.0634), '90017': np.float16(0.2517), '2588': np.float16(0.3333)})]\\n\",\n \"multi-vector:\\n\",\n \"[array([[-8.68966337e-03, -4.89266850e-02, -3.03634931e-03, ...,\\n\",\n \" -2.21243706e-02, 5.72856329e-02, 1.28355855e-02],\\n\",\n \" [-8.92937183e-03, -4.67235669e-02, -9.52814799e-03, ...,\\n\",\n \" -3.14785317e-02, 5.39088845e-02, 6.96671568e-03],\\n\",\n \" [ 1.84195358e-02, -4.22310382e-02, 8.55499704e-04, ...,\\n\",\n \" -1.97946690e-02, 3.84313315e-02, 7.71250250e-03],\\n\",\n \" ...,\\n\",\n \" [-2.55824160e-02, -1.65533274e-02, -4.21357416e-02, ...,\\n\",\n \" -4.50234264e-02, 4.41286489e-02, -1.00052059e-02],\\n\",\n \" [ 5.90990965e-07, -5.53734899e-02, 8.51499755e-03, ...,\\n\",\n \" -2.29209941e-02, 6.04418293e-02, 9.39912070e-03],\\n\",\n \" [ 2.57394509e-03, -2.92690992e-02, -1.89342294e-02, ...,\\n\",\n \" -8.04431178e-03, 3.28964666e-02, 4.38723788e-02]], dtype=float32), array([[ 0.01724418, 0.03835401, -0.02309308, ..., 0.00141706,\\n\",\n \" 0.02995041, -0.05990082],\\n\",\n \" [ 0.00996325, 0.03922409, -0.03849588, ..., 0.00591671,\\n\",\n \" 0.02722516, -0.06510868],\\n\",\n \" [ 0.01781915, 0.03925728, -0.01710397, ..., 0.00801776,\\n\",\n \" 0.03987768, -0.05070014],\\n\",\n \" ...,\\n\",\n \" [ 0.05478653, 0.00755799, 0.00328444, ..., -0.01648209,\\n\",\n \" 0.02405782, 0.00363262],\\n\",\n \" [ 0.00936953, 0.05028074, -0.02388872, ..., 0.02567679,\\n\",\n \" 0.00791224, -0.03257877],\\n\",\n \" [ 0.01803976, 0.0133922 , 0.00019365, ..., 0.0184015 ,\\n\",\n \" 0.01373822, 0.00315539]], dtype=float32)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"dense embedding:\\\\n{embeddings['dense_vecs']}\\\")\\n\",\n \"print(f\\\"sparse embedding:\\\\n{embeddings['lexical_weights']}\\\")\\n\",\n \"print(f\\\"multi-vector:\\\\n{embeddings['colbert_vecs']}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"14d83caa\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 BGE Multilingual Gemma2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fd4c67df\",\n \"metadata\": {},\n \"source\": [\n \"BGE Multilingual Gemma2 is a LLM-based Multi-Lingual embedding model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"abdca22e\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | 9.24B | 37 GB | LLM-based multilingual embedding model with SOTA results on multilingual benchmarks | Gemma2-9B |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"8ec545bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 4/4 [00:00<00:00, 6.34it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 816.49it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 718.33it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.559 0.01685 ]\\n\",\n \" [0.0008683 0.5015 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagLLMModel\\n\",\n \"\\n\",\n \"queries = [\\\"how much protein should a female eat\\\", \\\"summit define\\\"]\\n\",\n \"documents = [\\n\",\n \" \\\"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\\\",\\n\",\n \" \\\"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\\\"\\n\",\n \"]\\n\",\n \"\\n\",\n \"model = FlagLLMModel('BAAI/bge-multilingual-gemma2', \\n\",\n \" query_instruction_for_retrieval=\\\"Given a web search query, retrieve relevant passages that answer the query.\\\",\\n\",\n \" use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\\n\",\n \"\\n\",\n \"embeddings_1 = model.encode_queries(queries)\\n\",\n \"embeddings_2 = model.encode_corpus(documents)\\n\",\n \"similarity = embeddings_1 @ embeddings_2.T\\n\",\n \"print(similarity)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b7b2aa4\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 BGE ICL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7c9acb92\",\n \"metadata\": {},\n \"source\": [\n \"BGE ICL stands for in-context learning. By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cf6c9345\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | 7.11B | 28.5 GB | LLM-based English embedding model with excellent in-context learning ability. | Mistral-7B |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"4595bae7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"documents = [\\n\",\n \" \\\"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\\\",\\n\",\n \" \\\"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\\\"\\n\",\n \"]\\n\",\n \"\\n\",\n \"examples = [\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'what is a virtual interface',\\n\",\n \" 'response': \\\"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\\\"\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'causes of back pain in female for a week',\\n\",\n \" 'response': \\\"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\\\"\\n\",\n \" }\\n\",\n \"]\\n\",\n \"\\n\",\n \"queries = [\\\"how much protein should a female eat\\\", \\\"summit define\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ffb586c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 6.55it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 366.09it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 623.69it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6064 0.3018]\\n\",\n \" [0.257 0.537 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagICLModel\\n\",\n \"import os\\n\",\n \"\\n\",\n \"model = FlagICLModel('BAAI/bge-en-icl', \\n\",\n \" examples_for_task=examples, # set `examples_for_task=None` to use model without examples\\n\",\n \" # examples_instruction_format=\\\"{}\\\\n{}\\\\n{}\\\" # specify the format to use examples_for_task\\n\",\n \" )\\n\",\n \"\\n\",\n \"embeddings_1 = model.encode_queries(queries)\\n\",\n \"embeddings_2 = model.encode_corpus(documents)\\n\",\n \"similarity = embeddings_1 @ embeddings_2.T\\n\",\n \"\\n\",\n \"print(similarity)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.2_Auto_Embedder.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Auto Embedder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"FlagEmbedding provides a high level class `FlagAutoModel` that unify the inference of embedding models. Besides BGE series, it also supports other popular open-source embedding models such as E5, GTE, SFR, etc. In this tutorial, we will have an idea how to use it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Usage\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, import `FlagAutoModel` from FlagEmbedding, and use the `from_finetuned()` function to initialize the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from FlagEmbedding import FlagAutoModel\\n\",\n \"\\n\",\n \"model = FlagAutoModel.from_finetuned(\\n\",\n \" 'BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages: \\\",\\n\",\n \" devices=\\\"cuda:0\\\", # if not specified, will use all available gpus or cpu when no gpu available\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then use the model exactly same to `FlagModel` (`BGEM3FlagModel` if using BGE M3, `FlagLLMModel` if using BGE Multilingual Gemma2, `FlagICLModel` if using BGE ICL)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.76 0.6714]\\n\",\n \" [0.6177 0.7603]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"queries = [\\\"query 1\\\", \\\"query 2\\\"]\\n\",\n \"corpus = [\\\"passage 1\\\", \\\"passage 2\\\"]\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"q_embeddings = model.encode_queries(queries)\\n\",\n \"p_embeddings = model.encode_corpus(corpus)\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"scores = q_embeddings @ p_embeddings.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Explanation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`FlagAutoModel` use an OrderedDict `MODEL_MAPPING` to store all the supported models configuration:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['bge-en-icl',\\n\",\n \" 'bge-multilingual-gemma2',\\n\",\n \" 'bge-m3',\\n\",\n \" 'bge-large-en-v1.5',\\n\",\n \" 'bge-base-en-v1.5',\\n\",\n \" 'bge-small-en-v1.5',\\n\",\n \" 'bge-large-zh-v1.5',\\n\",\n \" 'bge-base-zh-v1.5',\\n\",\n \" 'bge-small-zh-v1.5',\\n\",\n \" 'bge-large-en',\\n\",\n \" 'bge-base-en',\\n\",\n \" 'bge-small-en',\\n\",\n \" 'bge-large-zh',\\n\",\n \" 'bge-base-zh',\\n\",\n \" 'bge-small-zh',\\n\",\n \" 'e5-mistral-7b-instruct',\\n\",\n \" 'e5-large-v2',\\n\",\n \" 'e5-base-v2',\\n\",\n \" 'e5-small-v2',\\n\",\n \" 'multilingual-e5-large-instruct',\\n\",\n \" 'multilingual-e5-large',\\n\",\n \" 'multilingual-e5-base',\\n\",\n \" 'multilingual-e5-small',\\n\",\n \" 'e5-large',\\n\",\n \" 'e5-base',\\n\",\n \" 'e5-small',\\n\",\n \" 'gte-Qwen2-7B-instruct',\\n\",\n \" 'gte-Qwen2-1.5B-instruct',\\n\",\n \" 'gte-Qwen1.5-7B-instruct',\\n\",\n \" 'gte-multilingual-base',\\n\",\n \" 'gte-large-en-v1.5',\\n\",\n \" 'gte-base-en-v1.5',\\n\",\n \" 'gte-large',\\n\",\n \" 'gte-base',\\n\",\n \" 'gte-small',\\n\",\n \" 'gte-large-zh',\\n\",\n \" 'gte-base-zh',\\n\",\n \" 'gte-small-zh',\\n\",\n \" 'SFR-Embedding-2_R',\\n\",\n \" 'SFR-Embedding-Mistral',\\n\",\n \" 'Linq-Embed-Mistral']\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from FlagEmbedding.inference.embedder.model_mapping import AUTO_EMBEDDER_MAPPING\\n\",\n \"\\n\",\n \"list(AUTO_EMBEDDER_MAPPING.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"EmbedderConfig(model_class=, pooling_method=, trust_remote_code=False, query_instruction_format='{}\\\\n{}')\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(AUTO_EMBEDDER_MAPPING['bge-en-icl'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Taking a look at the value of each key, which is an object of `EmbedderConfig`. It consists four attributes:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```python\\n\",\n \"@dataclass\\n\",\n \"class EmbedderConfig:\\n\",\n \" model_class: Type[AbsEmbedder]\\n\",\n \" pooling_method: PoolingMethod\\n\",\n \" trust_remote_code: bool = False\\n\",\n \" query_instruction_format: str = \\\"{}{}\\\"\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Not only the BGE series, it supports other models such as E5 similarly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"EmbedderConfig(model_class=, pooling_method=, trust_remote_code=False, query_instruction_format='{}\\\\n{}')\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(AUTO_EMBEDDER_MAPPING['bge-en-icl'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Customization\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you want to use your own models through `FlagAutoModel`, consider the following steps:\\n\",\n \"\\n\",\n \"1. Check the type of your embedding model and choose the appropriate model class, is it an encoder or a decoder?\\n\",\n \"2. What kind of pooling method it uses? CLS token, mean pooling, or last token?\\n\",\n \"3. Does your model needs `trust_remote_code=Ture` to ran?\\n\",\n \"4. Is there a query instruction format for retrieval?\\n\",\n \"\\n\",\n \"After these four attributes are assured, add your model name as the key and corresponding EmbedderConfig as the value to `MODEL_MAPPING`. Now have a try!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.3_BGE_v1&1.5.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Explanation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this section, we will go through BGE and BGE-v1.5's structure and how they generate embeddings.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U transformers FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Encode sentences\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To know how exactly a sentence is encoded, let's first load the tokenizer and model from HF transformers instead of FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-base-en-v1.5\\\")\\n\",\n \"model = AutoModel.from_pretrained(\\\"BAAI/bge-base-en-v1.5\\\")\\n\",\n \"\\n\",\n \"sentences = [\\\"embedding\\\", \\\"I love machine learning and nlp\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the following cell to check the model of bge-base-en-v1.5. It uses BERT-base as base model, with 12 encoder layers and hidden dimension of 768.\\n\",\n \"\\n\",\n \"Note that the corresponding models of BGE and BGE-v1.5 have same structures. For example, bge-base-en and bge-base-en-v1.5 have the same structure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"BertModel(\\n\",\n \" (embeddings): BertEmbeddings(\\n\",\n \" (word_embeddings): Embedding(30522, 768, padding_idx=0)\\n\",\n \" (position_embeddings): Embedding(512, 768)\\n\",\n \" (token_type_embeddings): Embedding(2, 768)\\n\",\n \" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (encoder): BertEncoder(\\n\",\n \" (layer): ModuleList(\\n\",\n \" (0-11): 12 x BertLayer(\\n\",\n \" (attention): BertAttention(\\n\",\n \" (self): BertSelfAttention(\\n\",\n \" (query): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (key): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (value): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (output): BertSelfOutput(\\n\",\n \" (dense): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (intermediate): BertIntermediate(\\n\",\n \" (dense): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n \" (intermediate_act_fn): GELUActivation()\\n\",\n \" )\\n\",\n \" (output): BertOutput(\\n\",\n \" (dense): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (pooler): BertPooler(\\n\",\n \" (dense): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (activation): Tanh()\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's tokenize the sentences.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'input_ids': tensor([[ 101, 7861, 8270, 4667, 102, 0, 0, 0, 0],\\n\",\n \" [ 101, 1045, 2293, 3698, 4083, 1998, 17953, 2361, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],\\n\",\n \" [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 0, 0, 0, 0],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1]])}\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inputs = tokenizer(\\n\",\n \" sentences, \\n\",\n \" padding=True, \\n\",\n \" truncation=True, \\n\",\n \" return_tensors='pt', \\n\",\n \" max_length=512\\n\",\n \")\\n\",\n \"inputs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the results, we can see that each sentence begins with token 101 and ends with 102, which are the `[CLS]` and `[SEP]` special token used in BERT.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 9, 768])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"last_hidden_state = model(**inputs, return_dict=True).last_hidden_state\\n\",\n \"last_hidden_state.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we implement the pooling function, with two choices of using `[CLS]`'s last hidden state, or the mean pooling of the whole last hidden state.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def pooling(last_hidden_state: torch.Tensor, pooling_method='cls', attention_mask: torch.Tensor = None):\\n\",\n \" if pooling_method == 'cls':\\n\",\n \" return last_hidden_state[:, 0]\\n\",\n \" elif pooling_method == 'mean':\\n\",\n \" s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)\\n\",\n \" d = attention_mask.sum(dim=1, keepdim=True).float()\\n\",\n \" return s / d\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Different from more commonly used mean pooling, BGE is trained to use the last hidden state of `[CLS]` as the sentence embedding: \\n\",\n \"\\n\",\n \"`sentence_embeddings = model_output[0][:, 0]`\\n\",\n \"\\n\",\n \"If you use mean pooling, there will be a significant decrease in performance. Therefore, make sure to use the correct method to obtain sentence vectors.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 768])\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"embeddings = pooling(\\n\",\n \" last_hidden_state, \\n\",\n \" pooling_method='cls', \\n\",\n \" attention_mask=inputs['attention_mask']\\n\",\n \")\\n\",\n \"embeddings.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Assembling them together, we get the whole encoding function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def _encode(sentences, max_length=512, convert_to_numpy=True):\\n\",\n \"\\n\",\n \" # handle the case of single sentence and a list of sentences\\n\",\n \" input_was_string = False\\n\",\n \" if isinstance(sentences, str):\\n\",\n \" sentences = [sentences]\\n\",\n \" input_was_string = True\\n\",\n \"\\n\",\n \" inputs = tokenizer(\\n\",\n \" sentences, \\n\",\n \" padding=True, \\n\",\n \" truncation=True, \\n\",\n \" return_tensors='pt', \\n\",\n \" max_length=max_length\\n\",\n \" )\\n\",\n \"\\n\",\n \" last_hidden_state = model(**inputs, return_dict=True).last_hidden_state\\n\",\n \" \\n\",\n \" embeddings = pooling(\\n\",\n \" last_hidden_state, \\n\",\n \" pooling_method='cls', \\n\",\n \" attention_mask=inputs['attention_mask']\\n\",\n \" )\\n\",\n \"\\n\",\n \" # normalize the embedding vectors\\n\",\n \" embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\\n\",\n \"\\n\",\n \" # convert to numpy if needed\\n\",\n \" if convert_to_numpy:\\n\",\n \" embeddings = embeddings.detach().numpy()\\n\",\n \"\\n\",\n \" return embeddings[0] if input_was_string else embeddings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Comparison\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's run the function we wrote to get the embeddings of the two sentences:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"[[ 1.4549762e-02 -9.6840411e-03 3.7761475e-03 ... -8.5092714e-04\\n\",\n \" 2.8417887e-02 6.3214332e-02]\\n\",\n \" [ 3.3924331e-05 -3.2998275e-03 1.7206438e-02 ... 3.5703944e-03\\n\",\n \" 1.8721525e-02 -2.0371782e-02]]\\n\",\n \"Similarity scores:\\n\",\n \"[[0.9999997 0.6077381]\\n\",\n \" [0.6077381 0.9999999]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = _encode(sentences)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, run the API provided in FlagEmbedding:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"[[ 1.4549762e-02 -9.6840411e-03 3.7761475e-03 ... -8.5092714e-04\\n\",\n \" 2.8417887e-02 6.3214332e-02]\\n\",\n \" [ 3.3924331e-05 -3.2998275e-03 1.7206438e-02 ... 3.5703944e-03\\n\",\n \" 1.8721525e-02 -2.0371782e-02]]\\n\",\n \"Similarity scores:\\n\",\n \"[[0.9999997 0.6077381]\\n\",\n \" [0.6077381 0.9999999]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\\n\",\n \"\\n\",\n \"embeddings = model.encode(sentences)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we expect, the two encoding functions return exactly the same results. The full implementation in FlagEmbedding handles large datasets by batching and contains GPU support and parallelization. Feel free to check the [source code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/inference/embedder/encoder_only/base.py) for more details.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.4_BGE-M3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE-M3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U transformers FlagEmbedding accelerate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BGE-M3 structure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch, os\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-m3\\\")\\n\",\n \"raw_model = AutoModel.from_pretrained(\\\"BAAI/bge-m3\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The base model of BGE-M3 is [XLM-RoBERTa-large](https://huggingface.co/FacebookAI/xlm-roberta-large), which is a multilingual version of RoBERTa.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"XLMRobertaModel(\\n\",\n \" (embeddings): XLMRobertaEmbeddings(\\n\",\n \" (word_embeddings): Embedding(250002, 1024, padding_idx=1)\\n\",\n \" (position_embeddings): Embedding(8194, 1024, padding_idx=1)\\n\",\n \" (token_type_embeddings): Embedding(1, 1024)\\n\",\n \" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (encoder): XLMRobertaEncoder(\\n\",\n \" (layer): ModuleList(\\n\",\n \" (0-23): 24 x XLMRobertaLayer(\\n\",\n \" (attention): XLMRobertaAttention(\\n\",\n \" (self): XLMRobertaSelfAttention(\\n\",\n \" (query): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (key): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (value): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (output): XLMRobertaSelfOutput(\\n\",\n \" (dense): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (intermediate): XLMRobertaIntermediate(\\n\",\n \" (dense): Linear(in_features=1024, out_features=4096, bias=True)\\n\",\n \" (intermediate_act_fn): GELUActivation()\\n\",\n \" )\\n\",\n \" (output): XLMRobertaOutput(\\n\",\n \" (dense): Linear(in_features=4096, out_features=1024, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (pooler): XLMRobertaPooler(\\n\",\n \" (dense): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (activation): Tanh()\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"raw_model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Multi-Functionality\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fetching 30 files: 100%|██████████| 30/30 [00:00<00:00, 240131.91it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import BGEM3FlagModel\\n\",\n \"\\n\",\n \"model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)\\n\",\n \"\\n\",\n \"sentences_1 = [\\\"What is BGE M3?\\\", \\\"Defination of BM25\\\"]\\n\",\n \"sentences_2 = [\\\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\\\", \\n\",\n \" \\\"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Dense Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using BGE M3 for dense embedding has similar steps to BGE or BGE 1.5 models.\\n\",\n \"\\n\",\n \"Use the normalized hidden state of the special token [CLS] as the embedding:\\n\",\n \"\\n\",\n \"$$e_q = norm(H_q[0])$$\\n\",\n \"\\n\",\n \"Then compute the relevance score between the query and passage:\\n\",\n \"\\n\",\n \"$$s_{dense}=f_{sim}(e_p, e_q)$$\\n\",\n \"\\n\",\n \"where $e_p, e_q$ are the embedding vectors of passage and query, respectively.\\n\",\n \"\\n\",\n \"$f_{sim}$ is the score function (such as inner product and L2 distance) for comupting two embeddings' similarity.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6259035 0.34749585]\\n\",\n \" [0.349868 0.6782462 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# If you don't need such a long length of 8192 input tokens, you can set max_length to a smaller value to speed up encoding.\\n\",\n \"embeddings_1 = model.encode(sentences_1, max_length=10)['dense_vecs']\\n\",\n \"embeddings_2 = model.encode(sentences_2, max_length=100)['dense_vecs']\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"s_dense = embeddings_1 @ embeddings_2.T\\n\",\n \"print(s_dense)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Sparse Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set `return_sparse` to true to make the model return sparse vector. If a term token appears multiple times in the sentence, we only retain its max weight.\\n\",\n \"\\n\",\n \"BGE-M3 generates sparce embeddings by adding a linear layer and a ReLU activation function following the hidden states:\\n\",\n \"\\n\",\n \"$$w_{qt} = \\\\text{Relu}(W_{lex}^T H_q [i])$$\\n\",\n \"\\n\",\n \"where $W_{lex}$ representes the weights of linear layer and $H_q[i]$ is the encoder's output of the $i^{th}$ token.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[{'What': 0.08362077, 'is': 0.081469566, 'B': 0.12964639, 'GE': 0.25186998, 'M': 0.17001738, '3': 0.26957875, '?': 0.040755156}, {'De': 0.050144322, 'fin': 0.13689369, 'ation': 0.045134712, 'of': 0.06342201, 'BM': 0.25167602, '25': 0.33353207}]\\n\"\n ]\n }\n ],\n \"source\": [\n \"output_1 = model.encode(sentences_1, return_sparse=True)\\n\",\n \"output_2 = model.encode(sentences_2, return_sparse=True)\\n\",\n \"\\n\",\n \"# you can see the weight for each token:\\n\",\n \"print(model.convert_id_to_token(output_1['lexical_weights']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Based on the tokens' weights of query and passage, the relevance score between them is computed by the joint importance of the co-existed terms within the query and passage:\\n\",\n \"\\n\",\n \"$$s_{lex} = \\\\sum_{t\\\\in q\\\\cap p}(w_{qt} * w_{pt})$$\\n\",\n \"\\n\",\n \"where $w_{qt}, w_{pt}$ are the importance weights of each co-existed term $t$ in query and passage, respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.19554448500275612\\n\",\n \"0.00880391988903284\\n\"\n ]\n }\n ],\n \"source\": [\n \"# compute the scores via lexical mathcing\\n\",\n \"s_lex_10_20 = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])\\n\",\n \"s_lex_10_21 = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][1])\\n\",\n \"\\n\",\n \"print(s_lex_10_20)\\n\",\n \"print(s_lex_10_21)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Multi-Vector\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The multi-vector method utilizes the entire output embeddings for the representation of query $E_q$ and passage $E_p$.\\n\",\n \"\\n\",\n \"$$E_q = norm(W_{mul}^T H_q)$$\\n\",\n \"$$E_p = norm(W_{mul}^T H_p)$$\\n\",\n \"\\n\",\n \"where $W_{mul}$ is the learnable projection matrix.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(8, 1024)\\n\",\n \"(30, 1024)\\n\"\n ]\n }\n ],\n \"source\": [\n \"output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)\\n\",\n \"output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)\\n\",\n \"\\n\",\n \"print(f\\\"({len(output_1['colbert_vecs'][0])}, {len(output_1['colbert_vecs'][0][0])})\\\")\\n\",\n \"print(f\\\"({len(output_2['colbert_vecs'][0])}, {len(output_2['colbert_vecs'][0][0])})\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Following ColBert, we use late-interaction to compute the fine-grained relevance score:\\n\",\n \"\\n\",\n \"$$s_{mul}=\\\\frac{1}{N}\\\\sum_{i=1}^N\\\\max_{j=1}^M E_q[i]\\\\cdot E_p^T[j]$$\\n\",\n \"\\n\",\n \"where $E_q, E_p$ are the entire output embeddings of query and passage, respectively.\\n\",\n \"\\n\",\n \"This is a summation of average of maximum similarity of each $v\\\\in E_q$ with vectors in $E_p$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7796662449836731\\n\",\n \"0.4621177911758423\\n\"\n ]\n }\n ],\n \"source\": [\n \"s_mul_10_20 = model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]).item()\\n\",\n \"s_mul_10_21 = model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]).item()\\n\",\n \"\\n\",\n \"print(s_mul_10_20)\\n\",\n \"print(s_mul_10_21)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 Hybrid Ranking\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-M3's multi-functionality gives the possibility of hybrid ranking to improve retrieval. Firstly, due to the heavy cost of multi-vector method, we can retrieve the candidate results by either of the dense or sparse method. Then, to get the final result, we can rerank the candidates based on the integrated relevance score:\\n\",\n \"\\n\",\n \"$$s_{rank} = w_1\\\\cdot s_{dense}+w_2\\\\cdot s_{lex} + w_3\\\\cdot s_{mul}$$\\n\",\n \"\\n\",\n \"where the values chosen for $w_1, w_2$ and $w_3$ varies depending on the downstream scenario (here 1/3 is just for demonstration).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.5337047390639782\\n\",\n \"0.27280585498859483\\n\"\n ]\n }\n ],\n \"source\": [\n \"s_rank_10_20 = 1/3 * s_dense[0][0] + 1/3 * s_lex_10_20 + 1/3 * s_mul_10_20\\n\",\n \"s_rank_10_21 = 1/3 * s_dense[0][1] + 1/3 * s_lex_10_21 + 1/3 * s_mul_10_21\\n\",\n \"\\n\",\n \"print(s_rank_10_20)\\n\",\n \"print(s_rank_10_21)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.5_BGE_EN_ICL.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE-EN-ICL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will go through BGE-EN-ICL, an LLM based embedding model with both strong zero-shot and few-shot embedding capability.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0.Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U transformers FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BGE-EN-ICL structure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 9.94it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch, os\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-en-icl\\\")\\n\",\n \"raw_model = AutoModel.from_pretrained(\\\"BAAI/bge-en-icl\\\")\\n\",\n \"\\n\",\n \"sentences = [\\\"embedding\\\", \\\"I love machine learning and nlp\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Different from the previous BGE embedding models which are encoder only models, BGE-EN-ICL use decoder only LLM, Mistral-7B, as the base model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MistralModel(\\n\",\n \" (embed_tokens): Embedding(32003, 4096)\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-31): 32 x MistralDecoderLayer(\\n\",\n \" (self_attn): MistralSdpaAttention(\\n\",\n \" (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\\n\",\n \" (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\\n\",\n \" (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\\n\",\n \" (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\\n\",\n \" (rotary_emb): MistralRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (mlp): MistralMLP(\\n\",\n \" (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\\n\",\n \" (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\\n\",\n \" (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\\n\",\n \" (act_fn): SiLU()\\n\",\n \" )\\n\",\n \" (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"raw_model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. New Pooling Method\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BERT-like encoder only networks are considered with strong capacity for representation learning because of their bidirectional attention structure. Some previous work replace unidirectional attention with bidirectional attention during the embedding training phase. But this might creates a mismatch with the model's pre-training design, which could potentially undermine its in-context learning and generative properties.\\n\",\n \"\\n\",\n \"Thus BGE-EN-ICL introduces a [EOS] token's output embedding to address this issue.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'input_ids': tensor([[ 0, 0, 0, 0, 0, 0, 1, 28643, 2],\\n\",\n \" [ 1, 315, 2016, 5599, 5168, 304, 307, 12312, 2]]), 'attention_mask': tensor([[0, 0, 0, 0, 0, 0, 1, 1, 1],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1]])}\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inputs = tokenizer(\\n\",\n \" sentences,\\n\",\n \" padding=True,\\n\",\n \" return_tensors='pt',\\n\",\n \")\\n\",\n \"inputs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 9, 4096])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"last_hidden_state = raw_model(**inputs, return_dict=True).last_hidden_state\\n\",\n \"last_hidden_state.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The last token/[EOS] pooling method can be described as:\\n\",\n \"\\n\",\n \"Given the tokenized input sequence $T: [\\\\text{BOS}], t_1, ..., t_N$ is sent into the LLM:\\n\",\n \"$$h_t = \\\\text{LLM}(T)[\\\\text{EOS}]$$\\n\",\n \"where $h_t$ represents the text embedding taken from the output embedding of the special token [EOS]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def last_token_pool(last_hidden_states: torch.Tensor,\\n\",\n \" attention_mask: torch.Tensor) -> torch.Tensor:\\n\",\n \" \\n\",\n \" left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\\n\",\n \" if left_padding:\\n\",\n \" return last_hidden_states[:, -1]\\n\",\n \" else:\\n\",\n \" sequence_lengths = attention_mask.sum(dim=1) - 1\\n\",\n \" batch_size = last_hidden_states.shape[0]\\n\",\n \" return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 4096])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"embeddings = last_token_pool(\\n\",\n \" last_hidden_state, \\n\",\n \" attention_mask=inputs['attention_mask']\\n\",\n \")\\n\",\n \"embeddings.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. In-Context Learning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-EN-ICL integrate strong in-context learning of LLM into embedding model while still persisting strong zero-shot embedding capability.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For zero-shot inference, it's exactly same to BGE v1&1.5. For few-shot inference, use the following way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"examples = [\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'what is a virtual interface',\\n\",\n \" 'response': \\\"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\\\"\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'causes of back pain in female for a week',\\n\",\n \" 'response': \\\"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\\\"\\n\",\n \" }\\n\",\n \"]\\n\",\n \"\\n\",\n \"queries = [\\\"how much protein should a female eat\\\", \\\"summit define\\\"]\\n\",\n \"documents = [\\n\",\n \" \\\"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\\\",\\n\",\n \" \\\"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\\\"\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 4.59it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 501.41it/s]\\n\",\n \"You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6064 0.302 ]\\n\",\n \" [0.257 0.5366]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagICLModel\\n\",\n \"\\n\",\n \"model = FlagICLModel('BAAI/bge-en-icl', \\n\",\n \" examples_for_task=examples, # set `examples_for_task=None` to use model without examples\\n\",\n \" examples_instruction_format=\\\"{}\\\\n{}\\\\n{}\\\", # specify the format to use examples_for_task\\n\",\n \" devices=[0],\\n\",\n \" )\\n\",\n \"\\n\",\n \"embeddings_1 = model.encode_queries(queries)\\n\",\n \"embeddings_2 = model.encode_corpus(documents)\\n\",\n \"similarity = embeddings_1 @ embeddings_2.T\\n\",\n \"\\n\",\n \"print(similarity)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.6_BGE_VL.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE-VL-v1&v1.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will go through the multimodel retrieval models BGE-VL series, which achieved state-of-the-art performance on four popular zero-shot composed image retrieval benchmarks and the massive multimodal embedding benchmark (MMEB).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\\n\",\n \"\\n\",\n \"- Our model works well on transformers==4.45.2, and we recommend using this version.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install numpy torch transformers pillow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BGE-VL-CLIP\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-vl-base](https://huggingface.co/BAAI/BGE-VL-base) | English | 150M | 299 MB | Light weight multimodel embedder among image and text | CLIP-base |\\n\",\n \"| [BAAI/bge-vl-large](https://huggingface.co/BAAI/BGE-VL-large) | English | 428M | 855 MB | Large scale multimodel embedder among image and text | CLIP-large |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-VL-base and BGE-VL-large are trained based on CLIP base and CLIP large, which both contain a vision transformer and a text transformer:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"CLIPModel(\\n\",\n \" (text_model): CLIPTextTransformer(\\n\",\n \" (embeddings): CLIPTextEmbeddings(\\n\",\n \" (token_embedding): Embedding(49408, 512)\\n\",\n \" (position_embedding): Embedding(77, 512)\\n\",\n \" )\\n\",\n \" (encoder): CLIPEncoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-11): 12 x CLIPEncoderLayer(\\n\",\n \" (self_attn): CLIPSdpaAttention(\\n\",\n \" (k_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n \" (v_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n \" (q_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n \" (out_proj): Linear(in_features=512, out_features=512, bias=True)\\n\",\n \" )\\n\",\n \" (layer_norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (mlp): CLIPMLP(\\n\",\n \" (activation_fn): QuickGELUActivation()\\n\",\n \" (fc1): Linear(in_features=512, out_features=2048, bias=True)\\n\",\n \" (fc2): Linear(in_features=2048, out_features=512, bias=True)\\n\",\n \" )\\n\",\n \" (layer_norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" (vision_model): CLIPVisionTransformer(\\n\",\n \" (embeddings): CLIPVisionEmbeddings(\\n\",\n \" (patch_embedding): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False)\\n\",\n \" (position_embedding): Embedding(197, 768)\\n\",\n \" )\\n\",\n \" (pre_layrnorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (encoder): CLIPEncoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-11): 12 x CLIPEncoderLayer(\\n\",\n \" (self_attn): CLIPSdpaAttention(\\n\",\n \" (k_proj): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (v_proj): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (q_proj): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (out_proj): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" )\\n\",\n \" (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (mlp): CLIPMLP(\\n\",\n \" (activation_fn): QuickGELUActivation()\\n\",\n \" (fc1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n \" (fc2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n \" )\\n\",\n \" (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (post_layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" (visual_projection): Linear(in_features=768, out_features=512, bias=False)\\n\",\n \" (text_projection): Linear(in_features=512, out_features=512, bias=False)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"from transformers import AutoModel\\n\",\n \"\\n\",\n \"MODEL_NAME = \\\"BAAI/BGE-VL-base\\\" # or \\\"BAAI/BGE-VL-base\\\"\\n\",\n \"\\n\",\n \"model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True) # You must set trust_remote_code=True\\n\",\n \"model.set_processor(MODEL_NAME)\\n\",\n \"model.eval()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tensor([[0.2647, 0.1242]])\\n\"\n ]\n }\n ],\n \"source\": [\n \"with torch.no_grad():\\n\",\n \" query = model.encode(\\n\",\n \" images = \\\"../../imgs/cir_query.png\\\", \\n\",\n \" text = \\\"Make the background dark, as if the camera has taken the photo at night\\\"\\n\",\n \" )\\n\",\n \"\\n\",\n \" candidates = model.encode(\\n\",\n \" images = [\\\"../../imgs/cir_candi_1.png\\\", \\\"../../imgs/cir_candi_2.png\\\"]\\n\",\n \" )\\n\",\n \" \\n\",\n \" scores = query @ candidates.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. BGE-VL-MLLM\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-vl-MLLM-S1](https://huggingface.co/BAAI/BGE-VL-MLLM-S1) | English | 7.57B | 15.14 GB | SOTA in composed image retrieval, trained on MegaPairs dataset | LLaVA-1.6 |\\n\",\n \"| [BAAI/bge-vl-MLLM-S2](https://huggingface.co/BAAI/BGE-VL-MLLM-S2) | English | 7.57B | 15.14 GB | Finetune BGE-VL-MLLM-S1 with one epoch on MMEB training set | LLaVA-1.6 |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Loading checkpoint shards: 100%|██████████| 4/4 [00:03<00:00, 1.28it/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"LLaVANextForEmbedding(\\n\",\n \" (vision_tower): CLIPVisionModel(\\n\",\n \" (vision_model): CLIPVisionTransformer(\\n\",\n \" (embeddings): CLIPVisionEmbeddings(\\n\",\n \" (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)\\n\",\n \" (position_embedding): Embedding(577, 1024)\\n\",\n \" )\\n\",\n \" (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (encoder): CLIPEncoder(\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-23): 24 x CLIPEncoderLayer(\\n\",\n \" (self_attn): CLIPSdpaAttention(\\n\",\n \" (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" )\\n\",\n \" (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (mlp): CLIPMLP(\\n\",\n \" (activation_fn): QuickGELUActivation()\\n\",\n \" (fc1): Linear(in_features=1024, out_features=4096, bias=True)\\n\",\n \" (fc2): Linear(in_features=4096, out_features=1024, bias=True)\\n\",\n \" )\\n\",\n \" (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (multi_modal_projector): LlavaNextMultiModalProjector(\\n\",\n \" (linear_1): Linear(in_features=1024, out_features=4096, bias=True)\\n\",\n \" (act): GELUActivation()\\n\",\n \" (linear_2): Linear(in_features=4096, out_features=4096, bias=True)\\n\",\n \" )\\n\",\n \" (language_model): MistralForCausalLM(\\n\",\n \" (model): MistralModel(\\n\",\n \" (embed_tokens): Embedding(32005, 4096)\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-31): 32 x MistralDecoderLayer(\\n\",\n \" (self_attn): MistralSdpaAttention(\\n\",\n \" (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\\n\",\n \" (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\\n\",\n \" (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\\n\",\n \" (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\\n\",\n \" (rotary_emb): MistralRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (mlp): MistralMLP(\\n\",\n \" (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\\n\",\n \" (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\\n\",\n \" (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\\n\",\n \" (act_fn): SiLU()\\n\",\n \" )\\n\",\n \" (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" )\\n\",\n \" (lm_head): Linear(in_features=4096, out_features=32005, bias=False)\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"from transformers import AutoModel\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"MODEL_NAME= \\\"BAAI/BGE-VL-MLLM-S1\\\"\\n\",\n \"\\n\",\n \"model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\\n\",\n \"model.eval()\\n\",\n \"model.cuda()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tensor([[0.4109, 0.1807]], device='cuda:0')\\n\"\n ]\n }\n ],\n \"source\": [\n \"with torch.no_grad():\\n\",\n \" model.set_processor(MODEL_NAME)\\n\",\n \"\\n\",\n \" query_inputs = model.data_process(\\n\",\n \" text=\\\"Make the background dark, as if the camera has taken the photo at night\\\", \\n\",\n \" images=\\\"../../imgs/cir_query.png\\\",\\n\",\n \" q_or_c=\\\"q\\\",\\n\",\n \" task_instruction=\\\"Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: \\\"\\n\",\n \" )\\n\",\n \"\\n\",\n \" candidate_inputs = model.data_process(\\n\",\n \" images=[\\\"../../imgs/cir_candi_1.png\\\", \\\"../../imgs/cir_candi_2.png\\\"],\\n\",\n \" q_or_c=\\\"c\\\",\\n\",\n \" )\\n\",\n \"\\n\",\n \" query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]\\n\",\n \" candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]\\n\",\n \" \\n\",\n \" query_embs = torch.nn.functional.normalize(query_embs, dim=-1)\\n\",\n \" candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)\\n\",\n \"\\n\",\n \" scores = torch.matmul(query_embs, candi_embs.T)\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. BGE-VL-v1.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-VL-v1.5 series is a new version of BGE-VL, bringing better performance on both retrieval and multi-modal understanding. It is trained on 30M MegaPairs data and extra 10M natural and synthetic data.\\n\",\n \"\\n\",\n \"`bge-vl-v1.5-zs` is a zero-shot model, only trained on the data mentioned above. `bge-vl-v1.5-mmeb` is the fine-tuned version on MMEB training set.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/BGE-VL-v1.5-zs](https://huggingface.co/BAAI/BGE-VL-v1.5-zs) | English | 7.57B | 15.14 GB | Better multi-modal retrieval model with performs well in all kinds of tasks | LLaVA-1.6 |\\n\",\n \"| [BAAI/BGE-VL-v1.5-mmeb](https://huggingface.co/BAAI/BGE-VL-v1.5-mmeb) | English | 7.57B | 15.14 GB | Better multi-modal retrieval model, additionally fine-tuned on MMEB training set | LLaVA-1.6 |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use BGE-VL-v1.5 models in the exact same way as BGE-VL-MLLM.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 4/4 [00:01<00:00, 2.26it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tensor([[0.3880, 0.1815]], device='cuda:0')\\n\"\n ]\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"from transformers import AutoModel\\n\",\n \"from PIL import Image\\n\",\n \"\\n\",\n \"MODEL_NAME= \\\"BAAI/BGE-VL-v1.5-mmeb\\\" # \\\"BAAI/BGE-VL-v1.5-zs\\\"\\n\",\n \"\\n\",\n \"model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\\n\",\n \"model.eval()\\n\",\n \"model.cuda()\\n\",\n \"\\n\",\n \"with torch.no_grad():\\n\",\n \" model.set_processor(MODEL_NAME)\\n\",\n \"\\n\",\n \" query_inputs = model.data_process(\\n\",\n \" text=\\\"Make the background dark, as if the camera has taken the photo at night\\\", \\n\",\n \" images=\\\"../../imgs/cir_query.png\\\",\\n\",\n \" q_or_c=\\\"q\\\",\\n\",\n \" task_instruction=\\\"Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: \\\"\\n\",\n \" )\\n\",\n \"\\n\",\n \" candidate_inputs = model.data_process(\\n\",\n \" images=[\\\"../../imgs/cir_candi_1.png\\\", \\\"../../imgs/cir_candi_2.png\\\"],\\n\",\n \" q_or_c=\\\"c\\\",\\n\",\n \" )\\n\",\n \"\\n\",\n \" query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]\\n\",\n \" candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]\\n\",\n \" \\n\",\n \" query_embs = torch.nn.functional.normalize(query_embs, dim=-1)\\n\",\n \" candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)\\n\",\n \"\\n\",\n \" scores = torch.matmul(query_embs, candi_embs.T)\\n\",\n \"print(scores)\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.16\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/1_Embedding/1.2.7_BGE_Code_v1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE-Code-v1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Introduction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-code-v1](https://huggingface.co/BAAI/bge-code-v1) | Multi-lingual | 1.54B | 6.18 GB | LLM-based code embedding model with strong text retrieval and multilingual capabilities. | Qwen-2.5-Coder-1.5B |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**[BGE-Code-v1](https://huggingface.co/BAAI/bge-code-v1)** is an LLM-based code embedding model that supports code retrieval, text retrieval, and multilingual retrieval. It primarily demonstrates the following capabilities:\\n\",\n \"- Superior Code Retrieval Performance: The model demonstrates exceptional code retrieval capabilities, supporting natural language queries in both English and Chinese, as well as 20 programming languages.\\n\",\n \"- Robust Text Retrieval Capabilities: The model maintains strong text retrieval capabilities comparable to text embedding models of similar scale.\\n\",\n \"- Extensive Multilingual Support: BGE-Code-v1 offers comprehensive multilingual retrieval capabilities, excelling in languages such as English, Chinese, Japanese, French, and more.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch, os\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-code-v1\\\")\\n\",\n \"raw_model = AutoModel.from_pretrained(\\\"BAAI/bge-code-v1\\\")\\n\",\n \"\\n\",\n \"raw_model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Usage\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Given the following tiny corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = [\\\"\\\"\\\"\\n\",\n \"def func_1(arr, target):\\n\",\n \" low, high = 0, len(arr) - 1\\n\",\n \" while low <= high:\\n\",\n \" mid = (low + high) // 2\\n\",\n \" if arr[mid] == target: return mid\\n\",\n \" elif arr[mid] < target: low = mid + 1\\n\",\n \" else: high = mid - 1\\n\",\n \" return -1\\n\",\n \"\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"def func_2(n, memo={}):\\n\",\n \" if n <= 1: return n\\n\",\n \" if n not in memo:\\n\",\n \" memo[n] = fib(n-1, memo) + fib(n-2, memo)\\n\",\n \" return memo[n]\\n\",\n \"\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"def func_3(a, b):\\n\",\n \" while b:\\n\",\n \" a, b = b, a % b\\n\",\n \" return a\\n\",\n \"\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"def func_4(n):\\n\",\n \" if n < 2: return False\\n\",\n \" for i in range(2, int(n**0.5) + 1):\\n\",\n \" if n % i == 0: return False\\n\",\n \" return True\\n\",\n \"\\\"\\\"\\\",\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"int func_5(const vector& arr, int target) {\\n\",\n \" int low = 0, high = arr.size() - 1;\\n\",\n \" while (low <= high) {\\n\",\n \" int mid = low + (high - low) / 2;\\n\",\n \" if (arr[mid] == target) return mid;\\n\",\n \" else if (arr[mid] < target) low = mid + 1;\\n\",\n \" else high = mid - 1;\\n\",\n \" }\\n\",\n \" return -1;\\n\",\n \"}\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We want to find the answer to the following question:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query = \\\"The fastest way to find an element in a sorted array\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00, 6.08it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagLLMModel\\n\",\n \"\\n\",\n \"model = FlagLLMModel('BAAI/bge-code-v1', \\n\",\n \" query_instruction_format=\\\"{}\\\\n{}\\\",\\n\",\n \" query_instruction_for_retrieval=\\\"Given a question in text, retrieve SQL queries that are appropriate responses to the question.\\\",\\n\",\n \" trust_remote_code=True,\\n\",\n \" devices=0,\\n\",\n \" use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"((1536,), (5, 1536))\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query_emb = model.encode_queries(query)\\n\",\n \"corpus_emb = model.encode_corpus(corpus)\\n\",\n \"query_emb.shape, corpus_emb.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.4553 0.2172 0.2277 0.196 0.4355]\\n\"\n ]\n }\n ],\n \"source\": [\n \"similarity = query_emb @ corpus_emb.T\\n\",\n \"print(similarity)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that the elements with index 0 and 5, which are the implementation of binary search in Python and C++, have conspicuously higher similarity than other candidates.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.16\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/2_Metrics/2.1_Similarity_Metrics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d0f87e9-657d-46b9-a3f0-ebc1bf0656bd\",\n \"metadata\": {},\n \"source\": [\n \"# Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"00c817d5\",\n \"metadata\": {},\n \"source\": [\n \"In this section, we will introduce several different ways to measure similarity.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dae49384-2450-425c-b050-c27d3c07d8e7\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## 1. Jaccard Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03266267-2d6d-4124-9702-f61e0510586c\",\n \"metadata\": {},\n \"source\": [\n \"Before directly calculate the similarity between embedding vectors, let's first take a look at the primal method for measuring how similar two sentenses are: Jaccard similarity.\\n\",\n \"\\n\",\n \"**Definition:** For sets $A$ and $B$, the Jaccard index, or the Jaccard similarity coefficient between them is the size of their intersection divided by the size of their union:\\n\",\n \"$$J(A,B)=\\\\frac{|A\\\\cap B|}{|A\\\\cup B|}$$\\n\",\n \"\\n\",\n \"The value of $J(A,B)$ falls in the range of $[0, 1]$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"bed533e1-a17c-4595-bdff-7f4a29e4deb3\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:12:47.091346Z\",\n \"iopub.status.busy\": \"2024-07-17T03:12:47.091019Z\",\n \"iopub.status.idle\": \"2024-07-17T03:12:47.094401Z\",\n \"shell.execute_reply\": \"2024-07-17T03:12:47.093967Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:12:47.091327Z\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"def jaccard_similarity(sentence1, sentence2):\\n\",\n \" set1 = set(sentence1.split(\\\" \\\"))\\n\",\n \" set2 = set(sentence2.split(\\\" \\\"))\\n\",\n \" intersection = set1.intersection(set2)\\n\",\n \" union = set1.union(set2)\\n\",\n \" return len(intersection)/len(union)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"ea766de8-572d-4eca-91f7-284a121e8edb\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:14:06.133012Z\",\n \"iopub.status.busy\": \"2024-07-17T03:14:06.132502Z\",\n \"iopub.status.idle\": \"2024-07-17T03:14:06.135483Z\",\n \"shell.execute_reply\": \"2024-07-17T03:14:06.135044Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:14:06.132992Z\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"s1 = \\\"Hawaii is a wonderful place for holiday\\\"\\n\",\n \"s2 = \\\"Peter's favorite place to spend his holiday is Hawaii\\\"\\n\",\n \"s3 = \\\"Anna enjoys baking during her holiday\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"b359ff4e-21a1-489a-ad46-ba53e974dc48\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:13:34.646320Z\",\n \"iopub.status.busy\": \"2024-07-17T03:13:34.645942Z\",\n \"iopub.status.idle\": \"2024-07-17T03:13:34.649389Z\",\n \"shell.execute_reply\": \"2024-07-17T03:13:34.648998Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:13:34.646302Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.3333333333333333\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"jaccard_similarity(s1, s2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"069868a9-d379-4d55-8a23-835a2972d079\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:14:13.727400Z\",\n \"iopub.status.busy\": \"2024-07-17T03:14:13.726949Z\",\n \"iopub.status.idle\": \"2024-07-17T03:14:13.730545Z\",\n \"shell.execute_reply\": \"2024-07-17T03:14:13.730121Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:14:13.727381Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.08333333333333333\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"jaccard_similarity(s1, s3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b0323128\",\n \"metadata\": {},\n \"source\": [\n \"We can see that sentence 1 and 2 are sharing 'Hawaii', 'place', and 'holiday'. Thus getting a larger score of similarity (0.333) than that (0.083) of the sentence 1 and 3 that only share 'holiday'.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b509fa6c-87ac-4c59-b40e-fda95fd036d9\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## 2. Euclidean Distance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"9da366b8-427f-4e8f-b3e6-b453050f0591\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:30:37.643857Z\",\n \"iopub.status.busy\": \"2024-07-17T02:30:37.643302Z\",\n \"iopub.status.idle\": \"2024-07-17T02:30:37.647921Z\",\n \"shell.execute_reply\": \"2024-07-17T02:30:37.647513Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:30:37.643840Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tensor([[5., 2., 2., 6.]]) tensor([[4., 6., 6., 4.]])\\n\"\n ]\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"\\n\",\n \"A = torch.randint(1, 7, (1, 4), dtype=torch.float32)\\n\",\n \"B = torch.randint(1, 7, (1, 4), dtype=torch.float32)\\n\",\n \"print(A, B)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c068bb3-90ce-4266-8335-e3fb2ad3e996\",\n \"metadata\": {},\n \"source\": [\n \"**Definition:** For vectors $A$ and $B$, the Euclidean distance or L2 distance between them is defined as:\\n\",\n \"$$d(A, B) = \\\\|A-B\\\\|_2 = \\\\sqrt{\\\\sum_{i=1}^n (A_i-B_i)^2}$$\\n\",\n \"\\n\",\n \"The value of $d(A, B)$ falls in the range of [0, $+\\\\infty$). Since this is the measurement of distance, the closer the value is to 0, the more similar the two vector is. And the larger the value is, the two vectors are more dissimilar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d6c734d-cc03-4dd1-bb9e-3243006dcff4\",\n \"metadata\": {},\n \"source\": [\n \"You can calculate Euclidean distance step by step or directly call *torch.cdist()*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"0773acf4-eb53-4058-85da-af82af20c469\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:32:45.240684Z\",\n \"iopub.status.busy\": \"2024-07-17T02:32:45.240216Z\",\n \"iopub.status.idle\": \"2024-07-17T02:32:45.244248Z\",\n \"shell.execute_reply\": \"2024-07-17T02:32:45.243843Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:32:45.240665Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6.082762718200684\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dist = torch.sqrt(torch.sum(torch.pow(torch.subtract(A, B), 2), dim=-1))\\n\",\n \"dist.item()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"1dd45446-f7d6-4aab-b078-1d34f0a949e4\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:32:57.551560Z\",\n \"iopub.status.busy\": \"2024-07-17T02:32:57.550896Z\",\n \"iopub.status.idle\": \"2024-07-17T02:32:57.555031Z\",\n \"shell.execute_reply\": \"2024-07-17T02:32:57.554638Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:32:57.551536Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6.082762718200684\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"torch.cdist(A, B, p=2).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"da4435c0-98da-4397-8a45-c954dd3ada56\",\n \"metadata\": {},\n \"source\": [\n \"### (Maximum inner-product search)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0e0fa5c2-e619-4a0f-a785-9cc209f1503b\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## 3. Cosine Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"790e1ce3-1468-4819-a956-fc8eac690d89\",\n \"metadata\": {},\n \"source\": [\n \"For vectors $A$ and $B$, their cosine similarity is defined as:\\n\",\n \"$$\\\\cos(\\\\theta)=\\\\frac{A\\\\cdot B}{\\\\|A\\\\|\\\\|B\\\\|}$$\\n\",\n \"\\n\",\n \"The value of $\\\\cos(\\\\theta)$ falls in the range of $[-1, 1]$. Different from Euclidean distance, close to -1 denotes not similar at all and close to +1 means very similar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d0a64b4b-5caf-4bee-be0f-2e26b1c7ed6e\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### 3.1 Naive Approach\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"350cc48d-6e73-4e20-86dd-c05d1238ef60\",\n \"metadata\": {},\n \"source\": [\n \"The naive approach is just expanding the expression:\\n\",\n \"$$\\\\frac{A\\\\cdot B}{\\\\|A\\\\|\\\\|B\\\\|}=\\\\frac{\\\\sum_{i=1}^{i=n}A_i B_i}{\\\\sqrt{\\\\sum_{i=1}^{n}A_i^2}\\\\cdot\\\\sqrt{\\\\sum_{i=1}^{n}B_i^2}}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"20c7cff0-55a7-4222-9e5a-f5450171fb00\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:35.239550Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:35.239073Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:35.242844Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:35.242417Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:35.239531Z\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Compute the dot product of A and B\\n\",\n \"dot_prod = sum(a*b for a, b in zip(A[0], B[0]))\\n\",\n \"\\n\",\n \"# Compute the magnitude of A and B\\n\",\n \"A_norm = torch.sqrt(sum(a*a for a in A[0]))\\n\",\n \"B_norm = torch.sqrt(sum(b*b for b in B[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"f4dce1fb-9cff-4a0d-bc7f-a503be6a37ae\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:36.533667Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:36.533224Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:36.536611Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:36.536181Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:36.533650Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.802726686000824\\n\"\n ]\n }\n ],\n \"source\": [\n \"cos_1 = dot_prod / (A_norm * B_norm)\\n\",\n \"print(cos_1.item())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4665f38f-c1f1-42dd-914d-d1d69c038e88\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### 3.2 PyTorch Implementation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6154391d-1dea-4673-8502-b496cf87d4b0\",\n \"metadata\": {},\n \"source\": [\n \"The naive approach has few issues:\\n\",\n \"- There are chances of losing precision in the numerator and the denominator\\n\",\n \"- Losing precision may cause the computed cosine similarity > 1.0\\n\",\n \"\\n\",\n \"Thus PyTorch uses the following way:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\frac{A\\\\cdot B}{\\\\|A\\\\|\\\\|B\\\\|}=\\\\frac{A}{\\\\|A\\\\|}\\\\cdot\\\\frac{B}{\\\\|B\\\\|}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"b8be02be-3ac3-4e5f-a450-c53f05781ab4\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:38.945105Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:38.944403Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:38.948117Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:38.947698Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:38.945085Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.802726686000824\\n\"\n ]\n }\n ],\n \"source\": [\n \"res = torch.mm(A / A.norm(dim=1), B.T / B.norm(dim=1))\\n\",\n \"print(res.item())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"988acff0-e6b5-41db-92d6-8f175dd3e272\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### 3.3 PyTorch Function Call\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a61b4871-4039-4c6e-b5ee-f66a12156be9\",\n \"metadata\": {},\n \"source\": [\n \"In practice, the most convinient way is directly use *cosine_similarity()* in torch.nn.functional:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"1ac4012e-b90a-4e60-97b8-e42636fde1c9\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:55.804298Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:55.803810Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:55.807551Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:55.807146Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:55.804278Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.802726686000824\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import torch.nn.functional as F\\n\",\n \"\\n\",\n \"F.cosine_similarity(A, B).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f4ab87cc\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Inner Product/Dot Product\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e3c025ab\",\n \"metadata\": {},\n \"source\": [\n \"Coordinate definition:\\n\",\n \"$$A\\\\cdot B = \\\\sum_{i=1}^{i=n}A_i B_i$$\\n\",\n \"\\n\",\n \"Geometric definition:\\n\",\n \"$$A\\\\cdot B = \\\\|A\\\\|\\\\|B\\\\|\\\\cos(\\\\theta)$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"f0291d42\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"68.0\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dot_prod = A @ B.T\\n\",\n \"dot_prod.item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"33099a2e\",\n \"metadata\": {},\n \"source\": [\n \"### Relationship with Cosine similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2790e183\",\n \"metadata\": {},\n \"source\": [\n \"For computing the distance/similarity between two vectors, dot product and Cos similarity are closely related. Cos similarity only cares about the angle difference (because it is normalized by the product of two vectors' magnitude), while dot product takes both magnitude and angle into consideration. So the two metrics are preferred in different use cases.\\n\",\n \"\\n\",\n \"The BGE series models already normalized the output embedding vector to have the magnitude of 1. Thus using dot product and cos similarity will have the same result.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"e0f40534\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"model = FlagModel('BAAI/bge-large-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"78445a86\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sentence = \\\"I am very interested in natural language processing\\\"\\n\",\n \"embedding = torch.tensor(model.encode(sentence))\\n\",\n \"torch.norm(embedding).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9e1822ee\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Examples\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c665e3a\",\n \"metadata\": {},\n \"source\": [\n \"Now we've learned the mechanism of different types of similarity. Let's look at a real example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"73012cbb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sentence_1 = \\\"I will watch a show tonight\\\"\\n\",\n \"sentence_2 = \\\"I will show you my watch tonight\\\"\\n\",\n \"sentence_3 = \\\"I'm going to enjoy a performance this evening\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3cb79a47\",\n \"metadata\": {},\n \"source\": [\n \"It's clear to us that in sentence 1, 'watch' is a verb and 'show' is a noun. \\n\",\n \"\\n\",\n \"But in sentence 2, 'show' is a verb and 'watch' is a noun, which leads to different meaning of the two sentences.\\n\",\n \"\\n\",\n \"While sentence 3 has very similar meaning to sentence 1.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dc44dee9\",\n \"metadata\": {},\n \"source\": [\n \"Now let's see how does different similarity metrics tell us the relationship of the sentences.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"98bfcc6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.625\\n\",\n \"0.07692307692307693\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(jaccard_similarity(sentence_1, sentence_2))\\n\",\n \"print(jaccard_similarity(sentence_1, sentence_3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b7e4cd15\",\n \"metadata\": {},\n \"source\": [\n \"The results show that sentence 1 and 2 (0.625) are way more similar than sentence 1 and 3 (0.077), which indicate the opposite conclusion compare to what we have made.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cff73692\",\n \"metadata\": {},\n \"source\": [\n \"Now let's first get the embeddings of these sentences.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"426c0b42\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"torch.Size([1, 1024])\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = torch.from_numpy(model.encode([sentence_1, sentence_2, sentence_3]))\\n\",\n \"embedding_1 = embeddings[0].view(1, -1)\\n\",\n \"embedding_2 = embeddings[1].view(1, -1)\\n\",\n \"embedding_3 = embeddings[2].view(1, -1)\\n\",\n \"\\n\",\n \"print(embedding_1.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"63fe1b31\",\n \"metadata\": {},\n \"source\": [\n \"Then let's compute the Euclidean distance:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"d9bb35cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.714613139629364\\n\",\n \"0.5931472182273865\\n\"\n ]\n }\n ],\n \"source\": [\n \"euc_dist1_2 = torch.cdist(embedding_1, embedding_2, p=2).item()\\n\",\n \"euc_dist1_3 = torch.cdist(embedding_1, embedding_3, p=2).item()\\n\",\n \"print(euc_dist1_2)\\n\",\n \"print(euc_dist1_3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"402e6ea8\",\n \"metadata\": {},\n \"source\": [\n \"Then, let's see the cosine similarity:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"29e70bbc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7446640729904175\\n\",\n \"0.8240882158279419\\n\"\n ]\n }\n ],\n \"source\": [\n \"cos_dist1_2 = F.cosine_similarity(embedding_1, embedding_2).item()\\n\",\n \"cos_dist1_3 = F.cosine_similarity(embedding_1, embedding_3).item()\\n\",\n \"print(cos_dist1_2)\\n\",\n \"print(cos_dist1_3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c353d8cc\",\n \"metadata\": {},\n \"source\": [\n \"Using embedding, we can get the correct result different from Jaccard similarity that sentence 1 and 2 should be more similar than sentence 1 and 3 using either Euclidean distance or cos similarity as the metric.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "Tutorials/2_Metrics/2.2_Eval_Metrics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation Metrics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we'll cover a list of metrics that are widely used for evaluating embedding model's performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Preparation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install numpy scikit-learn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Suppose we have a corpus with document ids from 0 - 30. \\n\",\n \"- `ground_truth` contains the actual relevant document ids to each query.\\n\",\n \"- `results` contains the search results of each query by some retrieval system.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"ground_truth = [\\n\",\n \" [11, 1, 7, 17, 21],\\n\",\n \" [ 4, 16, 1],\\n\",\n \" [26, 10, 22, 8],\\n\",\n \"]\\n\",\n \"\\n\",\n \"results = [\\n\",\n \" [11, 1, 17, 7, 21, 8, 0, 28, 9, 20],\\n\",\n \" [16, 1, 6, 18, 3, 4, 25, 19, 8, 14],\\n\",\n \" [24, 10, 26, 2, 8, 28, 4, 23, 13, 21],\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19,\\n\",\n \" 21, 22, 24, 25, 26, 28])\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.intersect1d(ground_truth, results)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.isin(ground_truth, results).astype(int)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And we are interested in the following cutoffs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cutoffs = [1, 5, 10]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will use the above small example to show how different metrics evaluate the retrieval system's quality.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Recall\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Recall represents the model's capability of correctly predicting positive instances from all the actual positive samples in the dataset.\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{\\\\text{True Positives}}{\\\\text{True Positives}+\\\\text{False Negatives}}$$\\n\",\n \"\\n\",\n \"to write it in the form of information retrieval, which is the ratio of relevant documents retrieved to the total number of relevant documents in the corpus. In practice, we usually make the denominator to be the minimum between the current cutoff (usually 1, 5, 10, 100, etc) and the total number of relevant documents in the corpus:\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{|\\\\text{\\\\{Relevant docs\\\\}}\\\\cap\\\\text{\\\\{Retrieved docs\\\\}}|}{\\\\text{min}(|\\\\text{\\\\{Retrieved docs\\\\}}|, |\\\\text{\\\\{Relevant docs\\\\}}|)}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_recall(preds, truths, cutoffs):\\n\",\n \" recalls = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" hits = np.intersect1d(truth, text[:c])\\n\",\n \" recalls[i] += len(hits) / max(min(c, len(truth)), 1)\\n\",\n \" recalls /= len(preds)\\n\",\n \" return recalls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1: 0.6666666666666666\\n\",\n \"recall@5: 0.8055555555555555\\n\",\n \"recall@10: 0.9166666666666666\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls = calc_recall(results, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"recall@{c}: {recalls[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. MRR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. It measures the rank position of the first relevant result in a list of search results.\\n\",\n \"\\n\",\n \"$$MRR=\\\\frac{1}{|Q|}\\\\sum_{i=1}^{|Q|}\\\\frac{1}{rank_i}$$\\n\",\n \"\\n\",\n \"where \\n\",\n \"- $|Q|$ is the total number of queries.\\n\",\n \"- $rank_i$ is the rank position of the first relevant document of the i-th query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_MRR(preds, truth, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, t in zip(preds, truth):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, p in enumerate(pred):\\n\",\n \" if j < c and p in t:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1: 0.6666666666666666\\n\",\n \"MRR@5: 0.8333333333333334\\n\",\n \"MRR@10: 0.8333333333333334\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr = calc_MRR(results, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"MRR@{c}: {mrr[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. nDCG\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normalized Discounted Cumulative Gain (nDCG) measures the quality of a ranked list of search results by considering both the position of the relevant documents and their graded relevance scores. The calculation of nDCG involves two main steps:\\n\",\n \"\\n\",\n \"1. Discounted cumulative gain (DCG) measures the ranking quality in retrieval tasks.\\n\",\n \"\\n\",\n \"$$DCG_p=\\\\sum_{i=1}^p\\\\frac{2^{rel_i}-1}{\\\\log_2(i+1)}$$\\n\",\n \"\\n\",\n \"2. Normalized by ideal DCG to make it comparable across queries.\\n\",\n \"$$nDCG_p=\\\\frac{DCG_p}{IDCG_p}$$\\n\",\n \"where $IDCG$ is the maximum possible DCG for a given set of documents, assuming they are perfectly ranked in order of relevance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pred_hard_encodings = []\\n\",\n \"for pred, label in zip(results, ground_truth):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings.append(pred_hard_encoding)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.0\\n\",\n \"nDCG@5: 0.3298163165186628\\n\",\n \"nDCG@10: 0.5955665344840209\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import ndcg_score\\n\",\n \"\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings, results, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Precision\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Precision \\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{\\\\text{True Positives}}{\\\\text{True Positives}+\\\\text{False Positive}}$$\\n\",\n \"\\n\",\n \"in information retrieval, it's the ratio of relevant documents retrieved to the totoal number of documents retrieved:\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{|\\\\text{\\\\{Relevant docs\\\\}}\\\\cap\\\\text{\\\\{Retrieved docs\\\\}}|}{|\\\\text{\\\\{Retrieved docs\\\\}}|}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_precision(preds, truths, cutoffs):\\n\",\n \" prec = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" hits = np.intersect1d(truth, text[:c])\\n\",\n \" prec[i] += len(hits) / c\\n\",\n \" prec /= len(preds)\\n\",\n \" return prec\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"precision@1: 0.6666666666666666\\n\",\n \"precision@5: 0.6666666666666666\\n\",\n \"precision@10: 0.3666666666666667\\n\"\n ]\n }\n ],\n \"source\": [\n \"precisions = calc_precision(results, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"precision@{c}: {precisions[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. MAP\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Average Precision (MAP) measures the effectiveness of a system at returning relevant documents across multiple queries. \\n\",\n \"\\n\",\n \"First, Average Precision (AP) evals how well relevant documents are ranked within the retrieved documents. It's computed by averaging the precision values for each position of relevant document in the ranking of all the retrieved documents:\\n\",\n \"\\n\",\n \"$$\\\\textbf{AP}=\\\\frac{\\\\sum_{k=1}^{M}\\\\text{Relevance}(k) \\\\times \\\\text{Precision}(k)}{|\\\\{\\\\text{Relevant Docs}\\\\}|}$$\\n\",\n \"\\n\",\n \"where \\n\",\n \"- $M$ is the total number of documents retrieved.\\n\",\n \"- $\\\\text{Relevance}(k)$ is a binary value, indicating whether document at position $k$ is relevant (=1) or not (=0).\\n\",\n \"- $\\\\text{Precision}(k)$ is the precision when considering only top $k$ retrieved items.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then calculate the average AP across multiple queries to get the MAP:\\n\",\n \"\\n\",\n \"$$\\\\textbf{MAP}=\\\\frac{1}{N}\\\\sum_{i=1}^{N}\\\\text{AP}_i$$\\n\",\n \"\\n\",\n \"where\\n\",\n \"- $N$ is the total number of queries.\\n\",\n \"- $\\\\text{AP}_i$ is the average precision of the $i^{th}$ query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_AP(encoding):\\n\",\n \" rel = 0\\n\",\n \" precs = 0.0\\n\",\n \" for k, hit in enumerate(encoding, start=1):\\n\",\n \" if hit == 1:\\n\",\n \" rel += 1\\n\",\n \" precs += rel/k\\n\",\n \"\\n\",\n \" return 0 if rel == 0 else precs/rel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_MAP(encodings, cutoffs):\\n\",\n \" res = []\\n\",\n \" for c in cutoffs:\\n\",\n \" ap_sum = 0.0\\n\",\n \" for encoding in encodings:\\n\",\n \" ap_sum += calc_AP(encoding[:c])\\n\",\n \" res.append(ap_sum/len(encodings))\\n\",\n \" \\n\",\n \" return res\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MAP@1: 0.6666666666666666\\n\",\n \"MAP@5: 0.862962962962963\\n\",\n \"MAP@10: 0.8074074074074075\\n\"\n ]\n }\n ],\n \"source\": [\n \"maps = calc_MAP(pred_hard_encodings, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"MAP@{c}: {maps[i]}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"test\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/3_Indexing/3.1.1_Intro_to_Faiss.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Indexing Using Faiss\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In practical cases, datasets contain thousands or millions of rows. Looping through the whole corpus to find the best answer to a query is very time and space consuming. In this tutorial, we'll introduce how to use indexing to make our retrieval fast and neat.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 0: Setup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### faiss-gpu on Linux (x86_64)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss maintain the latest updates on conda. So if you have GPUs on Linux x86_64, create a conda virtual environment and run:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\\n\",\n \"\\n\",\n \"and make sure you select that conda env as the kernel for this notebook.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### faiss-cpu\\n\",\n \"\\n\",\n \"Otherwise it's simple, just run the following cell to install `faiss-cpu`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is a super tiny courpus with only 10 sentences, which will be the dataset we use.\\n\",\n \"\\n\",\n \"Each sentence is a concise discription of a famous people in specific domain.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = [\\n\",\n \" \\\"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\\\",\\n\",\n \" \\\"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\\\",\\n\",\n \" \\\"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\\\",\\n\",\n \" \\\"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\\",\\n\",\n \" \\\"Eminem is a renowned rapper and one of the best-selling music artists of all time.\\\",\\n\",\n \" \\\"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\\",\\n\",\n \" \\\"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\\\",\\n\",\n \" \\\"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\\\",\\n\",\n \" \\\"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\\\",\\n\",\n \" \\\"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And a few queries (add your own queries and check the result!): \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = [\\n\",\n \" \\\"Who is Robert Downey Jr.?\\\",\\n\",\n \" \\\"An expert of neural network\\\",\\n\",\n \" \\\"A famous female singer\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Text Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, for the sake of speed, we just embed the first 500 docs in the corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the corpus embeddings: (10, 768)\\n\",\n \"data type of the embeddings: float32\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss only accepts float32 inputs.\\n\",\n \"\\n\",\n \"So make sure the dtype of corpus_embeddings is float32 before adding them to the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this step, we build an index and add the embedding vectors to it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"\\n\",\n \"# if you installed faiss-gpu, uncomment the following lines to make the index on your GPUs.\\n\",\n \"\\n\",\n \"# co = faiss.GpuMultipleClonerOptions()\\n\",\n \"# index = faiss.index_cpu_to_all_gpus(index, co)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"No need to train if we use \\\"Flat\\\" quantizer and METRIC_INNER_PRODUCT as metric. Some other indices that using quantization might need training.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\",\n \"total number of vectors: 10\\n\"\n ]\n }\n ],\n \"source\": [\n \"# check if the index is trained\\n\",\n \"print(index.is_trained) \\n\",\n \"# index.train(corpus_embeddings)\\n\",\n \"\\n\",\n \"# add all the vectors to the index\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3.5 (Optional): Saving Faiss index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once you have your index with the embedding vectors, you can save it locally for future usage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# change the path to where you want to save the index\\n\",\n \"path = \\\"./index.bin\\\"\\n\",\n \"faiss.write_index(index, path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you already have stored index in your local directory, you can load it by:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = faiss.read_index(\\\"./index.bin\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Find answers to the query\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, get the embeddings of all the queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_embeddings = model.encode_queries(queries)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, use the Faiss index to do a knn search in the vector space:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6686779 0.37858668 0.3767978 ]\\n\",\n \" [0.6062041 0.59364545 0.527691 ]\\n\",\n \" [0.5409331 0.5097007 0.42427146]]\\n\",\n \"[[9 7 2]\\n\",\n \" [3 1 8]\\n\",\n \" [5 0 4]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"dists, ids = index.search(query_embeddings, k=3)\\n\",\n \"print(dists)\\n\",\n \"print(ids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's see the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"query:\\tWho is Robert Downey Jr.?\\n\",\n \"answer:\\tRobert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\n\",\n \"\\n\",\n \"query:\\tAn expert of neural network\\n\",\n \"answer:\\tGeoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\n\",\n \"\\n\",\n \"query:\\tA famous female singer\\n\",\n \"answer:\\tTaylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i, q in enumerate(queries):\\n\",\n \" print(f\\\"query:\\\\t{q}\\\\nanswer:\\\\t{corpus[ids[i][0]]}\\\\n\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/3_Indexing/3.1.2_Faiss_GPU.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Faiss GPU\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the last tutorial, we went through the basics of indexing using faiss-cpu. While for the use cases in research and industry. The size of dataset for indexing will be extremely large, the frequency of searching might also be very high. In this tutorial we'll see how to combine Faiss and GPU almost seamlessly.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss maintain the latest updates on conda. And its gpu version only supports Linux x86_64\\n\",\n \"\\n\",\n \"create a conda virtual environment and run:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\\n\",\n \"\\n\",\n \"make sure you select that conda env as the kernel for this notebook. After installation, restart the kernal.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If your system does not satisfy the requirement, install faiss-cpu and just skip the steps with gpu related codes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Data Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First let's create two datasets with \\\"fake embeddings\\\" of corpus and queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"dim = 768\\n\",\n \"corpus_size = 1000\\n\",\n \"# np.random.seed(111)\\n\",\n \"\\n\",\n \"corpus = np.random.random((corpus_size, dim)).astype('float32')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Create Index on CPU\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option 1:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss provides a great amount of choices of indexes by initializing directly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# first build a flat index (on CPU)\\n\",\n \"index = faiss.IndexFlatIP(dim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option 2:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Besides the basic index class, we can also use the index_factory function to produce composite Faiss index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = faiss.index_factory(dim, \\\"Flat\\\", faiss.METRIC_L2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Build GPU Index and Search\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All the GPU indexes are built with `StandardGpuResources` object. It contains all the needed resources for each GPU in use. By default it will allocate 18% of the total VRAM as a temporary scratch space.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `GpuClonerOptions` and `GpuMultipleClonerOptions` objects are optional when creating index from cpu to gpu. They are used to adjust the way the GPUs stores the objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Single GPU:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# use a single GPU\\n\",\n \"rs = faiss.StandardGpuResources()\\n\",\n \"co = faiss.GpuClonerOptions()\\n\",\n \"\\n\",\n \"# then make it to gpu index\\n\",\n \"index_gpu = faiss.index_cpu_to_gpu(provider=rs, device=0, index=index, options=co)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 5.31 ms, sys: 6.26 ms, total: 11.6 ms\\n\",\n \"Wall time: 8.94 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index_gpu.add(corpus)\\n\",\n \"D, I = index_gpu.search(corpus, 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### All Available GPUs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If your system contains multiple GPUs, Faiss provides the option to deploy al available GPUs. You can control their usages through `GpuMultipleClonerOptions`, e.g. whether to shard or replicate the index acrross GPUs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# cloner options for multiple GPUs\\n\",\n \"co = faiss.GpuMultipleClonerOptions()\\n\",\n \"\\n\",\n \"index_gpu = faiss.index_cpu_to_all_gpus(index=index, co=co)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 29.8 ms, sys: 26.8 ms, total: 56.6 ms\\n\",\n \"Wall time: 33.9 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index_gpu.add(corpus)\\n\",\n \"D, I = index_gpu.search(corpus, 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multiple GPUs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's also option that use multiple GPUs but not all:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ngpu = 4\\n\",\n \"resources = [faiss.StandardGpuResources() for _ in range(ngpu)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create vectors for the GpuResources and divices, then pass them to the index_cpu_to_gpu_multiple() function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vres = faiss.GpuResourcesVector()\\n\",\n \"vdev = faiss.Int32Vector()\\n\",\n \"for i, res in zip(range(ngpu), resources):\\n\",\n \" vdev.push_back(i)\\n\",\n \" vres.push_back(res)\\n\",\n \"index_gpu = faiss.index_cpu_to_gpu_multiple(vres, vdev, index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 3.49 ms, sys: 13.4 ms, total: 16.9 ms\\n\",\n \"Wall time: 9.03 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index_gpu.add(corpus)\\n\",\n \"D, I = index_gpu.search(corpus, 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All the three approaches should lead to identical result. Now let's do a quick sanity check:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The nearest neighbor of each vector in the corpus is itself\\n\",\n \"assert np.all(corpus[:] == corpus[I[:, 0]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And the corresponding distance should be 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0. 111.30057 113.2251 113.342316]\\n\",\n \" [ 0. 111.158875 111.742325 112.09038 ]\\n\",\n \" [ 0. 116.44429 116.849915 117.30502 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(D[:3])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"faiss\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/3_Indexing/3.1.3_Faiss_Indexes.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Faiss Indexes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tutorial will go through several widely used indexes in Faiss that fits different requirements, and how to use them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For CPU usage, use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For GPU on Linux x86_64 system, use Conda:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.random.seed(768)\\n\",\n \"\\n\",\n \"data = np.random.random((1000, 128))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. `IndexFlat*`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat index is the very fundamental index structure. It does not do any preprocess for the incoming vectors. All the vectors are stored directly without compression or quantization. Thus no training is need for flat indexes.\\n\",\n \"\\n\",\n \"When searching, Flat index will decode all the vectors sequentially and compute the similarity score to the query vectors. Thus, Flat Index guarantees the global optimum of results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat index family is small: just `IndexFlatL2` and `IndexFlatIP`, which are just different by the similarity metrics of Euclidean distance and inner product.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Usage:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = 128 # dimension of the vector\\n\",\n \"k = 3 # number of nearest neighbors to search\\n\",\n \"\\n\",\n \"# just simply create the index and add all the data\\n\",\n \"index = faiss.IndexFlatL2(d)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sanity check:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[ 0. 16.257435 16.658928]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# search for the k nearest neighbor for the first element in data\\n\",\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat Indexes guarantee the perfect quality but with terrible speed. It works well on small datasets or the cases that speed is not a crucial factor. \\n\",\n \"\\n\",\n \"But what about the cases that speed is important? There's no way to have it all. So we want some indexes that only sacrifice as small as possible quality to speed up. That's why approximate nearest-neighbors (ANN) algorithms are widely accepted. Now we will go through a few popular ANN methods used in vector searching.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. `IndexIVF*`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"Inverted File Flat (IVF) Index is a widely accepted technique to speed up searching by using k-means or Voronoi diagram to create a number of cells (or say, clusters) in the whole space. Then when given a query, an amount of closest cells will be searched. After that, `k` closest elements to the query will be searched in those cells.\\n\",\n \"\\n\",\n \"- `quantizer` is another index/quantizer to assign vectors to inverted lists.\\n\",\n \"- `nlist` is the number of cells the space to be partitioned.\\n\",\n \"- `nprob` is the nuber of closest cells to visit for searching in query time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tradeoff\\n\",\n \"\\n\",\n \"Increasing `nlist` will shrink the size of each cell, which speed up the search process. But the smaller coverage will sacrifice accuracy and increase the possibility of the edge/surface problem discribed above.\\n\",\n \"\\n\",\n \"Increasing `nprob` will have a greater scope, preferring search quality by the tradeoff of slower speed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Shortage\\n\",\n \"\\n\",\n \"There could be a problem when the query vector lands on the edge/surface of the cell. It is possible that the closest element falls into the neighbor cell, which may not be considered due to `nprob` is not large enough.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nlist = 5\\n\",\n \"nprob = 2\\n\",\n \"\\n\",\n \"# the quantizer defines how to store and compare the vectors\\n\",\n \"quantizer = faiss.IndexFlatL2(d)\\n\",\n \"index = faiss.IndexIVFFlat(quantizer, d, nlist)\\n\",\n \"\\n\",\n \"# note different from flat index, IVF index first needs training to create the cells\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[ 0. 16.257435 16.658928]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set nprob before searching\\n\",\n \"index.nprobe = 8\\n\",\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. `IndexHNSW*`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"Hierarchical Navigable Small World (HNSW) indexing is a graph based method, which is an extension of navigable small world (NSW). It builds a multi-layered graph where nodes (vectors) are connected based on their proximity, forming \\\"small-world\\\" structures that allow efficient navigation through the space.\\n\",\n \"\\n\",\n \"- `M` is the number of neighbors each vector has in the graph.\\n\",\n \"- `efConstruction` is the number of entry points to explore when building the index.\\n\",\n \"- `efSearch` is the number of entry points to explore when searching.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tradeoff\\n\",\n \"\\n\",\n \"Increasing `M` or `efSearch` will make greater fidelity with reasonable longer time. Larger `efConstruction` mainly increases the index construction time.\\n\",\n \"\\n\",\n \"HNSW has great searching quality and speed. But it is memory-consuming due to the graph structure. Scaling up `M` will cause a linear increase of memory usage.\\n\",\n \"\\n\",\n \"Note that HNSW index does not support vector's removal because removing nodes will distroy graph structure.\\n\",\n \"\\n\",\n \"Thus HNSW is a great index to choose when RAM is not a limiting factor.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"M = 32\\n\",\n \"ef_search = 16\\n\",\n \"ef_construction = 32\\n\",\n \"\\n\",\n \"index = faiss.IndexHNSWFlat(d, M)\\n\",\n \"# set the two parameters before adding data\\n\",\n \"index.hnsw.efConstruction = ef_construction\\n\",\n \"index.hnsw.efSearch = ef_search\\n\",\n \"\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[ 0. 16.257435 16.658928]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4. `IndexLSH`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"Locality Sensitive Hashing (LSH) is an ANN method that hashing data points into buckets. While well known use cases of hash function such as dictionary/hashtabel are trying to avoid hashing collisions, LSH trys to maximize hashing collisions. Similar vectors will be grouped into same hash bucket.\\n\",\n \"\\n\",\n \"In Faiss, `IndexLSH` is a Flat index with binary codes. Vectors are hashed into binary codes and compared by Hamming distances.\\n\",\n \"\\n\",\n \"- `nbits` can be seen as the \\\"resolution\\\" of hashed vectors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tradeoff\\n\",\n \"\\n\",\n \"Increasing `nbits` can get higher fidelity with the cost of more memory and longer searching time.\\n\",\n \"\\n\",\n \"LSH suffers the curse of dimensionality when using a larger `d`. In order to get similar search quality, the `nbits` value needs to be scaled up to maintain the search quality.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Shortage\\n\",\n \"\\n\",\n \"LSH speeds up searching time with a reasonable sacrifice of quality. But that only applies to small dimension `d`. Even 128 is already too large for LSH. Thus for vectors generated by transformer based embedding models, LSH index is not a common choice.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nbits = d * 8\\n\",\n \"\\n\",\n \"index = faiss.IndexLSH(d, nbits)\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 392]]\\n\",\n \"distance: [[ 0. 197. 199.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"faiss\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/3_Indexing/3.1.4_Faiss_Quantizers.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Faiss Quantizers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we will introduce the quantizer object in Faiss and how to use them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For CPU usage, run:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For GPU on Linux x86_64 system, use Conda:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.random.seed(768)\\n\",\n \"\\n\",\n \"data = np.random.random((1000, 128))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Scalar Quantizer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normal data type of vector embeedings is usually 32 bit floats. Scalar quantization is transforming the 32 float representation to, for example, 8 bit interger. Thus with a 4x reduction in size. In this way, it can be seen as we distribute each dimension into 256 buckets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Name | Class | Parameters |\\n\",\n \"|:------------:|:--------:|:-----------|\\n\",\n \"| `ScalarQuantizer` | Quantizer class | `d`: dimension of vectors
`qtype`: map dimension into $2^\\\\text{qtype}$ clusters |\\n\",\n \"| `IndexScalarQuantizer` | Flat index class | `d`: dimension of vectors
`qtype`: map dimension into $2^\\\\text{qtype}$ clusters
`metric`: similarity metric (L2 or IP) |\\n\",\n \"| `IndexIVFScalarQuantizer` | IVF index class | `d`: dimension of vectors
`nlist`: number of cells/clusters to partition the inverted file space
`qtype`: map dimension into $2^\\\\text{qtype}$ clusters
`metric`: similarity metric (L2 or IP)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Quantizer class objects are used to compress the data before adding into indexes. Flat index class objects and IVF index class objects can be used direct as and index. Quantization will be done automatically.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Scalar Quantizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[156 180 46 226 13 130 41 187 63 251 16 199 205 166 117 122 214 2\\n\",\n \" 206 137 71 186 20 131 59 57 68 114 35 45 28 210 27 93 74 245\\n\",\n \" 167 5 32 42 44 128 10 189 10 13 42 162 179 221 241 104 205 21\\n\",\n \" 70 87 52 219 172 138 193 0 228 175 144 34 59 88 170 1 233 220\\n\",\n \" 20 64 245 241 5 161 41 55 30 247 107 8 229 90 201 10 43 158\\n\",\n \" 238 184 187 114 232 90 116 205 14 214 135 158 237 192 205 141 232 176\\n\",\n \" 124 176 163 68 49 91 125 70 6 170 55 44 215 84 46 48 218 56\\n\",\n \" 107 176]\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = 128\\n\",\n \"qtype = faiss.ScalarQuantizer.QT_8bit\\n\",\n \"\\n\",\n \"quantizer = faiss.ScalarQuantizer(d, qtype)\\n\",\n \"\\n\",\n \"quantizer.train(data)\\n\",\n \"new_data = quantizer.compute_codes(data)\\n\",\n \"\\n\",\n \"print(new_data[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Scalar Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = 128\\n\",\n \"k = 3\\n\",\n \"qtype = faiss.ScalarQuantizer.QT_8bit\\n\",\n \"# nlist = 5\\n\",\n \"\\n\",\n \"index = faiss.IndexScalarQuantizer(d, qtype, faiss.METRIC_L2)\\n\",\n \"# index = faiss.IndexIVFScalarQuantizer(d, nlist, faiss.ScalarQuantizer.QT_8bit, faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[1.6511828e-04 1.6252808e+01 1.6658131e+01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Product Quantizer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When speed and memory are crucial factors in searching, product quantizer becomes a top choice. It is one of the effective quantizer on reducing memory size. \\n\",\n \"\\n\",\n \"The first step of PQ is dividing the original vectors with dimension `d` into smaller, low-dimensional sub-vectors with dimension `d/m`. Here `m` is the number of sub-vectors.\\n\",\n \"\\n\",\n \"Then clustering algorithms are used to create codebook of a fixed number of centroids.\\n\",\n \"\\n\",\n \"Next, each sub-vector of a vector is replaced by the index of the closest centroid from its corresponding codebook. Now each vector will be stored with only the indices instead of the full vector.\\n\",\n \"\\n\",\n \"When comuputing the distance between a query vector. Only the distances to the centroids in the codebooks are calculated, thus enable the quick approximate nearest neighbor searches.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Name | Class | Parameters |\\n\",\n \"|:------------:|:--------:|:-----------|\\n\",\n \"| `ProductQuantizer` | Quantizer class | `d`: dimension of vectors
`M`: number of sub-vectors that D % M == 0
`nbits`: number of bits per subquantizer, so each contain $2^\\\\text{nbits}$ centroids |\\n\",\n \"| `IndexPQ` | Flat index class | `d`: dimension of vectors
`M`: number of sub-vectors that D % M == 0
`nbits`: number of bits per subquantizer, so each contain $2^\\\\text{nbits}$ centroids
`metric`: similarity metric (L2 or IP) |\\n\",\n \"| `IndexIVFPQ` | IVF index class | `quantizer`: the quantizer used in computing distance phase.
`d`: dimension of vectors
`nlist`: number of cells/clusters to partition the inverted file space
`M`: number of sub-vectors that D % M == 0
`nbits`: number of bits per subquantizer, so each contain $2^\\\\text{nbits}$ centroids
`metric`: similarity metric (L2 or IP) |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Product Quantizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"255\\n\",\n \"[[ 90 169 226 45]\\n\",\n \" [ 33 51 34 15]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = 128\\n\",\n \"M = 8\\n\",\n \"nbits = 4\\n\",\n \"\\n\",\n \"quantizer = faiss.ProductQuantizer(d, M, nbits)\\n\",\n \"\\n\",\n \"quantizer.train(data)\\n\",\n \"new_data = quantizer.compute_codes(data)\\n\",\n \"\\n\",\n \"print(new_data.max())\\n\",\n \"print(new_data[:2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Product Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = faiss.IndexPQ(d, M, nbits, faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 946 330]]\\n\",\n \"distance: [[ 8.823908 11.602461 11.746731]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Product Quantizer IVF Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nlist = 5\\n\",\n \"\\n\",\n \"quantizer = faiss.IndexFlat(d, faiss.METRIC_L2)\\n\",\n \"index = faiss.IndexIVFPQ(quantizer, d, nlist, M, nbits, faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 899 521]]\\n\",\n \"distance: [[ 8.911423 12.088312 12.104569]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/3_Indexing/3.1.5_Faiss_Index_Choosing.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Choosing Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Give a great amount of indexes and quantizers, how to choose the one in the experiment/application? In this part, we will give a general suggestion on how to choose the one fits your need.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Packages\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For CPU usage, run:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U faiss-cpu numpy h5py\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For GPU on Linux x86_64 system, use Conda:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from urllib.request import urlretrieve\\n\",\n \"import h5py\\n\",\n \"import faiss\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we'll use [SIFT1M](http://corpus-texmex.irisa.fr/), a very popular dataset for ANN evaluation, as our dataset to demonstrate the comparison.\\n\",\n \"\\n\",\n \"Run the following cell to download the dataset or you can also manually download from the repo [ann-benchmarks](https://github.com/erikbern/ann-benchmarks?tab=readme-ov-file#data-sets))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data_url = \\\"http://ann-benchmarks.com/sift-128-euclidean.hdf5\\\"\\n\",\n \"destination = \\\"data.hdf5\\\"\\n\",\n \"urlretrieve(data_url, destination)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then load the data from the hdf5 file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1000000, 128) float32\\n\",\n \"(10000, 128) float32\\n\"\n ]\n }\n ],\n \"source\": [\n \"with h5py.File('data.hdf5', 'r') as f:\\n\",\n \" corpus = f['train'][:]\\n\",\n \" query = f['test'][:]\\n\",\n \"\\n\",\n \"print(corpus.shape, corpus.dtype)\\n\",\n \"print(query.shape, corpus.dtype)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = corpus[0].shape[0]\\n\",\n \"k = 100\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Helper function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following is a helper function for computing recall.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# compute recall from the prediction results and ground truth\\n\",\n \"def compute_recall(res, truth):\\n\",\n \" recall = 0\\n\",\n \" for i in range(len(res)):\\n\",\n \" intersect = np.intersect1d(res[i], truth[i])\\n\",\n \" recall += len(intersect) / len(res[i])\\n\",\n \" recall /= len(res)\\n\",\n \"\\n\",\n \" return recall\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Flat Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat index use brute force to search neighbors for each query. It guarantees the optimal result with 100% recall. Thus we use the result from it as the ground truth.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 69.2 ms, sys: 80.6 ms, total: 150 ms\\n\",\n \"Wall time: 149 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index = faiss.IndexFlatL2(d)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 17min 30s, sys: 1.62 s, total: 17min 31s\\n\",\n \"Wall time: 2min 1s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I_truth = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. IVF Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 10.6 s, sys: 831 ms, total: 11.4 s\\n\",\n \"Wall time: 419 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"nlist = 5\\n\",\n \"nprob = 3\\n\",\n \"\\n\",\n \"quantizer = faiss.IndexFlatL2(d)\\n\",\n \"index = faiss.IndexIVFFlat(quantizer, d, nlist)\\n\",\n \"index.nprobe = nprob\\n\",\n \"\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 9min 15s, sys: 598 ms, total: 9min 16s\\n\",\n \"Wall time: 12.5 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.9999189999999997\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the test we can see that IVFFlatL2 has a pretty good promotion for the searching speed with a very tiny loss of recall.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. HNSW Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 11min 21s, sys: 595 ms, total: 11min 22s\\n\",\n \"Wall time: 17 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"M = 64\\n\",\n \"ef_search = 32\\n\",\n \"ef_construction = 64\\n\",\n \"\\n\",\n \"index = faiss.IndexHNSWFlat(d, M)\\n\",\n \"# set the two parameters before adding data\\n\",\n \"index.hnsw.efConstruction = ef_construction\\n\",\n \"index.hnsw.efSearch = ef_search\\n\",\n \"\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 5.14 s, sys: 3.94 ms, total: 5.14 s\\n\",\n \"Wall time: 110 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.8963409999999716\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the searching time of less than 1 second, we can see why HNSW is one of the best choice when looking for an extreme speed during searching phase. The reduction of recall is acceptable. But the longer time during creation of index and large memory footprint need to be considered.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. LSH\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 13.7 s, sys: 660 ms, total: 14.4 s\\n\",\n \"Wall time: 12.1 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"nbits = d * 8\\n\",\n \"\\n\",\n \"index = faiss.IndexLSH(d, nbits)\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 3min 20s, sys: 84.2 ms, total: 3min 20s\\n\",\n \"Wall time: 5.64 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.5856720000000037\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we covered in the last notebook, LSH is not a good choice when the data dimension is large. Here 128 is already burdened for LSH. As we can see, even we choose a relatively small `nbits` of d * 8, the index creating time and search time are still pretty long. And the recall of about 58.6% is not satisfactory.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Scalar Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 550 ms, sys: 18 ms, total: 568 ms\\n\",\n \"Wall time: 87.4 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"qtype = faiss.ScalarQuantizer.QT_8bit\\n\",\n \"metric = faiss.METRIC_L2\\n\",\n \"\\n\",\n \"index = faiss.IndexScalarQuantizer(d, qtype, metric)\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 7min 36s, sys: 169 ms, total: 7min 36s\\n\",\n \"Wall time: 12.7 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.990444999999872\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here scalar quantizer index's performance looks very similar to the Flat index. Because the elements of vectors in the SIFT dataset are integers in the range of [0, 218]. Thus the index does not lose to much information during scalar quantization. For the dataset with more complex distribution in float32. The difference will be more obvious.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Product Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 46.7 s, sys: 22.3 ms, total: 46.7 s\\n\",\n \"Wall time: 1.36 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"M = 16\\n\",\n \"nbits = 8\\n\",\n \"metric = faiss.METRIC_L2\\n\",\n \"\\n\",\n \"index = faiss.IndexPQ(d, M, nbits, metric)\\n\",\n \"\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 1min 37s, sys: 106 ms, total: 1min 37s\\n\",\n \"Wall time: 2.8 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.630898999999999\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Product quantizer index is not standout in any one of the aspect. But it somewhat balance the tradeoffs. It is widely used in real applications with the combination of other indexes such as IVF or HNSW.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.1.1_Evaluation_MSMARCO.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Evaluation is a crucial part in all machine learning tasks. In this notebook, we will walk through the whole pipeline of evaluating the performance of an embedding model on [MS Marco](https://microsoft.github.io/msmarco/), and use three metrics to show its performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 0: Setup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Load Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, download the queries and MS Marco from Huggingface Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"data = load_dataset(\\\"namespace-Pt/msmarco\\\", split=\\\"dev\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Considering time cost, we will use the truncated dataset in this tutorial. `queries` contains the first 100 queries from the dataset. `corpus` is formed by the positives of the the first 5,000 queries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = np.array(data[:100][\\\"query\\\"])\\n\",\n \"corpus = sum(data[:5000][\\\"positive\\\"], [])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you have GPU and would like to try out the full evaluation of MS Marco, uncomment and run the following cell:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# data = load_dataset(\\\"namespace-Pt/msmarco\\\", split=\\\"dev\\\")\\n\",\n \"# queries = np.array(data[\\\"query\\\"])\\n\",\n \"\\n\",\n \"# corpus = load_dataset(\\\"namespace-PT/msmarco-corpus\\\", split=\\\"train\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Choose the embedding model that we would like to evaluate, and encode the corpus to embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Inference Embeddings: 100%|██████████| 21/21 [02:10<00:00, 6.22s/it]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the corpus embeddings: (5331, 768)\\n\",\n \"data type of the embeddings: float32\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use the index_factory() functions to create a Faiss index we want:\\n\",\n \"\\n\",\n \"- The first argument `dim` is the dimension of the vector space, in this case is 768 if you're using bge-base-en-v1.5.\\n\",\n \"\\n\",\n \"- The second argument `'Flat'` makes the index do exhaustive search.\\n\",\n \"\\n\",\n \"- The thrid argument `faiss.METRIC_INNER_PRODUCT` tells the index to use inner product as the distance metric.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 5331\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"# train and add the embeddings to the index\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since the embedding process is time consuming, it's a good choice to save the index for reproduction or other experiments.\\n\",\n \"\\n\",\n \"Uncomment the following lines to save the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# path = \\\"./index.bin\\\"\\n\",\n \"# faiss.write_index(index, path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you already have stored index in your local directory, you can load it by:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# index = faiss.read_index(\\\"./index.bin\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get the embeddings of all the queries, and get their corresponding ground truth answers for evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_embeddings = model.encode_queries(queries)\\n\",\n \"ground_truths = [d[\\\"positive\\\"] for d in data]\\n\",\n \"corpus = np.asarray(corpus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the faiss index to search top $k$ answers of each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 1/1 [00:00<00:00, 20.91it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"res_scores, res_ids, res_text = [], [], []\\n\",\n \"query_size = len(query_embeddings)\\n\",\n \"batch_size = 256\\n\",\n \"# The cutoffs we will use during evaluation, and set k to be the maximum of the cutoffs.\\n\",\n \"cut_offs = [1, 10]\\n\",\n \"k = max(cut_offs)\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, batch_size), desc=\\\"Searching\\\"):\\n\",\n \" q_embedding = query_embeddings[i: min(i+batch_size, query_size)].astype(np.float32)\\n\",\n \" # search the top k answers for each of the queries\\n\",\n \" score, idx = index.search(q_embedding, k=k)\\n\",\n \" res_scores += list(score)\\n\",\n \" res_ids += list(idx)\\n\",\n \" res_text += list(corpus[idx])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.1 Recall\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Recall represents the model's capability of correctly predicting positive instances from all the actual positive samples in the dataset.\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{\\\\text{True Positives}}{\\\\text{True Positives}+\\\\text{False Negatives}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Recall is useful when the cost of false negatives is high. In other words, we are trying to find all objects of the positive class, even if this results in some false positives. This attribute makes recall a useful metric for text retrieval tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1: 0.97\\n\",\n \"recall@10: 1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"def calc_recall(preds, truths, cutoffs):\\n\",\n \" recalls = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" recall = np.intersect1d(truth, text[:c])\\n\",\n \" recalls[i] += len(recall) / max(min(c, len(truth)), 1)\\n\",\n \" recalls /= len(preds)\\n\",\n \" return recalls\\n\",\n \"\\n\",\n \"recalls = calc_recall(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"recall@{c}: {recalls[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.2 MRR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. It measures the rank position of the first relevant result in a list of search results.\\n\",\n \"\\n\",\n \"$$MRR=\\\\frac{1}{|Q|}\\\\sum_{i=1}^{|Q|}\\\\frac{1}{rank_i}$$\\n\",\n \"\\n\",\n \"where \\n\",\n \"- $|Q|$ is the total number of queries.\\n\",\n \"- $rank_i$ is the rank position of the first relevant document of the i-th query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def MRR(preds, truth, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, t in zip(preds, truth):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, p in enumerate(pred):\\n\",\n \" if j < c and p in t:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1: 0.97\\n\",\n \"MRR@10: 0.9825\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr = MRR(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"MRR@{c}: {mrr[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.3 nDCG\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normalized Discounted cumulative gain (nDCG) measures the quality of a ranked list of search results by considering both the position of the relevant documents and their graded relevance scores. The calculation of nDCG involves two main steps:\\n\",\n \"\\n\",\n \"1. Discounted cumulative gain (DCG) measures the ranking quality in retrieval tasks.\\n\",\n \"\\n\",\n \"$$DCG_p=\\\\sum_{i=1}^p\\\\frac{2^{rel_i}-1}{\\\\log_2(i+1)}$$\\n\",\n \"\\n\",\n \"2. Normalized by ideal DCG to make it comparable across queries.\\n\",\n \"$$nDCG_p=\\\\frac{DCG_p}{IDCG_p}$$\\n\",\n \"where $IDCG$ is the maximum possible DCG for a given set of documents, assuming they are perfectly ranked in order of relevance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pred_hard_encodings = []\\n\",\n \"for pred, label in zip(res_text, ground_truths):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings.append(pred_hard_encoding)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.97\\n\",\n \"nDCG@10: 0.9869253606521631\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import ndcg_score\\n\",\n \"\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings, res_scores, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Congrats! You have walked through a full pipeline of evaluating an embedding model. Feel free to play with different datasets and models!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.2.1_MTEB_Intro.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# MTEB\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For evaluation of embedding models, MTEB is one of the most well-known benchmark. In this tutorial, we'll introduce MTEB, its basic usage, and evaluate how your model performs on the MTEB leaderboard.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the packages we will use in your environment:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install sentence_transformers mteb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Intro\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The [Massive Text Embedding Benchmark (MTEB)](https://github.com/embeddings-benchmark/mteb) is a large-scale evaluation framework designed to assess the performance of text embedding models across a wide variety of natural language processing (NLP) tasks. Introduced to standardize and improve the evaluation of text embeddings, MTEB is crucial for assessing how well these models generalize across various real-world applications. It contains a wide range of datasets in eight main NLP tasks and different languages, and provides an easy pipeline for evaluation.\\n\",\n \"\\n\",\n \"MTEB is also well known for the MTEB leaderboard, which contains a ranking of the latest first-class embedding models. We'll cover that in the next tutorial. Now let's have a look on how to use MTEB to do evaluation easily.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import mteb\\n\",\n \"from sentence_transformers import SentenceTransformer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's take a look at how to use MTEB to do a quick evaluation.\\n\",\n \"\\n\",\n \"First we load the model that we would like to evaluate on:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model_name = \\\"BAAI/bge-base-en-v1.5\\\"\\n\",\n \"model = SentenceTransformer(model_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is the list of datasets of retrieval used by MTEB's English leaderboard.\\n\",\n \"\\n\",\n \"MTEB directly use the open source benchmark BEIR in its retrieval part, which contains 15 datasets (note there are 12 subsets of CQADupstack).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"retrieval_tasks = [\\n\",\n \" \\\"ArguAna\\\",\\n\",\n \" \\\"ClimateFEVER\\\",\\n\",\n \" \\\"CQADupstackAndroidRetrieval\\\",\\n\",\n \" \\\"CQADupstackEnglishRetrieval\\\",\\n\",\n \" \\\"CQADupstackGamingRetrieval\\\",\\n\",\n \" \\\"CQADupstackGisRetrieval\\\",\\n\",\n \" \\\"CQADupstackMathematicaRetrieval\\\",\\n\",\n \" \\\"CQADupstackPhysicsRetrieval\\\",\\n\",\n \" \\\"CQADupstackProgrammersRetrieval\\\",\\n\",\n \" \\\"CQADupstackStatsRetrieval\\\",\\n\",\n \" \\\"CQADupstackTexRetrieval\\\",\\n\",\n \" \\\"CQADupstackUnixRetrieval\\\",\\n\",\n \" \\\"CQADupstackWebmastersRetrieval\\\",\\n\",\n \" \\\"CQADupstackWordpressRetrieval\\\",\\n\",\n \" \\\"DBPedia\\\",\\n\",\n \" \\\"FEVER\\\",\\n\",\n \" \\\"FiQA2018\\\",\\n\",\n \" \\\"HotpotQA\\\",\\n\",\n \" \\\"MSMARCO\\\",\\n\",\n \" \\\"NFCorpus\\\",\\n\",\n \" \\\"NQ\\\",\\n\",\n \" \\\"QuoraRetrieval\\\",\\n\",\n \" \\\"SCIDOCS\\\",\\n\",\n \" \\\"SciFact\\\",\\n\",\n \" \\\"Touche2020\\\",\\n\",\n \" \\\"TRECCOVID\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For demonstration, let's just run the first one, \\\"ArguAna\\\".\\n\",\n \"\\n\",\n \"For a full list of tasks and languages that MTEB supports, check the [page](https://github.com/embeddings-benchmark/mteb/blob/18662380f0f476db3d170d0926892045aa9f74ee/docs/tasks.md).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tasks = mteb.get_tasks(tasks=retrieval_tasks[:1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, create and initialize an MTEB instance with our chosen tasks, and run the evaluation process.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
───────────────────────────────────────────────── Selected tasks  ─────────────────────────────────────────────────\\n\",\n       \"
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\\n\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Batches: 100%|██████████| 44/44 [00:41<00:00, 1.06it/s]\\n\",\n \"Batches: 100%|██████████| 272/272 [03:36<00:00, 1.26it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use the tasks we chose to initialize the MTEB instance\\n\",\n \"evaluation = mteb.MTEB(tasks=tasks)\\n\",\n \"\\n\",\n \"# call run() with the model and output_folder\\n\",\n \"results = evaluation.run(model, output_folder=\\\"results\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The results should be stored in `{output_folder}/{model_name}/{model_revision}/{task_name}.json`.\\n\",\n \"\\n\",\n \"Openning the json file you should see contents as below, which are the evaluation results on \\\"ArguAna\\\" with different metrics on cutoffs from 1 to 1000.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```python\\n\",\n \"{\\n\",\n \" \\\"dataset_revision\\\": \\\"c22ab2a51041ffd869aaddef7af8d8215647e41a\\\",\\n\",\n \" \\\"evaluation_time\\\": 260.14976954460144,\\n\",\n \" \\\"kg_co2_emissions\\\": null,\\n\",\n \" \\\"mteb_version\\\": \\\"1.14.17\\\",\\n\",\n \" \\\"scores\\\": {\\n\",\n \" \\\"test\\\": [\\n\",\n \" {\\n\",\n \" \\\"hf_subset\\\": \\\"default\\\",\\n\",\n \" \\\"languages\\\": [\\n\",\n \" \\\"eng-Latn\\\"\\n\",\n \" ],\\n\",\n \" \\\"main_score\\\": 0.63616,\\n\",\n \" \\\"map_at_1\\\": 0.40754,\\n\",\n \" \\\"map_at_10\\\": 0.55773,\\n\",\n \" \\\"map_at_100\\\": 0.56344,\\n\",\n \" \\\"map_at_1000\\\": 0.56347,\\n\",\n \" \\\"map_at_20\\\": 0.56202,\\n\",\n \" \\\"map_at_3\\\": 0.51932,\\n\",\n \" \\\"map_at_5\\\": 0.54023,\\n\",\n \" \\\"mrr_at_1\\\": 0.4139402560455192,\\n\",\n \" \\\"mrr_at_10\\\": 0.5603739077423295,\\n\",\n \" \\\"mrr_at_100\\\": 0.5660817425350153,\\n\",\n \" \\\"mrr_at_1000\\\": 0.5661121884705748,\\n\",\n \" \\\"mrr_at_20\\\": 0.564661930998293,\\n\",\n \" \\\"mrr_at_3\\\": 0.5208629682313899,\\n\",\n \" \\\"mrr_at_5\\\": 0.5429113323850182,\\n\",\n \" \\\"nauc_map_at_1000_diff1\\\": 0.15930478114759905,\\n\",\n \" \\\"nauc_map_at_1000_max\\\": -0.06396189194646361,\\n\",\n \" \\\"nauc_map_at_1000_std\\\": -0.13168797291549253,\\n\",\n \" \\\"nauc_map_at_100_diff1\\\": 0.15934819555197366,\\n\",\n \" \\\"nauc_map_at_100_max\\\": -0.06389635013430676,\\n\",\n \" \\\"nauc_map_at_100_std\\\": -0.13164524259533786,\\n\",\n \" \\\"nauc_map_at_10_diff1\\\": 0.16057318234658585,\\n\",\n \" \\\"nauc_map_at_10_max\\\": -0.060962623117325254,\\n\",\n \" \\\"nauc_map_at_10_std\\\": -0.1300413865104607,\\n\",\n \" \\\"nauc_map_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_map_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_map_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_map_at_20_diff1\\\": 0.15956349246366208,\\n\",\n \" \\\"nauc_map_at_20_max\\\": -0.06259296677860492,\\n\",\n \" \\\"nauc_map_at_20_std\\\": -0.13097093150054095,\\n\",\n \" \\\"nauc_map_at_3_diff1\\\": 0.15620049317363813,\\n\",\n \" \\\"nauc_map_at_3_max\\\": -0.06690213479396273,\\n\",\n \" \\\"nauc_map_at_3_std\\\": -0.13440904793529648,\\n\",\n \" \\\"nauc_map_at_5_diff1\\\": 0.1557795701081579,\\n\",\n \" \\\"nauc_map_at_5_max\\\": -0.06255283252590663,\\n\",\n \" \\\"nauc_map_at_5_std\\\": -0.1355361594910923,\\n\",\n \" \\\"nauc_mrr_at_1000_diff1\\\": 0.1378988612808882,\\n\",\n \" \\\"nauc_mrr_at_1000_max\\\": -0.07507962333910836,\\n\",\n \" \\\"nauc_mrr_at_1000_std\\\": -0.12969109830101241,\\n\",\n \" \\\"nauc_mrr_at_100_diff1\\\": 0.13794450668758515,\\n\",\n \" \\\"nauc_mrr_at_100_max\\\": -0.07501290390362861,\\n\",\n \" \\\"nauc_mrr_at_100_std\\\": -0.12964855554504057,\\n\",\n \" \\\"nauc_mrr_at_10_diff1\\\": 0.1396047981645623,\\n\",\n \" \\\"nauc_mrr_at_10_max\\\": -0.07185174301688693,\\n\",\n \" \\\"nauc_mrr_at_10_std\\\": -0.12807325096717753,\\n\",\n \" \\\"nauc_mrr_at_1_diff1\\\": 0.15610387932529113,\\n\",\n \" \\\"nauc_mrr_at_1_max\\\": -0.09824591983546396,\\n\",\n \" \\\"nauc_mrr_at_1_std\\\": -0.13914318784294258,\\n\",\n \" \\\"nauc_mrr_at_20_diff1\\\": 0.1382786098284509,\\n\",\n \" \\\"nauc_mrr_at_20_max\\\": -0.07364476417961506,\\n\",\n \" \\\"nauc_mrr_at_20_std\\\": -0.12898192060943495,\\n\",\n \" \\\"nauc_mrr_at_3_diff1\\\": 0.13118224861025093,\\n\",\n \" \\\"nauc_mrr_at_3_max\\\": -0.08164985279853691,\\n\",\n \" \\\"nauc_mrr_at_3_std\\\": -0.13241573571401533,\\n\",\n \" \\\"nauc_mrr_at_5_diff1\\\": 0.1346130730317385,\\n\",\n \" \\\"nauc_mrr_at_5_max\\\": -0.07404093236468848,\\n\",\n \" \\\"nauc_mrr_at_5_std\\\": -0.1340775377068567,\\n\",\n \" \\\"nauc_ndcg_at_1000_diff1\\\": 0.15919987960292029,\\n\",\n \" \\\"nauc_ndcg_at_1000_max\\\": -0.05457945565481172,\\n\",\n \" \\\"nauc_ndcg_at_1000_std\\\": -0.12457339152558143,\\n\",\n \" \\\"nauc_ndcg_at_100_diff1\\\": 0.1604091882521101,\\n\",\n \" \\\"nauc_ndcg_at_100_max\\\": -0.05281549383775287,\\n\",\n \" \\\"nauc_ndcg_at_100_std\\\": -0.12347288098914058,\\n\",\n \" \\\"nauc_ndcg_at_10_diff1\\\": 0.1657018523692905,\\n\",\n \" \\\"nauc_ndcg_at_10_max\\\": -0.036222943297402846,\\n\",\n \" \\\"nauc_ndcg_at_10_std\\\": -0.11284619565817842,\\n\",\n \" \\\"nauc_ndcg_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_ndcg_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_ndcg_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_ndcg_at_20_diff1\\\": 0.16231721725673165,\\n\",\n \" \\\"nauc_ndcg_at_20_max\\\": -0.04147115653921931,\\n\",\n \" \\\"nauc_ndcg_at_20_std\\\": -0.11598700704312062,\\n\",\n \" \\\"nauc_ndcg_at_3_diff1\\\": 0.15256475371124711,\\n\",\n \" \\\"nauc_ndcg_at_3_max\\\": -0.05432154580979357,\\n\",\n \" \\\"nauc_ndcg_at_3_std\\\": -0.12841084787822227,\\n\",\n \" \\\"nauc_ndcg_at_5_diff1\\\": 0.15236205846534961,\\n\",\n \" \\\"nauc_ndcg_at_5_max\\\": -0.04356123278888682,\\n\",\n \" \\\"nauc_ndcg_at_5_std\\\": -0.12942556865700913,\\n\",\n \" \\\"nauc_precision_at_1000_diff1\\\": -0.038790629929866066,\\n\",\n \" \\\"nauc_precision_at_1000_max\\\": 0.3630826341915611,\\n\",\n \" \\\"nauc_precision_at_1000_std\\\": 0.4772189839676386,\\n\",\n \" \\\"nauc_precision_at_100_diff1\\\": 0.32118609204433185,\\n\",\n \" \\\"nauc_precision_at_100_max\\\": 0.4740132817600036,\\n\",\n \" \\\"nauc_precision_at_100_std\\\": 0.3456396169952022,\\n\",\n \" \\\"nauc_precision_at_10_diff1\\\": 0.22279659689895104,\\n\",\n \" \\\"nauc_precision_at_10_max\\\": 0.16823918613191954,\\n\",\n \" \\\"nauc_precision_at_10_std\\\": 0.0377209694331257,\\n\",\n \" \\\"nauc_precision_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_precision_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_precision_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_precision_at_20_diff1\\\": 0.23025740175221762,\\n\",\n \" \\\"nauc_precision_at_20_max\\\": 0.2892313928157665,\\n\",\n \" \\\"nauc_precision_at_20_std\\\": 0.13522755012490692,\\n\",\n \" \\\"nauc_precision_at_3_diff1\\\": 0.1410889527057097,\\n\",\n \" \\\"nauc_precision_at_3_max\\\": -0.010771302313530132,\\n\",\n \" \\\"nauc_precision_at_3_std\\\": -0.10744937823276193,\\n\",\n \" \\\"nauc_precision_at_5_diff1\\\": 0.14012953903010988,\\n\",\n \" \\\"nauc_precision_at_5_max\\\": 0.03977485677045894,\\n\",\n \" \\\"nauc_precision_at_5_std\\\": -0.10292184602358977,\\n\",\n \" \\\"nauc_recall_at_1000_diff1\\\": -0.03879062992990034,\\n\",\n \" \\\"nauc_recall_at_1000_max\\\": 0.36308263419153386,\\n\",\n \" \\\"nauc_recall_at_1000_std\\\": 0.47721898396760526,\\n\",\n \" \\\"nauc_recall_at_100_diff1\\\": 0.3211860920443005,\\n\",\n \" \\\"nauc_recall_at_100_max\\\": 0.4740132817599919,\\n\",\n \" \\\"nauc_recall_at_100_std\\\": 0.345639616995194,\\n\",\n \" \\\"nauc_recall_at_10_diff1\\\": 0.22279659689895054,\\n\",\n \" \\\"nauc_recall_at_10_max\\\": 0.16823918613192046,\\n\",\n \" \\\"nauc_recall_at_10_std\\\": 0.037720969433127145,\\n\",\n \" \\\"nauc_recall_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_recall_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_recall_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_recall_at_20_diff1\\\": 0.23025740175221865,\\n\",\n \" \\\"nauc_recall_at_20_max\\\": 0.2892313928157675,\\n\",\n \" \\\"nauc_recall_at_20_std\\\": 0.13522755012490456,\\n\",\n \" \\\"nauc_recall_at_3_diff1\\\": 0.14108895270570979,\\n\",\n \" \\\"nauc_recall_at_3_max\\\": -0.010771302313529425,\\n\",\n \" \\\"nauc_recall_at_3_std\\\": -0.10744937823276134,\\n\",\n \" \\\"nauc_recall_at_5_diff1\\\": 0.14012953903010958,\\n\",\n \" \\\"nauc_recall_at_5_max\\\": 0.039774856770459645,\\n\",\n \" \\\"nauc_recall_at_5_std\\\": -0.10292184602358935,\\n\",\n \" \\\"ndcg_at_1\\\": 0.40754,\\n\",\n \" \\\"ndcg_at_10\\\": 0.63616,\\n\",\n \" \\\"ndcg_at_100\\\": 0.66063,\\n\",\n \" \\\"ndcg_at_1000\\\": 0.6613,\\n\",\n \" \\\"ndcg_at_20\\\": 0.65131,\\n\",\n \" \\\"ndcg_at_3\\\": 0.55717,\\n\",\n \" \\\"ndcg_at_5\\\": 0.59461,\\n\",\n \" \\\"precision_at_1\\\": 0.40754,\\n\",\n \" \\\"precision_at_10\\\": 0.08841,\\n\",\n \" \\\"precision_at_100\\\": 0.00991,\\n\",\n \" \\\"precision_at_1000\\\": 0.001,\\n\",\n \" \\\"precision_at_20\\\": 0.04716,\\n\",\n \" \\\"precision_at_3\\\": 0.22238,\\n\",\n \" \\\"precision_at_5\\\": 0.15149,\\n\",\n \" \\\"recall_at_1\\\": 0.40754,\\n\",\n \" \\\"recall_at_10\\\": 0.88407,\\n\",\n \" \\\"recall_at_100\\\": 0.99147,\\n\",\n \" \\\"recall_at_1000\\\": 0.99644,\\n\",\n \" \\\"recall_at_20\\\": 0.9431,\\n\",\n \" \\\"recall_at_3\\\": 0.66714,\\n\",\n \" \\\"recall_at_5\\\": 0.75747\\n\",\n \" }\\n\",\n \" ]\\n\",\n \" },\\n\",\n \" \\\"task_name\\\": \\\"ArguAna\\\"\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we've successfully run the evaluation using mteb! In the next tutorial, we'll show how to evaluate your model on the whole 56 tasks of English MTEB and compete with models on the leaderboard.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.2.2_MTEB_Leaderboard.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# MTEB Leaderboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the last tutorial we show how to evaluate an embedding model on an dataset supported by MTEB. In this tutorial, we will go through how to do a full evaluation and compare the results with MTEB English leaderboard.\\n\",\n \"\\n\",\n \"Caution: Evaluation on the full Eng MTEB is very time consuming even with GPU. So we encourage you to go through the notebook to have an idea. And run the experiment when you have enough computing resource and time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the packages we will use in your environment:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install sentence_transformers mteb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Run the Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The MTEB English leaderboard contains 56 datasets on 7 tasks:\\n\",\n \"1. **Classification**: Use the embeddings to train a logistic regression on the train set and is scored on the test set. F1 is the main metric.\\n\",\n \"2. **Clustering**: Train a mini-batch k-means model with batch size 32 and k equals to the number of different labels. Then score using v-measure.\\n\",\n \"3. **Pair Classification**: A pair of text inputs is provided and a label which is a binary variable needs to be assigned. The main metric is average precision score.\\n\",\n \"4. **Reranking**: Rank a list of relevant and irrelevant reference texts according to a query. Metrics are mean MRR@k and MAP.\\n\",\n \"5. **Retrieval**: Each dataset comprises corpus, queries, and a mapping that links each query to its relevant documents within the corpus. The goal is to retrieve relevant documents for each query. The main metric is nDCG@k. MTEB directly adopts BEIR for the retrieval task.\\n\",\n \"6. **Semantic Textual Similarity (STS)**: Determine the similarity between each sentence pair. Spearman correlation based on cosine\\n\",\n \"similarity serves as the main metric.\\n\",\n \"7. **Summarization**: Only 1 dataset is used in this task. Score the machine-generated summaries to human-written summaries by computing distances of their embeddings. The main metric is also Spearman correlation based on cosine similarity.\\n\",\n \"\\n\",\n \"The benchmark is widely accepted by researchers and engineers to fairly evaluate and compare the performance of the models they train. Now let's take a look at the whole evaluation pipeline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the `MTEB_MAIN_EN` to check the all 56 datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['AmazonCounterfactualClassification', 'AmazonPolarityClassification', 'AmazonReviewsClassification', 'ArguAna', 'ArxivClusteringP2P', 'ArxivClusteringS2S', 'AskUbuntuDupQuestions', 'BIOSSES', 'Banking77Classification', 'BiorxivClusteringP2P', 'BiorxivClusteringS2S', 'CQADupstackAndroidRetrieval', 'CQADupstackEnglishRetrieval', 'CQADupstackGamingRetrieval', 'CQADupstackGisRetrieval', 'CQADupstackMathematicaRetrieval', 'CQADupstackPhysicsRetrieval', 'CQADupstackProgrammersRetrieval', 'CQADupstackStatsRetrieval', 'CQADupstackTexRetrieval', 'CQADupstackUnixRetrieval', 'CQADupstackWebmastersRetrieval', 'CQADupstackWordpressRetrieval', 'ClimateFEVER', 'DBPedia', 'EmotionClassification', 'FEVER', 'FiQA2018', 'HotpotQA', 'ImdbClassification', 'MSMARCO', 'MTOPDomainClassification', 'MTOPIntentClassification', 'MassiveIntentClassification', 'MassiveScenarioClassification', 'MedrxivClusteringP2P', 'MedrxivClusteringS2S', 'MindSmallReranking', 'NFCorpus', 'NQ', 'QuoraRetrieval', 'RedditClustering', 'RedditClusteringP2P', 'SCIDOCS', 'SICK-R', 'STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STS17', 'STS22', 'STSBenchmark', 'SciDocsRR', 'SciFact', 'SprintDuplicateQuestions', 'StackExchangeClustering', 'StackExchangeClusteringP2P', 'StackOverflowDupQuestions', 'SummEval', 'TRECCOVID', 'Touche2020', 'ToxicConversationsClassification', 'TweetSentimentExtractionClassification', 'TwentyNewsgroupsClustering', 'TwitterSemEval2015', 'TwitterURLCorpus']\\n\"\n ]\n }\n ],\n \"source\": [\n \"import mteb\\n\",\n \"from mteb.benchmarks import MTEB_MAIN_EN\\n\",\n \"\\n\",\n \"print(MTEB_MAIN_EN.tasks)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load the model we want to evaluate:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"model_name = \\\"BAAI/bge-base-en-v1.5\\\"\\n\",\n \"model = SentenceTransformer(model_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, MTEB provides popular models on their leaderboard in order to reproduce their results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model_name = \\\"BAAI/bge-base-en-v1.5\\\"\\n\",\n \"model = mteb.get_model(model_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then start to evaluate on each dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for task in MTEB_MAIN_EN.tasks:\\n\",\n \" # get the test set to evaluate on\\n\",\n \" eval_splits = [\\\"dev\\\"] if task == \\\"MSMARCO\\\" else [\\\"test\\\"]\\n\",\n \" evaluation = mteb.MTEB(\\n\",\n \" tasks=[task], task_langs=[\\\"en\\\"]\\n\",\n \" ) # Remove \\\"en\\\" to run all available languages\\n\",\n \" evaluation.run(\\n\",\n \" model, output_folder=\\\"results\\\", eval_splits=eval_splits\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Submit to MTEB Leaderboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the evaluation is done, all the evaluation results should be stored in `results/{model_name}/{model_revision}`.\\n\",\n \"\\n\",\n \"Then run the following shell command to create the model_card.md. Change {model_name} and {model_revision} to your path.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!mteb create_meta --results_folder results/{model_name}/{model_revision} --output_path model_card.md\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the case that the readme of that model already exists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !mteb create_meta --results_folder results/{model_name}/{model_revision} --output_path model_card.md --from_existing your_existing_readme.md \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copy and paste the contents of model_card.md to the top of README.md of your model on HF Hub. Now relax and wait for the daily refresh of leaderboard. Your model will show up soon!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Partially Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that you don't need to finish all the tasks to get on to the leaderboard.\\n\",\n \"\\n\",\n \"For example you fine-tune a model's ability on clustering. And you only care about how your model performs with respoect to clustering, but not the other tasks. Then you can just test its performance on the clustering tasks of MTEB and submit to the leaderboard.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"TASK_LIST_CLUSTERING = [\\n\",\n \" \\\"ArxivClusteringP2P\\\",\\n\",\n \" \\\"ArxivClusteringS2S\\\",\\n\",\n \" \\\"BiorxivClusteringP2P\\\",\\n\",\n \" \\\"BiorxivClusteringS2S\\\",\\n\",\n \" \\\"MedrxivClusteringP2P\\\",\\n\",\n \" \\\"MedrxivClusteringS2S\\\",\\n\",\n \" \\\"RedditClustering\\\",\\n\",\n \" \\\"RedditClusteringP2P\\\",\\n\",\n \" \\\"StackExchangeClustering\\\",\\n\",\n \" \\\"StackExchangeClusteringP2P\\\",\\n\",\n \" \\\"TwentyNewsgroupsClustering\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the evaluation with only clustering tasks:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"evaluation = mteb.MTEB(tasks=TASK_LIST_CLUSTERING)\\n\",\n \"\\n\",\n \"results = evaluation.run(model, output_folder=\\\"results\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then repeat Step 2 to submit your model. After the leaderboard refresh, you can find your model in the \\\"Clustering\\\" section of the leaderboard.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Future Work\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"MTEB is working on a new version of English benchmark. It contains updated and concise tasks and will make the evaluation process faster.\\n\",\n \"\\n\",\n \"Please check out their [GitHub](https://github.com/embeddings-benchmark/mteb) page for future updates and releases.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.2.3_C-MTEB.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# C-MTEB\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"C-MTEB is the largest benchmark for Chinese text embeddings, similar to MTEB. In this tutorial, we will go through how to evaluate an embedding model's ability on Chinese tasks in C-MTEB.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install dependent packages:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install FlagEmbedding mteb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Datasets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"C-MTEB uses similar task splits and metrics as English MTEB. It contains 35 datasets in 6 different tasks: Classification, Clustering, Pair Classification, Reranking, Retrieval, and Semantic Textual Similarity (STS). \\n\",\n \"\\n\",\n \"1. **Classification**: Use the embeddings to train a logistic regression on the train set and is scored on the test set. F1 is the main metric.\\n\",\n \"2. **Clustering**: Train a mini-batch k-means model with batch size 32 and k equals to the number of different labels. Then score using v-measure.\\n\",\n \"3. **Pair Classification**: A pair of text inputs is provided and a label which is a binary variable needs to be assigned. The main metric is average precision score.\\n\",\n \"4. **Reranking**: Rank a list of relevant and irrelevant reference texts according to a query. Metrics are mean MRR@k and MAP.\\n\",\n \"5. **Retrieval**: Each dataset comprises corpus, queries, and a mapping that links each query to its relevant documents within the corpus. The goal is to retrieve relevant documents for each query. The main metric is nDCG@k. MTEB directly adopts BEIR for the retrieval task.\\n\",\n \"6. **Semantic Textual Similarity (STS)**: Determine the similarity between each sentence pair. Spearman correlation based on cosine\\n\",\n \"similarity serves as the main metric.\\n\",\n \"\\n\",\n \"\\n\",\n \"Check the [HF page](https://huggingface.co/C-MTEB) for the details of each dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ChineseTaskList = [\\n\",\n \" 'TNews', 'IFlyTek', 'MultilingualSentiment', 'JDReview', 'OnlineShopping', 'Waimai',\\n\",\n \" 'CLSClusteringS2S.v2', 'CLSClusteringP2P.v2', 'ThuNewsClusteringS2S.v2', 'ThuNewsClusteringP2P.v2',\\n\",\n \" 'Ocnli', 'Cmnli',\\n\",\n \" 'T2Reranking', 'MMarcoReranking', 'CMedQAv1-reranking', 'CMedQAv2-reranking',\\n\",\n \" 'T2Retrieval', 'MMarcoRetrieval', 'DuRetrieval', 'CovidRetrieval', 'CmedqaRetrieval', 'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',\\n\",\n \" 'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STSB', 'AFQMC', 'QBQTC'\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, load the model for evaluation. Note that the instruction here is used for retreival tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from ...C_MTEB.flag_dres_model import FlagDRESModel\\n\",\n \"\\n\",\n \"instruction = \\\"为这个句子生成表示以用于检索相关文章：\\\"\\n\",\n \"model_name = \\\"BAAI/bge-base-zh-v1.5\\\"\\n\",\n \"\\n\",\n \"model = FlagDRESModel(model_name_or_path=\\\"BAAI/bge-base-zh-v1.5\\\",\\n\",\n \" query_instruction_for_retrieval=instruction,\\n\",\n \" pooling_method=\\\"cls\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Otherwise, you can load a model using sentence_transformers:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"model = SentenceTransformer(\\\"PATH_TO_MODEL\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or implement a class following the structure below:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"class MyModel():\\n\",\n \" def __init__(self):\\n\",\n \" \\\"\\\"\\\"initialize the tokenizer and model\\\"\\\"\\\"\\n\",\n \" pass\\n\",\n \"\\n\",\n \" def encode(self, sentences, batch_size=32, **kwargs):\\n\",\n \" \\\"\\\"\\\" Returns a list of embeddings for the given sentences.\\n\",\n \" Args:\\n\",\n \" sentences (`List[str]`): List of sentences to encode\\n\",\n \" batch_size (`int`): Batch size for the encoding\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" pass\\n\",\n \"\\n\",\n \"model = MyModel()\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we've prepared the dataset and model, we can start the evaluation. For time efficiency, we highly recommend to use GPU for evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import mteb\\n\",\n \"from mteb import MTEB\\n\",\n \"\\n\",\n \"tasks = mteb.get_tasks(ChineseTaskList)\\n\",\n \"\\n\",\n \"for task in tasks:\\n\",\n \" evaluation = MTEB(tasks=[task])\\n\",\n \" evaluation.run(model, output_folder=f\\\"zh_results/{model_name.split('/')[-1]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Submit to MTEB Leaderboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the evaluation is done, all the evaluation results should be stored in `zh_results/{model_name}/`.\\n\",\n \"\\n\",\n \"Then run the following shell command to create the model_card.md. Change {model_name} and its following to your path.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!!mteb create_meta --results_folder results/{model_name}/ --output_path model_card.md\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copy and paste the contents of model_card.md to the top of README.md of your model on HF Hub. Then goto the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and choose the Chinese leaderboard to find your model! It will appear soon after the website's daily refresh.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.3.1_Sentence_Transformers_Eval.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation Using Sentence Transformers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will go through how to use the Sentence Tranformers library to do evaluation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U sentence-transformers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"# Load a model\\n\",\n \"model = SentenceTransformer('all-MiniLM-L6-v2')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's choose retrieval as the first task\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"\\n\",\n \"from sentence_transformers.evaluation import InformationRetrievalEvaluator\\n\",\n \"\\n\",\n \"from datasets import load_dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BeIR is a well known benchmark for retrieval. Let's use the xxx dataset for our evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Quora IR dataset (https://huggingface.co/datasets/BeIR/quora, https://huggingface.co/datasets/BeIR/quora-qrels)\\n\",\n \"corpus = load_dataset(\\\"BeIR/quora\\\", \\\"corpus\\\", split=\\\"corpus\\\")\\n\",\n \"queries = load_dataset(\\\"BeIR/quora\\\", \\\"queries\\\", split=\\\"queries\\\")\\n\",\n \"relevant_docs_data = load_dataset(\\\"BeIR/quora-qrels\\\", split=\\\"validation\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Shrink the corpus size heavily to only the relevant documents + 10,000 random documents\\n\",\n \"required_corpus_ids = list(map(str, relevant_docs_data[\\\"corpus-id\\\"]))\\n\",\n \"required_corpus_ids += random.sample(corpus[\\\"_id\\\"], k=10_000)\\n\",\n \"corpus = corpus.filter(lambda x: x[\\\"_id\\\"] in required_corpus_ids)\\n\",\n \"\\n\",\n \"# Convert the datasets to dictionaries\\n\",\n \"corpus = dict(zip(corpus[\\\"_id\\\"], corpus[\\\"text\\\"])) # Our corpus (cid => document)\\n\",\n \"queries = dict(zip(queries[\\\"_id\\\"], queries[\\\"text\\\"])) # Our queries (qid => question)\\n\",\n \"relevant_docs = {} # Query ID to relevant documents (qid => set([relevant_cids])\\n\",\n \"for qid, corpus_ids in zip(relevant_docs_data[\\\"query-id\\\"], relevant_docs_data[\\\"corpus-id\\\"]):\\n\",\n \" qid = str(qid)\\n\",\n \" corpus_ids = str(corpus_ids)\\n\",\n \" if qid not in relevant_docs:\\n\",\n \" relevant_docs[qid] = set()\\n\",\n \" relevant_docs[qid].add(corpus_ids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally we are ready to do the evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Given queries, a corpus and a mapping with relevant documents, the InformationRetrievalEvaluator computes different IR metrics.\\n\",\n \"ir_evaluator = InformationRetrievalEvaluator(\\n\",\n \" queries=queries,\\n\",\n \" corpus=corpus,\\n\",\n \" relevant_docs=relevant_docs,\\n\",\n \" name=\\\"BeIR-quora-dev\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"results = ir_evaluator(model)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\",\n \"version\": \"3.12.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.4.1_BEIR.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate on BEIR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[BEIR](https://github.com/beir-cellar/beir) (Benchmarking-IR) is a heterogeneous evaluation benchmark for information retrieval. \\n\",\n \"It is designed for evaluating the performance of NLP-based retrieval models and widely used by research of modern embedding models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the libraries we are using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install beir FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Evaluate using BEIR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BEIR contains 18 datasets which can be downloaded from the [link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/), while 4 of them are private datasets that need appropriate licences. If you want to access to those 4 datasets, take a look at their [wiki](https://github.com/beir-cellar/beir/wiki/Datasets-available) for more information. Information collected and codes adapted from BEIR GitHub [repo](https://github.com/beir-cellar/beir).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Dataset Name | Type | Queries | Documents | Avg. Docs/Q | Public | \\n\",\n \"| ---------| :-----------: | ---------| --------- | ------| :------------:| \\n\",\n \"| ``msmarco`` | `Train` `Dev` `Test` | 6,980 | 8.84M | 1.1 | Yes | \\n\",\n \"| ``trec-covid``| `Test` | 50| 171K| 493.5 | Yes | \\n\",\n \"| ``nfcorpus`` | `Train` `Dev` `Test` | 323 | 3.6K | 38.2 | Yes |\\n\",\n \"| ``bioasq``| `Train` `Test` | 500 | 14.91M | 8.05 | No | \\n\",\n \"| ``nq``| `Train` `Test` | 3,452 | 2.68M | 1.2 | Yes | \\n\",\n \"| ``hotpotqa``| `Train` `Dev` `Test` | 7,405 | 5.23M | 2.0 | Yes |\\n\",\n \"| ``fiqa`` | `Train` `Dev` `Test` | 648 | 57K | 2.6 | Yes | \\n\",\n \"| ``signal1m`` | `Test` | 97 | 2.86M | 19.6 | No |\\n\",\n \"| ``trec-news`` | `Test` | 57 | 595K | 19.6 | No |\\n\",\n \"| ``arguana`` | `Test` | 1,406 | 8.67K | 1.0 | Yes |\\n\",\n \"| ``webis-touche2020``| `Test` | 49 | 382K | 49.2 | Yes |\\n\",\n \"| ``cqadupstack``| `Test` | 13,145 | 457K | 1.4 | Yes |\\n\",\n \"| ``quora``| `Dev` `Test` | 10,000 | 523K | 1.6 | Yes | \\n\",\n \"| ``dbpedia-entity``| `Dev` `Test` | 400 | 4.63M | 38.2 | Yes | \\n\",\n \"| ``scidocs``| `Test` | 1,000 | 25K | 4.9 | Yes | \\n\",\n \"| ``fever``| `Train` `Dev` `Test` | 6,666 | 5.42M | 1.2| Yes | \\n\",\n \"| ``climate-fever``| `Test` | 1,535 | 5.42M | 3.0 | Yes |\\n\",\n \"| ``scifact``| `Train` `Test` | 300 | 5K | 1.1 | Yes |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 Load Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First prepare the logging setup.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import logging\\n\",\n \"from beir import LoggingHandler\\n\",\n \"\\n\",\n \"logging.basicConfig(format='%(message)s',\\n\",\n \" level=logging.INFO,\\n\",\n \" handlers=[LoggingHandler()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this demo, we choose the `arguana` dataset for a quick demonstration.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Dataset downloaded here: /share/project/xzy/Projects/FlagEmbedding/Tutorials/4_Evaluation/data/arguana\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"from beir import util\\n\",\n \"\\n\",\n \"url = \\\"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip\\\"\\n\",\n \"out_dir = os.path.join(os.getcwd(), \\\"data\\\")\\n\",\n \"data_path = util.download_and_unzip(url, out_dir)\\n\",\n \"print(f\\\"Dataset is stored at: {data_path}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 03:54:55,809 - Loading Corpus...\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 8674/8674 [00:00<00:00, 158928.31it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 03:54:55,891 - Loaded 8674 TEST Documents.\\n\",\n \"2024-11-15 03:54:55,891 - Doc Example: {'text': \\\"You don’t have to be vegetarian to be green. Many special environments have been created by livestock farming – for example chalk down land in England and mountain pastures in many countries. Ending livestock farming would see these areas go back to woodland with a loss of many unique plants and animals. Growing crops can also be very bad for the planet, with fertilisers and pesticides polluting rivers, lakes and seas. Most tropical forests are now cut down for timber, or to allow oil palm trees to be grown in plantations, not to create space for meat production. British farmer and former editor Simon Farrell also states: “Many vegans and vegetarians rely on one source from the U.N. calculation that livestock generates 18% of global carbon emissions, but this figure contains basic mistakes. It attributes all deforestation from ranching to cattle, rather than logging or development. It also muddles up one-off emissions from deforestation with on-going pollution.” He also refutes the statement of meat production inefficiency: “Scientists have calculated that globally the ratio between the amounts of useful plant food used to produce meat is about 5 to 1. If you feed animals only food that humans can eat — which is, indeed, largely the case in the Western world — that may be true. But animals also eat food we can't eat, such as grass. So the real conversion figure is 1.4 to 1.” [1] At the same time eating a vegetarian diet may be no more environmentally friendly than a meat based diet if it is not sustainably sourced or uses perishable fruit and vegetables that are flown in from around the world. Eating locally sourced food can has as big an impact as being vegetarian. [2] [1] Tara Kelly, Simon Fairlie: How Eating Meat Can Save the World, 12 October 2010 [2] Lucy Siegle, ‘It is time to become a vegetarian?’ The Observer, 18th May 2008\\\", 'title': 'animals environment general health health general weight philosophy ethics'}\\n\",\n \"2024-11-15 03:54:55,891 - Loading Queries...\\n\",\n \"2024-11-15 03:54:55,903 - Loaded 1406 TEST Queries.\\n\",\n \"2024-11-15 03:54:55,903 - Query Example: Being vegetarian helps the environment Becoming a vegetarian is an environmentally friendly thing to do. Modern farming is one of the main sources of pollution in our rivers. Beef farming is one of the main causes of deforestation, and as long as people continue to buy fast food in their billions, there will be a financial incentive to continue cutting down trees to make room for cattle. Because of our desire to eat fish, our rivers and seas are being emptied of fish and many species are facing extinction. Energy resources are used up much more greedily by meat farming than my farming cereals, pulses etc. Eating meat and fish not only causes cruelty to animals, it causes serious harm to the environment and to biodiversity. For example consider Meat production related pollution and deforestation At Toronto’s 1992 Royal Agricultural Winter Fair, Agriculture Canada displayed two contrasting statistics: “it takes four football fields of land (about 1.6 hectares) to feed each Canadian” and “one apple tree produces enough fruit to make 320 pies.” Think about it — a couple of apple trees and a few rows of wheat on a mere fraction of a hectare could produce enough food for one person! [1] The 2006 U.N. Food and Agriculture Organization (FAO) report concluded that worldwide livestock farming generates 18% of the planet's greenhouse gas emissions — by comparison, all the world's cars, trains, planes and boats account for a combined 13% of greenhouse gas emissions. [2] As a result of the above point producing meat damages the environment. The demand for meat drives deforestation. Daniel Cesar Avelino of Brazil's Federal Public Prosecution Office says “We know that the single biggest driver of deforestation in the Amazon is cattle.” This clearing of tropical rainforests such as the Amazon for agriculture is estimated to produce 17% of the world's greenhouse gas emissions. [3] Not only this but the production of meat takes a lot more energy than it ultimately gives us chicken meat production consumes energy in a 4:1 ratio to protein output; beef cattle production requires an energy input to protein output ratio of 54:1. The same is true with water use due to the same phenomenon of meat being inefficient to produce in terms of the amount of grain needed to produce the same weight of meat, production requires a lot of water. Water is another scarce resource that we will soon not have enough of in various areas of the globe. Grain-fed beef production takes 100,000 liters of water for every kilogram of food. Raising broiler chickens takes 3,500 liters of water to make a kilogram of meat. In comparison, soybean production uses 2,000 liters for kilogram of food produced; rice, 1,912; wheat, 900; and potatoes, 500 liters. [4] This is while there are areas of the globe that have severe water shortages. With farming using up to 70 times more water than is used for domestic purposes: cooking and washing. A third of the population of the world is already suffering from a shortage of water. [5] Groundwater levels are falling all over the world and rivers are beginning to dry up. Already some of the biggest rivers such as China’s Yellow river do not reach the sea. [6] With a rising population becoming vegetarian is the only responsible way to eat. [1] Stephen Leckie, ‘How Meat-centred Eating Patterns Affect Food Security and the Environment’, International development research center [2] Bryan Walsh, Meat: Making Global Warming Worse, Time magazine, 10 September 2008 . [3] David Adam, Supermarket suppliers ‘helping to destroy Amazon rainforest’, The Guardian, 21st June 2009. [4] Roger Segelken, U.S. could feed 800 million people with grain that livestock eat, Cornell Science News, 7th August 1997. [5] Fiona Harvey, Water scarcity affects one in three, FT.com, 21st August 2003 [6] Rupert Wingfield-Hayes, Yellow river ‘drying up’, BBC News, 29th July 2004\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from beir.datasets.data_loader import GenericDataLoader\\n\",\n \"\\n\",\n \"corpus, queries, qrels = GenericDataLoader(\\\"data/arguana\\\").load(split=\\\"test\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we load `bge-base-en-v1.5` from huggingface and evaluate its performance on arguana.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 04:00:45,253 - Use pytorch device_name: cuda\\n\",\n \"2024-11-15 04:00:45,254 - Load pretrained SentenceTransformer: BAAI/bge-base-en-v1.5\\n\",\n \"2024-11-15 04:00:48,750 - Encoding Queries...\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Batches: 100%|██████████| 11/11 [00:01<00:00, 8.27it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 04:00:50,177 - Sorting Corpus by document length (Longest first)...\\n\",\n \"2024-11-15 04:00:50,183 - Encoding Corpus in batches... Warning: This might take a while!\\n\",\n \"2024-11-15 04:00:50,183 - Scoring Function: Cosine Similarity (cos_sim)\\n\",\n \"2024-11-15 04:00:50,184 - Encoding Batch 1/1...\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Batches: 100%|██████████| 68/68 [00:07<00:00, 9.43it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from beir.retrieval.evaluation import EvaluateRetrieval\\n\",\n \"from beir.retrieval import models\\n\",\n \"from beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES\\n\",\n \"\\n\",\n \"\\n\",\n \"# Load bge model using Sentence Transformers\\n\",\n \"model = DRES(models.SentenceBERT(\\\"BAAI/bge-base-en-v1.5\\\"), batch_size=128)\\n\",\n \"retriever = EvaluateRetrieval(model, score_function=\\\"cos_sim\\\")\\n\",\n \"\\n\",\n \"# Get the searching results\\n\",\n \"results = retriever.retrieve(corpus, queries)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 04:00:58,514 - Retriever evaluation for k in: [1, 3, 5, 10, 100, 1000]\\n\",\n \"2024-11-15 04:00:58,514 - For evaluation, we ignore identical query and document ids (default), please explicitly set ``ignore_identical_ids=False`` to ignore this.\\n\",\n \"2024-11-15 04:00:59,184 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@3: 0.5572\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@5: 0.5946\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@10: 0.6361\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@100: 0.6606\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@1000: 0.6613\\n\",\n \"2024-11-15 04:00:59,188 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,188 - MAP@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,188 - MAP@3: 0.5193\\n\",\n \"2024-11-15 04:00:59,188 - MAP@5: 0.5402\\n\",\n \"2024-11-15 04:00:59,188 - MAP@10: 0.5577\\n\",\n \"2024-11-15 04:00:59,188 - MAP@100: 0.5634\\n\",\n \"2024-11-15 04:00:59,188 - MAP@1000: 0.5635\\n\",\n \"2024-11-15 04:00:59,188 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,188 - Recall@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,188 - Recall@3: 0.6671\\n\",\n \"2024-11-15 04:00:59,188 - Recall@5: 0.7575\\n\",\n \"2024-11-15 04:00:59,188 - Recall@10: 0.8841\\n\",\n \"2024-11-15 04:00:59,188 - Recall@100: 0.9915\\n\",\n \"2024-11-15 04:00:59,189 - Recall@1000: 0.9964\\n\",\n \"2024-11-15 04:00:59,189 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,189 - P@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,189 - P@3: 0.2224\\n\",\n \"2024-11-15 04:00:59,189 - P@5: 0.1515\\n\",\n \"2024-11-15 04:00:59,189 - P@10: 0.0884\\n\",\n \"2024-11-15 04:00:59,189 - P@100: 0.0099\\n\",\n \"2024-11-15 04:00:59,189 - P@1000: 0.0010\\n\"\n ]\n }\n ],\n \"source\": [\n \"logging.info(\\\"Retriever evaluation for k in: {}\\\".format(retriever.k_values))\\n\",\n \"ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Evaluate using FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide independent evaluation for popular datasets and benchmarks. Try the following code to run the evaluation, or run the shell script provided in [example](../../examples/evaluation/beir/eval_beir.sh) folder.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load the arguments:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"arguments = \\\"\\\"\\\"-\\n\",\n \" --eval_name beir \\n\",\n \" --dataset_dir ./beir/data \\n\",\n \" --dataset_names arguana\\n\",\n \" --splits test dev \\n\",\n \" --corpus_embd_save_dir ./beir/corpus_embd \\n\",\n \" --output_dir ./beir/search_results \\n\",\n \" --search_top_k 1000 \\n\",\n \" --rerank_top_k 100 \\n\",\n \" --cache_path /root/.cache/huggingface/hub \\n\",\n \" --overwrite True \\n\",\n \" --k_values 10 100 \\n\",\n \" --eval_output_method markdown \\n\",\n \" --eval_output_path ./beir/beir_eval_results.md \\n\",\n \" --eval_metrics ndcg_at_10 recall_at_100 \\n\",\n \" --ignore_identical_ids True \\n\",\n \" --embedder_name_or_path BAAI/bge-base-en-v1.5 \\n\",\n \" --embedder_batch_size 1024\\n\",\n \" --devices cuda:4\\n\",\n \"\\\"\\\"\\\".replace('\\\\n','')\\n\",\n \"\\n\",\n \"sys.argv = arguments.split()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then pass the arguments to HFArgumentParser and run the evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Split 'dev' not found in the dataset. Removing it from the list.\\n\",\n \"ignore_identical_ids is set to True. This means that the search results will not contain identical ids. Note: Dataset such as MIRACL should NOT set this to True.\\n\",\n \"pre tokenize: 100%|██████████| 9/9 [00:00<00:00, 16.19it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 100%|██████████| 9/9 [00:11<00:00, 1.27s/it]\\n\",\n \"pre tokenize: 100%|██████████| 2/2 [00:00<00:00, 19.54it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 2/2 [00:02<00:00, 1.29s/it]\\n\",\n \"Searching: 100%|██████████| 44/44 [00:00<00:00, 208.73it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import HfArgumentParser\\n\",\n \"\\n\",\n \"from FlagEmbedding.evaluation.beir import (\\n\",\n \" BEIREvalArgs, BEIREvalModelArgs,\\n\",\n \" BEIREvalRunner\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"parser = HfArgumentParser((\\n\",\n \" BEIREvalArgs,\\n\",\n \" BEIREvalModelArgs\\n\",\n \"))\\n\",\n \"\\n\",\n \"eval_args, model_args = parser.parse_args_into_dataclasses()\\n\",\n \"eval_args: BEIREvalArgs\\n\",\n \"model_args: BEIREvalModelArgs\\n\",\n \"\\n\",\n \"runner = BEIREvalRunner(\\n\",\n \" eval_args=eval_args,\\n\",\n \" model_args=model_args\\n\",\n \")\\n\",\n \"\\n\",\n \"runner.run()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Take a look at the results and choose the way you prefer!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"arguana-test\\\": {\\n\",\n \" \\\"ndcg_at_10\\\": 0.63668,\\n\",\n \" \\\"ndcg_at_100\\\": 0.66075,\\n\",\n \" \\\"map_at_10\\\": 0.55801,\\n\",\n \" \\\"map_at_100\\\": 0.56358,\\n\",\n \" \\\"recall_at_10\\\": 0.88549,\\n\",\n \" \\\"recall_at_100\\\": 0.99147,\\n\",\n \" \\\"precision_at_10\\\": 0.08855,\\n\",\n \" \\\"precision_at_100\\\": 0.00991,\\n\",\n \" \\\"mrr_at_10\\\": 0.55809,\\n\",\n \" \\\"mrr_at_100\\\": 0.56366\\n\",\n \" }\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('beir/search_results/bge-base-en-v1.5/NoReranker/EVAL/eval_results.json', 'r') as content_file:\\n\",\n \" print(content_file.read())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.5.1_MIRACL.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate on MIRACL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[MIRACL](https://project-miracl.github.io/) (Multilingual Information Retrieval Across a Continuum of Languages) is an WSDM 2023 Cup challenge that focuses on search across 18 different languages. They release a multilingual retrieval dataset containing the train and dev set for 16 “known languages” and only dev set for 2 “surprise languages”. The topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list. You can found the dataset on HuggingFace.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note: We highly recommend you to run the evaluation of MIRACL on GPU. For reference, it takes about an hour for the whole process on a 8xA100 40G node.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the libraries we are using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install FlagEmbedding pytrec_eval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the great number of passages and articles in the 18 languages. MIRACL is a resourceful dataset for training or evaluating multi-lingual model. The data can be downloaded from [Hugging Face](https://huggingface.co/datasets/miracl/miracl).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Language | # of Passages | # of Articles |\\n\",\n \"|:----------------|--------------:|--------------:|\\n\",\n \"| Arabic (ar) | 2,061,414 | 656,982 |\\n\",\n \"| Bengali (bn) | 297,265 | 63,762 |\\n\",\n \"| English (en) | 32,893,221 | 5,758,285 |\\n\",\n \"| Spanish (es) | 10,373,953 | 1,669,181 |\\n\",\n \"| Persian (fa) | 2,207,172 | 857,827 |\\n\",\n \"| Finnish (fi) | 1,883,509 | 447,815 |\\n\",\n \"| French (fr) | 14,636,953 | 2,325,608 |\\n\",\n \"| Hindi (hi) | 506,264 | 148,107 |\\n\",\n \"| Indonesian (id) | 1,446,315 | 446,330 |\\n\",\n \"| Japanese (ja) | 6,953,614 | 1,133,444 |\\n\",\n \"| Korean (ko) | 1,486,752 | 437,373 |\\n\",\n \"| Russian (ru) | 9,543,918 | 1,476,045 |\\n\",\n \"| Swahili (sw) | 131,924 | 47,793 |\\n\",\n \"| Telugu (te) | 518,079 | 66,353 |\\n\",\n \"| Thai (th) | 542,166 | 128,179 |\\n\",\n \"| Chinese (zh) | 4,934,368 | 1,246,389 |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"lang = \\\"en\\\"\\n\",\n \"corpus = load_dataset(\\\"miracl/miracl-corpus\\\", lang, trust_remote_code=True)['train']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each passage in the corpus has three parts: `docid`, `title`, and `text`. In the structure of document with docid `x#y`, `x` indicates the id of Wikipedia article, and `y` is the number of passage within that article. The title is the name of the article with id `x` that passage belongs to. The text is the text body of the passage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'docid': '56672809#4',\\n\",\n \" 'title': 'Glen Tomasetti',\\n\",\n \" 'text': 'In 1967 Tomasetti was prosecuted after refusing to pay one sixth of her taxes on the grounds that one sixth of the federal budget was funding Australia\\\\'s military presence in Vietnam. In court she argued that Australia\\\\'s participation in the Vietnam War violated its international legal obligations as a member of the United Nations. Public figures such as Joan Baez had made similar protests in the USA, but Tomasetti\\\\'s prosecution was \\\"believed to be the first case of its kind in Australia\\\", according to a contemporary news report. Tomasetti was eventually ordered to pay the unpaid taxes.'}\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"corpus[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The qrels have following form:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dev = load_dataset('miracl/miracl', lang, trust_remote_code=True)['dev']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query_id': '0',\\n\",\n \" 'query': 'Is Creole a pidgin of French?',\\n\",\n \" 'positive_passages': [{'docid': '462221#4',\\n\",\n \" 'text': \\\"At the end of World War II in 1945, Korea was divided into North Korea and South Korea with North Korea (assisted by the Soviet Union), becoming a communist government after 1946, known as the Democratic People's Republic, followed by South Korea becoming the Republic of Korea. China became the communist People's Republic of China in 1949. In 1950, the Soviet Union backed North Korea while the United States backed South Korea, and China allied with the Soviet Union in what was to become the first military action of the Cold War.\\\",\\n\",\n \" 'title': 'Eighth United States Army'},\\n\",\n \" {'docid': '29810#23',\\n\",\n \" 'text': 'The large size of Texas and its location at the intersection of multiple climate zones gives the state highly variable weather. The Panhandle of the state has colder winters than North Texas, while the Gulf Coast has mild winters. Texas has wide variations in precipitation patterns. El Paso, on the western end of the state, averages of annual rainfall, while parts of southeast Texas average as much as per year. Dallas in the North Central region averages a more moderate per year.',\\n\",\n \" 'title': 'Texas'},\\n\",\n \" {'docid': '3716905#0',\\n\",\n \" 'text': 'A French creole, or French-based creole language, is a creole language (contact language with native speakers) for which French is the \\\"lexifier\\\". Most often this lexifier is not modern French but rather a 17th-century koiné of French from Paris, the French Atlantic harbors, and the nascent French colonies. French-based creole languages are spoken natively by millions of people worldwide, primarily in the Americas and on archipelagos throughout the Indian Ocean. This article also contains information on French pidgin languages, contact languages that lack native speakers.',\\n\",\n \" 'title': 'French-based creole languages'},\\n\",\n \" {'docid': '22399755#18',\\n\",\n \" 'text': 'There are many hypotheses on the origins of Haitian Creole. Linguist John Singler suggests that it most likely emerged under French control in colonial years when shifted its economy focused heavily on sugar production. This resulted in a much larger population of enslaved Africans, whose interaction with the French created the circumstances for the dialect to evolve from a pidgin to a Creole. His research and the research of Claire Lefebvre of the Université du Québec à Montréal suggests that Creole, despite drawing 90% of its lexicon from French, is the syntactic cousin of Fon, a Gbe language of the Niger-Congo family spoken in Benin. At the time of the emergence of Haitian Creole, 50% of the enslaved Africans in Haiti were Gbe speakers.',\\n\",\n \" 'title': 'Haitian literature'}],\\n\",\n \" 'negative_passages': [{'docid': '1170520#2',\\n\",\n \" 'text': 'Louisiana Creole is a contact language that arose in the 18th century from interactions between speakers of the lexifier language of Standard French and several substrate or adstrate languages from Africa. Prior to its establishment as a Creole, the precursor was considered a pidgin language. The social situation that gave rise to the Louisiana Creole language was unique, in that the lexifier language was the language found at the contact site. More often the lexifier is the language that arrives at the contact site belonging to the substrate/adstrate languages. Neither the French, the French-Canadians, nor the African slaves were native to the area; this fact categorizes Louisiana Creole as a contact language that arose between exogenous ethnicities. Once the pidgin tongue was transmitted to the next generation as a \\\"lingua franca\\\" (who were then considered the first native speakers of the new grammar), it could effectively be classified as a creole language.',\\n\",\n \" 'title': 'Louisiana Creole'},\\n\",\n \" {'docid': '49823#1',\\n\",\n \" 'text': 'The precise number of creole languages is not known, particularly as many are poorly attested or documented. About one hundred creole languages have arisen since 1500. These are predominantly based on European languages such as English and French due to the European Age of Discovery and the Atlantic slave trade that arose at that time. With the improvements in ship-building and navigation, traders had to learn to communicate with people around the world, and the quickest way to do this was to develop a pidgin, or simplified language suited to the purpose; in turn, full creole languages developed from these pidgins. In addition to creoles that have European languages as their base, there are, for example, creoles based on Arabic, Chinese, and Malay. The creole with the largest number of speakers is Haitian Creole, with almost ten million native speakers, followed by Tok Pisin with about 4 million, most of whom are second-language speakers.',\\n\",\n \" 'title': 'Creole language'},\\n\",\n \" {'docid': '1651722#10',\\n\",\n \" 'text': 'Krio is an English-based creole from which descend Nigerian Pidgin English and Cameroonian Pidgin English and Pichinglis. It is also similar to English-based creole languages spoken in the Americas, especially the Gullah language, Jamaican Patois (Jamaican Creole), and Bajan Creole but it has its own distinctive character. It also shares some linguistic similarities with non-English creoles, such as the French-based creole languages in the Caribbean.',\\n\",\n \" 'title': 'Krio language'},\\n\",\n \" {'docid': '540382#4',\\n\",\n \" 'text': 'Until recently creoles were considered \\\"degenerate\\\" dialects of Portuguese unworthy of attention. As a consequence, there is little documentation on the details of their formation. Since the 20th century, increased study of creoles by linguists led to several theories being advanced. The monogenetic theory of pidgins assumes that some type of pidgin language — dubbed West African Pidgin Portuguese — based on Portuguese was spoken from the 15th to 18th centuries in the forts established by the Portuguese on the West African coast. According to this theory, this variety may have been the starting point of all the pidgin and creole languages. This may explain to some extent why Portuguese lexical items can be found in many creoles, but more importantly, it would account for the numerous grammatical similarities shared by such languages, such as the preposition \\\"na\\\", meaning \\\"in\\\" and/or \\\"on\\\", which would come from the Portuguese contraction \\\"na\\\" meaning \\\"in the\\\" (feminine singular).',\\n\",\n \" 'title': 'Portuguese-based creole languages'},\\n\",\n \" {'docid': '49823#7',\\n\",\n \" 'text': 'Other scholars, such as Salikoko Mufwene, argue that pidgins and creoles arise independently under different circumstances, and that a pidgin need not always precede a creole nor a creole evolve from a pidgin. Pidgins, according to Mufwene, emerged in trade colonies among \\\"users who preserved their native vernaculars for their day-to-day interactions.\\\" Creoles, meanwhile, developed in settlement colonies in which speakers of a European language, often indentured servants whose language would be far from the standard in the first place, interacted extensively with non-European slaves, absorbing certain words and features from the slaves\\\\' non-European native languages, resulting in a heavily basilectalized version of the original language. These servants and slaves would come to use the creole as an everyday vernacular, rather than merely in situations in which contact with a speaker of the superstrate was necessary.',\\n\",\n \" 'title': 'Creole language'},\\n\",\n \" {'docid': '11236157#2',\\n\",\n \" 'text': 'While many creoles around the world have lexicons based on languages other than Portuguese (e.g. English, French, Spanish, Dutch), it was hypothesized that such creoles were derived from this lingua franca by means of relexification, i.e. the process in which a pidgin or creole incorporates a significant amount of its lexicon from another language while keeping the grammar intact. There is some evidence that relexification is a real process. Pieter Muysken and show that there are languages which derive their grammar and lexicon from two different languages respectively, which could be easily explained with the relexification hypothesis. Also, Saramaccan seems to be a pidgin frozen in the middle of relexification from Portuguese to English. However, in cases of such mixed languages, as call them, there is never a one-to-one relationship between the grammar or lexicon of the mixed language and the grammar or lexicon of the language they attribute it to.',\\n\",\n \" 'title': 'Monogenetic theory of pidgins'},\\n\",\n \" {'docid': '1612877#8',\\n\",\n \" 'text': 'A mixed language differs from pidgins, creoles and code-switching in very fundamental ways. In most cases, mixed language speakers are fluent, even native, speakers of both languages; however, speakers of Michif (a N-V mixed language) are unique in that many are not fluent in both of the sources languages. Pidgins, on the other hand, develop in a situation, usually in the context of trade, where speakers of two (or more) different languages come into contact and need to find some way to communicate with each other. Creoles develop when a pidgin language becomes a first language for young speakers. While creoles tend to have drastically simplified morphologies, mixed languages often retain the inflectional complexities of one, or both, of parent languages. For instance, Michif retains the complexities of its French nouns and its Cree verbs.',\\n\",\n \" 'title': 'Mixed language'},\\n\",\n \" {'docid': '9606120#4',\\n\",\n \" 'text': 'While it is classified as a pidgin language, this is inaccurate. Speakers are already fluent in either English and French, and as such it is not used in situations where both parties lack a common tongue. As a whole, Camfranglais sets itself apart from other pidgins and creoles in that it consists of an array of languages, at least one of which is already known by those speaking it. For instance, while it contains elements of borrowing, code-switching, and pidgin languages, it is not a contact language as both parties can be presumed to speak French, the lexifer. Numerous other classifications have been proposed, like ‘pidgin’, ‘argot’, ‘youth language’, a ‘sabir camerounais’, an ‘appropriation vernaculaire du français’ or a ‘hybrid slang’. However, as Camfranglais is more developed than a slang, this too is insufficient. Kießling proposes it be classified as a \\\\'highly hybrid sociolect of the urban youth type\\\", a definition that Stein-Kanjora agrees with.',\\n\",\n \" 'title': 'Camfranglais'}]}\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dev[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each item has four parts: `query_id`, `query`, `positive_passages`, and `negative_passages`. Here, `query_id` and `query` correspond to the id and text content of the qeury. `positive_passages` and `negative_passages` are list of passages with their corresponding `docid`, `title`, and `text`. \\n\",\n \"\\n\",\n \"This structure is the same in the `train`, `dev`, `testA` and `testB` sets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we process the ids and text of queries and corpus, and get the qrels of the dev set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus_ids = corpus['docid']\\n\",\n \"corpus_text = []\\n\",\n \"for doc in corpus:\\n\",\n \" corpus_text.append(f\\\"{doc['title']} {doc['text']}\\\".strip())\\n\",\n \"\\n\",\n \"queries_ids = dev['query_id']\\n\",\n \"queries_text = dev['query']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Evaluate from scratch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the demo we use bge-base-en-v1.5, feel free to change to the model you prefer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"os.environ['SETUPTOOLS_USE_DISTUTILS'] = ''\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"initial target device: 100%|██████████| 8/8 [00:29<00:00, 3.66s/it]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 52.84it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 55.15it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 56.49it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 55.22it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 49.22it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 54.69it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 49.16it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 50.77it/s]\\n\",\n \"Chunks: 100%|██████████| 8/8 [00:10<00:00, 1.27s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:12<00:00, 32.58it/s] \\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:44<00:00, 30.60it/s]68s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:39<00:00, 30.90it/s]41s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:04<00:00, 29.49it/s]43s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:27<00:00, 28.29it/s]it/s]t]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:08<00:00, 29.30it/s]32s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:59<00:00, 29.77it/s]it/s]t]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:04<00:00, 29.50it/s]29s/it]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:10<00:00, 15.59it/s] \\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:04<00:00, 15.68it/s]]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:01<00:00, 15.72it/s]s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:28<00:00, 15.32it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:43<00:00, 15.10it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:27<00:00, 15.34it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:36<00:00, 15.20it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:31<00:00, 15.28it/s]\\n\",\n \"Chunks: 100%|██████████| 8/8 [27:49<00:00, 208.64s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the embeddings: (32893221, 768)\\n\",\n \"data type of the embeddings: float16\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\\n\",\n \"\\n\",\n \"# get the embedding of the queries and corpus\\n\",\n \"queries_embeddings = model.encode_queries(queries_text)\\n\",\n \"corpus_embeddings = model.encode_corpus(corpus_text)\\n\",\n \"\\n\",\n \"print(\\\"shape of the embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a Faiss index to store the embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 32893221\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"# train and add the embeddings to the index\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Searching\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the Faiss index to search for each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 25/25 [15:03<00:00, 36.15s/it]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"query_size = len(queries_embeddings)\\n\",\n \"\\n\",\n \"all_scores = []\\n\",\n \"all_indices = []\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, 32), desc=\\\"Searching\\\"):\\n\",\n \" j = min(i + 32, query_size)\\n\",\n \" query_embedding = queries_embeddings[i: j]\\n\",\n \" score, indice = index.search(query_embedding.astype(np.float32), k=100)\\n\",\n \" all_scores.append(score)\\n\",\n \" all_indices.append(indice)\\n\",\n \"\\n\",\n \"all_scores = np.concatenate(all_scores, axis=0)\\n\",\n \"all_indices = np.concatenate(all_indices, axis=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then map the search results back to the indices in the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = {}\\n\",\n \"for idx, (scores, indices) in enumerate(zip(all_scores, all_indices)):\\n\",\n \" results[queries_ids[idx]] = {}\\n\",\n \" for score, index in zip(scores, indices):\\n\",\n \" if index != -1:\\n\",\n \" results[queries_ids[idx]][corpus_ids[index]] = float(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 Evaluating\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download the qrels file for evaluation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"--2024-11-21 10:26:16-- https://hf-mirror.com/datasets/miracl/miracl/resolve/main/miracl-v1.0-en/qrels/qrels.miracl-v1.0-en-dev.tsv\\n\",\n \"Resolving hf-mirror.com (hf-mirror.com)... 153.121.57.40, 133.242.169.68, 160.16.199.204\\n\",\n \"Connecting to hf-mirror.com (hf-mirror.com)|153.121.57.40|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 167817 (164K) [text/plain]\\n\",\n \"Saving to: ‘qrels.miracl-v1.0-en-dev.tsv’\\n\",\n \"\\n\",\n \" 0K .......... .......... .......... .......... .......... 30% 109K 1s\\n\",\n \" 50K .......... .......... .......... .......... .......... 61% 44.5K 1s\\n\",\n \" 100K .......... .......... .......... .......... .......... 91% 69.6K 0s\\n\",\n \" 150K .......... ... 100% 28.0K=2.8s\\n\",\n \"\\n\",\n \"2024-11-21 10:26:20 (58.6 KB/s) - ‘qrels.miracl-v1.0-en-dev.tsv’ saved [167817/167817]\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"endpoint = os.getenv('HF_ENDPOINT', 'https://huggingface.co')\\n\",\n \"file_name = \\\"qrels.miracl-v1.0-en-dev.tsv\\\"\\n\",\n \"qrel_url = f\\\"wget {endpoint}/datasets/miracl/miracl/resolve/main/miracl-v1.0-en/qrels/{file_name}\\\"\\n\",\n \"\\n\",\n \"os.system(qrel_url)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Read the qrels from the file:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qrels_dict = {}\\n\",\n \"with open(file_name, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n\",\n \" for line in f.readlines():\\n\",\n \" qid, _, docid, rel = line.strip().split(\\\"\\\\t\\\")\\n\",\n \" qid, docid, rel = str(qid), str(docid), int(rel)\\n\",\n \" if qid not in qrels_dict:\\n\",\n \" qrels_dict[qid] = {}\\n\",\n \" qrels_dict[qid][docid] = rel\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, use [pytrec_eval](https://github.com/cvangysel/pytrec_eval) library to help us calculate the scores of selected metrics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {'NDCG@10': 0.46073, 'NDCG@100': 0.54336})\\n\",\n \"defaultdict(, {'Recall@10': 0.55972, 'Recall@100': 0.83827})\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pytrec_eval\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"ndcg_string = \\\"ndcg_cut.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"recall_string = \\\"recall.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"\\n\",\n \"evaluator = pytrec_eval.RelevanceEvaluator(\\n\",\n \" qrels_dict, {ndcg_string, recall_string}\\n\",\n \")\\n\",\n \"scores = evaluator.evaluate(results)\\n\",\n \"\\n\",\n \"all_ndcgs, all_recalls = defaultdict(list), defaultdict(list)\\n\",\n \"for query_id in scores.keys():\\n\",\n \" for k in [10,100]:\\n\",\n \" all_ndcgs[f\\\"NDCG@{k}\\\"].append(scores[query_id][\\\"ndcg_cut_\\\" + str(k)])\\n\",\n \" all_recalls[f\\\"Recall@{k}\\\"].append(scores[query_id][\\\"recall_\\\" + str(k)])\\n\",\n \"\\n\",\n \"ndcg, recall = (\\n\",\n \" all_ndcgs.copy(),\\n\",\n \" all_recalls.copy(),\\n\",\n \")\\n\",\n \"\\n\",\n \"for k in [10,100]:\\n\",\n \" ndcg[f\\\"NDCG@{k}\\\"] = round(sum(ndcg[f\\\"NDCG@{k}\\\"]) / len(scores), 5)\\n\",\n \" recall[f\\\"Recall@{k}\\\"] = round(sum(recall[f\\\"Recall@{k}\\\"]) / len(scores), 5)\\n\",\n \"\\n\",\n \"print(ndcg)\\n\",\n \"print(recall)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Evaluate using FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide independent evaluation for popular datasets and benchmarks. Try the following code to run the evaluation, or run the shell script provided in [example](../../examples/evaluation/miracl/eval_miracl.sh) folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"arguments = \\\"\\\"\\\"- \\\\\\n\",\n \" --eval_name miracl \\\\\\n\",\n \" --dataset_dir ./miracl/data \\\\\\n\",\n \" --dataset_names en \\\\\\n\",\n \" --splits dev \\\\\\n\",\n \" --corpus_embd_save_dir ./miracl/corpus_embd \\\\\\n\",\n \" --output_dir ./miracl/search_results \\\\\\n\",\n \" --search_top_k 100 \\\\\\n\",\n \" --cache_path ./cache/data \\\\\\n\",\n \" --overwrite True \\\\\\n\",\n \" --k_values 10 100 \\\\\\n\",\n \" --eval_output_method markdown \\\\\\n\",\n \" --eval_output_path ./miracl/miracl_eval_results.md \\\\\\n\",\n \" --eval_metrics ndcg_at_10 recall_at_100 \\\\\\n\",\n \" --embedder_name_or_path BAAI/bge-base-en-v1.5 \\\\\\n\",\n \" --devices cuda:0 cuda:1 \\\\\\n\",\n \" --embedder_batch_size 1024\\n\",\n \"\\\"\\\"\\\".replace('\\\\n','')\\n\",\n \"\\n\",\n \"sys.argv = arguments.split()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/root/anaconda3/envs/dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"initial target device: 100%|██████████| 2/2 [00:09<00:00, 4.98s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [18:01<00:00, 14.85it/s] \\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"/root/anaconda3/envs/dev/lib/python3.12/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [18:44<00:00, 14.29it/s]92s/it]\\n\",\n \"Inference Embeddings: 0%| | 42/16062 [00:54<8:28:19, 1.90s/it]You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 0%| | 43/16062 [00:56<8:22:03, 1.88s/it]/root/anaconda3/envs/dev/lib/python3.12/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [48:29<00:00, 5.52it/s] \\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [48:55<00:00, 5.47it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [1:10:57<00:00, 2128.54s/it] \\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:11<00:00, 11.06s/it]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:12<00:00, 12.72s/it]\\n\",\n \"Inference Embeddings: 100%|██████████| 1/1 [00:00<00:00, 32.15it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 1/1 [00:00<00:00, 39.80it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [00:31<00:00, 15.79s/it]\\n\",\n \"Searching: 100%|██████████| 25/25 [00:00<00:00, 26.24it/s]\\n\",\n \"Qrels not found in ./miracl/data/en/dev_qrels.jsonl. Trying to download the qrels from the remote and save it to ./miracl/data/en.\\n\",\n \"--2024-11-20 13:00:40-- https://hf-mirror.com/datasets/miracl/miracl/resolve/main/miracl-v1.0-en/qrels/qrels.miracl-v1.0-en-dev.tsv\\n\",\n \"Resolving hf-mirror.com (hf-mirror.com)... 133.242.169.68, 153.121.57.40, 160.16.199.204\\n\",\n \"Connecting to hf-mirror.com (hf-mirror.com)|133.242.169.68|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 167817 (164K) [text/plain]\\n\",\n \"Saving to: ‘./cache/data/miracl/qrels.miracl-v1.0-en-dev.tsv’\\n\",\n \"\\n\",\n \" 0K .......... .......... .......... .......... .......... 30% 336K 0s\\n\",\n \" 50K .......... .......... .......... .......... .......... 61% 678K 0s\\n\",\n \" 100K .......... .......... .......... .......... .......... 91% 362K 0s\\n\",\n \" 150K .......... ... 100% 39.8K=0.7s\\n\",\n \"\\n\",\n \"2024-11-20 13:00:42 (231 KB/s) - ‘./cache/data/miracl/qrels.miracl-v1.0-en-dev.tsv’ saved [167817/167817]\\n\",\n \"\\n\",\n \"Loading and Saving qrels: 100%|██████████| 8350/8350 [00:00<00:00, 184554.95it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import HfArgumentParser\\n\",\n \"\\n\",\n \"from FlagEmbedding.evaluation.miracl import (\\n\",\n \" MIRACLEvalArgs, MIRACLEvalModelArgs,\\n\",\n \" MIRACLEvalRunner\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"parser = HfArgumentParser((\\n\",\n \" MIRACLEvalArgs,\\n\",\n \" MIRACLEvalModelArgs\\n\",\n \"))\\n\",\n \"\\n\",\n \"eval_args, model_args = parser.parse_args_into_dataclasses()\\n\",\n \"eval_args: MIRACLEvalArgs\\n\",\n \"model_args: MIRACLEvalModelArgs\\n\",\n \"\\n\",\n \"runner = MIRACLEvalRunner(\\n\",\n \" eval_args=eval_args,\\n\",\n \" model_args=model_args\\n\",\n \")\\n\",\n \"\\n\",\n \"runner.run()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"en-dev\\\": {\\n\",\n \" \\\"ndcg_at_10\\\": 0.46053,\\n\",\n \" \\\"ndcg_at_100\\\": 0.54313,\\n\",\n \" \\\"map_at_10\\\": 0.35928,\\n\",\n \" \\\"map_at_100\\\": 0.38726,\\n\",\n \" \\\"recall_at_10\\\": 0.55972,\\n\",\n \" \\\"recall_at_100\\\": 0.83809,\\n\",\n \" \\\"precision_at_10\\\": 0.14018,\\n\",\n \" \\\"precision_at_100\\\": 0.02347,\\n\",\n \" \\\"mrr_at_10\\\": 0.54328,\\n\",\n \" \\\"mrr_at_100\\\": 0.54929\\n\",\n \" }\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('miracl/search_results/bge-base-en-v1.5/NoReranker/EVAL/eval_results.json', 'r') as content_file:\\n\",\n \" print(content_file.read())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/4.5.2_MLDR.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate on MLDR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[MLDR](https://huggingface.co/datasets/Shitao/MLDR) is a Multilingual Long-Document Retrieval dataset built on Wikipeida, Wudao and mC4, covering 13 typologically diverse languages. Specifically, we sample lengthy articles from Wikipedia, Wudao and mC4 datasets and randomly choose paragraphs from them. Then we use GPT-3.5 to generate questions based on these paragraphs. The generated question and the sampled article constitute a new text pair to the dataset.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the libraries we are using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install FlagEmbedding pytrec_eval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download the dataset of 13 different languages from [Hugging Face](https://huggingface.co/datasets/Shitao/MLDR).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Language Code | Language | Source | #train | #dev | #test | #corpus | Avg. Length of Docs |\\n\",\n \"| :-----------: | :--------: | :--------------: | :-----: | :---: | :---: | :-----: | :-----------------: |\\n\",\n \"| ar | Arabic | Wikipedia | 1,817 | 200 | 200 | 7,607 | 9,428 |\\n\",\n \"| de | German | Wikipedia, mC4 | 1,847 | 200 | 200 | 10,000 | 9,039 |\\n\",\n \"| en | English | Wikipedia | 10,000 | 200 | 800 | 200,000 | 3,308 |\\n\",\n \"| es | Spanish | Wikipedia, mc4 | 2,254 | 200 | 200 | 9,551 | 8,771 |\\n\",\n \"| fr | French | Wikipedia | 1,608 | 200 | 200 | 10,000 | 9,659 |\\n\",\n \"| hi | Hindi | Wikipedia | 1,618 | 200 | 200 | 3,806 | 5,555 |\\n\",\n \"| it | Italian | Wikipedia | 2,151 | 200 | 200 | 10,000 | 9,195 |\\n\",\n \"| ja | Japanese | Wikipedia | 2,262 | 200 | 200 | 10,000 | 9,297 |\\n\",\n \"| ko | Korean | Wikipedia | 2,198 | 200 | 200 | 6,176 | 7,832 |\\n\",\n \"| pt | Portuguese | Wikipedia | 1,845 | 200 | 200 | 6,569 | 7,922 |\\n\",\n \"| ru | Russian | Wikipedia | 1,864 | 200 | 200 | 10,000 | 9,723 |\\n\",\n \"| th | Thai | mC4 | 1,970 | 200 | 200 | 10,000 | 8,089 |\\n\",\n \"| zh | Chinese | Wikipedia, Wudao | 10,000 | 200 | 800 | 200,000 | 4,249 |\\n\",\n \"| Total | - | - | 41,434 | 2,600 | 3,800 | 493,709 | 4,737 |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First download the queries and corresponding qrels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"lang = \\\"en\\\"\\n\",\n \"dataset = load_dataset('Shitao/MLDR', lang, trust_remote_code=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each item has four parts: `query_id`, `query`, `positive_passages`, and `negative_passages`. `query_id` and `query` correspond to the id and text content of the qeury. `positive_passages` and `negative_passages` are list of passages with their corresponding `docid` and `text`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query_id': 'q-en-1',\\n\",\n \" 'query': 'What is the syntax for the shorthand of the conditional operator in PHP 5.3?',\\n\",\n \" 'positive_passages': [{'docid': 'doc-en-8',\\n\",\n \" 'text': 'In computer programming, is a ternary operator that is part of the syntax for basic conditional expressions in several programming languages. It is commonly referred to as the conditional operator, inline if (iif), or ternary if. An expression evaluates to if the value of is true, and otherwise to . One can read it aloud as \\\"if a then b otherwise c\\\".\\\\n\\\\nIt originally comes from CPL, in which equivalent syntax for e1 ? e2 : e3 was e1 → e2, e3.\\\\n\\\\nAlthough many ternary operators are possible, the conditional operator is so common, and other ternary operators so rare, that the conditional operator is commonly referred to as the ternary operator.\\\\n\\\\nVariations\\\\nThe detailed semantics of \\\"the\\\" ternary operator as well as its syntax differs significantly from language to language.\\\\n\\\\nA top level distinction from one language to another is whether the expressions permit side effects (as in most procedural languages) and whether the language provides short-circuit evaluation semantics, whereby only the selected expression is evaluated (most standard operators in most languages evaluate all arguments).\\\\n\\\\nIf the language supports expressions with side effects but does not specify short-circuit evaluation, then a further distinction exists about which expression evaluates first—if the language guarantees any specific order (bear in mind that the conditional also counts as an expression).\\\\n\\\\nFurthermore, if no order is guaranteed, a distinction exists about whether the result is then classified as indeterminate (the value obtained from some order) or undefined (any value at all at the whim of the compiler in the face of side effects, or even a crash).\\\\n\\\\nIf the language does not permit side-effects in expressions (common in functional languages), then the order of evaluation has no value semantics—though it may yet bear on whether an infinite recursion terminates, or have other performance implications (in a functional language with match expressions, short-circuit evaluation is inherent, and natural uses for the ternary operator arise less often, so this point is of limited concern).\\\\n\\\\nFor these reasons, in some languages the statement form can have subtly different semantics than the block conditional form } (in the C language—the syntax of the example given—these are in fact equivalent).\\\\n\\\\nThe associativity of nested ternary operators can also differ from language to language. In almost all languages, the ternary operator is right associative so that evaluates intuitively as , but PHP in particular is notoriously left-associative, and evaluates as follows: , which is rarely what any programmer expects. (The given examples assume that the ternary operator has low operator precedence, which is true in all C-family languages, and many others.)\\\\n\\\\nEquivalence to map\\\\nThe ternary operator can also be viewed as a binary map operation.\\\\n\\\\nIn R—and other languages with literal expression tuples—one can simulate the ternary operator with something like the R expression (this idiom is slightly more natural in languages with 0-origin subscripts).\\\\n\\\\nHowever, in this idiom it is almost certain that the entire tuple expression will evaluate prior to the subscript expression, so there will be no short-circuit semantics.\\\\n\\\\nNested ternaries can be simulated as where the function returns the index of the first true value in the condition vector. Note that both of these map equivalents are binary operators, revealing that the ternary operator is ternary in syntax, rather than semantics. These constructions can be regarded as a weak form of currying based on data concatenation rather than function composition.\\\\n\\\\nIf the language provides a mechanism of futures or promises, then short-circuit evaluation can sometimes also be simulated in the context of a binary map operation.\\\\n\\\\nConditional assignment\\\\n is used as follows:\\\\n\\\\n condition ? value_if_true : value_if_false\\\\n\\\\nThe condition is evaluated true or false as a Boolean expression. On the basis of the evaluation of the Boolean condition, the entire expression returns value_if_true if condition is true, but value_if_false otherwise. Usually the two sub-expressions value_if_true and value_if_false must have the same type, which determines the type of the whole expression. The importance of this type-checking lies in the operator\\\\'s most common use—in conditional assignment statements. In this usage it appears as an expression on the right side of an assignment statement, as follows:\\\\n\\\\n variable = condition ? value_if_true : value_if_false\\\\n\\\\nThe ?: operator is similar to the way conditional expressions (if-then-else constructs) work in functional programming languages, like Scheme, ML, and Haskell, since if-then-else forms an expression instead of a statement in those languages.\\\\n\\\\nUsage\\\\nThe conditional operator\\\\'s most common usage is to make a terse simple conditional assignment statement. For example, if we wish to implement some C code to change a shop\\\\'s normal opening hours from 9 o\\\\'clock to 12 o\\\\'clock on Sundays, we may use\\\\n\\\\nint opening_time = (day == SUNDAY) ? 12 : 9;\\\\n\\\\ninstead of the more verbose\\\\n\\\\nint opening_time;\\\\n\\\\nif (day == SUNDAY)\\\\n opening_time = 12;\\\\nelse\\\\n opening_time = 9;\\\\n\\\\nThe two forms are nearly equivalent. Keep in mind that the is an expression and if-then-else is a statement. Note that neither the true nor false portions can be omitted from the conditional operator without an error report upon parsing. This contrasts with if-then-else statements, where the else clause can be omitted.\\\\n\\\\nMost of the languages emphasizing functional programming don\\\\'t need such an operator as their regular conditional expression(s) is an expression in the first place e.g. the Scheme expression is equivalent in semantics to the C expression . This is also the case in many imperative languages, starting with ALGOL where it is possible to write , or Smalltalk () or Ruby (, although works as well).\\\\n\\\\nNote that some languages may evaluate both the true- and false-expressions, even though only one or the other will be assigned to the variable. This means that if the true- or false-expression contain a function call, that function may be called and executed (causing any related side-effects due to the function\\\\'s execution), regardless of whether or not its result will be used. Programmers should consult their programming language specifications or test the ternary operator to determine whether or not the language will evaluate both expressions in this way. If it does, and this is not the desired behaviour, then an if-then-else statement should be used.\\\\n\\\\nActionScript 3\\\\ncondition ? value_if_true : value_if_false\\\\n\\\\nAda\\\\nThe 2012 edition of Ada has introduced conditional expressions (using and ), as part of an enlarged set of expressions including quantified expressions and expression functions. The Rationale for Ada 2012 states motives for Ada not having had them before, as well as motives for now adding them, such as to support \\\"contracts\\\" (also new).\\\\n\\\\nPay_per_Hour := (if Day = Sunday\\\\n then 12.50\\\\n else 10.00);\\\\n\\\\nWhen the value of an if_expression is itself of Boolean type, then the part may be omitted, the value being True. Multiple conditions may chained using .\\\\n\\\\nALGOL 68\\\\nBoth ALGOL 68\\\\'s choice clauses (if and the case clauses) provide the coder with a choice of either the \\\"bold\\\" syntax or the \\\"brief\\\" form.\\\\n\\\\n Single if choice clause:\\\\n if condition then statements [ else statements ] fi\\\\n \\\"brief\\\" form: ( condition | statements | statements )\\\\n\\\\n Chained if choice clause:\\\\n if condition1 then statements elif condition2 then statements [ else statements ] fi\\\\n \\\"brief\\\" form: ( condition1 | statements |: condition2 | statements | statements )\\\\n\\\\nAPL\\\\nWith the following syntax, both expressions are evaluated (with evaluated first, then , then ):\\\\n\\\\nresult ← value_if_true ⊣⍣ condition ⊢ value_if_false\\\\n\\\\nThis alternative syntax provides short-circuit evaluation:\\\\n\\\\nresult ← { condition : expression_if_true ⋄ expression_if_false } ⍬\\\\n\\\\nAWK\\\\nresult = condition ? value_if_true : value_if_false\\\\n\\\\nBash\\\\nA true ternary operator only exists for arithmetic expressions:\\\\n\\\\n((result = condition ? value_if_true : value_if_false))\\\\n\\\\nFor strings there only exist workarounds, like e.g.:\\\\n\\\\nresult=$([[ \\\"$a\\\" = \\\"$b\\\" ]] && echo \\\"value_if_true\\\" || echo \\\"value_if_false\\\")\\\\n\\\\nWhere can be any condition construct can evaluate. Instead of the there can be any other bash command. When it exits with success, the first echo command is executed, otherwise the second one is executed.\\\\n\\\\nC\\\\nA traditional if-else construct in C, Java and JavaScript is written:\\\\n\\\\nif (a > b) {\\\\n result = x;\\\\n}\\\\nelse {\\\\n result = y;\\\\n}\\\\n\\\\nThis can be rewritten as the following statement:\\\\n\\\\nresult = a > b ? x : y;\\\\n\\\\nAs in the if-else construct only one of the expressions \\\\'x\\\\' and \\\\'y\\\\' is evaluated. This is significant if the evaluation of \\\\'x\\\\' or \\\\'y\\\\' has side effects. The behaviour is undefined if an attempt is made to use the result of the conditional operator as an lvalue.\\\\n\\\\nA GNU extension to C allows omitting the second operand, and using implicitly the first operand as the second also:\\\\n\\\\na == x ? : y;\\\\n\\\\nThe expression is equivalent to\\\\n\\\\na == x ? (a == x) : y;\\\\n\\\\nexcept that if x is an expression, it is evaluated only once. The difference is significant if evaluating the expression has side effects. This shorthand form is sometimes known as the Elvis operator in other languages.\\\\n\\\\nC#\\\\nIn C#, if condition is true, first expression is evaluated and becomes the result; if false, the second expression is evaluated and becomes the result. As with Java only one of two expressions is ever evaluated.\\\\n\\\\n// condition ? first_expression : second_expression;\\\\n\\\\nstatic double sinc(double x) \\\\n{\\\\n return x != 0.0 ? Math.Sin(x) / x : 1.0;\\\\n}\\\\n\\\\nC++\\\\nUnlike in C, the precedence of the operator in C++ is the same as that of the assignment operator ( or ), and it can return an lvalue. This means that expressions like and are both legal and are parsed differently, the former being equivalent to .\\\\n\\\\nIn C++ there are conditional assignment situations where use of the if-else statement is impossible, since this language explicitly distinguishes between initialization and assignment. In such case it is always possible to use a function call, but this can be cumbersome and inelegant. For example, to pass conditionally different values as an argument for a constructor of a field or a base class, it is impossible to use a plain if-else statement; in this case we can use a conditional assignment expression, or a function call. Bear in mind also that some types allow initialization, but do not allow assignment, or even that the assignment operator and the constructor do totally different things. This last is true for reference types, for example:\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nint main(int argc, char *argv[])\\\\n{\\\\n std::string name;\\\\n std::ofstream fout;\\\\n\\\\n if (argc > 1 && argv[1])\\\\n {\\\\n name = argv[1];\\\\n fout.open(name.c_str(), std::ios::out | std::ios::app);\\\\n }\\\\n\\\\n std::ostream &sout = name.empty() ? std::cout : fout;\\\\n\\\\n sout << \\\"Hello, world!\\\\\\\\n\\\";\\\\n\\\\n return 0;\\\\n}\\\\n\\\\nIn this case there is no possibility of using an if-else statement in place of the operator (Although we can replace the use of with a function call, inside of which can be an if-else statement).\\\\n\\\\nFurthermore, the conditional operator can yield an lvalue, i.e. a value to which another value can be assigned. Consider the following example:\\\\n\\\\n#include \\\\n\\\\nint main(int argc, char *argv[]) \\\\n{\\\\n int a = 0;\\\\n int b = 0;\\\\n\\\\n (argc > 1 ? a : b) = 1;\\\\n\\\\n std::cout << \\\"a: \\\" << a\\\\n << \\\" b: \\\" << b\\\\n << \\\\'\\\\\\\\n\\\\';\\\\n\\\\n return 0;\\\\n}\\\\n\\\\nIn this example, if the boolean expression yields the value on line 8, the value is assigned to the variable , otherwise the value is assigned to the variable .\\\\n\\\\nIn C++ and other various languages, ternary operators like are also possible but are very rare.\\\\n\\\\nCFML\\\\nExample of the operator in CFML:\\\\n\\\\nresult = randRange(0,1) ? \\\"heads\\\" : \\\"tails\\\";\\\\n\\\\nRoughly 50% of the time the expression will return 1 (true) or 0 (false); meaning result will take the value \\\"heads\\\" or \\\"tails\\\" respectively.\\\\n\\\\nLucee, Railo, and ColdFusion 11-specific\\\\nLucee, Railo, and ColdFusion 11 also implement the Elvis operator, which will return the value of the expression if it is not-null, otherwise the specified default.\\\\n\\\\nSyntax:\\\\n\\\\nresult = expression ?: value_if_expression_is_null\\\\n\\\\nExample:\\\\n\\\\nresult = f() ?: \\\"default\\\";\\\\n\\\\n// where...\\\\nfunction f(){\\\\n if (randRange(0,1)){ // either 0 or 1 (false / true)\\\\n return \\\"value\\\";\\\\n }\\\\n}\\\\n\\\\nwriteOutput(result);\\\\n\\\\nThe function will return roughly 50% of the time, otherwise will not return anything. If returns \\\"value\\\", will take that value, otherwise will take the value \\\"default\\\".\\\\n\\\\nCoffeeScript\\\\nExample of using this operator in CoffeeScript:\\\\n\\\\nif 1 is 2 then \\\"true value\\\" else \\\"false value\\\"\\\\n\\\\nReturns \\\"false value\\\".\\\\n\\\\nCommon Lisp\\\\nAssignment using a conditional expression in Common Lisp:\\\\n\\\\n(setf result (if (> a b) x y))\\\\n\\\\nAlternative form:\\\\n\\\\n(if (> a b)\\\\n (setf result x)\\\\n (setf result y))\\\\n\\\\nCrystal\\\\nExample of using this operator in Crystal:\\\\n\\\\n1 == 2 ? \\\"true value\\\" : \\\"false value\\\"\\\\n\\\\nReturns .\\\\n\\\\nThe Crystal compiler transforms conditional operators to expressions, so the above is semantically identical to:\\\\n\\\\nif 1 == 2\\\\n \\\"true value\\\"\\\\nelse\\\\n \\\"false value\\\"\\\\nend\\\\n\\\\nDart\\\\nThe Dart programming language\\\\'s syntax belongs to the C family, primarily inspired by languages like Java, C# and JavaScript, which means it has inherited the traditional syntax for its conditional expression.\\\\n\\\\nExample:\\\\n\\\\nreturn x.isEven ? x ~/ 2 : x * 3 + 1;\\\\n\\\\nLike other conditions in Dart, the expression before the must evaluate to a Boolean value.\\\\n\\\\nThe Dart syntax uses both and in various other ways, which causes ambiguities in the language grammar. An expression like:\\\\n\\\\n{ x as T ? [1] : [2] }\\\\n\\\\ncould be parsed as either a \\\"set literal\\\" containing one of two lists or as a \\\"map literal\\\" {((x as T?)[1]) : [2]}. The language always chooses the conditional expression in such situations.\\\\n\\\\nDart also has a second ternary operator, the operator commonly used for setting values in lists or maps, which makes the term \\\"the ternary operator\\\" ambiguous in a Dart context.\\\\n\\\\nDelphi\\\\nIn Delphi the function can be used to achieve the same as . If the library is used, the function returns a numeric value such as an Integer, Double or Extended. If the library is used, this function can also return a string value.\\\\n\\\\nUsing \\\\n\\\\nfunction IfThen(AValue: Boolean; const ATrue: Integer; const AFalse: Integer): Integer;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Int64; const AFalse: Int64): Int64;\\\\nfunction IfThen(AValue: Boolean; const ATrue: UInt64; const AFalse: UInt64): UInt64;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Single; const AFalse: Single): Single;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Double; const AFalse: Double): Double;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Extended; const AFalse: Extended): Extended;\\\\n\\\\nUsing the library\\\\n\\\\nfunction IfThen(AValue: Boolean; const ATrue: string; AFalse: string = \\\\'\\\\'): string;\\\\n\\\\nUsage example:\\\\n\\\\nfunction GetOpeningTime(Weekday: Integer): Integer;\\\\nbegin\\\\n { This function will return the opening time for the given weekday: 12 for Sundays, 9 for other days }\\\\n Result := IfThen((Weekday = 1) or (Weekday = 7), 12, 9);\\\\nend;\\\\n\\\\nUnlike a true ternary operator however, both of the results are evaluated prior to performing the comparison. For example, if one of the results is a call to a function which inserts a row into a database table, that function will be called whether or not the condition to return that specific result is met.\\\\n\\\\nF#\\\\n\\\\nIn F# the built-in syntax for if-then-else is already an expression that always must return a value.\\\\n\\\\nlet num = if x = 10 then 42 else 24\\\\n\\\\nF# has a special case where you can omit the else branch if the return value is of type unit. This way you can do side-effects, without using a else branch.\\\\n\\\\nif x = 10 then\\\\n printfn \\\"It is 10\\\"\\\\n\\\\nBut even in this case, the if expression would return unit. You don\\\\'t need to write the else branch, because the compiler will assume the unit type on else.\\\\n\\\\nFORTH\\\\nSince FORTH is a stack-oriented language, and any expression can leave a value on the stack, all // sequences can generate values:\\\\n\\\\n: test ( n -- n ) 1 AND IF 22 ELSE 42 THEN ;\\\\n\\\\nThis word takes 1 parameter on the stack, and if that number is odd, leaves 22. If it\\\\'s even, 42 is left on the stack.\\\\n\\\\nFortran\\\\nWith the additions to the code in the 1995 release, the ternary operator was added to the Fortran compiler as the intrinsic function :\\\\n\\\\nvariable = merge(x,y,a>b)\\\\n\\\\nNote that both x and y are evaluated before the results of one or the other are returned from the function. Here, x is returned if the condition holds true and y otherwise.\\\\n\\\\nFreeMarker \\\\nThis built-in exists since FreeMarker 2.3.20.\\\\n\\\\nUsed like booleanExp?then(whenTrue, whenFalse), fills the same role as the ternary operator in C-like languages.\\\\n\\\\n<#assign x = 10>\\\\n<#assign y = 20>\\\\n<#-- Prints the maximum of x and y: -->\\\\n${(x > y)?then(x, y)}\\\\n\\\\nGo\\\\nThere is no ternary if in Go, so use of the full if statement is always required.\\\\n\\\\nHaskell\\\\nThe built-in if-then-else syntax is inline: the expression\\\\n\\\\nif predicate then expr1 else expr2\\\\n\\\\nhas type\\\\n\\\\nBool -> a -> a -> a\\\\n\\\\nThe base library also provides the function :\\\\n\\\\nbool :: a -> a -> Bool -> a\\\\n\\\\nIn both cases, no special treatment is needed to ensure that only the selected expression is evaluated, since Haskell is non-strict by default. This also means an operator can be defined that, when used in combination with the operator, functions exactly like in most languages:\\\\n\\\\n(?) :: Bool -> a -> a -> a\\\\n(?) pred x y = if pred then x else y\\\\ninfix 1 ?\\\\n\\\\n-- example (vehicle will evaluate to \\\"airplane\\\"):\\\\narg = \\\\'A\\\\'\\\\nvehicle = arg == \\\\'B\\\\' ? \\\"boat\\\" $\\\\n arg == \\\\'A\\\\' ? \\\"airplane\\\" $\\\\n arg == \\\\'T\\\\' ? \\\"train\\\" $\\\\n \\\"car\\\"\\\\n\\\\nHowever, it is more idiomatic to use pattern guards\\\\n\\\\n-- example (vehicle will evaluate to \\\"airplane\\\"):\\\\narg = \\\\'A\\\\'\\\\nvehicle | arg == \\\\'B\\\\' = \\\"boat\\\"\\\\n | arg == \\\\'A\\\\' = \\\"airplane\\\"\\\\n | arg == \\\\'T\\\\' = \\\"train\\\"\\\\n | otherwise = \\\"car\\\"\\\\n\\\\nJava\\\\nIn Java this expression evaluates to:\\\\n\\\\n// If foo is selected, assign selected foo to bar. If not, assign baz to bar.\\\\nObject bar = foo.isSelected() ? foo : baz; \\\\n\\\\nNote that Java, in a manner similar to C#, only evaluates the used expression and will not evaluate the unused expression.\\\\n\\\\nJulia\\\\nIn Julia, \\\"Note that the spaces around and are mandatory: an expression like is not a valid ternary expression (but a newline is acceptable after both the and the ).\\\"\\\\n\\\\nJavaScript\\\\nThe conditional operator in JavaScript is similar to that of C++ and Java, except for the fact the middle expression cannot be a comma expression. Also, as in C++, but unlike in C or Perl, it will not bind tighter than an assignment to its right— is equivalent to instead of .\\\\n\\\\nvar timeout = settings !== null ? settings.timeout : 1000;\\\\n\\\\nJust like C# and Java, the expression will only be evaluated if, and only if, the expression is the matching one for the condition given; the other expression will not be evaluated.\\\\n\\\\nKotlin \\\\nKotlin does not include the traditional ternary operator, however, s can be used as expressions that can be assigned, achieving the same results. Note that, as the complexity of your conditional statement grows, you might consider replacing your - expression with a expression.\\\\n\\\\nval max = if (a > b) a else b\\\\n\\\\nLua \\\\nLua does not have a traditional conditional operator. However, the short-circuiting behaviour of its and operators allows the emulation of this behaviour:\\\\n\\\\n-- equivalent to var = cond ? a : b;\\\\nvar = cond and a or b\\\\n\\\\nThis will succeed unless is logically false (i.e. or ); in this case, the expression will always result in . This can result in some surprising behaviour if ignored.\\\\n\\\\nSQL\\\\nThe SQL expression is a generalization of the ternary operator. Instead of one conditional and two results, n conditionals and n+1 results can be specified.\\\\n\\\\nWith one conditional it is equivalent (although more verbose) to the ternary operator:\\\\n\\\\nSELECT (CASE WHEN a > b THEN x ELSE y END) AS CONDITIONAL_EXAMPLE\\\\n FROM tab;\\\\n\\\\nThis can be expanded to several conditionals:\\\\n\\\\nSELECT (CASE WHEN a > b THEN x WHEN a < b THEN y ELSE z END) AS CONDITIONAL_EXAMPLE\\\\n FROM tab;\\\\n\\\\nMySQL\\\\nIn addition to the standard expression, MySQL provides an function as an extension:\\\\n\\\\nIF(cond, a, b);\\\\n\\\\nSQL Server\\\\nIn addition to the standard expression, SQL Server (from 2012) provides an function:\\\\n\\\\nIIF(condition, true_value, false_value)\\\\n\\\\nOracle SQL\\\\nIn addition to the standard expression, Oracle has a variadic functional counterpart which operates similarly to a switch statement and can be used to emulate the conditional operator when testing for equality.\\\\n\\\\n-- General syntax takes case-result pairs, comparing against an expression, followed by a fall-back result:\\\\nDECODE(expression, case1, result1,\\\\n ...\\\\n caseN, resultN,\\\\n resultElse)\\\\n\\\\n-- We can emulate the conditional operator by just selecting one case:\\\\nDECODE(expression, condition, true, false)\\\\n\\\\nThe function is, today, deprecated in favour of the standard expression. This can be used in both Oracle SQL queries as well as PL/SQL blocks, whereas can only be used in the former.\\\\n\\\\nPerl\\\\nA traditional if-else construct in Perl is written:\\\\n\\\\nif ($a > $b) {\\\\n $result = $x;\\\\n} else {\\\\n $result = $y;\\\\n}\\\\n\\\\nRewritten to use the conditional operator:\\\\n\\\\n$result = $a > $b ? $x : $y;\\\\n\\\\nThe precedence of the conditional operator in perl is the same as in C, not as in C++. This is conveniently of higher precedence than a comma operator but lower than the precedence of most operators used in expressions within the ternary operator, so the use of parentheses is rarely required.\\\\n\\\\nIts associativity matches that of C and C++, not that of PHP. Unlike C but like C++, perl allows the use of the conditional expression as an L-value; for example:\\\\n\\\\n$a > $b ? $x : $y = $result;\\\\n\\\\nwill assign to either or depending on the logical expression\\\\'s boolean result.\\\\n\\\\nThe respective precedence rules and associativities of the operators used guarantee that the version absent any parentheses is equivalent to this explicitly parenthesized version:\\\\n\\\\n(($a > $b) ? $x : $y) = $result;\\\\n\\\\nThis is equivalent to the if-else version:\\\\n\\\\nif ($a > $b) {\\\\n $x = $result;\\\\n} else {\\\\n $y = $result;\\\\n}\\\\n\\\\nPHP\\\\nA simple PHP implementation is this:\\\\n\\\\n$abs = $value >= 0 ? $value : -$value;\\\\n\\\\nDue to an unfortunate design of the language grammar, the conditional operator in PHP is left associative in contrast to other languages, thus given a value of T for arg, the PHP code in the following example would yield the value horse instead of train as one might expect:\\\\n\\\\n b else y\\\\n\\\\nThis form invites considering as the normal value and as an exceptional case. \\\\n\\\\nPrior to Python\\\\xa02.5 there were a number of ways to approximate a conditional operator (for example by indexing into a two element array), all of which have drawbacks as compared to the built-in operator.\\\\n\\\\nR\\\\nThe traditional if-else construct in R (which is an implementation of S) is:\\\\n\\\\nif (a < b) {\\\\n x <- \\\"true\\\"\\\\n} else {\\\\n x <- \\\"false\\\"\\\\n}\\\\n\\\\nIf there is only one statement in each block, braces can be omitted, like in C:\\\\n\\\\nif (a < b)\\\\n x <- \\\"true\\\"\\\\nelse\\\\n x <- \\\"false\\\"\\\\n\\\\nThe code above can be written in the following non-standard condensed way:\\\\n\\\\nx <- if (a < b) \\\"true\\\" else \\\"false\\\"\\\\n\\\\nThere exists also the function that allows rewriting the expression above as:\\\\n\\\\nx <- ifelse(a < b, \\\"true\\\", \\\"false\\\")\\\\n\\\\nThe function is automatically vectorized. For instance:\\\\n\\\\n> ifelse(c (0, 2) < 1, \\\"true\\\", \\\"false\\\")\\\\n[1] \\\"true\\\" \\\"false\\\"\\\\n\\\\nRaku\\\\nRaku uses a doubled symbol instead of single \\\\nand a doubled symbol instead of \\\\n\\\\n$result = $a > $b ?? $x !! $y;\\\\n\\\\nRuby\\\\nExample of using this operator in Ruby:\\\\n\\\\n1 == 2 ? \\\"true value\\\" : \\\"false value\\\"\\\\n\\\\nReturns \\\"false value\\\".\\\\n\\\\nA traditional if-else construct in Ruby is written:\\\\n\\\\nif a > b\\\\n result = x\\\\nelse\\\\n result = y\\\\nend\\\\n\\\\nThis could also be written as:\\\\n\\\\nresult = if a > b\\\\n x\\\\nelse\\\\n y\\\\nend\\\\n\\\\nThese can be rewritten as the following statement:\\\\n\\\\nresult = a > b ? x : y\\\\n\\\\nRust\\\\nBeing an expression-oriented programming language, Rust\\\\'s existing if expr1 else expr2 syntax can behave as the traditional ternary operator does. Earlier versions of the language did have the operator but it was removed due to duplication with .\\\\n\\\\nNote the lack of semi-colons in the code below compared to a more declarative ... block, and the semi-colon at the end of the assignment to .\\\\n\\\\nlet x = 5;\\\\n\\\\nlet y = if x == 5 {\\\\n 10\\\\n} else {\\\\n 15\\\\n};\\\\n\\\\nThis could also be written as:\\\\n\\\\nlet y = if x == 5 { 10 } else { 15 };\\\\n\\\\nNote that curly braces are mandatory in Rust conditional expressions.\\\\n\\\\nYou could also use a expression:\\\\n\\\\nlet y = match x {\\\\n 5 => 10,\\\\n _ => 15,\\\\n};\\\\n\\\\nScheme\\\\nSame as in Common Lisp. Every expression has a value. Thus the builtin can be used:\\\\n\\\\n(let* ((x 5)\\\\n (y (if (= x 5) 10 15)))\\\\n ...)\\\\n\\\\nSmalltalk\\\\nEvery expression (message send) has a value. Thus can be used:\\\\n\\\\n|x y|\\\\n\\\\nx := 5.\\\\ny := (x == 5) ifTrue:[10] ifFalse:[15].\\\\n\\\\nSwift\\\\nThe ternary conditional operator of Swift is written in the usual way of the C tradition, and is used within expressions.\\\\n\\\\nlet result = a > b ? a : b\\\\n\\\\nTcl\\\\nIn Tcl, this operator is available in expr expressions only:\\\\n\\\\nset x 5\\\\nset y [expr {$x == 5 ? 10 : 15}]\\\\n\\\\nOutside of expr, if can be used for a similar purpose, as it also returns a value:\\\\npackage require math\\\\n\\\\nset x 5\\\\nset y [if {$x == 5} {\\\\n ::math::random $x\\\\n} else {\\\\n ::math::fibonacci $x\\\\n}]\\\\n\\\\nTestStand\\\\nIn a National Instruments TestStand expression, if condition is true, the first expression is evaluated and becomes the output of the conditional operation; if false, the second expression is evaluated and becomes the result. Only one of two expressions is ever evaluated.\\\\n\\\\ncondition ? first_expression : second_expression\\\\n\\\\nFor example:\\\\n\\\\nRunState.Root.Parameters.TestSocket.Index == 3 ? Locals.UUTIndex = 3 : Locals.UUTIndex = 0\\\\n\\\\nSets the local variable to 3 if is 3, otherwise it sets to 0.\\\\n\\\\nSimilar to other languages, first_expression and second_expression do not need to be autonomous expressions, allowing the operator to be used for variable assignment:\\\\n\\\\nLocals.UUTIndex = ( RunState.Root.Parameters.TestSocket.Index == 3 ? 3 : 0 )\\\\n\\\\nVerilog\\\\nVerilog is technically a hardware description language, not a programming language though the semantics of both are very similar. It uses the syntax for the ternary operator.\\\\n\\\\n// using blocking assignment\\\\nwire out;\\\\nassign out = sel ? a : b;\\\\n\\\\nThis is equivalent to the more verbose Verilog code:\\\\n\\\\n// using blocking assignment\\\\nwire out;\\\\nif (sel === 1) // sel is 1, not 0, x or z\\\\n assign out = a;\\\\nelse if (sel === 0) // sel is 0, x or z (1 checked above)\\\\n assign out = b;\\\\nelse // sel is x or z (0 and 1 checked above)\\\\n assign out = [comment]; // a and b are compared bit by bit, and return for each bit\\\\n // an x if bits are different, and the bit value if the same\\\\n\\\\nVisual Basic\\\\nVisual Basic doesn\\\\'t use per se, but has a very similar implementation of this shorthand statement. Using the first example provided in this article, it can do:\\\\n\\\\n\\\\' variable = IIf(condition, value_if_true, value_if_false)\\\\nDim opening_time As Integer = IIf((day = SUNDAY), 12, 9)\\\\n\\\\nIn the above example, is a ternary function, but not a ternary operator. As a function, the values of all three portions are evaluated before the function call occurs. This imposed limitations, and in Visual Basic .Net 9.0, released with Visual Studio 2008, an actual conditional operator was introduced, using the keyword instead of . This allows the following example code to work:\\\\n\\\\nDim name As String = If(person Is Nothing, \\\"\\\", person.Name)\\\\n\\\\nUsing , would be evaluated even if person is (Nothing), causing an exception. With a true short-circuiting conditional operator, is not evaluated unless person is not .\\\\n\\\\nVisual Basic Version 9 has added the operator in addition to the existing function that existed previously. As a true operator, it does not have the side effects and potential inefficiencies of the function.\\\\n\\\\nThe syntaxes of the tokens are similar: vs . As mentioned above, the function call has significant disadvantages, because the sub-expressions must all be evaluated, according to Visual Basic\\\\'s evaluation strategy for function calls and the result will always be of type variant (VB) or object (VB.NET). The operator however does not suffer from these problems as it supports conditional evaluation and determines the type of the expression based on the types of its operands.\\\\n\\\\nResult type\\\\nClearly the type of the result of the operator must be in some sense the type unification of the types of its second and third operands. In C this is accomplished for numeric types by arithmetic promotion; since C does not have a type hierarchy for pointer types, pointer operands may only be used if they are of the same type (ignoring type qualifiers) or one is void or NULL. It is undefined behaviour to mix pointer and integral or incompatible pointer types; thus\\\\n\\\\nnumber = spell_out_numbers ? \\\"forty-two\\\" : 42;\\\\n\\\\nwill result in a compile-time error in most compilers.\\\\n\\\\n?: in style guidelines\\\\nConditional operators are widely used and can be useful in certain circumstances to avoid the use of an statement, either because the extra verbiage would be too lengthy or because the syntactic context does not permit a statement. For example:\\\\n\\\\n #define MAX(a, b) (((a)>(b)) ? (a) : (b))\\\\n\\\\nor\\\\n\\\\n for (i = 0; i < MAX_PATTERNS; i++)\\\\n c_patterns[i].ShowWindow(m_data.fOn[i] ? SW_SHOW : SW_HIDE);\\\\n\\\\n(The latter example uses the Microsoft Foundation Classes Framework for Win32.)\\\\n\\\\nInitialization\\\\nAn important use of the conditional operator is in allowing a single initialization statement, rather than multiple initialization statements. In many cases this also allows single assignment and for an identifier to be a constant.\\\\n\\\\nThe simplest benefit is avoiding duplicating the variable name, as in Python:\\\\n\\\\nx = \\\\'foo\\\\' if b else \\\\'bar\\\\'\\\\n\\\\ninstead of:\\\\n\\\\nif b:\\\\n x = \\\\'foo\\\\'\\\\nelse:\\\\n x = \\\\'bar\\\\'\\\\n\\\\nMore importantly, in languages with block scope, such as C++, the blocks of an if/else statement create new scopes, and thus variables must be declared before the if/else statement, as:\\\\n\\\\nstd::string s;\\\\nif (b)\\\\n s = \\\"foo\\\";\\\\nelse\\\\n s = \\\"bar\\\";\\\\n\\\\nUse of the conditional operator simplifies this:\\\\n\\\\nstd::string s = b ? \\\"foo\\\" : \\\"bar\\\";\\\\n\\\\nFurthermore, since initialization is now part of the declaration, rather than a separate statement, the identifier can be a constant (formally, of type):\\\\n\\\\nconst std::string s = b ? \\\"foo\\\" : \\\"bar\\\";\\\\n\\\\nCase selectors\\\\nWhen properly formatted, the conditional operator can be used to write simple and coherent case selectors. For example:\\\\n\\\\nvehicle = arg == \\\\'B\\\\' ? bus :\\\\n arg == \\\\'A\\\\' ? airplane :\\\\n arg == \\\\'T\\\\' ? train :\\\\n arg == \\\\'C\\\\' ? car :\\\\n arg == \\\\'H\\\\' ? horse :\\\\n feet;\\\\n\\\\nAppropriate use of the conditional operator in a variable assignment context reduces the probability of a bug from a faulty assignment as the assigned variable is stated just once as opposed to multiple times.\\\\n\\\\nProgramming languages without the conditional operator\\\\nThe following are examples of notable general-purpose programming languages that don\\\\'t provide a conditional operator:\\\\n\\\\n CoffeeScript\\\\n Go programming language\\\\n MATLAB\\\\n Pascal although Object Pascal / Delphi do have a function to do the same (with caveats)\\\\n Rust The construct is an expression and can be used to get the same functionality.\\\\n \\\\n PowerShell (in old versions) an elegant workaround is to use (,)[!()]\\\\n\\\\nSee also\\\\n IIf, inline if function\\\\n Null coalescing operator, operator\\\\n Elvis operator, , or sometimes , as a shorthand binary operator\\\\n Conditioned disjunction, equivalent ternary logical connective.\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n Description of If operator in Visual Basic\\\\n Description of Conditional Expression in Python (PEP 308)\\\\n Description in the Java Language Specification\\\\n Description in the PHP Language Documentation\\\\n\\\\nConditional constructs\\\\nOperators (programming)\\\\nTernary operations\\\\nArticles with example code\\\\n\\\\nde:Bedingte Anweisung und Verzweigung#Auswahloperator'}],\\n\",\n \" 'negative_passages': [{'docid': 'doc-en-9',\\n\",\n \" 'text': 'The Pirates of Penzance; or, The Slave of Duty is a comic opera in two acts, with music by Arthur Sullivan and libretto by W.\\\\xa0S.\\\\xa0Gilbert. The opera\\\\'s official premiere was at the Fifth Avenue Theatre in New York City on 31 December 1879, where the show was well received by both audiences and critics. Its London debut was on 3 April 1880, at the Opera Comique, where it ran for 363 performances.\\\\n\\\\nThe story concerns Frederic, who, having completed his 21st year, is released from his apprenticeship to a band of tender-hearted pirates. He meets the daughters of Major-General Stanley, including Mabel, and the two young people fall instantly in love. Frederic soon learns, however, that he was born on the 29th of February, and so, technically, he has a birthday only once each leap year. His indenture specifies that he remain apprenticed to the pirates until his \\\"twenty-first birthday\\\", meaning that he must serve for another 63 years. Bound by his own sense of duty, Frederic\\\\'s only solace is that Mabel agrees to wait for him faithfully.\\\\n\\\\nPirates was the fifth Gilbert and Sullivan collaboration and introduced the much-parodied \\\"Major-General\\\\'s Song\\\". The opera was performed for over a century by the D\\\\'Oyly Carte Opera Company in Britain and by many other opera companies and repertory companies worldwide. Modernized productions include Joseph Papp\\\\'s 1981 Broadway production, which ran for 787 performances, winning the Tony Award for Best Revival and the Drama Desk Award for Outstanding Musical, and spawning many imitations and a 1983 film adaptation. Pirates remains popular today, taking its place along with The Mikado and H.M.S. Pinafore as one of the most frequently played Gilbert and Sullivan operas.\\\\n\\\\nBackground\\\\n\\\\nThe Pirates of Penzance was the only Gilbert and Sullivan opera to have its official premiere in the United States. At the time, American law offered no copyright protection to foreigners. After the pair\\\\'s previous opera, H.M.S. Pinafore, achieved success in London in 1878, approximately 150 American companies quickly mounted unauthorised productions that often took considerable liberties with the text and paid no royalties to the creators. Gilbert and Sullivan hoped to forestall further \\\"copyright piracy\\\" by mounting the first production of their next opera in America, before others could copy it, and by delaying publication of the score and libretto. They succeeded in keeping for themselves the direct profits of the first American production of The Pirates of Penzance by opening the production themselves on Broadway, prior to the London production, and they also operated profitable US touring companies of Pirates and Pinafore. However, Gilbert, Sullivan, and their producer, Richard D\\\\'Oyly Carte, failed in their efforts, over the next decade, to control the American performance copyrights to Pirates and their other operas.\\\\n\\\\nFiction and plays about pirates were ubiquitous in the 19th century. Walter Scott\\\\'s The Pirate (1822) and James Fenimore Cooper\\\\'s The Red Rover were key sources for the romanticised, dashing pirate image and the idea of repentant pirates. Both Gilbert and Sullivan had parodied these ideas early in their careers. Sullivan had written a comic opera called The Contrabandista, in 1867, about a hapless British tourist who is captured by bandits and forced to become their chief. Gilbert had written several comic works that involved pirates or bandits. In Gilbert\\\\'s 1876 opera Princess Toto, the title character is eager to be captured by a brigand chief. Gilbert had translated Jacques Offenbach\\\\'s operetta Les brigands, in 1871. As in Les brigands, The Pirates of Penzance absurdly treats stealing as a professional career path, with apprentices and tools of the trade such as the crowbar and life preserver.\\\\n\\\\nGenesis\\\\nWhile Pinafore was running strongly at the Opera Comique in London, Gilbert was eager to get started on his and Sullivan\\\\'s next opera, and he began working on the libretto in December 1878. He re-used several elements of his 1870 one-act piece, Our Island Home, which had introduced a pirate \\\"chief\\\", Captain Bang. Bang was mistakenly apprenticed to a pirate band as a child by his deaf nursemaid. Also, Bang, like Frederic in The Pirates of Penzance, had never seen a woman before and felt a keen sense of duty, as an apprenticed pirate, until the passage of his twenty-first birthday freed him from his articles of indenture. Bernard Shaw believed that Gilbert drew on ideas in Les brigands for his new libretto, including the businesslike bandits and the bumbling police. Gilbert and Sullivan also inserted into Act II an idea they first considered for a one-act opera parody in 1876 about burglars meeting police, while their conflict escapes the notice of the oblivious father of a large family of girls. As in Pinafore, \\\"there was a wordful self-descriptive set-piece for Stanley [\\\"The Major-General\\\\'s Song\\\"], introducing himself much as Sir Joseph Porter had done ... a lugubrious comic number for the Sergeant of Police ... a song of confession for Ruth, the successor [to] Little Buttercup\\\", romantic material for Frederic and Mabel, and \\\"ensemble and chorus music in turn pretty, parodic and atmospheric.\\\"\\\\n\\\\nGilbert, Sullivan and Carte met by 24 April 1879 to make plans for a production of Pinafore and the new opera in America. Carte travelled to New York in the summer of 1879 and made arrangements with theatre manager John T. Ford to present, at the Fifth Avenue Theatre, the authorised productions. He then returned to London. Meanwhile, once Pinafore became a hit in London, the author, composer and producer had the financial resources to produce future shows themselves, and they executed a plan to free themselves from their financial backers in the \\\"Comedy Opera Company\\\". Carte formed a new partnership with Gilbert and Sullivan to divide profits equally among themselves after the expenses of each of their shows.\\\\n\\\\nIn November 1879, Gilbert, Sullivan and Carte sailed to America with a company of singing actors, to play both Pinafore and the new opera, including J. H. Ryley as Sir Joseph, Blanche Roosevelt as Josephine, Alice Barnett as Little Buttercup, Furneaux Cook as Dick Deadeye, Hugh Talbot as Ralph Rackstraw and Jessie Bond as Cousin Hebe, some of whom had been in the Pinafore cast in London. To these, he added some American singers, including Signor Brocolini as Captain Corcoran. Alfred Cellier came to assist Sullivan, while his brother François Cellier remained in London to conduct Pinafore there. Gilbert and Sullivan cast talented actors who were not well-known stars and did not command high fees. They then tailored their operas to the particular abilities of these performers. The skill with which Gilbert and Sullivan used their performers had an effect on the audience: as critic Herman Klein wrote, \\\"we secretly marvelled at the naturalness and ease with which [the Gilbertian quips and absurdities] were said and done. For until then no living soul had seen upon the stage such weird, eccentric, yet intensely human beings\\\\xa0.... [They] conjured into existence a hitherto unknown comic world of sheer delight.\\\" Gilbert acted as stage director for his own plays and operas. He sought naturalism in acting, which was unusual at the time, just as he strove for realistic visual elements. He deprecated self-conscious interaction with the audience and insisted on a style of portrayal in which the characters were never aware of their own absurdity but were coherent internal wholes. Sullivan conducted the music rehearsals.\\\\n\\\\nSullivan had sketched out the music for Pirates in England. When he arrived in New York, however, he found that he had left the sketches for Act I behind, and he had to reconstruct the first act from memory, or compose new numbers. Gilbert told a correspondent many years later that Sullivan was unable to recall his setting of the entrance of the women\\\\'s chorus, so they substituted the chorus \\\"Climbing over rocky mountain\\\" from their earlier opera, Thespis. Sullivan\\\\'s manuscript for Pirates contains pages removed from a Thespis score, with the vocal parts of this chorus altered from their original arrangement as a four-part chorus. Some scholars (e.g. Tillett and Spencer, 2000) have suggested that Gilbert and Sullivan had planned all along to re-use \\\"Climbing over rocky mountain,\\\" and perhaps other parts of Thespis. They argue that Sullivan\\\\'s having brought the unpublished Thespis score to New York, when there were no plans to revive Thespis, might not have been accidental. In any case, on 10 December 1879, Sullivan wrote a letter to his mother about the new opera, upon which he was hard at work in New York. \\\"I think it will be a great success, for it is exquisitely funny, and the music is strikingly tuneful and catching.\\\" As was his usual practice in his operas, Sullivan left the overture for the last moment, often sketching it out and entrusting completion of \\\"the details\\\" to an assistant, in this case the company\\\\'s music director, Alfred Cellier.\\\\n\\\\nPinafore opened in New York on 1 December 1879 and ran for the rest of December. After a reasonably strong first week, audiences quickly fell off, since most New Yorkers had already seen local productions of Pinafore. In the meantime, Gilbert and Sullivan raced to complete and rehearse The Pirates of Penzance. The work\\\\'s title is a multi-layered joke. On the one hand, Penzance was a docile seaside resort in 1879, and not the place where one would expect to encounter pirates. On the other hand, the title was also a jab at the theatrical \\\"pirates\\\" who had staged unlicensed productions of H.M.S. Pinafore in America. To secure the British copyright, a D\\\\'Oyly Carte touring company gave a perfunctory copyright performance of Pirates the afternoon before the New York premiere, at the Royal Bijou Theatre in Paignton, Devon, organised by Helen Lenoir, who would later marry Richard D\\\\'Oyly Carte. The cast, which was performing Pinafore in the evenings in Torquay, received some of the music for Pirates only two days beforehand. Having had only one rehearsal, they travelled to nearby Paignton for the matinee, where they read their parts from scripts carried onto the stage, making do with whatever costumes they had on hand.\\\\n\\\\nOriginal production and aftermath\\\\n\\\\nPirates opened on 31 December 1879 in New York and was an immediate hit. On 2 January 1880, Sullivan wrote, in another letter to his mother from New York, \\\"The libretto is ingenious, clever, wonderfully funny in parts, and sometimes brilliant in dialogue – beautifully written for music, as is all Gilbert does. ... The music is infinitely superior in every way to the Pinafore – \\\\'tunier\\\\' and more developed, of a higher class altogether. I think that in time it will be very popular.\\\" Shortly thereafter, Carte sent three touring companies around the United States East Coast and Midwest, playing Pirates and Pinafore. Sullivan\\\\'s prediction was correct. After a strong run in New York and several American tours, Pirates opened in London on 3 April 1880, running for 363 performances there. It remains one of the most popular G&S works. The London sets were designed by John O\\\\'Connor.\\\\n\\\\nThe critics\\\\' notices were generally excellent in both New York and London. The character of Major-General Stanley was widely taken to be a caricature of the popular general Sir Garnet Wolseley. The biographer Michael Ainger, however, doubts that Gilbert intended a caricature of Wolseley, identifying instead General Henry Turner, uncle of Gilbert\\\\'s wife, as the pattern for the \\\"modern Major-General\\\". Gilbert disliked Turner, who, unlike the progressive Wolseley, was of the old school of officers. Nevertheless, in the original London production, George Grossmith imitated Wolseley\\\\'s mannerisms and appearance, particularly his large moustache, and the audience recognised the allusion. Wolseley himself, according to his biographer, took no offence at the caricature and sometimes sang \\\"I am the very model of a modern Major-General\\\" for the private amusement of his family and friends.\\\\n\\\\nRoles\\\\n Major-General Stanley (comic baritone)\\\\n The Pirate King (bass-baritone)\\\\n Samuel, his Lieutenant (baritone)\\\\n Frederic, the Pirate Apprentice (tenor)\\\\n Sergeant of Police (bass)\\\\nGeneral Stanley\\\\'s daughters\\\\n Mabel (soprano)\\\\n Edith (mezzo-soprano)\\\\n Kate (mezzo-soprano)\\\\n Isabel (speaking role)\\\\n Ruth, a Piratical Maid of all work (contralto)\\\\n Chorus of Pirates, Police and General Stanley\\\\'s Daughters\\\\n\\\\nSynopsis\\\\n\\\\nAct I\\\\nOn the coast of Cornwall, during Queen Victoria\\\\'s reign, Frederic celebrates the completion of his twenty-first year and the end of his apprenticeship to a gentlemanly band of pirates (\\\"Pour, oh pour the pirate sherry\\\"). The pirates\\\\' maid of all work, Ruth, appears and reveals that, as Frederic\\\\'s nursemaid long ago, she made a mistake \\\"through being hard of hearing\\\": Mishearing Frederic\\\\'s father\\\\'s instructions, she apprenticed him to a pirate, instead of to a ship\\\\'s pilot (\\\"When Frederic was a little lad\\\").\\\\n\\\\nFrederic has never seen any woman other than Ruth, and he believes her to be beautiful. The pirates know better and suggest that Frederic take Ruth with him when he returns to civilisation. Frederic announces that, although it pains him, so strong is his sense of duty that, once free from his apprenticeship, he will be forced to devote himself to the pirates\\\\' extermination. He also points out that they are not successful pirates: since they are all orphans, they allow their prey to go free if they too are orphans. Frederic notes that word of this has got about, so captured ships\\\\' companies routinely claim to be orphans. Frederic invites the pirates to give up piracy and go with him, so that he need not destroy them, but the Pirate King says that, contrasted with respectability, piracy is comparatively honest (\\\"Oh! better far to live and die\\\"). The pirates depart, leaving Frederic and Ruth. Frederic sees a group of beautiful young girls approaching the pirate lair, and realises that Ruth misled him about her appearance (\\\"Oh false one! You have deceived me!\\\"). Sending Ruth away, Frederic hides before the girls arrive.\\\\n\\\\nThe girls burst exuberantly upon the secluded spot (\\\"Climbing over rocky mountain\\\"). Frederic reveals himself (\\\"Stop, ladies, pray!\\\"), startling them. He appeals to them to help him reform (\\\"Oh! is there not one maiden breast?\\\"). The girls are fascinated by him, but all reject him, except one: Mabel responds to his plea, chiding her sisters for their lack of charity (\\\"Oh sisters deaf to pity\\\\'s name for shame!\\\"). She offers Frederic her pity (\\\"Poor wand\\\\'ring one\\\"), and the two quickly fall in love. The other girls discuss whether to eavesdrop or to leave the new couple alone (\\\"What ought we to do?\\\"), deciding to \\\"talk about the weather,\\\" although they steal glances at the affectionate couple (\\\"How beautifully blue the sky\\\").\\\\n\\\\nFrederic warns the young ladies that his old associates will soon return (\\\"Stay, we must not lose our senses\\\"), but before they can flee, the pirates arrive and capture the girls, intending to marry them (\\\"Here\\\\'s a first rate opportunity\\\"). Mabel warns the pirates that the girls\\\\' father is a Major-General (\\\"Hold, monsters!\\\"), who soon arrives and introduces himself (\\\"I am the very model of a modern Major-General\\\"). He appeals to the pirates not to take his daughters, leaving him to face his old age alone. Having heard of the famous Pirates of Penzance, he pretends that he is an orphan to elicit their sympathy (\\\"Oh, men of dark and dismal fate\\\"). The soft-hearted pirates release the girls (\\\"Hail, Poetry!\\\"), making Major-General Stanley and his daughters honorary members of their band (\\\"Pray observe the magnanimity\\\").\\\\n\\\\nAct II\\\\nThe Major-General sits in a ruined chapel on his estate, surrounded by his daughters. His conscience is tortured by the lie that he told the pirates, and the girls attempt to console him (\\\"Oh dry the glist\\\\'ning tear\\\"). The Sergeant of Police and his corps arrive to announce their readiness to arrest the pirates (\\\"When the foeman bares his steel\\\"). The girls loudly express their admiration of the police for facing likely slaughter by fierce and merciless foes. The police are unnerved by this and leave reluctantly.\\\\n\\\\nLeft alone, Frederic, who is to lead the police, reflects on his opportunity to atone for a life of piracy (\\\"Now for the pirates\\\\' lair\\\"), at which point he encounters Ruth and the Pirate King. They have realised that Frederic\\\\'s apprenticeship was worded so as to bind him to them until his twenty-first birthday – and, because that birthday happens to be on the 29th of February (in a leap year), it means that technically only five birthdays have passed (\\\"When you had left our pirate fold\\\"), and he will not reach his twenty-first birthday until he is in his eighties. Frederic is convinced by this logic and agrees to rejoin the pirates. He then sees it as his duty to inform the Pirate King of the Major-General\\\\'s deception. The outraged outlaw declares that the pirates\\\\' \\\"revenge will be swift and terrible\\\" (\\\"Away, away, my heart\\\\'s on fire\\\").\\\\n\\\\nFrederic meets Mabel (\\\"All is prepared\\\"), and she pleads with him to stay (\\\"Stay Frederic, stay\\\"), but he feels bound by his duty to the pirates until his 21st birthday – in 1940. They agree to be faithful to each other until then, though to Mabel \\\"It seems so long\\\" (\\\"Oh, here is love, and here is truth\\\"); Frederic departs. Mabel steels herself (\\\"No, I\\\\'ll be brave\\\") and tells the police that they must go alone to face the pirates. They muse that an outlaw might be just like any other man, and it is a shame to deprive him of \\\"that liberty which is so dear to all\\\" (\\\"When a felon\\\\'s not engaged in his employment\\\"). The police hide on hearing the approach of the pirates (\\\"A rollicking band of pirates we\\\"), who have stolen onto the estate, intending to take revenge for the Major-General\\\\'s lie (\\\"With cat-like tread\\\").\\\\n\\\\nJust then, Major-General Stanley appears, sleepless with guilt, and the pirates also hide (\\\"Hush, hush! not a word\\\"), while the Major-General listens to the soothing breeze (\\\"Sighing softly to the river\\\"). The girls come looking for him. The pirates leap out to seize them, and the police rush to their defense; but the police are easily defeated, and the Pirate King urges the captured Major-General to prepare for death. The Sergeant has one stratagem left: he demands that the pirates yield \\\"in Queen Victoria\\\\'s name\\\"; the pirates, overcome with loyalty to their Queen, do so. Ruth appears and reveals that the pirates are \\\"all noblemen who have gone wrong\\\". The Major-General is impressed by this and all is forgiven. Frederic and Mabel are reunited, and the Major-General is happy to marry his daughters to the noble ex-pirates after all (\\\"Poor Wand\\\\'ring Ones\\\" (reprise)).\\\\n\\\\nMusical numbers\\\\n Overture (includes \\\"With cat-like tread\\\", \\\"Ah, leave me not to pine\\\", \\\"Pray observe the magnanimity\\\", \\\"When you had left our pirate fold\\\", \\\"Climbing over rocky mountain\\\", and \\\"How beautifully blue the sky\\\")\\\\n\\\\nAct I\\\\n\\\\n 1. \\\"Pour, oh pour, the pirate sherry\\\" (Samuel and Chorus of Pirates)\\\\n 2. \\\"When Fred\\\\'ric was a little lad\\\" (Ruth)\\\\n 3. \\\"Oh, better far to live and die\\\" (Pirate King and Chorus of Pirates)\\\\n 4. \\\"Oh! false one, you have deceiv\\\\'d me\\\" (Frederic and Ruth)\\\\n 5. \\\"Climbing over rocky mountain\\\" (Chorus of Girls)\\\\n 6. \\\"Stop, ladies, pray\\\" (Edith, Kate, Frederic, and Chorus of Girls)\\\\n 7. \\\"Oh, is there not one maiden breast?\\\" (Frederic and Chorus of Girls)\\\\n 8. \\\"Poor wand\\\\'ring one\\\" (Mabel and Chorus of Girls)\\\\n 9. \\\"What ought we to do?\\\" (Edith, Kate, and Chorus of Girls)\\\\n 10. \\\"How beautifully blue the sky\\\" (Mabel, Frederic, and Chorus of Girls)\\\\n 11. \\\"Stay, we must not lose our senses\\\" ... \\\"Here\\\\'s a first-rate opportunity to get married with impunity\\\" (Frederic and Chorus of Girls and Pirates)\\\\n 12. \\\"Hold, monsters\\\" (Mabel, Major-General, Samuel, and Chorus)\\\\n 13. \\\"I am the very model of a modern Major-General\\\" (Major-General and Chorus)\\\\n 14. Finale Act I (Mabel, Kate, Edith, Ruth, Frederic, Samuel, King, Major-General, and Chorus)\\\\n \\\"Oh, men of dark and dismal fate\\\"\\\\n \\\"I’m telling a terrible story\\\"\\\\n \\\"Hail, Poetry\\\"\\\\n \\\"Oh, happy day, with joyous glee\\\"\\\\n \\\"Pray observe the magnanimity\\\" (reprise of \\\"Here\\\\'s a first-rate opportunity\\\")\\\\n\\\\nAct II\\\\n 15. \\\"Oh, dry the glist\\\\'ning tear\\\" (Mabel and Chorus of Girls)\\\\n 16. \\\"Then, Frederic, let your escort lion-hearted\\\" (Frederic and Major-General)\\\\n 17. \\\"When the foeman bares his steel\\\" (Mabel, Edith, Sergeant, and Chorus of Policemen and Girls)\\\\n 18. \\\"Now for the pirates\\\\' lair!\\\" (Frederic, Ruth, and King)\\\\n 19. \\\"When you had left our pirate fold\\\" [The \\\"paradox\\\" trio] (Ruth, Frederic, and King)\\\\n 20. \\\"Away, away! My heart\\\\'s on fire!\\\" (Ruth, Frederic, and King)\\\\n 21. \\\"All is prepar\\\\'d; your gallant crew await you\\\" (Mabel and Frederic)\\\\n 22. \\\"Stay, Fred\\\\'ric, stay\\\" ... \\\"Ah, leave me not to pine\\\" ... \\\"Oh, here is love, and here is truth\\\" (Mabel and Frederic)\\\\n 23. \\\"No, I\\\\'ll be brave\\\" ... \\\"Though in body and in mind\\\" (Reprise of \\\"When the foeman bares his steel\\\") (Mabel, Sergeant, and Chorus of Police)\\\\n 23a. \\\"Sergeant, approach!\\\" (Mabel, Sergeant of Police, and Chorus of Police)\\\\n 24. \\\"When a felon\\\\'s not engaged in his employment\\\" (Sergeant and Chorus of Police)\\\\n 25. \\\"A rollicking band of pirates we\\\" (Sergeant and Chorus of Pirates and Police)\\\\n 26. \\\"With cat-like tread, upon our prey we steal\\\" (Samuel and Chorus of Pirates and Police)\\\\n 27. \\\"Hush, hush, not a word!\\\" (Frederic, King, Major-General, and Chorus of Police and Pirates)\\\\n 28. Finale, Act II (Ensemble)\\\\n \\\"Sighing softly to the river\\\"\\\\n \\\"Now what is this, and what is that?\\\"\\\\n \\\"You/We triumph now\\\"\\\\n \\\"Away with them, and place them at the bar!\\\"\\\\n \\\"Poor wandering ones!\\\"\\\\n\\\\nCritical reception\\\\nThe notices from critics were generally excellent in both New York and London in 1880. In New York, the Herald and the Tribune both dedicated considerable space to their reviews. The Herald took the view that \\\"the new work is in every respect superior to the Pinafore, the text more humorous, the music more elegant and more elaborate.\\\" The Tribune called it \\\"a brilliant and complete success\\\", commenting, \\\"The humor of the Pirates is richer, but more recondite. It demands a closer attention to the words [but] there are great stores of wit and drollery ... which will well repay exploration. ... The music is fresh, bright, elegant and merry, and much of it belongs to a higher order of art than the most popular of the tunes of Pinafore.\\\" The New York Times also praised the work, writing, \\\"it would be impossible for a confirmed misanthrope to refrain from merriment over it\\\", though the paper doubted if Pirates could repeat the prodigious success of Pinafore.\\\\n\\\\nAfter the London premiere, the critical consensus, led by the theatrical newspaper The Era, was that the new work marked a distinct advance on Gilbert and Sullivan\\\\'s earlier works. The Pall Mall Gazette said, \\\"Of Mr. Sullivan\\\\'s music we must speak in detail on some other occasion. Suffice it for the present to say that in the new style which he has marked out for himself it is the best he has written.\\\" The Graphic wrote:\\\\n\\\\nThere were a few dissenting comments: The Manchester Guardian thought both author and composer had drawn on the works of their predecessors: \\\"Mr. Gilbert ... seems to have borrowed an idea from Sheridan\\\\'s The Critic; Mr. Sullivan\\\\'s music is sprightly, tuneful and full of \\\\'go\\\\', although it is certainly lacking in originality.\\\" The Sporting Times noted, \\\"It doesn\\\\'t appear to have struck any of the critics yet that the central idea in The Pirates of Penzance is taken from Our Island Home, which was played by the German Reeds some ten years ago.\\\" The Times thought Gilbert\\\\'s wit outran his dramatic invention, and Sullivan\\\\'s music for the new work was not quite as good as his score for The Sorcerer, which the Times critic called a masterpiece.\\\\n\\\\nMusical analysis\\\\nThe overture to The Pirates of Penzance was composed by Sullivan and his musical assistant Alfred Cellier. It follows the pattern of most Savoy opera overtures: a lively opening (the melody of \\\"With cat-like tread\\\"), a slow middle section (\\\"Ah, leave me not to pine alone\\\"), and a concluding allegro in a compressed sonata form, in which the themes of \\\"How beautifully blue the sky\\\" and \\\"A paradox, a paradox\\\" are combined.\\\\n\\\\nParody\\\\nThe score parodies several composers, most conspicuously Verdi. \\\"Come, friends, who plough the sea\\\" and \\\"You triumph now\\\" are burlesques of Il trovatore, and one of the best-known choral passages from the finale to Act\\\\xa0I, \\\"Hail Poetry\\\", is, according to the Sullivan scholar, Arthur Jacobs, a burlesque of the prayer scene, \\\"La Vergine degli Angeli\\\", in Verdi\\\\'s La forza del destino. However, another musicologist, Nicholas Temperley, writes, \\\"The choral outburst \\\\'Hail, Poetry\\\\' in The Pirates of Penzance would need very little alteration to turn it into a Mozart string quartet.\\\" Another well-known parody number from the work is the song for coloratura, \\\"Poor wand\\\\'ring one\\\", which is generally thought to burlesque Gounod\\\\'s waltz-songs, though the music critic of The Times called it \\\"mock-Donizetti\\\". In a scene in Act\\\\xa0II, Mabel addresses the police, who chant their response in the manner of an Anglican church service.\\\\n\\\\nSullivan even managed to parody two composers at once. The critic Rodney Milnes describes the Major-General\\\\'s Act\\\\xa0II song, \\\"Sighing softly to the river\\\", \\\"as plainly inspired by – and indeed worthy of – Sullivan\\\\'s hero Schubert\\\", and Amanda Holden speaks of the song\\\\'s \\\"Schubertian water-rippling accompaniment\\\", but adds that it simultaneously spoofs Verdi\\\\'s Il trovatore, with the soloist unaware of a concealed male chorus singing behind him.\\\\n\\\\nPatter, counterpoint, and vocal writing\\\\n\\\\nWriting about patter songs, Shaw, in his capacity as a music critic, praised \\\"the time-honored lilt which Sir Arthur Sullivan, following the example of Mozart and Rossini, chose for the lists of accomplishments of the Major-General in The Pirates or the Colonel in Patience.\\\"\\\\n\\\\nThis opera contains two well-known examples of Sullivan\\\\'s characteristic combination of two seemingly disparate melodies. Jacobs suggests that Berlioz\\\\'s La damnation de Faust, a great favourite in Sullivan\\\\'s formative years, may have been the model for Sullivan\\\\'s trademark contrapuntal mingling of the rapid prattle of the women\\\\'s chorus in Act I (\\\"How beautifully blue the sky\\\") in 2/4 time with the lovers\\\\' duet in waltz time. Jacobs writes that \\\"the whole number [shifts] with Schubertian ease from B to G and back again.\\\" In Act II, a double chorus combines the policemen\\\\'s dogged tune, \\\"When the foeman bares his steel\\\" and the soaring line for the women, \\\"Go, ye heroes, go to glory\\\". In adapting the four-part chorus \\\"Climbing over rocky mountain\\\" from Thespis for re-use in Pirates, Sullivan took less trouble: he wrote only a single vocal line, suitable for soprano voices. Despite this, the number ends with another example of Sullivan\\\\'s counterpoint, with the chorus singing the second melody of the piece (\\\"Let us gaily tread the measure\\\") while the orchestra plays the first (\\\"Climbing over rocky mountain\\\").\\\\n\\\\nSullivan set a particular vocal challenge for the soprano who portrays Mabel. The Sullivan scholar Gervase Hughes wrote, \\\"Mabel ... must be a coloratura because of \\\\'Poor wand\\\\'ring one!\\\\', yet \\\\'Dear father, why leave your bed\\\\' demands steady beauty of tone throughout the octave F to F, and \\\\'Ah, leave me not to pine\\\\' goes a third lower still.\\\" In The Music of Arthur Sullivan (1959), Hughes quoted four extracts from Pirates, saying that if hearing each out of context one might attribute it to Schubert, Mendelssohn, Gounod or Bizet respectively, \\\"yet on learning the truth one would kick oneself for not having recognised Sullivan\\\\'s touch in all four.\\\" Hughes concluded by quoting the introductory bars of \\\"When a felon\\\\'s not engaged in his employment\\\", adding, \\\"There could never be any doubt as to who wrote that, and it is as English as our wonderful police themselves.\\\"\\\\n\\\\nVersions\\\\n\\\\nBecause the work was premiered in three different places (the Paignton performance and the full productions in New York and London), there are more variations in the early libretto and score of The Pirates of Penzance than in other Gilbert and Sullivan works. Songs sent from New York to the D\\\\'Oyly Carte touring company in England for the Paignton premiere were then altered or omitted during Broadway rehearsals. Gilbert and Sullivan trimmed the work for the London premiere, and Gilbert made further alterations up to and including the 1908 Savoy revival. For example, early versions depicted the Pirate King as the servant of the pirate band, and the words of the opening chorus were, \\\"Pour, O King, the pirate sherry\\\". In the original New York production the revelation by Ruth that the pirates are \\\"all noblemen who have gone wrong\\\" prompted the following exchange (recalling a famous passage in H.M.S. Pinafore):\\\\n\\\\nIn the original London production, this exchange was shortened to the following:\\\\n\\\\nGilbert deleted the exchange in the 1900 revival, and the Chappell vocal score was revised accordingly. For the 1908 revival Gilbert had the pirates yielding \\\"in good King Edward\\\\'s name\\\". Despite Helen Carte\\\\'s repeated urging, Gilbert did not prepare an authorised version of the libretti of the Savoy operas.\\\\n\\\\nIn its 1989 production, the D\\\\'Oyly Carte Opera Company restored one of the original versions of the finale, which finishes with a variation of \\\"I am the very model of a modern major-general\\\", rather than with the customary reprise of \\\"Poor wand\\\\'ring one\\\", but in later revivals, it reverted to the more familiar text.\\\\n\\\\nProduction history\\\\n\\\\nThe Pirates of Penzance has been one of Gilbert and Sullivan\\\\'s most popular comic operas. After its unique triple opening in 1879–80, it was revived in London at the Savoy Theatre in 1888 and in 1900, and for the Savoy\\\\'s repertory season of 1908–09. In the British provinces, the D\\\\'Oyly Carte Opera Company toured it almost continuously from 1880–1884, and again in 1888. It re-entered the D\\\\'Oyly Carte touring repertory in 1893 and was never again absent until the company\\\\'s closure in 1982. New costumes were designed by Percy Anderson in 1919 and George Sheringham in 1929 (who also executed a new Act I set). Peter Goffin created a new touring set in 1957.\\\\n\\\\nIn America, after the New York opening on New Year\\\\'s Eve, 1879, Richard D\\\\'Oyly Carte launched four companies that covered the United States on tours that lasted through the following summer. Gilbert and Sullivan themselves trained each of the touring companies through January and early February 1880, and each company\\\\'s first performance – whether it was in Philadelphia, Newark, or Buffalo – was conducted by the composer. In Australia, its first authorised performance was on 19 March 1881 at the Theatre Royal, Sydney, produced by J. C. Williamson. There was still no international copyright law in 1880, and the first unauthorised New York production was given by the Boston Ideal Opera Company at Booth\\\\'s Theatre in September of that year. The opera premiered in a German translation by Richard Genée and Camillo Walzel (Die Piraten) in Austria at the Theater an der Wien on 1 March 1889, and in Düsseldorf, Germany, on 1 December 1936.\\\\n\\\\nThe first non-D\\\\'Oyly Carte professional production in a country that had been subject to Gilbert\\\\'s copyright (other than Williamsons\\\\' authorised productions) was in Stratford, Ontario, Canada, in September 1961, as the copyright expired. In 1979, the Torbay branch of the Gilbert and Sullivan Society presented a centenary tribute to the world premiere performance of Pirates in Paignton, with a production at the Palace Avenue Theatre (situated a few metres from the former Bijou Theatre).\\\\n\\\\nNew York has seen over forty major revivals since the premiere. One of these, produced and directed by Winthrop Ames in 1926 at the Plymouth Theatre, ran for 128 performances and gained good notices. A brief 1952 Broadway staging starring Martyn Green, earned Lehman Engel a Tony Award as conductor. Repertory companies that have mounted Pirates numerous times Off-Broadway and on tour in the US have included the American Savoyards (1953–67), the Light Opera of Manhattan (1968–89) and the New York Gilbert and Sullivan Players (1976–present).\\\\n\\\\nAs discussed below, Joseph Papp\\\\'s 1980–83 Pirates ran for nearly two years each on Broadway and in the West End, boosting the opera\\\\'s popularity. Professional and amateur productions of the opera continue with frequency. For example, the Chicago Lyric Opera and English National Opera each staged the work in 2004, and in 2007, the New York City Opera and Opera Australia both mounted new productions. In 2013, Scottish Opera produced a British touring production of The Pirates of Penzance co-produced by the trustees of the D\\\\'Oyly Carte Opera Company. Richard Suart played Major-General Stanley and Nicholas Sharratt played Frederic.\\\\n\\\\nThe following table shows the history of the D\\\\'Oyly Carte productions in Gilbert\\\\'s lifetime (excluding tours):\\\\n\\\\nHistorical casting\\\\nThe following tables show the casts of the principal original productions and D\\\\'Oyly Carte Opera Company touring repertory at various times through to the company\\\\'s 1982 closure:\\\\n\\\\nJoseph Papp\\\\'s Pirates\\\\n\\\\nIn 1980, Joseph Papp and the Public Theater of New York City produced a new version of Pirates, directed by Wilford Leach and choreographed by Graciela Daniele, at the Delacorte Theatre in Central Park, as a Shakespeare in the Park summer event. Musical direction and arrangements were by William Elliott. The show played for 10 previews and 35 performances. It then transferred to Broadway, opening on 8 January 1981 for a run of 20 previews and 787 regular performances at the Uris and Minskoff Theatres, the longest run for any Gilbert and Sullivan production in history. This take on Pirates earned enthusiastic reviews and seven Tony Award nominations, winning three, including the award for Best Revival and for Leach as director. It was also nominated for eight Drama Desk Awards, winning five, including Outstanding Musical and director.\\\\n\\\\nCompared with traditional productions of the opera, Papp\\\\'s Pirates featured a more swashbuckling Pirate King and Frederic, and a broader, more musical comedy style of singing and humour. It did not significantly change the libretto, but it used a new orchestration and arrangements that changed some keys, added repeats, lengthened dance music and made other minor changes in the score. The \\\"Matter Patter\\\" trio from Ruddigore and \\\"Sorry her lot\\\" from H.M.S. Pinafore, two other Gilbert and Sullivan operas, were interpolated into the show. The production also restored Gilbert and Sullivan\\\\'s original New York ending, with a reprise of the Major-General\\\\'s song in the Act II finale. Linda Ronstadt starred as Mabel, Rex Smith as Frederic, Kevin Kline as the Pirate King, Patricia Routledge as Ruth (replaced by Estelle Parsons for the Broadway transfer), George Rose as the Major-General, and Tony Azito as the Sergeant of Police. Kline won a Tony Award for his performance. Smith won a Theatre World Award, and Kline and Azito won Drama Desk Awards. Notable replacements during the Broadway run included Karla DeVito, Maureen McGovern and Pam Dawber as Mabel; Robby Benson, Patrick Cassidy and Peter Noone as Frederic; Treat Williams, Gary Sandy, James Belushi and Wally Kurth as the Pirate King; David Garrison as the Sergeant; George S. Irving as the Major-General; and Kaye Ballard as Ruth. The Los Angeles cast of the production featured Barry Bostwick as the Pirate King, Jo Anne Worley as Ruth, Clive Revill as the Major-General, Dawber as Mabel, Paxton Whitehead as the Sergeant, Caroline Peyton as Edith and Andy Gibb as Frederic.\\\\n\\\\nThe production opened at the Theatre Royal, Drury Lane, London, on 26 May 1982, to generally warm reviews, for a run of 601 performances, earning an Olivier Award nomination as Outstanding Musical and another for Curry. Notable among the cast were George Cole and Ronald Fraser as the Major-General; Pamela Stephenson as Mabel; Michael Praed and Peter Noone as Frederic; Tim Curry, Timothy Bentinck, Oliver Tobias and Paul Nicholas as the Pirate King; Chris Langham as the Sergeant of Police; Annie Ross as Ruth; Bonnie Langford as Kate; and Louise Gold as Isabel. The Australian production opened in Melbourne in January 1984, opening the new Victorian Arts Centre, directed by John Feraro. It starred Jon English as the Pirate King, Simon Gallaher as Frederic, June Bronhill as Ruth, David Atkins as the Sergeant of Police and Marina Prior as Mabel. The six-week limited season was followed by an Australian national tour from 1984 to 1986 and another tour with same cast in the mid-1990s. In 1985, Papp\\\\'s Pirates opened the new Queensland Performing Arts Centre in Brisbane, setting attendance records that were not surpassed until many years later by The Phantom of the Opera. Gallaher\\\\'s Essgee Entertainment version of Pirates was inspired by the Papp version. The Papp version also inspired foreign-language productions in Germany and elsewhere in Europe.\\\\n\\\\nThe Papp production was turned into a film in 1983, with the original Broadway principal cast reprising their roles, except that Angela Lansbury replaced Estelle Parsons as Ruth. The minor roles used British actors miming to their Broadway counterparts. The film has been shown occasionally on television. Another film based loosely on the opera and inspired by the success of the Papp version, The Pirate Movie, was released during the Broadway run.\\\\n\\\\nThe Papp production design has been widely imitated in later productions of Pirates, even where traditional orchestration and the standard score are used. Some modern productions are also influenced by the Disney film franchise Pirates of the Caribbean, combining aspects of the Papp production with the Disney design concepts. Not all of these revivals have generated the same enthusiasm as Papp\\\\'s 1980s productions. A 1999 UK touring production received this critique: \\\"No doubt when Papp first staged this show in New York and London it had some quality of cheek or chutzpah or pizzazz or irony or something that accounted for its success. But all that\\\\'s left now ... is a crass Broadway-style musical arrangement ground out by a seven-piece band, and the worst kind of smutty send-up of a historic piece of art.\\\"\\\\n\\\\nRecordings\\\\nThe Pirates of Penzance has been recorded many times, and the critical consensus is that it has fared well on record. The first complete recording of the score was in 1921, under the direction of Rupert D\\\\'Oyly Carte, but with established recording singers rather than D\\\\'Oyly Carte Opera Company performers. In 1929, The Gramophone said of a new set with a mainly D\\\\'Oyly Carte cast, \\\"This new recording represents the high-water mark so far as Gilbert and Sullivan opera is concerned. In each of the previous Savoy albums there have been occasional lapses which prevented one from awarding them unqualified praise; but with the Pirates it is happily otherwise; from first to last, and in every bar, a simply delightful production.\\\" Of later recordings by the D\\\\'Oyly Carte Opera Company, the 1968 recording (with complete dialogue) is highly regarded: The online Gilbert and Sullivan Discography says, \\\"This recording is one of the best D\\\\'Oyly Carte sets of all time, and certainly the best Pirates\\\", and the Penguin Guide to Opera on Compact Disc also recommends it. So too does the Penguin Guide to Recorded Classical Music, alongside the 1993 Mackerras recording. The opera critic Alan Blyth recommended the D\\\\'Oyly Carte recording of 1990: \\\"a performance full of the kind of life that can only come from the experience of stage performances\\\". The online Discography site also mentions the 1981 Papp recording as \\\"excellent\\\", despite its inauthentic 1980 re-orchestrations that \\\"changed some of the timbres so as to appeal to a rock-oriented public\\\".\\\\n\\\\nOf the available commercial videos, the Discography site considers the Brent Walker better than the Papp version. More recent professional productions have been recorded on video by the International Gilbert and Sullivan Festival.\\\\n\\\\nSelected recordings\\\\n 1929 D\\\\'Oyly Carte – Conductor: Malcolm Sargent\\\\n 1957 D\\\\'Oyly Carte – New Symphony Orchestra of London; Conductor: Isidore Godfrey\\\\n 1961 Sargent/Glyndebourne – Pro Arte Orchestra, Glyndebourne Festival Chorus; Conductor: Sir Malcolm Sargent\\\\n 1968 D\\\\'Oyly Carte (with dialogue) – Royal Philharmonic Orchestra; Conductor: Isidore Godfrey\\\\n 1981; 1983 Papp\\\\'s Pirates (with dialogue) – Director: Wilford Leach; Musical Director: William Elliott; Choreographer: Graciela Daniele\\\\n 1982 Brent Walker Productions (with dialogue) – Ambrosian Opera Chorus, London Symphony Orchestra; Conductor: Alexander Faris; Stage Director: Michael Geliot\\\\n 1990 New D\\\\'Oyly Carte – Conductor: John Pryce-Jones\\\\n 1993 Mackerras/Telarc – Orchestra and Chorus of the Welsh National Opera; Conductor: Sir Charles Mackerras\\\\n 1994 Essgee Entertainment (video adaptation) – Director and Choreographer: Craig Schaefer; Orchestrator and Conductor: Kevin Hocking; Additional Lyrics: Melvyn Morrow\\\\n\\\\nCultural impact\\\\n\\\\nMajor-General\\\\'s Song\\\\n\\\\nPirates is one of the most frequently referenced works of Gilbert and Sullivan. The Major-General\\\\'s Song, in particular, is frequently parodied, pastiched and used in advertising. Parody versions have been used in political commentary as well as entertainment media. Its challenging patter has proved interesting to comedians; notable examples include Tom Lehrer\\\\'s song \\\"The Elements\\\" and David Hyde Pierce\\\\'s monologue, as host of Saturday Night Live. In 2010, comedian Ron Butler released a YouTube pastiche of the song in character as President Obama which, as of September 2021, had garnered more than 1.9 million views.\\\\n\\\\nPastiche examples include the Animaniacs version, \\\"I am the very model of a cartoon individual\\\", in the episode \\\"H.M.S. Yakko\\\"; the Doctor Who audio \\\"I am the very model of a Gallifreyan buccaneer\\\" in Doctor Who and the Pirates; the Studio 60 on the Sunset Strip version in the episode \\\"The Cold Open\\\" (2006), where the cast performs \\\"We\\\\'ll be the very model of a modern network TV show\\\"; and the Mass Effect 2 video game version, where the character Mordin Solus sings: \\\"I am the very model of a scientist Salarian\\\".\\\\n\\\\nThe song is often used in film and on television, unchanged in many instances, as a character\\\\'s audition piece, or seen in a \\\"school play\\\" scene. Examples include a VeggieTales episode entitled \\\"The Wonderful World of Auto-Tainment!\\\"; the Frasier episode \\\"Fathers and Sons\\\"; The Simpsons episode \\\"Deep Space Homer\\\"; and the Mad About You episode \\\"Moody Blues\\\", where Paul directs a charity production of Penzance starring his father, Burt, as the Major-General. In The Muppet Show (season 3, episode 4) guest host, comedian Gilda Radner, sings the song with a talking carrot (Parodying the pilot/pirate confusion in Pirates, Radner had requested a talking parrot, but was misheard). In an episode of Home Improvement, Al Borland begins to sing the song when tricked into thinking he is in a soundproof booth. In the Babylon 5 episode \\\"Atonement\\\", Marcus Cole uses the song to drive Dr Stephen Franklin crazy on a long journey to Mars.\\\\n\\\\nExamples of the use of the song in advertising include Martyn Green\\\\'s pastiche of the song listing all of the varieties of Campbell\\\\'s Soup and a 2011 Geico commercial in which a couple that wants to save money, but still listen to musicals, finds a roommate, dressed as the Major-General, who awkwardly begins the song while dancing on a coffee table. Gimbels department store had a campaign sung to the tune of the Major-General\\\\'s Song that began, \\\"We are the very model of a modern big department store.\\\" George Washington, in the number \\\"Right Hand Man\\\" from the 2015 musical Hamilton by Lin-Manuel Miranda, refers to himself with irony as \\\"The model of a modern major general\\\", which he rhymes with \\\"men are all\\\" and \\\"pedestal\\\". Miranda commented: \\\"I always felt like ‘mineral’ wasn\\\\'t the best possible rhyme.\\\"\\\\n\\\\nFilm and television\\\\nOther film references to Pirates include Kate & Leopold, where there are multiple references, including a scene where Leopold sings \\\"I Am The Very Model of A Modern Major-General\\\" while accompanying himself on the piano; and in Pretty Woman, Edward Lewis (Richard Gere) covers a social gaffe by prostitute Vivian Ward (Julia Roberts), who comments that the opera La traviata was so good that she almost \\\"peed [her] pants\\\", by saying that she had said that she liked it better than The Pirates of Penzance\\\". In Walt Disney\\\\'s cartoon Mickey, Donald, Goofy: The Three Musketeers (2004), there is a performance of Pirates that becomes the setting for the climactic battle between the Musketeers and Captain Pete. Pirates songs sung in the cartoon are \\\"With cat-like tread\\\", \\\"Poor wand\\\\'ring one\\\", \\\"Climbing over rocky mountain\\\" and the Major-General\\\\'s song. \\\"Poor wand\\\\'ring one\\\" was used in the movie An American Tail. The soundtrack of the 1992 film The Hand That Rocks the Cradle includes \\\"Poor Wand\\\\'ring One\\\" and \\\"Oh Dry the Glistening Tear\\\".\\\\n\\\\nTelevision references, in addition to those mentioned above, included the series The West Wing, where Pirates and other Gilbert and Sullivan operas are mentioned in several episodes, especially by Deputy Communications Director, Sam Seaborn, who was recording secretary of his school\\\\'s Gilbert and Sullivan society. In Studio 60 on the Sunset Strip, a poster from Pirates hangs on Matt Albie\\\\'s office wall. Both TV series were created by Aaron Sorkin. In the pilot episode of the 2008 CTV series Flashpoint, a police officer and his partner sing the policeman\\\\'s song. In an Assy McGee episode entitled \\\"Pegfinger\\\", Detective Sanchez\\\\'s wife is a member of a community theatre that performs the opera. In a 1986 episode of the animated television adaptation of The Wind in the Willows entitled A Producer\\\\'s Lot, several characters put on a production of Pirates. In a 2005 Family Guy episode \\\"Peter\\\\'s Got Woods\\\", Brian Griffin sings \\\"Sighing Softly\\\", with Peter Griffin\\\\'s assistance. In a 2012 episode, \\\"Killer Queen\\\", Peter gives a garbled rendition of the Major-General\\\\'s Song. In the 2009 Criminal Minds episode \\\"The Slave of Duty\\\", Hotch quotes \\\"Oh dry the glist\\\\'ning tear\\\". In the 1992 episode \\\"The Understudy\\\" of Clarissa Explains it All, the title character is chosen to understudy Mabel in a school production of Pirates and is unprepared when she must go on; a scene from The Mikado is also heard.\\\\n\\\\nOther references\\\\n\\\\nOther notable instances of references to Pirates include a New York Times article on 29 February 1940, memorialising that Frederic was finally out of his indentures. Six years previously, the arms granted to the municipal borough of Penzance in 1934 contain a pirate dressed in Gilbert\\\\'s original costuming, and Penzance had a rugby team called the Penzance Pirates, which is now called the Cornish Pirates. In 1980, Isaac Asimov wrote a short story called \\\"The Year of the Action\\\", concerning whether the action of Pirates took place on 1 March 1873, or 1 March 1877 (depending on whether Gilbert took into account the fact that 1900 was not a leap year). The plot of Laurie R. King\\\\'s 2011 novel Pirate King centers on a 1924 silent movie adaptation of The Pirates of Penzance.\\\\n\\\\nThe music from the chorus of \\\"With cat-like tread\\\", which begins \\\"Come, friends, who plough the sea,\\\" was used in the popular American song, \\\"Hail, Hail, the Gang\\\\'s All Here.\\\" \\\"With cat-like tread\\\" is also part of the soundtrack, along with other Gilbert and Sullivan songs, in the 1981 film, Chariots of Fire, and it was pastiched in the \\\"HMS Yakko\\\" episode of Animaniacs in a song about surfing a whale.\\\\n\\\\nAdaptations\\\\nStage\\\\n Di Yam Gazlonim, a Yiddish adaptation of Pirates by Al Grand that continues to be performed in North America. The 2006 production at the National Yiddish Theater Folksbiene was nominated for the 2007 Drama Desk Award for Outstanding Revival. The Montreal Express wrote in 2009, \\\"Grand\\\\'s adaptation is a delightfully whimsical treatment\\\".\\\\n The Parson\\\\'s Pirates by Opera della Luna premiered in 1995.\\\\n Pirates! Or, Gilbert and Sullivan Plunder\\\\'d (2006), is a musical comedy set on a Caribbean island, involving a voodoo curse that makes the pirates \\\"landsick\\\". It was first presented 1 November 2006 at Goodspeed Opera House in East Haddam, Connecticut, then in 2007 at the Paper Mill Playhouse in Millburn, New Jersey, in 2009 at the Huntington Theatre Company in Boston, Massachusetts, and at The Muny in St Louis, Missouri in 2012. Other Gilbert and Sullivan numbers, such as the Nightmare song from Iolanthe are interpolated.\\\\n Pirates of Penzance – The Ballet! premiered in 1991\\\\n Essgee Entertainment produced an adapted version in 1994 in Australia and New Zealand. Their producer, Simon Gallaher (Frederic in the Australian Papp production), produced another adaptation of Pirates that toured Australia from 2001 to 2003\\\\n All-male versions of the opera include a long-running adaptation by Sasha Regan at the Union Theatre in 2009, which transferred to Wilton\\\\'s Music Hall in London in 2010 and toured in Australia in 2012.\\\\n\\\\nFilm and TV\\\\n The Pirate Movie, a 1982 musical romantic comedy film loosely based on the opera.\\\\n The Pirates of Penzance, a 1983 film adaptation of Papp\\\\'s Broadway production.\\\\n Die Piraten, a German-language version, was premiered on German television in 1968 and starred Arleen Auger as Mabel, Gerd Nienstedt as the Pirate King and Martha Mödl as Ruth, with Franz Marszalek conducting. Mabel falls in love with the Pirate King, among other plot changes. A 2-CD set of the broadcast was issued by Gala Records in 2000.\\\\n Several other television adaptations of the opera have been made, beginning in 1939.\\\\n\\\\nSee also\\\\n Our Island Home, one of the sources of the libretto for Pirates\\\\n\\\\nReferences\\\\n\\\\nSources\\\\n \\\\n \\\\n \\\\n (Chapters 5 and 6)\\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n Also, five supplements, privately printed\\\\n\\\\nExternal links\\\\n\\\\nGeneral\\\\n The Pirates of Penzance at The Gilbert & Sullivan Archive\\\\n Sullivan\\\\'s autograph manuscript, 1879\\\\n 1880 London theatre programme\\\\n Review of the opening night by Clement Scott\\\\n Papp\\\\'s version of The Pirates of Penzance at the Music Theatre International website\\\\n D\\\\'Oyly Carte Prompt Books at The Victoria and Albert Museum\\\\n Televised scenes from Pirates, D\\\\'Oyly Carte Opera Company, 1955\\\\n \\\\n\\\\nLists of productions\\\\n The Pirates of Penzance. Production list at Floormic.com\\\\n The Pirates of Penzance at The Internet Broadway Database\\\\n The Pirates of Penzance at IMDb\\\\n\\\\n1879 operas\\\\nCornwall in fiction\\\\nDrama Desk Award-winning musicals\\\\nEnglish comic operas\\\\nEnglish-language operas\\\\nNautical fiction\\\\nOperas adapted into films\\\\nOperas by Gilbert and Sullivan\\\\nOperas set in England\\\\nOperas\\\\nPenzance\\\\nPiracy in fiction\\\\nTony Award-winning musicals'},\\n\",\n \" {'docid': 'doc-en-10',\\n\",\n \" 'text': 'Follies is a musical with music and lyrics by Stephen Sondheim and a book by James Goldman.\\\\n\\\\nThe story concerns a reunion in a crumbling Broadway theater, scheduled for demolition, of the past performers of the \\\"Weismann\\\\'s Follies\\\", a musical revue (based on the Ziegfeld Follies), that played in that theater between the world wars. It focuses on two couples, Buddy and Sally Durant Plummer and Benjamin and Phyllis Rogers Stone, who are attending the reunion. Sally and Phyllis were showgirls in the Follies. Both couples are deeply unhappy with their marriages. Buddy, a traveling salesman, is having an affair with a girl on the road; Sally is still as much in love with Ben as she was years ago; and Ben is so self-absorbed that Phyllis feels emotionally abandoned. Several of the former showgirls perform their old numbers, sometimes accompanied by the ghosts of their former selves. The musical numbers in the show have been interpreted as pastiches of the styles of the leading Broadway composers of the 1920s and 1930s, and sometimes as parodies of specific songs.\\\\n\\\\nThe Broadway production opened on April 4, 1971, directed by Harold Prince and Michael Bennett, and with choreography by Bennett. The musical was nominated for 11 Tony Awards and won seven. The original production, the second-most costly performed on Broadway to that date, ran for over 500 performances but ultimately lost its entire investment. The musical has had a number of major revivals, and several of its songs have become standards, including \\\"Broadway Baby\\\", \\\"I\\\\'m Still Here\\\", \\\"Too Many Mornings\\\", \\\"Could I Leave You?\\\", and \\\"Losing My Mind\\\".\\\\n\\\\nBackground\\\\nAfter the failure of Do I Hear a Waltz? (1965), for which he had written the lyrics to Richard Rodgers\\\\'s music, Sondheim decided that he would henceforth work only on projects where he could write both the music and lyrics himself. He asked author and playwright James Goldman to join him as bookwriter for a new musical. Inspired by a New York Times article about a gathering of former showgirls from the Ziegfeld Follies, they decided upon a story about ex-showgirls.\\\\n\\\\nOriginally titled The Girls Upstairs, the musical was to be produced by David Merrick and Leland Hayward in late 1967, but the plans ultimately fell through, and Stuart Ostrow became the producer, with Joseph Hardy as director. These plans also did not work out, and finally Harold Prince, who had worked previously with Sondheim, became the producer and director. He had agreed to work on The Girls Upstairs if Sondheim agreed to work on Company; Michael Bennett, the young choreographer of Company, was also brought onto the project. It was Prince who changed the title to Follies; he was \\\"intrigued by the psychology of a reunion of old chorus dancers and loved the play on the word \\\\'follies.\\\\n\\\\nPlot\\\\nIn 1971, on the soon-to-be-demolished stage of the Weismann Theatre, a reunion is being held to honor the Weismann\\\\'s Follies shows past and the beautiful chorus girls who performed there every year between the two world wars. The once resplendent theater is now little but planks and scaffolding (\\\"Prologue\\\"/\\\"Overture\\\"). As the ghosts of the young showgirls slowly drift through the theater, a majordomo enters with his entourage of waiters and waitresses. They pass through the spectral showgirls without seeing them.\\\\n\\\\nSally Durant Plummer, \\\"blond, petite, sweet-faced\\\" and at 49 \\\"still remarkably like the girl she was thirty years ago\\\", a former Weismann girl, is the first guest to arrive, and her ghostly youthful counterpart moves towards her. Phyllis Rogers Stone, a stylish and elegant woman, arrives with her husband Ben, a renowned philanthropist and politician. As their younger counterparts approach them, Phyllis comments to Ben about their past. He feigns a lack of interest; there is an underlying tension in their relationship. As more guests arrive, Sally\\\\'s husband, Buddy, enters. He is a salesman, in his early 50s, appealing and lively, whose smiles cover inner disappointment.\\\\n\\\\nFinally, Weismann enters to greet his guests. Roscoe, the old master of ceremonies, introduces the former showgirls (\\\"Beautiful Girls\\\"). Former Weismann performers at the reunion include Max and Stella Deems, who lost their radio jobs and became store owners in Miami; Solange La Fitte, a coquette, who is vibrant and flirtatious even at 66; Hattie Walker, who has outlived five younger husbands; Vincent and Vanessa, former dancers who now own an Arthur Murray franchise; Heidi Schiller, for whom Franz Lehár once wrote a waltz (\\\"or was it Oscar Straus?\\\" Facts never interest her; what matters is the song!); and Carlotta Campion, a film star who has embraced life and benefited from every experience.\\\\n\\\\nAs the guests reminisce, the stories of Ben, Phyllis, Buddy, and Sally unfold. Phyllis and Sally were roommates while in the Follies, and Ben and Buddy were best friends at school in New York. When Sally sees Ben, her former lover, she greets him self-consciously (\\\"Don\\\\'t Look at Me\\\"). Buddy and Phyllis join their spouses and the foursome reminisces about the old days of their courtship and the theater, their memories vividly coming to life in the apparitions of their young counterparts (\\\"Waiting For The Girls Upstairs\\\"). Each of the four is shaken at the realization of how life has changed them. Elsewhere, Willy Wheeler (portly, in his sixties) cartwheels for a photographer. Emily and Theodore Whitman, ex-vaudevillians in their seventies, perform an old routine (\\\"The Rain on the Roof\\\"). Solange proves she is still fashionable at what she claims is 66 (\\\"Ah, Paris!\\\"), and Hattie Walker performs her old showstopping number (\\\"Broadway Baby\\\").\\\\n\\\\nBuddy warns Phyllis that Sally is still in love with Ben, and she is shaken by how the past threatens to repeat itself. Sally is awed by Ben\\\\'s apparently glamorous life, but Ben wonders if he made the right choices and considers how things might have been (\\\"The Road You Didn\\\\'t Take\\\"). Sally tells Ben how her days have been spent with Buddy, trying to convince him (and herself) (\\\"In Buddy\\\\'s Eyes\\\"). However, it is clear that Sally is still in love with Ben – even though their affair ended badly when Ben decided to marry Phyllis. She shakes loose from the memory and begins to dance with Ben, who is touched by the memory of the Sally he once cast aside.\\\\n\\\\nPhyllis interrupts this tender moment and has a biting encounter with Sally. Before she has a chance to really let loose, they are both called on to participate in another performance – Stella Deems and the ex-chorines line up to perform an old number (\\\"Who\\\\'s That Woman?\\\"), as they are mirrored by their younger selves. Afterwards, Phyllis and Ben angrily discuss their lives and relationship, which has become numb and emotionless. Sally is bitter and has never been happy with Buddy, although he has always adored her. She accuses him of having affairs while he is on the road, and he admits he has a steady girlfriend, Margie, in another town, but always returns home. Carlotta amuses a throng of admirers with a tale of how her dramatic solo was cut from the Follies because the audience found it humorous, transforming it as she sings it into a toast to her own hard-won survival (\\\"I\\\\'m Still Here\\\").\\\\n\\\\nBen confides to Sally that his life is empty. She yearns for him to hold her, but young Sally slips between them and the three move together (\\\"Too Many Mornings\\\"). Ben, caught in the passion of memories, kisses Sally as Buddy watches from the shadows. Sally thinks this is a sign that the two will finally get married, and Ben is about to protest until Sally interrupts him with a kiss and runs off to gather her things, thinking that the two will leave together. Buddy leaves the shadows furious, and fantasizes about the girl he should have married, Margie, who loves him and makes him feel like \\\"a somebody\\\", but bitterly concludes he does not love her back (\\\"The Right Girl\\\"). He tells Sally that he\\\\'s done, but she is lost in a fantasy world and tells him that Ben has asked her to marry him. Buddy tells her she must be either crazy or drunk, but he\\\\'s already supported Sally through rehab clinics and mental hospitals and cannot take any more. Ben drunkenly propositions Carlotta, with whom he once had a fling, but she has a young lover and coolly turns him down. Heidi Schiller, joined by her younger counterpart, performs \\\"One More Kiss\\\", her aged voice a stark contrast to the sparkling coloratura of her younger self. Phyllis kisses a waiter and confesses to him that she had always wanted a son. She then tells Ben that their marriage can\\\\'t continue the way it has been. Ben replies by saying that he wants a divorce, and Phyllis assumes the request is due to his love for Sally. Ben denies this, but still wants Phyllis out. Angry and hurt, Phyllis considers whether to grant his request (\\\"Could I Leave You?\\\").\\\\n\\\\nPhyllis begins wondering at her younger self, who worked so hard to become the socialite that Ben needed. Ben yells at his younger self for not appreciating all the work that Phyllis did. Both Buddys enter to confront the Bens about how they stole Sally. Sally and her younger self enter and Ben firmly tells Sally that he never loved her. All the voices begin speaking and yelling at each other. Suddenly, at the peak of madness and confusion, the couples are engulfed by their follies, which transform the rundown theater into a fantastical \\\"Loveland\\\", an extravaganza even more grand and opulent than the gaudiest Weismann confection: \\\"the place where lovers are always young and beautiful, and everyone lives only for love\\\". Sally, Phyllis, Ben, and Buddy show their \\\"real and emotional lives\\\" in \\\"a sort of group nervous breakdown\\\".\\\\n\\\\nWhat follows is a series of musical numbers performed by the principal characters, each exploring their biggest desires. The two younger couples sing in a counterpoint of their hopes for the future (\\\"You\\\\'re Gonna Love Tomorrow/Love Will See Us Through\\\"). Buddy then appears, dressed in \\\"plaid baggy pants, garish jacket, and a shiny derby hat\\\", and performs a high-energy vaudeville routine depicting how he is caught between his love for Sally and Margie\\\\'s love for him (\\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\"). Sally appears next, dressed as a torch singer, singing of her passion for Ben from the past - and her obsession with him now (\\\"Losing My Mind\\\"). In a jazzy dance number, accompanied by a squadron of chorus boys, Phyllis reflects on the two sides of her personality, one naive and passionate and the other jaded and sophisticated and her desire to combine them (\\\"The Story of Lucy and Jessie\\\"). Resplendent in top hat and tails, Ben begins to offer his devil-may-care philosophy (\\\"Live, Laugh, Love\\\"), but stumbles and anxiously calls to the conductor for the lyrics, as he frantically tries to keep going. Ben becomes frenzied, while the dancing ensemble continues as if nothing was wrong. Amidst a deafening discord, Ben screams at all the figures from his past and collapses as he cries out for Phyllis.\\\\n\\\\n\\\"Loveland\\\" has dissolved back into the reality of the crumbling and half-demolished theater; dawn is approaching. Ben admits to Phyllis his admiration for her, and Phyllis shushes him and helps Ben regain his dignity before they leave. After exiting, Buddy escorts the emotionally devastated Sally back to their hotel with the promise to work things out later. Their ghostly younger selves appear, watching them go. The younger Ben and Buddy softly call to their \\\"girls upstairs\\\", and the Follies end.\\\\n\\\\nSongs\\\\nSource: Follies score\\\\n \\\"Prologue\\\" – Orchestra\\\\n \\\"Overture\\\" – Orchestra\\\\n \\\"Beautiful Girls\\\" – Roscoe and Company\\\\n \\\"Don\\\\'t Look at Me\\\" – Sally and Ben\\\\n \\\"Waiting for the Girls Upstairs\\\" – Ben, Sally, Phyllis and Buddy, Young Ben, Young Sally, Young Phyllis and Young Buddy\\\\n \\\"Montage\\\" (\\\"Rain on the Roof\\\"/\\\"Ah, Paris!\\\"/\\\"\\\") – Emily, Theodore, Solange, and Hattie\\\\n \\\"The Road You Didn\\\\'t Take\\\" – Ben\\\\n \\\"Bolero d\\\\'Amour\\\" – Danced by Vincent and Vanessa ≠≠\\\\n \\\"In Buddy\\\\'s Eyes\\\" – Sally\\\\n \\\"Who\\\\'s That Woman?\\\" – Stella and Company\\\\n \\\"I\\\\'m Still Here\\\" – Carlotta\\\\n \\\"Too Many Mornings\\\" – Ben and Sally\\\\n \\\"The Right Girl\\\" – Buddy\\\\n \\\"One More Kiss\\\" – Heidi and Young Heidi\\\\n \\\"Could I Leave You?\\\" – Phyllis\\\\n \\\"Loveland\\\" – Company\\\\n \\\"You\\\\'re Gonna Love Tomorrow\\\" / \\\"Love Will See Us Through\\\" – Young Ben, Young Sally, Young Phyllis and Young Buddy\\\\n \\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\" – Buddy, \\\"Margie\\\", \\\"Sally\\\"\\\\n \\\"Losing My Mind\\\" – Sally\\\\n \\\"The Story of Lucy and Jessie\\\" ≠ – Phyllis and backup male dancers\\\\n \\\"Live, Laugh, Love\\\" – Ben and Company\\\\n \\\"Chaos\\\" – Ben and Company\\\\n \\\"Finale\\\" – Young Buddy and Young Ben\\\\n≠ Some productions substitute \\\"Ah, but Underneath\\\" when the actress portraying Phyllis is not primarily a dancer.\\\\n\\\\n≠≠ Omitted from some productions\\\\n\\\\nNote: This is the song list from the original Broadway production in 1971. Variations are discussed in Versions.\\\\n\\\\nSongs cut before the Broadway premiere include \\\"All Things Bright and Beautiful\\\" (used in the prologue), \\\"Can That Boy Foxtrot!\\\", \\\"Who Could Be Blue?\\\", \\\"Little White House\\\", \\\"So Many People\\\", \\\"It Wasn\\\\'t Meant to Happen\\\", \\\"Pleasant Little Kingdom\\\", and \\\"Uptown Downtown\\\". The musical numbers \\\"Ah, but Underneath\\\" (replacing \\\"The Story of Lucy and Jessie\\\"), \\\"Country House\\\", \\\"Make the Most of Your Music\\\" (replacing \\\"Live, Laugh, Love\\\"), \\\"Social Dancing\\\" and a new version of \\\"Loveland\\\" have been incorporated into various productions.\\\\n\\\\nAnalysis\\\\nHal Prince said: \\\"Follies examines obsessive behavior, neurosis and self-indulgence more microscopically than anything I know of.\\\" Bernadette Peters quoted Sondheim on the character of \\\"Sally\\\": \\\"He said early on that [Sally] is off-balance, to put it mildly. He thinks she\\\\'s very neurotic, and she is very neurotic, so he said to me \\\\'Congratulations. She\\\\'s crazy. Martin Gottfried wrote: \\\"The concept behind Follies is theatre nostalgia, representing the rose-colored glasses through which we face the fact of age\\\\xa0... the show is conceived in ghostliness. At its very start, ghosts of Follies showgirls stalk the stage, mythic giants in winged, feathered, black and white opulence. Similarly, ghosts of the Twenties shows slip through the evening as the characters try desperately to regain their youth through re-creations of their performances and inane theatre sentiments of their past.\\\"\\\\n\\\\nJoanne Gordon, author and chair and artistic director, Theatre, at California State University, Long Beach, wrote \\\"Follies is in part an affectionate look at the American musical theatre between the two World Wars and provides Sondheim with an opportunity to use the traditional conventions of the genre to reveal the hollowness and falsity of his characters\\\\' dreams and illusions. The emotional high generated by the reunion of the Follies girls ultimately gives way to anger, disappointment, and weary resignation to reality.\\\" \\\"Follies contains two scores: the Follies pastiche numbers and the book numbers.\\\" Some of the Follies numbers imitate the style of particular composers of the early 20th century: \\\"Losing My Mind\\\" is in the style of a George Gershwin ballad \\\"The Man I Love\\\". Sondheim noted that the song \\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\" is \\\"another generic pastiche: vaudeville music for chases and low comics, but with a patter lyric\\\\xa0... I tried to give it the sardonic knowingness of Lorenz Hart or Frank Loesser.\\\"\\\\n\\\\n\\\"Loveland\\\", the final musical sequence, (that \\\"consumed the last half-hour of the original\\\" production) is akin to an imaginary 1941 Ziegfeld Follies sequence, with Sally, Phyllis, Ben and Buddy performing \\\"like comics and torch singers from a Broadway of yore.\\\" \\\"Loveland\\\" features a string of vaudeville-style numbers, reflecting the leading characters\\\\' emotional problems, before returning to the theater for the end of the reunion party. The four characters are \\\"whisked into a dream show in which each acts out his or her own principal \\\\'folly.\\\\n\\\\nVersions\\\\nGoldman continued to revise the book of the musical right up to his death, which occurred shortly before the 1998 Paper Mill Playhouse production. Sondheim, too, has added and removed songs that he judged to be problematic in various productions. Ted Chapin explains: \\\"Today, Follies is rarely performed twice in exactly the same version. James Goldman\\\\'s widow made the observation that the show has morphed throughout its entire life\\\\xa0... The London production had new songs and dialogue. The Paper Mill Playhouse production used some elements from London but stayed close to the original. The 2001 Roundabout Broadway revival, the first major production following Goldman\\\\'s death in 1998, was again a combination of previous versions.\\\"\\\\n\\\\nMajor changes were made for the original production in London, which attempted to establish a lighter tone and favored a happier ending than the original Broadway production. According to Joanne Gordon, \\\"When Follies opened in London\\\\xa0... it had an entirely different, and significantly more optimistic, tone. Goldman\\\\'s revised book offered some small improvements over the original.\\\"\\\\n\\\\nAccording to Sondheim, the producer Cameron Mackintosh asked for changes for the 1987 London production. \\\"I was reluctantly happy to comply, my only serious balk being at his request that I cut \\\"The Road You Didn\\\\'t Take\\\"\\\\xa0... I saw no reason not to try new things, knowing we could always revert to the original (which we eventually did). The net result was four new songs\\\\xa0... For reasons which I\\\\'ve forgotten, I rewrote \\\"Loveland\\\" for the London production. There were only four showgirls in this version, and each one carried a shepherd\\\\'s crook with a letter of the alphabet on it.\\\"\\\\n\\\\nThe musical was written in one act, and the original director, Prince, did not want an intermission, while the co-director, Bennett, wanted two acts. It originally was performed in one act. The 1987 West End, 2005 Barrington Stage Company, the 2001 Broadway revival and Kennedy Center 2011 productions were performed in two acts. However, August 23, 2011, Broadway preview performance was performed without an intermission. By opening, the 2011 Broadway revival was performed with the intermission, in two acts. The 2017 National Theatre production is performed without an interval.\\\\n\\\\nProductions\\\\n\\\\n1971 original Broadway\\\\nFollies had its pre-Broadway tryout at the Colonial Theatre, Boston, from February 20 through March 20, 1971.\\\\n\\\\nFollies premiered on Broadway on April 4, 1971, at the Winter Garden Theatre. It was directed by Harold Prince and Michael Bennett, with choreography by Bennett, scenic design by Boris Aronson, costumes by Florence Klotz, and lighting by Tharon Musser. It starred Alexis Smith (Phyllis), John McMartin (Ben), Dorothy Collins (Sally), Gene Nelson (Buddy), along with several veterans of the Broadway and vaudeville stage. The supporting role of Carlotta was created by Yvonne De Carlo and usually is given to a well-known veteran performer who can belt out a song. Other notable performers in the original productions were Fifi D\\\\'Orsay as Solange LaFitte, Justine Johnston as Heidi Schiller, Mary McCarty as Stella Deems, Arnold Moss as Dimitri Weismann, Ethel Shutta as Hattie Walker, and Marcie Stringer and Charles Welch as Emily and Theodore Whitman.\\\\n\\\\nThe show closed on July 1, 1972, after 522 performances and 12 previews. According to Variety, the production was a \\\"total financial failure, with a cumulative loss of $792,000.\\\" Prince planned to present the musical on the West Coast and then on a national tour. However, the show did not do well in its Los Angeles engagement and plans for a tour ended.\\\\n\\\\nFrank Rich, for many years the chief drama critic for The New York Times, had first garnered attention, while an undergraduate at Harvard University, with a lengthy essay for the Harvard Crimson about the show, which he had seen during its pre-Broadway run in Boston. He predicted that the show eventually would achieve recognition as a Broadway classic. Rich later wrote that audiences at the original production were baffled and restless.\\\\n\\\\nFor commercial reasons, the cast album was cut from two LPs to one early in production. Most songs were therefore heavily abridged and several were left entirely unrecorded. According to Craig Zadan, \\\"It\\\\'s generally felt that\\\\xa0... Prince made a mistake by giving the recording rights of Follies to Capitol Records, which in order to squeeze the unusually long score onto one disc, mutilated the songs by condensing some and omitting others.\\\" Chapin confirms this: \\\"Alas\\\\xa0... final word came from Capitol that they would not go for two records\\\\xa0... [Dick Jones] now had to propose cuts throughout the score in consultation with Steve.\\\" \\\"One More Kiss\\\" was omitted from the final release but was restored for CD release. Chapin relates that \\\"there was one song that Dick Jones [producer of the cast album] didn\\\\'t want to include on the album but which Steve Sondheim most definitely did. The song was \\\"One More Kiss\\\", and the compromise was that if there was time, it would be recorded, even if Jones couldn\\\\'t promise it would end up on the album. (It did get recorded but didn\\\\'t make its way onto the album until the CD reissue years later.)\\\"\\\\n\\\\n1972 Los Angeles\\\\nThe musical was produced at The Muny, St. Louis, Missouri in July 1972 and then transferred to the Shubert Theatre, Century City, California, running from July 22, 1972, through October 1, 1972. It was directed by Prince and starred Dorothy Collins (Sally; replaced by Janet Blair), Alexis Smith (Phyllis), John McMartin (Ben; replaced by Edward Winter), Gene Nelson (Buddy), and Yvonne De Carlo (Carlotta) reprising their original roles. The production was the premiere attraction at the newly constructed 1,800-seat theater, which, coincidentally, was itself razed thirty years later (in 2002, in order to build a new office building), thus mirroring the Follies plot line upon which the musical is based.\\\\n\\\\n1985 Wythenshawe and Lincoln Center\\\\nA full production ran at the Forum Theatre, Wythenshawe, England, from April 30, 1985, directed by Howard Lloyd-Lewis, design by Chris Kinman, costumes by Charles Cusick-Smith, lighting by Tim Wratten, musical direction by Simon Lowe, and choreographed by Paul Kerryson. The cast included Mary Millar (Sally Durant Plummer), Liz Izen (Young Sally), Meg Johnson (Stella Deems), Les Want (Max Deems), Betty Benfield (Heidi Schiller), Joseph Powell (Roscoe), Chili Bouchier (Hattie Walker), Shirley Greenwood (Emily Whitman), Bryan Burdon (Theodore Whitman), Monica Dell (Solange LaFitte), Jeannie Harris (Carlotta Campion), Josephine Blake (Phyllis Rogers Stone), Kevin Colson (Ben), Debbie Snook (Young Phyllis), Stephen Hale (Young Ben), Bill Bradley (Buddy Plummer), Paul Burton (Young Buddy), David Scase (Dimitri Weismann), Mitch Sebastian (Young Vincent), Kim Ismay (Young Vanessa), Lorraine Croft (Young Stella), and Meryl Richardson (Young Heidi).\\\\n\\\\nA staged concert at Avery Fisher Hall, Lincoln Center, was performed on September 6 and 7, 1985. The concert starred Barbara Cook (Sally), George Hearn (Ben), Mandy Patinkin (Buddy), and Lee Remick (Phyllis), and featured Carol Burnett (Carlotta), Betty Comden (Emily), Adolph Green (Theodore), Liliane Montevecchi (Solange LaFitte), Elaine Stritch (Hattie Walker), Phyllis Newman (Stella Deems), Jim Walton (Young Buddy), Howard McGillin (Young Ben), Liz Callaway (Young Sally), Daisy Prince (Young Phyllis), Andre Gregory (Dmitri), Arthur Rubin (Roscoe), and Licia Albanese (Heidi Schiller). Rich, in his review, noted that \\\"As performed at Avery Fisher Hall, the score emerged as an original whole, in which the \\\\'modern\\\\' music and mock vintage tunes constantly comment on each other, much as the script\\\\'s action unfolds simultaneously in 1971 (the year of the reunion) and 1941 (the year the Follies disbanded).\\\"\\\\n\\\\nAmong the reasons the concert was staged was to provide an opportunity to record the entire score. The resulting album was more complete than the original cast album. However, director Herbert Ross took some liberties in adapting the book and score for the concert format—dance music was changed, songs were given false endings, the new dialogue was spoken, reprises were added, and Patinkin was allowed to sing \\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\" as a solo instead of a trio with two chorus girls. Portions of the concert were seen by audiences worldwide in the televised documentary about the making of the concert, also released on videotape and DVD, of \\\\'Follies\\\\' in Concert.\\\\n\\\\n1987 West End\\\\n\\\\nThe musical played in the West End at the Shaftesbury Theatre on July 21, 1987, and closed on February 4, 1989, after 644 performances. The producer was Cameron Mackintosh, the direction was by Mike Ockrent, with choreography by Bob Avian and design by Maria Björnson. The cast featured Diana Rigg (Phyllis), Daniel Massey (Ben), Julia McKenzie (Sally), David Healy (Buddy), Lynda Baron, Leonard Sachs, Maria Charles, Pearl Carr & Teddy Johnson. Dolores Gray was praised as Carlotta, continuing to perform after breaking her ankle, although in a reduced version of the part. During the run, Eartha Kitt replaced Gray, sparking somewhat of a comeback (she went on to perform her own one-woman show at The Shaftesbury Theatre to sell-out houses for three weeks from March 18, 1989, after Follies closed). Other cast replacements included Millicent Martin as Phyllis. Julia McKenzie returned to the production for the final four performances.\\\\n\\\\nThe book \\\"was extensively reworked by James Goldman, with Sondheim\\\\'s cooperation and also given an intermission.\\\" The producer Cameron Mackintosh did not like \\\"that there was no change in the characters from beginning to end\\\\xa0... In the London production\\\\xa0... the characters come to understand each other.\\\" Sondheim \\\"did not think the London script was as good as the original.\\\" However, he thought that it was \\\"wonderful\\\" that, at the end of the first act, \\\"the principal characters recognized their younger selves and were able to acknowledge them throughout the last thirty minutes of the piece.\\\" Sondheim wrote four new songs: \\\"Country House\\\" (replacing \\\"The Road You Didn\\\\'t Take\\\"), \\\"Loveland\\\" (replacing the song of the same title), \\\"Ah, But Underneath\\\" (replacing \\\"The Story of Lucy and Jessie\\\", for the non-dancer Diana Rigg), and \\\"Make the Most of Your Music\\\" (replacing \\\"Live, Laugh, Love\\\").\\\\n\\\\nCritics who had seen the production in New York (such as Frank Rich) found it substantially more \\\"upbeat\\\" and lacking in the atmosphere it had originally possessed. According to the Associated Press (AP) reviewer, \\\"A revised version of the Broadway hit Follies received a standing ovation from its opening-night audience and raves from British critics, who stated the show was worth a 16-year wait.\\\" The AP quoted Michael Coveney of the Financial Times, who wrote: \\\"Follies is a great deal more than a camp love-in for old burlesque buffs and Sondheim aficionados.\\\" In The New York Times, the critic Francis X. Clines wrote: \\\"The initial critics\\\\' reviews ranged from unqualified raves to some doubts whether the reworked book of James Goldman is up to the inventiveness of Sondheim\\\\'s songs. \\\\'A truly fantastic evening,\\\\' The Financial Times concluded, while the London Daily News stated \\\\'The musical is inspired,\\\\' and The Times described the evening as \\\\'a wonderful idea for a show which has failed to grow into a story. The Times critic Irving Wardle stated \\\"It is not much of a story, and whatever possibilities it may have had in theory are scuppered by James Goldman\\\\'s book\\\\xa0... a blend of lifeless small-talk, bitching and dreadful gags\\\". Clines further commented: \\\"In part, the show is a tribute to musical stage history, in which the 57-year-old Mr Sondheim is steeped, for he first learned song writing at the knee of Oscar Hammerstein II and became the acknowledged master songwriter who bridged past musical stage romance into the modern musical era of irony and neurosis. Follies is a blend of both, and the new production is rounded out with production numbers celebrating love\\\\'s simple hope for young lovers, its extravagant fantasies for Ziegfeld aficionados, and its fresh lesson for the graying principals.\\\"\\\\n\\\\nThis production was also recorded on two CDs and was the first full recording.\\\\n\\\\nFollies was voted ninth in a BBC Radio 2 listener poll of the UK\\\\'s \\\"Nation\\\\'s Number One Essential Musicals\\\".\\\\n\\\\nU.S. regional productions\\\\nMichigan Opera Theatre (MOT) was the first major American opera company to present Follies as part of their main stage repertoire, running from October 21, 1988, through November 6. The MOT production starred Nancy Dussault (Sally), John-Charles Kelly (Buddy), Juliet Prowse (Phyllis) and Ron Raines (Ben), Edie Adams (Carlotta), Thelma Lee (Hattie), and Dennis Grimaldi (Vincent).\\\\n\\\\nA production also ran from March to April 1995 at the Theatre Under the Stars, Houston, Texas, and in April to May 1995 at the 5th Avenue Theatre, Seattle with Constance Towers (Phyllis), Judy Kaye (Sally), Edie Adams, Denise Darcel, Virginia Mayo and Karen Morrow (Carlotta). The 1998 Paper Mill Playhouse production (Millburn, New Jersey) was directed by Robert Johanson with choreography by Jerry Mitchell and starred Donna McKechnie (Sally), Dee Hoty (Phyllis), Laurence Guittard (Ben), Tony Roberts (Buddy), Kaye Ballard (Hattie ), Eddie Bracken (Weismann), and Ann Miller (Carlotta). Phyllis Newman and Liliane Montevecchi reprised the roles they played in the Lincoln Center production. \\\"Ah, but Underneath\\\" was substituted for \\\"The Story of Lucy and Jessie\\\" in order to accommodate non-dancer Hoty. This production received a full-length recording on two CDs, including not only the entire score as originally written but a lengthy appendix of songs cut from the original production in tryouts.\\\\n\\\\nJulianne Boyd directed a fully staged version of Follies in 2005 by the Barrington Stage Company (Massachusetts) in June–July 2005. The principal cast included Kim Crosby (Sally), Leslie Denniston (Phyllis), Jeff McCarthy (Ben), Lara Teeter (Buddy), Joy Franz (Solange), Marni Nixon (Heidi), and Donna McKechnie (Carlotta). Stephen Sondheim attended one of the performances.\\\\n\\\\n1996 and 1998 concerts\\\\nDublin concert\\\\nThe Dublin Concert was held in May 1996 at the National Concert Hall. Directed by Michael Scott, the cast included Lorna Luft, Millicent Martin, Mary Millar, Dave Willetts, Trevor Jones Bryan Smyth, Alex Sharpe, Christine Scarry, Aidan Conway and Enda Markey.\\\\n\\\\nLondon concert\\\\nA concert was held at Theatre Royal, Drury Lane, London, on December 8, 1996, and broadcast on BBC Radio 2 on February 15, 1997. The cast starred Julia McKenzie (Sally), Donna McKechnie (Phyllis), Denis Quilley (Ben) and Ron Moody (Buddy). This show recreated the original Broadway score.\\\\n\\\\nSydney concert\\\\nFollies was performed in concert at the Sydney Opera House with the Sydney Symphony Orchestra in February 1998 as the highlight of the Sydney Gay and Lesbian Mardi Gras and had three performances. It was directed and staged by Stephen Lloyd Helper and produced by Helper and Alistair Thomson for Mardi Gras. It starred Toni Lamond (Sally), Jill Perryman(Carlotta), Judi Connelli (Phyllis), Terence Donovan (Ben), Nancye Hayes (Hattie), Glenn Butcher (Buddy), Ron Haddrick (Dimitri), Susan Johnston (Heidi), and Leonie Page, Maree Johnson, Mitchell Butel, Maureen Howard. The Sydney Symphony was conducted by Maestro Tommy Tycho. It followed a similar presentation at the 1995 Melbourne Festival of Arts with a different cast and orchestra.\\\\n\\\\n2001 Broadway revival\\\\nA Broadway revival opened at the Belasco Theatre on April 5, 2001, and closed on July 14, 2001, after 117 performances and 32 previews. This Roundabout Theatre limited engagement had been expected to close on September 30, 2001. Directed by Matthew Warchus with choreography by Kathleen Marshall, it starred Blythe Danner (Phyllis), Judith Ivey (Sally), Treat Williams (Buddy), Gregory Harrison (Ben), Marge Champion, Polly Bergen (Carlotta), Joan Roberts (Laurey from the original Broadway production of Oklahoma!; later replaced by Marni Nixon), Larry Raiken (Roscoe) and an assortment of famous names from the past. Former MGM and onetime Broadway star Betty Garrett, best known to younger audiences for her television work, played Hattie. It was significantly stripped down (earlier productions had featured extravagant sets and costumes) and was not a success critically.\\\\n\\\\nAccording to an article in The Hollywood Reporter, \\\"almost every performance of the show played to a full house, more often than not to standing-room-only. Tickets always were tough to come by. The reason the final curtain came down Saturday was that being a production by the Roundabout Theatre Company – a subscription-based \\\\'not-for-profit\\\\' theater company – it was presented under special Equity terms, with its actors paid a minimal fee. To extend the show, it would have been necessary to negotiate new contracts with the entire company\\\\xa0... because of the Belasco\\\\'s limited seating, it wasn\\\\'t deemed financially feasible to do so.\\\"\\\\n\\\\nTheater writer and historian John Kenrick wrote \\\"the bad news is that this Follies is a dramatic and conceptual failure. The good news is that it also features some of the most exciting musical moments Broadway has seen in several seasons. Since you don\\\\'t get those moments from the production, the book or the leads, that leaves the featured ensemble, and in Follies that amounts to a small army\\\\xa0... Marge Champion and Donald Saddler are endearing as the old hoofers\\\\xa0... I dare you not to fall in love with Betty Garrett\\\\'s understated \\\"Broadway Baby\\\" – you just want to pick her up and hug her. Polly Bergen stops everything cold with \\\"I\\\\'m Still Here\\\", bringing a rare degree of introspection to a song that is too often a mere belt-fest\\\\xa0... [T]he emotional highpoint comes when Joan Roberts sings \\\\'One More Kiss\\\\'.\\\"\\\\n\\\\n2002 London revival\\\\nA production was mounted at London\\\\'s Royal Festival Hall in a limited engagement. After previews from August 3, 2002, it opened officially on August 6, and closed on August 31, 2002. Paul Kerryson directed, and the cast starred David Durham as Ben, Kathryn Evans as Sally, Louise Gold as Phyllis, Julia Goss as Heidi and Henry Goodman as Buddy. Variety singer and performer Joan Savage sang \\\"Broadway Baby\\\". This production conducted by Julian Kelly featured the original Broadway score.\\\\n\\\\n2002 Los Angeles\\\\nFollies was part of L.A.\\\\'s Reprise series, and it was housed at the Wadsworth Theatre, presented as a staged concert, running from June 15 to 23, 2002. The production was directed by Arthur Allan Seidelman, set design by Ray Klausen, lighting design by Tom Ruzika, costumes by Randy Gardell, sound design by Philip G. Allen, choreography by Kay Cole, musical director Gerald Sternbach.\\\\n\\\\nThe production starred Bob Gunton (Ben), Warren Berlinger (Dimitri Weismann), Patty Duke (Phyllis), Vikki Carr (Sally), Harry Groener (Buddy), Carole Cook (Hattie), Carol Lawrence (Vanessa), Ken Page (Roscoe), Liz Torres (Stella), Amanda McBroom (Solange), Grover Dale (Vincent), Donna McKechnie (Carlotta), Carole Swarbrick (Christine), Stella Stevens (Dee Dee), Mary Jo Catlett (Emily), Justine Johnston (Heidi), Jean Louisa Kelly (Young Sally), Austin Miller (Young Buddy), Tia Riebling (Young Phyllis), Kevin Earley (Young Ben), Abby Feldman (Young Stella), Barbara Chiofalo (Young Heidi), Trevor Brackney (Young Vincent), Melissa Driscoll (Young Vanessa), Stephen Reed (Kevin), and Billy Barnes (Theodore). Hal Linden originally was going to play Ben, but left because he was cast in the Broadway revival of Cabaret as Herr Schultz. Tom Bosley originally was cast as Dimitri Weismann.\\\\n\\\\n2003 Ann Arbor\\\\nA concert production at the Michigan Theater in January 2003 reunited the four principal young ghosts of the original Broadway cast: Kurt Peterson, Harvey Evans, Virginia Sandifur, and Marti Rolph. Having originated the young ghosts over 30 years prior, the actors portrayed the older versions of their Broadway roles. Donna McKechnie enjoyed top billing as Carlotta.\\\\n\\\\n2007 New York City Center Encores!\\\\nNew York City Center\\\\'s Encores! \\\"Great American Musicals in Concert\\\" series featured Follies as its 40th production for six performances in February 2007 in a sold out semi-staged concert. The cast starred Donna Murphy (Phyllis), Victoria Clark (Sally), Victor Garber (Ben) and Michael McGrath (Buddy). Christine Baranski played Carlotta, and Lucine Amara sang Heidi. The cast included Anne Rogers, Jo Anne Worley and Philip Bosco. The director and choreographer was Casey Nicholaw. This production used the original text, and the \\\"Loveland\\\" lyrics performed in the 1987 London production.\\\\n\\\\n2011 Kennedy Center and Broadway\\\\nThe Kennedy Center for the Performing Arts production at the Eisenhower Theater started previews on May 7, 2011, with an official opening on May 21, and closed on June 19, 2011. The cast starred Bernadette Peters as Sally, Jan Maxwell as Phyllis, Elaine Paige as Carlotta, Linda Lavin as Hattie, Ron Raines as Ben and Danny Burstein as Buddy. The production was directed by Eric Schaeffer, with choreography by Warren Carlyle, costumes by Gregg Barnes, set by Derek McLane and lighting by Natasha Katz. Also featured were Rosalind Elias as Heidi, Régine as Solange, Susan Watson as Emily, and Terri White as Stella. The budget was reported to be $7.3 million. The production played to 95% capacity.\\\\n\\\\nReviews were mixed, with Ben Brantley of The New York Times writing \\\"It wasn\\\\'t until the second act that I fell in love all over again with Follies\\\". Peter Marks of The Washington Post wrote that the revival \\\"takes an audience halfway to paradise.\\\" He praised a \\\"broodingly luminous Jan Maxwell\\\" and Burstein\\\\'s \\\"hapless onetime stage-door Johnny\\\", as well as \\\"the show\\\\'s final 20 minutes, when we ascend with the main characters into an ironic vaudeville dreamscape of assorted neuroses - the most intoxicating articulation of the musical\\\\'s \\\\'Loveland\\\\' sequence that I\\\\'ve ever seen.\\\" Variety gave a very favorable review to the \\\"lavish and entirely satisfying production\\\", saying that Schaeffer directs \\\"in methodical fashion, building progressively to a crescendo exactly as Sondheim does with so many of his stirring melodies. Several show-stopping routines are provided by choreographer Warren Carlyle.\\\" Terry Teachout of the Wall Street Journal noted that \\\"One of the signal achievements of this Follies is that it succeeds in untangling each and every strand of the show\\\\'s knotty plot\\\\xa0... Mr. Schaeffer is clearly unafraid of the darkness of Follies, so much so that the first act is bitter enough to sting. Yet he and Warren Carlyle\\\\xa0... just as clearly revel in the richness of the knowing pastiche songs with which Mr. Sondheim evokes the popular music of the prerock era.\\\"\\\\n\\\\nThe production transferred to Broadway at the Marquis Theatre in a limited engagement starting previews on August 7, 2011, with the official opening on September 12, and closing on January 22, 2012, after 151 performances and 38 previews. The four principal performers reprised their roles, as well as Paige as Carlotta. Jayne Houdyshell as Hattie, Mary Beth Peil as Solange LaFitte, and Don Correia as Theodore joined the Broadway cast. A two-disc cast album of this production was recorded by PS Classics and was released on November 29, 2011.\\\\n\\\\nBrantley reviewed the Broadway revival for The New York Times, writing: \\\"Somewhere along the road from Washington to Broadway, the Kennedy Center production of Follies picked up a pulse\\\\xa0... I am happy to report that since then, Ms Peters has connected with her inner frump, Mr. Raines has found the brittle skeleton within his solid flesh, and Ms. Maxwell and Mr. Burstein have only improved. Two new additions to the cast, Jayne Houdyshell and Mary Beth Peil, are terrific. This production has taken on the glint of crystalline sharpness.\\\" The production\\\\'s run was extended, and its grosses exceeded expectations, but it did not recoup its investment.\\\\n\\\\nThe Broadway production won the Drama League Award, Distinguished Production of a Musical Revival for 2011-2012 and the Drama Desk Award for Outstanding Revival of a Musical, Outstanding Actor in a Musical (Burstein) and Outstanding Costume Design (Barnes). Out of seven Tony Award nominations, including Best Revival of a Musical, it won only one, for Barnes\\\\' costumes.\\\\n\\\\n2012 Los Angeles\\\\nThe 2011 Broadway and Kennedy Center production transferred to the Ahmanson Theatre, Los Angeles, California, in a limited engagement, from May 3, 2012, through June 9. The majority of the Broadway cast reprised their roles, with the exception of Bernadette Peters, who had prior concert commitments and was replaced by Victoria Clark in the role of Sally, a role she has previously played in New York. Other new cast members included Carol Neblett as Heidi, Sammy Williams as Theodore and Obba Babatunde as Max.\\\\n\\\\n2013 Toulon Opera House (France)\\\\nFor its first production in France, Follies was presented at the Toulon Opera House in March 2013. This English-language production, using the full original orchestration, was directed by Olivier Bénézech and conducted by David Charles Abell. The cast featured Charlotte Page (Sally), Liz Robertson (Phyllis), Graham Bickley (Ben), Jérôme Pradon (Buddy), Nicole Croisille (Carlotta), Julia Sutton (Hattie) and Fra Fee (Young Buddy).\\\\n\\\\n2016 Australian concert version \\\\nA concert version at the Melbourne Recital Centre, staged with a full 23-piece orchestra and Australian actors Philip Quast (Ben), David Hobson (Buddy), Lisa McCune (Sally), Anne Wood (Phyllis), Rowan Witt (Young Buddy), Sophie Wright (Young Sally), Nancy Hayes (Hattie), Debra Byrne (Carlotta), and Queenie van de Zandt (Stella). The production was directed by Tyran Parke and produced by StoreyBoard Entertainment.\\\\n\\\\n2017 London revival \\\\nA London revival was performed in the Olivier Theatre at the National Theatre (August 22 until November 4, 2017 - later extended to January 3, 2018, as extensions are common practice at the National Theatre). The production was directed by Dominic Cooke, choreographed by Bill Deamer and starred Peter Forbes as Buddy, Imelda Staunton as Sally, Janie Dee as Phyllis, Philip Quast as Ben and Tracie Bennett as Carlotta. This production notably goes back to the original plan of a one-act performance. The production was broadcast live to cinemas worldwide on November 16 through the National Theatre Live program.\\\\n\\\\nThe production returned to the Olivier Theatre on February 14, 2019, playing until May 11. Janie Dee and Peter Forbes returned as Phyllis and Buddy, while Joanna Riding and Alexander Hanson replaced Staunton and Quast as Sally and Ben. Bennett also reprised her Olivier-nominated performance. A recording of the National Theatre production was released on January 18, 2019.\\\\n\\\\nThe 2017 production was nominated for 10 Laurence Olivier Awards and won 2 for Best Musical Revival and Best Costume Design (by Vicki Mortimer).\\\\n\\\\nCharacters and original cast\\\\n\\\\nThe characters and original cast:\\\\n\\\\nCritical response\\\\nIn the foreword to \\\"Everything Was Possible\\\", Frank Rich wrote: \\\"From the start, critics have been divided about Follies, passionately pro or con but rarely on the fence\\\\xa0... Is it really a great musical, or merely the greatest of all cult musicals?\\\" (Chapin, p. xi) Ted Chapin wrote, \\\"Taken as a whole, the collection of reviews Follies received was as rangy as possible.\\\" (Chapin, p.\\\\xa0300) In his The New York Times review of the original Broadway production, Clive Barnes wrote: \\\"it is stylish, innovative, it has some of the best lyrics I have ever encountered, and above all it is a serious attempt to deal with the musical form.\\\" Barnes also called the story shallow and Sondheim\\\\'s words a joy \\\"even when his music sends shivers of indifference up your spine.\\\"\\\\n\\\\nWalter Kerr wrote in The New York Times about the original production: \\\"Follies is intermissionless and exhausting, an extravaganza that becomes so tedious\\\\xa0... because its extravaganzas have nothing to do with its pebble of a plot.\\\" On the other hand, Martin Gottfried wrote: \\\"Follies is truly awesome and, if it is not consistently good, it is always great.\\\"\\\\n\\\\nTime magazine wrote about the original Broadway production: \\\"At its worst moments, Follies is mannered and pretentious, overreaching for Significance. At its best moments—and there are many—it is the most imaginative and original new musical that Broadway has seen in years.\\\"\\\\n\\\\nFrank Rich, in reviewing the 1985 concert in The New York Times, wrote: \\\"Friday\\\\'s performance made the case that this Broadway musical\\\\xa0... can take its place among our musical theater\\\\'s very finest achievements.\\\" Ben Brantley, reviewing the 1998 Paper Mill Playhouse production in The New York Times, concluded that it was a \\\"fine, heartfelt production, which confirms Follies as a landmark musical and a work of art\\\\xa0...\\\".\\\\n\\\\nThe Time reviewer wrote of the 2001 Broadway revival: \\\"Even in its more modest incarnation, Follies has, no question, the best score on Broadway.\\\" He noted, though, that \\\"I\\\\'m sorry the cast was reduced from 52 to 38, the orchestra from 26 players to 14\\\\xa0... To appreciate the revival, you must buy into James Goldman\\\\'s book, which is peddling a panoramically bleak take on marriage.\\\" Finally, he wrote: \\\"But Follies never makes fun of the honorable musical tradition to which it belongs. The show and the score have a double vision: simultaneously squinting at the messes people make of their lives and wide-eyed at the lingering grace and lift of the music they want to hear. Sondheim\\\\'s songs aren\\\\'t parodies or deconstructions; they are evocations that recognize the power of a love song. In 1971 or 2001, Follies validates the legend that a Broadway show can be an event worth dressing up for.\\\"\\\\n\\\\nBrantley, reviewing the 2007 Encores! concert for The New York Times, wrote: \\\"I have never felt the splendid sadness of Follies as acutely as I did watching the emotionally transparent concert production\\\\xa0... At almost any moment, to look at the faces of any of the principal performers\\\\xa0... is to be aware of people both bewitched and wounded by the contemplation of who they used to be. When they sing, in voices layered with ambivalence and anger and longing, it is clear that it is their past selves whom they are serenading.\\\"\\\\n\\\\nRecordings\\\\nThere have been six recordings of Follies released: the original 1971 Broadway cast album; Follies in Concert, Avery Fisher Hall (1985); the original London production (1987); the Paper Mill Playhouse (1998); the 2011 Broadway revival; and the 2017 London revival. The original cast album has always been controversial, because significant portions of the score were cut to fit onto one LP. However, as Kritzerland Records head Bruce Kimmel wrote in his liner notes to Kritzerland\\\\'s remixed version of the album, \\\"What it did have made it something that, despite the frustrations, meant it would never be bettered – the original cast.\\\"\\\\nThe cast recording of the 2011 Broadway revival, by PS Classics, was released officially on November 29, 2011, and was in pre-sale before the store release. PS Classics co-founder Tommy Krasker stated \\\"We\\\\'ve never had the kind of reaction that we\\\\'ve had for Follies. Not only has it already outsold every other album at our website, but the steady stream of emails from customers has been amazing.\\\" This recording includes \\\"extended segments of the show\\\\'s dialogue\\\". The theatermania.com reviewer wrote that \\\"The result is an album that, more so than any of the other existing recordings, allows listeners to re-experience the heartbreaking collision of past and present that\\\\'s at the core of the piece.\\\" The recording of the 2011 revival was nominated for a Grammy Award in the Musical Theater Album category. The 2017 London revival cast was recorded after the production closed in January 2018, and was released in early 2019.\\\\n\\\\nFilm adaptation\\\\nIn January 2015, it was reported that Rob Marshall is set to direct the movie, and Meryl Streep was rumored to star in it. Tony Award-winning playwright and Oscar-nominated screenwriter John Logan has expressed interest in writing a film adaptation of Follies.\\\\n\\\\nIn November 2019, it was announced that Dominic Cooke will adapt the screenplay and direct the film, after having directed the successful 2017 revival in the National Theatre in London, which returned in 2019 because of popular demand.\\\\n\\\\nAwards and nominations\\\\n\\\\nOriginal Broadway production\\\\n\\\\nOriginal London production\\\\n\\\\n2001 Broadway revival\\\\n\\\\n2011 Broadway revival\\\\n\\\\n2017 London revival\\\\n\\\\nNotes\\\\n\\\\nReferences\\\\n Chapin, Ted (2003). Everything Was Possible: The Birth of the Musical Follies. New York, New York: Alfred A. Knopf. \\\\n Secrest, Meryle (1998). Stephen Sondheim: A Life. Dell Publishing, Alfred A. Knopf (reprint). \\\\n Sondheim, Stephen and Goldman, James (2001). Follies. New York, New York: Theatre Communications Group. \\\\nSondheim, Stephen (2010). Finishing the Hat. Alfred A. Knopf.\\\\n\\\\nFurther reading\\\\n Prince, Harold (1974). Contradictions: Notes on Twenty-six Years in the Theatre. Dodd, Mead. \\\\n Ilson, Carol (2004). Harold Prince: A Director\\\\'s Journey, Limelight Editions. \\\\n Mandelbaum, Ken (1990). A Chorus Line and the Musicals of Michael Bennett. St. Martins Press.\\\\n\\\\nExternal links\\\\n \\\\n Follies on The Stephen Sondheim Reference Guide\\\\n \\\\n Follies at the Music Theatre International website\\\\n\\\\n1971 musicals\\\\nBroadway musicals\\\\nLaurence Olivier Award-winning musicals\\\\nOriginal musicals\\\\nMusicals by James Goldman\\\\nMusicals by Stephen Sondheim\\\\nWest End musicals\\\\nPlays set in New York City\\\\nTony Award-winning musicals\\\\nBackstage musicals'},\\n\",\n \" {'docid': 'doc-en-11',\\n\",\n \" 'text': 'Cleopatra in Space is an American animated television series produced by DreamWorks Animation and animated by Titmouse, Inc., based on the graphic novel series of the same name by Mike Maihack. The showrunners for the series are Doug Langdale and Fitzy Fitzmaurice.\\\\n\\\\nIn the United States, the first five episodes were released on NBCUniversal\\\\'s streaming service Peacock for Xfinity customers on April 15, 2020, making this the first DreamWorks Animation series to be released on a streaming device other than Netflix or Amazon Video. On July 15, 2020, the first season was officially released when the service launched nationwide. Prior to its release in the United States, the series was first broadcast in Southeast Asia on DreamWorks Channel beginning on November 25, 2019. The show is geared toward those between ages 6 and 11. Langdale, in an interview, said that he is attempting to make sure the show is \\\"accessible to a younger audience,\\\" even as he doesn\\\\'t give much thought to what age demographic the show is aiming towards.\\\\n\\\\nOn July 15, the show premiered on Peacock, with episodes 1–5 and 7–13 of the first season made available to viewers who subscribed to \\\"Peacock Premium\\\", and a more limited selection for those who chose a free plan. It was one of the three animated Peacock Originals streaming on the platform, with the other two being season 13 of Curious George and season 2 of Where\\\\'s Waldo?. The show can only be watched using the streaming service\\\\'s premium plan. On November 19, 2020, Season 2 premiered on Peacock. On January 14, 2021, Season 3 was released on Peacock. On July 14, 2021, all three seasons were added to Hulu.\\\\n\\\\nPlot\\\\nCleopatra in Space is a comedic adventure focusing on Cleopatra\\\\'s teenage years, as she deals with the ups and downs of being a high school teenager, after she transported 30,000 years into her future to a planet with Egyptian themes ruled by talking cats, and she is said to be the savior of a galaxy. Cleopatra and her newfound friends work to try and return her to her own time, in Ancient Egypt, as she gains new combat skills in the process. Showrunner Doug Langdale described the show as a \\\"real move-forward story\\\" which continues forward without interruption.\\\\n\\\\nCharacters\\\\n\\\\nMain\\\\n Cleopatra \\\"Cleo\\\" (voiced by Lilimar Hernandez) - The fearless and confident protagonist of the series. The 15-year-old princess of ancient Egypt, whose father is Pharaoh King Ptolemy (Sendhil Ramamurthy), she ends up sucked into a portal that sends her 30,000 years into the future where she learns she is the prophesied \\\"Savior of the Nile Galaxy\\\", destined to defeat the evil space tyrant Octavian. She ends up attending the futuristic intergalactic academy named P.Y.R.A.M.I.D. to obtain the proper training and skills to fulfill her role. She is sometimes reckless and impulsive, but has a good heart and wants peace. She has also gained strange and powerful powers from her time-travel, which manifests in pink and can be used to drain energy and project it into energy waves and beams. Lilimar called Cleo a character who is not completely mature or responsible, but a young girl who is on the road to becoming a hero, a person who is courageous and brave, seeing \\\"a lot of positivity in the world, no matter how dark things seem to be,\\\" even as she seeks adventure all the time.\\\\n Akila (voiced by Katie Crown) - A pink-eyed fish girl from another planet and the first of Cleopatra\\\\'s teammates. She is very friendly and optimistic, but over-enthusiastic. She may have a crush on Brian. She has two moms: Theoda (voiced by Cissy Jones) and Pothina (voiced by Kari Wahlgren), who are scholars at The Savior Institute, use dated social expressions and love their daughter. They are the first confirmed LGBTQ characters in the series.\\\\n Brian (voiced by Jorge Diaz) - A cyborg teenage boy, and Cleopatra\\\\'s second teammate. His body is mostly robotic and is also sensitive of the fact that he was transformed into a cyborg. He is rather worrisome, paranoid, nervous, and self-conscious at times. He has a crush on Akila. He and Akila later have a foster child of sorts named Eyeball, who is voiced by Brian Posehn.\\\\n Khensu (voiced by Sendhil Ramamurthy) - An intelligent, long-eared cat with the ability to speak. He serves as the leader of the group, and a teacher at P.Y.R.A.M.I.D. He becomes, as noted by showrunner Doug Langdale, like a surrogate father to Cleo, balancing out her action and acrobatics with his intellectual, sensible, and down-to-Earth nature.\\\\n Mihos - The cute and lovable pet of Cleo. Later Doctor Queed says that Mihos can\\\\'t be native to the planet but rather is a \\\"lost pet.\\\" Boop, a female pirate and space squirrel, is his doppelgänger. Lilimar, in an interview, said that while she likes Brian and Akila as characters, Mihos is her favorite, even making a request to make him into a plushie.\\\\n\\\\nSupporting\\\\n Callie (voiced by Kari Wahlgren) - An arrogant and snobby student at P.Y.R.A.M.I.D. who becomes Cleopatra\\\\'s academic rival upon her arrival in the school. \\\\n Xerxs (voiced by Dee Bradley Baker) - Legions of alien robots who are the soldiers and servants of Octavian.\\\\n Zaid Antonius (voiced by Xolo Maridueña) - A student at P.Y.R.A.M.I.D., whom Cleopatra is attracted to. He is later revealed to be a spy of Octavian, due to the latter holding his parents, Dahab (Candi Milo) and Askari (Dee Bradley Baker), hostages. His family name is a hint towards the historical figure Marcus Antonius.\\\\n Administrant Khepra (voiced by Sumalee Montano) - The head of the cat-council.\\\\n Octavian (voiced by Jonathan Kite) - The main antagonist of the series. The evil ruler of the Xerxs who has destroyed or enslaved many worlds and Cleopatra\\\\'s arch-nemesis. He is bent on capturing and getting rid of Cleopatra so the prophecy about her defeating him cannot be fulfilled. Named after the historical figure Gaius Octavian.\\\\n E\\\\'Geke-Ek\\\\'Gek (voiced by Alex Cazares) - an alien student who requires a translator torque to communicate.\\\\n Yosira (voiced by Wahlgren) - The young Pharaoh of P.Y.R.A.M.I.D. She is the granddaughter of the founder of P.Y.R.A.M.I.D., the late Pharaoh Yosiro. \\\\n Zuzz (voiced by Zach Callison) - A student whose body consists of a swarm of insect-shaped \\\"bits\\\".\\\\n Omnia (voiced by Elise Dubois) - A robot representing a planet and part of the debate club at P.Y.R.A.M.I.D.\\\\n\\\\nOther characters\\\\n Professor Sitre (voiced by Amy Hill) - a middle-age cat teacher at P.Y.R.A.M.I.D.\\\\n Philo (voiced by Gunnar Sizemore) - A young apprentice at P.Y.R.A.M.I.D., whom Cleopatra mentors in one episode in order to attain the cadets\\\\' Level 2.\\\\n Zedge (voiced by Lucas Grabeel) - A popular intergalactic rockstar, whom Akila has a crush on and claims to be his \\\"biggest fan\\\". He was briefly mind-controlled by Octavian to capture Cleopatra. \\\\n Cyrano (voiced by Greg Cipes) - An evil artificial intelligence created by Octavius to counter Brian. \\\\n Gozi (voiced by Karan Brar) - A young Egyptian boy who was Cleopatra\\\\'s best friend back on Earth in her own time. \\\\n Msamaki (voiced by David Shaughnessy) - An intelligent, long-eared cat with the ability to speak, and a member of the Cat council.\\\\n Professor Klabrax V (voiced by Dawnn Lewis) - An intelligent, fish-like being who is a professor at P.Y.R.A.M.I.D.\\\\n Dr. Queed (voiced by Paul Rugg) - A former doctor at P.Y.R.A.M.I.D. and an acquaintance of Khensu whose catchphrase is \\\"uncanny scientific brilliance!\\\"\\\\n Debbie (voiced by Candi Milo) - A lonely planet who can manifest into various forms and later a student at P.Y.R.A.M.I.D.\\\\n Generator A.I. (voiced by Dee Bradley Baker) - An A.I. located nearby the academy which is used to generate electricity for the campus after Cleo sucks out all the power from the academy.\\\\n Damaris (voiced by Marieve Herington) - A space scavenger, part of a group led by Dave.\\\\n Dave (voiced by Ace Gibson) - Leader of the space scavengers and has a pet named Precious.\\\\n Simon (voiced by Rhys Darby) - A conniving snake who turns on Akila, with Cleo and her parents working together to stop it.\\\\n Gurbo Gorbage (voiced by Kay Bess) - A purported television personality who kidnaps Cleo and almost sends her to Octavian.\\\\n Amsaja (voiced by Kimberly D. Brooks) - The self-declared \\\"Queen of the Space Pirates,\\\" who heading a crew of three other pirates, and Cleo\\\\'s doppelgänger. She previously had the telepathic space shark ninja as her ex-boyfriend, and Octavian might be her ex-boyfriend as well.\\\\n Cyborg Dwayne - (voiced by Andrew Morgado) - The doppelgänger of Brian who is also on the pirate ship.\\\\n Medjed (voiced by Ken Pringle) - Ruler of Dargham who tries to convince Cleo and Zaid to stay there indefinitely.\\\\n Gled (voiced by John DiMaggio) - Chief of the Tawrisians on the planet Tawris in the Nile Galaxy. \\\\n Commodore Winifred (voiced by Toks Olagundoye) - The commander of a ship of Parvites atop the head of Mihos, whose full name is Commodore Winifred Blurvington the Third.\\\\n Mortimer (voiced by Damian O\\\\'Hare) - A lieutenant who serves under Commodore Winifred and resembles a proper British gentleman.\\\\n\\\\nProduction\\\\nIn January 2018, DreamWorks Animation filed trademark applications for the show to the United States Patent and Trademark Office. Since then, DreamWorks has filed for four extensions on their trademark for Cleopatra in Space, two times in 2019, and two times in 2020, all of which were granted.\\\\n\\\\nMike Maihack said that the series is in retroactive continuity to his comics because Cleo is a teenager and there is time travel. This differs from his comic book series which is \\\"rooted from stories and research of the actual Cleopatra.\\\" He also may have been influenced by Kipo and the Age of Wonderbeasts, Avatar: The Last Airbender, Star Trek: The Next Generation, Buffy the Vampire Slayer, and Legends of Tomorrow. In an interview with Charles C. Dowd on I Heart Webcomics on July 22, Maihack said that he consulted in the early stages of the show, letting DreamWorks know the upcoming details of his book and remained supportive, admitting he did not want \\\"a direct adaption of the novels.\\\" He further said that he saw the animated series as an opportunity to work on the Cleo in Space concept in another way who had worked on Ben 10: Omniverse and DuckTales, noting that while it was clear that those working on the series understood \\\"the core components of the story,\\\" he stepped back, letting the \\\"amazingly talented folks\\\" involved in the show do their work.\\\\n\\\\nIn an interview with Jackson Murphy of Animation Scoop, showrunner Doug Langdale said the story lends itself to \\\"serialized storytelling\\\" rather than an animated feature film, and that developing the show has been a \\\"pretty involved process.\\\" He also stated that the different uniforms at the Academy are \\\"different colors for different divisions in the school and emblems,\\\" that they used common science-fiction tropes, that the show is not lesson-based but is just entertainment, and that Mike Maihack was ok with deviating from the original graphic novels, so they could create something that fans would enjoy. Langdale expanded on this in an interview with Screen Rant where he noted that they found Maihack\\\\'s books, noted that DreamWorks had been trying to create a feature film about it, which was abandoned so they could do a series. He further described the differences between the books and the series, which are on a \\\"day-to-day basis,\\\" with the series not following the books closely at all, even as they used the \\\"same set up, [and] many of the same characters.\\\" Langdale explained how Egyptian history was an inspiration for many character names, sometimes by coincidence, and how Lilimar Herendez was the choice for the main role of Cleopatra from the beginning, while stating how Sendhil Ramamurthy, Jorge Diaz, and Katie Crown influenced the show through their voice acting. He finally told the interview that the show ended up with a \\\"predominantly female cast,\\\" with DreamWorks seeing this as a \\\"good time to make a show with a female lead,\\\" and explained that the \\\"first 12 episodes take place within a few months.\\\"\\\\n\\\\nIn an interview with CBR, Langdale said that he enjoyed Maihack\\\\'s books, and agreed with DreamWorks to create the show, using Maihack\\\\'s characters as a starting point, but then \\\"went off in some different directions.\\\" He again reiterated that the episodes track the characters on a day-to-day basis, and differed from the original books because Brian and Akila are humans, rather than a cyborg and an alien, with the voice actors shaping their characters. At the same time, he sidestepped the historical debate over her origins.\\\\n\\\\nIn an August 6, 2020 interview with ComicBook, Langdale further explained the development of the show. He said that they didn\\\\'t \\\"literally translate the books,\\\" but took the characters Mike Maihack created, some character bits, and did their \\\"our own thing.\\\" He added that they wanted to show a \\\"day-to-day story about the characters,\\\" different from the books, tried to work in some \\\"some ancient Egyptian motifs\\\" and said some inspiration could have drawn from 1970s French comic books, and three divisions in the school: \\\"command, combat, and science.\\\" He stated he didn\\\\'t think about the age range DreamWorks gave him for the show, rather aiming to make an enjoyable show which is \\\"pretty serialized.\\\"\\\\n\\\\nMusic\\\\nThe music of the series is composed by Jay Vincent and Ryan Lofty. It was described by Courtney Lofty, the score production manager, as \\\"an epic cocktail of electronic beats, Egyptian melodies, and orchestral dramatics,\\\" with other melodies, with \\\"an extreme amount of time\\\" researching for the music, which references Paramore, M.I.A., and the score of The Prince of Egypt. The music was attuned to the specific scenes in each episode.\\\\n\\\\nThe series opening theme song was sung by Lilimar Hernandez, the voice actress for Cleopatra. Additionally, Matt Barrios worked on the main title. Jackson Murphy of Animation Scoop described the song as making it clear that Cleo\\\\'s story is about \\\"meaning, purpose and destiny.\\\" In an August 13, 2020 interview with Sergio Burstein of the Los Angeles Times, Lilimar describes the show as the first thing she has done in the animation field, and was surprised to by their proposal for her to sing the title song. While she admitted she was nervous to sing the song at first, because she hadn\\\\'t \\\"done anything with music in ten years,\\\" she said that working on the show helped her regain her \\\"voice as a singer,\\\" while encouraging her to do things out of her \\\"comfort zone,\\\" and remained grateful of the positive responses on Twitter. She said something similar in an interview with a Spanish language publication, Siempre Mujer and an interview with a Spanish-language YouTuber, saying the project surprised her because she never expected to sing the theme song and that working on the show was a learning experience. On August 21, in an interview with Alfonso Díaz of RCN Nuestra Tele Internacional, Lilimar called working on the show a \\\"very nice experience,\\\" noted that the show was her second job for DreamWorks (her first was Spirit Riding Free), and how she sometimes recorded the lines for the show alone, while other times she did it with the rest of the cast. She also explained the struggles with recording lines, how this show is the first time she has had such a big role, and its relevance during the COVID-19 pandemic with the main cast having to work together under extraordinary circumstances.\\\\n\\\\nIn an interview with CBR in January 2021, she said that singing the opening theme was nowhere in her contract and they called her saying they\\\\'d like her to sing the song. She decided to do so, even though she was \\\"not in a confident place\\\" with her singing, and didn\\\\'t think much of it. She wasn\\\\'t told they were going to use it, then they had a premiere for those on the team, and she brought her mom along, who pointed out it was her. She said that the fact that they used her voice meant \\\"they liked it\\\" and called it \\\"really cool\\\" that she sang the opening.\\\\n\\\\nDesign\\\\nAccording to Langdale, the drawn-out visual development of the show allowed them to have a style close to Mailhack\\\\'s original graphic novels. He added that the show\\\\'s crew wanted a visual style which \\\"was going to be fun to look at,\\\" with the Academy divided into \\\"three areas of specialization...identified by color\\\" which is not directly noted in the show. He further pointed out that one challenge was with the digital 2D animation and they received help from the animators. In a later interview he said that the show\\\\'s animation sometimes mirrored the scenes in the graphic novels.\\\\n\\\\nCharacter design\\\\nOn September 16, 2020, Bertrand Todesco, the series\\\\' character designer, was interviewed by VoyageLA. In the interview, he described how he imagines the \\\"shapes and colors of the characters based on the descriptions\\\" he reads in a script or other document, saying that in draw something differently depending on the age of the audience, and the excitement of working collaboratively with others on various animated shows. He also talked about developing the show\\\\'s main characters and that with the help of DreamWorks, and others like Angela Mueller, Art Director for the show, his O-1 visa was approved, allowing him to stay in Los Angeles. According to Langdale, Todesco came up with Akila as a \\\"fish-based character\\\" while they went back-and-forth as to whether Brian would be a human or a cyborg, and that they shaped Cleopatra\\\\'s character by what \\\"someone with a passing familiarity\\\" of her might think she was as a person.\\\\n\\\\nStorytelling\\\\nIn early January 2021, Wei Li, who storyboarded eight episodes for the show, shared an animatic from an episode he storyboarded, titled \\\"My Pharaoh Lady,\\\" adding that the \\\"show never took off.\\\" He later explained what he meant was that the series did not get the \\\"proper distribution,\\\" said that he personally thought \\\"the story could be a lot better,\\\" and argued the original comic deals with subjects with more seriousness, stated that, \\\"it seems like they radically changed Cleo\\\\'s personality\\\" from the comics. He later explained that personally, if he could, he would change the episodes \\\"where Cleo supposedly learns from her mistakes,\\\" so that the viewers see a \\\"change in her from that lesson in the episodes afterward\\\".\\\\n\\\\nVoice acting\\\\nIn an exclusive interview with CBR, Lilimar Hernandez said that this was the first show where she had a major role and that she used her \\\"natural voice\\\" when voicing Cleopatra. She described trying to found out what that voice was and attempting to be as consistent as possible with that voice. She tried her best to keep her \\\"fun-loving nature\\\" and called the voice acting a \\\"cool journey.\\\" Furthermore, she said that the team she worked with, like Doug Langdale, and a voice director Sirena Irwin, made her feel excited and comfortable, as she explored the character and the world of the show. In the same interview, she said that the experience was \\\"nice,\\\" even as a new person to voice acting, adding that working with people who more skilled in the industry inspired and motivated her. She later said that it was cool \\\"tapping into the fanbase from the books themselves,\\\" that she received a lot of \\\"really, really cool feedback\\\" and noted that the production schedule was consistent. She described the group sessions as having the highest energy, with everyone having fun \\\"seeing each other become the characters,\\\" with none of it seen as draining.\\\\n\\\\nEpisodes\\\\n\\\\nSeries overview\\\\n\\\\nSeason 1 (2020-21)\\\\n\\\\nSeason 2 (2020)\\\\n\\\\nSeason 3 (2021)\\\\n\\\\nRelease\\\\nIn the United States, NBCUniversal\\\\'s advertisement sales website previously suggested that Cleopatra in Space would be broadcast on Universal Kids. Later, it emerged in January 2020 that the series would instead be included in the launch line-up of Peacock, NBCUniversal\\\\'s streaming service on April 15, 2020 to Xfinity customers, and July 15 to all customers nationwide.\\\\n\\\\nPrior to the scheduled release in the United States, the series premiered on November 25, 2019 on DreamWorks Channel, which is available in Southeast Asia and select other areas of Asia Pacific (specifically, Hong Kong, Indonesia, South Korea, Malaysia, Maldives, Myanmar, the Philippines, Pakistan, Singapore, and Taiwan). 19 episodes have since aired on the channel. The series also premiered in Poland on Teletoon+ on February 15, 2020 with a Polish dub. The series was also available in South Africa on Showmax, including all episodes in season one by February 23, 2020. By June 9, 2020, all 26 episodes of the show\\\\'s first season were made available on the Viaplay service in Scandinavia because of an agreement between NBCUniversal and the Nordic Entertainment Group (NENT Group).\\\\n\\\\nOn May 1, 2020, the entire first season of Cleopatra in Space was released on Globoplay, a Brazilian service and subsidiary of Grupo Globo, with the name Cleópatra no Espaço.\\\\n\\\\nOn July 15, when the show premiered on Peacock to all those in the United States, Mike Maihack praised the show\\\\'s release and all the hard work put in, giving it his endorsement. At the same time, he called the release of only 12 episodes \\\"disappointing,\\\" and lamented the absence of the sixth episode, \\\"Quarantine,\\\" which \\\"deals with a zombie-like flu and the consequences of Cleo avoiding quarantine,\\\" saying that it is something the whole world should \\\"be able to see right now.\\\" Two days later, when a fan asked about the missing sixth episode and the misspelled title of one of the episodes, an official Peacock account responded, saying they had corrected the episode title, but that for episode 6, \\\"this content is temporarily unavailable on the platform\\\" and that they appreciated the feedback, saying they \\\"will pass it along to the proper team.\\\" Later the same day, the same account said that there was \\\"no news on that at the moment.\\\" A few months later, on September 1, another fan asked about the episode, and an official Peacock account stated that the episode is \\\"not actively on Peacock\\\" but gave no further explanation as to why that was the case. The episode was eventually released on June 25, 2021.\\\\n\\\\nOn August 27, Bertrand Todesco called on Netflix France, and their main office in the United States, to broadcast the show outside the U.S., noting that \\\"international fans\\\" are asking about it every day, and that they could negotiate the rights with DreamWorks. The same day, Todesco thanked the fans of the show, saying he had seen \\\"a lot of incredible\\\" fan art from \\\"all over the web.\\\"\\\\n\\\\nIn September 2020, the show began airing on the Disney Channel in Russia with the name Клеопатра в космосе.\\\\n\\\\nIn October, in the UK, the show began airing on Sky One as part of a partnership with NBCUniversal. In a tweet, Mike Maihack hoped it was \\\"good news\\\" for fans of the show in the United Kingdom who have wanted to watch the show. Currently, the Sky One website allows subscribers to their Sky Go service to watch 25 episodes, but not episode 6, \\\"Quarantine\\\". When asked about this, Sky UK stated that this was not included because of the \\\"licensing on the episode.\\\"\\\\n\\\\nIn November, a new poster for Season 2 was released, as was a summary for the season, saying it would focus on Cleo and her friends \\\"embarking on a mission to search the galaxy for an ancient artifact that could defeat Octavian for good,\\\" and a preview video. On November 19, 2020, Season 2, premiered on Peacock.\\\\n\\\\nOn January 9, 2021, in Canada, the series started airing on Family CHRGD. Then on January 14, 2021, Season 3 was released on Peacock.\\\\n\\\\nOn July 14, 2021, the \\\"Complete First Season\\\" of the series, along with the Spanish version, Cleopatra en el Espacio, was released on Hulu, consisting of all three seasons which were released on Peacock.\\\\n\\\\nReception\\\\nEncyclopedia of Science Fiction contributor Steven Pearce gave a short positive review of the show, though was critical about the show\\\\'s writing and Cleopatra\\\\'s personality, saying it is \\\"very much your feisty American teen.\\\" The entry praises the \\\"nice background details\\\" and calls the series fun, amusing, \\\"brightly animated and engaging.\\\" Courtney Lofty described the series as being about \\\"badass women, talking cats, [and] space,\\\" noting that the overall vibe is a \\\"classic Saturday morning cartoon, with extremely quotable moments\\\" which is like Invader Zim. Cheryl Eddy on Gizmodo described the show as one aimed at children, but \\\"looks like a fun ride for geeky grown-ups\\\", while Karen Han and Petrana Radulovic in Polygon and Sam Stone on CBR reviewed it positively. Additionally, others described the show as \\\"a fun take on the original Cleopatra story\\\" and a \\\"comedic adventure\\\" which focuses on Cleopatra\\\\'s teenage years, where she is transported into the future \\\"to an Egyptian-themed planet...ruled by talking cats\\\" while dealing with the pressures of being \\\"a teenager in high school\\\" as she tries to fit in even as Octavian tries to kill her. Later, Petrana Radulovic wrote a positive review of the show. She described the series as wacky and vibrant, using its \\\"zany concept effectively,\\\" having interesting adventures, and has main characters who have \\\"typical stock cartoon personalities.\\\" At the same time, she compared the impulsive and cocky behavior of Cleo to Lance in Voltron: Legendary Defender and Ben in Ben 10. She further contrasted Cleopatra to Korra in The Legend of Korra and Adora in She-Ra and the Princesses of Power in that she is not ready to accept her destiny but will have to \\\"confront her own laziness,\\\" remaining as a \\\"carefree, imperfect heroine\\\" in the meantime. Radulovic also said that the \\\"electronic-infused Egyptian melodies of the score\\\" make it stand out, as do the outfits of the characters, while noting that the show is episodic like Gravity Falls rather than something like She-Ra and the Princesses of Power.\\\\n\\\\nThere were a number of other reviews of the show. In an episode of Tooned Up on the Renegade Pop Culture Podcast, one of the guests described the show as having a stellar voice cast, sharp writing, which is \\\"almost too self aware,\\\" while saying that they wished that the animation budget \\\"was a little bit higher.\\\" The same guest said that the show skews to those \\\"a little bit younger\\\" and said that the show takes a \\\"few episodes to find its stride,\\\" but once it does that, it is \\\"one of the easiest shows to binge.\\\" Another reviewer took a different tack, focusing on themes of libraries in the show, writing in the ALA\\\\'s I Love Libraries, writing that the library at Cleopatra\\\\'s futuristic high school contains information saved from the show\\\\'s villain, who destroyed most of recorded knowledge, and noting that the library\\\\'s section on Ancient Egypt, would, if in a real library, \\\"be housed in a library\\\\'s special collections.\\\" In contrast, Ashley Moulton on Common Sense Media rated the series 3 out of 5, noting that there is \\\"a lot of fantasy violence,\\\" while saying that Cleopatra is a \\\"fearless female lead,\\\" with her potential as a role model \\\"offset by the fact that she can be impulsive, impatient, overconfident, and not so dedicated to her schoolwork,\\\" adding that there is \\\"mild language...and flirtation,\\\" saying that the show isn\\\\'t educational even though it \\\"features a historical character.\\\" Rather it is, in Moulton\\\\'s view, focused on entertainment \\\"in the vein of \\\\'80s Saturday morning cartoons,\\\" and she describes the series as \\\"light, fun tween sci-fi\\\" animation which explores the past and future, while praising the \\\"interesting alien species, exciting fight scenes, and fun gadgets like robots and hover boards,\\\" and the world they live in as \\\"pretty cool.\\\" Even so, she argued that the characters are flat, while the characters \\\"gleefully engage in moderately violent fight scenes\\\" to defeat villains, and calling the characters \\\"disappointing\\\".\\\\n\\\\nExplanatory notes\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n\\\\nDepictions of Cleopatra on television\\\\n2020 American television series debuts\\\\n2020s American animated television series\\\\nAmerican children\\\\'s animated action television series\\\\nAmerican children\\\\'s animated space adventure television series\\\\nAmerican children\\\\'s animated comic science fiction television series\\\\nAmerican children\\\\'s animated science fantasy television series\\\\nAmerican flash animated television series\\\\nTelevision series by DreamWorks Animation\\\\nTelevision series by Universal Television\\\\nAnimated television series about teenagers\\\\nPeacock (streaming service) original programming\\\\nTelevision shows based on comics\\\\nPeacock (streaming service) children\\\\'s programming\\\\nWorks about Cleopatra'},\\n\",\n \" {'docid': 'doc-en-12',\\n\",\n \" 'text': 'Impression Products, Inc. v. Lexmark International, Inc., 581 U.S. ___ (2017), is a decision of the Supreme Court of the United States on the exhaustion doctrine in patent law in which the Court held that after the sale of a patented item, the patent holder cannot sue for patent infringement relating to further use of that item, even when in violation of a contract with a customer or imported from outside the United States. The case concerned a patent infringement lawsuit brought by Lexmark against Impression Products, Inc., which bought used ink cartridges, refilled them, replaced a microchip on the cartridge to circumvent a digital rights management scheme, and then resold them. Lexmark argued that as they own several patents related to the ink cartridges, Impression Products was violating their patent rights. The U.S. Supreme Court, reversing a 2016 decision of the Federal Circuit, held that the exhaustion doctrine prevented Lexmark\\\\'s patent infringement lawsuit, although Lexmark could enforce restrictions on use or resale of its contracts with direct purchasers under regular contract law (but not as a patent infringement lawsuit). Besides printer and ink manufacturers, the decision of the case could affect the markets of high tech consumer goods and prescription drugs.\\\\n\\\\nBackground\\\\n\\\\nFactual setting\\\\n\\\\nLexmark International, Inc. makes and sells printers and toner cartridges for its printers. Lexmark owns a number of patents that cover its cartridges and their use. Lexmark sold the cartridges at issue in this case—some in the United States and some abroad.\\\\n\\\\nDomestic sales \\\\n\\\\nLexmark\\\\'s domestic sales were in two categories. A \\\"Regular Cartridge\\\" is sold at \\\"list price\\\" and confers an absolute title and property right on the buyer. A \\\"Return Program Cartridge\\\" is sold at a discount of about 20 percent, and is subject to post-sale restrictions: The buyer may not reuse the cartridge after the toner runs out and may not transfer it to anybody else. The first branch of the case turns on the legal status of these post-sale restrictions.\\\\n\\\\nLexmark manufactured the toner cartridges with microchips in them, which send signals to the printers indicating toner level. When the amount of toner in a cartridge falls below a certain level, the printer will not operate with that cartridge. Also, the printer will not operate with a Return Program Cartridge that has been refilled by a third party. Thus, Lexmark\\\\'s technology prevented violation of the post-sale restriction against refilling the Return Program Cartridges. The Regular Cartridges do not have this anti-refill feature and can therefore be refilled and reused (but they cost 20 percent more).\\\\n\\\\n\\\"To circumvent this technological measure,\\\" however, \\\"third parties have \\\\'hacked\\\\' the Lexmark microchips. They created their own \\\"unauthorized replacement\\\" microchips that, when installed in a Return Program cartridge, fool the printer into allowing reuse of that cartridge. Various companies purchase used Return Program Cartridges from the customers who bought them from Lexmark. They replace the microchips with \\\"unauthorized replacement\\\" microchips, refill the cartridges with toner, and sell the \\\"re-manufactured\\\" cartridges to resellers such as Impression Products for marketing to consumers for use with Lexmark printers. Lexmark had previously argued in Lexmark International, Inc. v. Static Control Components, Inc. that replacing these microchips violated copyright law and the Digital Millennium Copyright Act (DMCA), but both federal and the Supreme Court have ruled against Lexmark, affirming that replacing the microchips is not in violation of copyright.\\\\n\\\\nImported cartridges\\\\n\\\\nThe second branch of the case involves cartridges that Lexmark sold outside the US. While some of the foreign-sold cartridges were Regular Cartridges and some were Return Program Cartridges, this branch of the case does not involve any distinction among the two types of imported cartridges.\\\\n\\\\nTrial court decision\\\\n\\\\nThe district court granted Impression\\\\'s motion to dismiss Lexmark\\\\'s claim of infringement involving the single-use cartridges Lexmark had first sold in the United States. The district court concluded that the Supreme Court in Quanta Computer, Inc. v. LG Electronics, Inc. found exhaustion where \\\"the Supreme Court determined that the agreements [at issue] broadly authorized Intel [the seller] to sell the licensed products without restrictions or conditions.\\\" The district court said \\\"that Quanta overruled Mallinckrodt sub silentio,\\\" and therefore \\\"those post-sale use restrictions do not prevent patent rights from being exhausted given that the initial sales were authorized and unrestricted.\\\"\\\\n\\\\nThe district court held, however, that the exhaustion doctrine did not apply to the cartridges that Lexmark had sold abroad. It said that international exhaustion did not apply to patents because Kirtsaeng v. John Wiley & Sons, Inc., which established international exhaustion in at least some cases, applied only to copyrights. The court therefore denied Impression\\\\'s motion to dismiss Lexmark\\\\'s claim of infringement involving the cartridges Lexmark had sold abroad.\\\\n\\\\nGovernment amicus curiae position\\\\n\\\\nIn its amicus curiae brief, the US Government argued that Mallinckrodt had been wrongly decided in 1992 and in any case it had been overruled sub silentio in Quanta. It stated:\\\\nIn the view of the United States, the first authorized sale of a patented article in the United States wholly exhausts the patentee\\\\'s exclusive rights in that article, notwithstanding any post-sale restriction imposed by the patentee.\\\\nThe government also argued that the decision of Jazz Photo Corp. v. United States International Trade Commission (2001) should be partially overruled in light of Kirtsaeng insofar as it held that foreign sales can never exhaust US patent rights. When the patentee neither makes nor authorizes a foreign sale, as occurred in Boesch v. Graff, it is proper to say no exhaustion occurred. But when the patentee makes or authorizes a foreign sale, and fails expressly to reserve its US rights, then exhaustion should be found. In the present case, Lexmark made the foreign sales and failed to expressly reserve its US rights; therefore, the sale exhausted the patent rights.\\\\n\\\\nFederal Circuit decision\\\\n\\\\nThe parties each appealed. After a three judge panel had heard oral argument the Federal Circuit sua sponte set the case for argument en banc in the first instance and invited the filing of amicus curiae briefs.\\\\n\\\\nMajority opinion\\\\n\\\\nJudge Taranto, writing for a 10-2 majority, reaffirmed both of the prior Federal Circuit rulings. In summary, the court held:\\\\n\\\\nFirst, we adhere to the holding of Mallinckrodt, Inc. v. Medipart, Inc. that a patentee, when selling a patented article subject to a single-use/no-resale restriction that is lawful and clearly communicated to the purchaser, does not by that sale give the buyer, or downstream buyers, the resale/reuse authority that has been expressly denied. Such resale or reuse, when contrary to the known, lawful limits on the authority conferred at the time of the original sale, remains unauthorized and therefore remains infringing conduct under the terms of §\\\\xa0271. Under Supreme Court precedent, a patentee may preserve its §\\\\xa0271 rights through such restrictions when licensing others to make and sell patented articles; Mallinckrodt held that there is no sound legal basis for denying the same ability to the patentee that makes and sells the articles itself. We find Mallinckrodt\\\\'s principle to remain sound after the Supreme Court\\\\'s decision in Quanta Computer, Inc. v. LG Electronics, Inc.\\\\xa0.\\\\xa0.\\\\xa0.\\\\n\\\\nSecond, we adhere to the holding of Jazz Photo Corp. v. International Trade Comm\\\\'n, that a U.S. patentee, merely by selling or authorizing the sale of a U.S.-patented article abroad, does not authorize the buyer to import the article and sell and use it in the United States, which are infringing acts in the absence of patentee-conferred authority. Jazz Photo\\\\'s no-exhaustion ruling recognizes that foreign markets under foreign sovereign control are not equivalent to the U.S. markets under U.S. control in which a U.S. patentee\\\\'s sale presumptively exhausts its rights in the article sold. A buyer may still rely on a foreign sale as a defense to infringement, but only by establishing an express or implied license—a defense separate from exhaustion, as Quanta holds—based on patentee communications or other circumstances of the sale. We conclude that Jazz Photo\\\\'s no-exhaustion principle remains sound after the Supreme Court\\\\'s decision in Kirtsaeng v. John Wiley & Sons, Inc., in which the Court did not address patent law or whether a foreign sale should be viewed as conferring authority to engage in otherwise-infringing domestic acts. Kirtsaeng is a copyright case holding that 17 U.S.C. §\\\\xa0109(a) entitles owners of copyrighted articles to take certain acts \\\"without the authority\\\" of the copyright holder. There is no counterpart to that provision in the Patent Act, under which a foreign sale is properly treated as neither conclusively nor even presumptively exhausting the U.S. patentee\\\\'s rights in the United States.\\\\n\\\\nDomestic exhaustion\\\\n\\\\nIn this part of its opinion, the Federal Circuit reaffirmed its Mallincrodt decision and rejected contentions that Quanta had silently overruled it.\\\\n\\\\n§\\\\xa0271 abrogates common-law rule\\\\n\\\\nThe court began by distinguishing the Patent Act\\\\'s and Copyright Act\\\\'s respective approaches to infringement. in 17 U.S.C. §\\\\xa0109(a) the Copyright Act says, \\\"Notwithstanding the provisions of section 106(3),\\\" defining infringement by selling, a purchaser \\\"is entitled, without the authority of the copyright owner, to sell or otherwise dispose of the possession\\\" of a purchased copy of a work. In contrast, the Patent Act contains no exhaustion provision. Therefore, the Patent Act requires a \\\"conferral of \\\\'authority\\\\' by the patentee .\\\\xa0.\\\\xa0. in order for the actions listed in §\\\\xa0271(a) not to constitute infringement.\\\" This means there must be \\\"permission from the patentee\\\" to avoid infringement. The court does not accept exhaustion as a form of \\\"constructive\\\" permission. Hence, if the patentee places explicit limits or conditions on its permission, they qualify the scope of the permission. This has the effect of limiting the common law.\\\\n\\\\nGeneral Talking Pictures rule applies to \\\"conditional\\\" sale\\\\n\\\\nThe court turned to the General Talking Pictures decision, which holds \\\"that Lexmark would not have exhausted its patent rights in those cartridges, upon the manufacturing licensee\\\\'s sale (the first sale), if a buyer with knowledge of the restrictions resold or reused them in violation of the restrictions.\\\" Although the government in its amicus curiae brief and defendant Impression argue \\\"that a different result is required—that Lexmark automatically lost its patent rights—simply because Lexmark sold the Return Program cartridges itself, subject to the same communicated restriction, rather than having left the manufacture and sale to others under license,\\\" the court does not accept that:\\\\n\\\\nWe conclude otherwise, as we did in Mallinckrodt and subsequent decisions. A sale made under a clearly communicated, otherwise-lawful restriction as to post-sale use or resale does not confer on the buyer and a subsequent purchaser the \\\"authority\\\" to engage in the use or resale that the restriction precludes. And there is no sound reason, and no Supreme Court precedent, requiring a distinction that gives less control to a practicing-entity patentee that makes and sells its own product than to a non-practicing-entity patentee that licenses others to make and sell the product.\\\\n\\\\nQuanta distinguishable and inapplicable\\\\n\\\\nThe court turned to the Quanta decision and found it inapplicable to the present issues. \\\"\\\\'Quanta did not involve a patentee\\\\'s sale at all, let alone one subject to a restriction or, more particularly, a single-use/no-resale restriction.\\\" Rather, Quanta involved a patentee\\\\'s (LGE\\\\'s) license to a manufacturer (Intel) that sold to the accused infringer (Quanta). LGE had not limited Intel\\\\'s license to manufacture the patented product, although it imposed contractual obligations on Intel. \\\"No conditions limited Intel\\\\'s authority to sell products substantially embodying the patents.\\\" The Federal Circuit emphasized: \\\"There were no patentee sales, and there were no restrictions on the sales made by the licensee.\\\" Those facts were removed from the case at bar. Thus the Quanta \\\"Court\\\\'s discussion of that issue does not undermine Mallinckrodts ruling that a patentee can preserve its patent rights through restrictions on its sales.\\\" The Federal Circuit also emphasized as significant the failure of the Quanta Court to explicitly repudiate Mallinckrodt despite the fact that in its amicus brief \\\"the government prominently featured an argument that Mallinckrodt was incorrect and should be repudiated.\\\"\\\\n\\\\nPrior cases\\\\n\\\\nThe court then turned to the prior Supreme Court cases. Reviewing them, it found that although they used sweeping language that a patentee\\\\'s sale of the patented product placed it beyond the reach of the patent, so that no post-sale restriction could be enforced under the patent laws, that language went beyond the actual facts of the cases. First, the sales were in most cases without any condition or restriction on what the buyer might do with the product. Second, in the cases where an explicit condition or restriction was imposed, the case involved a tie-in or a price-fix.\\\\n\\\\nThe Court conceded that in the General Electric case, the Supreme Court had said: \\\"It is well settled, as already said, that where a patentee makes the patented article, and sells it, he can exercise no future control over what the purchaser may wish to do with the article after his purchase. It has passed beyond the scope of the patentee\\\\'s rights.\\\" But that case involved an antitrust challenge to GE\\\\'s distribution of lamps that did not meet that description. The case involved price restrictions on a licensed manufacturer. The Federal Circuit then explained that the word \\\"settled\\\" in the Supreme Court\\\\'s statement had a special, narrow meaning:\\\\n\\\"We read that language to deem \\\\'settled\\\\' only what was settled in the cited precedents—a patentee\\\\'s sales without restrictions exhaust patent rights in the item sold.\\\" Thus, the Supreme Court\\\\'s sweeping exhaustion language applies precedentially only to cases in which either the sale was without condition or restriction or else the sale was made with a tie-in or price-fixing condition. \\\"But the Court did not rule that all restrictions on a patentee\\\\'s sale were ineffective to preserve the patentee\\\\'s patent-law rights.\\\"\\\\n\\\\nSimilarly, in United States v. Univis Lens Co., the Supreme Court\\\\'s sweeping language must now be limited to the factual context of the case:\\\\n\\\\nMoreover, although some language in Univis, like language in other decisions in the area, can be taken out of context and read as going beyond the specific restrictions involved, the most the Court ruled, even as to patent law all by itself, was that a vertical price-control restriction was ineffective to preserve patent rights after sales of articles embodying the patents. While Univis is controlling on what it decided on the issues before it, we do not think it appropriate to give broad effect to language in Univis, taken out of context, to support an otherwise-unjustified conclusion here on a question not faced there.\\\\n\\\\nThe Federal Circuit therefore drew this conclusion from the past series of Supreme Court cases on exhaustion:\\\\n\\\\nFor the foregoing reasons, we think that the best lesson to draw from the Supreme Court\\\\'s precedents, as applied to the question before us, is that a patentee may preserve its patent rights by otherwise-proper restrictions when it makes and sells patented articles itself and not only when it contracts out manufacturing and sales.\\\\n\\\\nPatent law trumps common law\\\\n\\\\nThe Federal Circuit returned to the common law and Lord Coke\\\\'s commentary on it. Again, the court insisted that Congress had overridden the common law\\\\'s prohibitions on post-sale restraints, in order to promote technological progress:\\\\n\\\\n[W]hatever considerations might go into a jurisdiction\\\\'s choice as to the background rule for personal property in general, lawmaking authorities may reasonably make different choices for particular kinds of property. Notably, as to intellectual property in its various forms, Congress, implementing the Constitution, has long deemed it important to incentivize creation and disclosure through grants to the creator of rights to exclude others for a time.\\\\xa0.\\\\xa0.\\\\xa0. That overriding legislative prescription removes the patented-article sale from the scope of Lord Coke\\\\'s 1628 description of his country\\\\'s general judicially fashioned property law.\\\\xa0.\\\\xa0.\\\\xa0. In short, notwithstanding Lord Coke\\\\'s description of English general personal-property judge-made law, the patent-specific statutory analysis must govern here.\\\\n\\\\nLikely effects on public\\\\n\\\\nThe court then turned to what it called \\\"the likely real-world consequences of one answer or another to the exhaustion question presented here.\\\" The court noted that in Kirtsaeng the Supreme Court had envisioned serious adverse effects on competition unless Coke\\\\'s 1628 property law rules were followed. The Federal Circuit said that did not apply to patents:\\\\n\\\\n[W]e see no basis for predicting the extreme, lop-sided impacts the Court found plausible in Kirtsaeng in different circumstances. Mallinckrodt has been the governing case law since 1992 and has been reiterated in subsequent precedent. And yet we have been given no reliable demonstration of widespread problems not being solved in the marketplace. Given General Talking Pictures, the only question is about patentees\\\\' ability to do for their own sales what they already can do by contracting out their manufacturing and sales. Regarding the specific scenario we are addressing today—in which the patentee has sought to preserve its patent rights by conditioning its first sale on a single-use/no-resale restriction of which the accused infringer had adequate notice at the time of purchase—we have been given no proof of a significant problem with enforcing patent rights.\\\\n\\\\nFurthermore, the Federal Circuit maintained, the conduct challenged here can have benefits. Under Lexmark\\\\'s program, customers who agree to the restriction pay a lower price than those who do not. It could be that the companies that refill the cartridges use inferior products that could harm the Lexmark machines, which \\\"could harm Lexmark\\\\'s reputation.\\\" To assume that the restrictions are illegitimate would run counter to the trends \\\"over the last four decades, that have displaced the strict condemnation of various vertical restrictions that characterized\\\" earlier antitrust and patent-misuse law in the first part of the twentieth century. \\\"Field-of-use, territorial, and other limitations on intellectual property licenses may serve procompetitive ends by allowing the licensor to exploit its property as efficiently and effectively as possible.\\\" Therefore, the court concluded it is appropriate to apply to post-sale restrictions the same tolerance that the General Talking Pictures doctrine accords limitations in manufacturing licenses.\\\\n\\\\nInternational exhaustion\\\\n\\\\nIn this part of its opinion, the Federal Circuit reaffirmed its Jazz Photo opinion and rejected contentions that Kirtsaeng had undermined the basis for Jazz Photo. The Federal Circuit insisted that \\\"Kirtsaeng says nothing about patent law.\\\"\\\\n\\\\nThe court emphasized the differences between patent law and copyright law. For example, patent law gives patentees an exclusive right to use of the invention but copyright law gives no general exclusionary right as to use (it gives exclusive public performance and display \\\"use\\\" rights, but not others). Also, it is much more costly and time-consuming to obtain a patent than a copyright. The court did not explain, however, the way that or other differences between copyrights and patents called for contrary results as to international exhaustion.\\\\n\\\\nThe court did say that the US patent statute gives patentees the reward available from \\\"sales in American markets, not from sales in foreign markets.\\\" A sale in a foreign market therefore does not furnish a proper basis for finding exhaustion. \\\"American markets differ substantially from markets in many other countries, and not just because of disparities in wealth that can lead to dramatically different prices\\\" in this country and abroad (as was the case in Kirtsaeng). \\\"Government policies differ dramatically, including policies on price regulation and, most particularly, policies on the availability and scope of patent protection.\\\" The court did not explain further, however, whether and how such dramatic differences in policy applied to the toner cartridges at issue in the present case.\\\\n\\\\nThe court then turned to the only Supreme Court case on foreign exhaustion, Boesch v. Graff. In that case, Graff was the assignee of a US patent. Boesch bought the product from a German supplier who had a prior-user right under German law to make and sell the product, because the supplier had begun activity before the application for the German counterpart patent was filed. The US assignee and the inventor had no connection with Boesch. When Graff imported the product into the US, Boesch sued for infringement. The US courts found Boesch liable. The rights that Boesch had under German law did not entitle him to import the product into the US. That is governed by US law. The US patentee had never \\\"received any royalty or given any license to use the patented article in any part of the United States.\\\"\\\\nAccordingly, the court held, a foreign sale does not of its own force authorize importation into the US.\\\\n\\\\nThis does not mean, however, that a patentee by its conduct cannot waive its US rights, be estopped from asserting them, or be found to have granted an implied license.\\\\n\\\\nThe court expressed concern that overruling Jazz Photo would harm the US drug industry:\\\\n\\\\nThere seems to be no dispute that U.S.-patented medicines are often sold outside the United States at substantially lower prices than those charged here and, also, that the practice could be disrupted by the increased arbitrage opportunities that would come from deeming U.S. rights eliminated by a foreign sale made or authorized by the U.S. patentee.\\\\n\\\\nFinally, the court rejected a proposal that exhaustion should be presumed unless the patentee express states that it reserves its US rights. Foreign governments might \\\"prohibit sellers from stating reservations of rights that would make importation into and sale in the United States more difficult.\\\" Also: \\\"Intermediary companies between the foreign purchase and the importation into the United States may be created that make it difficult for the U.S. patentee to carry an affirmative burden of proving adequate notice of reservations attached to a foreign-sold article.\\\"\\\\n\\\\nDissenting opinion\\\\n\\\\nJudge Dyk, joined by Judge Hughes, dissented from both branches of the court\\\\'s exhaustion analysis. Judge Dyk summarized his dissent in these terms:\\\\n\\\\nI would overrule our decision in Mallinckrodt as inconsistent with governing Supreme Court authority and overrule Jazz Photo to the extent that it imposes a blanket ban on foreign exhaustion. I would recognize foreign exhaustion where the U.S. rights holder has not notified the buyer of its retention of the U.S. patent rights.\\\\n\\\\nDomestic exhaustion\\\\n\\\\nIn this part of the dissent, Judge Dyk argued that the majority had misunderstood the Supreme Court\\\\'s exhaustion jurisprudence in order to substitute its own ideas of the proper balance between patent rights and public rights. He began by saying:\\\\n\\\\nFirst, I agree with the government that Mallinckrodt was wrong when decided, and in any event cannot be reconciled with the Supreme Court\\\\'s recent decision in Quanta Computer, Inc. v. LG Electronics, Inc. We exceed our role as a subordinate court by declining to follow the explicit domestic exhaustion rule announced by the Supreme Court.\\\\n\\\\nHe argued that since 1850 the Supreme Court has held that a sale by the patentee or its licensee exhausts all patent rights. In such cases, \\\"The question of whether the seller has \\\\'authorized\\\\' the buyer to use or resell the item is simply irrelevant.\\\" Post-sale restrictions could not be enforced under federal patent law. The only Supreme Court case to depart from that principle was Henry v. A.B. Dick Co., and it was explicitly overruled five years later by Motion Picture Patents Co. v. Universal Film Mfg. Co. The principle of the overruled Dick case that a patentee could impost a post-sale restriction by giving a buyer notice of it was \\\"the same as the panel\\\\'s holding in Mallinckrodt and the majority\\\\'s holding in this case.\\\"\\\\n\\\\nHe insisted that the majority opinion misread the Motion Picture Patents decision by asserting \\\"that it only \\\\'held particular restrictions improper\\\\' .\\\\xa0.\\\\xa0. but \\\\'did not rule that all restrictions on a patentee\\\\'s sale were ineffective to preserve the patentee\\\\'s patent-law rights.\\\\'\\\" He explained:\\\\n\\\\nThat is not accurate. Motion Picture Patents did not leave behind the remnants of A.B. Dick—minus tie-ins and resale price maintenance. To the contrary, the Court in Motion Picture Patents found that \\\"[t]he patent law furnishes no warrant for\\\" the restrictions imposed by the patent owner.\\\\n\\\\nLater cases, such as Quanta, confirmed this \\\"broad patent exhaustion rule [in Motion Picture Patents] and left no room for a resurrection of A.B. Dick.\\\"\\\\n\\\\nHe next turned to the majority\\\\'s referenced to \\\"conditional sales\\\" and \\\"unconditional sales,\\\" and said that the majority misconstrued the terms. \\\"Conditional sales,\\\" he said, as used in pre-Mallinckrodt case law referred only to the retention of title for a security interest in installment purchases. \\\"In other words, a sale with restrictions could nonetheless be an \\\\'unconditional\\\\' sale in which title passes, with the restrictions invalid under the patent laws because of exhaustion.\\\"\\\\n\\\\nHe then criticized the majority for making up special rules for patent cases that differed from the common law and general legal principles, citing Supreme Court admonitions not to do that--\\\"The Supreme Court has repeatedly instructed us not to ignore traditional legal principles to fashion rules \\\\'unique to patent disputes.\\\\'\\\"\\\\n\\\\nFinally, Judge Dyk took issue on multiple grounds with the majority\\\\'s efforts to distinguish and limit the Supreme Court\\\\'s rulings. \\\"The majority\\\\'s justifications for refusing to follow Supreme Court authority establishing the exhaustion rule misconceive our role as a subordinate court.\\\" Each justification in the majority decision was unsupportable, he said.\\\\n\\\\n \\\"First, the majority characterizes the statement of the exhaustion rule in the Supreme Court cases as mere dictum because in those cases there was either no restriction imposed or the restriction would otherwise violate the antitrust laws. But the cases impose no such qualification on the rule announced. The Supreme Court has repeatedly advised the courts of appeals that our task is to follow the rules proclaimed by the Court, and not to attempt to distinguish Supreme Court cases on their facts.\\\"\\\\n \\\"Second, the majority relies on 35 U.S.C. §§\\\\xa0271(a) and 154(a)(1) to suggest that a broad reading of the exhaustion doctrine is inconsistent with statutory language making an act of infringement .\\\\xa0.\\\\xa0. any use or sale of a patented invention \\\\'without authority\\\\' of the patent owner, and providing the patent owner with a \\\\'right to exclude.\\\\'\\\" But the patent exhaustion doctrine is a limitation on the operation of those sections, and applies notwithstanding them.\\\\n \\\"Third, the majority claims that giving full sweep to the articulation of the exhaustion doctrine in Quanta and other cases would be inconsistent with the Supreme Court\\\\'s decision in General Talking Pictures Corp. v. Western Electric Co. .\\\\xa0.\\\\xa0. The majority suggests it would be incongruous if \\\\'a patentee cannot preserve its patent rights against uses of a patented article .\\\\xa0.\\\\xa0. if, instead of licensing someone else to make and sell the article, it chooses to make and sell the article itself.\\\\'\\\\xa0\\\"\\\\n\\\\nBut General Talking Pictures was a case of a license to manufacture in a limited field, not a sale with a post-sale restriction. The cases recognize that distinction. Thus, in Quanta the Supreme Court stated that General Talking Pictures \\\"held that exhaustion did not apply because the manufacturer had no authority to sell the amplifiers for commercial use.\\\" And where the manufacturer in that case (Intel) did have a general authority to make and sell, the Supreme Court held that exhaustion applied to the sale.\\\\n\\\\nThe majority found \\\"tension\\\" between \\\"the Supreme Court\\\\'s broad statement of the exhaustion rule and General Talking Pictures\\\" and sought to resolve it by extending the rule of General Talking Pictures and contracting the exhaustion doctrine in the area of possible conflict. But, Dyk maintained:\\\\n\\\\n[I]t is not our task to ignore Supreme Court rulings as \\\"unjustifi[ed]\\\" or \\\"unsound\\\" because they are purportedly inconsistent with other Supreme Court cases. The distinction between restrictions on sales (impermissible) and restrictions on licensees (permissible) exists in the Court\\\\'s precedent, and it is not for us to decide if it is a sound distinction.\\\\n\\\\n \\\"Finally, the majority proposes that we should somehow sustain the restriction here because it may be pro-competitive. Exhaustion does not turn on whether a particular post-sale restriction is desirable or undesirable, pro-competitive or anti-competitive, but whether the sale was authorized and the item has passed beyond the scope of the patent monopoly.\\\" Furthermore, the Supreme Court said in Kirtsaeng that a prohibition on resale is \\\"manifestly anti-competitive.\\\"\\\\n\\\\nDyk concluded his discussion of domestic exhaustion with the statement: \\\"There is, in sum, no colorable basis for the majority\\\\'s failure to follow the exhaustion rule for domestic sales as articulated by the Court in Quanta and numerous other cases.\\\"\\\\n\\\\nInternational exhaustion\\\\n\\\\nIn this part of the dissent, Judge Dyk argued for a nuanced balance that called for different results depending on whether the patentee was responsible for the sale abroad that was alleged to trigger exhaustion.\\\\n\\\\nHe began by pointing out that because Lexmark\\\\'s foreign sales were made without any restrictions or reservations, \\\"even under the majority\\\\'s cramped view of exhaustion, there is no question that the sales would have exhausted Lexmark\\\\'s domestic patent rights. The issue is whether the foreign location of the sale should lead to a different result, as we previously held in Jazz Photo.\\\"\\\\n\\\\nHe then turned to \\\"the centerpiece of the majority\\\\'s holding that there is a doctrinal blanket ban on foreign exhaustion, namely the Supreme Court\\\\'s decision in Boesch v. Graff. But \\\"Boesch announced no such blanket ban,\\\" he said. \\\"It did not even involve an authorized sale by the holder of U.S. patent rights but rather a sale by a third party under a foreign law\\\\'s prior use exception.\\\" But \\\"Boesch does not apply here because the foreign sales were made by Lexmark.\\\"\\\\n\\\\nIn every US lower court decision before Jazz Photo: \\\"When the sale was made by an entity not holding U.S. patent rights, as in Boesch, or when the authorized foreign seller clearly reserved U.S. rights, there was no exhaustion.\\\" In contrast, \\\"where the foreign sale was made by a seller holding U.S. patent rights without a contractual reservation of U.S. rights, exhaustion occurred as a result of an authorized foreign sale.\\\"\\\\n\\\\nDyk maintained that \\\"Kirtsaeng provides significant guidance and cannot be dismissed as simply a copyright case, or as limited to the \\\\'first sale\\\\' provision of the Copyright Act.\\\" Rather, the policies that animated Kirtsaeng typically apply to patent exhaustion. But because in some cases a difference may be significant, there should be abalanced approach. Dyk argued for \\\"put[ting] the burden on the U.S. rights holder to provide notice of a reservation of U.S. rights to the purchaser.\\\" Thus, he \\\"would recognize foreign exhaustion where the U.S. rights holder has not notified the buyer of its retention of the U.S. patent rights.\\\"\\\\n\\\\nSupreme Court\\\\nIn March 2016, Impression filed a petition for certiorari in the U.S. Supreme Court. Impression presented these questions in its petition:\\\\n\\\\n\\\\xa0\\\\xa0\\\\xa01. Whether a \\\"conditional sale\\\" that transfers title to the patented item while specifying post-sale restrictions on the article\\\\'s use or resale avoids application of the patent exhaustion doctrine and therefore permits the enforcement of such post-sale restrictions through the patent law\\\\'s infringement remedy.\\\\n\\\\xa0\\\\xa0\\\\xa02. Whether, in light of this Court\\\\'s holding in Kirtsaeng v. John Wiley & Sons, Inc., 133 S. Ct. 1351, 1363 (2013), that the common law doctrine barring restraints on alienation that is the basis of exhaustion doctrine \\\"makes no geographical distinctions,\\\" a sale of a patented article—authorized by the U.S. patentee—that takes place outside of the United States exhausts the U.S. patent rights in that article.\\\\n\\\\nOn June 20, 2016, the Court invited the Solicitor General to file briefs in this case expressing the views of the United States. In October 2016, the government filed the requested amicus curiae brief. It recommended grant of certiorari on both questions. The brief argues that the \\\"Federal Circuit\\\\'s decision misreads\\\" the Supreme Court\\\\'s precedents and \\\"would substantially erode the exhaustion doctrine.\\\" The Supreme Court granted certiorari on December 2, 2016 and heard oral argument in the case on March 21, 2017. The Court published its decisions on May 30, 2017.\\\\n\\\\nMajority\\\\nA unanimous Court found that Lexmark exhausted its patent rights upon first sale domestically, even with the single-use/no-resale restrictions imposed by Lexmark in contracts with its customers, although such restrictions could be enforced under contract law. The Court noted that the exhaustion doctrine has a long history and that any change would have significant effects on commerce in the modern world, noting that \\\"extending the patent rights beyond the first sale would clog the channels of commerce, with little benefit from the extra control that the patentees retain,\\\" noting that complex modern supply chains can involve large numbers of patents. Chief Justice Roberts, in his opinion, compared the situation to automobile repair shops: \\\"The business works because the shop can rest assured that, so long as those bringing in the cars own them, the shop is free to repair and resell those vehicles. That smooth flow of commerce would sputter if companies that make the thousands of parts that go into a vehicle could keep their patent rights after the first sale.\\\"\\\\n\\\\nSeven justices joined the Court\\\\'s opinion extending that reasoning to items imported from abroad. Lexmark had argued, and the Federal Circuit agreed, that sale abroad \\\"does not trigger patent exhaustion unless the patentee \\\\'expressly or implicitly transfers or licenses\\\\' its rights.\\\" The Court, however, ruled that \\\"[a]n authorized sale outside the United States, just as one within the United States, exhausts all rights under the Patent Act.\\\" The Court relied on its 2013 decision in Kirtsaeng v. John Wiley & Sons, Inc. on a nearly identical issue under copyright law. Because the underlying statute was not clear as to its geographical scope, the Court in Kirtsaeng decided that, because the statute was based in the common law exhaustion doctrine, which is not limited in geographic extent, the statute at issue was therefore not intended to be limited to only U.S. sales. Applying the same principle to patent law, which historically has a close connection with copyright law, was \\\"straightforward\\\" and \\\"the bond between [copyright and patent law] leaves no room for a rift on the question of international exhaustion\\\".\\\\n\\\\nPartial dissent\\\\nJustice Ginsburg dissented from the Court\\\\'s holding with respect to imported items. Adhering to substantially the same reasoning of her dissent in Kirtsaeng, Justice Ginsburg argued that because patent law is territorial and the sale of an item abroad is \\\"independent[] of the U.S. patent system, it makes little sense to say that such a sale exhausts an inventor\\\\'s U.S. patent rights.\\\" She would have upheld the Federal Circuit\\\\'s decision that sale abroad does not exhaust a patentee\\\\'s rights in the United States.\\\\n\\\\nCommentary\\\\n\\\\nGerstein\\\\n\\\\nRobert M. Gerstein concluded that further review in the Supreme Court was likely:\\\\nGiven the Supreme Court\\\\'s interest in patent cases, a vigorous dissent in Lexmark that relies on a number of Supreme Court precedents, including Quanta and Kirtsaeng, and the position of the Justice Department that Quanta overruled Mallinckrodt, it would not be surprising to see the Supreme Court take up Lexmark in its next term.\\\\n\\\\nDodd and Dowd\\\\n\\\\nJeff C. Dodd and Matthew J. Dowd viewed the decision as an affirmation of strong patent rights:\\\\nLexmark embraces a very strong view of patent rights and a narrow view of the scope of exhaustion. It affirms that patent holders have wide latitude to segment and control distribution in the market channels for products covered by patents. This latitude is particularly wide with respect to limiting the import into the United States of patented goods sold in authorized sales in foreign markets even where restrictions on resale were not proven to have been communicated to foreign buyers. Even so, the court left open the possibility that foreign sales, under the right circumstances, may incorporate an implied license to import and use the product within the United States.\\\\n\\\\nCukierski and Masia\\\\n\\\\nKevin J. Cukierski and Adam H. Masia see the decision as \\\"pro-patent owner\\\" but warn again premature celebration:\\\\nBut take caution—it is likely that the Supreme Court will be asked to hear the case. Given the tension between this case and the Supreme Court\\\\'s language in Quanta and Kirtsaeng, along with the discord at the district court level and among commentators before the Federal Circuit\\\\'s decision, there\\\\'s a good chance the Supreme Court will do so. Until the Supreme Court has its say, you should take precautions in case the Supreme Court takes an expansive view of patent exhaustion and decides to remove these exceptions.\\\\n\\\\n\\\"Without Precedent\\\"\\\\n\\\\nAnother commentator (unsigned comment) indicated a skeptical view of the Federal Circuit\\\\'s tendency to march to a different drummer. After quoting Judge Dyk\\\\'s admonition, \\\"We exceed our role as a subordinate court by declining to follow the explicit domestic exhaustion rule announced by the Supreme Court,\\\" he (or she) observed:\\\\nFor present purposes, it is simply worth noting that the Federal Circuit appears to be inching closer again to the concept that patent law is simply a unique beast, with unique rules and requirements. The Supreme Court has taken a skeptical view of that approach in the past. And may well again.\\\\n\\\\nJahn, Pichler, and Lo\\\\n\\\\nPaul Jahn, Rufus Pichler and Lincoln Lo raise many questions (mostly about \\\"clear communication\\\") about what the Lexmark majority opinion left unresolved:\\\\n\\\\n Conflict or tension with Quanta: \\\"Quanta expressly distinguished implied licenses and exhaustion, holding that disclaimers of license rights are \\\\'irrelevant\\\\' where \\\\'the right to practice the patents is based not on implied license but on exhaustion.\\\\'\\\\xa0\\\" But \\\"the Federal Circuit appears to treat exhaustion like an implied license—one that the patentee can disclaim by \\\\'clearly communicate[d]\\\\' restrictions.\\\" Quanta appears to hold that the patentee\\\\'s attempt to impose a post-sale restriction on a manufacturing licensee is ineffective if the license does not conform to the General Talking Pictures case.\\\\n \\\"[W]hat arrangement between a seller and buyer is sufficient to deny \\\\'authority.\\\\'? It was undisputed in Lexmark that there was \\\\'an express and enforceable contractual agreement\\\\' between Lexmark and each end-user, and that the no-resale and no-reuse restrictions were binding on end users. Yet throughout the Lexmark opinion, the majority suggests that restrictions may be sufficient if \\\\'clearly communicated\\\\'—even if well short of a contractual meeting of the minds.\\\"\\\\n Another way to put this is what is a \\\"clear communication\\\"? In Jazz Photo, the Federal Circuit noted that the \\\"package instructions [were] not in the form of a contractual agreement by the purchaser to limit reuse of the cameras.\\\" Accordingly, \\\"There was no showing of a \\\\'meeting of the minds\\\\' whereby the purchaser, and those obtaining the purchaser\\\\'s discarded camera, may be deemed to have breached a contract or violated a license limited to a single use of the camera.\\\" The writers conclude, therefore, \\\"It is unclear if the Federal Circuit intended an expansion of the patentee-seller\\\\'s ability to avoid exhaustion.\\\"\\\\n Also, how clear must a \\\"clear communication\\\" be? \\\"The Federal Circuit appears to limit infringement claims against subsequent downstream buyers to those \\\\'having knowledge of the restrictions.\\\\' The appellate court did not elaborate on what defenses a subsequent downstream purchaser without knowledge may have, assuming no exhaustion. The court only mentions in passing that \\\\'we do not have before us the questions that would arise, whether under principles governing bona fide purchasers or otherwise, if a downstream re-purchaser acquired a patented article with less than actual knowledge of the restriction.\\\\'\\\\xa0\\\"\\\\n Finally, does the court\\\\'s focus on \\\"clear communication\\\" have a negative impact on post-sale restrictions that a limited licensee under General Talking Pictures is required to impose? \\\"The Federal Circuit suggested repeatedly that buyers\\\\' knowledge of the licensee\\\\'s field of use limitation may be required for a licensee\\\\'s sale to be non-exhaustive. While General Talking Pictures did not clearly resolve this question, many licensors have assumed that sales by a licensee outside of its licensed field are unauthorized altogether and are therefore non-exhaustive regardless of the purchaser\\\\'s knowledge of the field of use limitation.\\\" Therefore, does the emphasis, here \\\"on the buyer\\\\'s knowledge, even if dicta, add to the uncertainty concerning this issue\\\"?\\\\n\\\\nCastanias, Nix, and Kazhdan\\\\n\\\\nGregory A. Castanias, Kelsey I. Nix, and Daniel Kazhdan also point to unresolved issues over which patent owners \\\"must still be cautious\\\":\\\\nLexmark explicitly left open several fact-specific questions, including (i) what happens if someone acquires a patented article with \\\"less than actual knowledge\\\" of the restrictions placed on the original sale by the patent owner and (ii) when would a foreign buyer have an \\\"implied license\\\" to sell in the United States, independent of patent exhaustion. These issues will surely be raised in future cases.\\\\n\\\\nCrouch\\\\n\\\\nDennis Crouch, in Patently-O commented on the issues and provided a summary of the merits briefs filed in the Supreme Court as of January 31, 2017. Crouch opposed the Federal Circuit\\\\'s ruling on these grounds:\\\\nWith personal property courts long ago rejected servitudes (such as use and resale restrictions) that bind subsequent purchasers. Unlike real property, personal property moves and is often transferred without substantial paperwork or record-keeping, and allowing a set of unique restrictions has the potential of gumming up the marketplace. The Federal Circuit in this case went all the way to the other side — holding that the presumption in foreign sales is that no US patent rights are exhausted. I purchased my last couple of smart phones through the used market – and have also repaired them several times. Under the law, I probably should have taken steps to ensure that all of the original equipment manufacturers affirmatively granted repair and resale rights. Coming together, the Federal Circuit\\\\'s approach here has the potential to limit the market for the repair and reselling of goods. I would suggest that those activities are incredibly beneficial to our society in terms of resource allocation and avoiding waste as well as empowering citizens and avoiding anticompetitive market behavior.\\\\n\\\\nNotes and references\\\\n\\\\nNotes\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n \\\\n SCOTUSblog coverage\\\\n Podcast – Interview with proprietor of Impression Products\\\\n\\\\nIntellectual property law\\\\nUnited States patent case law\\\\nUnited States Court of Appeals for the Federal Circuit cases\\\\nUnited States Supreme Court cases\\\\nUnited States Supreme Court cases of the Roberts Court\\\\n2015 in United States case law\\\\n2017 in United States case law\\\\nLexmark'},\\n\",\n \" {'docid': 'doc-en-13',\\n\",\n \" 'text': 'The Werewolf by Night is the name applied to two fictional characters who are werewolves appearing in American comic books published by Marvel Comics. The Werewolf by Night (usually referred to by other characters simply as the Werewolf) first appeared in Marvel Spotlight #2 (February 1972).\\\\n\\\\nPublication history\\\\nPrior to the formation of the Comics Code Authority in 1954, Marvel\\\\'s predecessor Atlas Comics published a five-page short story titled \\\"Werewolf by Night!\\\" in Marvel Tales #116 (July 1953). With the relaxation of the Comics Code Authority\\\\'s rules in 1971, it became possible for the first time to publish code-approved comic books with werewolves. The Jack Russell version of Werewolf by Night first appeared in Marvel Spotlight #2 (February 1972) and was based on an idea by Roy Thomas. The series name was suggested by Stan Lee and the initial creative team was Gerry Conway and Mike Ploog, who worked from a plot by Roy and Jeanie Thomas for the first issue. Readers have often pointed out that the lead character\\\\'s name, Jack Russell, is also a breed of dog. Conway has said that while he cannot remember how he came up with the name, it is unlikely that he was making this canine reference consciously, since he did not own a dog and never lived with one growing up. After the test run in Marvel Spotlight #2-4, the character graduated to his own eponymous series in September 1972. Conway described working on the series as \\\"a lot of fun\\\" because the horror genre made a refreshing change from the superhero stories that had been the staple of mainstream comics for years. Werewolf by Night was published for 43 issues and ran through March 1977. During the series\\\\' run, the editorship could not resist the opportunity to assign one of their most popular writers, Marv Wolfman, to write some stories for the series with a playful note: \\\"At last -- WEREWOLF -- written by a WOLFMAN.\\\"\\\\n\\\\nIssue #32 (August 1975) contains the first appearance of the Moon Knight. Jack Russell co-starred with Tigra in Giant-Size Creatures #1 (July 1974), which was the first appearance of Greer Grant Nelson as Tigra instead of as the Cat. That series was retitled Giant-Size Werewolf with its second issue. Jack Russell was dormant for most of the 1980s. The character\\\\'s appearance was radically revamped in Moon Knight #29 (March 1983). He guest-starred in various issues of Spider-Woman, West Coast Avengers, and Doctor Strange: Sorcerer Supreme. The Werewolf by Night was later revived in the pages of Marvel Comics Presents, where he appeared irregularly from 1991-1993. He made regular appearances as a supporting cast member in the pages of Morbius: The Living Vampire from 1993-1995. A letters page in an issue of Morbius mentioned that a Werewolf by Night miniseries by Len Kaminski and James Fry was in the works, but the miniseries was never published. Werewolf by Night vol. 2 ran for six issues in 1998. The series was written by Paul Jenkins and penciled by Leonardo Manco. After the book\\\\'s cancellation, the story was continued in the pages of Strange Tales, which also featured the Man-Thing. That volume of Strange Tales was canceled after only two issues due to poor sales. In early 2007, Marvel published a one-shot entitled Legion of Monsters: Werewolf by Night, with art by Greg Land. In January 2009, Jack Russell was featured in the four-issue limited series Dead of Night Featuring Werewolf by Night, from Marvel\\\\'s mature readers MAX imprint. The series was written by Duane Swierczynski, with art by Mico Suayan. He was featured as a member of Morbius\\\\' Midnight Sons in Marvel Zombies 4 in 2009.\\\\n\\\\nA second Werewolf by Night first appeared in the third volume of Werewolf by Night and was created by Taboo of the Black-Eyed Peas, Benjamin Jackendoff, and Scot Eaton.\\\\n\\\\nFictional character biography\\\\n\\\\nJack Russell\\\\n\\\\nWhile reports of lycanthropy (shapeshifting into a werewolf) in the Russoff line stretch back many centuries, the first confirmed manifestation is Grigori Russoff in 1795. Dracula slew Grigori\\\\'s wife Louisa after he refused to acknowledge Dracula\\\\'s primacy upon his return to Transylvania. Grigori then ambushed and destroyed Dracula, but was mutated into a werewolf by Lydia, a werewolf formerly imprisoned by the vampire lord. Grigori took a second wife, but accounts vary as to why lycanthropy failed to pass to his descendants. Sometime prior to May 1930, Grigori\\\\'s descendant, Gregor, obtained the legendary Darkhold scrolls, binding them back into book form. Reading lycanthropy\\\\'s origins in the Darkhold under a full moon triggered the dormant curse, mutating Gregor into a werewolf. Gregor further transcribed much of the Darkhold into Grigori\\\\'s diary, essentially creating a Darkhold copy, which he used as his own diary. Gregor sold part of his estate — including Wundagore Mountain — to Jonathon Drew, who shared it with partner Herbert Wyndham (the future High Evolutionary). The Russoff werewolf slew Jonathon\\\\'s wife, Merriem, and Wyndham designed a suit of silver-coated armor to protect himself, enabling Russoff\\\\'s capture. Russoff stayed with the Evolutionary, who kept the werewolf safely contained for decades. Russoff eventually used the Darkhold to summon Chthon to cure him and the Elder God nearly broke through the earthly plane; but Magnus the Sorcerer forced Russoff to banish Chthon, who lashed out with a parting blast that slew Gregor. Despite contrary accounts, the Gregor Russoff who stayed with the High Evolutionary seems to have been the grandfather (or great-grandfather) of Jack Russell. Having the same name and presumably using the same diary contributed to earlier confusion. It would seem more likely that the elder Gregor was the one who transcribed the Darkhold into the diary.\\\\n\\\\nDecades later, another Gregor Russoff married Laura, the former girlfriend of his younger brother Philip. Jacob (later Jack) was born in Mediaș, Transylvania, soon after, and Laura was pregnant with Lissa within two years of marriage; however, when lightning struck Russoff\\\\'s Transylvanian castle during a full moon, the werewolf Gregor escaped confinement and began attacking villagers. They tracked down and killed Russoff with silver bullets. Gregor\\\\'s mother, Maria, was stoned and driven from the village, living with gypsies and learning magic. After Gregor\\\\'s death, Laura found Philip - who had moved to Los Angeles and anglicized his name to Russell - and they married after a year; Jack and Lissa remained unaware of Philip\\\\'s past. Approximately 15 years later, the criminal organization known as the Committee learned of Gregor\\\\'s curse and blackmailed Philip, threatening to reveal his secrets. To protect Laura\\\\'s name, Philip paid them, but had second thoughts and canceled payment, causing the Committee to send Max Grant to kill Laura. Critically injured in a car crash on Jack\\\\'s 18th birthday, Laura barely had time to tell Jack about his true father and the curse of the werewolf, making Jack promise not to harm Philip, before dying. Having inherited lycanthropy the night before, Jack slew Grant, but wrongly blamed Philip for some time. Laura left Castle Russoff in Jack\\\\'s name, but Philip, the trustee, sold the castle to Miles Blackgar, who had it moved to an island off the California coast. Jack battled a motorcycle gang, infecting its members with lycanthropy.\\\\n\\\\nJack spent the next few years as a traveler, shapeshifting on the three nights of the full moon into a savage werewolf form. He learned of the Darkhold from Nathan and Agatha Timly, who briefly kidnapped the Werewolf and met grisly ends. Befriending writer Buck Cowan, Jack sneaked into Blackgar\\\\'s castle and stole the Darkhold, encountering Miles Blackgar and his daughter Marlene, whose petrifying power slew both Blackgars. After fighting off the deformed Cephalos\\\\' plot to drain his power to stabilize Cephalos\\\\' form, Jack had Father Ramon Joaquez translate the Darkhold. The priest died after being possessed by the Darkhold\\\\'s former custodian, the 12th-century mad monk Aelfric, and the indestructible Darkhold vanished. Jack encountered Joshua Kane, who hunted the Werewolf, and his brother, Luther Kane, who offered to prevent Lissa from mutating into a werewolf in exchange for Jack kidnapping billionaire-turned recluse Judson Hemp; he met mentalist Swami Rihva, who sought the Werewolf\\\\'s blood to reveal the treasure map of the ancient sorcerer Kaman-Ru on his \\\"Bloodstone\\\"; the possessing demon Krogg; and Spider-Man and Moondark the Magician. Jack then fought the sonic-weapons of Sarnak, his first brush with the criminal organization known as the Committee, who wished to enslave the Werewolf.\\\\n\\\\nAfter fighting the sociopathic Hangman (Harlan Krueger), Jack was entranced by Topaz, the familiar of the sorcerer Taboo, who sought the Darkhold. Taboo had used the tome decades before to grant his son, Algon, a golden touch, but had lost the book in mid-spell, trapping Algon in a mindless state. Lacking the Darkhold, Taboo transferred Philip Russell\\\\'s mind into Algon, but both Algon and Taboo died, restoring Philip, who explained Laura\\\\'s death and reconciled with both Jack and Lissa. Traveling to Transylvania alongside Topaz, with whom he had bonded, Jack discovered the Russoff diary/Darkhold copy, the Werewolf battled Dracula and the book was lost in the Alps. Jack and Topaz encountered the kyphotic Half-Mad before returning to the U.S. and Jack fought the Committee\\\\'s Behemoth robot and then Ma Mayhem, assisted by werewolf Raymond Coker. Jack joined the newly-mutated Tigra against HYDRA, battled vampires Louis Belski and Liza Pyne, opposed Ma Mayhem and her ally Baron Thunder, and joined Coker against Lou Hackett (a corrupt policeman who could also shapeshift into a werewolf by using a magic ring), who was killed in the struggle. The Werewolf joined the Frankenstein Monster against the Satanic Brotherhood of Baal who had abducted Lissa, then fought the disfigured Atlas and the Jekyll/Hyde-like DePrayve. Jack briefly returned to Transylvania following Topaz\\\\'s psychic summons and encountered Maria Russoff, who used Gypsy magic to raise zombies to slay the villagers who had driven her off. Maria sacrificed herself to save Jack from her zombies upon learning that he was her grandson.\\\\n\\\\nIn Blackgar\\\\'s castle, the Werewolf, Topaz and the repentant spirit fragment of Taboo battled the necromancer Doctor Glitternight, who mutated Lissa Russell into a were-demoness; the process of curing Lissa purged her of the threat of lycanthropy, though she would still pass it on to her children. After battling Morbius, the Living Vampire and slaying the demon worshipped by Brad Wrangle, the Werewolf was briefly transported to the divided dimension Biphasia by Satanist Joaquin Zaire and he aided Paingloss against the sorcerer Sardanus. During a subsequent ski trip, the Werewolf nearly slew Buck Cowan, after which he was captured by the Committee-paid mercenary known as the Moon Knight, who set him free when he realized Jack\\\\'s humanity and the Committee\\\\'s intentions. The Werewolf then joined the Ghost Rider, the Man-Thing, and Morbius in unwittingly slaying the benevolent alien Starseed, who had intended to cure them all.\\\\n\\\\nThe Werewolf, Topaz and others then battled and were nearly driven mad by the ghost of 19th-century black magician Belaric Marcosa, but they freed the trapped spirits of Marcosa\\\\'s victims, who destroyed him, and one of the grateful spirits, that of magician Gideon Blaine, healed Buck. The enigmatic Three Who Are All (the Hooded One, the Burning Snake and the Goat Child) — an ancient extra-dimensional group who had formerly included Glitternight and a fifth member, Fire-Eyes — sent Jack, Topaz, Raymond Coker and Brother Voodoo to Haiti, where the Werewolf and Fire-Eyes destroyed Glitternight once and for all. In the process, Jack gained control of his Werewolf persona, though he still only shapeshifted under moonlight and still lost control during the three nights of the full moon.\\\\n\\\\nThe Werewolf joined with Iron Man against the Maggia\\\\'s Masked Marauder and his Tri-Animan and he teamed with Spider-Woman against the mercenary the Enforcer. The mad scientist Dr. Karl Malus captured and performed scientific experiments on Russell to control him and use him against Spider-Woman; Russell escaped and apprehended Malus with the aid of Spider-Woman. Russell joined Spider-Man against the Tatterdemalion, a former agent of Sarnak. After being temporarily captured alongside a number of costumed adventurers by the Locksmith and Tick-Tock, Russell began mutating into a more savage and lupine form, a late effect from Malus\\\\' treatment. He fled Satanists Morning Star (Schuyler Belial) and his Left Hand Path, who wished to use his blood to mutate into werewolves, then sought aid from the now-human Michael Morbius in controlling his savage self, leading to a battle with the West Coast Avengers. With assistance from Iron Man, he later saved Lissa from Morgan Le Fay\\\\'s attempt to possess her.\\\\n\\\\nHe was subsequently mind-controlled into joining the mostly-criminal Night Shift by Dansen Macabre. Russell was the only member who knew their leader, the Shroud, was using the group to oppose other criminals and to prevent them from harming innocents. After encounters with Captain America, the Moon Knight and the Avengers, the Werewolf eventually developed resistance to Macabre\\\\'s powers and turned on the Night Shift, after which he went solo. After briefly battling the Hulk in the Midwest, Jack contacted his father Gregor\\\\'s spirit to cure his lycanthropy, but was told that he would die unless he accepted his beast. During the ensuing battle with the religious zealot Silver Dagger and the Braineaters, a cult of werewolves mutated in the past by Russell, Jack fully accepted his wolf-self and his personae merged, altering his powers and granting him full control and the best of both selves.\\\\n\\\\nRussell assisted Doctor Strange against the alien Possessors, the Night Shift against an L.A. street gang and the Ghost Rider against a new group of Braineaters; Jack battles with Sabretooth but before Jack can kill Sabretooth three locals show up with rifles and save Sabretooth by shooting at Jack. and fought an unidentified Wendigo in Canada. Russell was captured by criminal scientist Nightshade who used his blood to create the Night Patrol, a group of werewolves in Starkesboro, Massachusetts. Captain America - also mutated into a werewolf - freed Russell and led the werewolves to defeat Nightshade\\\\'s master, Dredmund the Druid, who had used the Godstone (the former gem of the Man-Wolf) to briefly mutate into the powerful Starwolf. The Night Patrol was cured, after which Russell was drawn into a conflict involving the Midnight Sons and was slain by Switchblade (the insane Darkhold-powered Blade), but Jack was revived once Professor Louise Hastings broke Switchblade\\\\'s spell. Russell befriended the again pseudo-vampiric (and now demon-possessed) Morbius, had a vision of advertisements on the moon causing mass insanity and fought the Lilin Goblins, Mr. Hyde and the sadist Morphine. Jack had an affair with Morbius\\\\' possessed former girlfriend Martine Bancroft.\\\\n\\\\nJack again began losing control of the Werewolf, locking himself in a cage while under the full moons, and even glimpsing visions of Hell as he shapeshifted. From the Cult of the Third Moon\\\\'s dying leader, Walter Clark, Russell learned that only the legendary Wolfblade could control his lupine self. With the aid of Smedley, a mysterious benefactor, Russell recovered all three parts of the Wolfblade, battled the original Wolf Demon in a branch of Hell, completed the puzzle by reaccepting both selves and seemingly regained control. However, after Jack visited friends Freddie and the disfigured Lump, Smedley sent him to investigate a series of killings in which the evidence pointed to Jack as the killer. As Russell began to mutate further, Smedley said Jack just had not been careful enough in his wish to be freed from the Wolf Demon and that he must embrace the disease, or it would destroy him. Uncertain how to accomplish this, Jack found a confidant in the Lump, who cared for the Werewolf as he hid out in the sewers. While Jack\\\\'s new girlfriend, Roxanna, remained blissfully unaware of his dual existence, the Werewolf was tracked down by a pair of detectives, escaping only after they were slain by the Cult of the Third Moon. Though Jack\\\\'s subsequent fate is unknown, he was later seen sensing the arrival of the mystic assassin Hellphyr.\\\\n\\\\nIn the Legion of Monsters: Werewolf by Night one-shot, Jack Russell came to Salvage, Alabama to save a family of law-abiding werewolves from a group of townsfolk led by Cal Escher. Young Rhonda was the only one left in the family after her mother and her sister Suzie chose death by gun or knife. The girl was drowning her sorrows in Sullivan\\\\'s bar next to the cemetery when the gang attacked her, revealing her werewolf nature by means of a tarot card (\\\"The Moon\\\") and then trying to kill her. Russell interfered, mutating into the Werewolf while Rhonda decided to do the same. After killing the violent gang, Russell and Rhonda left the town, determined to control their afflictions and live their lives without fear.\\\\n\\\\nThe Moon Knight rescues Jack from a criminal enterprise wherein samples of his blood are used to temporarily mutate homeless people into pseudo-werewolves, who are then provoked into fighting each other as a spectator sport. The Moon Knight frees Jack, who has degenerated into a near-mindless feral state, from his captors; the Werewolf proceeds to go on a rampage, attacking both his tormentors and the Moon Knight, who subdues him before restoring his freedom to him.\\\\n\\\\nThe Werewolf appears as part of the new Midnight Sons team to hunt down zombies who escaped A.R.M.O.R. headquarters and prevent the contagion from spreading. Prior to the team\\\\'s mission, he records a video will and testament telling his sister that he is happy in life. He was given a vaccine developed by Morbius, the Living Vampire. In their search for the missing zombie Deadpool, the team battles and kills zombie Men-Fish and their leader, the Piranha. After battling the Hood\\\\'s Night Shift and watching ally the Man-Thing seemingly die in a battle against Deadpool, Russell\\\\'s vaccine fails him and he becomes a zombie. He then confronts Jennifer Kale. He battles Morbius, who realizes that Jack\\\\'s werewolf form is not subject to the virus, and Jennifer Kale summons a moonlight spell to mutate him into the Werewolf. Jack is later restored to normal by Morbius, who developed a cure for the zombie virus using Spider-Man\\\\'s blood and samples of the zombie virus from different realities.\\\\n\\\\nAfter the death of Frank Castle, Morbius pieces the Punisher together again and enlists his help in saving monsters from extinction. Jack Russell, the Man-Thing and the Living Mummy are part of the Legion of Monsters, who fight those who would wipe out all monsters. The Punisher aids this group in protecting an underground city that has many innocent, sentient monsters.\\\\n\\\\nRussell appears among many mystical beings of lupine and feline nature drawn to the headquarters of X-Factor Investigations by the imminent birth of the mutant Wolfsbane\\\\'s child. While many of the gathered beings wish to acquire the child for their own ends, Russell seems intent on protecting mother and child. Once the child is born, it is rejected by a shaken Wolfsbane due to its vicious, feral nature and her own religious beliefs. The cub appears to be caught up in a convergence of the mystic forces seeking it, vanishing explosively from the Earth; however, Russell finds the child hiding in a cave and takes it under his care.\\\\n\\\\nDeadpool later discovered that Russell had an affair with his wife Shiklah. Deadpool then promptly blew off Jack\\\\'s head with a blunderbuss, but Shiklah revealed that Jack would survive.\\\\n\\\\nJake Gomez\\\\n\\\\nJake Gomez is a boy of Hopi descent who underwent his first werewolf transformation at the age of 13 due to a curse in his family. He was able to get control over his werewolf form with the help of his grandmother Rora and his sister Molly where the music helped with controlling his emotions unlike how Jake\\\\'s father operated. Jake first appeared when hunters were hunting rabbits on Hopi tribal lands and fought them off. Rora advised him to be careful in the mission at Life Pharmaceuticals where he worked in the day due to the government going after teenage superheroes. Jake attacks two vehicles leaving Life Pharmaceuticals and finds that one of them contains the people who have gone missing from the Rez in the past month. He is then confronted by three monstrous figures with cybernetic parts on them.\\\\n\\\\nPowers and abilities\\\\nJack Russell is a descendant of the mystically-altered offshoot of humans known as Lycanthropes. During the night of the full moon and the two nights surrounding it he is forced to mutate into a werewolf, a large, powerful form which is a hybrid of human and wolf, and loses his human intellect. Through a series of events, he is also capable of mutating voluntarily outside of the full moon, at which time he remains in control of himself. As a werewolf, Jack gains the proportionate physical advantages of a nearly wolf. In this form, he possesses superhuman strength, speed, stamina, durability, agility and reflexes. He possesses a superhuman sense of smell, which carries over to his human form. He has razor-sharp teeth and claws that are able to rend light metals. The Werewolf is resistant to many forms of conventional injury and very difficult to kill by conventional means. Though he can be severely wounded, he recovers from non-fatal wounds much faster than a human would. He is vulnerable to magical attacks and, like all werewolves, he can be killed by weapons made of silver, due to its inherent mystical \\\"purity\\\".\\\\n\\\\nJake Gomez has the same transformation abilities like Jack Russell.\\\\n\\\\nOther versions\\\\nIn Marvel\\\\'s Earth-666, a variation of the Jack Russell version of the Werewolf appeared in Supernatural Tourbook and Supernaturals #1-4.\\\\n\\\\nIn the Marvel Adventures continuity, Jack Russell\\\\'s family home is in Queens, New York. This brings him into conflict with Spider-Man after he reluctantly mutates the somewhat-innocent Flash Thompson into a werewolf. Fortunately, Dr. Strange\\\\'s knowledge of lycanthropy saves Flash.\\\\n\\\\nDuring \\\"Infinity Wars\\\", when the universe was folded, Jack Russell got fused with Norman Osborn to create the Goblin by Night. Norman Russell was cursed to be the Goblin by Night, killed Ben and May Spector and nearly killed Peter Spector, leaving Peter to become the ArachKnight. During a battle with Peter, Norman got injured and got saved by his son, Harry Russell. While Harry was taking care of his father, Norman lost control and bit Harry, passing the curse on to him. Harry, now as the new Goblin by Night, starts using the glider that Peter built prior to him to becoming the Goblin, leaving Norman free from the curse, being forgiven by Peter and deciding to find a way to cure Harry.\\\\n\\\\nIn other media\\\\n\\\\nTelevision\\\\n The Jack Russell incarnation of Werewolf by Night appears in The Super Hero Squad Show episode \\\"This Man-Thing, This Monster!\\\", voiced by Rob Paulsen. After his girlfriend, Ellen, is kidnapped by an army of mummies led by N\\\\'Kantu, the Living Mummy on Dracula\\\\'s behalf, the Werewolf by Night joins forces with the Man-Thing and a dimensionally-displaced Iron Man to rescue her. While they succeed in defeating the vampire and the mummies, they learn Ellen had been turned into a vampiress. Taking inspiration from Iron Man, Werewolf by Night, Ellen, and the Man-Thing form the Supernatural Hero Squad to defend their town from future monster attacks.\\\\n The Jack Russell incarnation of Werewolf by Night appears in the Ultimate Spider-Man episodes \\\"Blade\\\" and \\\"The Howling Commandos\\\", voiced by Ross Lynch. This version is a member of the Howling Commandos. Werewolf by Night and the Howling Commandos join forces with former member Blade and Spider-Man to retrieve a powerful ankh from Dracula.\\\\n The Jack Russell incarnation of Werewolf by Night appears in Hulk and the Agents of S.M.A.S.H., voiced by Nolan North. This version is a member of the Howling Commandos. In the episode \\\"Hulking Commandos\\\", he and the Howling Commandos are assigned to apprehend the agents of S.M.A.S.H., only to join forces with them to defeat Dormammu. In the episode \\\"Planet Monster: Part 2\\\", the Werewolf by Night joins the Howling Commandos in helping the agents of S.M.A.S.H. and the Avengers combat the Supreme Intelligence\\\\'s forces.\\\\n The Werewolf by Night\\\\'s unnamed grandfather appears in \\\"Days of Future Smash, Part 3: Dracula\\\", also voiced by North. While operating in 1890, he helps Frankenstein\\\\'s Monster, N\\\\'Kantu the Living Mummy, and a time-traveling Hulk thwart the Leader and Dracula\\\\'s plan to blanket the Earth in darkness with their Gamma Furnace.\\\\n A Halloween special based on Werewolf by Night is in development for Disney+. On November 4, 2021, actor Gael García Bernal was cast. On January 11, 2022, Laura Donnelly was cast in a undisclosed role. Michael Giacchino is set to direct Werewolf by Night.\\\\n\\\\nFilm\\\\n A film adaptation of Werewolf by Night, written by Robert Nelson Jacobs, was announced and due to begin filming in 2005. However, no further developments took place since.\\\\n\\\\nVideo games\\\\n The Jack Russell incarnation of Werewolf by Night makes a cameo appearance in Jill Valentine\\\\'s ending in Marvel vs. Capcom 3: Fate of Two Worlds and Ultimate Marvel vs. Capcom 3.\\\\n The Jack Russell incarnation of Werewolf by Night appeared as an unlockable playable character in Marvel Super Hero Squad Online.\\\\n The Jack Russell incarnation of Werewolf by Night appeared as an unlockable playable character in Marvel Avengers Academy. He could be first recruited during the event \\\"Avengers Halloween Event 2017\\\".\\\\n\\\\nReception\\\\nThe Werewolf by Night was ranked #6 on a listing of Marvel Comics\\\\' monster characters in 2015.\\\\n\\\\nCollected editions\\\\n Essential Werewolf by Night \\\\n Vol. 1 collects Marvel Spotlight #2-4, Werewolf By Night #1-21, Marvel Team-Up #12, Giant-Size Creatures #1, and The Tomb of Dracula #18, 576 pages, October 2005, \\\\n Vol. 2 collects Werewolf By Night #22-43, Giant-Size Werewolf #2-5, and Marvel Premiere #28, 576 pages, November 2007, \\\\n Essential The Tomb of Dracula Vol. 1 includes Werewolf by Night #15, 560 pages, 2004, \\\\n Essential Monster of Frankenstein includes Giant-Size Werewolf #2, 496 pages, October 2004, \\\\n Essential Moon Knight Vol. 1 includes Werewolf by Night #32-33, 560 pages, March 2006, \\\\n Werewolf by Night: In the Blood includes Werewolf by Night vol. 2 #1-4 \\\\n Werewolf by Night: The Complete Collection \\\\n Vol. 1: Marvel Spotlight #2-4, Werewolf by Night #1-15, Marvel Team-Up #12, Tomb of Dracula #18 (October 17, 2017)\\\\n Vol. 2: Werewolf by Night #16-30, Giant-Size Creatures #1, Giant-Size Werewolf #2-4, material from Monsters Unleashed #6-7 (February 13, 2018)\\\\n Vol. 3: Werewolf by Night #31-43, Giant-Size Werewolf #5, Marvel Premiere #28, Spider-Woman #6, 19, 32, Marvel Team-Up #93, Ghost Rider #55, Moon Knight #29-30, material from Marvel Premiere #59 (May 15, 2018)\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n Werewolf by Night at Marvel.com\\\\n \\\\n Werewolf by Night appearances in publication order\\\\n\\\\n1972 comics debuts\\\\nCharacters created by Gerry Conway\\\\nCharacters created by Mike Ploog\\\\nCharacters created by Roy Thomas\\\\nComics about werewolves\\\\nComics by Doug Moench\\\\nComics by Gerry Conway\\\\nComics by Marv Wolfman\\\\nComics characters introduced in 1972\\\\nDefunct American comics\\\\nFictional characters with superhuman senses\\\\nFictional Romanian people\\\\nFictional werewolves\\\\nHorror comics\\\\nMarvel Comics characters who are shapeshifters\\\\nMarvel Comics characters who can move at superhuman speeds\\\\nMarvel Comics characters with accelerated healing\\\\nMarvel Comics characters with superhuman strength\\\\nMarvel Comics superheroes\\\\nMarvel Comics titles\\\\nMidnight Sons'},\\n\",\n \" {'docid': 'doc-en-14',\\n\",\n \" 'text': 'The 2021 NHL Entry Draft was the 59th NHL Entry Draft. The draft was held on July 23–24, 2021, delayed by one month from its normally scheduled time of June due to the COVID-19 pandemic and the later-than-normal finish of the 2020–21 NHL season. It was thus the first draft held in July since 2005. For the second year in a row, the event was held in a remote format, with teams convening via videoconferencing, and Commissioner Gary Bettman announcing the selections in the opening round and deputy commissioner Bill Daly in all subsequent rounds from the NHL Network studios in Secaucus, New Jersey.\\\\n\\\\nThe first three selections were Owen Power going to the Buffalo Sabres, Matty Beniers being selected by the Seattle Kraken, and Mason McTavish being picked by the Anaheim Ducks.\\\\n\\\\nEligibility\\\\nIce hockey players born between January 1, 2001, and September 15, 2003, were eligible for selection in the 2021 NHL Entry Draft. Additionally, un-drafted, non-North American players born in 2000 were eligible for the draft; and those players who were drafted in the 2019 NHL Entry Draft, but not signed by an NHL team and who were born after June 30, 2001, were also eligible to re-enter the draft.\\\\n\\\\nDraft lottery\\\\nFrom the 2012–13 NHL season up to the 2020–21 NHL season all teams not qualifying for the Stanley Cup playoffs have had a \\\"weighted\\\" chance at winning the first overall selection. Beginning with the 2014–15 NHL season, the league changed the weighting system that was used in previous years. Under the new system, the odds of winning the draft lottery for the four lowest finishing teams in the league decreased, while the odds for the other non-playoff teams increased. The draft lottery took place on June 2, 2021. After changing the number of lottery drawings earlier in the season, the first two picks overall in this draft were awarded by lottery. The Buffalo Sabres and Seattle Kraken won the two draft lotteries that took place on June 2, 2021, giving them the first and second picks overall. Buffalo retained the first pick, while Seattle moved up one spot and Anaheim dropped one spot to third overall.\\\\n\\\\nThe expansion Seattle Kraken had the same odds of winning the lottery as the team that finished with the third fewest points (this ended up being the New Jersey Devils). Because the Arizona Coyotes\\\\' 2021 first-round pick was forfeited as the result of a penalty sanction due to violations of the NHL Combine Testing Policy during the 2019–20 NHL season, Arizona\\\\'s lottery odds were instead listed as re-draws.\\\\n\\\\n{| class=\\\"wikitable\\\"\\\\n|+ Complete draft position odds\\\\n! Team\\\\n! 1st\\\\n! 2nd\\\\n! 3rd\\\\n! 4th\\\\n! 5th\\\\n! 6th\\\\n! 7th\\\\n! 8th\\\\n! 9th\\\\n! 10th\\\\n! 11th\\\\n! 12th\\\\n! 13th\\\\n! 14th\\\\n! 15th\\\\n! 16th\\\\n|-\\\\n! Buffalo\\\\n| style=\\\"background:#A9D0F5;\\\"| 16.6% || 15.0% || 68.4% || || || || || || || || || || || || ||\\\\n|-\\\\n! Anaheim\\\\n| 12.1% || 11.7% || style=\\\"background:#DDDDDD;\\\"| 26.9% || 49.3% || || || || || || || || || || || ||\\\\n|-\\\\n! Seattle\\\\n| 10.3% || style=\\\"background:#F5A9BC;\\\"| 10.2% || 4.7% || 39.3% || 35.6% || || || || || || || || || || ||\\\\n|-\\\\n! New Jersey\\\\n| 10.3% || 10.2% || || style=\\\"background:#DDDDDD;\\\"| 11.5% || 43.9% || 24.2% || || || || || || || || || ||\\\\n|-\\\\n! Columbus\\\\n| 8.5% || 8.6% || || || style=\\\"background:#DDDDDD;\\\"| 20.6% || 45.8% || 16.5% || || || || || || || || ||\\\\n|-\\\\n! Detroit\\\\n| 7.6% || 7.8% || || || || style=\\\"background:#DDDDDD;\\\"| 30.0% || 43.8% || 10.9% || || || || || || || ||\\\\n|-\\\\n! San Jose\\\\n| 6.7% || 6.9% || || || || || style=\\\"background:#DDDDDD;\\\"| 39.7% || 39.7% || 6.9% || || || || || || ||\\\\n|-\\\\n! Los Angeles\\\\n| 5.8% || 6.0% || || || || || || style=\\\"background:#DDDDDD;\\\"| 49.4% || 34.5% || 4.3% || || || || || ||\\\\n|-\\\\n! Vancouver\\\\n| 5.4% || 5.7% || || || || || || || style=\\\"background:#DDDDDD;\\\"| 58.6% || 28.0% || 2.4% || || || || ||\\\\n|-\\\\n! Ottawa\\\\n| 4.5% || 4.8% || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 67.7% || 21.8% || 1.2% || || || ||\\\\n|-\\\\n! Arizona\\\\n| 3.1% || 3.3% || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 75.9% || 17.1% || 0.7% || || ||\\\\n|-\\\\n! Chicago\\\\n| 2.7% || 2.9% || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 81.7% || 12.4% || 0.3% || ||\\\\n|-\\\\n! Calgary\\\\n| 2.2% || 2.4% || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 87.0% || 8.4% || 0.1% ||\\\\n|-\\\\n! Philadelphia\\\\n| 1.8% || 2.0% || || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 91.3% || 4.9% || >0.0%\\\\n|-\\\\n! Dallas\\\\n| 1.4% || 1.5% || || || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 95.0% || 2.1%\\\\n|-\\\\n! NY Rangers\\\\n| 1.0% || 1.1% || || || || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 97.9%\\\\n|}\\\\n\\\\nTop prospects\\\\nSource: NHL Central Scouting (May 27, 2021) ranking.\\\\n\\\\nSelections by round\\\\nThe order of the 2021 Entry Draft is listed below.\\\\n\\\\nRound one\\\\n\\\\nNotes\\\\n The Vancouver Canucks\\\\' first-round pick went to the Arizona Coyotes as the result of a trade on July 23, 2021, that sent Oliver Ekman-Larsson and Conor Garland to Vancouver in exchange for Jay Beagle, Loui Eriksson, Antoine Roussel, a second-round pick in 2022, a seventh-round pick in 2023 and this pick.\\\\n The Arizona Coyotes\\\\' first-round pick was forfeited as the result of a penalty sanction due to violations of the NHL Combine Testing Policy during the 2019–20 NHL season. The penalty includes the forfeiture of a second-round pick in 2020 and this pick.\\\\n The Chicago Blackhawks\\\\' first-round pick went to the Columbus Blue Jackets as the result of a trade on July 23, 2021, that sent Seth Jones, Tampa Bay\\\\'s first-round-pick in 2021 (32nd overall) and a sixth-round pick in 2022 to Chicago in exchange for Adam Boqvist, a second-round pick in 2021 (44th overall), a conditional first-round pick in 2022 and this pick.\\\\n The Philadelphia Flyers\\\\' first-round pick went to the Buffalo Sabres as the result of a trade on July 23, 2021, that sent Rasmus Ristolainen to Philadelphia in exchange for Robert Hagg, a second-round pick in 2023 and this pick.\\\\n The Dallas Stars first-round pick went to the Detroit Red Wings as the result of a trade on July 23, 2021, that sent Washington\\\\'s first-round pick, the Rangers\\\\' second-round pick and Ottawa\\\\'s fifth-round pick all in 2021 (23rd, 48th and 138th overall) to Dallas in exchange for this pick.\\\\n The Edmonton Oilers\\\\' first-round pick went to the Minnesota Wild as the result of a trade on July 23, 2021, that sent a first-round pick and Pittsburgh\\\\'s third-round pick both in 2021 (22nd and 90th overall) to Edmonton in exchange for this pick.\\\\n The Minnesota Wild\\\\'s first-round pick went to the Edmonton Oilers as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (20th overall) to Minnesota in exchange for Pittsburgh\\\\'s third-round pick both in 2021 (90th overall) and this pick.\\\\n The Washington Capitals\\\\' first-round pick went to the Dallas Stars as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (15th overall) to Detroit in exchange for the Rangers\\\\' second-round pick and Ottawa\\\\'s sixth round pick both in 2021 (48th and 138th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on April 12, 2021, that sent Anthony Mantha to Washington in exchange for Richard Panik, Jakub Vrana, a second-round pick in 2022 and this pick.\\\\n The Toronto Maple Leafs\\\\' first-round pick went to the Columbus Blue Jackets as the result of a trade on April 11, 2021, that sent Nick Foligno and Stefan Noesen to Toronto in exchange for a fourth-round pick in 2022 and this pick.\\\\n The Pittsburgh Penguins\\\\' first-round pick went to the Minnesota Wild as the result of a trade on February 10, 2020, that sent Jason Zucker to Pittsburgh in exchange for Alex Galchenyuk, Calen Addison and this pick (being conditional at the time of the trade). The condition – Minnesota will receive a 2021 first-round pick at Pittsburgh\\\\'s choice if the Penguins fail to qualify for the 2020 Eastern Conference First Round – was converted on August 12, 2020, when the Penguins elected to defer the pick to 2021.\\\\n The Carolina Hurricanes\\\\' first-round pick went to the Nashville Predators as the result of a trade on July 23, 2021, that sent Los Angeles and Nashville\\\\'s second-round picks both in 2021 to Carolina in exchange for this pick.\\\\n The New York Islanders\\\\' first-round pick went to the New Jersey Devils as the result of a trade on April 7, 2021, that sent Kyle Palmieri and Travis Zajac to New York in exchange for A. J. Greer, Mason Jobst, a conditional fourth-round pick in 2022 and this pick.\\\\n The Tampa Bay Lightning\\\\'s first-round pick went to the Chicago Blackhawks as the result of a trade on July 23, 2021, that sent a first and second-round pick both in 2021 (12th and 44th overall) to Columbus in exchange for Seth Jones, a sixth-round pick in 2022 and this pick.\\\\nColumbus previously acquired this pick as the result of a trade on April 10, 2021, that sent Brian Lashoff to Tampa Bay in exchange for a third-round pick in 2022 and this pick.\\\\n\\\\nRound two\\\\n\\\\nNotes\\\\n The New Jersey Devils\\\\' second-round pick went to the Detroit Red Wings as the result of a trade on July 24, 2021, that sent a second-round pick and Tampa Bay\\\\'s fourth-round pick both in 2021 (38th and 128th overall) to Vegas in exchange for this pick.\\\\nVegas previously acquired this pick as the result of a trade on July 29, 2019, that sent Nikita Gusev to New Jersey in exchange for a third-round pick in 2020 and this pick.\\\\n The Columbus Blue Jackets\\\\' second-round pick went to the Arizona Coyotes as the result of a trade on December 26, 2020, that sent Derek Stepan to Ottawa in exchange for this pick.\\\\nOttawa previously acquired this pick as the result of a trade on February 23, 2019, that sent Ryan Dzingel and Calgary\\\\'s seventh-round pick in 2019 to Columbus in exchange for Anthony Duclair, a second-round pick in 2020, and this pick.\\\\n The Detroit Red Wings\\\\' second-round pick went to the Vegas Golden Knights as the result of a trade on July 24, 2021, that sent New Jersey\\\\'s second-round pick in 2021 (36th overall) to Detroit in exchange for Tampa Bay\\\\'s fourth-round pick in 2021 (128th overall) and this pick.\\\\n The San Jose Sharks\\\\' second-round pick went to the Ottawa Senators as the result of a trade on September 13, 2018, that sent Erik Karlsson and Francis Perron to San Jose in exchange for Chris Tierney, Dylan DeMelo, Josh Norris, Rudolfs Balcers, a conditional second-round pick in 2019, a conditional l first-round pick in 2019 or 2020, a conditional first-round pick no later than 2022, and this pick (being conditional at the time of the trade). The condition – Ottawa will receive a second-round pick in 2021 if Karlsson re-signs with the Sharks for the 2019–20 NHL season and the Sharks do not make the 2019 Stanley Cup Finals – was converted on June 17, 2019, when Karlsson re-signed with San Jose for the 2019–20 NHL season.\\\\n The Los Angeles Kings\\\\' second-round pick went to the Carolina Hurricanes as the result of a trade on July 23, 2021, that sent Carolina\\\\'s first-round pick (27th overall) in 2021 to Nashville in exchange for a second-round pick (51st overall) and this pick.\\\\nNashville previously acquired this pick as the result of a trade on July 1, 2021, that sent Viktor Arvidsson to Los Angeles in exchange for a third-round pick in 2022 and this pick.\\\\n The Ottawa Senators\\\\' second-round pick went to the Los Angeles Kings as the result of a trade on July 24, 2021, that sent St. Louis\\\\' second-round pick and a fifth-round pick both in 2021 (49th and 136th overall) to Ottawa in exchange for this pick.\\\\n The Chicago Blackhawks\\\\' second-round pick went to the Carolina Hurricanes as the result of a trade on July 23, 2021, that sent Jake Bean to Columbus in exchange for this pick.\\\\nColumbus previously acquired this pick as the result of a trade on July 23, 2021, that sent Seth Jones, Tampa Bay\\\\'s first-round-pick in 2021 (32nd overall) and a sixth-round pick in 2022 to Chicago in exchange for Adam Boqvist, a first-round pick in 2021 (12th overall), a conditional first-round pick in 2022 and this pick.\\\\n The New York Rangers\\\\' second-round pick went to the Dallas Stars as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (15th overall) to Detroit in exchange for Washington\\\\'s first-round pick and Ottawa\\\\'s fifth-round pick both in 2021 (23rd and 138th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on September 26, 2020, that sent future considerations to New York in exchange for Marc Staal and this pick.\\\\n The St. Louis Blues\\\\' second-round pick went to the Ottawa Senators as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (42nd overall) to Los Angeles in exchange for a fifth-round pick in 2021 (136th overall) and this pick.\\\\nLos Angeles previously acquired this as the result of a trade on February 19, 2020, that sent Alec Martinez to Vegas in exchange for a second-round pick in 2020 and this pick.\\\\nVegas previously acquired this pick as the result of a trade on June 28, 2019, that sent Colin Miller to Buffalo in exchange for a fifth-round pick in 2022 and this pick.\\\\nBuffalo previously acquired this pick as the result of a trade on July 1, 2018, that sent Ryan O\\\\'Reilly to St. Louis in exchange for Vladimir Sobotka, Patrik Berglund, Tage Thompson, a conditional first-round pick in 2019 or 2020 and this pick.\\\\n The Nashville Predators\\\\' second-round pick went to the Carolina Hurricanes as the result of a trade on July 23, 2021, that sent Carolina\\\\'s first-round pick (27th overall) in 2021 to Nashville in exchange for Los Angeles\\\\' second-round pick (40th overall) and this pick.\\\\n The Edmonton Oilers\\\\' second-round pick went to the New York Islanders as the result of a trade on July 16, 2021, that sent Nick Leddy to Detroit in exchange for Richard Panik and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on February 24, 2020, that sent Andreas Athanasiou and Ryan Kuffner to Edmonton in exchange for Sam Gagner, a second-round pick in 2020 and this pick.\\\\n The Boston Bruins\\\\' second-round pick went to the Buffalo Sabres as the result of a trade on April 12, 2021, that sent Taylor Hall and Curtis Lazar to Boston in exchange for Anders Bjork and this pick.\\\\n The Carolina Hurricanes\\\\' second-round pick went to the Los Angeles Kings as the result of a trade on July 24, 2021, that sent a third-round pick and Calgary\\\\'s fourth-round pick both in 2021 (72nd and 109th overall) to Carolina in exchange for this pick.\\\\n The Colorado Avalanche\\\\'s second-round pick went to the Arizona Coyotes as the result of a trade on July 17, 2021, that sent future considerations to the New York Islanders in exchange for Andrew Ladd, a conditional second-round pick in 2022, a conditional third-round pick in 2023, and this pick.\\\\nThe Islanders previously acquired this pick as the result of a trade on October 12, 2020, that sent Devon Toews to Colorado in exchange for a second-round pick in 2022 and this pick.\\\\n The New York Islanders\\\\' second-round pick went to the Colorado Avalanche as the result of a trade on July 15, 2021, that sent Ryan Graves to New Jersey in exchange for Mikhail Maltsev and this pick.\\\\nNew Jersey previously acquired this pick as the result of a trade on February 16, 2020, that sent Andy Greene to New York in exchange for David Quenneville and this pick.\\\\n The Vegas Golden Knights\\\\' second-round pick went to the Chicago Blackhawks as the result of a trade on April 12, 2021, that sent Nick DeSimone and a fifth-round pick in 2022 to Vegas in exchange for a third-round pick in 2022 and this pick.\\\\n The Tampa Bay Lightning\\\\'s second-round pick went to the Montreal Canadiens as the result of a trade on October 7, 2020, that sent St. Louis\\\\' second-round pick in 2020 (57th overall) to Tampa Bay in exchange for a fourth-round pick in 2020 (124th overall) and this pick.\\\\n\\\\nRound three\\\\n\\\\nNotes\\\\n The Buffalo Sabres\\\\' third-round pick went to the New York Rangers as the result of a trade on July 1, 2019, that sent Jimmy Vesey to Buffalo in exchange for this pick.\\\\n The San Jose Sharks\\\\' third-round pick went to the St. Louis Blues as the result of a trade on July 24, 2021, that sent a third and sixth-round pick both in 2021 (81st and 177th overall) to San Jose in exchange for this pick.\\\\n The Los Angeles Kings\\\\' third-round pick went to the Nashville Predators as the result of a trade on July 24, 2021, that sent a third and fifth-round pick both in 2021 (83rd and 147th overall) to Carolina in exchange for this pick.\\\\nCarolina previously acquired this pick as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (59th overall) to Los Angeles in exchange for Calgary\\\\'s fourth-round pick in 2021 (109th overall) and this pick.\\\\n The Vancouver Canucks\\\\' third-round pick went to the Dallas Stars as the result of a trade on July 17, 2021, that sent Jason Dickinson to Vancouver in exchange for this pick.\\\\n The Arizona Coyotes\\\\' third-round pick went to the New York Rangers as the result of a trade on July 24, 2021, that sent a third and sixth-round pick both in 2021 (80th and 176th overall) to Washington in exchange for this pick.\\\\nWashington previously acquired this pick as the result of a trade on April 11, 2021, that sent a Jonas Siegenthaler to New Jersey in exchange for this conditional pick. The condition – Washington will receive Arizona\\\\'s third-round pick in 2021 at New Jersey\\\\'s choice, if the pick is available before the time of the selection – the date of conversion is unknown.\\\\nNew Jersey previously acquired this pick as the result of a trade on December 16, 2019, that sent Taylor Hall and Blake Speers to Arizona in exchange for Nick Merkley, Kevin Bahl, Nate Schnarr, a conditional first-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – New Jersey will receive a third-round pick in 2021 if Arizona does not advance to the 2020 Western Conference Second Round and Hall does not re-sign with Arizona for the 2020–21 NHL season – was converted when Arizona was eliminated in the First Round of the playoffs on August 19, 2020 and when Hall signed with the Buffalo Sabres on October 11, 2020.\\\\n The Chicago Blackhawks\\\\' third-round pick went to the Anaheim Ducks as the result of a trade on July 24, 2021, that sent a third-round pick in 2022 to Montreal in exchange for this pick.\\\\nMontreal previously acquired this pick as the result of a trade on June 30, 2019, that sent Andrew Shaw and a seventh-round pick in 2021 to Chicago in exchange for second and seventh-round picks both in 2020 and this pick.\\\\n The New York Rangers\\\\' third-round pick went to the Washington Capitals as the result of a trade on July 24, 2021, that sent Arizona\\\\'s third-round pick in 2021 (75th overall) to New York in exchange for a sixth-round pick in 2021 (176th overall) and this pick.\\\\n The St. Louis Blues\\\\' third-round pick went to the San Jose Sharks as the result of a trade on July 24, 2021, that sent a third-round pick in 2021 (71st overall) to St. Louis in exchange for a sixth-round pick in 2021 (177th overall) and this pick.\\\\n The Nashville Predators\\\\' third-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent Los Angeles\\\\' third-round pick in 2021 (72nd overall) to Nashville in exchange for a fifth-round pick in 2021 (147th overall) and this pick.\\\\n The Edmonton Oilers\\\\' third-round pick went to the Los Angeles Kings as the result of a trade on July 24, 2021, that sent Toronto\\\\'s third-round pick and a sixth-round pick both in 2021 (89th and 168th overall) to Calgary in exchange for this pick.\\\\nCalgary previously acquired this pick as the result of a trade on July 19, 2019, that sent James Neal to Edmonton in exchange for Milan Lucic and this pick (being conditional at the time of the trade). The condition – Calgary will receive a third-round pick in 2020 or 2021 at Edmonton\\\\'s choice, after the league made a ruling on this conditional pick on July 31, 2020. The original condition on this pick was that Calgary will receive a 2020 third-round pick if Neal scores at least 21 goals during the 2019–20 NHL season and Lucic has at least ten fewer goals than Neal – was converted when the Oilers elected to keep their 2020 third-round pick on October 7, 2020.\\\\n The Washington Capitals\\\\' third-round pick went to the Montreal Canadiens as the result of a trade on October 7, 2020, that sent Anaheim\\\\'s fourth-round pick in 2020 (98th overall) to San Jose in exchange for this pick.\\\\nSan Jose previously acquired this pick as the result of a trade on February 18, 2020, that sent Brenden Dillon to Washington in exchange for Colorado\\\\'s second-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – San Jose will receive a third-round pick in 2021 if Washington does not win the Stanley Cup in 2020 – was converted when Washington was eliminated from the 2020 Stanley Cup playoffs on August 20, 2020.\\\\n The Florida Panthers\\\\' third-round pick went to the Buffalo Sabres as the result of a trade on April 10, 2021, that sent Brandon Montour to Florida in exchange for this pick.\\\\n The Toronto Maple Leafs\\\\' third-round pick went to the Calgary Flames as the result of a trade on July 24, 2021, that sent Edmonton\\\\'s third-round pick in 2021 (84th overall) to Los Angeles in exchange for a sixth-round pick in 2021 (168th overall) and this pick.\\\\nLos Angeles previously acquired this pick as the result of a trade on February 5, 2020, that sent Jack Campbell and Kyle Clifford to Toronto in exchange for Trevor Moore, Columbus\\\\' third-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – Los Angeles will receive a third-round pick in 2021 if Clifford does not re-sign with Toronto for the 2020–21 NHL season – was converted when Clifford signed with St. Louis.\\\\n The Pittsburgh Penguins\\\\' third-round pick went to the Edmonton Oilers as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (20th overall) to Minnesota in exchange for a first-round pick in 2021 (22nd overall) and this pick.\\\\nMinnesota previously acquired this pick as the result of a trade on October 5, 2020, that sent Ryan Donato to San Jose in exchange for this pick.\\\\nSan Jose previously acquired this pick as the result of a trade on February 24, 2020, that sent Patrick Marleau to Pittsburgh in exchange for this pick (being conditional at the time of the trade). The condition – San Jose will receive a third-round pick in 2021 if Pittsburgh does not win the Stanley Cup in 2020 – was converted when the Penguins were eliminated from the 2020 Stanley Cup playoffs on August 7, 2020.\\\\n The Carolina Hurricanes\\\\' third-round pick went to the Chicago Blackhawks as the result of a trade on July 24, 2021, that sent a third-round pick in 2022 to Carolina in exchange for this pick.\\\\n The Vegas Golden Knights third-round pick went to the Carolina Hurricanes as the result of a trade on July 22, 2021, that sent Alex Nedeljkovic to Detroit in exchange for Jonathan Bernier and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on February 26, 2018, that sent Tomas Tatar to Vegas in exchange for a first-round pick in 2018, the Islanders\\\\' second-round pick in 2019 and this pick.\\\\n The Montreal Canadiens\\\\' third-round pick went to the Buffalo Sabres as the result of a trade on March 26, 2021, that sent Eric Staal to Montreal in exchange for a fifth-round pick in 2021 and this pick.\\\\n\\\\nRound four\\\\n\\\\nNotes\\\\n The Detroit Red Wings\\\\' fourth-round pick went to the Vegas Golden Knights as the result of a trade on July 24, 2021, that sent Winnipeg\\\\'s fourth-round pick and Carolina\\\\'s fifth-round pick both in 2021 (114th and 155th overall) to Detroit in exchange for this pick.\\\\n The Los Angeles Kings\\\\' fourth-round pick went to the New York Rangers as the result of a trade on March 27, 2021, that sent Brendan Lemieux to Los Angeles in exchange for this pick.\\\\n The Vancouver Canucks\\\\' fourth-round pick went to the Chicago Blackhawks as the result of a trade on April 12, 2021, that sent Madison Bowey and a fifth-round pick in 2021 to Vancouver in exchange for this pick.\\\\n The Ottawa Senators\\\\' fourth-round pick went to the New York Rangers as the result of a trade on October 7, 2019, that sent Vladislav Namestnikov to Ottawa in exchange for Nick Ebert and this pick.\\\\n The Calgary Flames\\\\' fourth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (59th overall) to Los Angeles in exchange for a third-round pick in 2021 (72nd overall) and this pick.\\\\nLos Angeles previously acquired this pick as the result of a trade on February 24, 2020, that sent Derek Forbort to Calgary in exchange for this pick (being conditional at the time of the trade). The condition – Los Angeles will receive a fourth-round pick in 2021 if Forbort does not re-sign with Calgary for the 2020–21 NHL season – was converted when Forbort signed with the Winnipeg Jets on October 11, 2020.\\\\n The St. Louis Blues\\\\' fourth-round pick went to the Montreal Canadiens as the result of a trade on February 18, 2020, that sent Marco Scandella to St. Louis in exchange for a second-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – Montreal will receive a fourth-round pick in 2021 if Scandella re-signs with the Blues for the 2020–21 NHL season by October 7, 2020 – was converted when Scandella re-signed with the Blues on April 16, 2020.\\\\n The Winnipeg Jets\\\\' fourth-round pick went to the Detroit Red Wings as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (102nd overall) to Vegas in exchange for Carolina\\\\'s fifth-round pick in 2021 (155th overall) and this pick.\\\\nVegas previously acquired this pick as the result of a trade on February 21, 2020, that sent Cody Eakin to Winnipeg in exchange for this pick (being conditional at the time of the trade). The condition – Vegas will receive a fourth-round pick in 2021 if Eakin does not re-sign with the Jets for the 2020–21 NHL season – was converted when Eakin signed with the Buffalo Sabres for the 2020–21 NHL season on October 10, 2020.\\\\n The Toronto Maple Leafs\\\\' fourth-round pick went to the San Jose Sharks as the result of a trade on April 11, 2021, that sent Nick Foligno to Toronto in exchange for this pick.\\\\n The Pittsburgh Penguins\\\\' fourth-round pick went to the Arizona Coyotes as the result of a trade on June 29, 2019, that sent Alex Galchenyuk and Pierre-Olivier Joseph to Pittsburgh in exchange for Phil Kessel, Dane Birks and this pick.\\\\n The Carolina Hurricanes\\\\' fourth-round pick went to the Ottawa Senators as the result of a trade on July 24, 2021, that sent Los Angeles\\\\' fifth-round pick and a sixth-round pick both in 2021 (136th and 170th overall) to Carolina in exchange for this pick.\\\\n The Colorado Avalanche\\\\'s fourth-round pick went to the Nashville Predators as the result of a trade on October 10, 2020, that sent Austin Watson to Ottawa in exchange for this pick.\\\\nOttawa previously acquired this pick as the result of a trade on February 24, 2020, that sent Vladislav Namestnikov to Colorado in exchange for this pick.\\\\n The Vegas Golden Knights\\\\' fourth-round pick went to the Tampa Bay Lightning as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2022 to Montreal in exchange for this pick.\\\\nMontreal previously acquired this pick as the result of a trade on February 24, 2020, that sent Nick Cousins to Vegas in exchange for this pick.\\\\n The Montreal Canadiens\\\\' fourth-round pick went to the Minnesota Wild as the result of a trade on July 24, 2021, that sent a fifth and seventh-round pick both in 2021 (150th and 214th overall) to Montreal in exchange for this pick.\\\\n The Tampa Bay Lightning\\\\'s fourth-round pick went to the Vegas Golden Knights as the result of a trade on July 24, 2021, that sent New Jersey\\\\'s second-round pick in 2021 (36th overall) to Detroit in exchange for a second-round pick in 2021 (38th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on April 10, 2021, that sent David Savard to Tampa Bay in exchange for this pick.\\\\n\\\\nRound five\\\\n\\\\nNotes\\\\n The Buffalo Sabres\\\\' fifth-round pick went to the New Jersey Devils as the result of a trade on February 24, 2020, that sent Wayne Simmonds to Buffalo in exchange for this pick (being conditional at the time of the trade). The condition – New Jersey will receive a fifth-round pick in 2021 if the Sabres do not qualify for the 2020 Stanley Cup playoffs – was converted on May 26, 2020, when it was announced the Sabres would not participate in the 2020 Stanley Cup playoffs.\\\\n The New Jersey Devils\\\\' fifth-round pick went to the Columbus Blue Jackets as the result of a trade on October 8, 2020, that sent Ryan Murray to New Jersey in exchange for this pick.\\\\n The Los Angeles Kings\\\\' fifth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (123rd overall) to Ottawa in exchange for a sixth-round pick in 2021 (170th overall) and this pick.\\\\nOttawa previously acquired this pick as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (42nd overall) to Los Angeles in exchange for St. Louis\\\\' second-round pick in 2021 (49th overall) and this pick.\\\\n The Ottawa Senators\\\\' fifth-round pick went to the Dallas Stars as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (15th overall) to Detroit in exchange for Washington\\\\'s first-round pick and the Rangers\\\\' second-round pick both in 2021 (23rd and 48th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on April 11, 2021, that sent Jon Merrill to Montreal in exchange for Hayden Verbeek and this pick.\\\\nMontreal previously acquired this pick as the result of a trade on January 2, 2020, that sent Mike Reilly to Ottawa in exchange for Andrew Sturtz and this pick.\\\\n The Chicago Blackhawks\\\\' fifth-round pick went to the Vancouver Canucks as the result of a trade on April 12, 2021, that sent a fourth-round pick in 2021 to Chicago in exchange for Madison Bowey and this pick.\\\\n The Philadelphia Flyers\\\\' fifth-round pick went to the Montreal Canadiens as the result of a trade on February 24, 2020, that sent Nate Thompson to Philadelphia in exchange for this pick.\\\\n The Nashville Predators\\\\' fifth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent Los Angeles\\\\' third-round pick in 2021 (72nd overall) to Nashville in exchange for a third-round pick in 2021 (83rd overall) and this pick.\\\\n The Edmonton Oilers\\\\' fifth-round pick went to the Anaheim Ducks as the result of a trade on October 8, 2020, that sent Erik Gudbranson to Ottawa in exchange for this pick.\\\\nOttawa previously acquired this pick as the result of a trade on February 24, 2020 that sent Tyler Ennis to Edmonton in exchange for this pick.\\\\n The Minnesota Wild\\\\'s fifth-round pick went to the Montreal Canadiens as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (127th overall) to Minnesota in exchange for a seventh-round pick in 2021 (214th overall) and this pick.\\\\n The Carolina Hurricanes\\\\' fifth-round pick went to the Detroit Red Wings as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (102nd overall) to Vegas in exchange for Winnipeg\\\\'s fourth-round pick in 2021 (114th overall) and this pick.\\\\nVegas previously acquired this pick as the result of a trade on June 26, 2019, that sent Erik Haula to Carolina in exchange for Nicolas Roy and this pick (being conditional at the time of the trade). The condition – Vegas will receive a fifth-round pick in 2021 if Carolina trades Haula for a player, multiple draft picks or if he is traded for a draft pick in the first five rounds of any future draft – was converted when Haula was traded to the Florida Panthers on February 24, 2020.\\\\n The Colorado Avalanche\\\\'s fifth-round pick went to the San Jose Sharks as the result of a trade on April 10, 2021, that sent Devan Dubnyk to Colorado in exchange for Greg Pateryn and this pick.\\\\n The Vegas Golden Knights\\\\' fifth-round pick went to the Philadelphia Flyers as the result of a trade on April 12, 2021, that sent Michael Raffl to Washington in exchange for this pick.\\\\nWashington previously acquired this as the result of a trade on December 2, 2019, that sent Chandler Stephenson to Vegas in exchange for this pick.\\\\n The Montreal Canadiens\\\\' fifth-round pick went to the Buffalo Sabres as the result of a trade on March 26, 2021, that sent Eric Staal to Montreal in exchange for a third-round pick in 2021 and this pick.\\\\n\\\\nRound six\\\\n\\\\nNotes\\\\n The Los Angeles Kings\\\\' sixth-round pick went to the Calgary Flames as the result of a trade on July 24, 2021, that sent Edmonton\\\\'s third-round pick in 2021 (84th overall) to Los Angeles in exchange for Toronto\\\\'s third-round pick in 2021 (89th overall) and this pick.\\\\n The Ottawa Senators\\\\' sixth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (123rd overall) to Ottawa in exchange for Los Angeles\\\\' fifth-round pick in 2021 (136th overall) and this pick.\\\\n The New York Rangers\\\\' sith-round pick went to the Washington Capitals as the result of a trade on July 24, 2021, that sent Arizona\\\\'s third-round pick in 2021 (75th overall) to New York in exchange for a third-round pick in 2021 (80th overall) and this pick.\\\\n The St. Louis Blues\\\\' sixth-round pick went to the San Jose Sharks as the result of a trade on July 24, 2021, that sent a third-round pick in 2021 (71st overall) to St. Louis in exchange for a third-round pick in 2021 (81th overall) and this pick.\\\\n The Winnipeg Jets\\\\' sixth-round pick went to the Vancouver Canucks as the result of a trade on April 12, 2021, that sent Jordie Benn to Winnipeg in exchange for this pick.\\\\n The Pittsburgh Penguins\\\\' sixth-round pick went to the Edmonton Oilers as the result of a trade on July 26, 2019, that sent John Marino to Pittsburgh in exchange for this pick (being conditional at the time of the trade). The condition – Edmonton will receive a sixth-round pick in 2021 if Marino signs with the Penguins – was converted when Marino signed with the Penguins on August 8, 2019.\\\\n The Colorado Avalanche\\\\'s sixth-round pick went to the Buffalo Sabres as the result of a trade on March 20, 2021, that sent Jonas Johansson to Colorado in exchange for this pick.\\\\n\\\\nRound seven\\\\n\\\\nNotes\\\\n The Anaheim Ducks\\\\' seventh-round pick went to the Pittsburgh Penguins as the result of a trade on October 25, 2019, that sent Erik Gudbranson to Anaheim in exchange for Andreas Martinsen and this pick.\\\\n The New Jersey Devils\\\\' seventh-round pick went to the Tampa Bay Lightning as the result of a trade on November 1, 2019, that sent Louis Domingue to New Jersey in exchange for this pick (being conditional at the time of the trade). The condition – Tampa Bay will receive a seventh-round pick in 2021 if Domingue plays in seven games for the Devils during the 2019–20 NHL season – was converted on January 9, 2020.\\\\n The Detroit Red Wings\\\\' seventh-round pick went to the St. Louis Blues as the result of a trade on October 7, 2020, that sent Chicago\\\\'s seventh-round pick in 2020 (203rd overall) to Detroit in exchange for this pick.\\\\n The Los Angeles Kings\\\\' seventh-round pick went to the Carolina Hurricanes as the result of a trade on October 7, 2020, that sent Montreal\\\\'s fifth-round pick in 2020 (140th overall) to Los Angeles in exchange for a sixth-round pick in 2020 and this pick.\\\\n The Arizona Coyotes\\\\' seventh-round pick went to the New Jersey Devils as the result of a trade on October 7, 2020, that sent a seventh-round pick in 2020 (192nd overall) to Arizona in exchange for this pick.\\\\n The St. Louis Blues\\\\' seventh-round pick went to the Carolina Hurricanes as the result of a trade on September 24, 2019, that sent Justin Faulk and a fifth-round pick in 2020 to St. Louis in exchange for Joel Edmundson, Dominik Bokk and this pick.\\\\n The Winnipeg Jets\\\\' seventh-round pick went to the Florida Panthers as the result of a trade on February 25, 2019, that sent Bogdan Kiselevich to Winnipeg in exchange for this pick.\\\\n The Nashville Predators\\\\' seventh-round pick went to the Tampa Bay Lightning as the result of a trade on June 14, 2019, that sent Connor Ingram to Nashville in exchange for this pick.\\\\n The Minnesota Wild\\\\'s seventh-round pick went to the Montreal Canadiens as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (127th overall) to Minnesota in exchange for a fifth-round pick in 2021 (150th overall) and this pick.\\\\n The Washington Capitals\\\\' seventh-round pick went to the Pittsburgh Penguins as the result of a trade on October 7, 2020, that sent Colorado\\\\'s seventh-round pick in 2020 (211th overall) to Washington in exchange for this pick.\\\\n The Florida Panthers\\\\' seventh-round pick went to the Chicago Blackhawks as the result of a trade on April 8, 2021, that sent Lucas Carlsson and Lucas Wallmark to Florida in exchange for Brett Connolly, Riley Stillman, Henrik Borgstrom and this pick.\\\\n The Toronto Maple Leafs\\\\' seventh-round pick went to the Boston Bruins as the result of a trade on October 7, 2020, that sent a seventh-round pick in 2020 (213th overall) to Toronto in exchange for this pick.\\\\n The Montreal Canadiens\\\\' seventh-round pick went Arizona Coyotes as the result of a trade on July 24, 2021, that sent St. Louis\\\\' seventh-round pick in 2022 to Montreal in exchange for this pick.\\\\nMontreal previously re-acquired this pick as the result of a trade on October 7, 2020, that sent Ottawa\\\\'s seventh-round pick in 2020 to Chicago in exchange for this pick.\\\\nChicago previously acquired this pick as the result of a trade on June 30, 2019, that sent second and seventh-round picks both in 2020 and a third-round pick in 2021 to Montreal in exchange for Andrew Shaw and this pick.\\\\n\\\\nDraftees based on nationality\\\\n\\\\nNorth American draftees by state/province\\\\n\\\\nSee also\\\\n 2017–18 NHL transactions\\\\n 2018–19 NHL transactions\\\\n 2019–20 NHL transactions\\\\n 2020–21 NHL transactions\\\\n 2021–22 NHL transactions\\\\n 2020–21 NHL season\\\\n 2021 NHL Expansion Draft\\\\n List of first overall NHL draft picks\\\\n List of NHL players\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n2021 NHL Entry Draft player stats at The Internet Hockey Database\\\\n\\\\nEntry Draft\\\\nNHL Entry Draft\\\\nNHL Entry Draft\\\\nEvents in New Jersey\\\\nIce hockey in New Jersey\\\\nNHL\\\\nNational Hockey League in the New York metropolitan area\\\\nNational Hockey League Entry Draft'},\\n\",\n \" {'docid': 'doc-en-15',\\n\",\n \" 'text': 'The history of Christianity in Sussex includes all aspects of the Christianity in the region that is now Sussex from its introduction to the present day. Christianity is the most commonly practised religion in Sussex.\\\\n\\\\nEarly history\\\\n\\\\nAfter the Roman conquest of AD 43, the Celtic society of Sussex became heavily Romanized.\\\\n\\\\nThe first written account of Christianity in Britain comes from the early Christian Berber author, Tertullian, writing in the third century, who said that \\\"Christianity could even be found in Britain.\\\" Emperor Constantine (AD\\\\xa0306-337), granted official tolerance to Christianity with the Edict of Milan in AD\\\\xa0313. Then, in the reign of Emperor Theodosius \\\"the Great\\\" (AD\\\\xa0378–395), Christianity was made the official religion of the Roman Empire.\\\\n\\\\nWhen Roman rule eventually ceased, Christianity was probably confined to urban communities. At Wiggonholt, on a tributary of the River Arun, a large lead tank with repeated chi-rho motifs was discovered in 1943, the only Roman period artefact in Sussex found with a definite Christian association. It may represent a baptismal font or a container for holy water, or alternatively may have been used by pagans.\\\\n\\\\nMedieval\\\\n\\\\nSaxon\\\\n\\\\nAfter the departure of the Roman army, the Saxons arrived and founded the Kingdom of Sussex in the 5th century, bringing with them their polytheistic religion. The Saxon pagan culture probably caused a reversal of the spread of Christianity. According to Bede, Sussex was the last of the mainland Anglo Saxon kingdoms to be converted.\\\\n\\\\nÆðelwealh became Sussex\\\\'s first Christian king when he married Eafe, the daughter of Wulfhere, the Christian king of Mercia. In 681 St Wilfrid, the exiled Bishop of York, landed at Selsey and is credited with evangelising the local population and founding the church in Sussex. King Æðelwealh granted land to Wilfrid which became the site of Selsey Abbey. The seat of the Sussex bishopric was originally located here before the Normans moved it to Chichester Cathedral in 1075. According to Bede, Sussex was the last area of the country to be converted. However it is unlikely that Sussex was wholly heathen when Wilfrid arrived. Æðelwealh, Sussex\\\\'s king, had been baptised. Damianus, a South Saxon, was made Bishop of Rochester in the Kingdom of Kent in the 650s; this may indicate earlier missionary work in the first half of the 7th century. At the time of Wilfrid\\\\'s mission there was a monastery at Bosham containing a few monks led by an Irish monk named Dicul, which was probably part of the Hiberno-Scottish mission of the time. Wilfrid was a champion of Roman customs and it was these customs that were adopted by the church in Sussex rather than the Celtic customs that had taken root in Scotland and Ireland.\\\\n\\\\nShortly after Æðelwealh granted land to Wilfrid for the church, Cædwalla of Wessex killed Æðelwealh and conquered Sussex. Christianity in Sussex was put under control of the diocese of Winchester. It was not until c. 715 that Eadberht, Abbot of Selsey was consecrated the first bishop of the South Saxons.\\\\n\\\\nSt Lewinna, or St Leofwynn, was a female saint who lived around Seaford, probably at Bishopstone around the 7th century. According to the hagiography of the Secgan Manuscript, Lyminster is the burial place of St Cuthflæd of Lyminster. In the late 7th or early 8th century, St Cuthman, a shepherd who may have been born in Chidham and had been reduced to begging, set out from his home with his disabled mother using a one-wheeled cart. When he reached Steyning he saw a vision and stopped there to build a church. Cuthman was venerated as a saint and his church was in existence by 857 when King Æthelwulf of Wessex was buried there. Steyning was an important religious centre and St Cuthman\\\\'s grave became a place of pilgrimage in the 10th and 11th centuries. In 681, Bede records that an outbreak of the plague had devastated parts of England, including Sussex, and the monks at Selsey Abbey fasted and prayed for three days for an end to the outbreak. A young boy with the plague prayed to St Oswald and his prayers were answered, and a vision of St Peter and St Paul was said to have appeared to the boy, telling him that he would be the last to die.\\\\n\\\\nThe church built at Steyning was one of around 50 minster churches across Sussex and these churches supplied itinerant clergy to surrounding districts. Other examples are churches at Singleton, Lyminster, Findon and Bishopstone. The jurisdiction of each minster church in the pre-Viking era seems to match early land divisions that were replaced by hundreds in the 10th or 11th centuries. It was not until 200–300 years after its conversion to Christianity in the 680s that a network of local parish churches existed in Sussex.\\\\n\\\\nVarious monastic houses were established in the Saxon period in Sussex including at Selsey Abbey, Lyminster Priory, Aldingbourne, Beddingham, Bosham, Chichester, Ferring and South Malling, near Lewes.\\\\n\\\\nNorman and Angevin\\\\n\\\\nFollowing the Norman Conquest of 1066, there was a purge of the English episcopate in 1070. The Anglo-Saxon Bishop of Selsey was deposed and replaced with William the Conqueror\\\\'s personal chaplain, Stigand. During Stigand\\\\'s episcopate the see that had been established at Selsey was transferred to Chichester after the Council of London of 1075 decreed that sees should be centred in cities rather than vills. 1094 saw the completion of Battle Abbey, which had been founded on the site of the Battle of Hastings after Pope Alexander II had ordered the Normans to do penance for killing so many people during their conquest of England. Monks also planned out the nearby town of Battle shortly after the conquest. Many of the monastic houses of this period were founded by Sussex\\\\'s new Norman lords. Around 1081, the lord of Lewes Rape, William de Warenne and his wife Gundrada formed England\\\\'s first and largest Cluniac monastery at Lewes Priory. The lord of Arundel Rape, Roger de Montgomerie established Arundel Priory in 1102. Sele Priory in the Rape of Bramber was founded by the Braose family by 1126.\\\\n\\\\nBishop Ralph Luffa is credited with the foundation of the current Chichester Cathedral. \\\\nThe original structure that had been built by Stigand was largely destroyed by fire in 1114.\\\\n\\\\nThe medieval church also set up various hospitals and schools in Sussex, including St Mary\\\\'s Hospital in Chichester (c. 1290-1300); St Nicholas\\\\' Hospital in Lewes, which was run by the monks of Lewes Priory; and the Prebendal School close to Chichester Cathedral.\\\\n\\\\nThe archdeaconries of Chichester and Lewes were created in the 12th century under Ralph Luffa.\\\\n\\\\nSussex has strong links with the Knights Templar and the Knights Hospitaller including at Shipley, Poling and Sompting.\\\\n\\\\nIn the 13th century, Richard of Chichester was canonised as a saint, and a shrine dedicated to him at Chichester Cathedral became an important place of pilgrimage. St Richard later became Sussex\\\\'s patron saint.\\\\n\\\\nIn 1450 Adam Moleyns became the first and only bishop of Chichester to be assassinated. Troops had been gathered to send to the war in France, but bad weather delayed their departure, and troops raided several towns along the coast. Moleyns was sent to Portsmouth to pay troops their outstanding wages, but was beaten so severely by the mob of soldiers that he died.\\\\n\\\\nThere is very little evidence of Lollardy in Sussex in the 15th century. Only one person was burnt to death as a Lollard, Thomas Bageley. Goring argues that pockets of Lollardy existed in the High Weald for over a century before Henry VIII\\\\'s break with Rome. Lollards tended to congregate near diocesan boundaries so that they could flee across the boundary to safety. Reginald Pecock, bishop of Chichester from 1450–1459, was accused of heresy and only saved his life by privately and publicly renouncing his opinions.\\\\n\\\\nEarly modern\\\\nDuring this period Sussex has been described \\\"as an anomaly: a southern county with a religious dynamic more in keeping with those of the north, connected to the Continent as much as the rest of the country, an entity that resisted easy co-option into Elizabeth I\\\\'s \\\\'little Israel of England\\\\'.\\\" Rye was probably the most Protestant of all Sussex towns, gaining a reputation as a \\\\'godly commonwealth\\\\' well before the end of Henry VIII\\\\'s reign. There was also strong opposition to the imposition of mass by Mary I.\\\\n\\\\nThe Reformation\\\\n\\\\nAs in the rest of the country, the Church of England\\\\'s split with Rome during the reign of Henry VIII was felt in Sussex. In 1535, the king appointed Sir Thomas Cromwell as vicar-general. Cromwell visited Sussex later in 1535, as part of his national census of churches and monasteries. The census was intended to enable the more efficient taxing of church property. The following year, an Act was passed that decreed the dissolution of monasteries with an income of less than £200 per annum. This first phase was followed by the \\\"voluntary\\\" surrenders of the larger houses. Lewes Priory with Battle, was the first house in England, during the Dissolution, to surrender on a voluntary basis. The monks surrendered the house in November 1537 in return for either being given a small pension or a living as a priest. The site and possessions of Lewes Priory were granted to Henry VIII\\\\'s vicar-general, Thomas Cromwell, who passed Lewes Priory to his son, Gregory Cromwell. Sussex did not do too badly compared to the rest of the country, as it only had one person in 500 who was a member of a religious order, compared to the national average of one in 256.\\\\n \\\\nIn 1538 there was a royal order for the demolition of the shrine of St Richard of Chichester in Chichester Cathedral. Thomas Cromwell saying that there was \\\"a certain kind of idolatry about the shrine\\\".\\\\n\\\\nRichard Sampson, Bishop of Chichester, incurred the displeasure of Cromwell and ended up imprisoned in the Tower of London at the end of 1539. Sampson was released after Cromwell\\\\'s fall from favour and execution in 1540. Sampson then continued at the see of Chichester for a further two years. He was succeeded as Bishop of Chichester by George Day. Day opposed the changes, and incurred the displeasure of the royal commissioners, who promptly suspended him as Bishop and allowed him only to preach in his cathedral church.\\\\n\\\\nHenry VIII died in 1547; his son Edward VI continued on the path that his father had set. However his reign was only short-lived as he died after only six years.\\\\n\\\\nThe bishops of Chichester had not been in favour of the Reformation until the appointment of John Scory to the episcopate in 1552. During Henry VIII\\\\'s reign two of the canons of Chichester Cathedral had been executed for their opposition to the Reformation, and during Edward VI\\\\'s reign George Day was ultimately imprisoned for his opposition to the reforms.\\\\n\\\\nReign of Mary I\\\\nThere had been twenty years of religious reform when the Catholic, Mary Tudor succeeded to the throne of England in 1553. Mary expected her clergy to be unmarried, so Bishop Scory thought it prudent to retire as he was a married man, and George Day was released and restored to the see of Chichester.\\\\n\\\\nMary\\\\'s persecution of Protestants earned her the nickname \\\"Bloody Mary\\\". Nationally about 288 Protestants were burnt at the stake during her reign, including 41 in Sussex. Most of the executions in Sussex were at Lewes. Of these 41 burnings, 36 can be identified to have come from specific parishes, and the place of execution is known for 27 of them; because the details of the executions were recorded in the Book of Martyrs by John Foxe, published in 1563. Martyrs included Deryck Carver, a French-speaking Flemish man who had sought refuge in Brighton from persecution for his Calvinist beliefs; and Richard Woodman, an ironmaster from Buxted. There are Bonfire Societies in Sussex that still remember the 17 Protestant martyrs that burned in Lewes High Street, and in Lewes itself they have a procession of martyrs\\\\' crosses during the bonfire night celebration. According to Quinn, the authorities in Sussex during Mary\\\\'s reign were rather less bloodthirsty than is generally assumed, often allowing their opponents to slip the noose when they could. Carver\\\\'s meetings had been attended by many fishermen from both England and France, beginning the tradition of French Christian worship in Brighton.\\\\n\\\\nThere was a range of Protestant beliefs in Sussex during the reign of Queen Mary. Sussex\\\\'s proximity to the Continent left it particularly exposed to European Protestantism, while its proximity to large parts of the Weald also left it open to pre-Reformation Protestantism. This was particularly so in the east of the county, with its trade links to Protestant areas of northern Europe and it covering a large part of the Weald, as well as being close to the Kentish border.\\\\n\\\\nReign of Elizabeth I\\\\nWhen Mary died in 1558, she was replaced by her Protestant sister Elizabeth I. Elizabeth re-established the break with Rome when she passed the 1559 Acts of Supremacy and Uniformity: the clergy were expected to take statutory oaths, and those that did not were deprived of their living. In the county nearly half the cathedral clergy and about 40% of the parish clergy had to be replaced, although some of the vacancies were due to ill health or death.\\\\n\\\\nA case can be made for the Reformation as a religious phenomenon only arriving in Sussex with Bishop Richard Curteys from 1570. In the west, Curteys\\\\' reforms were hampered by the noble Catholic families, and in the east by more radical forms of Protestantism. Until then the loyal but conservative bishops Sherborne, Sampson and Day did not appear to enforce doctrinal orthodoxy. Through the influence of Richard Curteys, the Reformation in Sussex took on a Puritan tone from the 1570s and a tradition of \\\\'radical parochialism\\\\' developed with well-educated preachers supporting ministers, often sponsored by Puritan landowners. Curteys circumvented the existing clergy by bringing in \\\\'lecturers\\\\' or unbeneficed clergy who provided a new preaching tradition, and also gathered some existing clergy who were sympathetic to his aims. This was particularly strong in the Lewes area, in part because of its European trade links.\\\\n\\\\nDuring the 1570s Puritan Christian names like \\\"Feregod\\\" became common in the Weald. Far from the seat of the Bishop of Chichester, radical towns like Rye and Lewes became \\\"free-thinking\\\" Protestant towns, and numbers of Protestants increased, with Huguenots seeking refuge after the St Bartholomew\\\\'s Day massacre in France. In the 1560s and 1570s, there was a trend for giving Puritan children \\\"godly\\\" names, especially in East Sussex, signifying a Puritan counter-culture. Eighteen parishes in the east of Sussex record Puritan names, the highest concentration of which was in Warbleton, where around half the children were given Puritan names between 1587 and 1590. Such Puritan names included \\\"Be-courteous Cole\\\" (in Pevensey), \\\"Safely-on-High Snat\\\" (in Uckfield) and \\\"Fight-the-Good-Fight-of-Faith White\\\" (in Ewhurst. One child with a Puritan name, Accepted Frewen, later became Archbishop of York. Many Sussex Puritans emigrated across the Atlantic Ocean to New England, accounting for about 1% of New England\\\\'s immigrants. Puritan migrants from other English regions, such as East Anglia, had much lower usage of hortatory names, and Puritans in the US state of Massachusetts followed the East Anglian rather than the Sussex naming custom.\\\\n\\\\nIn the late 16th century, Sussex was a complicated and divided region. The countryside was largely Catholic, dominated by the ancient Catholic families: the Howards at Arundel, the Percys at Petworth House, the Gages at Firle, the Brownes (the Lords Montague) at Cowdray Park, the Palmers at Parham House, as well as other minor dynasties like the Carylls, Lewkenors, Shelleys and Kemps. At the start of Elizabeth\\\\'s reign all six of Sussex\\\\'s noble families were Catholic. The towns, including Rye and Lewes, were more likely to be controlled by Protestants if not Protestant in orientation. The Earl of Arundel, Henry FitzAlan had considerable influence as Lord Steward of the Royal Household, privy councillor and Lord Lieutenant of Sussex (1559-1569) until he was involved in the Ridolfi plot to marry his son-in-law, Thomas, Duke of Norfolk, to Mary Queen of Scots. Even after the 1580s when restrictions on Catholics were imposed, Sussex continued to be led by Catholic peers. The office of sheriff of Sussex was held by Catholics eleven times between 1558 and 1603.\\\\n\\\\nAt the end of Elizabeth\\\\'s reign, Catholicism continued to be tolerated. On the death of her husband, Lady Montague withdrew to Battle Abbey, the family\\\\'s seat in the east of the county. The establishment of what became known as \\\"Little Rome\\\" became a focal point for the local Catholic community, with as many as 120 people attending Mass. This shows that long-standing political loyalty by Catholics was repaid by a form of toleration.\\\\n\\\\nThe Catholic Sussex families which suffered imprisonment or financial ruin at this time were mostly those that were involved in conspiracies against Elizabeth. After the uprising of 1569, the eighth Earl of Northumberland was effectively sent into internal exile in Sussex, at his home at Petworth House. After 1577, central authorities mounted on a growing attack on Catholic recusants, forcing them to abandon apparent conformity at a greater cost. Fines for non-attendance at an Anglican church were increased from 12d per week to 20 pounds per month. In 1580 leading Sussex Catholics including John Gage of Firle and Richard Shelley of Warminghurst were imprisoned for recusancy and continued to pay the taxes and fines demanded. In 1583 Charles Paget was smuggled into England, meeting William Shelley at Patching to discuss a plan to land Spanish, German and Italian troops in Sussex and march to Petworth House, the home of Northumberland, and Arundel Castle, while a second force would land in Lancashire and be joined by an uprising of English Catholics. Shelley\\\\'s and Northumberland\\\\'s actions reveal there was some truth in the suspicions directed against Sussex Catholics.\\\\n\\\\nWith further legislation in the 1580s, Sussex Catholics caught harbouring priests were guilty of treason. Significantly, no member of the Sussex gentry or nobility was ever charged under these laws, and neither was there ever any uprising, even though there was a significant Catholic community in Sussex. In this, the west of Sussex was out of step with the rest of England, just as attempts to impose a \\\"Godly magistracy\\\" in Rye in the east of the county was out of step with the rest of Protestant England. During this period Sussex was often different from the rest of England, with east and west of the county often inversions of each other. West Grinstead Park, home of the Caryll family, became a Roman Catholic mission where priests arrived, generally at night up the River Adur to await \\\"posting\\\". he River Adur was extensively used by the many Catholics travelling covertly between London and the Continent. Thomas Pilchard was executed in 1587 for being a priest and Edward Shelley of Warminghurst died at Tyburn in London in 1588 for hiding a priest. In 1588 two Catholic priests, Ralph Crockett and Edward James, were arrested at Arundel Haven (now Littlehampton), taken to London and executed outside Chichester. Philip Howard, 20th Earl of Arundel, who was canonised in 1970 as one of the Forty Martyrs of England and Wales, spent much of his life at his family home of Arundel Castle. From a family of Catholic recusants, Howard was imprisoned in the Tower of London for leaving the country without the permission of Queen Elizabeth. He died there ten years later. Early in the 17th century, Bosham-born Benedictine priest, George Gervase, was executed in London.\\\\n\\\\n17th century\\\\n\\\\nIn the 17th century, the diocese of Chichester was home to several Arminian bishops, including Bishops Andrews, Harsnett, Montagu, Duppa and King.\\\\n\\\\nIn the 1620s and 1630s many communities had licensed preachers. Lectureships at Rye, Lewes, Horsham and Midhurst extended preaching to the towns with the full support of the local gentry. From this time, Sabbatarianism gained ground with suppression of games and disorder. Bishop Montagu put forward extreme views against Puritanism and stressed the importance of ritual. Anthony Stapley, chairman of the Michaelmas quarter sessions in Sussex, was persuaded by Puritans to develop a harangue against the bishops in 1639, and in 1641 Stapley and Thomas Pelham petitioned Parliament on this issue. Latent hostility towards Catholics increased; and although Sussex contained as large a proportion of recusant households as many of the northern counties, few Catholic gentry in the county openly supported the king.\\\\n\\\\nThere were no battles of national significance in Sussex, during the 1642–1651 English civil war; however there were small sieges at Chichester and Arundel. The west of the county was generally royalist, although Chichester was for parliament and the east of the county, with some exceptions, was also for parliament. A few churches were damaged, particularly in the Arundel area. Also, after the surrender of Chichester, the Cathedral was sacked by Sir William Wallers parliamentary troops. Bruno Ryves, Dean of Chichester Cathedral said of the troops that \\\"they deface and mangle [the monuments] with their swords as high as they could reach\\\". He also complained that Waller\\\\'s troops...\\\\n\\\"... brake down the Organs and dashing the pipes with their Pole-axes...\\\"\\\\nMercurius Rusticus p. 139\\\\nDestruction of the cathedrals\\\\' music seems to have been one of the objectives, as Ryves also said, of Waller\\\\'s men, that...\\\\n\\\"they force open all the locks, either of doors or desks wherein the Singing-men laid up their Common-Prayer Books, their singing-Books, their Gowns and Surplesses they rent the Books in pieces and scatter the torn leaves all over the Church, even to the covering of the Pavement..\\\"\\\\nMercurius Rusticus p. 140\\\\n\\\\nIn 1643, Francis Bell, one of the priests at the Catholic mission in West Grinstead, was executed, along with other priests. The Caryll family were frequently persecuted and fined.\\\\n\\\\nDuring Cromwell\\\\'s interregnum, Rye stood out as a Puritan \\\\'Common Wealth\\\\', a centre of social experiment and rigorous public morality under vicar Joseph Beeton and his successor John Allen. The people of Rye seem in general to have ignored the strict sabbatarianism enforced by the constables, particularly where \\\\'immoderate drinking\\\\' was concerned.\\\\n\\\\nSussex Quakers and emigration to British North America\\\\n\\\\nAbout a quarter of the incumbents were forced from their parishes and replaced with Puritans. Many people turned away from the traditional churches and in 1655 George Fox founded the Society of Friends at Horsham. Quakerism emerged in Sussex in the 1650s, to be firmly suppressed by a gentry concerned about its revolutionary tendencies. In 1656, Thomas Haycock of Horsham became the first person in Sussex to be sent to gaol for their Quaker beliefs. William Penn lived in the county for a while; in 1676 he bought the estate of Warminghurst, near Steyning. In 1677 a huge open air meeting of Quakers was held at Penn\\\\'s home in Warminghurst in defiance of the law, with several hundred Quakers attending. Then in 1681 Charles II granted Penn lands in what became Pennsylvania and Delaware. Amongst those whom he carried to Pennsylvania as colonists were 200 people from Sussex. In 1682 Penn left the Kent port of Deal for the Province of Pennsylvania with about 100 passengers, mostly Quakers and mostly from Sussex. Quakers to leave Sussex for Pennsylvania included Samuel Carpenter who founded Horsham Township, Pennsylvania; and in 1677 William Clayton left for Pennsylvania, where his family founded with others a township they called Chichester, and opened the Chichester Friends Meetinghouse. Penn also created Sussex County and renamed the settlement of Hoernkills as Lewes.\\\\n\\\\nFollowing the Rye House Plot of 1683 a new wave of religious persecution swept across England. Until the passing of the Toleration Act received royal assent in 1689 Quakers in Sussex and elsewhere had suffered considerable persecution, many of whom were imprisoned in Horsham Jail. While living at Warminghurst, Penn too was persecuted for his Quaker faith. The 1684 Chichester Quarter Sessions recorded that William Penn \\\"being a factitious and seditious person doth frequently entertain and keep an unlawful assemblage and conventicle in his dwelling house at Warminghurst to the terror of the King\\\\'s liege people.\\\" Penn sold the estate, at Warminghurst, to a James Butler in 1707.\\\\n\\\\nThe Quakers in Sussex debated with Matthew Caffyn, a General Baptist preacher and writer, including George Fox and William Penn. There is a well-known account in 1655 when two Quakers from the north of England, Thomas Lawson and John Slee, disputed doctrine with Caffyn. As a result of their debates, Lawson produced a pamphlet entitled An Untaught Teacher Witnessed Against (1655) and Caffyn produced a pamphlet Deceived and Deceiving Quakers Discovered, Their Damnable Heresies, Horrid Blasphemies, Mockings, Railings (1656). in 1696, Caffyn\\\\'s increasingly radical, unorthodox beliefs caused a schism in the General Baptist Assembly, and its response to his changing theology was significant in the development of Unitarianism. The attorney-general of Rye, Samuel Jeake was exiled from the town after being found guilty of preaching under the Five Mile Act 1665. He was forced to remain outside of Rye until 1687 when the toleration which James II extended to Protestant dissenters enabled him to return to Rye.\\\\n\\\\nThe Restoration of the English monarchy began in 1660 under Charles II. It took over a year, after the restoration of Charles II in May 1660, for Chichester cathedral to get its choir back to full strength.\\\\n\\\\nIn the late 17th century, Sussex was a stronghold of the General Baptists.\\\\n\\\\nIn 1676 the Sussex parishes with the highest proportion of Catholics were almost entirely in the two most westerly Rapes of Chichester and Arundel: at least ten per cent of the population were Catholic in the parishes of Burton, Clapham, Coates, Midhurst, Racton, Shipley and Westfield.\\\\n\\\\nIn 1678 a former Hastings rector, Titus Oates fabricated the \\\"Popish Plot\\\", a supposed Catholic conspiracy to assassinate King Charles II and replace him with James (later James II). The plot led to the false implication, imprisonment and execution of William Howard. As a \\\\'Catholic of distinction\\\\' the seventh John Caryll from Sussex was imprisoned in the Tower of London but was let out on bail. Following the persecutions and executions that followed the Titus Oates plot, the death penalty for being a priest was removed. Instead, unscheduled fines were doubled and all remaining civil rights were removed from people keeping the Roman Catholic faith. At this stage, most Sussex Catholic families conformed to the Anglican church, except notably for the Caryll family. In 1688 the seventh John Caryll went into exile to Saint-Germain in France with James II as private secretary to James\\\\' queen, Mary of Modena.\\\\n\\\\nLate modern\\\\n\\\\n18th century\\\\nThere was a significant decline in non-conformity in Sussex in the early 18th century. Between 1676 and 1724 the strength of non-conformity in the county was reduced by at least one quarter. Around a third of the parishes in Sussex in 1724 had no dissenters. For instance in 1676, Horsham had over 100 non-conformists but by 1724 there were just 34.\\\\n\\\\nThe number of dissenters fell from 4,300 in 1676 to around 3,300 in 1724. In the 18th century, the Sussex grocer, Thomas Turner left a diary which suggests a high level of theological literacy amongst laypeople. At this time, the Sussex Weald and bordering towns such as Lewes were home to a number of fundamentalist sects. Cade Street Chapel in Heathfield was founded in 1769 for the followers of George Gilbert, who was popularly styled as \\\\'The Apostle of Sussex\\\\'. Gilbert also preached in surrounding villages, often with great hardship and difficulty: at Ticehurst he was pelted with stones when the bells rang; at Bexhill he was plastered from head to toe in filth, and a large drum was played to drown out the sound of his voice until a woman put a knife into the drum.\\\\n\\\\nUnder Caffyn\\\\'s guidance a General Baptist chapel was founded in Horsham in 1719, bringing together Baptists who had met in small house-groups in the town since 1669 or possibly as early as 1645. Worshippers from across northern Sussex came to this chapel; many were from the village of Billingshurst a few miles away. This group later became large enough to split from the Horsham congregation and establish a chapel in their home village.\\\\n\\\\nMethodist pioneers came to the Rape of Hastings in 1756, with John Wesley visiting Rye in 1758. Wesley\\\\'s last open air sermon was held in nearby Winchelsea in 1790. The Countess of Huntingdon\\\\'s Connexion\\\\'s first church was set up in 1761 in North Street, Brighton in what was originally Selina, Countess of Huntingdon\\\\'s garden.\\\\n\\\\nSussex had a significantly larger proportion of Catholics than other southern counties. Between 1715 and 1720, 8 per cent of the population of Sussex were registered as Catholic, a proportion more in common with counties north of a line from the River Severn to the Wash. John Baptist Caryll, the last of the Caryll family, was penalised for his Catholic faith and was forced in 1754 to sell his Sussex homes including that at West Grinstead. He endowed the Priest\\\\'s House to the Catholic Church via Lewes-born bishop Richard Challoner so that Catholic mass could be continued in the locality. When Challoner visited the West Grinstead Mission in 1741 he found 80 Catholics at Mass. Finally, history cannot forget the famous recusant, Maria Fitzherbert, who during this period secretly married the Prince of Wales, Prince Regent, and future George IV in 1785. The British Constitution, however, did not accept it and George IV later moved on. Cast aside by the establishment, she was adopted by the town of Brighton, whose citizens, both Catholic and Protestant, called her \\\"Mrs. Prince.\\\" According to journalist, Richard Abbott, \\\"Before the town had a [Catholic] church of its own, she had a priest say Mass at her own house, and invited local Catholics\\\", suggesting the recusants of Brighton were not very undiscovered.\\\\n\\\\n19th Century\\\\n\\\\nRoman Catholic Church\\\\nBrighton\\\\'s Roman Catholic community at the time of the Relief Act was small, but two factors caused it to grow in the 1790s. Many refugees from the French Revolution settled in Brighton after escaping from France; and Maria Fitzherbert, a twice-widowed Catholic, began a relationship with the Prince Regent (and secretly married him in 1785 in a ceremony which was illegal according to the Act of Settlement 1701 and the Royal Marriages Act 1772). She accompanied the Prince Regent whenever he visited Brighton, and had her own house (Steine House on Old Steine).\\\\n\\\\nThe first Catholic place of worship since the Reformation in Brighton was established above a shop in 1798; it was one of the earliest in Britain. In 1805 the priest in charge, a French émigré, started to raise money for a permanent building; a site on High Street, east of the Royal Pavilion and Old Steine, was found, and the Classical-style church was completed in 1807. It was demolished in 1981.\\\\n\\\\nIn 1818 the new rector, a friend of Maria Fitzherbert, wanted to extend the church. Mrs Fitzherbert donated £1,000 for this purpose, but before any action could be taken the events of 1829, when Catholic emancipation was fully achieved, encouraged Brighton\\\\'s Catholic community to seek a new site for a larger, more elaborate church. A piece of undeveloped land on the estate of the Marquess of Bristol was bought for £1,050, and William Hallett, later a mayor of Brighton, designed and built the new church of St John the Baptist. It was consecrated on 7 July 1835 and opened on 9 July 1835. Many of the 900 Catholic churches opened in England since the 1791 Roman Catholic Relief Act had not been consecrated by that stage, so St John the Baptist\\\\'s was only the fourth new church to be consecrated in England since the Reformation in the 16th century.\\\\n\\\\nFounded in 1873, St. Hugh\\\\'s Charterhouse, Parkminster is the first and only post-Reformation Carthusian monastery in the United Kingdom. In 1876 the Shrine Church of Our Lady of Consolation of West Grinstead was established, becoming the first Catholic shrine in honour of Mary to be established in England since the Reformation. Sussex was covered by the new Roman Catholic diocese of Southwark, created in 1850. New priests for the Catholic diocese of Southwark began to train at West Grinstead until they could move to a larger domestic property at Henfield. The diocese then moved its seminary to a purpose-built seminary in Surrey.\\\\n\\\\nNon-conformist churches\\\\nDespite Methodism\\\\'s early progress around Rye and Winchelsea in the Rape of Hastings, Methodism took longer to gain ground in the rest of Sussex. Methodism in the coastal towns of Sussex had a very unusual origin in that it was Methodists in the army who were the main or contributory founders of Methodism in towns from Chichester to Bexhill, including Lewes. Michael Hickman has argued that it was not until 1803 when Methodists and others in the army were allowed to worship freely on Sundays that Methodist soldiers could support or found Methodist societies in Sussex. 1805 saw the timber-framed Jireh Chapel open in Lewes, for Calvinist William Huntington whose tomb is at the rear of the chapel.\\\\n\\\\nThe General Baptist congregations at Billingshurst, Ditchling and Horsham gradually moved from General Baptist beliefs towards Unitarianism in the early 19th century.\\\\n\\\\nIn the mid 19th century John Sirgood founded the Society of Dependants at Loxwood in the north of the county. Nicknamed the \\\\'Cokelers\\\\' their beliefs were largely derived from Wesleyan Arminianism. They believed in the people\\\\'s ability to exercise free will and thereby achieve salvation rather than the Calvinistic assertion of predestination. They first established themselves at Loxwood because it was outside of the control of the large estates whose Anglican owners would have denied them land or premises. As well as at Loxwood, the Society of Dependants went on to found places of worship at Chichester, Hove, Northchapel and Warnham, as well as at three locations in Surrey.\\\\n\\\\n1851 census\\\\nIn 1851 the authorities organised a census of places of worship in England and Wales. The figures for Sussex indicated that there were more Anglican than non-conformist places of worship. In the neighbouring counties of Hampshire and Kent, there were more non-conformist places than Anglican.\\\\n\\\\nThe 1851 census shows that the Anglican church was particularly strong in the west of the county. These were areas where settlements were predominantly nucleated, with small parishes. Thakeham had the second highest rate of Anglicans in England (96% Anglican). Steyning, Petworth, Westhampnett and Westbourne were also over 80% Anglican. Anglican churches did well in the coastal towns including Brighton. In parts of the Sussex Weald the Anglican church had fewer churches than many other denominations, but not in terms of attendances at these churches.\\\\n\\\\nJust over 40% of the places of worship in Sussex in 1851 were non-conformist, mainly Independents, Wesleyan Methodists and Baptists. There were also smaller congregations of Catholics, Quakers, Countess of Huntingdon\\\\'s Connexion and Unitarians. Non-conformist chapels did well particularly in the Weald.\\\\n\\\\nOld dissent - dating back to Lollardy, such as Baptists, Unitarians and Quakers - remained more popular than new dissent and could be seen particularly in the Weald and on the Downs. It was particularly noticeable in the towns such as Brighton, Shoreham, Hastings and Rye. Some parts of Sussex were areas of strength for Baptists, but the west was an area of relative weakness. Overall in Sussex, Wesleyan Methodism had some of the fewest adherents in Sussex in all of England. However Wesleyan Methodism was strong in the rape of Hastings along the border with Kent; it was weakest in the county west of Eastbourne. Primitive Methodists were almost absent from Sussex.\\\\n\\\\nPrimitive Methodists were almost completely absent from Sussex. Of the 44 Sussex parishes with Catholics in 1676, only two, Arundel and Slindon, also had a Catholic place of worship in 1851.\\\\n\\\\nAnglo-Catholic reform in the Anglican Church and subsequent protest\\\\nIn the mid 19th century, divine Frederick William Robertson became well-known and preached at the Holy Trinity Church, Brighton.\\\\n\\\\nFormed in the 19th century, the cult of the Sussex martyrs was instigated at a time of the restoration of the Catholic hierarchy in England, bolstered by an increase in the Irish Catholic population, as well as the high-profile conversion to Catholicism of members of the Oxford movement, including Cardinal Newman and former Archdeacon of Chichester, Henry Edward Manning. Mark Antony Lower, an anti-Catholic propagandist and schoolmaster from Lewes, inaugurated the cult of the Sussex martyrs after the publication of his 1851 book The Sussex Martyrs to recall the dire actions of Catholicism in Sussex. Hostility to the Roman Catholic church, strong shortly after the Reformation had virtually died out by the early 19th century when religious tolerance was dominant mood. This began to change with the Evangelical Revival. The first Methodists to preach in Lewes were Calvinist Methodists, who saw the world as a sharp contrast between good and evil, God and the devil. The natural recipients of their negative projections were Catholics, who were becoming tolerated in England. More petitions were to come out of Lewes against Catholic emancipation that any other town in southern England. They came not from the old dissenters who favoured toleration but from the newly-formed Calvinist congregations. The local press in Lewes pandered to these prejudices. The introduction of ritualist practices in the Anglican church further increased anti-Catholic attitudes in Lewes.\\\\n\\\\nIn the mid 19th century the practice of burning an effigy of Pope Paul V at the Lewes Bonfire celebrations began. Paul V was a peaceable man who happened to be pope at the time of the Gunpowder Plot in 1605 and who cannot be held responsible for the Gunpowder Plot or the persecution of Protestants in the reign of Mary I, which were linked at this time by a misunderstanding of the past. In 1893 William Richardson, rector of the Southover district of Lewes, held sermons on the Sunday before 5 November warning about the perils of Catholicism. Many attendees were members of the newly-formed Orange Lodge in Lewes.\\\\n\\\\nAt the end of the 19th century and beginning of the 20th century, memorials were erected across Sussex and several other English counties to honour people burnt to death as heretics in the reigns of Henry VIII and Mary I. These were largely a reminder of religious divisions of more than three centuries earlier which seemed remote from the public preoccupations of the day. The actions could only be seen an anti-Catholic or at least anti-papal. Whilst moderate supporters did not wish to offend the Catholic community, a memorial in Heathfield read \\\"burnt to death at Lewes by the Roman Catholics\\\". These monuments did not commemorate the martyrdoms of Catholics or the Protestant opponents of state-imposed orthodoxy, except where they were erected by nonconformists. Anger was directed against the Anglo-Catholic community more than Catholics.\\\\n\\\\nIn the Anglican church in the 19th century, the role of ritual became subject of great, often heated, debate. In Brighton the Anglican church became influenced by the Oxford Movement, to an extent unparalleled elsewhere in the country apart from London. In Anglo-Catholic circles, Brighton became associated with London, as in the collective title of \\\"London-Brighton and South Coast Religion\\\", a play on the name of the main railway company in Victorian Sussex, the \\\"London, Brighton and South Coast Railway\\\". The railway, coincidentally or otherwise, linked all the large and growing centres of Anglo-Catholic worship spreading from London to Brighton and then east and west along coast of Sussex to the neighbouring counties of Kent and Hampshire. Anglo-Catholic priests in Brighton, included Henry Michell Wagner whose churches included St Paul\\\\'s Church and there was a powerful Protestant reaction including a riot in 1880. Brighton vicar Rev John Purchas was charged and ritualism spread to churches in Hastings and Worthing. Various militant Protestant groups formed branches and lodges across the county. Richard Enraght was also tried, arrested and imprisoned. The prolific Anglo-Catholic hymnologist John Mason Neale was attacked by a mob and hostile demonstrations ensued at East Grinstead.\\\\n\\\\nIn 1884 rioting ensued in Worthing, Eastbourne and Shoreham as mobs of people including members of the Skeleton Army reacted to Salvation Army criticism.\\\\n\\\\nContemporary Christianity\\\\n\\\\nChurch of England\\\\nIn the Church of England in Sussex, the administration of the diocese of Chichester which covers the county was changed in 1912. In addition to the existing archdeaconries of Chichester and Lewes that date from the 12th century, a third archdeaconry of Hastings was created. This structure remained in place until the archdeaconries were reorganised under Eric Kemp in 1975. The archdeaconry of Hastings was dissolved and merged back into the archdeaconry of Lewes, which was renamed the archdeaconry of Lewes and Hastings. A new archdeaconry was created in the north of the county - the archdeaconry of Horsham. This structure remained until 2014 when the archdeaconry of Hastings was recreated in the east of the county and the archeaconry of Lewes and Hastings was renamed the archdeaconry of Brighton and Lewes. The suffragan Bishop of Horsham oversees the archdeaconries of Chichester and Horsham, while the suffragan Bishop of Lewes oversees the archdeaconries of Brighton & Lewes and Hastings. The bishop of Chichester retains oversight over the entire diocese of Chichester i.e. all of Sussex.\\\\n\\\\nOn 16 November 2001, Pat Sinton, became the first woman priest in Sussex to be ordained. Sinton was ordained by John Hind, the bishop of Chichester, following the departure of the previous bishop of Chichester, Eric Kemp. Although Kemp had encouraged women to serve in the permanent diaconate in his diocese he had been an opponent of the ordination of women to the priesthood and women priests were not licensed in the Diocese of Chichester during his episcopate. In September 2014 Fiona Windsor was made archdeacon of Horsham, making her the first female archdeacon in Sussex. \\\\nThe Church of England in Sussex was damaged by sexual abuse scandals in the early 2000s.\\\\n\\\\nRoman Catholic Church\\\\nIn 1900 the Roman Catholic nun Maude Petre began a friendship with the Jesuit priest George Tyrell, which resulted in Petre building a cottage for Tyrell in the garden of her Storrington home. Both Petre and Tyrell were major figures in the Modernist controversy of the early 20th century. The Roman Catholic Diocese of Arundel and Brighton was formed in 1965 out of part of the diocese of Southwark. It includes Sussex and Surrey. In the early 2000s, the sexual abuse scandal in the Arundel and Brighton diocese hurt the public\\\\'s trust in the work of local diocesan officials.\\\\n\\\\nRelations with Sussex churches\\\\nAppointed as Bishop of Chichester in 1929, George Bell was a vocal supporter of the German resistance to Nazism and a pioneer of the Ecumenical Movement that aimed for greater co-operation between churches. Bell established in 1955 the first ever County Council of Churches in Sussex, since which similar structures have been formed in other parts of England.\\\\n\\\\nThere is a history of religious antagonism and anti-popery around the bonfire celebrations in Lewes. In the 1930s the mayor of Lewes requested that \\\\'no popery\\\\' banners be removed and an end to the burning of effigies of Pope Paul V. In the 1950s the Cliffe Bonfire Society was banned from the Bonfire Council from taking part in the United Grand Procession for its refusal to stop carrying a \\\\'no popery\\\\' banner and banners commemorating the 16th century Protestant martyrs burned at Lewes. In Lewes, women were to a significant degree responsible for using the spirit of ecumenism to build bridges between the denominations that had until then continued to be anti-Catholic. In 1984 Sussex church leaders were invited to Lewes to discuss Protestant-Catholic relations. Attendees included Eric Kemp, Bishop of Chichester, Peter Ball, suffragan Bishop of Lewes and Cormac Murphy-O\\\\'Connor, Bishop of Arundel and Brighton, as well as their equivalent positions in the Baptist, Methodist and United Reformed churches. In a historic gesture after the meeting the leaders walked to the Martyrs\\\\' memorial and prayed for peace and reconciliation. The owners of the memorial, associated with Jireh Chapel, subsequently threatened the intruders for trespassing. The LDCC later persuaded BBC to make a Songs of Praise TV programme in Lewes on the theme of religious tolerance, broadcast on 5 November 1989. To many though, the bonfire celebrations have lost much of their religious meaning, with many Catholics taking part. There are parallels with the carnival celebrations that took place across western Europe when the established order was turned upside down and the lord of misrule held sway for the day. In 1981 Ian Paisley visited Lewes on Bonfire Night and tried to fan the flames of conflict by handing out anti-Catholic pamphlets. His intervention back-fired and the following year he was burned in effigy. Today, anti-Catholic attitudes are rare and the militant Calvinism that continues in Northern Ireland is all but extinct in Lewes.\\\\n\\\\nIn the 21st century, controversy continues to be associated around the Bonfire societies and competing definitions of tradition and bigotry. For instance, the burning in effigy of Pope Paul V was described in 2012 as \\\"a scandalous piece of stone-cold bigotry\\\"\\\\n\\\\nOther Christian denominations\\\\nEstablished in 1971 the Anabaptist Bruderhof community was founded near Robertsbridge, the earliest such community remaining in Europe. From the 1980s, Sussex has three Greek Orthodox churches - at Brighton, Hastings and Eastbourne.\\\\n\\\\nFollowing the Second Sudanese Civil War, many refugees came to Brighton and Hove and neighbouring areas. Hove and Worthing are now home to Coptic Orthodox Churches, two of 28 such churches in the British Isles. The churches were visited in 2017 by Pope Tawadros II of Alexandria and Bishop Paula of Tanta. In 1998 the congregation at Jireh Chapel in Lewes took the decision to affiliate with the Free Presbyterian Church of Ulster. The church is one of seven such churches established in England.\\\\n\\\\nIn the Old Roman Catholic Church in Europe in 2012, Jerome Lloyd was made Metropolitan Archbishop of Selsey (officially \\\"Archbishop Metropolitan of the Isle of the Seals (Selsey) and the New Market of the Regnenses (i.e. of the Celtic tribe the Romans conquered in AD43, now called Chichester) in the Kingdom of the South Saxons (i.e. Sussex)\\\". Based in Brighton, the archbishop is one of a small number of priests who broadcast the traditional Mass in Latin live, via the internet and is the only priest to do so in Europe. The archbishop works on various projects to help homeless people in Brighton.\\\\n\\\\nThe turn of the 21st century saw the rise of so-called mega-churches and neo-charismatic and evangelical churches including Kingdom Faith in Horsham, set up by Colin Urquhart and the Newfrontiers group founded by Terry Virgo.\\\\n\\\\nCurrent and former places of worship\\\\nLists of all current and former places of worship in Sussex by district are as follows:\\\\n\\\\n Adur District\\\\n Arun District\\\\n Brighton and Hove\\\\n Chichester (current)\\\\n Chichester (former)\\\\n Crawley\\\\n Eastbourne\\\\n Hastings\\\\n Horsham District\\\\n Lewes District\\\\n Mid Sussex\\\\n Rother\\\\n Wealden (current)\\\\n Wealden (former)\\\\n Worthing\\\\n\\\\nSee also\\\\n History of Christianity in England\\\\n History of Sussex\\\\n Religion in Sussex\\\\n List of monastic houses in East Sussex\\\\n List of monastic houses in West Sussex\\\\n History of local government in Sussex\\\\n\\\\nBibliography\\\\n\\\\nReferences\\\\n\\\\nChristianity in Sussex\\\\nHistory of Sussex\\\\nHistory of Christianity in England'}]}\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset['dev'][0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each passage in the corpus has two parts: `docid` and `text`. `docid` has the form of `doc--`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = load_dataset('Shitao/MLDR', f\\\"corpus-{lang}\\\", trust_remote_code=True)['corpus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'docid': 'doc-en-9633',\\n\",\n \" 'text': 'Mars Hill Church was a Christian megachurch, founded by Mark Driscoll, Lief Moi, and Mike Gunn. It was a multi-site church based in Seattle, Washington and grew from a home Bible study to 15 locations in 4 U.S. states. Services were offered at its 15 locations; the church also podcast content of weekend services, and of conferences, on the Internet with more than 260,000 sermon views online every week. In 2013, Mars Hill had a membership of 6,489 and average weekly attendance of 12,329. Following controversy in 2014 involving founding pastor Mark Driscoll, attendance dropped to 8,0009,000 people per week.\\\\n\\\\nAt the end of September, 2014, an investigation by the church elders found \\\"bullying\\\" and \\\"patterns of persistent sinful behavior\\\" by Driscoll. The church elders crafted a \\\"restoration\\\" plan to help Driscoll and save the church. Instead, Driscoll declined the restoration plan and resigned. On October 31, 2014, lead pastor Dave Bruskas announced plans to dissolve the church\\\\'s 13 remaining campuses into autonomous entities, with the option of continuing, merging with other congregations, or disbanding, effective January 1, 2015. The Mars Hill network dissolved on January 1, 2015.\\\\n\\\\nHistory\\\\n\\\\nEarly years \\\\nMars Hill Church was founded in spring 1996 by Mark Driscoll, Lief Moi and Mike Gunn. The church started at the rental house of Driscoll and his wife Grace with the blessing of Antioch Bible Church and the exodus of about 30 of its students. They outgrew the apartment and started meeting in the youth rooms of another church. The church had its first official service October 1996, with 160 people attending; attendance quickly fell to around 60 because of discussions about the visions and mission of the church.\\\\n\\\\nIn the spring of 1997, the church expanded to two evening services. The transition to two different congregations resulted in some anxiety and stir by members who didn\\\\'t want the church to grow bigger, but it resulted in growing attendance. Later that same year Mark Driscoll was invited to speak at a pastors\\\\' conference in California. Driscoll\\\\'s speech influenced the emerging church movement, and changed the focus from reaching Generation X to reaching the postmodern world. The speech resulted in media coverage of Mars Hill Church and Mark Driscoll, and put Driscoll in connection with Leadership Network.\\\\n\\\\nThe church continued growing. Inspired by Alan Roxburgh, Driscoll settled on an emerging and missional ecclesiology, and a complementarian view on women in ministry. The church installed the first team of elders and they took over much of the work teaching classes, counseling and training new leaders. Furthermore, the church started a course for new members, called the Gospel Class, to ensure that members were focused on the mission of the church and that they agreed with the central doctrinal statements of the church. The class had been running every quarter since. In the fall of 1999 the church had grown to 350 in attendance every week and was able to pay Driscoll full-time. Prior to 1999, Driscoll operated as an unpaid pastor for three years.\\\\n\\\\nMultisite church \\\\n\\\\nIn 2003, Mars Hill Church moved into a renovated hardware store in the Ballard neighborhood of Seattle. In 2006, in an effort to reduce the overcrowding at its services, Mars Hill opened its first satellite campus in Shoreline. This change also marked their transition to a multi-site church, using video sermons and other multimedia improvements to the church\\\\'s web site to connect the campuses. Later in 2006 Mars Hill acquired two new properties in West Seattle and Wedgwood, which became their West Seattle and Lake City campuses.\\\\n\\\\nSince then, new Mars Hill locations were added using a multi-campus \\\"meta-church\\\" structure, connecting Driscoll\\\\'s sermons via high-definition video to the remote campuses during weekly worship services. This format allowed each location to retain local leadership and ministries while under the leadership of the main campus. A fourth and fifth Mars Hill location opened in 2007, and in 2008 a sixth location was added in downtown Seattle. A seventh campus, in Olympia, Washington, opened in Fall 2008 and an eighth campus, the first outside of Washington state, opened in Albuquerque, New Mexico in Fall 2009. The church launched four new churches on January 15 in Portland (Oregon), Rainier Valley (Seattle), Sammamish (near Seattle), and Orange County (California), the same day as the first sermon in the \\\"Real Marriage\\\" sermon series, based on Mark and Grace Driscoll\\\\'s book, Real Marriage.\\\\n\\\\nOn October 16, \\\"black-clad demonstrators\\\" gathered in front of the Mars Hill Church in Southeast Portland to \\\"protest the church\\\\'s stance on homosexuality.\\\" Approximately 20 protesters, \\\"some of whom wore kerchiefs to cover their faces, shouted profanities at adults and children,\\\" and briefly blocked the entrance of the church. Mars Hill Church Portland lead pastor Tim Smith expressed disagreement with the conduct of the protesters, but expressed defense of their right to free speech.\\\\n\\\\nIn 2008, the church launched an online community-building network, called The City, to improve communication on all levels in the church. The City was purchased by the Christian publishing brand, Zondervan, before Christmas 2008.\\\\n\\\\nGrowth and influence \\\\n\\\\nIn 2013, The Church Guide released a list of the \\\"Top Churches to Watch in America\\\". The link ranked churches according to how much churches could learn from the ranked churches on particular topics. They ranked Mars Hill Church as #3 to learn from about church growth, #3 for innovation, #2 for church planting, and #4 overall. The list considered data from Outreach magazine\\\\'s annual lists from 2004–2012 and other sources.\\\\n\\\\nIn 2006, Mars Hill Church claimed $31,110,000 in assets.\\\\n\\\\nActs 29 Church Planting Network \\\\n\\\\nActs 29 Church Planting Network is a separate 501(c)(3) from Mars Hill Church but was founded by Mars Hill in 2001. It is an interdenominational network of pastors and churches from around the world whose focus is to assess and equip qualified leaders, plant new churches, and rejuvenate declining churches. The current president of Acts 29 is Matt Chandler. The offices and leadership of Acts 29 moved from Mars Hill Church in Seattle to The Village Church in Texas in March 2012.\\\\nIn August 2014, Acts 29 removed Mark Driscoll and Mars Hill Church from the network.\\\\n\\\\nChurch leadership controversies\\\\n\\\\nDealing with dissent \\\\n\\\\nAs a result of the large growth of the church, its bylaws were rewritten more than once. The outcome of this process led to changes in leadership organization in November 2007. The new bylaws installed lead pastor Jamie Munson, preaching pastor Mark Driscoll, and pastors Scott Thomas and Tim Beltz as \\\"executive pastors\\\" who led the objectives of the church \\\"under the authority of the Board of Directors,\\\" on which the executive pastors also served as directors. This change precipitated the firing of two pastors.\\\\n\\\\nMars Hill leaders said in forum postings that one fired pastor was removed, in part, for \\\"displaying an unhealthy distrust in the senior leadership.\\\" They said the other was removed for \\\"disregarding the accepted elder protocol for the bylaw deliberation period\\\" and \\\"verbally attacking the lead pastor\\\"\\\\xa0— charges the fired pastor denied, the leaders added.\\\\n\\\\nChurch leadership instructed members of the congregation to shun the two former elders as unrepentant. Former Mars Hill Church elders and members have criticized the church for its harshness in dealing with dissent within its leadership. Additionally, members who have openly questioned or dissented with Mars Hill leaders have been asked to leave the church. This policy of church discipline was discussed during a lecture given on April 20, 2009 by Mark Driscoll for The Gospel Coalition.\\\\n\\\\nIn early 2012, the church once again became a source of controversy over shunning and disciplinary proceedings when a young man under discipline released documents from his disciplinary contract to blogger and author Mathew Paul Turner. The documents included a discipline contract and an email from church leaders to the congregation directing them to shun him.\\\\n\\\\nResultSource contract for the Real Marriage Book \\\\nOn March 5, 2014, evangelical magazine World published an article claiming that Mars Hill Church paid a $25,000 fee to marketing firm ResultSource, to manipulate sales numbers of Mark Driscoll\\\\'s book Real Marriage and thereby attain a place on the New York Times bestseller list. ResultSource accomplished this objective—the book briefly reached #1 in the \\\"Advice How-to\\\" category—by buying 11,000 copies of the book, using $210,000 of Mars Hill Church\\\\'s money, from a variety of online sources and payment methods.\\\\n\\\\nThe Evangelical Council for Financial Accountability stated that buying a place on bestseller lists violates its ethical standards, but that because this happened before Mars Hill Church joined they were unable to take action. Christianity Today described the arrangement as \\\"ethically questionable\\\", and Carl Trueman of religion journal First Things decried the revelation, writing, \\\"the overall picture is one of disaster\\\" and \\\"[it] has raised questions not simply about personal integrity but also the very culture of American Evangelicalism.\\\"\\\\n\\\\nDriscoll had used the apparent success of Real Marriage to negotiate a multi-book deal with Christian publisher Tyndale House. The first book under Driscoll\\\\'s \\\"Resurgence\\\" imprint was A Call to Resurgence, with plans to publish five to seven books per year. Tyndale House defended Driscoll\\\\'s alleged plagiarism in A Call to Resurgence, and affirmed their continuing relationship with Driscoll.\\\\n\\\\nMars Hill Church responded with a statement, writing, \\\"while not uncommon or illegal, this unwise strategy is not one we had used before or since, and not one we will use again.\\\" Mars Hill also claimed that the \\\"true cost\\\" of the effort was less than \\\"what has been reported.\\\"\\\\n\\\\nOn March 17, 2014, Driscoll posted an open letter of apology in response to this controversy and others, writing that he will no longer claim to be a New York Times bestselling author, and that he now sees the ResultSource marketing campaign as \\\"manipulating a book sales reporting system, which is wrong.\\\" He wrote that he was giving up his status as a \\\"celebrity pastor\\\", that he considered his \\\"angry young prophet\\\" days to be over, and that he was reducing his public presence in speaking engagements and on social media.\\\\n\\\\nOn March 28, 2015, Sutton Turner, a former elder of the church who signed the Result Source contract, explained that he disapproved of the marketing plan to use Result Source, but the decision to use it had already been made before he began work at Mars Hill, so he signed the contract anyway. Turner revealed that Driscoll had not been involved in initiating nor signing the contract with Result Source. Turner stated that the business relationship with the marketing firm was initiated by a pastor who resigned shortly thereafter, and remaining church leaders disagreed over the completion of the contract, stating that it would reflect badly on the church and Mark Driscoll.\\\\n\\\\nPlagiarism allegations \\\\nOn November 21, 2013, radio host Janet Mefferd accused Driscoll of plagiarism. Mefferd claimed that 14 pages of Driscoll\\\\'s book A Call to Resurgence quoted \\\"extensively and without citation\\\" from Peter Jones\\\\' 1999 book, Gospel Truth/Pagan Lies: Can You Tell the Difference? and Jones\\\\' 2010 book One or Two: Seeing a World of Difference. Driscoll\\\\'s publisher Tyndale House stated that they performed a \\\"thorough in-house review\\\" and disagreed that this was a case of plagiarism. Neil Holdway, a plagiarism expert with the American Copy Editors Society, concluded that \\\"Driscoll had not adequately indicated the extent to which he had borrowed Jones\\\\' work.\\\"\\\\n\\\\nMore allegations of plagiarism in other Driscoll works soon surfaced, including passages from a sermon series companion text, Trial: 8 Witnesses From 1&2 Peter, which were copied verbatim from passages written by David Wheaton in the New Bible Commentary. InterVarsity Press, publisher of the New Bible Commentary, stated that Driscoll failed to properly provide quotation or attribution for the material. The relevant passages were posted online. The allegations soon expanded to include claims that Driscoll used ghostwriters and researchers without giving them proper attribution. As of December 2013, neither Peter Jones, D.A. Carson, nor Janet Mefferd had made any further statements pertaining the case.\\\\n\\\\nSyndicator Salem Radio subsequently removed both the broadcast interview with Driscoll and associated materials from Mefferd\\\\'s program website and apologized for raising the matter in a broadcast interview. This attempt to shut down the story provoked the resignation of Mefferd\\\\'s producer, Ingrid Schlueter. In explaining her resignation, Schlueter wrote the following regarding herself and Mefferd:\\\\n\\\\nDriscoll apologized for \\\"mistakes\\\" related to the allegations in a statement released to The Christian Post on December 18, 2013. Mefferd eventually left Salem Radio in April 2015.\\\\n\\\\nMars Hill Global Fund \\\\nIn June 2014 an online petition asked Sutton Turner of Mars Hill Church and Dan Busby of the Evangelical Council for Financial Accountability where the money raised through Mars Hill Global Fund actually went. The church reported that \\\"Mars Hill Church began to use the term \\\\'Global Fund\\\\' to solicit gifts restricted for \\\\'capital development and expansion\\\\'. As communicated in the Global Newsletter on July 7, 2009, the Global Fund was used to raise resources for the following purposes: \\\\'start new Mars Hill campuses, plant new Acts 29 churches, and equip leaders at the Resurgence Training Center\\\\'. In the 2009-2011 time frame, over 80% of the funds given to the \\\"Global Fund\\\" went to Acts 29 church planting, with additional funds used for the Resurgence Training Center and church planting in India.\\\" Additionally, \\\"subsequent to June 1, 2012, in early July 2014, Mars Hill Church sent approximately 6,000 letters and 3,765 emails to individuals who had made gifts as a global donor subsequent to June 1, 2012. In these communications, Mars Hill Church offered to redirect the donor\\\\'s gifts, made as a global donor during this time period, specifically for planting churches in Ethiopia or India.\\\"\\\\n\\\\nFormer leaders and members protest Mark Driscoll (2014) \\\\nMichael Paulson, writing for The New York Times, wrote that while Driscoll had endured criticism from the American political left and liberal Christianity for many years, recent years leading up to and including 2014 saw the rise of criticism from conservative Christians, including Driscoll\\\\'s former \\\"allies and supporters.\\\" According to the Seattle Times, plagiarism accusations against Driscoll made by Janet Mefferd were a \\\"crucial turning point\\\" that drew outside interest into Mars Hill\\\\'s internal affairs, and prompted inquiries from new critics about the church and how it handled its finances. After hearing of Mefferd\\\\'s plagiarism accusations, evangelical Christian and Grove City College psychology professor Warren Throckmorton took interest and became a prominent critic of Driscoll and Mars Hill, documenting other examples of perceived plagiarism, abuse reported by former Mars Hill members, and questionable uses of church finances.\\\\n\\\\n\\\"Repentant Pastors\\\" \\\\nOn March 29, 2014, four former Mars Hill elders (Kyle Firstenberg, Dave Kraft, Scott Mitchell, and co-founder Lief Moi) created a blog titled \\\"Repentant Pastor\\\" and posted online \\\"confessions and apologies\\\" related to their leadership roles in Mars Hill. In a joint statement, they wrote, \\\"we recognize and confess that Mars Hill has hurt many people within the Mars Hill community, as well as those outside the community.\\\" Salon summarized the statements, writing that the former leaders emphasized their failures to \\\"rein Driscoll in\\\" and their complicity with Driscoll\\\\'s \\\"autocratic\\\" management style. Firstenberg wrote that while the church appeared to flourish, employees lived in constant stress, and \\\"success was to be attained regardless of human and moral cost.\\\"\\\\n\\\\nMegachurch pastors come to Driscoll\\\\'s defense \\\\nSeveral prominent pastors publicly defended Driscoll from allegations made against him. Those pastors included mega-church pastor Rick Warren, author of The Purpose Driven Life, and Gateway Church\\\\'s founding pastor Robert Morris. At the 2014 Gateway Conference, Morris told the audience that he counseled Mark Driscoll directly, and that media reports were largely untrue. Morris cited recent media reports of lead pastor Steven Furtick of Elevation Church as experiencing similar coverage. At the conference, Mark Driscoll was invited up to the stage where he told the audience that he received death threats and that his children allegedly had rocks thrown at them. Driscoll stated that \\\"I\\\\'m just trying to figure out how to be a good pastor to my family first.\\\"\\\\n\\\\nDriscoll addresses former members\\\\' complaints \\\\nIn a recorded message shown to church members on July 27, 2014, Driscoll discussed the various controversies of 2014. He said that he could \\\"not address some members\\\\' discontent ... because the complaints were anonymous.\\\" According to Rob Smith, former program director at the church, the anonymity assertion \\\"really touched a nerve\\\" with former members. In response, dissenters organized a Facebook group called \\\"Dear Pastor Mark & Mars Hill: We Are Not Anonymous.\\\"\\\\n\\\\nThe following Sunday, \\\"dozens of demonstrators\\\" organized and picketed the Mars Hill Church Bellevue campus (where Driscoll preached live), calling for Driscoll\\\\'s resignation. Demonstrators carried placards reading \\\"We Are Not Anonymous\\\" and \\\"Question Mark\\\", and accused Driscoll of bullying, misogyny, inadequate transparency in church finances, and harsh discipline of members. Driscoll was away for his annual summer vacation. A church elder, Anthony Iannicielo, responded that the criticism of Driscoll and Mars Hill \\\"goes with the territory\\\" of running a large church with a long history. In a pre-recorded message, Driscoll said that he had been deliberately \\\"rather silent\\\" during the criticism, that he found it \\\"a little overwhelming and a bit confusing\\\", and that he had no intention of resigning.\\\\n\\\\nRemoval from Acts 29 Network \\\\nOn August 8, 2014, the board of Acts 29 Network removed both Driscoll and Mars Hill Church from membership. Chairman Matt Chandler wrote, \\\"it is our conviction that the nature of the accusations against Mark, most of which have been confirmed by him, make it untenable and unhelpful to keep Mark [Driscoll] and Mars Hill [Church] in our network.\\\"\\\\xa0The board of directors of Acts 29 expressed gratitude for Driscoll\\\\'s work with the Network as co-founder and former President, but declared his recent actions \\\"ungodly and disqualifying behavior.\\\" To Driscoll, they wrote, \\\"our board and network have been the recipients of ... dozens of fires directly linked to you ... we are naturally associated with you and feel that this association discredits the network and is a major distraction.\\\" They further advised him to \\\"step down from ministry for an extended time and seek help.\\\"\\\\n\\\\nActs 29 had attempted to \\\"lean on\\\" the Mars Hill\\\\'s Board of Advisors and Accountability (BOAA) to discipline Driscoll, but lost confidence in the board. The BOAA had been set up by Driscoll as his accountability board, rather than the elders of the church. (Members of the BOAA were for the most part professional clergy and businessmen who were not members of the church and hand picked by Driscoll.) The previous month, evangelical leaders and Acts 29 associates Paul Tripp and James MacDonald resigned from the BOAA. Religion correspondent Sarah Pulliam Bailey described Acts 29\\\\'s decision as \\\"unusual\\\" since \\\"ministries usually leave matters of church discipline up to local churches.\\\"\\\\n\\\\nBOAA Chairman Michael Van Skaik responded, \\\"Men, I told the lead pastors ... that we are making real progress in addressing the serious reconciliation and unhealthy culture issues that have been part of Mars Hill Church for way too long. And we are. ... \\\" He further added that Acts 29 leaders did not contact Mars Hill before acting, and that Driscoll had \\\"changed his ways\\\", and described Acts 29\\\\'s actions as \\\"divisive.\\\" Van Skaik also addressed the formal charges brought against Driscoll under the Mars Hill bylaws, writing \\\"the formal charges that were filed were serious, were taken seriously, and were not dismissed by the board lightly.\\\"\\\\n\\\\nDriscoll\\\\'s hiatus from ministry \\\\nOn August 24, 2014, Driscoll announced he would take a six-week \\\"extended focus break\\\" from his pastorship while charges against him were investigated. Later that week, a letter signed by nine current Mars Hill pastors which severely criticized Driscoll was leaked to the public. The letter, written days before Driscoll stepped down, urged him to step down from all aspects of ministry. It included a quote from \\\"internationally recognized\\\" author, pastor and former BOAA member Paul Tripp saying, \\\"This is without a doubt, the most abusive, coercive ministry culture I\\\\'ve ever been involved with.\\\" One of the pastors who signed the letter was fired five days later for \\\"rebellion against the church.\\\" By September 9, eight of the nine pastors who signed the letter had resigned or been terminated, including worship director Dustin Kensrue. The last of the nine pastors was demoted from pastor to lay elder.\\\\n\\\\nStaff layoffs and closure of church branches \\\\n\\\\nOn September 7, 2014 (the second week of Driscoll\\\\'s hiatus), Mars Hill officials, citing \\\"financial pressures in the wake of recent negative media attention\\\", announced layoffs and closures of a few church branches. Weekly attendance at the start of the year for all branches was 12,000–13,000, but had dropped to 8,000–9,000. Donations also had a \\\"steep decline.\\\" In response, the church planned to lay off \\\"30 to 40 percent\\\" of their 100 paid staff members, and close their downtown Seattle branch and University District branch, consolidating both congregations into the Ballard location. Two other branches outside Washington state were marked for possible closure if their finances did not improve. Mars Hill also announced the resignation of Sutton Turner, executive elder since 2011, effective at the end of September 2014.\\\\n\\\\nDriscoll\\\\'s resignation \\\\nIn the fall of 2014, a group of elders released a report on an investigation into accusations of bullying and intimidating behavior by Driscoll made by 21 former church elders. The investigation involved \\\"some 1,000 hours of research, interviewing more than 50 people and preparing 200 pages of information.\\\" The report concluded that Driscoll had never been charged with \\\"immorality, illegality or heresy,\\\" and considered \\\"some of the accusations against Pastor Mark to be altogether unfair or untrue.\\\" Additionally, the report found that many of the \\\"other charges had previously been addressed by Pastor Mark, privately and publicly. Indeed, he had publicly confessed and apologized for a number of the charges against him, some of which occurred as long as 14 years ago.\\\" However, elders did find \\\"bullying\\\" and \\\"patterns of persistent sinful behavior\\\" by Driscoll. The Board also concluded that Driscoll had \\\"been guilty of arrogance, responding to conflict with a quick temper and harsh speech, and leading the staff and elders in a domineering manner\\\", but was not charged with anything immoral or illegal. Driscoll maintained that he had not disqualified himself from ministry.\\\\n\\\\nChurch leadership crafted a \\\"restoration\\\" plan to help Driscoll and save the church. Instead, Driscoll declined the restoration plan and resigned on October 14, 2014, citing concerns for his health and safety. His resignation came as a \\\"surprise\\\" to the church\\\\'s Board of Overseers, who said in a statement that they had not asked Driscoll for his resignation.\\\\n\\\\nIn 2015, after the disbanding of Mars Hill, an executive elder of the church stated that \\\"There has been much talk about the abusive and coercive culture at Mars Hill. What many people do not realize is that some of the very people who were calling for an end to this type of abuse were using abusive tactics.\\\" The executive elder stated that he was blackmailed by a staff who asked for more severance pay. He also stated that \\\"former Mars Hill elders were working to file formal charges against me also. I was told that a former lead pastor was approached to lead a group of people who hoped to force my resignation so that I \\\\'could not help Pastor Mark Driscoll\\\\'.\\\"\\\\n\\\\nPastor and theologian John Piper referred to the controversies and subsequent church closure as a \\\"Satanic victory.\\\"\\\\n\\\\nIt was a defeat for the gospel, it was a defeat for Mark [Driscoll], it was a defeat for evangelicalism, for Reformed Theology, for complementarianism ... It was a colossal Satanic victory.Driscoll\\\\'s resignation is thoroughly investigated in the podcast The Rise and Fall of Mars Hill.\\\\n\\\\nClosing \\\\nOn October 31, 2014, lead pastor Dave Bruskas announced plans to dissolve the church\\\\'s 13 remaining campuses into autonomous entities, with the option of continuing, merging with other congregations, or disbanding, effective January 1, 2015.\\\\n\\\\nOn December 28, 2014, Rick Warren gave the final Sunday sermon at Mars Hill, encouraging its remaining members to \\\"give grace\\\" to its leaders, \\\"You need to be grateful for all the ways that God used Mars Hill Church. Be grateful for all the ways God used Mark Driscoll.\\\" Driscoll had previously delivered a sermon at Saddleback Church the weekend Rick Warren grieved the loss of his son.\\\\n\\\\nThe Mars Hill Church network officially disbanded Thursday, January 1, 2015. Eleven of the Mars Hill Churches became independent churches and the remaining churches were dissolved. Prior to the churches disbanding, Mars Hill transferred the majority of its content from its website to where the church\\\\'s sermons remain. The Mars Hill website now contains a history of the church and a church directory of the previous Mars Hill churches locations with their new names and websites.\\\\n\\\\nPrior to disbanding on January 1, 2015, Mars Hill Church met at twelve locations, mostly in Seattle and Washington state, with three out of state locations in New Mexico, California, and Oregon. A few locations were closed or consolidated on October 12, 2014. After January 1, 2015, each church location dissolved into an independent congregation. The remaining members of Mars Hill Ballard reorganized as Cross and Crown Church Seattle, led by former Mars Hill Downtown pastor Matthias Haeusel at Mars Hill\\\\'s former Ballard location.\\\\n\\\\nIn February 2016, a federal racketeering lawsuit was filed by former Mars Hill members against both Mars Hill and Driscoll. That lawsuit was dismissed in November 2016 after the plaintiffs said they did not have the money to continue the suit. The plaintiffs\\\\' online fundraising campaign on GoFundMe had raised $34,660, which was approximately half of its goal.\\\\n\\\\nReferences\\\\n\\\\nFurther reading\\\\n\\\\n Pastor Dude\\\\'s Mega-Church Draws Crowds - ABC Nightline story about Mars Hill Church\\\\n Tempers Flare at Debate on the Devil - ABC Nightline debate at Mars Hill Church on the Devil\\\\n\\\\nExternal links\\\\n \\\\n\\\\n \\\\nEmerging church movement\\\\nEvangelical churches in Washington (state)\\\\nFormer megachurches\\\\nChurches in Seattle\\\\nChristian organizations established in 1996\\\\nReligious organizations disestablished in 2015'}\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"corpus[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we process the ids and text of queries and corpus for preparation of embedding and searching.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus_ids = corpus['docid']\\n\",\n \"corpus_text = corpus['text']\\n\",\n \"\\n\",\n \"queries_ids = dataset['dev']['query_id']\\n\",\n \"queries_text = dataset['dev']['query']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Evaluate from scratch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the demo we use bge-base-en-v1.5, feel free to change to the model you prefer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 60.08it/s]\\n\",\n \"pre tokenize: 100%|██████████| 782/782 [02:22<00:00, 5.50it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 782/782 [02:47<00:00, 4.66it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the embeddings: (200000, 768)\\n\",\n \"data type of the embeddings: float16\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',)\\n\",\n \" # query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\")\\n\",\n \"\\n\",\n \"# get the embedding of the queries and corpus\\n\",\n \"queries_embeddings = model.encode_queries(queries_text)\\n\",\n \"corpus_embeddings = model.encode_corpus(corpus_text)\\n\",\n \"\\n\",\n \"print(\\\"shape of the embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a Faiss index to store the embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 200000\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"# train and add the embeddings to the index\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Searching\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the Faiss index to search answers for each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 7/7 [00:01<00:00, 5.15it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"query_size = len(queries_embeddings)\\n\",\n \"\\n\",\n \"all_scores = []\\n\",\n \"all_indices = []\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, 32), desc=\\\"Searching\\\"):\\n\",\n \" j = min(i + 32, query_size)\\n\",\n \" query_embedding = queries_embeddings[i: j]\\n\",\n \" score, indice = index.search(query_embedding.astype(np.float32), k=100)\\n\",\n \" all_scores.append(score)\\n\",\n \" all_indices.append(indice)\\n\",\n \"\\n\",\n \"all_scores = np.concatenate(all_scores, axis=0)\\n\",\n \"all_indices = np.concatenate(all_indices, axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = {}\\n\",\n \"for idx, (scores, indices) in enumerate(zip(all_scores, all_indices)):\\n\",\n \" results[queries_ids[idx]] = {}\\n\",\n \" for score, index in zip(scores, indices):\\n\",\n \" if index != -1:\\n\",\n \" results[queries_ids[idx]][corpus_ids[index]] = float(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 Evaluating\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Process the qrels into a dictionary with qid-docid pairs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qrels_dict = {}\\n\",\n \"for data in dataset['dev']:\\n\",\n \" qid = str(data[\\\"query_id\\\"])\\n\",\n \" if qid not in qrels_dict:\\n\",\n \" qrels_dict[qid] = {}\\n\",\n \" for doc in data[\\\"positive_passages\\\"]:\\n\",\n \" docid = str(doc[\\\"docid\\\"])\\n\",\n \" qrels_dict[qid][docid] = 1\\n\",\n \" for doc in data[\\\"negative_passages\\\"]:\\n\",\n \" docid = str(doc[\\\"docid\\\"])\\n\",\n \" qrels_dict[qid][docid] = 0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, use [pytrec_eval](https://github.com/cvangysel/pytrec_eval) library to help us calculate the scores of selected metrics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {'NDCG@10': 0.35304, 'NDCG@100': 0.38694})\\n\",\n \"defaultdict(, {'Recall@10': 0.465, 'Recall@100': 0.625})\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pytrec_eval\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"ndcg_string = \\\"ndcg_cut.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"recall_string = \\\"recall.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"\\n\",\n \"evaluator = pytrec_eval.RelevanceEvaluator(\\n\",\n \" qrels_dict, {ndcg_string, recall_string}\\n\",\n \")\\n\",\n \"scores = evaluator.evaluate(results)\\n\",\n \"\\n\",\n \"all_ndcgs, all_recalls = defaultdict(list), defaultdict(list)\\n\",\n \"for query_id in scores.keys():\\n\",\n \" for k in [10,100]:\\n\",\n \" all_ndcgs[f\\\"NDCG@{k}\\\"].append(scores[query_id][\\\"ndcg_cut_\\\" + str(k)])\\n\",\n \" all_recalls[f\\\"Recall@{k}\\\"].append(scores[query_id][\\\"recall_\\\" + str(k)])\\n\",\n \"\\n\",\n \"ndcg, recall = (\\n\",\n \" all_ndcgs.copy(),\\n\",\n \" all_recalls.copy(),\\n\",\n \")\\n\",\n \"\\n\",\n \"for k in [10,100]:\\n\",\n \" ndcg[f\\\"NDCG@{k}\\\"] = round(sum(ndcg[f\\\"NDCG@{k}\\\"]) / len(scores), 5)\\n\",\n \" recall[f\\\"Recall@{k}\\\"] = round(sum(recall[f\\\"Recall@{k}\\\"]) / len(scores), 5)\\n\",\n \"\\n\",\n \"print(ndcg)\\n\",\n \"print(recall)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Evaluate using FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide independent evaluation for popular datasets and benchmarks. Try the following code to run the evaluation, or run the shell script provided in [example](../../examples/evaluation/mldr/eval_mldr.sh) folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"arguments = \\\"\\\"\\\"- \\\\\\n\",\n \" --eval_name mldr \\\\\\n\",\n \" --dataset_dir ./mldr/data \\\\\\n\",\n \" --dataset_names en \\\\\\n\",\n \" --splits dev \\\\\\n\",\n \" --corpus_embd_save_dir ./mldr/corpus_embd \\\\\\n\",\n \" --output_dir ./mldr/search_results \\\\\\n\",\n \" --search_top_k 1000 \\\\\\n\",\n \" --cache_path ./cache/data \\\\\\n\",\n \" --overwrite False \\\\\\n\",\n \" --k_values 10 100 \\\\\\n\",\n \" --eval_output_method markdown \\\\\\n\",\n \" --eval_output_path ./mldr/mldr_eval_results.md \\\\\\n\",\n \" --eval_metrics ndcg_at_10 \\\\\\n\",\n \" --embedder_name_or_path BAAI/bge-base-en-v1.5 \\\\\\n\",\n \" --devices cuda:0 cuda:1 \\\\\\n\",\n \" --embedder_batch_size 1024\\n\",\n \"\\\"\\\"\\\".replace('\\\\n','')\\n\",\n \"\\n\",\n \"sys.argv = arguments.split()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/root/anaconda3/envs/dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"initial target device: 100%|██████████| 2/2 [00:07<00:00, 3.54s/it]\\n\",\n \"pre tokenize: 100%|██████████| 98/98 [01:01<00:00, 1.58it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"pre tokenize: 100%|██████████| 98/98 [01:07<00:00, 1.44it/s]09it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 100%|██████████| 98/98 [01:22<00:00, 1.19it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 98/98 [01:23<00:00, 1.17it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [02:40<00:00, 80.21s/it] \\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 2.16it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 2.21it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [00:01<00:00, 1.13it/s]\\n\",\n \"Searching: 100%|██████████| 7/7 [00:01<00:00, 6.79it/s]\\n\",\n \"Qrels not found in ./mldr/data/en/dev_qrels.jsonl. Trying to download the qrels from the remote and save it to ./mldr/data/en.\\n\",\n \"Loading and Saving qrels: 100%|██████████| 200/200 [00:00<00:00, 598.03it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import HfArgumentParser\\n\",\n \"\\n\",\n \"from FlagEmbedding.evaluation.mldr import (\\n\",\n \" MLDREvalArgs, MLDREvalModelArgs,\\n\",\n \" MLDREvalRunner\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"parser = HfArgumentParser((\\n\",\n \" MLDREvalArgs,\\n\",\n \" MLDREvalModelArgs\\n\",\n \"))\\n\",\n \"\\n\",\n \"eval_args, model_args = parser.parse_args_into_dataclasses()\\n\",\n \"eval_args: MLDREvalArgs\\n\",\n \"model_args: MLDREvalModelArgs\\n\",\n \"\\n\",\n \"runner = MLDREvalRunner(\\n\",\n \" eval_args=eval_args,\\n\",\n \" model_args=model_args\\n\",\n \")\\n\",\n \"\\n\",\n \"runner.run()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"en-dev\\\": {\\n\",\n \" \\\"ndcg_at_10\\\": 0.35304,\\n\",\n \" \\\"ndcg_at_100\\\": 0.38694,\\n\",\n \" \\\"map_at_10\\\": 0.31783,\\n\",\n \" \\\"map_at_100\\\": 0.32469,\\n\",\n \" \\\"recall_at_10\\\": 0.465,\\n\",\n \" \\\"recall_at_100\\\": 0.625,\\n\",\n \" \\\"precision_at_10\\\": 0.0465,\\n\",\n \" \\\"precision_at_100\\\": 0.00625,\\n\",\n \" \\\"mrr_at_10\\\": 0.31783,\\n\",\n \" \\\"mrr_at_100\\\": 0.32469\\n\",\n \" }\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('mldr/search_results/bge-base-en-v1.5/NoReranker/EVAL/eval_results.json', 'r') as content_file:\\n\",\n \" print(content_file.read())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/4_Evaluation/utils/compute_metrics.py", "content": "\"\"\"\nRef: https://github.com/facebookresearch/contriever\n\"\"\"\nimport regex\nimport unicodedata\nfrom functools import partial\nfrom typing import List, Union\n\n\nclass SimpleTokenizer:\n ALPHA_NUM = r'[\\p{L}\\p{N}\\p{M}]+'\n NON_WS = r'[^\\p{Z}\\p{C}]'\n\n def __init__(self):\n \"\"\"\n Args:\n annotators: None or empty set (only tokenizes).\n \"\"\"\n self._regexp = regex.compile(\n '(%s)|(%s)' % (self.ALPHA_NUM, self.NON_WS),\n flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE\n )\n\n def tokenize(self, text, uncased=False):\n matches = [m for m in self._regexp.finditer(text)]\n if uncased:\n tokens = [m.group().lower() for m in matches]\n else:\n tokens = [m.group() for m in matches]\n return tokens\n\n\ndef _normalize(text):\n return unicodedata.normalize('NFD', text)\n\n\ndef has_answer(answers, text, tokenizer) -> bool:\n \"\"\"Check if a document contains an answer string.\"\"\"\n text = _normalize(text)\n text = tokenizer.tokenize(text, uncased=True)\n\n for answer in answers:\n answer = _normalize(answer)\n answer = tokenizer.tokenize(answer, uncased=True)\n for i in range(0, len(text) - len(answer) + 1):\n if answer == text[i: i + len(answer)]:\n return True\n return False\n\n\ndef check_answer(example, tokenizer) -> List[bool]:\n \"\"\"Search through all the top docs to see if they have any of the answers.\"\"\"\n answers = example['answers']\n ctxs = example['ctxs']\n\n hits = []\n for i, text in enumerate(ctxs):\n if text is None: # cannot find the document for some reason\n hits.append(False)\n continue\n hits.append(has_answer(answers, text, tokenizer))\n return hits\n\n\ndef evaluate_qa_recall(ctxs, answers, k_values: Union[int, List[int]]=100):\n # compute Recall@k for QA task\n data = []\n assert len(ctxs) == len(answers)\n for i in range(len(ctxs)):\n _ctxs, _answers = ctxs[i], answers[i]\n data.append({\n 'answers': _answers,\n 'ctxs': _ctxs,\n })\n tokenizer = SimpleTokenizer()\n get_score_partial = partial(check_answer, tokenizer=tokenizer)\n\n scores = map(get_score_partial, data)\n\n n_docs = len(data[0]['ctxs'])\n top_k_hits = [0] * n_docs\n for question_hits in scores:\n best_hit = next((i for i, x in enumerate(question_hits) if x), None)\n if best_hit is not None:\n top_k_hits[best_hit:] = [v + 1 for v in top_k_hits[best_hit:]]\n\n if isinstance(k_values, int):\n k = min(k_values, len(top_k_hits))\n return top_k_hits[k - 1] / len(data)\n else:\n scores = []\n for k in k_values:\n k = min(k, len(top_k_hits))\n scores.append(top_k_hits[k - 1] / len(data))\n return scores\n" }, { "path": "Tutorials/4_Evaluation/utils/normalize_text.py", "content": "\"\"\"\nadapted from chemdataextractor.text.normalize\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nTools for normalizing text.\nhttps://github.com/mcs07/ChemDataExtractor\n:copyright: Copyright 2016 by Matt Swain.\n:license: MIT\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n'Software'), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\n#: Control characters.\nCONTROLS = {\n '\\u0001', '\\u0002', '\\u0003', '\\u0004', '\\u0005', '\\u0006', '\\u0007', '\\u0008', '\\u000e', '\\u000f', '\\u0011',\n '\\u0012', '\\u0013', '\\u0014', '\\u0015', '\\u0016', '\\u0017', '\\u0018', '\\u0019', '\\u001a', '\\u001b',\n}\n# There are further control characters, but they are instead replaced with a space by unicode normalization\n# '\\u0009', '\\u000a', '\\u000b', '\\u000c', '\\u000d', '\\u001c', '\\u001d', '\\u001e', '\\u001f'\n\n\n#: Hyphen and dash characters.\nHYPHENS = {\n '-', # \\u002d Hyphen-minus\n '‐', # \\u2010 Hyphen\n '‑', # \\u2011 Non-breaking hyphen\n '⁃', # \\u2043 Hyphen bullet\n '‒', # \\u2012 figure dash\n '–', # \\u2013 en dash\n '—', # \\u2014 em dash\n '―', # \\u2015 horizontal bar\n}\n\n#: Minus characters.\nMINUSES = {\n '-', # \\u002d Hyphen-minus\n '−', # \\u2212 Minus\n '－', # \\uff0d Full-width Hyphen-minus\n '⁻', # \\u207b Superscript minus\n}\n\n#: Plus characters.\nPLUSES = {\n '+', # \\u002b Plus\n '＋', # \\uff0b Full-width Plus\n '⁺', # \\u207a Superscript plus\n}\n\n#: Slash characters.\nSLASHES = {\n '/', # \\u002f Solidus\n '⁄', # \\u2044 Fraction slash\n '∕', # \\u2215 Division slash\n}\n\n#: Tilde characters.\nTILDES = {\n '~', # \\u007e Tilde\n '˜', # \\u02dc Small tilde\n '⁓', # \\u2053 Swung dash\n '∼', # \\u223c Tilde operator #in mbert vocab\n '∽', # \\u223d Reversed tilde\n '∿', # \\u223f Sine wave\n '〜', # \\u301c Wave dash #in mbert vocab\n '～', # \\uff5e Full-width tilde #in mbert vocab\n}\n\n#: Apostrophe characters.\nAPOSTROPHES = {\n \"'\", # \\u0027\n '’', # \\u2019\n '՚', # \\u055a\n 'Ꞌ', # \\ua78b\n 'ꞌ', # \\ua78c\n '＇', # \\uff07\n}\n\n#: Single quote characters.\nSINGLE_QUOTES = {\n \"'\", # \\u0027\n '‘', # \\u2018\n '’', # \\u2019\n '‚', # \\u201a\n '‛', # \\u201b\n\n}\n\n#: Double quote characters.\nDOUBLE_QUOTES = {\n '\"', # \\u0022\n '“', # \\u201c\n '”', # \\u201d\n '„', # \\u201e\n '‟', # \\u201f\n}\n\n#: Accent characters.\nACCENTS = {\n '`', # \\u0060\n '´', # \\u00b4\n}\n\n#: Prime characters.\nPRIMES = {\n '′', # \\u2032\n '″', # \\u2033\n '‴', # \\u2034\n '‵', # \\u2035\n '‶', # \\u2036\n '‷', # \\u2037\n '⁗', # \\u2057\n}\n\n#: Quote characters, including apostrophes, single quotes, double quotes, accents and primes.\nQUOTES = APOSTROPHES | SINGLE_QUOTES | DOUBLE_QUOTES | ACCENTS | PRIMES\n\ndef normalize_text(text: str):\n for control in CONTROLS:\n text = text.replace(control, '')\n text = text.replace('\\u000b', ' ').replace('\\u000c', ' ').replace(u'\\u0085', ' ')\n\n for hyphen in HYPHENS | MINUSES:\n text = text.replace(hyphen, '-')\n text = text.replace('\\u00ad', '')\n\n for double_quote in DOUBLE_QUOTES:\n text = text.replace(double_quote, '\"') # \\u0022\n for single_quote in (SINGLE_QUOTES | APOSTROPHES | ACCENTS):\n text = text.replace(single_quote, \"'\") # \\u0027\n text = text.replace('′', \"'\") # \\u2032 prime\n text = text.replace('‵', \"'\") # \\u2035 reversed prime\n text = text.replace('″', \"''\") # \\u2033 double prime\n text = text.replace('‶', \"''\") # \\u2036 reversed double prime\n text = text.replace('‴', \"'''\") # \\u2034 triple prime\n text = text.replace('‷', \"'''\") # \\u2037 reversed triple prime\n text = text.replace('⁗', \"''''\") # \\u2057 quadruple prime\n\n text = text.replace('…', '...').replace(' . . . ', ' ... ') # \\u2026\n\n for slash in SLASHES:\n text = text.replace(slash, '/')\n\n #for tilde in TILDES:\n # text = text.replace(tilde, '~')\n\n return text\n" }, { "path": "Tutorials/5_Reranking/5.1_Intro.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reranker is designed in cross-encoder architecture that takes the query and text at the same time and directly output their score of similarity. It is more capable of scoring the query-text relevance, but with the tradeoff of slower speed. Thus, a complete retrieval system usually contains retrievers in the first stage to do a large scope retrieval, and then followed by rerankers to rerank the results more precisely.\\n\",\n \"\\n\",\n \"In this tutorial, we will go through text retrieval pipeline with reranker and evaluate the results before and after reranking.\\n\",\n \"\\n\",\n \"Note: Steps 1-4 are identical to the tutorial of [evaluation](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/4_Evaluation). We suggest to first go through that if you are not familiar with retrieval.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Setup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download and preprocess the MS Marco dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"data = load_dataset(\\\"namespace-Pt/msmarco\\\", split=\\\"dev\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = np.array(data[:100][\\\"query\\\"])\\n\",\n \"corpus = sum(data[:5000][\\\"positive\\\"], [])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Embedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Inference Embeddings: 100%|██████████| 21/21 [01:59<00:00, 5.68s/it]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the corpus embeddings: (5331, 768)\\n\",\n \"data type of the embeddings: float32\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Indexing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 5331\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Retrieval\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_embeddings = model.encode_queries(queries)\\n\",\n \"ground_truths = [d[\\\"positive\\\"] for d in data]\\n\",\n \"corpus = np.asarray(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 1/1 [00:00<00:00, 22.35it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"res_scores, res_ids, res_text = [], [], []\\n\",\n \"query_size = len(query_embeddings)\\n\",\n \"batch_size = 256\\n\",\n \"# The cutoffs we will use during evaluation, and set k to be the maximum of the cutoffs.\\n\",\n \"cut_offs = [1, 10]\\n\",\n \"k = max(cut_offs)\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, batch_size), desc=\\\"Searching\\\"):\\n\",\n \" q_embedding = query_embeddings[i: min(i+batch_size, query_size)].astype(np.float32)\\n\",\n \" # search the top k answers for each of the queries\\n\",\n \" score, idx = index.search(q_embedding, k=k)\\n\",\n \" res_scores += list(score)\\n\",\n \" res_ids += list(idx)\\n\",\n \" res_text += list(corpus[idx])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Reranking\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we will use a reranker to rerank the list of answers we retrieved using our index. Hopefully, this will lead to better results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following table lists the available BGE rerankers. Feel free to try out to see their differences!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Description | Base Model |\\n\",\n \"|:-------|:--------:|:----:|:-----------------:|:--------------------------------------:|\\n\",\n \"| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | 568M | a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | XLM-RoBERTa-Large |\\n\",\n \"| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | 2.51B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | Gemma2-2B |\\n\",\n \"| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | 2.72B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | MiniCPM |\\n\",\n \"| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | 9.24B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. | Gemma2-9B |\\n\",\n \"| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | 560M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Large |\\n\",\n \"| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | 278M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Base |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's use a small example to see how reranker works:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-9.474676132202148, -2.823843240737915, 5.76226806640625]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagReranker\\n\",\n \"\\n\",\n \"reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) \\n\",\n \"# Setting use_fp16 to True speeds up computation with a slight performance degradation\\n\",\n \"\\n\",\n \"# use the compute_score() function to calculate scores for each input sentence pair\\n\",\n \"scores = reranker.compute_score([\\n\",\n \" ['what is panda?', 'Today is a sunny day'], \\n\",\n \" ['what is panda?', 'The tiger (Panthera tigris) is a member of the genus Panthera and the largest living cat species native to Asia.'],\\n\",\n \" ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']\\n\",\n \" ])\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, let's use the reranker to rerank our previously retrieved results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_ids, new_scores, new_text = [], [], []\\n\",\n \"for i in range(len(queries)):\\n\",\n \" # get the new scores of the previously retrieved results\\n\",\n \" new_score = reranker.compute_score([[queries[i], text] for text in res_text[i]])\\n\",\n \" # sort the lists of ids and scores by the new scores\\n\",\n \" new_id = [tup[1] for tup in sorted(list(zip(new_score, res_ids[i])), reverse=True)]\\n\",\n \" new_scores.append(sorted(new_score, reverse=True))\\n\",\n \" new_ids.append(new_id)\\n\",\n \" new_text.append(corpus[new_id])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For details of these metrics, please check out the tutorial of [evaluation](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/4_Evaluation).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.1 Recall\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_recall(preds, truths, cutoffs):\\n\",\n \" recalls = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" recall = np.intersect1d(truth, text[:c])\\n\",\n \" recalls[i] += len(recall) / max(min(len(recall), len(truth)), 1)\\n\",\n \" recalls /= len(preds)\\n\",\n \" return recalls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Before reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1:\\t0.97\\n\",\n \"recall@10:\\t1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls_init = calc_recall(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"recall@{c}:\\\\t{recalls_init[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1:\\t0.99\\n\",\n \"recall@10:\\t1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls_rerank = calc_recall(new_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"recall@{c}:\\\\t{recalls_rerank[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.2 MRR\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def MRR(preds, truth, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, t in zip(preds, truth):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, p in enumerate(pred):\\n\",\n \" if j < c and p in t:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Before reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1:\\t0.97\\n\",\n \"MRR@10:\\t0.9825\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr_init = MRR(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"MRR@{c}:\\\\t{mrr_init[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1:\\t0.99\\n\",\n \"MRR@10:\\t0.995\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr_rerank = MRR(new_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"MRR@{c}:\\\\t{mrr_rerank[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.3 nDCG\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Before reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.97\\n\",\n \"nDCG@10: 0.9869253606521631\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import ndcg_score\\n\",\n \"\\n\",\n \"pred_hard_encodings = []\\n\",\n \"for pred, label in zip(res_text, ground_truths):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings.append(pred_hard_encoding)\\n\",\n \"\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings, res_scores, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.99\\n\",\n \"nDCG@10: 0.9963092975357145\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_hard_encodings_rerank = []\\n\",\n \"for pred, label in zip(new_text, ground_truths):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings_rerank.append(pred_hard_encoding)\\n\",\n \"\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings_rerank, new_scores, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/5_Reranking/5.2_BGE_Reranker.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like embedding models, BGE has a group of rerankers with various sizes and functionalities. In this tutorial, we will introduce the BGE rerankers series.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. bge-reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first generation of BGE reranker contains two models:\\n\",\n \"\\n\",\n \"| Model | Language | Parameters | Description | Base Model |\\n\",\n \"|:-------|:--------:|:----:|:-----------------:|:--------------------------------------:|\\n\",\n \"| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | 278M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Base |\\n\",\n \"| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | 560M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Large |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[7.984375, -6.84375, -7.15234375, 5.44921875]\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagReranker\\n\",\n \"\\n\",\n \"model = FlagReranker(\\n\",\n \" 'BAAI/bge-reranker-large',\\n\",\n \" use_fp16=True,\\n\",\n \" devices=[\\\"cuda:0\\\"], # if you don't have GPUs, you can use \\\"cpu\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"pairs = [\\n\",\n \" [\\\"What is the capital of France?\\\", \\\"Paris is the capital of France.\\\"],\\n\",\n \" [\\\"What is the capital of France?\\\", \\\"The population of China is over 1.4 billion people.\\\"],\\n\",\n \" [\\\"What is the population of China?\\\", \\\"Paris is the capital of France.\\\"],\\n\",\n \" [\\\"What is the population of China?\\\", \\\"The population of China is over 1.4 billion people.\\\"]\\n\",\n \"]\\n\",\n \"\\n\",\n \"scores = model.compute_score(pairs)\\n\",\n \"scores\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. bge-reranker v2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Description | Base Model |\\n\",\n \"|:-------|:--------:|:----:|:-----------------:|:--------------------------------------:|\\n\",\n \"| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | 568M | a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | XLM-RoBERTa-Large |\\n\",\n \"| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | 2.51B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | Gemma2-2B |\\n\",\n \"| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | 2.72B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | MiniCPM |\\n\",\n \"| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | 9.24B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. | Gemma2-9B |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2-m3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2-m3 is trained based on bge-m3, introducing great multi-lingual capability as keeping a slim model size.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.003483424193080668]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagReranker\\n\",\n \"\\n\",\n \"# Setting use_fp16 to True speeds up computation with a slight performance degradation (if using gpu)\\n\",\n \"reranker = FlagReranker('BAAI/bge-reranker-v2-m3', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"score = reranker.compute_score(['query', 'passage'])\\n\",\n \"# or set \\\"normalize=True\\\" to apply a sigmoid function to the score for 0-1 range\\n\",\n \"score = reranker.compute_score(['query', 'passage'], normalize=True)\\n\",\n \"\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2-gemma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2-gemma is trained based on gemma-2b. It has excellent performances with both English proficiency and multilingual capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 5.29it/s]\\n\",\n \"You're using a GemmaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"100%|██████████| 1/1 [00:00<00:00, 45.99it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1.974609375]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagLLMReranker\\n\",\n \"\\n\",\n \"reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"score = reranker.compute_score(['query', 'passage'])\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2-minicpm-layerwise\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2-minicpm-layerwise is trained based on minicpm-2b-dpo-bf16. It's suitable for multi-lingual contexts, performs well in Both English and Chinese proficiency.\\n\",\n \"\\n\",\n \"Another special functionality is the layerwise design gives user freedom to select layers for output, facilitating accelerated inference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\\n\",\n \"You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"100%|██████████| 1/1 [00:00<00:00, 24.51it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-7.06640625]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import LayerWiseFlagLLMReranker\\n\",\n \"\\n\",\n \"reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"# Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\\n\",\n \"score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28])\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2.5-gemma2-lightweight\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2.5-gemma2-lightweight is trained based on gemma2-9b. It's also suitable for multi-lingual contexts.\\n\",\n \"\\n\",\n \"Besides the layerwise reduction functionality, bge-reranker-v2.5-gemma2-lightweight integrates token compression capabilities to further save more resources while maintaining outstanding performances.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 4/4 [00:01<00:00, 3.60it/s]\\n\",\n \"You're using a GemmaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"100%|██████████| 1/1 [00:00<00:00, 23.95it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[14.734375]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import LightWeightFlagLLMReranker\\n\",\n \"\\n\",\n \"reranker = LightWeightFlagLLMReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"# Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\\n\",\n \"score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparison\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE reranker series provides a great number of choices for all kinds of functionalities. You can select the model according your senario and resource:\\n\",\n \"\\n\",\n \"- For multilingual, utilize `BAAI/bge-reranker-v2-m3`, `BAAI/bge-reranker-v2-gemma` and `BAAI/bge-reranker-v2.5-gemma2-lightweight`.\\n\",\n \"\\n\",\n \"- For Chinese or English, utilize `BAAI/bge-reranker-v2-m3` and `BAAI/bge-reranker-v2-minicpm-layerwise`.\\n\",\n \"\\n\",\n \"- For efficiency, utilize `BAAI/bge-reranker-v2-m3` and the low layer of `BAAI/bge-reranker-v2-minicpm-layerwise`.\\n\",\n \"\\n\",\n \"- For saving resources and extreme efficiency, utilize `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`.\\n\",\n \"\\n\",\n \"- For better performance, recommand `BAAI/bge-reranker-v2-minicpm-layerwise` and B`AAI/bge-reranker-v2-gemma`.\\n\",\n \"\\n\",\n \"Make sure always test on your real use case and choose the one with best speed-quality balance!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/5_Reranking/5.3_Reranker_Eval.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate Reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reranker usually better captures the latent semantic meanings between sentences. But comparing to using an embedding model, it will take quadratic $O(N^2)$ running time for the whole dataset. Thus the most common use cases of rerankers in information retrieval or RAG is reranking the top k answers retrieved according to the embedding similarities.\\n\",\n \"\\n\",\n \"The evaluation of reranker has the similar idea. We compare how much better the rerankers can rerank the candidates searched by a same embedder. In this tutorial, we will evaluate two rerankers' performances on BEIR benchmark, with bge-large-en-v1.5 as the base embedding model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note: We highly recommend to run this notebook with GPU. The whole pipeline is very time consuming. For simplicity, we only use a single task FiQA in BEIR.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the required dependency\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. bge-reranker-large\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first model is bge-reranker-large, a BERT like reranker with about 560M parameters.\\n\",\n \"\\n\",\n \"We can use the evaluation pipeline of FlagEmbedding to directly run the whole process:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Split 'dev' not found in the dataset. Removing it from the list.\\n\",\n \"ignore_identical_ids is set to True. This means that the search results will not contain identical ids. Note: Dataset such as MIRACL should NOT set this to True.\\n\",\n \"pre tokenize: 100%|██████████| 57/57 [00:03<00:00, 14.68it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Inference Embeddings: 100%|██████████| 57/57 [00:44<00:00, 1.28it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 61.59it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 1/1 [00:00<00:00, 6.22it/s]\\n\",\n \"Searching: 100%|██████████| 21/21 [00:00<00:00, 68.26it/s]\\n\",\n \"pre tokenize: 0%| | 0/64 [00:00\\n\",\n \"{context}\\n\",\n \"\\n\",\n \"\\n\",\n \"Question: {input}\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"# Create a prompt template\\n\",\n \"prompt = ChatPromptTemplate.from_template(template)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now everything is ready. Assemble them to a chain and let the magic happen!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain.chains.combine_documents import create_stuff_documents_chain\\n\",\n \"from langchain.chains import create_retrieval_chain\\n\",\n \"\\n\",\n \"doc_chain = create_stuff_documents_chain(llm, prompt)\\n\",\n \"chain = create_retrieval_chain(retriever, doc_chain)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the following cell, we can see that the chatbot can answer the question correctly!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"M3-Embedding stands for a new embedding model that is distinguished for its versatility in multi-linguality, multi-functionality, and multi-granularity.\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = chain.invoke({\\\"input\\\": \\\"What does M3-Embedding stands for?\\\"})\\n\",\n \"\\n\",\n \"# print the answer only\\n\",\n \"print(response['answer'])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/6_RAG/6.3_RAG_LlamaIndex.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# RAG with LlamaIndex\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"LlamaIndex is a very popular framework to help build connections between data sources and LLMs. It is also a top choice when people would like to build an RAG framework. In this tutorial, we will go through how to use LlamaIndex to aggregate bge-base-en-v1.5 and GPT-4o-mini to an RAG application.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the required packages in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install llama-index-llms-openai llama-index-embeddings-huggingface llama-index-vector-stores-faiss\\n\",\n \"%pip install llama_index \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then fill the OpenAI API key below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# For openai key\\n\",\n \"import os\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"YOUR_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-M3 is a very powerful embedding model, We would like to know what does that 'M3' stands for.\\n\",\n \"\\n\",\n \"Let's first ask GPT the question:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"M3-Embedding stands for Multimodal Multiscale Embedding. It is a technique used in machine learning and data analysis to embed high-dimensional data into a lower-dimensional space while preserving the structure and relationships within the data. This technique is particularly useful for analyzing complex datasets that contain multiple modalities or scales of information.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from llama_index.llms.openai import OpenAI\\n\",\n \"\\n\",\n \"# non-streaming\\n\",\n \"response = OpenAI().complete(\\\"What does M3-Embedding stands for?\\\")\\n\",\n \"print(response)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By checking the description in GitHub [repo](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) of BGE-M3, we are pretty sure that GPT is giving us hallucination. Let's build an RAG pipeline to solve the problem!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, download BGE-M3 [paper](https://arxiv.org/pdf/2402.03216) to a directory, and load it through `SimpleDirectoryReader`. \\n\",\n \"\\n\",\n \"Note that `SimpleDirectoryReader` can read all the documents under that directory and supports a lot of commonly used [file types](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader/#supported-file-types).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from llama_index.core import SimpleDirectoryReader\\n\",\n \"\\n\",\n \"reader = SimpleDirectoryReader(\\\"data\\\")\\n\",\n \"# reader = SimpleDirectoryReader(\\\"DIR_TO_FILE\\\")\\n\",\n \"documents = reader.load_data()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `Settings` object is a global settings for the RAG pipeline. Attributes in it have default settings and can be modified by users (OpenAI's GPT and embedding model). Large attributes like models will be only loaded when being used.\\n\",\n \"\\n\",\n \"Here, we specify the `node_parser` to `SentenceSplitter()` with our chosen parameters, use the open-source `bge-base-en-v1.5` as our embedding model, and `gpt-4o-mini` as our llm.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from llama_index.core import Settings\\n\",\n \"from llama_index.core.node_parser import SentenceSplitter\\n\",\n \"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\\n\",\n \"from llama_index.llms.openai import OpenAI\\n\",\n \"\\n\",\n \"# set the parser with parameters\\n\",\n \"Settings.node_parser = SentenceSplitter(\\n\",\n \" chunk_size=1000, # Maximum size of chunks to return\\n\",\n \" chunk_overlap=150, # number of overlap characters between chunks\\n\",\n \")\\n\",\n \"\\n\",\n \"# set the specific embedding model\\n\",\n \"Settings.embed_model = HuggingFaceEmbedding(model_name=\\\"BAAI/bge-base-en-v1.5\\\")\\n\",\n \"\\n\",\n \"# set the llm we want to use\\n\",\n \"Settings.llm = OpenAI(model=\\\"gpt-4o-mini\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Indexing is one of the most important part in RAG. LlamaIndex integrates a great amount of vector databases. Here we will use Faiss as an example.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First check the dimension of the embeddings, which will need for initializing a Faiss index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"768\\n\"\n ]\n }\n ],\n \"source\": [\n \"embedding = Settings.embed_model.get_text_embedding(\\\"Hello world\\\")\\n\",\n \"dim = len(embedding)\\n\",\n \"print(dim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then create the index with Faiss and our documents. Here LlamaIndex help capsulate the Faiss function calls. If you would like to know more about Faiss, refer to the tutorial of [Faiss and indexing](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/3_Indexing).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"from llama_index.vector_stores.faiss import FaissVectorStore\\n\",\n \"from llama_index.core import StorageContext, VectorStoreIndex\\n\",\n \"\\n\",\n \"# init Faiss and create a vector store\\n\",\n \"faiss_index = faiss.IndexFlatL2(dim)\\n\",\n \"vector_store = FaissVectorStore(faiss_index=faiss_index)\\n\",\n \"\\n\",\n \"# customize the storage context using our vector store\\n\",\n \"storage_context = StorageContext.from_defaults(\\n\",\n \" vector_store=vector_store\\n\",\n \")\\n\",\n \"\\n\",\n \"# use the loaded documents to build the index\\n\",\n \"index = VectorStoreIndex.from_documents(\\n\",\n \" documents, storage_context=storage_context\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Retrieve and Generate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With a well constructed index, we can now build the query engine to accomplish our task:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_engine = index.as_query_engine()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following cell displays the default prompt template for Q&A in our pipeline:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Context information is below.\\n\",\n \"---------------------\\n\",\n \"{context_str}\\n\",\n \"---------------------\\n\",\n \"Given the context information and not prior knowledge, answer the query.\\n\",\n \"Query: {query_str}\\n\",\n \"Answer: \\n\"\n ]\n }\n ],\n \"source\": [\n \"# check the default promt template\\n\",\n \"prompt_template = query_engine.get_prompts()['response_synthesizer:text_qa_template']\\n\",\n \"print(prompt_template.get_template())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"(Optional) You could modify the prompt to match your use cases:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"You are a Q&A chat bot.\\n\",\n \"Use the given context only, answer the question.\\n\",\n \"\\n\",\n \"\\n\",\n \"{context_str}\\n\",\n \"\\n\",\n \"\\n\",\n \"Question: {query_str}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from llama_index.core import PromptTemplate\\n\",\n \"\\n\",\n \"template = \\\"\\\"\\\"\\n\",\n \"You are a Q&A chat bot.\\n\",\n \"Use the given context only, answer the question.\\n\",\n \"\\n\",\n \"\\n\",\n \"{context_str}\\n\",\n \"\\n\",\n \"\\n\",\n \"Question: {query_str}\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"new_template = PromptTemplate(template)\\n\",\n \"query_engine.update_prompts(\\n\",\n \" {\\\"response_synthesizer:text_qa_template\\\": new_template}\\n\",\n \")\\n\",\n \"\\n\",\n \"prompt_template = query_engine.get_prompts()['response_synthesizer:text_qa_template']\\n\",\n \"print(prompt_template.get_template())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, let's see how does the RAG application performs on our query!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"M3-Embedding stands for Multi-Linguality, Multi-Functionality, and Multi-Granularity.\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = query_engine.query(\\\"What does M3-Embedding stands for?\\\")\\n\",\n \"print(response)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"test\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/7_Fine-tuning/7.1.1_Data_preparation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Data Preparation for Fine-tuning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will show an example of the first step for fine-tuning: dataset preparation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install -U datasets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Suppose we are willing to fine-tune our model for financial tasks. We found an open-source dataset that could be useful: [financial-qa-10k](https://huggingface.co/datasets/virattt/financial-qa-10K). Let's see how to properly prepare our dataset for fine-tuning.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The raw dataset has the following structure:\\n\",\n \"- 5 columns of: 'question', 'answer', 'context', 'ticker', and 'filing'.\\n\",\n \"- 7000 rows.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Dataset({\\n\",\n \" features: ['question', 'answer', 'context', 'ticker', 'filing'],\\n\",\n \" num_rows: 7000\\n\",\n \"})\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"ds = load_dataset(\\\"virattt/financial-qa-10K\\\", split=\\\"train\\\")\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Data for Fine-tuning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Construct the dataset to the following format:\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"{\\\"query\\\": str, \\\"pos\\\": List[str], \\\"neg\\\":List[str], \\\"pos_scores\\\": List[int], \\\"neg_scores\\\": List[int], \\\"prompt\\\": str, \\\"type\\\": str}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. `pos_scores` is a list of scores corresponding to the query and pos, `neg_scores` is a list of scores corresponding to the `query` and `neg`, if you don't use knowledge distillation, it can be ignored. `prompt` is the prompt used for the query, it will cover query_instruction_for_retrieval. `type` is used for bge-en-icl, it includes `normal`, `symmetric_class`, `symmetric_clustering`, .etc. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We select the columns 'question' and 'context' as our query and answer(pos), and rename the columns. Then add the 'id' column for later evaluation use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\\n\",\n \" 'pos': 'Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.',\\n\",\n \" 'id': '0'}\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = ds.select_columns(column_names=[\\\"question\\\", \\\"context\\\"])\\n\",\n \"ds = ds.rename_column(\\\"question\\\", \\\"query\\\")\\n\",\n \"ds = ds.rename_column(\\\"context\\\", \\\"pos\\\")\\n\",\n \"ds = ds.add_column(\\\"id\\\", [str(i) for i in range(len(ds))])\\n\",\n \"ds[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Negative examples are important during the training of embedding models. Our initial dataset does not come with negative texts. Thus we directly sample a few from the whole corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Map: 100%|██████████| 7000/7000 [00:00<00:00, 22336.83 examples/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.random.seed(520)\\n\",\n \"neg_num = 10\\n\",\n \"\\n\",\n \"def str_to_lst(data):\\n\",\n \" data[\\\"pos\\\"] = [data[\\\"pos\\\"]]\\n\",\n \" return data\\n\",\n \"\\n\",\n \"# sample negative texts\\n\",\n \"new_col = []\\n\",\n \"for i in range(len(ds)):\\n\",\n \" ids = np.random.randint(0, len(ds), size=neg_num)\\n\",\n \" while i in ids:\\n\",\n \" ids = np.random.randint(0, len(ds), size=neg_num)\\n\",\n \" neg = [ds[i.item()][\\\"pos\\\"] for i in ids]\\n\",\n \" new_col.append(neg)\\n\",\n \"ds = ds.add_column(\\\"neg\\\", new_col)\\n\",\n \"\\n\",\n \"# change the key of 'pos' to a list\\n\",\n \"ds = ds.map(str_to_lst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lastly, we add the prompt which is used for query. It will be the `query_instruction_for_retrieval` during inference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"instruction = \\\"Represent this sentence for searching relevant passages: \\\"\\n\",\n \"ds = ds.add_column(\\\"prompt\\\", [instruction]*len(ds))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now a single row of the dataset is:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\\n\",\n \" 'pos': ['Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.'],\\n\",\n \" 'id': '0',\\n\",\n \" 'neg': ['Kroger expects that its value creation model will deliver total shareholder return within a target range of 8% to 11% over time.',\\n\",\n \" 'CSB purchased First Mortgages of $2.9 billion during 2023.',\\n\",\n \" 'See Note 13 to our Consolidated Financial Statements for information on certain legal proceedings for which there are contingencies.',\\n\",\n \" 'Diluted earnings per share were $16.69 in fiscal 2022 compared to $15.53 in fiscal 2021.',\\n\",\n \" 'In the year ended December 31, 2023, Total net sales and revenue increased primarily due to: (1) increased net wholesale volumes primarily due to increased sales of crossover vehicles and full-size pickup trucks, partially offset by decreased sales of mid-size pickup trucks; (2) favorable Price as a result of low dealer inventory levels and strong demand for our products; (3) favorable Mix associated with increased sales of full-size pickup trucks and full-size SUVs and decreased sales of vans, passenger cars and mid-size pickup trucks, partially offset by increased sales of crossover vehicles; and (4) favorable Other due to increased sales of parts and accessories.',\\n\",\n \" 'As of December 31, 2023, we had 3,157 full-time employees.',\\n\",\n \" 'Item 3. Legal Proceedings. The information contained in Note 18 ‘‘Commitments and Contingencies’’ included in Item 8 of this 10-K is incorporated herein by reference.',\\n\",\n \" 'Under the amended 2019 Secured Facility, the maturity date is set to July 20, 2026.',\\n\",\n \" 'Accounts receivable for Las Vegas Sands Corp. on December 31, 2023, totaled $685 million, with a provision for credit losses of $201 million, resulting in a net balance of $484 million.',\\n\",\n \" 'Operating expenses as a percentage of segment net sales decreased 25 basis points for fiscal 2023 when compared to the previous fiscal year, primarily driven by strong sales growth and lower incremental COVID-19 related costs, partially offset by increased wage costs.'],\\n\",\n \" 'prompt': 'Represent this sentence for searching relevant passages: '}\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we split the dataset into training set and testing set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"split = ds.train_test_split(test_size=0.1, shuffle=True, seed=520)\\n\",\n \"train = split[\\\"train\\\"]\\n\",\n \"test = split[\\\"test\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we are ready to store the data for later fine-tuning:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Creating json from Arrow format: 100%|██████████| 7/7 [00:00<00:00, 39.73ba/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"16583481\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train.to_json(\\\"ft_data/training.json\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Test Data for Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The last step is to construct the testing dataset for evaluaton.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Dataset({\\n\",\n \" features: ['query', 'pos', 'id', 'neg', 'prompt'],\\n\",\n \" num_rows: 700\\n\",\n \"})\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First select the columns for queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'id': '1289',\\n\",\n \" 'text': 'How does Starbucks recognize the interest and penalties related to income tax matters on their financial statements?'}\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"queries = test.select_columns(column_names=[\\\"id\\\", \\\"query\\\"])\\n\",\n \"queries = queries.rename_column(\\\"query\\\", \\\"text\\\")\\n\",\n \"queries[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then select the columns for corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = ds.select_columns(column_names=[\\\"id\\\", \\\"pos\\\"])\\n\",\n \"corpus = corpus.rename_column(\\\"pos\\\", \\\"text\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, make the qrels that indicating the relations of queries and corresponding corpus\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Flattening the indices: 100%|██████████| 700/700 [00:00<00:00, 180956.10 examples/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"{'qid': '1289', 'docid': '1289', 'relevance': 1}\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qrels = test.select_columns([\\\"id\\\"])\\n\",\n \"qrels = qrels.rename_column(\\\"id\\\", \\\"qid\\\")\\n\",\n \"qrels = qrels.add_column(\\\"docid\\\", list(test[\\\"id\\\"]))\\n\",\n \"qrels = qrels.add_column(\\\"relevance\\\", [1]*len(test))\\n\",\n \"qrels[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Store the training set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Creating json from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 210.42ba/s]\\n\",\n \"Creating json from Arrow format: 100%|██████████| 7/7 [00:00<00:00, 261.19ba/s]\\n\",\n \"Creating json from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 591.08ba/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"30574\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"queries.to_json(\\\"ft_data/test_queries.jsonl\\\")\\n\",\n \"corpus.to_json(\\\"ft_data/corpus.jsonl\\\")\\n\",\n \"qrels.to_json(\\\"ft_data/test_qrels.jsonl\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/7_Fine-tuning/7.1.2_Fine-tune.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Fine-tuning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the previous section, we went through how to construct training and testing data properly. In this tutorial, we will actually fine-tune the model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note to fine-tune BGE models using FlagEmbedding, we need to install the package with the finetune dependency:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install -U FlagEmbedding[finetune]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fine-tune\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below are the arguments for fine-tuning:\\n\",\n \"\\n\",\n \"The following arguments are for model:\\n\",\n \"- `model_name_or_path`: The model checkpoint for initialization.\\n\",\n \"- `config_name`: Pretrained config name or path if not the same as model_name.\\n\",\n \"- `tokenizer_name`: Pretrained tokenizer name or path if not the same as model_name.\\n\",\n \"- `cache_dir`: Where do you want to store the pre-trained models downloaded from s3.\\n\",\n \"- `trust_remote_code`: Trust remote code\\n\",\n \"- `token`: The token to use when accessing the model.\\n\",\n \"\\n\",\n \"The following arguments are for data:\\n\",\n \"- `train_data`: One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data. Argument type: multiple.\\n\",\n \"- `cache_path`: Where do you want to store the cached data.\\n\",\n \"- `train_group_size`: (No metadata provided)\\n\",\n \"- `query_max_len`: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\\n\",\n \"- `passage_max_len`: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\\n\",\n \"- `pad_to_multiple_of`: If set will pad the sequence to be a multiple of the provided value.\\n\",\n \"- `max_example_num_per_dataset`: The max number of examples for each dataset.\\n\",\n \"- `query_instruction_for_retrieval`: Instruction for query.\\n\",\n \"- `query_instruction_format`: Format for query instruction.\\n\",\n \"- `knowledge_distillation`: Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data.\\n\",\n \"- `passage_instruction_for_retrieval`: Instruction for passage.\\n\",\n \"- `passage_instruction_format`: Format for passage instruction.\\n\",\n \"- `shuffle_ratio`: The ratio of shuffling the text.\\n\",\n \"- `same_dataset_within_batch`: All samples in the same batch comes from the same dataset.\\n\",\n \"- `small_threshold`: The threshold of small dataset. All small dataset in the same directory will be merged into one dataset.\\n\",\n \"- `drop_threshold`: The threshold for dropping merged small dataset. If the number of examples in the merged small dataset is less than this threshold, it will be dropped.\\n\",\n \"\\n\",\n \"And the following extra arguments:\\n\",\n \"- `negatives_cross_device`: Share negatives across devices.\\n\",\n \"- `temperature`: Temperature used for similarity score.\\n\",\n \"- `fix_position_embedding`: Freeze the parameters of position embeddings.\\n\",\n \"- `sentence_pooling_method`: The pooling method. Available options: cls, mean, last_token. Default: cls.\\n\",\n \"- `normalize_embeddings`: Whether to normalize the embeddings.\\n\",\n \"- `sub_batch_size`: Sub batch size for training.\\n\",\n \"- `kd_loss_type`: The loss type for knowledge distillation. Available options: kl_div, m3_kd_loss. Default: kl_div.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] \\n\",\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] *****************************************\\n\",\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \\n\",\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] *****************************************\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2024-12-23 06:27:31,423] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)\\n\",\n \"[2024-12-23 06:27:31,424] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)\\n\",\n \"[2024-12-23 06:27:40,529] [INFO] [comm.py:652:init_distributed] cdb=None\\n\",\n \"[2024-12-23 06:27:40,529] [INFO] [comm.py:652:init_distributed] cdb=None\\n\",\n \"[2024-12-23 06:27:40,529] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"12/23/2024 06:27:40 - WARNING - FlagEmbedding.abc.finetune.embedder.AbsRunner - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: True, 16-bits training: True\\n\",\n \"12/23/2024 06:27:40 - INFO - FlagEmbedding.abc.finetune.embedder.AbsRunner - Training/evaluation parameters AbsEmbedderTrainingArguments(\\n\",\n \"_n_gpu=1,\\n\",\n \"accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},\\n\",\n \"adafactor=False,\\n\",\n \"adam_beta1=0.9,\\n\",\n \"adam_beta2=0.999,\\n\",\n \"adam_epsilon=1e-08,\\n\",\n \"auto_find_batch_size=False,\\n\",\n \"batch_eval_metrics=False,\\n\",\n \"bf16=False,\\n\",\n \"bf16_full_eval=False,\\n\",\n \"data_seed=None,\\n\",\n \"dataloader_drop_last=True,\\n\",\n \"dataloader_num_workers=0,\\n\",\n \"dataloader_persistent_workers=False,\\n\",\n \"dataloader_pin_memory=True,\\n\",\n \"dataloader_prefetch_factor=None,\\n\",\n \"ddp_backend=None,\\n\",\n \"ddp_broadcast_buffers=None,\\n\",\n \"ddp_bucket_cap_mb=None,\\n\",\n \"ddp_find_unused_parameters=None,\\n\",\n \"ddp_timeout=1800,\\n\",\n \"debug=[],\\n\",\n \"deepspeed=config/ds_stage0.json,\\n\",\n \"disable_tqdm=False,\\n\",\n \"dispatch_batches=None,\\n\",\n \"do_eval=False,\\n\",\n \"do_predict=False,\\n\",\n \"do_train=False,\\n\",\n \"eval_accumulation_steps=None,\\n\",\n \"eval_delay=0,\\n\",\n \"eval_do_concat_batches=True,\\n\",\n \"eval_on_start=False,\\n\",\n \"eval_steps=None,\\n\",\n \"eval_strategy=IntervalStrategy.NO,\\n\",\n \"eval_use_gather_object=False,\\n\",\n \"evaluation_strategy=None,\\n\",\n \"fix_position_embedding=False,\\n\",\n \"fp16=True,\\n\",\n \"fp16_backend=auto,\\n\",\n \"fp16_full_eval=False,\\n\",\n \"fp16_opt_level=O1,\\n\",\n \"fsdp=[],\\n\",\n \"fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},\\n\",\n \"fsdp_min_num_params=0,\\n\",\n \"fsdp_transformer_layer_cls_to_wrap=None,\\n\",\n \"full_determinism=False,\\n\",\n \"gradient_accumulation_steps=1,\\n\",\n \"gradient_checkpointing=True,\\n\",\n \"gradient_checkpointing_kwargs=None,\\n\",\n \"greater_is_better=None,\\n\",\n \"group_by_length=False,\\n\",\n \"half_precision_backend=auto,\\n\",\n \"hub_always_push=False,\\n\",\n \"hub_model_id=None,\\n\",\n \"hub_private_repo=False,\\n\",\n \"hub_strategy=HubStrategy.EVERY_SAVE,\\n\",\n \"hub_token=,\\n\",\n \"ignore_data_skip=False,\\n\",\n \"include_inputs_for_metrics=False,\\n\",\n \"include_num_input_tokens_seen=False,\\n\",\n \"include_tokens_per_second=False,\\n\",\n \"jit_mode_eval=False,\\n\",\n \"kd_loss_type=kl_div,\\n\",\n \"label_names=None,\\n\",\n \"label_smoothing_factor=0.0,\\n\",\n \"learning_rate=1e-05,\\n\",\n \"length_column_name=length,\\n\",\n \"load_best_model_at_end=False,\\n\",\n \"local_rank=0,\\n\",\n \"log_level=passive,\\n\",\n \"log_level_replica=warning,\\n\",\n \"log_on_each_node=True,\\n\",\n \"logging_dir=./test_encoder_only_base_bge-large-en-v1.5/runs/Dec23_06-27-30_job-40fb0ce3-8bfb-46ea-b409-0a2e2a1a3163-master-0,\\n\",\n \"logging_first_step=False,\\n\",\n \"logging_nan_inf_filter=True,\\n\",\n \"logging_steps=1.0,\\n\",\n \"logging_strategy=IntervalStrategy.STEPS,\\n\",\n \"lr_scheduler_kwargs={},\\n\",\n \"lr_scheduler_type=SchedulerType.LINEAR,\\n\",\n \"max_grad_norm=1.0,\\n\",\n \"max_steps=-1,\\n\",\n \"metric_for_best_model=None,\\n\",\n \"mp_parameters=,\\n\",\n \"neftune_noise_alpha=None,\\n\",\n \"negatives_cross_device=True,\\n\",\n \"no_cuda=False,\\n\",\n \"normalize_embeddings=True,\\n\",\n \"num_train_epochs=2.0,\\n\",\n \"optim=OptimizerNames.ADAMW_TORCH,\\n\",\n \"optim_args=None,\\n\",\n \"optim_target_modules=None,\\n\",\n \"output_dir=./test_encoder_only_base_bge-large-en-v1.5,\\n\",\n \"overwrite_output_dir=True,\\n\",\n \"past_index=-1,\\n\",\n \"per_device_eval_batch_size=8,\\n\",\n \"per_device_train_batch_size=2,\\n\",\n \"prediction_loss_only=False,\\n\",\n \"push_to_hub=False,\\n\",\n \"push_to_hub_model_id=None,\\n\",\n \"push_to_hub_organization=None,\\n\",\n \"push_to_hub_token=,\\n\",\n \"ray_scope=last,\\n\",\n \"remove_unused_columns=True,\\n\",\n \"report_to=[],\\n\",\n \"restore_callback_states_from_checkpoint=False,\\n\",\n \"resume_from_checkpoint=None,\\n\",\n \"run_name=./test_encoder_only_base_bge-large-en-v1.5,\\n\",\n \"save_on_each_node=False,\\n\",\n \"save_only_model=False,\\n\",\n \"save_safetensors=True,\\n\",\n \"save_steps=1000,\\n\",\n \"save_strategy=IntervalStrategy.STEPS,\\n\",\n \"save_total_limit=None,\\n\",\n \"seed=42,\\n\",\n \"sentence_pooling_method=cls,\\n\",\n \"skip_memory_metrics=True,\\n\",\n \"split_batches=None,\\n\",\n \"sub_batch_size=None,\\n\",\n \"temperature=0.02,\\n\",\n \"tf32=None,\\n\",\n \"torch_compile=False,\\n\",\n \"torch_compile_backend=None,\\n\",\n \"torch_compile_mode=None,\\n\",\n \"torch_empty_cache_steps=None,\\n\",\n \"torchdynamo=None,\\n\",\n \"tpu_metrics_debug=False,\\n\",\n \"tpu_num_cores=None,\\n\",\n \"use_cpu=False,\\n\",\n \"use_ipex=False,\\n\",\n \"use_legacy_prediction_loop=False,\\n\",\n \"use_mps_device=False,\\n\",\n \"warmup_ratio=0.1,\\n\",\n \"warmup_steps=0,\\n\",\n \"weight_decay=0.0,\\n\",\n \")\\n\",\n \"12/23/2024 06:27:40 - INFO - FlagEmbedding.abc.finetune.embedder.AbsRunner - Model parameters AbsEmbedderModelArguments(model_name_or_path='BAAI/bge-large-en-v1.5', config_name=None, tokenizer_name=None, cache_dir='./cache/model', trust_remote_code=False, token=None)\\n\",\n \"12/23/2024 06:27:40 - INFO - FlagEmbedding.abc.finetune.embedder.AbsRunner - Data parameters AbsEmbedderDataArguments(train_data=['./ft_data/training.json'], cache_path='./cache/data', train_group_size=8, query_max_len=512, passage_max_len=512, pad_to_multiple_of=8, max_example_num_per_dataset=100000000, query_instruction_for_retrieval='Represent this sentence for searching relevant passages: ', query_instruction_format='{}{}', knowledge_distillation=False, passage_instruction_for_retrieval=None, passage_instruction_format='{}{}', shuffle_ratio=0.0, same_dataset_within_batch=False, small_threshold=0, drop_threshold=0)\\n\",\n \"12/23/2024 06:27:40 - WARNING - FlagEmbedding.abc.finetune.embedder.AbsRunner - Process rank: 1, device: cuda:1, n_gpu: 1, distributed training: True, 16-bits training: True\\n\",\n \"12/23/2024 06:35:01 - INFO - FlagEmbedding.finetune.embedder.encoder_only.base.runner - Config: BertConfig {\\n\",\n \" \\\"_name_or_path\\\": \\\"BAAI/bge-large-en-v1.5\\\",\\n\",\n \" \\\"architectures\\\": [\\n\",\n \" \\\"BertModel\\\"\\n\",\n \" ],\\n\",\n \" \\\"attention_probs_dropout_prob\\\": 0.1,\\n\",\n \" \\\"classifier_dropout\\\": null,\\n\",\n \" \\\"gradient_checkpointing\\\": false,\\n\",\n \" \\\"hidden_act\\\": \\\"gelu\\\",\\n\",\n \" \\\"hidden_dropout_prob\\\": 0.1,\\n\",\n \" \\\"hidden_size\\\": 1024,\\n\",\n \" \\\"id2label\\\": {\\n\",\n \" \\\"0\\\": \\\"LABEL_0\\\"\\n\",\n \" },\\n\",\n \" \\\"initializer_range\\\": 0.02,\\n\",\n \" \\\"intermediate_size\\\": 4096,\\n\",\n \" \\\"label2id\\\": {\\n\",\n \" \\\"LABEL_0\\\": 0\\n\",\n \" },\\n\",\n \" \\\"layer_norm_eps\\\": 1e-12,\\n\",\n \" \\\"max_position_embeddings\\\": 512,\\n\",\n \" \\\"model_type\\\": \\\"bert\\\",\\n\",\n \" \\\"num_attention_heads\\\": 16,\\n\",\n \" \\\"num_hidden_layers\\\": 24,\\n\",\n \" \\\"pad_token_id\\\": 0,\\n\",\n \" \\\"position_embedding_type\\\": \\\"absolute\\\",\\n\",\n \" \\\"torch_dtype\\\": \\\"float32\\\",\\n\",\n \" \\\"transformers_version\\\": \\\"4.44.2\\\",\\n\",\n \" \\\"type_vocab_size\\\": 2,\\n\",\n \" \\\"use_cache\\\": true,\\n\",\n \" \\\"vocab_size\\\": 30522\\n\",\n \"}\\n\",\n \"\\n\",\n \"12/23/2024 06:35:01 - INFO - FlagEmbedding.abc.finetune.embedder.AbsDataset - loading data from ./ft_data/training.json ...\\n\",\n \"Generating train split: 6300 examples [00:00, 46043.95 examples/s]\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\\n\",\n \" warnings.warn(\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\\n\",\n \" warnings.warn(\\n\",\n \"12/23/2024 06:35:02 - WARNING - accelerate.utils.other - Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1734935704.354551] [job-40fb0ce3-8bfb-46ea-b409-0a2e2a1a3163-master-0:1362491:f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\\n\",\n \"[1734935704.383634] [job-40fb0ce3-8bfb-46ea-b409-0a2e2a1a3163-master-0:1362492:f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using /root/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...\\n\",\n \"Using /root/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...\\n\",\n \"Detected CUDA files, patching ldflags\\n\",\n \"Emitting ninja build file /root/.cache/torch_extensions/py311_cu124/fused_adam/build.ninja...\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/torch/utils/cpp_extension.py:1964: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \\n\",\n \"If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].\\n\",\n \" warnings.warn(\\n\",\n \"Building extension module fused_adam...\\n\",\n \"Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ninja: no work to do.\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading extension module fused_adam...\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time to load fused_adam op: 1.1966907978057861 seconds\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading extension module fused_adam...\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time to load fused_adam op: 1.2037739753723145 seconds\\n\",\n \"[2024-12-23 06:35:06,883] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started\\n\",\n \"[2024-12-23 06:35:06,888] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \" 0%| | 0/3150 [00:00, {'NDCG@10': 0.70405, 'NDCG@100': 0.73528})\\n\",\n \"defaultdict(, {'MAP@10': 0.666, 'MAP@100': 0.67213})\\n\",\n \"defaultdict(, {'Recall@10': 0.82286, 'Recall@100': 0.97286})\\n\",\n \"defaultdict(, {'P@10': 0.08229, 'P@100': 0.00973})\\n\",\n \"defaultdict(, {'MRR@10': 0.666, 'MRR@100': 0.67213})\\n\"\n ]\n }\n ],\n \"source\": [\n \"raw_model = FlagModel(\\n\",\n \" raw_name, \\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" devices=[0],\\n\",\n \" use_fp16=False\\n\",\n \")\\n\",\n \"\\n\",\n \"results = search(raw_model, queries_text, corpus_text)\\n\",\n \"\\n\",\n \"eval_res = evaluate_metrics(qrels_dict, results, k_values)\\n\",\n \"mrr = evaluate_mrr(qrels_dict, results, k_values)\\n\",\n \"\\n\",\n \"for res in eval_res:\\n\",\n \" print(res)\\n\",\n \"print(mrr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then the result for the model after fine-tuning:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 3/3 [00:00<00:00, 164.72it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 100%|██████████| 3/3 [00:00<00:00, 9.45it/s]\\n\",\n \"pre tokenize: 100%|██████████| 28/28 [00:00<00:00, 160.19it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 28/28 [00:04<00:00, 6.06it/s]\\n\",\n \"Searching: 100%|██████████| 22/22 [00:07<00:00, 2.80it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {'NDCG@10': 0.84392, 'NDCG@100': 0.85792})\\n\",\n \"defaultdict(, {'MAP@10': 0.81562, 'MAP@100': 0.81875})\\n\",\n \"defaultdict(, {'Recall@10': 0.93143, 'Recall@100': 0.99429})\\n\",\n \"defaultdict(, {'P@10': 0.09314, 'P@100': 0.00994})\\n\",\n \"defaultdict(, {'MRR@10': 0.81562, 'MRR@100': 0.81875})\\n\"\n ]\n }\n ],\n \"source\": [\n \"ft_model = FlagModel(\\n\",\n \" finetuned_path, \\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" devices=[0],\\n\",\n \" use_fp16=False\\n\",\n \")\\n\",\n \"\\n\",\n \"results = search(ft_model, queries_text, corpus_text)\\n\",\n \"\\n\",\n \"eval_res = evaluate_metrics(qrels_dict, results, k_values)\\n\",\n \"mrr = evaluate_mrr(qrels_dict, results, k_values)\\n\",\n \"\\n\",\n \"for res in eval_res:\\n\",\n \" print(res)\\n\",\n \"print(mrr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see an obvious improvement in all the metrics.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "Tutorials/7_Fine-tuning/7.2.1_Hard_Negative_Mining.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hard Negatives\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Hard negatives are those negative samples that are particularly challenging for the model to distinguish from the positive ones. They are often close to the decision boundary or exhibit features that make them highly similar to the positive samples. Thus hard negative mining is widely used in machine learning tasks to make the model focus on subtle differences between similar instances, leading to better discrimination.\\n\",\n \"\\n\",\n \"In text retrieval system, a hard negative could be document that share some feature similarities with the query but does not truly satisfy the query's intent. During retrieval, those documents could rank higher than the real answers. Thus it's valuable to explicitly train the model on these hard negatives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, load an embedding model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, load the queries and corpus from dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"corpus = load_dataset(\\\"BeIR/scifact\\\", \\\"corpus\\\")[\\\"corpus\\\"]\\n\",\n \"queries = load_dataset(\\\"BeIR/scifact\\\", \\\"queries\\\")[\\\"queries\\\"]\\n\",\n \"\\n\",\n \"corpus_ids = corpus.select_columns([\\\"_id\\\"])[\\\"_id\\\"]\\n\",\n \"corpus = corpus.select_columns([\\\"text\\\"])[\\\"text\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We create a dictionary maping auto generated ids (starting from 0) used by FAISS index, for later use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus_ids_map = {}\\n\",\n \"for i in range(len(corpus)):\\n\",\n \" corpus_ids_map[i] = corpus_ids[i]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the embedding model to encode the queries and corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 21/21 [00:00<00:00, 46.18it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Attempting to cast a BatchEncoding to type None. This is not supported.\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Inference Embeddings: 0%| | 0/21 [00:00\n Tutorial roadmap\n \n\n\n## [Embedding](./1_Embedding)\n\nThis module includes tutorials and demos showing how to use BGE and Sentence Transformers, as well as other embedding related topics.\n\n- [x] Intro to embedding model\n- [x] BGE series\n- [x] Usage of BGE\n- [x] BGE-M3\n- [ ] BGE-ICL\n- [ ] ...\n\n## [Metrics](./2_Metrics)\n\nIn this part, we show popular similarity functions and techniques about searching.\n\n- [x] Similarity metrics\n- [x] Evaluation metrics\n- [ ] ...\n\n## [Indexing](./3_Indexing)\n\nAlthough not included in the quick start, indexing is a very important part in practical cases. This module shows how to use popular libraries like Faiss and Milvus to do indexing.\n\n- [x] Intro to Faiss\n- [x] Using GPU in Faiss\n- [x] Indexes\n- [x] Quantizers\n- [x] Faiss Index Choosing\n- [ ] Milvus\n- [ ] ...\n\n## [Evaluation](./4_Evaluation)\n\nIn this module, we'll show the full pipeline of evaluating an embedding model, as well as popular benchmarks like MTEB and C-MTEB.\n\n- [x] Evaluate MSMARCO\n- [x] Intro to MTEB\n- [x] MTEB Leaderboard Eval\n- [x] C-MTEB intro\n- [x] C-MTEB leaderboard\n- [x] Evaluation using Sentence Transformers\n- [x] BEIR benchmark\n- [x] MIRACL\n- [x] MLDR\n- [ ] MKQA\n- [ ] ...\n\n## [Reranking](./5_Reranking/)\n\nTo balance accuracy and efficiency tradeoff, many retrieval system use a more efficient retriever to quickly narrow down the candidates. Then use more accurate models do reranking for the final results.\n\n- [x] Intro to reranker\n- [ ] ...\n\n## [RAG](./6_RAG/)\n\nRAG is one of the most popular approach to enchance the capabilities of LLMs by integrating information retrieval with them. In this module, we will cover the implementation, popular tools and libraries, and more advanced techniques.\n\n- [x] RAG from scratch\n- [x] RAG with LangChain\n- [x] RAG with LlamaIndex\n- [ ] ..." }, { "path": "Tutorials/quick_start.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Quick Start\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will show how to use BGE models on a text retrieval task in 5 minutes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 0: Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, install FlagEmbedding in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is a super tiny courpus with only 10 sentences, which will be the dataset we use.\\n\",\n \"\\n\",\n \"Each sentence is a concise discription of a famous people in specific domain.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = [\\n\",\n \" \\\"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\\\",\\n\",\n \" \\\"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\\\",\\n\",\n \" \\\"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\\\",\\n\",\n \" \\\"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\\",\\n\",\n \" \\\"Eminem is a renowned rapper and one of the best-selling music artists of all time.\\\",\\n\",\n \" \\\"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\\",\\n\",\n \" \\\"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\\\",\\n\",\n \" \\\"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\\\",\\n\",\n \" \\\"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\\\",\\n\",\n \" \\\"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We want to know which one of these people could be an expert of neural network and who he/she is. \\n\",\n \"\\n\",\n \"Thus we generate the following query:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query = \\\"Who could be an expert of neural network?\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Text -> Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's use a [BGE embedding model](https://huggingface.co/BAAI/bge-base-en-v1.5) to create sentence embedding for the corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the query and corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"query_embedding = model.encode(query)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The embedding of each sentence is a vector with length 768. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the query embedding: (768,)\\n\",\n \"shape of the corpus embeddings: (10, 768)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"shape of the query embedding: \\\", query_embedding.shape)\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the following print line to take a look at the first 10 elements of the query embedding vector.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-0.00790005 -0.00683443 -0.00806659 0.00756918 0.04374858 0.02838556\\n\",\n \" 0.02357143 -0.02270943 -0.03611493 -0.03038301]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(query_embedding[:10])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Calculate Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, we have the embeddings of the query and the corpus. The next step is to calculate the similarity between the query and each sentence in the corpus. Here we use the dot product/inner product as our similarity metric.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.39290053 0.6031525 0.32672375 0.6082418 0.39446455 0.35350388\\n\",\n \" 0.4626108 0.40196604 0.5284606 0.36792332]\\n\"\n ]\n }\n ],\n \"source\": [\n \"sim_scores = query_embedding @ corpus_embeddings.T\\n\",\n \"print(sim_scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a list of score representing the query's similarity to: [sentence 0, sentence 1, sentence 2, ...]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Ranking\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we have the similarity score of the query to each sentence in the corpus, we can rank them from large to small.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3, 1, 8, 6, 7, 4, 0, 9, 5, 2]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get the indices in sorted order\\n\",\n \"sorted_indices = sorted(range(len(sim_scores)), key=lambda k: sim_scores[k], reverse=True)\\n\",\n \"print(sorted_indices)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now from the ranking, the sentence with index 3 is the best answer to our query \\\"Who could be an expert of neural network?\\\"\\n\",\n \"\\n\",\n \"And that person is Geoffrey Hinton!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(corpus[3])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"According to the order of indecies, we can print out the ranking of people that our little retriever got.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Score of 0.608: \\\"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\\"\\n\",\n \"Score of 0.603: \\\"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\\\"\\n\",\n \"Score of 0.528: \\\"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\\\"\\n\",\n \"Score of 0.463: \\\"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\\\"\\n\",\n \"Score of 0.402: \\\"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\\\"\\n\",\n \"Score of 0.394: \\\"Eminem is a renowned rapper and one of the best-selling music artists of all time.\\\"\\n\",\n \"Score of 0.393: \\\"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\\\"\\n\",\n \"Score of 0.368: \\\"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\\"\\n\",\n \"Score of 0.354: \\\"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\\"\\n\",\n \"Score of 0.327: \\\"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\\\"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# iteratively print the score and corresponding sentences in descending order\\n\",\n \"\\n\",\n \"for i in sorted_indices:\\n\",\n \" print(f\\\"Score of {sim_scores[i]:.3f}: \\\\\\\"{corpus[i]}\\\\\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the ranking, not surprisingly, the similarity scores of the query and the discriptions of Geoffrey Hinton and Fei-Fei Li is way higher than others, following by those of Andrew Ng and Sam Altman. \\n\",\n \"\\n\",\n \"While the key phrase \\\"neural network\\\" in the query does not appear in any of those discriptions, the BGE embedding model is still powerful enough to get the semantic meaning of query and corpus well.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've seen the embedding model performed pretty well on the \\\"neural network\\\" query. What about the more general quality?\\n\",\n \"\\n\",\n \"Let's generate a very small dataset of queries and corresponding ground truth answers. Note that the ground truth answers are the indices of sentences in the corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = [\\n\",\n \" \\\"Who could be an expert of neural network?\\\",\\n\",\n \" \\\"Who might had won Grammy?\\\",\\n\",\n \" \\\"Won Academy Awards\\\",\\n\",\n \" \\\"One of the most famous female singers.\\\",\\n\",\n \" \\\"Inventor of AlexNet\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ground_truth = [\\n\",\n \" [1, 3],\\n\",\n \" [0, 4, 5],\\n\",\n \" [2, 7, 9],\\n\",\n \" [5],\\n\",\n \" [3],\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we repeat the steps we covered above to get the predicted ranking of each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[[3, 1, 8, 6, 7, 4, 0, 9, 5, 2],\\n\",\n \" [5, 0, 3, 4, 1, 9, 7, 2, 6, 8],\\n\",\n \" [3, 2, 7, 5, 9, 0, 1, 4, 6, 8],\\n\",\n \" [5, 0, 4, 7, 1, 9, 2, 3, 6, 8],\\n\",\n \" [3, 1, 8, 6, 0, 7, 5, 9, 4, 2]]\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# use bge model to generate embeddings for all the queries\\n\",\n \"queries_embedding = model.encode(queries)\\n\",\n \"# compute similarity scores\\n\",\n \"scores = queries_embedding @ corpus_embeddings.T\\n\",\n \"# get he final rankings\\n\",\n \"rankings = [sorted(range(len(sim_scores)), key=lambda k: sim_scores[k], reverse=True) for sim_scores in scores]\\n\",\n \"rankings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. Here we use that to have a very rough idea how our system performs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def MRR(preds, labels, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, label in zip(preds, labels):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, index in enumerate(pred):\\n\",\n \" if j < c and index in label:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We choose to use 1 and 5 as our cutoffs, with the result of 0.8 and 0.9 respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1: 0.8\\n\",\n \"MRR@5: 0.9\\n\"\n ]\n }\n ],\n \"source\": [\n \"cutoffs = [1, 5]\\n\",\n \"mrrs = MRR(rankings, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"MRR@{c}: {mrrs[i]}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "dataset/README.md", "content": "# DataSet\n\nThis will point to the training data we use for training various models.\n\n| Dataset | Introduction |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| [MLDR](https://huggingface.co/datasets/Shitao/MLDR) | Document Retrieval Dataset, covering 13 languages |\n| [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) | Fine-tuning data used by [bge-m3](https://huggingface.co/BAAI/bge-m3) |\n| [public-data](https://huggingface.co/datasets/cfli/bge-e5data) | Public data identical to [e5-mistral](https://huggingface.co/intfloat/e5-mistral-7b-instruct) |\n| [full-data](https://huggingface.co/datasets/cfli/bge-full-data) | The full dataset we used for training [bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) |\n| [bge-multilingual-gemma2-data](https://huggingface.co/datasets/hanhainebula/bge-multilingual-gemma2-data) | The full multilingual dataset we used for training [bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) |\n| [reranker-data](https://huggingface.co/datasets/Shitao/bge-reranker-data) | a mixture of multilingual datasets |\n\n\n" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the environment for the first two.\nSPHINXOPTS ?=\nSPHINXBUILD ?= sphinx-build\nSOURCEDIR = source\nBUILDDIR = build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/README.md", "content": "install the required pkgs:\n```\npip install -r requirements.txt\n```\n\n\nto host the webpages locally:\n```\npython -m http.server\n```" }, { "path": "docs/make.bat", "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sphinx-build\r\n)\r\nset SOURCEDIR=source\r\nset BUILDDIR=build\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\r\n\techo.installed, then set the SPHINXBUILD environment variable to point\r\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\r\n\techo.may add the Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.https://www.sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%\r\n\r\n:end\r\npopd\r\n" }, { "path": "docs/requirements.txt", "content": "sphinx\nmyst-nb\nmyst_parser\nsphinx-design\npydata-sphinx-theme\n# furo" }, { "path": "docs/source/API/abc/evaluation/arguments.rst", "content": "Arguments\n=========\n\n.. autoclass:: FlagEmbedding.abc.evaluation.AbsEvalArgs\n\n\n.. autoclass:: FlagEmbedding.abc.evaluation.AbsEvalModelArgs" }, { "path": "docs/source/API/abc/evaluation/data_loader.rst", "content": "dataset loader\n==============\n\n.. autoclass:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.available_dataset_names\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.available_splits\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.check_dataset_names\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.check_splits\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.load_corpus\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.load_qrels\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader.load_queries\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._load_remote_corpus\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._load_remote_qrels\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._load_remote_queries\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._load_local_corpus\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._load_local_qrels\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._load_local_queries\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._download_file\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._get_fpath_size\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._download_gz_file\n.. automethod:: FlagEmbedding.abc.evaluation.AbsEvalDataLoader._download_zip_file" }, { "path": "docs/source/API/abc/evaluation/evaluator.rst", "content": "Evaluator\n=========\n\n.. autoclass:: FlagEmbedding.abc.evaluation.AbsEvaluator" }, { "path": "docs/source/API/abc/evaluation/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.abc.evaluation.AbsEvalRunner" }, { "path": "docs/source/API/abc/evaluation/searcher.rst", "content": "========\nsearcher\n========\n\nEvalRetriever\n=============\n\n.. autoclass:: FlagEmbedding.abc.evaluation.EvalRetriever\n\nEvalDenseRetriever\n==================\n\n.. autoclass:: FlagEmbedding.abc.evaluation.EvalDenseRetriever\n\nEvalReranker\n============\n\n.. autoclass:: FlagEmbedding.abc.evaluation.EvalReranker" }, { "path": "docs/source/API/abc/evaluation.rst", "content": "Evaluation\n==========\n\n.. toctree::\n evaluation/arguments\n evaluation/data_loader\n evaluation/searcher\n evaluation/evaluator\n evaluation/runner" }, { "path": "docs/source/API/abc/finetune/embedder/AbsArguments.rst", "content": "AbsArguments\n============\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModelArguments\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerDataArguments\n" }, { "path": "docs/source/API/abc/finetune/embedder/AbsDataset.rst", "content": "==========\nAbsDataset\n==========\n\nAbsEmbedderTrainDataset\n=======================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderTrainDataset\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderTrainDataset._load_dataset\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderTrainDataset._shuffle_text\n\nAbsEmbedderCollator\n===================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderCollator\n\nAbsEmbedderSameDatasetTrainDataset\n==================================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetTrainDataset\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetTrainDataset.refresh_epoch\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetTrainDataset._load_dataset\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetTrainDataset._get_file_batch_size\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetTrainDataset._get_train_group_size\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetTrainDataset._create_batch_data\n\nAbsEmbedderSameDatasetCollator\n==============================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderSameDatasetCollator\n\nEmbedderTrainerCallbackForDataRefresh\n=====================================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.EmbedderTrainerCallbackForDataRefresh\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.EmbedderTrainerCallbackForDataRefresh.on_epoch_end" }, { "path": "docs/source/API/abc/finetune/embedder/AbsModeling.rst", "content": "===========\nAbsModeling\n===========\n\nAbsEmbedderModel\n================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.encode\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.compute_loss\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.compute_score\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.save\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.get_local_score\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.compute_local_score\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.forward\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel.distill_loss\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel._compute_no_in_batch_neg_loss\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel._compute_in_batch_neg_loss\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel._compute_cross_device_neg_loss\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModel._dist_gather_tensor\n\n\nEmbedderOutput\n==============\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.EmbedderOutput" }, { "path": "docs/source/API/abc/finetune/embedder/AbsRunner.rst", "content": "=========\nAbsRunner\n=========\n\nAbsEmbedderTrainer\n==================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderRunner\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderRunner.load_tokenizer_and_model\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderRunner.load_trainer\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderRunner.load_train_dataset\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderRunner.load_data_collator\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderRunner.run" }, { "path": "docs/source/API/abc/finetune/embedder/AbsTrainer.rst", "content": "==========\nAbsTrainer\n==========\n\nAbsEmbedderTrainer\n==================\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderTrainer\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderTrainer.compute_loss" }, { "path": "docs/source/API/abc/finetune/embedder.rst", "content": "Embedder\n========\n\n.. toctree::\n embedder/AbsArguments\n embedder/AbsDataset\n embedder/AbsModeling\n embedder/AbsTrainer\n embedder/AbsRunner" }, { "path": "docs/source/API/abc/finetune/reranker/AbsArguments.rst", "content": "AbsArguments\n============\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderModelArguments\n\n.. autoclass:: FlagEmbedding.abc.finetune.embedder.AbsEmbedderDataArguments\n" }, { "path": "docs/source/API/abc/finetune/reranker/AbsDataset.rst", "content": "==========\nAbsDataset\n==========\n\nAbsRerankerTrainDataset\n=======================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerTrainDataset\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerTrainDataset.create_one_example\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerTrainDataset._load_dataset\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerTrainDataset._shuffle_text\n\nAbsRerankerCollator\n===================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerCollator\n\nAbsLLMRerankerTrainDataset\n==========================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsLLMRerankerTrainDataset\n\nAbsLLMRerankerCollator\n======================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsLLMRerankerCollator\n" }, { "path": "docs/source/API/abc/finetune/reranker/AbsModeling.rst", "content": "===========\nAbsModeling\n===========\n\nAbsRerankerModel\n================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.encode\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.gradient_checkpointing_enable\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.enable_input_require_grads\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.forward\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.compute_loss\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.save\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerModel.save_pretrained\n\n\nRerankerOutput\n==============\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.RerankerOutput" }, { "path": "docs/source/API/abc/finetune/reranker/AbsRunner.rst", "content": "=========\nAbsRunner\n=========\n\nAbsRerankerTrainer\n==================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerRunner\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerRunner.load_tokenizer_and_model\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerRunner.load_trainer\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerRunner.load_train_dataset\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerRunner.load_data_collator\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerRunner.run" }, { "path": "docs/source/API/abc/finetune/reranker/AbsTrainer.rst", "content": "==========\nAbsTrainer\n==========\n\nAbsRerankerTrainer\n==================\n\n.. autoclass:: FlagEmbedding.abc.finetune.reranker.AbsRerankerTrainer\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.finetune.reranker.AbsRerankerTrainer.compute_loss" }, { "path": "docs/source/API/abc/finetune/reranker.rst", "content": "Reranker\n========\n\n.. toctree::\n reranker/AbsArguments\n reranker/AbsDataset\n reranker/AbsModeling\n reranker/AbsTrainer\n reranker/AbsRunner" }, { "path": "docs/source/API/abc/finetune.rst", "content": "Finetune\n========\n\n.. toctree::\n finetune/embedder\n finetune/reranker" }, { "path": "docs/source/API/abc/inference/AbsEmbedder.rst", "content": "AbsEmbedder\n===========\n\n.. autoclass:: FlagEmbedding.abc.inference.AbsEmbedder\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.get_target_devices\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.get_detailed_instruct\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.encode_queries\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.encode_corpus\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.encode\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.encode_single_device\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.start_multi_process_pool\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder._encode_multi_process_worker\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.stop_multi_process_pool\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder.encode_multi_process\n\n.. automethod:: FlagEmbedding.abc.inference.AbsEmbedder._concatenate_results_from_multi_process" }, { "path": "docs/source/API/abc/inference/AbsReranker.rst", "content": "AbsReranker\n===========\n\n.. autoclass:: FlagEmbedding.abc.inference.AbsReranker\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.get_target_devices\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.get_detailed_instruct\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.get_detailed_inputs\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.compute_score\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.compute_score_single_gpu\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.start_multi_process_pool\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.encode_multi_process\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker._encode_multi_process_worker\n\n.. automethod:: FlagEmbedding.abc.inference.AbsReranker.stop_multi_process_pool" }, { "path": "docs/source/API/abc/inference.rst", "content": "Inference\n=========\n\n.. toctree::\n inference/AbsEmbedder\n inference/AbsReranker" }, { "path": "docs/source/API/abc.rst", "content": "Abstract Class\n==============\n\n.. toctree::\n abc/inference\n abc/evaluation\n abc/finetune" }, { "path": "docs/source/API/evaluation/airbench/arguments.rst", "content": "arguments\n=========\n\n.. autoclass:: FlagEmbedding.evaluation.air_bench.AIRBenchEvalModelArgs" }, { "path": "docs/source/API/evaluation/airbench/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.evaluation.air_bench.AIRBenchEvalRunner" }, { "path": "docs/source/API/evaluation/airbench.rst", "content": "AIR-Bench\n=========\n\n`AIR-Bench `_ (Automated heterogeneous Information Retrieval Benchmark) is a dynamic (actively being updated) benchmark for information retrieval.\nNow the benchmark contains two versions. Notice that the testing data is generated by LLMs with out human intervention. \nThis helps the evaluation of new domains easier and faster to be updated. It also makes it impossible for any models to have the test data covered in their training sets.\n\nYou can evaluate model's performance on AIR-Bench by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/air_bench/eval_air_bench.sh\n ./examples/evaluation/air_bench/eval_air_bench.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.air_bench \\\n --benchmark_version AIR-Bench_24.05 \\\n --task_types qa long-doc \\\n --domains arxiv \\\n --languages en \\\n --splits dev test \\\n --output_dir ./air_bench/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_dir /root/.cache/huggingface/hub \\\n --overwrite False \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --model_cache_dir /root/.cache/huggingface/hub \\\n --reranker_max_length 1024\n\nchange the embedder, reranker, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n airbench/arguments\n airbench/runner" }, { "path": "docs/source/API/evaluation/beir/arguments.rst", "content": "arguments\n=========\n\n.. autoclass:: FlagEmbedding.evaluation.beir.arguments.BEIREvalArgs" }, { "path": "docs/source/API/evaluation/beir/data_loader.rst", "content": "data loader\n===========\n\n.. autoclass:: FlagEmbedding.evaluation.beir.data_loader.BEIREvalDataLoader" }, { "path": "docs/source/API/evaluation/beir/evaluator.rst", "content": "evaluator\n=========\n\n.. autoclass:: FlagEmbedding.evaluation.beir.evaluator.BEIREvaluator" }, { "path": "docs/source/API/evaluation/beir/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.evaluation.beir.BEIREvalRunner" }, { "path": "docs/source/API/evaluation/beir.rst", "content": "BEIR\n====\n\n`BEIR `_ (Benchmarking-IR) is a heterogeneous evaluation benchmark for information retrieval. \nIt is designed for evaluating the performance of NLP-based retrieval models and widely used by research of modern embedding models.\n\nYou can evaluate model's performance on the BEIR benchmark by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/beir/eval_beir.sh\n ./examples/evaluation/beir/eval_beir.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.beir \\\n --eval_name beir \\\n --dataset_dir ./beir/data \\\n --dataset_names fiqa arguana cqadupstack \\\n --splits test dev \\\n --corpus_embd_save_dir ./beir/corpus_embd \\\n --output_dir ./beir/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path /root/.cache/huggingface/hub \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./beir/beir_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --ignore_identical_ids True \\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --reranker_max_length 1024 \\\n\nchange the embedder, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n beir/arguments\n beir/data_loader\n beir/evaluator\n beir/runner" }, { "path": "docs/source/API/evaluation/miracl/data_loader.rst", "content": "data_loader\n===========\n\n.. autoclass:: FlagEmbedding.evaluation.miracl.MIRACLEvalDataLoader\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.evaluation.miracl.MIRACLEvalDataLoader.available_dataset_names\n.. automethod:: FlagEmbedding.evaluation.miracl.MIRACLEvalDataLoader.available_splits\n.. automethod:: FlagEmbedding.evaluation.miracl.MIRACLEvalDataLoader._load_remote_corpus\n.. automethod:: FlagEmbedding.evaluation.miracl.MIRACLEvalDataLoader._load_remote_qrels\n.. automethod:: FlagEmbedding.evaluation.miracl.MIRACLEvalDataLoader._load_remote_queries" }, { "path": "docs/source/API/evaluation/miracl/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.evaluation.miracl.MIRACLEvalRunner\n :members:" }, { "path": "docs/source/API/evaluation/miracl.rst", "content": "MIRACL\n======\n\n`MIRACL `_ (Multilingual Information Retrieval Across a Continuum of Languages) \nis an WSDM 2023 Cup challenge that focuses on search across 18 different languages.\nThey release a multilingual retrieval dataset containing the train and dev set for 16 \"known languages\" and only dev set for 2 \"surprise languages\".\nThe topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list.\nYou can found the `dataset `_ on HuggingFace.\n\nYou can evaluate model's performance on MIRACL simply by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/miracl/eval_miracl.sh\n ./examples/evaluation/miracl/eval_miracl.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.miracl \\\n --eval_name miracl \\\n --dataset_dir ./miracl/data \\\n --dataset_names bn hi sw te th yo \\\n --splits dev \\\n --corpus_embd_save_dir ./miracl/corpus_embd \\\n --output_dir ./miracl/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path /root/.cache/huggingface/hub \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./miracl/miracl_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir /root/.cache/huggingface/hub \\\n --reranker_max_length 1024\n\nchange the embedder, reranker, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n miracl/data_loader\n miracl/runner" }, { "path": "docs/source/API/evaluation/mkqa/data_loader.rst", "content": "data_loader\n===========\n\n.. autoclass:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader.available_dataset_names\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader.available_splits\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader.load_corpus\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader._load_local_qrels\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader._load_remote_corpus\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader._load_remote_qrels\n.. automethod:: FlagEmbedding.evaluation.mkqa.MKQAEvalDataLoader._load_remote_queries" }, { "path": "docs/source/API/evaluation/mkqa/evaluator.rst", "content": "evaluator\n=========\n\n.. autoclass:: FlagEmbedding.evaluation.mkqa.MKQAEvaluator\n :members:" }, { "path": "docs/source/API/evaluation/mkqa/runner.rst", "content": "runner\n======\n.. autoclass:: FlagEmbedding.evaluation.mkqa.MKQAEvalRunner\n :members:" }, { "path": "docs/source/API/evaluation/mkqa.rst", "content": "MKQA\n====\n\n`MKQA `_ is an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages.\nThe queries are sampled from the [Google Natural Questions Dataset](https://github.com/google-research-datasets/natural-questions). \n\nEach example in the dataset has the following structure:\n\n.. code:: bash\n\n {\n 'example_id': 563260143484355911,\n 'queries': {\n 'en': \"who sings i hear you knocking but you can't come in\",\n 'ru': \"кто поет i hear you knocking but you can't come in\",\n 'ja': '「 I hear you knocking」は誰が歌っていますか',\n 'zh_cn': \"《i hear you knocking but you can't come in》是谁演唱的\",\n ...\n },\n 'query': \"who sings i hear you knocking but you can't come in\",\n 'answers': {\n 'en': [{\n 'type': 'entity',\n 'entity': 'Q545186',\n 'text': 'Dave Edmunds',\n 'aliases': [],\n }],\n 'ru': [{\n 'type': 'entity',\n 'entity': 'Q545186',\n 'text': 'Эдмундс, Дэйв',\n 'aliases': ['Эдмундс', 'Дэйв Эдмундс', 'Эдмундс Дэйв', 'Dave Edmunds'],\n }],\n 'ja': [{\n 'type': 'entity',\n 'entity': 'Q545186',\n 'text': 'デイヴ・エドモンズ',\n 'aliases': ['デーブ・エドモンズ', 'デイブ・エドモンズ'],\n }],\n 'zh_cn': [{\n 'type': 'entity', \n 'text': '戴维·埃德蒙兹 ', \n 'entity': 'Q545186',\n }],\n ...\n },\n }\n\n\nYou can evaluate model's performance on MKQA simply by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/mkqa/eval_mkqa.sh\n ./examples/evaluation/mkqa/eval_mkqa.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.mkqa \\\n --eval_name mkqa \\\n --dataset_dir ./mkqa/data \\\n --dataset_names en zh_cn \\\n --splits test \\\n --corpus_embd_save_dir ./mkqa/corpus_embd \\\n --output_dir ./mkqa/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path /root/.cache/huggingface/hub \\\n --overwrite False \\\n --k_values 20 \\\n --eval_output_method markdown \\\n --eval_output_path ./mkqa/mkqa_eval_results.md \\\n --eval_metrics qa_recall_at_20 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir /root/.cache/huggingface/hub \\\n --reranker_max_length 1024\n\nchange the embedder, reranker, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n mkqa/data_loader\n mkqa/evaluator\n mkqa/runner" }, { "path": "docs/source/API/evaluation/mldr/data_loader.rst", "content": "data_loader\n===========\n\n.. autoclass:: FlagEmbedding.evaluation.mldr.MLDREvalDataLoader\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.evaluation.mldr.MLDREvalDataLoader.available_dataset_names\n.. automethod:: FlagEmbedding.evaluation.mldr.MLDREvalDataLoader.available_splits\n.. automethod:: FlagEmbedding.evaluation.mldr.MLDREvalDataLoader._load_remote_corpus\n.. automethod:: FlagEmbedding.evaluation.mldr.MLDREvalDataLoader._load_remote_qrels\n.. automethod:: FlagEmbedding.evaluation.mldr.MLDREvalDataLoader._load_remote_queries" }, { "path": "docs/source/API/evaluation/mldr/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.evaluation.mldr.MLDREvalRunner\n :members:" }, { "path": "docs/source/API/evaluation/mldr.rst", "content": "MLDR\n====\n\n`MLDR `_ is a Multilingual Long-Document Retrieval dataset built on Wikipeida, Wudao and mC4, covering 13 typologically diverse languages. \nSpecifically, we sample lengthy articles from Wikipedia, Wudao and mC4 datasets and randomly choose paragraphs from them. \nThen we use GPT-3.5 to generate questions based on these paragraphs. \nThe generated question and the sampled article constitute a new text pair to the dataset.\n\nAn example of ``train`` set looks like:\n\n.. code:: bash\n\n {\n 'query_id': 'q-zh-<...>', \n 'query': '...', \n 'positive_passages': [\n {\n 'docid': 'doc-zh-<...>',\n 'text': '...'\n }\n ],\n 'negative_passages': [\n {\n 'docid': 'doc-zh-<...>',\n 'text': '...'\n },\n ...\n ]\n }\n\nAn example of ``dev`` and ``test`` set looks like:\n\n.. code:: bash\n\n {\n 'query_id': 'q-zh-<...>', \n 'query': '...', \n 'positive_passages': [\n {\n 'docid': 'doc-zh-<...>',\n 'text': '...'\n }\n ],\n 'negative_passages': []\n }\n\nAn example of ``corpus`` looks like:\n\n.. code:: bash\n\n {\n 'docid': 'doc-zh-<...>', \n 'text': '...'\n }\n\nYou can evaluate model's performance on MLDR simply by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/mldr/eval_mldr.sh\n ./examples/evaluation/mldr/eval_mldr.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.mldr \\\n --eval_name mldr \\\n --dataset_dir ./mldr/data \\\n --dataset_names hi \\\n --splits test \\\n --corpus_embd_save_dir ./mldr/corpus_embd \\\n --output_dir ./mldr/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path /root/.cache/huggingface/hub \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./mldr/mldr_eval_results.md \\\n --eval_metrics ndcg_at_10 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir /root/.cache/huggingface/hub \\\n --embedder_passage_max_length 8192 \\\n --reranker_max_length 8192\n\nchange the args of embedder, reranker, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n mldr/data_loader\n mldr/runner" }, { "path": "docs/source/API/evaluation/msmarco/data_loader.rst", "content": "data_loader\n===========\n\n.. autoclass:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalDataLoader\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalDataLoader.available_dataset_names\n.. automethod:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalDataLoader.available_splits\n.. automethod:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalDataLoader._load_remote_corpus\n.. automethod:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalDataLoader._load_remote_qrels\n.. automethod:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalDataLoader._load_remote_queries" }, { "path": "docs/source/API/evaluation/msmarco/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.evaluation.msmarco.MSMARCOEvalRunner\n :members:" }, { "path": "docs/source/API/evaluation/msmarco.rst", "content": "MSMARCO\n=======\n\n`MS Marco `_ (Microsoft MAchine Reading Comprehension) is a large scale real-world reading comprehension dataset.\nIt is widely used in information retrieval, question answering, and natural language processing research.\n\n\nYou can evaluate model's performance on MS MARCO simply by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/msmarco/eval_msmarco.sh\n ./examples/evaluation/msmarco/eval_msmarco.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.msmarco \\\n --eval_name msmarco \\\n --dataset_dir ./msmarco/data \\\n --dataset_names passage \\\n --splits dev \\\n --corpus_embd_save_dir ./msmarco/corpus_embd \\\n --output_dir ./msmarco/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path /root/.cache/huggingface/hub \\\n --overwrite True \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./msmarco/msmarco_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 cuda:2 cuda:3 cuda:4 cuda:5 cuda:6 cuda:7 \\\n --cache_dir /root/.cache/huggingface/hub \\\n --reranker_max_length 1024\n\nchange the embedder, reranker, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n msmarco/data_loader\n msmarco/runner" }, { "path": "docs/source/API/evaluation/mteb/arguments.rst", "content": "arguments\n=========\n\n.. autoclass:: FlagEmbedding.evaluation.mteb.arguments.MTEBEvalArgs" }, { "path": "docs/source/API/evaluation/mteb/runner.rst", "content": "runner\n======\n\n.. autoclass:: FlagEmbedding.evaluation.mteb.runner.MTEBEvalRunner" }, { "path": "docs/source/API/evaluation/mteb/searcher.rst", "content": "searcher\n========\n\n.. autoclass:: FlagEmbedding.evaluation.mteb.searcher.MTEBEvalDenseRetriever\n\n.. autoclass:: FlagEmbedding.evaluation.mteb.searcher.MTEBEvalReranker" }, { "path": "docs/source/API/evaluation/mteb.rst", "content": "MTEB\n====\n\n`MTEB `_ (The Massive Text Embedding Benchmark) is a large-scale evaluation framework designed to assess the performance of text embedding models across a wide variety of NLP tasks. \nIntroduced to standardize and improve the evaluation of text embeddings, MTEB is crucial for assessing how well these models generalize across various real-world applications. \nIt contains a wide range of datasets in eight main NLP tasks and different languages, and provides an easy pipeline for evaluation.\nIt also holds the well known MTEB `leaderboard `_, which contains a ranking of the latest first-class embedding models.\n\nYou can evaluate model's performance on the whole MTEB benchmark by running our provided shell script:\n\n.. code:: bash\n\n chmod +x /examples/evaluation/mteb/eval_mteb.sh\n ./examples/evaluation/mteb/eval_mteb.sh\n\nOr by running:\n\n.. code:: bash\n\n python -m FlagEmbedding.evaluation.mteb \\\n --eval_name mteb \\\n --output_dir ./mteb/search_results \\\n --languages eng \\\n --tasks NFCorpus BiorxivClusteringS2S SciDocsRR \\\n --eval_output_path ./mteb/mteb_eval_results.json \\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --devices cuda:7 \\\n --cache_dir /root/.cache/huggingface/hub\n\nchange the embedder, devices and cache directory to your preference.\n\n.. toctree::\n :hidden:\n\n mteb/arguments\n mteb/searcher\n mteb/runner" }, { "path": "docs/source/API/evaluation.rst", "content": "Evaluation\n==========\n\n.. toctree::\n evaluation/mteb\n evaluation/airbench\n evaluation/msmarco\n evaluation/beir\n evaluation/miracl\n evaluation/mkqa\n evaluation/mldr" }, { "path": "docs/source/API/finetune/embedder/decoder_only/base/arguments.rst", "content": "Arguments\n=========\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.base.DecoderOnlyEmbedderModelArguments\n" }, { "path": "docs/source/API/finetune/embedder/decoder_only/base/modeling.rst", "content": "========\nModeling\n========\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.base.CrossDecoderModel\n\nMethods\n=======\n\n.. automethod:: FlagEmbedding.finetune.reranker.decoder_only.base.CrossDecoderModel.encode\n" }, { "path": "docs/source/API/finetune/embedder/decoder_only/base/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.base.DecoderOnlyRerankerRunner\n :members:" }, { "path": "docs/source/API/finetune/embedder/decoder_only/base/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.base.DecoderOnlyRerankerTrainer\n :members:" }, { "path": "docs/source/API/finetune/embedder/decoder_only/base.rst", "content": "Base\n====\n\n.. toctree::\n base/arguments\n base/modeling\n base/runner\n base/trainer" }, { "path": "docs/source/API/finetune/embedder/decoder_only/icl/arguments.rst", "content": "Arguments\n=========\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.DecoderOnlyEmbedderICLModelArguments\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.DecoderOnlyEmbedderICLDataArguments" }, { "path": "docs/source/API/finetune/embedder/decoder_only/icl/dataset.rst", "content": "=======\nDataset\n=======\n\nDecoderOnlyEmbedderICLSameDatasetTrainDataset\n=============================================\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.DecoderOnlyEmbedderICLSameDatasetTrainDataset\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.DecoderOnlyEmbedderICLSameDatasetTrainDataset._create_batch_data\n\nAbsEmbedderSameDatasetCollator\n==============================\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.AbsEmbedderSameDatasetCollator" }, { "path": "docs/source/API/finetune/embedder/decoder_only/icl/modeling.rst", "content": "========\nModeling\n========\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel\n\nMethods\n=======\n\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel.encode\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel.compute_score\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel.compute_loss\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel.gradient_checkpointing_enable\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel.enable_input_require_grads\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel.save\n\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel._sentence_embedding\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.icl.BiDecoderOnlyEmbedderICLModel._compute_similarity\n" }, { "path": "docs/source/API/finetune/embedder/decoder_only/icl/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.DecoderOnlyEmbedderICLRunner\n :members:" }, { "path": "docs/source/API/finetune/embedder/decoder_only/icl/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.icl.DecoderOnlyEmbedderICLTrainer\n :members:" }, { "path": "docs/source/API/finetune/embedder/decoder_only/icl.rst", "content": "ICL\n===\n\n.. toctree::\n icl/arguments\n icl/dataset\n icl/modeling\n icl/runner\n icl/trainer" }, { "path": "docs/source/API/finetune/embedder/decoder_only.rst", "content": "Decoder Only\n============\n\n.. toctree::\n decoder_only/base\n decoder_only/icl" }, { "path": "docs/source/API/finetune/embedder/encoder_only/base/modeling.rst", "content": "Modeling\n========\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel.encode\n\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel.compute_score\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel.compute_loss\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel.gradient_checkpointing_enable\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel.enable_input_require_grads\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel.save\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel._sentence_embedding\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.base.BiEncoderOnlyEmbedderModel._compute_similarity" }, { "path": "docs/source/API/finetune/embedder/encoder_only/base/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.base.EncoderOnlyEmbedderRunner\n :members:" }, { "path": "docs/source/API/finetune/embedder/encoder_only/base/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.base.EncoderOnlyEmbedderTrainer\n :members:" }, { "path": "docs/source/API/finetune/embedder/encoder_only/base.rst", "content": "Base\n====\n\n.. toctree::\n base/modeling\n base/runner\n base/trainer" }, { "path": "docs/source/API/finetune/embedder/encoder_only/m3/arguments.rst", "content": "Arguments\n=========\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3ModelArguments\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3TrainingArguments" }, { "path": "docs/source/API/finetune/embedder/encoder_only/m3/modeling.rst", "content": "========\nModeling\n========\n\nEncoderOnlyEmbedderM3Model\n============================\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.encode\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.compute_score\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.compute_dense_score\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.compute_sparse_score\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.compute_colbert_score\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.ensemble_score\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.forward\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.compute_loss\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.gradient_checkpointing_enable\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.enable_input_require_grads\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model.save\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model._dense_embedding\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model._sparse_embedding\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model._colbert_embedding\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model._encode\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model._compute_similarity\n.. automethod:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Model._get_queries_attention_mask\n\nEncoderOnlyEmbedderM3ModelForInference\n======================================\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3ModelForInference\n :members:" }, { "path": "docs/source/API/finetune/embedder/encoder_only/m3/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Runner\n :members:" }, { "path": "docs/source/API/finetune/embedder/encoder_only/m3/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.encoder_only.m3.EncoderOnlyEmbedderM3Trainer\n :members:" }, { "path": "docs/source/API/finetune/embedder/encoder_only/m3.rst", "content": "M3\n==\n\n.. toctree::\n m3/arguments\n m3/modeling\n m3/runner\n m3/trainer" }, { "path": "docs/source/API/finetune/embedder/encoder_only.rst", "content": "Encoder Only\n============\n\n.. toctree::\n encoder_only/base\n encoder_only/m3" }, { "path": "docs/source/API/finetune/embedder.rst", "content": "Embedder\n========\n\n.. toctree::\n embedder/encoder_only\n embedder/decoder_only" }, { "path": "docs/source/API/finetune/reranker/decoder_only/base/arguments.rst", "content": "Arguments\n=========\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.base.RerankerModelArguments\n" }, { "path": "docs/source/API/finetune/reranker/decoder_only/base/modeling.rst", "content": "========\nModeling\n========\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel\n\nMethods\n=======\n\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel.encode\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel.compute_score\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel.compute_loss\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel.gradient_checkpointing_enable\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel.enable_input_require_grads\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel.save\n\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel._sentence_embedding\n.. automethod:: FlagEmbedding.finetune.embedder.decoder_only.base.BiDecoderOnlyEmbedderModel._compute_similarity\n" }, { "path": "docs/source/API/finetune/reranker/decoder_only/base/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.base.DecoderOnlyEmbedderRunner\n :members:" }, { "path": "docs/source/API/finetune/reranker/decoder_only/base/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.embedder.decoder_only.base.DecoderOnlyEmbedderTrainer\n :members:" }, { "path": "docs/source/API/finetune/reranker/decoder_only/base.rst", "content": "Base\n====\n\n.. toctree::\n base/arguments\n base/modeling\n base/runner\n base/trainer" }, { "path": "docs/source/API/finetune/reranker/decoder_only/layerwise/arguments.rst", "content": "Arguments\n=========\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.layerwise.RerankerModelArguments\n" }, { "path": "docs/source/API/finetune/reranker/decoder_only/layerwise/modeling.rst", "content": "========\nModeling\n========\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.layerwise.CrossDecoderModel\n\nMethods\n=======\n\n.. automethod:: FlagEmbedding.finetune.reranker.decoder_only.layerwise.CrossDecoderModel.encode\n.. automethod:: FlagEmbedding.finetune.reranker.decoder_only.layerwise.CrossDecoderModel.forward\n" }, { "path": "docs/source/API/finetune/reranker/decoder_only/layerwise/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.layerwise.DecoderOnlyRerankerRunner\n :members:" }, { "path": "docs/source/API/finetune/reranker/decoder_only/layerwise/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.reranker.decoder_only.layerwise.DecoderOnlyRerankerTrainer\n :members:" }, { "path": "docs/source/API/finetune/reranker/decoder_only/layerwise.rst", "content": "Layerwise\n=========\n\n.. toctree::\n layerwise/arguments\n layerwise/modeling\n layerwise/runner\n layerwise/trainer" }, { "path": "docs/source/API/finetune/reranker/decoder_only.rst", "content": "Decoder Only\n============\n\n.. toctree::\n decoder_only/base\n decoder_only/layerwise" }, { "path": "docs/source/API/finetune/reranker/encoder_only/base/modeling.rst", "content": "Modeling\n========\n\n.. autoclass:: FlagEmbedding.finetune.reranker.encoder_only.base.CrossEncoderModel\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.finetune.reranker.encoder_only.base.CrossEncoderModel.encode\n" }, { "path": "docs/source/API/finetune/reranker/encoder_only/base/runner.rst", "content": "Runner\n======\n\n.. autoclass:: FlagEmbedding.finetune.reranker.encoder_only.base.EncoderOnlyRerankerRunner\n :members:" }, { "path": "docs/source/API/finetune/reranker/encoder_only/base/trainer.rst", "content": "Trainer\n=======\n\n.. autoclass:: FlagEmbedding.finetune.reranker.encoder_only.base.EncoderOnlyRerankerTrainer\n :members:" }, { "path": "docs/source/API/finetune/reranker/encoder_only/base.rst", "content": "Base\n====\n\n.. toctree::\n base/modeling\n base/runner\n base/trainer" }, { "path": "docs/source/API/finetune/reranker/encoder_only.rst", "content": "Encoder Only\n============\n\n.. toctree::\n encoder_only/base" }, { "path": "docs/source/API/finetune/reranker.rst", "content": "Reranker\n========\n\n.. toctree::\n reranker/encoder_only\n reranker/decoder_only" }, { "path": "docs/source/API/finetune.rst", "content": "Finetune\n========\n\n.. toctree::\n finetune/embedder\n finetune/reranker" }, { "path": "docs/source/API/index.rst", "content": "API\n===\n\n.. toctree::\n :maxdepth: 1\n\n abc\n inference\n evaluation\n finetune" }, { "path": "docs/source/API/inference/FlagAutoModel.rst", "content": "FlagAutoModel\n=============\n\n.. autoclass:: FlagEmbedding.inference.FlagAutoModel\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.inference.FlagAutoModel.from_finetuned" }, { "path": "docs/source/API/inference/FlagAutoReranker.rst", "content": "FlagAutoReranker\n================\n\n.. autoclass:: FlagEmbedding.inference.FlagAutoReranker\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.inference.FlagAutoReranker.from_finetuned" }, { "path": "docs/source/API/inference/embedder/decoder_only/BaseLLMEmbedder.rst", "content": "BaseEmbedder\n============\n\n.. autoclass:: FlagEmbedding.inference.embedder.decoder_only.base.BaseLLMEmbedder\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.base.BaseLLMEmbedder.encode_queries\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.base.BaseLLMEmbedder.encode_corpus\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.base.BaseLLMEmbedder.encode\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.base.BaseLLMEmbedder.encode_single_device" }, { "path": "docs/source/API/inference/embedder/decoder_only/ICLLLMEmbedder.rst", "content": "ICLLLMEmbedder\n==============\n\n.. autoclass:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.encode_queries\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.encode_corpus\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.encode\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.set_examples\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.get_detailed_example\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.encode_queries_single_device\n\n.. automethod:: FlagEmbedding.inference.embedder.decoder_only.icl.ICLLLMEmbedder.encode_single_device" }, { "path": "docs/source/API/inference/embedder/embedder.rst", "content": "Embedder\n========\n\n.. toctree::\n encoder_only/BaseEmbedder\n encoder_only/M3Embedder\n decoder_only/BaseLLMEmbedder\n decoder_only/ICLLLMEmbedder" }, { "path": "docs/source/API/inference/embedder/encoder_only/BaseEmbedder.rst", "content": "BaseEmbedder\n============\n\n.. autoclass:: FlagEmbedding.inference.embedder.encoder_only.base.BaseEmbedder\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.base.BaseEmbedder.encode_queries\n :no-index:\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.base.BaseEmbedder.encode_corpus\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.base.BaseEmbedder.encode\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.base.BaseEmbedder.encode_single_device\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.base.BaseEmbedder.pooling" }, { "path": "docs/source/API/inference/embedder/encoder_only/M3Embedder.rst", "content": "M3Embedder\n============\n\n.. autoclass:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder\n\nMethods\n-------\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.encode_queries\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.encode_corpus\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.encode\n \n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.convert_id_to_token\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.compute_lexical_matching_score\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.colbert_score\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.encode_single_device\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.compute_score\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.compute_score_multi_process\n\n.. automethod:: FlagEmbedding.inference.embedder.encoder_only.m3.M3Embedder.compute_score_single_device" }, { "path": "docs/source/API/inference/reranker/decoder_only/BaseLLMReranker.rst", "content": "BaseLLMReranker\n===============\n\n.. autoclass:: FlagEmbedding.inference.reranker.decoder_only.base.BaseLLMReranker\n\nMethods\n-------\n\n.. autoclass:: FlagEmbedding.inference.reranker.decoder_only.base.BaseLLMReranker.compute_score_single_gpu\n" }, { "path": "docs/source/API/inference/reranker/decoder_only/LayerWiseLLMReranker.rst", "content": "LayerWiseLLMReranker\n====================\n\n.. autoclass:: FlagEmbedding.inference.reranker.decoder_only.layerwise.LayerWiseLLMReranker\n\nMethods\n-------\n\n.. autoclass:: FlagEmbedding.inference.reranker.decoder_only.layerwise.LayerWiseLLMReranker.compute_score_single_gpu\n" }, { "path": "docs/source/API/inference/reranker/decoder_only/LightweightLLMReranker.rst", "content": "LightweightLLMReranker\n======================\n\n.. autoclass:: FlagEmbedding.inference.reranker.decoder_only.lightweight.LightweightLLMReranker\n\nMethods\n-------\n\n.. autoclass:: FlagEmbedding.inference.reranker.decoder_only.lightweight.LightweightLLMReranker.compute_score_single_gpu\n" }, { "path": "docs/source/API/inference/reranker/encoder_only/BaseReranker.rst", "content": "BaseReranker\n============\n\n.. autoclass:: FlagEmbedding.inference.reranker.encoder_only.base.BaseReranker\n\nMethods\n-------\n\n.. autoclass:: FlagEmbedding.inference.reranker.encoder_only.base.BaseReranker.compute_score_single_gpu\n" }, { "path": "docs/source/API/inference/reranker/reranker.rst", "content": "Reranker\n========\n\n.. toctree::\n encoder_only/BaseReranker\n decoder_only/BaseLLMReranker\n decoder_only/LayerWiseLLMReranker\n decoder_only/LightweightLLMReranker" }, { "path": "docs/source/API/inference.rst", "content": "Inference\n=========\n\n.. toctree::\n inference/FlagAutoModel\n inference/FlagAutoReranker\n inference/embedder/embedder\n inference/reranker/reranker" }, { "path": "docs/source/C-MTEB.rst", "content": ".. C-MTEB\n.. ======\n\n.. Introduction\n.. ------------\n\n.. `C-MTEB `_ is a benchmark for chinese text embedding. It contains 35\n.. datasets in 6 different tasks, providing a comprehensive evaluation to the quality of an embedding model on Chinese.\n\n\n.. .. image:: ../_static/img/C_MTEB.png\n.. :width: 700\n.. :align: center\n\n\n.. Installation\n.. ------------\n\n.. C-MTEB is developed based on MTEB, you can install C-MTEB by:\n\n.. .. code:: bash\n\n.. pip install -U C_MTEB\n\n.. or install by FlagEmbedding's repo:\n\n.. .. code:: bash\n\n.. git clone https://github.com/FlagOpen/FlagEmbedding.git\n.. cd FlagEmbedding/C_MTEB\n.. pip install -e .\n\n.. Citing the Work\n.. ---------------\n\n.. There are more details in our publication. If you find C-MTEB useful, you can cite it by:\n\n.. .. code::\n\n.. @misc{c-pack,\n.. title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n.. author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n.. year={2023},\n.. eprint={2309.07597},\n.. archivePrefix={arXiv},\n.. primaryClass={cs.CL}\n.. }" }, { "path": "docs/source/FAQ/index.rst", "content": "FAQ\n===\n\nBelow are some commonly asked questions.\n\n.. tip::\n\n For more questions, search in issues on GitHub or join our community!\n\n.. dropdown:: Having network issue when connecting to Hugging Face?\n :animate: fade-in-slide-down\n\n Try to set the :code:`HF_ENDPOINT` to `HF mirror `_ instead.\n\n .. code:: bash\n\n export HF_ENDPOINT=https://hf-mirror.com\n\n.. dropdown:: When does the query instruction need to be used?\n :animate: fade-in-slide-down\n\n For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. \n The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task. \n In all cases, the documents/passages do not need to add the instruction.\n\n.. dropdown:: Why it takes quite long to just encode 1 sentence?\n :animate: fade-in-slide-down\n\n Note that if you have multiple CUDA GPUs, FlagEmbedding will automatically use all of them. \n Then the time used to start the multi-process will cost way longer than the actual encoding.\n Try to just use CPU or just single GPU for simple tasks.\n\n.. dropdown:: The embedding results are different for CPU and GPU?\n :animate: fade-in-slide-down\n\n The encode function will use FP16 by default if GPU is available, which leads to different precision. \n Set :code:`fp16=False` to get full precision.\n\n.. dropdown:: How many languages do the multi-lingual models support?\n :animate: fade-in-slide-down\n\n The training datasets cover up to 170+ languages. \n But note that due to the unbalanced distribution of languages, the performances will be different.\n Please further test refer to the real application scenario.\n\n.. dropdown:: How does the different retrieval method works in bge-m3?\n :animate: fade-in-slide-down\n\n - Dense retrieval: map the text into a single embedding, e.g., `DPR `_, `BGE-v1.5 <../bge/bge_v1_v1.5>`_\n - Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, `unicoil `_, and `splade `_\n - Multi-vector retrieval: use multiple vectors to represent a text, e.g., `ColBERT `_.\n\n.. dropdown:: Recommended vector database?\n :animate: fade-in-slide-down\n\n Generally you can use any vector database (open-sourced, commercial). We use `Faiss `_ by default in our evaluation pipeline and tutorials.\n\n.. dropdown:: No enough VRAM or OOM error during evaluation?\n :animate: fade-in-slide-down\n\n The default values of :code:`embedder_batch_size` and :code:`reranker_batch_size` are both 3000. Try a smaller value.\n" }, { "path": "docs/source/Introduction/IR.rst", "content": "Information Retrieval\n=====================\n\nWhat is Information Retrieval?\n------------------------------\n\nSimply put, Information Retrieval (IR) is the science of searching and retrieving information from a large collection of data based on a user's query. \nThe goal of an IR system is not just to return a list of documents but to ensure that the most relevant ones appear at the top of the results.\n\nA very straightforward example of IR is library catalog. One wants to find the book that best matches the query, but there are thousands or millions of books on the shelf.\nThe library's catalog system helps you find the best matches based on your search terms. \nIn modern digital world, search engines and databases work in a similar way, using sophisticated algorithms and models to retrieve, rank and return the most relevant results.\nAnd the resource categories are expanding from text to more modalities such as images, videos, 3D objects, music, etc.\n\nIR and Embedding Model\n----------------------\n\nTraditional IR methods, like TF-IDF and BM25, rely on statistical and heuristic techniques to rank documents based on term frequency and document relevance.\nThese methods are efficient and effective for keyword-based search but often struggle with understanding the deeper context or semantics of the text.\n\n.. seealso::\n \n Take a very simple example with two sentences:\n\n .. code:: python\n\n sentence_1 = \"watch a play\"\n sentence_2 = \"play with a watch\"\n\n Sentence 1 means going for a show/performance, which has watch as a verb and play as a noun.\n\n However sentence 2 means someone is interacting with a timepiece on wrist, which has play as a verb and watch as a noun.\n\nThese two sentences could be regard as very similar to each other when using the traditional IR methods though they actually have totally different semantic meaning. \nThen how could we solve this? The best answer up until now is embedding models.\n\nEmbedding models have revolutionized IR by representing text as dense vectors in a high-dimensional space, capturing the semantic meaning of words, sentences, or even entire documents. \nThis allows for more sophisticated search capabilities, such as semantic search, where results are ranked based on meaning rather than simple keyword matching." }, { "path": "docs/source/Introduction/embedder.rst", "content": "Embedder\n========\n\n.. tip::\n \n If you are already familiar with the concepts, take a look at the :doc:`BGE models <../bge/index>`!\n\nEmbedder, or embedding model, bi-encoder, is a model designed to convert data, usually text, codes, or images, into sparse or dense numerical vectors (embeddings) in a high dimensional vector space.\nThese embeddings capture the semantic meaning or key features of the input, which enable efficient comparison and analysis.\n\nA very famous demonstration is the example from `word2vec `_. It shows how word embeddings capture semantic relationships through vector arithmetic:\n\n.. image:: ../_static/img/word2vec.png\n :width: 500\n :align: center\n\nNowadays, embedders are capable of mapping sentences and even passages into vector space.\nThey are widely used in real world tasks such as retrieval, clustering, etc.\nIn the era of LLMs, embedding models play a pivot role in RAG, enables LLMs to access and integrate relevant context from vast external datasets.\n\n\nSparse Vector\n-------------\n\nSparse vectors usually have structure of high dimensionality with only a few non-zero values, which usually effective for tasks like keyword matching.\nTypically, though not always, the number of dimensions in sparse vectors corresponds to the different tokens present in the language.\nEach dimension is assigned a value representing the token's relative importance within the document.\nSome well known algorithms for sparse vector embedding includes `bag-of-words `_, `TF-IDF `_, `BM25 `_, etc.\nSparse vector embeddings have great ability to extract the information of key terms and their corresponding importance within documents.\n\nDense Vector\n------------\n\nDense vectors typically use neural networks to map words, sentences, and passages into a fixed dimension latent vector space. \nThen we can compare the similarity between two objects using certain metrics like Euclidean distance or Cos similarity.\nThose vectors can represent deeper meaning of the sentences.\nThus we can distinguish sentences using similar words but actually have different meaning. \nAnd also understand different ways in speaking and writing that express the same thing.\nDense vector embeddings, instead of keywords counting and matching, directly capture the semantics." }, { "path": "docs/source/Introduction/index.rst", "content": "Introduction\n============\n\nBGE builds one-stop retrieval toolkit for search and RAG. We provide inference, evaluation, and fine-tuning for embedding models and reranker.\n\n.. figure:: ../_static/img/RAG_pipeline.png\n :width: 700\n :align: center\n\n BGE embedder and reranker in an RAG pipeline. `Source `_\n\nQuickly get started with:\n\n.. toctree::\n :maxdepth: 1\n :caption: Start\n\n overview\n installation\n quick_start\n \n\n.. toctree::\n :maxdepth: 1\n :caption: Concept\n\n IR\n embedder\n reranker\n similarity\n retrieval_demo" }, { "path": "docs/source/Introduction/installation.rst", "content": ":github_url: https://github.com/AI4Finance-Foundation/FinRL\n\nInstallation\n============\n\nUsing pip:\n----------\n\nIf you do not need to finetune the models, you can install the package without the finetune dependency:\n\n.. code:: bash\n\n pip install -U FlagEmbedding\n\nIf you want to finetune the models, you can install the package with the finetune dependency:\n\n.. code:: bash\n\n pip install -U FlagEmbedding[finetune]\n\n\nInstall from sources:\n---------------------\n\nClone the repository and install\n\n.. code:: bash\n\n git clone https://github.com/FlagOpen/FlagEmbedding.git\n cd FlagEmbedding\n # If you do not need to finetune the models, you can install the package without the finetune dependency:\n pip install .\n # If you want to finetune the models, install the package with the finetune dependency:\n pip install .[finetune]\n\nFor development in editable mode:\n\n.. code:: bash\n\n # If you do not need to finetune the models, you can install the package without the finetune dependency:\n pip install -e .\n # If you want to finetune the models, install the package with the finetune dependency:\n pip install -e .[finetune]\n\nPyTorch-CUDA\n------------\n\nIf you want to use CUDA GPUs during inference and finetuning, please install appropriate version of `PyTorch `_ with CUDA support." }, { "path": "docs/source/Introduction/overview.rst", "content": "Overview\n========\n\nOur repository provides well-structured `APIs `_ for the inference, evaluation, and fine-tuning of BGE series models.\nBesides that, there are abundant resources of and for users to quickly get a hands-on experience.\n\n.. figure:: https://raw.githubusercontent.com/FlagOpen/FlagEmbedding/refs/heads/master/imgs/projects.png\n :width: 700\n :align: center\n\n Structure of contents in our `repo `_\n\nOur repository provides well-structured contents for information retrieval and RAG:\n\n- The core `APIs <../API>`_ for embedding models' inference, evaluation, and fine-tuning.\n- Hands-on `examples `_ for the three mentioned use cases.\n- Detailed `tutorials `_ covering topics in retrieval to help you learn from scratch." }, { "path": "docs/source/Introduction/quick_start.rst", "content": "Quick Start\n===========\n\nFirst, load one of the BGE embedding model:\n\n.. code:: python\n\n from FlagEmbedding import FlagAutoModel\n\n model = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5')\n\n.. tip::\n\n If there's difficulty connecting to Hugging Face, you can use the `HF mirror `_ instead.\n\n .. code:: bash\n\n export HF_ENDPOINT=https://hf-mirror.com\n\nThen, feed some sentences to the model and get their embeddings:\n\n.. code:: python\n\n sentences_1 = [\"I love NLP\", \"I love machine learning\"]\n sentences_2 = [\"I love BGE\", \"I love text retrieval\"]\n embeddings_1 = model.encode(sentences_1)\n embeddings_2 = model.encode(sentences_2)\n\nOnce we get the embeddings, we can compute similarity by inner product:\n\n.. code:: python\n\n similarity = embeddings_1 @ embeddings_2.T\n print(similarity)\n" }, { "path": "docs/source/Introduction/reranker.rst", "content": "Reranker\n========\n\n.. tip::\n \n If you are already familiar with the concepts, take a look at the :doc:`BGE rerankers <../bge/index>`!\n\nReranker, or Cross-Encoder, is a model that refines the ranking of candidate pairs (e.g., query-document pairs) by jointly encoding and scoring them.\n\nTypically, we use embedder as a Bi-Encoder. It first computes the embeddings of two input sentences, then compute their similarity using metrics such as cosine similarity or Euclidean distance.\nWhereas a reranker takes two sentences at the same time and directly computer a score representing their similarity.\n\nThe following figure shows their difference:\n\n.. figure:: https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/Bi_vs_Cross-Encoder.png\n :width: 500\n :align: center\n \n Bi-Encoder & Cross-Encoder (from Sentence Transformers)\n\nAlthough Cross-Encoder usually has better performances than Bi-Encoder, it is extremly time consuming to use Cross-Encoder if we have a great amount of data. \nThus a widely accepted approach is to use a Bi-Encoder for initial retrieval (e.g., selecting the top 100 candidates from 100,000 sentences) and then refine the ranking of the selected candidates using a Cross-Encoder for more accurate results.\n" }, { "path": "docs/source/Introduction/retrieval_demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Retrieval Demo\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will show how to use BGE models on a text retrieval task in 5 minutes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 0: Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, install FlagEmbedding in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is a super tiny courpus with only 10 sentences, which will be the dataset we use.\\n\",\n \"\\n\",\n \"Each sentence is a concise discription of a famous people in specific domain.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = [\\n\",\n \" \\\"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\\\",\\n\",\n \" \\\"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\\\",\\n\",\n \" \\\"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\\\",\\n\",\n \" \\\"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\\",\\n\",\n \" \\\"Eminem is a renowned rapper and one of the best-selling music artists of all time.\\\",\\n\",\n \" \\\"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\\",\\n\",\n \" \\\"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\\\",\\n\",\n \" \\\"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\\\",\\n\",\n \" \\\"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\\\",\\n\",\n \" \\\"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We want to know which one of these people could be an expert of neural network and who he/she is. \\n\",\n \"\\n\",\n \"Thus we generate the following query:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query = \\\"Who could be an expert of neural network?\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Text -> Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's use a [BGE embedding model](https://huggingface.co/BAAI/bge-base-en-v1.5) to create sentence embedding for the corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the query and corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"query_embedding = model.encode(query)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The embedding of each sentence is a vector with length 768. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the query embedding: (768,)\\n\",\n \"shape of the corpus embeddings: (10, 768)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"shape of the query embedding: \\\", query_embedding.shape)\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the following print line to take a look at the first 10 elements of the query embedding vector.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-0.00790005 -0.00683443 -0.00806659 0.00756918 0.04374858 0.02838556\\n\",\n \" 0.02357143 -0.02270943 -0.03611493 -0.03038301]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(query_embedding[:10])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Calculate Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, we have the embeddings of the query and the corpus. The next step is to calculate the similarity between the query and each sentence in the corpus. Here we use the dot product/inner product as our similarity metric.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.39290053 0.6031525 0.32672375 0.6082418 0.39446455 0.35350388\\n\",\n \" 0.4626108 0.40196604 0.5284606 0.36792332]\\n\"\n ]\n }\n ],\n \"source\": [\n \"sim_scores = query_embedding @ corpus_embeddings.T\\n\",\n \"print(sim_scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The result is a list of score representing the query's similarity to: [sentence 0, sentence 1, sentence 2, ...]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Ranking\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we have the similarity score of the query to each sentence in the corpus, we can rank them from large to small.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3, 1, 8, 6, 7, 4, 0, 9, 5, 2]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get the indices in sorted order\\n\",\n \"sorted_indices = sorted(range(len(sim_scores)), key=lambda k: sim_scores[k], reverse=True)\\n\",\n \"print(sorted_indices)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now from the ranking, the sentence with index 3 is the best answer to our query \\\"Who could be an expert of neural network?\\\"\\n\",\n \"\\n\",\n \"And that person is Geoffrey Hinton!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(corpus[3])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"According to the order of indecies, we can print out the ranking of people that our little retriever got.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Score of 0.608: \\\"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\\"\\n\",\n \"Score of 0.603: \\\"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\\\"\\n\",\n \"Score of 0.528: \\\"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\\\"\\n\",\n \"Score of 0.463: \\\"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\\\"\\n\",\n \"Score of 0.402: \\\"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\\\"\\n\",\n \"Score of 0.394: \\\"Eminem is a renowned rapper and one of the best-selling music artists of all time.\\\"\\n\",\n \"Score of 0.393: \\\"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\\\"\\n\",\n \"Score of 0.368: \\\"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\\"\\n\",\n \"Score of 0.354: \\\"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\\"\\n\",\n \"Score of 0.327: \\\"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\\\"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# iteratively print the score and corresponding sentences in descending order\\n\",\n \"\\n\",\n \"for i in sorted_indices:\\n\",\n \" print(f\\\"Score of {sim_scores[i]:.3f}: \\\\\\\"{corpus[i]}\\\\\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the ranking, not surprisingly, the similarity scores of the query and the discriptions of Geoffrey Hinton and Fei-Fei Li is way higher than others, following by those of Andrew Ng and Sam Altman. \\n\",\n \"\\n\",\n \"While the key phrase \\\"neural network\\\" in the query does not appear in any of those discriptions, the BGE embedding model is still powerful enough to get the semantic meaning of query and corpus well.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've seen the embedding model performed pretty well on the \\\"neural network\\\" query. What about the more general quality?\\n\",\n \"\\n\",\n \"Let's generate a very small dataset of queries and corresponding ground truth answers. Note that the ground truth answers are the indices of sentences in the corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = [\\n\",\n \" \\\"Who could be an expert of neural network?\\\",\\n\",\n \" \\\"Who might had won Grammy?\\\",\\n\",\n \" \\\"Won Academy Awards\\\",\\n\",\n \" \\\"One of the most famous female singers.\\\",\\n\",\n \" \\\"Inventor of AlexNet\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ground_truth = [\\n\",\n \" [1, 3],\\n\",\n \" [0, 4, 5],\\n\",\n \" [2, 7, 9],\\n\",\n \" [5],\\n\",\n \" [3],\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we repeat the steps we covered above to get the predicted ranking of each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[[3, 1, 8, 6, 7, 4, 0, 9, 5, 2],\\n\",\n \" [5, 0, 3, 4, 1, 9, 7, 2, 6, 8],\\n\",\n \" [3, 2, 7, 5, 9, 0, 1, 4, 6, 8],\\n\",\n \" [5, 0, 4, 7, 1, 9, 2, 3, 6, 8],\\n\",\n \" [3, 1, 8, 6, 0, 7, 5, 9, 4, 2]]\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# use bge model to generate embeddings for all the queries\\n\",\n \"queries_embedding = model.encode(queries)\\n\",\n \"# compute similarity scores\\n\",\n \"scores = queries_embedding @ corpus_embeddings.T\\n\",\n \"# get he final rankings\\n\",\n \"rankings = [sorted(range(len(sim_scores)), key=lambda k: sim_scores[k], reverse=True) for sim_scores in scores]\\n\",\n \"rankings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. Here we use that to have a very rough idea how our system performs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def MRR(preds, labels, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, label in zip(preds, labels):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, index in enumerate(pred):\\n\",\n \" if j < c and index in label:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We choose to use 1 and 5 as our cutoffs, with the result of 0.8 and 0.9 respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1: 0.8\\n\",\n \"MRR@5: 0.9\\n\"\n ]\n }\n ],\n \"source\": [\n \"cutoffs = [1, 5]\\n\",\n \"mrrs = MRR(rankings, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"MRR@{c}: {mrrs[i]}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/Introduction/similarity.rst", "content": "Similarity\n==========\n\nA primary goal of retrieval is to find the most relevant documents in response to a user's query. \nOne of the core components of this process is measuring similarity between the query and candidates.\nSimilarity metrics quantify how closely related two pieces of data are, and guide the retrieval system in ranking results.\n\nJaccard Similarity\n------------------\n\n.. math::\n\n J(A,B)=\\frac{|A\\cap B|}{|A\\cup B|}\n\nThe Jaccard similarity or Jaccard index is commonly used for set-based similarity, particularly in binary data (e.g., whether a term appears in a document or not). \nIt is calculated as the size of the intersection of two sets divided by the size of their union. \nIn information retrieval, it's often used to compare sets of keywords or phrases, with higher values indicating more similarity.\n\nEuclidean Distance\n------------------\n\n.. math::\n\n d(A, B) = \\|A-B\\|_2 = \\sqrt{\\sum_{i=1}^n (A_i-B_i)^2}\n\nEuclidean distance measures the straight-line distance between two points in a vector space. \nIn IR, this can be used to assess the difference between document or query vectors. \nA smaller distance indicates greater similarity. \nThis metric is intuitive but can sometimes be sensitive to the scale of the data, especially in high-dimensional spaces like text embeddings.\n\nCosine Similarity\n-----------------\n\n.. math::\n\n \\cos(\\theta)=\\frac{A\\cdot B}{\\|A\\|\\|B\\|}\n\nCosine similarity is one of the most widely used metrics in information retrieval, especially for text. \nIt measures the cosine of the angle between two vectors in a multi-dimensional space (typically representing term frequency vectors of documents and queries). \nIf the cosine similarity is closer to 1, the vectors are more similar. \nA value of 0 indicates orthogonality, meaning no similarity. \nIt's a simple yet effective measure for text-based retrieval, as it considers the orientation but not the magnitude of vectors.\n\nDot Product\n-----------\n\nCoordinate definition:\n.. math::\n\n A\\cdot B = \\sum_{i=1}^{i=n}A_i B_i\n\nGeometric definition:\n.. math::\n\n A\\cdot B = \\|A\\|\\|B\\|\\cos(\\theta)\n\nThe dot product between two vectors provides a measure of how similar the vectors are in terms of direction and magnitude. \nIn information retrieval, the dot product is often used in vector space models, particularly when dealing with pre-trained word or sentence embeddings. \nA higher dot product indicates that the query and document are closely aligned in the vector space.\n\n" }, { "path": "docs/source/_static/css/custom.css", "content": ".bd-sidebar-primary {\n width: 22%;\n line-height: 1.4;\n}\n\n.col-lg-3 {\n flex: 0 0 auto;\n width: 22%;\n}" }, { "path": "docs/source/bge/bge_code.rst", "content": "BGE-Code-v1\n===========\n\n**`BGE-Code-v1 `_** is an LLM-based code embedding model that supports code retrieval, text retrieval, and multilingual retrieval. It primarily demonstrates the following capabilities:\n- Superior Code Retrieval Performance: The model demonstrates exceptional code retrieval capabilities, supporting natural language queries in both English and Chinese, as well as 20 programming languages.\n- Robust Text Retrieval Capabilities: The model maintains strong text retrieval capabilities comparable to text embedding models of similar scale.\n- Extensive Multilingual Support: BGE-Code-v1 offers comprehensive multilingual retrieval capabilities, excelling in languages such as English, Chinese, Japanese, French, and more.\n\n+-------------------------------------------------------------------+-----------------+------------+--------------+----------------------------------------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+===================================================================+=================+============+==============+====================================================================================================+\n| `BAAI/bge-code-v1 `_ | Multilingual | 1.5B | 6.18 GB | SOTA code retrieval model, with exceptional multilingual text retrieval performance as well |\n+-------------------------------------------------------------------+-----------------+------------+--------------+----------------------------------------------------------------------------------------------------+\n\n\n.. code:: python\n from FlagEmbedding import FlagLLMModel\n\n queries = [\n \"Delete the record with ID 4 from the 'Staff' table.\", \n 'Delete all records in the \"Livestock\" table where age is greater than 5'\n ]\n documents = [\n \"DELETE FROM Staff WHERE StaffID = 4;\",\n \"DELETE FROM Livestock WHERE age > 5;\"\n ]\n \n model = FlagLLMModel('BAAI/bge-code-v1', \n query_instruction_format=\"{}\\n{}\",\n query_instruction_for_retrieval=\"Given a question in text, retrieve SQL queries that are appropriate responses to the question.\",\n trust_remote_code=True,\n use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n embeddings_1 = model.encode_queries(queries)\n embeddings_2 = model.encode_corpus(documents)\n similarity = embeddings_1 @ embeddings_2.T\n print(similarity)\n" }, { "path": "docs/source/bge/bge_icl.rst", "content": "BGE-EN-ICL\n==========\n\nBGE-EN-ICL is the new SoTA embedding model in BGE series with capabilities:\n- In-context learning ability: By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.\n- Outstanding performance: The model has achieved state-of-the-art (SOTA) performance on MTEB and AIR-Bench.\n\n+-------------------------------------------------------------------+-----------------+------------+--------------+----------------------------------------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+===================================================================+=================+============+==============+====================================================================================================+\n| `BAAI/bge-en-icl `_ | English | 7.1B | 28.5 GB | In-context learning capabilities, fully leverage the model's potential based on a few shot examples|\n+-------------------------------------------------------------------+-----------------+------------+--------------+----------------------------------------------------------------------------------------------------+\n\n\n\nUsage\n-----\n\n.. code:: python\n\n from FlagEmbedding import FlagICLModel\n\n documents = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ]\n\n examples = [\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"\n },\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"\n }\n ]\n\n queries = [\"how much protein should a female eat\", \"summit define\"]\n\n model = FlagICLModel('BAAI/bge-en-icl', \n examples_for_task=examples, # set `examples_for_task=None` to use model without examples\n examples_instruction_format=\"{}\\n{}\\n{}\") # specify the format to use examples_for_task\n\n embeddings_1 = model.encode_queries(queries)\n embeddings_2 = model.encode_corpus(documents)\n similarity = embeddings_1 @ embeddings_2.T\n\n print(similarity)" }, { "path": "docs/source/bge/bge_m3.rst", "content": "======\nBGE-M3\n======\n\nBGE-M3 is a compound and powerful embedding model distinguished for its versatility in:\n\n- **Multi-Functionality**: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.\n- **Multi-Linguality**: It can support more than 100 working languages.\n- **Multi-Granularity**: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.\n\n+-------------------------------------------------------------------+-----------------+------------+--------------+-----------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+===================================================================+=================+============+==============+=======================================================================+\n| `BAAI/bge-m3 `_ | Multi-Lingual | 569M | 2.27 GB | Multi-Functionality, Multi-Linguality, and Multi-Granularity |\n+-------------------------------------------------------------------+-----------------+------------+--------------+-----------------------------------------------------------------------+\n\nMulti-Linguality\n================\n\nBGE-M3 was trained on multiple datasets covering up to 170+ different languages. \nWhile the amount of training data on languages are highly unbalanced, the actual model performance on different languages will have difference.\n\nFor more information of datasets and evaluation results, please check out our `paper `_ for details.\n\nMulti-Granularity\n=================\n\nWe extend the max position to 8192, enabling the embedding of larger corpus. \nProposing a simple but effective method: MCLS (Multiple CLS) to enhance the model's ability on long text without additional fine-tuning.\n\nMulti-Functionality\n===================\n\n.. code:: python\n\n from FlagEmbedding import BGEM3FlagModel\n\n model = BGEM3FlagModel('BAAI/bge-m3')\n sentences_1 = [\"What is BGE M3?\", \"Defination of BM25\"]\n sentences_2 = [\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"]\n\nDense Retrieval\n---------------\n\nSimilar to BGE v1 or v1.5 models, BGE-M3 use the normalized hidden state of the special token [CLS] as the dense embedding:\n\n.. math:: e_q = norm(H_q[0])\n\nNext, to compute the relevance score between the query and passage:\n\n.. math:: s_{dense}=f_{sim}(e_p, e_q)\n\nwhere :math:`e_p, e_q` are the embedding vectors of passage and query, respectively.\n\n:math:`f_{sim}` is the score function (such as inner product and L2 distance) for comupting two embeddings' similarity.\n\nSparse Retrieval\n----------------\n\nBGE-M3 generates sparce embeddings by adding a linear layer and a ReLU activation function following the hidden states:\n\n.. math:: w_{qt} = \\text{Relu}(W_{lex}^T H_q [i])\n\nwhere :math:`W_{lex}` representes the weights of linear layer and :math:`H_q[i]` is the encoder's output of the :math:`i^{th}` token.\n\nBased on the tokens' weights of query and passage, the relevance score between them is computed by the joint importance of the co-existed terms within the query and passage:\n\n.. math:: s_{lex} = \\sum_{t\\in q\\cap p}(w_{qt} * w_{pt})\n\nwhere :math:`w_{qt}, w_{pt}` are the importance weights of each co-existed term :math:`t` in query and passage, respectively.\n\nMulti-Vector\n------------\n\nThe multi-vector method utilizes the entire output embeddings for the representation of query :math:`E_q` and passage :math:`E_p`.\n\n.. math:: \n\n E_q = norm(W_{mul}^T H_q)\n\n E_p = norm(W_{mul}^T H_p)\n\nwhere :math:`W_{mul}` is the learnable projection matrix.\n\nFollowing ColBert, BGE-M3 use late-interaction to compute the fine-grained relevance score:\n\n.. math:: s_{mul}=\\frac{1}{N}\\sum_{i=1}^N\\max_{j=1}^M E_q[i]\\cdot E_p^T[j]\n\nwhere :math:`E_q, E_p` are the entire output embeddings of query and passage, respectively.\n\nThis is a summation of average of maximum similarity of each :math:`v\\in E_q` with vectors in :math:`E_p`.\n\nHybrid Ranking\n--------------\n\nBGE-M3's multi-functionality gives the possibility of hybrid ranking to improve retrieval. \nFirstly, due to the heavy cost of multi-vector method, we can retrieve the candidate results by either of the dense or sparse method. \nThen, to get the final result, we can rerank the candidates based on the integrated relevance score:\n\n.. math:: s_{rank} = w_1\\cdot s_{dense}+w_2\\cdot s_{lex} + w_3\\cdot s_{mul}\n\nwhere the values chosen for :math:`w_1`, :math:`w_2` and :math:`w_3` varies depending on the downstream scenario.\n\n\nUsage\n=====\n\n.. code:: python\n\n from FlagEmbedding import BGEM3FlagModel\n\n model = BGEM3FlagModel('BAAI/bge-m3')\n\n sentences_1 = [\"What is BGE M3?\", \"Defination of BM25\"]\n\n output = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)\n dense, sparse, multiv = output['dense_vecs'], output['lexical_weights'], output['colbert_vecs']\n\nUseful Links:\n\n`API <../API/inference/embedder/encoder_only/M3Embedder>`_\n`Tutorial `_\n`Example `_" }, { "path": "docs/source/bge/bge_reranker.rst", "content": "BGE-Reranker\n============\n\nDifferent from embedding model, reranker, or cross-encoder uses question and document as input and directly output similarity instead of embedding. \nTo balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. \nFor examples, use a bge embedding model to first retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.\n\nThe first series of BGE-Reranker contains two models, large and base.\n\n+-------------------------------------------------------------------------------+-----------------------+------------+--------------+-----------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+===============================================================================+=======================+============+==============+=======================================================================+\n| `BAAI/bge-reranker-large `_ | English & Chinese | 560M | 2.24 GB | Larger reranker model, easy to deploy with better inference |\n+-------------------------------------------------------------------------------+-----------------------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-reranker-base `_ | English & Chinese | 278M | 1.11 GB | Lightweight reranker model, easy to deploy with fast inference |\n+-------------------------------------------------------------------------------+-----------------------+------------+--------------+-----------------------------------------------------------------------+\n\nbge-reranker-large and bge-reranker-base used `XLM-RoBERTa-Large `_ and `XLM-RoBERTa-Base `_ respectively as the base model. \nThey were trained on high quality English and Chinese data, and acheived State-of-The-Art performance in the level of same size models at the time released.\n\nUsage\n-----\n \n\n.. code:: python\n\n from FlagEmbedding import FlagReranker\n\n reranker = FlagReranker(\n 'BAAI/bge-reranker-base', \n query_max_length=256,\n use_fp16=True,\n devices=['cuda:1'],\n )\n\n score = reranker.compute_score(['I am happy to help', 'Assisting you is my pleasure'])\n print(score)" }, { "path": "docs/source/bge/bge_reranker_v2.rst", "content": "BGE-Reranker-v2\n===============\n\n+------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+==================================================================================================================+=======================+=============+==============+=========================================================================================================================================================+\n| `BAAI/bge-reranker-v2-m3 `_ | Multilingual | 568M | 2.27 GB | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. |\n+------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| `BAAI/bge-reranker-v2-gemma `_ | Multilingual | 2.51B | 10 GB | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. |\n+------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| `BAAI/bge-reranker-v2-minicpm-layerwise `_ | Multilingual | 2.72B | 10.9 GB | Suitable for multilingual contexts, allows freedom to select layers for output, facilitating accelerated inference. |\n+------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n| `BAAI/bge-reranker-v2.5-gemma2-lightweight `_ | Multilingual | 2.72B | 10.9 GB | Suitable for multilingual contexts, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. |\n+------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+\n\n\n.. tip::\n\n You can select the model according your senario and resource:\n\n - For multilingual, utilize :code:`BAAI/bge-reranker-v2-m3`, :code:`BAAI/bge-reranker-v2-gemma` and :code:`BAAI/bge-reranker-v2.5-gemma2-lightweight`.\n - For Chinese or English, utilize :code:`BAAI/bge-reranker-v2-m3` and :code:`BAAI/bge-reranker-v2-minicpm-layerwise`.\n - For efficiency, utilize :code:`BAAI/bge-reranker-v2-m3` and the low layer of :code:`BAAI/bge-reranker-v2-minicpm-layerwise`.\n - For better performance, recommand :code:`BAAI/bge-reranker-v2-minicpm-layerwise` and :code:`BAAI/bge-reranker-v2-gemma`.\n\n Make sure always test on your real use case and choose the one with best speed-quality balance!\n\nUsage\n-----\n\n**bge-reranker-v2-m3**\n\nUse :code:`bge-reranker-v2-m3` in the same way as bge-reranker-base and bge-reranker-large.\n\n.. code:: python\n\n from FlagEmbedding import FlagReranker\n\n # Setting use_fp16 to True speeds up computation with a slight performance degradation\n reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True)\n\n score = reranker.compute_score(['query', 'passage'])\n # or set \"normalize=True\" to apply a sigmoid function to the score for 0-1 range\n score = reranker.compute_score(['query', 'passage'], normalize=True)\n\n print(score)\n\n**bge-reranker-v2-gemma**\n\nUse the :code:`FlagLLMReranker` class for bge-reranker-v2-gemma.\n\n.. code:: python\n\n from FlagEmbedding import FlagLLMReranker\n\n # Setting use_fp16 to True speeds up computation with a slight performance degradation\n reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True)\n\n score = reranker.compute_score(['query', 'passage'])\n print(score)\n\n**bge-reranker-v2-minicpm-layerwise**\n\nUse the :code:`LayerWiseFlagLLMReranker` class for bge-reranker-v2-minicpm-layerwise.\n\n.. code:: python\n\n from FlagEmbedding import LayerWiseFlagLLMReranker\n\n # Setting use_fp16 to True speeds up computation with a slight performance degradation\n reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True)\n\n # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\n score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) \n print(score)\n\n**bge-reranker-v2.5-gemma2-lightweight**\n\nUse the :code:`LightWeightFlagLLMReranker` class for bge-reranker-v2.5-gemma2-lightweight.\n\n.. code:: python\n\n from FlagEmbedding import LightWeightFlagLLMReranker\n\n # Setting use_fp16 to True speeds up computation with a slight performance degradation\n reranker = LightWeightFlagLLMReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', use_fp16=True)\n\n # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\n score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40])\n print(score)" }, { "path": "docs/source/bge/bge_v1_v1.5.rst", "content": "BGE v1 & v1.5\n=============\n\nBGE v1 and v1.5 are series of encoder only models base on BERT. They achieved best performance among the models of the same size at the time of release.\n\nBGE\n---\n\nThe first group of BGE models was released in Aug 2023. The :code:`bge-large-en` and :code:`bge-large-zh` ranked 1st on MTEB and \nC-MTEB benchmarks at the time released.\n\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+===================================================================+===========+============+==============+=======================================================================+\n| `BAAI/bge-large-en `_ | English | 335M | 1.34 GB | Embedding Model which map text into vector |\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-base-en `_ | English | 109M | 438 MB | a base-scale model but with similar ability to `BAAI/bge-large-en` |\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-small-en `_ | English | 33.4M | 133 MB | a small-scale model but with competitive performance |\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-large-zh `_ | Chinese | 326M | 1.3 GB | Embedding Model which map text into vector |\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-base-zh `_ | Chinese | 102M | 409 MB | a base-scale model but with similar ability to `BAAI/bge-large-zh` |\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-small-zh `_ | Chinese | 24M | 95.8 MB | a small-scale model but with competitive performance |\n+-------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n\nBGE-v1.5\n--------\n\nThen to enhance its retrieval ability without instruction and alleviate the issue of the similarity distribution, :code:`bge-*-v1.5` models \nwere released in Sep 2023. They are still the most popular embedding models that balanced well between embedding quality and model sizes.\n\n+-----------------------------------------------------------------------------+-----------+------------+--------------+--------------+\n| Model | Language | Parameters | Model Size | Description |\n+=============================================================================+===========+============+==============+==============+\n| `BAAI/bge-large-en-v1.5 `_ | English | 335M | 1.34 GB | version 1.5 |\n+-----------------------------------------------------------------------------+-----------+------------+--------------+ with more +\n| `BAAI/bge-base-en-v1.5 `_ | English | 109M | 438 MB | reasonable |\n+-----------------------------------------------------------------------------+-----------+------------+--------------+ similarity +\n| `BAAI/bge-small-en-v1.5 `_ | English | 33.4M | 133 MB | distribution |\n+-----------------------------------------------------------------------------+-----------+------------+--------------+ and better +\n| `BAAI/bge-large-zh-v1.5 `_ | Chinese | 326M | 1.3 GB | performance |\n+-----------------------------------------------------------------------------+-----------+------------+--------------+ +\n| `BAAI/bge-base-zh-v1.5 `_ | Chinese | 102M | 409 MB | |\n+-----------------------------------------------------------------------------+-----------+------------+--------------+ +\n| `BAAI/bge-small-zh-v1.5 `_ | Chinese | 24M | 95.8 MB | |\n+-----------------------------------------------------------------------------+-----------+------------+--------------+--------------+\n\n\nUsage\n-----\n\nTo use BGE v1 or v1.5 model for inference, load model through\n\n.. code:: python\n\n from FlagEmbedding import FlagModel\n\n model = FlagModel('BAAI/bge-base-en-v1.5')\n\n sentences = [\"Hello world\", \"I am inevitable\"]\n embeddings = model.encode(sentences)\n\n.. tip::\n\n For simple tasks that only encode a few sentences like above, it's faster to use CPU or a single GPU instead of multi-GPUs\n\nTo use CPU:\n\n.. code:: python\n\n # make no GPU visible\n import os\n os.environ['CUDA_VISIBLE_DEVICES'] = ''\n \n # or claim the devices during initialize the model\n model = FlagModel('BAAI/bge-base-en-v1.5', devices='cpu')\n\nTo use a single GPU:\n\n.. code:: python\n\n # select one sigle card to be visible\n import os\n os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n \n # or claim the devices during initialize the model\n model = FlagModel('BAAI/bge-base-en-v1.5', devices=0)\n\n|\n\nUseful Links:\n\n`API <../API/inference/embedder/encoder_only/BaseEmbedder>`_\n\n`Tutorial `_\n\n`Example `_" }, { "path": "docs/source/bge/bge_vl.rst", "content": "BGE-VL\n======\n\nBGE-VL is a series of multimodel retrieval models training on `MegaPairs `_ \n\nBGE-VL contains light weight CLIP based models as well as more powerful LLAVA-NeXT based MLLM models:\n\n+----------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| Model | Language | Parameters | Model Size | Description |\n+======================================================================+===========+============+==============+=======================================================================+\n| `BAAI/bge-vl-base `_ | English | 150M | 299 MB | Light weight multimodel embedder among image and text |\n+----------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-vl-large `_ | English | 428M | 855 MB | Large scale multimodel embedder among image and text |\n+----------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-vl-MLLM-S1 `_ | English | 7.57B | 15.14 GB | SOTA in composed image retrieval, trained on MegaPairs dataset |\n+----------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/bge-vl-MLLM-S2 `_ | English | 7.57B | 15.14 GB | Finetune BGE-VL-MLLM-S1 with one epoch on MMEB training set |\n+----------------------------------------------------------------------+-----------+------------+--------------+-----------------------------------------------------------------------+\n| `BAAI/BGE-VL-v1.5-zs `_ | English | 7.57B | 15.14 GB | Better multi-modal retrieval model with performs well in all kinds of tasks |\n| `BAAI/BGE-VL-v1.5-mmeb `_ | English | 7.57B | 15.14 GB | Better multi-modal retrieval model, additionally fine-tuned on MMEB training set |\n\n\nBGE-VL-CLIP\n-----------\n\nThe base and large model are trained based on CLIP-vit-base-patch16 and CLIP-vit-large-patch14. \nFor composed image-text data, the model directly use score-fusion to sum up the outputs of visual encoder and text encoder and get the final embedding.\n\n.. tip::\n\n Our code works well on transformers==4.45.2, and we recommend using this version.\n\nYou can easily use BGE-VL-CLIP models based on transformers:\n\n.. code:: python\n\n import torch\n from transformers import AutoModel\n\n MODEL_NAME = \"BAAI/BGE-VL-base\" # or \"BAAI/BGE-VL-large\"\n model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True) # You must set trust_remote_code=True\n model.set_processor(MODEL_NAME)\n model.eval()\n\n with torch.no_grad():\n query = model.encode(\n images = \"./assets/cir_query.png\", \n text = \"Make the background dark, as if the camera has taken the photo at night\"\n )\n candidates = model.encode(\n images = [\"./assets/cir_candi_1.png\", \"./assets/cir_candi_2.png\"]\n )\n \n scores = query @ candidates.T\n print(scores)\n\n\nBGE-VL-MLLM\n-----------\n\nThe multimodal large language models (MLLMs) incorporate a visual encoder, typically based on a vision transformer, into a large language model (LLM). \nThis integration allows image tokens to be directly processed by the LLM. \nConsequently, MLLMs can effectively handle diverse multimodal inputs by converting any type of input into a sequence of tokens.\n\nBGE-VL-MLLM builds upon the LLaVA1.6. In both training and inference stages, MMRet uses task-specific instructions for query inputs to improve generalization, aligning\nwith standard practices in LLM-based embedding models. \nA typical multimodal query input is structured as follows:\n\n.. math:: \n\n ⟨\\text{instruct}⟩{\\{task\\_ inst\\}} \\space⟨\\text{query}⟩\\{q_t\\} \\{q_i\\}\\space[\\text{EOS}]\n\nwhere :math:`{task_inst}` represents the task-specific instruction, :math:`{qt}` denotes the input query text, and\n:math:`{qi}` is the input query image. \nThe normalized last hidden state of the [EOS] token in the MLLM is used as the embedding of any given input sequence.\n\n.. code:: python\n\n import torch\n from transformers import AutoModel\n from PIL import Image\n\n MODEL_NAME= \"BAAI/BGE-VL-MLLM-S1\"\n model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\n model.eval()\n model.cuda()\n\n with torch.no_grad():\n model.set_processor(MODEL_NAME)\n\n query_inputs = model.data_process(\n text=\"Make the background dark, as if the camera has taken the photo at night\", \n images=\"./assets/cir_query.png\",\n q_or_c=\"q\",\n task_instruction=\"Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: \"\n )\n candidate_inputs = model.data_process(\n images=[\"./assets/cir_candi_1.png\", \"./assets/cir_candi_2.png\"],\n q_or_c=\"c\",\n )\n\n query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]\n candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]\n \n query_embs = torch.nn.functional.normalize(query_embs, dim=-1)\n candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)\n\n scores = torch.matmul(query_embs, candi_embs.T)\n print(scores)\n\n\nBGE-VL-v1.5\n-----------\n\nBGE-VL-v1.5 series is the updated version of BGE-VL, bringing better performance on both retrieval and multi-modal understanding. The models were trained on 30M MegaPairs data and extra 10M natural and synthetic data.\n\n`bge-vl-v1.5-zs` is a zero-shot model, only trained on the data mentioned above. `bge-vl-v1.5-mmeb` is the fine-tuned version on MMEB training set.\n\n\n.. code:: python\n\n import torch\n from transformers import AutoModel\n from PIL import Image\n\n MODEL_NAME= \"BAAI/BGE-VL-v1.5-mmeb\" # \"BAAI/BGE-VL-v1.5-zs\"\n\n model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\n model.eval()\n model.cuda()\n\n with torch.no_grad():\n model.set_processor(MODEL_NAME)\n\n query_inputs = model.data_process(\n text=\"Make the background dark, as if the camera has taken the photo at night\", \n images=\"../../imgs/cir_query.png\",\n q_or_c=\"q\",\n task_instruction=\"Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: \"\n )\n\n candidate_inputs = model.data_process(\n images=[\"../../imgs/cir_candi_1.png\", \"../../imgs/cir_candi_2.png\"],\n q_or_c=\"c\",\n )\n\n query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]\n candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]\n \n query_embs = torch.nn.functional.normalize(query_embs, dim=-1)\n candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)\n\n scores = torch.matmul(query_embs, candi_embs.T)\n print(scores)\n\n\n\nFor more details, check out the repo of `MegaPairs `_" }, { "path": "docs/source/bge/index.rst", "content": "BGE\n===\n\n.. figure:: ../_static/img/bge_logo.jpeg\n :width: 250\n :align: center\n\n**BGE** stands for **BAAI General Embeddings**, which is a series of embedding models released by BAAI.\n\n.. toctree::\n :maxdepth: 1\n :caption: Embedder\n\n bge_v1_v1.5\n bge_m3\n bge_icl\n bge_vl\n bge_code\n\n.. toctree::\n :maxdepth: 1\n :caption: Reranker\n\n bge_reranker\n bge_reranker_v2" }, { "path": "docs/source/community/index.rst", "content": "Community\n=========\n\nVisit our `GitHub repo `_ and \n`Hugging Face collection `_ for more materials!\n\nWe are also holding WeChat groups for for BGE. Scan the QR code to join the group chat! \nTo get the first hand message about our updates and new release, or having any questions or ideas, join us now!\n\n.. figure:: ../_static/img/BGE_WeChat_Group.png\n :width: 400\n :align: center" }, { "path": "docs/source/conf.py", "content": "# Configuration file for the Sphinx documentation builder.\n#\n# For the full list of built-in configuration values, see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\n# -- Project information -----------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information\nimport os\nimport sys\n\nsys.path.insert(0, os.path.abspath(\"..\"))\nsys.path.insert(0, os.path.abspath(\"../..\"))\n\nproject = 'BGE'\ncopyright = '2024, BAAI'\nauthor = 'BAAI'\n\n# -- General configuration ---------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration\n\nextensions = [\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.githubpages\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.coverage\",\n \"sphinx_design\",\n \"myst_nb\",\n \"sphinxcontrib.googleanalytics\",\n]\n\ntemplates_path = ['_templates']\nexclude_patterns = []\n\n# -- Options for HTML output -------------------------------------------------\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output\n\n# html_theme = 'furo'\nhtml_theme = \"pydata_sphinx_theme\"\nhtml_logo = \"_static/img/bge_logo.jpeg\"\nhtml_static_path = ['_static']\nhtml_css_files = [\"css/custom.css\"]\n\n# MyST-NB conf\nnb_execution_mode = \"off\"\n\nhtml_theme_options = {\n \"logo\": {\n \"text\": \"BGE\",\n },\n \"external_links\": [\n {\n \"url\": \"https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d\",\n \"name\": \"HF Models\",\n },\n ],\n \"icon_links\":[\n {\n \"name\": \"GitHub\",\n \"url\": \"https://github.com/FlagOpen/FlagEmbedding\",\n \"icon\": \"fa-brands fa-github\",\n },\n {\n \"name\": \"PyPI\",\n \"url\": \"https://pypi.org/project/FlagEmbedding/\",\n \"icon\": \"fa-brands fa-python\",\n },\n {\n \"name\": \"HF Models\",\n \"url\": \"https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d\",\n \"icon\": \"fa-solid fa-cube\",\n }\n ],\n \"navigation_depth\": 5,\n \"header_links_before_dropdown\": 5,\n}\n\nhtml_context = {\n \"default_mode\": \"light\"\n}\n\ngoogleanalytics_id = 'G-X4B1E1Q35K'" }, { "path": "docs/source/index.rst", "content": ".. FlagEmbedding documentation master file, created by\n sphinx-quickstart on Sat Oct 12 13:27:49 2024.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\n:html_theme.sidebar_secondary.remove: True\n\n\nWelcome to BGE!\n===============\n\n.. Welcome to BGE documentation!\n\n.. figure:: _static/img/bge_panda.jpg\n :width: 400\n :align: center\n\n.. grid:: 3\n :gutter: 3\n\n .. grid-item-card:: :octicon:`milestone` Introduction\n\n New to BGE? Quickly get hands-on information.\n\n +++\n\n .. button-ref:: Introduction/index\n :expand:\n :color: primary\n :click-parent:\n\n To Introduction\n\n\n .. grid-item-card:: :octicon:`package` BGE Models\n\n Get to know BGE embedding models and rerankers.\n\n +++\n\n .. button-ref:: bge/index\n :expand:\n :color: primary\n :click-parent:\n\n To BGE\n\n\n .. grid-item-card:: :octicon:`log` Tutorials\n\n Find useful tutorials to start with if you are looking for guidance\n\n +++\n\n .. button-ref:: tutorial/index\n :expand:\n :color: primary\n :click-parent:\n\n To Tutorials\n\n .. grid-item-card:: :octicon:`codescan` API\n\n Check the API of classes and functions in FlagEmbedding.\n\n +++\n\n .. button-ref:: API/index\n :expand:\n :color: primary\n :click-parent:\n\n To APIs\n\n .. grid-item-card:: :octicon:`question` FAQ\n\n Take a look of questions people frequently asked.\n\n +++\n\n .. button-ref:: FAQ/index\n :expand:\n :color: primary\n :click-parent:\n\n To FAQ\n \n .. grid-item-card:: :octicon:`people` Community\n\n Welcome to join BGE community!\n\n +++\n\n .. button-ref:: community/index\n :expand:\n :color: primary\n :click-parent:\n\n To Community\n\nBesides the resources we provide here in this documentation, please visit our `GitHub repo `_ for more contents including:\n\n- Want to get familiar with BGE quickly? There are hands-on `examples `_ to run for embedder and reranker's inference, evaluation, and finetuning.\n- Unfamiliar with some area, keywords or techniques of retrieval and RAG? We provide `tutorials `_ to teach you basic knowledge and coding tips.\n- Interested in research topics that expanding from BGE and retrieval? Our `research `_ folder contains many exciting topics for you to explore.\n\nBGE is developed by Beijing Academy of Artificial Intelligence (BAAI).\n\n|\n\n.. image:: _static/img/BAAI_logo.png\n :target: https://github.com/FlagOpen/FlagEmbedding\n :width: 300\n :align: center\n\n\n.. toctree::\n :maxdepth: 1\n :hidden:\n\n Home \n\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: Introduction\n\n Introduction/index\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: BGE\n\n bge/index\n\n.. toctree::\n :hidden:\n :maxdepth: 2\n :caption: Tutorials\n\n tutorial/index\n\n.. toctree::\n :hidden:\n :maxdepth: 5\n :caption: API\n\n API/index\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: FAQ\n\n FAQ/index\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: Community\n\n community/index" }, { "path": "docs/source/tutorial/1_Embedding/1.1.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Intro to Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For text retrieval, pattern matching is the most intuitive way. People would use certain characters, words, phrases, or sentence patterns. However, not only for human, it is also extremely inefficient for computer to do pattern matching between a query and a collection of text files to find the possible results. \\n\",\n \"\\n\",\n \"For images and acoustic waves, there are rgb pixels and digital signals. Similarly, in order to accomplish more sophisticated tasks of natural language such as retrieval, classification, clustering, or semantic search, we need a way to represent text data. That's how text embedding comes in front of the stage.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Background\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Traditional text embedding methods like one-hot encoding and bag-of-words (BoW) represent words and sentences as sparse vectors based on their statistical features, such as word appearance and frequency within a document. More advanced methods like TF-IDF and BM25 improve on these by considering a word's importance across an entire corpus, while n-gram techniques capture word order in small groups. However, these approaches suffer from the \\\"curse of dimensionality\\\" and fail to capture semantic similarity like \\\"cat\\\" and \\\"kitty\\\", difference like \\\"play the watch\\\" and \\\"watch the play\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# example of bag-of-words\\n\",\n \"sentence1 = \\\"I love basketball\\\"\\n\",\n \"sentence2 = \\\"I have a basketball match\\\"\\n\",\n \"\\n\",\n \"words = ['I', 'love', 'basketball', 'have', 'a', 'match']\\n\",\n \"sen1_vec = [1, 1, 1, 0, 0, 0]\\n\",\n \"sen2_vec = [1, 0, 1, 1, 1, 1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To overcome these limitations, dense word embeddings were developed, mapping words to vectors in a low-dimensional space that captures semantic and relational information. Early models like Word2Vec demonstrated the power of dense embeddings using neural networks. Subsequent advancements with neural network architectures like RNNs, LSTMs, and Transformers have enabled more sophisticated models such as BERT, RoBERTa, and GPT to excel in capturing complex word relationships and contexts. **BAAI General Embedding (BGE)** provide a series of open-source models that could satisfy all kinds of demands.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Get Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first step of modern text retrieval is embedding the text. So let's take a look at how to use the embedding models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the packages:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U FlagEmbedding sentence_transformers openai cohere\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"# single GPU is better for small tasks\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll use the following three sentences as the inputs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sentences = [\\n\",\n \" \\\"That is a happy dog\\\",\\n\",\n \" \\\"That is a very happy person\\\",\\n\",\n \" \\\"Today is a sunny day\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Open-source Models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A huge portion of embedding models are in the open source community. The advantages of open-source models include:\\n\",\n \"- Free, no extra cost. But make sure to check the License and your use case before using.\\n\",\n \"- No frequency limit, can accelerate a lot if you have enough GPUs to parallelize.\\n\",\n \"- Transparent and might be reproducible.\\n\",\n \"\\n\",\n \"Let's take a look at two representatives:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### BGE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE is a series of embedding models and rerankers published by BAAI. Several of them reached SOTA at the time they released.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"initial target device: 100%|██████████| 8/8 [00:31<00:00, 3.89s/it]\\n\",\n \"Chunks: 100%|██████████| 3/3 [00:04<00:00, 1.61s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 768)\\n\",\n \"Similarity scores:\\n\",\n \"[[1. 0.79 0.575 ]\\n\",\n \" [0.79 0.9995 0.592 ]\\n\",\n \" [0.575 0.592 0.999 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# Load BGE model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"embeddings = model.encode(sentences)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Sentence Transformers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sentence Transformers is a library for sentence embeddings with a huge amount of embedding models and datasets for related tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 384)\\n\",\n \"Similarity scores:\\n\",\n \"[[0.99999976 0.6210502 0.24906276]\\n\",\n \" [0.6210502 0.9999997 0.21061528]\\n\",\n \" [0.24906276 0.21061528 0.9999999 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"model = SentenceTransformer(\\\"all-MiniLM-L6-v2\\\")\\n\",\n \"\\n\",\n \"embeddings = model.encode(sentences, normalize_embeddings=True)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Commercial Models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are also plenty choices of commercial models. They have the advantages of:\\n\",\n \"- Efficient memory usage, fast inference with no need of GPUs.\\n\",\n \"- Systematic support, commercial models have closer connections with their other products.\\n\",\n \"- Better training data, commercial models might be trained on larger, higher-quality datasets than some open-source models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### OpenAI\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Along with GPT series, OpenAI has their own embedding models. Make sure to fill in your own API key in the field `\\\"YOUR_API_KEY\\\"`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"YOUR_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then run the following cells to get the embeddings. Check their official [documentation](https://platform.openai.com/docs/guides/embeddings) for more details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from openai import OpenAI\\n\",\n \"\\n\",\n \"client = OpenAI()\\n\",\n \"\\n\",\n \"response = client.embeddings.create(input = sentences, model=\\\"text-embedding-3-small\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 1536)\\n\",\n \"Similarity scores:\\n\",\n \"[[1.00000004 0.697673 0.34739798]\\n\",\n \" [0.697673 1.00000005 0.31969923]\\n\",\n \" [0.34739798 0.31969923 0.99999998]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = np.asarray([response.data[i].embedding for i in range(len(sentences))])\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Voyage AI\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Voyage AI provides embedding models and rerankers for different purpus and in various fields. Their API keys can be freely used in low frequency and token length.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.environ[\\\"VOYAGE_API_KEY\\\"] = \\\"YOUR_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check their official [documentation](https://docs.voyageai.com/docs/api-key-and-installation) for more details.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import voyageai\\n\",\n \"\\n\",\n \"vo = voyageai.Client()\\n\",\n \"\\n\",\n \"result = vo.embed(sentences, model=\\\"voyage-large-2-instruct\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"(3, 1024)\\n\",\n \"Similarity scores:\\n\",\n \"[[0.99999997 0.87282517 0.63276503]\\n\",\n \" [0.87282517 0.99999998 0.64720015]\\n\",\n \" [0.63276503 0.64720015 0.99999999]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = np.asarray(result.embeddings)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings.shape}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/1_Embedding/1.2.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"06cff9e4\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Series\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"880e229d\",\n \"metadata\": {},\n \"source\": [\n \"In this Part, we will walk through the BGE series and introduce how to use the BGE embedding models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2516fd49\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BAAI General Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2113ee71\",\n \"metadata\": {},\n \"source\": [\n \"BGE stands for BAAI General Embedding, it's a series of embeddings models developed and published by Beijing Academy of Artificial Intelligence (BAAI).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"16515b99\",\n \"metadata\": {},\n \"source\": [\n \"A full support of APIs and related usages of BGE is maintained in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) on GitHub.\\n\",\n \"\\n\",\n \"Run the following cell to install FlagEmbedding in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"88095fd0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"a2376217\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"# single GPU is better for small tasks\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bc6e30a0\",\n \"metadata\": {},\n \"source\": [\n \"The collection of BGE models can be found in [Huggingface collection](https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"67a16ccf\",\n \"metadata\": {},\n \"source\": [\n \"## 2. BGE Series Models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2e10034a\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 BGE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0cdc6702\",\n \"metadata\": {},\n \"source\": [\n \"The very first version of BGE has 6 models, with 'large', 'base', and 'small' for English and Chinese. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04b75f72\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | 500M | 1.34 GB | Embedding Model which map text into vector | BERT |\\n\",\n \"| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | 109M | 438 MB | a base-scale model but with similar ability to `bge-large-en` | BERT |\\n\",\n \"| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | 33.4M | 133 MB | a small-scale model but with competitive performance | BERT |\\n\",\n \"| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | 326M | 1.3 GB | Embedding Model which map text into vector | BERT |\\n\",\n \"| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | 102M | 409 MB | a base-scale model but with similar ability to `bge-large-zh` | BERT |\\n\",\n \"| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | 24M | 95.8 MB | a small-scale model but with competitive performance | BERT |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c9c45d17\",\n \"metadata\": {},\n \"source\": [\n \"For inference, simply import FlagModel from FlagEmbedding and initialize the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"89e07751\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.84864 0.7946737 ]\\n\",\n \" [0.760097 0.85449743]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# Load BGE model\\n\",\n \"model = FlagModel(\\n\",\n \" 'BAAI/bge-base-en',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" query_instruction_format='{}{}',\\n\",\n \")\\n\",\n \"\\n\",\n \"queries = [\\\"query 1\\\", \\\"query 2\\\"]\\n\",\n \"corpus = [\\\"passage 1\\\", \\\"passage 2\\\"]\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"q_embeddings = model.encode_queries(queries)\\n\",\n \"p_embeddings = model.encode_corpus(corpus)\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"scores = q_embeddings @ p_embeddings.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c8e69ed\",\n \"metadata\": {},\n \"source\": [\n \"For general encoding, use either `encode()`:\\n\",\n \"```python\\n\",\n \"FlagModel.encode(sentences, batch_size=256, max_length=512, convert_to_numpy=True)\\n\",\n \"```\\n\",\n \"or `encode_corpus()` that directly calls `encode()`:\\n\",\n \"```python\\n\",\n \"FlagModel.encode_corpus(corpus, batch_size=256, max_length=512, convert_to_numpy=True)\\n\",\n \"```\\n\",\n \"The *encode_queries()* function concatenate the `query_instruction_for_retrieval` with each of the input query to form the new sentences and then feed them to `encode()`.\\n\",\n \"```python\\n\",\n \"FlagModel.encode_queries(queries, batch_size=256, max_length=512, convert_to_numpy=True)\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2c86a5a3\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 BGE v1.5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"454ff7aa\",\n \"metadata\": {},\n \"source\": [\n \"BGE 1.5 alleviate the issue of the similarity distribution, and enhance retrieval ability without instruction.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"30b1f897\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | 335M | 1.34 GB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | 109M | 438 MB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | 33.4M | 133 MB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | 326M | 1.3 GB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | 102M | 409 MB | version 1.5 with more reasonable similarity distribution | BERT |\\n\",\n \"| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | 24M | 95.8 MB | version 1.5 with more reasonable similarity distribution | BERT |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ed00c504\",\n \"metadata\": {},\n \"source\": [\n \"You can use BGE 1.5 models exactly same to BGE v1 models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"9b17afcc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 2252.58it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 3575.71it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.76 0.6714]\\n\",\n \" [0.6177 0.7603]]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"model = FlagModel(\\n\",\n \" 'BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" query_instruction_format='{}{}'\\n\",\n \")\\n\",\n \"\\n\",\n \"queries = [\\\"query 1\\\", \\\"query 2\\\"]\\n\",\n \"corpus = [\\\"passage 1\\\", \\\"passage 2\\\"]\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"q_embeddings = model.encode_queries(queries)\\n\",\n \"p_embeddings = model.encode_corpus(corpus)\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"scores = q_embeddings @ p_embeddings.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dcf2a82b\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 BGE M3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cc5b5a5e\",\n \"metadata\": {},\n \"source\": [\n \"BGE-M3 is the new version of BGE models that is distinguished for its versatility in:\\n\",\n \"- Multi-Functionality: Simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.\\n\",\n \"- Multi-Linguality: Supports more than 100 working languages.\\n\",\n \"- Multi-Granularity: Can proces inputs with different granularityies, spanning from short sentences to long documents of up to 8192 tokens.\\n\",\n \"\\n\",\n \"For more details, feel free to check out the [paper](https://arxiv.org/pdf/2402.03216).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"41348e03\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | 568M | 2.27 GB | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | XLM-RoBERTa |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"d4647625\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fetching 30 files: 100%|██████████| 30/30 [00:00<00:00, 194180.74it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import BGEM3FlagModel\\n\",\n \"\\n\",\n \"model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)\\n\",\n \"\\n\",\n \"sentences = [\\\"What is BGE M3?\\\", \\\"Defination of BM25\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f89f1a9\",\n \"metadata\": {},\n \"source\": [\n \"```python\\n\",\n \"BGEM3FlagModel.encode(\\n\",\n \" sentences, \\n\",\n \" batch_size=12, \\n\",\n \" max_length=8192, \\n\",\n \" return_dense=True, \\n\",\n \" return_sparse=False, \\n\",\n \" return_colbert_vecs=False\\n\",\n \")\\n\",\n \"```\\n\",\n \"It returns a dictionary like:\\n\",\n \"```python\\n\",\n \"{\\n\",\n \" 'dense_vecs': # array of dense embeddings of inputs if return_dense=True, otherwise None,\\n\",\n \" 'lexical_weights': # array of dictionaries with keys and values are ids of tokens and their corresponding weights if return_sparse=True, otherwise None,\\n\",\n \" 'colbert_vecs': # array of multi-vector embeddings of inputs if return_cobert_vecs=True, otherwise None,'\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"f0b11cf0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 1148.18it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# If you don't need such a long length of 8192 input tokens, you can set max_length to a smaller value to speed up encoding.\\n\",\n \"embeddings = model.encode(\\n\",\n \" sentences, \\n\",\n \" max_length=10,\\n\",\n \" return_dense=True, \\n\",\n \" return_sparse=True, \\n\",\n \" return_colbert_vecs=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"72cba126\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dense embedding:\\n\",\n \"[[-0.03412 -0.04706 -0.00087 ... 0.04822 0.007614 -0.02957 ]\\n\",\n \" [-0.01035 -0.04483 -0.02434 ... -0.008224 0.01497 0.011055]]\\n\",\n \"sparse embedding:\\n\",\n \"[defaultdict(, {'4865': np.float16(0.0836), '83': np.float16(0.0814), '335': np.float16(0.1296), '11679': np.float16(0.2517), '276': np.float16(0.1699), '363': np.float16(0.2695), '32': np.float16(0.04077)}), defaultdict(, {'262': np.float16(0.05014), '5983': np.float16(0.1367), '2320': np.float16(0.04517), '111': np.float16(0.0634), '90017': np.float16(0.2517), '2588': np.float16(0.3333)})]\\n\",\n \"multi-vector:\\n\",\n \"[array([[-8.68966337e-03, -4.89266850e-02, -3.03634931e-03, ...,\\n\",\n \" -2.21243706e-02, 5.72856329e-02, 1.28355855e-02],\\n\",\n \" [-8.92937183e-03, -4.67235669e-02, -9.52814799e-03, ...,\\n\",\n \" -3.14785317e-02, 5.39088845e-02, 6.96671568e-03],\\n\",\n \" [ 1.84195358e-02, -4.22310382e-02, 8.55499704e-04, ...,\\n\",\n \" -1.97946690e-02, 3.84313315e-02, 7.71250250e-03],\\n\",\n \" ...,\\n\",\n \" [-2.55824160e-02, -1.65533274e-02, -4.21357416e-02, ...,\\n\",\n \" -4.50234264e-02, 4.41286489e-02, -1.00052059e-02],\\n\",\n \" [ 5.90990965e-07, -5.53734899e-02, 8.51499755e-03, ...,\\n\",\n \" -2.29209941e-02, 6.04418293e-02, 9.39912070e-03],\\n\",\n \" [ 2.57394509e-03, -2.92690992e-02, -1.89342294e-02, ...,\\n\",\n \" -8.04431178e-03, 3.28964666e-02, 4.38723788e-02]], dtype=float32), array([[ 0.01724418, 0.03835401, -0.02309308, ..., 0.00141706,\\n\",\n \" 0.02995041, -0.05990082],\\n\",\n \" [ 0.00996325, 0.03922409, -0.03849588, ..., 0.00591671,\\n\",\n \" 0.02722516, -0.06510868],\\n\",\n \" [ 0.01781915, 0.03925728, -0.01710397, ..., 0.00801776,\\n\",\n \" 0.03987768, -0.05070014],\\n\",\n \" ...,\\n\",\n \" [ 0.05478653, 0.00755799, 0.00328444, ..., -0.01648209,\\n\",\n \" 0.02405782, 0.00363262],\\n\",\n \" [ 0.00936953, 0.05028074, -0.02388872, ..., 0.02567679,\\n\",\n \" 0.00791224, -0.03257877],\\n\",\n \" [ 0.01803976, 0.0133922 , 0.00019365, ..., 0.0184015 ,\\n\",\n \" 0.01373822, 0.00315539]], dtype=float32)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"dense embedding:\\\\n{embeddings['dense_vecs']}\\\")\\n\",\n \"print(f\\\"sparse embedding:\\\\n{embeddings['lexical_weights']}\\\")\\n\",\n \"print(f\\\"multi-vector:\\\\n{embeddings['colbert_vecs']}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"14d83caa\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 BGE Multilingual Gemma2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fd4c67df\",\n \"metadata\": {},\n \"source\": [\n \"BGE Multilingual Gemma2 is a LLM-based Multi-Lingual embedding model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"abdca22e\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | 9.24B | 37 GB | LLM-based multilingual embedding model with SOTA results on multilingual benchmarks | Gemma2-9B |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"8ec545bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 4/4 [00:00<00:00, 6.34it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 816.49it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 718.33it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.559 0.01685 ]\\n\",\n \" [0.0008683 0.5015 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagLLMModel\\n\",\n \"\\n\",\n \"queries = [\\\"how much protein should a female eat\\\", \\\"summit define\\\"]\\n\",\n \"documents = [\\n\",\n \" \\\"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\\\",\\n\",\n \" \\\"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\\\"\\n\",\n \"]\\n\",\n \"\\n\",\n \"model = FlagLLMModel('BAAI/bge-multilingual-gemma2', \\n\",\n \" query_instruction_for_retrieval=\\\"Given a web search query, retrieve relevant passages that answer the query.\\\",\\n\",\n \" use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\\n\",\n \"\\n\",\n \"embeddings_1 = model.encode_queries(queries)\\n\",\n \"embeddings_2 = model.encode_corpus(documents)\\n\",\n \"similarity = embeddings_1 @ embeddings_2.T\\n\",\n \"print(similarity)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8b7b2aa4\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 BGE ICL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7c9acb92\",\n \"metadata\": {},\n \"source\": [\n \"BGE ICL stands for in-context learning. By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cf6c9345\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Model Size | Description | Base Model |\\n\",\n \"|:-------|:--------:|:--------------:|:--------------:|:-----------------:|:----------------:|\\n\",\n \"| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | 7.11B | 28.5 GB | LLM-based English embedding model with excellent in-context learning ability. | Mistral-7B |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"4595bae7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"documents = [\\n\",\n \" \\\"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\\\",\\n\",\n \" \\\"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\\\"\\n\",\n \"]\\n\",\n \"\\n\",\n \"examples = [\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'what is a virtual interface',\\n\",\n \" 'response': \\\"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\\\"\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'causes of back pain in female for a week',\\n\",\n \" 'response': \\\"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\\\"\\n\",\n \" }\\n\",\n \"]\\n\",\n \"\\n\",\n \"queries = [\\\"how much protein should a female eat\\\", \\\"summit define\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ffb586c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 6.55it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 366.09it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 623.69it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6064 0.3018]\\n\",\n \" [0.257 0.537 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagICLModel\\n\",\n \"import os\\n\",\n \"\\n\",\n \"model = FlagICLModel('BAAI/bge-en-icl', \\n\",\n \" examples_for_task=examples, # set `examples_for_task=None` to use model without examples\\n\",\n \" # examples_instruction_format=\\\"{}\\\\n{}\\\\n{}\\\" # specify the format to use examples_for_task\\n\",\n \" )\\n\",\n \"\\n\",\n \"embeddings_1 = model.encode_queries(queries)\\n\",\n \"embeddings_2 = model.encode_corpus(documents)\\n\",\n \"similarity = embeddings_1 @ embeddings_2.T\\n\",\n \"\\n\",\n \"print(similarity)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/source/tutorial/1_Embedding/1.2.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Auto Embedder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"FlagEmbedding provides a high level class `FlagAutoModel` that unify the inference of embedding models. Besides BGE series, it also supports other popular open-source embedding models such as E5, GTE, SFR, etc. In this tutorial, we will have an idea how to use it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Usage\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, import `FlagAutoModel` from FlagEmbedding, and use the `from_finetuned()` function to initialize the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from FlagEmbedding import FlagAutoModel\\n\",\n \"\\n\",\n \"model = FlagAutoModel.from_finetuned(\\n\",\n \" 'BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages: \\\",\\n\",\n \" devices=\\\"cuda:0\\\", # if not specified, will use all available gpus or cpu when no gpu available\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then use the model exactly same to `FlagModel` (`FlagM3Model` if using BGE M3, `FlagLLMModel` if using BGE Multilingual Gemma2, `FlagICLModel` if using BGE ICL)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.76 0.6714]\\n\",\n \" [0.6177 0.7603]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"queries = [\\\"query 1\\\", \\\"query 2\\\"]\\n\",\n \"corpus = [\\\"passage 1\\\", \\\"passage 2\\\"]\\n\",\n \"\\n\",\n \"# encode the queries and corpus\\n\",\n \"q_embeddings = model.encode_queries(queries)\\n\",\n \"p_embeddings = model.encode_corpus(corpus)\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"scores = q_embeddings @ p_embeddings.T\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Explanation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`FlagAutoModel` use an OrderedDict `MODEL_MAPPING` to store all the supported models configuration:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['bge-en-icl',\\n\",\n \" 'bge-multilingual-gemma2',\\n\",\n \" 'bge-m3',\\n\",\n \" 'bge-large-en-v1.5',\\n\",\n \" 'bge-base-en-v1.5',\\n\",\n \" 'bge-small-en-v1.5',\\n\",\n \" 'bge-large-zh-v1.5',\\n\",\n \" 'bge-base-zh-v1.5',\\n\",\n \" 'bge-small-zh-v1.5',\\n\",\n \" 'bge-large-en',\\n\",\n \" 'bge-base-en',\\n\",\n \" 'bge-small-en',\\n\",\n \" 'bge-large-zh',\\n\",\n \" 'bge-base-zh',\\n\",\n \" 'bge-small-zh',\\n\",\n \" 'e5-mistral-7b-instruct',\\n\",\n \" 'e5-large-v2',\\n\",\n \" 'e5-base-v2',\\n\",\n \" 'e5-small-v2',\\n\",\n \" 'multilingual-e5-large-instruct',\\n\",\n \" 'multilingual-e5-large',\\n\",\n \" 'multilingual-e5-base',\\n\",\n \" 'multilingual-e5-small',\\n\",\n \" 'e5-large',\\n\",\n \" 'e5-base',\\n\",\n \" 'e5-small',\\n\",\n \" 'gte-Qwen2-7B-instruct',\\n\",\n \" 'gte-Qwen2-1.5B-instruct',\\n\",\n \" 'gte-Qwen1.5-7B-instruct',\\n\",\n \" 'gte-multilingual-base',\\n\",\n \" 'gte-large-en-v1.5',\\n\",\n \" 'gte-base-en-v1.5',\\n\",\n \" 'gte-large',\\n\",\n \" 'gte-base',\\n\",\n \" 'gte-small',\\n\",\n \" 'gte-large-zh',\\n\",\n \" 'gte-base-zh',\\n\",\n \" 'gte-small-zh',\\n\",\n \" 'SFR-Embedding-2_R',\\n\",\n \" 'SFR-Embedding-Mistral',\\n\",\n \" 'Linq-Embed-Mistral']\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from FlagEmbedding.inference.embedder.model_mapping import AUTO_EMBEDDER_MAPPING\\n\",\n \"\\n\",\n \"list(AUTO_EMBEDDER_MAPPING.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"EmbedderConfig(model_class=, pooling_method=, trust_remote_code=False, query_instruction_format='{}\\\\n{}')\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(AUTO_EMBEDDER_MAPPING['bge-en-icl'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Taking a look at the value of each key, which is an object of `EmbedderConfig`. It consists four attributes:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```python\\n\",\n \"@dataclass\\n\",\n \"class EmbedderConfig:\\n\",\n \" model_class: Type[AbsEmbedder]\\n\",\n \" pooling_method: PoolingMethod\\n\",\n \" trust_remote_code: bool = False\\n\",\n \" query_instruction_format: str = \\\"{}{}\\\"\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Not only the BGE series, it supports other models such as E5 similarly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"EmbedderConfig(model_class=, pooling_method=, trust_remote_code=False, query_instruction_format='{}\\\\n{}')\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(AUTO_EMBEDDER_MAPPING['bge-en-icl'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Customization\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you want to use your own models through `FlagAutoModel`, consider the following steps:\\n\",\n \"\\n\",\n \"1. Check the type of your embedding model and choose the appropriate model class, is it an encoder or a decoder?\\n\",\n \"2. What kind of pooling method it uses? CLS token, mean pooling, or last token?\\n\",\n \"3. Does your model needs `trust_remote_code=Ture` to ran?\\n\",\n \"4. Is there a query instruction format for retrieval?\\n\",\n \"\\n\",\n \"After these four attributes are assured, add your model name as the key and corresponding EmbedderConfig as the value to `MODEL_MAPPING`. Now have a try!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/1_Embedding/1.2.3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Explanation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this section, we will go through BGE and BGE-v1.5's structure and how they generate embeddings.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U transformers FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Encode sentences\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To know how exactly a sentence is encoded, let's first load the tokenizer and model from HF transformers instead of FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-base-en-v1.5\\\")\\n\",\n \"model = AutoModel.from_pretrained(\\\"BAAI/bge-base-en-v1.5\\\")\\n\",\n \"\\n\",\n \"sentences = [\\\"embedding\\\", \\\"I love machine learning and nlp\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the following cell to check the model of bge-base-en-v1.5. It uses BERT-base as base model, with 12 encoder layers and hidden dimension of 768.\\n\",\n \"\\n\",\n \"Note that the corresponding models of BGE and BGE-v1.5 have same structures. For example, bge-base-en and bge-base-en-v1.5 have the same structure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"BertModel(\\n\",\n \" (embeddings): BertEmbeddings(\\n\",\n \" (word_embeddings): Embedding(30522, 768, padding_idx=0)\\n\",\n \" (position_embeddings): Embedding(512, 768)\\n\",\n \" (token_type_embeddings): Embedding(2, 768)\\n\",\n \" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (encoder): BertEncoder(\\n\",\n \" (layer): ModuleList(\\n\",\n \" (0-11): 12 x BertLayer(\\n\",\n \" (attention): BertAttention(\\n\",\n \" (self): BertSelfAttention(\\n\",\n \" (query): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (key): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (value): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (output): BertSelfOutput(\\n\",\n \" (dense): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (intermediate): BertIntermediate(\\n\",\n \" (dense): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n \" (intermediate_act_fn): GELUActivation()\\n\",\n \" )\\n\",\n \" (output): BertOutput(\\n\",\n \" (dense): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (pooler): BertPooler(\\n\",\n \" (dense): Linear(in_features=768, out_features=768, bias=True)\\n\",\n \" (activation): Tanh()\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's tokenize the sentences.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'input_ids': tensor([[ 101, 7861, 8270, 4667, 102, 0, 0, 0, 0],\\n\",\n \" [ 101, 1045, 2293, 3698, 4083, 1998, 17953, 2361, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0],\\n\",\n \" [0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 0, 0, 0, 0],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1]])}\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inputs = tokenizer(\\n\",\n \" sentences, \\n\",\n \" padding=True, \\n\",\n \" truncation=True, \\n\",\n \" return_tensors='pt', \\n\",\n \" max_length=512\\n\",\n \")\\n\",\n \"inputs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the results, we can see that each sentence begins with token 101 and ends with 102, which are the `[CLS]` and `[SEP]` special token used in BERT.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 9, 768])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"last_hidden_state = model(**inputs, return_dict=True).last_hidden_state\\n\",\n \"last_hidden_state.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we implement the pooling function, with two choices of using `[CLS]`'s last hidden state, or the mean pooling of the whole last hidden state.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def pooling(last_hidden_state: torch.Tensor, pooling_method='cls', attention_mask: torch.Tensor = None):\\n\",\n \" if pooling_method == 'cls':\\n\",\n \" return last_hidden_state[:, 0]\\n\",\n \" elif pooling_method == 'mean':\\n\",\n \" s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)\\n\",\n \" d = attention_mask.sum(dim=1, keepdim=True).float()\\n\",\n \" return s / d\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Different from more commonly used mean pooling, BGE is trained to use the last hidden state of `[CLS]` as the sentence embedding: \\n\",\n \"\\n\",\n \"`sentence_embeddings = model_output[0][:, 0]`\\n\",\n \"\\n\",\n \"If you use mean pooling, there will be a significant decrease in performance. Therefore, make sure to use the correct method to obtain sentence vectors.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 768])\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"embeddings = pooling(\\n\",\n \" last_hidden_state, \\n\",\n \" pooling_method='cls', \\n\",\n \" attention_mask=inputs['attention_mask']\\n\",\n \")\\n\",\n \"embeddings.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Assembling them together, we get the whole encoding function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def _encode(sentences, max_length=512, convert_to_numpy=True):\\n\",\n \"\\n\",\n \" # handle the case of single sentence and a list of sentences\\n\",\n \" input_was_string = False\\n\",\n \" if isinstance(sentences, str):\\n\",\n \" sentences = [sentences]\\n\",\n \" input_was_string = True\\n\",\n \"\\n\",\n \" inputs = tokenizer(\\n\",\n \" sentences, \\n\",\n \" padding=True, \\n\",\n \" truncation=True, \\n\",\n \" return_tensors='pt', \\n\",\n \" max_length=max_length\\n\",\n \" )\\n\",\n \"\\n\",\n \" last_hidden_state = model(**inputs, return_dict=True).last_hidden_state\\n\",\n \" \\n\",\n \" embeddings = pooling(\\n\",\n \" last_hidden_state, \\n\",\n \" pooling_method='cls', \\n\",\n \" attention_mask=inputs['attention_mask']\\n\",\n \" )\\n\",\n \"\\n\",\n \" # normalize the embedding vectors\\n\",\n \" embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\\n\",\n \"\\n\",\n \" # convert to numpy if needed\\n\",\n \" if convert_to_numpy:\\n\",\n \" embeddings = embeddings.detach().numpy()\\n\",\n \"\\n\",\n \" return embeddings[0] if input_was_string else embeddings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Comparison\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's run the function we wrote to get the embeddings of the two sentences:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"[[ 1.4549762e-02 -9.6840411e-03 3.7761475e-03 ... -8.5092714e-04\\n\",\n \" 2.8417887e-02 6.3214332e-02]\\n\",\n \" [ 3.3924331e-05 -3.2998275e-03 1.7206438e-02 ... 3.5703944e-03\\n\",\n \" 1.8721525e-02 -2.0371782e-02]]\\n\",\n \"Similarity scores:\\n\",\n \"[[0.9999997 0.6077381]\\n\",\n \" [0.6077381 0.9999999]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = _encode(sentences)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, run the API provided in FlagEmbedding:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embeddings:\\n\",\n \"[[ 1.4549762e-02 -9.6840411e-03 3.7761475e-03 ... -8.5092714e-04\\n\",\n \" 2.8417887e-02 6.3214332e-02]\\n\",\n \" [ 3.3924331e-05 -3.2998275e-03 1.7206438e-02 ... 3.5703944e-03\\n\",\n \" 1.8721525e-02 -2.0371782e-02]]\\n\",\n \"Similarity scores:\\n\",\n \"[[0.9999997 0.6077381]\\n\",\n \" [0.6077381 0.9999999]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\\n\",\n \"\\n\",\n \"embeddings = model.encode(sentences)\\n\",\n \"print(f\\\"Embeddings:\\\\n{embeddings}\\\")\\n\",\n \"\\n\",\n \"scores = embeddings @ embeddings.T\\n\",\n \"print(f\\\"Similarity scores:\\\\n{scores}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we expect, the two encoding functions return exactly the same results. The full implementation in FlagEmbedding handles large datasets by batching and contains GPU support and parallelization. Feel free to check the [source code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/inference/embedder/encoder_only/base.py) for more details.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.13.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/1_Embedding/1.2.4.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE-M3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install -U transformers FlagEmbedding accelerate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BGE-M3 structure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch, os\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-m3\\\")\\n\",\n \"raw_model = AutoModel.from_pretrained(\\\"BAAI/bge-m3\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The base model of BGE-M3 is [XLM-RoBERTa-large](https://huggingface.co/FacebookAI/xlm-roberta-large), which is a multilingual version of RoBERTa.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"XLMRobertaModel(\\n\",\n \" (embeddings): XLMRobertaEmbeddings(\\n\",\n \" (word_embeddings): Embedding(250002, 1024, padding_idx=1)\\n\",\n \" (position_embeddings): Embedding(8194, 1024, padding_idx=1)\\n\",\n \" (token_type_embeddings): Embedding(1, 1024)\\n\",\n \" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (encoder): XLMRobertaEncoder(\\n\",\n \" (layer): ModuleList(\\n\",\n \" (0-23): 24 x XLMRobertaLayer(\\n\",\n \" (attention): XLMRobertaAttention(\\n\",\n \" (self): XLMRobertaSelfAttention(\\n\",\n \" (query): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (key): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (value): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" (output): XLMRobertaSelfOutput(\\n\",\n \" (dense): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (intermediate): XLMRobertaIntermediate(\\n\",\n \" (dense): Linear(in_features=1024, out_features=4096, bias=True)\\n\",\n \" (intermediate_act_fn): GELUActivation()\\n\",\n \" )\\n\",\n \" (output): XLMRobertaOutput(\\n\",\n \" (dense): Linear(in_features=4096, out_features=1024, bias=True)\\n\",\n \" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\\n\",\n \" (dropout): Dropout(p=0.1, inplace=False)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (pooler): XLMRobertaPooler(\\n\",\n \" (dense): Linear(in_features=1024, out_features=1024, bias=True)\\n\",\n \" (activation): Tanh()\\n\",\n \" )\\n\",\n \")\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"raw_model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Multi-Functionality\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Fetching 30 files: 100%|██████████| 30/30 [00:00<00:00, 240131.91it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import BGEM3FlagModel\\n\",\n \"\\n\",\n \"model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)\\n\",\n \"\\n\",\n \"sentences_1 = [\\\"What is BGE M3?\\\", \\\"Defination of BM25\\\"]\\n\",\n \"sentences_2 = [\\\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\\\", \\n\",\n \" \\\"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Dense Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using BGE M3 for dense embedding has similar steps to BGE or BGE 1.5 models.\\n\",\n \"\\n\",\n \"Use the normalized hidden state of the special token [CLS] as the embedding:\\n\",\n \"\\n\",\n \"$$e_q = norm(H_q[0])$$\\n\",\n \"\\n\",\n \"Then compute the relevance score between the query and passage:\\n\",\n \"\\n\",\n \"$$s_{dense}=f_{sim}(e_p, e_q)$$\\n\",\n \"\\n\",\n \"where $e_p, e_q$ are the embedding vectors of passage and query, respectively.\\n\",\n \"\\n\",\n \"$f_{sim}$ is the score function (such as inner product and L2 distance) for comupting two embeddings' similarity.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6259035 0.34749585]\\n\",\n \" [0.349868 0.6782462 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# If you don't need such a long length of 8192 input tokens, you can set max_length to a smaller value to speed up encoding.\\n\",\n \"embeddings_1 = model.encode(sentences_1, max_length=10)['dense_vecs']\\n\",\n \"embeddings_2 = model.encode(sentences_2, max_length=100)['dense_vecs']\\n\",\n \"\\n\",\n \"# compute the similarity scores\\n\",\n \"s_dense = embeddings_1 @ embeddings_2.T\\n\",\n \"print(s_dense)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Sparse Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set `return_sparse` to true to make the model return sparse vector. If a term token appears multiple times in the sentence, we only retain its max weight.\\n\",\n \"\\n\",\n \"BGE-M3 generates sparce embeddings by adding a linear layer and a ReLU activation function following the hidden states:\\n\",\n \"\\n\",\n \"$$w_{qt} = \\\\text{Relu}(W_{lex}^T H_q [i])$$\\n\",\n \"\\n\",\n \"where $W_{lex}$ representes the weights of linear layer and $H_q[i]$ is the encoder's output of the $i^{th}$ token.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[{'What': 0.08362077, 'is': 0.081469566, 'B': 0.12964639, 'GE': 0.25186998, 'M': 0.17001738, '3': 0.26957875, '?': 0.040755156}, {'De': 0.050144322, 'fin': 0.13689369, 'ation': 0.045134712, 'of': 0.06342201, 'BM': 0.25167602, '25': 0.33353207}]\\n\"\n ]\n }\n ],\n \"source\": [\n \"output_1 = model.encode(sentences_1, return_sparse=True)\\n\",\n \"output_2 = model.encode(sentences_2, return_sparse=True)\\n\",\n \"\\n\",\n \"# you can see the weight for each token:\\n\",\n \"print(model.convert_id_to_token(output_1['lexical_weights']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Based on the tokens' weights of query and passage, the relevance score between them is computed by the joint importance of the co-existed terms within the query and passage:\\n\",\n \"\\n\",\n \"$$s_{lex} = \\\\sum_{t\\\\in q\\\\cap p}(w_{qt} * w_{pt})$$\\n\",\n \"\\n\",\n \"where $w_{qt}, w_{pt}$ are the importance weights of each co-existed term $t$ in query and passage, respectively.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.19554448500275612\\n\",\n \"0.00880391988903284\\n\"\n ]\n }\n ],\n \"source\": [\n \"# compute the scores via lexical mathcing\\n\",\n \"s_lex_10_20 = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])\\n\",\n \"s_lex_10_21 = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][1])\\n\",\n \"\\n\",\n \"print(s_lex_10_20)\\n\",\n \"print(s_lex_10_21)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Multi-Vector\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The multi-vector method utilizes the entire output embeddings for the representation of query $E_q$ and passage $E_p$.\\n\",\n \"\\n\",\n \"$$E_q = norm(W_{mul}^T H_q)$$\\n\",\n \"$$E_p = norm(W_{mul}^T H_p)$$\\n\",\n \"\\n\",\n \"where $W_{mul}$ is the learnable projection matrix.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(8, 1024)\\n\",\n \"(30, 1024)\\n\"\n ]\n }\n ],\n \"source\": [\n \"output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)\\n\",\n \"output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)\\n\",\n \"\\n\",\n \"print(f\\\"({len(output_1['colbert_vecs'][0])}, {len(output_1['colbert_vecs'][0][0])})\\\")\\n\",\n \"print(f\\\"({len(output_2['colbert_vecs'][0])}, {len(output_2['colbert_vecs'][0][0])})\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Following ColBert, we use late-interaction to compute the fine-grained relevance score:\\n\",\n \"\\n\",\n \"$$s_{mul}=\\\\frac{1}{N}\\\\sum_{i=1}^N\\\\max_{j=1}^M E_q[i]\\\\cdot E_p^T[j]$$\\n\",\n \"\\n\",\n \"where $E_q, E_p$ are the entire output embeddings of query and passage, respectively.\\n\",\n \"\\n\",\n \"This is a summation of average of maximum similarity of each $v\\\\in E_q$ with vectors in $E_p$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7796662449836731\\n\",\n \"0.4621177911758423\\n\"\n ]\n }\n ],\n \"source\": [\n \"s_mul_10_20 = model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]).item()\\n\",\n \"s_mul_10_21 = model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]).item()\\n\",\n \"\\n\",\n \"print(s_mul_10_20)\\n\",\n \"print(s_mul_10_21)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 Hybrid Ranking\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-M3's multi-functionality gives the possibility of hybrid ranking to improve retrieval. Firstly, due to the heavy cost of multi-vector method, we can retrieve the candidate results by either of the dense or sparse method. Then, to get the final result, we can rerank the candidates based on the integrated relevance score:\\n\",\n \"\\n\",\n \"$$s_{rank} = w_1\\\\cdot s_{dense}+w_2\\\\cdot s_{lex} + w_3\\\\cdot s_{mul}$$\\n\",\n \"\\n\",\n \"where the values chosen for $w_1, w_2$ and $w_3$ varies depending on the downstream scenario (here 1/3 is just for demonstration).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.5337047390639782\\n\",\n \"0.27280585498859483\\n\"\n ]\n }\n ],\n \"source\": [\n \"s_rank_10_20 = 1/3 * s_dense[0][0] + 1/3 * s_lex_10_20 + 1/3 * s_mul_10_20\\n\",\n \"s_rank_10_21 = 1/3 * s_dense[0][1] + 1/3 * s_lex_10_21 + 1/3 * s_mul_10_21\\n\",\n \"\\n\",\n \"print(s_rank_10_20)\\n\",\n \"print(s_rank_10_21)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/1_Embedding/1.2.5.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE-EN-ICL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will go through BGE-EN-ICL, an LLM based embedding model with both strong zero-shot and few-shot embedding capability.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0.Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the required packages in your environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U transformers FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. BGE-EN-ICL structure\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 9.94it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import AutoTokenizer, AutoModel\\n\",\n \"import torch, os\\n\",\n \"\\n\",\n \"tokenizer = AutoTokenizer.from_pretrained(\\\"BAAI/bge-en-icl\\\")\\n\",\n \"raw_model = AutoModel.from_pretrained(\\\"BAAI/bge-en-icl\\\")\\n\",\n \"\\n\",\n \"sentences = [\\\"embedding\\\", \\\"I love machine learning and nlp\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Different from the previous BGE embedding models which are encoder only models, BGE-EN-ICL use decoder only LLM, Mistral-7B, as the base model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"MistralModel(\\n\",\n \" (embed_tokens): Embedding(32003, 4096)\\n\",\n \" (layers): ModuleList(\\n\",\n \" (0-31): 32 x MistralDecoderLayer(\\n\",\n \" (self_attn): MistralSdpaAttention(\\n\",\n \" (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\\n\",\n \" (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\\n\",\n \" (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\\n\",\n \" (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\\n\",\n \" (rotary_emb): MistralRotaryEmbedding()\\n\",\n \" )\\n\",\n \" (mlp): MistralMLP(\\n\",\n \" (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\\n\",\n \" (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\\n\",\n \" (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\\n\",\n \" (act_fn): SiLU()\\n\",\n \" )\\n\",\n \" (input_layernorm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" (post_attention_layernorm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (norm): MistralRMSNorm((4096,), eps=1e-05)\\n\",\n \")\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"raw_model.eval()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. New Pooling Method\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BERT-like encoder only networks are considered with strong capacity for representation learning because of their bidirectional attention structure. Some previous work replace unidirectional attention with bidirectional attention during the embedding training phase. But this might creates a mismatch with the model's pre-training design, which could potentially undermine its in-context learning and generative properties.\\n\",\n \"\\n\",\n \"Thus BGE-EN-ICL introduces a [EOS] token's output embedding to address this issue.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'input_ids': tensor([[ 0, 0, 0, 0, 0, 0, 1, 28643, 2],\\n\",\n \" [ 1, 315, 2016, 5599, 5168, 304, 307, 12312, 2]]), 'attention_mask': tensor([[0, 0, 0, 0, 0, 0, 1, 1, 1],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1]])}\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"inputs = tokenizer(\\n\",\n \" sentences,\\n\",\n \" padding=True,\\n\",\n \" return_tensors='pt',\\n\",\n \")\\n\",\n \"inputs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 9, 4096])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"last_hidden_state = raw_model(**inputs, return_dict=True).last_hidden_state\\n\",\n \"last_hidden_state.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The last token/[EOS] pooling method can be described as:\\n\",\n \"\\n\",\n \"Given the tokenized input sequence $T: [\\\\text{BOS}], t_1, ..., t_N$ is sent into the LLM:\\n\",\n \"$$h_t = \\\\text{LLM}(T)[\\\\text{EOS}]$$\\n\",\n \"where $h_t$ represents the text embedding taken from the output embedding of the special token [EOS]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def last_token_pool(last_hidden_states: torch.Tensor,\\n\",\n \" attention_mask: torch.Tensor) -> torch.Tensor:\\n\",\n \" \\n\",\n \" left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\\n\",\n \" if left_padding:\\n\",\n \" return last_hidden_states[:, -1]\\n\",\n \" else:\\n\",\n \" sequence_lengths = attention_mask.sum(dim=1) - 1\\n\",\n \" batch_size = last_hidden_states.shape[0]\\n\",\n \" return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"torch.Size([2, 4096])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"embeddings = last_token_pool(\\n\",\n \" last_hidden_state, \\n\",\n \" attention_mask=inputs['attention_mask']\\n\",\n \")\\n\",\n \"embeddings.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. In-Context Learning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-EN-ICL integrate strong in-context learning of LLM into embedding model while still persisting strong zero-shot embedding capability.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For zero-shot inference, it's exactly same to BGE v1&1.5. For few-shot inference, use the following way:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"examples = [\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'what is a virtual interface',\\n\",\n \" 'response': \\\"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\\\"\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\\n\",\n \" 'query': 'causes of back pain in female for a week',\\n\",\n \" 'response': \\\"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\\\"\\n\",\n \" }\\n\",\n \"]\\n\",\n \"\\n\",\n \"queries = [\\\"how much protein should a female eat\\\", \\\"summit define\\\"]\\n\",\n \"documents = [\\n\",\n \" \\\"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\\\",\\n\",\n \" \\\"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\\\"\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 4.59it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 501.41it/s]\\n\",\n \"You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6064 0.302 ]\\n\",\n \" [0.257 0.5366]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagICLModel\\n\",\n \"\\n\",\n \"model = FlagICLModel('BAAI/bge-en-icl', \\n\",\n \" examples_for_task=examples, # set `examples_for_task=None` to use model without examples\\n\",\n \" examples_instruction_format=\\\"{}\\\\n{}\\\\n{}\\\", # specify the format to use examples_for_task\\n\",\n \" devices=[0],\\n\",\n \" )\\n\",\n \"\\n\",\n \"embeddings_1 = model.encode_queries(queries)\\n\",\n \"embeddings_2 = model.encode_corpus(documents)\\n\",\n \"similarity = embeddings_1 @ embeddings_2.T\\n\",\n \"\\n\",\n \"print(similarity)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/1_Embedding.rst", "content": "1. Embedding\n============\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: Embedding\n\n 1_Embedding/1.1.1\n 1_Embedding/1.2.1\n 1_Embedding/1.2.2\n 1_Embedding/1.2.3\n 1_Embedding/1.2.4\n 1_Embedding/1.2.5" }, { "path": "docs/source/tutorial/2_Metrics/2.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d0f87e9-657d-46b9-a3f0-ebc1bf0656bd\",\n \"metadata\": {},\n \"source\": [\n \"# Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"00c817d5\",\n \"metadata\": {},\n \"source\": [\n \"In this section, we will introduce several different ways to measure similarity.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dae49384-2450-425c-b050-c27d3c07d8e7\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## 1. Jaccard Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"03266267-2d6d-4124-9702-f61e0510586c\",\n \"metadata\": {},\n \"source\": [\n \"Before directly calculate the similarity between embedding vectors, let's first take a look at the primal method for measuring how similar two sentenses are: Jaccard similarity.\\n\",\n \"\\n\",\n \"**Definition:** For sets $A$ and $B$, the Jaccard index, or the Jaccard similarity coefficient between them is the size of their intersection divided by the size of their union:\\n\",\n \"$$J(A,B)=\\\\frac{|A\\\\cap B|}{|A\\\\cup B|}$$\\n\",\n \"\\n\",\n \"The value of $J(A,B)$ falls in the range of $[0, 1]$.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"bed533e1-a17c-4595-bdff-7f4a29e4deb3\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:12:47.091346Z\",\n \"iopub.status.busy\": \"2024-07-17T03:12:47.091019Z\",\n \"iopub.status.idle\": \"2024-07-17T03:12:47.094401Z\",\n \"shell.execute_reply\": \"2024-07-17T03:12:47.093967Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:12:47.091327Z\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"def jaccard_similarity(sentence1, sentence2):\\n\",\n \" set1 = set(sentence1.split(\\\" \\\"))\\n\",\n \" set2 = set(sentence2.split(\\\" \\\"))\\n\",\n \" intersection = set1.intersection(set2)\\n\",\n \" union = set1.union(set2)\\n\",\n \" return len(intersection)/len(union)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"ea766de8-572d-4eca-91f7-284a121e8edb\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:14:06.133012Z\",\n \"iopub.status.busy\": \"2024-07-17T03:14:06.132502Z\",\n \"iopub.status.idle\": \"2024-07-17T03:14:06.135483Z\",\n \"shell.execute_reply\": \"2024-07-17T03:14:06.135044Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:14:06.132992Z\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"s1 = \\\"Hawaii is a wonderful place for holiday\\\"\\n\",\n \"s2 = \\\"Peter's favorite place to spend his holiday is Hawaii\\\"\\n\",\n \"s3 = \\\"Anna enjoys baking during her holiday\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"b359ff4e-21a1-489a-ad46-ba53e974dc48\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:13:34.646320Z\",\n \"iopub.status.busy\": \"2024-07-17T03:13:34.645942Z\",\n \"iopub.status.idle\": \"2024-07-17T03:13:34.649389Z\",\n \"shell.execute_reply\": \"2024-07-17T03:13:34.648998Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:13:34.646302Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.3333333333333333\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"jaccard_similarity(s1, s2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"069868a9-d379-4d55-8a23-835a2972d079\",\n \"metadata\": {\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T03:14:13.727400Z\",\n \"iopub.status.busy\": \"2024-07-17T03:14:13.726949Z\",\n \"iopub.status.idle\": \"2024-07-17T03:14:13.730545Z\",\n \"shell.execute_reply\": \"2024-07-17T03:14:13.730121Z\",\n \"shell.execute_reply.started\": \"2024-07-17T03:14:13.727381Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.08333333333333333\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"jaccard_similarity(s1, s3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b0323128\",\n \"metadata\": {},\n \"source\": [\n \"We can see that sentence 1 and 2 are sharing 'Hawaii', 'place', and 'holiday'. Thus getting a larger score of similarity (0.333) than that (0.083) of the sentence 1 and 3 that only share 'holiday'.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b509fa6c-87ac-4c59-b40e-fda95fd036d9\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## 2. Euclidean Distance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"9da366b8-427f-4e8f-b3e6-b453050f0591\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:30:37.643857Z\",\n \"iopub.status.busy\": \"2024-07-17T02:30:37.643302Z\",\n \"iopub.status.idle\": \"2024-07-17T02:30:37.647921Z\",\n \"shell.execute_reply\": \"2024-07-17T02:30:37.647513Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:30:37.643840Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"tensor([[5., 2., 2., 6.]]) tensor([[4., 6., 6., 4.]])\\n\"\n ]\n }\n ],\n \"source\": [\n \"import torch\\n\",\n \"\\n\",\n \"A = torch.randint(1, 7, (1, 4), dtype=torch.float32)\\n\",\n \"B = torch.randint(1, 7, (1, 4), dtype=torch.float32)\\n\",\n \"print(A, B)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c068bb3-90ce-4266-8335-e3fb2ad3e996\",\n \"metadata\": {},\n \"source\": [\n \"**Definition:** For vectors $A$ and $B$, the Euclidean distance or L2 distance between them is defined as:\\n\",\n \"$$d(A, B) = \\\\|A-B\\\\|_2 = \\\\sqrt{\\\\sum_{i=1}^n (A_i-B_i)^2}$$\\n\",\n \"\\n\",\n \"The value of $d(A, B)$ falls in the range of [0, $+\\\\infty$). Since this is the measurement of distance, the closer the value is to 0, the more similar the two vector is. And the larger the value is, the two vectors are more dissimilar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d6c734d-cc03-4dd1-bb9e-3243006dcff4\",\n \"metadata\": {},\n \"source\": [\n \"You can calculate Euclidean distance step by step or directly call *torch.cdist()*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"0773acf4-eb53-4058-85da-af82af20c469\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:32:45.240684Z\",\n \"iopub.status.busy\": \"2024-07-17T02:32:45.240216Z\",\n \"iopub.status.idle\": \"2024-07-17T02:32:45.244248Z\",\n \"shell.execute_reply\": \"2024-07-17T02:32:45.243843Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:32:45.240665Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6.082762718200684\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dist = torch.sqrt(torch.sum(torch.pow(torch.subtract(A, B), 2), dim=-1))\\n\",\n \"dist.item()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"1dd45446-f7d6-4aab-b078-1d34f0a949e4\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:32:57.551560Z\",\n \"iopub.status.busy\": \"2024-07-17T02:32:57.550896Z\",\n \"iopub.status.idle\": \"2024-07-17T02:32:57.555031Z\",\n \"shell.execute_reply\": \"2024-07-17T02:32:57.554638Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:32:57.551536Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6.082762718200684\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"torch.cdist(A, B, p=2).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"da4435c0-98da-4397-8a45-c954dd3ada56\",\n \"metadata\": {},\n \"source\": [\n \"### (Maximum inner-product search)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0e0fa5c2-e619-4a0f-a785-9cc209f1503b\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"## 3. Cosine Similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"790e1ce3-1468-4819-a956-fc8eac690d89\",\n \"metadata\": {},\n \"source\": [\n \"For vectors $A$ and $B$, their cosine similarity is defined as:\\n\",\n \"$$\\\\cos(\\\\theta)=\\\\frac{A\\\\cdot B}{\\\\|A\\\\|\\\\|B\\\\|}$$\\n\",\n \"\\n\",\n \"The value of $\\\\cos(\\\\theta)$ falls in the range of $[-1, 1]$. Different from Euclidean distance, close to -1 denotes not similar at all and close to +1 means very similar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d0a64b4b-5caf-4bee-be0f-2e26b1c7ed6e\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### 3.1 Naive Approach\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"350cc48d-6e73-4e20-86dd-c05d1238ef60\",\n \"metadata\": {},\n \"source\": [\n \"The naive approach is just expanding the expression:\\n\",\n \"$$\\\\frac{A\\\\cdot B}{\\\\|A\\\\|\\\\|B\\\\|}=\\\\frac{\\\\sum_{i=1}^{i=n}A_i B_i}{\\\\sqrt{\\\\sum_{i=1}^{n}A_i^2}\\\\cdot\\\\sqrt{\\\\sum_{i=1}^{n}B_i^2}}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"20c7cff0-55a7-4222-9e5a-f5450171fb00\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:35.239550Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:35.239073Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:35.242844Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:35.242417Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:35.239531Z\"\n },\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Compute the dot product of A and B\\n\",\n \"dot_prod = sum(a*b for a, b in zip(A[0], B[0]))\\n\",\n \"\\n\",\n \"# Compute the magnitude of A and B\\n\",\n \"A_norm = torch.sqrt(sum(a*a for a in A[0]))\\n\",\n \"B_norm = torch.sqrt(sum(b*b for b in B[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"f4dce1fb-9cff-4a0d-bc7f-a503be6a37ae\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:36.533667Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:36.533224Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:36.536611Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:36.536181Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:36.533650Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.802726686000824\\n\"\n ]\n }\n ],\n \"source\": [\n \"cos_1 = dot_prod / (A_norm * B_norm)\\n\",\n \"print(cos_1.item())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4665f38f-c1f1-42dd-914d-d1d69c038e88\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### 3.2 PyTorch Implementation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6154391d-1dea-4673-8502-b496cf87d4b0\",\n \"metadata\": {},\n \"source\": [\n \"The naive approach has few issues:\\n\",\n \"- There are chances of losing precision in the numerator and the denominator\\n\",\n \"- Losing precision may cause the computed cosine similarity > 1.0\\n\",\n \"\\n\",\n \"Thus PyTorch uses the following way:\\n\",\n \"\\n\",\n \"$$\\n\",\n \"\\\\frac{A\\\\cdot B}{\\\\|A\\\\|\\\\|B\\\\|}=\\\\frac{A}{\\\\|A\\\\|}\\\\cdot\\\\frac{B}{\\\\|B\\\\|}\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"b8be02be-3ac3-4e5f-a450-c53f05781ab4\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:38.945105Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:38.944403Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:38.948117Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:38.947698Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:38.945085Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.802726686000824\\n\"\n ]\n }\n ],\n \"source\": [\n \"res = torch.mm(A / A.norm(dim=1), B.T / B.norm(dim=1))\\n\",\n \"print(res.item())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"988acff0-e6b5-41db-92d6-8f175dd3e272\",\n \"metadata\": {\n \"tags\": []\n },\n \"source\": [\n \"### 3.3 PyTorch Function Call\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a61b4871-4039-4c6e-b5ee-f66a12156be9\",\n \"metadata\": {},\n \"source\": [\n \"In practice, the most convinient way is directly use *cosine_similarity()* in torch.nn.functional:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"1ac4012e-b90a-4e60-97b8-e42636fde1c9\",\n \"metadata\": {\n \"ExecutionIndicator\": {\n \"show\": true\n },\n \"execution\": {\n \"iopub.execute_input\": \"2024-07-17T02:24:55.804298Z\",\n \"iopub.status.busy\": \"2024-07-17T02:24:55.803810Z\",\n \"iopub.status.idle\": \"2024-07-17T02:24:55.807551Z\",\n \"shell.execute_reply\": \"2024-07-17T02:24:55.807146Z\",\n \"shell.execute_reply.started\": \"2024-07-17T02:24:55.804278Z\"\n },\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.802726686000824\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import torch.nn.functional as F\\n\",\n \"\\n\",\n \"F.cosine_similarity(A, B).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f4ab87cc\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Inner Product/Dot Product\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e3c025ab\",\n \"metadata\": {},\n \"source\": [\n \"Coordinate definition:\\n\",\n \"$$A\\\\cdot B = \\\\sum_{i=1}^{i=n}A_i B_i$$\\n\",\n \"\\n\",\n \"Geometric definition:\\n\",\n \"$$A\\\\cdot B = \\\\|A\\\\|\\\\|B\\\\|\\\\cos(\\\\theta)$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"f0291d42\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"68.0\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dot_prod = A @ B.T\\n\",\n \"dot_prod.item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"33099a2e\",\n \"metadata\": {},\n \"source\": [\n \"### Relationship with Cosine similarity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2790e183\",\n \"metadata\": {},\n \"source\": [\n \"For computing the distance/similarity between two vectors, dot product and Cos similarity are closely related. Cos similarity only cares about the angle difference (because it is normalized by the product of two vectors' magnitude), while dot product takes both magnitude and angle into consideration. So the two metrics are preferred in different use cases.\\n\",\n \"\\n\",\n \"The BGE series models already normalized the output embedding vector to have the magnitude of 1. Thus using dot product and cos similarity will have the same result.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"e0f40534\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"model = FlagModel('BAAI/bge-large-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"78445a86\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sentence = \\\"I am very interested in natural language processing\\\"\\n\",\n \"embedding = torch.tensor(model.encode(sentence))\\n\",\n \"torch.norm(embedding).item()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9e1822ee\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Examples\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c665e3a\",\n \"metadata\": {},\n \"source\": [\n \"Now we've learned the mechanism of different types of similarity. Let's look at a real example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"73012cbb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sentence_1 = \\\"I will watch a show tonight\\\"\\n\",\n \"sentence_2 = \\\"I will show you my watch tonight\\\"\\n\",\n \"sentence_3 = \\\"I'm going to enjoy a performance this evening\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3cb79a47\",\n \"metadata\": {},\n \"source\": [\n \"It's clear to us that in sentence 1, 'watch' is a verb and 'show' is a noun. \\n\",\n \"\\n\",\n \"But in sentence 2, 'show' is a verb and 'watch' is a noun, which leads to different meaning of the two sentences.\\n\",\n \"\\n\",\n \"While sentence 3 has very similar meaning to sentence 1.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dc44dee9\",\n \"metadata\": {},\n \"source\": [\n \"Now let's see how does different similarity metrics tell us the relationship of the sentences.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"98bfcc6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.625\\n\",\n \"0.07692307692307693\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(jaccard_similarity(sentence_1, sentence_2))\\n\",\n \"print(jaccard_similarity(sentence_1, sentence_3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b7e4cd15\",\n \"metadata\": {},\n \"source\": [\n \"The results show that sentence 1 and 2 (0.625) are way more similar than sentence 1 and 3 (0.077), which indicate the opposite conclusion compare to what we have made.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cff73692\",\n \"metadata\": {},\n \"source\": [\n \"Now let's first get the embeddings of these sentences.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"426c0b42\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"torch.Size([1, 1024])\\n\"\n ]\n }\n ],\n \"source\": [\n \"embeddings = torch.from_numpy(model.encode([sentence_1, sentence_2, sentence_3]))\\n\",\n \"embedding_1 = embeddings[0].view(1, -1)\\n\",\n \"embedding_2 = embeddings[1].view(1, -1)\\n\",\n \"embedding_3 = embeddings[2].view(1, -1)\\n\",\n \"\\n\",\n \"print(embedding_1.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"63fe1b31\",\n \"metadata\": {},\n \"source\": [\n \"Then let's compute the Euclidean distance:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"d9bb35cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.714613139629364\\n\",\n \"0.5931472182273865\\n\"\n ]\n }\n ],\n \"source\": [\n \"euc_dist1_2 = torch.cdist(embedding_1, embedding_2, p=2).item()\\n\",\n \"euc_dist1_3 = torch.cdist(embedding_1, embedding_3, p=2).item()\\n\",\n \"print(euc_dist1_2)\\n\",\n \"print(euc_dist1_3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"402e6ea8\",\n \"metadata\": {},\n \"source\": [\n \"Then, let's see the cosine similarity:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"29e70bbc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7446640729904175\\n\",\n \"0.8240882158279419\\n\"\n ]\n }\n ],\n \"source\": [\n \"cos_dist1_2 = F.cosine_similarity(embedding_1, embedding_2).item()\\n\",\n \"cos_dist1_3 = F.cosine_similarity(embedding_1, embedding_3).item()\\n\",\n \"print(cos_dist1_2)\\n\",\n \"print(cos_dist1_3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c353d8cc\",\n \"metadata\": {},\n \"source\": [\n \"Using embedding, we can get the correct result different from Jaccard similarity that sentence 1 and 2 should be more similar than sentence 1 and 3 using either Euclidean distance or cos similarity as the metric.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/source/tutorial/2_Metrics/2.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation Metrics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we'll cover a list of metrics that are widely used for evaluating embedding model's performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Preparation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install numpy scikit-learn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Suppose we have a corpus with document ids from 0 - 30. \\n\",\n \"- `ground_truth` contains the actual relevant document ids to each query.\\n\",\n \"- `results` contains the search results of each query by some retrieval system.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"ground_truth = [\\n\",\n \" [11, 1, 7, 17, 21],\\n\",\n \" [ 4, 16, 1],\\n\",\n \" [26, 10, 22, 8],\\n\",\n \"]\\n\",\n \"\\n\",\n \"results = [\\n\",\n \" [11, 1, 17, 7, 21, 8, 0, 28, 9, 20],\\n\",\n \" [16, 1, 6, 18, 3, 4, 25, 19, 8, 14],\\n\",\n \" [24, 10, 26, 2, 8, 28, 4, 23, 13, 21],\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19,\\n\",\n \" 21, 22, 24, 25, 26, 28])\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.intersect1d(ground_truth, results)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\\n\",\n \" [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.isin(ground_truth, results).astype(int)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And we are interested in the following cutoffs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cutoffs = [1, 5, 10]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will use the above small example to show how different metrics evaluate the retrieval system's quality.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Recall\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Recall represents the model's capability of correctly predicting positive instances from all the actual positive samples in the dataset.\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{\\\\text{True Positives}}{\\\\text{True Positives}+\\\\text{False Negatives}}$$\\n\",\n \"\\n\",\n \"to write it in the form of information retrieval, which is the ratio of relevant documents retrieved to the total number of relevant documents in the corpus. In practice, we usually make the denominator to be the minimum between the current cutoff (usually 1, 5, 10, 100, etc) and the total number of relevant documents in the corpus:\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{|\\\\text{\\\\{Relevant docs\\\\}}\\\\cap\\\\text{\\\\{Retrieved docs\\\\}}|}{\\\\text{min}(|\\\\text{\\\\{Retrieved docs\\\\}}|, |\\\\text{\\\\{Relevant docs\\\\}}|)}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_recall(preds, truths, cutoffs):\\n\",\n \" recalls = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" hits = np.intersect1d(truth, text[:c])\\n\",\n \" recalls[i] += len(hits) / max(min(c, len(truth)), 1)\\n\",\n \" recalls /= len(preds)\\n\",\n \" return recalls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1: 0.6666666666666666\\n\",\n \"recall@5: 0.8055555555555555\\n\",\n \"recall@10: 0.9166666666666666\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls = calc_recall(results, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"recall@{c}: {recalls[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. MRR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. It measures the rank position of the first relevant result in a list of search results.\\n\",\n \"\\n\",\n \"$$MRR=\\\\frac{1}{|Q|}\\\\sum_{i=1}^{|Q|}\\\\frac{1}{rank_i}$$\\n\",\n \"\\n\",\n \"where \\n\",\n \"- $|Q|$ is the total number of queries.\\n\",\n \"- $rank_i$ is the rank position of the first relevant document of the i-th query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_MRR(preds, truth, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, t in zip(preds, truth):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, p in enumerate(pred):\\n\",\n \" if j < c and p in t:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1: 0.6666666666666666\\n\",\n \"MRR@5: 0.8333333333333334\\n\",\n \"MRR@10: 0.8333333333333334\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr = calc_MRR(results, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"MRR@{c}: {mrr[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. nDCG\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normalized Discounted Cumulative Gain (nDCG) measures the quality of a ranked list of search results by considering both the position of the relevant documents and their graded relevance scores. The calculation of nDCG involves two main steps:\\n\",\n \"\\n\",\n \"1. Discounted cumulative gain (DCG) measures the ranking quality in retrieval tasks.\\n\",\n \"\\n\",\n \"$$DCG_p=\\\\sum_{i=1}^p\\\\frac{2^{rel_i}-1}{\\\\log_2(i+1)}$$\\n\",\n \"\\n\",\n \"2. Normalized by ideal DCG to make it comparable across queries.\\n\",\n \"$$nDCG_p=\\\\frac{DCG_p}{IDCG_p}$$\\n\",\n \"where $IDCG$ is the maximum possible DCG for a given set of documents, assuming they are perfectly ranked in order of relevance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pred_hard_encodings = []\\n\",\n \"for pred, label in zip(results, ground_truth):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings.append(pred_hard_encoding)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.0\\n\",\n \"nDCG@5: 0.3298163165186628\\n\",\n \"nDCG@10: 0.5955665344840209\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import ndcg_score\\n\",\n \"\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings, results, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Precision\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Precision \\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{\\\\text{True Positives}}{\\\\text{True Positives}+\\\\text{False Positive}}$$\\n\",\n \"\\n\",\n \"in information retrieval, it's the ratio of relevant documents retrieved to the totoal number of documents retrieved:\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{|\\\\text{\\\\{Relevant docs\\\\}}\\\\cap\\\\text{\\\\{Retrieved docs\\\\}}|}{|\\\\text{\\\\{Retrieved docs\\\\}}|}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_precision(preds, truths, cutoffs):\\n\",\n \" prec = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" hits = np.intersect1d(truth, text[:c])\\n\",\n \" prec[i] += len(hits) / c\\n\",\n \" prec /= len(preds)\\n\",\n \" return prec\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"precision@1: 0.6666666666666666\\n\",\n \"precision@5: 0.6666666666666666\\n\",\n \"precision@10: 0.3666666666666667\\n\"\n ]\n }\n ],\n \"source\": [\n \"precisions = calc_precision(results, ground_truth, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"precision@{c}: {precisions[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. MAP\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Average Precision (MAP) measures the effectiveness of a system at returning relevant documents across multiple queries. \\n\",\n \"\\n\",\n \"First, Average Precision (AP) evals how well relevant documents are ranked within the retrieved documents. It's computed by averaging the precision values for each position of relevant document in the ranking of all the retrieved documents:\\n\",\n \"\\n\",\n \"$$\\\\textbf{AP}=\\\\frac{\\\\sum_{k=1}^{M}\\\\text{Relevance}(k) \\\\times \\\\text{Precision}(k)}{|\\\\{\\\\text{Relevant Docs}\\\\}|}$$\\n\",\n \"\\n\",\n \"where \\n\",\n \"- $M$ is the total number of documents retrieved.\\n\",\n \"- $\\\\text{Relevance}(k)$ is a binary value, indicating whether document at position $k$ is relevant (=1) or not (=0).\\n\",\n \"- $\\\\text{Precision}(k)$ is the precision when considering only top $k$ retrieved items.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then calculate the average AP across multiple queries to get the MAP:\\n\",\n \"\\n\",\n \"$$\\\\textbf{MAP}=\\\\frac{1}{N}\\\\sum_{i=1}^{N}\\\\text{AP}_i$$\\n\",\n \"\\n\",\n \"where\\n\",\n \"- $N$ is the total number of queries.\\n\",\n \"- $\\\\text{AP}_i$ is the average precision of the $i^{th}$ query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_AP(encoding):\\n\",\n \" rel = 0\\n\",\n \" precs = 0.0\\n\",\n \" for k, hit in enumerate(encoding, start=1):\\n\",\n \" if hit == 1:\\n\",\n \" rel += 1\\n\",\n \" precs += rel/k\\n\",\n \"\\n\",\n \" return 0 if rel == 0 else precs/rel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_MAP(encodings, cutoffs):\\n\",\n \" res = []\\n\",\n \" for c in cutoffs:\\n\",\n \" ap_sum = 0.0\\n\",\n \" for encoding in encodings:\\n\",\n \" ap_sum += calc_AP(encoding[:c])\\n\",\n \" res.append(ap_sum/len(encodings))\\n\",\n \" \\n\",\n \" return res\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MAP@1: 0.6666666666666666\\n\",\n \"MAP@5: 0.862962962962963\\n\",\n \"MAP@10: 0.8074074074074075\\n\"\n ]\n }\n ],\n \"source\": [\n \"maps = calc_MAP(pred_hard_encodings, cutoffs)\\n\",\n \"for i, c in enumerate(cutoffs):\\n\",\n \" print(f\\\"MAP@{c}: {maps[i]}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"test\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/2_Metrics.rst", "content": "2. Metrics\n==========\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: Metrics\n\n 2_Metrics/2.1\n 2_Metrics/2.2" }, { "path": "docs/source/tutorial/3_Indexing/3.1.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Indexing Using Faiss\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In practical cases, datasets contain thousands or millions of rows. Looping through the whole corpus to find the best answer to a query is very time and space consuming. In this tutorial, we'll introduce how to use indexing to make our retrieval fast and neat.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 0: Setup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### faiss-gpu on Linux (x86_64)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss maintain the latest updates on conda. So if you have GPUs on Linux x86_64, create a conda virtual environment and run:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\\n\",\n \"\\n\",\n \"and make sure you select that conda env as the kernel for this notebook.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### faiss-cpu\\n\",\n \"\\n\",\n \"Otherwise it's simple, just run the following cell to install `faiss-cpu`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is a super tiny courpus with only 10 sentences, which will be the dataset we use.\\n\",\n \"\\n\",\n \"Each sentence is a concise discription of a famous people in specific domain.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = [\\n\",\n \" \\\"Michael Jackson was a legendary pop icon known for his record-breaking music and dance innovations.\\\",\\n\",\n \" \\\"Fei-Fei Li is a professor in Stanford University, revolutionized computer vision with the ImageNet project.\\\",\\n\",\n \" \\\"Brad Pitt is a versatile actor and producer known for his roles in films like 'Fight Club' and 'Once Upon a Time in Hollywood.'\\\",\\n\",\n \" \\\"Geoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\\",\\n\",\n \" \\\"Eminem is a renowned rapper and one of the best-selling music artists of all time.\\\",\\n\",\n \" \\\"Taylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\\",\\n\",\n \" \\\"Sam Altman leads OpenAI as its CEO, with astonishing works of GPT series and pursuing safe and beneficial AI.\\\",\\n\",\n \" \\\"Morgan Freeman is an acclaimed actor famous for his distinctive voice and diverse roles.\\\",\\n\",\n \" \\\"Andrew Ng spread AI knowledge globally via public courses on Coursera and Stanford University.\\\",\\n\",\n \" \\\"Robert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And a few queries (add your own queries and check the result!): \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = [\\n\",\n \" \\\"Who is Robert Downey Jr.?\\\",\\n\",\n \" \\\"An expert of neural network\\\",\\n\",\n \" \\\"A famous female singer\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Text Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here, for the sake of speed, we just embed the first 500 docs in the corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the corpus embeddings: (10, 768)\\n\",\n \"data type of the embeddings: float32\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss only accepts float32 inputs.\\n\",\n \"\\n\",\n \"So make sure the dtype of corpus_embeddings is float32 before adding them to the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this step, we build an index and add the embedding vectors to it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"\\n\",\n \"# if you installed faiss-gpu, uncomment the following lines to make the index on your GPUs.\\n\",\n \"\\n\",\n \"# co = faiss.GpuMultipleClonerOptions()\\n\",\n \"# index = faiss.index_cpu_to_all_gpus(index, co)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"No need to train if we use \\\"Flat\\\" quantizer and METRIC_INNER_PRODUCT as metric. Some other indices that using quantization might need training.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\",\n \"total number of vectors: 10\\n\"\n ]\n }\n ],\n \"source\": [\n \"# check if the index is trained\\n\",\n \"print(index.is_trained) \\n\",\n \"# index.train(corpus_embeddings)\\n\",\n \"\\n\",\n \"# add all the vectors to the index\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Step 3.5 (Optional): Saving Faiss index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once you have your index with the embedding vectors, you can save it locally for future usage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# change the path to where you want to save the index\\n\",\n \"path = \\\"./index.bin\\\"\\n\",\n \"faiss.write_index(index, path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you already have stored index in your local directory, you can load it by:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = faiss.read_index(\\\"./index.bin\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Find answers to the query\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, get the embeddings of all the queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_embeddings = model.encode_queries(queries)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, use the Faiss index to do a knn search in the vector space:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.6686779 0.37858668 0.3767978 ]\\n\",\n \" [0.6062041 0.59364545 0.527691 ]\\n\",\n \" [0.5409331 0.5097007 0.42427146]]\\n\",\n \"[[9 7 2]\\n\",\n \" [3 1 8]\\n\",\n \" [5 0 4]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"dists, ids = index.search(query_embeddings, k=3)\\n\",\n \"print(dists)\\n\",\n \"print(ids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's see the result:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"query:\\tWho is Robert Downey Jr.?\\n\",\n \"answer:\\tRobert Downey Jr. is an iconic actor best known for playing Iron Man in the Marvel Cinematic Universe.\\n\",\n \"\\n\",\n \"query:\\tAn expert of neural network\\n\",\n \"answer:\\tGeoffrey Hinton, as a foundational figure in AI, received Turing Award for his contribution in deep learning.\\n\",\n \"\\n\",\n \"query:\\tA famous female singer\\n\",\n \"answer:\\tTaylor Swift is a Grammy-winning singer-songwriter known for her narrative-driven music.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i, q in enumerate(queries):\\n\",\n \" print(f\\\"query:\\\\t{q}\\\\nanswer:\\\\t{corpus[ids[i][0]]}\\\\n\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/3_Indexing/3.1.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Faiss GPU\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the last tutorial, we went through the basics of indexing using faiss-cpu. While for the use cases in research and industry. The size of dataset for indexing will be extremely large, the frequency of searching might also be very high. In this tutorial we'll see how to combine Faiss and GPU almost seamlessly.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss maintain the latest updates on conda. And its gpu version only supports Linux x86_64\\n\",\n \"\\n\",\n \"create a conda virtual environment and run:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\\n\",\n \"\\n\",\n \"make sure you select that conda env as the kernel for this notebook. After installation, restart the kernal.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If your system does not satisfy the requirement, install faiss-cpu and just skip the steps with gpu related codes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Data Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First let's create two datasets with \\\"fake embeddings\\\" of corpus and queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"dim = 768\\n\",\n \"corpus_size = 1000\\n\",\n \"# np.random.seed(111)\\n\",\n \"\\n\",\n \"corpus = np.random.random((corpus_size, dim)).astype('float32')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Create Index on CPU\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option 1:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Faiss provides a great amount of choices of indexes by initializing directly:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# first build a flat index (on CPU)\\n\",\n \"index = faiss.IndexFlatIP(dim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Option 2:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Besides the basic index class, we can also use the index_factory function to produce composite Faiss index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = faiss.index_factory(dim, \\\"Flat\\\", faiss.METRIC_L2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Build GPU Index and Search\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All the GPU indexes are built with `StandardGpuResources` object. It contains all the needed resources for each GPU in use. By default it will allocate 18% of the total VRAM as a temporary scratch space.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `GpuClonerOptions` and `GpuMultipleClonerOptions` objects are optional when creating index from cpu to gpu. They are used to adjust the way the GPUs stores the objects.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Single GPU:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# use a single GPU\\n\",\n \"rs = faiss.StandardGpuResources()\\n\",\n \"co = faiss.GpuClonerOptions()\\n\",\n \"\\n\",\n \"# then make it to gpu index\\n\",\n \"index_gpu = faiss.index_cpu_to_gpu(provider=rs, device=0, index=index, options=co)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 5.31 ms, sys: 6.26 ms, total: 11.6 ms\\n\",\n \"Wall time: 8.94 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index_gpu.add(corpus)\\n\",\n \"D, I = index_gpu.search(corpus, 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### All Available GPUs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If your system contains multiple GPUs, Faiss provides the option to deploy al available GPUs. You can control their usages through `GpuMultipleClonerOptions`, e.g. whether to shard or replicate the index acrross GPUs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# cloner options for multiple GPUs\\n\",\n \"co = faiss.GpuMultipleClonerOptions()\\n\",\n \"\\n\",\n \"index_gpu = faiss.index_cpu_to_all_gpus(index=index, co=co)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 29.8 ms, sys: 26.8 ms, total: 56.6 ms\\n\",\n \"Wall time: 33.9 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index_gpu.add(corpus)\\n\",\n \"D, I = index_gpu.search(corpus, 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Multiple GPUs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's also option that use multiple GPUs but not all:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ngpu = 4\\n\",\n \"resources = [faiss.StandardGpuResources() for _ in range(ngpu)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create vectors for the GpuResources and divices, then pass them to the index_cpu_to_gpu_multiple() function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"vres = faiss.GpuResourcesVector()\\n\",\n \"vdev = faiss.Int32Vector()\\n\",\n \"for i, res in zip(range(ngpu), resources):\\n\",\n \" vdev.push_back(i)\\n\",\n \" vres.push_back(res)\\n\",\n \"index_gpu = faiss.index_cpu_to_gpu_multiple(vres, vdev, index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 3.49 ms, sys: 13.4 ms, total: 16.9 ms\\n\",\n \"Wall time: 9.03 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index_gpu.add(corpus)\\n\",\n \"D, I = index_gpu.search(corpus, 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All the three approaches should lead to identical result. Now let's do a quick sanity check:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# The nearest neighbor of each vector in the corpus is itself\\n\",\n \"assert np.all(corpus[:] == corpus[I[:, 0]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And the corresponding distance should be 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0. 111.30057 113.2251 113.342316]\\n\",\n \" [ 0. 111.158875 111.742325 112.09038 ]\\n\",\n \" [ 0. 116.44429 116.849915 117.30502 ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(D[:3])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"faiss\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/3_Indexing/3.1.3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Faiss Indexes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This tutorial will go through several widely used indexes in Faiss that fits different requirements, and how to use them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For CPU usage, use:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For GPU on Linux x86_64 system, use Conda:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.random.seed(768)\\n\",\n \"\\n\",\n \"data = np.random.random((1000, 128))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. `IndexFlat*`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat index is the very fundamental index structure. It does not do any preprocess for the incoming vectors. All the vectors are stored directly without compression or quantization. Thus no training is need for flat indexes.\\n\",\n \"\\n\",\n \"When searching, Flat index will decode all the vectors sequentially and compute the similarity score to the query vectors. Thus, Flat Index guarantees the global optimum of results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat index family is small: just `IndexFlatL2` and `IndexFlatIP`, which are just different by the similarity metrics of Euclidean distance and inner product.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Usage:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = 128 # dimension of the vector\\n\",\n \"k = 3 # number of nearest neighbors to search\\n\",\n \"\\n\",\n \"# just simply create the index and add all the data\\n\",\n \"index = faiss.IndexFlatL2(d)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sanity check:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[ 0. 16.257435 16.658928]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# search for the k nearest neighbor for the first element in data\\n\",\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat Indexes guarantee the perfect quality but with terrible speed. It works well on small datasets or the cases that speed is not a crucial factor. \\n\",\n \"\\n\",\n \"But what about the cases that speed is important? There's no way to have it all. So we want some indexes that only sacrifice as small as possible quality to speed up. That's why approximate nearest-neighbors (ANN) algorithms are widely accepted. Now we will go through a few popular ANN methods used in vector searching.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. `IndexIVF*`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"Inverted File Flat (IVF) Index is a widely accepted technique to speed up searching by using k-means or Voronoi diagram to create a number of cells (or say, clusters) in the whole space. Then when given a query, an amount of closest cells will be searched. After that, `k` closest elements to the query will be searched in those cells.\\n\",\n \"\\n\",\n \"- `quantizer` is another index/quantizer to assign vectors to inverted lists.\\n\",\n \"- `nlist` is the number of cells the space to be partitioned.\\n\",\n \"- `nprob` is the nuber of closest cells to visit for searching in query time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tradeoff\\n\",\n \"\\n\",\n \"Increasing `nlist` will shrink the size of each cell, which speed up the search process. But the smaller coverage will sacrifice accuracy and increase the possibility of the edge/surface problem discribed above.\\n\",\n \"\\n\",\n \"Increasing `nprob` will have a greater scope, preferring search quality by the tradeoff of slower speed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Shortage\\n\",\n \"\\n\",\n \"There could be a problem when the query vector lands on the edge/surface of the cell. It is possible that the closest element falls into the neighbor cell, which may not be considered due to `nprob` is not large enough.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nlist = 5\\n\",\n \"nprob = 2\\n\",\n \"\\n\",\n \"# the quantizer defines how to store and compare the vectors\\n\",\n \"quantizer = faiss.IndexFlatL2(d)\\n\",\n \"index = faiss.IndexIVFFlat(quantizer, d, nlist)\\n\",\n \"\\n\",\n \"# note different from flat index, IVF index first needs training to create the cells\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[ 0. 16.257435 16.658928]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set nprob before searching\\n\",\n \"index.nprobe = 8\\n\",\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. `IndexHNSW*`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"Hierarchical Navigable Small World (HNSW) indexing is a graph based method, which is an extension of navigable small world (NSW). It builds a multi-layered graph where nodes (vectors) are connected based on their proximity, forming \\\"small-world\\\" structures that allow efficient navigation through the space.\\n\",\n \"\\n\",\n \"- `M` is the number of neighbors each vector has in the graph.\\n\",\n \"- `efConstruction` is the number of entry points to explore when building the index.\\n\",\n \"- `efSearch` is the number of entry points to explore when searching.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tradeoff\\n\",\n \"\\n\",\n \"Increasing `M` or `efSearch` will make greater fidelity with reasonable longer time. Larger `efConstruction` mainly increases the index construction time.\\n\",\n \"\\n\",\n \"HNSW has great searching quality and speed. But it is memory-consuming due to the graph structure. Scaling up `M` will cause a linear increase of memory usage.\\n\",\n \"\\n\",\n \"Note that HNSW index does not support vector's removal because removing nodes will distroy graph structure.\\n\",\n \"\\n\",\n \"Thus HNSW is a great index to choose when RAM is not a limiting factor.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"M = 32\\n\",\n \"ef_search = 16\\n\",\n \"ef_construction = 32\\n\",\n \"\\n\",\n \"index = faiss.IndexHNSWFlat(d, M)\\n\",\n \"# set the two parameters before adding data\\n\",\n \"index.hnsw.efConstruction = ef_construction\\n\",\n \"index.hnsw.efSearch = ef_search\\n\",\n \"\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[ 0. 16.257435 16.658928]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4. `IndexLSH`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Intro\\n\",\n \"\\n\",\n \"Locality Sensitive Hashing (LSH) is an ANN method that hashing data points into buckets. While well known use cases of hash function such as dictionary/hashtabel are trying to avoid hashing collisions, LSH trys to maximize hashing collisions. Similar vectors will be grouped into same hash bucket.\\n\",\n \"\\n\",\n \"In Faiss, `IndexLSH` is a Flat index with binary codes. Vectors are hashed into binary codes and compared by Hamming distances.\\n\",\n \"\\n\",\n \"- `nbits` can be seen as the \\\"resolution\\\" of hashed vectors.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tradeoff\\n\",\n \"\\n\",\n \"Increasing `nbits` can get higher fidelity with the cost of more memory and longer searching time.\\n\",\n \"\\n\",\n \"LSH suffers the curse of dimensionality when using a larger `d`. In order to get similar search quality, the `nbits` value needs to be scaled up to maintain the search quality.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Shortage\\n\",\n \"\\n\",\n \"LSH speeds up searching time with a reasonable sacrifice of quality. But that only applies to small dimension `d`. Even 128 is already too large for LSH. Thus for vectors generated by transformer based embedding models, LSH index is not a common choice.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nbits = d * 8\\n\",\n \"\\n\",\n \"index = faiss.IndexLSH(d, nbits)\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 392]]\\n\",\n \"distance: [[ 0. 197. 199.]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"faiss\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/3_Indexing/3.1.4.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Faiss Quantizers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we will introduce the quantizer object in Faiss and how to use them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For CPU usage, run:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For GPU on Linux x86_64 system, use Conda:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.random.seed(768)\\n\",\n \"\\n\",\n \"data = np.random.random((1000, 128))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Scalar Quantizer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normal data type of vector embeedings is usually 32 bit floats. Scalar quantization is transforming the 32 float representation to, for example, 8 bit interger. Thus with a 4x reduction in size. In this way, it can be seen as we distribute each dimension into 256 buckets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Name | Class | Parameters |\\n\",\n \"|:------------:|:--------:|:-----------|\\n\",\n \"| `ScalarQuantizer` | Quantizer class | `d`: dimension of vectors
`qtype`: map dimension into $2^\\\\text{qtype}$ clusters |\\n\",\n \"| `IndexScalarQuantizer` | Flat index class | `d`: dimension of vectors
`qtype`: map dimension into $2^\\\\text{qtype}$ clusters
`metric`: similarity metric (L2 or IP) |\\n\",\n \"| `IndexIVFScalarQuantizer` | IVF index class | `d`: dimension of vectors
`nlist`: number of cells/clusters to partition the inverted file space
`qtype`: map dimension into $2^\\\\text{qtype}$ clusters
`metric`: similarity metric (L2 or IP)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Quantizer class objects are used to compress the data before adding into indexes. Flat index class objects and IVF index class objects can be used direct as and index. Quantization will be done automatically.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Scalar Quantizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[156 180 46 226 13 130 41 187 63 251 16 199 205 166 117 122 214 2\\n\",\n \" 206 137 71 186 20 131 59 57 68 114 35 45 28 210 27 93 74 245\\n\",\n \" 167 5 32 42 44 128 10 189 10 13 42 162 179 221 241 104 205 21\\n\",\n \" 70 87 52 219 172 138 193 0 228 175 144 34 59 88 170 1 233 220\\n\",\n \" 20 64 245 241 5 161 41 55 30 247 107 8 229 90 201 10 43 158\\n\",\n \" 238 184 187 114 232 90 116 205 14 214 135 158 237 192 205 141 232 176\\n\",\n \" 124 176 163 68 49 91 125 70 6 170 55 44 215 84 46 48 218 56\\n\",\n \" 107 176]\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = 128\\n\",\n \"qtype = faiss.ScalarQuantizer.QT_8bit\\n\",\n \"\\n\",\n \"quantizer = faiss.ScalarQuantizer(d, qtype)\\n\",\n \"\\n\",\n \"quantizer.train(data)\\n\",\n \"new_data = quantizer.compute_codes(data)\\n\",\n \"\\n\",\n \"print(new_data[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Scalar Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = 128\\n\",\n \"k = 3\\n\",\n \"qtype = faiss.ScalarQuantizer.QT_8bit\\n\",\n \"# nlist = 5\\n\",\n \"\\n\",\n \"index = faiss.IndexScalarQuantizer(d, qtype, faiss.METRIC_L2)\\n\",\n \"# index = faiss.IndexIVFScalarQuantizer(d, nlist, faiss.ScalarQuantizer.QT_8bit, faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 471 188]]\\n\",\n \"distance: [[1.6511828e-04 1.6252808e+01 1.6658131e+01]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Product Quantizer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When speed and memory are crucial factors in searching, product quantizer becomes a top choice. It is one of the effective quantizer on reducing memory size. \\n\",\n \"\\n\",\n \"The first step of PQ is dividing the original vectors with dimension `d` into smaller, low-dimensional sub-vectors with dimension `d/m`. Here `m` is the number of sub-vectors.\\n\",\n \"\\n\",\n \"Then clustering algorithms are used to create codebook of a fixed number of centroids.\\n\",\n \"\\n\",\n \"Next, each sub-vector of a vector is replaced by the index of the closest centroid from its corresponding codebook. Now each vector will be stored with only the indices instead of the full vector.\\n\",\n \"\\n\",\n \"When comuputing the distance between a query vector. Only the distances to the centroids in the codebooks are calculated, thus enable the quick approximate nearest neighbor searches.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Name | Class | Parameters |\\n\",\n \"|:------------:|:--------:|:-----------|\\n\",\n \"| `ProductQuantizer` | Quantizer class | `d`: dimension of vectors
`M`: number of sub-vectors that D % M == 0
`nbits`: number of bits per subquantizer, so each contain $2^\\\\text{nbits}$ centroids |\\n\",\n \"| `IndexPQ` | Flat index class | `d`: dimension of vectors
`M`: number of sub-vectors that D % M == 0
`nbits`: number of bits per subquantizer, so each contain $2^\\\\text{nbits}$ centroids
`metric`: similarity metric (L2 or IP) |\\n\",\n \"| `IndexIVFPQ` | IVF index class | `quantizer`: the quantizer used in computing distance phase.
`d`: dimension of vectors
`nlist`: number of cells/clusters to partition the inverted file space
`M`: number of sub-vectors that D % M == 0
`nbits`: number of bits per subquantizer, so each contain $2^\\\\text{nbits}$ centroids
`metric`: similarity metric (L2 or IP) |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Product Quantizer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"255\\n\",\n \"[[ 90 169 226 45]\\n\",\n \" [ 33 51 34 15]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = 128\\n\",\n \"M = 8\\n\",\n \"nbits = 4\\n\",\n \"\\n\",\n \"quantizer = faiss.ProductQuantizer(d, M, nbits)\\n\",\n \"\\n\",\n \"quantizer.train(data)\\n\",\n \"new_data = quantizer.compute_codes(data)\\n\",\n \"\\n\",\n \"print(new_data.max())\\n\",\n \"print(new_data[:2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Product Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"index = faiss.IndexPQ(d, M, nbits, faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 946 330]]\\n\",\n \"distance: [[ 8.823908 11.602461 11.746731]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Product Quantizer IVF Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nlist = 5\\n\",\n \"\\n\",\n \"quantizer = faiss.IndexFlat(d, faiss.METRIC_L2)\\n\",\n \"index = faiss.IndexIVFPQ(quantizer, d, nlist, M, nbits, faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.train(data)\\n\",\n \"index.add(data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"closest elements: [[ 0 899 521]]\\n\",\n \"distance: [[ 8.911423 12.088312 12.104569]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"D, I = index.search(data[:1], k)\\n\",\n \"\\n\",\n \"print(f\\\"closest elements: {I}\\\")\\n\",\n \"print(f\\\"distance: {D}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/3_Indexing/3.1.5.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Choosing Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Give a great amount of indexes and quantizers, how to choose the one in the experiment/application? In this part, we will give a general suggestion on how to choose the one fits your need.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Packages\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For CPU usage, run:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %pip install -U faiss-cpu numpy h5py\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For GPU on Linux x86_64 system, use Conda:\\n\",\n \"\\n\",\n \"```conda install -c pytorch -c nvidia faiss-gpu=1.8.0```\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from urllib.request import urlretrieve\\n\",\n \"import h5py\\n\",\n \"import faiss\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we'll use [SIFT1M](http://corpus-texmex.irisa.fr/), a very popular dataset for ANN evaluation, as our dataset to demonstrate the comparison.\\n\",\n \"\\n\",\n \"Run the following cell to download the dataset or you can also manually download from the repo [ann-benchmarks](https://github.com/erikbern/ann-benchmarks?tab=readme-ov-file#data-sets))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data_url = \\\"http://ann-benchmarks.com/sift-128-euclidean.hdf5\\\"\\n\",\n \"destination = \\\"data.hdf5\\\"\\n\",\n \"urlretrieve(data_url, destination)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then load the data from the hdf5 file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1000000, 128) float32\\n\",\n \"(10000, 128) float32\\n\"\n ]\n }\n ],\n \"source\": [\n \"with h5py.File('data.hdf5', 'r') as f:\\n\",\n \" corpus = f['train'][:]\\n\",\n \" query = f['test'][:]\\n\",\n \"\\n\",\n \"print(corpus.shape, corpus.dtype)\\n\",\n \"print(query.shape, corpus.dtype)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"d = corpus[0].shape[0]\\n\",\n \"k = 100\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Helper function\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following is a helper function for computing recall.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# compute recall from the prediction results and ground truth\\n\",\n \"def compute_recall(res, truth):\\n\",\n \" recall = 0\\n\",\n \" for i in range(len(res)):\\n\",\n \" intersect = np.intersect1d(res[i], truth[i])\\n\",\n \" recall += len(intersect) / len(res[i])\\n\",\n \" recall /= len(res)\\n\",\n \"\\n\",\n \" return recall\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Flat Index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Flat index use brute force to search neighbors for each query. It guarantees the optimal result with 100% recall. Thus we use the result from it as the ground truth.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 69.2 ms, sys: 80.6 ms, total: 150 ms\\n\",\n \"Wall time: 149 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"index = faiss.IndexFlatL2(d)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 17min 30s, sys: 1.62 s, total: 17min 31s\\n\",\n \"Wall time: 2min 1s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I_truth = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. IVF Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 10.6 s, sys: 831 ms, total: 11.4 s\\n\",\n \"Wall time: 419 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"nlist = 5\\n\",\n \"nprob = 3\\n\",\n \"\\n\",\n \"quantizer = faiss.IndexFlatL2(d)\\n\",\n \"index = faiss.IndexIVFFlat(quantizer, d, nlist)\\n\",\n \"index.nprobe = nprob\\n\",\n \"\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 9min 15s, sys: 598 ms, total: 9min 16s\\n\",\n \"Wall time: 12.5 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.9999189999999997\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the test we can see that IVFFlatL2 has a pretty good promotion for the searching speed with a very tiny loss of recall.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. HNSW Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 11min 21s, sys: 595 ms, total: 11min 22s\\n\",\n \"Wall time: 17 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"M = 64\\n\",\n \"ef_search = 32\\n\",\n \"ef_construction = 64\\n\",\n \"\\n\",\n \"index = faiss.IndexHNSWFlat(d, M)\\n\",\n \"# set the two parameters before adding data\\n\",\n \"index.hnsw.efConstruction = ef_construction\\n\",\n \"index.hnsw.efSearch = ef_search\\n\",\n \"\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 5.14 s, sys: 3.94 ms, total: 5.14 s\\n\",\n \"Wall time: 110 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.8963409999999716\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the searching time of less than 1 second, we can see why HNSW is one of the best choice when looking for an extreme speed during searching phase. The reduction of recall is acceptable. But the longer time during creation of index and large memory footprint need to be considered.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. LSH\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 13.7 s, sys: 660 ms, total: 14.4 s\\n\",\n \"Wall time: 12.1 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"nbits = d * 8\\n\",\n \"\\n\",\n \"index = faiss.IndexLSH(d, nbits)\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 3min 20s, sys: 84.2 ms, total: 3min 20s\\n\",\n \"Wall time: 5.64 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.5856720000000037\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we covered in the last notebook, LSH is not a good choice when the data dimension is large. Here 128 is already burdened for LSH. As we can see, even we choose a relatively small `nbits` of d * 8, the index creating time and search time are still pretty long. And the recall of about 58.6% is not satisfactory.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Scalar Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 550 ms, sys: 18 ms, total: 568 ms\\n\",\n \"Wall time: 87.4 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"qtype = faiss.ScalarQuantizer.QT_8bit\\n\",\n \"metric = faiss.METRIC_L2\\n\",\n \"\\n\",\n \"index = faiss.IndexScalarQuantizer(d, qtype, metric)\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 7min 36s, sys: 169 ms, total: 7min 36s\\n\",\n \"Wall time: 12.7 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.990444999999872\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here scalar quantizer index's performance looks very similar to the Flat index. Because the elements of vectors in the SIFT dataset are integers in the range of [0, 218]. Thus the index does not lose to much information during scalar quantization. For the dataset with more complex distribution in float32. The difference will be more obvious.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Product Quantizer Index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 46.7 s, sys: 22.3 ms, total: 46.7 s\\n\",\n \"Wall time: 1.36 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"M = 16\\n\",\n \"nbits = 8\\n\",\n \"metric = faiss.METRIC_L2\\n\",\n \"\\n\",\n \"index = faiss.IndexPQ(d, M, nbits, metric)\\n\",\n \"\\n\",\n \"index.train(corpus)\\n\",\n \"index.add(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 1min 37s, sys: 106 ms, total: 1min 37s\\n\",\n \"Wall time: 2.8 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"D, I = index.search(query, k)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Recall: 0.630898999999999\\n\"\n ]\n }\n ],\n \"source\": [\n \"recall = compute_recall(I, I_truth)\\n\",\n \"print(f\\\"Recall: {recall}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Product quantizer index is not standout in any one of the aspect. But it somewhat balance the tradeoffs. It is widely used in real applications with the combination of other indexes such as IVF or HNSW.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/3_Indexing.rst", "content": "3. Indexing\n===========\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: Indexing\n\n 3_Indexing/3.1.1\n 3_Indexing/3.1.2\n 3_Indexing/3.1.3\n 3_Indexing/3.1.4\n 3_Indexing/3.1.5" }, { "path": "docs/source/tutorial/4_Evaluation/4.1.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Evaluation is a crucial part in all machine learning tasks. In this notebook, we will walk through the whole pipeline of evaluating the performance of an embedding model on [MS Marco](https://microsoft.github.io/msmarco/), and use three metrics to show its performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 0: Setup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 1: Load Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, download the queries and MS Marco from Huggingface Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"data = load_dataset(\\\"namespace-Pt/msmarco\\\", split=\\\"dev\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Considering time cost, we will use the truncated dataset in this tutorial. `queries` contains the first 100 queries from the dataset. `corpus` is formed by the positives of the the first 5,000 queries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = np.array(data[:100][\\\"query\\\"])\\n\",\n \"corpus = sum(data[:5000][\\\"positive\\\"], [])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you have GPU and would like to try out the full evaluation of MS Marco, uncomment and run the following cell:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# data = load_dataset(\\\"namespace-Pt/msmarco\\\", split=\\\"dev\\\")\\n\",\n \"# queries = np.array(data[\\\"query\\\"])\\n\",\n \"\\n\",\n \"# corpus = load_dataset(\\\"namespace-PT/msmarco-corpus\\\", split=\\\"train\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 2: Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Choose the embedding model that we would like to evaluate, and encode the corpus to embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Inference Embeddings: 100%|██████████| 21/21 [02:10<00:00, 6.22s/it]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the corpus embeddings: (5331, 768)\\n\",\n \"data type of the embeddings: float32\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 3: Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use the index_factory() functions to create a Faiss index we want:\\n\",\n \"\\n\",\n \"- The first argument `dim` is the dimension of the vector space, in this case is 768 if you're using bge-base-en-v1.5.\\n\",\n \"\\n\",\n \"- The second argument `'Flat'` makes the index do exhaustive search.\\n\",\n \"\\n\",\n \"- The thrid argument `faiss.METRIC_INNER_PRODUCT` tells the index to use inner product as the distance metric.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 5331\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"# train and add the embeddings to the index\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since the embedding process is time consuming, it's a good choice to save the index for reproduction or other experiments.\\n\",\n \"\\n\",\n \"Uncomment the following lines to save the index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# path = \\\"./index.bin\\\"\\n\",\n \"# faiss.write_index(index, path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you already have stored index in your local directory, you can load it by:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# index = faiss.read_index(\\\"./index.bin\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 4: Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get the embeddings of all the queries, and get their corresponding ground truth answers for evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_embeddings = model.encode_queries(queries)\\n\",\n \"ground_truths = [d[\\\"positive\\\"] for d in data]\\n\",\n \"corpus = np.asarray(corpus)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the faiss index to search top $k$ answers of each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 1/1 [00:00<00:00, 20.91it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"res_scores, res_ids, res_text = [], [], []\\n\",\n \"query_size = len(query_embeddings)\\n\",\n \"batch_size = 256\\n\",\n \"# The cutoffs we will use during evaluation, and set k to be the maximum of the cutoffs.\\n\",\n \"cut_offs = [1, 10]\\n\",\n \"k = max(cut_offs)\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, batch_size), desc=\\\"Searching\\\"):\\n\",\n \" q_embedding = query_embeddings[i: min(i+batch_size, query_size)].astype(np.float32)\\n\",\n \" # search the top k answers for each of the queries\\n\",\n \" score, idx = index.search(q_embedding, k=k)\\n\",\n \" res_scores += list(score)\\n\",\n \" res_ids += list(idx)\\n\",\n \" res_text += list(corpus[idx])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Step 5: Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.1 Recall\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Recall represents the model's capability of correctly predicting positive instances from all the actual positive samples in the dataset.\\n\",\n \"\\n\",\n \"$$\\\\textbf{Recall}=\\\\frac{\\\\text{True Positives}}{\\\\text{True Positives}+\\\\text{False Negatives}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Recall is useful when the cost of false negatives is high. In other words, we are trying to find all objects of the positive class, even if this results in some false positives. This attribute makes recall a useful metric for text retrieval tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1: 0.97\\n\",\n \"recall@10: 1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"def calc_recall(preds, truths, cutoffs):\\n\",\n \" recalls = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" recall = np.intersect1d(truth, text[:c])\\n\",\n \" recalls[i] += len(recall) / max(min(c, len(truth)), 1)\\n\",\n \" recalls /= len(preds)\\n\",\n \" return recalls\\n\",\n \"\\n\",\n \"recalls = calc_recall(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"recall@{c}: {recalls[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.2 MRR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Mean Reciprocal Rank ([MRR](https://en.wikipedia.org/wiki/Mean_reciprocal_rank)) is a widely used metric in information retrieval to evaluate the effectiveness of a system. It measures the rank position of the first relevant result in a list of search results.\\n\",\n \"\\n\",\n \"$$MRR=\\\\frac{1}{|Q|}\\\\sum_{i=1}^{|Q|}\\\\frac{1}{rank_i}$$\\n\",\n \"\\n\",\n \"where \\n\",\n \"- $|Q|$ is the total number of queries.\\n\",\n \"- $rank_i$ is the rank position of the first relevant document of the i-th query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def MRR(preds, truth, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, t in zip(preds, truth):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, p in enumerate(pred):\\n\",\n \" if j < c and p in t:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1: 0.97\\n\",\n \"MRR@10: 0.9825\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr = MRR(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"MRR@{c}: {mrr[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.3 nDCG\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normalized Discounted cumulative gain (nDCG) measures the quality of a ranked list of search results by considering both the position of the relevant documents and their graded relevance scores. The calculation of nDCG involves two main steps:\\n\",\n \"\\n\",\n \"1. Discounted cumulative gain (DCG) measures the ranking quality in retrieval tasks.\\n\",\n \"\\n\",\n \"$$DCG_p=\\\\sum_{i=1}^p\\\\frac{2^{rel_i}-1}{\\\\log_2(i+1)}$$\\n\",\n \"\\n\",\n \"2. Normalized by ideal DCG to make it comparable across queries.\\n\",\n \"$$nDCG_p=\\\\frac{DCG_p}{IDCG_p}$$\\n\",\n \"where $IDCG$ is the maximum possible DCG for a given set of documents, assuming they are perfectly ranked in order of relevance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pred_hard_encodings = []\\n\",\n \"for pred, label in zip(res_text, ground_truths):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings.append(pred_hard_encoding)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.97\\n\",\n \"nDCG@10: 0.9869253606521631\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import ndcg_score\\n\",\n \"\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings, res_scores, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Congrats! You have walked through a full pipeline of evaluating an embedding model. Feel free to play with different datasets and models!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.2.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# MTEB\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For evaluation of embedding models, MTEB is one of the most well-known benchmark. In this tutorial, we'll introduce MTEB, its basic usage, and evaluate how your model performs on the MTEB leaderboard.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the packages we will use in your environment:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install sentence_transformers mteb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Intro\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The [Massive Text Embedding Benchmark (MTEB)](https://github.com/embeddings-benchmark/mteb) is a large-scale evaluation framework designed to assess the performance of text embedding models across a wide variety of natural language processing (NLP) tasks. Introduced to standardize and improve the evaluation of text embeddings, MTEB is crucial for assessing how well these models generalize across various real-world applications. It contains a wide range of datasets in eight main NLP tasks and different languages, and provides an easy pipeline for evaluation.\\n\",\n \"\\n\",\n \"MTEB is also well known for the MTEB leaderboard, which contains a ranking of the latest first-class embedding models. We'll cover that in the next tutorial. Now let's have a look on how to use MTEB to do evaluation easily.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import mteb\\n\",\n \"from sentence_transformers import SentenceTransformer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's take a look at how to use MTEB to do a quick evaluation.\\n\",\n \"\\n\",\n \"First we load the model that we would like to evaluate on:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model_name = \\\"BAAI/bge-base-en-v1.5\\\"\\n\",\n \"model = SentenceTransformer(model_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below is the list of datasets of retrieval used by MTEB's English leaderboard.\\n\",\n \"\\n\",\n \"MTEB directly use the open source benchmark BEIR in its retrieval part, which contains 15 datasets (note there are 12 subsets of CQADupstack).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"retrieval_tasks = [\\n\",\n \" \\\"ArguAna\\\",\\n\",\n \" \\\"ClimateFEVER\\\",\\n\",\n \" \\\"CQADupstackAndroidRetrieval\\\",\\n\",\n \" \\\"CQADupstackEnglishRetrieval\\\",\\n\",\n \" \\\"CQADupstackGamingRetrieval\\\",\\n\",\n \" \\\"CQADupstackGisRetrieval\\\",\\n\",\n \" \\\"CQADupstackMathematicaRetrieval\\\",\\n\",\n \" \\\"CQADupstackPhysicsRetrieval\\\",\\n\",\n \" \\\"CQADupstackProgrammersRetrieval\\\",\\n\",\n \" \\\"CQADupstackStatsRetrieval\\\",\\n\",\n \" \\\"CQADupstackTexRetrieval\\\",\\n\",\n \" \\\"CQADupstackUnixRetrieval\\\",\\n\",\n \" \\\"CQADupstackWebmastersRetrieval\\\",\\n\",\n \" \\\"CQADupstackWordpressRetrieval\\\",\\n\",\n \" \\\"DBPedia\\\",\\n\",\n \" \\\"FEVER\\\",\\n\",\n \" \\\"FiQA2018\\\",\\n\",\n \" \\\"HotpotQA\\\",\\n\",\n \" \\\"MSMARCO\\\",\\n\",\n \" \\\"NFCorpus\\\",\\n\",\n \" \\\"NQ\\\",\\n\",\n \" \\\"QuoraRetrieval\\\",\\n\",\n \" \\\"SCIDOCS\\\",\\n\",\n \" \\\"SciFact\\\",\\n\",\n \" \\\"Touche2020\\\",\\n\",\n \" \\\"TRECCOVID\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For demonstration, let's just run the first one, \\\"ArguAna\\\".\\n\",\n \"\\n\",\n \"For a full list of tasks and languages that MTEB supports, check the [page](https://github.com/embeddings-benchmark/mteb/blob/18662380f0f476db3d170d0926892045aa9f74ee/docs/tasks.md).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tasks = mteb.get_tasks(tasks=retrieval_tasks[:1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, create and initialize an MTEB instance with our chosen tasks, and run the evaluation process.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
───────────────────────────────────────────────── Selected tasks  ─────────────────────────────────────────────────\\n\",\n       \"
\\n\"\n ],\n \"text/plain\": [\n \"\\u001b[38;5;235m───────────────────────────────────────────────── \\u001b[0m\\u001b[1mSelected tasks \\u001b[0m\\u001b[38;5;235m ─────────────────────────────────────────────────\\u001b[0m\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
Retrieval\\n\",\n       \"
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    - ArguAna, s2p\\n\",\n       \"
\\n\"\n ],\n \"text/plain\": [\n \" - ArguAna, \\u001b[3;38;5;241ms2p\\u001b[0m\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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\\n\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\n\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Batches: 100%|██████████| 44/44 [00:41<00:00, 1.06it/s]\\n\",\n \"Batches: 100%|██████████| 272/272 [03:36<00:00, 1.26it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# use the tasks we chose to initialize the MTEB instance\\n\",\n \"evaluation = mteb.MTEB(tasks=tasks)\\n\",\n \"\\n\",\n \"# call run() with the model and output_folder\\n\",\n \"results = evaluation.run(model, output_folder=\\\"results\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The results should be stored in `{output_folder}/{model_name}/{model_revision}/{task_name}.json`.\\n\",\n \"\\n\",\n \"Openning the json file you should see contents as below, which are the evaluation results on \\\"ArguAna\\\" with different metrics on cutoffs from 1 to 1000.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"```python\\n\",\n \"{\\n\",\n \" \\\"dataset_revision\\\": \\\"c22ab2a51041ffd869aaddef7af8d8215647e41a\\\",\\n\",\n \" \\\"evaluation_time\\\": 260.14976954460144,\\n\",\n \" \\\"kg_co2_emissions\\\": null,\\n\",\n \" \\\"mteb_version\\\": \\\"1.14.17\\\",\\n\",\n \" \\\"scores\\\": {\\n\",\n \" \\\"test\\\": [\\n\",\n \" {\\n\",\n \" \\\"hf_subset\\\": \\\"default\\\",\\n\",\n \" \\\"languages\\\": [\\n\",\n \" \\\"eng-Latn\\\"\\n\",\n \" ],\\n\",\n \" \\\"main_score\\\": 0.63616,\\n\",\n \" \\\"map_at_1\\\": 0.40754,\\n\",\n \" \\\"map_at_10\\\": 0.55773,\\n\",\n \" \\\"map_at_100\\\": 0.56344,\\n\",\n \" \\\"map_at_1000\\\": 0.56347,\\n\",\n \" \\\"map_at_20\\\": 0.56202,\\n\",\n \" \\\"map_at_3\\\": 0.51932,\\n\",\n \" \\\"map_at_5\\\": 0.54023,\\n\",\n \" \\\"mrr_at_1\\\": 0.4139402560455192,\\n\",\n \" \\\"mrr_at_10\\\": 0.5603739077423295,\\n\",\n \" \\\"mrr_at_100\\\": 0.5660817425350153,\\n\",\n \" \\\"mrr_at_1000\\\": 0.5661121884705748,\\n\",\n \" \\\"mrr_at_20\\\": 0.564661930998293,\\n\",\n \" \\\"mrr_at_3\\\": 0.5208629682313899,\\n\",\n \" \\\"mrr_at_5\\\": 0.5429113323850182,\\n\",\n \" \\\"nauc_map_at_1000_diff1\\\": 0.15930478114759905,\\n\",\n \" \\\"nauc_map_at_1000_max\\\": -0.06396189194646361,\\n\",\n \" \\\"nauc_map_at_1000_std\\\": -0.13168797291549253,\\n\",\n \" \\\"nauc_map_at_100_diff1\\\": 0.15934819555197366,\\n\",\n \" \\\"nauc_map_at_100_max\\\": -0.06389635013430676,\\n\",\n \" \\\"nauc_map_at_100_std\\\": -0.13164524259533786,\\n\",\n \" \\\"nauc_map_at_10_diff1\\\": 0.16057318234658585,\\n\",\n \" \\\"nauc_map_at_10_max\\\": -0.060962623117325254,\\n\",\n \" \\\"nauc_map_at_10_std\\\": -0.1300413865104607,\\n\",\n \" \\\"nauc_map_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_map_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_map_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_map_at_20_diff1\\\": 0.15956349246366208,\\n\",\n \" \\\"nauc_map_at_20_max\\\": -0.06259296677860492,\\n\",\n \" \\\"nauc_map_at_20_std\\\": -0.13097093150054095,\\n\",\n \" \\\"nauc_map_at_3_diff1\\\": 0.15620049317363813,\\n\",\n \" \\\"nauc_map_at_3_max\\\": -0.06690213479396273,\\n\",\n \" \\\"nauc_map_at_3_std\\\": -0.13440904793529648,\\n\",\n \" \\\"nauc_map_at_5_diff1\\\": 0.1557795701081579,\\n\",\n \" \\\"nauc_map_at_5_max\\\": -0.06255283252590663,\\n\",\n \" \\\"nauc_map_at_5_std\\\": -0.1355361594910923,\\n\",\n \" \\\"nauc_mrr_at_1000_diff1\\\": 0.1378988612808882,\\n\",\n \" \\\"nauc_mrr_at_1000_max\\\": -0.07507962333910836,\\n\",\n \" \\\"nauc_mrr_at_1000_std\\\": -0.12969109830101241,\\n\",\n \" \\\"nauc_mrr_at_100_diff1\\\": 0.13794450668758515,\\n\",\n \" \\\"nauc_mrr_at_100_max\\\": -0.07501290390362861,\\n\",\n \" \\\"nauc_mrr_at_100_std\\\": -0.12964855554504057,\\n\",\n \" \\\"nauc_mrr_at_10_diff1\\\": 0.1396047981645623,\\n\",\n \" \\\"nauc_mrr_at_10_max\\\": -0.07185174301688693,\\n\",\n \" \\\"nauc_mrr_at_10_std\\\": -0.12807325096717753,\\n\",\n \" \\\"nauc_mrr_at_1_diff1\\\": 0.15610387932529113,\\n\",\n \" \\\"nauc_mrr_at_1_max\\\": -0.09824591983546396,\\n\",\n \" \\\"nauc_mrr_at_1_std\\\": -0.13914318784294258,\\n\",\n \" \\\"nauc_mrr_at_20_diff1\\\": 0.1382786098284509,\\n\",\n \" \\\"nauc_mrr_at_20_max\\\": -0.07364476417961506,\\n\",\n \" \\\"nauc_mrr_at_20_std\\\": -0.12898192060943495,\\n\",\n \" \\\"nauc_mrr_at_3_diff1\\\": 0.13118224861025093,\\n\",\n \" \\\"nauc_mrr_at_3_max\\\": -0.08164985279853691,\\n\",\n \" \\\"nauc_mrr_at_3_std\\\": -0.13241573571401533,\\n\",\n \" \\\"nauc_mrr_at_5_diff1\\\": 0.1346130730317385,\\n\",\n \" \\\"nauc_mrr_at_5_max\\\": -0.07404093236468848,\\n\",\n \" \\\"nauc_mrr_at_5_std\\\": -0.1340775377068567,\\n\",\n \" \\\"nauc_ndcg_at_1000_diff1\\\": 0.15919987960292029,\\n\",\n \" \\\"nauc_ndcg_at_1000_max\\\": -0.05457945565481172,\\n\",\n \" \\\"nauc_ndcg_at_1000_std\\\": -0.12457339152558143,\\n\",\n \" \\\"nauc_ndcg_at_100_diff1\\\": 0.1604091882521101,\\n\",\n \" \\\"nauc_ndcg_at_100_max\\\": -0.05281549383775287,\\n\",\n \" \\\"nauc_ndcg_at_100_std\\\": -0.12347288098914058,\\n\",\n \" \\\"nauc_ndcg_at_10_diff1\\\": 0.1657018523692905,\\n\",\n \" \\\"nauc_ndcg_at_10_max\\\": -0.036222943297402846,\\n\",\n \" \\\"nauc_ndcg_at_10_std\\\": -0.11284619565817842,\\n\",\n \" \\\"nauc_ndcg_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_ndcg_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_ndcg_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_ndcg_at_20_diff1\\\": 0.16231721725673165,\\n\",\n \" \\\"nauc_ndcg_at_20_max\\\": -0.04147115653921931,\\n\",\n \" \\\"nauc_ndcg_at_20_std\\\": -0.11598700704312062,\\n\",\n \" \\\"nauc_ndcg_at_3_diff1\\\": 0.15256475371124711,\\n\",\n \" \\\"nauc_ndcg_at_3_max\\\": -0.05432154580979357,\\n\",\n \" \\\"nauc_ndcg_at_3_std\\\": -0.12841084787822227,\\n\",\n \" \\\"nauc_ndcg_at_5_diff1\\\": 0.15236205846534961,\\n\",\n \" \\\"nauc_ndcg_at_5_max\\\": -0.04356123278888682,\\n\",\n \" \\\"nauc_ndcg_at_5_std\\\": -0.12942556865700913,\\n\",\n \" \\\"nauc_precision_at_1000_diff1\\\": -0.038790629929866066,\\n\",\n \" \\\"nauc_precision_at_1000_max\\\": 0.3630826341915611,\\n\",\n \" \\\"nauc_precision_at_1000_std\\\": 0.4772189839676386,\\n\",\n \" \\\"nauc_precision_at_100_diff1\\\": 0.32118609204433185,\\n\",\n \" \\\"nauc_precision_at_100_max\\\": 0.4740132817600036,\\n\",\n \" \\\"nauc_precision_at_100_std\\\": 0.3456396169952022,\\n\",\n \" \\\"nauc_precision_at_10_diff1\\\": 0.22279659689895104,\\n\",\n \" \\\"nauc_precision_at_10_max\\\": 0.16823918613191954,\\n\",\n \" \\\"nauc_precision_at_10_std\\\": 0.0377209694331257,\\n\",\n \" \\\"nauc_precision_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_precision_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_precision_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_precision_at_20_diff1\\\": 0.23025740175221762,\\n\",\n \" \\\"nauc_precision_at_20_max\\\": 0.2892313928157665,\\n\",\n \" \\\"nauc_precision_at_20_std\\\": 0.13522755012490692,\\n\",\n \" \\\"nauc_precision_at_3_diff1\\\": 0.1410889527057097,\\n\",\n \" \\\"nauc_precision_at_3_max\\\": -0.010771302313530132,\\n\",\n \" \\\"nauc_precision_at_3_std\\\": -0.10744937823276193,\\n\",\n \" \\\"nauc_precision_at_5_diff1\\\": 0.14012953903010988,\\n\",\n \" \\\"nauc_precision_at_5_max\\\": 0.03977485677045894,\\n\",\n \" \\\"nauc_precision_at_5_std\\\": -0.10292184602358977,\\n\",\n \" \\\"nauc_recall_at_1000_diff1\\\": -0.03879062992990034,\\n\",\n \" \\\"nauc_recall_at_1000_max\\\": 0.36308263419153386,\\n\",\n \" \\\"nauc_recall_at_1000_std\\\": 0.47721898396760526,\\n\",\n \" \\\"nauc_recall_at_100_diff1\\\": 0.3211860920443005,\\n\",\n \" \\\"nauc_recall_at_100_max\\\": 0.4740132817599919,\\n\",\n \" \\\"nauc_recall_at_100_std\\\": 0.345639616995194,\\n\",\n \" \\\"nauc_recall_at_10_diff1\\\": 0.22279659689895054,\\n\",\n \" \\\"nauc_recall_at_10_max\\\": 0.16823918613192046,\\n\",\n \" \\\"nauc_recall_at_10_std\\\": 0.037720969433127145,\\n\",\n \" \\\"nauc_recall_at_1_diff1\\\": 0.17346152653542332,\\n\",\n \" \\\"nauc_recall_at_1_max\\\": -0.09705499215630589,\\n\",\n \" \\\"nauc_recall_at_1_std\\\": -0.14726476953035533,\\n\",\n \" \\\"nauc_recall_at_20_diff1\\\": 0.23025740175221865,\\n\",\n \" \\\"nauc_recall_at_20_max\\\": 0.2892313928157675,\\n\",\n \" \\\"nauc_recall_at_20_std\\\": 0.13522755012490456,\\n\",\n \" \\\"nauc_recall_at_3_diff1\\\": 0.14108895270570979,\\n\",\n \" \\\"nauc_recall_at_3_max\\\": -0.010771302313529425,\\n\",\n \" \\\"nauc_recall_at_3_std\\\": -0.10744937823276134,\\n\",\n \" \\\"nauc_recall_at_5_diff1\\\": 0.14012953903010958,\\n\",\n \" \\\"nauc_recall_at_5_max\\\": 0.039774856770459645,\\n\",\n \" \\\"nauc_recall_at_5_std\\\": -0.10292184602358935,\\n\",\n \" \\\"ndcg_at_1\\\": 0.40754,\\n\",\n \" \\\"ndcg_at_10\\\": 0.63616,\\n\",\n \" \\\"ndcg_at_100\\\": 0.66063,\\n\",\n \" \\\"ndcg_at_1000\\\": 0.6613,\\n\",\n \" \\\"ndcg_at_20\\\": 0.65131,\\n\",\n \" \\\"ndcg_at_3\\\": 0.55717,\\n\",\n \" \\\"ndcg_at_5\\\": 0.59461,\\n\",\n \" \\\"precision_at_1\\\": 0.40754,\\n\",\n \" \\\"precision_at_10\\\": 0.08841,\\n\",\n \" \\\"precision_at_100\\\": 0.00991,\\n\",\n \" \\\"precision_at_1000\\\": 0.001,\\n\",\n \" \\\"precision_at_20\\\": 0.04716,\\n\",\n \" \\\"precision_at_3\\\": 0.22238,\\n\",\n \" \\\"precision_at_5\\\": 0.15149,\\n\",\n \" \\\"recall_at_1\\\": 0.40754,\\n\",\n \" \\\"recall_at_10\\\": 0.88407,\\n\",\n \" \\\"recall_at_100\\\": 0.99147,\\n\",\n \" \\\"recall_at_1000\\\": 0.99644,\\n\",\n \" \\\"recall_at_20\\\": 0.9431,\\n\",\n \" \\\"recall_at_3\\\": 0.66714,\\n\",\n \" \\\"recall_at_5\\\": 0.75747\\n\",\n \" }\\n\",\n \" ]\\n\",\n \" },\\n\",\n \" \\\"task_name\\\": \\\"ArguAna\\\"\\n\",\n \"}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we've successfully run the evaluation using mteb! In the next tutorial, we'll show how to evaluate your model on the whole 56 tasks of English MTEB and compete with models on the leaderboard.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.2.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# MTEB Leaderboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the last tutorial we show how to evaluate an embedding model on an dataset supported by MTEB. In this tutorial, we will go through how to do a full evaluation and compare the results with MTEB English leaderboard.\\n\",\n \"\\n\",\n \"Caution: Evaluation on the full Eng MTEB is very time consuming even with GPU. So we encourage you to go through the notebook to have an idea. And run the experiment when you have enough computing resource and time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the packages we will use in your environment:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%%capture\\n\",\n \"%pip install sentence_transformers mteb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Run the Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The MTEB English leaderboard contains 56 datasets on 7 tasks:\\n\",\n \"1. **Classification**: Use the embeddings to train a logistic regression on the train set and is scored on the test set. F1 is the main metric.\\n\",\n \"2. **Clustering**: Train a mini-batch k-means model with batch size 32 and k equals to the number of different labels. Then score using v-measure.\\n\",\n \"3. **Pair Classification**: A pair of text inputs is provided and a label which is a binary variable needs to be assigned. The main metric is average precision score.\\n\",\n \"4. **Reranking**: Rank a list of relevant and irrelevant reference texts according to a query. Metrics are mean MRR@k and MAP.\\n\",\n \"5. **Retrieval**: Each dataset comprises corpus, queries, and a mapping that links each query to its relevant documents within the corpus. The goal is to retrieve relevant documents for each query. The main metric is nDCG@k. MTEB directly adopts BEIR for the retrieval task.\\n\",\n \"6. **Semantic Textual Similarity (STS)**: Determine the similarity between each sentence pair. Spearman correlation based on cosine\\n\",\n \"similarity serves as the main metric.\\n\",\n \"7. **Summarization**: Only 1 dataset is used in this task. Score the machine-generated summaries to human-written summaries by computing distances of their embeddings. The main metric is also Spearman correlation based on cosine similarity.\\n\",\n \"\\n\",\n \"The benchmark is widely accepted by researchers and engineers to fairly evaluate and compare the performance of the models they train. Now let's take a look at the whole evaluation pipeline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the `MTEB_MAIN_EN` to check the all 56 datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['AmazonCounterfactualClassification', 'AmazonPolarityClassification', 'AmazonReviewsClassification', 'ArguAna', 'ArxivClusteringP2P', 'ArxivClusteringS2S', 'AskUbuntuDupQuestions', 'BIOSSES', 'Banking77Classification', 'BiorxivClusteringP2P', 'BiorxivClusteringS2S', 'CQADupstackAndroidRetrieval', 'CQADupstackEnglishRetrieval', 'CQADupstackGamingRetrieval', 'CQADupstackGisRetrieval', 'CQADupstackMathematicaRetrieval', 'CQADupstackPhysicsRetrieval', 'CQADupstackProgrammersRetrieval', 'CQADupstackStatsRetrieval', 'CQADupstackTexRetrieval', 'CQADupstackUnixRetrieval', 'CQADupstackWebmastersRetrieval', 'CQADupstackWordpressRetrieval', 'ClimateFEVER', 'DBPedia', 'EmotionClassification', 'FEVER', 'FiQA2018', 'HotpotQA', 'ImdbClassification', 'MSMARCO', 'MTOPDomainClassification', 'MTOPIntentClassification', 'MassiveIntentClassification', 'MassiveScenarioClassification', 'MedrxivClusteringP2P', 'MedrxivClusteringS2S', 'MindSmallReranking', 'NFCorpus', 'NQ', 'QuoraRetrieval', 'RedditClustering', 'RedditClusteringP2P', 'SCIDOCS', 'SICK-R', 'STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STS17', 'STS22', 'STSBenchmark', 'SciDocsRR', 'SciFact', 'SprintDuplicateQuestions', 'StackExchangeClustering', 'StackExchangeClusteringP2P', 'StackOverflowDupQuestions', 'SummEval', 'TRECCOVID', 'Touche2020', 'ToxicConversationsClassification', 'TweetSentimentExtractionClassification', 'TwentyNewsgroupsClustering', 'TwitterSemEval2015', 'TwitterURLCorpus']\\n\"\n ]\n }\n ],\n \"source\": [\n \"import mteb\\n\",\n \"from mteb.benchmarks import MTEB_MAIN_EN\\n\",\n \"\\n\",\n \"print(MTEB_MAIN_EN.tasks)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load the model we want to evaluate:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"model_name = \\\"BAAI/bge-base-en-v1.5\\\"\\n\",\n \"model = SentenceTransformer(model_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, MTEB provides popular models on their leaderboard in order to reproduce their results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"model_name = \\\"BAAI/bge-base-en-v1.5\\\"\\n\",\n \"model = mteb.get_model(model_name)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then start to evaluate on each dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for task in MTEB_MAIN_EN.tasks:\\n\",\n \" # get the test set to evaluate on\\n\",\n \" eval_splits = [\\\"dev\\\"] if task == \\\"MSMARCO\\\" else [\\\"test\\\"]\\n\",\n \" evaluation = mteb.MTEB(\\n\",\n \" tasks=[task], task_langs=[\\\"en\\\"]\\n\",\n \" ) # Remove \\\"en\\\" to run all available languages\\n\",\n \" evaluation.run(\\n\",\n \" model, output_folder=\\\"results\\\", eval_splits=eval_splits\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Submit to MTEB Leaderboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the evaluation is done, all the evaluation results should be stored in `results/{model_name}/{model_revision}`.\\n\",\n \"\\n\",\n \"Then run the following shell command to create the model_card.md. Change {model_name} and {model_revision} to your path.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!mteb create_meta --results_folder results/{model_name}/{model_revision} --output_path model_card.md\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the case that the readme of that model already exists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !mteb create_meta --results_folder results/{model_name}/{model_revision} --output_path model_card.md --from_existing your_existing_readme.md \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copy and paste the contents of model_card.md to the top of README.md of your model on HF Hub. Now relax and wait for the daily refresh of leaderboard. Your model will show up soon!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Partially Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that you don't need to finish all the tasks to get on to the leaderboard.\\n\",\n \"\\n\",\n \"For example you fine-tune a model's ability on clustering. And you only care about how your model performs with respoect to clustering, but not the other tasks. Then you can just test its performance on the clustering tasks of MTEB and submit to the leaderboard.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"TASK_LIST_CLUSTERING = [\\n\",\n \" \\\"ArxivClusteringP2P\\\",\\n\",\n \" \\\"ArxivClusteringS2S\\\",\\n\",\n \" \\\"BiorxivClusteringP2P\\\",\\n\",\n \" \\\"BiorxivClusteringS2S\\\",\\n\",\n \" \\\"MedrxivClusteringP2P\\\",\\n\",\n \" \\\"MedrxivClusteringS2S\\\",\\n\",\n \" \\\"RedditClustering\\\",\\n\",\n \" \\\"RedditClusteringP2P\\\",\\n\",\n \" \\\"StackExchangeClustering\\\",\\n\",\n \" \\\"StackExchangeClusteringP2P\\\",\\n\",\n \" \\\"TwentyNewsgroupsClustering\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the evaluation with only clustering tasks:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"evaluation = mteb.MTEB(tasks=TASK_LIST_CLUSTERING)\\n\",\n \"\\n\",\n \"results = evaluation.run(model, output_folder=\\\"results\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then repeat Step 2 to submit your model. After the leaderboard refresh, you can find your model in the \\\"Clustering\\\" section of the leaderboard.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Future Work\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"MTEB is working on a new version of English benchmark. It contains updated and concise tasks and will make the evaluation process faster.\\n\",\n \"\\n\",\n \"Please check out their [GitHub](https://github.com/embeddings-benchmark/mteb) page for future updates and releases.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.2.3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# C-MTEB\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"C-MTEB is the largest benchmark for Chinese text embeddings, similar to MTEB. In this tutorial, we will go through how to evaluate an embedding model's ability on Chinese tasks in C-MTEB.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install dependent packages:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install FlagEmbedding mteb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Datasets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"C-MTEB uses similar task splits and metrics as English MTEB. It contains 35 datasets in 6 different tasks: Classification, Clustering, Pair Classification, Reranking, Retrieval, and Semantic Textual Similarity (STS). \\n\",\n \"\\n\",\n \"1. **Classification**: Use the embeddings to train a logistic regression on the train set and is scored on the test set. F1 is the main metric.\\n\",\n \"2. **Clustering**: Train a mini-batch k-means model with batch size 32 and k equals to the number of different labels. Then score using v-measure.\\n\",\n \"3. **Pair Classification**: A pair of text inputs is provided and a label which is a binary variable needs to be assigned. The main metric is average precision score.\\n\",\n \"4. **Reranking**: Rank a list of relevant and irrelevant reference texts according to a query. Metrics are mean MRR@k and MAP.\\n\",\n \"5. **Retrieval**: Each dataset comprises corpus, queries, and a mapping that links each query to its relevant documents within the corpus. The goal is to retrieve relevant documents for each query. The main metric is nDCG@k. MTEB directly adopts BEIR for the retrieval task.\\n\",\n \"6. **Semantic Textual Similarity (STS)**: Determine the similarity between each sentence pair. Spearman correlation based on cosine\\n\",\n \"similarity serves as the main metric.\\n\",\n \"\\n\",\n \"\\n\",\n \"Check the [HF page](https://huggingface.co/C-MTEB) for the details of each dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ChineseTaskList = [\\n\",\n \" 'TNews', 'IFlyTek', 'MultilingualSentiment', 'JDReview', 'OnlineShopping', 'Waimai',\\n\",\n \" 'CLSClusteringS2S.v2', 'CLSClusteringP2P.v2', 'ThuNewsClusteringS2S.v2', 'ThuNewsClusteringP2P.v2',\\n\",\n \" 'Ocnli', 'Cmnli',\\n\",\n \" 'T2Reranking', 'MMarcoReranking', 'CMedQAv1-reranking', 'CMedQAv2-reranking',\\n\",\n \" 'T2Retrieval', 'MMarcoRetrieval', 'DuRetrieval', 'CovidRetrieval', 'CmedqaRetrieval', 'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',\\n\",\n \" 'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STSB', 'AFQMC', 'QBQTC'\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, load the model for evaluation. Note that the instruction here is used for retreival tasks.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from ...C_MTEB.flag_dres_model import FlagDRESModel\\n\",\n \"\\n\",\n \"instruction = \\\"为这个句子生成表示以用于检索相关文章：\\\"\\n\",\n \"model_name = \\\"BAAI/bge-base-zh-v1.5\\\"\\n\",\n \"\\n\",\n \"model = FlagDRESModel(model_name_or_path=\\\"BAAI/bge-base-zh-v1.5\\\",\\n\",\n \" query_instruction_for_retrieval=instruction,\\n\",\n \" pooling_method=\\\"cls\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Otherwise, you can load a model using sentence_transformers:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"model = SentenceTransformer(\\\"PATH_TO_MODEL\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or implement a class following the structure below:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"class MyModel():\\n\",\n \" def __init__(self):\\n\",\n \" \\\"\\\"\\\"initialize the tokenizer and model\\\"\\\"\\\"\\n\",\n \" pass\\n\",\n \"\\n\",\n \" def encode(self, sentences, batch_size=32, **kwargs):\\n\",\n \" \\\"\\\"\\\" Returns a list of embeddings for the given sentences.\\n\",\n \" Args:\\n\",\n \" sentences (`List[str]`): List of sentences to encode\\n\",\n \" batch_size (`int`): Batch size for the encoding\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" pass\\n\",\n \"\\n\",\n \"model = MyModel()\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we've prepared the dataset and model, we can start the evaluation. For time efficiency, we highly recommend to use GPU for evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import mteb\\n\",\n \"from mteb import MTEB\\n\",\n \"\\n\",\n \"tasks = mteb.get_tasks(ChineseTaskList)\\n\",\n \"\\n\",\n \"for task in tasks:\\n\",\n \" evaluation = MTEB(tasks=[task])\\n\",\n \" evaluation.run(model, output_folder=f\\\"zh_results/{model_name.split('/')[-1]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Submit to MTEB Leaderboard\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After the evaluation is done, all the evaluation results should be stored in `zh_results/{model_name}/`.\\n\",\n \"\\n\",\n \"Then run the following shell command to create the model_card.md. Change {model_name} and its following to your path.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"!!mteb create_meta --results_folder results/{model_name}/ --output_path model_card.md\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Copy and paste the contents of model_card.md to the top of README.md of your model on HF Hub. Then goto the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and choose the Chinese leaderboard to find your model! It will appear soon after the website's daily refresh.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.3.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation Using Sentence Transformers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will go through how to use the Sentence Tranformers library to do evaluation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U sentence-transformers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sentence_transformers import SentenceTransformer\\n\",\n \"\\n\",\n \"# Load a model\\n\",\n \"model = SentenceTransformer('all-MiniLM-L6-v2')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Retrieval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's choose retrieval as the first task\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"\\n\",\n \"from sentence_transformers.evaluation import InformationRetrievalEvaluator\\n\",\n \"\\n\",\n \"from datasets import load_dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BeIR is a well known benchmark for retrieval. Let's use the xxx dataset for our evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Load the Quora IR dataset (https://huggingface.co/datasets/BeIR/quora, https://huggingface.co/datasets/BeIR/quora-qrels)\\n\",\n \"corpus = load_dataset(\\\"BeIR/quora\\\", \\\"corpus\\\", split=\\\"corpus\\\")\\n\",\n \"queries = load_dataset(\\\"BeIR/quora\\\", \\\"queries\\\", split=\\\"queries\\\")\\n\",\n \"relevant_docs_data = load_dataset(\\\"BeIR/quora-qrels\\\", split=\\\"validation\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Shrink the corpus size heavily to only the relevant documents + 10,000 random documents\\n\",\n \"required_corpus_ids = list(map(str, relevant_docs_data[\\\"corpus-id\\\"]))\\n\",\n \"required_corpus_ids += random.sample(corpus[\\\"_id\\\"], k=10_000)\\n\",\n \"corpus = corpus.filter(lambda x: x[\\\"_id\\\"] in required_corpus_ids)\\n\",\n \"\\n\",\n \"# Convert the datasets to dictionaries\\n\",\n \"corpus = dict(zip(corpus[\\\"_id\\\"], corpus[\\\"text\\\"])) # Our corpus (cid => document)\\n\",\n \"queries = dict(zip(queries[\\\"_id\\\"], queries[\\\"text\\\"])) # Our queries (qid => question)\\n\",\n \"relevant_docs = {} # Query ID to relevant documents (qid => set([relevant_cids])\\n\",\n \"for qid, corpus_ids in zip(relevant_docs_data[\\\"query-id\\\"], relevant_docs_data[\\\"corpus-id\\\"]):\\n\",\n \" qid = str(qid)\\n\",\n \" corpus_ids = str(corpus_ids)\\n\",\n \" if qid not in relevant_docs:\\n\",\n \" relevant_docs[qid] = set()\\n\",\n \" relevant_docs[qid].add(corpus_ids)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally we are ready to do the evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Given queries, a corpus and a mapping with relevant documents, the InformationRetrievalEvaluator computes different IR metrics.\\n\",\n \"ir_evaluator = InformationRetrievalEvaluator(\\n\",\n \" queries=queries,\\n\",\n \" corpus=corpus,\\n\",\n \" relevant_docs=relevant_docs,\\n\",\n \" name=\\\"BeIR-quora-dev\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"results = ir_evaluator(model)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"name\": \"python\",\n \"version\": \"3.12.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.4.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate on BEIR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[BEIR](https://github.com/beir-cellar/beir) (Benchmarking-IR) is a heterogeneous evaluation benchmark for information retrieval. \\n\",\n \"It is designed for evaluating the performance of NLP-based retrieval models and widely used by research of modern embedding models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the libraries we are using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install beir FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Evaluate using BEIR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BEIR contains 18 datasets which can be downloaded from the [link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/), while 4 of them are private datasets that need appropriate licences. If you want to access to those 4 datasets, take a look at their [wiki](https://github.com/beir-cellar/beir/wiki/Datasets-available) for more information. Information collected and codes adapted from BEIR GitHub [repo](https://github.com/beir-cellar/beir).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Dataset Name | Type | Queries | Documents | Avg. Docs/Q | Public | \\n\",\n \"| ---------| :-----------: | ---------| --------- | ------| :------------:| \\n\",\n \"| ``msmarco`` | `Train` `Dev` `Test` | 6,980 | 8.84M | 1.1 | Yes | \\n\",\n \"| ``trec-covid``| `Test` | 50| 171K| 493.5 | Yes | \\n\",\n \"| ``nfcorpus`` | `Train` `Dev` `Test` | 323 | 3.6K | 38.2 | Yes |\\n\",\n \"| ``bioasq``| `Train` `Test` | 500 | 14.91M | 8.05 | No | \\n\",\n \"| ``nq``| `Train` `Test` | 3,452 | 2.68M | 1.2 | Yes | \\n\",\n \"| ``hotpotqa``| `Train` `Dev` `Test` | 7,405 | 5.23M | 2.0 | Yes |\\n\",\n \"| ``fiqa`` | `Train` `Dev` `Test` | 648 | 57K | 2.6 | Yes | \\n\",\n \"| ``signal1m`` | `Test` | 97 | 2.86M | 19.6 | No |\\n\",\n \"| ``trec-news`` | `Test` | 57 | 595K | 19.6 | No |\\n\",\n \"| ``arguana`` | `Test` | 1,406 | 8.67K | 1.0 | Yes |\\n\",\n \"| ``webis-touche2020``| `Test` | 49 | 382K | 49.2 | Yes |\\n\",\n \"| ``cqadupstack``| `Test` | 13,145 | 457K | 1.4 | Yes |\\n\",\n \"| ``quora``| `Dev` `Test` | 10,000 | 523K | 1.6 | Yes | \\n\",\n \"| ``dbpedia-entity``| `Dev` `Test` | 400 | 4.63M | 38.2 | Yes | \\n\",\n \"| ``scidocs``| `Test` | 1,000 | 25K | 4.9 | Yes | \\n\",\n \"| ``fever``| `Train` `Dev` `Test` | 6,666 | 5.42M | 1.2| Yes | \\n\",\n \"| ``climate-fever``| `Test` | 1,535 | 5.42M | 3.0 | Yes |\\n\",\n \"| ``scifact``| `Train` `Test` | 300 | 5K | 1.1 | Yes |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 Load Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First prepare the logging setup.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import logging\\n\",\n \"from beir import LoggingHandler\\n\",\n \"\\n\",\n \"logging.basicConfig(format='%(message)s',\\n\",\n \" level=logging.INFO,\\n\",\n \" handlers=[LoggingHandler()])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this demo, we choose the `arguana` dataset for a quick demonstration.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Dataset downloaded here: /share/project/xzy/Projects/FlagEmbedding/Tutorials/4_Evaluation/data/arguana\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"from beir import util\\n\",\n \"\\n\",\n \"url = \\\"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip\\\"\\n\",\n \"out_dir = os.path.join(os.getcwd(), \\\"data\\\")\\n\",\n \"data_path = util.download_and_unzip(url, out_dir)\\n\",\n \"print(f\\\"Dataset is stored at: {data_path}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 03:54:55,809 - Loading Corpus...\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100%|██████████| 8674/8674 [00:00<00:00, 158928.31it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 03:54:55,891 - Loaded 8674 TEST Documents.\\n\",\n \"2024-11-15 03:54:55,891 - Doc Example: {'text': \\\"You don’t have to be vegetarian to be green. Many special environments have been created by livestock farming – for example chalk down land in England and mountain pastures in many countries. Ending livestock farming would see these areas go back to woodland with a loss of many unique plants and animals. Growing crops can also be very bad for the planet, with fertilisers and pesticides polluting rivers, lakes and seas. Most tropical forests are now cut down for timber, or to allow oil palm trees to be grown in plantations, not to create space for meat production. British farmer and former editor Simon Farrell also states: “Many vegans and vegetarians rely on one source from the U.N. calculation that livestock generates 18% of global carbon emissions, but this figure contains basic mistakes. It attributes all deforestation from ranching to cattle, rather than logging or development. It also muddles up one-off emissions from deforestation with on-going pollution.” He also refutes the statement of meat production inefficiency: “Scientists have calculated that globally the ratio between the amounts of useful plant food used to produce meat is about 5 to 1. If you feed animals only food that humans can eat — which is, indeed, largely the case in the Western world — that may be true. But animals also eat food we can't eat, such as grass. So the real conversion figure is 1.4 to 1.” [1] At the same time eating a vegetarian diet may be no more environmentally friendly than a meat based diet if it is not sustainably sourced or uses perishable fruit and vegetables that are flown in from around the world. Eating locally sourced food can has as big an impact as being vegetarian. [2] [1] Tara Kelly, Simon Fairlie: How Eating Meat Can Save the World, 12 October 2010 [2] Lucy Siegle, ‘It is time to become a vegetarian?’ The Observer, 18th May 2008\\\", 'title': 'animals environment general health health general weight philosophy ethics'}\\n\",\n \"2024-11-15 03:54:55,891 - Loading Queries...\\n\",\n \"2024-11-15 03:54:55,903 - Loaded 1406 TEST Queries.\\n\",\n \"2024-11-15 03:54:55,903 - Query Example: Being vegetarian helps the environment Becoming a vegetarian is an environmentally friendly thing to do. Modern farming is one of the main sources of pollution in our rivers. Beef farming is one of the main causes of deforestation, and as long as people continue to buy fast food in their billions, there will be a financial incentive to continue cutting down trees to make room for cattle. Because of our desire to eat fish, our rivers and seas are being emptied of fish and many species are facing extinction. Energy resources are used up much more greedily by meat farming than my farming cereals, pulses etc. Eating meat and fish not only causes cruelty to animals, it causes serious harm to the environment and to biodiversity. For example consider Meat production related pollution and deforestation At Toronto’s 1992 Royal Agricultural Winter Fair, Agriculture Canada displayed two contrasting statistics: “it takes four football fields of land (about 1.6 hectares) to feed each Canadian” and “one apple tree produces enough fruit to make 320 pies.” Think about it — a couple of apple trees and a few rows of wheat on a mere fraction of a hectare could produce enough food for one person! [1] The 2006 U.N. Food and Agriculture Organization (FAO) report concluded that worldwide livestock farming generates 18% of the planet's greenhouse gas emissions — by comparison, all the world's cars, trains, planes and boats account for a combined 13% of greenhouse gas emissions. [2] As a result of the above point producing meat damages the environment. The demand for meat drives deforestation. Daniel Cesar Avelino of Brazil's Federal Public Prosecution Office says “We know that the single biggest driver of deforestation in the Amazon is cattle.” This clearing of tropical rainforests such as the Amazon for agriculture is estimated to produce 17% of the world's greenhouse gas emissions. [3] Not only this but the production of meat takes a lot more energy than it ultimately gives us chicken meat production consumes energy in a 4:1 ratio to protein output; beef cattle production requires an energy input to protein output ratio of 54:1. The same is true with water use due to the same phenomenon of meat being inefficient to produce in terms of the amount of grain needed to produce the same weight of meat, production requires a lot of water. Water is another scarce resource that we will soon not have enough of in various areas of the globe. Grain-fed beef production takes 100,000 liters of water for every kilogram of food. Raising broiler chickens takes 3,500 liters of water to make a kilogram of meat. In comparison, soybean production uses 2,000 liters for kilogram of food produced; rice, 1,912; wheat, 900; and potatoes, 500 liters. [4] This is while there are areas of the globe that have severe water shortages. With farming using up to 70 times more water than is used for domestic purposes: cooking and washing. A third of the population of the world is already suffering from a shortage of water. [5] Groundwater levels are falling all over the world and rivers are beginning to dry up. Already some of the biggest rivers such as China’s Yellow river do not reach the sea. [6] With a rising population becoming vegetarian is the only responsible way to eat. [1] Stephen Leckie, ‘How Meat-centred Eating Patterns Affect Food Security and the Environment’, International development research center [2] Bryan Walsh, Meat: Making Global Warming Worse, Time magazine, 10 September 2008 . [3] David Adam, Supermarket suppliers ‘helping to destroy Amazon rainforest’, The Guardian, 21st June 2009. [4] Roger Segelken, U.S. could feed 800 million people with grain that livestock eat, Cornell Science News, 7th August 1997. [5] Fiona Harvey, Water scarcity affects one in three, FT.com, 21st August 2003 [6] Rupert Wingfield-Hayes, Yellow river ‘drying up’, BBC News, 29th July 2004\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from beir.datasets.data_loader import GenericDataLoader\\n\",\n \"\\n\",\n \"corpus, queries, qrels = GenericDataLoader(\\\"data/arguana\\\").load(split=\\\"test\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we load `bge-base-en-v1.5` from huggingface and evaluate its performance on arguana.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 04:00:45,253 - Use pytorch device_name: cuda\\n\",\n \"2024-11-15 04:00:45,254 - Load pretrained SentenceTransformer: BAAI/bge-base-en-v1.5\\n\",\n \"2024-11-15 04:00:48,750 - Encoding Queries...\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Batches: 100%|██████████| 11/11 [00:01<00:00, 8.27it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 04:00:50,177 - Sorting Corpus by document length (Longest first)...\\n\",\n \"2024-11-15 04:00:50,183 - Encoding Corpus in batches... Warning: This might take a while!\\n\",\n \"2024-11-15 04:00:50,183 - Scoring Function: Cosine Similarity (cos_sim)\\n\",\n \"2024-11-15 04:00:50,184 - Encoding Batch 1/1...\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Batches: 100%|██████████| 68/68 [00:07<00:00, 9.43it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from beir.retrieval.evaluation import EvaluateRetrieval\\n\",\n \"from beir.retrieval import models\\n\",\n \"from beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES\\n\",\n \"\\n\",\n \"\\n\",\n \"# Load bge model using Sentence Transformers\\n\",\n \"model = DRES(models.SentenceBERT(\\\"BAAI/bge-base-en-v1.5\\\"), batch_size=128)\\n\",\n \"retriever = EvaluateRetrieval(model, score_function=\\\"cos_sim\\\")\\n\",\n \"\\n\",\n \"# Get the searching results\\n\",\n \"results = retriever.retrieve(corpus, queries)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2024-11-15 04:00:58,514 - Retriever evaluation for k in: [1, 3, 5, 10, 100, 1000]\\n\",\n \"2024-11-15 04:00:58,514 - For evaluation, we ignore identical query and document ids (default), please explicitly set ``ignore_identical_ids=False`` to ignore this.\\n\",\n \"2024-11-15 04:00:59,184 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@3: 0.5572\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@5: 0.5946\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@10: 0.6361\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@100: 0.6606\\n\",\n \"2024-11-15 04:00:59,188 - NDCG@1000: 0.6613\\n\",\n \"2024-11-15 04:00:59,188 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,188 - MAP@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,188 - MAP@3: 0.5193\\n\",\n \"2024-11-15 04:00:59,188 - MAP@5: 0.5402\\n\",\n \"2024-11-15 04:00:59,188 - MAP@10: 0.5577\\n\",\n \"2024-11-15 04:00:59,188 - MAP@100: 0.5634\\n\",\n \"2024-11-15 04:00:59,188 - MAP@1000: 0.5635\\n\",\n \"2024-11-15 04:00:59,188 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,188 - Recall@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,188 - Recall@3: 0.6671\\n\",\n \"2024-11-15 04:00:59,188 - Recall@5: 0.7575\\n\",\n \"2024-11-15 04:00:59,188 - Recall@10: 0.8841\\n\",\n \"2024-11-15 04:00:59,188 - Recall@100: 0.9915\\n\",\n \"2024-11-15 04:00:59,189 - Recall@1000: 0.9964\\n\",\n \"2024-11-15 04:00:59,189 - \\n\",\n \"\\n\",\n \"2024-11-15 04:00:59,189 - P@1: 0.4075\\n\",\n \"2024-11-15 04:00:59,189 - P@3: 0.2224\\n\",\n \"2024-11-15 04:00:59,189 - P@5: 0.1515\\n\",\n \"2024-11-15 04:00:59,189 - P@10: 0.0884\\n\",\n \"2024-11-15 04:00:59,189 - P@100: 0.0099\\n\",\n \"2024-11-15 04:00:59,189 - P@1000: 0.0010\\n\"\n ]\n }\n ],\n \"source\": [\n \"logging.info(\\\"Retriever evaluation for k in: {}\\\".format(retriever.k_values))\\n\",\n \"ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Evaluate using FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide independent evaluation for popular datasets and benchmarks. Try the following code to run the evaluation, or run the shell script provided in [example](../../examples/evaluation/beir/eval_beir.sh) folder.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load the arguments:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"arguments = \\\"\\\"\\\"-\\n\",\n \" --eval_name beir \\n\",\n \" --dataset_dir ./beir/data \\n\",\n \" --dataset_names arguana\\n\",\n \" --splits test dev \\n\",\n \" --corpus_embd_save_dir ./beir/corpus_embd \\n\",\n \" --output_dir ./beir/search_results \\n\",\n \" --search_top_k 1000 \\n\",\n \" --rerank_top_k 100 \\n\",\n \" --cache_path /root/.cache/huggingface/hub \\n\",\n \" --overwrite True \\n\",\n \" --k_values 10 100 \\n\",\n \" --eval_output_method markdown \\n\",\n \" --eval_output_path ./beir/beir_eval_results.md \\n\",\n \" --eval_metrics ndcg_at_10 recall_at_100 \\n\",\n \" --ignore_identical_ids True \\n\",\n \" --embedder_name_or_path BAAI/bge-base-en-v1.5 \\n\",\n \" --embedder_batch_size 1024\\n\",\n \" --devices cuda:4\\n\",\n \"\\\"\\\"\\\".replace('\\\\n','')\\n\",\n \"\\n\",\n \"sys.argv = arguments.split()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then pass the arguments to HFArgumentParser and run the evaluation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Split 'dev' not found in the dataset. Removing it from the list.\\n\",\n \"ignore_identical_ids is set to True. This means that the search results will not contain identical ids. Note: Dataset such as MIRACL should NOT set this to True.\\n\",\n \"pre tokenize: 100%|██████████| 9/9 [00:00<00:00, 16.19it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 100%|██████████| 9/9 [00:11<00:00, 1.27s/it]\\n\",\n \"pre tokenize: 100%|██████████| 2/2 [00:00<00:00, 19.54it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 2/2 [00:02<00:00, 1.29s/it]\\n\",\n \"Searching: 100%|██████████| 44/44 [00:00<00:00, 208.73it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import HfArgumentParser\\n\",\n \"\\n\",\n \"from FlagEmbedding.evaluation.beir import (\\n\",\n \" BEIREvalArgs, BEIREvalModelArgs,\\n\",\n \" BEIREvalRunner\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"parser = HfArgumentParser((\\n\",\n \" BEIREvalArgs,\\n\",\n \" BEIREvalModelArgs\\n\",\n \"))\\n\",\n \"\\n\",\n \"eval_args, model_args = parser.parse_args_into_dataclasses()\\n\",\n \"eval_args: BEIREvalArgs\\n\",\n \"model_args: BEIREvalModelArgs\\n\",\n \"\\n\",\n \"runner = BEIREvalRunner(\\n\",\n \" eval_args=eval_args,\\n\",\n \" model_args=model_args\\n\",\n \")\\n\",\n \"\\n\",\n \"runner.run()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Take a look at the results and choose the way you prefer!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"arguana-test\\\": {\\n\",\n \" \\\"ndcg_at_10\\\": 0.63668,\\n\",\n \" \\\"ndcg_at_100\\\": 0.66075,\\n\",\n \" \\\"map_at_10\\\": 0.55801,\\n\",\n \" \\\"map_at_100\\\": 0.56358,\\n\",\n \" \\\"recall_at_10\\\": 0.88549,\\n\",\n \" \\\"recall_at_100\\\": 0.99147,\\n\",\n \" \\\"precision_at_10\\\": 0.08855,\\n\",\n \" \\\"precision_at_100\\\": 0.00991,\\n\",\n \" \\\"mrr_at_10\\\": 0.55809,\\n\",\n \" \\\"mrr_at_100\\\": 0.56366\\n\",\n \" }\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('beir/search_results/bge-base-en-v1.5/NoReranker/EVAL/eval_results.json', 'r') as content_file:\\n\",\n \" print(content_file.read())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.5.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate on MIRACL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[MIRACL](https://project-miracl.github.io/) (Multilingual Information Retrieval Across a Continuum of Languages) is an WSDM 2023 Cup challenge that focuses on search across 18 different languages. They release a multilingual retrieval dataset containing the train and dev set for 16 “known languages” and only dev set for 2 “surprise languages”. The topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list. You can found the dataset on HuggingFace.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note: We highly recommend you to run the evaluation of MIRACL on GPU. For reference, it takes about an hour for the whole process on a 8xA100 40G node.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the libraries we are using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install FlagEmbedding pytrec_eval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the great number of passages and articles in the 18 languages. MIRACL is a resourceful dataset for training or evaluating multi-lingual model. The data can be downloaded from [Hugging Face](https://huggingface.co/datasets/miracl/miracl).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Language | # of Passages | # of Articles |\\n\",\n \"|:----------------|--------------:|--------------:|\\n\",\n \"| Arabic (ar) | 2,061,414 | 656,982 |\\n\",\n \"| Bengali (bn) | 297,265 | 63,762 |\\n\",\n \"| English (en) | 32,893,221 | 5,758,285 |\\n\",\n \"| Spanish (es) | 10,373,953 | 1,669,181 |\\n\",\n \"| Persian (fa) | 2,207,172 | 857,827 |\\n\",\n \"| Finnish (fi) | 1,883,509 | 447,815 |\\n\",\n \"| French (fr) | 14,636,953 | 2,325,608 |\\n\",\n \"| Hindi (hi) | 506,264 | 148,107 |\\n\",\n \"| Indonesian (id) | 1,446,315 | 446,330 |\\n\",\n \"| Japanese (ja) | 6,953,614 | 1,133,444 |\\n\",\n \"| Korean (ko) | 1,486,752 | 437,373 |\\n\",\n \"| Russian (ru) | 9,543,918 | 1,476,045 |\\n\",\n \"| Swahili (sw) | 131,924 | 47,793 |\\n\",\n \"| Telugu (te) | 518,079 | 66,353 |\\n\",\n \"| Thai (th) | 542,166 | 128,179 |\\n\",\n \"| Chinese (zh) | 4,934,368 | 1,246,389 |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"lang = \\\"en\\\"\\n\",\n \"corpus = load_dataset(\\\"miracl/miracl-corpus\\\", lang, trust_remote_code=True)['train']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each passage in the corpus has three parts: `docid`, `title`, and `text`. In the structure of document with docid `x#y`, `x` indicates the id of Wikipedia article, and `y` is the number of passage within that article. The title is the name of the article with id `x` that passage belongs to. The text is the text body of the passage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'docid': '56672809#4',\\n\",\n \" 'title': 'Glen Tomasetti',\\n\",\n \" 'text': 'In 1967 Tomasetti was prosecuted after refusing to pay one sixth of her taxes on the grounds that one sixth of the federal budget was funding Australia\\\\'s military presence in Vietnam. In court she argued that Australia\\\\'s participation in the Vietnam War violated its international legal obligations as a member of the United Nations. Public figures such as Joan Baez had made similar protests in the USA, but Tomasetti\\\\'s prosecution was \\\"believed to be the first case of its kind in Australia\\\", according to a contemporary news report. Tomasetti was eventually ordered to pay the unpaid taxes.'}\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"corpus[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The qrels have following form:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dev = load_dataset('miracl/miracl', lang, trust_remote_code=True)['dev']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query_id': '0',\\n\",\n \" 'query': 'Is Creole a pidgin of French?',\\n\",\n \" 'positive_passages': [{'docid': '462221#4',\\n\",\n \" 'text': \\\"At the end of World War II in 1945, Korea was divided into North Korea and South Korea with North Korea (assisted by the Soviet Union), becoming a communist government after 1946, known as the Democratic People's Republic, followed by South Korea becoming the Republic of Korea. China became the communist People's Republic of China in 1949. In 1950, the Soviet Union backed North Korea while the United States backed South Korea, and China allied with the Soviet Union in what was to become the first military action of the Cold War.\\\",\\n\",\n \" 'title': 'Eighth United States Army'},\\n\",\n \" {'docid': '29810#23',\\n\",\n \" 'text': 'The large size of Texas and its location at the intersection of multiple climate zones gives the state highly variable weather. The Panhandle of the state has colder winters than North Texas, while the Gulf Coast has mild winters. Texas has wide variations in precipitation patterns. El Paso, on the western end of the state, averages of annual rainfall, while parts of southeast Texas average as much as per year. Dallas in the North Central region averages a more moderate per year.',\\n\",\n \" 'title': 'Texas'},\\n\",\n \" {'docid': '3716905#0',\\n\",\n \" 'text': 'A French creole, or French-based creole language, is a creole language (contact language with native speakers) for which French is the \\\"lexifier\\\". Most often this lexifier is not modern French but rather a 17th-century koiné of French from Paris, the French Atlantic harbors, and the nascent French colonies. French-based creole languages are spoken natively by millions of people worldwide, primarily in the Americas and on archipelagos throughout the Indian Ocean. This article also contains information on French pidgin languages, contact languages that lack native speakers.',\\n\",\n \" 'title': 'French-based creole languages'},\\n\",\n \" {'docid': '22399755#18',\\n\",\n \" 'text': 'There are many hypotheses on the origins of Haitian Creole. Linguist John Singler suggests that it most likely emerged under French control in colonial years when shifted its economy focused heavily on sugar production. This resulted in a much larger population of enslaved Africans, whose interaction with the French created the circumstances for the dialect to evolve from a pidgin to a Creole. His research and the research of Claire Lefebvre of the Université du Québec à Montréal suggests that Creole, despite drawing 90% of its lexicon from French, is the syntactic cousin of Fon, a Gbe language of the Niger-Congo family spoken in Benin. At the time of the emergence of Haitian Creole, 50% of the enslaved Africans in Haiti were Gbe speakers.',\\n\",\n \" 'title': 'Haitian literature'}],\\n\",\n \" 'negative_passages': [{'docid': '1170520#2',\\n\",\n \" 'text': 'Louisiana Creole is a contact language that arose in the 18th century from interactions between speakers of the lexifier language of Standard French and several substrate or adstrate languages from Africa. Prior to its establishment as a Creole, the precursor was considered a pidgin language. The social situation that gave rise to the Louisiana Creole language was unique, in that the lexifier language was the language found at the contact site. More often the lexifier is the language that arrives at the contact site belonging to the substrate/adstrate languages. Neither the French, the French-Canadians, nor the African slaves were native to the area; this fact categorizes Louisiana Creole as a contact language that arose between exogenous ethnicities. Once the pidgin tongue was transmitted to the next generation as a \\\"lingua franca\\\" (who were then considered the first native speakers of the new grammar), it could effectively be classified as a creole language.',\\n\",\n \" 'title': 'Louisiana Creole'},\\n\",\n \" {'docid': '49823#1',\\n\",\n \" 'text': 'The precise number of creole languages is not known, particularly as many are poorly attested or documented. About one hundred creole languages have arisen since 1500. These are predominantly based on European languages such as English and French due to the European Age of Discovery and the Atlantic slave trade that arose at that time. With the improvements in ship-building and navigation, traders had to learn to communicate with people around the world, and the quickest way to do this was to develop a pidgin, or simplified language suited to the purpose; in turn, full creole languages developed from these pidgins. In addition to creoles that have European languages as their base, there are, for example, creoles based on Arabic, Chinese, and Malay. The creole with the largest number of speakers is Haitian Creole, with almost ten million native speakers, followed by Tok Pisin with about 4 million, most of whom are second-language speakers.',\\n\",\n \" 'title': 'Creole language'},\\n\",\n \" {'docid': '1651722#10',\\n\",\n \" 'text': 'Krio is an English-based creole from which descend Nigerian Pidgin English and Cameroonian Pidgin English and Pichinglis. It is also similar to English-based creole languages spoken in the Americas, especially the Gullah language, Jamaican Patois (Jamaican Creole), and Bajan Creole but it has its own distinctive character. It also shares some linguistic similarities with non-English creoles, such as the French-based creole languages in the Caribbean.',\\n\",\n \" 'title': 'Krio language'},\\n\",\n \" {'docid': '540382#4',\\n\",\n \" 'text': 'Until recently creoles were considered \\\"degenerate\\\" dialects of Portuguese unworthy of attention. As a consequence, there is little documentation on the details of their formation. Since the 20th century, increased study of creoles by linguists led to several theories being advanced. The monogenetic theory of pidgins assumes that some type of pidgin language — dubbed West African Pidgin Portuguese — based on Portuguese was spoken from the 15th to 18th centuries in the forts established by the Portuguese on the West African coast. According to this theory, this variety may have been the starting point of all the pidgin and creole languages. This may explain to some extent why Portuguese lexical items can be found in many creoles, but more importantly, it would account for the numerous grammatical similarities shared by such languages, such as the preposition \\\"na\\\", meaning \\\"in\\\" and/or \\\"on\\\", which would come from the Portuguese contraction \\\"na\\\" meaning \\\"in the\\\" (feminine singular).',\\n\",\n \" 'title': 'Portuguese-based creole languages'},\\n\",\n \" {'docid': '49823#7',\\n\",\n \" 'text': 'Other scholars, such as Salikoko Mufwene, argue that pidgins and creoles arise independently under different circumstances, and that a pidgin need not always precede a creole nor a creole evolve from a pidgin. Pidgins, according to Mufwene, emerged in trade colonies among \\\"users who preserved their native vernaculars for their day-to-day interactions.\\\" Creoles, meanwhile, developed in settlement colonies in which speakers of a European language, often indentured servants whose language would be far from the standard in the first place, interacted extensively with non-European slaves, absorbing certain words and features from the slaves\\\\' non-European native languages, resulting in a heavily basilectalized version of the original language. These servants and slaves would come to use the creole as an everyday vernacular, rather than merely in situations in which contact with a speaker of the superstrate was necessary.',\\n\",\n \" 'title': 'Creole language'},\\n\",\n \" {'docid': '11236157#2',\\n\",\n \" 'text': 'While many creoles around the world have lexicons based on languages other than Portuguese (e.g. English, French, Spanish, Dutch), it was hypothesized that such creoles were derived from this lingua franca by means of relexification, i.e. the process in which a pidgin or creole incorporates a significant amount of its lexicon from another language while keeping the grammar intact. There is some evidence that relexification is a real process. Pieter Muysken and show that there are languages which derive their grammar and lexicon from two different languages respectively, which could be easily explained with the relexification hypothesis. Also, Saramaccan seems to be a pidgin frozen in the middle of relexification from Portuguese to English. However, in cases of such mixed languages, as call them, there is never a one-to-one relationship between the grammar or lexicon of the mixed language and the grammar or lexicon of the language they attribute it to.',\\n\",\n \" 'title': 'Monogenetic theory of pidgins'},\\n\",\n \" {'docid': '1612877#8',\\n\",\n \" 'text': 'A mixed language differs from pidgins, creoles and code-switching in very fundamental ways. In most cases, mixed language speakers are fluent, even native, speakers of both languages; however, speakers of Michif (a N-V mixed language) are unique in that many are not fluent in both of the sources languages. Pidgins, on the other hand, develop in a situation, usually in the context of trade, where speakers of two (or more) different languages come into contact and need to find some way to communicate with each other. Creoles develop when a pidgin language becomes a first language for young speakers. While creoles tend to have drastically simplified morphologies, mixed languages often retain the inflectional complexities of one, or both, of parent languages. For instance, Michif retains the complexities of its French nouns and its Cree verbs.',\\n\",\n \" 'title': 'Mixed language'},\\n\",\n \" {'docid': '9606120#4',\\n\",\n \" 'text': 'While it is classified as a pidgin language, this is inaccurate. Speakers are already fluent in either English and French, and as such it is not used in situations where both parties lack a common tongue. As a whole, Camfranglais sets itself apart from other pidgins and creoles in that it consists of an array of languages, at least one of which is already known by those speaking it. For instance, while it contains elements of borrowing, code-switching, and pidgin languages, it is not a contact language as both parties can be presumed to speak French, the lexifer. Numerous other classifications have been proposed, like ‘pidgin’, ‘argot’, ‘youth language’, a ‘sabir camerounais’, an ‘appropriation vernaculaire du français’ or a ‘hybrid slang’. However, as Camfranglais is more developed than a slang, this too is insufficient. Kießling proposes it be classified as a \\\\'highly hybrid sociolect of the urban youth type\\\", a definition that Stein-Kanjora agrees with.',\\n\",\n \" 'title': 'Camfranglais'}]}\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dev[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each item has four parts: `query_id`, `query`, `positive_passages`, and `negative_passages`. Here, `query_id` and `query` correspond to the id and text content of the qeury. `positive_passages` and `negative_passages` are list of passages with their corresponding `docid`, `title`, and `text`. \\n\",\n \"\\n\",\n \"This structure is the same in the `train`, `dev`, `testA` and `testB` sets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we process the ids and text of queries and corpus, and get the qrels of the dev set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus_ids = corpus['docid']\\n\",\n \"corpus_text = []\\n\",\n \"for doc in corpus:\\n\",\n \" corpus_text.append(f\\\"{doc['title']} {doc['text']}\\\".strip())\\n\",\n \"\\n\",\n \"queries_ids = dev['query_id']\\n\",\n \"queries_text = dev['query']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Evaluate from scratch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the demo we use bge-base-en-v1.5, feel free to change to the model you prefer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"os.environ['SETUPTOOLS_USE_DISTUTILS'] = ''\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"initial target device: 100%|██████████| 8/8 [00:29<00:00, 3.66s/it]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 52.84it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 55.15it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 56.49it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 55.22it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 49.22it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 54.69it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 49.16it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 50.77it/s]\\n\",\n \"Chunks: 100%|██████████| 8/8 [00:10<00:00, 1.27s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:12<00:00, 32.58it/s] \\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:44<00:00, 30.60it/s]68s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:39<00:00, 30.90it/s]41s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:04<00:00, 29.49it/s]43s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:27<00:00, 28.29it/s]it/s]t]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:08<00:00, 29.30it/s]32s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [08:59<00:00, 29.77it/s]it/s]t]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [09:04<00:00, 29.50it/s]29s/it]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:10<00:00, 15.59it/s] \\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:04<00:00, 15.68it/s]]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:01<00:00, 15.72it/s]s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:28<00:00, 15.32it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:43<00:00, 15.10it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:27<00:00, 15.34it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:36<00:00, 15.20it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [17:31<00:00, 15.28it/s]\\n\",\n \"Chunks: 100%|██████████| 8/8 [27:49<00:00, 208.64s/it]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the embeddings: (32893221, 768)\\n\",\n \"data type of the embeddings: float16\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\\n\",\n \"\\n\",\n \"# get the embedding of the queries and corpus\\n\",\n \"queries_embeddings = model.encode_queries(queries_text)\\n\",\n \"corpus_embeddings = model.encode_corpus(corpus_text)\\n\",\n \"\\n\",\n \"print(\\\"shape of the embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a Faiss index to store the embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 32893221\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"# train and add the embeddings to the index\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Searching\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the Faiss index to search for each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 25/25 [15:03<00:00, 36.15s/it]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"query_size = len(queries_embeddings)\\n\",\n \"\\n\",\n \"all_scores = []\\n\",\n \"all_indices = []\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, 32), desc=\\\"Searching\\\"):\\n\",\n \" j = min(i + 32, query_size)\\n\",\n \" query_embedding = queries_embeddings[i: j]\\n\",\n \" score, indice = index.search(query_embedding.astype(np.float32), k=100)\\n\",\n \" all_scores.append(score)\\n\",\n \" all_indices.append(indice)\\n\",\n \"\\n\",\n \"all_scores = np.concatenate(all_scores, axis=0)\\n\",\n \"all_indices = np.concatenate(all_indices, axis=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then map the search results back to the indices in the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = {}\\n\",\n \"for idx, (scores, indices) in enumerate(zip(all_scores, all_indices)):\\n\",\n \" results[queries_ids[idx]] = {}\\n\",\n \" for score, index in zip(scores, indices):\\n\",\n \" if index != -1:\\n\",\n \" results[queries_ids[idx]][corpus_ids[index]] = float(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 Evaluating\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download the qrels file for evaluation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"--2024-11-21 10:26:16-- https://hf-mirror.com/datasets/miracl/miracl/resolve/main/miracl-v1.0-en/qrels/qrels.miracl-v1.0-en-dev.tsv\\n\",\n \"Resolving hf-mirror.com (hf-mirror.com)... 153.121.57.40, 133.242.169.68, 160.16.199.204\\n\",\n \"Connecting to hf-mirror.com (hf-mirror.com)|153.121.57.40|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 167817 (164K) [text/plain]\\n\",\n \"Saving to: ‘qrels.miracl-v1.0-en-dev.tsv’\\n\",\n \"\\n\",\n \" 0K .......... .......... .......... .......... .......... 30% 109K 1s\\n\",\n \" 50K .......... .......... .......... .......... .......... 61% 44.5K 1s\\n\",\n \" 100K .......... .......... .......... .......... .......... 91% 69.6K 0s\\n\",\n \" 150K .......... ... 100% 28.0K=2.8s\\n\",\n \"\\n\",\n \"2024-11-21 10:26:20 (58.6 KB/s) - ‘qrels.miracl-v1.0-en-dev.tsv’ saved [167817/167817]\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"endpoint = os.getenv('HF_ENDPOINT', 'https://huggingface.co')\\n\",\n \"file_name = \\\"qrels.miracl-v1.0-en-dev.tsv\\\"\\n\",\n \"qrel_url = f\\\"wget {endpoint}/datasets/miracl/miracl/resolve/main/miracl-v1.0-en/qrels/{file_name}\\\"\\n\",\n \"\\n\",\n \"os.system(qrel_url)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Read the qrels from the file:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qrels_dict = {}\\n\",\n \"with open(file_name, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n\",\n \" for line in f.readlines():\\n\",\n \" qid, _, docid, rel = line.strip().split(\\\"\\\\t\\\")\\n\",\n \" qid, docid, rel = str(qid), str(docid), int(rel)\\n\",\n \" if qid not in qrels_dict:\\n\",\n \" qrels_dict[qid] = {}\\n\",\n \" qrels_dict[qid][docid] = rel\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, use [pytrec_eval](https://github.com/cvangysel/pytrec_eval) library to help us calculate the scores of selected metrics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {'NDCG@10': 0.46073, 'NDCG@100': 0.54336})\\n\",\n \"defaultdict(, {'Recall@10': 0.55972, 'Recall@100': 0.83827})\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pytrec_eval\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"ndcg_string = \\\"ndcg_cut.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"recall_string = \\\"recall.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"\\n\",\n \"evaluator = pytrec_eval.RelevanceEvaluator(\\n\",\n \" qrels_dict, {ndcg_string, recall_string}\\n\",\n \")\\n\",\n \"scores = evaluator.evaluate(results)\\n\",\n \"\\n\",\n \"all_ndcgs, all_recalls = defaultdict(list), defaultdict(list)\\n\",\n \"for query_id in scores.keys():\\n\",\n \" for k in [10,100]:\\n\",\n \" all_ndcgs[f\\\"NDCG@{k}\\\"].append(scores[query_id][\\\"ndcg_cut_\\\" + str(k)])\\n\",\n \" all_recalls[f\\\"Recall@{k}\\\"].append(scores[query_id][\\\"recall_\\\" + str(k)])\\n\",\n \"\\n\",\n \"ndcg, recall = (\\n\",\n \" all_ndcgs.copy(),\\n\",\n \" all_recalls.copy(),\\n\",\n \")\\n\",\n \"\\n\",\n \"for k in [10,100]:\\n\",\n \" ndcg[f\\\"NDCG@{k}\\\"] = round(sum(ndcg[f\\\"NDCG@{k}\\\"]) / len(scores), 5)\\n\",\n \" recall[f\\\"Recall@{k}\\\"] = round(sum(recall[f\\\"Recall@{k}\\\"]) / len(scores), 5)\\n\",\n \"\\n\",\n \"print(ndcg)\\n\",\n \"print(recall)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Evaluate using FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide independent evaluation for popular datasets and benchmarks. Try the following code to run the evaluation, or run the shell script provided in [example](../../examples/evaluation/miracl/eval_miracl.sh) folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"arguments = \\\"\\\"\\\"- \\\\\\n\",\n \" --eval_name miracl \\\\\\n\",\n \" --dataset_dir ./miracl/data \\\\\\n\",\n \" --dataset_names en \\\\\\n\",\n \" --splits dev \\\\\\n\",\n \" --corpus_embd_save_dir ./miracl/corpus_embd \\\\\\n\",\n \" --output_dir ./miracl/search_results \\\\\\n\",\n \" --search_top_k 100 \\\\\\n\",\n \" --cache_path ./cache/data \\\\\\n\",\n \" --overwrite True \\\\\\n\",\n \" --k_values 10 100 \\\\\\n\",\n \" --eval_output_method markdown \\\\\\n\",\n \" --eval_output_path ./miracl/miracl_eval_results.md \\\\\\n\",\n \" --eval_metrics ndcg_at_10 recall_at_100 \\\\\\n\",\n \" --embedder_name_or_path BAAI/bge-base-en-v1.5 \\\\\\n\",\n \" --devices cuda:0 cuda:1 \\\\\\n\",\n \" --embedder_batch_size 1024\\n\",\n \"\\\"\\\"\\\".replace('\\\\n','')\\n\",\n \"\\n\",\n \"sys.argv = arguments.split()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/root/anaconda3/envs/dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"initial target device: 100%|██████████| 2/2 [00:09<00:00, 4.98s/it]\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [18:01<00:00, 14.85it/s] \\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"/root/anaconda3/envs/dev/lib/python3.12/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"pre tokenize: 100%|██████████| 16062/16062 [18:44<00:00, 14.29it/s]92s/it]\\n\",\n \"Inference Embeddings: 0%| | 42/16062 [00:54<8:28:19, 1.90s/it]You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 0%| | 43/16062 [00:56<8:22:03, 1.88s/it]/root/anaconda3/envs/dev/lib/python3.12/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [48:29<00:00, 5.52it/s] \\n\",\n \"Inference Embeddings: 100%|██████████| 16062/16062 [48:55<00:00, 5.47it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [1:10:57<00:00, 2128.54s/it] \\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:11<00:00, 11.06s/it]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:12<00:00, 12.72s/it]\\n\",\n \"Inference Embeddings: 100%|██████████| 1/1 [00:00<00:00, 32.15it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 1/1 [00:00<00:00, 39.80it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [00:31<00:00, 15.79s/it]\\n\",\n \"Searching: 100%|██████████| 25/25 [00:00<00:00, 26.24it/s]\\n\",\n \"Qrels not found in ./miracl/data/en/dev_qrels.jsonl. Trying to download the qrels from the remote and save it to ./miracl/data/en.\\n\",\n \"--2024-11-20 13:00:40-- https://hf-mirror.com/datasets/miracl/miracl/resolve/main/miracl-v1.0-en/qrels/qrels.miracl-v1.0-en-dev.tsv\\n\",\n \"Resolving hf-mirror.com (hf-mirror.com)... 133.242.169.68, 153.121.57.40, 160.16.199.204\\n\",\n \"Connecting to hf-mirror.com (hf-mirror.com)|133.242.169.68|:443... connected.\\n\",\n \"HTTP request sent, awaiting response... 200 OK\\n\",\n \"Length: 167817 (164K) [text/plain]\\n\",\n \"Saving to: ‘./cache/data/miracl/qrels.miracl-v1.0-en-dev.tsv’\\n\",\n \"\\n\",\n \" 0K .......... .......... .......... .......... .......... 30% 336K 0s\\n\",\n \" 50K .......... .......... .......... .......... .......... 61% 678K 0s\\n\",\n \" 100K .......... .......... .......... .......... .......... 91% 362K 0s\\n\",\n \" 150K .......... ... 100% 39.8K=0.7s\\n\",\n \"\\n\",\n \"2024-11-20 13:00:42 (231 KB/s) - ‘./cache/data/miracl/qrels.miracl-v1.0-en-dev.tsv’ saved [167817/167817]\\n\",\n \"\\n\",\n \"Loading and Saving qrels: 100%|██████████| 8350/8350 [00:00<00:00, 184554.95it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import HfArgumentParser\\n\",\n \"\\n\",\n \"from FlagEmbedding.evaluation.miracl import (\\n\",\n \" MIRACLEvalArgs, MIRACLEvalModelArgs,\\n\",\n \" MIRACLEvalRunner\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"parser = HfArgumentParser((\\n\",\n \" MIRACLEvalArgs,\\n\",\n \" MIRACLEvalModelArgs\\n\",\n \"))\\n\",\n \"\\n\",\n \"eval_args, model_args = parser.parse_args_into_dataclasses()\\n\",\n \"eval_args: MIRACLEvalArgs\\n\",\n \"model_args: MIRACLEvalModelArgs\\n\",\n \"\\n\",\n \"runner = MIRACLEvalRunner(\\n\",\n \" eval_args=eval_args,\\n\",\n \" model_args=model_args\\n\",\n \")\\n\",\n \"\\n\",\n \"runner.run()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"en-dev\\\": {\\n\",\n \" \\\"ndcg_at_10\\\": 0.46053,\\n\",\n \" \\\"ndcg_at_100\\\": 0.54313,\\n\",\n \" \\\"map_at_10\\\": 0.35928,\\n\",\n \" \\\"map_at_100\\\": 0.38726,\\n\",\n \" \\\"recall_at_10\\\": 0.55972,\\n\",\n \" \\\"recall_at_100\\\": 0.83809,\\n\",\n \" \\\"precision_at_10\\\": 0.14018,\\n\",\n \" \\\"precision_at_100\\\": 0.02347,\\n\",\n \" \\\"mrr_at_10\\\": 0.54328,\\n\",\n \" \\\"mrr_at_100\\\": 0.54929\\n\",\n \" }\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('miracl/search_results/bge-base-en-v1.5/NoReranker/EVAL/eval_results.json', 'r') as content_file:\\n\",\n \" print(content_file.read())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation/4.5.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate on MLDR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[MLDR](https://huggingface.co/datasets/Shitao/MLDR) is a Multilingual Long-Document Retrieval dataset built on Wikipeida, Wudao and mC4, covering 13 typologically diverse languages. Specifically, we sample lengthy articles from Wikipedia, Wudao and mC4 datasets and randomly choose paragraphs from them. Then we use GPT-3.5 to generate questions based on these paragraphs. The generated question and the sampled article constitute a new text pair to the dataset.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the libraries we are using:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install FlagEmbedding pytrec_eval\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download the dataset of 13 different languages from [Hugging Face](https://huggingface.co/datasets/Shitao/MLDR).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Language Code | Language | Source | #train | #dev | #test | #corpus | Avg. Length of Docs |\\n\",\n \"| :-----------: | :--------: | :--------------: | :-----: | :---: | :---: | :-----: | :-----------------: |\\n\",\n \"| ar | Arabic | Wikipedia | 1,817 | 200 | 200 | 7,607 | 9,428 |\\n\",\n \"| de | German | Wikipedia, mC4 | 1,847 | 200 | 200 | 10,000 | 9,039 |\\n\",\n \"| en | English | Wikipedia | 10,000 | 200 | 800 | 200,000 | 3,308 |\\n\",\n \"| es | Spanish | Wikipedia, mc4 | 2,254 | 200 | 200 | 9,551 | 8,771 |\\n\",\n \"| fr | French | Wikipedia | 1,608 | 200 | 200 | 10,000 | 9,659 |\\n\",\n \"| hi | Hindi | Wikipedia | 1,618 | 200 | 200 | 3,806 | 5,555 |\\n\",\n \"| it | Italian | Wikipedia | 2,151 | 200 | 200 | 10,000 | 9,195 |\\n\",\n \"| ja | Japanese | Wikipedia | 2,262 | 200 | 200 | 10,000 | 9,297 |\\n\",\n \"| ko | Korean | Wikipedia | 2,198 | 200 | 200 | 6,176 | 7,832 |\\n\",\n \"| pt | Portuguese | Wikipedia | 1,845 | 200 | 200 | 6,569 | 7,922 |\\n\",\n \"| ru | Russian | Wikipedia | 1,864 | 200 | 200 | 10,000 | 9,723 |\\n\",\n \"| th | Thai | mC4 | 1,970 | 200 | 200 | 10,000 | 8,089 |\\n\",\n \"| zh | Chinese | Wikipedia, Wudao | 10,000 | 200 | 800 | 200,000 | 4,249 |\\n\",\n \"| Total | - | - | 41,434 | 2,600 | 3,800 | 493,709 | 4,737 |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First download the queries and corresponding qrels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"lang = \\\"en\\\"\\n\",\n \"dataset = load_dataset('Shitao/MLDR', lang, trust_remote_code=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each item has four parts: `query_id`, `query`, `positive_passages`, and `negative_passages`. `query_id` and `query` correspond to the id and text content of the qeury. `positive_passages` and `negative_passages` are list of passages with their corresponding `docid` and `text`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query_id': 'q-en-1',\\n\",\n \" 'query': 'What is the syntax for the shorthand of the conditional operator in PHP 5.3?',\\n\",\n \" 'positive_passages': [{'docid': 'doc-en-8',\\n\",\n \" 'text': 'In computer programming, is a ternary operator that is part of the syntax for basic conditional expressions in several programming languages. It is commonly referred to as the conditional operator, inline if (iif), or ternary if. An expression evaluates to if the value of is true, and otherwise to . One can read it aloud as \\\"if a then b otherwise c\\\".\\\\n\\\\nIt originally comes from CPL, in which equivalent syntax for e1 ? e2 : e3 was e1 → e2, e3.\\\\n\\\\nAlthough many ternary operators are possible, the conditional operator is so common, and other ternary operators so rare, that the conditional operator is commonly referred to as the ternary operator.\\\\n\\\\nVariations\\\\nThe detailed semantics of \\\"the\\\" ternary operator as well as its syntax differs significantly from language to language.\\\\n\\\\nA top level distinction from one language to another is whether the expressions permit side effects (as in most procedural languages) and whether the language provides short-circuit evaluation semantics, whereby only the selected expression is evaluated (most standard operators in most languages evaluate all arguments).\\\\n\\\\nIf the language supports expressions with side effects but does not specify short-circuit evaluation, then a further distinction exists about which expression evaluates first—if the language guarantees any specific order (bear in mind that the conditional also counts as an expression).\\\\n\\\\nFurthermore, if no order is guaranteed, a distinction exists about whether the result is then classified as indeterminate (the value obtained from some order) or undefined (any value at all at the whim of the compiler in the face of side effects, or even a crash).\\\\n\\\\nIf the language does not permit side-effects in expressions (common in functional languages), then the order of evaluation has no value semantics—though it may yet bear on whether an infinite recursion terminates, or have other performance implications (in a functional language with match expressions, short-circuit evaluation is inherent, and natural uses for the ternary operator arise less often, so this point is of limited concern).\\\\n\\\\nFor these reasons, in some languages the statement form can have subtly different semantics than the block conditional form } (in the C language—the syntax of the example given—these are in fact equivalent).\\\\n\\\\nThe associativity of nested ternary operators can also differ from language to language. In almost all languages, the ternary operator is right associative so that evaluates intuitively as , but PHP in particular is notoriously left-associative, and evaluates as follows: , which is rarely what any programmer expects. (The given examples assume that the ternary operator has low operator precedence, which is true in all C-family languages, and many others.)\\\\n\\\\nEquivalence to map\\\\nThe ternary operator can also be viewed as a binary map operation.\\\\n\\\\nIn R—and other languages with literal expression tuples—one can simulate the ternary operator with something like the R expression (this idiom is slightly more natural in languages with 0-origin subscripts).\\\\n\\\\nHowever, in this idiom it is almost certain that the entire tuple expression will evaluate prior to the subscript expression, so there will be no short-circuit semantics.\\\\n\\\\nNested ternaries can be simulated as where the function returns the index of the first true value in the condition vector. Note that both of these map equivalents are binary operators, revealing that the ternary operator is ternary in syntax, rather than semantics. These constructions can be regarded as a weak form of currying based on data concatenation rather than function composition.\\\\n\\\\nIf the language provides a mechanism of futures or promises, then short-circuit evaluation can sometimes also be simulated in the context of a binary map operation.\\\\n\\\\nConditional assignment\\\\n is used as follows:\\\\n\\\\n condition ? value_if_true : value_if_false\\\\n\\\\nThe condition is evaluated true or false as a Boolean expression. On the basis of the evaluation of the Boolean condition, the entire expression returns value_if_true if condition is true, but value_if_false otherwise. Usually the two sub-expressions value_if_true and value_if_false must have the same type, which determines the type of the whole expression. The importance of this type-checking lies in the operator\\\\'s most common use—in conditional assignment statements. In this usage it appears as an expression on the right side of an assignment statement, as follows:\\\\n\\\\n variable = condition ? value_if_true : value_if_false\\\\n\\\\nThe ?: operator is similar to the way conditional expressions (if-then-else constructs) work in functional programming languages, like Scheme, ML, and Haskell, since if-then-else forms an expression instead of a statement in those languages.\\\\n\\\\nUsage\\\\nThe conditional operator\\\\'s most common usage is to make a terse simple conditional assignment statement. For example, if we wish to implement some C code to change a shop\\\\'s normal opening hours from 9 o\\\\'clock to 12 o\\\\'clock on Sundays, we may use\\\\n\\\\nint opening_time = (day == SUNDAY) ? 12 : 9;\\\\n\\\\ninstead of the more verbose\\\\n\\\\nint opening_time;\\\\n\\\\nif (day == SUNDAY)\\\\n opening_time = 12;\\\\nelse\\\\n opening_time = 9;\\\\n\\\\nThe two forms are nearly equivalent. Keep in mind that the is an expression and if-then-else is a statement. Note that neither the true nor false portions can be omitted from the conditional operator without an error report upon parsing. This contrasts with if-then-else statements, where the else clause can be omitted.\\\\n\\\\nMost of the languages emphasizing functional programming don\\\\'t need such an operator as their regular conditional expression(s) is an expression in the first place e.g. the Scheme expression is equivalent in semantics to the C expression . This is also the case in many imperative languages, starting with ALGOL where it is possible to write , or Smalltalk () or Ruby (, although works as well).\\\\n\\\\nNote that some languages may evaluate both the true- and false-expressions, even though only one or the other will be assigned to the variable. This means that if the true- or false-expression contain a function call, that function may be called and executed (causing any related side-effects due to the function\\\\'s execution), regardless of whether or not its result will be used. Programmers should consult their programming language specifications or test the ternary operator to determine whether or not the language will evaluate both expressions in this way. If it does, and this is not the desired behaviour, then an if-then-else statement should be used.\\\\n\\\\nActionScript 3\\\\ncondition ? value_if_true : value_if_false\\\\n\\\\nAda\\\\nThe 2012 edition of Ada has introduced conditional expressions (using and ), as part of an enlarged set of expressions including quantified expressions and expression functions. The Rationale for Ada 2012 states motives for Ada not having had them before, as well as motives for now adding them, such as to support \\\"contracts\\\" (also new).\\\\n\\\\nPay_per_Hour := (if Day = Sunday\\\\n then 12.50\\\\n else 10.00);\\\\n\\\\nWhen the value of an if_expression is itself of Boolean type, then the part may be omitted, the value being True. Multiple conditions may chained using .\\\\n\\\\nALGOL 68\\\\nBoth ALGOL 68\\\\'s choice clauses (if and the case clauses) provide the coder with a choice of either the \\\"bold\\\" syntax or the \\\"brief\\\" form.\\\\n\\\\n Single if choice clause:\\\\n if condition then statements [ else statements ] fi\\\\n \\\"brief\\\" form: ( condition | statements | statements )\\\\n\\\\n Chained if choice clause:\\\\n if condition1 then statements elif condition2 then statements [ else statements ] fi\\\\n \\\"brief\\\" form: ( condition1 | statements |: condition2 | statements | statements )\\\\n\\\\nAPL\\\\nWith the following syntax, both expressions are evaluated (with evaluated first, then , then ):\\\\n\\\\nresult ← value_if_true ⊣⍣ condition ⊢ value_if_false\\\\n\\\\nThis alternative syntax provides short-circuit evaluation:\\\\n\\\\nresult ← { condition : expression_if_true ⋄ expression_if_false } ⍬\\\\n\\\\nAWK\\\\nresult = condition ? value_if_true : value_if_false\\\\n\\\\nBash\\\\nA true ternary operator only exists for arithmetic expressions:\\\\n\\\\n((result = condition ? value_if_true : value_if_false))\\\\n\\\\nFor strings there only exist workarounds, like e.g.:\\\\n\\\\nresult=$([[ \\\"$a\\\" = \\\"$b\\\" ]] && echo \\\"value_if_true\\\" || echo \\\"value_if_false\\\")\\\\n\\\\nWhere can be any condition construct can evaluate. Instead of the there can be any other bash command. When it exits with success, the first echo command is executed, otherwise the second one is executed.\\\\n\\\\nC\\\\nA traditional if-else construct in C, Java and JavaScript is written:\\\\n\\\\nif (a > b) {\\\\n result = x;\\\\n}\\\\nelse {\\\\n result = y;\\\\n}\\\\n\\\\nThis can be rewritten as the following statement:\\\\n\\\\nresult = a > b ? x : y;\\\\n\\\\nAs in the if-else construct only one of the expressions \\\\'x\\\\' and \\\\'y\\\\' is evaluated. This is significant if the evaluation of \\\\'x\\\\' or \\\\'y\\\\' has side effects. The behaviour is undefined if an attempt is made to use the result of the conditional operator as an lvalue.\\\\n\\\\nA GNU extension to C allows omitting the second operand, and using implicitly the first operand as the second also:\\\\n\\\\na == x ? : y;\\\\n\\\\nThe expression is equivalent to\\\\n\\\\na == x ? (a == x) : y;\\\\n\\\\nexcept that if x is an expression, it is evaluated only once. The difference is significant if evaluating the expression has side effects. This shorthand form is sometimes known as the Elvis operator in other languages.\\\\n\\\\nC#\\\\nIn C#, if condition is true, first expression is evaluated and becomes the result; if false, the second expression is evaluated and becomes the result. As with Java only one of two expressions is ever evaluated.\\\\n\\\\n// condition ? first_expression : second_expression;\\\\n\\\\nstatic double sinc(double x) \\\\n{\\\\n return x != 0.0 ? Math.Sin(x) / x : 1.0;\\\\n}\\\\n\\\\nC++\\\\nUnlike in C, the precedence of the operator in C++ is the same as that of the assignment operator ( or ), and it can return an lvalue. This means that expressions like and are both legal and are parsed differently, the former being equivalent to .\\\\n\\\\nIn C++ there are conditional assignment situations where use of the if-else statement is impossible, since this language explicitly distinguishes between initialization and assignment. In such case it is always possible to use a function call, but this can be cumbersome and inelegant. For example, to pass conditionally different values as an argument for a constructor of a field or a base class, it is impossible to use a plain if-else statement; in this case we can use a conditional assignment expression, or a function call. Bear in mind also that some types allow initialization, but do not allow assignment, or even that the assignment operator and the constructor do totally different things. This last is true for reference types, for example:\\\\n\\\\n#include \\\\n#include \\\\n#include \\\\n\\\\nint main(int argc, char *argv[])\\\\n{\\\\n std::string name;\\\\n std::ofstream fout;\\\\n\\\\n if (argc > 1 && argv[1])\\\\n {\\\\n name = argv[1];\\\\n fout.open(name.c_str(), std::ios::out | std::ios::app);\\\\n }\\\\n\\\\n std::ostream &sout = name.empty() ? std::cout : fout;\\\\n\\\\n sout << \\\"Hello, world!\\\\\\\\n\\\";\\\\n\\\\n return 0;\\\\n}\\\\n\\\\nIn this case there is no possibility of using an if-else statement in place of the operator (Although we can replace the use of with a function call, inside of which can be an if-else statement).\\\\n\\\\nFurthermore, the conditional operator can yield an lvalue, i.e. a value to which another value can be assigned. Consider the following example:\\\\n\\\\n#include \\\\n\\\\nint main(int argc, char *argv[]) \\\\n{\\\\n int a = 0;\\\\n int b = 0;\\\\n\\\\n (argc > 1 ? a : b) = 1;\\\\n\\\\n std::cout << \\\"a: \\\" << a\\\\n << \\\" b: \\\" << b\\\\n << \\\\'\\\\\\\\n\\\\';\\\\n\\\\n return 0;\\\\n}\\\\n\\\\nIn this example, if the boolean expression yields the value on line 8, the value is assigned to the variable , otherwise the value is assigned to the variable .\\\\n\\\\nIn C++ and other various languages, ternary operators like are also possible but are very rare.\\\\n\\\\nCFML\\\\nExample of the operator in CFML:\\\\n\\\\nresult = randRange(0,1) ? \\\"heads\\\" : \\\"tails\\\";\\\\n\\\\nRoughly 50% of the time the expression will return 1 (true) or 0 (false); meaning result will take the value \\\"heads\\\" or \\\"tails\\\" respectively.\\\\n\\\\nLucee, Railo, and ColdFusion 11-specific\\\\nLucee, Railo, and ColdFusion 11 also implement the Elvis operator, which will return the value of the expression if it is not-null, otherwise the specified default.\\\\n\\\\nSyntax:\\\\n\\\\nresult = expression ?: value_if_expression_is_null\\\\n\\\\nExample:\\\\n\\\\nresult = f() ?: \\\"default\\\";\\\\n\\\\n// where...\\\\nfunction f(){\\\\n if (randRange(0,1)){ // either 0 or 1 (false / true)\\\\n return \\\"value\\\";\\\\n }\\\\n}\\\\n\\\\nwriteOutput(result);\\\\n\\\\nThe function will return roughly 50% of the time, otherwise will not return anything. If returns \\\"value\\\", will take that value, otherwise will take the value \\\"default\\\".\\\\n\\\\nCoffeeScript\\\\nExample of using this operator in CoffeeScript:\\\\n\\\\nif 1 is 2 then \\\"true value\\\" else \\\"false value\\\"\\\\n\\\\nReturns \\\"false value\\\".\\\\n\\\\nCommon Lisp\\\\nAssignment using a conditional expression in Common Lisp:\\\\n\\\\n(setf result (if (> a b) x y))\\\\n\\\\nAlternative form:\\\\n\\\\n(if (> a b)\\\\n (setf result x)\\\\n (setf result y))\\\\n\\\\nCrystal\\\\nExample of using this operator in Crystal:\\\\n\\\\n1 == 2 ? \\\"true value\\\" : \\\"false value\\\"\\\\n\\\\nReturns .\\\\n\\\\nThe Crystal compiler transforms conditional operators to expressions, so the above is semantically identical to:\\\\n\\\\nif 1 == 2\\\\n \\\"true value\\\"\\\\nelse\\\\n \\\"false value\\\"\\\\nend\\\\n\\\\nDart\\\\nThe Dart programming language\\\\'s syntax belongs to the C family, primarily inspired by languages like Java, C# and JavaScript, which means it has inherited the traditional syntax for its conditional expression.\\\\n\\\\nExample:\\\\n\\\\nreturn x.isEven ? x ~/ 2 : x * 3 + 1;\\\\n\\\\nLike other conditions in Dart, the expression before the must evaluate to a Boolean value.\\\\n\\\\nThe Dart syntax uses both and in various other ways, which causes ambiguities in the language grammar. An expression like:\\\\n\\\\n{ x as T ? [1] : [2] }\\\\n\\\\ncould be parsed as either a \\\"set literal\\\" containing one of two lists or as a \\\"map literal\\\" {((x as T?)[1]) : [2]}. The language always chooses the conditional expression in such situations.\\\\n\\\\nDart also has a second ternary operator, the operator commonly used for setting values in lists or maps, which makes the term \\\"the ternary operator\\\" ambiguous in a Dart context.\\\\n\\\\nDelphi\\\\nIn Delphi the function can be used to achieve the same as . If the library is used, the function returns a numeric value such as an Integer, Double or Extended. If the library is used, this function can also return a string value.\\\\n\\\\nUsing \\\\n\\\\nfunction IfThen(AValue: Boolean; const ATrue: Integer; const AFalse: Integer): Integer;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Int64; const AFalse: Int64): Int64;\\\\nfunction IfThen(AValue: Boolean; const ATrue: UInt64; const AFalse: UInt64): UInt64;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Single; const AFalse: Single): Single;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Double; const AFalse: Double): Double;\\\\nfunction IfThen(AValue: Boolean; const ATrue: Extended; const AFalse: Extended): Extended;\\\\n\\\\nUsing the library\\\\n\\\\nfunction IfThen(AValue: Boolean; const ATrue: string; AFalse: string = \\\\'\\\\'): string;\\\\n\\\\nUsage example:\\\\n\\\\nfunction GetOpeningTime(Weekday: Integer): Integer;\\\\nbegin\\\\n { This function will return the opening time for the given weekday: 12 for Sundays, 9 for other days }\\\\n Result := IfThen((Weekday = 1) or (Weekday = 7), 12, 9);\\\\nend;\\\\n\\\\nUnlike a true ternary operator however, both of the results are evaluated prior to performing the comparison. For example, if one of the results is a call to a function which inserts a row into a database table, that function will be called whether or not the condition to return that specific result is met.\\\\n\\\\nF#\\\\n\\\\nIn F# the built-in syntax for if-then-else is already an expression that always must return a value.\\\\n\\\\nlet num = if x = 10 then 42 else 24\\\\n\\\\nF# has a special case where you can omit the else branch if the return value is of type unit. This way you can do side-effects, without using a else branch.\\\\n\\\\nif x = 10 then\\\\n printfn \\\"It is 10\\\"\\\\n\\\\nBut even in this case, the if expression would return unit. You don\\\\'t need to write the else branch, because the compiler will assume the unit type on else.\\\\n\\\\nFORTH\\\\nSince FORTH is a stack-oriented language, and any expression can leave a value on the stack, all // sequences can generate values:\\\\n\\\\n: test ( n -- n ) 1 AND IF 22 ELSE 42 THEN ;\\\\n\\\\nThis word takes 1 parameter on the stack, and if that number is odd, leaves 22. If it\\\\'s even, 42 is left on the stack.\\\\n\\\\nFortran\\\\nWith the additions to the code in the 1995 release, the ternary operator was added to the Fortran compiler as the intrinsic function :\\\\n\\\\nvariable = merge(x,y,a>b)\\\\n\\\\nNote that both x and y are evaluated before the results of one or the other are returned from the function. Here, x is returned if the condition holds true and y otherwise.\\\\n\\\\nFreeMarker \\\\nThis built-in exists since FreeMarker 2.3.20.\\\\n\\\\nUsed like booleanExp?then(whenTrue, whenFalse), fills the same role as the ternary operator in C-like languages.\\\\n\\\\n<#assign x = 10>\\\\n<#assign y = 20>\\\\n<#-- Prints the maximum of x and y: -->\\\\n${(x > y)?then(x, y)}\\\\n\\\\nGo\\\\nThere is no ternary if in Go, so use of the full if statement is always required.\\\\n\\\\nHaskell\\\\nThe built-in if-then-else syntax is inline: the expression\\\\n\\\\nif predicate then expr1 else expr2\\\\n\\\\nhas type\\\\n\\\\nBool -> a -> a -> a\\\\n\\\\nThe base library also provides the function :\\\\n\\\\nbool :: a -> a -> Bool -> a\\\\n\\\\nIn both cases, no special treatment is needed to ensure that only the selected expression is evaluated, since Haskell is non-strict by default. This also means an operator can be defined that, when used in combination with the operator, functions exactly like in most languages:\\\\n\\\\n(?) :: Bool -> a -> a -> a\\\\n(?) pred x y = if pred then x else y\\\\ninfix 1 ?\\\\n\\\\n-- example (vehicle will evaluate to \\\"airplane\\\"):\\\\narg = \\\\'A\\\\'\\\\nvehicle = arg == \\\\'B\\\\' ? \\\"boat\\\" $\\\\n arg == \\\\'A\\\\' ? \\\"airplane\\\" $\\\\n arg == \\\\'T\\\\' ? \\\"train\\\" $\\\\n \\\"car\\\"\\\\n\\\\nHowever, it is more idiomatic to use pattern guards\\\\n\\\\n-- example (vehicle will evaluate to \\\"airplane\\\"):\\\\narg = \\\\'A\\\\'\\\\nvehicle | arg == \\\\'B\\\\' = \\\"boat\\\"\\\\n | arg == \\\\'A\\\\' = \\\"airplane\\\"\\\\n | arg == \\\\'T\\\\' = \\\"train\\\"\\\\n | otherwise = \\\"car\\\"\\\\n\\\\nJava\\\\nIn Java this expression evaluates to:\\\\n\\\\n// If foo is selected, assign selected foo to bar. If not, assign baz to bar.\\\\nObject bar = foo.isSelected() ? foo : baz; \\\\n\\\\nNote that Java, in a manner similar to C#, only evaluates the used expression and will not evaluate the unused expression.\\\\n\\\\nJulia\\\\nIn Julia, \\\"Note that the spaces around and are mandatory: an expression like is not a valid ternary expression (but a newline is acceptable after both the and the ).\\\"\\\\n\\\\nJavaScript\\\\nThe conditional operator in JavaScript is similar to that of C++ and Java, except for the fact the middle expression cannot be a comma expression. Also, as in C++, but unlike in C or Perl, it will not bind tighter than an assignment to its right— is equivalent to instead of .\\\\n\\\\nvar timeout = settings !== null ? settings.timeout : 1000;\\\\n\\\\nJust like C# and Java, the expression will only be evaluated if, and only if, the expression is the matching one for the condition given; the other expression will not be evaluated.\\\\n\\\\nKotlin \\\\nKotlin does not include the traditional ternary operator, however, s can be used as expressions that can be assigned, achieving the same results. Note that, as the complexity of your conditional statement grows, you might consider replacing your - expression with a expression.\\\\n\\\\nval max = if (a > b) a else b\\\\n\\\\nLua \\\\nLua does not have a traditional conditional operator. However, the short-circuiting behaviour of its and operators allows the emulation of this behaviour:\\\\n\\\\n-- equivalent to var = cond ? a : b;\\\\nvar = cond and a or b\\\\n\\\\nThis will succeed unless is logically false (i.e. or ); in this case, the expression will always result in . This can result in some surprising behaviour if ignored.\\\\n\\\\nSQL\\\\nThe SQL expression is a generalization of the ternary operator. Instead of one conditional and two results, n conditionals and n+1 results can be specified.\\\\n\\\\nWith one conditional it is equivalent (although more verbose) to the ternary operator:\\\\n\\\\nSELECT (CASE WHEN a > b THEN x ELSE y END) AS CONDITIONAL_EXAMPLE\\\\n FROM tab;\\\\n\\\\nThis can be expanded to several conditionals:\\\\n\\\\nSELECT (CASE WHEN a > b THEN x WHEN a < b THEN y ELSE z END) AS CONDITIONAL_EXAMPLE\\\\n FROM tab;\\\\n\\\\nMySQL\\\\nIn addition to the standard expression, MySQL provides an function as an extension:\\\\n\\\\nIF(cond, a, b);\\\\n\\\\nSQL Server\\\\nIn addition to the standard expression, SQL Server (from 2012) provides an function:\\\\n\\\\nIIF(condition, true_value, false_value)\\\\n\\\\nOracle SQL\\\\nIn addition to the standard expression, Oracle has a variadic functional counterpart which operates similarly to a switch statement and can be used to emulate the conditional operator when testing for equality.\\\\n\\\\n-- General syntax takes case-result pairs, comparing against an expression, followed by a fall-back result:\\\\nDECODE(expression, case1, result1,\\\\n ...\\\\n caseN, resultN,\\\\n resultElse)\\\\n\\\\n-- We can emulate the conditional operator by just selecting one case:\\\\nDECODE(expression, condition, true, false)\\\\n\\\\nThe function is, today, deprecated in favour of the standard expression. This can be used in both Oracle SQL queries as well as PL/SQL blocks, whereas can only be used in the former.\\\\n\\\\nPerl\\\\nA traditional if-else construct in Perl is written:\\\\n\\\\nif ($a > $b) {\\\\n $result = $x;\\\\n} else {\\\\n $result = $y;\\\\n}\\\\n\\\\nRewritten to use the conditional operator:\\\\n\\\\n$result = $a > $b ? $x : $y;\\\\n\\\\nThe precedence of the conditional operator in perl is the same as in C, not as in C++. This is conveniently of higher precedence than a comma operator but lower than the precedence of most operators used in expressions within the ternary operator, so the use of parentheses is rarely required.\\\\n\\\\nIts associativity matches that of C and C++, not that of PHP. Unlike C but like C++, perl allows the use of the conditional expression as an L-value; for example:\\\\n\\\\n$a > $b ? $x : $y = $result;\\\\n\\\\nwill assign to either or depending on the logical expression\\\\'s boolean result.\\\\n\\\\nThe respective precedence rules and associativities of the operators used guarantee that the version absent any parentheses is equivalent to this explicitly parenthesized version:\\\\n\\\\n(($a > $b) ? $x : $y) = $result;\\\\n\\\\nThis is equivalent to the if-else version:\\\\n\\\\nif ($a > $b) {\\\\n $x = $result;\\\\n} else {\\\\n $y = $result;\\\\n}\\\\n\\\\nPHP\\\\nA simple PHP implementation is this:\\\\n\\\\n$abs = $value >= 0 ? $value : -$value;\\\\n\\\\nDue to an unfortunate design of the language grammar, the conditional operator in PHP is left associative in contrast to other languages, thus given a value of T for arg, the PHP code in the following example would yield the value horse instead of train as one might expect:\\\\n\\\\n b else y\\\\n\\\\nThis form invites considering as the normal value and as an exceptional case. \\\\n\\\\nPrior to Python\\\\xa02.5 there were a number of ways to approximate a conditional operator (for example by indexing into a two element array), all of which have drawbacks as compared to the built-in operator.\\\\n\\\\nR\\\\nThe traditional if-else construct in R (which is an implementation of S) is:\\\\n\\\\nif (a < b) {\\\\n x <- \\\"true\\\"\\\\n} else {\\\\n x <- \\\"false\\\"\\\\n}\\\\n\\\\nIf there is only one statement in each block, braces can be omitted, like in C:\\\\n\\\\nif (a < b)\\\\n x <- \\\"true\\\"\\\\nelse\\\\n x <- \\\"false\\\"\\\\n\\\\nThe code above can be written in the following non-standard condensed way:\\\\n\\\\nx <- if (a < b) \\\"true\\\" else \\\"false\\\"\\\\n\\\\nThere exists also the function that allows rewriting the expression above as:\\\\n\\\\nx <- ifelse(a < b, \\\"true\\\", \\\"false\\\")\\\\n\\\\nThe function is automatically vectorized. For instance:\\\\n\\\\n> ifelse(c (0, 2) < 1, \\\"true\\\", \\\"false\\\")\\\\n[1] \\\"true\\\" \\\"false\\\"\\\\n\\\\nRaku\\\\nRaku uses a doubled symbol instead of single \\\\nand a doubled symbol instead of \\\\n\\\\n$result = $a > $b ?? $x !! $y;\\\\n\\\\nRuby\\\\nExample of using this operator in Ruby:\\\\n\\\\n1 == 2 ? \\\"true value\\\" : \\\"false value\\\"\\\\n\\\\nReturns \\\"false value\\\".\\\\n\\\\nA traditional if-else construct in Ruby is written:\\\\n\\\\nif a > b\\\\n result = x\\\\nelse\\\\n result = y\\\\nend\\\\n\\\\nThis could also be written as:\\\\n\\\\nresult = if a > b\\\\n x\\\\nelse\\\\n y\\\\nend\\\\n\\\\nThese can be rewritten as the following statement:\\\\n\\\\nresult = a > b ? x : y\\\\n\\\\nRust\\\\nBeing an expression-oriented programming language, Rust\\\\'s existing if expr1 else expr2 syntax can behave as the traditional ternary operator does. Earlier versions of the language did have the operator but it was removed due to duplication with .\\\\n\\\\nNote the lack of semi-colons in the code below compared to a more declarative ... block, and the semi-colon at the end of the assignment to .\\\\n\\\\nlet x = 5;\\\\n\\\\nlet y = if x == 5 {\\\\n 10\\\\n} else {\\\\n 15\\\\n};\\\\n\\\\nThis could also be written as:\\\\n\\\\nlet y = if x == 5 { 10 } else { 15 };\\\\n\\\\nNote that curly braces are mandatory in Rust conditional expressions.\\\\n\\\\nYou could also use a expression:\\\\n\\\\nlet y = match x {\\\\n 5 => 10,\\\\n _ => 15,\\\\n};\\\\n\\\\nScheme\\\\nSame as in Common Lisp. Every expression has a value. Thus the builtin can be used:\\\\n\\\\n(let* ((x 5)\\\\n (y (if (= x 5) 10 15)))\\\\n ...)\\\\n\\\\nSmalltalk\\\\nEvery expression (message send) has a value. Thus can be used:\\\\n\\\\n|x y|\\\\n\\\\nx := 5.\\\\ny := (x == 5) ifTrue:[10] ifFalse:[15].\\\\n\\\\nSwift\\\\nThe ternary conditional operator of Swift is written in the usual way of the C tradition, and is used within expressions.\\\\n\\\\nlet result = a > b ? a : b\\\\n\\\\nTcl\\\\nIn Tcl, this operator is available in expr expressions only:\\\\n\\\\nset x 5\\\\nset y [expr {$x == 5 ? 10 : 15}]\\\\n\\\\nOutside of expr, if can be used for a similar purpose, as it also returns a value:\\\\npackage require math\\\\n\\\\nset x 5\\\\nset y [if {$x == 5} {\\\\n ::math::random $x\\\\n} else {\\\\n ::math::fibonacci $x\\\\n}]\\\\n\\\\nTestStand\\\\nIn a National Instruments TestStand expression, if condition is true, the first expression is evaluated and becomes the output of the conditional operation; if false, the second expression is evaluated and becomes the result. Only one of two expressions is ever evaluated.\\\\n\\\\ncondition ? first_expression : second_expression\\\\n\\\\nFor example:\\\\n\\\\nRunState.Root.Parameters.TestSocket.Index == 3 ? Locals.UUTIndex = 3 : Locals.UUTIndex = 0\\\\n\\\\nSets the local variable to 3 if is 3, otherwise it sets to 0.\\\\n\\\\nSimilar to other languages, first_expression and second_expression do not need to be autonomous expressions, allowing the operator to be used for variable assignment:\\\\n\\\\nLocals.UUTIndex = ( RunState.Root.Parameters.TestSocket.Index == 3 ? 3 : 0 )\\\\n\\\\nVerilog\\\\nVerilog is technically a hardware description language, not a programming language though the semantics of both are very similar. It uses the syntax for the ternary operator.\\\\n\\\\n// using blocking assignment\\\\nwire out;\\\\nassign out = sel ? a : b;\\\\n\\\\nThis is equivalent to the more verbose Verilog code:\\\\n\\\\n// using blocking assignment\\\\nwire out;\\\\nif (sel === 1) // sel is 1, not 0, x or z\\\\n assign out = a;\\\\nelse if (sel === 0) // sel is 0, x or z (1 checked above)\\\\n assign out = b;\\\\nelse // sel is x or z (0 and 1 checked above)\\\\n assign out = [comment]; // a and b are compared bit by bit, and return for each bit\\\\n // an x if bits are different, and the bit value if the same\\\\n\\\\nVisual Basic\\\\nVisual Basic doesn\\\\'t use per se, but has a very similar implementation of this shorthand statement. Using the first example provided in this article, it can do:\\\\n\\\\n\\\\' variable = IIf(condition, value_if_true, value_if_false)\\\\nDim opening_time As Integer = IIf((day = SUNDAY), 12, 9)\\\\n\\\\nIn the above example, is a ternary function, but not a ternary operator. As a function, the values of all three portions are evaluated before the function call occurs. This imposed limitations, and in Visual Basic .Net 9.0, released with Visual Studio 2008, an actual conditional operator was introduced, using the keyword instead of . This allows the following example code to work:\\\\n\\\\nDim name As String = If(person Is Nothing, \\\"\\\", person.Name)\\\\n\\\\nUsing , would be evaluated even if person is (Nothing), causing an exception. With a true short-circuiting conditional operator, is not evaluated unless person is not .\\\\n\\\\nVisual Basic Version 9 has added the operator in addition to the existing function that existed previously. As a true operator, it does not have the side effects and potential inefficiencies of the function.\\\\n\\\\nThe syntaxes of the tokens are similar: vs . As mentioned above, the function call has significant disadvantages, because the sub-expressions must all be evaluated, according to Visual Basic\\\\'s evaluation strategy for function calls and the result will always be of type variant (VB) or object (VB.NET). The operator however does not suffer from these problems as it supports conditional evaluation and determines the type of the expression based on the types of its operands.\\\\n\\\\nResult type\\\\nClearly the type of the result of the operator must be in some sense the type unification of the types of its second and third operands. In C this is accomplished for numeric types by arithmetic promotion; since C does not have a type hierarchy for pointer types, pointer operands may only be used if they are of the same type (ignoring type qualifiers) or one is void or NULL. It is undefined behaviour to mix pointer and integral or incompatible pointer types; thus\\\\n\\\\nnumber = spell_out_numbers ? \\\"forty-two\\\" : 42;\\\\n\\\\nwill result in a compile-time error in most compilers.\\\\n\\\\n?: in style guidelines\\\\nConditional operators are widely used and can be useful in certain circumstances to avoid the use of an statement, either because the extra verbiage would be too lengthy or because the syntactic context does not permit a statement. For example:\\\\n\\\\n #define MAX(a, b) (((a)>(b)) ? (a) : (b))\\\\n\\\\nor\\\\n\\\\n for (i = 0; i < MAX_PATTERNS; i++)\\\\n c_patterns[i].ShowWindow(m_data.fOn[i] ? SW_SHOW : SW_HIDE);\\\\n\\\\n(The latter example uses the Microsoft Foundation Classes Framework for Win32.)\\\\n\\\\nInitialization\\\\nAn important use of the conditional operator is in allowing a single initialization statement, rather than multiple initialization statements. In many cases this also allows single assignment and for an identifier to be a constant.\\\\n\\\\nThe simplest benefit is avoiding duplicating the variable name, as in Python:\\\\n\\\\nx = \\\\'foo\\\\' if b else \\\\'bar\\\\'\\\\n\\\\ninstead of:\\\\n\\\\nif b:\\\\n x = \\\\'foo\\\\'\\\\nelse:\\\\n x = \\\\'bar\\\\'\\\\n\\\\nMore importantly, in languages with block scope, such as C++, the blocks of an if/else statement create new scopes, and thus variables must be declared before the if/else statement, as:\\\\n\\\\nstd::string s;\\\\nif (b)\\\\n s = \\\"foo\\\";\\\\nelse\\\\n s = \\\"bar\\\";\\\\n\\\\nUse of the conditional operator simplifies this:\\\\n\\\\nstd::string s = b ? \\\"foo\\\" : \\\"bar\\\";\\\\n\\\\nFurthermore, since initialization is now part of the declaration, rather than a separate statement, the identifier can be a constant (formally, of type):\\\\n\\\\nconst std::string s = b ? \\\"foo\\\" : \\\"bar\\\";\\\\n\\\\nCase selectors\\\\nWhen properly formatted, the conditional operator can be used to write simple and coherent case selectors. For example:\\\\n\\\\nvehicle = arg == \\\\'B\\\\' ? bus :\\\\n arg == \\\\'A\\\\' ? airplane :\\\\n arg == \\\\'T\\\\' ? train :\\\\n arg == \\\\'C\\\\' ? car :\\\\n arg == \\\\'H\\\\' ? horse :\\\\n feet;\\\\n\\\\nAppropriate use of the conditional operator in a variable assignment context reduces the probability of a bug from a faulty assignment as the assigned variable is stated just once as opposed to multiple times.\\\\n\\\\nProgramming languages without the conditional operator\\\\nThe following are examples of notable general-purpose programming languages that don\\\\'t provide a conditional operator:\\\\n\\\\n CoffeeScript\\\\n Go programming language\\\\n MATLAB\\\\n Pascal although Object Pascal / Delphi do have a function to do the same (with caveats)\\\\n Rust The construct is an expression and can be used to get the same functionality.\\\\n \\\\n PowerShell (in old versions) an elegant workaround is to use (,)[!()]\\\\n\\\\nSee also\\\\n IIf, inline if function\\\\n Null coalescing operator, operator\\\\n Elvis operator, , or sometimes , as a shorthand binary operator\\\\n Conditioned disjunction, equivalent ternary logical connective.\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n Description of If operator in Visual Basic\\\\n Description of Conditional Expression in Python (PEP 308)\\\\n Description in the Java Language Specification\\\\n Description in the PHP Language Documentation\\\\n\\\\nConditional constructs\\\\nOperators (programming)\\\\nTernary operations\\\\nArticles with example code\\\\n\\\\nde:Bedingte Anweisung und Verzweigung#Auswahloperator'}],\\n\",\n \" 'negative_passages': [{'docid': 'doc-en-9',\\n\",\n \" 'text': 'The Pirates of Penzance; or, The Slave of Duty is a comic opera in two acts, with music by Arthur Sullivan and libretto by W.\\\\xa0S.\\\\xa0Gilbert. The opera\\\\'s official premiere was at the Fifth Avenue Theatre in New York City on 31 December 1879, where the show was well received by both audiences and critics. Its London debut was on 3 April 1880, at the Opera Comique, where it ran for 363 performances.\\\\n\\\\nThe story concerns Frederic, who, having completed his 21st year, is released from his apprenticeship to a band of tender-hearted pirates. He meets the daughters of Major-General Stanley, including Mabel, and the two young people fall instantly in love. Frederic soon learns, however, that he was born on the 29th of February, and so, technically, he has a birthday only once each leap year. His indenture specifies that he remain apprenticed to the pirates until his \\\"twenty-first birthday\\\", meaning that he must serve for another 63 years. Bound by his own sense of duty, Frederic\\\\'s only solace is that Mabel agrees to wait for him faithfully.\\\\n\\\\nPirates was the fifth Gilbert and Sullivan collaboration and introduced the much-parodied \\\"Major-General\\\\'s Song\\\". The opera was performed for over a century by the D\\\\'Oyly Carte Opera Company in Britain and by many other opera companies and repertory companies worldwide. Modernized productions include Joseph Papp\\\\'s 1981 Broadway production, which ran for 787 performances, winning the Tony Award for Best Revival and the Drama Desk Award for Outstanding Musical, and spawning many imitations and a 1983 film adaptation. Pirates remains popular today, taking its place along with The Mikado and H.M.S. Pinafore as one of the most frequently played Gilbert and Sullivan operas.\\\\n\\\\nBackground\\\\n\\\\nThe Pirates of Penzance was the only Gilbert and Sullivan opera to have its official premiere in the United States. At the time, American law offered no copyright protection to foreigners. After the pair\\\\'s previous opera, H.M.S. Pinafore, achieved success in London in 1878, approximately 150 American companies quickly mounted unauthorised productions that often took considerable liberties with the text and paid no royalties to the creators. Gilbert and Sullivan hoped to forestall further \\\"copyright piracy\\\" by mounting the first production of their next opera in America, before others could copy it, and by delaying publication of the score and libretto. They succeeded in keeping for themselves the direct profits of the first American production of The Pirates of Penzance by opening the production themselves on Broadway, prior to the London production, and they also operated profitable US touring companies of Pirates and Pinafore. However, Gilbert, Sullivan, and their producer, Richard D\\\\'Oyly Carte, failed in their efforts, over the next decade, to control the American performance copyrights to Pirates and their other operas.\\\\n\\\\nFiction and plays about pirates were ubiquitous in the 19th century. Walter Scott\\\\'s The Pirate (1822) and James Fenimore Cooper\\\\'s The Red Rover were key sources for the romanticised, dashing pirate image and the idea of repentant pirates. Both Gilbert and Sullivan had parodied these ideas early in their careers. Sullivan had written a comic opera called The Contrabandista, in 1867, about a hapless British tourist who is captured by bandits and forced to become their chief. Gilbert had written several comic works that involved pirates or bandits. In Gilbert\\\\'s 1876 opera Princess Toto, the title character is eager to be captured by a brigand chief. Gilbert had translated Jacques Offenbach\\\\'s operetta Les brigands, in 1871. As in Les brigands, The Pirates of Penzance absurdly treats stealing as a professional career path, with apprentices and tools of the trade such as the crowbar and life preserver.\\\\n\\\\nGenesis\\\\nWhile Pinafore was running strongly at the Opera Comique in London, Gilbert was eager to get started on his and Sullivan\\\\'s next opera, and he began working on the libretto in December 1878. He re-used several elements of his 1870 one-act piece, Our Island Home, which had introduced a pirate \\\"chief\\\", Captain Bang. Bang was mistakenly apprenticed to a pirate band as a child by his deaf nursemaid. Also, Bang, like Frederic in The Pirates of Penzance, had never seen a woman before and felt a keen sense of duty, as an apprenticed pirate, until the passage of his twenty-first birthday freed him from his articles of indenture. Bernard Shaw believed that Gilbert drew on ideas in Les brigands for his new libretto, including the businesslike bandits and the bumbling police. Gilbert and Sullivan also inserted into Act II an idea they first considered for a one-act opera parody in 1876 about burglars meeting police, while their conflict escapes the notice of the oblivious father of a large family of girls. As in Pinafore, \\\"there was a wordful self-descriptive set-piece for Stanley [\\\"The Major-General\\\\'s Song\\\"], introducing himself much as Sir Joseph Porter had done ... a lugubrious comic number for the Sergeant of Police ... a song of confession for Ruth, the successor [to] Little Buttercup\\\", romantic material for Frederic and Mabel, and \\\"ensemble and chorus music in turn pretty, parodic and atmospheric.\\\"\\\\n\\\\nGilbert, Sullivan and Carte met by 24 April 1879 to make plans for a production of Pinafore and the new opera in America. Carte travelled to New York in the summer of 1879 and made arrangements with theatre manager John T. Ford to present, at the Fifth Avenue Theatre, the authorised productions. He then returned to London. Meanwhile, once Pinafore became a hit in London, the author, composer and producer had the financial resources to produce future shows themselves, and they executed a plan to free themselves from their financial backers in the \\\"Comedy Opera Company\\\". Carte formed a new partnership with Gilbert and Sullivan to divide profits equally among themselves after the expenses of each of their shows.\\\\n\\\\nIn November 1879, Gilbert, Sullivan and Carte sailed to America with a company of singing actors, to play both Pinafore and the new opera, including J. H. Ryley as Sir Joseph, Blanche Roosevelt as Josephine, Alice Barnett as Little Buttercup, Furneaux Cook as Dick Deadeye, Hugh Talbot as Ralph Rackstraw and Jessie Bond as Cousin Hebe, some of whom had been in the Pinafore cast in London. To these, he added some American singers, including Signor Brocolini as Captain Corcoran. Alfred Cellier came to assist Sullivan, while his brother François Cellier remained in London to conduct Pinafore there. Gilbert and Sullivan cast talented actors who were not well-known stars and did not command high fees. They then tailored their operas to the particular abilities of these performers. The skill with which Gilbert and Sullivan used their performers had an effect on the audience: as critic Herman Klein wrote, \\\"we secretly marvelled at the naturalness and ease with which [the Gilbertian quips and absurdities] were said and done. For until then no living soul had seen upon the stage such weird, eccentric, yet intensely human beings\\\\xa0.... [They] conjured into existence a hitherto unknown comic world of sheer delight.\\\" Gilbert acted as stage director for his own plays and operas. He sought naturalism in acting, which was unusual at the time, just as he strove for realistic visual elements. He deprecated self-conscious interaction with the audience and insisted on a style of portrayal in which the characters were never aware of their own absurdity but were coherent internal wholes. Sullivan conducted the music rehearsals.\\\\n\\\\nSullivan had sketched out the music for Pirates in England. When he arrived in New York, however, he found that he had left the sketches for Act I behind, and he had to reconstruct the first act from memory, or compose new numbers. Gilbert told a correspondent many years later that Sullivan was unable to recall his setting of the entrance of the women\\\\'s chorus, so they substituted the chorus \\\"Climbing over rocky mountain\\\" from their earlier opera, Thespis. Sullivan\\\\'s manuscript for Pirates contains pages removed from a Thespis score, with the vocal parts of this chorus altered from their original arrangement as a four-part chorus. Some scholars (e.g. Tillett and Spencer, 2000) have suggested that Gilbert and Sullivan had planned all along to re-use \\\"Climbing over rocky mountain,\\\" and perhaps other parts of Thespis. They argue that Sullivan\\\\'s having brought the unpublished Thespis score to New York, when there were no plans to revive Thespis, might not have been accidental. In any case, on 10 December 1879, Sullivan wrote a letter to his mother about the new opera, upon which he was hard at work in New York. \\\"I think it will be a great success, for it is exquisitely funny, and the music is strikingly tuneful and catching.\\\" As was his usual practice in his operas, Sullivan left the overture for the last moment, often sketching it out and entrusting completion of \\\"the details\\\" to an assistant, in this case the company\\\\'s music director, Alfred Cellier.\\\\n\\\\nPinafore opened in New York on 1 December 1879 and ran for the rest of December. After a reasonably strong first week, audiences quickly fell off, since most New Yorkers had already seen local productions of Pinafore. In the meantime, Gilbert and Sullivan raced to complete and rehearse The Pirates of Penzance. The work\\\\'s title is a multi-layered joke. On the one hand, Penzance was a docile seaside resort in 1879, and not the place where one would expect to encounter pirates. On the other hand, the title was also a jab at the theatrical \\\"pirates\\\" who had staged unlicensed productions of H.M.S. Pinafore in America. To secure the British copyright, a D\\\\'Oyly Carte touring company gave a perfunctory copyright performance of Pirates the afternoon before the New York premiere, at the Royal Bijou Theatre in Paignton, Devon, organised by Helen Lenoir, who would later marry Richard D\\\\'Oyly Carte. The cast, which was performing Pinafore in the evenings in Torquay, received some of the music for Pirates only two days beforehand. Having had only one rehearsal, they travelled to nearby Paignton for the matinee, where they read their parts from scripts carried onto the stage, making do with whatever costumes they had on hand.\\\\n\\\\nOriginal production and aftermath\\\\n\\\\nPirates opened on 31 December 1879 in New York and was an immediate hit. On 2 January 1880, Sullivan wrote, in another letter to his mother from New York, \\\"The libretto is ingenious, clever, wonderfully funny in parts, and sometimes brilliant in dialogue – beautifully written for music, as is all Gilbert does. ... The music is infinitely superior in every way to the Pinafore – \\\\'tunier\\\\' and more developed, of a higher class altogether. I think that in time it will be very popular.\\\" Shortly thereafter, Carte sent three touring companies around the United States East Coast and Midwest, playing Pirates and Pinafore. Sullivan\\\\'s prediction was correct. After a strong run in New York and several American tours, Pirates opened in London on 3 April 1880, running for 363 performances there. It remains one of the most popular G&S works. The London sets were designed by John O\\\\'Connor.\\\\n\\\\nThe critics\\\\' notices were generally excellent in both New York and London. The character of Major-General Stanley was widely taken to be a caricature of the popular general Sir Garnet Wolseley. The biographer Michael Ainger, however, doubts that Gilbert intended a caricature of Wolseley, identifying instead General Henry Turner, uncle of Gilbert\\\\'s wife, as the pattern for the \\\"modern Major-General\\\". Gilbert disliked Turner, who, unlike the progressive Wolseley, was of the old school of officers. Nevertheless, in the original London production, George Grossmith imitated Wolseley\\\\'s mannerisms and appearance, particularly his large moustache, and the audience recognised the allusion. Wolseley himself, according to his biographer, took no offence at the caricature and sometimes sang \\\"I am the very model of a modern Major-General\\\" for the private amusement of his family and friends.\\\\n\\\\nRoles\\\\n Major-General Stanley (comic baritone)\\\\n The Pirate King (bass-baritone)\\\\n Samuel, his Lieutenant (baritone)\\\\n Frederic, the Pirate Apprentice (tenor)\\\\n Sergeant of Police (bass)\\\\nGeneral Stanley\\\\'s daughters\\\\n Mabel (soprano)\\\\n Edith (mezzo-soprano)\\\\n Kate (mezzo-soprano)\\\\n Isabel (speaking role)\\\\n Ruth, a Piratical Maid of all work (contralto)\\\\n Chorus of Pirates, Police and General Stanley\\\\'s Daughters\\\\n\\\\nSynopsis\\\\n\\\\nAct I\\\\nOn the coast of Cornwall, during Queen Victoria\\\\'s reign, Frederic celebrates the completion of his twenty-first year and the end of his apprenticeship to a gentlemanly band of pirates (\\\"Pour, oh pour the pirate sherry\\\"). The pirates\\\\' maid of all work, Ruth, appears and reveals that, as Frederic\\\\'s nursemaid long ago, she made a mistake \\\"through being hard of hearing\\\": Mishearing Frederic\\\\'s father\\\\'s instructions, she apprenticed him to a pirate, instead of to a ship\\\\'s pilot (\\\"When Frederic was a little lad\\\").\\\\n\\\\nFrederic has never seen any woman other than Ruth, and he believes her to be beautiful. The pirates know better and suggest that Frederic take Ruth with him when he returns to civilisation. Frederic announces that, although it pains him, so strong is his sense of duty that, once free from his apprenticeship, he will be forced to devote himself to the pirates\\\\' extermination. He also points out that they are not successful pirates: since they are all orphans, they allow their prey to go free if they too are orphans. Frederic notes that word of this has got about, so captured ships\\\\' companies routinely claim to be orphans. Frederic invites the pirates to give up piracy and go with him, so that he need not destroy them, but the Pirate King says that, contrasted with respectability, piracy is comparatively honest (\\\"Oh! better far to live and die\\\"). The pirates depart, leaving Frederic and Ruth. Frederic sees a group of beautiful young girls approaching the pirate lair, and realises that Ruth misled him about her appearance (\\\"Oh false one! You have deceived me!\\\"). Sending Ruth away, Frederic hides before the girls arrive.\\\\n\\\\nThe girls burst exuberantly upon the secluded spot (\\\"Climbing over rocky mountain\\\"). Frederic reveals himself (\\\"Stop, ladies, pray!\\\"), startling them. He appeals to them to help him reform (\\\"Oh! is there not one maiden breast?\\\"). The girls are fascinated by him, but all reject him, except one: Mabel responds to his plea, chiding her sisters for their lack of charity (\\\"Oh sisters deaf to pity\\\\'s name for shame!\\\"). She offers Frederic her pity (\\\"Poor wand\\\\'ring one\\\"), and the two quickly fall in love. The other girls discuss whether to eavesdrop or to leave the new couple alone (\\\"What ought we to do?\\\"), deciding to \\\"talk about the weather,\\\" although they steal glances at the affectionate couple (\\\"How beautifully blue the sky\\\").\\\\n\\\\nFrederic warns the young ladies that his old associates will soon return (\\\"Stay, we must not lose our senses\\\"), but before they can flee, the pirates arrive and capture the girls, intending to marry them (\\\"Here\\\\'s a first rate opportunity\\\"). Mabel warns the pirates that the girls\\\\' father is a Major-General (\\\"Hold, monsters!\\\"), who soon arrives and introduces himself (\\\"I am the very model of a modern Major-General\\\"). He appeals to the pirates not to take his daughters, leaving him to face his old age alone. Having heard of the famous Pirates of Penzance, he pretends that he is an orphan to elicit their sympathy (\\\"Oh, men of dark and dismal fate\\\"). The soft-hearted pirates release the girls (\\\"Hail, Poetry!\\\"), making Major-General Stanley and his daughters honorary members of their band (\\\"Pray observe the magnanimity\\\").\\\\n\\\\nAct II\\\\nThe Major-General sits in a ruined chapel on his estate, surrounded by his daughters. His conscience is tortured by the lie that he told the pirates, and the girls attempt to console him (\\\"Oh dry the glist\\\\'ning tear\\\"). The Sergeant of Police and his corps arrive to announce their readiness to arrest the pirates (\\\"When the foeman bares his steel\\\"). The girls loudly express their admiration of the police for facing likely slaughter by fierce and merciless foes. The police are unnerved by this and leave reluctantly.\\\\n\\\\nLeft alone, Frederic, who is to lead the police, reflects on his opportunity to atone for a life of piracy (\\\"Now for the pirates\\\\' lair\\\"), at which point he encounters Ruth and the Pirate King. They have realised that Frederic\\\\'s apprenticeship was worded so as to bind him to them until his twenty-first birthday – and, because that birthday happens to be on the 29th of February (in a leap year), it means that technically only five birthdays have passed (\\\"When you had left our pirate fold\\\"), and he will not reach his twenty-first birthday until he is in his eighties. Frederic is convinced by this logic and agrees to rejoin the pirates. He then sees it as his duty to inform the Pirate King of the Major-General\\\\'s deception. The outraged outlaw declares that the pirates\\\\' \\\"revenge will be swift and terrible\\\" (\\\"Away, away, my heart\\\\'s on fire\\\").\\\\n\\\\nFrederic meets Mabel (\\\"All is prepared\\\"), and she pleads with him to stay (\\\"Stay Frederic, stay\\\"), but he feels bound by his duty to the pirates until his 21st birthday – in 1940. They agree to be faithful to each other until then, though to Mabel \\\"It seems so long\\\" (\\\"Oh, here is love, and here is truth\\\"); Frederic departs. Mabel steels herself (\\\"No, I\\\\'ll be brave\\\") and tells the police that they must go alone to face the pirates. They muse that an outlaw might be just like any other man, and it is a shame to deprive him of \\\"that liberty which is so dear to all\\\" (\\\"When a felon\\\\'s not engaged in his employment\\\"). The police hide on hearing the approach of the pirates (\\\"A rollicking band of pirates we\\\"), who have stolen onto the estate, intending to take revenge for the Major-General\\\\'s lie (\\\"With cat-like tread\\\").\\\\n\\\\nJust then, Major-General Stanley appears, sleepless with guilt, and the pirates also hide (\\\"Hush, hush! not a word\\\"), while the Major-General listens to the soothing breeze (\\\"Sighing softly to the river\\\"). The girls come looking for him. The pirates leap out to seize them, and the police rush to their defense; but the police are easily defeated, and the Pirate King urges the captured Major-General to prepare for death. The Sergeant has one stratagem left: he demands that the pirates yield \\\"in Queen Victoria\\\\'s name\\\"; the pirates, overcome with loyalty to their Queen, do so. Ruth appears and reveals that the pirates are \\\"all noblemen who have gone wrong\\\". The Major-General is impressed by this and all is forgiven. Frederic and Mabel are reunited, and the Major-General is happy to marry his daughters to the noble ex-pirates after all (\\\"Poor Wand\\\\'ring Ones\\\" (reprise)).\\\\n\\\\nMusical numbers\\\\n Overture (includes \\\"With cat-like tread\\\", \\\"Ah, leave me not to pine\\\", \\\"Pray observe the magnanimity\\\", \\\"When you had left our pirate fold\\\", \\\"Climbing over rocky mountain\\\", and \\\"How beautifully blue the sky\\\")\\\\n\\\\nAct I\\\\n\\\\n 1. \\\"Pour, oh pour, the pirate sherry\\\" (Samuel and Chorus of Pirates)\\\\n 2. \\\"When Fred\\\\'ric was a little lad\\\" (Ruth)\\\\n 3. \\\"Oh, better far to live and die\\\" (Pirate King and Chorus of Pirates)\\\\n 4. \\\"Oh! false one, you have deceiv\\\\'d me\\\" (Frederic and Ruth)\\\\n 5. \\\"Climbing over rocky mountain\\\" (Chorus of Girls)\\\\n 6. \\\"Stop, ladies, pray\\\" (Edith, Kate, Frederic, and Chorus of Girls)\\\\n 7. \\\"Oh, is there not one maiden breast?\\\" (Frederic and Chorus of Girls)\\\\n 8. \\\"Poor wand\\\\'ring one\\\" (Mabel and Chorus of Girls)\\\\n 9. \\\"What ought we to do?\\\" (Edith, Kate, and Chorus of Girls)\\\\n 10. \\\"How beautifully blue the sky\\\" (Mabel, Frederic, and Chorus of Girls)\\\\n 11. \\\"Stay, we must not lose our senses\\\" ... \\\"Here\\\\'s a first-rate opportunity to get married with impunity\\\" (Frederic and Chorus of Girls and Pirates)\\\\n 12. \\\"Hold, monsters\\\" (Mabel, Major-General, Samuel, and Chorus)\\\\n 13. \\\"I am the very model of a modern Major-General\\\" (Major-General and Chorus)\\\\n 14. Finale Act I (Mabel, Kate, Edith, Ruth, Frederic, Samuel, King, Major-General, and Chorus)\\\\n \\\"Oh, men of dark and dismal fate\\\"\\\\n \\\"I’m telling a terrible story\\\"\\\\n \\\"Hail, Poetry\\\"\\\\n \\\"Oh, happy day, with joyous glee\\\"\\\\n \\\"Pray observe the magnanimity\\\" (reprise of \\\"Here\\\\'s a first-rate opportunity\\\")\\\\n\\\\nAct II\\\\n 15. \\\"Oh, dry the glist\\\\'ning tear\\\" (Mabel and Chorus of Girls)\\\\n 16. \\\"Then, Frederic, let your escort lion-hearted\\\" (Frederic and Major-General)\\\\n 17. \\\"When the foeman bares his steel\\\" (Mabel, Edith, Sergeant, and Chorus of Policemen and Girls)\\\\n 18. \\\"Now for the pirates\\\\' lair!\\\" (Frederic, Ruth, and King)\\\\n 19. \\\"When you had left our pirate fold\\\" [The \\\"paradox\\\" trio] (Ruth, Frederic, and King)\\\\n 20. \\\"Away, away! My heart\\\\'s on fire!\\\" (Ruth, Frederic, and King)\\\\n 21. \\\"All is prepar\\\\'d; your gallant crew await you\\\" (Mabel and Frederic)\\\\n 22. \\\"Stay, Fred\\\\'ric, stay\\\" ... \\\"Ah, leave me not to pine\\\" ... \\\"Oh, here is love, and here is truth\\\" (Mabel and Frederic)\\\\n 23. \\\"No, I\\\\'ll be brave\\\" ... \\\"Though in body and in mind\\\" (Reprise of \\\"When the foeman bares his steel\\\") (Mabel, Sergeant, and Chorus of Police)\\\\n 23a. \\\"Sergeant, approach!\\\" (Mabel, Sergeant of Police, and Chorus of Police)\\\\n 24. \\\"When a felon\\\\'s not engaged in his employment\\\" (Sergeant and Chorus of Police)\\\\n 25. \\\"A rollicking band of pirates we\\\" (Sergeant and Chorus of Pirates and Police)\\\\n 26. \\\"With cat-like tread, upon our prey we steal\\\" (Samuel and Chorus of Pirates and Police)\\\\n 27. \\\"Hush, hush, not a word!\\\" (Frederic, King, Major-General, and Chorus of Police and Pirates)\\\\n 28. Finale, Act II (Ensemble)\\\\n \\\"Sighing softly to the river\\\"\\\\n \\\"Now what is this, and what is that?\\\"\\\\n \\\"You/We triumph now\\\"\\\\n \\\"Away with them, and place them at the bar!\\\"\\\\n \\\"Poor wandering ones!\\\"\\\\n\\\\nCritical reception\\\\nThe notices from critics were generally excellent in both New York and London in 1880. In New York, the Herald and the Tribune both dedicated considerable space to their reviews. The Herald took the view that \\\"the new work is in every respect superior to the Pinafore, the text more humorous, the music more elegant and more elaborate.\\\" The Tribune called it \\\"a brilliant and complete success\\\", commenting, \\\"The humor of the Pirates is richer, but more recondite. It demands a closer attention to the words [but] there are great stores of wit and drollery ... which will well repay exploration. ... The music is fresh, bright, elegant and merry, and much of it belongs to a higher order of art than the most popular of the tunes of Pinafore.\\\" The New York Times also praised the work, writing, \\\"it would be impossible for a confirmed misanthrope to refrain from merriment over it\\\", though the paper doubted if Pirates could repeat the prodigious success of Pinafore.\\\\n\\\\nAfter the London premiere, the critical consensus, led by the theatrical newspaper The Era, was that the new work marked a distinct advance on Gilbert and Sullivan\\\\'s earlier works. The Pall Mall Gazette said, \\\"Of Mr. Sullivan\\\\'s music we must speak in detail on some other occasion. Suffice it for the present to say that in the new style which he has marked out for himself it is the best he has written.\\\" The Graphic wrote:\\\\n\\\\nThere were a few dissenting comments: The Manchester Guardian thought both author and composer had drawn on the works of their predecessors: \\\"Mr. Gilbert ... seems to have borrowed an idea from Sheridan\\\\'s The Critic; Mr. Sullivan\\\\'s music is sprightly, tuneful and full of \\\\'go\\\\', although it is certainly lacking in originality.\\\" The Sporting Times noted, \\\"It doesn\\\\'t appear to have struck any of the critics yet that the central idea in The Pirates of Penzance is taken from Our Island Home, which was played by the German Reeds some ten years ago.\\\" The Times thought Gilbert\\\\'s wit outran his dramatic invention, and Sullivan\\\\'s music for the new work was not quite as good as his score for The Sorcerer, which the Times critic called a masterpiece.\\\\n\\\\nMusical analysis\\\\nThe overture to The Pirates of Penzance was composed by Sullivan and his musical assistant Alfred Cellier. It follows the pattern of most Savoy opera overtures: a lively opening (the melody of \\\"With cat-like tread\\\"), a slow middle section (\\\"Ah, leave me not to pine alone\\\"), and a concluding allegro in a compressed sonata form, in which the themes of \\\"How beautifully blue the sky\\\" and \\\"A paradox, a paradox\\\" are combined.\\\\n\\\\nParody\\\\nThe score parodies several composers, most conspicuously Verdi. \\\"Come, friends, who plough the sea\\\" and \\\"You triumph now\\\" are burlesques of Il trovatore, and one of the best-known choral passages from the finale to Act\\\\xa0I, \\\"Hail Poetry\\\", is, according to the Sullivan scholar, Arthur Jacobs, a burlesque of the prayer scene, \\\"La Vergine degli Angeli\\\", in Verdi\\\\'s La forza del destino. However, another musicologist, Nicholas Temperley, writes, \\\"The choral outburst \\\\'Hail, Poetry\\\\' in The Pirates of Penzance would need very little alteration to turn it into a Mozart string quartet.\\\" Another well-known parody number from the work is the song for coloratura, \\\"Poor wand\\\\'ring one\\\", which is generally thought to burlesque Gounod\\\\'s waltz-songs, though the music critic of The Times called it \\\"mock-Donizetti\\\". In a scene in Act\\\\xa0II, Mabel addresses the police, who chant their response in the manner of an Anglican church service.\\\\n\\\\nSullivan even managed to parody two composers at once. The critic Rodney Milnes describes the Major-General\\\\'s Act\\\\xa0II song, \\\"Sighing softly to the river\\\", \\\"as plainly inspired by – and indeed worthy of – Sullivan\\\\'s hero Schubert\\\", and Amanda Holden speaks of the song\\\\'s \\\"Schubertian water-rippling accompaniment\\\", but adds that it simultaneously spoofs Verdi\\\\'s Il trovatore, with the soloist unaware of a concealed male chorus singing behind him.\\\\n\\\\nPatter, counterpoint, and vocal writing\\\\n\\\\nWriting about patter songs, Shaw, in his capacity as a music critic, praised \\\"the time-honored lilt which Sir Arthur Sullivan, following the example of Mozart and Rossini, chose for the lists of accomplishments of the Major-General in The Pirates or the Colonel in Patience.\\\"\\\\n\\\\nThis opera contains two well-known examples of Sullivan\\\\'s characteristic combination of two seemingly disparate melodies. Jacobs suggests that Berlioz\\\\'s La damnation de Faust, a great favourite in Sullivan\\\\'s formative years, may have been the model for Sullivan\\\\'s trademark contrapuntal mingling of the rapid prattle of the women\\\\'s chorus in Act I (\\\"How beautifully blue the sky\\\") in 2/4 time with the lovers\\\\' duet in waltz time. Jacobs writes that \\\"the whole number [shifts] with Schubertian ease from B to G and back again.\\\" In Act II, a double chorus combines the policemen\\\\'s dogged tune, \\\"When the foeman bares his steel\\\" and the soaring line for the women, \\\"Go, ye heroes, go to glory\\\". In adapting the four-part chorus \\\"Climbing over rocky mountain\\\" from Thespis for re-use in Pirates, Sullivan took less trouble: he wrote only a single vocal line, suitable for soprano voices. Despite this, the number ends with another example of Sullivan\\\\'s counterpoint, with the chorus singing the second melody of the piece (\\\"Let us gaily tread the measure\\\") while the orchestra plays the first (\\\"Climbing over rocky mountain\\\").\\\\n\\\\nSullivan set a particular vocal challenge for the soprano who portrays Mabel. The Sullivan scholar Gervase Hughes wrote, \\\"Mabel ... must be a coloratura because of \\\\'Poor wand\\\\'ring one!\\\\', yet \\\\'Dear father, why leave your bed\\\\' demands steady beauty of tone throughout the octave F to F, and \\\\'Ah, leave me not to pine\\\\' goes a third lower still.\\\" In The Music of Arthur Sullivan (1959), Hughes quoted four extracts from Pirates, saying that if hearing each out of context one might attribute it to Schubert, Mendelssohn, Gounod or Bizet respectively, \\\"yet on learning the truth one would kick oneself for not having recognised Sullivan\\\\'s touch in all four.\\\" Hughes concluded by quoting the introductory bars of \\\"When a felon\\\\'s not engaged in his employment\\\", adding, \\\"There could never be any doubt as to who wrote that, and it is as English as our wonderful police themselves.\\\"\\\\n\\\\nVersions\\\\n\\\\nBecause the work was premiered in three different places (the Paignton performance and the full productions in New York and London), there are more variations in the early libretto and score of The Pirates of Penzance than in other Gilbert and Sullivan works. Songs sent from New York to the D\\\\'Oyly Carte touring company in England for the Paignton premiere were then altered or omitted during Broadway rehearsals. Gilbert and Sullivan trimmed the work for the London premiere, and Gilbert made further alterations up to and including the 1908 Savoy revival. For example, early versions depicted the Pirate King as the servant of the pirate band, and the words of the opening chorus were, \\\"Pour, O King, the pirate sherry\\\". In the original New York production the revelation by Ruth that the pirates are \\\"all noblemen who have gone wrong\\\" prompted the following exchange (recalling a famous passage in H.M.S. Pinafore):\\\\n\\\\nIn the original London production, this exchange was shortened to the following:\\\\n\\\\nGilbert deleted the exchange in the 1900 revival, and the Chappell vocal score was revised accordingly. For the 1908 revival Gilbert had the pirates yielding \\\"in good King Edward\\\\'s name\\\". Despite Helen Carte\\\\'s repeated urging, Gilbert did not prepare an authorised version of the libretti of the Savoy operas.\\\\n\\\\nIn its 1989 production, the D\\\\'Oyly Carte Opera Company restored one of the original versions of the finale, which finishes with a variation of \\\"I am the very model of a modern major-general\\\", rather than with the customary reprise of \\\"Poor wand\\\\'ring one\\\", but in later revivals, it reverted to the more familiar text.\\\\n\\\\nProduction history\\\\n\\\\nThe Pirates of Penzance has been one of Gilbert and Sullivan\\\\'s most popular comic operas. After its unique triple opening in 1879–80, it was revived in London at the Savoy Theatre in 1888 and in 1900, and for the Savoy\\\\'s repertory season of 1908–09. In the British provinces, the D\\\\'Oyly Carte Opera Company toured it almost continuously from 1880–1884, and again in 1888. It re-entered the D\\\\'Oyly Carte touring repertory in 1893 and was never again absent until the company\\\\'s closure in 1982. New costumes were designed by Percy Anderson in 1919 and George Sheringham in 1929 (who also executed a new Act I set). Peter Goffin created a new touring set in 1957.\\\\n\\\\nIn America, after the New York opening on New Year\\\\'s Eve, 1879, Richard D\\\\'Oyly Carte launched four companies that covered the United States on tours that lasted through the following summer. Gilbert and Sullivan themselves trained each of the touring companies through January and early February 1880, and each company\\\\'s first performance – whether it was in Philadelphia, Newark, or Buffalo – was conducted by the composer. In Australia, its first authorised performance was on 19 March 1881 at the Theatre Royal, Sydney, produced by J. C. Williamson. There was still no international copyright law in 1880, and the first unauthorised New York production was given by the Boston Ideal Opera Company at Booth\\\\'s Theatre in September of that year. The opera premiered in a German translation by Richard Genée and Camillo Walzel (Die Piraten) in Austria at the Theater an der Wien on 1 March 1889, and in Düsseldorf, Germany, on 1 December 1936.\\\\n\\\\nThe first non-D\\\\'Oyly Carte professional production in a country that had been subject to Gilbert\\\\'s copyright (other than Williamsons\\\\' authorised productions) was in Stratford, Ontario, Canada, in September 1961, as the copyright expired. In 1979, the Torbay branch of the Gilbert and Sullivan Society presented a centenary tribute to the world premiere performance of Pirates in Paignton, with a production at the Palace Avenue Theatre (situated a few metres from the former Bijou Theatre).\\\\n\\\\nNew York has seen over forty major revivals since the premiere. One of these, produced and directed by Winthrop Ames in 1926 at the Plymouth Theatre, ran for 128 performances and gained good notices. A brief 1952 Broadway staging starring Martyn Green, earned Lehman Engel a Tony Award as conductor. Repertory companies that have mounted Pirates numerous times Off-Broadway and on tour in the US have included the American Savoyards (1953–67), the Light Opera of Manhattan (1968–89) and the New York Gilbert and Sullivan Players (1976–present).\\\\n\\\\nAs discussed below, Joseph Papp\\\\'s 1980–83 Pirates ran for nearly two years each on Broadway and in the West End, boosting the opera\\\\'s popularity. Professional and amateur productions of the opera continue with frequency. For example, the Chicago Lyric Opera and English National Opera each staged the work in 2004, and in 2007, the New York City Opera and Opera Australia both mounted new productions. In 2013, Scottish Opera produced a British touring production of The Pirates of Penzance co-produced by the trustees of the D\\\\'Oyly Carte Opera Company. Richard Suart played Major-General Stanley and Nicholas Sharratt played Frederic.\\\\n\\\\nThe following table shows the history of the D\\\\'Oyly Carte productions in Gilbert\\\\'s lifetime (excluding tours):\\\\n\\\\nHistorical casting\\\\nThe following tables show the casts of the principal original productions and D\\\\'Oyly Carte Opera Company touring repertory at various times through to the company\\\\'s 1982 closure:\\\\n\\\\nJoseph Papp\\\\'s Pirates\\\\n\\\\nIn 1980, Joseph Papp and the Public Theater of New York City produced a new version of Pirates, directed by Wilford Leach and choreographed by Graciela Daniele, at the Delacorte Theatre in Central Park, as a Shakespeare in the Park summer event. Musical direction and arrangements were by William Elliott. The show played for 10 previews and 35 performances. It then transferred to Broadway, opening on 8 January 1981 for a run of 20 previews and 787 regular performances at the Uris and Minskoff Theatres, the longest run for any Gilbert and Sullivan production in history. This take on Pirates earned enthusiastic reviews and seven Tony Award nominations, winning three, including the award for Best Revival and for Leach as director. It was also nominated for eight Drama Desk Awards, winning five, including Outstanding Musical and director.\\\\n\\\\nCompared with traditional productions of the opera, Papp\\\\'s Pirates featured a more swashbuckling Pirate King and Frederic, and a broader, more musical comedy style of singing and humour. It did not significantly change the libretto, but it used a new orchestration and arrangements that changed some keys, added repeats, lengthened dance music and made other minor changes in the score. The \\\"Matter Patter\\\" trio from Ruddigore and \\\"Sorry her lot\\\" from H.M.S. Pinafore, two other Gilbert and Sullivan operas, were interpolated into the show. The production also restored Gilbert and Sullivan\\\\'s original New York ending, with a reprise of the Major-General\\\\'s song in the Act II finale. Linda Ronstadt starred as Mabel, Rex Smith as Frederic, Kevin Kline as the Pirate King, Patricia Routledge as Ruth (replaced by Estelle Parsons for the Broadway transfer), George Rose as the Major-General, and Tony Azito as the Sergeant of Police. Kline won a Tony Award for his performance. Smith won a Theatre World Award, and Kline and Azito won Drama Desk Awards. Notable replacements during the Broadway run included Karla DeVito, Maureen McGovern and Pam Dawber as Mabel; Robby Benson, Patrick Cassidy and Peter Noone as Frederic; Treat Williams, Gary Sandy, James Belushi and Wally Kurth as the Pirate King; David Garrison as the Sergeant; George S. Irving as the Major-General; and Kaye Ballard as Ruth. The Los Angeles cast of the production featured Barry Bostwick as the Pirate King, Jo Anne Worley as Ruth, Clive Revill as the Major-General, Dawber as Mabel, Paxton Whitehead as the Sergeant, Caroline Peyton as Edith and Andy Gibb as Frederic.\\\\n\\\\nThe production opened at the Theatre Royal, Drury Lane, London, on 26 May 1982, to generally warm reviews, for a run of 601 performances, earning an Olivier Award nomination as Outstanding Musical and another for Curry. Notable among the cast were George Cole and Ronald Fraser as the Major-General; Pamela Stephenson as Mabel; Michael Praed and Peter Noone as Frederic; Tim Curry, Timothy Bentinck, Oliver Tobias and Paul Nicholas as the Pirate King; Chris Langham as the Sergeant of Police; Annie Ross as Ruth; Bonnie Langford as Kate; and Louise Gold as Isabel. The Australian production opened in Melbourne in January 1984, opening the new Victorian Arts Centre, directed by John Feraro. It starred Jon English as the Pirate King, Simon Gallaher as Frederic, June Bronhill as Ruth, David Atkins as the Sergeant of Police and Marina Prior as Mabel. The six-week limited season was followed by an Australian national tour from 1984 to 1986 and another tour with same cast in the mid-1990s. In 1985, Papp\\\\'s Pirates opened the new Queensland Performing Arts Centre in Brisbane, setting attendance records that were not surpassed until many years later by The Phantom of the Opera. Gallaher\\\\'s Essgee Entertainment version of Pirates was inspired by the Papp version. The Papp version also inspired foreign-language productions in Germany and elsewhere in Europe.\\\\n\\\\nThe Papp production was turned into a film in 1983, with the original Broadway principal cast reprising their roles, except that Angela Lansbury replaced Estelle Parsons as Ruth. The minor roles used British actors miming to their Broadway counterparts. The film has been shown occasionally on television. Another film based loosely on the opera and inspired by the success of the Papp version, The Pirate Movie, was released during the Broadway run.\\\\n\\\\nThe Papp production design has been widely imitated in later productions of Pirates, even where traditional orchestration and the standard score are used. Some modern productions are also influenced by the Disney film franchise Pirates of the Caribbean, combining aspects of the Papp production with the Disney design concepts. Not all of these revivals have generated the same enthusiasm as Papp\\\\'s 1980s productions. A 1999 UK touring production received this critique: \\\"No doubt when Papp first staged this show in New York and London it had some quality of cheek or chutzpah or pizzazz or irony or something that accounted for its success. But all that\\\\'s left now ... is a crass Broadway-style musical arrangement ground out by a seven-piece band, and the worst kind of smutty send-up of a historic piece of art.\\\"\\\\n\\\\nRecordings\\\\nThe Pirates of Penzance has been recorded many times, and the critical consensus is that it has fared well on record. The first complete recording of the score was in 1921, under the direction of Rupert D\\\\'Oyly Carte, but with established recording singers rather than D\\\\'Oyly Carte Opera Company performers. In 1929, The Gramophone said of a new set with a mainly D\\\\'Oyly Carte cast, \\\"This new recording represents the high-water mark so far as Gilbert and Sullivan opera is concerned. In each of the previous Savoy albums there have been occasional lapses which prevented one from awarding them unqualified praise; but with the Pirates it is happily otherwise; from first to last, and in every bar, a simply delightful production.\\\" Of later recordings by the D\\\\'Oyly Carte Opera Company, the 1968 recording (with complete dialogue) is highly regarded: The online Gilbert and Sullivan Discography says, \\\"This recording is one of the best D\\\\'Oyly Carte sets of all time, and certainly the best Pirates\\\", and the Penguin Guide to Opera on Compact Disc also recommends it. So too does the Penguin Guide to Recorded Classical Music, alongside the 1993 Mackerras recording. The opera critic Alan Blyth recommended the D\\\\'Oyly Carte recording of 1990: \\\"a performance full of the kind of life that can only come from the experience of stage performances\\\". The online Discography site also mentions the 1981 Papp recording as \\\"excellent\\\", despite its inauthentic 1980 re-orchestrations that \\\"changed some of the timbres so as to appeal to a rock-oriented public\\\".\\\\n\\\\nOf the available commercial videos, the Discography site considers the Brent Walker better than the Papp version. More recent professional productions have been recorded on video by the International Gilbert and Sullivan Festival.\\\\n\\\\nSelected recordings\\\\n 1929 D\\\\'Oyly Carte – Conductor: Malcolm Sargent\\\\n 1957 D\\\\'Oyly Carte – New Symphony Orchestra of London; Conductor: Isidore Godfrey\\\\n 1961 Sargent/Glyndebourne – Pro Arte Orchestra, Glyndebourne Festival Chorus; Conductor: Sir Malcolm Sargent\\\\n 1968 D\\\\'Oyly Carte (with dialogue) – Royal Philharmonic Orchestra; Conductor: Isidore Godfrey\\\\n 1981; 1983 Papp\\\\'s Pirates (with dialogue) – Director: Wilford Leach; Musical Director: William Elliott; Choreographer: Graciela Daniele\\\\n 1982 Brent Walker Productions (with dialogue) – Ambrosian Opera Chorus, London Symphony Orchestra; Conductor: Alexander Faris; Stage Director: Michael Geliot\\\\n 1990 New D\\\\'Oyly Carte – Conductor: John Pryce-Jones\\\\n 1993 Mackerras/Telarc – Orchestra and Chorus of the Welsh National Opera; Conductor: Sir Charles Mackerras\\\\n 1994 Essgee Entertainment (video adaptation) – Director and Choreographer: Craig Schaefer; Orchestrator and Conductor: Kevin Hocking; Additional Lyrics: Melvyn Morrow\\\\n\\\\nCultural impact\\\\n\\\\nMajor-General\\\\'s Song\\\\n\\\\nPirates is one of the most frequently referenced works of Gilbert and Sullivan. The Major-General\\\\'s Song, in particular, is frequently parodied, pastiched and used in advertising. Parody versions have been used in political commentary as well as entertainment media. Its challenging patter has proved interesting to comedians; notable examples include Tom Lehrer\\\\'s song \\\"The Elements\\\" and David Hyde Pierce\\\\'s monologue, as host of Saturday Night Live. In 2010, comedian Ron Butler released a YouTube pastiche of the song in character as President Obama which, as of September 2021, had garnered more than 1.9 million views.\\\\n\\\\nPastiche examples include the Animaniacs version, \\\"I am the very model of a cartoon individual\\\", in the episode \\\"H.M.S. Yakko\\\"; the Doctor Who audio \\\"I am the very model of a Gallifreyan buccaneer\\\" in Doctor Who and the Pirates; the Studio 60 on the Sunset Strip version in the episode \\\"The Cold Open\\\" (2006), where the cast performs \\\"We\\\\'ll be the very model of a modern network TV show\\\"; and the Mass Effect 2 video game version, where the character Mordin Solus sings: \\\"I am the very model of a scientist Salarian\\\".\\\\n\\\\nThe song is often used in film and on television, unchanged in many instances, as a character\\\\'s audition piece, or seen in a \\\"school play\\\" scene. Examples include a VeggieTales episode entitled \\\"The Wonderful World of Auto-Tainment!\\\"; the Frasier episode \\\"Fathers and Sons\\\"; The Simpsons episode \\\"Deep Space Homer\\\"; and the Mad About You episode \\\"Moody Blues\\\", where Paul directs a charity production of Penzance starring his father, Burt, as the Major-General. In The Muppet Show (season 3, episode 4) guest host, comedian Gilda Radner, sings the song with a talking carrot (Parodying the pilot/pirate confusion in Pirates, Radner had requested a talking parrot, but was misheard). In an episode of Home Improvement, Al Borland begins to sing the song when tricked into thinking he is in a soundproof booth. In the Babylon 5 episode \\\"Atonement\\\", Marcus Cole uses the song to drive Dr Stephen Franklin crazy on a long journey to Mars.\\\\n\\\\nExamples of the use of the song in advertising include Martyn Green\\\\'s pastiche of the song listing all of the varieties of Campbell\\\\'s Soup and a 2011 Geico commercial in which a couple that wants to save money, but still listen to musicals, finds a roommate, dressed as the Major-General, who awkwardly begins the song while dancing on a coffee table. Gimbels department store had a campaign sung to the tune of the Major-General\\\\'s Song that began, \\\"We are the very model of a modern big department store.\\\" George Washington, in the number \\\"Right Hand Man\\\" from the 2015 musical Hamilton by Lin-Manuel Miranda, refers to himself with irony as \\\"The model of a modern major general\\\", which he rhymes with \\\"men are all\\\" and \\\"pedestal\\\". Miranda commented: \\\"I always felt like ‘mineral’ wasn\\\\'t the best possible rhyme.\\\"\\\\n\\\\nFilm and television\\\\nOther film references to Pirates include Kate & Leopold, where there are multiple references, including a scene where Leopold sings \\\"I Am The Very Model of A Modern Major-General\\\" while accompanying himself on the piano; and in Pretty Woman, Edward Lewis (Richard Gere) covers a social gaffe by prostitute Vivian Ward (Julia Roberts), who comments that the opera La traviata was so good that she almost \\\"peed [her] pants\\\", by saying that she had said that she liked it better than The Pirates of Penzance\\\". In Walt Disney\\\\'s cartoon Mickey, Donald, Goofy: The Three Musketeers (2004), there is a performance of Pirates that becomes the setting for the climactic battle between the Musketeers and Captain Pete. Pirates songs sung in the cartoon are \\\"With cat-like tread\\\", \\\"Poor wand\\\\'ring one\\\", \\\"Climbing over rocky mountain\\\" and the Major-General\\\\'s song. \\\"Poor wand\\\\'ring one\\\" was used in the movie An American Tail. The soundtrack of the 1992 film The Hand That Rocks the Cradle includes \\\"Poor Wand\\\\'ring One\\\" and \\\"Oh Dry the Glistening Tear\\\".\\\\n\\\\nTelevision references, in addition to those mentioned above, included the series The West Wing, where Pirates and other Gilbert and Sullivan operas are mentioned in several episodes, especially by Deputy Communications Director, Sam Seaborn, who was recording secretary of his school\\\\'s Gilbert and Sullivan society. In Studio 60 on the Sunset Strip, a poster from Pirates hangs on Matt Albie\\\\'s office wall. Both TV series were created by Aaron Sorkin. In the pilot episode of the 2008 CTV series Flashpoint, a police officer and his partner sing the policeman\\\\'s song. In an Assy McGee episode entitled \\\"Pegfinger\\\", Detective Sanchez\\\\'s wife is a member of a community theatre that performs the opera. In a 1986 episode of the animated television adaptation of The Wind in the Willows entitled A Producer\\\\'s Lot, several characters put on a production of Pirates. In a 2005 Family Guy episode \\\"Peter\\\\'s Got Woods\\\", Brian Griffin sings \\\"Sighing Softly\\\", with Peter Griffin\\\\'s assistance. In a 2012 episode, \\\"Killer Queen\\\", Peter gives a garbled rendition of the Major-General\\\\'s Song. In the 2009 Criminal Minds episode \\\"The Slave of Duty\\\", Hotch quotes \\\"Oh dry the glist\\\\'ning tear\\\". In the 1992 episode \\\"The Understudy\\\" of Clarissa Explains it All, the title character is chosen to understudy Mabel in a school production of Pirates and is unprepared when she must go on; a scene from The Mikado is also heard.\\\\n\\\\nOther references\\\\n\\\\nOther notable instances of references to Pirates include a New York Times article on 29 February 1940, memorialising that Frederic was finally out of his indentures. Six years previously, the arms granted to the municipal borough of Penzance in 1934 contain a pirate dressed in Gilbert\\\\'s original costuming, and Penzance had a rugby team called the Penzance Pirates, which is now called the Cornish Pirates. In 1980, Isaac Asimov wrote a short story called \\\"The Year of the Action\\\", concerning whether the action of Pirates took place on 1 March 1873, or 1 March 1877 (depending on whether Gilbert took into account the fact that 1900 was not a leap year). The plot of Laurie R. King\\\\'s 2011 novel Pirate King centers on a 1924 silent movie adaptation of The Pirates of Penzance.\\\\n\\\\nThe music from the chorus of \\\"With cat-like tread\\\", which begins \\\"Come, friends, who plough the sea,\\\" was used in the popular American song, \\\"Hail, Hail, the Gang\\\\'s All Here.\\\" \\\"With cat-like tread\\\" is also part of the soundtrack, along with other Gilbert and Sullivan songs, in the 1981 film, Chariots of Fire, and it was pastiched in the \\\"HMS Yakko\\\" episode of Animaniacs in a song about surfing a whale.\\\\n\\\\nAdaptations\\\\nStage\\\\n Di Yam Gazlonim, a Yiddish adaptation of Pirates by Al Grand that continues to be performed in North America. The 2006 production at the National Yiddish Theater Folksbiene was nominated for the 2007 Drama Desk Award for Outstanding Revival. The Montreal Express wrote in 2009, \\\"Grand\\\\'s adaptation is a delightfully whimsical treatment\\\".\\\\n The Parson\\\\'s Pirates by Opera della Luna premiered in 1995.\\\\n Pirates! Or, Gilbert and Sullivan Plunder\\\\'d (2006), is a musical comedy set on a Caribbean island, involving a voodoo curse that makes the pirates \\\"landsick\\\". It was first presented 1 November 2006 at Goodspeed Opera House in East Haddam, Connecticut, then in 2007 at the Paper Mill Playhouse in Millburn, New Jersey, in 2009 at the Huntington Theatre Company in Boston, Massachusetts, and at The Muny in St Louis, Missouri in 2012. Other Gilbert and Sullivan numbers, such as the Nightmare song from Iolanthe are interpolated.\\\\n Pirates of Penzance – The Ballet! premiered in 1991\\\\n Essgee Entertainment produced an adapted version in 1994 in Australia and New Zealand. Their producer, Simon Gallaher (Frederic in the Australian Papp production), produced another adaptation of Pirates that toured Australia from 2001 to 2003\\\\n All-male versions of the opera include a long-running adaptation by Sasha Regan at the Union Theatre in 2009, which transferred to Wilton\\\\'s Music Hall in London in 2010 and toured in Australia in 2012.\\\\n\\\\nFilm and TV\\\\n The Pirate Movie, a 1982 musical romantic comedy film loosely based on the opera.\\\\n The Pirates of Penzance, a 1983 film adaptation of Papp\\\\'s Broadway production.\\\\n Die Piraten, a German-language version, was premiered on German television in 1968 and starred Arleen Auger as Mabel, Gerd Nienstedt as the Pirate King and Martha Mödl as Ruth, with Franz Marszalek conducting. Mabel falls in love with the Pirate King, among other plot changes. A 2-CD set of the broadcast was issued by Gala Records in 2000.\\\\n Several other television adaptations of the opera have been made, beginning in 1939.\\\\n\\\\nSee also\\\\n Our Island Home, one of the sources of the libretto for Pirates\\\\n\\\\nReferences\\\\n\\\\nSources\\\\n \\\\n \\\\n \\\\n (Chapters 5 and 6)\\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n \\\\n Also, five supplements, privately printed\\\\n\\\\nExternal links\\\\n\\\\nGeneral\\\\n The Pirates of Penzance at The Gilbert & Sullivan Archive\\\\n Sullivan\\\\'s autograph manuscript, 1879\\\\n 1880 London theatre programme\\\\n Review of the opening night by Clement Scott\\\\n Papp\\\\'s version of The Pirates of Penzance at the Music Theatre International website\\\\n D\\\\'Oyly Carte Prompt Books at The Victoria and Albert Museum\\\\n Televised scenes from Pirates, D\\\\'Oyly Carte Opera Company, 1955\\\\n \\\\n\\\\nLists of productions\\\\n The Pirates of Penzance. Production list at Floormic.com\\\\n The Pirates of Penzance at The Internet Broadway Database\\\\n The Pirates of Penzance at IMDb\\\\n\\\\n1879 operas\\\\nCornwall in fiction\\\\nDrama Desk Award-winning musicals\\\\nEnglish comic operas\\\\nEnglish-language operas\\\\nNautical fiction\\\\nOperas adapted into films\\\\nOperas by Gilbert and Sullivan\\\\nOperas set in England\\\\nOperas\\\\nPenzance\\\\nPiracy in fiction\\\\nTony Award-winning musicals'},\\n\",\n \" {'docid': 'doc-en-10',\\n\",\n \" 'text': 'Follies is a musical with music and lyrics by Stephen Sondheim and a book by James Goldman.\\\\n\\\\nThe story concerns a reunion in a crumbling Broadway theater, scheduled for demolition, of the past performers of the \\\"Weismann\\\\'s Follies\\\", a musical revue (based on the Ziegfeld Follies), that played in that theater between the world wars. It focuses on two couples, Buddy and Sally Durant Plummer and Benjamin and Phyllis Rogers Stone, who are attending the reunion. Sally and Phyllis were showgirls in the Follies. Both couples are deeply unhappy with their marriages. Buddy, a traveling salesman, is having an affair with a girl on the road; Sally is still as much in love with Ben as she was years ago; and Ben is so self-absorbed that Phyllis feels emotionally abandoned. Several of the former showgirls perform their old numbers, sometimes accompanied by the ghosts of their former selves. The musical numbers in the show have been interpreted as pastiches of the styles of the leading Broadway composers of the 1920s and 1930s, and sometimes as parodies of specific songs.\\\\n\\\\nThe Broadway production opened on April 4, 1971, directed by Harold Prince and Michael Bennett, and with choreography by Bennett. The musical was nominated for 11 Tony Awards and won seven. The original production, the second-most costly performed on Broadway to that date, ran for over 500 performances but ultimately lost its entire investment. The musical has had a number of major revivals, and several of its songs have become standards, including \\\"Broadway Baby\\\", \\\"I\\\\'m Still Here\\\", \\\"Too Many Mornings\\\", \\\"Could I Leave You?\\\", and \\\"Losing My Mind\\\".\\\\n\\\\nBackground\\\\nAfter the failure of Do I Hear a Waltz? (1965), for which he had written the lyrics to Richard Rodgers\\\\'s music, Sondheim decided that he would henceforth work only on projects where he could write both the music and lyrics himself. He asked author and playwright James Goldman to join him as bookwriter for a new musical. Inspired by a New York Times article about a gathering of former showgirls from the Ziegfeld Follies, they decided upon a story about ex-showgirls.\\\\n\\\\nOriginally titled The Girls Upstairs, the musical was to be produced by David Merrick and Leland Hayward in late 1967, but the plans ultimately fell through, and Stuart Ostrow became the producer, with Joseph Hardy as director. These plans also did not work out, and finally Harold Prince, who had worked previously with Sondheim, became the producer and director. He had agreed to work on The Girls Upstairs if Sondheim agreed to work on Company; Michael Bennett, the young choreographer of Company, was also brought onto the project. It was Prince who changed the title to Follies; he was \\\"intrigued by the psychology of a reunion of old chorus dancers and loved the play on the word \\\\'follies.\\\\n\\\\nPlot\\\\nIn 1971, on the soon-to-be-demolished stage of the Weismann Theatre, a reunion is being held to honor the Weismann\\\\'s Follies shows past and the beautiful chorus girls who performed there every year between the two world wars. The once resplendent theater is now little but planks and scaffolding (\\\"Prologue\\\"/\\\"Overture\\\"). As the ghosts of the young showgirls slowly drift through the theater, a majordomo enters with his entourage of waiters and waitresses. They pass through the spectral showgirls without seeing them.\\\\n\\\\nSally Durant Plummer, \\\"blond, petite, sweet-faced\\\" and at 49 \\\"still remarkably like the girl she was thirty years ago\\\", a former Weismann girl, is the first guest to arrive, and her ghostly youthful counterpart moves towards her. Phyllis Rogers Stone, a stylish and elegant woman, arrives with her husband Ben, a renowned philanthropist and politician. As their younger counterparts approach them, Phyllis comments to Ben about their past. He feigns a lack of interest; there is an underlying tension in their relationship. As more guests arrive, Sally\\\\'s husband, Buddy, enters. He is a salesman, in his early 50s, appealing and lively, whose smiles cover inner disappointment.\\\\n\\\\nFinally, Weismann enters to greet his guests. Roscoe, the old master of ceremonies, introduces the former showgirls (\\\"Beautiful Girls\\\"). Former Weismann performers at the reunion include Max and Stella Deems, who lost their radio jobs and became store owners in Miami; Solange La Fitte, a coquette, who is vibrant and flirtatious even at 66; Hattie Walker, who has outlived five younger husbands; Vincent and Vanessa, former dancers who now own an Arthur Murray franchise; Heidi Schiller, for whom Franz Lehár once wrote a waltz (\\\"or was it Oscar Straus?\\\" Facts never interest her; what matters is the song!); and Carlotta Campion, a film star who has embraced life and benefited from every experience.\\\\n\\\\nAs the guests reminisce, the stories of Ben, Phyllis, Buddy, and Sally unfold. Phyllis and Sally were roommates while in the Follies, and Ben and Buddy were best friends at school in New York. When Sally sees Ben, her former lover, she greets him self-consciously (\\\"Don\\\\'t Look at Me\\\"). Buddy and Phyllis join their spouses and the foursome reminisces about the old days of their courtship and the theater, their memories vividly coming to life in the apparitions of their young counterparts (\\\"Waiting For The Girls Upstairs\\\"). Each of the four is shaken at the realization of how life has changed them. Elsewhere, Willy Wheeler (portly, in his sixties) cartwheels for a photographer. Emily and Theodore Whitman, ex-vaudevillians in their seventies, perform an old routine (\\\"The Rain on the Roof\\\"). Solange proves she is still fashionable at what she claims is 66 (\\\"Ah, Paris!\\\"), and Hattie Walker performs her old showstopping number (\\\"Broadway Baby\\\").\\\\n\\\\nBuddy warns Phyllis that Sally is still in love with Ben, and she is shaken by how the past threatens to repeat itself. Sally is awed by Ben\\\\'s apparently glamorous life, but Ben wonders if he made the right choices and considers how things might have been (\\\"The Road You Didn\\\\'t Take\\\"). Sally tells Ben how her days have been spent with Buddy, trying to convince him (and herself) (\\\"In Buddy\\\\'s Eyes\\\"). However, it is clear that Sally is still in love with Ben – even though their affair ended badly when Ben decided to marry Phyllis. She shakes loose from the memory and begins to dance with Ben, who is touched by the memory of the Sally he once cast aside.\\\\n\\\\nPhyllis interrupts this tender moment and has a biting encounter with Sally. Before she has a chance to really let loose, they are both called on to participate in another performance – Stella Deems and the ex-chorines line up to perform an old number (\\\"Who\\\\'s That Woman?\\\"), as they are mirrored by their younger selves. Afterwards, Phyllis and Ben angrily discuss their lives and relationship, which has become numb and emotionless. Sally is bitter and has never been happy with Buddy, although he has always adored her. She accuses him of having affairs while he is on the road, and he admits he has a steady girlfriend, Margie, in another town, but always returns home. Carlotta amuses a throng of admirers with a tale of how her dramatic solo was cut from the Follies because the audience found it humorous, transforming it as she sings it into a toast to her own hard-won survival (\\\"I\\\\'m Still Here\\\").\\\\n\\\\nBen confides to Sally that his life is empty. She yearns for him to hold her, but young Sally slips between them and the three move together (\\\"Too Many Mornings\\\"). Ben, caught in the passion of memories, kisses Sally as Buddy watches from the shadows. Sally thinks this is a sign that the two will finally get married, and Ben is about to protest until Sally interrupts him with a kiss and runs off to gather her things, thinking that the two will leave together. Buddy leaves the shadows furious, and fantasizes about the girl he should have married, Margie, who loves him and makes him feel like \\\"a somebody\\\", but bitterly concludes he does not love her back (\\\"The Right Girl\\\"). He tells Sally that he\\\\'s done, but she is lost in a fantasy world and tells him that Ben has asked her to marry him. Buddy tells her she must be either crazy or drunk, but he\\\\'s already supported Sally through rehab clinics and mental hospitals and cannot take any more. Ben drunkenly propositions Carlotta, with whom he once had a fling, but she has a young lover and coolly turns him down. Heidi Schiller, joined by her younger counterpart, performs \\\"One More Kiss\\\", her aged voice a stark contrast to the sparkling coloratura of her younger self. Phyllis kisses a waiter and confesses to him that she had always wanted a son. She then tells Ben that their marriage can\\\\'t continue the way it has been. Ben replies by saying that he wants a divorce, and Phyllis assumes the request is due to his love for Sally. Ben denies this, but still wants Phyllis out. Angry and hurt, Phyllis considers whether to grant his request (\\\"Could I Leave You?\\\").\\\\n\\\\nPhyllis begins wondering at her younger self, who worked so hard to become the socialite that Ben needed. Ben yells at his younger self for not appreciating all the work that Phyllis did. Both Buddys enter to confront the Bens about how they stole Sally. Sally and her younger self enter and Ben firmly tells Sally that he never loved her. All the voices begin speaking and yelling at each other. Suddenly, at the peak of madness and confusion, the couples are engulfed by their follies, which transform the rundown theater into a fantastical \\\"Loveland\\\", an extravaganza even more grand and opulent than the gaudiest Weismann confection: \\\"the place where lovers are always young and beautiful, and everyone lives only for love\\\". Sally, Phyllis, Ben, and Buddy show their \\\"real and emotional lives\\\" in \\\"a sort of group nervous breakdown\\\".\\\\n\\\\nWhat follows is a series of musical numbers performed by the principal characters, each exploring their biggest desires. The two younger couples sing in a counterpoint of their hopes for the future (\\\"You\\\\'re Gonna Love Tomorrow/Love Will See Us Through\\\"). Buddy then appears, dressed in \\\"plaid baggy pants, garish jacket, and a shiny derby hat\\\", and performs a high-energy vaudeville routine depicting how he is caught between his love for Sally and Margie\\\\'s love for him (\\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\"). Sally appears next, dressed as a torch singer, singing of her passion for Ben from the past - and her obsession with him now (\\\"Losing My Mind\\\"). In a jazzy dance number, accompanied by a squadron of chorus boys, Phyllis reflects on the two sides of her personality, one naive and passionate and the other jaded and sophisticated and her desire to combine them (\\\"The Story of Lucy and Jessie\\\"). Resplendent in top hat and tails, Ben begins to offer his devil-may-care philosophy (\\\"Live, Laugh, Love\\\"), but stumbles and anxiously calls to the conductor for the lyrics, as he frantically tries to keep going. Ben becomes frenzied, while the dancing ensemble continues as if nothing was wrong. Amidst a deafening discord, Ben screams at all the figures from his past and collapses as he cries out for Phyllis.\\\\n\\\\n\\\"Loveland\\\" has dissolved back into the reality of the crumbling and half-demolished theater; dawn is approaching. Ben admits to Phyllis his admiration for her, and Phyllis shushes him and helps Ben regain his dignity before they leave. After exiting, Buddy escorts the emotionally devastated Sally back to their hotel with the promise to work things out later. Their ghostly younger selves appear, watching them go. The younger Ben and Buddy softly call to their \\\"girls upstairs\\\", and the Follies end.\\\\n\\\\nSongs\\\\nSource: Follies score\\\\n \\\"Prologue\\\" – Orchestra\\\\n \\\"Overture\\\" – Orchestra\\\\n \\\"Beautiful Girls\\\" – Roscoe and Company\\\\n \\\"Don\\\\'t Look at Me\\\" – Sally and Ben\\\\n \\\"Waiting for the Girls Upstairs\\\" – Ben, Sally, Phyllis and Buddy, Young Ben, Young Sally, Young Phyllis and Young Buddy\\\\n \\\"Montage\\\" (\\\"Rain on the Roof\\\"/\\\"Ah, Paris!\\\"/\\\"\\\") – Emily, Theodore, Solange, and Hattie\\\\n \\\"The Road You Didn\\\\'t Take\\\" – Ben\\\\n \\\"Bolero d\\\\'Amour\\\" – Danced by Vincent and Vanessa ≠≠\\\\n \\\"In Buddy\\\\'s Eyes\\\" – Sally\\\\n \\\"Who\\\\'s That Woman?\\\" – Stella and Company\\\\n \\\"I\\\\'m Still Here\\\" – Carlotta\\\\n \\\"Too Many Mornings\\\" – Ben and Sally\\\\n \\\"The Right Girl\\\" – Buddy\\\\n \\\"One More Kiss\\\" – Heidi and Young Heidi\\\\n \\\"Could I Leave You?\\\" – Phyllis\\\\n \\\"Loveland\\\" – Company\\\\n \\\"You\\\\'re Gonna Love Tomorrow\\\" / \\\"Love Will See Us Through\\\" – Young Ben, Young Sally, Young Phyllis and Young Buddy\\\\n \\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\" – Buddy, \\\"Margie\\\", \\\"Sally\\\"\\\\n \\\"Losing My Mind\\\" – Sally\\\\n \\\"The Story of Lucy and Jessie\\\" ≠ – Phyllis and backup male dancers\\\\n \\\"Live, Laugh, Love\\\" – Ben and Company\\\\n \\\"Chaos\\\" – Ben and Company\\\\n \\\"Finale\\\" – Young Buddy and Young Ben\\\\n≠ Some productions substitute \\\"Ah, but Underneath\\\" when the actress portraying Phyllis is not primarily a dancer.\\\\n\\\\n≠≠ Omitted from some productions\\\\n\\\\nNote: This is the song list from the original Broadway production in 1971. Variations are discussed in Versions.\\\\n\\\\nSongs cut before the Broadway premiere include \\\"All Things Bright and Beautiful\\\" (used in the prologue), \\\"Can That Boy Foxtrot!\\\", \\\"Who Could Be Blue?\\\", \\\"Little White House\\\", \\\"So Many People\\\", \\\"It Wasn\\\\'t Meant to Happen\\\", \\\"Pleasant Little Kingdom\\\", and \\\"Uptown Downtown\\\". The musical numbers \\\"Ah, but Underneath\\\" (replacing \\\"The Story of Lucy and Jessie\\\"), \\\"Country House\\\", \\\"Make the Most of Your Music\\\" (replacing \\\"Live, Laugh, Love\\\"), \\\"Social Dancing\\\" and a new version of \\\"Loveland\\\" have been incorporated into various productions.\\\\n\\\\nAnalysis\\\\nHal Prince said: \\\"Follies examines obsessive behavior, neurosis and self-indulgence more microscopically than anything I know of.\\\" Bernadette Peters quoted Sondheim on the character of \\\"Sally\\\": \\\"He said early on that [Sally] is off-balance, to put it mildly. He thinks she\\\\'s very neurotic, and she is very neurotic, so he said to me \\\\'Congratulations. She\\\\'s crazy. Martin Gottfried wrote: \\\"The concept behind Follies is theatre nostalgia, representing the rose-colored glasses through which we face the fact of age\\\\xa0... the show is conceived in ghostliness. At its very start, ghosts of Follies showgirls stalk the stage, mythic giants in winged, feathered, black and white opulence. Similarly, ghosts of the Twenties shows slip through the evening as the characters try desperately to regain their youth through re-creations of their performances and inane theatre sentiments of their past.\\\"\\\\n\\\\nJoanne Gordon, author and chair and artistic director, Theatre, at California State University, Long Beach, wrote \\\"Follies is in part an affectionate look at the American musical theatre between the two World Wars and provides Sondheim with an opportunity to use the traditional conventions of the genre to reveal the hollowness and falsity of his characters\\\\' dreams and illusions. The emotional high generated by the reunion of the Follies girls ultimately gives way to anger, disappointment, and weary resignation to reality.\\\" \\\"Follies contains two scores: the Follies pastiche numbers and the book numbers.\\\" Some of the Follies numbers imitate the style of particular composers of the early 20th century: \\\"Losing My Mind\\\" is in the style of a George Gershwin ballad \\\"The Man I Love\\\". Sondheim noted that the song \\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\" is \\\"another generic pastiche: vaudeville music for chases and low comics, but with a patter lyric\\\\xa0... I tried to give it the sardonic knowingness of Lorenz Hart or Frank Loesser.\\\"\\\\n\\\\n\\\"Loveland\\\", the final musical sequence, (that \\\"consumed the last half-hour of the original\\\" production) is akin to an imaginary 1941 Ziegfeld Follies sequence, with Sally, Phyllis, Ben and Buddy performing \\\"like comics and torch singers from a Broadway of yore.\\\" \\\"Loveland\\\" features a string of vaudeville-style numbers, reflecting the leading characters\\\\' emotional problems, before returning to the theater for the end of the reunion party. The four characters are \\\"whisked into a dream show in which each acts out his or her own principal \\\\'folly.\\\\n\\\\nVersions\\\\nGoldman continued to revise the book of the musical right up to his death, which occurred shortly before the 1998 Paper Mill Playhouse production. Sondheim, too, has added and removed songs that he judged to be problematic in various productions. Ted Chapin explains: \\\"Today, Follies is rarely performed twice in exactly the same version. James Goldman\\\\'s widow made the observation that the show has morphed throughout its entire life\\\\xa0... The London production had new songs and dialogue. The Paper Mill Playhouse production used some elements from London but stayed close to the original. The 2001 Roundabout Broadway revival, the first major production following Goldman\\\\'s death in 1998, was again a combination of previous versions.\\\"\\\\n\\\\nMajor changes were made for the original production in London, which attempted to establish a lighter tone and favored a happier ending than the original Broadway production. According to Joanne Gordon, \\\"When Follies opened in London\\\\xa0... it had an entirely different, and significantly more optimistic, tone. Goldman\\\\'s revised book offered some small improvements over the original.\\\"\\\\n\\\\nAccording to Sondheim, the producer Cameron Mackintosh asked for changes for the 1987 London production. \\\"I was reluctantly happy to comply, my only serious balk being at his request that I cut \\\"The Road You Didn\\\\'t Take\\\"\\\\xa0... I saw no reason not to try new things, knowing we could always revert to the original (which we eventually did). The net result was four new songs\\\\xa0... For reasons which I\\\\'ve forgotten, I rewrote \\\"Loveland\\\" for the London production. There were only four showgirls in this version, and each one carried a shepherd\\\\'s crook with a letter of the alphabet on it.\\\"\\\\n\\\\nThe musical was written in one act, and the original director, Prince, did not want an intermission, while the co-director, Bennett, wanted two acts. It originally was performed in one act. The 1987 West End, 2005 Barrington Stage Company, the 2001 Broadway revival and Kennedy Center 2011 productions were performed in two acts. However, August 23, 2011, Broadway preview performance was performed without an intermission. By opening, the 2011 Broadway revival was performed with the intermission, in two acts. The 2017 National Theatre production is performed without an interval.\\\\n\\\\nProductions\\\\n\\\\n1971 original Broadway\\\\nFollies had its pre-Broadway tryout at the Colonial Theatre, Boston, from February 20 through March 20, 1971.\\\\n\\\\nFollies premiered on Broadway on April 4, 1971, at the Winter Garden Theatre. It was directed by Harold Prince and Michael Bennett, with choreography by Bennett, scenic design by Boris Aronson, costumes by Florence Klotz, and lighting by Tharon Musser. It starred Alexis Smith (Phyllis), John McMartin (Ben), Dorothy Collins (Sally), Gene Nelson (Buddy), along with several veterans of the Broadway and vaudeville stage. The supporting role of Carlotta was created by Yvonne De Carlo and usually is given to a well-known veteran performer who can belt out a song. Other notable performers in the original productions were Fifi D\\\\'Orsay as Solange LaFitte, Justine Johnston as Heidi Schiller, Mary McCarty as Stella Deems, Arnold Moss as Dimitri Weismann, Ethel Shutta as Hattie Walker, and Marcie Stringer and Charles Welch as Emily and Theodore Whitman.\\\\n\\\\nThe show closed on July 1, 1972, after 522 performances and 12 previews. According to Variety, the production was a \\\"total financial failure, with a cumulative loss of $792,000.\\\" Prince planned to present the musical on the West Coast and then on a national tour. However, the show did not do well in its Los Angeles engagement and plans for a tour ended.\\\\n\\\\nFrank Rich, for many years the chief drama critic for The New York Times, had first garnered attention, while an undergraduate at Harvard University, with a lengthy essay for the Harvard Crimson about the show, which he had seen during its pre-Broadway run in Boston. He predicted that the show eventually would achieve recognition as a Broadway classic. Rich later wrote that audiences at the original production were baffled and restless.\\\\n\\\\nFor commercial reasons, the cast album was cut from two LPs to one early in production. Most songs were therefore heavily abridged and several were left entirely unrecorded. According to Craig Zadan, \\\"It\\\\'s generally felt that\\\\xa0... Prince made a mistake by giving the recording rights of Follies to Capitol Records, which in order to squeeze the unusually long score onto one disc, mutilated the songs by condensing some and omitting others.\\\" Chapin confirms this: \\\"Alas\\\\xa0... final word came from Capitol that they would not go for two records\\\\xa0... [Dick Jones] now had to propose cuts throughout the score in consultation with Steve.\\\" \\\"One More Kiss\\\" was omitted from the final release but was restored for CD release. Chapin relates that \\\"there was one song that Dick Jones [producer of the cast album] didn\\\\'t want to include on the album but which Steve Sondheim most definitely did. The song was \\\"One More Kiss\\\", and the compromise was that if there was time, it would be recorded, even if Jones couldn\\\\'t promise it would end up on the album. (It did get recorded but didn\\\\'t make its way onto the album until the CD reissue years later.)\\\"\\\\n\\\\n1972 Los Angeles\\\\nThe musical was produced at The Muny, St. Louis, Missouri in July 1972 and then transferred to the Shubert Theatre, Century City, California, running from July 22, 1972, through October 1, 1972. It was directed by Prince and starred Dorothy Collins (Sally; replaced by Janet Blair), Alexis Smith (Phyllis), John McMartin (Ben; replaced by Edward Winter), Gene Nelson (Buddy), and Yvonne De Carlo (Carlotta) reprising their original roles. The production was the premiere attraction at the newly constructed 1,800-seat theater, which, coincidentally, was itself razed thirty years later (in 2002, in order to build a new office building), thus mirroring the Follies plot line upon which the musical is based.\\\\n\\\\n1985 Wythenshawe and Lincoln Center\\\\nA full production ran at the Forum Theatre, Wythenshawe, England, from April 30, 1985, directed by Howard Lloyd-Lewis, design by Chris Kinman, costumes by Charles Cusick-Smith, lighting by Tim Wratten, musical direction by Simon Lowe, and choreographed by Paul Kerryson. The cast included Mary Millar (Sally Durant Plummer), Liz Izen (Young Sally), Meg Johnson (Stella Deems), Les Want (Max Deems), Betty Benfield (Heidi Schiller), Joseph Powell (Roscoe), Chili Bouchier (Hattie Walker), Shirley Greenwood (Emily Whitman), Bryan Burdon (Theodore Whitman), Monica Dell (Solange LaFitte), Jeannie Harris (Carlotta Campion), Josephine Blake (Phyllis Rogers Stone), Kevin Colson (Ben), Debbie Snook (Young Phyllis), Stephen Hale (Young Ben), Bill Bradley (Buddy Plummer), Paul Burton (Young Buddy), David Scase (Dimitri Weismann), Mitch Sebastian (Young Vincent), Kim Ismay (Young Vanessa), Lorraine Croft (Young Stella), and Meryl Richardson (Young Heidi).\\\\n\\\\nA staged concert at Avery Fisher Hall, Lincoln Center, was performed on September 6 and 7, 1985. The concert starred Barbara Cook (Sally), George Hearn (Ben), Mandy Patinkin (Buddy), and Lee Remick (Phyllis), and featured Carol Burnett (Carlotta), Betty Comden (Emily), Adolph Green (Theodore), Liliane Montevecchi (Solange LaFitte), Elaine Stritch (Hattie Walker), Phyllis Newman (Stella Deems), Jim Walton (Young Buddy), Howard McGillin (Young Ben), Liz Callaway (Young Sally), Daisy Prince (Young Phyllis), Andre Gregory (Dmitri), Arthur Rubin (Roscoe), and Licia Albanese (Heidi Schiller). Rich, in his review, noted that \\\"As performed at Avery Fisher Hall, the score emerged as an original whole, in which the \\\\'modern\\\\' music and mock vintage tunes constantly comment on each other, much as the script\\\\'s action unfolds simultaneously in 1971 (the year of the reunion) and 1941 (the year the Follies disbanded).\\\"\\\\n\\\\nAmong the reasons the concert was staged was to provide an opportunity to record the entire score. The resulting album was more complete than the original cast album. However, director Herbert Ross took some liberties in adapting the book and score for the concert format—dance music was changed, songs were given false endings, the new dialogue was spoken, reprises were added, and Patinkin was allowed to sing \\\"The God-Why-Don\\\\'t-You-Love-Me Blues\\\" as a solo instead of a trio with two chorus girls. Portions of the concert were seen by audiences worldwide in the televised documentary about the making of the concert, also released on videotape and DVD, of \\\\'Follies\\\\' in Concert.\\\\n\\\\n1987 West End\\\\n\\\\nThe musical played in the West End at the Shaftesbury Theatre on July 21, 1987, and closed on February 4, 1989, after 644 performances. The producer was Cameron Mackintosh, the direction was by Mike Ockrent, with choreography by Bob Avian and design by Maria Björnson. The cast featured Diana Rigg (Phyllis), Daniel Massey (Ben), Julia McKenzie (Sally), David Healy (Buddy), Lynda Baron, Leonard Sachs, Maria Charles, Pearl Carr & Teddy Johnson. Dolores Gray was praised as Carlotta, continuing to perform after breaking her ankle, although in a reduced version of the part. During the run, Eartha Kitt replaced Gray, sparking somewhat of a comeback (she went on to perform her own one-woman show at The Shaftesbury Theatre to sell-out houses for three weeks from March 18, 1989, after Follies closed). Other cast replacements included Millicent Martin as Phyllis. Julia McKenzie returned to the production for the final four performances.\\\\n\\\\nThe book \\\"was extensively reworked by James Goldman, with Sondheim\\\\'s cooperation and also given an intermission.\\\" The producer Cameron Mackintosh did not like \\\"that there was no change in the characters from beginning to end\\\\xa0... In the London production\\\\xa0... the characters come to understand each other.\\\" Sondheim \\\"did not think the London script was as good as the original.\\\" However, he thought that it was \\\"wonderful\\\" that, at the end of the first act, \\\"the principal characters recognized their younger selves and were able to acknowledge them throughout the last thirty minutes of the piece.\\\" Sondheim wrote four new songs: \\\"Country House\\\" (replacing \\\"The Road You Didn\\\\'t Take\\\"), \\\"Loveland\\\" (replacing the song of the same title), \\\"Ah, But Underneath\\\" (replacing \\\"The Story of Lucy and Jessie\\\", for the non-dancer Diana Rigg), and \\\"Make the Most of Your Music\\\" (replacing \\\"Live, Laugh, Love\\\").\\\\n\\\\nCritics who had seen the production in New York (such as Frank Rich) found it substantially more \\\"upbeat\\\" and lacking in the atmosphere it had originally possessed. According to the Associated Press (AP) reviewer, \\\"A revised version of the Broadway hit Follies received a standing ovation from its opening-night audience and raves from British critics, who stated the show was worth a 16-year wait.\\\" The AP quoted Michael Coveney of the Financial Times, who wrote: \\\"Follies is a great deal more than a camp love-in for old burlesque buffs and Sondheim aficionados.\\\" In The New York Times, the critic Francis X. Clines wrote: \\\"The initial critics\\\\' reviews ranged from unqualified raves to some doubts whether the reworked book of James Goldman is up to the inventiveness of Sondheim\\\\'s songs. \\\\'A truly fantastic evening,\\\\' The Financial Times concluded, while the London Daily News stated \\\\'The musical is inspired,\\\\' and The Times described the evening as \\\\'a wonderful idea for a show which has failed to grow into a story. The Times critic Irving Wardle stated \\\"It is not much of a story, and whatever possibilities it may have had in theory are scuppered by James Goldman\\\\'s book\\\\xa0... a blend of lifeless small-talk, bitching and dreadful gags\\\". Clines further commented: \\\"In part, the show is a tribute to musical stage history, in which the 57-year-old Mr Sondheim is steeped, for he first learned song writing at the knee of Oscar Hammerstein II and became the acknowledged master songwriter who bridged past musical stage romance into the modern musical era of irony and neurosis. Follies is a blend of both, and the new production is rounded out with production numbers celebrating love\\\\'s simple hope for young lovers, its extravagant fantasies for Ziegfeld aficionados, and its fresh lesson for the graying principals.\\\"\\\\n\\\\nThis production was also recorded on two CDs and was the first full recording.\\\\n\\\\nFollies was voted ninth in a BBC Radio 2 listener poll of the UK\\\\'s \\\"Nation\\\\'s Number One Essential Musicals\\\".\\\\n\\\\nU.S. regional productions\\\\nMichigan Opera Theatre (MOT) was the first major American opera company to present Follies as part of their main stage repertoire, running from October 21, 1988, through November 6. The MOT production starred Nancy Dussault (Sally), John-Charles Kelly (Buddy), Juliet Prowse (Phyllis) and Ron Raines (Ben), Edie Adams (Carlotta), Thelma Lee (Hattie), and Dennis Grimaldi (Vincent).\\\\n\\\\nA production also ran from March to April 1995 at the Theatre Under the Stars, Houston, Texas, and in April to May 1995 at the 5th Avenue Theatre, Seattle with Constance Towers (Phyllis), Judy Kaye (Sally), Edie Adams, Denise Darcel, Virginia Mayo and Karen Morrow (Carlotta). The 1998 Paper Mill Playhouse production (Millburn, New Jersey) was directed by Robert Johanson with choreography by Jerry Mitchell and starred Donna McKechnie (Sally), Dee Hoty (Phyllis), Laurence Guittard (Ben), Tony Roberts (Buddy), Kaye Ballard (Hattie ), Eddie Bracken (Weismann), and Ann Miller (Carlotta). Phyllis Newman and Liliane Montevecchi reprised the roles they played in the Lincoln Center production. \\\"Ah, but Underneath\\\" was substituted for \\\"The Story of Lucy and Jessie\\\" in order to accommodate non-dancer Hoty. This production received a full-length recording on two CDs, including not only the entire score as originally written but a lengthy appendix of songs cut from the original production in tryouts.\\\\n\\\\nJulianne Boyd directed a fully staged version of Follies in 2005 by the Barrington Stage Company (Massachusetts) in June–July 2005. The principal cast included Kim Crosby (Sally), Leslie Denniston (Phyllis), Jeff McCarthy (Ben), Lara Teeter (Buddy), Joy Franz (Solange), Marni Nixon (Heidi), and Donna McKechnie (Carlotta). Stephen Sondheim attended one of the performances.\\\\n\\\\n1996 and 1998 concerts\\\\nDublin concert\\\\nThe Dublin Concert was held in May 1996 at the National Concert Hall. Directed by Michael Scott, the cast included Lorna Luft, Millicent Martin, Mary Millar, Dave Willetts, Trevor Jones Bryan Smyth, Alex Sharpe, Christine Scarry, Aidan Conway and Enda Markey.\\\\n\\\\nLondon concert\\\\nA concert was held at Theatre Royal, Drury Lane, London, on December 8, 1996, and broadcast on BBC Radio 2 on February 15, 1997. The cast starred Julia McKenzie (Sally), Donna McKechnie (Phyllis), Denis Quilley (Ben) and Ron Moody (Buddy). This show recreated the original Broadway score.\\\\n\\\\nSydney concert\\\\nFollies was performed in concert at the Sydney Opera House with the Sydney Symphony Orchestra in February 1998 as the highlight of the Sydney Gay and Lesbian Mardi Gras and had three performances. It was directed and staged by Stephen Lloyd Helper and produced by Helper and Alistair Thomson for Mardi Gras. It starred Toni Lamond (Sally), Jill Perryman(Carlotta), Judi Connelli (Phyllis), Terence Donovan (Ben), Nancye Hayes (Hattie), Glenn Butcher (Buddy), Ron Haddrick (Dimitri), Susan Johnston (Heidi), and Leonie Page, Maree Johnson, Mitchell Butel, Maureen Howard. The Sydney Symphony was conducted by Maestro Tommy Tycho. It followed a similar presentation at the 1995 Melbourne Festival of Arts with a different cast and orchestra.\\\\n\\\\n2001 Broadway revival\\\\nA Broadway revival opened at the Belasco Theatre on April 5, 2001, and closed on July 14, 2001, after 117 performances and 32 previews. This Roundabout Theatre limited engagement had been expected to close on September 30, 2001. Directed by Matthew Warchus with choreography by Kathleen Marshall, it starred Blythe Danner (Phyllis), Judith Ivey (Sally), Treat Williams (Buddy), Gregory Harrison (Ben), Marge Champion, Polly Bergen (Carlotta), Joan Roberts (Laurey from the original Broadway production of Oklahoma!; later replaced by Marni Nixon), Larry Raiken (Roscoe) and an assortment of famous names from the past. Former MGM and onetime Broadway star Betty Garrett, best known to younger audiences for her television work, played Hattie. It was significantly stripped down (earlier productions had featured extravagant sets and costumes) and was not a success critically.\\\\n\\\\nAccording to an article in The Hollywood Reporter, \\\"almost every performance of the show played to a full house, more often than not to standing-room-only. Tickets always were tough to come by. The reason the final curtain came down Saturday was that being a production by the Roundabout Theatre Company – a subscription-based \\\\'not-for-profit\\\\' theater company – it was presented under special Equity terms, with its actors paid a minimal fee. To extend the show, it would have been necessary to negotiate new contracts with the entire company\\\\xa0... because of the Belasco\\\\'s limited seating, it wasn\\\\'t deemed financially feasible to do so.\\\"\\\\n\\\\nTheater writer and historian John Kenrick wrote \\\"the bad news is that this Follies is a dramatic and conceptual failure. The good news is that it also features some of the most exciting musical moments Broadway has seen in several seasons. Since you don\\\\'t get those moments from the production, the book or the leads, that leaves the featured ensemble, and in Follies that amounts to a small army\\\\xa0... Marge Champion and Donald Saddler are endearing as the old hoofers\\\\xa0... I dare you not to fall in love with Betty Garrett\\\\'s understated \\\"Broadway Baby\\\" – you just want to pick her up and hug her. Polly Bergen stops everything cold with \\\"I\\\\'m Still Here\\\", bringing a rare degree of introspection to a song that is too often a mere belt-fest\\\\xa0... [T]he emotional highpoint comes when Joan Roberts sings \\\\'One More Kiss\\\\'.\\\"\\\\n\\\\n2002 London revival\\\\nA production was mounted at London\\\\'s Royal Festival Hall in a limited engagement. After previews from August 3, 2002, it opened officially on August 6, and closed on August 31, 2002. Paul Kerryson directed, and the cast starred David Durham as Ben, Kathryn Evans as Sally, Louise Gold as Phyllis, Julia Goss as Heidi and Henry Goodman as Buddy. Variety singer and performer Joan Savage sang \\\"Broadway Baby\\\". This production conducted by Julian Kelly featured the original Broadway score.\\\\n\\\\n2002 Los Angeles\\\\nFollies was part of L.A.\\\\'s Reprise series, and it was housed at the Wadsworth Theatre, presented as a staged concert, running from June 15 to 23, 2002. The production was directed by Arthur Allan Seidelman, set design by Ray Klausen, lighting design by Tom Ruzika, costumes by Randy Gardell, sound design by Philip G. Allen, choreography by Kay Cole, musical director Gerald Sternbach.\\\\n\\\\nThe production starred Bob Gunton (Ben), Warren Berlinger (Dimitri Weismann), Patty Duke (Phyllis), Vikki Carr (Sally), Harry Groener (Buddy), Carole Cook (Hattie), Carol Lawrence (Vanessa), Ken Page (Roscoe), Liz Torres (Stella), Amanda McBroom (Solange), Grover Dale (Vincent), Donna McKechnie (Carlotta), Carole Swarbrick (Christine), Stella Stevens (Dee Dee), Mary Jo Catlett (Emily), Justine Johnston (Heidi), Jean Louisa Kelly (Young Sally), Austin Miller (Young Buddy), Tia Riebling (Young Phyllis), Kevin Earley (Young Ben), Abby Feldman (Young Stella), Barbara Chiofalo (Young Heidi), Trevor Brackney (Young Vincent), Melissa Driscoll (Young Vanessa), Stephen Reed (Kevin), and Billy Barnes (Theodore). Hal Linden originally was going to play Ben, but left because he was cast in the Broadway revival of Cabaret as Herr Schultz. Tom Bosley originally was cast as Dimitri Weismann.\\\\n\\\\n2003 Ann Arbor\\\\nA concert production at the Michigan Theater in January 2003 reunited the four principal young ghosts of the original Broadway cast: Kurt Peterson, Harvey Evans, Virginia Sandifur, and Marti Rolph. Having originated the young ghosts over 30 years prior, the actors portrayed the older versions of their Broadway roles. Donna McKechnie enjoyed top billing as Carlotta.\\\\n\\\\n2007 New York City Center Encores!\\\\nNew York City Center\\\\'s Encores! \\\"Great American Musicals in Concert\\\" series featured Follies as its 40th production for six performances in February 2007 in a sold out semi-staged concert. The cast starred Donna Murphy (Phyllis), Victoria Clark (Sally), Victor Garber (Ben) and Michael McGrath (Buddy). Christine Baranski played Carlotta, and Lucine Amara sang Heidi. The cast included Anne Rogers, Jo Anne Worley and Philip Bosco. The director and choreographer was Casey Nicholaw. This production used the original text, and the \\\"Loveland\\\" lyrics performed in the 1987 London production.\\\\n\\\\n2011 Kennedy Center and Broadway\\\\nThe Kennedy Center for the Performing Arts production at the Eisenhower Theater started previews on May 7, 2011, with an official opening on May 21, and closed on June 19, 2011. The cast starred Bernadette Peters as Sally, Jan Maxwell as Phyllis, Elaine Paige as Carlotta, Linda Lavin as Hattie, Ron Raines as Ben and Danny Burstein as Buddy. The production was directed by Eric Schaeffer, with choreography by Warren Carlyle, costumes by Gregg Barnes, set by Derek McLane and lighting by Natasha Katz. Also featured were Rosalind Elias as Heidi, Régine as Solange, Susan Watson as Emily, and Terri White as Stella. The budget was reported to be $7.3 million. The production played to 95% capacity.\\\\n\\\\nReviews were mixed, with Ben Brantley of The New York Times writing \\\"It wasn\\\\'t until the second act that I fell in love all over again with Follies\\\". Peter Marks of The Washington Post wrote that the revival \\\"takes an audience halfway to paradise.\\\" He praised a \\\"broodingly luminous Jan Maxwell\\\" and Burstein\\\\'s \\\"hapless onetime stage-door Johnny\\\", as well as \\\"the show\\\\'s final 20 minutes, when we ascend with the main characters into an ironic vaudeville dreamscape of assorted neuroses - the most intoxicating articulation of the musical\\\\'s \\\\'Loveland\\\\' sequence that I\\\\'ve ever seen.\\\" Variety gave a very favorable review to the \\\"lavish and entirely satisfying production\\\", saying that Schaeffer directs \\\"in methodical fashion, building progressively to a crescendo exactly as Sondheim does with so many of his stirring melodies. Several show-stopping routines are provided by choreographer Warren Carlyle.\\\" Terry Teachout of the Wall Street Journal noted that \\\"One of the signal achievements of this Follies is that it succeeds in untangling each and every strand of the show\\\\'s knotty plot\\\\xa0... Mr. Schaeffer is clearly unafraid of the darkness of Follies, so much so that the first act is bitter enough to sting. Yet he and Warren Carlyle\\\\xa0... just as clearly revel in the richness of the knowing pastiche songs with which Mr. Sondheim evokes the popular music of the prerock era.\\\"\\\\n\\\\nThe production transferred to Broadway at the Marquis Theatre in a limited engagement starting previews on August 7, 2011, with the official opening on September 12, and closing on January 22, 2012, after 151 performances and 38 previews. The four principal performers reprised their roles, as well as Paige as Carlotta. Jayne Houdyshell as Hattie, Mary Beth Peil as Solange LaFitte, and Don Correia as Theodore joined the Broadway cast. A two-disc cast album of this production was recorded by PS Classics and was released on November 29, 2011.\\\\n\\\\nBrantley reviewed the Broadway revival for The New York Times, writing: \\\"Somewhere along the road from Washington to Broadway, the Kennedy Center production of Follies picked up a pulse\\\\xa0... I am happy to report that since then, Ms Peters has connected with her inner frump, Mr. Raines has found the brittle skeleton within his solid flesh, and Ms. Maxwell and Mr. Burstein have only improved. Two new additions to the cast, Jayne Houdyshell and Mary Beth Peil, are terrific. This production has taken on the glint of crystalline sharpness.\\\" The production\\\\'s run was extended, and its grosses exceeded expectations, but it did not recoup its investment.\\\\n\\\\nThe Broadway production won the Drama League Award, Distinguished Production of a Musical Revival for 2011-2012 and the Drama Desk Award for Outstanding Revival of a Musical, Outstanding Actor in a Musical (Burstein) and Outstanding Costume Design (Barnes). Out of seven Tony Award nominations, including Best Revival of a Musical, it won only one, for Barnes\\\\' costumes.\\\\n\\\\n2012 Los Angeles\\\\nThe 2011 Broadway and Kennedy Center production transferred to the Ahmanson Theatre, Los Angeles, California, in a limited engagement, from May 3, 2012, through June 9. The majority of the Broadway cast reprised their roles, with the exception of Bernadette Peters, who had prior concert commitments and was replaced by Victoria Clark in the role of Sally, a role she has previously played in New York. Other new cast members included Carol Neblett as Heidi, Sammy Williams as Theodore and Obba Babatunde as Max.\\\\n\\\\n2013 Toulon Opera House (France)\\\\nFor its first production in France, Follies was presented at the Toulon Opera House in March 2013. This English-language production, using the full original orchestration, was directed by Olivier Bénézech and conducted by David Charles Abell. The cast featured Charlotte Page (Sally), Liz Robertson (Phyllis), Graham Bickley (Ben), Jérôme Pradon (Buddy), Nicole Croisille (Carlotta), Julia Sutton (Hattie) and Fra Fee (Young Buddy).\\\\n\\\\n2016 Australian concert version \\\\nA concert version at the Melbourne Recital Centre, staged with a full 23-piece orchestra and Australian actors Philip Quast (Ben), David Hobson (Buddy), Lisa McCune (Sally), Anne Wood (Phyllis), Rowan Witt (Young Buddy), Sophie Wright (Young Sally), Nancy Hayes (Hattie), Debra Byrne (Carlotta), and Queenie van de Zandt (Stella). The production was directed by Tyran Parke and produced by StoreyBoard Entertainment.\\\\n\\\\n2017 London revival \\\\nA London revival was performed in the Olivier Theatre at the National Theatre (August 22 until November 4, 2017 - later extended to January 3, 2018, as extensions are common practice at the National Theatre). The production was directed by Dominic Cooke, choreographed by Bill Deamer and starred Peter Forbes as Buddy, Imelda Staunton as Sally, Janie Dee as Phyllis, Philip Quast as Ben and Tracie Bennett as Carlotta. This production notably goes back to the original plan of a one-act performance. The production was broadcast live to cinemas worldwide on November 16 through the National Theatre Live program.\\\\n\\\\nThe production returned to the Olivier Theatre on February 14, 2019, playing until May 11. Janie Dee and Peter Forbes returned as Phyllis and Buddy, while Joanna Riding and Alexander Hanson replaced Staunton and Quast as Sally and Ben. Bennett also reprised her Olivier-nominated performance. A recording of the National Theatre production was released on January 18, 2019.\\\\n\\\\nThe 2017 production was nominated for 10 Laurence Olivier Awards and won 2 for Best Musical Revival and Best Costume Design (by Vicki Mortimer).\\\\n\\\\nCharacters and original cast\\\\n\\\\nThe characters and original cast:\\\\n\\\\nCritical response\\\\nIn the foreword to \\\"Everything Was Possible\\\", Frank Rich wrote: \\\"From the start, critics have been divided about Follies, passionately pro or con but rarely on the fence\\\\xa0... Is it really a great musical, or merely the greatest of all cult musicals?\\\" (Chapin, p. xi) Ted Chapin wrote, \\\"Taken as a whole, the collection of reviews Follies received was as rangy as possible.\\\" (Chapin, p.\\\\xa0300) In his The New York Times review of the original Broadway production, Clive Barnes wrote: \\\"it is stylish, innovative, it has some of the best lyrics I have ever encountered, and above all it is a serious attempt to deal with the musical form.\\\" Barnes also called the story shallow and Sondheim\\\\'s words a joy \\\"even when his music sends shivers of indifference up your spine.\\\"\\\\n\\\\nWalter Kerr wrote in The New York Times about the original production: \\\"Follies is intermissionless and exhausting, an extravaganza that becomes so tedious\\\\xa0... because its extravaganzas have nothing to do with its pebble of a plot.\\\" On the other hand, Martin Gottfried wrote: \\\"Follies is truly awesome and, if it is not consistently good, it is always great.\\\"\\\\n\\\\nTime magazine wrote about the original Broadway production: \\\"At its worst moments, Follies is mannered and pretentious, overreaching for Significance. At its best moments—and there are many—it is the most imaginative and original new musical that Broadway has seen in years.\\\"\\\\n\\\\nFrank Rich, in reviewing the 1985 concert in The New York Times, wrote: \\\"Friday\\\\'s performance made the case that this Broadway musical\\\\xa0... can take its place among our musical theater\\\\'s very finest achievements.\\\" Ben Brantley, reviewing the 1998 Paper Mill Playhouse production in The New York Times, concluded that it was a \\\"fine, heartfelt production, which confirms Follies as a landmark musical and a work of art\\\\xa0...\\\".\\\\n\\\\nThe Time reviewer wrote of the 2001 Broadway revival: \\\"Even in its more modest incarnation, Follies has, no question, the best score on Broadway.\\\" He noted, though, that \\\"I\\\\'m sorry the cast was reduced from 52 to 38, the orchestra from 26 players to 14\\\\xa0... To appreciate the revival, you must buy into James Goldman\\\\'s book, which is peddling a panoramically bleak take on marriage.\\\" Finally, he wrote: \\\"But Follies never makes fun of the honorable musical tradition to which it belongs. The show and the score have a double vision: simultaneously squinting at the messes people make of their lives and wide-eyed at the lingering grace and lift of the music they want to hear. Sondheim\\\\'s songs aren\\\\'t parodies or deconstructions; they are evocations that recognize the power of a love song. In 1971 or 2001, Follies validates the legend that a Broadway show can be an event worth dressing up for.\\\"\\\\n\\\\nBrantley, reviewing the 2007 Encores! concert for The New York Times, wrote: \\\"I have never felt the splendid sadness of Follies as acutely as I did watching the emotionally transparent concert production\\\\xa0... At almost any moment, to look at the faces of any of the principal performers\\\\xa0... is to be aware of people both bewitched and wounded by the contemplation of who they used to be. When they sing, in voices layered with ambivalence and anger and longing, it is clear that it is their past selves whom they are serenading.\\\"\\\\n\\\\nRecordings\\\\nThere have been six recordings of Follies released: the original 1971 Broadway cast album; Follies in Concert, Avery Fisher Hall (1985); the original London production (1987); the Paper Mill Playhouse (1998); the 2011 Broadway revival; and the 2017 London revival. The original cast album has always been controversial, because significant portions of the score were cut to fit onto one LP. However, as Kritzerland Records head Bruce Kimmel wrote in his liner notes to Kritzerland\\\\'s remixed version of the album, \\\"What it did have made it something that, despite the frustrations, meant it would never be bettered – the original cast.\\\"\\\\nThe cast recording of the 2011 Broadway revival, by PS Classics, was released officially on November 29, 2011, and was in pre-sale before the store release. PS Classics co-founder Tommy Krasker stated \\\"We\\\\'ve never had the kind of reaction that we\\\\'ve had for Follies. Not only has it already outsold every other album at our website, but the steady stream of emails from customers has been amazing.\\\" This recording includes \\\"extended segments of the show\\\\'s dialogue\\\". The theatermania.com reviewer wrote that \\\"The result is an album that, more so than any of the other existing recordings, allows listeners to re-experience the heartbreaking collision of past and present that\\\\'s at the core of the piece.\\\" The recording of the 2011 revival was nominated for a Grammy Award in the Musical Theater Album category. The 2017 London revival cast was recorded after the production closed in January 2018, and was released in early 2019.\\\\n\\\\nFilm adaptation\\\\nIn January 2015, it was reported that Rob Marshall is set to direct the movie, and Meryl Streep was rumored to star in it. Tony Award-winning playwright and Oscar-nominated screenwriter John Logan has expressed interest in writing a film adaptation of Follies.\\\\n\\\\nIn November 2019, it was announced that Dominic Cooke will adapt the screenplay and direct the film, after having directed the successful 2017 revival in the National Theatre in London, which returned in 2019 because of popular demand.\\\\n\\\\nAwards and nominations\\\\n\\\\nOriginal Broadway production\\\\n\\\\nOriginal London production\\\\n\\\\n2001 Broadway revival\\\\n\\\\n2011 Broadway revival\\\\n\\\\n2017 London revival\\\\n\\\\nNotes\\\\n\\\\nReferences\\\\n Chapin, Ted (2003). Everything Was Possible: The Birth of the Musical Follies. New York, New York: Alfred A. Knopf. \\\\n Secrest, Meryle (1998). Stephen Sondheim: A Life. Dell Publishing, Alfred A. Knopf (reprint). \\\\n Sondheim, Stephen and Goldman, James (2001). Follies. New York, New York: Theatre Communications Group. \\\\nSondheim, Stephen (2010). Finishing the Hat. Alfred A. Knopf.\\\\n\\\\nFurther reading\\\\n Prince, Harold (1974). Contradictions: Notes on Twenty-six Years in the Theatre. Dodd, Mead. \\\\n Ilson, Carol (2004). Harold Prince: A Director\\\\'s Journey, Limelight Editions. \\\\n Mandelbaum, Ken (1990). A Chorus Line and the Musicals of Michael Bennett. St. Martins Press.\\\\n\\\\nExternal links\\\\n \\\\n Follies on The Stephen Sondheim Reference Guide\\\\n \\\\n Follies at the Music Theatre International website\\\\n\\\\n1971 musicals\\\\nBroadway musicals\\\\nLaurence Olivier Award-winning musicals\\\\nOriginal musicals\\\\nMusicals by James Goldman\\\\nMusicals by Stephen Sondheim\\\\nWest End musicals\\\\nPlays set in New York City\\\\nTony Award-winning musicals\\\\nBackstage musicals'},\\n\",\n \" {'docid': 'doc-en-11',\\n\",\n \" 'text': 'Cleopatra in Space is an American animated television series produced by DreamWorks Animation and animated by Titmouse, Inc., based on the graphic novel series of the same name by Mike Maihack. The showrunners for the series are Doug Langdale and Fitzy Fitzmaurice.\\\\n\\\\nIn the United States, the first five episodes were released on NBCUniversal\\\\'s streaming service Peacock for Xfinity customers on April 15, 2020, making this the first DreamWorks Animation series to be released on a streaming device other than Netflix or Amazon Video. On July 15, 2020, the first season was officially released when the service launched nationwide. Prior to its release in the United States, the series was first broadcast in Southeast Asia on DreamWorks Channel beginning on November 25, 2019. The show is geared toward those between ages 6 and 11. Langdale, in an interview, said that he is attempting to make sure the show is \\\"accessible to a younger audience,\\\" even as he doesn\\\\'t give much thought to what age demographic the show is aiming towards.\\\\n\\\\nOn July 15, the show premiered on Peacock, with episodes 1–5 and 7–13 of the first season made available to viewers who subscribed to \\\"Peacock Premium\\\", and a more limited selection for those who chose a free plan. It was one of the three animated Peacock Originals streaming on the platform, with the other two being season 13 of Curious George and season 2 of Where\\\\'s Waldo?. The show can only be watched using the streaming service\\\\'s premium plan. On November 19, 2020, Season 2 premiered on Peacock. On January 14, 2021, Season 3 was released on Peacock. On July 14, 2021, all three seasons were added to Hulu.\\\\n\\\\nPlot\\\\nCleopatra in Space is a comedic adventure focusing on Cleopatra\\\\'s teenage years, as she deals with the ups and downs of being a high school teenager, after she transported 30,000 years into her future to a planet with Egyptian themes ruled by talking cats, and she is said to be the savior of a galaxy. Cleopatra and her newfound friends work to try and return her to her own time, in Ancient Egypt, as she gains new combat skills in the process. Showrunner Doug Langdale described the show as a \\\"real move-forward story\\\" which continues forward without interruption.\\\\n\\\\nCharacters\\\\n\\\\nMain\\\\n Cleopatra \\\"Cleo\\\" (voiced by Lilimar Hernandez) - The fearless and confident protagonist of the series. The 15-year-old princess of ancient Egypt, whose father is Pharaoh King Ptolemy (Sendhil Ramamurthy), she ends up sucked into a portal that sends her 30,000 years into the future where she learns she is the prophesied \\\"Savior of the Nile Galaxy\\\", destined to defeat the evil space tyrant Octavian. She ends up attending the futuristic intergalactic academy named P.Y.R.A.M.I.D. to obtain the proper training and skills to fulfill her role. She is sometimes reckless and impulsive, but has a good heart and wants peace. She has also gained strange and powerful powers from her time-travel, which manifests in pink and can be used to drain energy and project it into energy waves and beams. Lilimar called Cleo a character who is not completely mature or responsible, but a young girl who is on the road to becoming a hero, a person who is courageous and brave, seeing \\\"a lot of positivity in the world, no matter how dark things seem to be,\\\" even as she seeks adventure all the time.\\\\n Akila (voiced by Katie Crown) - A pink-eyed fish girl from another planet and the first of Cleopatra\\\\'s teammates. She is very friendly and optimistic, but over-enthusiastic. She may have a crush on Brian. She has two moms: Theoda (voiced by Cissy Jones) and Pothina (voiced by Kari Wahlgren), who are scholars at The Savior Institute, use dated social expressions and love their daughter. They are the first confirmed LGBTQ characters in the series.\\\\n Brian (voiced by Jorge Diaz) - A cyborg teenage boy, and Cleopatra\\\\'s second teammate. His body is mostly robotic and is also sensitive of the fact that he was transformed into a cyborg. He is rather worrisome, paranoid, nervous, and self-conscious at times. He has a crush on Akila. He and Akila later have a foster child of sorts named Eyeball, who is voiced by Brian Posehn.\\\\n Khensu (voiced by Sendhil Ramamurthy) - An intelligent, long-eared cat with the ability to speak. He serves as the leader of the group, and a teacher at P.Y.R.A.M.I.D. He becomes, as noted by showrunner Doug Langdale, like a surrogate father to Cleo, balancing out her action and acrobatics with his intellectual, sensible, and down-to-Earth nature.\\\\n Mihos - The cute and lovable pet of Cleo. Later Doctor Queed says that Mihos can\\\\'t be native to the planet but rather is a \\\"lost pet.\\\" Boop, a female pirate and space squirrel, is his doppelgänger. Lilimar, in an interview, said that while she likes Brian and Akila as characters, Mihos is her favorite, even making a request to make him into a plushie.\\\\n\\\\nSupporting\\\\n Callie (voiced by Kari Wahlgren) - An arrogant and snobby student at P.Y.R.A.M.I.D. who becomes Cleopatra\\\\'s academic rival upon her arrival in the school. \\\\n Xerxs (voiced by Dee Bradley Baker) - Legions of alien robots who are the soldiers and servants of Octavian.\\\\n Zaid Antonius (voiced by Xolo Maridueña) - A student at P.Y.R.A.M.I.D., whom Cleopatra is attracted to. He is later revealed to be a spy of Octavian, due to the latter holding his parents, Dahab (Candi Milo) and Askari (Dee Bradley Baker), hostages. His family name is a hint towards the historical figure Marcus Antonius.\\\\n Administrant Khepra (voiced by Sumalee Montano) - The head of the cat-council.\\\\n Octavian (voiced by Jonathan Kite) - The main antagonist of the series. The evil ruler of the Xerxs who has destroyed or enslaved many worlds and Cleopatra\\\\'s arch-nemesis. He is bent on capturing and getting rid of Cleopatra so the prophecy about her defeating him cannot be fulfilled. Named after the historical figure Gaius Octavian.\\\\n E\\\\'Geke-Ek\\\\'Gek (voiced by Alex Cazares) - an alien student who requires a translator torque to communicate.\\\\n Yosira (voiced by Wahlgren) - The young Pharaoh of P.Y.R.A.M.I.D. She is the granddaughter of the founder of P.Y.R.A.M.I.D., the late Pharaoh Yosiro. \\\\n Zuzz (voiced by Zach Callison) - A student whose body consists of a swarm of insect-shaped \\\"bits\\\".\\\\n Omnia (voiced by Elise Dubois) - A robot representing a planet and part of the debate club at P.Y.R.A.M.I.D.\\\\n\\\\nOther characters\\\\n Professor Sitre (voiced by Amy Hill) - a middle-age cat teacher at P.Y.R.A.M.I.D.\\\\n Philo (voiced by Gunnar Sizemore) - A young apprentice at P.Y.R.A.M.I.D., whom Cleopatra mentors in one episode in order to attain the cadets\\\\' Level 2.\\\\n Zedge (voiced by Lucas Grabeel) - A popular intergalactic rockstar, whom Akila has a crush on and claims to be his \\\"biggest fan\\\". He was briefly mind-controlled by Octavian to capture Cleopatra. \\\\n Cyrano (voiced by Greg Cipes) - An evil artificial intelligence created by Octavius to counter Brian. \\\\n Gozi (voiced by Karan Brar) - A young Egyptian boy who was Cleopatra\\\\'s best friend back on Earth in her own time. \\\\n Msamaki (voiced by David Shaughnessy) - An intelligent, long-eared cat with the ability to speak, and a member of the Cat council.\\\\n Professor Klabrax V (voiced by Dawnn Lewis) - An intelligent, fish-like being who is a professor at P.Y.R.A.M.I.D.\\\\n Dr. Queed (voiced by Paul Rugg) - A former doctor at P.Y.R.A.M.I.D. and an acquaintance of Khensu whose catchphrase is \\\"uncanny scientific brilliance!\\\"\\\\n Debbie (voiced by Candi Milo) - A lonely planet who can manifest into various forms and later a student at P.Y.R.A.M.I.D.\\\\n Generator A.I. (voiced by Dee Bradley Baker) - An A.I. located nearby the academy which is used to generate electricity for the campus after Cleo sucks out all the power from the academy.\\\\n Damaris (voiced by Marieve Herington) - A space scavenger, part of a group led by Dave.\\\\n Dave (voiced by Ace Gibson) - Leader of the space scavengers and has a pet named Precious.\\\\n Simon (voiced by Rhys Darby) - A conniving snake who turns on Akila, with Cleo and her parents working together to stop it.\\\\n Gurbo Gorbage (voiced by Kay Bess) - A purported television personality who kidnaps Cleo and almost sends her to Octavian.\\\\n Amsaja (voiced by Kimberly D. Brooks) - The self-declared \\\"Queen of the Space Pirates,\\\" who heading a crew of three other pirates, and Cleo\\\\'s doppelgänger. She previously had the telepathic space shark ninja as her ex-boyfriend, and Octavian might be her ex-boyfriend as well.\\\\n Cyborg Dwayne - (voiced by Andrew Morgado) - The doppelgänger of Brian who is also on the pirate ship.\\\\n Medjed (voiced by Ken Pringle) - Ruler of Dargham who tries to convince Cleo and Zaid to stay there indefinitely.\\\\n Gled (voiced by John DiMaggio) - Chief of the Tawrisians on the planet Tawris in the Nile Galaxy. \\\\n Commodore Winifred (voiced by Toks Olagundoye) - The commander of a ship of Parvites atop the head of Mihos, whose full name is Commodore Winifred Blurvington the Third.\\\\n Mortimer (voiced by Damian O\\\\'Hare) - A lieutenant who serves under Commodore Winifred and resembles a proper British gentleman.\\\\n\\\\nProduction\\\\nIn January 2018, DreamWorks Animation filed trademark applications for the show to the United States Patent and Trademark Office. Since then, DreamWorks has filed for four extensions on their trademark for Cleopatra in Space, two times in 2019, and two times in 2020, all of which were granted.\\\\n\\\\nMike Maihack said that the series is in retroactive continuity to his comics because Cleo is a teenager and there is time travel. This differs from his comic book series which is \\\"rooted from stories and research of the actual Cleopatra.\\\" He also may have been influenced by Kipo and the Age of Wonderbeasts, Avatar: The Last Airbender, Star Trek: The Next Generation, Buffy the Vampire Slayer, and Legends of Tomorrow. In an interview with Charles C. Dowd on I Heart Webcomics on July 22, Maihack said that he consulted in the early stages of the show, letting DreamWorks know the upcoming details of his book and remained supportive, admitting he did not want \\\"a direct adaption of the novels.\\\" He further said that he saw the animated series as an opportunity to work on the Cleo in Space concept in another way who had worked on Ben 10: Omniverse and DuckTales, noting that while it was clear that those working on the series understood \\\"the core components of the story,\\\" he stepped back, letting the \\\"amazingly talented folks\\\" involved in the show do their work.\\\\n\\\\nIn an interview with Jackson Murphy of Animation Scoop, showrunner Doug Langdale said the story lends itself to \\\"serialized storytelling\\\" rather than an animated feature film, and that developing the show has been a \\\"pretty involved process.\\\" He also stated that the different uniforms at the Academy are \\\"different colors for different divisions in the school and emblems,\\\" that they used common science-fiction tropes, that the show is not lesson-based but is just entertainment, and that Mike Maihack was ok with deviating from the original graphic novels, so they could create something that fans would enjoy. Langdale expanded on this in an interview with Screen Rant where he noted that they found Maihack\\\\'s books, noted that DreamWorks had been trying to create a feature film about it, which was abandoned so they could do a series. He further described the differences between the books and the series, which are on a \\\"day-to-day basis,\\\" with the series not following the books closely at all, even as they used the \\\"same set up, [and] many of the same characters.\\\" Langdale explained how Egyptian history was an inspiration for many character names, sometimes by coincidence, and how Lilimar Herendez was the choice for the main role of Cleopatra from the beginning, while stating how Sendhil Ramamurthy, Jorge Diaz, and Katie Crown influenced the show through their voice acting. He finally told the interview that the show ended up with a \\\"predominantly female cast,\\\" with DreamWorks seeing this as a \\\"good time to make a show with a female lead,\\\" and explained that the \\\"first 12 episodes take place within a few months.\\\"\\\\n\\\\nIn an interview with CBR, Langdale said that he enjoyed Maihack\\\\'s books, and agreed with DreamWorks to create the show, using Maihack\\\\'s characters as a starting point, but then \\\"went off in some different directions.\\\" He again reiterated that the episodes track the characters on a day-to-day basis, and differed from the original books because Brian and Akila are humans, rather than a cyborg and an alien, with the voice actors shaping their characters. At the same time, he sidestepped the historical debate over her origins.\\\\n\\\\nIn an August 6, 2020 interview with ComicBook, Langdale further explained the development of the show. He said that they didn\\\\'t \\\"literally translate the books,\\\" but took the characters Mike Maihack created, some character bits, and did their \\\"our own thing.\\\" He added that they wanted to show a \\\"day-to-day story about the characters,\\\" different from the books, tried to work in some \\\"some ancient Egyptian motifs\\\" and said some inspiration could have drawn from 1970s French comic books, and three divisions in the school: \\\"command, combat, and science.\\\" He stated he didn\\\\'t think about the age range DreamWorks gave him for the show, rather aiming to make an enjoyable show which is \\\"pretty serialized.\\\"\\\\n\\\\nMusic\\\\nThe music of the series is composed by Jay Vincent and Ryan Lofty. It was described by Courtney Lofty, the score production manager, as \\\"an epic cocktail of electronic beats, Egyptian melodies, and orchestral dramatics,\\\" with other melodies, with \\\"an extreme amount of time\\\" researching for the music, which references Paramore, M.I.A., and the score of The Prince of Egypt. The music was attuned to the specific scenes in each episode.\\\\n\\\\nThe series opening theme song was sung by Lilimar Hernandez, the voice actress for Cleopatra. Additionally, Matt Barrios worked on the main title. Jackson Murphy of Animation Scoop described the song as making it clear that Cleo\\\\'s story is about \\\"meaning, purpose and destiny.\\\" In an August 13, 2020 interview with Sergio Burstein of the Los Angeles Times, Lilimar describes the show as the first thing she has done in the animation field, and was surprised to by their proposal for her to sing the title song. While she admitted she was nervous to sing the song at first, because she hadn\\\\'t \\\"done anything with music in ten years,\\\" she said that working on the show helped her regain her \\\"voice as a singer,\\\" while encouraging her to do things out of her \\\"comfort zone,\\\" and remained grateful of the positive responses on Twitter. She said something similar in an interview with a Spanish language publication, Siempre Mujer and an interview with a Spanish-language YouTuber, saying the project surprised her because she never expected to sing the theme song and that working on the show was a learning experience. On August 21, in an interview with Alfonso Díaz of RCN Nuestra Tele Internacional, Lilimar called working on the show a \\\"very nice experience,\\\" noted that the show was her second job for DreamWorks (her first was Spirit Riding Free), and how she sometimes recorded the lines for the show alone, while other times she did it with the rest of the cast. She also explained the struggles with recording lines, how this show is the first time she has had such a big role, and its relevance during the COVID-19 pandemic with the main cast having to work together under extraordinary circumstances.\\\\n\\\\nIn an interview with CBR in January 2021, she said that singing the opening theme was nowhere in her contract and they called her saying they\\\\'d like her to sing the song. She decided to do so, even though she was \\\"not in a confident place\\\" with her singing, and didn\\\\'t think much of it. She wasn\\\\'t told they were going to use it, then they had a premiere for those on the team, and she brought her mom along, who pointed out it was her. She said that the fact that they used her voice meant \\\"they liked it\\\" and called it \\\"really cool\\\" that she sang the opening.\\\\n\\\\nDesign\\\\nAccording to Langdale, the drawn-out visual development of the show allowed them to have a style close to Mailhack\\\\'s original graphic novels. He added that the show\\\\'s crew wanted a visual style which \\\"was going to be fun to look at,\\\" with the Academy divided into \\\"three areas of specialization...identified by color\\\" which is not directly noted in the show. He further pointed out that one challenge was with the digital 2D animation and they received help from the animators. In a later interview he said that the show\\\\'s animation sometimes mirrored the scenes in the graphic novels.\\\\n\\\\nCharacter design\\\\nOn September 16, 2020, Bertrand Todesco, the series\\\\' character designer, was interviewed by VoyageLA. In the interview, he described how he imagines the \\\"shapes and colors of the characters based on the descriptions\\\" he reads in a script or other document, saying that in draw something differently depending on the age of the audience, and the excitement of working collaboratively with others on various animated shows. He also talked about developing the show\\\\'s main characters and that with the help of DreamWorks, and others like Angela Mueller, Art Director for the show, his O-1 visa was approved, allowing him to stay in Los Angeles. According to Langdale, Todesco came up with Akila as a \\\"fish-based character\\\" while they went back-and-forth as to whether Brian would be a human or a cyborg, and that they shaped Cleopatra\\\\'s character by what \\\"someone with a passing familiarity\\\" of her might think she was as a person.\\\\n\\\\nStorytelling\\\\nIn early January 2021, Wei Li, who storyboarded eight episodes for the show, shared an animatic from an episode he storyboarded, titled \\\"My Pharaoh Lady,\\\" adding that the \\\"show never took off.\\\" He later explained what he meant was that the series did not get the \\\"proper distribution,\\\" said that he personally thought \\\"the story could be a lot better,\\\" and argued the original comic deals with subjects with more seriousness, stated that, \\\"it seems like they radically changed Cleo\\\\'s personality\\\" from the comics. He later explained that personally, if he could, he would change the episodes \\\"where Cleo supposedly learns from her mistakes,\\\" so that the viewers see a \\\"change in her from that lesson in the episodes afterward\\\".\\\\n\\\\nVoice acting\\\\nIn an exclusive interview with CBR, Lilimar Hernandez said that this was the first show where she had a major role and that she used her \\\"natural voice\\\" when voicing Cleopatra. She described trying to found out what that voice was and attempting to be as consistent as possible with that voice. She tried her best to keep her \\\"fun-loving nature\\\" and called the voice acting a \\\"cool journey.\\\" Furthermore, she said that the team she worked with, like Doug Langdale, and a voice director Sirena Irwin, made her feel excited and comfortable, as she explored the character and the world of the show. In the same interview, she said that the experience was \\\"nice,\\\" even as a new person to voice acting, adding that working with people who more skilled in the industry inspired and motivated her. She later said that it was cool \\\"tapping into the fanbase from the books themselves,\\\" that she received a lot of \\\"really, really cool feedback\\\" and noted that the production schedule was consistent. She described the group sessions as having the highest energy, with everyone having fun \\\"seeing each other become the characters,\\\" with none of it seen as draining.\\\\n\\\\nEpisodes\\\\n\\\\nSeries overview\\\\n\\\\nSeason 1 (2020-21)\\\\n\\\\nSeason 2 (2020)\\\\n\\\\nSeason 3 (2021)\\\\n\\\\nRelease\\\\nIn the United States, NBCUniversal\\\\'s advertisement sales website previously suggested that Cleopatra in Space would be broadcast on Universal Kids. Later, it emerged in January 2020 that the series would instead be included in the launch line-up of Peacock, NBCUniversal\\\\'s streaming service on April 15, 2020 to Xfinity customers, and July 15 to all customers nationwide.\\\\n\\\\nPrior to the scheduled release in the United States, the series premiered on November 25, 2019 on DreamWorks Channel, which is available in Southeast Asia and select other areas of Asia Pacific (specifically, Hong Kong, Indonesia, South Korea, Malaysia, Maldives, Myanmar, the Philippines, Pakistan, Singapore, and Taiwan). 19 episodes have since aired on the channel. The series also premiered in Poland on Teletoon+ on February 15, 2020 with a Polish dub. The series was also available in South Africa on Showmax, including all episodes in season one by February 23, 2020. By June 9, 2020, all 26 episodes of the show\\\\'s first season were made available on the Viaplay service in Scandinavia because of an agreement between NBCUniversal and the Nordic Entertainment Group (NENT Group).\\\\n\\\\nOn May 1, 2020, the entire first season of Cleopatra in Space was released on Globoplay, a Brazilian service and subsidiary of Grupo Globo, with the name Cleópatra no Espaço.\\\\n\\\\nOn July 15, when the show premiered on Peacock to all those in the United States, Mike Maihack praised the show\\\\'s release and all the hard work put in, giving it his endorsement. At the same time, he called the release of only 12 episodes \\\"disappointing,\\\" and lamented the absence of the sixth episode, \\\"Quarantine,\\\" which \\\"deals with a zombie-like flu and the consequences of Cleo avoiding quarantine,\\\" saying that it is something the whole world should \\\"be able to see right now.\\\" Two days later, when a fan asked about the missing sixth episode and the misspelled title of one of the episodes, an official Peacock account responded, saying they had corrected the episode title, but that for episode 6, \\\"this content is temporarily unavailable on the platform\\\" and that they appreciated the feedback, saying they \\\"will pass it along to the proper team.\\\" Later the same day, the same account said that there was \\\"no news on that at the moment.\\\" A few months later, on September 1, another fan asked about the episode, and an official Peacock account stated that the episode is \\\"not actively on Peacock\\\" but gave no further explanation as to why that was the case. The episode was eventually released on June 25, 2021.\\\\n\\\\nOn August 27, Bertrand Todesco called on Netflix France, and their main office in the United States, to broadcast the show outside the U.S., noting that \\\"international fans\\\" are asking about it every day, and that they could negotiate the rights with DreamWorks. The same day, Todesco thanked the fans of the show, saying he had seen \\\"a lot of incredible\\\" fan art from \\\"all over the web.\\\"\\\\n\\\\nIn September 2020, the show began airing on the Disney Channel in Russia with the name Клеопатра в космосе.\\\\n\\\\nIn October, in the UK, the show began airing on Sky One as part of a partnership with NBCUniversal. In a tweet, Mike Maihack hoped it was \\\"good news\\\" for fans of the show in the United Kingdom who have wanted to watch the show. Currently, the Sky One website allows subscribers to their Sky Go service to watch 25 episodes, but not episode 6, \\\"Quarantine\\\". When asked about this, Sky UK stated that this was not included because of the \\\"licensing on the episode.\\\"\\\\n\\\\nIn November, a new poster for Season 2 was released, as was a summary for the season, saying it would focus on Cleo and her friends \\\"embarking on a mission to search the galaxy for an ancient artifact that could defeat Octavian for good,\\\" and a preview video. On November 19, 2020, Season 2, premiered on Peacock.\\\\n\\\\nOn January 9, 2021, in Canada, the series started airing on Family CHRGD. Then on January 14, 2021, Season 3 was released on Peacock.\\\\n\\\\nOn July 14, 2021, the \\\"Complete First Season\\\" of the series, along with the Spanish version, Cleopatra en el Espacio, was released on Hulu, consisting of all three seasons which were released on Peacock.\\\\n\\\\nReception\\\\nEncyclopedia of Science Fiction contributor Steven Pearce gave a short positive review of the show, though was critical about the show\\\\'s writing and Cleopatra\\\\'s personality, saying it is \\\"very much your feisty American teen.\\\" The entry praises the \\\"nice background details\\\" and calls the series fun, amusing, \\\"brightly animated and engaging.\\\" Courtney Lofty described the series as being about \\\"badass women, talking cats, [and] space,\\\" noting that the overall vibe is a \\\"classic Saturday morning cartoon, with extremely quotable moments\\\" which is like Invader Zim. Cheryl Eddy on Gizmodo described the show as one aimed at children, but \\\"looks like a fun ride for geeky grown-ups\\\", while Karen Han and Petrana Radulovic in Polygon and Sam Stone on CBR reviewed it positively. Additionally, others described the show as \\\"a fun take on the original Cleopatra story\\\" and a \\\"comedic adventure\\\" which focuses on Cleopatra\\\\'s teenage years, where she is transported into the future \\\"to an Egyptian-themed planet...ruled by talking cats\\\" while dealing with the pressures of being \\\"a teenager in high school\\\" as she tries to fit in even as Octavian tries to kill her. Later, Petrana Radulovic wrote a positive review of the show. She described the series as wacky and vibrant, using its \\\"zany concept effectively,\\\" having interesting adventures, and has main characters who have \\\"typical stock cartoon personalities.\\\" At the same time, she compared the impulsive and cocky behavior of Cleo to Lance in Voltron: Legendary Defender and Ben in Ben 10. She further contrasted Cleopatra to Korra in The Legend of Korra and Adora in She-Ra and the Princesses of Power in that she is not ready to accept her destiny but will have to \\\"confront her own laziness,\\\" remaining as a \\\"carefree, imperfect heroine\\\" in the meantime. Radulovic also said that the \\\"electronic-infused Egyptian melodies of the score\\\" make it stand out, as do the outfits of the characters, while noting that the show is episodic like Gravity Falls rather than something like She-Ra and the Princesses of Power.\\\\n\\\\nThere were a number of other reviews of the show. In an episode of Tooned Up on the Renegade Pop Culture Podcast, one of the guests described the show as having a stellar voice cast, sharp writing, which is \\\"almost too self aware,\\\" while saying that they wished that the animation budget \\\"was a little bit higher.\\\" The same guest said that the show skews to those \\\"a little bit younger\\\" and said that the show takes a \\\"few episodes to find its stride,\\\" but once it does that, it is \\\"one of the easiest shows to binge.\\\" Another reviewer took a different tack, focusing on themes of libraries in the show, writing in the ALA\\\\'s I Love Libraries, writing that the library at Cleopatra\\\\'s futuristic high school contains information saved from the show\\\\'s villain, who destroyed most of recorded knowledge, and noting that the library\\\\'s section on Ancient Egypt, would, if in a real library, \\\"be housed in a library\\\\'s special collections.\\\" In contrast, Ashley Moulton on Common Sense Media rated the series 3 out of 5, noting that there is \\\"a lot of fantasy violence,\\\" while saying that Cleopatra is a \\\"fearless female lead,\\\" with her potential as a role model \\\"offset by the fact that she can be impulsive, impatient, overconfident, and not so dedicated to her schoolwork,\\\" adding that there is \\\"mild language...and flirtation,\\\" saying that the show isn\\\\'t educational even though it \\\"features a historical character.\\\" Rather it is, in Moulton\\\\'s view, focused on entertainment \\\"in the vein of \\\\'80s Saturday morning cartoons,\\\" and she describes the series as \\\"light, fun tween sci-fi\\\" animation which explores the past and future, while praising the \\\"interesting alien species, exciting fight scenes, and fun gadgets like robots and hover boards,\\\" and the world they live in as \\\"pretty cool.\\\" Even so, she argued that the characters are flat, while the characters \\\"gleefully engage in moderately violent fight scenes\\\" to defeat villains, and calling the characters \\\"disappointing\\\".\\\\n\\\\nExplanatory notes\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n\\\\nDepictions of Cleopatra on television\\\\n2020 American television series debuts\\\\n2020s American animated television series\\\\nAmerican children\\\\'s animated action television series\\\\nAmerican children\\\\'s animated space adventure television series\\\\nAmerican children\\\\'s animated comic science fiction television series\\\\nAmerican children\\\\'s animated science fantasy television series\\\\nAmerican flash animated television series\\\\nTelevision series by DreamWorks Animation\\\\nTelevision series by Universal Television\\\\nAnimated television series about teenagers\\\\nPeacock (streaming service) original programming\\\\nTelevision shows based on comics\\\\nPeacock (streaming service) children\\\\'s programming\\\\nWorks about Cleopatra'},\\n\",\n \" {'docid': 'doc-en-12',\\n\",\n \" 'text': 'Impression Products, Inc. v. Lexmark International, Inc., 581 U.S. ___ (2017), is a decision of the Supreme Court of the United States on the exhaustion doctrine in patent law in which the Court held that after the sale of a patented item, the patent holder cannot sue for patent infringement relating to further use of that item, even when in violation of a contract with a customer or imported from outside the United States. The case concerned a patent infringement lawsuit brought by Lexmark against Impression Products, Inc., which bought used ink cartridges, refilled them, replaced a microchip on the cartridge to circumvent a digital rights management scheme, and then resold them. Lexmark argued that as they own several patents related to the ink cartridges, Impression Products was violating their patent rights. The U.S. Supreme Court, reversing a 2016 decision of the Federal Circuit, held that the exhaustion doctrine prevented Lexmark\\\\'s patent infringement lawsuit, although Lexmark could enforce restrictions on use or resale of its contracts with direct purchasers under regular contract law (but not as a patent infringement lawsuit). Besides printer and ink manufacturers, the decision of the case could affect the markets of high tech consumer goods and prescription drugs.\\\\n\\\\nBackground\\\\n\\\\nFactual setting\\\\n\\\\nLexmark International, Inc. makes and sells printers and toner cartridges for its printers. Lexmark owns a number of patents that cover its cartridges and their use. Lexmark sold the cartridges at issue in this case—some in the United States and some abroad.\\\\n\\\\nDomestic sales \\\\n\\\\nLexmark\\\\'s domestic sales were in two categories. A \\\"Regular Cartridge\\\" is sold at \\\"list price\\\" and confers an absolute title and property right on the buyer. A \\\"Return Program Cartridge\\\" is sold at a discount of about 20 percent, and is subject to post-sale restrictions: The buyer may not reuse the cartridge after the toner runs out and may not transfer it to anybody else. The first branch of the case turns on the legal status of these post-sale restrictions.\\\\n\\\\nLexmark manufactured the toner cartridges with microchips in them, which send signals to the printers indicating toner level. When the amount of toner in a cartridge falls below a certain level, the printer will not operate with that cartridge. Also, the printer will not operate with a Return Program Cartridge that has been refilled by a third party. Thus, Lexmark\\\\'s technology prevented violation of the post-sale restriction against refilling the Return Program Cartridges. The Regular Cartridges do not have this anti-refill feature and can therefore be refilled and reused (but they cost 20 percent more).\\\\n\\\\n\\\"To circumvent this technological measure,\\\" however, \\\"third parties have \\\\'hacked\\\\' the Lexmark microchips. They created their own \\\"unauthorized replacement\\\" microchips that, when installed in a Return Program cartridge, fool the printer into allowing reuse of that cartridge. Various companies purchase used Return Program Cartridges from the customers who bought them from Lexmark. They replace the microchips with \\\"unauthorized replacement\\\" microchips, refill the cartridges with toner, and sell the \\\"re-manufactured\\\" cartridges to resellers such as Impression Products for marketing to consumers for use with Lexmark printers. Lexmark had previously argued in Lexmark International, Inc. v. Static Control Components, Inc. that replacing these microchips violated copyright law and the Digital Millennium Copyright Act (DMCA), but both federal and the Supreme Court have ruled against Lexmark, affirming that replacing the microchips is not in violation of copyright.\\\\n\\\\nImported cartridges\\\\n\\\\nThe second branch of the case involves cartridges that Lexmark sold outside the US. While some of the foreign-sold cartridges were Regular Cartridges and some were Return Program Cartridges, this branch of the case does not involve any distinction among the two types of imported cartridges.\\\\n\\\\nTrial court decision\\\\n\\\\nThe district court granted Impression\\\\'s motion to dismiss Lexmark\\\\'s claim of infringement involving the single-use cartridges Lexmark had first sold in the United States. The district court concluded that the Supreme Court in Quanta Computer, Inc. v. LG Electronics, Inc. found exhaustion where \\\"the Supreme Court determined that the agreements [at issue] broadly authorized Intel [the seller] to sell the licensed products without restrictions or conditions.\\\" The district court said \\\"that Quanta overruled Mallinckrodt sub silentio,\\\" and therefore \\\"those post-sale use restrictions do not prevent patent rights from being exhausted given that the initial sales were authorized and unrestricted.\\\"\\\\n\\\\nThe district court held, however, that the exhaustion doctrine did not apply to the cartridges that Lexmark had sold abroad. It said that international exhaustion did not apply to patents because Kirtsaeng v. John Wiley & Sons, Inc., which established international exhaustion in at least some cases, applied only to copyrights. The court therefore denied Impression\\\\'s motion to dismiss Lexmark\\\\'s claim of infringement involving the cartridges Lexmark had sold abroad.\\\\n\\\\nGovernment amicus curiae position\\\\n\\\\nIn its amicus curiae brief, the US Government argued that Mallinckrodt had been wrongly decided in 1992 and in any case it had been overruled sub silentio in Quanta. It stated:\\\\nIn the view of the United States, the first authorized sale of a patented article in the United States wholly exhausts the patentee\\\\'s exclusive rights in that article, notwithstanding any post-sale restriction imposed by the patentee.\\\\nThe government also argued that the decision of Jazz Photo Corp. v. United States International Trade Commission (2001) should be partially overruled in light of Kirtsaeng insofar as it held that foreign sales can never exhaust US patent rights. When the patentee neither makes nor authorizes a foreign sale, as occurred in Boesch v. Graff, it is proper to say no exhaustion occurred. But when the patentee makes or authorizes a foreign sale, and fails expressly to reserve its US rights, then exhaustion should be found. In the present case, Lexmark made the foreign sales and failed to expressly reserve its US rights; therefore, the sale exhausted the patent rights.\\\\n\\\\nFederal Circuit decision\\\\n\\\\nThe parties each appealed. After a three judge panel had heard oral argument the Federal Circuit sua sponte set the case for argument en banc in the first instance and invited the filing of amicus curiae briefs.\\\\n\\\\nMajority opinion\\\\n\\\\nJudge Taranto, writing for a 10-2 majority, reaffirmed both of the prior Federal Circuit rulings. In summary, the court held:\\\\n\\\\nFirst, we adhere to the holding of Mallinckrodt, Inc. v. Medipart, Inc. that a patentee, when selling a patented article subject to a single-use/no-resale restriction that is lawful and clearly communicated to the purchaser, does not by that sale give the buyer, or downstream buyers, the resale/reuse authority that has been expressly denied. Such resale or reuse, when contrary to the known, lawful limits on the authority conferred at the time of the original sale, remains unauthorized and therefore remains infringing conduct under the terms of §\\\\xa0271. Under Supreme Court precedent, a patentee may preserve its §\\\\xa0271 rights through such restrictions when licensing others to make and sell patented articles; Mallinckrodt held that there is no sound legal basis for denying the same ability to the patentee that makes and sells the articles itself. We find Mallinckrodt\\\\'s principle to remain sound after the Supreme Court\\\\'s decision in Quanta Computer, Inc. v. LG Electronics, Inc.\\\\xa0.\\\\xa0.\\\\xa0.\\\\n\\\\nSecond, we adhere to the holding of Jazz Photo Corp. v. International Trade Comm\\\\'n, that a U.S. patentee, merely by selling or authorizing the sale of a U.S.-patented article abroad, does not authorize the buyer to import the article and sell and use it in the United States, which are infringing acts in the absence of patentee-conferred authority. Jazz Photo\\\\'s no-exhaustion ruling recognizes that foreign markets under foreign sovereign control are not equivalent to the U.S. markets under U.S. control in which a U.S. patentee\\\\'s sale presumptively exhausts its rights in the article sold. A buyer may still rely on a foreign sale as a defense to infringement, but only by establishing an express or implied license—a defense separate from exhaustion, as Quanta holds—based on patentee communications or other circumstances of the sale. We conclude that Jazz Photo\\\\'s no-exhaustion principle remains sound after the Supreme Court\\\\'s decision in Kirtsaeng v. John Wiley & Sons, Inc., in which the Court did not address patent law or whether a foreign sale should be viewed as conferring authority to engage in otherwise-infringing domestic acts. Kirtsaeng is a copyright case holding that 17 U.S.C. §\\\\xa0109(a) entitles owners of copyrighted articles to take certain acts \\\"without the authority\\\" of the copyright holder. There is no counterpart to that provision in the Patent Act, under which a foreign sale is properly treated as neither conclusively nor even presumptively exhausting the U.S. patentee\\\\'s rights in the United States.\\\\n\\\\nDomestic exhaustion\\\\n\\\\nIn this part of its opinion, the Federal Circuit reaffirmed its Mallincrodt decision and rejected contentions that Quanta had silently overruled it.\\\\n\\\\n§\\\\xa0271 abrogates common-law rule\\\\n\\\\nThe court began by distinguishing the Patent Act\\\\'s and Copyright Act\\\\'s respective approaches to infringement. in 17 U.S.C. §\\\\xa0109(a) the Copyright Act says, \\\"Notwithstanding the provisions of section 106(3),\\\" defining infringement by selling, a purchaser \\\"is entitled, without the authority of the copyright owner, to sell or otherwise dispose of the possession\\\" of a purchased copy of a work. In contrast, the Patent Act contains no exhaustion provision. Therefore, the Patent Act requires a \\\"conferral of \\\\'authority\\\\' by the patentee .\\\\xa0.\\\\xa0. in order for the actions listed in §\\\\xa0271(a) not to constitute infringement.\\\" This means there must be \\\"permission from the patentee\\\" to avoid infringement. The court does not accept exhaustion as a form of \\\"constructive\\\" permission. Hence, if the patentee places explicit limits or conditions on its permission, they qualify the scope of the permission. This has the effect of limiting the common law.\\\\n\\\\nGeneral Talking Pictures rule applies to \\\"conditional\\\" sale\\\\n\\\\nThe court turned to the General Talking Pictures decision, which holds \\\"that Lexmark would not have exhausted its patent rights in those cartridges, upon the manufacturing licensee\\\\'s sale (the first sale), if a buyer with knowledge of the restrictions resold or reused them in violation of the restrictions.\\\" Although the government in its amicus curiae brief and defendant Impression argue \\\"that a different result is required—that Lexmark automatically lost its patent rights—simply because Lexmark sold the Return Program cartridges itself, subject to the same communicated restriction, rather than having left the manufacture and sale to others under license,\\\" the court does not accept that:\\\\n\\\\nWe conclude otherwise, as we did in Mallinckrodt and subsequent decisions. A sale made under a clearly communicated, otherwise-lawful restriction as to post-sale use or resale does not confer on the buyer and a subsequent purchaser the \\\"authority\\\" to engage in the use or resale that the restriction precludes. And there is no sound reason, and no Supreme Court precedent, requiring a distinction that gives less control to a practicing-entity patentee that makes and sells its own product than to a non-practicing-entity patentee that licenses others to make and sell the product.\\\\n\\\\nQuanta distinguishable and inapplicable\\\\n\\\\nThe court turned to the Quanta decision and found it inapplicable to the present issues. \\\"\\\\'Quanta did not involve a patentee\\\\'s sale at all, let alone one subject to a restriction or, more particularly, a single-use/no-resale restriction.\\\" Rather, Quanta involved a patentee\\\\'s (LGE\\\\'s) license to a manufacturer (Intel) that sold to the accused infringer (Quanta). LGE had not limited Intel\\\\'s license to manufacture the patented product, although it imposed contractual obligations on Intel. \\\"No conditions limited Intel\\\\'s authority to sell products substantially embodying the patents.\\\" The Federal Circuit emphasized: \\\"There were no patentee sales, and there were no restrictions on the sales made by the licensee.\\\" Those facts were removed from the case at bar. Thus the Quanta \\\"Court\\\\'s discussion of that issue does not undermine Mallinckrodts ruling that a patentee can preserve its patent rights through restrictions on its sales.\\\" The Federal Circuit also emphasized as significant the failure of the Quanta Court to explicitly repudiate Mallinckrodt despite the fact that in its amicus brief \\\"the government prominently featured an argument that Mallinckrodt was incorrect and should be repudiated.\\\"\\\\n\\\\nPrior cases\\\\n\\\\nThe court then turned to the prior Supreme Court cases. Reviewing them, it found that although they used sweeping language that a patentee\\\\'s sale of the patented product placed it beyond the reach of the patent, so that no post-sale restriction could be enforced under the patent laws, that language went beyond the actual facts of the cases. First, the sales were in most cases without any condition or restriction on what the buyer might do with the product. Second, in the cases where an explicit condition or restriction was imposed, the case involved a tie-in or a price-fix.\\\\n\\\\nThe Court conceded that in the General Electric case, the Supreme Court had said: \\\"It is well settled, as already said, that where a patentee makes the patented article, and sells it, he can exercise no future control over what the purchaser may wish to do with the article after his purchase. It has passed beyond the scope of the patentee\\\\'s rights.\\\" But that case involved an antitrust challenge to GE\\\\'s distribution of lamps that did not meet that description. The case involved price restrictions on a licensed manufacturer. The Federal Circuit then explained that the word \\\"settled\\\" in the Supreme Court\\\\'s statement had a special, narrow meaning:\\\\n\\\"We read that language to deem \\\\'settled\\\\' only what was settled in the cited precedents—a patentee\\\\'s sales without restrictions exhaust patent rights in the item sold.\\\" Thus, the Supreme Court\\\\'s sweeping exhaustion language applies precedentially only to cases in which either the sale was without condition or restriction or else the sale was made with a tie-in or price-fixing condition. \\\"But the Court did not rule that all restrictions on a patentee\\\\'s sale were ineffective to preserve the patentee\\\\'s patent-law rights.\\\"\\\\n\\\\nSimilarly, in United States v. Univis Lens Co., the Supreme Court\\\\'s sweeping language must now be limited to the factual context of the case:\\\\n\\\\nMoreover, although some language in Univis, like language in other decisions in the area, can be taken out of context and read as going beyond the specific restrictions involved, the most the Court ruled, even as to patent law all by itself, was that a vertical price-control restriction was ineffective to preserve patent rights after sales of articles embodying the patents. While Univis is controlling on what it decided on the issues before it, we do not think it appropriate to give broad effect to language in Univis, taken out of context, to support an otherwise-unjustified conclusion here on a question not faced there.\\\\n\\\\nThe Federal Circuit therefore drew this conclusion from the past series of Supreme Court cases on exhaustion:\\\\n\\\\nFor the foregoing reasons, we think that the best lesson to draw from the Supreme Court\\\\'s precedents, as applied to the question before us, is that a patentee may preserve its patent rights by otherwise-proper restrictions when it makes and sells patented articles itself and not only when it contracts out manufacturing and sales.\\\\n\\\\nPatent law trumps common law\\\\n\\\\nThe Federal Circuit returned to the common law and Lord Coke\\\\'s commentary on it. Again, the court insisted that Congress had overridden the common law\\\\'s prohibitions on post-sale restraints, in order to promote technological progress:\\\\n\\\\n[W]hatever considerations might go into a jurisdiction\\\\'s choice as to the background rule for personal property in general, lawmaking authorities may reasonably make different choices for particular kinds of property. Notably, as to intellectual property in its various forms, Congress, implementing the Constitution, has long deemed it important to incentivize creation and disclosure through grants to the creator of rights to exclude others for a time.\\\\xa0.\\\\xa0.\\\\xa0. That overriding legislative prescription removes the patented-article sale from the scope of Lord Coke\\\\'s 1628 description of his country\\\\'s general judicially fashioned property law.\\\\xa0.\\\\xa0.\\\\xa0. In short, notwithstanding Lord Coke\\\\'s description of English general personal-property judge-made law, the patent-specific statutory analysis must govern here.\\\\n\\\\nLikely effects on public\\\\n\\\\nThe court then turned to what it called \\\"the likely real-world consequences of one answer or another to the exhaustion question presented here.\\\" The court noted that in Kirtsaeng the Supreme Court had envisioned serious adverse effects on competition unless Coke\\\\'s 1628 property law rules were followed. The Federal Circuit said that did not apply to patents:\\\\n\\\\n[W]e see no basis for predicting the extreme, lop-sided impacts the Court found plausible in Kirtsaeng in different circumstances. Mallinckrodt has been the governing case law since 1992 and has been reiterated in subsequent precedent. And yet we have been given no reliable demonstration of widespread problems not being solved in the marketplace. Given General Talking Pictures, the only question is about patentees\\\\' ability to do for their own sales what they already can do by contracting out their manufacturing and sales. Regarding the specific scenario we are addressing today—in which the patentee has sought to preserve its patent rights by conditioning its first sale on a single-use/no-resale restriction of which the accused infringer had adequate notice at the time of purchase—we have been given no proof of a significant problem with enforcing patent rights.\\\\n\\\\nFurthermore, the Federal Circuit maintained, the conduct challenged here can have benefits. Under Lexmark\\\\'s program, customers who agree to the restriction pay a lower price than those who do not. It could be that the companies that refill the cartridges use inferior products that could harm the Lexmark machines, which \\\"could harm Lexmark\\\\'s reputation.\\\" To assume that the restrictions are illegitimate would run counter to the trends \\\"over the last four decades, that have displaced the strict condemnation of various vertical restrictions that characterized\\\" earlier antitrust and patent-misuse law in the first part of the twentieth century. \\\"Field-of-use, territorial, and other limitations on intellectual property licenses may serve procompetitive ends by allowing the licensor to exploit its property as efficiently and effectively as possible.\\\" Therefore, the court concluded it is appropriate to apply to post-sale restrictions the same tolerance that the General Talking Pictures doctrine accords limitations in manufacturing licenses.\\\\n\\\\nInternational exhaustion\\\\n\\\\nIn this part of its opinion, the Federal Circuit reaffirmed its Jazz Photo opinion and rejected contentions that Kirtsaeng had undermined the basis for Jazz Photo. The Federal Circuit insisted that \\\"Kirtsaeng says nothing about patent law.\\\"\\\\n\\\\nThe court emphasized the differences between patent law and copyright law. For example, patent law gives patentees an exclusive right to use of the invention but copyright law gives no general exclusionary right as to use (it gives exclusive public performance and display \\\"use\\\" rights, but not others). Also, it is much more costly and time-consuming to obtain a patent than a copyright. The court did not explain, however, the way that or other differences between copyrights and patents called for contrary results as to international exhaustion.\\\\n\\\\nThe court did say that the US patent statute gives patentees the reward available from \\\"sales in American markets, not from sales in foreign markets.\\\" A sale in a foreign market therefore does not furnish a proper basis for finding exhaustion. \\\"American markets differ substantially from markets in many other countries, and not just because of disparities in wealth that can lead to dramatically different prices\\\" in this country and abroad (as was the case in Kirtsaeng). \\\"Government policies differ dramatically, including policies on price regulation and, most particularly, policies on the availability and scope of patent protection.\\\" The court did not explain further, however, whether and how such dramatic differences in policy applied to the toner cartridges at issue in the present case.\\\\n\\\\nThe court then turned to the only Supreme Court case on foreign exhaustion, Boesch v. Graff. In that case, Graff was the assignee of a US patent. Boesch bought the product from a German supplier who had a prior-user right under German law to make and sell the product, because the supplier had begun activity before the application for the German counterpart patent was filed. The US assignee and the inventor had no connection with Boesch. When Graff imported the product into the US, Boesch sued for infringement. The US courts found Boesch liable. The rights that Boesch had under German law did not entitle him to import the product into the US. That is governed by US law. The US patentee had never \\\"received any royalty or given any license to use the patented article in any part of the United States.\\\"\\\\nAccordingly, the court held, a foreign sale does not of its own force authorize importation into the US.\\\\n\\\\nThis does not mean, however, that a patentee by its conduct cannot waive its US rights, be estopped from asserting them, or be found to have granted an implied license.\\\\n\\\\nThe court expressed concern that overruling Jazz Photo would harm the US drug industry:\\\\n\\\\nThere seems to be no dispute that U.S.-patented medicines are often sold outside the United States at substantially lower prices than those charged here and, also, that the practice could be disrupted by the increased arbitrage opportunities that would come from deeming U.S. rights eliminated by a foreign sale made or authorized by the U.S. patentee.\\\\n\\\\nFinally, the court rejected a proposal that exhaustion should be presumed unless the patentee express states that it reserves its US rights. Foreign governments might \\\"prohibit sellers from stating reservations of rights that would make importation into and sale in the United States more difficult.\\\" Also: \\\"Intermediary companies between the foreign purchase and the importation into the United States may be created that make it difficult for the U.S. patentee to carry an affirmative burden of proving adequate notice of reservations attached to a foreign-sold article.\\\"\\\\n\\\\nDissenting opinion\\\\n\\\\nJudge Dyk, joined by Judge Hughes, dissented from both branches of the court\\\\'s exhaustion analysis. Judge Dyk summarized his dissent in these terms:\\\\n\\\\nI would overrule our decision in Mallinckrodt as inconsistent with governing Supreme Court authority and overrule Jazz Photo to the extent that it imposes a blanket ban on foreign exhaustion. I would recognize foreign exhaustion where the U.S. rights holder has not notified the buyer of its retention of the U.S. patent rights.\\\\n\\\\nDomestic exhaustion\\\\n\\\\nIn this part of the dissent, Judge Dyk argued that the majority had misunderstood the Supreme Court\\\\'s exhaustion jurisprudence in order to substitute its own ideas of the proper balance between patent rights and public rights. He began by saying:\\\\n\\\\nFirst, I agree with the government that Mallinckrodt was wrong when decided, and in any event cannot be reconciled with the Supreme Court\\\\'s recent decision in Quanta Computer, Inc. v. LG Electronics, Inc. We exceed our role as a subordinate court by declining to follow the explicit domestic exhaustion rule announced by the Supreme Court.\\\\n\\\\nHe argued that since 1850 the Supreme Court has held that a sale by the patentee or its licensee exhausts all patent rights. In such cases, \\\"The question of whether the seller has \\\\'authorized\\\\' the buyer to use or resell the item is simply irrelevant.\\\" Post-sale restrictions could not be enforced under federal patent law. The only Supreme Court case to depart from that principle was Henry v. A.B. Dick Co., and it was explicitly overruled five years later by Motion Picture Patents Co. v. Universal Film Mfg. Co. The principle of the overruled Dick case that a patentee could impost a post-sale restriction by giving a buyer notice of it was \\\"the same as the panel\\\\'s holding in Mallinckrodt and the majority\\\\'s holding in this case.\\\"\\\\n\\\\nHe insisted that the majority opinion misread the Motion Picture Patents decision by asserting \\\"that it only \\\\'held particular restrictions improper\\\\' .\\\\xa0.\\\\xa0. but \\\\'did not rule that all restrictions on a patentee\\\\'s sale were ineffective to preserve the patentee\\\\'s patent-law rights.\\\\'\\\" He explained:\\\\n\\\\nThat is not accurate. Motion Picture Patents did not leave behind the remnants of A.B. Dick—minus tie-ins and resale price maintenance. To the contrary, the Court in Motion Picture Patents found that \\\"[t]he patent law furnishes no warrant for\\\" the restrictions imposed by the patent owner.\\\\n\\\\nLater cases, such as Quanta, confirmed this \\\"broad patent exhaustion rule [in Motion Picture Patents] and left no room for a resurrection of A.B. Dick.\\\"\\\\n\\\\nHe next turned to the majority\\\\'s referenced to \\\"conditional sales\\\" and \\\"unconditional sales,\\\" and said that the majority misconstrued the terms. \\\"Conditional sales,\\\" he said, as used in pre-Mallinckrodt case law referred only to the retention of title for a security interest in installment purchases. \\\"In other words, a sale with restrictions could nonetheless be an \\\\'unconditional\\\\' sale in which title passes, with the restrictions invalid under the patent laws because of exhaustion.\\\"\\\\n\\\\nHe then criticized the majority for making up special rules for patent cases that differed from the common law and general legal principles, citing Supreme Court admonitions not to do that--\\\"The Supreme Court has repeatedly instructed us not to ignore traditional legal principles to fashion rules \\\\'unique to patent disputes.\\\\'\\\"\\\\n\\\\nFinally, Judge Dyk took issue on multiple grounds with the majority\\\\'s efforts to distinguish and limit the Supreme Court\\\\'s rulings. \\\"The majority\\\\'s justifications for refusing to follow Supreme Court authority establishing the exhaustion rule misconceive our role as a subordinate court.\\\" Each justification in the majority decision was unsupportable, he said.\\\\n\\\\n \\\"First, the majority characterizes the statement of the exhaustion rule in the Supreme Court cases as mere dictum because in those cases there was either no restriction imposed or the restriction would otherwise violate the antitrust laws. But the cases impose no such qualification on the rule announced. The Supreme Court has repeatedly advised the courts of appeals that our task is to follow the rules proclaimed by the Court, and not to attempt to distinguish Supreme Court cases on their facts.\\\"\\\\n \\\"Second, the majority relies on 35 U.S.C. §§\\\\xa0271(a) and 154(a)(1) to suggest that a broad reading of the exhaustion doctrine is inconsistent with statutory language making an act of infringement .\\\\xa0.\\\\xa0. any use or sale of a patented invention \\\\'without authority\\\\' of the patent owner, and providing the patent owner with a \\\\'right to exclude.\\\\'\\\" But the patent exhaustion doctrine is a limitation on the operation of those sections, and applies notwithstanding them.\\\\n \\\"Third, the majority claims that giving full sweep to the articulation of the exhaustion doctrine in Quanta and other cases would be inconsistent with the Supreme Court\\\\'s decision in General Talking Pictures Corp. v. Western Electric Co. .\\\\xa0.\\\\xa0. The majority suggests it would be incongruous if \\\\'a patentee cannot preserve its patent rights against uses of a patented article .\\\\xa0.\\\\xa0. if, instead of licensing someone else to make and sell the article, it chooses to make and sell the article itself.\\\\'\\\\xa0\\\"\\\\n\\\\nBut General Talking Pictures was a case of a license to manufacture in a limited field, not a sale with a post-sale restriction. The cases recognize that distinction. Thus, in Quanta the Supreme Court stated that General Talking Pictures \\\"held that exhaustion did not apply because the manufacturer had no authority to sell the amplifiers for commercial use.\\\" And where the manufacturer in that case (Intel) did have a general authority to make and sell, the Supreme Court held that exhaustion applied to the sale.\\\\n\\\\nThe majority found \\\"tension\\\" between \\\"the Supreme Court\\\\'s broad statement of the exhaustion rule and General Talking Pictures\\\" and sought to resolve it by extending the rule of General Talking Pictures and contracting the exhaustion doctrine in the area of possible conflict. But, Dyk maintained:\\\\n\\\\n[I]t is not our task to ignore Supreme Court rulings as \\\"unjustifi[ed]\\\" or \\\"unsound\\\" because they are purportedly inconsistent with other Supreme Court cases. The distinction between restrictions on sales (impermissible) and restrictions on licensees (permissible) exists in the Court\\\\'s precedent, and it is not for us to decide if it is a sound distinction.\\\\n\\\\n \\\"Finally, the majority proposes that we should somehow sustain the restriction here because it may be pro-competitive. Exhaustion does not turn on whether a particular post-sale restriction is desirable or undesirable, pro-competitive or anti-competitive, but whether the sale was authorized and the item has passed beyond the scope of the patent monopoly.\\\" Furthermore, the Supreme Court said in Kirtsaeng that a prohibition on resale is \\\"manifestly anti-competitive.\\\"\\\\n\\\\nDyk concluded his discussion of domestic exhaustion with the statement: \\\"There is, in sum, no colorable basis for the majority\\\\'s failure to follow the exhaustion rule for domestic sales as articulated by the Court in Quanta and numerous other cases.\\\"\\\\n\\\\nInternational exhaustion\\\\n\\\\nIn this part of the dissent, Judge Dyk argued for a nuanced balance that called for different results depending on whether the patentee was responsible for the sale abroad that was alleged to trigger exhaustion.\\\\n\\\\nHe began by pointing out that because Lexmark\\\\'s foreign sales were made without any restrictions or reservations, \\\"even under the majority\\\\'s cramped view of exhaustion, there is no question that the sales would have exhausted Lexmark\\\\'s domestic patent rights. The issue is whether the foreign location of the sale should lead to a different result, as we previously held in Jazz Photo.\\\"\\\\n\\\\nHe then turned to \\\"the centerpiece of the majority\\\\'s holding that there is a doctrinal blanket ban on foreign exhaustion, namely the Supreme Court\\\\'s decision in Boesch v. Graff. But \\\"Boesch announced no such blanket ban,\\\" he said. \\\"It did not even involve an authorized sale by the holder of U.S. patent rights but rather a sale by a third party under a foreign law\\\\'s prior use exception.\\\" But \\\"Boesch does not apply here because the foreign sales were made by Lexmark.\\\"\\\\n\\\\nIn every US lower court decision before Jazz Photo: \\\"When the sale was made by an entity not holding U.S. patent rights, as in Boesch, or when the authorized foreign seller clearly reserved U.S. rights, there was no exhaustion.\\\" In contrast, \\\"where the foreign sale was made by a seller holding U.S. patent rights without a contractual reservation of U.S. rights, exhaustion occurred as a result of an authorized foreign sale.\\\"\\\\n\\\\nDyk maintained that \\\"Kirtsaeng provides significant guidance and cannot be dismissed as simply a copyright case, or as limited to the \\\\'first sale\\\\' provision of the Copyright Act.\\\" Rather, the policies that animated Kirtsaeng typically apply to patent exhaustion. But because in some cases a difference may be significant, there should be abalanced approach. Dyk argued for \\\"put[ting] the burden on the U.S. rights holder to provide notice of a reservation of U.S. rights to the purchaser.\\\" Thus, he \\\"would recognize foreign exhaustion where the U.S. rights holder has not notified the buyer of its retention of the U.S. patent rights.\\\"\\\\n\\\\nSupreme Court\\\\nIn March 2016, Impression filed a petition for certiorari in the U.S. Supreme Court. Impression presented these questions in its petition:\\\\n\\\\n\\\\xa0\\\\xa0\\\\xa01. Whether a \\\"conditional sale\\\" that transfers title to the patented item while specifying post-sale restrictions on the article\\\\'s use or resale avoids application of the patent exhaustion doctrine and therefore permits the enforcement of such post-sale restrictions through the patent law\\\\'s infringement remedy.\\\\n\\\\xa0\\\\xa0\\\\xa02. Whether, in light of this Court\\\\'s holding in Kirtsaeng v. John Wiley & Sons, Inc., 133 S. Ct. 1351, 1363 (2013), that the common law doctrine barring restraints on alienation that is the basis of exhaustion doctrine \\\"makes no geographical distinctions,\\\" a sale of a patented article—authorized by the U.S. patentee—that takes place outside of the United States exhausts the U.S. patent rights in that article.\\\\n\\\\nOn June 20, 2016, the Court invited the Solicitor General to file briefs in this case expressing the views of the United States. In October 2016, the government filed the requested amicus curiae brief. It recommended grant of certiorari on both questions. The brief argues that the \\\"Federal Circuit\\\\'s decision misreads\\\" the Supreme Court\\\\'s precedents and \\\"would substantially erode the exhaustion doctrine.\\\" The Supreme Court granted certiorari on December 2, 2016 and heard oral argument in the case on March 21, 2017. The Court published its decisions on May 30, 2017.\\\\n\\\\nMajority\\\\nA unanimous Court found that Lexmark exhausted its patent rights upon first sale domestically, even with the single-use/no-resale restrictions imposed by Lexmark in contracts with its customers, although such restrictions could be enforced under contract law. The Court noted that the exhaustion doctrine has a long history and that any change would have significant effects on commerce in the modern world, noting that \\\"extending the patent rights beyond the first sale would clog the channels of commerce, with little benefit from the extra control that the patentees retain,\\\" noting that complex modern supply chains can involve large numbers of patents. Chief Justice Roberts, in his opinion, compared the situation to automobile repair shops: \\\"The business works because the shop can rest assured that, so long as those bringing in the cars own them, the shop is free to repair and resell those vehicles. That smooth flow of commerce would sputter if companies that make the thousands of parts that go into a vehicle could keep their patent rights after the first sale.\\\"\\\\n\\\\nSeven justices joined the Court\\\\'s opinion extending that reasoning to items imported from abroad. Lexmark had argued, and the Federal Circuit agreed, that sale abroad \\\"does not trigger patent exhaustion unless the patentee \\\\'expressly or implicitly transfers or licenses\\\\' its rights.\\\" The Court, however, ruled that \\\"[a]n authorized sale outside the United States, just as one within the United States, exhausts all rights under the Patent Act.\\\" The Court relied on its 2013 decision in Kirtsaeng v. John Wiley & Sons, Inc. on a nearly identical issue under copyright law. Because the underlying statute was not clear as to its geographical scope, the Court in Kirtsaeng decided that, because the statute was based in the common law exhaustion doctrine, which is not limited in geographic extent, the statute at issue was therefore not intended to be limited to only U.S. sales. Applying the same principle to patent law, which historically has a close connection with copyright law, was \\\"straightforward\\\" and \\\"the bond between [copyright and patent law] leaves no room for a rift on the question of international exhaustion\\\".\\\\n\\\\nPartial dissent\\\\nJustice Ginsburg dissented from the Court\\\\'s holding with respect to imported items. Adhering to substantially the same reasoning of her dissent in Kirtsaeng, Justice Ginsburg argued that because patent law is territorial and the sale of an item abroad is \\\"independent[] of the U.S. patent system, it makes little sense to say that such a sale exhausts an inventor\\\\'s U.S. patent rights.\\\" She would have upheld the Federal Circuit\\\\'s decision that sale abroad does not exhaust a patentee\\\\'s rights in the United States.\\\\n\\\\nCommentary\\\\n\\\\nGerstein\\\\n\\\\nRobert M. Gerstein concluded that further review in the Supreme Court was likely:\\\\nGiven the Supreme Court\\\\'s interest in patent cases, a vigorous dissent in Lexmark that relies on a number of Supreme Court precedents, including Quanta and Kirtsaeng, and the position of the Justice Department that Quanta overruled Mallinckrodt, it would not be surprising to see the Supreme Court take up Lexmark in its next term.\\\\n\\\\nDodd and Dowd\\\\n\\\\nJeff C. Dodd and Matthew J. Dowd viewed the decision as an affirmation of strong patent rights:\\\\nLexmark embraces a very strong view of patent rights and a narrow view of the scope of exhaustion. It affirms that patent holders have wide latitude to segment and control distribution in the market channels for products covered by patents. This latitude is particularly wide with respect to limiting the import into the United States of patented goods sold in authorized sales in foreign markets even where restrictions on resale were not proven to have been communicated to foreign buyers. Even so, the court left open the possibility that foreign sales, under the right circumstances, may incorporate an implied license to import and use the product within the United States.\\\\n\\\\nCukierski and Masia\\\\n\\\\nKevin J. Cukierski and Adam H. Masia see the decision as \\\"pro-patent owner\\\" but warn again premature celebration:\\\\nBut take caution—it is likely that the Supreme Court will be asked to hear the case. Given the tension between this case and the Supreme Court\\\\'s language in Quanta and Kirtsaeng, along with the discord at the district court level and among commentators before the Federal Circuit\\\\'s decision, there\\\\'s a good chance the Supreme Court will do so. Until the Supreme Court has its say, you should take precautions in case the Supreme Court takes an expansive view of patent exhaustion and decides to remove these exceptions.\\\\n\\\\n\\\"Without Precedent\\\"\\\\n\\\\nAnother commentator (unsigned comment) indicated a skeptical view of the Federal Circuit\\\\'s tendency to march to a different drummer. After quoting Judge Dyk\\\\'s admonition, \\\"We exceed our role as a subordinate court by declining to follow the explicit domestic exhaustion rule announced by the Supreme Court,\\\" he (or she) observed:\\\\nFor present purposes, it is simply worth noting that the Federal Circuit appears to be inching closer again to the concept that patent law is simply a unique beast, with unique rules and requirements. The Supreme Court has taken a skeptical view of that approach in the past. And may well again.\\\\n\\\\nJahn, Pichler, and Lo\\\\n\\\\nPaul Jahn, Rufus Pichler and Lincoln Lo raise many questions (mostly about \\\"clear communication\\\") about what the Lexmark majority opinion left unresolved:\\\\n\\\\n Conflict or tension with Quanta: \\\"Quanta expressly distinguished implied licenses and exhaustion, holding that disclaimers of license rights are \\\\'irrelevant\\\\' where \\\\'the right to practice the patents is based not on implied license but on exhaustion.\\\\'\\\\xa0\\\" But \\\"the Federal Circuit appears to treat exhaustion like an implied license—one that the patentee can disclaim by \\\\'clearly communicate[d]\\\\' restrictions.\\\" Quanta appears to hold that the patentee\\\\'s attempt to impose a post-sale restriction on a manufacturing licensee is ineffective if the license does not conform to the General Talking Pictures case.\\\\n \\\"[W]hat arrangement between a seller and buyer is sufficient to deny \\\\'authority.\\\\'? It was undisputed in Lexmark that there was \\\\'an express and enforceable contractual agreement\\\\' between Lexmark and each end-user, and that the no-resale and no-reuse restrictions were binding on end users. Yet throughout the Lexmark opinion, the majority suggests that restrictions may be sufficient if \\\\'clearly communicated\\\\'—even if well short of a contractual meeting of the minds.\\\"\\\\n Another way to put this is what is a \\\"clear communication\\\"? In Jazz Photo, the Federal Circuit noted that the \\\"package instructions [were] not in the form of a contractual agreement by the purchaser to limit reuse of the cameras.\\\" Accordingly, \\\"There was no showing of a \\\\'meeting of the minds\\\\' whereby the purchaser, and those obtaining the purchaser\\\\'s discarded camera, may be deemed to have breached a contract or violated a license limited to a single use of the camera.\\\" The writers conclude, therefore, \\\"It is unclear if the Federal Circuit intended an expansion of the patentee-seller\\\\'s ability to avoid exhaustion.\\\"\\\\n Also, how clear must a \\\"clear communication\\\" be? \\\"The Federal Circuit appears to limit infringement claims against subsequent downstream buyers to those \\\\'having knowledge of the restrictions.\\\\' The appellate court did not elaborate on what defenses a subsequent downstream purchaser without knowledge may have, assuming no exhaustion. The court only mentions in passing that \\\\'we do not have before us the questions that would arise, whether under principles governing bona fide purchasers or otherwise, if a downstream re-purchaser acquired a patented article with less than actual knowledge of the restriction.\\\\'\\\\xa0\\\"\\\\n Finally, does the court\\\\'s focus on \\\"clear communication\\\" have a negative impact on post-sale restrictions that a limited licensee under General Talking Pictures is required to impose? \\\"The Federal Circuit suggested repeatedly that buyers\\\\' knowledge of the licensee\\\\'s field of use limitation may be required for a licensee\\\\'s sale to be non-exhaustive. While General Talking Pictures did not clearly resolve this question, many licensors have assumed that sales by a licensee outside of its licensed field are unauthorized altogether and are therefore non-exhaustive regardless of the purchaser\\\\'s knowledge of the field of use limitation.\\\" Therefore, does the emphasis, here \\\"on the buyer\\\\'s knowledge, even if dicta, add to the uncertainty concerning this issue\\\"?\\\\n\\\\nCastanias, Nix, and Kazhdan\\\\n\\\\nGregory A. Castanias, Kelsey I. Nix, and Daniel Kazhdan also point to unresolved issues over which patent owners \\\"must still be cautious\\\":\\\\nLexmark explicitly left open several fact-specific questions, including (i) what happens if someone acquires a patented article with \\\"less than actual knowledge\\\" of the restrictions placed on the original sale by the patent owner and (ii) when would a foreign buyer have an \\\"implied license\\\" to sell in the United States, independent of patent exhaustion. These issues will surely be raised in future cases.\\\\n\\\\nCrouch\\\\n\\\\nDennis Crouch, in Patently-O commented on the issues and provided a summary of the merits briefs filed in the Supreme Court as of January 31, 2017. Crouch opposed the Federal Circuit\\\\'s ruling on these grounds:\\\\nWith personal property courts long ago rejected servitudes (such as use and resale restrictions) that bind subsequent purchasers. Unlike real property, personal property moves and is often transferred without substantial paperwork or record-keeping, and allowing a set of unique restrictions has the potential of gumming up the marketplace. The Federal Circuit in this case went all the way to the other side — holding that the presumption in foreign sales is that no US patent rights are exhausted. I purchased my last couple of smart phones through the used market – and have also repaired them several times. Under the law, I probably should have taken steps to ensure that all of the original equipment manufacturers affirmatively granted repair and resale rights. Coming together, the Federal Circuit\\\\'s approach here has the potential to limit the market for the repair and reselling of goods. I would suggest that those activities are incredibly beneficial to our society in terms of resource allocation and avoiding waste as well as empowering citizens and avoiding anticompetitive market behavior.\\\\n\\\\nNotes and references\\\\n\\\\nNotes\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n \\\\n SCOTUSblog coverage\\\\n Podcast – Interview with proprietor of Impression Products\\\\n\\\\nIntellectual property law\\\\nUnited States patent case law\\\\nUnited States Court of Appeals for the Federal Circuit cases\\\\nUnited States Supreme Court cases\\\\nUnited States Supreme Court cases of the Roberts Court\\\\n2015 in United States case law\\\\n2017 in United States case law\\\\nLexmark'},\\n\",\n \" {'docid': 'doc-en-13',\\n\",\n \" 'text': 'The Werewolf by Night is the name applied to two fictional characters who are werewolves appearing in American comic books published by Marvel Comics. The Werewolf by Night (usually referred to by other characters simply as the Werewolf) first appeared in Marvel Spotlight #2 (February 1972).\\\\n\\\\nPublication history\\\\nPrior to the formation of the Comics Code Authority in 1954, Marvel\\\\'s predecessor Atlas Comics published a five-page short story titled \\\"Werewolf by Night!\\\" in Marvel Tales #116 (July 1953). With the relaxation of the Comics Code Authority\\\\'s rules in 1971, it became possible for the first time to publish code-approved comic books with werewolves. The Jack Russell version of Werewolf by Night first appeared in Marvel Spotlight #2 (February 1972) and was based on an idea by Roy Thomas. The series name was suggested by Stan Lee and the initial creative team was Gerry Conway and Mike Ploog, who worked from a plot by Roy and Jeanie Thomas for the first issue. Readers have often pointed out that the lead character\\\\'s name, Jack Russell, is also a breed of dog. Conway has said that while he cannot remember how he came up with the name, it is unlikely that he was making this canine reference consciously, since he did not own a dog and never lived with one growing up. After the test run in Marvel Spotlight #2-4, the character graduated to his own eponymous series in September 1972. Conway described working on the series as \\\"a lot of fun\\\" because the horror genre made a refreshing change from the superhero stories that had been the staple of mainstream comics for years. Werewolf by Night was published for 43 issues and ran through March 1977. During the series\\\\' run, the editorship could not resist the opportunity to assign one of their most popular writers, Marv Wolfman, to write some stories for the series with a playful note: \\\"At last -- WEREWOLF -- written by a WOLFMAN.\\\"\\\\n\\\\nIssue #32 (August 1975) contains the first appearance of the Moon Knight. Jack Russell co-starred with Tigra in Giant-Size Creatures #1 (July 1974), which was the first appearance of Greer Grant Nelson as Tigra instead of as the Cat. That series was retitled Giant-Size Werewolf with its second issue. Jack Russell was dormant for most of the 1980s. The character\\\\'s appearance was radically revamped in Moon Knight #29 (March 1983). He guest-starred in various issues of Spider-Woman, West Coast Avengers, and Doctor Strange: Sorcerer Supreme. The Werewolf by Night was later revived in the pages of Marvel Comics Presents, where he appeared irregularly from 1991-1993. He made regular appearances as a supporting cast member in the pages of Morbius: The Living Vampire from 1993-1995. A letters page in an issue of Morbius mentioned that a Werewolf by Night miniseries by Len Kaminski and James Fry was in the works, but the miniseries was never published. Werewolf by Night vol. 2 ran for six issues in 1998. The series was written by Paul Jenkins and penciled by Leonardo Manco. After the book\\\\'s cancellation, the story was continued in the pages of Strange Tales, which also featured the Man-Thing. That volume of Strange Tales was canceled after only two issues due to poor sales. In early 2007, Marvel published a one-shot entitled Legion of Monsters: Werewolf by Night, with art by Greg Land. In January 2009, Jack Russell was featured in the four-issue limited series Dead of Night Featuring Werewolf by Night, from Marvel\\\\'s mature readers MAX imprint. The series was written by Duane Swierczynski, with art by Mico Suayan. He was featured as a member of Morbius\\\\' Midnight Sons in Marvel Zombies 4 in 2009.\\\\n\\\\nA second Werewolf by Night first appeared in the third volume of Werewolf by Night and was created by Taboo of the Black-Eyed Peas, Benjamin Jackendoff, and Scot Eaton.\\\\n\\\\nFictional character biography\\\\n\\\\nJack Russell\\\\n\\\\nWhile reports of lycanthropy (shapeshifting into a werewolf) in the Russoff line stretch back many centuries, the first confirmed manifestation is Grigori Russoff in 1795. Dracula slew Grigori\\\\'s wife Louisa after he refused to acknowledge Dracula\\\\'s primacy upon his return to Transylvania. Grigori then ambushed and destroyed Dracula, but was mutated into a werewolf by Lydia, a werewolf formerly imprisoned by the vampire lord. Grigori took a second wife, but accounts vary as to why lycanthropy failed to pass to his descendants. Sometime prior to May 1930, Grigori\\\\'s descendant, Gregor, obtained the legendary Darkhold scrolls, binding them back into book form. Reading lycanthropy\\\\'s origins in the Darkhold under a full moon triggered the dormant curse, mutating Gregor into a werewolf. Gregor further transcribed much of the Darkhold into Grigori\\\\'s diary, essentially creating a Darkhold copy, which he used as his own diary. Gregor sold part of his estate — including Wundagore Mountain — to Jonathon Drew, who shared it with partner Herbert Wyndham (the future High Evolutionary). The Russoff werewolf slew Jonathon\\\\'s wife, Merriem, and Wyndham designed a suit of silver-coated armor to protect himself, enabling Russoff\\\\'s capture. Russoff stayed with the Evolutionary, who kept the werewolf safely contained for decades. Russoff eventually used the Darkhold to summon Chthon to cure him and the Elder God nearly broke through the earthly plane; but Magnus the Sorcerer forced Russoff to banish Chthon, who lashed out with a parting blast that slew Gregor. Despite contrary accounts, the Gregor Russoff who stayed with the High Evolutionary seems to have been the grandfather (or great-grandfather) of Jack Russell. Having the same name and presumably using the same diary contributed to earlier confusion. It would seem more likely that the elder Gregor was the one who transcribed the Darkhold into the diary.\\\\n\\\\nDecades later, another Gregor Russoff married Laura, the former girlfriend of his younger brother Philip. Jacob (later Jack) was born in Mediaș, Transylvania, soon after, and Laura was pregnant with Lissa within two years of marriage; however, when lightning struck Russoff\\\\'s Transylvanian castle during a full moon, the werewolf Gregor escaped confinement and began attacking villagers. They tracked down and killed Russoff with silver bullets. Gregor\\\\'s mother, Maria, was stoned and driven from the village, living with gypsies and learning magic. After Gregor\\\\'s death, Laura found Philip - who had moved to Los Angeles and anglicized his name to Russell - and they married after a year; Jack and Lissa remained unaware of Philip\\\\'s past. Approximately 15 years later, the criminal organization known as the Committee learned of Gregor\\\\'s curse and blackmailed Philip, threatening to reveal his secrets. To protect Laura\\\\'s name, Philip paid them, but had second thoughts and canceled payment, causing the Committee to send Max Grant to kill Laura. Critically injured in a car crash on Jack\\\\'s 18th birthday, Laura barely had time to tell Jack about his true father and the curse of the werewolf, making Jack promise not to harm Philip, before dying. Having inherited lycanthropy the night before, Jack slew Grant, but wrongly blamed Philip for some time. Laura left Castle Russoff in Jack\\\\'s name, but Philip, the trustee, sold the castle to Miles Blackgar, who had it moved to an island off the California coast. Jack battled a motorcycle gang, infecting its members with lycanthropy.\\\\n\\\\nJack spent the next few years as a traveler, shapeshifting on the three nights of the full moon into a savage werewolf form. He learned of the Darkhold from Nathan and Agatha Timly, who briefly kidnapped the Werewolf and met grisly ends. Befriending writer Buck Cowan, Jack sneaked into Blackgar\\\\'s castle and stole the Darkhold, encountering Miles Blackgar and his daughter Marlene, whose petrifying power slew both Blackgars. After fighting off the deformed Cephalos\\\\' plot to drain his power to stabilize Cephalos\\\\' form, Jack had Father Ramon Joaquez translate the Darkhold. The priest died after being possessed by the Darkhold\\\\'s former custodian, the 12th-century mad monk Aelfric, and the indestructible Darkhold vanished. Jack encountered Joshua Kane, who hunted the Werewolf, and his brother, Luther Kane, who offered to prevent Lissa from mutating into a werewolf in exchange for Jack kidnapping billionaire-turned recluse Judson Hemp; he met mentalist Swami Rihva, who sought the Werewolf\\\\'s blood to reveal the treasure map of the ancient sorcerer Kaman-Ru on his \\\"Bloodstone\\\"; the possessing demon Krogg; and Spider-Man and Moondark the Magician. Jack then fought the sonic-weapons of Sarnak, his first brush with the criminal organization known as the Committee, who wished to enslave the Werewolf.\\\\n\\\\nAfter fighting the sociopathic Hangman (Harlan Krueger), Jack was entranced by Topaz, the familiar of the sorcerer Taboo, who sought the Darkhold. Taboo had used the tome decades before to grant his son, Algon, a golden touch, but had lost the book in mid-spell, trapping Algon in a mindless state. Lacking the Darkhold, Taboo transferred Philip Russell\\\\'s mind into Algon, but both Algon and Taboo died, restoring Philip, who explained Laura\\\\'s death and reconciled with both Jack and Lissa. Traveling to Transylvania alongside Topaz, with whom he had bonded, Jack discovered the Russoff diary/Darkhold copy, the Werewolf battled Dracula and the book was lost in the Alps. Jack and Topaz encountered the kyphotic Half-Mad before returning to the U.S. and Jack fought the Committee\\\\'s Behemoth robot and then Ma Mayhem, assisted by werewolf Raymond Coker. Jack joined the newly-mutated Tigra against HYDRA, battled vampires Louis Belski and Liza Pyne, opposed Ma Mayhem and her ally Baron Thunder, and joined Coker against Lou Hackett (a corrupt policeman who could also shapeshift into a werewolf by using a magic ring), who was killed in the struggle. The Werewolf joined the Frankenstein Monster against the Satanic Brotherhood of Baal who had abducted Lissa, then fought the disfigured Atlas and the Jekyll/Hyde-like DePrayve. Jack briefly returned to Transylvania following Topaz\\\\'s psychic summons and encountered Maria Russoff, who used Gypsy magic to raise zombies to slay the villagers who had driven her off. Maria sacrificed herself to save Jack from her zombies upon learning that he was her grandson.\\\\n\\\\nIn Blackgar\\\\'s castle, the Werewolf, Topaz and the repentant spirit fragment of Taboo battled the necromancer Doctor Glitternight, who mutated Lissa Russell into a were-demoness; the process of curing Lissa purged her of the threat of lycanthropy, though she would still pass it on to her children. After battling Morbius, the Living Vampire and slaying the demon worshipped by Brad Wrangle, the Werewolf was briefly transported to the divided dimension Biphasia by Satanist Joaquin Zaire and he aided Paingloss against the sorcerer Sardanus. During a subsequent ski trip, the Werewolf nearly slew Buck Cowan, after which he was captured by the Committee-paid mercenary known as the Moon Knight, who set him free when he realized Jack\\\\'s humanity and the Committee\\\\'s intentions. The Werewolf then joined the Ghost Rider, the Man-Thing, and Morbius in unwittingly slaying the benevolent alien Starseed, who had intended to cure them all.\\\\n\\\\nThe Werewolf, Topaz and others then battled and were nearly driven mad by the ghost of 19th-century black magician Belaric Marcosa, but they freed the trapped spirits of Marcosa\\\\'s victims, who destroyed him, and one of the grateful spirits, that of magician Gideon Blaine, healed Buck. The enigmatic Three Who Are All (the Hooded One, the Burning Snake and the Goat Child) — an ancient extra-dimensional group who had formerly included Glitternight and a fifth member, Fire-Eyes — sent Jack, Topaz, Raymond Coker and Brother Voodoo to Haiti, where the Werewolf and Fire-Eyes destroyed Glitternight once and for all. In the process, Jack gained control of his Werewolf persona, though he still only shapeshifted under moonlight and still lost control during the three nights of the full moon.\\\\n\\\\nThe Werewolf joined with Iron Man against the Maggia\\\\'s Masked Marauder and his Tri-Animan and he teamed with Spider-Woman against the mercenary the Enforcer. The mad scientist Dr. Karl Malus captured and performed scientific experiments on Russell to control him and use him against Spider-Woman; Russell escaped and apprehended Malus with the aid of Spider-Woman. Russell joined Spider-Man against the Tatterdemalion, a former agent of Sarnak. After being temporarily captured alongside a number of costumed adventurers by the Locksmith and Tick-Tock, Russell began mutating into a more savage and lupine form, a late effect from Malus\\\\' treatment. He fled Satanists Morning Star (Schuyler Belial) and his Left Hand Path, who wished to use his blood to mutate into werewolves, then sought aid from the now-human Michael Morbius in controlling his savage self, leading to a battle with the West Coast Avengers. With assistance from Iron Man, he later saved Lissa from Morgan Le Fay\\\\'s attempt to possess her.\\\\n\\\\nHe was subsequently mind-controlled into joining the mostly-criminal Night Shift by Dansen Macabre. Russell was the only member who knew their leader, the Shroud, was using the group to oppose other criminals and to prevent them from harming innocents. After encounters with Captain America, the Moon Knight and the Avengers, the Werewolf eventually developed resistance to Macabre\\\\'s powers and turned on the Night Shift, after which he went solo. After briefly battling the Hulk in the Midwest, Jack contacted his father Gregor\\\\'s spirit to cure his lycanthropy, but was told that he would die unless he accepted his beast. During the ensuing battle with the religious zealot Silver Dagger and the Braineaters, a cult of werewolves mutated in the past by Russell, Jack fully accepted his wolf-self and his personae merged, altering his powers and granting him full control and the best of both selves.\\\\n\\\\nRussell assisted Doctor Strange against the alien Possessors, the Night Shift against an L.A. street gang and the Ghost Rider against a new group of Braineaters; Jack battles with Sabretooth but before Jack can kill Sabretooth three locals show up with rifles and save Sabretooth by shooting at Jack. and fought an unidentified Wendigo in Canada. Russell was captured by criminal scientist Nightshade who used his blood to create the Night Patrol, a group of werewolves in Starkesboro, Massachusetts. Captain America - also mutated into a werewolf - freed Russell and led the werewolves to defeat Nightshade\\\\'s master, Dredmund the Druid, who had used the Godstone (the former gem of the Man-Wolf) to briefly mutate into the powerful Starwolf. The Night Patrol was cured, after which Russell was drawn into a conflict involving the Midnight Sons and was slain by Switchblade (the insane Darkhold-powered Blade), but Jack was revived once Professor Louise Hastings broke Switchblade\\\\'s spell. Russell befriended the again pseudo-vampiric (and now demon-possessed) Morbius, had a vision of advertisements on the moon causing mass insanity and fought the Lilin Goblins, Mr. Hyde and the sadist Morphine. Jack had an affair with Morbius\\\\' possessed former girlfriend Martine Bancroft.\\\\n\\\\nJack again began losing control of the Werewolf, locking himself in a cage while under the full moons, and even glimpsing visions of Hell as he shapeshifted. From the Cult of the Third Moon\\\\'s dying leader, Walter Clark, Russell learned that only the legendary Wolfblade could control his lupine self. With the aid of Smedley, a mysterious benefactor, Russell recovered all three parts of the Wolfblade, battled the original Wolf Demon in a branch of Hell, completed the puzzle by reaccepting both selves and seemingly regained control. However, after Jack visited friends Freddie and the disfigured Lump, Smedley sent him to investigate a series of killings in which the evidence pointed to Jack as the killer. As Russell began to mutate further, Smedley said Jack just had not been careful enough in his wish to be freed from the Wolf Demon and that he must embrace the disease, or it would destroy him. Uncertain how to accomplish this, Jack found a confidant in the Lump, who cared for the Werewolf as he hid out in the sewers. While Jack\\\\'s new girlfriend, Roxanna, remained blissfully unaware of his dual existence, the Werewolf was tracked down by a pair of detectives, escaping only after they were slain by the Cult of the Third Moon. Though Jack\\\\'s subsequent fate is unknown, he was later seen sensing the arrival of the mystic assassin Hellphyr.\\\\n\\\\nIn the Legion of Monsters: Werewolf by Night one-shot, Jack Russell came to Salvage, Alabama to save a family of law-abiding werewolves from a group of townsfolk led by Cal Escher. Young Rhonda was the only one left in the family after her mother and her sister Suzie chose death by gun or knife. The girl was drowning her sorrows in Sullivan\\\\'s bar next to the cemetery when the gang attacked her, revealing her werewolf nature by means of a tarot card (\\\"The Moon\\\") and then trying to kill her. Russell interfered, mutating into the Werewolf while Rhonda decided to do the same. After killing the violent gang, Russell and Rhonda left the town, determined to control their afflictions and live their lives without fear.\\\\n\\\\nThe Moon Knight rescues Jack from a criminal enterprise wherein samples of his blood are used to temporarily mutate homeless people into pseudo-werewolves, who are then provoked into fighting each other as a spectator sport. The Moon Knight frees Jack, who has degenerated into a near-mindless feral state, from his captors; the Werewolf proceeds to go on a rampage, attacking both his tormentors and the Moon Knight, who subdues him before restoring his freedom to him.\\\\n\\\\nThe Werewolf appears as part of the new Midnight Sons team to hunt down zombies who escaped A.R.M.O.R. headquarters and prevent the contagion from spreading. Prior to the team\\\\'s mission, he records a video will and testament telling his sister that he is happy in life. He was given a vaccine developed by Morbius, the Living Vampire. In their search for the missing zombie Deadpool, the team battles and kills zombie Men-Fish and their leader, the Piranha. After battling the Hood\\\\'s Night Shift and watching ally the Man-Thing seemingly die in a battle against Deadpool, Russell\\\\'s vaccine fails him and he becomes a zombie. He then confronts Jennifer Kale. He battles Morbius, who realizes that Jack\\\\'s werewolf form is not subject to the virus, and Jennifer Kale summons a moonlight spell to mutate him into the Werewolf. Jack is later restored to normal by Morbius, who developed a cure for the zombie virus using Spider-Man\\\\'s blood and samples of the zombie virus from different realities.\\\\n\\\\nAfter the death of Frank Castle, Morbius pieces the Punisher together again and enlists his help in saving monsters from extinction. Jack Russell, the Man-Thing and the Living Mummy are part of the Legion of Monsters, who fight those who would wipe out all monsters. The Punisher aids this group in protecting an underground city that has many innocent, sentient monsters.\\\\n\\\\nRussell appears among many mystical beings of lupine and feline nature drawn to the headquarters of X-Factor Investigations by the imminent birth of the mutant Wolfsbane\\\\'s child. While many of the gathered beings wish to acquire the child for their own ends, Russell seems intent on protecting mother and child. Once the child is born, it is rejected by a shaken Wolfsbane due to its vicious, feral nature and her own religious beliefs. The cub appears to be caught up in a convergence of the mystic forces seeking it, vanishing explosively from the Earth; however, Russell finds the child hiding in a cave and takes it under his care.\\\\n\\\\nDeadpool later discovered that Russell had an affair with his wife Shiklah. Deadpool then promptly blew off Jack\\\\'s head with a blunderbuss, but Shiklah revealed that Jack would survive.\\\\n\\\\nJake Gomez\\\\n\\\\nJake Gomez is a boy of Hopi descent who underwent his first werewolf transformation at the age of 13 due to a curse in his family. He was able to get control over his werewolf form with the help of his grandmother Rora and his sister Molly where the music helped with controlling his emotions unlike how Jake\\\\'s father operated. Jake first appeared when hunters were hunting rabbits on Hopi tribal lands and fought them off. Rora advised him to be careful in the mission at Life Pharmaceuticals where he worked in the day due to the government going after teenage superheroes. Jake attacks two vehicles leaving Life Pharmaceuticals and finds that one of them contains the people who have gone missing from the Rez in the past month. He is then confronted by three monstrous figures with cybernetic parts on them.\\\\n\\\\nPowers and abilities\\\\nJack Russell is a descendant of the mystically-altered offshoot of humans known as Lycanthropes. During the night of the full moon and the two nights surrounding it he is forced to mutate into a werewolf, a large, powerful form which is a hybrid of human and wolf, and loses his human intellect. Through a series of events, he is also capable of mutating voluntarily outside of the full moon, at which time he remains in control of himself. As a werewolf, Jack gains the proportionate physical advantages of a nearly wolf. In this form, he possesses superhuman strength, speed, stamina, durability, agility and reflexes. He possesses a superhuman sense of smell, which carries over to his human form. He has razor-sharp teeth and claws that are able to rend light metals. The Werewolf is resistant to many forms of conventional injury and very difficult to kill by conventional means. Though he can be severely wounded, he recovers from non-fatal wounds much faster than a human would. He is vulnerable to magical attacks and, like all werewolves, he can be killed by weapons made of silver, due to its inherent mystical \\\"purity\\\".\\\\n\\\\nJake Gomez has the same transformation abilities like Jack Russell.\\\\n\\\\nOther versions\\\\nIn Marvel\\\\'s Earth-666, a variation of the Jack Russell version of the Werewolf appeared in Supernatural Tourbook and Supernaturals #1-4.\\\\n\\\\nIn the Marvel Adventures continuity, Jack Russell\\\\'s family home is in Queens, New York. This brings him into conflict with Spider-Man after he reluctantly mutates the somewhat-innocent Flash Thompson into a werewolf. Fortunately, Dr. Strange\\\\'s knowledge of lycanthropy saves Flash.\\\\n\\\\nDuring \\\"Infinity Wars\\\", when the universe was folded, Jack Russell got fused with Norman Osborn to create the Goblin by Night. Norman Russell was cursed to be the Goblin by Night, killed Ben and May Spector and nearly killed Peter Spector, leaving Peter to become the ArachKnight. During a battle with Peter, Norman got injured and got saved by his son, Harry Russell. While Harry was taking care of his father, Norman lost control and bit Harry, passing the curse on to him. Harry, now as the new Goblin by Night, starts using the glider that Peter built prior to him to becoming the Goblin, leaving Norman free from the curse, being forgiven by Peter and deciding to find a way to cure Harry.\\\\n\\\\nIn other media\\\\n\\\\nTelevision\\\\n The Jack Russell incarnation of Werewolf by Night appears in The Super Hero Squad Show episode \\\"This Man-Thing, This Monster!\\\", voiced by Rob Paulsen. After his girlfriend, Ellen, is kidnapped by an army of mummies led by N\\\\'Kantu, the Living Mummy on Dracula\\\\'s behalf, the Werewolf by Night joins forces with the Man-Thing and a dimensionally-displaced Iron Man to rescue her. While they succeed in defeating the vampire and the mummies, they learn Ellen had been turned into a vampiress. Taking inspiration from Iron Man, Werewolf by Night, Ellen, and the Man-Thing form the Supernatural Hero Squad to defend their town from future monster attacks.\\\\n The Jack Russell incarnation of Werewolf by Night appears in the Ultimate Spider-Man episodes \\\"Blade\\\" and \\\"The Howling Commandos\\\", voiced by Ross Lynch. This version is a member of the Howling Commandos. Werewolf by Night and the Howling Commandos join forces with former member Blade and Spider-Man to retrieve a powerful ankh from Dracula.\\\\n The Jack Russell incarnation of Werewolf by Night appears in Hulk and the Agents of S.M.A.S.H., voiced by Nolan North. This version is a member of the Howling Commandos. In the episode \\\"Hulking Commandos\\\", he and the Howling Commandos are assigned to apprehend the agents of S.M.A.S.H., only to join forces with them to defeat Dormammu. In the episode \\\"Planet Monster: Part 2\\\", the Werewolf by Night joins the Howling Commandos in helping the agents of S.M.A.S.H. and the Avengers combat the Supreme Intelligence\\\\'s forces.\\\\n The Werewolf by Night\\\\'s unnamed grandfather appears in \\\"Days of Future Smash, Part 3: Dracula\\\", also voiced by North. While operating in 1890, he helps Frankenstein\\\\'s Monster, N\\\\'Kantu the Living Mummy, and a time-traveling Hulk thwart the Leader and Dracula\\\\'s plan to blanket the Earth in darkness with their Gamma Furnace.\\\\n A Halloween special based on Werewolf by Night is in development for Disney+. On November 4, 2021, actor Gael García Bernal was cast. On January 11, 2022, Laura Donnelly was cast in a undisclosed role. Michael Giacchino is set to direct Werewolf by Night.\\\\n\\\\nFilm\\\\n A film adaptation of Werewolf by Night, written by Robert Nelson Jacobs, was announced and due to begin filming in 2005. However, no further developments took place since.\\\\n\\\\nVideo games\\\\n The Jack Russell incarnation of Werewolf by Night makes a cameo appearance in Jill Valentine\\\\'s ending in Marvel vs. Capcom 3: Fate of Two Worlds and Ultimate Marvel vs. Capcom 3.\\\\n The Jack Russell incarnation of Werewolf by Night appeared as an unlockable playable character in Marvel Super Hero Squad Online.\\\\n The Jack Russell incarnation of Werewolf by Night appeared as an unlockable playable character in Marvel Avengers Academy. He could be first recruited during the event \\\"Avengers Halloween Event 2017\\\".\\\\n\\\\nReception\\\\nThe Werewolf by Night was ranked #6 on a listing of Marvel Comics\\\\' monster characters in 2015.\\\\n\\\\nCollected editions\\\\n Essential Werewolf by Night \\\\n Vol. 1 collects Marvel Spotlight #2-4, Werewolf By Night #1-21, Marvel Team-Up #12, Giant-Size Creatures #1, and The Tomb of Dracula #18, 576 pages, October 2005, \\\\n Vol. 2 collects Werewolf By Night #22-43, Giant-Size Werewolf #2-5, and Marvel Premiere #28, 576 pages, November 2007, \\\\n Essential The Tomb of Dracula Vol. 1 includes Werewolf by Night #15, 560 pages, 2004, \\\\n Essential Monster of Frankenstein includes Giant-Size Werewolf #2, 496 pages, October 2004, \\\\n Essential Moon Knight Vol. 1 includes Werewolf by Night #32-33, 560 pages, March 2006, \\\\n Werewolf by Night: In the Blood includes Werewolf by Night vol. 2 #1-4 \\\\n Werewolf by Night: The Complete Collection \\\\n Vol. 1: Marvel Spotlight #2-4, Werewolf by Night #1-15, Marvel Team-Up #12, Tomb of Dracula #18 (October 17, 2017)\\\\n Vol. 2: Werewolf by Night #16-30, Giant-Size Creatures #1, Giant-Size Werewolf #2-4, material from Monsters Unleashed #6-7 (February 13, 2018)\\\\n Vol. 3: Werewolf by Night #31-43, Giant-Size Werewolf #5, Marvel Premiere #28, Spider-Woman #6, 19, 32, Marvel Team-Up #93, Ghost Rider #55, Moon Knight #29-30, material from Marvel Premiere #59 (May 15, 2018)\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n Werewolf by Night at Marvel.com\\\\n \\\\n Werewolf by Night appearances in publication order\\\\n\\\\n1972 comics debuts\\\\nCharacters created by Gerry Conway\\\\nCharacters created by Mike Ploog\\\\nCharacters created by Roy Thomas\\\\nComics about werewolves\\\\nComics by Doug Moench\\\\nComics by Gerry Conway\\\\nComics by Marv Wolfman\\\\nComics characters introduced in 1972\\\\nDefunct American comics\\\\nFictional characters with superhuman senses\\\\nFictional Romanian people\\\\nFictional werewolves\\\\nHorror comics\\\\nMarvel Comics characters who are shapeshifters\\\\nMarvel Comics characters who can move at superhuman speeds\\\\nMarvel Comics characters with accelerated healing\\\\nMarvel Comics characters with superhuman strength\\\\nMarvel Comics superheroes\\\\nMarvel Comics titles\\\\nMidnight Sons'},\\n\",\n \" {'docid': 'doc-en-14',\\n\",\n \" 'text': 'The 2021 NHL Entry Draft was the 59th NHL Entry Draft. The draft was held on July 23–24, 2021, delayed by one month from its normally scheduled time of June due to the COVID-19 pandemic and the later-than-normal finish of the 2020–21 NHL season. It was thus the first draft held in July since 2005. For the second year in a row, the event was held in a remote format, with teams convening via videoconferencing, and Commissioner Gary Bettman announcing the selections in the opening round and deputy commissioner Bill Daly in all subsequent rounds from the NHL Network studios in Secaucus, New Jersey.\\\\n\\\\nThe first three selections were Owen Power going to the Buffalo Sabres, Matty Beniers being selected by the Seattle Kraken, and Mason McTavish being picked by the Anaheim Ducks.\\\\n\\\\nEligibility\\\\nIce hockey players born between January 1, 2001, and September 15, 2003, were eligible for selection in the 2021 NHL Entry Draft. Additionally, un-drafted, non-North American players born in 2000 were eligible for the draft; and those players who were drafted in the 2019 NHL Entry Draft, but not signed by an NHL team and who were born after June 30, 2001, were also eligible to re-enter the draft.\\\\n\\\\nDraft lottery\\\\nFrom the 2012–13 NHL season up to the 2020–21 NHL season all teams not qualifying for the Stanley Cup playoffs have had a \\\"weighted\\\" chance at winning the first overall selection. Beginning with the 2014–15 NHL season, the league changed the weighting system that was used in previous years. Under the new system, the odds of winning the draft lottery for the four lowest finishing teams in the league decreased, while the odds for the other non-playoff teams increased. The draft lottery took place on June 2, 2021. After changing the number of lottery drawings earlier in the season, the first two picks overall in this draft were awarded by lottery. The Buffalo Sabres and Seattle Kraken won the two draft lotteries that took place on June 2, 2021, giving them the first and second picks overall. Buffalo retained the first pick, while Seattle moved up one spot and Anaheim dropped one spot to third overall.\\\\n\\\\nThe expansion Seattle Kraken had the same odds of winning the lottery as the team that finished with the third fewest points (this ended up being the New Jersey Devils). Because the Arizona Coyotes\\\\' 2021 first-round pick was forfeited as the result of a penalty sanction due to violations of the NHL Combine Testing Policy during the 2019–20 NHL season, Arizona\\\\'s lottery odds were instead listed as re-draws.\\\\n\\\\n{| class=\\\"wikitable\\\"\\\\n|+ Complete draft position odds\\\\n! Team\\\\n! 1st\\\\n! 2nd\\\\n! 3rd\\\\n! 4th\\\\n! 5th\\\\n! 6th\\\\n! 7th\\\\n! 8th\\\\n! 9th\\\\n! 10th\\\\n! 11th\\\\n! 12th\\\\n! 13th\\\\n! 14th\\\\n! 15th\\\\n! 16th\\\\n|-\\\\n! Buffalo\\\\n| style=\\\"background:#A9D0F5;\\\"| 16.6% || 15.0% || 68.4% || || || || || || || || || || || || ||\\\\n|-\\\\n! Anaheim\\\\n| 12.1% || 11.7% || style=\\\"background:#DDDDDD;\\\"| 26.9% || 49.3% || || || || || || || || || || || ||\\\\n|-\\\\n! Seattle\\\\n| 10.3% || style=\\\"background:#F5A9BC;\\\"| 10.2% || 4.7% || 39.3% || 35.6% || || || || || || || || || || ||\\\\n|-\\\\n! New Jersey\\\\n| 10.3% || 10.2% || || style=\\\"background:#DDDDDD;\\\"| 11.5% || 43.9% || 24.2% || || || || || || || || || ||\\\\n|-\\\\n! Columbus\\\\n| 8.5% || 8.6% || || || style=\\\"background:#DDDDDD;\\\"| 20.6% || 45.8% || 16.5% || || || || || || || || ||\\\\n|-\\\\n! Detroit\\\\n| 7.6% || 7.8% || || || || style=\\\"background:#DDDDDD;\\\"| 30.0% || 43.8% || 10.9% || || || || || || || ||\\\\n|-\\\\n! San Jose\\\\n| 6.7% || 6.9% || || || || || style=\\\"background:#DDDDDD;\\\"| 39.7% || 39.7% || 6.9% || || || || || || ||\\\\n|-\\\\n! Los Angeles\\\\n| 5.8% || 6.0% || || || || || || style=\\\"background:#DDDDDD;\\\"| 49.4% || 34.5% || 4.3% || || || || || ||\\\\n|-\\\\n! Vancouver\\\\n| 5.4% || 5.7% || || || || || || || style=\\\"background:#DDDDDD;\\\"| 58.6% || 28.0% || 2.4% || || || || ||\\\\n|-\\\\n! Ottawa\\\\n| 4.5% || 4.8% || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 67.7% || 21.8% || 1.2% || || || ||\\\\n|-\\\\n! Arizona\\\\n| 3.1% || 3.3% || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 75.9% || 17.1% || 0.7% || || ||\\\\n|-\\\\n! Chicago\\\\n| 2.7% || 2.9% || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 81.7% || 12.4% || 0.3% || ||\\\\n|-\\\\n! Calgary\\\\n| 2.2% || 2.4% || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 87.0% || 8.4% || 0.1% ||\\\\n|-\\\\n! Philadelphia\\\\n| 1.8% || 2.0% || || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 91.3% || 4.9% || >0.0%\\\\n|-\\\\n! Dallas\\\\n| 1.4% || 1.5% || || || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 95.0% || 2.1%\\\\n|-\\\\n! NY Rangers\\\\n| 1.0% || 1.1% || || || || || || || || || || || || || || style=\\\"background:#DDDDDD;\\\"| 97.9%\\\\n|}\\\\n\\\\nTop prospects\\\\nSource: NHL Central Scouting (May 27, 2021) ranking.\\\\n\\\\nSelections by round\\\\nThe order of the 2021 Entry Draft is listed below.\\\\n\\\\nRound one\\\\n\\\\nNotes\\\\n The Vancouver Canucks\\\\' first-round pick went to the Arizona Coyotes as the result of a trade on July 23, 2021, that sent Oliver Ekman-Larsson and Conor Garland to Vancouver in exchange for Jay Beagle, Loui Eriksson, Antoine Roussel, a second-round pick in 2022, a seventh-round pick in 2023 and this pick.\\\\n The Arizona Coyotes\\\\' first-round pick was forfeited as the result of a penalty sanction due to violations of the NHL Combine Testing Policy during the 2019–20 NHL season. The penalty includes the forfeiture of a second-round pick in 2020 and this pick.\\\\n The Chicago Blackhawks\\\\' first-round pick went to the Columbus Blue Jackets as the result of a trade on July 23, 2021, that sent Seth Jones, Tampa Bay\\\\'s first-round-pick in 2021 (32nd overall) and a sixth-round pick in 2022 to Chicago in exchange for Adam Boqvist, a second-round pick in 2021 (44th overall), a conditional first-round pick in 2022 and this pick.\\\\n The Philadelphia Flyers\\\\' first-round pick went to the Buffalo Sabres as the result of a trade on July 23, 2021, that sent Rasmus Ristolainen to Philadelphia in exchange for Robert Hagg, a second-round pick in 2023 and this pick.\\\\n The Dallas Stars first-round pick went to the Detroit Red Wings as the result of a trade on July 23, 2021, that sent Washington\\\\'s first-round pick, the Rangers\\\\' second-round pick and Ottawa\\\\'s fifth-round pick all in 2021 (23rd, 48th and 138th overall) to Dallas in exchange for this pick.\\\\n The Edmonton Oilers\\\\' first-round pick went to the Minnesota Wild as the result of a trade on July 23, 2021, that sent a first-round pick and Pittsburgh\\\\'s third-round pick both in 2021 (22nd and 90th overall) to Edmonton in exchange for this pick.\\\\n The Minnesota Wild\\\\'s first-round pick went to the Edmonton Oilers as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (20th overall) to Minnesota in exchange for Pittsburgh\\\\'s third-round pick both in 2021 (90th overall) and this pick.\\\\n The Washington Capitals\\\\' first-round pick went to the Dallas Stars as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (15th overall) to Detroit in exchange for the Rangers\\\\' second-round pick and Ottawa\\\\'s sixth round pick both in 2021 (48th and 138th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on April 12, 2021, that sent Anthony Mantha to Washington in exchange for Richard Panik, Jakub Vrana, a second-round pick in 2022 and this pick.\\\\n The Toronto Maple Leafs\\\\' first-round pick went to the Columbus Blue Jackets as the result of a trade on April 11, 2021, that sent Nick Foligno and Stefan Noesen to Toronto in exchange for a fourth-round pick in 2022 and this pick.\\\\n The Pittsburgh Penguins\\\\' first-round pick went to the Minnesota Wild as the result of a trade on February 10, 2020, that sent Jason Zucker to Pittsburgh in exchange for Alex Galchenyuk, Calen Addison and this pick (being conditional at the time of the trade). The condition – Minnesota will receive a 2021 first-round pick at Pittsburgh\\\\'s choice if the Penguins fail to qualify for the 2020 Eastern Conference First Round – was converted on August 12, 2020, when the Penguins elected to defer the pick to 2021.\\\\n The Carolina Hurricanes\\\\' first-round pick went to the Nashville Predators as the result of a trade on July 23, 2021, that sent Los Angeles and Nashville\\\\'s second-round picks both in 2021 to Carolina in exchange for this pick.\\\\n The New York Islanders\\\\' first-round pick went to the New Jersey Devils as the result of a trade on April 7, 2021, that sent Kyle Palmieri and Travis Zajac to New York in exchange for A. J. Greer, Mason Jobst, a conditional fourth-round pick in 2022 and this pick.\\\\n The Tampa Bay Lightning\\\\'s first-round pick went to the Chicago Blackhawks as the result of a trade on July 23, 2021, that sent a first and second-round pick both in 2021 (12th and 44th overall) to Columbus in exchange for Seth Jones, a sixth-round pick in 2022 and this pick.\\\\nColumbus previously acquired this pick as the result of a trade on April 10, 2021, that sent Brian Lashoff to Tampa Bay in exchange for a third-round pick in 2022 and this pick.\\\\n\\\\nRound two\\\\n\\\\nNotes\\\\n The New Jersey Devils\\\\' second-round pick went to the Detroit Red Wings as the result of a trade on July 24, 2021, that sent a second-round pick and Tampa Bay\\\\'s fourth-round pick both in 2021 (38th and 128th overall) to Vegas in exchange for this pick.\\\\nVegas previously acquired this pick as the result of a trade on July 29, 2019, that sent Nikita Gusev to New Jersey in exchange for a third-round pick in 2020 and this pick.\\\\n The Columbus Blue Jackets\\\\' second-round pick went to the Arizona Coyotes as the result of a trade on December 26, 2020, that sent Derek Stepan to Ottawa in exchange for this pick.\\\\nOttawa previously acquired this pick as the result of a trade on February 23, 2019, that sent Ryan Dzingel and Calgary\\\\'s seventh-round pick in 2019 to Columbus in exchange for Anthony Duclair, a second-round pick in 2020, and this pick.\\\\n The Detroit Red Wings\\\\' second-round pick went to the Vegas Golden Knights as the result of a trade on July 24, 2021, that sent New Jersey\\\\'s second-round pick in 2021 (36th overall) to Detroit in exchange for Tampa Bay\\\\'s fourth-round pick in 2021 (128th overall) and this pick.\\\\n The San Jose Sharks\\\\' second-round pick went to the Ottawa Senators as the result of a trade on September 13, 2018, that sent Erik Karlsson and Francis Perron to San Jose in exchange for Chris Tierney, Dylan DeMelo, Josh Norris, Rudolfs Balcers, a conditional second-round pick in 2019, a conditional l first-round pick in 2019 or 2020, a conditional first-round pick no later than 2022, and this pick (being conditional at the time of the trade). The condition – Ottawa will receive a second-round pick in 2021 if Karlsson re-signs with the Sharks for the 2019–20 NHL season and the Sharks do not make the 2019 Stanley Cup Finals – was converted on June 17, 2019, when Karlsson re-signed with San Jose for the 2019–20 NHL season.\\\\n The Los Angeles Kings\\\\' second-round pick went to the Carolina Hurricanes as the result of a trade on July 23, 2021, that sent Carolina\\\\'s first-round pick (27th overall) in 2021 to Nashville in exchange for a second-round pick (51st overall) and this pick.\\\\nNashville previously acquired this pick as the result of a trade on July 1, 2021, that sent Viktor Arvidsson to Los Angeles in exchange for a third-round pick in 2022 and this pick.\\\\n The Ottawa Senators\\\\' second-round pick went to the Los Angeles Kings as the result of a trade on July 24, 2021, that sent St. Louis\\\\' second-round pick and a fifth-round pick both in 2021 (49th and 136th overall) to Ottawa in exchange for this pick.\\\\n The Chicago Blackhawks\\\\' second-round pick went to the Carolina Hurricanes as the result of a trade on July 23, 2021, that sent Jake Bean to Columbus in exchange for this pick.\\\\nColumbus previously acquired this pick as the result of a trade on July 23, 2021, that sent Seth Jones, Tampa Bay\\\\'s first-round-pick in 2021 (32nd overall) and a sixth-round pick in 2022 to Chicago in exchange for Adam Boqvist, a first-round pick in 2021 (12th overall), a conditional first-round pick in 2022 and this pick.\\\\n The New York Rangers\\\\' second-round pick went to the Dallas Stars as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (15th overall) to Detroit in exchange for Washington\\\\'s first-round pick and Ottawa\\\\'s fifth-round pick both in 2021 (23rd and 138th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on September 26, 2020, that sent future considerations to New York in exchange for Marc Staal and this pick.\\\\n The St. Louis Blues\\\\' second-round pick went to the Ottawa Senators as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (42nd overall) to Los Angeles in exchange for a fifth-round pick in 2021 (136th overall) and this pick.\\\\nLos Angeles previously acquired this as the result of a trade on February 19, 2020, that sent Alec Martinez to Vegas in exchange for a second-round pick in 2020 and this pick.\\\\nVegas previously acquired this pick as the result of a trade on June 28, 2019, that sent Colin Miller to Buffalo in exchange for a fifth-round pick in 2022 and this pick.\\\\nBuffalo previously acquired this pick as the result of a trade on July 1, 2018, that sent Ryan O\\\\'Reilly to St. Louis in exchange for Vladimir Sobotka, Patrik Berglund, Tage Thompson, a conditional first-round pick in 2019 or 2020 and this pick.\\\\n The Nashville Predators\\\\' second-round pick went to the Carolina Hurricanes as the result of a trade on July 23, 2021, that sent Carolina\\\\'s first-round pick (27th overall) in 2021 to Nashville in exchange for Los Angeles\\\\' second-round pick (40th overall) and this pick.\\\\n The Edmonton Oilers\\\\' second-round pick went to the New York Islanders as the result of a trade on July 16, 2021, that sent Nick Leddy to Detroit in exchange for Richard Panik and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on February 24, 2020, that sent Andreas Athanasiou and Ryan Kuffner to Edmonton in exchange for Sam Gagner, a second-round pick in 2020 and this pick.\\\\n The Boston Bruins\\\\' second-round pick went to the Buffalo Sabres as the result of a trade on April 12, 2021, that sent Taylor Hall and Curtis Lazar to Boston in exchange for Anders Bjork and this pick.\\\\n The Carolina Hurricanes\\\\' second-round pick went to the Los Angeles Kings as the result of a trade on July 24, 2021, that sent a third-round pick and Calgary\\\\'s fourth-round pick both in 2021 (72nd and 109th overall) to Carolina in exchange for this pick.\\\\n The Colorado Avalanche\\\\'s second-round pick went to the Arizona Coyotes as the result of a trade on July 17, 2021, that sent future considerations to the New York Islanders in exchange for Andrew Ladd, a conditional second-round pick in 2022, a conditional third-round pick in 2023, and this pick.\\\\nThe Islanders previously acquired this pick as the result of a trade on October 12, 2020, that sent Devon Toews to Colorado in exchange for a second-round pick in 2022 and this pick.\\\\n The New York Islanders\\\\' second-round pick went to the Colorado Avalanche as the result of a trade on July 15, 2021, that sent Ryan Graves to New Jersey in exchange for Mikhail Maltsev and this pick.\\\\nNew Jersey previously acquired this pick as the result of a trade on February 16, 2020, that sent Andy Greene to New York in exchange for David Quenneville and this pick.\\\\n The Vegas Golden Knights\\\\' second-round pick went to the Chicago Blackhawks as the result of a trade on April 12, 2021, that sent Nick DeSimone and a fifth-round pick in 2022 to Vegas in exchange for a third-round pick in 2022 and this pick.\\\\n The Tampa Bay Lightning\\\\'s second-round pick went to the Montreal Canadiens as the result of a trade on October 7, 2020, that sent St. Louis\\\\' second-round pick in 2020 (57th overall) to Tampa Bay in exchange for a fourth-round pick in 2020 (124th overall) and this pick.\\\\n\\\\nRound three\\\\n\\\\nNotes\\\\n The Buffalo Sabres\\\\' third-round pick went to the New York Rangers as the result of a trade on July 1, 2019, that sent Jimmy Vesey to Buffalo in exchange for this pick.\\\\n The San Jose Sharks\\\\' third-round pick went to the St. Louis Blues as the result of a trade on July 24, 2021, that sent a third and sixth-round pick both in 2021 (81st and 177th overall) to San Jose in exchange for this pick.\\\\n The Los Angeles Kings\\\\' third-round pick went to the Nashville Predators as the result of a trade on July 24, 2021, that sent a third and fifth-round pick both in 2021 (83rd and 147th overall) to Carolina in exchange for this pick.\\\\nCarolina previously acquired this pick as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (59th overall) to Los Angeles in exchange for Calgary\\\\'s fourth-round pick in 2021 (109th overall) and this pick.\\\\n The Vancouver Canucks\\\\' third-round pick went to the Dallas Stars as the result of a trade on July 17, 2021, that sent Jason Dickinson to Vancouver in exchange for this pick.\\\\n The Arizona Coyotes\\\\' third-round pick went to the New York Rangers as the result of a trade on July 24, 2021, that sent a third and sixth-round pick both in 2021 (80th and 176th overall) to Washington in exchange for this pick.\\\\nWashington previously acquired this pick as the result of a trade on April 11, 2021, that sent a Jonas Siegenthaler to New Jersey in exchange for this conditional pick. The condition – Washington will receive Arizona\\\\'s third-round pick in 2021 at New Jersey\\\\'s choice, if the pick is available before the time of the selection – the date of conversion is unknown.\\\\nNew Jersey previously acquired this pick as the result of a trade on December 16, 2019, that sent Taylor Hall and Blake Speers to Arizona in exchange for Nick Merkley, Kevin Bahl, Nate Schnarr, a conditional first-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – New Jersey will receive a third-round pick in 2021 if Arizona does not advance to the 2020 Western Conference Second Round and Hall does not re-sign with Arizona for the 2020–21 NHL season – was converted when Arizona was eliminated in the First Round of the playoffs on August 19, 2020 and when Hall signed with the Buffalo Sabres on October 11, 2020.\\\\n The Chicago Blackhawks\\\\' third-round pick went to the Anaheim Ducks as the result of a trade on July 24, 2021, that sent a third-round pick in 2022 to Montreal in exchange for this pick.\\\\nMontreal previously acquired this pick as the result of a trade on June 30, 2019, that sent Andrew Shaw and a seventh-round pick in 2021 to Chicago in exchange for second and seventh-round picks both in 2020 and this pick.\\\\n The New York Rangers\\\\' third-round pick went to the Washington Capitals as the result of a trade on July 24, 2021, that sent Arizona\\\\'s third-round pick in 2021 (75th overall) to New York in exchange for a sixth-round pick in 2021 (176th overall) and this pick.\\\\n The St. Louis Blues\\\\' third-round pick went to the San Jose Sharks as the result of a trade on July 24, 2021, that sent a third-round pick in 2021 (71st overall) to St. Louis in exchange for a sixth-round pick in 2021 (177th overall) and this pick.\\\\n The Nashville Predators\\\\' third-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent Los Angeles\\\\' third-round pick in 2021 (72nd overall) to Nashville in exchange for a fifth-round pick in 2021 (147th overall) and this pick.\\\\n The Edmonton Oilers\\\\' third-round pick went to the Los Angeles Kings as the result of a trade on July 24, 2021, that sent Toronto\\\\'s third-round pick and a sixth-round pick both in 2021 (89th and 168th overall) to Calgary in exchange for this pick.\\\\nCalgary previously acquired this pick as the result of a trade on July 19, 2019, that sent James Neal to Edmonton in exchange for Milan Lucic and this pick (being conditional at the time of the trade). The condition – Calgary will receive a third-round pick in 2020 or 2021 at Edmonton\\\\'s choice, after the league made a ruling on this conditional pick on July 31, 2020. The original condition on this pick was that Calgary will receive a 2020 third-round pick if Neal scores at least 21 goals during the 2019–20 NHL season and Lucic has at least ten fewer goals than Neal – was converted when the Oilers elected to keep their 2020 third-round pick on October 7, 2020.\\\\n The Washington Capitals\\\\' third-round pick went to the Montreal Canadiens as the result of a trade on October 7, 2020, that sent Anaheim\\\\'s fourth-round pick in 2020 (98th overall) to San Jose in exchange for this pick.\\\\nSan Jose previously acquired this pick as the result of a trade on February 18, 2020, that sent Brenden Dillon to Washington in exchange for Colorado\\\\'s second-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – San Jose will receive a third-round pick in 2021 if Washington does not win the Stanley Cup in 2020 – was converted when Washington was eliminated from the 2020 Stanley Cup playoffs on August 20, 2020.\\\\n The Florida Panthers\\\\' third-round pick went to the Buffalo Sabres as the result of a trade on April 10, 2021, that sent Brandon Montour to Florida in exchange for this pick.\\\\n The Toronto Maple Leafs\\\\' third-round pick went to the Calgary Flames as the result of a trade on July 24, 2021, that sent Edmonton\\\\'s third-round pick in 2021 (84th overall) to Los Angeles in exchange for a sixth-round pick in 2021 (168th overall) and this pick.\\\\nLos Angeles previously acquired this pick as the result of a trade on February 5, 2020, that sent Jack Campbell and Kyle Clifford to Toronto in exchange for Trevor Moore, Columbus\\\\' third-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – Los Angeles will receive a third-round pick in 2021 if Clifford does not re-sign with Toronto for the 2020–21 NHL season – was converted when Clifford signed with St. Louis.\\\\n The Pittsburgh Penguins\\\\' third-round pick went to the Edmonton Oilers as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (20th overall) to Minnesota in exchange for a first-round pick in 2021 (22nd overall) and this pick.\\\\nMinnesota previously acquired this pick as the result of a trade on October 5, 2020, that sent Ryan Donato to San Jose in exchange for this pick.\\\\nSan Jose previously acquired this pick as the result of a trade on February 24, 2020, that sent Patrick Marleau to Pittsburgh in exchange for this pick (being conditional at the time of the trade). The condition – San Jose will receive a third-round pick in 2021 if Pittsburgh does not win the Stanley Cup in 2020 – was converted when the Penguins were eliminated from the 2020 Stanley Cup playoffs on August 7, 2020.\\\\n The Carolina Hurricanes\\\\' third-round pick went to the Chicago Blackhawks as the result of a trade on July 24, 2021, that sent a third-round pick in 2022 to Carolina in exchange for this pick.\\\\n The Vegas Golden Knights third-round pick went to the Carolina Hurricanes as the result of a trade on July 22, 2021, that sent Alex Nedeljkovic to Detroit in exchange for Jonathan Bernier and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on February 26, 2018, that sent Tomas Tatar to Vegas in exchange for a first-round pick in 2018, the Islanders\\\\' second-round pick in 2019 and this pick.\\\\n The Montreal Canadiens\\\\' third-round pick went to the Buffalo Sabres as the result of a trade on March 26, 2021, that sent Eric Staal to Montreal in exchange for a fifth-round pick in 2021 and this pick.\\\\n\\\\nRound four\\\\n\\\\nNotes\\\\n The Detroit Red Wings\\\\' fourth-round pick went to the Vegas Golden Knights as the result of a trade on July 24, 2021, that sent Winnipeg\\\\'s fourth-round pick and Carolina\\\\'s fifth-round pick both in 2021 (114th and 155th overall) to Detroit in exchange for this pick.\\\\n The Los Angeles Kings\\\\' fourth-round pick went to the New York Rangers as the result of a trade on March 27, 2021, that sent Brendan Lemieux to Los Angeles in exchange for this pick.\\\\n The Vancouver Canucks\\\\' fourth-round pick went to the Chicago Blackhawks as the result of a trade on April 12, 2021, that sent Madison Bowey and a fifth-round pick in 2021 to Vancouver in exchange for this pick.\\\\n The Ottawa Senators\\\\' fourth-round pick went to the New York Rangers as the result of a trade on October 7, 2019, that sent Vladislav Namestnikov to Ottawa in exchange for Nick Ebert and this pick.\\\\n The Calgary Flames\\\\' fourth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (59th overall) to Los Angeles in exchange for a third-round pick in 2021 (72nd overall) and this pick.\\\\nLos Angeles previously acquired this pick as the result of a trade on February 24, 2020, that sent Derek Forbort to Calgary in exchange for this pick (being conditional at the time of the trade). The condition – Los Angeles will receive a fourth-round pick in 2021 if Forbort does not re-sign with Calgary for the 2020–21 NHL season – was converted when Forbort signed with the Winnipeg Jets on October 11, 2020.\\\\n The St. Louis Blues\\\\' fourth-round pick went to the Montreal Canadiens as the result of a trade on February 18, 2020, that sent Marco Scandella to St. Louis in exchange for a second-round pick in 2020 and this pick (being conditional at the time of the trade). The condition – Montreal will receive a fourth-round pick in 2021 if Scandella re-signs with the Blues for the 2020–21 NHL season by October 7, 2020 – was converted when Scandella re-signed with the Blues on April 16, 2020.\\\\n The Winnipeg Jets\\\\' fourth-round pick went to the Detroit Red Wings as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (102nd overall) to Vegas in exchange for Carolina\\\\'s fifth-round pick in 2021 (155th overall) and this pick.\\\\nVegas previously acquired this pick as the result of a trade on February 21, 2020, that sent Cody Eakin to Winnipeg in exchange for this pick (being conditional at the time of the trade). The condition – Vegas will receive a fourth-round pick in 2021 if Eakin does not re-sign with the Jets for the 2020–21 NHL season – was converted when Eakin signed with the Buffalo Sabres for the 2020–21 NHL season on October 10, 2020.\\\\n The Toronto Maple Leafs\\\\' fourth-round pick went to the San Jose Sharks as the result of a trade on April 11, 2021, that sent Nick Foligno to Toronto in exchange for this pick.\\\\n The Pittsburgh Penguins\\\\' fourth-round pick went to the Arizona Coyotes as the result of a trade on June 29, 2019, that sent Alex Galchenyuk and Pierre-Olivier Joseph to Pittsburgh in exchange for Phil Kessel, Dane Birks and this pick.\\\\n The Carolina Hurricanes\\\\' fourth-round pick went to the Ottawa Senators as the result of a trade on July 24, 2021, that sent Los Angeles\\\\' fifth-round pick and a sixth-round pick both in 2021 (136th and 170th overall) to Carolina in exchange for this pick.\\\\n The Colorado Avalanche\\\\'s fourth-round pick went to the Nashville Predators as the result of a trade on October 10, 2020, that sent Austin Watson to Ottawa in exchange for this pick.\\\\nOttawa previously acquired this pick as the result of a trade on February 24, 2020, that sent Vladislav Namestnikov to Colorado in exchange for this pick.\\\\n The Vegas Golden Knights\\\\' fourth-round pick went to the Tampa Bay Lightning as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2022 to Montreal in exchange for this pick.\\\\nMontreal previously acquired this pick as the result of a trade on February 24, 2020, that sent Nick Cousins to Vegas in exchange for this pick.\\\\n The Montreal Canadiens\\\\' fourth-round pick went to the Minnesota Wild as the result of a trade on July 24, 2021, that sent a fifth and seventh-round pick both in 2021 (150th and 214th overall) to Montreal in exchange for this pick.\\\\n The Tampa Bay Lightning\\\\'s fourth-round pick went to the Vegas Golden Knights as the result of a trade on July 24, 2021, that sent New Jersey\\\\'s second-round pick in 2021 (36th overall) to Detroit in exchange for a second-round pick in 2021 (38th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on April 10, 2021, that sent David Savard to Tampa Bay in exchange for this pick.\\\\n\\\\nRound five\\\\n\\\\nNotes\\\\n The Buffalo Sabres\\\\' fifth-round pick went to the New Jersey Devils as the result of a trade on February 24, 2020, that sent Wayne Simmonds to Buffalo in exchange for this pick (being conditional at the time of the trade). The condition – New Jersey will receive a fifth-round pick in 2021 if the Sabres do not qualify for the 2020 Stanley Cup playoffs – was converted on May 26, 2020, when it was announced the Sabres would not participate in the 2020 Stanley Cup playoffs.\\\\n The New Jersey Devils\\\\' fifth-round pick went to the Columbus Blue Jackets as the result of a trade on October 8, 2020, that sent Ryan Murray to New Jersey in exchange for this pick.\\\\n The Los Angeles Kings\\\\' fifth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (123rd overall) to Ottawa in exchange for a sixth-round pick in 2021 (170th overall) and this pick.\\\\nOttawa previously acquired this pick as the result of a trade on July 24, 2021, that sent a second-round pick in 2021 (42nd overall) to Los Angeles in exchange for St. Louis\\\\' second-round pick in 2021 (49th overall) and this pick.\\\\n The Ottawa Senators\\\\' fifth-round pick went to the Dallas Stars as the result of a trade on July 23, 2021, that sent a first-round pick in 2021 (15th overall) to Detroit in exchange for Washington\\\\'s first-round pick and the Rangers\\\\' second-round pick both in 2021 (23rd and 48th overall) and this pick.\\\\nDetroit previously acquired this pick as the result of a trade on April 11, 2021, that sent Jon Merrill to Montreal in exchange for Hayden Verbeek and this pick.\\\\nMontreal previously acquired this pick as the result of a trade on January 2, 2020, that sent Mike Reilly to Ottawa in exchange for Andrew Sturtz and this pick.\\\\n The Chicago Blackhawks\\\\' fifth-round pick went to the Vancouver Canucks as the result of a trade on April 12, 2021, that sent a fourth-round pick in 2021 to Chicago in exchange for Madison Bowey and this pick.\\\\n The Philadelphia Flyers\\\\' fifth-round pick went to the Montreal Canadiens as the result of a trade on February 24, 2020, that sent Nate Thompson to Philadelphia in exchange for this pick.\\\\n The Nashville Predators\\\\' fifth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent Los Angeles\\\\' third-round pick in 2021 (72nd overall) to Nashville in exchange for a third-round pick in 2021 (83rd overall) and this pick.\\\\n The Edmonton Oilers\\\\' fifth-round pick went to the Anaheim Ducks as the result of a trade on October 8, 2020, that sent Erik Gudbranson to Ottawa in exchange for this pick.\\\\nOttawa previously acquired this pick as the result of a trade on February 24, 2020 that sent Tyler Ennis to Edmonton in exchange for this pick.\\\\n The Minnesota Wild\\\\'s fifth-round pick went to the Montreal Canadiens as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (127th overall) to Minnesota in exchange for a seventh-round pick in 2021 (214th overall) and this pick.\\\\n The Carolina Hurricanes\\\\' fifth-round pick went to the Detroit Red Wings as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (102nd overall) to Vegas in exchange for Winnipeg\\\\'s fourth-round pick in 2021 (114th overall) and this pick.\\\\nVegas previously acquired this pick as the result of a trade on June 26, 2019, that sent Erik Haula to Carolina in exchange for Nicolas Roy and this pick (being conditional at the time of the trade). The condition – Vegas will receive a fifth-round pick in 2021 if Carolina trades Haula for a player, multiple draft picks or if he is traded for a draft pick in the first five rounds of any future draft – was converted when Haula was traded to the Florida Panthers on February 24, 2020.\\\\n The Colorado Avalanche\\\\'s fifth-round pick went to the San Jose Sharks as the result of a trade on April 10, 2021, that sent Devan Dubnyk to Colorado in exchange for Greg Pateryn and this pick.\\\\n The Vegas Golden Knights\\\\' fifth-round pick went to the Philadelphia Flyers as the result of a trade on April 12, 2021, that sent Michael Raffl to Washington in exchange for this pick.\\\\nWashington previously acquired this as the result of a trade on December 2, 2019, that sent Chandler Stephenson to Vegas in exchange for this pick.\\\\n The Montreal Canadiens\\\\' fifth-round pick went to the Buffalo Sabres as the result of a trade on March 26, 2021, that sent Eric Staal to Montreal in exchange for a third-round pick in 2021 and this pick.\\\\n\\\\nRound six\\\\n\\\\nNotes\\\\n The Los Angeles Kings\\\\' sixth-round pick went to the Calgary Flames as the result of a trade on July 24, 2021, that sent Edmonton\\\\'s third-round pick in 2021 (84th overall) to Los Angeles in exchange for Toronto\\\\'s third-round pick in 2021 (89th overall) and this pick.\\\\n The Ottawa Senators\\\\' sixth-round pick went to the Carolina Hurricanes as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (123rd overall) to Ottawa in exchange for Los Angeles\\\\' fifth-round pick in 2021 (136th overall) and this pick.\\\\n The New York Rangers\\\\' sith-round pick went to the Washington Capitals as the result of a trade on July 24, 2021, that sent Arizona\\\\'s third-round pick in 2021 (75th overall) to New York in exchange for a third-round pick in 2021 (80th overall) and this pick.\\\\n The St. Louis Blues\\\\' sixth-round pick went to the San Jose Sharks as the result of a trade on July 24, 2021, that sent a third-round pick in 2021 (71st overall) to St. Louis in exchange for a third-round pick in 2021 (81th overall) and this pick.\\\\n The Winnipeg Jets\\\\' sixth-round pick went to the Vancouver Canucks as the result of a trade on April 12, 2021, that sent Jordie Benn to Winnipeg in exchange for this pick.\\\\n The Pittsburgh Penguins\\\\' sixth-round pick went to the Edmonton Oilers as the result of a trade on July 26, 2019, that sent John Marino to Pittsburgh in exchange for this pick (being conditional at the time of the trade). The condition – Edmonton will receive a sixth-round pick in 2021 if Marino signs with the Penguins – was converted when Marino signed with the Penguins on August 8, 2019.\\\\n The Colorado Avalanche\\\\'s sixth-round pick went to the Buffalo Sabres as the result of a trade on March 20, 2021, that sent Jonas Johansson to Colorado in exchange for this pick.\\\\n\\\\nRound seven\\\\n\\\\nNotes\\\\n The Anaheim Ducks\\\\' seventh-round pick went to the Pittsburgh Penguins as the result of a trade on October 25, 2019, that sent Erik Gudbranson to Anaheim in exchange for Andreas Martinsen and this pick.\\\\n The New Jersey Devils\\\\' seventh-round pick went to the Tampa Bay Lightning as the result of a trade on November 1, 2019, that sent Louis Domingue to New Jersey in exchange for this pick (being conditional at the time of the trade). The condition – Tampa Bay will receive a seventh-round pick in 2021 if Domingue plays in seven games for the Devils during the 2019–20 NHL season – was converted on January 9, 2020.\\\\n The Detroit Red Wings\\\\' seventh-round pick went to the St. Louis Blues as the result of a trade on October 7, 2020, that sent Chicago\\\\'s seventh-round pick in 2020 (203rd overall) to Detroit in exchange for this pick.\\\\n The Los Angeles Kings\\\\' seventh-round pick went to the Carolina Hurricanes as the result of a trade on October 7, 2020, that sent Montreal\\\\'s fifth-round pick in 2020 (140th overall) to Los Angeles in exchange for a sixth-round pick in 2020 and this pick.\\\\n The Arizona Coyotes\\\\' seventh-round pick went to the New Jersey Devils as the result of a trade on October 7, 2020, that sent a seventh-round pick in 2020 (192nd overall) to Arizona in exchange for this pick.\\\\n The St. Louis Blues\\\\' seventh-round pick went to the Carolina Hurricanes as the result of a trade on September 24, 2019, that sent Justin Faulk and a fifth-round pick in 2020 to St. Louis in exchange for Joel Edmundson, Dominik Bokk and this pick.\\\\n The Winnipeg Jets\\\\' seventh-round pick went to the Florida Panthers as the result of a trade on February 25, 2019, that sent Bogdan Kiselevich to Winnipeg in exchange for this pick.\\\\n The Nashville Predators\\\\' seventh-round pick went to the Tampa Bay Lightning as the result of a trade on June 14, 2019, that sent Connor Ingram to Nashville in exchange for this pick.\\\\n The Minnesota Wild\\\\'s seventh-round pick went to the Montreal Canadiens as the result of a trade on July 24, 2021, that sent a fourth-round pick in 2021 (127th overall) to Minnesota in exchange for a fifth-round pick in 2021 (150th overall) and this pick.\\\\n The Washington Capitals\\\\' seventh-round pick went to the Pittsburgh Penguins as the result of a trade on October 7, 2020, that sent Colorado\\\\'s seventh-round pick in 2020 (211th overall) to Washington in exchange for this pick.\\\\n The Florida Panthers\\\\' seventh-round pick went to the Chicago Blackhawks as the result of a trade on April 8, 2021, that sent Lucas Carlsson and Lucas Wallmark to Florida in exchange for Brett Connolly, Riley Stillman, Henrik Borgstrom and this pick.\\\\n The Toronto Maple Leafs\\\\' seventh-round pick went to the Boston Bruins as the result of a trade on October 7, 2020, that sent a seventh-round pick in 2020 (213th overall) to Toronto in exchange for this pick.\\\\n The Montreal Canadiens\\\\' seventh-round pick went Arizona Coyotes as the result of a trade on July 24, 2021, that sent St. Louis\\\\' seventh-round pick in 2022 to Montreal in exchange for this pick.\\\\nMontreal previously re-acquired this pick as the result of a trade on October 7, 2020, that sent Ottawa\\\\'s seventh-round pick in 2020 to Chicago in exchange for this pick.\\\\nChicago previously acquired this pick as the result of a trade on June 30, 2019, that sent second and seventh-round picks both in 2020 and a third-round pick in 2021 to Montreal in exchange for Andrew Shaw and this pick.\\\\n\\\\nDraftees based on nationality\\\\n\\\\nNorth American draftees by state/province\\\\n\\\\nSee also\\\\n 2017–18 NHL transactions\\\\n 2018–19 NHL transactions\\\\n 2019–20 NHL transactions\\\\n 2020–21 NHL transactions\\\\n 2021–22 NHL transactions\\\\n 2020–21 NHL season\\\\n 2021 NHL Expansion Draft\\\\n List of first overall NHL draft picks\\\\n List of NHL players\\\\n\\\\nReferences\\\\n\\\\nExternal links\\\\n2021 NHL Entry Draft player stats at The Internet Hockey Database\\\\n\\\\nEntry Draft\\\\nNHL Entry Draft\\\\nNHL Entry Draft\\\\nEvents in New Jersey\\\\nIce hockey in New Jersey\\\\nNHL\\\\nNational Hockey League in the New York metropolitan area\\\\nNational Hockey League Entry Draft'},\\n\",\n \" {'docid': 'doc-en-15',\\n\",\n \" 'text': 'The history of Christianity in Sussex includes all aspects of the Christianity in the region that is now Sussex from its introduction to the present day. Christianity is the most commonly practised religion in Sussex.\\\\n\\\\nEarly history\\\\n\\\\nAfter the Roman conquest of AD 43, the Celtic society of Sussex became heavily Romanized.\\\\n\\\\nThe first written account of Christianity in Britain comes from the early Christian Berber author, Tertullian, writing in the third century, who said that \\\"Christianity could even be found in Britain.\\\" Emperor Constantine (AD\\\\xa0306-337), granted official tolerance to Christianity with the Edict of Milan in AD\\\\xa0313. Then, in the reign of Emperor Theodosius \\\"the Great\\\" (AD\\\\xa0378–395), Christianity was made the official religion of the Roman Empire.\\\\n\\\\nWhen Roman rule eventually ceased, Christianity was probably confined to urban communities. At Wiggonholt, on a tributary of the River Arun, a large lead tank with repeated chi-rho motifs was discovered in 1943, the only Roman period artefact in Sussex found with a definite Christian association. It may represent a baptismal font or a container for holy water, or alternatively may have been used by pagans.\\\\n\\\\nMedieval\\\\n\\\\nSaxon\\\\n\\\\nAfter the departure of the Roman army, the Saxons arrived and founded the Kingdom of Sussex in the 5th century, bringing with them their polytheistic religion. The Saxon pagan culture probably caused a reversal of the spread of Christianity. According to Bede, Sussex was the last of the mainland Anglo Saxon kingdoms to be converted.\\\\n\\\\nÆðelwealh became Sussex\\\\'s first Christian king when he married Eafe, the daughter of Wulfhere, the Christian king of Mercia. In 681 St Wilfrid, the exiled Bishop of York, landed at Selsey and is credited with evangelising the local population and founding the church in Sussex. King Æðelwealh granted land to Wilfrid which became the site of Selsey Abbey. The seat of the Sussex bishopric was originally located here before the Normans moved it to Chichester Cathedral in 1075. According to Bede, Sussex was the last area of the country to be converted. However it is unlikely that Sussex was wholly heathen when Wilfrid arrived. Æðelwealh, Sussex\\\\'s king, had been baptised. Damianus, a South Saxon, was made Bishop of Rochester in the Kingdom of Kent in the 650s; this may indicate earlier missionary work in the first half of the 7th century. At the time of Wilfrid\\\\'s mission there was a monastery at Bosham containing a few monks led by an Irish monk named Dicul, which was probably part of the Hiberno-Scottish mission of the time. Wilfrid was a champion of Roman customs and it was these customs that were adopted by the church in Sussex rather than the Celtic customs that had taken root in Scotland and Ireland.\\\\n\\\\nShortly after Æðelwealh granted land to Wilfrid for the church, Cædwalla of Wessex killed Æðelwealh and conquered Sussex. Christianity in Sussex was put under control of the diocese of Winchester. It was not until c. 715 that Eadberht, Abbot of Selsey was consecrated the first bishop of the South Saxons.\\\\n\\\\nSt Lewinna, or St Leofwynn, was a female saint who lived around Seaford, probably at Bishopstone around the 7th century. According to the hagiography of the Secgan Manuscript, Lyminster is the burial place of St Cuthflæd of Lyminster. In the late 7th or early 8th century, St Cuthman, a shepherd who may have been born in Chidham and had been reduced to begging, set out from his home with his disabled mother using a one-wheeled cart. When he reached Steyning he saw a vision and stopped there to build a church. Cuthman was venerated as a saint and his church was in existence by 857 when King Æthelwulf of Wessex was buried there. Steyning was an important religious centre and St Cuthman\\\\'s grave became a place of pilgrimage in the 10th and 11th centuries. In 681, Bede records that an outbreak of the plague had devastated parts of England, including Sussex, and the monks at Selsey Abbey fasted and prayed for three days for an end to the outbreak. A young boy with the plague prayed to St Oswald and his prayers were answered, and a vision of St Peter and St Paul was said to have appeared to the boy, telling him that he would be the last to die.\\\\n\\\\nThe church built at Steyning was one of around 50 minster churches across Sussex and these churches supplied itinerant clergy to surrounding districts. Other examples are churches at Singleton, Lyminster, Findon and Bishopstone. The jurisdiction of each minster church in the pre-Viking era seems to match early land divisions that were replaced by hundreds in the 10th or 11th centuries. It was not until 200–300 years after its conversion to Christianity in the 680s that a network of local parish churches existed in Sussex.\\\\n\\\\nVarious monastic houses were established in the Saxon period in Sussex including at Selsey Abbey, Lyminster Priory, Aldingbourne, Beddingham, Bosham, Chichester, Ferring and South Malling, near Lewes.\\\\n\\\\nNorman and Angevin\\\\n\\\\nFollowing the Norman Conquest of 1066, there was a purge of the English episcopate in 1070. The Anglo-Saxon Bishop of Selsey was deposed and replaced with William the Conqueror\\\\'s personal chaplain, Stigand. During Stigand\\\\'s episcopate the see that had been established at Selsey was transferred to Chichester after the Council of London of 1075 decreed that sees should be centred in cities rather than vills. 1094 saw the completion of Battle Abbey, which had been founded on the site of the Battle of Hastings after Pope Alexander II had ordered the Normans to do penance for killing so many people during their conquest of England. Monks also planned out the nearby town of Battle shortly after the conquest. Many of the monastic houses of this period were founded by Sussex\\\\'s new Norman lords. Around 1081, the lord of Lewes Rape, William de Warenne and his wife Gundrada formed England\\\\'s first and largest Cluniac monastery at Lewes Priory. The lord of Arundel Rape, Roger de Montgomerie established Arundel Priory in 1102. Sele Priory in the Rape of Bramber was founded by the Braose family by 1126.\\\\n\\\\nBishop Ralph Luffa is credited with the foundation of the current Chichester Cathedral. \\\\nThe original structure that had been built by Stigand was largely destroyed by fire in 1114.\\\\n\\\\nThe medieval church also set up various hospitals and schools in Sussex, including St Mary\\\\'s Hospital in Chichester (c. 1290-1300); St Nicholas\\\\' Hospital in Lewes, which was run by the monks of Lewes Priory; and the Prebendal School close to Chichester Cathedral.\\\\n\\\\nThe archdeaconries of Chichester and Lewes were created in the 12th century under Ralph Luffa.\\\\n\\\\nSussex has strong links with the Knights Templar and the Knights Hospitaller including at Shipley, Poling and Sompting.\\\\n\\\\nIn the 13th century, Richard of Chichester was canonised as a saint, and a shrine dedicated to him at Chichester Cathedral became an important place of pilgrimage. St Richard later became Sussex\\\\'s patron saint.\\\\n\\\\nIn 1450 Adam Moleyns became the first and only bishop of Chichester to be assassinated. Troops had been gathered to send to the war in France, but bad weather delayed their departure, and troops raided several towns along the coast. Moleyns was sent to Portsmouth to pay troops their outstanding wages, but was beaten so severely by the mob of soldiers that he died.\\\\n\\\\nThere is very little evidence of Lollardy in Sussex in the 15th century. Only one person was burnt to death as a Lollard, Thomas Bageley. Goring argues that pockets of Lollardy existed in the High Weald for over a century before Henry VIII\\\\'s break with Rome. Lollards tended to congregate near diocesan boundaries so that they could flee across the boundary to safety. Reginald Pecock, bishop of Chichester from 1450–1459, was accused of heresy and only saved his life by privately and publicly renouncing his opinions.\\\\n\\\\nEarly modern\\\\nDuring this period Sussex has been described \\\"as an anomaly: a southern county with a religious dynamic more in keeping with those of the north, connected to the Continent as much as the rest of the country, an entity that resisted easy co-option into Elizabeth I\\\\'s \\\\'little Israel of England\\\\'.\\\" Rye was probably the most Protestant of all Sussex towns, gaining a reputation as a \\\\'godly commonwealth\\\\' well before the end of Henry VIII\\\\'s reign. There was also strong opposition to the imposition of mass by Mary I.\\\\n\\\\nThe Reformation\\\\n\\\\nAs in the rest of the country, the Church of England\\\\'s split with Rome during the reign of Henry VIII was felt in Sussex. In 1535, the king appointed Sir Thomas Cromwell as vicar-general. Cromwell visited Sussex later in 1535, as part of his national census of churches and monasteries. The census was intended to enable the more efficient taxing of church property. The following year, an Act was passed that decreed the dissolution of monasteries with an income of less than £200 per annum. This first phase was followed by the \\\"voluntary\\\" surrenders of the larger houses. Lewes Priory with Battle, was the first house in England, during the Dissolution, to surrender on a voluntary basis. The monks surrendered the house in November 1537 in return for either being given a small pension or a living as a priest. The site and possessions of Lewes Priory were granted to Henry VIII\\\\'s vicar-general, Thomas Cromwell, who passed Lewes Priory to his son, Gregory Cromwell. Sussex did not do too badly compared to the rest of the country, as it only had one person in 500 who was a member of a religious order, compared to the national average of one in 256.\\\\n \\\\nIn 1538 there was a royal order for the demolition of the shrine of St Richard of Chichester in Chichester Cathedral. Thomas Cromwell saying that there was \\\"a certain kind of idolatry about the shrine\\\".\\\\n\\\\nRichard Sampson, Bishop of Chichester, incurred the displeasure of Cromwell and ended up imprisoned in the Tower of London at the end of 1539. Sampson was released after Cromwell\\\\'s fall from favour and execution in 1540. Sampson then continued at the see of Chichester for a further two years. He was succeeded as Bishop of Chichester by George Day. Day opposed the changes, and incurred the displeasure of the royal commissioners, who promptly suspended him as Bishop and allowed him only to preach in his cathedral church.\\\\n\\\\nHenry VIII died in 1547; his son Edward VI continued on the path that his father had set. However his reign was only short-lived as he died after only six years.\\\\n\\\\nThe bishops of Chichester had not been in favour of the Reformation until the appointment of John Scory to the episcopate in 1552. During Henry VIII\\\\'s reign two of the canons of Chichester Cathedral had been executed for their opposition to the Reformation, and during Edward VI\\\\'s reign George Day was ultimately imprisoned for his opposition to the reforms.\\\\n\\\\nReign of Mary I\\\\nThere had been twenty years of religious reform when the Catholic, Mary Tudor succeeded to the throne of England in 1553. Mary expected her clergy to be unmarried, so Bishop Scory thought it prudent to retire as he was a married man, and George Day was released and restored to the see of Chichester.\\\\n\\\\nMary\\\\'s persecution of Protestants earned her the nickname \\\"Bloody Mary\\\". Nationally about 288 Protestants were burnt at the stake during her reign, including 41 in Sussex. Most of the executions in Sussex were at Lewes. Of these 41 burnings, 36 can be identified to have come from specific parishes, and the place of execution is known for 27 of them; because the details of the executions were recorded in the Book of Martyrs by John Foxe, published in 1563. Martyrs included Deryck Carver, a French-speaking Flemish man who had sought refuge in Brighton from persecution for his Calvinist beliefs; and Richard Woodman, an ironmaster from Buxted. There are Bonfire Societies in Sussex that still remember the 17 Protestant martyrs that burned in Lewes High Street, and in Lewes itself they have a procession of martyrs\\\\' crosses during the bonfire night celebration. According to Quinn, the authorities in Sussex during Mary\\\\'s reign were rather less bloodthirsty than is generally assumed, often allowing their opponents to slip the noose when they could. Carver\\\\'s meetings had been attended by many fishermen from both England and France, beginning the tradition of French Christian worship in Brighton.\\\\n\\\\nThere was a range of Protestant beliefs in Sussex during the reign of Queen Mary. Sussex\\\\'s proximity to the Continent left it particularly exposed to European Protestantism, while its proximity to large parts of the Weald also left it open to pre-Reformation Protestantism. This was particularly so in the east of the county, with its trade links to Protestant areas of northern Europe and it covering a large part of the Weald, as well as being close to the Kentish border.\\\\n\\\\nReign of Elizabeth I\\\\nWhen Mary died in 1558, she was replaced by her Protestant sister Elizabeth I. Elizabeth re-established the break with Rome when she passed the 1559 Acts of Supremacy and Uniformity: the clergy were expected to take statutory oaths, and those that did not were deprived of their living. In the county nearly half the cathedral clergy and about 40% of the parish clergy had to be replaced, although some of the vacancies were due to ill health or death.\\\\n\\\\nA case can be made for the Reformation as a religious phenomenon only arriving in Sussex with Bishop Richard Curteys from 1570. In the west, Curteys\\\\' reforms were hampered by the noble Catholic families, and in the east by more radical forms of Protestantism. Until then the loyal but conservative bishops Sherborne, Sampson and Day did not appear to enforce doctrinal orthodoxy. Through the influence of Richard Curteys, the Reformation in Sussex took on a Puritan tone from the 1570s and a tradition of \\\\'radical parochialism\\\\' developed with well-educated preachers supporting ministers, often sponsored by Puritan landowners. Curteys circumvented the existing clergy by bringing in \\\\'lecturers\\\\' or unbeneficed clergy who provided a new preaching tradition, and also gathered some existing clergy who were sympathetic to his aims. This was particularly strong in the Lewes area, in part because of its European trade links.\\\\n\\\\nDuring the 1570s Puritan Christian names like \\\"Feregod\\\" became common in the Weald. Far from the seat of the Bishop of Chichester, radical towns like Rye and Lewes became \\\"free-thinking\\\" Protestant towns, and numbers of Protestants increased, with Huguenots seeking refuge after the St Bartholomew\\\\'s Day massacre in France. In the 1560s and 1570s, there was a trend for giving Puritan children \\\"godly\\\" names, especially in East Sussex, signifying a Puritan counter-culture. Eighteen parishes in the east of Sussex record Puritan names, the highest concentration of which was in Warbleton, where around half the children were given Puritan names between 1587 and 1590. Such Puritan names included \\\"Be-courteous Cole\\\" (in Pevensey), \\\"Safely-on-High Snat\\\" (in Uckfield) and \\\"Fight-the-Good-Fight-of-Faith White\\\" (in Ewhurst. One child with a Puritan name, Accepted Frewen, later became Archbishop of York. Many Sussex Puritans emigrated across the Atlantic Ocean to New England, accounting for about 1% of New England\\\\'s immigrants. Puritan migrants from other English regions, such as East Anglia, had much lower usage of hortatory names, and Puritans in the US state of Massachusetts followed the East Anglian rather than the Sussex naming custom.\\\\n\\\\nIn the late 16th century, Sussex was a complicated and divided region. The countryside was largely Catholic, dominated by the ancient Catholic families: the Howards at Arundel, the Percys at Petworth House, the Gages at Firle, the Brownes (the Lords Montague) at Cowdray Park, the Palmers at Parham House, as well as other minor dynasties like the Carylls, Lewkenors, Shelleys and Kemps. At the start of Elizabeth\\\\'s reign all six of Sussex\\\\'s noble families were Catholic. The towns, including Rye and Lewes, were more likely to be controlled by Protestants if not Protestant in orientation. The Earl of Arundel, Henry FitzAlan had considerable influence as Lord Steward of the Royal Household, privy councillor and Lord Lieutenant of Sussex (1559-1569) until he was involved in the Ridolfi plot to marry his son-in-law, Thomas, Duke of Norfolk, to Mary Queen of Scots. Even after the 1580s when restrictions on Catholics were imposed, Sussex continued to be led by Catholic peers. The office of sheriff of Sussex was held by Catholics eleven times between 1558 and 1603.\\\\n\\\\nAt the end of Elizabeth\\\\'s reign, Catholicism continued to be tolerated. On the death of her husband, Lady Montague withdrew to Battle Abbey, the family\\\\'s seat in the east of the county. The establishment of what became known as \\\"Little Rome\\\" became a focal point for the local Catholic community, with as many as 120 people attending Mass. This shows that long-standing political loyalty by Catholics was repaid by a form of toleration.\\\\n\\\\nThe Catholic Sussex families which suffered imprisonment or financial ruin at this time were mostly those that were involved in conspiracies against Elizabeth. After the uprising of 1569, the eighth Earl of Northumberland was effectively sent into internal exile in Sussex, at his home at Petworth House. After 1577, central authorities mounted on a growing attack on Catholic recusants, forcing them to abandon apparent conformity at a greater cost. Fines for non-attendance at an Anglican church were increased from 12d per week to 20 pounds per month. In 1580 leading Sussex Catholics including John Gage of Firle and Richard Shelley of Warminghurst were imprisoned for recusancy and continued to pay the taxes and fines demanded. In 1583 Charles Paget was smuggled into England, meeting William Shelley at Patching to discuss a plan to land Spanish, German and Italian troops in Sussex and march to Petworth House, the home of Northumberland, and Arundel Castle, while a second force would land in Lancashire and be joined by an uprising of English Catholics. Shelley\\\\'s and Northumberland\\\\'s actions reveal there was some truth in the suspicions directed against Sussex Catholics.\\\\n\\\\nWith further legislation in the 1580s, Sussex Catholics caught harbouring priests were guilty of treason. Significantly, no member of the Sussex gentry or nobility was ever charged under these laws, and neither was there ever any uprising, even though there was a significant Catholic community in Sussex. In this, the west of Sussex was out of step with the rest of England, just as attempts to impose a \\\"Godly magistracy\\\" in Rye in the east of the county was out of step with the rest of Protestant England. During this period Sussex was often different from the rest of England, with east and west of the county often inversions of each other. West Grinstead Park, home of the Caryll family, became a Roman Catholic mission where priests arrived, generally at night up the River Adur to await \\\"posting\\\". he River Adur was extensively used by the many Catholics travelling covertly between London and the Continent. Thomas Pilchard was executed in 1587 for being a priest and Edward Shelley of Warminghurst died at Tyburn in London in 1588 for hiding a priest. In 1588 two Catholic priests, Ralph Crockett and Edward James, were arrested at Arundel Haven (now Littlehampton), taken to London and executed outside Chichester. Philip Howard, 20th Earl of Arundel, who was canonised in 1970 as one of the Forty Martyrs of England and Wales, spent much of his life at his family home of Arundel Castle. From a family of Catholic recusants, Howard was imprisoned in the Tower of London for leaving the country without the permission of Queen Elizabeth. He died there ten years later. Early in the 17th century, Bosham-born Benedictine priest, George Gervase, was executed in London.\\\\n\\\\n17th century\\\\n\\\\nIn the 17th century, the diocese of Chichester was home to several Arminian bishops, including Bishops Andrews, Harsnett, Montagu, Duppa and King.\\\\n\\\\nIn the 1620s and 1630s many communities had licensed preachers. Lectureships at Rye, Lewes, Horsham and Midhurst extended preaching to the towns with the full support of the local gentry. From this time, Sabbatarianism gained ground with suppression of games and disorder. Bishop Montagu put forward extreme views against Puritanism and stressed the importance of ritual. Anthony Stapley, chairman of the Michaelmas quarter sessions in Sussex, was persuaded by Puritans to develop a harangue against the bishops in 1639, and in 1641 Stapley and Thomas Pelham petitioned Parliament on this issue. Latent hostility towards Catholics increased; and although Sussex contained as large a proportion of recusant households as many of the northern counties, few Catholic gentry in the county openly supported the king.\\\\n\\\\nThere were no battles of national significance in Sussex, during the 1642–1651 English civil war; however there were small sieges at Chichester and Arundel. The west of the county was generally royalist, although Chichester was for parliament and the east of the county, with some exceptions, was also for parliament. A few churches were damaged, particularly in the Arundel area. Also, after the surrender of Chichester, the Cathedral was sacked by Sir William Wallers parliamentary troops. Bruno Ryves, Dean of Chichester Cathedral said of the troops that \\\"they deface and mangle [the monuments] with their swords as high as they could reach\\\". He also complained that Waller\\\\'s troops...\\\\n\\\"... brake down the Organs and dashing the pipes with their Pole-axes...\\\"\\\\nMercurius Rusticus p. 139\\\\nDestruction of the cathedrals\\\\' music seems to have been one of the objectives, as Ryves also said, of Waller\\\\'s men, that...\\\\n\\\"they force open all the locks, either of doors or desks wherein the Singing-men laid up their Common-Prayer Books, their singing-Books, their Gowns and Surplesses they rent the Books in pieces and scatter the torn leaves all over the Church, even to the covering of the Pavement..\\\"\\\\nMercurius Rusticus p. 140\\\\n\\\\nIn 1643, Francis Bell, one of the priests at the Catholic mission in West Grinstead, was executed, along with other priests. The Caryll family were frequently persecuted and fined.\\\\n\\\\nDuring Cromwell\\\\'s interregnum, Rye stood out as a Puritan \\\\'Common Wealth\\\\', a centre of social experiment and rigorous public morality under vicar Joseph Beeton and his successor John Allen. The people of Rye seem in general to have ignored the strict sabbatarianism enforced by the constables, particularly where \\\\'immoderate drinking\\\\' was concerned.\\\\n\\\\nSussex Quakers and emigration to British North America\\\\n\\\\nAbout a quarter of the incumbents were forced from their parishes and replaced with Puritans. Many people turned away from the traditional churches and in 1655 George Fox founded the Society of Friends at Horsham. Quakerism emerged in Sussex in the 1650s, to be firmly suppressed by a gentry concerned about its revolutionary tendencies. In 1656, Thomas Haycock of Horsham became the first person in Sussex to be sent to gaol for their Quaker beliefs. William Penn lived in the county for a while; in 1676 he bought the estate of Warminghurst, near Steyning. In 1677 a huge open air meeting of Quakers was held at Penn\\\\'s home in Warminghurst in defiance of the law, with several hundred Quakers attending. Then in 1681 Charles II granted Penn lands in what became Pennsylvania and Delaware. Amongst those whom he carried to Pennsylvania as colonists were 200 people from Sussex. In 1682 Penn left the Kent port of Deal for the Province of Pennsylvania with about 100 passengers, mostly Quakers and mostly from Sussex. Quakers to leave Sussex for Pennsylvania included Samuel Carpenter who founded Horsham Township, Pennsylvania; and in 1677 William Clayton left for Pennsylvania, where his family founded with others a township they called Chichester, and opened the Chichester Friends Meetinghouse. Penn also created Sussex County and renamed the settlement of Hoernkills as Lewes.\\\\n\\\\nFollowing the Rye House Plot of 1683 a new wave of religious persecution swept across England. Until the passing of the Toleration Act received royal assent in 1689 Quakers in Sussex and elsewhere had suffered considerable persecution, many of whom were imprisoned in Horsham Jail. While living at Warminghurst, Penn too was persecuted for his Quaker faith. The 1684 Chichester Quarter Sessions recorded that William Penn \\\"being a factitious and seditious person doth frequently entertain and keep an unlawful assemblage and conventicle in his dwelling house at Warminghurst to the terror of the King\\\\'s liege people.\\\" Penn sold the estate, at Warminghurst, to a James Butler in 1707.\\\\n\\\\nThe Quakers in Sussex debated with Matthew Caffyn, a General Baptist preacher and writer, including George Fox and William Penn. There is a well-known account in 1655 when two Quakers from the north of England, Thomas Lawson and John Slee, disputed doctrine with Caffyn. As a result of their debates, Lawson produced a pamphlet entitled An Untaught Teacher Witnessed Against (1655) and Caffyn produced a pamphlet Deceived and Deceiving Quakers Discovered, Their Damnable Heresies, Horrid Blasphemies, Mockings, Railings (1656). in 1696, Caffyn\\\\'s increasingly radical, unorthodox beliefs caused a schism in the General Baptist Assembly, and its response to his changing theology was significant in the development of Unitarianism. The attorney-general of Rye, Samuel Jeake was exiled from the town after being found guilty of preaching under the Five Mile Act 1665. He was forced to remain outside of Rye until 1687 when the toleration which James II extended to Protestant dissenters enabled him to return to Rye.\\\\n\\\\nThe Restoration of the English monarchy began in 1660 under Charles II. It took over a year, after the restoration of Charles II in May 1660, for Chichester cathedral to get its choir back to full strength.\\\\n\\\\nIn the late 17th century, Sussex was a stronghold of the General Baptists.\\\\n\\\\nIn 1676 the Sussex parishes with the highest proportion of Catholics were almost entirely in the two most westerly Rapes of Chichester and Arundel: at least ten per cent of the population were Catholic in the parishes of Burton, Clapham, Coates, Midhurst, Racton, Shipley and Westfield.\\\\n\\\\nIn 1678 a former Hastings rector, Titus Oates fabricated the \\\"Popish Plot\\\", a supposed Catholic conspiracy to assassinate King Charles II and replace him with James (later James II). The plot led to the false implication, imprisonment and execution of William Howard. As a \\\\'Catholic of distinction\\\\' the seventh John Caryll from Sussex was imprisoned in the Tower of London but was let out on bail. Following the persecutions and executions that followed the Titus Oates plot, the death penalty for being a priest was removed. Instead, unscheduled fines were doubled and all remaining civil rights were removed from people keeping the Roman Catholic faith. At this stage, most Sussex Catholic families conformed to the Anglican church, except notably for the Caryll family. In 1688 the seventh John Caryll went into exile to Saint-Germain in France with James II as private secretary to James\\\\' queen, Mary of Modena.\\\\n\\\\nLate modern\\\\n\\\\n18th century\\\\nThere was a significant decline in non-conformity in Sussex in the early 18th century. Between 1676 and 1724 the strength of non-conformity in the county was reduced by at least one quarter. Around a third of the parishes in Sussex in 1724 had no dissenters. For instance in 1676, Horsham had over 100 non-conformists but by 1724 there were just 34.\\\\n\\\\nThe number of dissenters fell from 4,300 in 1676 to around 3,300 in 1724. In the 18th century, the Sussex grocer, Thomas Turner left a diary which suggests a high level of theological literacy amongst laypeople. At this time, the Sussex Weald and bordering towns such as Lewes were home to a number of fundamentalist sects. Cade Street Chapel in Heathfield was founded in 1769 for the followers of George Gilbert, who was popularly styled as \\\\'The Apostle of Sussex\\\\'. Gilbert also preached in surrounding villages, often with great hardship and difficulty: at Ticehurst he was pelted with stones when the bells rang; at Bexhill he was plastered from head to toe in filth, and a large drum was played to drown out the sound of his voice until a woman put a knife into the drum.\\\\n\\\\nUnder Caffyn\\\\'s guidance a General Baptist chapel was founded in Horsham in 1719, bringing together Baptists who had met in small house-groups in the town since 1669 or possibly as early as 1645. Worshippers from across northern Sussex came to this chapel; many were from the village of Billingshurst a few miles away. This group later became large enough to split from the Horsham congregation and establish a chapel in their home village.\\\\n\\\\nMethodist pioneers came to the Rape of Hastings in 1756, with John Wesley visiting Rye in 1758. Wesley\\\\'s last open air sermon was held in nearby Winchelsea in 1790. The Countess of Huntingdon\\\\'s Connexion\\\\'s first church was set up in 1761 in North Street, Brighton in what was originally Selina, Countess of Huntingdon\\\\'s garden.\\\\n\\\\nSussex had a significantly larger proportion of Catholics than other southern counties. Between 1715 and 1720, 8 per cent of the population of Sussex were registered as Catholic, a proportion more in common with counties north of a line from the River Severn to the Wash. John Baptist Caryll, the last of the Caryll family, was penalised for his Catholic faith and was forced in 1754 to sell his Sussex homes including that at West Grinstead. He endowed the Priest\\\\'s House to the Catholic Church via Lewes-born bishop Richard Challoner so that Catholic mass could be continued in the locality. When Challoner visited the West Grinstead Mission in 1741 he found 80 Catholics at Mass. Finally, history cannot forget the famous recusant, Maria Fitzherbert, who during this period secretly married the Prince of Wales, Prince Regent, and future George IV in 1785. The British Constitution, however, did not accept it and George IV later moved on. Cast aside by the establishment, she was adopted by the town of Brighton, whose citizens, both Catholic and Protestant, called her \\\"Mrs. Prince.\\\" According to journalist, Richard Abbott, \\\"Before the town had a [Catholic] church of its own, she had a priest say Mass at her own house, and invited local Catholics\\\", suggesting the recusants of Brighton were not very undiscovered.\\\\n\\\\n19th Century\\\\n\\\\nRoman Catholic Church\\\\nBrighton\\\\'s Roman Catholic community at the time of the Relief Act was small, but two factors caused it to grow in the 1790s. Many refugees from the French Revolution settled in Brighton after escaping from France; and Maria Fitzherbert, a twice-widowed Catholic, began a relationship with the Prince Regent (and secretly married him in 1785 in a ceremony which was illegal according to the Act of Settlement 1701 and the Royal Marriages Act 1772). She accompanied the Prince Regent whenever he visited Brighton, and had her own house (Steine House on Old Steine).\\\\n\\\\nThe first Catholic place of worship since the Reformation in Brighton was established above a shop in 1798; it was one of the earliest in Britain. In 1805 the priest in charge, a French émigré, started to raise money for a permanent building; a site on High Street, east of the Royal Pavilion and Old Steine, was found, and the Classical-style church was completed in 1807. It was demolished in 1981.\\\\n\\\\nIn 1818 the new rector, a friend of Maria Fitzherbert, wanted to extend the church. Mrs Fitzherbert donated £1,000 for this purpose, but before any action could be taken the events of 1829, when Catholic emancipation was fully achieved, encouraged Brighton\\\\'s Catholic community to seek a new site for a larger, more elaborate church. A piece of undeveloped land on the estate of the Marquess of Bristol was bought for £1,050, and William Hallett, later a mayor of Brighton, designed and built the new church of St John the Baptist. It was consecrated on 7 July 1835 and opened on 9 July 1835. Many of the 900 Catholic churches opened in England since the 1791 Roman Catholic Relief Act had not been consecrated by that stage, so St John the Baptist\\\\'s was only the fourth new church to be consecrated in England since the Reformation in the 16th century.\\\\n\\\\nFounded in 1873, St. Hugh\\\\'s Charterhouse, Parkminster is the first and only post-Reformation Carthusian monastery in the United Kingdom. In 1876 the Shrine Church of Our Lady of Consolation of West Grinstead was established, becoming the first Catholic shrine in honour of Mary to be established in England since the Reformation. Sussex was covered by the new Roman Catholic diocese of Southwark, created in 1850. New priests for the Catholic diocese of Southwark began to train at West Grinstead until they could move to a larger domestic property at Henfield. The diocese then moved its seminary to a purpose-built seminary in Surrey.\\\\n\\\\nNon-conformist churches\\\\nDespite Methodism\\\\'s early progress around Rye and Winchelsea in the Rape of Hastings, Methodism took longer to gain ground in the rest of Sussex. Methodism in the coastal towns of Sussex had a very unusual origin in that it was Methodists in the army who were the main or contributory founders of Methodism in towns from Chichester to Bexhill, including Lewes. Michael Hickman has argued that it was not until 1803 when Methodists and others in the army were allowed to worship freely on Sundays that Methodist soldiers could support or found Methodist societies in Sussex. 1805 saw the timber-framed Jireh Chapel open in Lewes, for Calvinist William Huntington whose tomb is at the rear of the chapel.\\\\n\\\\nThe General Baptist congregations at Billingshurst, Ditchling and Horsham gradually moved from General Baptist beliefs towards Unitarianism in the early 19th century.\\\\n\\\\nIn the mid 19th century John Sirgood founded the Society of Dependants at Loxwood in the north of the county. Nicknamed the \\\\'Cokelers\\\\' their beliefs were largely derived from Wesleyan Arminianism. They believed in the people\\\\'s ability to exercise free will and thereby achieve salvation rather than the Calvinistic assertion of predestination. They first established themselves at Loxwood because it was outside of the control of the large estates whose Anglican owners would have denied them land or premises. As well as at Loxwood, the Society of Dependants went on to found places of worship at Chichester, Hove, Northchapel and Warnham, as well as at three locations in Surrey.\\\\n\\\\n1851 census\\\\nIn 1851 the authorities organised a census of places of worship in England and Wales. The figures for Sussex indicated that there were more Anglican than non-conformist places of worship. In the neighbouring counties of Hampshire and Kent, there were more non-conformist places than Anglican.\\\\n\\\\nThe 1851 census shows that the Anglican church was particularly strong in the west of the county. These were areas where settlements were predominantly nucleated, with small parishes. Thakeham had the second highest rate of Anglicans in England (96% Anglican). Steyning, Petworth, Westhampnett and Westbourne were also over 80% Anglican. Anglican churches did well in the coastal towns including Brighton. In parts of the Sussex Weald the Anglican church had fewer churches than many other denominations, but not in terms of attendances at these churches.\\\\n\\\\nJust over 40% of the places of worship in Sussex in 1851 were non-conformist, mainly Independents, Wesleyan Methodists and Baptists. There were also smaller congregations of Catholics, Quakers, Countess of Huntingdon\\\\'s Connexion and Unitarians. Non-conformist chapels did well particularly in the Weald.\\\\n\\\\nOld dissent - dating back to Lollardy, such as Baptists, Unitarians and Quakers - remained more popular than new dissent and could be seen particularly in the Weald and on the Downs. It was particularly noticeable in the towns such as Brighton, Shoreham, Hastings and Rye. Some parts of Sussex were areas of strength for Baptists, but the west was an area of relative weakness. Overall in Sussex, Wesleyan Methodism had some of the fewest adherents in Sussex in all of England. However Wesleyan Methodism was strong in the rape of Hastings along the border with Kent; it was weakest in the county west of Eastbourne. Primitive Methodists were almost absent from Sussex.\\\\n\\\\nPrimitive Methodists were almost completely absent from Sussex. Of the 44 Sussex parishes with Catholics in 1676, only two, Arundel and Slindon, also had a Catholic place of worship in 1851.\\\\n\\\\nAnglo-Catholic reform in the Anglican Church and subsequent protest\\\\nIn the mid 19th century, divine Frederick William Robertson became well-known and preached at the Holy Trinity Church, Brighton.\\\\n\\\\nFormed in the 19th century, the cult of the Sussex martyrs was instigated at a time of the restoration of the Catholic hierarchy in England, bolstered by an increase in the Irish Catholic population, as well as the high-profile conversion to Catholicism of members of the Oxford movement, including Cardinal Newman and former Archdeacon of Chichester, Henry Edward Manning. Mark Antony Lower, an anti-Catholic propagandist and schoolmaster from Lewes, inaugurated the cult of the Sussex martyrs after the publication of his 1851 book The Sussex Martyrs to recall the dire actions of Catholicism in Sussex. Hostility to the Roman Catholic church, strong shortly after the Reformation had virtually died out by the early 19th century when religious tolerance was dominant mood. This began to change with the Evangelical Revival. The first Methodists to preach in Lewes were Calvinist Methodists, who saw the world as a sharp contrast between good and evil, God and the devil. The natural recipients of their negative projections were Catholics, who were becoming tolerated in England. More petitions were to come out of Lewes against Catholic emancipation that any other town in southern England. They came not from the old dissenters who favoured toleration but from the newly-formed Calvinist congregations. The local press in Lewes pandered to these prejudices. The introduction of ritualist practices in the Anglican church further increased anti-Catholic attitudes in Lewes.\\\\n\\\\nIn the mid 19th century the practice of burning an effigy of Pope Paul V at the Lewes Bonfire celebrations began. Paul V was a peaceable man who happened to be pope at the time of the Gunpowder Plot in 1605 and who cannot be held responsible for the Gunpowder Plot or the persecution of Protestants in the reign of Mary I, which were linked at this time by a misunderstanding of the past. In 1893 William Richardson, rector of the Southover district of Lewes, held sermons on the Sunday before 5 November warning about the perils of Catholicism. Many attendees were members of the newly-formed Orange Lodge in Lewes.\\\\n\\\\nAt the end of the 19th century and beginning of the 20th century, memorials were erected across Sussex and several other English counties to honour people burnt to death as heretics in the reigns of Henry VIII and Mary I. These were largely a reminder of religious divisions of more than three centuries earlier which seemed remote from the public preoccupations of the day. The actions could only be seen an anti-Catholic or at least anti-papal. Whilst moderate supporters did not wish to offend the Catholic community, a memorial in Heathfield read \\\"burnt to death at Lewes by the Roman Catholics\\\". These monuments did not commemorate the martyrdoms of Catholics or the Protestant opponents of state-imposed orthodoxy, except where they were erected by nonconformists. Anger was directed against the Anglo-Catholic community more than Catholics.\\\\n\\\\nIn the Anglican church in the 19th century, the role of ritual became subject of great, often heated, debate. In Brighton the Anglican church became influenced by the Oxford Movement, to an extent unparalleled elsewhere in the country apart from London. In Anglo-Catholic circles, Brighton became associated with London, as in the collective title of \\\"London-Brighton and South Coast Religion\\\", a play on the name of the main railway company in Victorian Sussex, the \\\"London, Brighton and South Coast Railway\\\". The railway, coincidentally or otherwise, linked all the large and growing centres of Anglo-Catholic worship spreading from London to Brighton and then east and west along coast of Sussex to the neighbouring counties of Kent and Hampshire. Anglo-Catholic priests in Brighton, included Henry Michell Wagner whose churches included St Paul\\\\'s Church and there was a powerful Protestant reaction including a riot in 1880. Brighton vicar Rev John Purchas was charged and ritualism spread to churches in Hastings and Worthing. Various militant Protestant groups formed branches and lodges across the county. Richard Enraght was also tried, arrested and imprisoned. The prolific Anglo-Catholic hymnologist John Mason Neale was attacked by a mob and hostile demonstrations ensued at East Grinstead.\\\\n\\\\nIn 1884 rioting ensued in Worthing, Eastbourne and Shoreham as mobs of people including members of the Skeleton Army reacted to Salvation Army criticism.\\\\n\\\\nContemporary Christianity\\\\n\\\\nChurch of England\\\\nIn the Church of England in Sussex, the administration of the diocese of Chichester which covers the county was changed in 1912. In addition to the existing archdeaconries of Chichester and Lewes that date from the 12th century, a third archdeaconry of Hastings was created. This structure remained in place until the archdeaconries were reorganised under Eric Kemp in 1975. The archdeaconry of Hastings was dissolved and merged back into the archdeaconry of Lewes, which was renamed the archdeaconry of Lewes and Hastings. A new archdeaconry was created in the north of the county - the archdeaconry of Horsham. This structure remained until 2014 when the archdeaconry of Hastings was recreated in the east of the county and the archeaconry of Lewes and Hastings was renamed the archdeaconry of Brighton and Lewes. The suffragan Bishop of Horsham oversees the archdeaconries of Chichester and Horsham, while the suffragan Bishop of Lewes oversees the archdeaconries of Brighton & Lewes and Hastings. The bishop of Chichester retains oversight over the entire diocese of Chichester i.e. all of Sussex.\\\\n\\\\nOn 16 November 2001, Pat Sinton, became the first woman priest in Sussex to be ordained. Sinton was ordained by John Hind, the bishop of Chichester, following the departure of the previous bishop of Chichester, Eric Kemp. Although Kemp had encouraged women to serve in the permanent diaconate in his diocese he had been an opponent of the ordination of women to the priesthood and women priests were not licensed in the Diocese of Chichester during his episcopate. In September 2014 Fiona Windsor was made archdeacon of Horsham, making her the first female archdeacon in Sussex. \\\\nThe Church of England in Sussex was damaged by sexual abuse scandals in the early 2000s.\\\\n\\\\nRoman Catholic Church\\\\nIn 1900 the Roman Catholic nun Maude Petre began a friendship with the Jesuit priest George Tyrell, which resulted in Petre building a cottage for Tyrell in the garden of her Storrington home. Both Petre and Tyrell were major figures in the Modernist controversy of the early 20th century. The Roman Catholic Diocese of Arundel and Brighton was formed in 1965 out of part of the diocese of Southwark. It includes Sussex and Surrey. In the early 2000s, the sexual abuse scandal in the Arundel and Brighton diocese hurt the public\\\\'s trust in the work of local diocesan officials.\\\\n\\\\nRelations with Sussex churches\\\\nAppointed as Bishop of Chichester in 1929, George Bell was a vocal supporter of the German resistance to Nazism and a pioneer of the Ecumenical Movement that aimed for greater co-operation between churches. Bell established in 1955 the first ever County Council of Churches in Sussex, since which similar structures have been formed in other parts of England.\\\\n\\\\nThere is a history of religious antagonism and anti-popery around the bonfire celebrations in Lewes. In the 1930s the mayor of Lewes requested that \\\\'no popery\\\\' banners be removed and an end to the burning of effigies of Pope Paul V. In the 1950s the Cliffe Bonfire Society was banned from the Bonfire Council from taking part in the United Grand Procession for its refusal to stop carrying a \\\\'no popery\\\\' banner and banners commemorating the 16th century Protestant martyrs burned at Lewes. In Lewes, women were to a significant degree responsible for using the spirit of ecumenism to build bridges between the denominations that had until then continued to be anti-Catholic. In 1984 Sussex church leaders were invited to Lewes to discuss Protestant-Catholic relations. Attendees included Eric Kemp, Bishop of Chichester, Peter Ball, suffragan Bishop of Lewes and Cormac Murphy-O\\\\'Connor, Bishop of Arundel and Brighton, as well as their equivalent positions in the Baptist, Methodist and United Reformed churches. In a historic gesture after the meeting the leaders walked to the Martyrs\\\\' memorial and prayed for peace and reconciliation. The owners of the memorial, associated with Jireh Chapel, subsequently threatened the intruders for trespassing. The LDCC later persuaded BBC to make a Songs of Praise TV programme in Lewes on the theme of religious tolerance, broadcast on 5 November 1989. To many though, the bonfire celebrations have lost much of their religious meaning, with many Catholics taking part. There are parallels with the carnival celebrations that took place across western Europe when the established order was turned upside down and the lord of misrule held sway for the day. In 1981 Ian Paisley visited Lewes on Bonfire Night and tried to fan the flames of conflict by handing out anti-Catholic pamphlets. His intervention back-fired and the following year he was burned in effigy. Today, anti-Catholic attitudes are rare and the militant Calvinism that continues in Northern Ireland is all but extinct in Lewes.\\\\n\\\\nIn the 21st century, controversy continues to be associated around the Bonfire societies and competing definitions of tradition and bigotry. For instance, the burning in effigy of Pope Paul V was described in 2012 as \\\"a scandalous piece of stone-cold bigotry\\\"\\\\n\\\\nOther Christian denominations\\\\nEstablished in 1971 the Anabaptist Bruderhof community was founded near Robertsbridge, the earliest such community remaining in Europe. From the 1980s, Sussex has three Greek Orthodox churches - at Brighton, Hastings and Eastbourne.\\\\n\\\\nFollowing the Second Sudanese Civil War, many refugees came to Brighton and Hove and neighbouring areas. Hove and Worthing are now home to Coptic Orthodox Churches, two of 28 such churches in the British Isles. The churches were visited in 2017 by Pope Tawadros II of Alexandria and Bishop Paula of Tanta. In 1998 the congregation at Jireh Chapel in Lewes took the decision to affiliate with the Free Presbyterian Church of Ulster. The church is one of seven such churches established in England.\\\\n\\\\nIn the Old Roman Catholic Church in Europe in 2012, Jerome Lloyd was made Metropolitan Archbishop of Selsey (officially \\\"Archbishop Metropolitan of the Isle of the Seals (Selsey) and the New Market of the Regnenses (i.e. of the Celtic tribe the Romans conquered in AD43, now called Chichester) in the Kingdom of the South Saxons (i.e. Sussex)\\\". Based in Brighton, the archbishop is one of a small number of priests who broadcast the traditional Mass in Latin live, via the internet and is the only priest to do so in Europe. The archbishop works on various projects to help homeless people in Brighton.\\\\n\\\\nThe turn of the 21st century saw the rise of so-called mega-churches and neo-charismatic and evangelical churches including Kingdom Faith in Horsham, set up by Colin Urquhart and the Newfrontiers group founded by Terry Virgo.\\\\n\\\\nCurrent and former places of worship\\\\nLists of all current and former places of worship in Sussex by district are as follows:\\\\n\\\\n Adur District\\\\n Arun District\\\\n Brighton and Hove\\\\n Chichester (current)\\\\n Chichester (former)\\\\n Crawley\\\\n Eastbourne\\\\n Hastings\\\\n Horsham District\\\\n Lewes District\\\\n Mid Sussex\\\\n Rother\\\\n Wealden (current)\\\\n Wealden (former)\\\\n Worthing\\\\n\\\\nSee also\\\\n History of Christianity in England\\\\n History of Sussex\\\\n Religion in Sussex\\\\n List of monastic houses in East Sussex\\\\n List of monastic houses in West Sussex\\\\n History of local government in Sussex\\\\n\\\\nBibliography\\\\n\\\\nReferences\\\\n\\\\nChristianity in Sussex\\\\nHistory of Sussex\\\\nHistory of Christianity in England'}]}\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset['dev'][0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Each passage in the corpus has two parts: `docid` and `text`. `docid` has the form of `doc--`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = load_dataset('Shitao/MLDR', f\\\"corpus-{lang}\\\", trust_remote_code=True)['corpus']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'docid': 'doc-en-9633',\\n\",\n \" 'text': 'Mars Hill Church was a Christian megachurch, founded by Mark Driscoll, Lief Moi, and Mike Gunn. It was a multi-site church based in Seattle, Washington and grew from a home Bible study to 15 locations in 4 U.S. states. Services were offered at its 15 locations; the church also podcast content of weekend services, and of conferences, on the Internet with more than 260,000 sermon views online every week. In 2013, Mars Hill had a membership of 6,489 and average weekly attendance of 12,329. Following controversy in 2014 involving founding pastor Mark Driscoll, attendance dropped to 8,0009,000 people per week.\\\\n\\\\nAt the end of September, 2014, an investigation by the church elders found \\\"bullying\\\" and \\\"patterns of persistent sinful behavior\\\" by Driscoll. The church elders crafted a \\\"restoration\\\" plan to help Driscoll and save the church. Instead, Driscoll declined the restoration plan and resigned. On October 31, 2014, lead pastor Dave Bruskas announced plans to dissolve the church\\\\'s 13 remaining campuses into autonomous entities, with the option of continuing, merging with other congregations, or disbanding, effective January 1, 2015. The Mars Hill network dissolved on January 1, 2015.\\\\n\\\\nHistory\\\\n\\\\nEarly years \\\\nMars Hill Church was founded in spring 1996 by Mark Driscoll, Lief Moi and Mike Gunn. The church started at the rental house of Driscoll and his wife Grace with the blessing of Antioch Bible Church and the exodus of about 30 of its students. They outgrew the apartment and started meeting in the youth rooms of another church. The church had its first official service October 1996, with 160 people attending; attendance quickly fell to around 60 because of discussions about the visions and mission of the church.\\\\n\\\\nIn the spring of 1997, the church expanded to two evening services. The transition to two different congregations resulted in some anxiety and stir by members who didn\\\\'t want the church to grow bigger, but it resulted in growing attendance. Later that same year Mark Driscoll was invited to speak at a pastors\\\\' conference in California. Driscoll\\\\'s speech influenced the emerging church movement, and changed the focus from reaching Generation X to reaching the postmodern world. The speech resulted in media coverage of Mars Hill Church and Mark Driscoll, and put Driscoll in connection with Leadership Network.\\\\n\\\\nThe church continued growing. Inspired by Alan Roxburgh, Driscoll settled on an emerging and missional ecclesiology, and a complementarian view on women in ministry. The church installed the first team of elders and they took over much of the work teaching classes, counseling and training new leaders. Furthermore, the church started a course for new members, called the Gospel Class, to ensure that members were focused on the mission of the church and that they agreed with the central doctrinal statements of the church. The class had been running every quarter since. In the fall of 1999 the church had grown to 350 in attendance every week and was able to pay Driscoll full-time. Prior to 1999, Driscoll operated as an unpaid pastor for three years.\\\\n\\\\nMultisite church \\\\n\\\\nIn 2003, Mars Hill Church moved into a renovated hardware store in the Ballard neighborhood of Seattle. In 2006, in an effort to reduce the overcrowding at its services, Mars Hill opened its first satellite campus in Shoreline. This change also marked their transition to a multi-site church, using video sermons and other multimedia improvements to the church\\\\'s web site to connect the campuses. Later in 2006 Mars Hill acquired two new properties in West Seattle and Wedgwood, which became their West Seattle and Lake City campuses.\\\\n\\\\nSince then, new Mars Hill locations were added using a multi-campus \\\"meta-church\\\" structure, connecting Driscoll\\\\'s sermons via high-definition video to the remote campuses during weekly worship services. This format allowed each location to retain local leadership and ministries while under the leadership of the main campus. A fourth and fifth Mars Hill location opened in 2007, and in 2008 a sixth location was added in downtown Seattle. A seventh campus, in Olympia, Washington, opened in Fall 2008 and an eighth campus, the first outside of Washington state, opened in Albuquerque, New Mexico in Fall 2009. The church launched four new churches on January 15 in Portland (Oregon), Rainier Valley (Seattle), Sammamish (near Seattle), and Orange County (California), the same day as the first sermon in the \\\"Real Marriage\\\" sermon series, based on Mark and Grace Driscoll\\\\'s book, Real Marriage.\\\\n\\\\nOn October 16, \\\"black-clad demonstrators\\\" gathered in front of the Mars Hill Church in Southeast Portland to \\\"protest the church\\\\'s stance on homosexuality.\\\" Approximately 20 protesters, \\\"some of whom wore kerchiefs to cover their faces, shouted profanities at adults and children,\\\" and briefly blocked the entrance of the church. Mars Hill Church Portland lead pastor Tim Smith expressed disagreement with the conduct of the protesters, but expressed defense of their right to free speech.\\\\n\\\\nIn 2008, the church launched an online community-building network, called The City, to improve communication on all levels in the church. The City was purchased by the Christian publishing brand, Zondervan, before Christmas 2008.\\\\n\\\\nGrowth and influence \\\\n\\\\nIn 2013, The Church Guide released a list of the \\\"Top Churches to Watch in America\\\". The link ranked churches according to how much churches could learn from the ranked churches on particular topics. They ranked Mars Hill Church as #3 to learn from about church growth, #3 for innovation, #2 for church planting, and #4 overall. The list considered data from Outreach magazine\\\\'s annual lists from 2004–2012 and other sources.\\\\n\\\\nIn 2006, Mars Hill Church claimed $31,110,000 in assets.\\\\n\\\\nActs 29 Church Planting Network \\\\n\\\\nActs 29 Church Planting Network is a separate 501(c)(3) from Mars Hill Church but was founded by Mars Hill in 2001. It is an interdenominational network of pastors and churches from around the world whose focus is to assess and equip qualified leaders, plant new churches, and rejuvenate declining churches. The current president of Acts 29 is Matt Chandler. The offices and leadership of Acts 29 moved from Mars Hill Church in Seattle to The Village Church in Texas in March 2012.\\\\nIn August 2014, Acts 29 removed Mark Driscoll and Mars Hill Church from the network.\\\\n\\\\nChurch leadership controversies\\\\n\\\\nDealing with dissent \\\\n\\\\nAs a result of the large growth of the church, its bylaws were rewritten more than once. The outcome of this process led to changes in leadership organization in November 2007. The new bylaws installed lead pastor Jamie Munson, preaching pastor Mark Driscoll, and pastors Scott Thomas and Tim Beltz as \\\"executive pastors\\\" who led the objectives of the church \\\"under the authority of the Board of Directors,\\\" on which the executive pastors also served as directors. This change precipitated the firing of two pastors.\\\\n\\\\nMars Hill leaders said in forum postings that one fired pastor was removed, in part, for \\\"displaying an unhealthy distrust in the senior leadership.\\\" They said the other was removed for \\\"disregarding the accepted elder protocol for the bylaw deliberation period\\\" and \\\"verbally attacking the lead pastor\\\"\\\\xa0— charges the fired pastor denied, the leaders added.\\\\n\\\\nChurch leadership instructed members of the congregation to shun the two former elders as unrepentant. Former Mars Hill Church elders and members have criticized the church for its harshness in dealing with dissent within its leadership. Additionally, members who have openly questioned or dissented with Mars Hill leaders have been asked to leave the church. This policy of church discipline was discussed during a lecture given on April 20, 2009 by Mark Driscoll for The Gospel Coalition.\\\\n\\\\nIn early 2012, the church once again became a source of controversy over shunning and disciplinary proceedings when a young man under discipline released documents from his disciplinary contract to blogger and author Mathew Paul Turner. The documents included a discipline contract and an email from church leaders to the congregation directing them to shun him.\\\\n\\\\nResultSource contract for the Real Marriage Book \\\\nOn March 5, 2014, evangelical magazine World published an article claiming that Mars Hill Church paid a $25,000 fee to marketing firm ResultSource, to manipulate sales numbers of Mark Driscoll\\\\'s book Real Marriage and thereby attain a place on the New York Times bestseller list. ResultSource accomplished this objective—the book briefly reached #1 in the \\\"Advice How-to\\\" category—by buying 11,000 copies of the book, using $210,000 of Mars Hill Church\\\\'s money, from a variety of online sources and payment methods.\\\\n\\\\nThe Evangelical Council for Financial Accountability stated that buying a place on bestseller lists violates its ethical standards, but that because this happened before Mars Hill Church joined they were unable to take action. Christianity Today described the arrangement as \\\"ethically questionable\\\", and Carl Trueman of religion journal First Things decried the revelation, writing, \\\"the overall picture is one of disaster\\\" and \\\"[it] has raised questions not simply about personal integrity but also the very culture of American Evangelicalism.\\\"\\\\n\\\\nDriscoll had used the apparent success of Real Marriage to negotiate a multi-book deal with Christian publisher Tyndale House. The first book under Driscoll\\\\'s \\\"Resurgence\\\" imprint was A Call to Resurgence, with plans to publish five to seven books per year. Tyndale House defended Driscoll\\\\'s alleged plagiarism in A Call to Resurgence, and affirmed their continuing relationship with Driscoll.\\\\n\\\\nMars Hill Church responded with a statement, writing, \\\"while not uncommon or illegal, this unwise strategy is not one we had used before or since, and not one we will use again.\\\" Mars Hill also claimed that the \\\"true cost\\\" of the effort was less than \\\"what has been reported.\\\"\\\\n\\\\nOn March 17, 2014, Driscoll posted an open letter of apology in response to this controversy and others, writing that he will no longer claim to be a New York Times bestselling author, and that he now sees the ResultSource marketing campaign as \\\"manipulating a book sales reporting system, which is wrong.\\\" He wrote that he was giving up his status as a \\\"celebrity pastor\\\", that he considered his \\\"angry young prophet\\\" days to be over, and that he was reducing his public presence in speaking engagements and on social media.\\\\n\\\\nOn March 28, 2015, Sutton Turner, a former elder of the church who signed the Result Source contract, explained that he disapproved of the marketing plan to use Result Source, but the decision to use it had already been made before he began work at Mars Hill, so he signed the contract anyway. Turner revealed that Driscoll had not been involved in initiating nor signing the contract with Result Source. Turner stated that the business relationship with the marketing firm was initiated by a pastor who resigned shortly thereafter, and remaining church leaders disagreed over the completion of the contract, stating that it would reflect badly on the church and Mark Driscoll.\\\\n\\\\nPlagiarism allegations \\\\nOn November 21, 2013, radio host Janet Mefferd accused Driscoll of plagiarism. Mefferd claimed that 14 pages of Driscoll\\\\'s book A Call to Resurgence quoted \\\"extensively and without citation\\\" from Peter Jones\\\\' 1999 book, Gospel Truth/Pagan Lies: Can You Tell the Difference? and Jones\\\\' 2010 book One or Two: Seeing a World of Difference. Driscoll\\\\'s publisher Tyndale House stated that they performed a \\\"thorough in-house review\\\" and disagreed that this was a case of plagiarism. Neil Holdway, a plagiarism expert with the American Copy Editors Society, concluded that \\\"Driscoll had not adequately indicated the extent to which he had borrowed Jones\\\\' work.\\\"\\\\n\\\\nMore allegations of plagiarism in other Driscoll works soon surfaced, including passages from a sermon series companion text, Trial: 8 Witnesses From 1&2 Peter, which were copied verbatim from passages written by David Wheaton in the New Bible Commentary. InterVarsity Press, publisher of the New Bible Commentary, stated that Driscoll failed to properly provide quotation or attribution for the material. The relevant passages were posted online. The allegations soon expanded to include claims that Driscoll used ghostwriters and researchers without giving them proper attribution. As of December 2013, neither Peter Jones, D.A. Carson, nor Janet Mefferd had made any further statements pertaining the case.\\\\n\\\\nSyndicator Salem Radio subsequently removed both the broadcast interview with Driscoll and associated materials from Mefferd\\\\'s program website and apologized for raising the matter in a broadcast interview. This attempt to shut down the story provoked the resignation of Mefferd\\\\'s producer, Ingrid Schlueter. In explaining her resignation, Schlueter wrote the following regarding herself and Mefferd:\\\\n\\\\nDriscoll apologized for \\\"mistakes\\\" related to the allegations in a statement released to The Christian Post on December 18, 2013. Mefferd eventually left Salem Radio in April 2015.\\\\n\\\\nMars Hill Global Fund \\\\nIn June 2014 an online petition asked Sutton Turner of Mars Hill Church and Dan Busby of the Evangelical Council for Financial Accountability where the money raised through Mars Hill Global Fund actually went. The church reported that \\\"Mars Hill Church began to use the term \\\\'Global Fund\\\\' to solicit gifts restricted for \\\\'capital development and expansion\\\\'. As communicated in the Global Newsletter on July 7, 2009, the Global Fund was used to raise resources for the following purposes: \\\\'start new Mars Hill campuses, plant new Acts 29 churches, and equip leaders at the Resurgence Training Center\\\\'. In the 2009-2011 time frame, over 80% of the funds given to the \\\"Global Fund\\\" went to Acts 29 church planting, with additional funds used for the Resurgence Training Center and church planting in India.\\\" Additionally, \\\"subsequent to June 1, 2012, in early July 2014, Mars Hill Church sent approximately 6,000 letters and 3,765 emails to individuals who had made gifts as a global donor subsequent to June 1, 2012. In these communications, Mars Hill Church offered to redirect the donor\\\\'s gifts, made as a global donor during this time period, specifically for planting churches in Ethiopia or India.\\\"\\\\n\\\\nFormer leaders and members protest Mark Driscoll (2014) \\\\nMichael Paulson, writing for The New York Times, wrote that while Driscoll had endured criticism from the American political left and liberal Christianity for many years, recent years leading up to and including 2014 saw the rise of criticism from conservative Christians, including Driscoll\\\\'s former \\\"allies and supporters.\\\" According to the Seattle Times, plagiarism accusations against Driscoll made by Janet Mefferd were a \\\"crucial turning point\\\" that drew outside interest into Mars Hill\\\\'s internal affairs, and prompted inquiries from new critics about the church and how it handled its finances. After hearing of Mefferd\\\\'s plagiarism accusations, evangelical Christian and Grove City College psychology professor Warren Throckmorton took interest and became a prominent critic of Driscoll and Mars Hill, documenting other examples of perceived plagiarism, abuse reported by former Mars Hill members, and questionable uses of church finances.\\\\n\\\\n\\\"Repentant Pastors\\\" \\\\nOn March 29, 2014, four former Mars Hill elders (Kyle Firstenberg, Dave Kraft, Scott Mitchell, and co-founder Lief Moi) created a blog titled \\\"Repentant Pastor\\\" and posted online \\\"confessions and apologies\\\" related to their leadership roles in Mars Hill. In a joint statement, they wrote, \\\"we recognize and confess that Mars Hill has hurt many people within the Mars Hill community, as well as those outside the community.\\\" Salon summarized the statements, writing that the former leaders emphasized their failures to \\\"rein Driscoll in\\\" and their complicity with Driscoll\\\\'s \\\"autocratic\\\" management style. Firstenberg wrote that while the church appeared to flourish, employees lived in constant stress, and \\\"success was to be attained regardless of human and moral cost.\\\"\\\\n\\\\nMegachurch pastors come to Driscoll\\\\'s defense \\\\nSeveral prominent pastors publicly defended Driscoll from allegations made against him. Those pastors included mega-church pastor Rick Warren, author of The Purpose Driven Life, and Gateway Church\\\\'s founding pastor Robert Morris. At the 2014 Gateway Conference, Morris told the audience that he counseled Mark Driscoll directly, and that media reports were largely untrue. Morris cited recent media reports of lead pastor Steven Furtick of Elevation Church as experiencing similar coverage. At the conference, Mark Driscoll was invited up to the stage where he told the audience that he received death threats and that his children allegedly had rocks thrown at them. Driscoll stated that \\\"I\\\\'m just trying to figure out how to be a good pastor to my family first.\\\"\\\\n\\\\nDriscoll addresses former members\\\\' complaints \\\\nIn a recorded message shown to church members on July 27, 2014, Driscoll discussed the various controversies of 2014. He said that he could \\\"not address some members\\\\' discontent ... because the complaints were anonymous.\\\" According to Rob Smith, former program director at the church, the anonymity assertion \\\"really touched a nerve\\\" with former members. In response, dissenters organized a Facebook group called \\\"Dear Pastor Mark & Mars Hill: We Are Not Anonymous.\\\"\\\\n\\\\nThe following Sunday, \\\"dozens of demonstrators\\\" organized and picketed the Mars Hill Church Bellevue campus (where Driscoll preached live), calling for Driscoll\\\\'s resignation. Demonstrators carried placards reading \\\"We Are Not Anonymous\\\" and \\\"Question Mark\\\", and accused Driscoll of bullying, misogyny, inadequate transparency in church finances, and harsh discipline of members. Driscoll was away for his annual summer vacation. A church elder, Anthony Iannicielo, responded that the criticism of Driscoll and Mars Hill \\\"goes with the territory\\\" of running a large church with a long history. In a pre-recorded message, Driscoll said that he had been deliberately \\\"rather silent\\\" during the criticism, that he found it \\\"a little overwhelming and a bit confusing\\\", and that he had no intention of resigning.\\\\n\\\\nRemoval from Acts 29 Network \\\\nOn August 8, 2014, the board of Acts 29 Network removed both Driscoll and Mars Hill Church from membership. Chairman Matt Chandler wrote, \\\"it is our conviction that the nature of the accusations against Mark, most of which have been confirmed by him, make it untenable and unhelpful to keep Mark [Driscoll] and Mars Hill [Church] in our network.\\\"\\\\xa0The board of directors of Acts 29 expressed gratitude for Driscoll\\\\'s work with the Network as co-founder and former President, but declared his recent actions \\\"ungodly and disqualifying behavior.\\\" To Driscoll, they wrote, \\\"our board and network have been the recipients of ... dozens of fires directly linked to you ... we are naturally associated with you and feel that this association discredits the network and is a major distraction.\\\" They further advised him to \\\"step down from ministry for an extended time and seek help.\\\"\\\\n\\\\nActs 29 had attempted to \\\"lean on\\\" the Mars Hill\\\\'s Board of Advisors and Accountability (BOAA) to discipline Driscoll, but lost confidence in the board. The BOAA had been set up by Driscoll as his accountability board, rather than the elders of the church. (Members of the BOAA were for the most part professional clergy and businessmen who were not members of the church and hand picked by Driscoll.) The previous month, evangelical leaders and Acts 29 associates Paul Tripp and James MacDonald resigned from the BOAA. Religion correspondent Sarah Pulliam Bailey described Acts 29\\\\'s decision as \\\"unusual\\\" since \\\"ministries usually leave matters of church discipline up to local churches.\\\"\\\\n\\\\nBOAA Chairman Michael Van Skaik responded, \\\"Men, I told the lead pastors ... that we are making real progress in addressing the serious reconciliation and unhealthy culture issues that have been part of Mars Hill Church for way too long. And we are. ... \\\" He further added that Acts 29 leaders did not contact Mars Hill before acting, and that Driscoll had \\\"changed his ways\\\", and described Acts 29\\\\'s actions as \\\"divisive.\\\" Van Skaik also addressed the formal charges brought against Driscoll under the Mars Hill bylaws, writing \\\"the formal charges that were filed were serious, were taken seriously, and were not dismissed by the board lightly.\\\"\\\\n\\\\nDriscoll\\\\'s hiatus from ministry \\\\nOn August 24, 2014, Driscoll announced he would take a six-week \\\"extended focus break\\\" from his pastorship while charges against him were investigated. Later that week, a letter signed by nine current Mars Hill pastors which severely criticized Driscoll was leaked to the public. The letter, written days before Driscoll stepped down, urged him to step down from all aspects of ministry. It included a quote from \\\"internationally recognized\\\" author, pastor and former BOAA member Paul Tripp saying, \\\"This is without a doubt, the most abusive, coercive ministry culture I\\\\'ve ever been involved with.\\\" One of the pastors who signed the letter was fired five days later for \\\"rebellion against the church.\\\" By September 9, eight of the nine pastors who signed the letter had resigned or been terminated, including worship director Dustin Kensrue. The last of the nine pastors was demoted from pastor to lay elder.\\\\n\\\\nStaff layoffs and closure of church branches \\\\n\\\\nOn September 7, 2014 (the second week of Driscoll\\\\'s hiatus), Mars Hill officials, citing \\\"financial pressures in the wake of recent negative media attention\\\", announced layoffs and closures of a few church branches. Weekly attendance at the start of the year for all branches was 12,000–13,000, but had dropped to 8,000–9,000. Donations also had a \\\"steep decline.\\\" In response, the church planned to lay off \\\"30 to 40 percent\\\" of their 100 paid staff members, and close their downtown Seattle branch and University District branch, consolidating both congregations into the Ballard location. Two other branches outside Washington state were marked for possible closure if their finances did not improve. Mars Hill also announced the resignation of Sutton Turner, executive elder since 2011, effective at the end of September 2014.\\\\n\\\\nDriscoll\\\\'s resignation \\\\nIn the fall of 2014, a group of elders released a report on an investigation into accusations of bullying and intimidating behavior by Driscoll made by 21 former church elders. The investigation involved \\\"some 1,000 hours of research, interviewing more than 50 people and preparing 200 pages of information.\\\" The report concluded that Driscoll had never been charged with \\\"immorality, illegality or heresy,\\\" and considered \\\"some of the accusations against Pastor Mark to be altogether unfair or untrue.\\\" Additionally, the report found that many of the \\\"other charges had previously been addressed by Pastor Mark, privately and publicly. Indeed, he had publicly confessed and apologized for a number of the charges against him, some of which occurred as long as 14 years ago.\\\" However, elders did find \\\"bullying\\\" and \\\"patterns of persistent sinful behavior\\\" by Driscoll. The Board also concluded that Driscoll had \\\"been guilty of arrogance, responding to conflict with a quick temper and harsh speech, and leading the staff and elders in a domineering manner\\\", but was not charged with anything immoral or illegal. Driscoll maintained that he had not disqualified himself from ministry.\\\\n\\\\nChurch leadership crafted a \\\"restoration\\\" plan to help Driscoll and save the church. Instead, Driscoll declined the restoration plan and resigned on October 14, 2014, citing concerns for his health and safety. His resignation came as a \\\"surprise\\\" to the church\\\\'s Board of Overseers, who said in a statement that they had not asked Driscoll for his resignation.\\\\n\\\\nIn 2015, after the disbanding of Mars Hill, an executive elder of the church stated that \\\"There has been much talk about the abusive and coercive culture at Mars Hill. What many people do not realize is that some of the very people who were calling for an end to this type of abuse were using abusive tactics.\\\" The executive elder stated that he was blackmailed by a staff who asked for more severance pay. He also stated that \\\"former Mars Hill elders were working to file formal charges against me also. I was told that a former lead pastor was approached to lead a group of people who hoped to force my resignation so that I \\\\'could not help Pastor Mark Driscoll\\\\'.\\\"\\\\n\\\\nPastor and theologian John Piper referred to the controversies and subsequent church closure as a \\\"Satanic victory.\\\"\\\\n\\\\nIt was a defeat for the gospel, it was a defeat for Mark [Driscoll], it was a defeat for evangelicalism, for Reformed Theology, for complementarianism ... It was a colossal Satanic victory.Driscoll\\\\'s resignation is thoroughly investigated in the podcast The Rise and Fall of Mars Hill.\\\\n\\\\nClosing \\\\nOn October 31, 2014, lead pastor Dave Bruskas announced plans to dissolve the church\\\\'s 13 remaining campuses into autonomous entities, with the option of continuing, merging with other congregations, or disbanding, effective January 1, 2015.\\\\n\\\\nOn December 28, 2014, Rick Warren gave the final Sunday sermon at Mars Hill, encouraging its remaining members to \\\"give grace\\\" to its leaders, \\\"You need to be grateful for all the ways that God used Mars Hill Church. Be grateful for all the ways God used Mark Driscoll.\\\" Driscoll had previously delivered a sermon at Saddleback Church the weekend Rick Warren grieved the loss of his son.\\\\n\\\\nThe Mars Hill Church network officially disbanded Thursday, January 1, 2015. Eleven of the Mars Hill Churches became independent churches and the remaining churches were dissolved. Prior to the churches disbanding, Mars Hill transferred the majority of its content from its website to where the church\\\\'s sermons remain. The Mars Hill website now contains a history of the church and a church directory of the previous Mars Hill churches locations with their new names and websites.\\\\n\\\\nPrior to disbanding on January 1, 2015, Mars Hill Church met at twelve locations, mostly in Seattle and Washington state, with three out of state locations in New Mexico, California, and Oregon. A few locations were closed or consolidated on October 12, 2014. After January 1, 2015, each church location dissolved into an independent congregation. The remaining members of Mars Hill Ballard reorganized as Cross and Crown Church Seattle, led by former Mars Hill Downtown pastor Matthias Haeusel at Mars Hill\\\\'s former Ballard location.\\\\n\\\\nIn February 2016, a federal racketeering lawsuit was filed by former Mars Hill members against both Mars Hill and Driscoll. That lawsuit was dismissed in November 2016 after the plaintiffs said they did not have the money to continue the suit. The plaintiffs\\\\' online fundraising campaign on GoFundMe had raised $34,660, which was approximately half of its goal.\\\\n\\\\nReferences\\\\n\\\\nFurther reading\\\\n\\\\n Pastor Dude\\\\'s Mega-Church Draws Crowds - ABC Nightline story about Mars Hill Church\\\\n Tempers Flare at Debate on the Devil - ABC Nightline debate at Mars Hill Church on the Devil\\\\n\\\\nExternal links\\\\n \\\\n\\\\n \\\\nEmerging church movement\\\\nEvangelical churches in Washington (state)\\\\nFormer megachurches\\\\nChurches in Seattle\\\\nChristian organizations established in 1996\\\\nReligious organizations disestablished in 2015'}\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"corpus[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we process the ids and text of queries and corpus for preparation of embedding and searching.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus_ids = corpus['docid']\\n\",\n \"corpus_text = corpus['text']\\n\",\n \"\\n\",\n \"queries_ids = dataset['dev']['query_id']\\n\",\n \"queries_text = dataset['dev']['query']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Evaluate from scratch\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Embedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the demo we use bge-base-en-v1.5, feel free to change to the model you prefer.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os \\n\",\n \"os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'\\n\",\n \"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 60.08it/s]\\n\",\n \"pre tokenize: 100%|██████████| 782/782 [02:22<00:00, 5.50it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 782/782 [02:47<00:00, 4.66it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the embeddings: (200000, 768)\\n\",\n \"data type of the embeddings: float16\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',)\\n\",\n \" # query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\")\\n\",\n \"\\n\",\n \"# get the embedding of the queries and corpus\\n\",\n \"queries_embeddings = model.encode_queries(queries_text)\\n\",\n \"corpus_embeddings = model.encode_corpus(corpus_text)\\n\",\n \"\\n\",\n \"print(\\\"shape of the embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a Faiss index to store the embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 200000\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"# train and add the embeddings to the index\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Searching\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the Faiss index to search answers for each query.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 7/7 [00:01<00:00, 5.15it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"query_size = len(queries_embeddings)\\n\",\n \"\\n\",\n \"all_scores = []\\n\",\n \"all_indices = []\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, 32), desc=\\\"Searching\\\"):\\n\",\n \" j = min(i + 32, query_size)\\n\",\n \" query_embedding = queries_embeddings[i: j]\\n\",\n \" score, indice = index.search(query_embedding.astype(np.float32), k=100)\\n\",\n \" all_scores.append(score)\\n\",\n \" all_indices.append(indice)\\n\",\n \"\\n\",\n \"all_scores = np.concatenate(all_scores, axis=0)\\n\",\n \"all_indices = np.concatenate(all_indices, axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = {}\\n\",\n \"for idx, (scores, indices) in enumerate(zip(all_scores, all_indices)):\\n\",\n \" results[queries_ids[idx]] = {}\\n\",\n \" for score, index in zip(scores, indices):\\n\",\n \" if index != -1:\\n\",\n \" results[queries_ids[idx]][corpus_ids[index]] = float(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.4 Evaluating\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Process the qrels into a dictionary with qid-docid pairs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"qrels_dict = {}\\n\",\n \"for data in dataset['dev']:\\n\",\n \" qid = str(data[\\\"query_id\\\"])\\n\",\n \" if qid not in qrels_dict:\\n\",\n \" qrels_dict[qid] = {}\\n\",\n \" for doc in data[\\\"positive_passages\\\"]:\\n\",\n \" docid = str(doc[\\\"docid\\\"])\\n\",\n \" qrels_dict[qid][docid] = 1\\n\",\n \" for doc in data[\\\"negative_passages\\\"]:\\n\",\n \" docid = str(doc[\\\"docid\\\"])\\n\",\n \" qrels_dict[qid][docid] = 0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, use [pytrec_eval](https://github.com/cvangysel/pytrec_eval) library to help us calculate the scores of selected metrics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {'NDCG@10': 0.35304, 'NDCG@100': 0.38694})\\n\",\n \"defaultdict(, {'Recall@10': 0.465, 'Recall@100': 0.625})\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pytrec_eval\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"ndcg_string = \\\"ndcg_cut.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"recall_string = \\\"recall.\\\" + \\\",\\\".join([str(k) for k in [10,100]])\\n\",\n \"\\n\",\n \"evaluator = pytrec_eval.RelevanceEvaluator(\\n\",\n \" qrels_dict, {ndcg_string, recall_string}\\n\",\n \")\\n\",\n \"scores = evaluator.evaluate(results)\\n\",\n \"\\n\",\n \"all_ndcgs, all_recalls = defaultdict(list), defaultdict(list)\\n\",\n \"for query_id in scores.keys():\\n\",\n \" for k in [10,100]:\\n\",\n \" all_ndcgs[f\\\"NDCG@{k}\\\"].append(scores[query_id][\\\"ndcg_cut_\\\" + str(k)])\\n\",\n \" all_recalls[f\\\"Recall@{k}\\\"].append(scores[query_id][\\\"recall_\\\" + str(k)])\\n\",\n \"\\n\",\n \"ndcg, recall = (\\n\",\n \" all_ndcgs.copy(),\\n\",\n \" all_recalls.copy(),\\n\",\n \")\\n\",\n \"\\n\",\n \"for k in [10,100]:\\n\",\n \" ndcg[f\\\"NDCG@{k}\\\"] = round(sum(ndcg[f\\\"NDCG@{k}\\\"]) / len(scores), 5)\\n\",\n \" recall[f\\\"Recall@{k}\\\"] = round(sum(recall[f\\\"Recall@{k}\\\"]) / len(scores), 5)\\n\",\n \"\\n\",\n \"print(ndcg)\\n\",\n \"print(recall)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Evaluate using FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We provide independent evaluation for popular datasets and benchmarks. Try the following code to run the evaluation, or run the shell script provided in [example](../../examples/evaluation/mldr/eval_mldr.sh) folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"arguments = \\\"\\\"\\\"- \\\\\\n\",\n \" --eval_name mldr \\\\\\n\",\n \" --dataset_dir ./mldr/data \\\\\\n\",\n \" --dataset_names en \\\\\\n\",\n \" --splits dev \\\\\\n\",\n \" --corpus_embd_save_dir ./mldr/corpus_embd \\\\\\n\",\n \" --output_dir ./mldr/search_results \\\\\\n\",\n \" --search_top_k 1000 \\\\\\n\",\n \" --cache_path ./cache/data \\\\\\n\",\n \" --overwrite False \\\\\\n\",\n \" --k_values 10 100 \\\\\\n\",\n \" --eval_output_method markdown \\\\\\n\",\n \" --eval_output_path ./mldr/mldr_eval_results.md \\\\\\n\",\n \" --eval_metrics ndcg_at_10 \\\\\\n\",\n \" --embedder_name_or_path BAAI/bge-base-en-v1.5 \\\\\\n\",\n \" --devices cuda:0 cuda:1 \\\\\\n\",\n \" --embedder_batch_size 1024\\n\",\n \"\\\"\\\"\\\".replace('\\\\n','')\\n\",\n \"\\n\",\n \"sys.argv = arguments.split()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/root/anaconda3/envs/dev/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"initial target device: 100%|██████████| 2/2 [00:07<00:00, 3.54s/it]\\n\",\n \"pre tokenize: 100%|██████████| 98/98 [01:01<00:00, 1.58it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"pre tokenize: 100%|██████████| 98/98 [01:07<00:00, 1.44it/s]09it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 100%|██████████| 98/98 [01:22<00:00, 1.19it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 98/98 [01:23<00:00, 1.17it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [02:40<00:00, 80.21s/it] \\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 2.16it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 2.21it/s]\\n\",\n \"Chunks: 100%|██████████| 2/2 [00:01<00:00, 1.13it/s]\\n\",\n \"Searching: 100%|██████████| 7/7 [00:01<00:00, 6.79it/s]\\n\",\n \"Qrels not found in ./mldr/data/en/dev_qrels.jsonl. Trying to download the qrels from the remote and save it to ./mldr/data/en.\\n\",\n \"Loading and Saving qrels: 100%|██████████| 200/200 [00:00<00:00, 598.03it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from transformers import HfArgumentParser\\n\",\n \"\\n\",\n \"from FlagEmbedding.evaluation.mldr import (\\n\",\n \" MLDREvalArgs, MLDREvalModelArgs,\\n\",\n \" MLDREvalRunner\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"parser = HfArgumentParser((\\n\",\n \" MLDREvalArgs,\\n\",\n \" MLDREvalModelArgs\\n\",\n \"))\\n\",\n \"\\n\",\n \"eval_args, model_args = parser.parse_args_into_dataclasses()\\n\",\n \"eval_args: MLDREvalArgs\\n\",\n \"model_args: MLDREvalModelArgs\\n\",\n \"\\n\",\n \"runner = MLDREvalRunner(\\n\",\n \" eval_args=eval_args,\\n\",\n \" model_args=model_args\\n\",\n \")\\n\",\n \"\\n\",\n \"runner.run()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{\\n\",\n \" \\\"en-dev\\\": {\\n\",\n \" \\\"ndcg_at_10\\\": 0.35304,\\n\",\n \" \\\"ndcg_at_100\\\": 0.38694,\\n\",\n \" \\\"map_at_10\\\": 0.31783,\\n\",\n \" \\\"map_at_100\\\": 0.32469,\\n\",\n \" \\\"recall_at_10\\\": 0.465,\\n\",\n \" \\\"recall_at_100\\\": 0.625,\\n\",\n \" \\\"precision_at_10\\\": 0.0465,\\n\",\n \" \\\"precision_at_100\\\": 0.00625,\\n\",\n \" \\\"mrr_at_10\\\": 0.31783,\\n\",\n \" \\\"mrr_at_100\\\": 0.32469\\n\",\n \" }\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"with open('mldr/search_results/bge-base-en-v1.5/NoReranker/EVAL/eval_results.json', 'r') as content_file:\\n\",\n \" print(content_file.read())\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"dev\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/4_Evaluation.rst", "content": "4. Evaluation\n=============\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: Evaluation\n\n 4_Evaluation/4.1.1\n 4_Evaluation/4.2.1\n 4_Evaluation/4.2.2\n 4_Evaluation/4.2.3\n 4_Evaluation/4.3.1\n 4_Evaluation/4.4.1\n 4_Evaluation/4.5.1\n 4_Evaluation/4.5.2\n" }, { "path": "docs/source/tutorial/5_Reranking/5.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reranker is disigned in cross-encoder architecture that takes the query and text at the same time and directly output their score of similarity. It is more capable on scoring the query-text relevance, but with the tradeoff of slower speed. Thus, complete retrieval system usually contains retrievers in the first stage to do a large scope retrieval, and then follows by rerankers to rerank the results more precisely.\\n\",\n \"\\n\",\n \"In this tutorial, we will go through text retrieval pipeline with reranker and evaluate the results before and after reranking.\\n\",\n \"\\n\",\n \"Note: Step 1-4 are identical to the tutorial of [evaluation](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/4_Evaluation). We suggest to first go through that if you are not familiar with retrieval.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Setup\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding faiss-cpu\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download and preprocess the MS Marco dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"data = load_dataset(\\\"namespace-Pt/msmarco\\\", split=\\\"dev\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = np.array(data[:100][\\\"query\\\"])\\n\",\n \"corpus = sum(data[:5000][\\\"positive\\\"], [])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Embedding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Inference Embeddings: 100%|██████████| 21/21 [01:59<00:00, 5.68s/it]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"shape of the corpus embeddings: (5331, 768)\\n\",\n \"data type of the embeddings: float32\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"# get the BGE embedding model\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5',\\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" use_fp16=True)\\n\",\n \"\\n\",\n \"# get the embedding of the corpus\\n\",\n \"corpus_embeddings = model.encode(corpus)\\n\",\n \"\\n\",\n \"print(\\\"shape of the corpus embeddings:\\\", corpus_embeddings.shape)\\n\",\n \"print(\\\"data type of the embeddings: \\\", corpus_embeddings.dtype)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Indexing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"total number of vectors: 5331\\n\"\n ]\n }\n ],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"# get the length of our embedding vectors, vectors by bge-base-en-v1.5 have length 768\\n\",\n \"dim = corpus_embeddings.shape[-1]\\n\",\n \"\\n\",\n \"# create the faiss index and store the corpus embeddings into the vector space\\n\",\n \"index = faiss.index_factory(dim, 'Flat', faiss.METRIC_INNER_PRODUCT)\\n\",\n \"corpus_embeddings = corpus_embeddings.astype(np.float32)\\n\",\n \"index.train(corpus_embeddings)\\n\",\n \"index.add(corpus_embeddings)\\n\",\n \"\\n\",\n \"print(f\\\"total number of vectors: {index.ntotal}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Retrieval\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_embeddings = model.encode_queries(queries)\\n\",\n \"ground_truths = [d[\\\"positive\\\"] for d in data]\\n\",\n \"corpus = np.asarray(corpus)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Searching: 100%|██████████| 1/1 [00:00<00:00, 22.35it/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from tqdm import tqdm\\n\",\n \"\\n\",\n \"res_scores, res_ids, res_text = [], [], []\\n\",\n \"query_size = len(query_embeddings)\\n\",\n \"batch_size = 256\\n\",\n \"# The cutoffs we will use during evaluation, and set k to be the maximum of the cutoffs.\\n\",\n \"cut_offs = [1, 10]\\n\",\n \"k = max(cut_offs)\\n\",\n \"\\n\",\n \"for i in tqdm(range(0, query_size, batch_size), desc=\\\"Searching\\\"):\\n\",\n \" q_embedding = query_embeddings[i: min(i+batch_size, query_size)].astype(np.float32)\\n\",\n \" # search the top k answers for each of the queries\\n\",\n \" score, idx = index.search(q_embedding, k=k)\\n\",\n \" res_scores += list(score)\\n\",\n \" res_ids += list(idx)\\n\",\n \" res_text += list(corpus[idx])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Reranking\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we will use a reranker to rerank the list of answers we retrieved using our index. Hopefully, this will lead to better results.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following table lists the available BGE rerankers. Feel free to try out to see their differences!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Description | Base Model |\\n\",\n \"|:-------|:--------:|:----:|:-----------------:|:--------------------------------------:|\\n\",\n \"| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | 568M | a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | XLM-RoBERTa-Large |\\n\",\n \"| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | 2.51B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | Gemma2-2B |\\n\",\n \"| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | 2.72B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | MiniCPM |\\n\",\n \"| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | 9.24B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. | Gemma2-9B |\\n\",\n \"| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | 560M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Large |\\n\",\n \"| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | 278M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Base |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, let's use a small example to see how reranker works:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-9.474676132202148, -2.823843240737915, 5.76226806640625]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagReranker\\n\",\n \"\\n\",\n \"reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) \\n\",\n \"# Setting use_fp16 to True speeds up computation with a slight performance degradation\\n\",\n \"\\n\",\n \"# use the compute_score() function to calculate scores for each input sentence pair\\n\",\n \"scores = reranker.compute_score([\\n\",\n \" ['what is panda?', 'Today is a sunny day'], \\n\",\n \" ['what is panda?', 'The tiger (Panthera tigris) is a member of the genus Panthera and the largest living cat species native to Asia.'],\\n\",\n \" ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']\\n\",\n \" ])\\n\",\n \"print(scores)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, let's use the reranker to rerank our previously retrieved results:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_ids, new_scores, new_text = [], [], []\\n\",\n \"for i in range(len(queries)):\\n\",\n \" # get the new scores of the previously retrieved results\\n\",\n \" new_score = reranker.compute_score([[queries[i], text] for text in res_text[i]])\\n\",\n \" # sort the lists of ids and scores by the new scores\\n\",\n \" new_id = [tup[1] for tup in sorted(list(zip(new_score, res_ids[i])), reverse=True)]\\n\",\n \" new_scores.append(sorted(new_score, reverse=True))\\n\",\n \" new_ids.append(new_id)\\n\",\n \" new_text.append(corpus[new_id])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Evaluate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For details of these metrics, please checkout the tutorial of [evaluation](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/4_Evaluation).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.1 Recall\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_recall(preds, truths, cutoffs):\\n\",\n \" recalls = np.zeros(len(cutoffs))\\n\",\n \" for text, truth in zip(preds, truths):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" recall = np.intersect1d(truth, text[:c])\\n\",\n \" recalls[i] += len(recall) / max(min(len(recall), len(truth)), 1)\\n\",\n \" recalls /= len(preds)\\n\",\n \" return recalls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Before reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1:\\t0.97\\n\",\n \"recall@10:\\t1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls_init = calc_recall(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"recall@{c}:\\\\t{recalls_init[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"recall@1:\\t0.99\\n\",\n \"recall@10:\\t1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls_rerank = calc_recall(new_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"recall@{c}:\\\\t{recalls_rerank[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.2 MRR\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def MRR(preds, truth, cutoffs):\\n\",\n \" mrr = [0 for _ in range(len(cutoffs))]\\n\",\n \" for pred, t in zip(preds, truth):\\n\",\n \" for i, c in enumerate(cutoffs):\\n\",\n \" for j, p in enumerate(pred):\\n\",\n \" if j < c and p in t:\\n\",\n \" mrr[i] += 1/(j+1)\\n\",\n \" break\\n\",\n \" mrr = [k/len(preds) for k in mrr]\\n\",\n \" return mrr\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Before reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1:\\t0.97\\n\",\n \"MRR@10:\\t0.9825\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr_init = MRR(res_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"MRR@{c}:\\\\t{mrr_init[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MRR@1:\\t0.99\\n\",\n \"MRR@10:\\t0.995\\n\"\n ]\n }\n ],\n \"source\": [\n \"mrr_rerank = MRR(new_text, ground_truths, cut_offs)\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" print(f\\\"MRR@{c}:\\\\t{mrr_rerank[i]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.3 nDCG\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Before reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.97\\n\",\n \"nDCG@10: 0.9869253606521631\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import ndcg_score\\n\",\n \"\\n\",\n \"pred_hard_encodings = []\\n\",\n \"for pred, label in zip(res_text, ground_truths):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings.append(pred_hard_encoding)\\n\",\n \"\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings, res_scores, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After reranking:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"nDCG@1: 0.99\\n\",\n \"nDCG@10: 0.9963092975357145\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_hard_encodings_rerank = []\\n\",\n \"for pred, label in zip(new_text, ground_truths):\\n\",\n \" pred_hard_encoding = list(np.isin(pred, label).astype(int))\\n\",\n \" pred_hard_encodings_rerank.append(pred_hard_encoding)\\n\",\n \"\\n\",\n \"for i, c in enumerate(cut_offs):\\n\",\n \" nDCG = ndcg_score(pred_hard_encodings_rerank, new_scores, k=c)\\n\",\n \" print(f\\\"nDCG@{c}: {nDCG}\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/5_Reranking/5.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# BGE Reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like embedding models, BGE has a group of rerankers with various sizes and functionalities. In this tutorial, we will introduce the BGE rerankers series.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Install the dependencies in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install -U FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. bge-reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first generation of BGE reranker contains two models:\\n\",\n \"\\n\",\n \"| Model | Language | Parameters | Description | Base Model |\\n\",\n \"|:-------|:--------:|:----:|:-----------------:|:--------------------------------------:|\\n\",\n \"| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | 278M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Base |\\n\",\n \"| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | 560M | a cross-encoder model which is more accurate but less efficient | XLM-RoBERTa-Large |\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\",\n \"You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"[7.984375, -6.84375, -7.15234375, 5.44921875]\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagReranker\\n\",\n \"\\n\",\n \"model = FlagReranker(\\n\",\n \" 'BAAI/bge-reranker-large',\\n\",\n \" use_fp16=True,\\n\",\n \" devices=[\\\"cuda:0\\\"], # if you don't have GPUs, you can use \\\"cpu\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"pairs = [\\n\",\n \" [\\\"What is the capital of France?\\\", \\\"Paris is the capital of France.\\\"],\\n\",\n \" [\\\"What is the capital of France?\\\", \\\"The population of China is over 1.4 billion people.\\\"],\\n\",\n \" [\\\"What is the population of China?\\\", \\\"Paris is the capital of France.\\\"],\\n\",\n \" [\\\"What is the population of China?\\\", \\\"The population of China is over 1.4 billion people.\\\"]\\n\",\n \"]\\n\",\n \"\\n\",\n \"scores = model.compute_score(pairs)\\n\",\n \"scores\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. bge-reranker v2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"| Model | Language | Parameters | Description | Base Model |\\n\",\n \"|:-------|:--------:|:----:|:-----------------:|:--------------------------------------:|\\n\",\n \"| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | 568M | a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | XLM-RoBERTa-Large |\\n\",\n \"| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | 2.51B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | Gemma2-2B |\\n\",\n \"| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | 2.72B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | MiniCPM |\\n\",\n \"| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | 9.24B | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. | Gemma2-9B |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2-m3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2-m3 is trained based on bge-m3, introducing great multi-lingual capability as keeping a slim model size.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.003483424193080668]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagReranker\\n\",\n \"\\n\",\n \"# Setting use_fp16 to True speeds up computation with a slight performance degradation (if using gpu)\\n\",\n \"reranker = FlagReranker('BAAI/bge-reranker-v2-m3', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"score = reranker.compute_score(['query', 'passage'])\\n\",\n \"# or set \\\"normalize=True\\\" to apply a sigmoid function to the score for 0-1 range\\n\",\n \"score = reranker.compute_score(['query', 'passage'], normalize=True)\\n\",\n \"\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2-gemma\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2-gemma is trained based on gemma-2b. It has excellent performances with both English proficiency and multilingual capabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 5.29it/s]\\n\",\n \"You're using a GemmaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"100%|██████████| 1/1 [00:00<00:00, 45.99it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1.974609375]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagLLMReranker\\n\",\n \"\\n\",\n \"reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"score = reranker.compute_score(['query', 'passage'])\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2-minicpm-layerwise\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2-minicpm-layerwise is trained based on minicpm-2b-dpo-bf16. It's suitable for multi-lingual contexts, performs well in Both English and Chinese proficiency.\\n\",\n \"\\n\",\n \"Another special functionality is the layerwise design gives user freedom to select layers for output, facilitating accelerated inference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 3.85it/s]\\n\",\n \"You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"100%|██████████| 1/1 [00:00<00:00, 24.51it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[-7.06640625]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import LayerWiseFlagLLMReranker\\n\",\n \"\\n\",\n \"reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"# Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\\n\",\n \"score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28])\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### bge-reranker-v2.5-gemma2-lightweight\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"bge-reranker-v2.5-gemma2-lightweight is trained based on gemma2-9b. It's also suitable for multi-lingual contexts.\\n\",\n \"\\n\",\n \"Besides the layerwise reduction functionality, bge-reranker-v2.5-gemma2-lightweight integrates token compression capabilities to further save more resources while maintaining outstanding performances.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading checkpoint shards: 100%|██████████| 4/4 [00:01<00:00, 3.60it/s]\\n\",\n \"You're using a GemmaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"100%|██████████| 1/1 [00:00<00:00, 23.95it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[14.734375]\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import LightWeightFlagLLMReranker\\n\",\n \"\\n\",\n \"reranker = LightWeightFlagLLMReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', devices=[\\\"cuda:0\\\"], use_fp16=True)\\n\",\n \"\\n\",\n \"# Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\\n\",\n \"score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\\n\",\n \"print(score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparison\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE reranker series provides a great number of choices for all kinds of functionalities. You can select the model according your senario and resource:\\n\",\n \"\\n\",\n \"- For multilingual, utilize `BAAI/bge-reranker-v2-m3`, `BAAI/bge-reranker-v2-gemma` and `BAAI/bge-reranker-v2.5-gemma2-lightweight`.\\n\",\n \"\\n\",\n \"- For Chinese or English, utilize `BAAI/bge-reranker-v2-m3` and `BAAI/bge-reranker-v2-minicpm-layerwise`.\\n\",\n \"\\n\",\n \"- For efficiency, utilize `BAAI/bge-reranker-v2-m3` and the low layer of `BAAI/bge-reranker-v2-minicpm-layerwise`.\\n\",\n \"\\n\",\n \"- For saving resources and extreme efficiency, utilize `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`.\\n\",\n \"\\n\",\n \"- For better performance, recommand `BAAI/bge-reranker-v2-minicpm-layerwise` and B`AAI/bge-reranker-v2-gemma`.\\n\",\n \"\\n\",\n \"Make sure always test on your real use case and choose the one with best speed-quality balance!\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/5_Reranking/5.3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluate Reranker\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reranker usually better captures the latent semantic meanings between sentences. But comparing to using an embedding model, it will take quadratic $O(N^2)$ running time for the whole dataset. Thus the most common use cases of rerankers in information retrieval or RAG is reranking the top k answers retrieved according to the embedding similarities.\\n\",\n \"\\n\",\n \"The evaluation of reranker has the similar idea. We compare how much better the rerankers can rerank the candidates searched by a same embedder. In this tutorial, we will evaluate two rerankers' performances on BEIR benchmark, with bge-large-en-v1.5 as the base embedding model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note: We highly recommend to run this notebook with GPU. The whole pipeline is very time consuming. For simplicity, we only use a single task FiQA in BEIR.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the required dependency\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install FlagEmbedding\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. bge-reranker-large\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first model is bge-reranker-large, a BERT like reranker with about 560M parameters.\\n\",\n \"\\n\",\n \"We can use the evaluation pipeline of FlagEmbedding to directly run the whole process:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Split 'dev' not found in the dataset. Removing it from the list.\\n\",\n \"ignore_identical_ids is set to True. This means that the search results will not contain identical ids. Note: Dataset such as MIRACL should NOT set this to True.\\n\",\n \"pre tokenize: 100%|██████████| 57/57 [00:03<00:00, 14.68it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Inference Embeddings: 100%|██████████| 57/57 [00:44<00:00, 1.28it/s]\\n\",\n \"pre tokenize: 100%|██████████| 1/1 [00:00<00:00, 61.59it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 1/1 [00:00<00:00, 6.22it/s]\\n\",\n \"Searching: 100%|██████████| 21/21 [00:00<00:00, 68.26it/s]\\n\",\n \"pre tokenize: 0%| | 0/64 [00:00\\n\",\n \"{context}\\n\",\n \"\\n\",\n \"\\n\",\n \"Question: {input}\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"# Create a prompt template\\n\",\n \"prompt = ChatPromptTemplate.from_template(template)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now everything is ready. Assemble them to a chain and let the magic happen!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from langchain.chains.combine_documents import create_stuff_documents_chain\\n\",\n \"from langchain.chains import create_retrieval_chain\\n\",\n \"\\n\",\n \"doc_chain = create_stuff_documents_chain(llm, prompt)\\n\",\n \"chain = create_retrieval_chain(retriever, doc_chain)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the following cell, we can see that the chatbot can answer the question correctly!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"M3-Embedding stands for a new embedding model that is distinguished for its versatility in multi-linguality, multi-functionality, and multi-granularity.\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = chain.invoke({\\\"input\\\": \\\"What does M3-Embedding stands for?\\\"})\\n\",\n \"\\n\",\n \"# print the answer only\\n\",\n \"print(response['answer'])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"base\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/6_RAG/6.3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# RAG with LlamaIndex\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"LlamaIndex is a very popular framework to help build connections between data sources and LLMs. It is also a top choice when people would like to build an RAG framework. In this tutorial, we will go through how to use LlamaIndex to aggregate bge-base-en-v1.5 and GPT-4o-mini to an RAG application.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First install the required packages in the environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install llama-index-llms-openai llama-index-embeddings-huggingface llama-index-vector-stores-faiss\\n\",\n \"%pip install llama_index \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then fill the OpenAI API key below:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# For openai key\\n\",\n \"import os\\n\",\n \"os.environ[\\\"OPENAI_API_KEY\\\"] = \\\"YOUR_API_KEY\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"BGE-M3 is a very powerful embedding model, We would like to know what does that 'M3' stands for.\\n\",\n \"\\n\",\n \"Let's first ask GPT the question:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"M3-Embedding stands for Multimodal Multiscale Embedding. It is a technique used in machine learning and data analysis to embed high-dimensional data into a lower-dimensional space while preserving the structure and relationships within the data. This technique is particularly useful for analyzing complex datasets that contain multiple modalities or scales of information.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from llama_index.llms.openai import OpenAI\\n\",\n \"\\n\",\n \"# non-streaming\\n\",\n \"response = OpenAI().complete(\\\"What does M3-Embedding stands for?\\\")\\n\",\n \"print(response)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By checking the description in GitHub [repo](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) of BGE-M3, we are pretty sure that GPT is giving us hallucination. Let's build an RAG pipeline to solve the problem!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, download BGE-M3 [paper](https://arxiv.org/pdf/2402.03216) to a directory, and load it through `SimpleDirectoryReader`. \\n\",\n \"\\n\",\n \"Note that `SimpleDirectoryReader` can read all the documents under that directory and supports a lot of commonly used [file types](https://docs.llamaindex.ai/en/stable/module_guides/loading/simpledirectoryreader/#supported-file-types).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from llama_index.core import SimpleDirectoryReader\\n\",\n \"\\n\",\n \"reader = SimpleDirectoryReader(\\\"data\\\")\\n\",\n \"# reader = SimpleDirectoryReader(\\\"DIR_TO_FILE\\\")\\n\",\n \"documents = reader.load_data()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `Settings` object is a global settings for the RAG pipeline. Attributes in it have default settings and can be modified by users (OpenAI's GPT and embedding model). Large attributes like models will be only loaded when being used.\\n\",\n \"\\n\",\n \"Here, we specify the `node_parser` to `SentenceSplitter()` with our chosen parameters, use the open-source `bge-base-en-v1.5` as our embedding model, and `gpt-4o-mini` as our llm.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from llama_index.core import Settings\\n\",\n \"from llama_index.core.node_parser import SentenceSplitter\\n\",\n \"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\\n\",\n \"from llama_index.llms.openai import OpenAI\\n\",\n \"\\n\",\n \"# set the parser with parameters\\n\",\n \"Settings.node_parser = SentenceSplitter(\\n\",\n \" chunk_size=1000, # Maximum size of chunks to return\\n\",\n \" chunk_overlap=150, # number of overlap characters between chunks\\n\",\n \")\\n\",\n \"\\n\",\n \"# set the specific embedding model\\n\",\n \"Settings.embed_model = HuggingFaceEmbedding(model_name=\\\"BAAI/bge-base-en-v1.5\\\")\\n\",\n \"\\n\",\n \"# set the llm we want to use\\n\",\n \"Settings.llm = OpenAI(model=\\\"gpt-4o-mini\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Indexing is one of the most important part in RAG. LlamaIndex integrates a great amount of vector databases. Here we will use Faiss as an example.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First check the dimension of the embeddings, which will need for initializing a Faiss index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"768\\n\"\n ]\n }\n ],\n \"source\": [\n \"embedding = Settings.embed_model.get_text_embedding(\\\"Hello world\\\")\\n\",\n \"dim = len(embedding)\\n\",\n \"print(dim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then create the index with Faiss and our documents. Here LlamaIndex help capsulate the Faiss function calls. If you would like to know more about Faiss, refer to the tutorial of [Faiss and indexing](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials/3_Indexing).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"from llama_index.vector_stores.faiss import FaissVectorStore\\n\",\n \"from llama_index.core import StorageContext, VectorStoreIndex\\n\",\n \"\\n\",\n \"# init Faiss and create a vector store\\n\",\n \"faiss_index = faiss.IndexFlatL2(dim)\\n\",\n \"vector_store = FaissVectorStore(faiss_index=faiss_index)\\n\",\n \"\\n\",\n \"# customize the storage context using our vector store\\n\",\n \"storage_context = StorageContext.from_defaults(\\n\",\n \" vector_store=vector_store\\n\",\n \")\\n\",\n \"\\n\",\n \"# use the loaded documents to build the index\\n\",\n \"index = VectorStoreIndex.from_documents(\\n\",\n \" documents, storage_context=storage_context\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Retrieve and Generate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With a well constructed index, we can now build the query engine to accomplish our task:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"query_engine = index.as_query_engine()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following cell displays the default prompt template for Q&A in our pipeline:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Context information is below.\\n\",\n \"---------------------\\n\",\n \"{context_str}\\n\",\n \"---------------------\\n\",\n \"Given the context information and not prior knowledge, answer the query.\\n\",\n \"Query: {query_str}\\n\",\n \"Answer: \\n\"\n ]\n }\n ],\n \"source\": [\n \"# check the default promt template\\n\",\n \"prompt_template = query_engine.get_prompts()['response_synthesizer:text_qa_template']\\n\",\n \"print(prompt_template.get_template())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"(Optional) You could modify the prompt to match your use cases:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"You are a Q&A chat bot.\\n\",\n \"Use the given context only, answer the question.\\n\",\n \"\\n\",\n \"\\n\",\n \"{context_str}\\n\",\n \"\\n\",\n \"\\n\",\n \"Question: {query_str}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from llama_index.core import PromptTemplate\\n\",\n \"\\n\",\n \"template = \\\"\\\"\\\"\\n\",\n \"You are a Q&A chat bot.\\n\",\n \"Use the given context only, answer the question.\\n\",\n \"\\n\",\n \"\\n\",\n \"{context_str}\\n\",\n \"\\n\",\n \"\\n\",\n \"Question: {query_str}\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"new_template = PromptTemplate(template)\\n\",\n \"query_engine.update_prompts(\\n\",\n \" {\\\"response_synthesizer:text_qa_template\\\": new_template}\\n\",\n \")\\n\",\n \"\\n\",\n \"prompt_template = query_engine.get_prompts()['response_synthesizer:text_qa_template']\\n\",\n \"print(prompt_template.get_template())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, let's see how does the RAG application performs on our query!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"M3-Embedding stands for Multi-Linguality, Multi-Functionality, and Multi-Granularity.\\n\"\n ]\n }\n ],\n \"source\": [\n \"response = query_engine.query(\\\"What does M3-Embedding stands for?\\\")\\n\",\n \"print(response)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"test\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/6_RAG.rst", "content": "6. RAG\n======\n\n.. toctree::\n :hidden:\n :maxdepth: 1\n :caption: RAG\n\n 6_RAG/6.1\n 6_RAG/6.2\n 6_RAG/6.3" }, { "path": "docs/source/tutorial/7_Finetuning/7.1.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Data Preparation for Fine-tuning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this tutorial, we will show an example of the first step for fine-tuning: dataset preparation.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 0. Installation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install -U datasets\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Suppose we are willing to fine-tune our model for financial tasks. We found an open-source dataset that could be useful: [financial-qa-10k](https://huggingface.co/datasets/virattt/financial-qa-10K). Let's see how to properly prepare our dataset for fine-tuning.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The raw dataset has the following structure:\\n\",\n \"- 5 columns of: 'question', 'answer', 'context', 'ticker', and 'filing'.\\n\",\n \"- 7000 rows.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Dataset({\\n\",\n \" features: ['question', 'answer', 'context', 'ticker', 'filing'],\\n\",\n \" num_rows: 7000\\n\",\n \"})\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"ds = load_dataset(\\\"virattt/financial-qa-10K\\\", split=\\\"train\\\")\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Data for Fine-tuning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Construct the dataset to the following format:\\n\",\n \"\\n\",\n \"``` python\\n\",\n \"{\\\"query\\\": str, \\\"pos\\\": List[str], \\\"neg\\\":List[str], \\\"pos_scores\\\": List[int], \\\"neg_scores\\\": List[int], \\\"prompt\\\": str, \\\"type\\\": str}\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. `pos_scores` is a list of scores corresponding to the query and pos, `neg_scores` is a list of scores corresponding to the `query` and `neg`, if you don't use knowledge distillation, it can be ignored. `prompt` is the prompt used for the query, it will cover query_instruction_for_retrieval. `type` is used for bge-en-icl, it includes `normal`, `symmetric_class`, `symmetric_clustering`, .etc. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We select the columns 'question' and 'context' as our query and answer(pos), and rename the columns. Then add the 'id' column for later evaluation use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\\n\",\n \" 'pos': 'Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.',\\n\",\n \" 'id': '0'}\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = ds.select_columns(column_names=[\\\"question\\\", \\\"context\\\"])\\n\",\n \"ds = ds.rename_column(\\\"question\\\", \\\"query\\\")\\n\",\n \"ds = ds.rename_column(\\\"context\\\", \\\"pos\\\")\\n\",\n \"ds = ds.add_column(\\\"id\\\", [str(i) for i in range(len(ds))])\\n\",\n \"ds[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Negative examples are important during the training of embedding models. Our initial dataset does not come with negative texts. Thus we directly sample a few from the whole corpus.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Map: 100%|██████████| 7000/7000 [00:00<00:00, 22336.83 examples/s]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"np.random.seed(520)\\n\",\n \"neg_num = 10\\n\",\n \"\\n\",\n \"def str_to_lst(data):\\n\",\n \" data[\\\"pos\\\"] = [data[\\\"pos\\\"]]\\n\",\n \" return data\\n\",\n \"\\n\",\n \"# sample negative texts\\n\",\n \"new_col = []\\n\",\n \"for i in range(len(ds)):\\n\",\n \" ids = np.random.randint(0, len(ds), size=neg_num)\\n\",\n \" while i in ids:\\n\",\n \" ids = np.random.randint(0, len(ds), size=neg_num)\\n\",\n \" neg = [ds[i.item()][\\\"pos\\\"] for i in ids]\\n\",\n \" new_col.append(neg)\\n\",\n \"ds = ds.add_column(\\\"neg\\\", new_col)\\n\",\n \"\\n\",\n \"# change the key of 'pos' to a list\\n\",\n \"ds = ds.map(str_to_lst)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lastly, we add the prompt which is used for query. It will be the `query_instruction_for_retrieval` during inference.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"instruction = \\\"Represent this sentence for searching relevant passages: \\\"\\n\",\n \"ds = ds.add_column(\\\"prompt\\\", [instruction]*len(ds))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now a single row of the dataset is:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'query': 'What area did NVIDIA initially focus on before expanding to other computationally intensive fields?',\\n\",\n \" 'pos': ['Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.'],\\n\",\n \" 'id': '0',\\n\",\n \" 'neg': ['Kroger expects that its value creation model will deliver total shareholder return within a target range of 8% to 11% over time.',\\n\",\n \" 'CSB purchased First Mortgages of $2.9 billion during 2023.',\\n\",\n \" 'See Note 13 to our Consolidated Financial Statements for information on certain legal proceedings for which there are contingencies.',\\n\",\n \" 'Diluted earnings per share were $16.69 in fiscal 2022 compared to $15.53 in fiscal 2021.',\\n\",\n \" 'In the year ended December 31, 2023, Total net sales and revenue increased primarily due to: (1) increased net wholesale volumes primarily due to increased sales of crossover vehicles and full-size pickup trucks, partially offset by decreased sales of mid-size pickup trucks; (2) favorable Price as a result of low dealer inventory levels and strong demand for our products; (3) favorable Mix associated with increased sales of full-size pickup trucks and full-size SUVs and decreased sales of vans, passenger cars and mid-size pickup trucks, partially offset by increased sales of crossover vehicles; and (4) favorable Other due to increased sales of parts and accessories.',\\n\",\n \" 'As of December 31, 2023, we had 3,157 full-time employees.',\\n\",\n \" 'Item 3. Legal Proceedings. The information contained in Note 18 ‘‘Commitments and Contingencies’’ included in Item 8 of this 10-K is incorporated herein by reference.',\\n\",\n \" 'Under the amended 2019 Secured Facility, the maturity date is set to July 20, 2026.',\\n\",\n \" 'Accounts receivable for Las Vegas Sands Corp. on December 31, 2023, totaled $685 million, with a provision for credit losses of $201 million, resulting in a net balance of $484 million.',\\n\",\n \" 'Operating expenses as a percentage of segment net sales decreased 25 basis points for fiscal 2023 when compared to the previous fiscal year, primarily driven by strong sales growth and lower incremental COVID-19 related costs, partially offset by increased wage costs.'],\\n\",\n \" 'prompt': 'Represent this sentence for searching relevant passages: '}\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then we split the dataset into training set and testing set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"split = ds.train_test_split(test_size=0.1, shuffle=True, seed=520)\\n\",\n \"train = split[\\\"train\\\"]\\n\",\n \"test = split[\\\"test\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we are ready to store the data for later fine-tuning:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Creating json from Arrow format: 100%|██████████| 7/7 [00:00<00:00, 39.73ba/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"16583481\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train.to_json(\\\"ft_data/training.json\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Test Data for Evaluation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The last step is to construct the testing dataset for evaluaton.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Dataset({\\n\",\n \" features: ['query', 'pos', 'id', 'neg', 'prompt'],\\n\",\n \" num_rows: 700\\n\",\n \"})\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First select the columns for queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'id': '1289',\\n\",\n \" 'text': 'How does Starbucks recognize the interest and penalties related to income tax matters on their financial statements?'}\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"queries = test.select_columns(column_names=[\\\"id\\\", \\\"query\\\"])\\n\",\n \"queries = queries.rename_column(\\\"query\\\", \\\"text\\\")\\n\",\n \"queries[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then select the columns for corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus = ds.select_columns(column_names=[\\\"id\\\", \\\"pos\\\"])\\n\",\n \"corpus = corpus.rename_column(\\\"pos\\\", \\\"text\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, make the qrels that indicating the relations of queries and corresponding corpus\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Flattening the indices: 100%|██████████| 700/700 [00:00<00:00, 180956.10 examples/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"{'qid': '1289', 'docid': '1289', 'relevance': 1}\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qrels = test.select_columns([\\\"id\\\"])\\n\",\n \"qrels = qrels.rename_column(\\\"id\\\", \\\"qid\\\")\\n\",\n \"qrels = qrels.add_column(\\\"docid\\\", list(test[\\\"id\\\"]))\\n\",\n \"qrels = qrels.add_column(\\\"relevance\\\", [1]*len(test))\\n\",\n \"qrels[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Store the training set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Creating json from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 210.42ba/s]\\n\",\n \"Creating json from Arrow format: 100%|██████████| 7/7 [00:00<00:00, 261.19ba/s]\\n\",\n \"Creating json from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 591.08ba/s]\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"30574\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"queries.to_json(\\\"ft_data/test_queries.jsonl\\\")\\n\",\n \"corpus.to_json(\\\"ft_data/corpus.jsonl\\\")\\n\",\n \"qrels.to_json(\\\"ft_data/test_qrels.jsonl\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/7_Finetuning/7.1.2.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Fine-tuning\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the previous section, we went through how to construct training and testing data properly. In this tutorial, we will actually fine-tune the model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Installation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note to fine-tune BGE models using FlagEmbedding, we need to install the package with the finetune dependency:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"% pip install -U FlagEmbedding[finetune]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fine-tune\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below are the arguments for fine-tuning:\\n\",\n \"\\n\",\n \"The following arguments are for model:\\n\",\n \"- `model_name_or_path`: The model checkpoint for initialization.\\n\",\n \"- `config_name`: Pretrained config name or path if not the same as model_name.\\n\",\n \"- `tokenizer_name`: Pretrained tokenizer name or path if not the same as model_name.\\n\",\n \"- `cache_dir`: Where do you want to store the pre-trained models downloaded from s3.\\n\",\n \"- `trust_remote_code`: Trust remote code\\n\",\n \"- `token`: The token to use when accessing the model.\\n\",\n \"\\n\",\n \"The following arguments are for data:\\n\",\n \"- `train_data`: One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data. Argument type: multiple.\\n\",\n \"- `cache_path`: Where do you want to store the cached data.\\n\",\n \"- `train_group_size`: (No metadata provided)\\n\",\n \"- `query_max_len`: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\\n\",\n \"- `passage_max_len`: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\\n\",\n \"- `pad_to_multiple_of`: If set will pad the sequence to be a multiple of the provided value.\\n\",\n \"- `max_example_num_per_dataset`: The max number of examples for each dataset.\\n\",\n \"- `query_instruction_for_retrieval`: Instruction for query.\\n\",\n \"- `query_instruction_format`: Format for query instruction.\\n\",\n \"- `knowledge_distillation`: Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data.\\n\",\n \"- `passage_instruction_for_retrieval`: Instruction for passage.\\n\",\n \"- `passage_instruction_format`: Format for passage instruction.\\n\",\n \"- `shuffle_ratio`: The ratio of shuffling the text.\\n\",\n \"- `same_dataset_within_batch`: All samples in the same batch comes from the same dataset.\\n\",\n \"- `small_threshold`: The threshold of small dataset. All small dataset in the same directory will be merged into one dataset.\\n\",\n \"- `drop_threshold`: The threshold for dropping merged small dataset. If the number of examples in the merged small dataset is less than this threshold, it will be dropped.\\n\",\n \"\\n\",\n \"And the following extra arguments:\\n\",\n \"- `negatives_cross_device`: Share negatives across devices.\\n\",\n \"- `temperature`: Temperature used for similarity score.\\n\",\n \"- `fix_position_embedding`: Freeze the parameters of position embeddings.\\n\",\n \"- `sentence_pooling_method`: The pooling method. Available options: cls, mean, last_token. Default: cls.\\n\",\n \"- `normalize_embeddings`: Whether to normalize the embeddings.\\n\",\n \"- `sub_batch_size`: Sub batch size for training.\\n\",\n \"- `kd_loss_type`: The loss type for knowledge distillation. Available options: kl_div, m3_kd_loss. Default: kl_div.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] \\n\",\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] *****************************************\\n\",\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \\n\",\n \"W1223 06:27:06.807000 1362426 site-packages/torch/distributed/run.py:793] *****************************************\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2024-12-23 06:27:31,423] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)\\n\",\n \"[2024-12-23 06:27:31,424] [INFO] [real_accelerator.py:219:get_accelerator] Setting ds_accelerator to cuda (auto detect)\\n\",\n \"[2024-12-23 06:27:40,529] [INFO] [comm.py:652:init_distributed] cdb=None\\n\",\n \"[2024-12-23 06:27:40,529] [INFO] [comm.py:652:init_distributed] cdb=None\\n\",\n \"[2024-12-23 06:27:40,529] [INFO] [comm.py:683:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"12/23/2024 06:27:40 - WARNING - FlagEmbedding.abc.finetune.embedder.AbsRunner - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: True, 16-bits training: True\\n\",\n \"12/23/2024 06:27:40 - INFO - FlagEmbedding.abc.finetune.embedder.AbsRunner - Training/evaluation parameters AbsEmbedderTrainingArguments(\\n\",\n \"_n_gpu=1,\\n\",\n \"accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},\\n\",\n \"adafactor=False,\\n\",\n \"adam_beta1=0.9,\\n\",\n \"adam_beta2=0.999,\\n\",\n \"adam_epsilon=1e-08,\\n\",\n \"auto_find_batch_size=False,\\n\",\n \"batch_eval_metrics=False,\\n\",\n \"bf16=False,\\n\",\n \"bf16_full_eval=False,\\n\",\n \"data_seed=None,\\n\",\n \"dataloader_drop_last=True,\\n\",\n \"dataloader_num_workers=0,\\n\",\n \"dataloader_persistent_workers=False,\\n\",\n \"dataloader_pin_memory=True,\\n\",\n \"dataloader_prefetch_factor=None,\\n\",\n \"ddp_backend=None,\\n\",\n \"ddp_broadcast_buffers=None,\\n\",\n \"ddp_bucket_cap_mb=None,\\n\",\n \"ddp_find_unused_parameters=None,\\n\",\n \"ddp_timeout=1800,\\n\",\n \"debug=[],\\n\",\n \"deepspeed=config/ds_stage0.json,\\n\",\n \"disable_tqdm=False,\\n\",\n \"dispatch_batches=None,\\n\",\n \"do_eval=False,\\n\",\n \"do_predict=False,\\n\",\n \"do_train=False,\\n\",\n \"eval_accumulation_steps=None,\\n\",\n \"eval_delay=0,\\n\",\n \"eval_do_concat_batches=True,\\n\",\n \"eval_on_start=False,\\n\",\n \"eval_steps=None,\\n\",\n \"eval_strategy=IntervalStrategy.NO,\\n\",\n \"eval_use_gather_object=False,\\n\",\n \"evaluation_strategy=None,\\n\",\n \"fix_position_embedding=False,\\n\",\n \"fp16=True,\\n\",\n \"fp16_backend=auto,\\n\",\n \"fp16_full_eval=False,\\n\",\n \"fp16_opt_level=O1,\\n\",\n \"fsdp=[],\\n\",\n \"fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},\\n\",\n \"fsdp_min_num_params=0,\\n\",\n \"fsdp_transformer_layer_cls_to_wrap=None,\\n\",\n \"full_determinism=False,\\n\",\n \"gradient_accumulation_steps=1,\\n\",\n \"gradient_checkpointing=True,\\n\",\n \"gradient_checkpointing_kwargs=None,\\n\",\n \"greater_is_better=None,\\n\",\n \"group_by_length=False,\\n\",\n \"half_precision_backend=auto,\\n\",\n \"hub_always_push=False,\\n\",\n \"hub_model_id=None,\\n\",\n \"hub_private_repo=False,\\n\",\n \"hub_strategy=HubStrategy.EVERY_SAVE,\\n\",\n \"hub_token=,\\n\",\n \"ignore_data_skip=False,\\n\",\n \"include_inputs_for_metrics=False,\\n\",\n \"include_num_input_tokens_seen=False,\\n\",\n \"include_tokens_per_second=False,\\n\",\n \"jit_mode_eval=False,\\n\",\n \"kd_loss_type=kl_div,\\n\",\n \"label_names=None,\\n\",\n \"label_smoothing_factor=0.0,\\n\",\n \"learning_rate=1e-05,\\n\",\n \"length_column_name=length,\\n\",\n \"load_best_model_at_end=False,\\n\",\n \"local_rank=0,\\n\",\n \"log_level=passive,\\n\",\n \"log_level_replica=warning,\\n\",\n \"log_on_each_node=True,\\n\",\n \"logging_dir=./test_encoder_only_base_bge-large-en-v1.5/runs/Dec23_06-27-30_job-40fb0ce3-8bfb-46ea-b409-0a2e2a1a3163-master-0,\\n\",\n \"logging_first_step=False,\\n\",\n \"logging_nan_inf_filter=True,\\n\",\n \"logging_steps=1.0,\\n\",\n \"logging_strategy=IntervalStrategy.STEPS,\\n\",\n \"lr_scheduler_kwargs={},\\n\",\n \"lr_scheduler_type=SchedulerType.LINEAR,\\n\",\n \"max_grad_norm=1.0,\\n\",\n \"max_steps=-1,\\n\",\n \"metric_for_best_model=None,\\n\",\n \"mp_parameters=,\\n\",\n \"neftune_noise_alpha=None,\\n\",\n \"negatives_cross_device=True,\\n\",\n \"no_cuda=False,\\n\",\n \"normalize_embeddings=True,\\n\",\n \"num_train_epochs=2.0,\\n\",\n \"optim=OptimizerNames.ADAMW_TORCH,\\n\",\n \"optim_args=None,\\n\",\n \"optim_target_modules=None,\\n\",\n \"output_dir=./test_encoder_only_base_bge-large-en-v1.5,\\n\",\n \"overwrite_output_dir=True,\\n\",\n \"past_index=-1,\\n\",\n \"per_device_eval_batch_size=8,\\n\",\n \"per_device_train_batch_size=2,\\n\",\n \"prediction_loss_only=False,\\n\",\n \"push_to_hub=False,\\n\",\n \"push_to_hub_model_id=None,\\n\",\n \"push_to_hub_organization=None,\\n\",\n \"push_to_hub_token=,\\n\",\n \"ray_scope=last,\\n\",\n \"remove_unused_columns=True,\\n\",\n \"report_to=[],\\n\",\n \"restore_callback_states_from_checkpoint=False,\\n\",\n \"resume_from_checkpoint=None,\\n\",\n \"run_name=./test_encoder_only_base_bge-large-en-v1.5,\\n\",\n \"save_on_each_node=False,\\n\",\n \"save_only_model=False,\\n\",\n \"save_safetensors=True,\\n\",\n \"save_steps=1000,\\n\",\n \"save_strategy=IntervalStrategy.STEPS,\\n\",\n \"save_total_limit=None,\\n\",\n \"seed=42,\\n\",\n \"sentence_pooling_method=cls,\\n\",\n \"skip_memory_metrics=True,\\n\",\n \"split_batches=None,\\n\",\n \"sub_batch_size=None,\\n\",\n \"temperature=0.02,\\n\",\n \"tf32=None,\\n\",\n \"torch_compile=False,\\n\",\n \"torch_compile_backend=None,\\n\",\n \"torch_compile_mode=None,\\n\",\n \"torch_empty_cache_steps=None,\\n\",\n \"torchdynamo=None,\\n\",\n \"tpu_metrics_debug=False,\\n\",\n \"tpu_num_cores=None,\\n\",\n \"use_cpu=False,\\n\",\n \"use_ipex=False,\\n\",\n \"use_legacy_prediction_loop=False,\\n\",\n \"use_mps_device=False,\\n\",\n \"warmup_ratio=0.1,\\n\",\n \"warmup_steps=0,\\n\",\n \"weight_decay=0.0,\\n\",\n \")\\n\",\n \"12/23/2024 06:27:40 - INFO - FlagEmbedding.abc.finetune.embedder.AbsRunner - Model parameters AbsEmbedderModelArguments(model_name_or_path='BAAI/bge-large-en-v1.5', config_name=None, tokenizer_name=None, cache_dir='./cache/model', trust_remote_code=False, token=None)\\n\",\n \"12/23/2024 06:27:40 - INFO - FlagEmbedding.abc.finetune.embedder.AbsRunner - Data parameters AbsEmbedderDataArguments(train_data=['./ft_data/training.json'], cache_path='./cache/data', train_group_size=8, query_max_len=512, passage_max_len=512, pad_to_multiple_of=8, max_example_num_per_dataset=100000000, query_instruction_for_retrieval='Represent this sentence for searching relevant passages: ', query_instruction_format='{}{}', knowledge_distillation=False, passage_instruction_for_retrieval=None, passage_instruction_format='{}{}', shuffle_ratio=0.0, same_dataset_within_batch=False, small_threshold=0, drop_threshold=0)\\n\",\n \"12/23/2024 06:27:40 - WARNING - FlagEmbedding.abc.finetune.embedder.AbsRunner - Process rank: 1, device: cuda:1, n_gpu: 1, distributed training: True, 16-bits training: True\\n\",\n \"12/23/2024 06:35:01 - INFO - FlagEmbedding.finetune.embedder.encoder_only.base.runner - Config: BertConfig {\\n\",\n \" \\\"_name_or_path\\\": \\\"BAAI/bge-large-en-v1.5\\\",\\n\",\n \" \\\"architectures\\\": [\\n\",\n \" \\\"BertModel\\\"\\n\",\n \" ],\\n\",\n \" \\\"attention_probs_dropout_prob\\\": 0.1,\\n\",\n \" \\\"classifier_dropout\\\": null,\\n\",\n \" \\\"gradient_checkpointing\\\": false,\\n\",\n \" \\\"hidden_act\\\": \\\"gelu\\\",\\n\",\n \" \\\"hidden_dropout_prob\\\": 0.1,\\n\",\n \" \\\"hidden_size\\\": 1024,\\n\",\n \" \\\"id2label\\\": {\\n\",\n \" \\\"0\\\": \\\"LABEL_0\\\"\\n\",\n \" },\\n\",\n \" \\\"initializer_range\\\": 0.02,\\n\",\n \" \\\"intermediate_size\\\": 4096,\\n\",\n \" \\\"label2id\\\": {\\n\",\n \" \\\"LABEL_0\\\": 0\\n\",\n \" },\\n\",\n \" \\\"layer_norm_eps\\\": 1e-12,\\n\",\n \" \\\"max_position_embeddings\\\": 512,\\n\",\n \" \\\"model_type\\\": \\\"bert\\\",\\n\",\n \" \\\"num_attention_heads\\\": 16,\\n\",\n \" \\\"num_hidden_layers\\\": 24,\\n\",\n \" \\\"pad_token_id\\\": 0,\\n\",\n \" \\\"position_embedding_type\\\": \\\"absolute\\\",\\n\",\n \" \\\"torch_dtype\\\": \\\"float32\\\",\\n\",\n \" \\\"transformers_version\\\": \\\"4.44.2\\\",\\n\",\n \" \\\"type_vocab_size\\\": 2,\\n\",\n \" \\\"use_cache\\\": true,\\n\",\n \" \\\"vocab_size\\\": 30522\\n\",\n \"}\\n\",\n \"\\n\",\n \"12/23/2024 06:35:01 - INFO - FlagEmbedding.abc.finetune.embedder.AbsDataset - loading data from ./ft_data/training.json ...\\n\",\n \"Generating train split: 6300 examples [00:00, 46043.95 examples/s]\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\\n\",\n \" warnings.warn(\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations\\n\",\n \" warnings.warn(\\n\",\n \"12/23/2024 06:35:02 - WARNING - accelerate.utils.other - Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1734935704.354551] [job-40fb0ce3-8bfb-46ea-b409-0a2e2a1a3163-master-0:1362491:f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\\n\",\n \"[1734935704.383634] [job-40fb0ce3-8bfb-46ea-b409-0a2e2a1a3163-master-0:1362492:f] vfs_fuse.c:281 UCX ERROR inotify_add_watch(/tmp) failed: No space left on device\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using /root/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...\\n\",\n \"Using /root/.cache/torch_extensions/py311_cu124 as PyTorch extensions root...\\n\",\n \"Detected CUDA files, patching ldflags\\n\",\n \"Emitting ninja build file /root/.cache/torch_extensions/py311_cu124/fused_adam/build.ninja...\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/torch/utils/cpp_extension.py:1964: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation. \\n\",\n \"If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].\\n\",\n \" warnings.warn(\\n\",\n \"Building extension module fused_adam...\\n\",\n \"Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ninja: no work to do.\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading extension module fused_adam...\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time to load fused_adam op: 1.1966907978057861 seconds\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loading extension module fused_adam...\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Time to load fused_adam op: 1.2037739753723145 seconds\\n\",\n \"[2024-12-23 06:35:06,883] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started\\n\",\n \"[2024-12-23 06:35:06,888] [WARNING] [lr_schedules.py:683:get_lr] Attempting to get learning rate from scheduler before it has started\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \" 0%| | 0/3150 [00:00, {'NDCG@10': 0.70405, 'NDCG@100': 0.73528})\\n\",\n \"defaultdict(, {'MAP@10': 0.666, 'MAP@100': 0.67213})\\n\",\n \"defaultdict(, {'Recall@10': 0.82286, 'Recall@100': 0.97286})\\n\",\n \"defaultdict(, {'P@10': 0.08229, 'P@100': 0.00973})\\n\",\n \"defaultdict(, {'MRR@10': 0.666, 'MRR@100': 0.67213})\\n\"\n ]\n }\n ],\n \"source\": [\n \"raw_model = FlagModel(\\n\",\n \" raw_name, \\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" devices=[0],\\n\",\n \" use_fp16=False\\n\",\n \")\\n\",\n \"\\n\",\n \"results = search(raw_model, queries_text, corpus_text)\\n\",\n \"\\n\",\n \"eval_res = evaluate_metrics(qrels_dict, results, k_values)\\n\",\n \"mrr = evaluate_mrr(qrels_dict, results, k_values)\\n\",\n \"\\n\",\n \"for res in eval_res:\\n\",\n \" print(res)\\n\",\n \"print(mrr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then the result for the model after fine-tuning:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 3/3 [00:00<00:00, 164.72it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Inference Embeddings: 100%|██████████| 3/3 [00:00<00:00, 9.45it/s]\\n\",\n \"pre tokenize: 100%|██████████| 28/28 [00:00<00:00, 160.19it/s]\\n\",\n \"Inference Embeddings: 100%|██████████| 28/28 [00:04<00:00, 6.06it/s]\\n\",\n \"Searching: 100%|██████████| 22/22 [00:07<00:00, 2.80it/s]\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"defaultdict(, {'NDCG@10': 0.84392, 'NDCG@100': 0.85792})\\n\",\n \"defaultdict(, {'MAP@10': 0.81562, 'MAP@100': 0.81875})\\n\",\n \"defaultdict(, {'Recall@10': 0.93143, 'Recall@100': 0.99429})\\n\",\n \"defaultdict(, {'P@10': 0.09314, 'P@100': 0.00994})\\n\",\n \"defaultdict(, {'MRR@10': 0.81562, 'MRR@100': 0.81875})\\n\"\n ]\n }\n ],\n \"source\": [\n \"ft_model = FlagModel(\\n\",\n \" finetuned_path, \\n\",\n \" query_instruction_for_retrieval=\\\"Represent this sentence for searching relevant passages:\\\",\\n\",\n \" devices=[0],\\n\",\n \" use_fp16=False\\n\",\n \")\\n\",\n \"\\n\",\n \"results = search(ft_model, queries_text, corpus_text)\\n\",\n \"\\n\",\n \"eval_res = evaluate_metrics(qrels_dict, results, k_values)\\n\",\n \"mrr = evaluate_mrr(qrels_dict, results, k_values)\\n\",\n \"\\n\",\n \"for res in eval_res:\\n\",\n \" print(res)\\n\",\n \"print(mrr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see an obvious improvement in all the metrics.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"ft\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "docs/source/tutorial/7_Finetuning/7.2.1.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Hard Negatives\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Hard negatives are those negative samples that are particularly challenging for the model to distinguish from the positive ones. They are often close to the decision boundary or exhibit features that make them highly similar to the positive samples. Thus hard negative mining is widely used in machine learning tasks to make the model focus on subtle differences between similar instances, leading to better discrimination.\\n\",\n \"\\n\",\n \"In text retrieval system, a hard negative could be document that share some feature similarities with the query but does not truly satisfy the query's intent. During retrieval, those documents could rank higher than the real answers. Thus it's valuable to explicitly train the model on these hard negatives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, load an embedding model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\"\n ]\n }\n ],\n \"source\": [\n \"from FlagEmbedding import FlagModel\\n\",\n \"\\n\",\n \"model = FlagModel('BAAI/bge-base-en-v1.5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then, load the queries and corpus from dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from datasets import load_dataset\\n\",\n \"\\n\",\n \"corpus = load_dataset(\\\"BeIR/scifact\\\", \\\"corpus\\\")[\\\"corpus\\\"]\\n\",\n \"queries = load_dataset(\\\"BeIR/scifact\\\", \\\"queries\\\")[\\\"queries\\\"]\\n\",\n \"\\n\",\n \"corpus_ids = corpus.select_columns([\\\"_id\\\"])[\\\"_id\\\"]\\n\",\n \"corpus = corpus.select_columns([\\\"text\\\"])[\\\"text\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We create a dictionary maping auto generated ids (starting from 0) used by FAISS index, for later use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corpus_ids_map = {}\\n\",\n \"for i in range(len(corpus)):\\n\",\n \" corpus_ids_map[i] = corpus_ids[i]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the embedding model to encode the queries and corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"pre tokenize: 100%|██████████| 21/21 [00:00<00:00, 46.18it/s]\\n\",\n \"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\\n\",\n \"Attempting to cast a BatchEncoding to type None. This is not supported.\\n\",\n \"/share/project/xzy/Envs/ft/lib/python3.11/site-packages/_distutils_hack/__init__.py:54: UserWarning: Reliance on distutils from stdlib is deprecated. Users must rely on setuptools to provide the distutils module. Avoid importing distutils or import setuptools first, and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml\\n\",\n \" warnings.warn(\\n\",\n \"Inference Embeddings: 0%| | 0/21 [00:00`_ folder.\n\n.. toctree::\n :maxdepth: 1\n :caption: Tutorials\n\n 1_Embedding\n 2_Metrics\n 3_Indexing\n 4_Evaluation\n 5_Reranking\n 6_RAG\n 7_Finetuning" }, { "path": "examples/README.md", "content": "# Examples\n\n- [1. Introduction](#1-Introduction)\n- [2. Installation](#2-Installation)\n- [3. Inference](#3-Inference)\n- [4. Finetune](#4-Finetune)\n- [5. Evaluation](#5-Evaluation)\n\n## 1. Introduction\n\nIn this example, we show how to **inference**, **finetune** and **evaluate** the baai-general-embedding.\n\n## 2. Installation\n\n* **with pip**\n\n```shell\npip install -U FlagEmbedding\n```\n\n* **from source**\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\npip install .\n```\n\nFor development, install as editable:\n\n```shell\npip install -e .\n```\n\n## 3. Inference\n\nWe have provided the inference code for two types of models: the **embedder** and the **reranker**. These can be loaded using `FlagAutoModel` and `FlagAutoReranker`, respectively. For more detailed instructions on their use, please refer to the documentation for the [embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder) and [reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker).\n\n### 1. Embedder\n\n```python\nfrom FlagEmbedding import FlagAutoModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagAutoModel.from_finetuned('BAAI/bge-large-zh-v1.5', \n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章：\",\n use_fp16=True,\n devices=['cuda:0']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode_corpus(sentences_1)\nembeddings_2 = model.encode_corpus(sentences_2)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n\n# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query\n# corpus in retrieval task can still use encode_corpus(), since they don't need instruction\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode_corpus(passages)\nscores = q_embeddings @ p_embeddings.T\nprint(scores)\n```\n\n### 2. Reranker\n\n```python\nfrom FlagEmbedding import FlagAutoReranker\npairs = [(\"样例数据-1\", \"样例数据-3\"), (\"样例数据-2\", \"样例数据-4\")]\nmodel = FlagAutoReranker.from_finetuned('BAAI/bge-reranker-large',\n use_fp16=True,\n devices=['cuda:0']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nsimilarity = model.compute_score(pairs, normalize=True)\nprint(similarity)\n\npairs = [(\"query_1\", \"样例文档-1\"), (\"query_2\", \"样例文档-2\")]\nscores = model.compute_score(pairs)\nprint(scores)\n```\n\n## 4. Finetune\n\nWe support fine-tuning a variety of BGE series models, including `bge-large-en-v1.5`, `bge-m3`, `bge-en-icl`, `bge-multilingual-gemma2`, `bge-reranker-v2-m3`, `bge-reranker-v2-gemma`, and `bge-reranker-v2-minicpm-layerwise`, among others. As examples, we use the basic models `bge-large-en-v1.5` and `bge-reranker-large`. For more details, please refer to the [embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder) and [reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker) sections.\n\nIf you do not have the `deepspeed` and `flash-attn` packages installed, you can install them with the following commands:\n```shell\npip install deepspeed\npip install flash-attn --no-build-isolation\n```\n\n### 1. Embedder\n\n```shell\ntorchrun --nproc_per_node 2 \\\n -m FlagEmbedding.finetune.embedder.encoder_only.base \\\n --model_name_or_path BAAI/bge-large-en-v1.5 \\\n --cache_dir ./cache/model \\\n --train_data ./finetune/embedder/example_data/retrieval \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Represent this sentence for searching relevant passages: ' \\\n --query_instruction_format '{}{}' \\\n --knowledge_distillation False \\\n --output_dir ./test_encoder_only_base_bge-large-en-v1.5 \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ./finetune/ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type kl_div\n```\n\n### 2. Reranker\n\n```shell\ntorchrun --nproc_per_node 2 \\\n -m FlagEmbedding.finetune.reranker.encoder_only.base \\\n --model_name_or_path BAAI/bge-reranker-large \\\n --cache_dir ./cache/model \\\n --train_data ./finetune/reranker/example_data/normal/examples.jsonl \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 256 \\\n --passage_max_len 256 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation False \\\n --output_dir ./test_encoder_only_base_bge-reranker-large \\\n --overwrite_output_dir \\\n --learning_rate 6e-5 \\\n --fp16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --gradient_accumulation_steps 1 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ./finetune/ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000\n```\n\n## 5. Evaluation\n\nWe support evaluations on [MTEB](https://github.com/embeddings-benchmark/mteb), [BEIR](https://github.com/beir-cellar/beir), [MSMARCO](https://microsoft.github.io/msmarco/), [MIRACL](https://github.com/project-miracl/miracl), [MLDR](https://huggingface.co/datasets/Shitao/MLDR), [MKQA](https://github.com/apple/ml-mkqa), [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench), [BRIGHT](https://brightbenchmark.github.io/), and custom datasets. Below is an example of evaluating MSMARCO passages. For more details, please refer to the [evaluation examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation).\n\n```shell\npip install pytrec_eval\n# if you fail to install pytrec_eval, try the following command\n# pip install pytrec-eval-terrier\npip install https://github.com/kyamagu/faiss-wheels/releases/download/v1.7.3/faiss_gpu-1.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\npython -m FlagEmbedding.evaluation.msmarco \\\n --eval_name msmarco \\\n --dataset_dir ./data/msmarco \\\n --dataset_names passage \\\n --splits dev dl19 dl20 \\\n --corpus_embd_save_dir ./data/msmarco/corpus_embd \\\n --output_dir ./data/msmarco/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite True \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./data/msmarco/msmarco_eval_results.md \\\n --eval_metrics ndcg_at_10 mrr_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --embedder_batch_size 512 \\\n --embedder_query_max_length 512 \\\n --embedder_passage_max_length 512 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --reranker_batch_size 512 \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \\\n --devices cuda:0 cuda:1 cuda:2 cuda:3 cuda:4 cuda:5 cuda:6 cuda:7 \\\n --cache_dir ./cache/model\n```\n\n" }, { "path": "examples/evaluation/README.md", "content": "# Evaluation\n\nAfter fine-tuning the model, it is essential to evaluate its performance. To facilitate this process, we have provided scripts for assessing the model on various datasets. These datasets include: [**MTEB**](https://github.com/embeddings-benchmark/mteb), [**BEIR**](https://github.com/beir-cellar/beir), [**MSMARCO**](https://microsoft.github.io/msmarco/), [**MIRACL**](https://github.com/project-miracl/miracl), [**MLDR**](https://huggingface.co/datasets/Shitao/MLDR), [**MKQA**](https://github.com/apple/ml-mkqa), [**AIR-Bench**](https://github.com/AIR-Bench/AIR-Bench), [**BRIGHT**](https://brightbenchmark.github.io/), and your **custom datasets**.\n\nTo evaluate the model on a specific dataset, you can find the corresponding bash scripts in the respective folders dedicated to each dataset. These scripts contain the necessary commands and configurations to run the evaluation process.\n\nThis document serves as an overview of the evaluation process and provides a brief introduction to each dataset.\n\nIn this section, we will first introduce the commonly used arguments across all datasets. Then, we will provide a more detailed explanation of the specific arguments used for each individual dataset.\n\n- [1. Introduction](#1-Introduction)\n - [(1) EvalArgs](#1-EvalArgs)\n - [(2) ModelArgs](#2-ModelArgs)\n- [2. Usage](#2-Usage)\n - [Requirements](#Requirements)\n - [(1) MTEB](#1-MTEB)\n - [(2) BEIR](#2-BEIR)\n - [(3) MSMARCO](#3-MSMARCO)\n - [(4) MIRACL](#4-MIRACL)\n - [(5) MLDR](#5-MLDR)\n - [(6) MKQA](#6-MKQA)\n - [(7) AIR-Bench](#7-AIR-Bench)\n - [(8) BRIGHT](#8-BRIGHT)\n - [(9) Custom Dataset](#9-Custom-Dataset)\n\n## Introduction\n\n### 1. EvalArgs\n\n**Arguments for evaluation setup:**\n\n- **`eval_name`**: Name of the evaluation task (e.g., msmarco, beir, miracl).\n\n- **`dataset_dir`**: Path to the dataset directory. This can be:\n 1. A local path to perform evaluation on your dataset (must exist). It should contain:\n - `corpus.jsonl`\n - `_queries.jsonl`\n - `_qrels.jsonl`\n 2. Path to store datasets downloaded via API. Provide `None` to use the cache directory.\n\n- **`force_redownload`**: Set to `True` to force redownload of the dataset. Default is `False`.\n\n- **`dataset_names`**: List of dataset names to evaluate or `None` to evaluate all available datasets. This can be the dataset name (BEIR, etc.) or language (MIRACL, etc.).\n\n- **`splits`**: Dataset splits to evaluate. Default is `test`.\n\n- **`corpus_embd_save_dir`**: Directory to save corpus embeddings. If `None`, embeddings will not be saved.\n\n- **`output_dir`**: Directory to save evaluation results.\n\n- **`search_top_k`**: Top-K results for initial retrieval. Default is `1000`.\n\n- **`rerank_top_k`**: Top-K results for reranking. Default is `100`.\n\n- **`cache_path`**: Cache directory for datasets. Default is `None`.\n\n- **`token`**: Token used for accessing the private data (datasets/models) in HF. Default is `None`, which means it will use the environment variable `HF_TOKEN`.\n\n- **`overwrite`**: Set to `True` to overwrite existing evaluation results. Default is `False`.\n\n- **`ignore_identical_ids`**: Set to `True` to ignore identical IDs in search results. Default is `False`.\n\n- **`k_values`**: List of K values for evaluation (e.g., [1, 3, 5, 10, 100, 1000]). Default is `[1, 3, 5, 10, 100, 1000]`.\n\n- **`eval_output_method`**: Format for outputting evaluation results (options: 'json', 'markdown'). Default is `markdown`.\n\n- **`eval_output_path`**: Path to save the evaluation output.\n\n- **`eval_metrics`**: Metrics used for evaluation (e.g., ['ndcg_at_10', 'recall_at_10']). Default is `[ndcg_at_10, recall_at_100]`.\n\n### 2. ModelArgs\n\n**Arguments for Model Configuration:**\n\n- **`embedder_name_or_path`**: The name or path to the embedder.\n- **`embedder_model_class`**: Class of the model used for embedding (current options include 'encoder-only-base', 'encoder-only-m3', 'decoder-only-base', 'decoder-only-icl'.). Default is None. For the custom model, you should set this argument.\n- **`normalize_embeddings`**: Set to `True` to normalize embeddings.\n- **`pooling_method`**: The pooling method for the embedder.\n- **`use_fp16`**: Use FP16 precision for inference.\n- **`devices`**: List of devices used for inference.\n- **`query_instruction_for_retrieval`**, **`query_instruction_format_for_retrieval`**: Instructions and format for query during retrieval.\n- **`examples_for_task`**, **`examples_instruction_format`**: Example tasks and their instructions format.\n- **`trust_remote_code`**: Set to `True` to trust remote code execution.\n- **`reranker_name_or_path`**: Name or path to the reranker.\n- **`reranker_model_class`**: Reranker model class (options include 'encoder-only-base', 'decoder-only-base', 'decoder-only-layerwise', 'decoder-only-lightweight'). Default is None. For the custom model, you should set this argument.\n- **`reranker_peft_path`**: Path for portable encoder fine-tuning of the reranker.\n- **`use_bf16`**: Use BF16 precision for inference.\n- **`query_instruction_for_rerank`**, **`query_instruction_format_for_rerank`**: Instructions and format for query during reranking.\n- **`passage_instruction_for_rerank`**, **`passage_instruction_format_for_rerank`**: Instructions and format for processing passages during reranking.\n- **`cache_dir`**: Cache directory for models.\n- **`embedder_batch_size`**, **`reranker_batch_size`**: Batch sizes for embedding and reranking.\n- **`embedder_query_max_length`**, **`embedder_passage_max_length`**: Maximum length for embedding queries and passages.\n- **`reranker_query_max_length`**, **`reranker_max_length`**: Maximum lengths for reranking queries and reranking in general.\n- **`normalize`**: Normalize the reranking scores.\n- **`prompt`**: Prompt for the reranker.\n- **`cutoff_layers`**, **`compress_ratio`**, **`compress_layers`**: arguments for configuring the output and compression of layerwise or lightweight rerankers.\n\n***Notice:*** If you evaluate your own model, please set `embedder_model_class` and `reranker_model_class`.\n\n## Usage\n\n### Requirements\n\nYou need install `pytrec_eval` and `faiss` for evaluation:\n\n```shell\npip install pytrec_eval\n# if you fail to install pytrec_eval, try the following command\n# pip install pytrec-eval-terrier\npip install https://github.com/kyamagu/faiss-wheels/releases/download/v1.7.3/faiss_gpu-1.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl\n```\n\n### 1. MTEB\n\nFor MTEB, we primarily use the official [MTEB](https://github.com/embeddings-benchmark/mteb) code, which only supports the assessment of embedders. Moreover, it restricts the output format of the evaluation results to JSON. We have introduced the following new arguments:\n\n- **`languages`**: Languages to evaluate. Default: eng\n- **`tasks`**: Tasks to evaluate. Default: None\n- **`task_types`**: The task types to evaluate. Default: None\n- **`use_special_instructions`**: Whether to use specific instructions in `prompts.py` for evaluation. Default: False\n- **`examples_path`**: Use specific examples in the path. Default: None\n\nHere is an example for evaluation:\n\n```shell\npip install mteb==1.15.0\npython -m FlagEmbedding.evaluation.mteb \\\n --eval_name mteb \\\n --output_dir ./data/mteb/search_results \\\n --languages eng \\\n --tasks NFCorpus BiorxivClusteringS2S SciDocsRR \\\n --eval_output_path ./mteb/mteb_eval_results.json \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --devices cuda:7 \\\n --cache_dir ./cache/model\n```\n\n### 2. BEIR\n\n[BEIR](https://github.com/beir-cellar/beir/) supports evaluations on datasets including `arguana`, `climate-fever`, `cqadupstack`, `dbpedia-entity`, `fever`, `fiqa`, `hotpotqa`, `msmarco`, `nfcorpus`, `nq`, `quora`, `scidocs`, `scifact`, `trec-covid`, `webis-touche2020`, with `msmarco` as the dev set and all others as test sets. The following new arguments have been introduced:\n\n- **`use_special_instructions`**: Whether to use specific instructions in `prompts.py` for evaluation. Default: False\n\nHere is an example for evaluation:\n\n```shell\npip install beir\nmkdir eval_beir\ncd eavl_beir\npython -m FlagEmbedding.evaluation.beir \\\n --eval_name beir \\\n --dataset_dir ./beir/data \\\n --dataset_names fiqa arguana cqadupstack \\\n --splits test dev \\\n --corpus_embd_save_dir ./beir/corpus_embd \\\n --output_dir ./beir/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./beir/beir_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --ignore_identical_ids True \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```\n\n### 3. MSMARCO\n\n[MSMARCO](https://microsoft.github.io/msmarco/) supports evaluations on both `passage` and `document`, providing evaluation splits for `dev`, `dl19`, and `dl20` respectively.\n\nHere is an example for evaluation:\n\n```shell\npython -m FlagEmbedding.evaluation.msmarco \\\n --eval_name msmarco \\\n --dataset_dir ./msmarco/data \\\n --dataset_names passage \\\n --splits dev dl19 dl20 \\\n --corpus_embd_save_dir ./msmarco/corpus_embd \\\n --output_dir ./msmarco/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite True \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./msmarco/msmarco_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```\n\n### 4. MIRACL\n\n[MIRACL](https://github.com/project-miracl/miracl) supports evaluations in multiple languages. We utilize different languages as dataset names, including `ar`, `bn`, `en`, `es`, `fa`, `fi`, `fr`, `hi`, `id`, `ja`, `ko`, `ru`, `sw`, `te`, `th`, `zh`, `de`, `yo`. For the languages `de` and `yo`, the supported splits are `dev`, while for the rest, the supported splits are `train` and `dev`.\n\nHere is an example for evaluation:\n\n```shell\npython -m FlagEmbedding.evaluation.miracl \\\n --eval_name miracl \\\n --dataset_dir ./miracl/data \\\n --dataset_names bn hi sw te th yo \\\n --splits dev \\\n --corpus_embd_save_dir ./miracl/corpus_embd \\\n --output_dir ./miracl/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./miracl/miracl_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```\n\n### 5. MLDR\n\n[MLDR](https://huggingface.co/datasets/Shitao/MLDR) supports evaluations in multiple languages. We have dataset names in various languages, including `ar`, `de`, `en`, `es`, `fr`, `hi`, `it`, `ja`, `ko`, `pt`, `ru`, `th`, `zh`. The available splits are `train`, `dev`, and `test`.\n\nHere is an example for evaluation:\n\n```shell\npython -m FlagEmbedding.evaluation.mldr \\\n --eval_name mldr \\\n --dataset_dir ./mldr/data \\\n --dataset_names hi \\\n --splits test \\\n --corpus_embd_save_dir ./mldr/corpus_embd \\\n --output_dir ./mldr/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./mldr/mldr_eval_results.md \\\n --eval_metrics ndcg_at_10 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```\n\n### 6. MKQA\n\n[MKQA](https://github.com/apple/ml-mkqa) supports cross-lingual retrieval evaluation (from the [paper of BGE-M3](https://arxiv.org/pdf/2402.03216)), using different languages as dataset names, including `en`, `ar`, `fi`, `ja`, `ko`, `ru`, `es`, `sv`, `he`, `th`, `da`, `de`, `fr`, `it`, `nl`, `pl`, `pt`, `hu`, `vi`, `ms`, `km`, `no`, `tr`, `zh_cn`, `zh_hk`, `zh_tw`. The supported split is `test`.\n\nHere is an example for evaluation:\n\n```shell\npython -m FlagEmbedding.evaluation.mkqa \\\n --eval_name mkqa \\\n --dataset_dir ./mkqa/data \\\n --dataset_names en zh_cn \\\n --splits test \\\n --corpus_embd_save_dir ./mkqa/corpus_embd \\\n --output_dir ./mkqa/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite False \\\n --k_values 20 \\\n --eval_output_method markdown \\\n --eval_output_path ./mkqa/mkqa_eval_results.md \\\n --eval_metrics qa_recall_at_20 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```\n\n### 7. AIR-Bench\n\nThe AIR-Bench is primarily based on the official [AIR-Bench repository](https://github.com/AIR-Bench/AIR-Bench/tree/main) and requires the use of its official evaluation codes. Below are some important arguments:\n\n- **`benchmark_version`**: Benchmark version.\n- **`task_types`**: Task types.\n- **`domains`**: Domains to evaluate.\n- **`languages`**: Languages to evaluate.\n\nHere is an example for evaluation:\n\n```shell\npip install air-benchmark\npython -m FlagEmbedding.evaluation.air_bench \\\n --benchmark_version AIR-Bench_24.05 \\\n --task_types qa long-doc \\\n --domains arxiv \\\n --languages en \\\n --splits dev test \\\n --output_dir ./air_bench/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_dir ./cache/data \\\n --overwrite False \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --model_cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```\n\n### 8. BRIGHT\n\n[BRIGHT](https://brightbenchmark.github.io/) supports evaluations on reasoning-intensive text retrieval tasks, and includes 12 datasets: `biology`, `earth_science`, `economics`, `psychology`, `robotics`, `stackoverflow`, `sustainable_living`, `leetcode`, `pony`, `aops`, `theoremqa_questions`, `theoremqa_theorems`.\n\nHere is an example for evaluation:\n\n```shell\n# Available splits (provided by BRIGHT): examples, gpt4_reason, claude-3-opus_reason, Gemini-1.0_reason, llama3-70b_reason, grit_reason\n# NOTE: must run single split each time to ensure correct format of output evaluation results\nsplit=\"examples\"\npython -m FlagEmbedding.evaluation.bright \\\n --task_type short \\\n --use_special_instructions True \\\n --eval_name bright_short \\\n --dataset_dir ./bright_short/data \\\n --dataset_names pony theoremqa_theorems \\\n --splits $split \\\n --corpus_embd_save_dir ./bright_short/corpus_embd \\\n --output_dir ./bright_short/search_results/$split \\\n --search_top_k 2000 \\\n --cache_path ./cache/data \\\n --overwrite False \\\n --k_values 1 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./bright_short/eval_results_$split.md \\\n --eval_metrics ndcg_at_10 recall_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-reasoner-embed-qwen3-8b-0923 \\\n --embedder_model_class decoder-only-base \\\n --query_instruction_format_for_retrieval 'Instruct: {}\\nQuery: {}' \\\n --pooling_method last_token \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --embedder_batch_size 2 \\\n --embedder_query_max_length 8192 \\\n --embedder_passage_max_length 8192 \\\n```\n\n### 9. Custom Dataset\n\nThe example data for `corpus.jsonl`:\n\n```json\n{\"id\": \"566392\", \"title\": \"\", \"text\": \"Have the check reissued to the proper payee.\"}\n{\"id\": \"65404\", \"title\": \"\", \"text\": \"Just have the associate sign the back and then deposit it. It's called a third party cheque and is perfectly legal. I wouldn't be surprised if it has a longer hold period and, as always, you don't get the money if the cheque doesn't clear. Now, you may have problems if it's a large amount or you're not very well known at the bank. In that case you can have the associate go to the bank and endorse it in front of the teller with some ID. You don't even technically have to be there. Anybody can deposit money to your account if they have the account number. He could also just deposit it in his account and write a cheque to the business.\"}\n{\"id\": \"325273\", \"title\": \"\", \"text\": \"Sure you can. You can fill in whatever you want in the From section of a money order, so your business name and address would be fine. The price only includes the money order itself. You can hand deliver it yourself if you want, but if you want to mail it, you'll have to provide an envelope and a stamp. Note that, since you won't have a bank record of this payment, you'll want to make sure you keep other records, such as the stub of the money order. You should probably also ask the contractor to give you a receipt.\"}\n{\"id\": \"88124\", \"title\": \"\", \"text\": \"You're confusing a lot of things here. Company B LLC will have it's sales run under Company A LLC, and cease operating as a separate entity These two are contradicting each other. If B LLC ceases to exist - it is not going to have it's sales run under A LLC, since there will be no sales to run for a non-existent company. What happens is that you merge B LLC into A LLC, and then convert A LLC into S Corp. So you're cancelling the EIN for B LLC, you're cancelling the EIN for A LLC - because both entities cease to exist. You then create a EIN for A Corp, which is the converted A LLC, and you create a DBA where A Corp DBA B Shop. You then go to the bank and open the account for A Corp DBA B Shop with the EIN you just created for A Corp. Get a better accountant. Before you convert to S-Corp.\"}\n{\"id\": \"285255\", \"title\": \"\", \"text\": \"\\\"I'm afraid the great myth of limited liability companies is that all such vehicles have instant access to credit. Limited liability on a company with few physical assets to underwrite the loan, or with insufficient revenue, will usually mean that the owners (or others) will be asked to stand surety on any credit. However, there is a particular form of \\\"\\\"credit\\\"\\\" available to businesses on terms with their clients. It is called factoring. Factoring is a financial transaction whereby a business sells its accounts receivable (i.e., invoices) to a third party (called a factor) at a discount in exchange for immediate money with which to finance continued business. Factoring differs from a bank loan in three main ways. First, the emphasis is on the value of the receivables (essentially a financial asset), not the firm’s credit worthiness. Secondly, factoring is not a loan – it is the purchase of a financial asset (the receivable). Finally, a bank loan involves two parties whereas factoring involves three. Recognise that this can be quite expensive. Most banks catering to small businesses will offer some form of factoring service, or will know of services that offer it. It isn't that different from cheque encashment services (pay-day services) where you offer a discount on future income for money now. An alternative is simply to ask his clients if they'll pay him faster if he offers a discount (since either of interest payments or factoring would reduce profitability anyway).\\\"\"}\n{\"id\": \"350819\", \"title\": \"\", \"text\": \"Banks will usually look at 2 years worth of tax returns for issuing business credit. If those aren't available (for instance, for recently formed businesses), they will look at the personal returns of the owners. Unfortunately, it sounds like your friend is in the latter category. Bringing in another partner isn't necessarily going to help, either; with only two partners / owners, the bank would probably look at both owners' personal tax returns and credit histories. It may be necessary to offer collateral. I'm sorry I can't offer any better solutions, but alternative funding such as personal loans from family & friends could be necessary. Perhaps making them partners in exchange for capital.\"}\n```\n\nThe example data for `test_queries.jsonl`:\n\n```json\n{\"id\": \"8\", \"text\": \"How to deposit a cheque issued to an associate in my business into my business account?\"}\n{\"id\": \"15\", \"text\": \"Can I send a money order from USPS as a business?\"}\n{\"id\": \"18\", \"text\": \"1 EIN doing business under multiple business names\"}\n{\"id\": \"26\", \"text\": \"Applying for and receiving business credit\"}\n```\n\nThe example data for `test_qrels.jsonl`:\n\n```json\n{\"qid\": \"8\", \"docid\": \"566392\", \"relevance\": 1}\n{\"qid\": \"8\", \"docid\": \"65404\", \"relevance\": 1}\n{\"qid\": \"15\", \"docid\": \"325273\", \"relevance\": 1}\n{\"qid\": \"18\", \"docid\": \"88124\", \"relevance\": 1}\n{\"qid\": \"26\", \"docid\": \"285255\", \"relevance\": 1}\n{\"qid\": \"26\", \"docid\": \"350819\", \"relevance\": 1}\n```\n\nPlease put the above file (`corpus.jsonl`, `test_queries.jsonl`, `test_qrels.jsonl`) in `dataset_dir`, and then you can use the following code:\n\n```shell\npython -m FlagEmbedding.evaluation.custom \\\n --eval_name your_data_name \\\n --dataset_dir ./your_data_path \\\n --splits test \\\n --corpus_embd_save_dir ./your_data_name/corpus_embd \\\n --output_dir ./your_data_name/search_results \\\n --search_top_k 1000 \\\n --rerank_top_k 100 \\\n --cache_path ./cache/data \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./your_data_name/eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir ./cache/model \\\n --reranker_query_max_length 512 \\\n --reranker_max_length 1024 \n```" }, { "path": "examples/evaluation/air_bench/eval_air_bench.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\neval_args=\"\\\n --benchmark_version AIR-Bench_24.05 \\\n --task_types qa long-doc \\\n --domains arxiv \\\n --languages en \\\n --splits dev test \\\n --output_dir ./air_bench/search_results \\\n --search_top_k 1000 --rerank_top_k 100 \\\n --cache_dir $HF_HUB_CACHE \\\n --overwrite False \\\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --model_cache_dir $HF_HUB_CACHE \\\n --reranker_max_length 1024 \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.air_bench \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/evaluation/beir/eval_beir.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\ndataset_names=\"fiqa arguana cqadupstack\"\n\neval_args=\"\\\n --eval_name beir \\\n --dataset_dir ./beir/data \\\n --dataset_names $dataset_names \\\n --splits test dev \\\n --corpus_embd_save_dir ./beir/corpus_embd \\\n --output_dir ./beir/search_results \\\n --search_top_k 1000 --rerank_top_k 100 \\\n --cache_path $HF_HUB_CACHE \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./beir/beir_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n --ignore_identical_ids True \\\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir $HF_MODEL_CACHE \\\n --reranker_max_length 1024 \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.beir \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/evaluation/bright/eval_bright_short.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\n# full datasets\n# dataset_names=\"biology earth_science economics psychology robotics stackoverflow sustainable_living leetcode pony aops theoremqa_questions theoremqa_theorems\"\n\n# small datasets for quick test\ndataset_names=\"pony theoremqa_theorems\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-reasoner-embed-qwen3-8b-0923 \\\n --embedder_model_class decoder-only-base \\\n --query_instruction_format_for_retrieval 'Instruct: {}\\nQuery: {}' \\\n --pooling_method last_token \\\n --devices cuda:0 cuda:1 \\\n --cache_dir $HF_HUB_CACHE \\\n --embedder_batch_size 2 \\\n --embedder_query_max_length 8192 \\\n --embedder_passage_max_length 8192 \\\n\"\n\nsplit_list=(\"examples\" \"gpt4_reason\")\n\nfor split in \"${split_list[@]}\"; do\n eval_args=\"\\\n --task_type short \\\n --use_special_instructions True \\\n --eval_name bright_short \\\n --dataset_dir ./bright_short/data \\\n --dataset_names $dataset_names \\\n --splits $split \\\n --corpus_embd_save_dir ./bright_short/corpus_embd \\\n --output_dir ./bright_short/search_results/$split \\\n --search_top_k 2000 \\\n --cache_path $HF_HUB_CACHE \\\n --overwrite False \\\n --k_values 1 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./bright_short/eval_results_$split.md \\\n --eval_metrics ndcg_at_10 recall_at_10 recall_at_100 \\\n \"\n\n cmd=\"python -m FlagEmbedding.evaluation.bright \\\n $eval_args \\\n $model_args \\\n \"\n\n echo $cmd\n eval $cmd\n\ndone\n" }, { "path": "examples/evaluation/miracl/eval_miracl.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\ndataset_names=\"bn hi sw te th yo\"\n\neval_args=\"\\\n --eval_name miracl \\\n --dataset_dir ./miracl/data \\\n --dataset_names $dataset_names \\\n --splits dev \\\n --corpus_embd_save_dir ./miracl/corpus_embd \\\n --output_dir ./miracl/search_results \\\n --search_top_k 1000 --rerank_top_k 100 \\\n --cache_path $HF_HUB_CACHE \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./miracl/miracl_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir $HF_HUB_CACHE \\\n --reranker_max_length 1024 \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.miracl \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/evaluation/mkqa/eval_mkqa.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\ndataset_names=\"en zh_cn\"\n\neval_args=\"\\\n --eval_name mkqa \\\n --dataset_dir ./mkqa/data \\\n --dataset_names $dataset_names \\\n --splits test \\\n --corpus_embd_save_dir ./mkqa/corpus_embd \\\n --output_dir ./mkqa/search_results \\\n --search_top_k 1000 --rerank_top_k 100 \\\n --cache_path $HF_HUB_CACHE \\\n --overwrite False \\\n --k_values 20 \\\n --eval_output_method markdown \\\n --eval_output_path ./mkqa/mkqa_eval_results.md \\\n --eval_metrics qa_recall_at_20 \\\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir $HF_HUB_CACHE \\\n --reranker_max_length 1024 \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.mkqa \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/evaluation/mldr/eval_mldr.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\ndataset_names=\"hi\"\n\neval_args=\"\\\n --eval_name mldr \\\n --dataset_dir ./mldr/data \\\n --dataset_names $dataset_names \\\n --splits test \\\n --corpus_embd_save_dir ./mldr/corpus_embd \\\n --output_dir ./mldr/search_results \\\n --search_top_k 1000 --rerank_top_k 100 \\\n --cache_path $HF_HUB_CACHE \\\n --overwrite False \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./mldr/mldr_eval_results.md \\\n --eval_metrics ndcg_at_10 \\\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-m3 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 \\\n --cache_dir $HF_HUB_CACHE \\\n --embedder_passage_max_length 8192 \\\n --reranker_max_length 8192 \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.mldr \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/evaluation/msmarco/eval_msmarco.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\ndataset_names=\"passage\"\n\neval_args=\"\\\n --eval_name msmarco \\\n --dataset_dir ./msmarco/data \\\n --dataset_names $dataset_names \\\n --splits dev \\\n --corpus_embd_save_dir ./msmarco/corpus_embd \\\n --output_dir ./msmarco/search_results \\\n --search_top_k 1000 --rerank_top_k 100 \\\n --cache_path $HF_HUB_CACHE \\\n --overwrite True \\\n --k_values 10 100 \\\n --eval_output_method markdown \\\n --eval_output_path ./msmarco/msmarco_eval_results.md \\\n --eval_metrics ndcg_at_10 recall_at_100 \\\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n --devices cuda:0 cuda:1 cuda:2 cuda:3 cuda:4 cuda:5 cuda:6 cuda:7 \\\n --cache_dir $HF_HUB_CACHE \\\n --reranker_max_length 1024 \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.msmarco \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/evaluation/mteb/eval_mteb.sh", "content": "if [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nlanguages=\"eng\"\ntasks=\"NFCorpus BiorxivClusteringS2S SciDocsRR\"\n\neval_args=\"\\\n --eval_name mteb \\\n --output_dir ./mteb/search_results \\\n --languages $languages \\\n --tasks $tasks \\\n --eval_output_path ./mteb/mteb_eval_results.json\n\"\n\nmodel_args=\"\\\n --embedder_name_or_path BAAI/bge-large-en-v1.5 \\\n --devices cuda:7 \\\n --cache_dir $HF_HUB_CACHE \\\n\"\n\ncmd=\"python -m FlagEmbedding.evaluation.mteb \\\n $eval_args \\\n $model_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/ds_stage0.json", "content": "{\n \"zero_optimization\": {\n \"stage\": 0\n },\n \n \"fp16\": {\n \"enabled\": \"auto\",\n \"loss_scale\": 0,\n \"loss_scale_window\": 1000,\n \"initial_scale_power\": 12,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n\n \"bf16\": {\n \"enabled\": \"auto\"\n },\n\n \"optimizer\": {\n \"type\": \"AdamW\",\n \"params\": {\n \"lr\": \"auto\",\n \"betas\": \"auto\",\n \"eps\": \"auto\",\n \"weight_decay\": \"auto\"\n }\n },\n\n \"scheduler\": {\n \"type\": \"WarmupDecayLR\",\n \"params\": {\n \"warmup_min_lr\": \"auto\",\n \"warmup_max_lr\": \"auto\",\n \"warmup_num_steps\": \"auto\",\n \"total_num_steps\": \"auto\"\n }\n },\n\n \"gradient_accumulation_steps\": \"auto\",\n \"gradient_clipping\": \"auto\",\n \"steps_per_print\": 100,\n \"train_batch_size\": \"auto\",\n \"train_micro_batch_size_per_gpu\": \"auto\",\n \"wall_clock_breakdown\": false\n}" }, { "path": "examples/finetune/ds_stage1.json", "content": "{\n \"zero_optimization\": {\n \"stage\": 1,\n \"reduce_bucket_size\": 5e8\n },\n\n \"fp16\": {\n \"enabled\": \"auto\",\n \"loss_scale\": 0,\n \"initial_scale_power\": 10,\n \"loss_scale_window\": 1000,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n \"bf16\": {\n \"enabled\": \"auto\",\n \"loss_scale\": 0,\n \"initial_scale_power\": 10,\n \"loss_scale_window\": 1000,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n \"optimizer\": {\n \"type\": \"AdamW\",\n \"params\": {\n \"lr\": \"auto\",\n \"betas\": \"auto\",\n \"eps\": \"auto\",\n \"weight_decay\": \"auto\",\n \"torch_adam\": true\n }\n },\n\n \"scheduler\": {\n \"type\": \"WarmupDecayLR\",\n \"params\": {\n \"warmup_min_lr\": \"auto\",\n \"warmup_max_lr\": \"auto\",\n \"warmup_num_steps\": \"auto\",\n \"total_num_steps\": \"auto\"\n }\n },\n\n \"gradient_accumulation_steps\": \"auto\",\n \"gradient_clipping\": \"auto\",\n \"steps_per_print\": 1000,\n \"train_batch_size\": \"auto\",\n \"train_micro_batch_size_per_gpu\": \"auto\",\n \"wall_clock_breakdown\": false\n}" }, { "path": "examples/finetune/embedder/README.md", "content": "# Finetune\n\nIn this example, we show how to finetune the embedder with your data.\n\n- [1. Installation](#1-Installation)\n- [2. Data format](#2-Data-format)\n - [Hard Negatives](#Hard-Negatives)\n - [Teacher Scores](#Teacher-Scores)\n- [3. Train](#3-Train)\n - [(1) standard model](#1-standard-model)\n - [(2) bge-m3](#2-bge-m3)\n - [(3) bge-multilingual-gemma2](#3-bge-multilingual-gemma2)\n - [(4) bge-en-icl](#4-bge-en-icl)\n\n## 1. Installation\n\n- **with pip**\n\n```shell\npip install -U FlagEmbedding[finetune]\n```\n\n- **from source**\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\npip install .[finetune]\n```\n\nFor development, install as editable:\n\n```shell\npip install -e .[finetune]\n```\n\n## 2. Data format\n\nTrain data should be a json file, where each line is a dict like this:\n\n```shell\n{\"query\": str, \"pos\": List[str], \"neg\":List[str], \"pos_scores\": List[int], \"neg_scores\": List[int], \"prompt\": str, \"type\": str}\n```\n\n`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. `pos_scores` is a list of scores corresponding to the `query` and `pos`, `neg_scores` is a list of scores corresponding to the `query` and `neg`, if you don't use knowledge distillation, it can be ignored. `prompt` is the prompt used for the query, it will cover `query_instruction_for_retrieval`. `type` is used for `bge-en-icl`, it includes `normal`, `symmetric_class`, `symmetric_clustering`, .etc. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.\n\nSee [example_data](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder/example_data) for more detailed files.\n\n### Hard Negatives\n\nHard negatives is a widely used method to improve the quality of sentence embedding. You can mine hard negatives following this command:\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding/scripts\n```\n\n```shell\npython hn_mine.py \\\n--input_file toy_finetune_data.jsonl \\\n--output_file toy_finetune_data_minedHN.jsonl \\\n--range_for_sampling 2-200 \\\n--negative_number 15 \\\n--use_gpu_for_searching \\\n--embedder_name_or_path BAAI/bge-base-en-v1.5\n```\n\n- **`input_file`**: json data for finetuning. This script will retrieve top-k documents for each query, and random sample negatives from the top-k documents (not including the positive documents).\n- **`output_file`**: path to save JSON data with mined hard negatives for finetuning\n- **`negative_number`**: the number of sampled negatives\n- **`range_for_sampling`**: where to sample negative. For example, `2-100` means sampling `negative_number` negatives from top2-top200 documents. **You can set larger value to reduce the difficulty of negatives (e.g., set it `60-300` to sample negatives from top60-300 passages)**\n- **`candidate_pool`**: The pool to retrieval. The default value is None, and this script will retrieve from the combination of all `neg` in `input_file`. If provided, it should be a jsonl file, each line is a dict with a key `text`. If input a candidate_pool, this script will retrieve negatives from this file.\n- **`use_gpu_for_searching`**: whether to use faiss-gpu to retrieve negatives.\n- **`search_batch_size`**: batch size for searching. Default is 64.\n- **`embedder_name_or_path`**: The name or path to the embedder.\n- **`embedder_model_class`**: Class of the model used for embedding (current options include 'encoder-only-base', 'encoder-only-m3', 'decoder-only-base', 'decoder-only-icl'.). Default is None. For the custom model, you should set this argument.\n- **`normalize_embeddings`**: Set to `True` to normalize embeddings.\n- **`pooling_method`**: The pooling method for the embedder.\n- **`use_fp16`**: Use FP16 precision for inference.\n- **`devices`**: List of devices used for inference.\n- **`query_instruction_for_retrieval`**, **`query_instruction_format_for_retrieval`**: Instructions and format for query during retrieval.\n- **`examples_for_task`**, **`examples_instruction_format`**: Example tasks and their instructions format. This is only used when `embedder_model_class` is set to `decoder-only-icl`.\n- **`trust_remote_code`**: Set to `True` to trust remote code execution.\n- **`cache_dir`**: Cache directory for models.\n- **`embedder_batch_size`**: Batch sizes for embedding and reranking.\n- **`embedder_query_max_length`**, **`embedder_passage_max_length`**: Maximum length for embedding queries and passages.\n\n### Teacher Scores\n\nTeacher scores can be used for model distillation. You can obtain the scores using the following command:\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding/scripts\n```\n\n```shell\npython add_reranker_score.py \\\n--input_file toy_finetune_data_minedHN.jsonl \\\n--output_file toy_finetune_data_score.jsonl \\\n--reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n--devices cuda:0 cuda:1 \\\n--cache_dir ./cache/model \\\n--reranker_query_max_length 512 \\\n--reranker_max_length 1024\n```\n\n- **`input_file`**: path to save JSON data with mined hard negatives for finetuning\n- **`output_file`**: path to save JSON data with scores for finetuning\n- **`use_fp16`**: Whether to use fp16 for inference. Default: True\n- **`devices`**: Devices to use for inference. Default: None, multiple values allowed\n- **`trust_remote_code`**: Trust remote code. Default: False\n- **`reranker_name_or_path`**: The reranker name or path. Default: None\n- **`reranker_model_class`**: The reranker model class. Available classes: ['auto', 'encoder-only-base', 'decoder-only-base', 'decoder-only-layerwise', 'decoder-only-lightweight']. Default: auto\n- **`reranker_peft_path`**: The reranker peft path. Default: None\n- **`use_bf16`**: Whether to use bf16 for inference. Default: False\n- **`query_instruction_for_rerank`**: Instruction for query. Default: None\n- **`query_instruction_format_for_rerank`**: Format for query instruction. Default: {{}{}}\n- **`passage_instruction_for_rerank`**: Instruction for passage. Default: None\n- **`passage_instruction_format_for_rerank`**: Format for passage instruction. Default: {{}{}}\n- **`cache_dir`**: Cache directory for models. Default: None\n- **`reranker_batch_size`**: Batch size for inference. Default: 3000\n- **`reranker_query_max_length`**: Max length for reranking queries. Default: None\n- **`reranker_max_length`**: Max length for reranking. Default: 512\n- **`normalize`**: Whether to normalize the reranking scores. Default: False\n- **`prompt`**: The prompt for the reranker. Default: None\n- **`cutoff_layers`**: The output layers of layerwise/lightweight reranker. Default: None\n- **`compress_ratio`**: The compress ratio of lightweight reranker. Default: 1\n- **`compress_layers`**: The compress layers of lightweight reranker. Default: None, multiple values allowed\n\n## 3. Train\n\nDetailed examples of various fine-tuning can be found in the bash files located in the corresponding folders. Here, we simply provide the training methods for the `standard model`, `bge-m3`, `bge-multilingual-gemma2` and `bge-en-icl`.\n\nHere are some import arguments:\n\n- **`model_name_or_path`**: The model checkpoint for initialization.\n- **`config_name`**: Pretrained config name or path if not the same as model_name.\n- **`tokenizer_name`**: Pretrained tokenizer name or path if not the same as model_name.\n- **`cache_dir`**: Where do you want to store the pre-trained models downloaded from s3.\n- **`trust_remote_code`**: Trust remote code\n- **`token`**: The token to use when accessing the model.\n- **`train_data`**: One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data. Argument type: multiple.\n- **`cache_path`**: Where do you want to store the cached data.\n- **`train_group_size`**: (No metadata provided)\n- **`query_max_len`**: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\n- **`passage_max_len`**: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated.\n- **`pad_to_multiple_of`**: If set will pad the sequence to be a multiple of the provided value.\n- **`max_example_num_per_dataset`**: The max number of examples for each dataset.\n- **`query_instruction_for_retrieval`**: Instruction for query.\n- **`query_instruction_format`**: Format for query instruction.\n- **`knowledge_distillation`**: Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data.\n- **`passage_instruction_for_retrieval`**: Instruction for passage.\n- **`passage_instruction_format`**: Format for passage instruction.\n- **`shuffle_ratio`**: The ratio of shuffling the text.\n- **`same_dataset_within_batch`**: All samples in the same batch comes from the same dataset.\n- **`small_threshold`**: The threshold of small dataset. All small dataset in the same directory will be merged into one dataset.\n- **`drop_threshold`**: The threshold for dropping merged small dataset. If the number of examples in the merged small dataset is less than this threshold, it will be dropped.\n- **`negatives_cross_device`**: Share negatives across devices.\n- **`temperature`**: Temperature used for similarity score.\n- **`fix_position_embedding`**: Freeze the parameters of position embeddings.\n- **`sentence_pooling_method`**: The pooling method. Available options: cls, mean, last_token. Default: cls.\n- **`normalize_embeddings`**: Whether to normalize the embeddings.\n- **`sub_batch_size`**: Sub batch size for training.\n- **`kd_loss_type`**: The loss type for knowledge distillation. Available options: kl_div, m3_kd_loss. Default: kl_div.\n\n### (1) standard model\n\n```shell\ntorchrun --nproc_per_node 2 \\\n\t-m FlagEmbedding.finetune.embedder.encoder_only.base \\\n\t--model_name_or_path BAAI/bge-large-en-v1.5 \\\n --cache_dir ./cache/model \\\n --train_data ./example_data/retrieval \\\n \t\t\t ./example_data/sts/sts.jsonl \\\n \t\t\t ./example_data/classification-no_in_batch_neg \\\n \t\t\t ./example_data/clustering-no_in_batch_neg \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Represent this sentence for searching relevant passages: ' \\\n --query_instruction_format '{}{}' \\\n --knowledge_distillation False \\\n\t--output_dir ./test_encoder_only_base_bge-large-en-v1.5 \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs 2 \\\n --per_device_train_batch_size 2 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type kl_div\n```\n\n### (2) bge-m3\n\n```shell\ntorchrun --nproc_per_node 2 \\\n\t-m FlagEmbedding.finetune.embedder.encoder_only.m3 \\\n\t--model_name_or_path BAAI/bge-m3 \\\n --cache_dir ./cache/model \\\n --train_data ./example_data/retrieval \\\n \t\t\t ./example_data/sts/sts.jsonl \\\n \t\t\t ./example_data/classification-no_in_batch_neg \\\n \t\t\t ./example_data/clustering-no_in_batch_neg \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n --output_dir ./test_encoder_only_m3_bge-m3_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs 2 \\\n --per_device_train_batch_size 2 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type m3_kd_loss \\\n --unified_finetuning True \\\n --use_self_distill True \\\n --fix_encoder False \\\n --self_distill_start_step 0\n```\n\nHere are some new arguments:\n\n- **`colbert_dim`**: Dim of colbert linear\n- **`unified_finetuning`**: Use unify fine-tuning\n- **`use_self_distill`**: Use self-distill when using unify fine-tuning\n- **`fix_encoder`**: Freeze the parameters of encoder\n- **`self_distill_start_step`**: Num of step when using self-distill\n\n### (3) bge-multilingual-gemma2\n\n```shell\ntorchrun --nproc_per_node 2 \\\n -m FlagEmbedding.finetune.embedder.decoder_only.base \\\n\t--model_name_or_path BAAI/bge-multilingual-gemma2 \\\n --cache_dir ./cache/model \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --target_modules q_proj k_proj v_proj o_proj gate_proj down_proj up_proj \\\n --additional_special_tokens '' '' \\\n --save_merged_lora_model True \\\n --train_data ./example_data/retrieval \\\n \t\t\t ./example_data/sts/sts.jsonl \\\n \t\t\t ./example_data/classification-no_in_batch_neg \\\n \t\t\t ./example_data/clustering-no_in_batch_neg \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Given a query, retrieve passages that are relevant to the query.' \\\n --query_instruction_format '{}\\n{}' \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n\t--output_dir ./test_decoder_only_base_bge-multilingual-gemma2_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-4 \\\n --fp16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../ds_stage1.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method last_token \\\n --normalize_embeddings True \\\n --kd_loss_type m3_kd_loss\n```\n\nHere are some new arguments:\n\n- **`peft_model_path`**: The peft model checkpoint for initialization.\n- **`use_lora`**: If passed, will use LORA (low-rank parameter-efficient training) to train the model.\n- **`lora_rank`**: The rank of lora.\n- **`lora_alpha`**: The alpha parameter of lora.\n- **`lora_dropout`**: The dropout rate of lora modules.\n- **`target_modules`**: The target modules to apply LORA.\n- **`use_flash_attn`**: If passed, will use flash attention to train the model.\n- **`use_slow_tokenizer`**: If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\n- **`additional_special_tokens`**: Additional special tokens.\n- **`save_merged_lora_model`**: If passed, will merge the lora modules and save the entire model.\n\n### (4) bge-en-icl\n\n```shell\ntorchrun --nproc_per_node 2 \\\n -m FlagEmbedding.finetune.embedder.decoder_only.icl \\\n\t--model_name_or_path BAAI/bge-en-icl \\\n --cache_dir ./cache/model \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --target_modules q_proj k_proj v_proj o_proj gate_proj down_proj up_proj \\\n --additional_special_tokens '' '' '' \\\n --save_merged_lora_model True \\\n --train_data ./example_data/retrieval \\\n \t\t\t ./example_data/sts/sts.jsonl \\\n \t\t\t ./example_data/classification-no_in_batch_neg \\\n \t\t\t ./example_data/clustering-no_in_batch_neg \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 2048 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Given a query, retrieve passages that are relevant to the query.' \\\n --query_instruction_format '{}\\n{}' \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n --example_query_max_len 256 \\\n --example_passage_max_len 256 \\\n --retrieval_use_examples True \\\n --icl_suffix_str '\\n' \\\n --output_dir ./test_decoder_only_base_bge-en-icl_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-4 \\\n --fp16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../ds_stage1.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method last_token \\\n --normalize_embeddings True \\\n --kd_loss_type kl_div\n```\n\nHere are some new arguments:\n\n- **`peft_model_path`**: The peft model checkpoint for initialization.\n- **`use_lora`**: If passed, will use LORA (low-rank parameter-efficient training) to train the model.\n- **`lora_rank`**: The rank of LORA.\n- **`lora_alpha`**: The alpha parameter of LORA.\n- **`lora_dropout`**: The dropout rate of LORA modules.\n- **`target_modules`**: The target modules to apply LORA.\n- **`use_flash_attn`**: If passed, will use flash attention to train the model.\n- **`use_slow_tokenizer`**: If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\n- **`from_peft`** (no metadata provided)\n- **`modules_to_save`** (no metadata provided)\n- **`raw_peft`** (no metadata provided)\n- **`additional_special_tokens`**: additional special tokens\n- **`save_merged_lora_model`**: If passed, will merge the LORA modules and save the entire model.\n- **`example_query_max_len`**: The max length of example query.\n- **`example_passage_max_len`**: The max length of example passage.\n- **`retrieval_use_examples`**: If passed, will use examples for retrieval.\n- **`icl_suffix_str`**: The suffix string for ICL dataset.\n\n" }, { "path": "examples/finetune/embedder/decoder_only/base.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-multilingual-gemma2 \\\n --cache_dir $HF_HUB_CACHE \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --target_modules q_proj k_proj v_proj o_proj gate_proj down_proj up_proj \\\n --additional_special_tokens '' '' \\\n --save_merged_lora_model True \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Given a query, retrieve passages that are relevant to the query.' \\\n --query_instruction_format '{}\\n{}' \\\n --knowledge_distillation False \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_decoder_only_base_bge-multilingual-gemma2 \\\n --overwrite_output_dir \\\n --learning_rate 1e-4 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage1.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method last_token \\\n --normalize_embeddings True \\\n --kd_loss_type m3_kd_loss \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.decoder_only.base \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/decoder_only/base_same_dataset.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-multilingual-gemma2 \\\n --cache_dir $HF_HUB_CACHE \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --target_modules q_proj k_proj v_proj o_proj gate_proj down_proj up_proj \\\n --additional_special_tokens '' '' \\\n --save_merged_lora_model True \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Given a query, retrieve passages that are relevant to the query.' \\\n --query_instruction_format '{}\\n{}' \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_decoder_only_base_bge-multilingual-gemma2_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-4 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage1.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method last_token \\\n --normalize_embeddings True \\\n --kd_loss_type m3_kd_loss \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.decoder_only.base \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/decoder_only/icl_same_dataset.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-en-icl \\\n --cache_dir $HF_HUB_CACHE \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --target_modules q_proj k_proj v_proj o_proj gate_proj down_proj up_proj \\\n --additional_special_tokens '' '' '' \\\n --save_merged_lora_model True \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 2048 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Given a query, retrieve passages that are relevant to the query.' \\\n --query_instruction_format '{}\\n{}' \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n --example_query_max_len 256 \\\n --example_passage_max_len 256 \\\n --retrieval_use_examples True \\\n --icl_suffix_str '\\n' \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_decoder_only_base_bge-en-icl_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-4 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage1.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method last_token \\\n --normalize_embeddings True \\\n --kd_loss_type kl_div \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.decoder_only.icl \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/encoder_only/base.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-large-en-v1.5 \\\n --cache_dir $HF_HUB_CACHE \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Represent this sentence for searching relevant passages: ' \\\n --query_instruction_format '{}{}' \\\n --knowledge_distillation False \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_encoder_only_base_bge-large-en-v1.5 \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type kl_div \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.encoder_only.base \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/encoder_only/base_same_dataset.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-large-en-v1.5 \\\n --cache_dir $HF_HUB_CACHE \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --query_instruction_for_retrieval 'Represent this sentence for searching relevant passages: ' \\\n --query_instruction_format '{}{}' \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_encoder_only_base_bge-large-en-v1.5_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type kl_div \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.encoder_only.base \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/encoder_only/m3.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-m3 \\\n --cache_dir $HF_HUB_CACHE \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation False \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_encoder_only_m3_bge-m3 \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type m3_kd_loss \\\n --unified_finetuning True \\\n --use_self_distill True \\\n --fix_encoder False \\\n --self_distill_start_step 0 \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.encoder_only.m3 \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/encoder_only/m3_same_dataset.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/retrieval \\\n ../example_data/sts/sts.jsonl \\\n ../example_data/classification-no_in_batch_neg \\\n ../example_data/clustering-no_in_batch_neg \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-m3 \\\n --cache_dir $HF_HUB_CACHE \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation True \\\n --same_dataset_within_batch True \\\n --small_threshold 0 \\\n --drop_threshold 0 \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_encoder_only_m3_bge-m3_sd \\\n --overwrite_output_dir \\\n --learning_rate 1e-5 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n --negatives_cross_device \\\n --temperature 0.02 \\\n --sentence_pooling_method cls \\\n --normalize_embeddings True \\\n --kd_loss_type m3_kd_loss \\\n --unified_finetuning True \\\n --use_self_distill True \\\n --fix_encoder False \\\n --self_distill_start_step 0 \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.embedder.encoder_only.m3 \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n" }, { "path": "examples/finetune/embedder/example_data/classification-no_in_batch_neg/AmazonClassification.jsonl", "content": "{\"query\": \"Okay\\n\\nGood quality but can\\u2019t really breathe in it.\", \"pos\": [\"This review was found to be below average, thus earning a rating of two stars.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was considered to be very poor, resulting in the lowest rating of one star.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Two Stars\\n\\nNot correct size as advertised\", \"pos\": [\"This review was considered to be very poor, resulting in the lowest rating of one star.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Well...\\n\\nI'd like to give this a better rating, but it does not shut off when the timer is up!! It will continue to play sounds until you manually shut the program down, or your device dies (if its not charging). Very annoying. I have this on my Kindle and my Phone, same issue on both devices.\", \"pos\": [\"This review was found to be below average, thus earning a rating of two stars.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was considered to be very poor, resulting in the lowest rating of one star.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"See through and horrible material.\\n\\nDidn't like it. Way too thin, literally see through, short on ankles.\", \"pos\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\"], \"neg\": [\"This review was excellent and received a high rating of four stars.\", \"This review was considered to be very poor, resulting in the lowest rating of one star.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Not the most comfortable.\\n\\nWas missing parts which is a little dangerous when riding the bicycle. Not the most comfortable.\", \"pos\": [\"This review was considered to be very poor, resulting in the lowest rating of one star.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Did not fit properly\\n\\nFilter was a very tight fit. Have difficulty removing it. Air filter cover did not fit properly and was difficult to close.\", \"pos\": [\"This review was considered to be very poor, resulting in the lowest rating of one star.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Somewhat useful?\\n\\nIt was able to change my home's tap water taste, but not my office's. As a standard carbon filter, this is not that impressive. Not to mention the whole wrap-around ad the manufacturer put on the bottle - it took me hours to get all the glue off, not a fun project at all\", \"pos\": [\"This review was found to be below average, thus earning a rating of two stars.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was considered to be very poor, resulting in the lowest rating of one star.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Functional\\n\\nNot very sturdy, works fine with slippers, sneakers though.\", \"pos\": [\"This review was considered to be very poor, resulting in the lowest rating of one star.\"], \"neg\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\", \"This review was excellent and received a high rating of four stars.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Not very attractive\\n\\nVery cheap looking. I could probably do better at the dollar store.\", \"pos\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\"], \"neg\": [\"This review was excellent and received a high rating of four stars.\", \"This review was considered to be very poor, resulting in the lowest rating of one star.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Terrible product. The color stains your hands\\n\\nTerrible product. The color stains your hands, fingernails and anything your hair comes in contact with. I unfortunately bought 3 for a discount and then found it\\u2019s not eligible for return. I\\u2019m stuck with 3 cans of this garbage.\", \"pos\": [\"This review was deemed to be not helpful at all, hence a rating of zero stars.\"], \"neg\": [\"This review was excellent and received a high rating of four stars.\", \"This review was considered to be very poor, resulting in the lowest rating of one star.\", \"This review was found to be below average, thus earning a rating of two stars.\", \"This review deserves the middle-of-the-road rating of three stars.\"], \"prompt\": \"Classify the given Amazon review into its appropriate rating category.\", \"type\": \"symmetric_class\"}\n" }, { "path": "examples/finetune/embedder/example_data/classification-no_in_batch_neg/Banking77Classification.jsonl", "content": "{\"query\": \"I am still waiting on my card?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"Why was my transfer declined?\", \"What is the timing for transfers?\", \"How can I exchange currency via the app?\", \"Was my card payment reverted?\", \"Is my card a Visa or Mastercard?\", \"Why is my top-up pending?\", \"What is the charge for currency exchange?\", \"Why is my cash withdrawal pending?\", \"How do I verify the source of my funds?\", \"Which countries are supported?\", \"How do I top up my account by card?\", \"What is the current exchange rate?\", \"What are the top-up limits?\", \"How do I cancel a transfer?\", \"Why was my cash withdrawal declined?\", \"How do I change my PIN?\", \"What should I do if I forgot my passcode?\", \"What should I do if my phone is lost or stolen?\", \"How do I verify my identity?\", \"Is ATM support available?\", \"How can I transfer money into my account?\", \"Why was a fee charged for my transfer?\", \"Why was my transaction charged twice?\", \"What should I do if my card is about to expire?\", \"Why was my card payment declined?\", \"What is the charge for topping up by bank transfer?\", \"How do I request a refund?\", \"How do I get a spare card?\", \"Why is my balance not updated after a bank transfer?\", \"How do I order a physical card?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"What can I do if my card still hasn't arrived after 2 weeks?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"How do I activate my card?\", \"What are the top-up limits?\", \"What should I do if my card is compromised?\", \"How do I top up my account by card?\", \"Why has my transfer not been received by the recipient?\", \"How do I get a virtual card?\", \"Why was my top-up reverted?\", \"What should I do if my phone is lost or stolen?\", \"Why is the beneficiary not allowed?\", \"Which cards and currencies are supported?\", \"Why is my top-up pending?\", \"Was my card payment reverted?\", \"Why am I unable to verify my identity?\", \"What should I do if I forgot my passcode?\", \"What should I do if my card is about to expire?\", \"How do I change my PIN?\", \"What is the charge for currency exchange?\", \"How do I get a physical card?\", \"Why is my refund not showing up?\", \"How do I terminate my account?\", \"Why was a fee charged for my transfer?\", \"Why is my cash withdrawal pending?\", \"How do I request a refund?\", \"What should I do if my card is swallowed?\", \"Is ATM support available?\", \"Why is the exchange rate wrong for my card payment?\", \"What should I do if my card is lost or stolen?\", \"How can I transfer money into my account?\", \"Why do I need to verify my identity?\", \"Is my card a Visa or Mastercard?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"I have been waiting over a week. Is the card still coming?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"Was my card payment reverted?\", \"What are the limits for disposable cards?\", \"Why is my direct debit payment not recognised?\", \"What should I do if my card is swallowed?\", \"Why is my refund not showing up?\", \"What should I do if my card is compromised?\", \"Is ATM support available?\", \"What is automatic top-up?\", \"How do I top up my account by card?\", \"What is the charge for topping up by card?\", \"How do I verify my top-up?\", \"Why was my transaction charged twice?\", \"What is the estimate for card delivery?\", \"Why is my top-up pending?\", \"Why was my card payment declined?\", \"What should I do if my card is lost or stolen?\", \"What should I do if my phone is lost or stolen?\", \"Why has my transfer not been received by the recipient?\", \"Why is there an extra charge on my statement?\", \"How do I request a refund?\", \"Is my card a Visa or Mastercard?\", \"Why is the beneficiary not allowed?\", \"How do I change my PIN?\", \"Why do I need to verify my identity?\", \"Why is my card not accepted?\", \"What is the charge for currency exchange?\", \"How can I transfer money into my account?\", \"What is the current exchange rate?\", \"What is the charge for cash withdrawal?\", \"Why did my top-up fail?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Can I track my card while it is in the process of delivery?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"How do I top up by cash or cheque?\", \"Why is my virtual card not working?\", \"What is the estimate for card delivery?\", \"Why has my transfer not been received by the recipient?\", \"How do I top up my account by card?\", \"What is the charge for topping up by card?\", \"Why was my transaction charged twice?\", \"Why is my PIN blocked?\", \"How do I verify my identity?\", \"Why was my card payment declined?\", \"What should I do if I forgot my passcode?\", \"What is the charge for topping up by bank transfer?\", \"Why is my contactless payment not working?\", \"Why is my refund not showing up?\", \"Why is my balance not updated after a bank transfer?\", \"What are the limits for disposable cards?\", \"How can I exchange currency via the app?\", \"What is the age limit for using the service?\", \"Was my card payment reverted?\", \"Is my card a Visa or Mastercard?\", \"Why did my top-up fail?\", \"Why is my top-up pending?\", \"Why was my cash withdrawal declined?\", \"How do I verify my top-up?\", \"How do I use Apple Pay or Google Pay?\", \"What is automatic top-up?\", \"Which countries are supported?\", \"How can I get a disposable virtual card?\", \"How can I transfer money into my account?\", \"How do I receive money?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"How do I know if I will get my card, or if it is lost?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"How can I transfer money into my account?\", \"What should I do if my card is compromised?\", \"How do I top up my account by card?\", \"What is the charge for topping up by bank transfer?\", \"Why is the exchange rate wrong for my cash withdrawal?\", \"Why is my contactless payment not working?\", \"How do I edit my personal details?\", \"How do I verify my identity?\", \"Why is the exchange rate wrong for my card payment?\", \"Why is my virtual card not working?\", \"Which countries are supported?\", \"Why is my transfer pending?\", \"What is the current exchange rate?\", \"How do I receive money?\", \"Why was a fee charged for my transfer?\", \"How do I terminate my account?\", \"Why is my cash withdrawal not recognised?\", \"Why was my transaction charged twice?\", \"Which fiat currencies are supported?\", \"How do I get a physical card?\", \"Why is my top-up pending?\", \"Why was my top-up reverted?\", \"Why was my cash withdrawal declined?\", \"How do I change my PIN?\", \"Why was my card payment declined?\", \"Why is my card not accepted?\", \"Why was a fee charged for my card payment?\", \"What should I do if my card is swallowed?\", \"How can I get a disposable virtual card?\", \"How do I request a refund?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"When did you send me my new card?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"What should I do if my card is about to expire?\", \"Which fiat currencies are supported?\", \"What is the estimate for card delivery?\", \"What is the charge for topping up by card?\", \"What should I do if my phone is lost or stolen?\", \"Why is my direct debit payment not recognised?\", \"What should I do if my card is compromised?\", \"How do I use Apple Pay or Google Pay?\", \"How do I verify my top-up?\", \"Why was my cash withdrawal declined?\", \"Why is my refund not showing up?\", \"What is the current exchange rate?\", \"What is the charge for cash withdrawal?\", \"How can I exchange currency via the app?\", \"Why is my contactless payment not working?\", \"What should I do if I forgot my passcode?\", \"Why is my PIN blocked?\", \"How do I link my card?\", \"Why is my transfer pending?\", \"Why did my top-up fail?\", \"Is my card a Visa or Mastercard?\", \"How do I activate my card?\", \"What should I do if my card is swallowed?\", \"How do I verify my identity?\", \"Why is my card payment not recognised?\", \"How do I get a spare card?\", \"What is automatic top-up?\", \"What should I do if my card is lost or stolen?\", \"Why is my cash withdrawal not recognised?\", \"Why did my transfer fail?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Do you have info about the card on delivery?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"How do I change my PIN?\", \"How do I use Apple Pay or Google Pay?\", \"What should I do if my card is lost or stolen?\", \"Why did my top-up fail?\", \"Why did my transfer fail?\", \"Why was my cash withdrawal declined?\", \"Why is my card not working?\", \"How do I request a refund?\", \"Is ATM support available?\", \"Is my card a Visa or Mastercard?\", \"Why is my cash withdrawal not recognised?\", \"What are the top-up limits?\", \"How do I order a physical card?\", \"Why is my cash withdrawal pending?\", \"Why is my top-up pending?\", \"How do I link my card?\", \"How do I verify my top-up?\", \"Was my card payment reverted?\", \"How do I top up my account by card?\", \"How do I top up by cash or cheque?\", \"Why was a fee charged for my transfer?\", \"What should I do if my phone is lost or stolen?\", \"Why do I need to verify my identity?\", \"Which cards and currencies are supported?\", \"Why is there an extra charge on my statement?\", \"Why is my card not accepted?\", \"Which fiat currencies are supported?\", \"How do I verify the source of my funds?\", \"What should I do if my card is compromised?\", \"Why is my refund not showing up?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"What do I do if I still have not received my new card?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"Why did my top-up fail?\", \"Why was my cash withdrawal declined?\", \"Why is my top-up pending?\", \"What is automatic top-up?\", \"What should I do if my card is compromised?\", \"Why was my transaction charged twice?\", \"How do I receive money?\", \"How do I verify my identity?\", \"How do I use Apple Pay or Google Pay?\", \"Why is my balance not updated after a bank transfer?\", \"How do I order a physical card?\", \"How can I get a disposable virtual card?\", \"Why is my card not accepted?\", \"Why is my contactless payment not working?\", \"Was my card payment reverted?\", \"How do I cancel a transfer?\", \"Why is my transfer pending?\", \"How do I get a physical card?\", \"Why am I unable to verify my identity?\", \"Which countries are supported?\", \"What should I do if my card is swallowed?\", \"How do I terminate my account?\", \"Why is my card payment pending?\", \"What should I do if my phone is lost or stolen?\", \"How do I verify the source of my funds?\", \"What is the timing for transfers?\", \"How can I exchange currency via the app?\", \"What should I do if I forgot my passcode?\", \"What should I do if my card is lost or stolen?\", \"How do I top up by cash or cheque?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"Does the package with my card have tracking?\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"What is automatic top-up?\", \"Why is my card payment not recognised?\", \"How can I exchange currency via the app?\", \"What should I do if I forgot my passcode?\", \"Why is my cash withdrawal not recognised?\", \"Why has my transfer not been received by the recipient?\", \"Why is the exchange rate wrong for my cash withdrawal?\", \"Why was my transfer declined?\", \"How can I transfer money into my account?\", \"Why is my transfer pending?\", \"Which cards and currencies are supported?\", \"What is the charge for topping up by card?\", \"Why is my cash withdrawal pending?\", \"Why was my cash withdrawal declined?\", \"How do I verify my identity?\", \"Why is my card not accepted?\", \"How do I verify my top-up?\", \"Which countries are supported?\", \"How do I top up by cash or cheque?\", \"What should I do if my card is about to expire?\", \"Why do I need to verify my identity?\", \"Why is my balance not updated after a cheque or cash deposit?\", \"Which fiat currencies are supported?\", \"How do I link my card?\", \"How do I change my PIN?\", \"What is the current exchange rate?\", \"Why is my contactless payment not working?\", \"Why am I unable to verify my identity?\", \"How do I terminate my account?\", \"What is the charge for currency exchange?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n{\"query\": \"I ordered my card but it still isn't here\", \"pos\": [\"When will my card arrive?\"], \"neg\": [\"How do I change my PIN?\", \"How do I cancel a transfer?\", \"How do I receive money?\", \"What is the timing for transfers?\", \"Why is my virtual card not working?\", \"How do I get a physical card?\", \"Why is there an extra charge on my statement?\", \"Why is my transfer pending?\", \"How do I top up my account by card?\", \"What are the top-up limits?\", \"Why was my top-up reverted?\", \"What are the limits for disposable cards?\", \"Why is my card payment pending?\", \"How do I verify my identity?\", \"Why did I receive the wrong amount of cash?\", \"Is my card a Visa or Mastercard?\", \"How do I terminate my account?\", \"How do I get a virtual card?\", \"Why is my cash withdrawal pending?\", \"Why is my direct debit payment not recognised?\", \"Why was my transaction charged twice?\", \"Why did my top-up fail?\", \"Why is my card not working?\", \"Why is my cash withdrawal not recognised?\", \"How do I verify the source of my funds?\", \"How do I order a physical card?\", \"Which cards and currencies are supported?\", \"Why was a fee charged for my transfer?\", \"Why was my transfer declined?\", \"How do I activate my card?\"], \"prompt\": \"Given a online banking query, find the corresponding intents.\", \"type\": \"symmetric_class\"}\n" }, { "path": "examples/finetune/embedder/example_data/clustering-no_in_batch_neg/arXiv_title.jsonl", "content": "{\"query\": \"Rational Solutions of the Painlev\\\\'e-II Equation Revisited\", \"pos\": [\"A Generalised Sextic Freud Weight\"], \"neg\": [\"Formal groups and quantum cohomology\", \"Hypergeometric heritage of W.N. Bailey. With an appendix: Bailey's\\n letters to F. Dyson\", \"Scaling-violation phenomena and fractality in the human posture control\\n systems\", \"Glucodensities: a new representation of glucose profiles using\\n distributional data analysis\", \"Minimum Description Length codes are critical\", \"Universal Axial Algebras and a Theorem of Sakuma\", \"Prediction and description of a chiral pseudogap phase\", \"Bifurcation analysis of a normal form for excitable media: Are stable\\n dynamical alternans on a ring possible?\", \"One-dimensional foliations on topological manifolds\", \"Multiclass Diffuse Interface Models for Semi-Supervised Learning on\\n Graphs\", \"Synchronization patterns in LIF Neural Networks: Merging Nonlocal and\\n Diagonal Connectivity\", \"Longitudinal b-operators, Blups and Index theorems\", \"A two-scale model for sheared fault gouge: Competition between\\n macroscopic disorder and local viscoplasticity\", \"Online monitoring for safe pedestrian-vehicle interactions\", \"Accelerating Laue Depth Reconstruction Algorithm with CUDA\", \"An Algebraic Programming Style for Numerical Software and its\\n Optimization\", \"Compressible Baker Maps and Their Inverses. A Memoir for Francis Hayin\\n Ree [ 1936-2020 ]\", \"Multi-task Neural Networks for Personalized Pain Recognition from\\n Physiological Signals\", \"The Speed of Gravity in General Relativity and Theoretical\\n Interpretation of the Jovian Deflection Experiment\"], \"category\": \"nlin.SI math-ph math.CA math.MP\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Linear perturbations in a universe with a cosmological constant\", \"pos\": [\"On the Shape of the First Collapsed Objects\"], \"neg\": [\"The Revival of White Holes as Small Bangs\", \"Plasmon-Exciton Coupling Effect on Plasmon Damping\", \"How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal\\n Usage Logs\", \"The covariance matrix of the potts model: A random cluster analysis\", \"Configurable Per-Query Data Minimization for Privacy-Compliant Web APIs\", \"Bilinear expansions of lattices of KP $\\\\tau$-functions in BKP\\n $\\\\tau$-functions: a fermionic approach\", \"The Universal Generating Function of Analytical Poisson Structures\", \"Applications of quantum Monte Carlo methods in condensed systems\", \"Study of the Kramers-Fokker-Planck quadratic operator with a constant\\n magnetic field\", \"xTras: a field-theory inspired xAct package for Mathematica\", \"Intersection of almost complex submanifolds\", \"Annealing schedule from population dynamics\", \"An Analytic Grothendieck Riemann Roch Theorem\", \"Complexity and cohomology of cohomological Mackey functors\", \"A Semi-Analytical Method for Calculating Revisit Time for Satellite\\n Constellations with Discontinuous Coverage\", \"Spectral Theory of Unsigned and Signed Graphs. Applications to Graph\\n Clustering: a Survey\"], \"category\": \"astro-ph gr-qc\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Hadron production in pA collisions at the LHC from the Color Glass\\n Condensate\", \"pos\": [\"Searching for Lepton Flavour (Universality) Violation and Collider\\n Signals from a Singly-Charged Scalar Singlet\"], \"neg\": [\"Anomaly detection in reconstructed quantum states using a\\n machine-learning technique\", \"Evidence for Non-Random Hydrophobicity Structures in Protein Chains\", \"A Borsuk-Ulam theorem for digital images\", \"Learning the ground state of a non-stoquastic quantum Hamiltonian in a\\n rugged neural network landscape\", \"A Random Matrix Approach to Differential Privacy and Structure Preserved\\n Social Network Graph Publishing\", \"AGNet: Weighing Black Holes with Deep Learning\", \"Towards automated patient data cleaning using deep learning: A\\n feasibility study on the standardization of organ labeling\", \"Intrinsically-limited timing jitter in molybdenum silicide\\n superconducting nanowire single-photon detectors\", \"Ionization of atoms by slow heavy particles, including dark matter\", \"Maxwell's equations in media as a contact Hamiltonian vector field and\\n its information geometry -- An approach with a bundle whose fiber is a\\n contact manifold\", \"Estimating country-specific space heating threshold temperatures from\\n national consumption data\", \"Agafonov's Proof of Agafonov's Theorem: A Modern Account and New\\n Insights\", \"Monotonicity of a relative R\\\\'enyi entropy\", \"Definable (co)homology, pro-torus rigidity, and (co)homological\\n classification\", \"Dynamical and Hamiltonian formulation of General Relativity\", \"Differential Expression Analysis of Dynamical Sequencing Count Data with\\n a Gamma Markov Chain\", \"Constraints on long range force from perihelion precession of planets in\\n a gauged $L_e-L_{\\\\mu,\\\\tau}$ scenario\", \"Deep image prior for 3D magnetic particle imaging: A quantitative\\n comparison of regularization techniques on Open MPI dataset\", \"Deep Learning for the Benes Filter\", \"Assessing the Causal Impact of COVID-19 Related Policies on Outbreak\\n Dynamics: A Case Study in the US\"], \"category\": \"hep-ph hep-ex nucl-ex nucl-th\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Topology of (some) tiling spaces without finite local complexity\", \"pos\": [\"A KAM theorem for generalized Hamiltonian systems without action-angle\\n variables\"], \"neg\": [\"Yet another proof of the Morse index theorem\", \"Convergence to the boundary for random walks on discrete quantum groups\\n and monoidal categories\", \"Computation with competing patterns in Life-like automaton\", \"Josephson-junction infrared single-photon detector\", \"Spectral Cross-Cumulants for Multicolor Super-resolved SOFI Imaging\", \"Modeling a Slicer Mirror Using Zemax User-Defined Surface\", \"The Universal Aspect Ratio of Vortices in Rotating Stratifi?ed Flows:\\n Experiments and Observations\", \"The Schottky Conjecture and beyond\", \"Noisy time series generation by feed-forward networks\", \"Return migration of German-affiliated researchers: Analyzing departure\\n and return by gender, cohort, and discipline using Scopus bibliometric data\\n 1996-2020\", \"Quantum Spin probabilities at positive temperature are H\\\\\\\"older Gibbs\\n probabilities\", \"Computing zeta functions of large polynomial systems over finite fields\", \"Exact solutions of the (2+1) Dimensional Dirac equation in a constant\\n magnetic field in the presence of a minimal length\", \"SL(3|N) Wigner quantum oscillators: examples of ferromagnetic-like\\n oscillators with noncommutative, square-commutative geometry\", \"Exact Results for the Kuramoto Model with a Bimodal Frequency\\n Distribution\", \"Functional Control of Oscillator Networks\", \"Modeling the Adoption of Good Tech: Extending TAM3 to Incorporate the\\n Phenomenon of Warm-Glow\"], \"category\": \"math.DS math-ph math.MP\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Some remarks on twin groups\", \"pos\": [\"On metric relative hyperbolicity\"], \"neg\": [\"Electrostatic T-matrix for a torus on bases of toroidal and spherical\\n harmonics\", \"Classical Anthropic Everett model: indeterminacy in a preordained\\n multiverse\", \"Emergence and Synchronization in Chaotic Oscillators and in the Human\\n Cortical Network\", \"Modeling Cascading Failures in the North American Power Grid\", \"Phase transitions in anisotropic superconducting and magnetic systems\\n with vector order parameters: Three-loop renormalization-group analysis\", \"Nina Nikolaevna Uraltseva\", \"Broadband model-based inversion enables optoacoustic microscopy beyond\\n the acoustic diffraction limit\", \"Distinguishing resonance symmetries with energy-resolved photoion\\n angular distributions from ion-pair formation in O$_2$ following two-photon\\n absorption of a 9.3 eV femtosecond pulse\", \"Large-scale multilingual audio visual dubbing\", \"Liquid Cloud Storage\", \"Unsupervised seismic facies classification using deep convolutional\\n autoencoder\", \"Detecting Fast solvability of equations via small powerful Galois groups\", \"Approximating Holant problems by winding\", \"Information-theoretic thresholds from the cavity method\", \"Wound Healing Modeling Using Partial Differential Equation and Deep\\n Learning\", \"The Einstein-Jordan conundrum and its relation to ongoing foundational\\n research in local quantum physics\", \"Solution of the classical Yang--Baxter equation with an exotic symmetry,\\n and integrability of a multi-species boson tunneling model\", \"On the State of Computing in Statistics Education: Tools for Learning\\n and for Doing\", \"Hierarchical Graph Convolutional Networks for Semi-supervised Node\\n Classification\", \"Agile, Antifragile, Artificial-Intelligence-Enabled, Command and Control\", \"Universal patterns in sound amplitudes of songs and music genres\", \"Compound-tunable embedding potential method and its application to\\n fersmite crystal\", \"Asymptotic Normality of Scrambled Geometric Net Quadrature\"], \"category\": \"math.GR math.GT\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Non-adiabatic coupling and adiabatic population transfer in quantum\\n molecular systems\", \"pos\": [\"Total angular momentum representation for state-to-state quantum\\n scattering of cold molecules in a magnetic field\"], \"neg\": [\"Symplectic fermions and a quasi-Hopf algebra structure on $\\\\bar{U}_i\\n sl(2)$\", \"Loewner's Differential Equation and Spidernets\", \"Cosmology with a decaying vacuum\", \"Aggregation of Sub-mm Particles in Strong Electric Fields under\\n Microgravity Conditions\", \"Comparative analysis of the structures and outcomes of geophysical flow\\n models and modeling assumptions using uncertainty quantification\", \"DOLPHIn - Dictionary Learning for Phase Retrieval\", \"Applications of Finite Fields to Dynamical Systems and Reverse\\n Engineering Problems\", \"Analytic self-similar solutions of the Kardar-Parisi-Zhang interface\\n growing equation with various noise term\", \"Proximity-Aware Balanced Allocations in Cache Networks\", \"Local Moduli of Semisimple Frobenius Coalescent Structures\", \"Cavity ring-down spectroscopy of CO$_2$ near $\\\\lambda$ = 2.06 $\\\\mu$m:\\n Accurate transition intensities for the Orbiting Carbon Observatory-2 (OCO-2)\\n \\\"strong band\\\"\", \"A Nonlinear Evolution Equation in an Ordered Banach Space, Reconsidered\", \"Intrinsic scales for high-dimensional LEVY-driven models with\\n non-Markovian synchronizing updates\", \"SpaceHub: A high-performance gravity integration toolkit for few-body\\n problems in astrophysics\", \"How to choose which explanation to use with students? Discussing the\\n tensiometer with beginning teachers\", \"Graph Product Multilayer Networks: Spectral Properties and Applications\", \"Optimization with learning-informed differential equation constraints\\n and its applications\", \"Total nonnegativity of GCD matrices and kernels\", \"SN1987A: Revisiting the Data and the Correlation between Neutrino and\\n Gravitational Detectors\", \"Hecke operators and analytic Langlands correspondence for curves over\\n local fields\", \"Green Application Placement in the Cloud-IoT Continuum\", \"Structured Filtering\"], \"category\": \"physics.chem-ph physics.atom-ph\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Emergence of Haldane pseudo-potentials in systems with short-range\\n interactions\", \"pos\": [\"A Hamiltonian Approach for Obtaining Irreducible Projective\\n Representations and the $k\\\\cdot p$ Perturbation for Anti-unitary Symmetry\\n Groups\"], \"neg\": [\"How Likely Are Large Elections Tied?\", \"Collisional cooling of light ions by co-trapped heavy atoms\", \"jVMC: Versatile and performant variational Monte Carlo leveraging\\n automated differentiation and GPU acceleration\", \"Contrast Enhancement of Binary Star System Using an Optical Vortex\\n Coronagraph\", \"Cover Story Management\", \"Primitive point packing\", \"Matrix Tile Analysis\", \"Berry Phase in Neutrino Oscillations\", \"Tunable quantum logic gate on photonic qubits with a ladder emitter\", \"Inherent Stochastic Linearization of Random Laser Modes\", \"Exact scaling in the expansion-modification system\", \"Poincare isomorphism in K-theory on manifolds with edges\", \"Improved test of Lorentz Invariance in Electrodynamics using Rotating\\n Cryogenic Sapphire Oscillators\", \"Large Batch Simulation for Deep Reinforcement Learning\", \"Solving Polynomial Systems by Penetrating Gradient Algorithm Applying\\n Deepest Descent Strategy\", \"MARS: Middleware for Adaptive Reflective Computer Systems\", \"Matching with Text Data: An Experimental Evaluation of Methods for\\n Matching Documents and of Measuring Match Quality\", \"Phase retrieval for wide-band signals\", \"Sparsity Preserving Algorithms for Octagons\", \"On unique continuation for solutions of the Schr{\\\\\\\"o}dinger equation on\\n trees\", \"Robust Binary Neural Network Operation from 233 K to 398 K via Gate\\n Stack and Bias Optimization of Ferroelectric FinFET Synapses\"], \"category\": \"math-ph cond-mat.str-el math.MP\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Noncommutative generalizations of theorems of Cohen and Kaplansky\", \"pos\": [\"Prime Principal Right Ideal Rings\"], \"neg\": [\"A small-world of weak ties provides optimal global integration of\\n self-similar modules in functional brain networks\", \"Solving the radial Dirac equations: a numerical odyssey\", \"Social cohesion V.S. task cohesion: An evolutionary game theory study\", \"Learning to Locomote with Deep Neural-Network and CPG-based Control in a\\n Soft Snake Robot\", \"Inverse Design and Experimental Verification of a Bianisotropic\\n Metasurface Using Optimization and Machine Learning\", \"Self-Supervised Graph Representation Learning via Topology\\n Transformations\", \"Fast and Scalable Image Search For Histology\", \"Rips complexes as nerves and a Functorial Dowker-Nerve Diagram\", \"Application of gradient descent algorithms based on geodesic distances\", \"How Developers Iterate on Machine Learning Workflows -- A Survey of the\\n Applied Machine Learning Literature\", \"Social Information Processing in Social News Aggregation\", \"Algebraic structures connected with pairs of compatible associative\\n algebras\", \"Solving 1ODEs with functions\", \"The dynamics of the HIV infection: a time-delay differential equation\\n approach\", \"Besov spaces in multifractal environment and the Frisch-Parisi\\n conjecture\", \"Logic Learning in Hopfield Networks\", \"Exact model comparisons in the plausibility framework\", \"A Multiscale Kinetic-Fluid Solver with Dynamic Localization of Kinetic\\n Effects\", \"Quantum dynamics of the avian compass\"], \"category\": \"math.RA math.AC\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Interplay between Kondo physics and spin-orbit coupling in carbon\\n nanotube quantum dots\", \"pos\": [\"Dirac's method for constraints - an application to quantum wires,the 0.7\\n conductance anomaly\"], \"neg\": [\"\\\"Building\\\" exact confidence nets\", \"HMC, an Algorithms in Data Mining, the Functional Analysis approach\", \"Ansatz for the Jahn-Teller triplet instability\", \"Distributed Submodular Maximization\", \"PhasePack User Guide\", \"Position sensitive resonant Schottky cavities for heavy ion storage\\n rings\", \"Higher-order effects in the dynamics of hierarchical triple systems.\\n Quadrupole-squared terms\", \"Stochastic Minority on Graphs\", \"Active learning using weakly supervised signals for quality inspection\", \"On zeros of self-reciprocal polynomials\", \"Infinite Dimensional Multipliers and Pontryagin Principles for\\n Discrete-Time Problems\", \"Is the atomic metal vapor a dielectric state?\", \"Control of chaotic systems by Deep Reinforcement Learning\", \"The symmetry structure of the heavenly equation\", \"Tensor computations in computer algebra systems\", \"Asynchronous Decentralized Federated Learning for Collaborative Fault\\n Diagnosis of PV Stations\", \"On the Critical Coupling of the Finite Kuramoto Model on Dense Networks\", \"PageRank for evolving link structures\", \"Conley-Morse-Forman theory for generalized combinatorial multivector\\n fields on finite topological spaces\"], \"category\": \"cond-mat.str-el cond-mat.mes-hall\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Quantum Inverse Scattering Method and (Super)Conformal Field Theory\", \"pos\": [\"Rigidity and defect actions in Landau-Ginzburg models\"], \"neg\": [\"Generalized elimination of the global translation from explicitly\\n correlated Gaussian functions\", \"Sparse Recovery With Non-Linear Fourier Features\", \"Pricing in Social Networks with Negative Externalities\", \"Inferring the size of the causal universe: features and fusion of causal\\n attribution networks\", \"Tracking Attosecond Electronic Coherences Using Phase-Manipulated\\n Extreme Ultraviolet Pulses\", \"Building fast Bayesian computing machines out of intentionally\\n stochastic, digital parts\", \"The Gerrymandering Jumble: Map Projections Permute Districts'\\n Compactness Scores\", \"Embedding Symbolic Knowledge into Deep Networks\", \"Solar and planetary oscillation control on climate change: hind-cast,\\n forecast and a comparison with the CMIP5 GCMs\", \"Lagrangian shadows of ample algebraic divisors\", \"Surgery on Lagrangian and Legendrian Singularities\", \"Distributed Processing for Encoding and Decoding of Binary LDPC codes\\n using MPI\", \"Satisfiability Modulo ODEs\", \"Categorial Minimalist Grammar\", \"Three-Dimensional Lattice Boltzmann Model for High-Speed Compressible\\n Flows\", \"QNLP in Practice: Running Compositional Models of Meaning on a Quantum\\n Computer\", \"The word problem for free adequate semigroups\", \"Bose-Einstein condensation of magnons in spin pumping systems\", \"Regularity Normalization: Neuroscience-Inspired Unsupervised Attention\\n across Neural Network Layers\", \"Gluon Transport Equations with Condensate in the Small Angle\\n Approximation\", \"The evolution of Jordan curves on $\\\\mathbb{S}^2$ by curve shortening\\n flow\"], \"category\": \"hep-th math-ph math.MP math.QA\", \"prompt\": \"Identify the main and secondary category of Arxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n" }, { "path": "examples/finetune/embedder/example_data/clustering-no_in_batch_neg/bioRXiv_title.jsonl", "content": "{\"query\": \"Information processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.\", \"pos\": [\"Here's the twist: How the brain updates the representations of naturalistic events as our understanding of the past changes\"], \"neg\": [\"A network approach to genetic circuit designs\", \"Citation needed? Wikipedia and the COVID-19 pandemic\", \"Fishing for biodiversity by balanced harvesting\", \"XRN2-mediated regulation of let-7 microRNAs is of critical pathophysiological significance in Humans\", \"Active enhancers strengthen insulation by RNA-mediated CTCF binding at TAD boundaries\", \"Tardigrade CAHS Proteins Act as Molecular Swiss Army Knives to Mediate Desiccation Tolerance Through Multiple Mechanisms\", \"Protein-protein interactions on membrane surfaces analysed using pull-downs with supported bilayers on silica beads\", \"RapidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset\", \"Antennapedia and optix regulate metallic silver wing scale development and cell shape in Bicyclus anynana butterflies\", \"Conformist social learning leads to self-organised prevention against adverse bias in risky decision making\", \"Microorganisms Characterization in Anaerobic Solventogenic Processing of Volatile Fatty Acids\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Maternal Western-Style Diet Impairs Bone Marrow Development and Drives a Hyperinflammatory Phenotype in Hematopoietic Stem and Progenitor Cells in Fetal Rhesus Macaques\", \"A novel two-generation approach to the population dynamics of gregarious parasitoids\", \"Plant inositol-phosphate-glycans and a fucosylated xyloglucan oligosaccharide are accumulated upon Arabidopsis thaliana/ Botrytis cinerea infection\", \"Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic andtargetable tissue states\", \"Localization of signaling receptors maximizes cellular informationacquisition in spatially-structured natural environments\", \"Evolution of phenotypic variance during adaptation to high temperature in Drosophila\", \"Virus-host interactions and genetic diversity of Antarctic sea ice bacteriophages\", \"INPP4B drives lysosome biogenesis to restrict leukemic stem cell differentiation and promote leukemogenesis\", \"Structural effects driven by rare point mutations in amylin hormone, the type II diabetes-associated peptide\", \"Adipose triglyceride lipase promotes prostaglandin-dependent actin remodeling by regulating substrate release from lipid droplets\", \"Leaf Aqueous Extract of Manilkara hexandra influenced Glucose Metabolism in fish: An observation with Labeo rohita Fingerlings as model organism\", \"Neuronal Activation of the Gastrointestinal Tract Shapes the Gut Environment in Mice\"], \"category\": \"neuroscience\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Structural basis for the inhibition of the Bacillus subtilis c-di-AMP cyclase CdaA by the phosphoglucomutase GlmM\", \"pos\": [\"Morphogenesis and cell ordering in confined bacterial biofilms\"], \"neg\": [\"Estrogen-related receptor alpha and Rplp1-dependent translation coordinately regulate starvation response and decrease NASH progression\", \"Bayesian Additive Regression Trees for Genotype by Environment Interaction Models\", \"Identifying critical transcriptional targets of the MYC oncogene using a novel competitive precision genome editing (CGE) assay\", \"Ecological correlates of gene family size in a pine-feeding sawfly genome and across Hymenoptera\", \"Structure-Functional-Selectivity Relationship Studies on A-86929 Analogs and Small biaryl Fragments toward Discovery of biased D1 agonists\", \"Common variants in breast cancer risk loci predispose to distinct tumor subtypes\", \"mTORC2 contributes to murine systemic autoimmunity\", \"EEG and behavioral correlates of attentional processing while walking and navigating naturalistic environments\", \"Frequency modulated timer regulates mammalian hibernation\", \"Small extracellular vesicles from young mice prevent frailty, improve healthspan and decrease epigenetic age in old mice.\", \"A chemical screen based on an interruption of zebrafish gastrulation identifies the HTR2C inhibitor Pizotifen as a suppressor of EMT-mediated metastasis\", \"Cytokinin oxidase/dehydrogenase family genes exhibit functional divergence and overlap in rice growth and development\", \"The extracellular matrix controls stem cell specification and crypt morphology in the developing and adult gut\", \"Evaluation of US state pollinator plans using 3 evidence-based policymaking frameworks\", \"Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments and Multiscale Models\", \"Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning\", \"A Novel Paradigm for Deep Reinforcement Learning of Biomimetic Systems\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"TeamTree analysis: a new approach to evaluate scientific production\", \"Neuroinvasion and neurotropism by SARS-CoV-2 variants in the K18-hACE2 mouse\", \"Single molecule tracking the uncoupling of assembly and membrane insertion in Perfringolysin O\", \"Bulk-assembly of monodisperse coacervates and giant unilamellar vesicles with programmable hierarchical complexity\", \"Effects of a 33-ion sequential beam galactic cosmic ray analog on male mouse behavior and evaluation of CDDO-EA as a radiation countermeasure\", \"Cell Intrinsic Control of Death and Proliferation of Lymphocytes in Immune Response and Tumor Cells by pH\"], \"category\": \"microbiology\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Thalamic control of sensory enhancement and sleep spindle properties in a biophysical model of thalamoreticular microcircuitry\", \"pos\": [\"Face dissimilarity judgements are predicted by representational distance in 3D-morphable and image-computable models\"], \"neg\": [\"DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer\", \"Nanoscale ligand density modulates gap junction intercellular communication of cell condensates during chondrogenesis\", \"Microbiota-instructed regulatory T cell differentiation is mediated by a distinct ROR\\u03b3t+ antigen presenting cell subset\", \"ALLOPURINOL BLOCKS THE AORTIC ANEURYSM IN A MOUSE MODEL OF MARFAN SYNDROME VIA REDUCING AORTIC OXIDATIVE STRESS\", \"Super-enhancer signature reveals key mechanisms associated with resistance to non-alcoholicsteatohepatitis in humans with obesity\", \"Modular Dynamic Biomolecular Modelling with Bond Graphs: The Unification of Stoichiometry, Thermodynamics, Kinetics and Data.\", \"Conformist social learning leads to self-organised prevention against adverse bias in risky decision making\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Utility of SARS-CoV-2 infection models using in vitro and in vivo ACE2-lentivirus transduction\", \"ZIKV disrupts placental ultrastructure and drug transporter expression in mice\", \"Synthetic periphyton as a model system to understand species dynamics in complex microbial freshwater communities\", \"Strain and rupture of HIV-1 capsids during uncoating\", \"First evidence of in vitro cytotoxic effects of marine microlitter on Merluccius merluccius and Mullus barbatus, two Mediterranean commercial fish species\", \"Identifying and testing marker-trait associations for growth and phenology in three pine species: implications for genomic prediction\", \"Imbalanced segregation of recombinant haplotypes in hybrid populations reveals inter- and intrachromosomal Dobzhansky-Muller incompatibilities\", \"AFFORDABLE, REPLICABLE ROBOTIC MINIRHIZOTRON SAMPLING FOR ROOT DYNAMICS STUDIES\", \"Propionate ameliorates diabetes-induced neurological dysfunction through regulating the PI3K/Akt/eNOS signaling pathway\", \"The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans\", \"Serological fingerprints link antiviral activity of therapeutic antibodies to affinity and concentration\", \"Mapping the genetic and environmental aetiology of autistic traits in Sweden and the United Kingdom\", \"Analysis of science journalism reveals gender and regional disparities in coverage\", \"Single-nucleus RNA Sequencing and Spatial Transcriptomics Reveal the Immunological Microenvironment of Cervical Squamous Cell Carcinoma\", \"Tetraspanin Cd9b and Cxcl12a/Cxcr4b have a synergistic effect on the control of collective cell migration\", \"Non-targeted multimodal metabolomics and lipidomics data from ovine rumen fluid fractions\"], \"category\": \"neuroscience\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Emerging experience-dependent dynamics in primary somatosensory cortex reflect behavioral adaptation\", \"pos\": [\"The organizational principles of de-differentiated topographic maps in somatosensory cortex\"], \"neg\": [\"An hidden Markov model to estimate homozygous-by-descent probabilities associated with nested layers of ancestors\", \"Actin isovariant ACT7 controls root meristem development in Arabidopsis through modulating auxin and ethylene responses\", \"Tackling polypharmacology of kinase inhibitors and selecting chemical kinase probes by transcription factor activity profiling.\", \"Multi-layered regulation of neuroectoderm differentiation by retinoic acid in a primitive streak-like context\", \"kmtricks: Efficient and flexible construction of Bloom filters for large sequencing data collections\", \"A Hierarchy of Biomolecular Proportional-Integral-Derivative Feedback Controllers for Robust Perfect Adaptation and Dynamic Performance\", \"Alkaline phosphatase activity of serum affects osteogenic differentiation cultures\", \"CRISPR-based kinome-screening revealed MINK1 as a druggable player to rewire 5FU-resistance in OSCC through AKT/MDM2/p53 axis\", \"Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning\", \"Erythropoietin supplementation ameliorates the immunological and hematological deficiencies of lysinuric protein intolerance in mice\", \"Designer Scaffolds for Interfacial Bioengineering\", \"Microbial populations are shaped by dispersal and recombination in a low biomass subseafloor habitat\", \"Tree encroachment threatens the conservation potential and sustainability of U.S. rangelands\", \"Genome editing excisase origins illuminated by somatic genome of Blepharisma\", \"Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic andtargetable tissue states\", \"Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures\", \"Naming of human diseases on the wrong side of history\", \"An Extended Motif in the SARS-CoV-2 Spike Modulates Binding and Release of Host Coatomer in Retrograde Trafficking\", \"Resolving when (and where) the Thylacine went extinct\", \"Reduced glutathione decreases cell adhesion and increases cell volume.\", \"An estimate of the deepest branches of the tree of life from ancient vertically-evolving genes\", \"A TIR-1/SARM1 phase transition underlies p38 immune pathway activation in the C. elegans intestine\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Effect of a low concentration in chloride, sodium and potassium on oocyte maturation, oocyte hydration, ovulation and egg quality in rainbow trout.\"], \"category\": \"neuroscience\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Dissociating the Neural Correlates of Planning and Executing Tasks with Nested Task Sets\", \"pos\": [\"Performance of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector and BIANCA\"], \"neg\": [\"Limited selection towards lighter seeds in Danish grasslands over an eight-year period\", \"The origins and drivers of Neotropical diversity\", \"Elucidation of SARS-CoV-2 budding mechanisms through molecular dynamics simulations of M and E protein complexes\", \"The unfolded protein response triggers the immune deficiency pathway in ticks\", \"Co-infection of Candidatus Piscichlamydia trichopodus (order Chlamydiales) and Henneguya sp (Myxosporea, Myxobolidae) in snakeskin gourami Trichopodus pectoralis (Regan 1910)\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Small extracellular vesicles from young mice prevent frailty, improve healthspan and decrease epigenetic age in old mice.\", \"Distinct mutations and lineages of SARS-CoV-2 virus in the early phase of COVID-19 pandemic and subsequent one-year global expansion\", \"The Mitochondrial RNA Granule Modulates Manganese-Dependent Cell Toxicity\", \"Groping in the fog: soaring migrants exhibit wider scatter in flight directions and respond differently to wind under low visibility conditions\", \"A cell cycle-dependent GARP-like transcriptional repressor regulates the initiation of differentiation in Giardia lamblia\", \"Structural basis for cell-type specific evolution of viral fitness by SARS-CoV-2\", \"Integrative Modelling of Signalling Network Dynamics Identifies Cell Type-selective Therapeutic Strategies for FGFR4-driven Cancers\", \"Faster short-read mapping with strobemer seeds constructed from syncmers\", \"Reconstitution of minimal motility system based on Spiroplasma swimming by expressing two bacterial actins in synthetic minimal bacterium\", \"TeamTree analysis: a new approach to evaluate scientific production\", \"Enhancers regulate 3' end processing activityto control expression of alternative 3'UTR isoforms\", \"F1F0 ATP Hydrolysis is a Determinant of Metabolic Rate, a Correlate of Lifespan, and a Weakness of Cancer\", \"The perceptual relevance of the speech envelope-following response in normal hearing and age-related hearing impaired listeners\", \"Poly(rC)-binding protein 1 limits hepatitis C virus assembly and secretion\", \"An epigenetic memory of inflammation controls context-dependent lineage plasticity in the pancreas\", \"PIN-FORMED1 polarity in the shoot is insensitive to the polarity of neighbouring cells\", \"Effects of Ancestry and Hypopigmentation Alleles on Skin Color in a Caribbean Native American Population\", \"First evidence of in vitro cytotoxic effects of marine microlitter on Merluccius merluccius and Mullus barbatus, two Mediterranean commercial fish species\"], \"category\": \"neuroscience\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Migrators Within Migrators: Exploring Transposable Element Landscapes, Activity, and Host Gene Associations in the Monarch Butterfly, Danaus plexippus\", \"pos\": [\"H2A.B is a cancer/testis factor involved in activation of ribosome biogenesis in Hodgkin Lymphoma\"], \"neg\": [\"Analysis of science journalism reveals gender and regional disparities in coverage\", \"Royal knifefish generate powerful suction feeding through large neurocranial elevation and high epaxial muscle power\", \"Cascade screening following a polygenic risk score test: what is the risk of a relative conditional on a high score of a proband?\", \"Using networks to analyze and visualize the distribution of overlapping reading frames in virus genomes\", \"Differential regulation of degradation and immune pathways underlies adaptation of the ectosymbiotic nematode Laxus oneistus to oxic-anoxic interfaces\", \"Differential fate of acellular vascular scaffolds in vivo involves macrophages and T-cell subsets\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Titers of antibodies the receptor-binding domain (RBD) of ancestral SARS-CoV-2 are predictive for levels of neutralizing antibodies to multiple variants\", \"Cell Cycle Delay, Pro-metaphase Arrest and C-metaphase Inducing Effects of Petroleum Ether Fraction of Leaf Aqueous Extract of Clerodendrum viscosum Vent.\", \"The evolution of stomatal traits along the trajectory towards C4 photosynthesis\", \"Limitations of composability of cis-regulatory elements in messenger RNA\", \"Integrated analysis of cervical squamous cell carcinoma cohorts from three continents reveals conserved subtypes of prognostic significance.\", \"The initial response of females towards congeneric males matches the propensity to hybridise in Ophthalmotilapia.\", \"Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques\", \"A chemical screen based on an interruption of zebrafish gastrulation identifies the HTR2C inhibitor Pizotifen as a suppressor of EMT-mediated metastasis\", \"Only 2 of 12 now-deleted genes of 19 identified and predicted Myb family DNA binding proteins are directly involved in rice Magnaporthe oryzae pathogenicity\", \"Stress-dependent cell stiffening by tardigrade tolerance proteins through reversible formation of cytoskeleton-like filamentous network and gel-transition\", \"Analysis of the leaf metabolome in Arabidopsis thaliana mutation accumulation lines reveals association of pleiotropy and fitness consequences\", \"Genome editing in the unicellular holozoan Capsaspora owczarzaki suggests a premetazoan role for the Hippo pathway in multicellular morphogenesis\", \"Expression and subcellular localization of USH1C/harmonin in the human retina provides insights into pathophysiology and therapy of Usher syndrome\", \"Hotspots of pest-induced US urban tree death, 2020-2050\", \"A humanized nanobody phage display library yields potent binders of SARS CoV-2 spike\", \"Eye movements reveal spatiotemporal dynamics of visually-informed planning in navigation\", \"Rho Kinase regulates neutrophil NET formation that is involved in UVB-induced skin inflammation\"], \"category\": \"genomics\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"PRDM9 losses in vertebrates are coupled to those of paralogs ZCWPW1 and ZCWPW2\", \"pos\": [\"Recurring adaptive introgression of a supergene variant that determines social organization\"], \"neg\": [\"ZAF -- The First Open Source Fully Automated Feeder for Aquatic Facilities\", \"The pH Dependence of Niclosamide Solubility, Dissolution, and Morphology Motivates Potentially Universal Mucin-Penetrating Nasal and Throat Sprays for COVID19, its Contagious Variants, and Other Respiratory Viral Infections\", \"Suppression of sost/sclerostin and dickkopf-1 promote intervertebral disc structure in mice\", \"Raman Signal Denoising using Fully Convolutional Encoder Decoder Network\", \"A temporal parcellation of the sensory-evoked responses during the rubber hand illusion reveals manipulation- and illusion-specific correlates\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Oxytocin promotes social grooming in bonobos\", \"The unfolded protein response triggers the immune deficiency pathway in ticks\", \"LPS-TLR4 pathway exaggerates alcohol-induced acute-on-chronic liver failure via provoking NETs formation\", \"Monitoring fish communities through environmental DNA metabarcoding in the fish pass system of the second largest hydropower plant in the world\", \"Fungicide resistance characterised across seven modes of action in a Botrytis cinerea isolated from Australian vineyards\", \"Thirteen independent genetic loci associated with preserved processing speed in a study of cognitive resilience in 330,097 individuals in the UK Biobank\", \"Deep Transfer Learning of Drug Responses by Integrating Bulk and Single-cell RNA-seq data\", \"The obstacles and potential clues of prime editing applications in tomato, a dicot plant\", \"Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits\", \"3D Reconstructions of Mouse Skeletal Muscle Reveal a Decrease in the MICOS Complex and Altered Mitochondrial Networks\", \"Obesity affects mitochondrial metabolism and proliferative potential of equine endometrial progenitor cells.\", \"Engineered disulfide reveals structural dynamics of locked SARS-CoV-2 spike\", \"Synergy of chemotherapy and macrophage depletion leads to B cell-mediated T cell memory activation and durable triple negative breast cancer regression\", \"\\\"Adopt a Microorganism\\\" methodology enhances the discourse richness of high school students in both hybrid learning and remote education\", \"A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions\", \"Mouse lemur, a new animal model to investigate cardiac pacemaker activity in primates.\", \"Identifying and testing marker-trait associations for growth and phenology in three pine species: implications for genomic prediction\", \"Conserved organ-specific microbial assemblages in different populations of a terrestrial crab\"], \"category\": \"evolutionary biology\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Distinct corticostriatal compartments drive competition between adaptive and automatized behavior\", \"pos\": [\"Behavioral distractibility in preschoolers from different socio-economic backgrounds\"], \"neg\": [\"The number of cytokinesis nodes in mitotic fission yeast scales with cell size\", \"A Wnt/\\u03b2-catenin signaling driven adipocyte population required for beiging and glucose homeostasis\", \"Co-infection of Candidatus Piscichlamydia trichopodus (order Chlamydiales) and Henneguya sp (Myxosporea, Myxobolidae) in snakeskin gourami Trichopodus pectoralis (Regan 1910)\", \"\\\"Adopt a Microorganism\\\" methodology enhances the discourse richness of high school students in both hybrid learning and remote education\", \"The Ensemble of Gene Regulatory Networks at Mutation-Selection Balance\", \"Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data\", \"In vitro pectate lyase activity and carbon uptake assays and whole genome sequencing of Bacillus amyloliquefaciens subsp. plantarum strains for a pectin defective pathway\", \"Phenotypic heterogeneity driven by plasticity of the intermediate EMT state governs disease progression and metastasis in breast cancer\", \"Host factors drive the within-host competition between highly and low pathogenic H5N8 avian influenza viruses\", \"Induction of exaggerated cytokine production in human peripheral blood mononuclear cells by a recombinant SARS-CoV-2 spike glycoprotein S1 and its inhibition by dexamethasone\", \"Exploring functional protein covariation across single cells using nPOP\", \"Correlational networking guides the discovery of cryptic natural product biosynthetic enzymes\", \"A humanized nanobody phage display library yields potent binders of SARS CoV-2 spike\", \"Diversity of SARS-CoV-2 genome among various strains identified in Lucknow, Uttar Pradesh\", \"Nascent alt-protein chemoproteomics reveals a repressor of ribosome biogenesis\", \"Targeting SARS-CoV-2 infection through CAR-T like bispecific T cell engagers incorporating ACE2\", \"A novel two-generation approach to the population dynamics of gregarious parasitoids\", \"A rat model of pregnancy in the male parabiont\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Force-correction Analysis Method for Derivation of Multidimensional Free Energy Landscapes from Adaptively Biased Replica Simulations\", \"A Physiometric Investigation of Inflammatory Composites: Comparison of A Priori Aggregates, Empirically-identified Factors, and Individual Proteins\", \"A multivariate view of cognitive performance reveals positive correlation in the Trinidadian Guppy (Poecilia reticulata)\", \"Challenges in modelling the sediment retention ecosystem service for an ecosystem account, with examples from the Mitchell catchment\", \"Clonal evolution in serially passaged Cryptococcus neoformans x deneoformans hybrids reveals a heterogenous landscape of genomic change\"], \"category\": \"neuroscience\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Identification of two cancer stem cell-like populations in triple-negative breast cancer xenografts\", \"pos\": [\"Striatin 3 and MAP4K4 cooperate towards oncogenic growth and tissue invasion in medulloblastoma\"], \"neg\": [\"A novel two-generation approach to the population dynamics of gregarious parasitoids\", \"ConVarT: a search engine for orthologous variants and functional inference of human genetic variants\", \"Naming of human diseases on the wrong side of history\", \"Tunable Cellular Localization and Extensive Cytoskeleton-Interplay of Reflectins\", \"Embracing curiosity eliminates the exploration-exploitation dilemma\", \"Admp regulates tail bending by controlling ventral epidermal cell polarity via phosphorylated myosin translocation.\", \"ALLOPURINOL BLOCKS THE AORTIC ANEURYSM IN A MOUSE MODEL OF MARFAN SYNDROME VIA REDUCING AORTIC OXIDATIVE STRESS\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Atrazine induced In vivo immunotoxicity in Bivalves\", \"A novel, computationally tractable algorithm flags in big matrices every column associated in any way with others or a dependent variable, with much higher power when columns are linked like mutations in chromosomes\", \"Omicron-specific mRNA vaccination alone and as a heterologous booster against SARS-CoV-2\", \"T606-phosphorylation deprives the function of Kaiso as a transcription factor and cancer repressor\", \"Outer hair cell electro-mechanical power conversion\", \"Computation of the mitochondrial age distribution along the axon length\", \"The physics of liquid-to-solid transitions in multi-domain protein condensates\", \"A bacterial effector counteracts host autophagy by promoting degradation of an autophagy component\", \"Evolution of phenotypic variance during adaptation to high temperature in Drosophila\", \"Structural predictions of the SNX-RGS proteins suggest they belong to a new class of lipid transfer proteins\", \"Fine-scale adaptations to environmental variation and growth strategies drive phyllosphere Methylobacterium diversity.\", \"De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet\", \"In Vivo Multi-Day Calcium Imaging of CA1 Hippocampus in Freely Moving Rats Reveals a High Preponderance of Place Cells with Consistent Place Fields\", \"Reconstitution of minimal motility system based on Spiroplasma swimming by expressing two bacterial actins in synthetic minimal bacterium\", \"Hot and cold spots of pest-induced US urban tree death, 2020-2050\", \"Genome-wide mechanisms of rediploidization in paleopolyploid teleosts revealed by a high-resolution comparative atlas across 74 fishes\"], \"category\": \"cancer biology\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n{\"query\": \"Motor attention to the tactile modality induces sensory attenuation for sounds\", \"pos\": [\"Serotonin signals through postsynaptic G\\u03b1q, Trio RhoGEF, and diacylglycerol to promote C. elegans egg-laying circuit activity and behavior\"], \"neg\": [\"ALLOPURINOL BLOCKS THE AORTIC ANEURYSM IN A MOUSE MODEL OF MARFAN SYNDROME VIA REDUCING AORTIC OXIDATIVE STRESS\", \"Reduced glutathione decreases cell adhesion and increases cell volume.\", \"Transcriptional condensates formed by phase-separated ALOG family proteins control shoot meristem maturation for flowering\", \"A terrain treadmill to study animal locomotion through large obstacles\", \"Structural basis for the inhibition of the Bacillus subtilis c-di-AMP cyclase CdaA by the phosphoglucomutase GlmM\", \"Similarities Between Bacterial GAD and Human GAD65: Implications in Gut Mediated Autoimmune Type 1 Diabetes\", \"A novel two-generation approach to the population dynamics of gregarious parasitoids\", \"The role of Kabuki Syndrome genes KMT2D and KDM6A in development: Analysis in Human sequencing data and compared to mice and zebrafish\", \"Fusobacterium nucleatum induces pancreatic cancer cell proliferation and migration by regulating host autocrine and paracrine signaling\", \"Structural Analysis of Spike Protein Mutations in the SARS-CoV-2 P.3 Variant\", \"Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition\", \"Changes to the mtDNA copy number during yeast culture growth\", \"Differential fate of acellular vascular scaffolds in vivo involves macrophages and T-cell subsets\", \"C. elegans PEZO-1 is a Mechanosensitive Ion Channel Involved in Food Sensation\", \"Omicron BA.2 specifically evades broad sarbecovirus neutralizing antibodies\", \"Centimeter-scale quorum sensing dictates collective survival of differentiating embryonic stem cells\", \"Cell-specific Bioorthogonal Tagging of Glycoproteins\", \"Missing the sweet spot: one of the two N-glycans on human Gb3/CD77 synthase is expendable\", \"Impaired activation of Transposable Elements in SARS-CoV-2 infection\", \"Host phenology can drive the evolution of intermediate virulence strategies in some parasites\", \"Identification and characterization of the WYL BrxR protein and its gene asseparable regulatory elements of a BREX phage restriction system\", \"Naming of human diseases on the wrong side of history\", \"Mapping the planet's critical natural assets\", \"Cellular feedback to organotelluranes displays modulation of antioxidant proteins gene expression\"], \"category\": \"neuroscience\", \"prompt\": \"Identify the main category of Biorxiv papers based on the titles.\", \"type\": \"symmetric_clustering\"}\n" }, { "path": "examples/finetune/embedder/example_data/retrieval/msmarco.jsonl", "content": "{\"query\": \")what was the immediate impact of the success of the manhattan project?\", \"pos\": [\"Introduction The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.\"], \"neg\": [\"The Launch of Sputnik, 1957 The Soviets responded with yet another launch, and the space race continued. The success of Sputnik had a major impact on the Cold War and the United States. Fear that they had fallen behind led U.S. policymakers to accelerate space and weapons programs.he Soviets responded with yet another launch, and the space race continued. The success of Sputnik had a major impact on the Cold War and the United States. Fear that they had fallen behind led U.S. policymakers to accelerate space and weapons programs.\", \"Important result of the Marshall plan? Effected by the United States after World War II, the Marshall Plan produced the quite important result of building up nations shattered during the war. It did so quite quickly, as well.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first nuclear weapons during World War II.he United States had already spent more than $1 billion ($13,600,000,000 today), while in 1943, the United Kingdom had spent about \\u00a30.5 million. Chadwick thus pressed for British involvement in the Manhattan Project to the fullest extent and abandon any hopes of a British project during the war.\", \"- Los Alamos is where the first known Manhattan Project bombs were built and tested. On July 16, 1945, in a remote desert location near Alamogordo, New Mexico, the first atomic bomb was successfully detonated\\u2014the Trinity Test\\u2014creating an enormous mushroom cloud some 40,000 feet high and ushering in the Atomic Age.\", \"Holocauste Literature Meanwhile, President Truman was told of the successful test of the Manhattan Project (atomic bomb) in Alamogordo, New Mexico on Jul 16, 1945. Diary of President Truman of Jul 18, 1945 shows Discussed Manhattan (it is a success).t the end of World War II, few questioned Truman's decision to drop the atomic bombs on Hiroshima and Nagasaki. Most Americans accepted the obvious reasoning: the atomic bomb \\u2026 ings brought the war to a more timely end.\", \"- The Soviet project to develop an atomic bomb (Russian: \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u0441\\u043e\\u0432\\u0435\\u0442\\u0441\\u043a\\u043e\\u0439 \\u0430\\u0442\\u043e\\u043c\\u043d\\u043e\\u0439 \\u0431\\u043e\\u043c\\u0431\\u044b) was a top secret research and development program begun during World War II, in the wake of the Soviet Union 's discovery of the American, British, and Canadian nuclear project.hrough sources in the Manhattan Project, notably Klaus Fuchs, the Soviet intelligence obtained important information on the progress of the United States atomic bomb effort. Intelligence reports were shown to the head of the Soviet atomic project and had a significant impact on the direction of Soviet research.\", \"Monroe Doctrine The immediate impact of the Monroe Doctrine was mixed. It was successful to the extent that the continental powers did not attempt to revive the Spanish empire, but this was on account of the strength of the British Navy, not American military might, which was relatively limited.\", \"What impacts did benjamin harrison have on america while in office? Sorry, something has gone wrong. Best Answer: The greatest positive impact during his administration was the passing of the Sherman Anti-Trust Act of 1890, the first federal act to regulate trusts.\", \"The Manhattan Project Manhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first atomic bombs during World War II.It was led by the United States with the support of the United Kingdom and Canada.he Manhattan Project began modestly in 1939, but grew to employ more than 130,000 people and cost nearly US$2 billion (about $26 billion in 2015 dollars). Over 90% of the cost was for building factories and producing the fissionable materials, with less than 10% for development and production of the weapons.\", \"- Roosevelt agreed and placed General Leslie Groves and physicist J. Robert Oppenheimer in charge of the Manhattan Project two years later. The name Manhattan Project was the code word for the development of the atomic bomb. On July 16, 1945, the first atomic bomb was tested at the Trinity Site in New Mexico.The weapon was later used against the Japanese to end World War II.eginning in June 1942 during World War II, the United States' Manhattan Project brought together scientists and military experts to create the world's first atomic bomb.\", \"51f. The Manhattan Project In late 1941, the American effort to design and build an atomic bomb received its code name \\u2014 the Manhattan Project. At first the research was based at only a few universities \\u2014 Columbia University, the University of Chicago and the University of California at Berkeley.\", \"The Manhattan Project Manhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.anhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.\", \"- The Manhattan Project was an effort during World War II in the United States to develop the first nuclear weapon.\", \"Atomic Glossary The Manhattan Project was the code name for America's atomic bomb development efforts during World War II. Its name originated from the fact that it was part of the U. S. Army Corps of Engineers and organized under the Manhattan Engineer District (MED) in New York City.\", \"Introduction While communication led to the overall scientific triumph of the Manhattan project, an intentional lack of communication between military and government superiors and the Los Alamos laboratory led to one of the most devastating moral disasters of the twentieth century.\", \"- At the end of the unit the children will be quizzed on the different senses which will have to be matched to their source. The children will have to name the five senses and which part of the body is linked with that particular sense. Also, the story incorporating the five senses will also be used as a way to assess what the children comprehended ...\", \"- This project used hundreds of scientist and thousands of people to develop the first Atomic Bomb. The Manhattan Project was implemented by President Franklin Roosevelt in no small part due to two letters sent to him by Albert Einstein.\", \"The Cold War Museum In June of 1942 the War Department\\u2019s Army Corps of Engineers took charge of the effort to develop an atomic bomb. The subsequent top-secret project (code named Manhattan) cultivated a complex, but cooperative relationship between science, industry, the government, and the army.\", \"Moments in U.S. Diplomatic History Only later did we learn through Gorge that we had a ringside view of the Doolittle raid [the daring air attack on Tokyo on April 18, 1942, which was designed to show that Japan was vulnerable and to boost U.S. morale after Pearl Harbor]\\u2026.\", \"Car Crashes Kill 40,000 in U.S. Every Year Car Crashes Kill 40,000 in U.S. Every Year. Imagine a plane full of people crashing, killing everyone on board, every single day. That\\u2019s how many people die on America\\u2019s roads daily, says the Insurance Institute for Highway Safety. \\u201cMotor vehicle crashes in the United States result in more than 40,000 deaths per year,\\u201d says the Institute in the journal Injury Prevention. \\u201cThat is, on each of the 6,209 consecutive days included in this study, an equivalent of a plane load or more of people died on the roads.\\u201d. But not all days are alike.\", \"arrest Definition of arrest. 1 1 : the taking or detaining in custody by authority of law The investigation led to his arrest. 2 2a : the act of stoppingb : the condition of being stopped or inactive \\u2014 compare cardiac arrest.\", \"The Atomic Bomb The Desert Test By July, 1945 the Manhattan Project work was almost completed; they had developed a working nuclear bomb. The only obstacle that stood in their way is the actual testing of the bomb. The test, code name Trinity occurred on July 16, 1945 in the New Mexico desert town of Alamogordo.\", \"Nuclear explosion in Hiroshima: details and facts Right after the bomb blast in Hiroshima, President Truman officially announced the yield of the bomb as 18 kilotons, and the media rounded it to 20. Later on, after a survey, conducted by the Manhattan Project, it was calculated, that the yield of the Little Boy equaled to 12 kilotons.\", \"- The Manhattan Project was a US government project used to develop a nuclear bomb. The undertaking lasted form 1942 to 1946, when...\"], \"pos_scores\": [96.6875], \"neg_scores\": [90.5625, 89.0, 92.9375, 93.3125, 93.5, 91.75, 92.375, 86.75, 93.4375, 93.1875, 92.5, 92.375, 92.375, 93.3125, 92.3125, 94.0625, 88.0, 92.375, 91.9375, 89.3125, 83.1875, 87.5625, 92.0, 90.4375, 92.625], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"_________ justice is designed to repair the harm to victim, the community and the offender caused by the offender criminal act. question 19 options:\", \"pos\": [\"Restorative justice The approach is based on a theory of justice that considers crime and wrongdoing to be an offense against an individual or community, rather than the State. Restorative justice that fosters dialogue between victim and offender has shown the highest rates of victim satisfaction and offender accountability.\"], \"neg\": [\"What is Restorative Justice? Restorative Justice is a community-based approach to dealing with crime, the effects of crime, and the prevention of crime. Most people who move through the current system of criminal justice do not find it a healing or satisfying experience. Victims often feel re-victimized and their need for justice unmet.\", \"California\\u00e2s Criminal Justice System: A Primer The criminal justice system is based on criminal sentencing law, the body of laws that define crimes and specify the punishments for such crimes. The majority of sentencing law is set at the state level.\", \"- will serve the community and enhance public trust and confidence in the administration of justice through: 1 The impartial and timely resolution of disputes, 2 Ensuring compliance with the law and court orders, and. 3 Fostering a vital court-community relationship that promotes equal access to the courts.\", \"- CRIME VICTIM'S GUIDE. A Crime Victim's Guide to the Justice System in Arkansas was written to assist victims of crime in better understanding the Arkansas criminal justice system so they are more able to exercise their rights.\", \"- CORRECTIVE JUSTICE. The doer is directly liable to the sufferer, so that reparation of the. loss occurs through disgorgement of the gain. By directly linking the parties, corrective justice categorically. differs from distributive justice, the other form of justice that Aris-.\", \"3 \\u00e2 Restorative Justice: Justice That Promotes Healing Each of these types of communities\\u2014the geographic community of the victim, offender, or crime; the community of care; and civil society\\u2014may be injured by crime in different ways and degrees, but all will be affected in common ways as well: The sense of safety and confidence of their members is threatened, order within the community is threatened, and (depending on the kind of crime) common values of the community are challenged and perhaps eroded.\", \"- Mission is to protect the public from criminal offenders through a system of incarceration and supervision which securely segregates offenders from society, assures offenders of their constitutional rights and maintains programs to enhance the success of offenders' reentry into society.\", \"How Does the Criminal Justice System Work? Throughout each stage of the process, constitutional protections exist to ensure that the rights of the accused and convicted are respected. These protections balance the need of the criminal justice system to investigate and prosecute criminals with the fundamental rights of the accused (who are presumed innocent).\", \"Restorative Justice \\u2022 Restorative Justice can be utilized in crimes of severe violence or non-violent crimes as well as with juvenile offenders. \\u2022 Restorative Justice is an approach to crime which puts the victim or the victim\\u2019s family first and fully acknowledges the harm caused by the offender.\", \"- Criminal Justice. An effective criminal justice system is a key aspect of the rule of law, as it constitutes the natural mechanism to redress grievances and bring action against individuals for offenses against...\", \"- Punishment should be swift and certain. The purpose of the criminal justice system is to prevent crime through deterrence. According to this line of thinking, a potential criminal will decide against committing a crime because the punishment would be too costly.\", \"- As you have learned, distributive justice deals with the fairness of the distribution of benefits or burdens among two or more people or groups in society. Benefits may be such things as pay for work or the right to speak or to vote.xamining Issues of Justice We think of the essence of justice as fairness, and the essence of fairness as treating people equally. However, issues of justice can arise even if everyone is subjected to the same rules, and even if everyone who breaks the rules receives the same punishment.\", \"PUBLICATIONS Consequently, the equations of actuarial justice tend to be more simplified and intended for application to broad groupings of offenders. The more groups can be narrowed, the fairer and more effective will be the criminal justice policies toward offenders.\", \"- Social justice is justice in terms of the distribution of wealth, opportunities, and privileges within a society. Classically, justice (especially corrective justice or distributive justice) ensured that individuals both fulfilled their societal roles and received what was their due from society.\", \"\\\"\\\"\\\"How Can Law Enforcement Professionals Use The Social Justice Principles Of Equality Solidarity And Human Rights To Build A More Just Society\\\"\\\" Essays and Research Papers\\\" Justice in Law Enforcement The true concept of justice is a concept involving moral, fair, and... impartial treatment of all individuals. Justice is a concept that has many different translations and a concept that can be changed on a case-by-case basis.\", \"Criminal justice The criminal justice system consists of three main parts: (1) Legislative (create laws); (2) adjudication (courts); and (3) corrections (jails, prisons, probation and parole).\", \"- Distributive justice concerns the nature of a socially just allocation of goods in a society. A society in which incidental inequalities in outcome do not arise would be considered a society guided by the principles of distributive justice.\", \"Victims of Crime This environment began to change in the 1970s with the establishment of victim compensation funds. Not until the 1980s, however, did a national movement for victims' rights spark wholesale changes in the criminal justice system.\", \"- Economic justice is a component of social justice. It's a set of moral principles for building economic institutions, the ultimate goal of which is to create an opportunity for each person to create a sufficient material foundation upon which to have a dignified, productive, and creative life beyond economics.\", \"- Criminal law serves several purposes and benefits society in the following ways: 1 Maintaining order. 2 Resolving disputes. 3 Protecting individuals and property. 4 Providing for smooth functioning of society. 5 Safeguarding civil liberties.\", \"Victims bill of rights would compel spouses to testify 'Victims deserve fair and equal treatment by the justice system and they need to be assured that the system itself doesn't re-victimize' \\u2014 Sheldon Kennedy, former NHL player. Crime victims would have more say as their cases wind their way through the justice system under a newly proposed victims bill of rights.The long-awaited legislation, part of the government's law-and-order agenda, aims to fix what Prime Minister Stephen Harper said Thursday was a broken part of the justice system.Victims deserve fair and equal treatment by the justice system and they need to be assured that the system itself doesn't re-victimize' \\u2014 Sheldon Kennedy, former NHL player. Crime victims would have more say as their cases wind their way through the justice system under a newly proposed victims bill of rights.\", \"Social justice Social justice is a concept of fair and just relations between the individual and society. This is measured by the explicit and tacit terms for the distribution of wealth, opportunities for personal activity and social privileges. In Western as well as in older Asian cultures, the concept of social justice has often referred to the process of ensuring that individuals fulfill their societal roles and receive what was their due from society. In the current global grassroots movements for social j\", \"Procedural justice The idea of the outcomes model of procedural justice is that the fairness of process depends on the procedure producing correct outcomes. For example, if the procedure is a criminal trial, then the correct outcome would be conviction of the guilty and exonerating the innocent.\", \"Retribution Retribution is perhaps the most intuitive - and the most questionable - aim of punishment in the criminal law. Quite contrary to the idea of rehabilitation and distinct from the utilitarian purposes of restraint and deterrence, the purpose of retribution is actively to injure criminal offenders, ideally in proportion with their injuries to society, and so expiate them of guilt.\", \"Retributive Justice The idea of retributive justice has played a dominant role in theorizing about punishment over the past few decades, but many features of it\\u2014especially the notions of desert and proportionality, the normative status of suffering, and the ultimate justification for retribution\\u2014remain contested and problematic. 1 1.\"], \"pos_scores\": [95.3125], \"neg_scores\": [95.0, 90.25, 88.1875, 90.25, 92.6875, 94.5625, 88.0, 90.3125, 95.6875, 90.6875, 90.0, 90.0625, 89.875, 90.9375, 89.75, 90.6875, 90.3125, 90.0, 89.875, 89.125, 89.6875, 89.75, 89.625, 90.25, 91.0625], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"what color is amber urine\", \"pos\": [\"- Color\\u2014urine can be a variety of colors, most often shades of yellow, from very pale or colorless to very dark or amber. Unusual or abnormal urine colors can be the result of a disease process, several medications (e.g., multivitamins can turn urine bright yellow), or the result of eating certain foods.\"], \"neg\": [\"- ISO 9001:2008 certified manufacturer of standard & metric washers. Types include shoulder washers, finishing washers, cup washers, retaining washers, split flat washers & standard & special flat washers.\", \"Urine color Orange urine may result from: Medical conditions. In some cases, orange urine can indicate a problem with your liver or bile duct, especially if you also have light-colored stools. Orange urine may also be caused by dehydration, which can concentrate your urine and make it much deeper in color.\", \"- Normal urine color varies, depending on how much water you drink. Fluids dilute the yellow pigments in urine, so the more you drink, the clearer your urine looks. When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white. When to see a doctor\", \"How Long After Intercourse Can I Take a Pregnancy Test? A pregnancy test will detect this hormone about one week after fertilization. Most women tend to wait to take a pregnancy test until after they have missed one menstrual period. If you take a pregnancy test one week after you were supposed to get your period, the result will be about 95% accurate.If you take it too soon after fertilization, it may show up negative when in fact you really are pregnant. Pregnancy test are meant to be considered screening tests.f you take a pregnancy test one week after you were supposed to get your period, the result will be about 95% accurate. If you take it too soon after fertilization, it may show up negative when in fact you really are pregnant.\", \"Urine 1 Reddish or brown urine may be caused by porphyria (not to be confused with the harmless, temporary pink or reddish tint caused by beeturia). 2 Blue urine can be caused by the ingestion of methylene blue (e.g., in medications) or foods or beverages with blue dyes. Blue urine stains can be caused by blue diaper syndrome.\", \"What does bright yellow urine indicate? When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But sometimes urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white.\", \"- 4. Imitation Urine. In general, the physical appearance of urine should be yellow to yellow-tan in color. Unfortunately, there are liquid substances available such as apple juice, cranberry juice, etc., that have the same color as urine. Noting the temperature of the urine is important. 5. Length of time in the bathroom\", \"- Darkened urine is urine that is dark yellow, brown, dark red, or red, and it may range from slightly dark to considerably dark. A change in urine color may be temporary, or it may be persistent.\", \"- Amber Color The color of amber fossil varies from yellow to dark brown to almost black. Very rarely this gem may be found in green and blue-gray colors and hence green amber can be very rare. In addition, it is dyed in many colors like green, blue, pink etc.mber Color The color of amber fossil varies from yellow to dark brown to almost black. Very rarely this gem may be found in green and blue-gray colors and hence green amber can be very rare. In addition, it is dyed in many colors like green, blue, pink etc.\", \"Colorless Urine And Kidney Disease Colorless Urine And Kidney Disease. Due to urochrome, the normal urine color can range from pale yellow to deep amber. Abnormal urine color can be caused by foods, drugs, pigment, blood, urinary tract infection, kidney problems and other factors. Urine color has much to do with fluid intake.The more water we drink, the more light the color is.olorless Urine And Kidney Disease. Due to urochrome, the normal urine color can range from pale yellow to deep amber. Abnormal urine color can be caused by foods, drugs, pigment, blood, urinary tract infection, kidney problems and other factors. Urine color has much to do with fluid intake.\", \"Urine color and odor changes Rhubarb can also turn urine dark brown or tea-colored, as can fava beans and aloe. Carrots, carrot juice, and vitamin C can color urine orange, and B vitamins can turn it a fluorescent yellow-green. Asparagus sometimes gives urine a greenish tinge and a distinctive smell, said to resemble rotting cabbage. The cause of this smell is a matter for speculation.\", \"Urine Color Normal urine color ranges from pale yellow to deep amber \\u2014 the result of a pigment called urochrome and how diluted or concentrated the urine is.Pigments and other compounds in certain foods and medications may change your urine color.Beets, berries and fava beans are among the foods most likely to affect urine color. Many over-the-counter and prescription medications give urine vivid tones, such as raspberry red, lemon yellow or greenish blue.An unusual urine color can be a sign of disease. For instance, deep red to brown urine is an identifying characteristic of porphyria, a rare, inherited disorder of red blood cells.igments and other compounds in certain foods and medications may change your urine color. Beets, berries and fava beans are among the foods most likely to affect urine color.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.Most changes in urine color are harmless and the color will go back to normal within a day or two.rine with a bluish tint can mean some type of bacterial infection or may just mean that you have a high level of calcium. If this persists, give your health care professional a call. Brown urine can indicate a potentially serious condition and you should contact your doctor's office for help.\", \"- ask the disney parks moms panel discover the magic of a disney parks family vacation from one of our knowledgeable online moms this is a question and answer forum to help you plan the best possible disney vacation due to a high volume of submissions we cannot guarantee that all questions will be answered also please keep the number of questions in each submission to a limit of 3\", \"- The results can be urine that is either pink or cola-colored. Sometimes it\\u2019s difficult to tell the difference between dark urine due to dehydration or due to other causes. Dark urine due to dehydration is usually amber or honey-colored. Dark urine due to other causes can be tinged with brown or red. Some people have urine that appears almost syrup-like. This is the case when a person has liver or kidney disease. If you\\u2019re dehydrated, you can have additional symptoms besides dark urine.\", \"Alkaptonuria Alkaptonuria is a rare condition in which a person's urine turns a dark brownish-black color when exposed to air. Alkaptonuria is part of a group of conditions known as an inborn error of metabolism.\", \"Urine color Orange urine may also be caused by dehydration, which can concentrate your urine and make it much deeper in color. Blue or green urine Blue or green urine may be caused by: Dyes. Some brightly colored food dyes can cause green urine. Dyes used for some tests of kidney and bladder function can turn urine blue.\", \"- Urinalysis begins with a macroscopic examination of the urine which describes the color and clarity of the urine. In healthy individuals urine color ranges from pale yellow to amber, depending on their state of hydration. Many factors affect urine color including fluid balance, diet, medications and disease.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.ormal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.\", \"50 Shades Of Yellow: What Color Should Your Pee Be? Here's what your pee might be telling you: 1 Straw-Colored To Transparent-Yellow Pee: This is the normal urine color of a healthy, well-hydrated body. 2 Transparent Or Clear Pee: You should always be properly hydrated, but you can actually drink too much water, which will make your urine virtually colorless.\", \"Aging Skin: Do You Look Older Than You Should? Another Top Cause of Wrinkles: Smoking. Beyond question, smoking is bad for your skin. Smoking accelerates the aging process, wrinkling skin and making you look old beyond your years. Early wrinkling is visible under a microscope in smokers as young as 20.he more years and packs smoked, the more likely wrinkles will occur. Wrinkles are also more likely to be deeper in smokers. Tobacco smoke gives skin an unhealthy color and coarse texture, as well. 1 What you can do: Stop smoking 2 ! Effective tools are available at WebMD and throughout the Internet.\", \"Why Is Pee Yellow? Urine\\u2019s characteristic sunny hue comes primarily from a substance called urobilin. When your body is properly hydrated, the urobilin is diluted with plenty of water, and your pee is a nice, normal buttercup color. Amber or ale-colored urine is a reliable sign of dehydration, and clear pee is a signal that it\\u2019s time to put down the Nalgene.\", \"- The first of the physical properties to be considered is color. The color of urine often varies with its concentration and is most often reported as some shade of yellow (straw, light yellow, yellow, dark yellow, and amber). Normal urine can be found in any of these colors, with the exception of the \\u2018amber\\u2019 color.\", \"Urine color Normal urine color ranges from pale yellow to deep amber \\u2014 the result of a pigment called urochrome and how diluted or concentrated the urine is. Pigments and other compounds in certain foods and medications may change your urine color. Beets, berries and fava beans are among the foods most likely to affect urine color. Many over-the-counter and prescription medications give urine vivid tones, such as raspberry red, lemon yellow or greenish blue.\", \"Foamy Urine \\u00e2 Causes, Treatment, Diagnoses Foamy Urine. Urine is produced by the kidneys are discharged through urethra. Like any other metabolic wastes, urine is toxic and if retained in the body could produce undesirable illness. Normal color of the urine is amber or pale yellow.\"], \"pos_scores\": [97.5], \"neg_scores\": [86.5, 91.6875, 97.1875, 89.75, 90.875, 97.4375, 92.5625, 93.75, 92.5, 96.1875, 92.125, 97.375, 97.6875, 89.125, 96.6875, 90.4375, 91.0625, 97.125, 97.625, 92.0, 90.25, 96.875, 97.5625, 97.6875, 95.125], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"is autoimmune hepatitis a bile acid synthesis disorder\", \"pos\": [\"- Inborn errors of bile acid synthesis can produce life-threatening cholestatic liver disease (which usually presents in infancy) and progressive neurological disease presenting later in childhood or in adult life.he neurological presentation often includes signs of upper motor neurone damage (spastic paraparesis). The most useful screening test for many of these disorders is analysis of urinary cholanoids (bile acids and bile alcohols); this is usually now achieved by electrospray ionisation tandem mass spectrometry.\"], \"neg\": [\"Bile acid Bile acid. Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming primary bile acids. The sodium and potassium salts of bile acids are called bile salts. Primary bile acids are those synthesized by the liver.\", \"Overview When bile ducts are damaged, as in primary biliary cirrhosis, harmful substances can build up in your liver and sometimes lead to irreversible scarring of liver tissue (cirrhosis). Primary biliary cirrhosis is considered an autoimmune disease, in which the body turns against its own cells. Researchers think it is triggered by a combination of genetic and environmental factors.\", \"Bile acid malabsorption Bile acid malabsorption was first recognized in patients with ileal disease. When other causes were recognized, and an idiopathic, primary form described, a classification into three types was proposed: 1 Type 1: Bile acid malabsorption, secondary to ileal resection, or ileal inflammation (e.g. in Crohn's disease).ile acid malabsorption was first recognized in patients with ileal disease. When other causes were recognized, and an idiopathic, primary form described, a classification into three types was proposed: 1 Type 1: Bile acid malabsorption, secondary to ileal resection, or ileal inflammation (e.g. in Crohn's disease).\", \"- Czaja et al have shown that patients with autoimmune hepatitis who have positive test results for actin antibody are younger, more commonly test positive for human leukocyte antigen (HLA)\\u2013DR3, and required transplantation more frequently than patients with ANAs who test negative for actin antibody.\", \"- An autoimmune liver disease panel is a group of tests that is done to check for autoimmune liver disease. An autoimmune liver disease means that the body's immune system attacks the liver. These tests include: Anti-liver/kidney microsomal antibodies. Anti-mitochondrial antibodies. Anti-nuclear antibodies.\", \"- Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming bile salts.\", \"- Autoimmune hepatitis is a type of liver inflammation in which the body\\u2019s immune cells attack healthy liver cells after mistaking them for disease-causing foreign substances.\", \"- Worldwide, viral hepatitis is the most common cause of liver inflammation. Other causes include autoimmune diseases and ingestion of toxic substances (notably alcohol), certain medications (such as paracetamol), some industrial organic solvents, and plants.n autoimmune hepatitis, the immune system attacks the liver due to the autoimmune disease. In some hepatitis, often including hepatitis caused by alcoholism, fat deposits accumulate in the liver, resulting in fatty liver disease, also called steatohepatitis.\", \"Recent advances in the understanding of bile acid malabsorption Introduction. In patients with bile acid malabsorption (BAM), a larger amount of bile acids than normal spill into the colon, where they stimulate electrolyte and water secretion which results in the typical symptoms of BAM: chronic watery diarrhoea.ntroduction Bile acid malabsorption (BAM) is a syndrome of chronic watery diarrhoea with excess faecal bile acids. Disruption of the enterohepatic circulation of bile acids following surgical resection is a common cause of BAM.\", \"Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk However, the fact that there is a link between the two is not in dispute; multiple studies have shown that patients who suffer from celiac disease have a significantly higher risk of having other types of autoimmune diseases, including autoimmune hepatitis.\", \"- Many of the agents that can induce autoimmune hepatitis can also induce other autoimmune conditions or symptoms such as a lupus-like syndrome (minocycline), pneumonitis (nitrofurantoin), hemolytic anemia (methyldopa) and acute thyroiditis (interferon alfa).\", \"Kubla Khan Some of Coleridge's contemporaries denounced the poem and questioned his story of its origin. It was not until years later that critics began to openly admire the poem. Most modern critics now view Kubla Khan as one of Coleridge's three great poems, along with The Rime of the Ancient Mariner and Christabel.\", \"The Celiac and Autoimmune Thyroid Disease Connection Celiac disease, which is sometimes referred to as celiac sprue or sprue, is an autoimmune disorder that attacks the cells lining the intestine. This attack is a reaction to the presence of gluten, a protein found in wheat, rye, and barley. Eating these foods causes inflammation, which in turn makes it difficult for the body to properly absorb nutrients from foods. Celiac disease is a that damages the small intestine. People with celiac disease cannot eat gluten, a protein found in wheat, barley, and rye. Some of the common symptoms of celiac disease include the following: Intestinal difficulties. Abdominal bloating and pain. Nausea. Gas. Diarrhea.\", \"Autoimmune Liver Diseases Doctors have identified two main forms of autoimmune hepatitis: 1 Type 1 autoimmune hepatitis. This is the most common type of the disease. 2 Type 2 autoimmune hepatitis. Although adults can develop type 2 autoimmune hepatitis, it's most common in children and young people.\", \"- Case Reports in Infectious Diseases. Acute Acalculous Cholecystitis due to Viral Hepatitis A. Copyright \\u00a9 2013 Safak Kaya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\", \"Autoimmune Hemolytic Anemia Autoimmune Hemolytic Anemia. Introduction. Autoimmune Hemolytic Anemia (AIHA) is characterized by production of antibodies that bind to antigens on the erythrocyte surface. These antibodies then cause the destruction of RBCs thus shortening their life span.\", \"Cirrhosis Survival rates after transplantation are excellent. Liver Transplantation for Patients with Autoimmune Hepatitis. The outlook is also good for patients who have autoimmune hepatitis who require a transplant. Survival rates are about 90% after 1 year, and 70 - 80% after 5 years.\", \"How Old Do You Have To Be To Rent A Car? There is an industry standard in how old do you have to be to rent a car. At almost every rental agency, the minimum age requirement to rent a car is 25. There is some variance, however. Depending on the region and the country, the minimum age for the driver could be between 21 and 24 years old. Be aware, though, that you will have to pay additional fees because of it. There are some companies that will rent to younger drivers. The age range is anywhere from 18 years old to 21 years old.\", \"Autoimmune Hepatitis Points to Remember. 1 Autoimmune hepatitis is a chronic\\u2014or long lasting\\u2014disease in which the body's immune system attacks the liver and causes inflammation and damage. 2 Autoimmune hepatitis is a serious condition that may worsen over time if not treated. Autoimmune hepatitis can lead to cirrhosis and liver failure. Autoimmune hepatitis is more common in females. The disease can occur at any age and affects all ethnic groups. Autoimmune hepatitis is classified as type 1 or type 2.\", \"SD School for the Blind & Visually Impaired - Eye Conditions Less often, chronic hepatitis results from alpha1-antitrypsin deficiency (a hereditary disorder), celiac disease, a thyroid disorder, or, in children and young adults, Wilson disease\\u2014a rare hereditary disorder involving abnormal retention of copper in the liver (see Wilson Disease).\", \"What is an anti-smooth muscle antibody (ASMA) test? An F-actin antibody test, in addition to an ASMA test, may improve the ability to detect autoimmune hepatitis over other conditions. Because test results require interpretation, especially in relation to other tests that may have been performed, it\\u2019s important to talk to your doctor about your specific results. A diagnosis of autoimmune hepatitis means that your immune system is mistakenly making antibodies that attack healthy cells in your liver.\", \"Autoimmune Hepatitis Outlook of Autoimmune Hepatitis. Autoimmune hepatitis (AH) is a dangerous disease and without any treatment, around 50% of patients with severe AH will die within 5 years and most patients will die within 10 years of disease onset. Treatment with immunosuppressant drugs improves the chances for survival significantly.\", \"SOLUBLE LIVER ANTIGEN Clinical Significance. Anti-soluble liver antigen antibodies are detected in 10-30% of patients with type 1 autoimmune hepatitis (AIH), but not in patients with type 2 AIH, primary sclerosing cholangitis or primary biliary cirrhosis. The antibody is directed against a UGA suppressor tRNA-associated protein.\", \"Despite popular belief, celiac disease is a SERIOUS GENETIC AUTOIMMUNE DISEASE, not the latest fad diet. Celiac disease is a serious genetic autoimmune disease. It is triggered by consuming a protein called gluten, which is found in wheat, barley and rye. When people with celiac disease eat foods containing gluten, their immune system responds by damaging the finger-like villi of the small intestine.\", \"Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk. As many as 9% of patients with autoimmune hepatitis also have CD. As a major organ, the liver performs many essential tasks in digestion, so it may come as no surprise that patients with celiac disease also frequently have liver ailments, including autoimmune hepatitis.\"], \"pos_scores\": [93.875], \"neg_scores\": [91.125, 91.0, 89.375, 89.75, 90.25, 91.0, 92.25, 90.625, 89.5, 90.25, 90.625, 83.8125, 89.25, 91.375, 86.9375, 87.8125, 89.125, 85.5, 91.5, 89.625, 89.5, 90.75, 89.5625, 89.0625, 90.5], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"elegxo meaning\", \"pos\": [\"The Ministry of the Holy Spirit The word convict here (elegcw /elegxo) means to bring to light or expose error often with the idea of reproving or rebuking. It brings about knowledge of believing or doing something wrong, but it does not mean that the person will respond properly to that knowledge. Our usage of the English word, convict, is similar.\"], \"neg\": [\"XOXO means \\u00e2hugs and kisses\\u00e2 but why? XOXO means \\u201chugs and kisses\\u201d but why? What's the reasoning behind abbreviating hugs and kisses as X's and O's? Some say X is for hugs and O is for kisses, and some say the other way around; but why X and O, and why are they doubled? In my experience 'X' for kiss is universal.\", \"Elotes preparados, Mexico Elotero is the name given to the person selling elotes, a word from the nahuatl n\\u00e1huatl, elotl which \\u2018 means tender ear of \\u2018. maize\", \"- Definition of ambidextrous. 1 1 : using both hands with equal ease an ambidextrous baseball player. 2 2 : unusually skillful : versatile. 3 3 : characterized by duplicity : double-dealing.\", \"- el\\u00b7e\\u00b7gi\\u00b7ac. adj. 1. Of, relating to, or involving elegy or mourning or expressing sorrow for that which is irrecoverably past: an elegiac lament for youthful ideals.2.legiac. adj. 1. (Literary & Literary Critical Terms) resembling, characteristic of, relating to, or appropriate to an elegy.\", \"Attributes of Elegu\\u00c3\\u00a1 Elegu\\u00e1, Lord of the Crossroads. Attributes of Elegu\\u00e1. Elegu\\u00e1 (Eleggu\\u00e1) is sometimes represented as a child, and sometimes as an old man. He represents the beginning and end of life, and the opening and closing of paths in life. Sometimes known as the trickster, he likes to play jokes on people. He enjoys candy and toys.\", \"- The prefix tropo-denoting turning, a change, is derived from the Greek word tropos, a turning. The troposphere is the lowest level of atmosphere in which the temperature fall \\u2026 s as height increases.ropo-comes from the Greek tropos which means turning.. Meso-comes from the Greek mesos which means middle.. Thermo-comes from the Greek thermos which means hot.. Exo- \\u2026 comes from the Greek ex\\u014d which means outside..\", \"\\u00c2\\u00a1Qu\\u00c3\\u00a9 oso! Hacer el oso Hacer el oso means create an embarrassing situation, for example, by falling down at a party and spilling your drink everywhere. No quiero llegar antes de tiempo para no hacer el oso. I don\\u2019t want to look stupid by arriving early. Note that you aren\\u2019t making yourself into a bear by doing something stupid.\", \"- Strato means a combining form representing stratus This means layer. here are some examples that have the word Strato in it The stratosphere is the next highest atmospheric \\u2026layer. There are no clouds in the stratosphere. Commercial jets fly in the lower part of the stratosphere.\", \"Eleuthero Root Extract Benefits, Uses & Side Effects Loading ... Eleuthero root is an adaptogenic herb in the ginseng family that is used to boost energy levels, enhance stamina, promote overall health and increase athletic performance. It has been used as a natural medicine in China, Korea and Russia for many hundreds of years.\", \"- Definition of Elegy. An elegy is a mournful poem, usually written in remembrance of a lost one for a funeral or as a lament.An elegy tells the traffic story of an individual, or an individual\\u2019s loss, rather than the collective story of a people, which can be found in epic poetry.ignificance of Elegy in Literature. The definition of elegy as we know it now only came about in the sixteenth century. In ancient Greece, an elegy was any poem written in elegiac couplets, and could deal with any subject matter, including love and battle, along with death.\", \"Glossary An elegy is a poem of mourning; this is often the poet mourning one person, but the definition also includes Thomas Gray's 'Elegy Written in a Country Churchyard', which mourns all the occupants of that churchyard, and looks into the future to mourn the poet's own death.n elegy is a poem of mourning; this is often the poet mourning one person, but the definition also includes Thomas Gray's 'Elegy Written in a Country Churchyard', which mourns all the occupants of that churchyard, and looks into the future to mourn the poet's own death.\", \"- !meaning1 meaning2!e.g. hard!\\u00d2difficult\\u00d3 \\u00d2durable, solid\\u00d3!Single lexical entry Homophony!Homophones!morpheme1 e r\\u00d5: p o u morpheme2 meaning1 meaning2!e.g. pass (\\u00d4I\\u00d5m going to pass\\u00d5)!\\u00d4abstain\\u00d5 \\u00d4succeed\\u00d5!Distinct lexical entries Puns and Zeugmas Ambiguous words used in different senses in parallel syntactic construction.\", \"- GIGO. GIGO is an acronym which stands for 'Garbage In, Garbage Out'. It tends to be used when talking about data entry and databases. Bascially it means that if you enter inaccurate or wrong data then you have really entered garbage. Then when you want to do some queries, searches or reports, you will only get incorrect, wrong or inaccurate data out-garbage.\", \"XEG-AM XEG-AM is a Class A radio station on clear-channel frequency 1050 kHz in the state of Nuevo Leon, Le\\u00f3n. mexicot is licensed for Guadalupe, Nuevo Leon le\\u00f3n and brands itself as Serving. Monterrey known for its border blaster status in the, 1950s it now uses the Name La ranchera De monterrey and broadcasts ranchera. music\", \"What does el oso mean? What does natory mean? &Menos el Oso Natory is the name of a Wine Store and furniture shop. It is also a surname. What does 'El oso es grande' mean? The translation of 'El oso es grande' from Spanish to English is 'The bear is... What does menos el oso mean in spanish? The Spanish phrase menos el oso means minus the bear in English. ! What does me amor el oso de peluche mean? Me amor el oso de peluche translates from Spanish to English and means I love... What does menos el oso mean in english? The literal translation of menos el oso from Spanish to English is unless the... See All Questions\", \"\\\"What does it mean when a girl always uses \\\"\\\"cx\\\"\\\" every time you message each other?\\\" Cx is a smiley face, but also an alternative shortening of the word sex. Used to slip sex into a conversation, while smiling. Also cx may be the awesome type of debate which has people speaking 250 words/minute and ensures an intensifying feeling throughout the round. Also cx means sincerely or the meaning of CX in text slang is cancelled. See more types What does \\u201ccx\\u201d mean?\", \"Root Words & Prefixes: Quick Reference Latin: ambidextrous - able to use both hands equally; ambiguous - having more than one meaning; ambivalence - conflicting or opposite feelings toward a person or thing: ambul: walk, move: Latin: amble - to walk in a slow, relaxed way; ambulant - walking or moving around; ambulance - a vehicle that moves a patient: ami/o: love: Latin\", \"elegiac If there's one song on your playlist that always brings tears to your eyes, maybe it's because it has an elegiac quality. The adjective elegiac is useful when you're talking about music, a movie, a book, or another work of art that has a sorrowful tone. Sometimes elegiac specifically refers to something or someone that's gone: a person who's died, or a time in the past, especially if you feel a sense of longing for it. You can speak in an elegiac way, or sing an elegiac tune. The word comes from the Greek elegos, poem or song of lament..\", \"Meaning of nameEleuterio Epicurian, Eleuterio is often a bit of a ladies man and is extremely sensual. In romance, he needs to be admired; one could say he is a little vain, noblesse oblige. Likewise, he needs to have equal admiration for his partner, who he likes to show off in public, almost like an accessory.\", \"elegy Definition of elegy for English Language Learners. : a sad poem or song : a poem or song that expresses sorrow for someone who is dead.\", \"- equi-Meaning equal. Related terms . igual; equi\\u00e1ngulo; equidad; equidistancia; equil\\u00e1tero; equilibrar; equilibrio; equimolecular; equimosis; equinoccial\", \"- Meaning & History. From the Hebrew name \\u05d9\\u05b8\\u05e2\\u05b5\\u05dc (Ya'el) meaning ibex, mountain goat. This name appears in the Old Testament belonging to a woman who kills the captain of the Canaanite army, giving the victory to Israel.eaning & History. From the Hebrew name \\u05d9\\u05b8\\u05e2\\u05b5\\u05dc (Ya'el) meaning ibex, mountain goat. This name appears in the Old Testament belonging to a woman who kills the captain of the Canaanite army, giving the victory to Israel.\", \"- es lo que hay exprexpresi\\u00f3n: Expresiones idiom\\u00e1ticas, dichos, refranes y frases hechas de tres o m\\u00e1s palabras (Dios nos libre, a lo hecho, pecho). (lo que tenemos) is what you get exprexpression: Prepositional phrase, adverbial phrase, or other phrase or expression--for example, behind the times, on your own..\", \"- see also autoeroticism autosexual autosexual noun autosexuality characterized by self sex contact usually as a genital act masturbation with or without an accompanying erotic fantasy or ritual or rarely as a long term sexuoerotic status without a partner from greek auto self sexee also limbic system cervix adjective cervical the neck or neck like part of an organ specifically the neck of the lower part of the uterus or womb where the vagina and uterus unite 2 the cervix of the uterus is at its lower end where the uterus and the vagina meet\", \"Thread regardingHewlett Packard Enterprise (HPE) layoffs DXC (dumb--s core) is derived from making fun of sXe (straightedge) It is a clique made up of cool kids that like good music, but are a bit ditzy at times.. Well, to tell yout he truth, they are dumb--ses.\"], \"pos_scores\": [99.25], \"neg_scores\": [87.0, 84.6875, 87.1875, 89.9375, 86.6875, 86.875, 85.3125, 85.9375, 85.9375, 88.125, 87.8125, 87.8125, 85.625, 84.375, 85.5625, 86.3125, 86.875, 88.0625, 86.0, 89.25, 86.25, 87.0, 86.25, 88.5, 87.25], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"how much does an average person make for tutoring\", \"pos\": [\"- In-home tutors can earn anywhere from $10 to $80 an hour, depending on the type of lesson, the student\\u2019s skill and age level and the tutor\\u2019s experience. Tutors often charge more for older students or those who require more advanced lessons.\"], \"neg\": [\"- The price of a math tutor depends on the ability of the tutor as well as the type of tutoring required for the student or apprentice. How much is it? 1 Depending on your location, math tutors\\u2019 charges will vary. 2 On average, a tutor can cost anywhere from $15 to as much as $150 per hour. math tutor is a person who specializes in math and is employed in helping educate others. Before the wide availability of the internet, tutoring required the personal presence of the tutor. Today, there are online tutoring sessions available with the help of the internet.\", \"- 1 Individuals generally charge according to their level of education and experience. 2 Expect to pay $10 to $15 per hour for a high school student, and up to $75 per hour for a certified teacher with experience. 3 A teacher trained and qualified to work with children with special needs will likely charge more. A tutoring agency will help match you with a tutor. 2 Most agencies charge a registration fee, plus a fee for individual tutors. 3 Rates could start at $25 per hour and go up according to the subject area and the tutor's level of expertise. 4 Ask about any additional fees, such as for extra testing.\", \"- For example the median expected annual pay for a typical Teaching Assistant (College) in the United States is $15,659, so 50% of the people who perform the job of Teaching Assistant (College) in the United States are expected to make less than $15,659.\", \"Kumon Group Tutor Hourly Pay The typical Kumon Group Tutor salary is $9. Tutor salaries at Kumon Group can range from $7-$14. This estimate is based upon 90 Kumon Group Tutor salary report(s) provided by \\u2026 employees or estimated based upon statistical methods. See all Tutor salaries to learn how this stacks up in the market.\", \"How much do SAT tutoring centers charge? Here is an example I found online: cost for SAT tutoring: Kaplan = $200 + per hour Princeton Review = $125 to $315 per hour SATtutors.com = Tutors set their own rates.ould be $25 per hour to $50 to $100 plus per hour Sylvan = $100 + per hour There is everything from one-on-one tutoring for individual attention to larger classes.\", \"- How much does a Online Tutor make? The average Online Tutor salary is $30,000. Filter by location to see Online Tutor salaries in your area. Salary estimates are based on 60 salaries submitted anonymously to Glassdoor by Online Tutor employees.\", \"- By state, California offers the highest pay at $13.00 per hour. Those who have five to nine years of work experience see average pay of $10.97 per hour. Overall, the greater share of Tutor Time Learning Centers folks have one to four years of experience and earn an average about $9.68 per hour.arly Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00.\", \"How Much Should Tutoring Cost? On the other, a general math tutor in Nebraska who is just getting started, may have to charge a rate of $40.00 to get new clients. Students, parents and tutors need to consider subject, location, experience, teaching background, frequency of tutoring, etc. when determining how much tutoring should cost.\", \"How Much Does Tutoring Cost? Average cost of tutoring: $48/hour. Although tutors can be found on Craigslist for $20/hour (that\\u2019s a cheaper deal), you may be swapping quantity for quality. Craigslist doesn\\u2019t specialize in providing tutors, so the site doesn\\u2019t screen their applicants.\", \"- Early Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00.The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.egarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00. The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.\", \"- A School Custodian earns an average wage of $12.24 per hour. Pay for this job does not change much by experience, with the most experienced earning only a bit more than the least. People in this job generally don't have more than 20 years' experience.\", \"How Much Do Tutors Charge? If you want to know how much a particular tutoring company costs, call up their sales center and ask about the costs of different programs. According to Payscale.com, in 2011, tutors in the 25th to 75th percentile range working for private companies made from $10.10-$20.35. However, these figures do not include the amount of money that goes to the company providing you with the tutor.\", \"- 1 On average, a tutor can cost anywhere from $15 to as much as $150 per hour. 2 Online tutoring can range from $15 to $75 per hour, depending on the service that is used. 3 Group tutoring that is ideal for those in a classroom can be significantly lower as the group will pay the tutor. math tutor is a person who specializes in math and is employed in helping educate others. Before the wide availability of the internet, tutoring required the personal presence of the tutor. Today, there are online tutoring sessions available with the help of the internet.\", \"What's the Going Rate for a Tutor? The cost of a private tutor who comes to your home varies according to the grade level of the student, the education and experience of the tutor, and what city you live in. The national average price is about $41 per hour, according to Tutorspree. A high school student might charge as little as $10 per hour, while an experienced tutor in the country\\u2019s most expensive market, Manhattan, is considered reasonable at $85 to $150 per hour, with Ivy League graduates earning $200 per hour or more. Tutors don\\u2019t have to live locally.\", \"How much do teachers make per hour? How much do teachers make per hour? The median annual salary of a teacher in 2012 was $55,418. The average school year lasted 180 days at 6.7 hours per day, giving an average hourly wage of $45.95, according to The Daily Tarheel and the National Center for Education Statistics.\", \"Tutor Salary Pay by Experience for a Tutor has a positive trend. An entry-level Tutor with less than 5 years of experience can expect to earn an average total compensation of $31,000 based on 1,027 salaries provided by anonymous users. Average total compensation includes tips, bonus, and overtime pay.\", \"How Much Do Tutors Charge? Tutors who teach unusual subjects, like Latin, usually charge more than tutors who teach common subjects such as reading. You can expect private tutors to charge anywhere from $20-$100 per hour depending on his or her qualifications, location and what your child needs.f your child needs more casual help, you can save money and time by hiring an online tutor. Online tutoring is often cheaper than in-person lessons. If your child only needs help with a math problem or reading a certain passage, some sites charge by the minute with rates typically around 46-60 cents a minute.\", \"How Much Do Tutors Charge? according to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutorccording to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutor\", \"- The average cost for a SAT tutor is $50/hr. You are likely to spend between $30 and $75 per hour. Exact price may vary depending on your area and project details. Next Step: Find out exactly how much your project will cost.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields. $50-$100 per hour. As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour. I could probably charge a little more now, if I were still working as a private tutor.\", \"How Much Does Tutoring Cost? 36 By Design 3.0/5. Average cost of tutoring: $81.25/hour. If you hadn\\u2019t guessed it by the name of the company, 36 By Design only does one kind of tutoring \\u2014 ACT Test Prep. It\\u2019s their opinion that by only focusing on one subject, they can perfect it.\", \"How Much To Charge For Tutoring How Much To Charge For Tutoring Factors that affect how much a tutor charges include location, education, and experience. A typical tutor will charge between $17 and $45 per hour, according to our tutor pricing calculator.\", \"- Excluding overtime pay (which bumps median pay up to $21.00), people who work for Tutor Time Learning Centers earn a median wage of $10.00 per hour.egarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00. The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields $50-$100 per hour As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour.\", \"Senator Armstrong 2016 Presidential Ad Fed up with the state of the country, Senator Armstrong decides to run for the oval office in 2016 instead of 2020. Disclaimer: This is for entertainment purposes only.All audio recordings are property of the artist(s), management, and/or music publishing companies.No copyright infringement is intended.ed up with the state of the country, Senator Armstrong decides to run for the oval office in 2016 instead of 2020. Disclaimer: This is for entertainment purposes only.\"], \"pos_scores\": [95.4375], \"neg_scores\": [94.5, 93.5625, 90.25, 91.75, 91.3125, 96.0625, 92.5, 92.25, 94.9375, 90.8125, 88.6875, 94.625, 95.8125, 94.375, 90.5, 94.8125, 93.625, 93.4375, 93.0625, 93.1875, 94.4375, 94.6875, 92.25, 93.0625, 87.0625], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"can you use a calculator on the compass test\", \"pos\": [\"Calculator Guidelines for COMPASS \\u00c2\\u00ae Testing Calculators may be used on the COMPASS Pre-Algebra, Algebra, College Algebra, Geometry, and Trigonometry tests provided they meet the requirements listed below. Electronic writing pads or pen-input devices\\u2014The Sharp EL 9600 is permitted. Models with paper tapes\\u2014The paper must be removed.\"], \"neg\": [\"North References for Navigating with Map, Compass and GPS Adjusting the North Reference on Your Compass to True or Grid North Many compasses allow you to adjust the position of the lines used to align the magnetic needle with respect to the angle measuring dial.\", \"Study Guide Your COMPASS\\u00ae Test Success Starts Here. Studying the material on this site will help you improve your score on the COMPASS Math Assessment Test. A higher score will let you skip the most basic university math classes, saving you money and time. This site will help you learn about test strategy, review test material, and practice for the exam; all for free!\", \"TI-84 Plus CE Graphing Calculator The TI-84 Plus CE is approved for use on the following exams: 1 PSAT*, SAT*, and ACT\\u00ae college entrance exams. 2 AP* Exams that allow or require a graphing calculator. 3 Approved for use on the IB exam.\", \"Which calculator do I need? A good calculator will probably cost you at least $100, but you may be able to rent, buy used, or even borrow from your school library. The TI-84+ is a good calculator for algebra and geometry (now available with a color screen), and is a newer version of the classic TI-83+.\", \"How to Use a Compass You can use a bearing to get to a location any time you know where you are on a map: 1 Set your compass on the map so that the straight side of the baseplate lines up between your current position (1a) and the map location for a destination like a campsite (1b). 1 Make sure the direction of travel arrow is pointing in the general direction of that campsite (in other words, it's not upside down).\", \"How to Use the iPhone Compass App The iPhone digital compass app is located within the utilities icon on your phone. Follow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options.2 Click on the compass icon to select the compass app. 3 Hold phone flat in the upwards-facing palm of your hand. 4 Extend your arm straight out in front of you, as you would when holding a magnetic compass.ollow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options. 2 Click on the compass icon to select the compass app.\", \"Calculators Multiple Power Options Choose calculators with multiple power options to maximize your time at the office. Some models can be plugged directly into the wall, so there's no need to remove the batteries and interrupt your work during the charging process.hether you're teaching a math class or running an accounting business, calculators can help you get the job done. Each one is built with a mix of simple and advanced functions for maximum efficiency. Choose the best model for your business from brands such as Casio, Canon, and Texas Instruments.\", \"- The COMPASS test is a self-adjusting, multiple choice test that is taken at the computer. The. answer to your current question will determine the next question; it will stop once it has determined. your level.\", \"How to Use the TI-84 Plus Calculator to Convert Sine, Tangent & Cosine to Angles You can easily convert the basic trigonometric functions into angles measured in degrees or radians using a TI-84 Plus calculator. The TI-84 Plus is capable of going in both directions -- from the angle to the trigonometric measure and back.et your calculator to Degrees mode by pressing the MODE key, pressing the down arrow until you reach the row with the options Degree and Radian, highlighting Degree using the right arrow key, and pressing ENTER.\", \"- with Answers-Sample 1. Math questions, with answers, similar to the questions in the compass math test are presented. The questions are designed to reflect the major topics covered in the compass test: Numerical skills/pre-Algebra, algebra, college algebra, geometry and trigonometry.he questions are designed to reflect the major topics covered in the compass test: Numerical skills/pre-Algebra, algebra, college algebra, geometry and trigonometry.\", \"- Nursing Compass Entrance Exam. COMPASS is a comprehensive, computer-adaptive testing program that quickly and accurately assesses students' skill levels in reading and mathematics. Because it is adaptive, the length of the test will vary based upon the individuals' knowledge.*If you are under 21 years of age and have taken the ACT, you may not be required to take the COMPASS exam if you scored a minimum of 19 in Math, 19 in Reading, and 19 in English (a 19 composite score is not accepted).\", \"- Compass is awesome! Powerful off-the-bat visualization and analytics that are actually actionable. There's no other benchmarking tool that's as effective out there and it has really helped us focus on improving those metrics that deviated too much from the norm. Really solid support from the developers as well.\", \"- Either way, here\\u2019s how to find and use the compass and level app. Launch the Compass app with a quick tap. If this is the first time, you\\u2019ll need to gyrate the iPhone around a bit to completely calibrate it.Now, just hold the iPhone out from your body, like you would reading a text message.ither way, here\\u2019s how to find and use the compass and level app. Launch the Compass app with a quick tap. If this is the first time, you\\u2019ll need to gyrate the iPhone around a bit to completely calibrate it.\", \"How do I reset the compass on an '04 Ford F150 upper console? 8. Press the RESET control to start the compass calibration function. 9. Slowly drive the vehicle in a circle (less than 3 mph [5 km/h]) until the CIRCLE SLOWLY TO CALIBRATE display changes to CALIBRATION COMPLETED. It will take up to five circles to complete calibration. 10. The compass is now calibrated. couldn't get the picture of the zones to come up, but it's on page 72 of your manual.\", \"- Compass practice tests are an effective way to study for your college placement exams. Our free Compass sample tests provide you with an opportunity to assess how well you are prepared for the real Compass test, and then focus on the areas you need to work on.ompass Test. Doing well on your Compass Test can help you start college on the right foot. Discover test taking tips, score information, and Compass Exam Prep. Use our free Compass sample exams.\", \"OH NO, I am not allowed a calculator on the MCAT OH NO, I am not allowed a calculator on the MCAT!!! If you reading this you have probably completed all the general requirements to take the MCAT. Thinking back over your semesters of chemistry you would of used your calculator a lot to complete the math problems ranging from the basics of stoichiometry to the complex problems of solving the pH of a weak acid.\", \"Can you use a calculator on GED test? Best Answer: yea u CAN use a Calculator on a GED test i think on the math part is everything u took in high school that was math.\", \"How to Use the iPhone Compass App 1 Click on the compass icon to select the compass app. 2 Hold phone flat in the upwards-facing palm of your hand. 3 Extend your arm straight out in front of you, as you would when holding a magnetic compass.ollow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options. 2 Click on the compass icon to select the compass app.\", \"Calculators during the TEAS test Jul 1, '12. No calculators allowed. The only pop up help you get is the periodic table in the science portion. I was kind of worried about math too -- especially because i usually freak out on timed exams and go blank on stupid things such as multiplication tables and conversion.\", \"- May I Use a Calculator? The ACT Calculator Policy (effective September 1, 2014) is designed to ensure fairness for all examinees, avoid disturbances in the testing room, and protect the security of the test materials. A permitted calculator may be used on the ACT mathematics test only.\", \"Calculators on the SAT: Tips from Experts Calculator Uses and Limitations. Calculators are allowed on the SAT, and not using them correctly can put you far behind. SAT experts Fred Zhang and Allen Cheng discuss which tips and strategies worked for them in getting perfect scores.\", \"- The COMPASS Placement Test is an untimed, adaptive, computer-based college placement exam. The test changes for each student based on performance. If a student gets a question correct, they next receive a question of greater difficulty and higher value.he COMPASS Placement Test is an untimed, adaptive, computer-based college placement exam. The test changes for each student based on performance. If a student gets a question correct, they next receive a question of greater difficulty and higher value.\", \"The Compass Test: What You Need to Know The Compass Test: What You Need to Know. The COMPASS test is actually a series of computerized tests which were written by ACT, Inc. It is administered by a number of colleges and universities and is used to determine course placement. Find out everything you need to know about the COMPASS test here.\", \"Compass A compass is an instrument used for navigation and orientation that shows direction relative to the geographic cardinal directions, or points. Usually, a diagram called a compass rose, shows the directions north, south, east, and west as abbreviated initials marked on the compass.When the compass is used, the rose can be aligned with the corresponding geographic directions, so, for example, the N mark on the rose really points to the north.Frequently, in addition to the rose or sometimes instead of it, angle markings in degrees are shown on the compass.hen the compass is used, the rose can be aligned with the corresponding geographic directions, so, for example, the N mark on the rose really points to the north. Frequently, in addition to the rose or sometimes instead of it, angle markings in degrees are shown on the compass.\", \"Q: I need help switching an Xbox live account to another Microsoft id, how can I do that? I had made an account a while back, now I can't get on to Xbox live because they're trying to do a proof and I've deleted that email address and there for cant retrieve anything. Now i made a new account is there a way I can transfer my Xbox live gamer tag to this new account so I can get back on Xbox live? 17 people had this question.\"], \"pos_scores\": [100.0625], \"neg_scores\": [87.125, 93.375, 89.375, 87.0, 89.375, 88.5, 86.875, 92.125, 88.4375, 93.125, 92.75, 87.625, 88.4375, 85.3125, 92.3125, 89.5, 93.3125, 88.5, 91.875, 92.4375, 91.25, 92.125, 92.375, 88.4375, 86.25], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"what does physical medicine do\", \"pos\": [\"What Is a Physiatrist? Doctor Directory. A physiatrist practices in the field of physiatry - also called physical medicine and rehabilitation - which is a branch of medicine that specializes in diagnosis, treatment, and management of disease primarily using physical means, such as physical therapy and medications.\"], \"neg\": [\"Types of Doctors Physiatry: A physiatrist is a physician who specializes in physical medicine, which is the curing of injuries and disease by natural methods. Measures that are used include physical therapy, massage, exercise, light and heat.\", \"Physical Therapy Office Forms It helps you move better and may relieve pain. It also helps improve or restore your physical function and your fitness level. The goal of physical therapy is to make daily tasks and activities easier. For example, it may help with walking, going up stairs, or getting in and out of bed.\", \"What Does Therapeutic Ultrasound Do in Physical Therapy? Therapeutic ultrasound is a treatment modality commonly used in physical therapy. It is used to provide deep heating to soft tissues in the body. These include muscles, tendons, joints, and ligaments. Ultrasound in physical therapy is not to be confused with diagnostic ultrasound, which is \\u200ban ultrasound that is used to see the inside of the body, such as checking on a fetus during pregnancy.\", \"physical medicine physical medicine. n. The branch of medicine that deals with the treatment, prevention, and diagnosis of disease by essentially physical means, including manipulation, massage, and exercise, often with mechanical devices, and the application of heat, cold, electricity, radiation, and water. Also called physiatrics, physiatry.\", \"List of photographic equipment makers Some camera makers design lenses but outsource manufacture. Some lens makers have cameras made to sell under their own brand name. A few companies are only in the lens business. Some camera companies make no lenses, but usually at least sell a lens from some lens maker with their cameras as part of a package.\", \"Fitness Trainers and Instructors Physical therapists use different forms of treatment depending on the type of patient they are caring for. Physical therapists, sometimes called PTs, help injured or ill people improve their movement and manage their pain. These therapists are often an important part of the rehabilitation, treatment, and prevention of patients with chronic conditions, illnesses, or injuries.\", \"Frequently Asked Questions How does physical therapy help pelvic pain? There are many reasons for developing pelvic pain issues through a lifespan. Muscles, connective tissue (fascia), and nerves in the abdomen, pelvis, and low back region may all contribute to pelvic pain. Pelvic Floor physical therapists are specifically trained to evaluate and treat muscle spasm and trigger points/tender points that can result from muscle imbalance, scar tissue tension, prolonged nerve compression or stretch injuries, and trauma.\", \"Physical Therapist Physical Therapist. Physical therapists are evidence-based, health care professionals who diagnose and treat individuals of all ages who have medical problems or other health-related conditions that limit their abilities to move and perform functional activities in their daily lives.\", \"Physical Therapists What Physical Therapists Do About this section. Physical therapists use different forms of treatment depending on the type of patient they are caring for. Physical therapists, sometimes called PTs, help injured or ill people improve their movement and manage their pain.These therapists are often an important part of rehabilitation and treatment of patients with chronic conditions or injuries.he work of physical therapists varies by type of patient. For example, a patient experiencing loss of mobility due to stroke needs different care from that given to an athlete recovering from an injury. Some physical therapists specialize in one type of care, such as orthopedics or geriatrics.\", \"What does a Physical Therapist do? Geriatric physical therapy focuses on the unique movement needs of older adults. This includes treatment for conditions such as arthritis, cancer, osteoporosis, Alzheimer's disease, joint replacement and balance disorders. The goal of geriatric physical therapy is to help restore mobility, reduce pain, accommodate physical limitations and increase physical fitness.\", \"What Do Physical Therapists Do? What Do Physical Therapists Do? As a physical therapist, you assess patients and determine treatment, as well as track patients' progress and evaluate the effectiveness of treatment. You also teach patients and families how to properly continue therapy once released from the hospital or medical office.\", \"What is a Physiatrist? What is a Physiatrist? Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons.\", \"Physical Medicine and Rehabilitation By Mayo Clinic Staff. Mayo Clinic specialists in Physical Medicine and Rehabilitation (PM&R) help restore movement and function to people disabled by disease or injury. PM&R physicians (called physiatrists \\u2014 pronounced fiz-e-AT-rists) create therapy plans that consider the unique needs, abilities and objectives of each patient.\", \"The Calorie Requirements for Female Bodybuilders & Weight Loss Calorie Balance. To lose weight, you need to create a calorie deficit, which involves consuming fewer calories than you burn. It takes a deficit of around 3,500 calories to burn 1 pound of fat, so if you're currently maintaining your weight, lowering your daily intake by 500 calories will lead to 1 pound of fat loss each week.\", \"Accredited Colleges Offering Sports Medicine Degrees Other Sports Medicine Specialties. 1 Physical Therapist: Physical therapists use a variety of methods to help patients deal with different physical issues. 2 Nurse: Nurses are an integral part of disease prevention, health promotion, and recovery from illness.\", \"Who Are Physical Therapists? Physical therapists (PTs) are highly-educated, licensed health care professionals who can help patients reduce pain and improve or restore mobility-in many cases without expensive surgery and often reducing the need for long-term use of prescription medications and their side effects.hysical therapists (PTs) are highly-educated, licensed health care professionals who can help patients reduce pain and improve or restore mobility-in many cases without expensive surgery and often reducing the need for long-term use of prescription medications and their side effects.\", \"Physical Therapy for Chronic Pain: What to Expect Reviewed by Melinda Ratini, DO, MS on July 22, 2016. Physical therapy is often one of the best choices you can make when you have long-term pain (also called chronic pain) or an injury. It can make you stronger and help you move and feel better. Ask your doctor to recommend a physical therapist.\", \"Physical Therapy Career Opportunities Physical Therapy and Rehabilitation. Physical therapists (PTs) provide services that help restore function, improve mobility, relieve pain, and prevent or limit permanent physical disabilities of patients suffering from injuries or disease. They restore, maintain, and promote overall fitness and health.\", \"- Physical therapists also are key health care. team members who address prevention initiatives, such as. reducing falls, improving physical activity to mitigate chronic. disease and secondary health conditions, and tailoring wellness. programs for populations that have chronic conditions and/. or disabilities.\", \"- Physical Therapy Basics. Doctors often recommend physical therapy (PT) for kids and teens who have been injured or who have movement problems from an illness, disease, or disability. After an injury, physical therapists work to decrease pain, improve movement, and help kids return to daily activities.\", \"- First, your therapist will try to reduce your pain and swelling. Your physical therapist also may use manual therapy, education, and techniques such as heat, cold, water, ultrasound, and electrical stimulation.Physical therapy almost always includes exercise.ome physical therapists are board-certified in areas such as orthopedics, sports, and neurology and may offer more specialized care. Physical therapists can also specialize in certain types of care, such as: 1 Back and neck pain. 2 Cardiac rehabilitation (rehab). 3 Wound care. 4 Cancer-related problems.\", \"Physical Therapy (PT) Physical therapy aims to improve joint and muscle function (eg, range of motion, strength) and thus improve the patient\\u2019s ability to stand, balance, walk, and climb stairs.For example, physical therapy is usually used to train lower-extremity amputees.hysical therapy aims to improve joint and muscle function (eg, range of motion, strength) and thus improve the patient\\u2019s ability to stand, balance, walk, and climb stairs.\", \"Why Visit a PM&R Physician Why Visit a PM&R Physician. 1 Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons. By taking the whole body into account, they are able to accurately pinpoint problems and enhance performance without surgery.\", \"Department of Physical Therapy A Physical Therapist (PT) is a health care professional who provides direct patient care to persons who have disorders of movement, mechanical, physiological and developmental impairments and functional limitations, whether caused by injury or disease, to help them achieve maximum physical function and mobility.\", \"physical medicine Definition of 'physical medicine'. Word Frequency. physical medicine. noun. the branch of medicine devoted to the management of physical disabilities, as resulting from rheumatic disease, asthma, poliomyelitis, etc. See also rehabilitation (sense 2)\"], \"pos_scores\": [95.5], \"neg_scores\": [94.3125, 93.3125, 91.625, 98.1875, 89.9375, 92.8125, 92.1875, 93.5, 94.1875, 93.0625, 94.1875, 96.125, 96.75, 92.6875, 91.8125, 93.5, 93.0625, 93.3125, 92.9375, 93.0, 92.75, 93.5, 96.6875, 93.5, 96.625], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"what does pending mean on listing\", \"pos\": [\"- Active/Pending = Usually that means the seller would like to get more offers. Savvy agents want back-up offers in place. If you made an offer to purchase a property and the seller agreed to the price, but already has a buyer in contract, you would be the 1st position back-up buyer.\"], \"neg\": [\"Hours Worked Hours Worked | Bleakley Platt & Schmidt, LLP | New York Labor and Employment Attorneys. Employees must be paid for all of the hours that they are required to be on the employer's premises, on duty or at a prescribed work place.\", \"What Does a Pending Status on a House Mean? What Does a Pending Status on a House Mean? Brokers must report when the status of a home for sale changes. Real estate brokers must accurately report the status of a property listing to the multiple listing service, usually spoken of as MLS. Brokers rely heavily on the MLS to market listings and find interested buyers. As a member of an MLS, a listing broker must comply with certain rules and regulations.\", \"- For Sale: Properties which are available for showings and purchase. Active Contingent: Properties which are available for showing but are under contract with another buyer. Pending: Properties which are under contract with a buyer and are no longer available for showings. Sold: Properties on which the sale has closed.\", \"What Does Sale Pending Mean in Real Estate? Typically, the buyer has a week or two to do the inspection and a few weeks to get an appraisal and loan. During this period, the seller can\\u2019t enter into an agreement with another buyer, even though the sale hasn\\u2019t closed. This means another interested buyer could make a \\u201cback-up\\u201d offer. That way, if the first offer collapses, the seller has a back-up offer ready to go. A True Home, Pending Sale. A pending sale has had all contingencies removed.\", \"What is a root canal? The cost of root canals varies depending on the tooth and whether it is being treated by a general dentist or an endodontist. Molars have more canals that need to be filled, so they are more expensive, and endodontists typically charge more due to their specialty training.\", \"What\\u00e2s the Difference Between Contingent and Pending Listings? When a listing has changed status to \\u201cpending\\u201d, the seller has accepted an offer. This is an off-market status. Pending listings are much more common than contingent listings, since it just means the seller and buyer have agreed to the initial terms in the residential purchase agreement.\", \"Active Status? Pending Status? This status may also be used to indicate the property is a short sale with an accepted offer. Pending w/o release, continue to show (PS) or (Pend w/S) \\u2013 Accepted offer on the property, still showing to prospective buyers for offers in back-up position to the first accepted offer.\", \"What Do All Those Real Estate Listing Terms Really Mean? If your offer is accepted as a backup, you\\u2019re in line to go under contract if the first sale falls through. Pending, showing for backup This means the property's owners are actively taking backup offers in case the first one falls through.\", \"Sale pending? \\u00b7 just now. 1 Sale pending means that there is a transaction that is going through Escrow but that has not closed yet. 2 Sale pending can mean anything from an offer being accepted or contracts signed. 3 It means the buyer and seller have agreed on a contract, but closing has not happened yet.\", \"\\\"What does \\\"\\\"option pending\\\"\\\" mean on a real estate listing?\\\" What does option pending mean on a real estate listing? I've been looking for houses on the internet, and came across several listings that said option pending. Does it mean there's an offer on the house and it's pending approval? 2 following.\", \"- Other types of active statuses may be New or Price Change.. New means that the listing has been on the market for 3 - 7 days; Price Change means that the listing experienced a price decrease or increase within the past 3 - 7 days. There may be different types of active listings, depending on the MLS.\", \"Does 'Pending Contact Request' under a contact in skype mean that they have blocked me? up vote 1 down vote. Pending request-It means that the person has just removed you from their Skype contact list but hasn't blocked you. You can however send them messages to them but can't call them. The person can call you if they want. You can send friend request again, if they removed you from contact list by mistake.\", \"\\\"What does \\\"\\\"Pending\\\"\\\" mean? Is there a problem?\\\" According to our documentation on payment statuses, Pending means: This is a payment that has begun, but is not complete. An example of this is someone who has filled out the checkout form and then gone to PayPal for payment. We have the record of sale, but they haven't completed their payment yet. This means that Pending isn't inherently a problem, but it can be an indication of some other problem.\", \"\\\"What does \\\"\\\"Contingent\\\"\\\" mean for a listing status?\\\" Most of these are self-explanatory\\u2026. 1 Active means that the property is for sale. 2 Come on over and make us an offer. 3 Subject to Inspection means that a buyer\\u2019s offer is pending their inspection.\", \"- Homes with a Make Me Move\\u00ae price indicate the amount the owner(s) would be willing to sell for. They are exclusive to Zillow and a great way to learn about homes before they hit the market. A pending listing means a seller has accepted an offer from a buyer. In some cases, the seller will accept backup offers.\", \"MLS Rules & Regulations FAQ YES, MLS rules require that if a seller has signed acceptance of an offer, the listing status must be changed to Contingent or Pending. Lender approval of a \\u201cShort Sale\\u201d is treated as a contingency and does. not change the timing of the required change to Contingent or Pending status.\", \"Under Contract: \\u00e2Contingent\\u00e2 vs. \\u00e2Pending\\u00e2 \\u201cContingent\\u201d means it can be shown. \\u201cPending\\u201d means it cannot be shown, although there may still be contingencies. Contingent : Contingent status indicates that a property under contract is subject to an additional contingency or condition, i.e. \\u201cContingent Sales Addendum\\u201d, Due Diligence Period, etc.\", \"- Payments to some eBay sellers may be shown as pending in your PayPal account, and may not be available for a period of time to allow for successful fulfillment. Common reasons your funds might be pending. These are the most common reasons your funds would be unavailable for a period of time: 1 You're a new seller or have limited selling history, which means you have not yet met all of the following criteria: It's been more than 90 days since your first successful sale. You've made more than 25 sales transactions.\", \"what is the definition of pending litigation Would that Pending litigation refers to any suits which have been filed, but remain unresolved. A notice of claim could be included, if that referenced a notice of claim which had been filed in court.It appears that you may be referring to lis pendens which is filed with real property records to place potential buyer's on notice of pending litigation involving the real property. Thank you.his is in reference to a Homeowners Association filing a claim with the builder under the 10 year time frame. (Sec 1375) No formal complaint has been filed. They are in the pre-litigation stages.\", \"Under Contract: \\u00e2Contingent\\u00e2 vs. \\u00e2Pending\\u00e2 A listing in Contingent status will not expire at the end of the listing contract term. Pending: Pending status indicates that a property is under contract and is not available for showings. Listings in this status may include contingencies. The listing will not expire at the end of the listing contract term.\", \"- What does pending disposition mean in legal terms? A pending disposition in legal terms means that no decision has been reached yet. When someone is arrested and they have a pending disposition, it means that it has not ye \\u2026 t been determined whether they have actually committed a crime or not.\", \"Why is the order status pending? For some purchases, such as services, rentals, or hotel stays, the seller will complete the payment after your purchase is fully delivered. The status of an order will continue to display as pending until it expires, is completed, or has been voided.\", \"What does pending mean? im asking what pending is because im installing some games on my laptop and all it says is its pending\", \"What is a pending transaction? Pending transactions include credits and debits that have not yet posted to your account. They may also include holds applied to certain transactions or your balance. Pending transactions are listed in lowest to highest dollar amount order, and are not listed in the order that they post to your account.\", \"Option Pending vs Pending continue to show OP - Option Pending - Listings that are under contract and the seller and buyer have agreed to use the \\u201cTermination Option\\u201d in paragraph 23 of the standard TREC contract. PS - Pending Continue to Show - Used for listings currently under contract but are still available to show.\"], \"pos_scores\": [96.9375], \"neg_scores\": [88.625, 96.75, 95.1875, 96.0, 87.8125, 99.125, 95.25, 97.0625, 96.8125, 95.4375, 91.6875, 92.5625, 93.9375, 95.0625, 98.25, 94.25, 95.875, 89.875, 93.0, 98.375, 92.0625, 90.6875, 89.125, 92.6875, 96.9375], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n{\"query\": \"feeding rice cereal how many times per day\", \"pos\": [\"FEEDING GUIDELINES AGES 4 TO 6 MONTHS 1. Begin with rice cereal on days 1,2 and 3, offering it twice daily at breakfast and dinnertime. Rice cereal can be mixed with water or milk(breast or formula) to make a thin oatmeal like consistency. The infant should be offered a rubberized spoon. During these first three days, offer 3-4 tablespoons at a time but be flexible. Some infants will be very hungry and want more- that's OK.\"], \"neg\": [\"How often do kittens need to eat? Kittens, at any age, have small stomachs, so the best method of feeding is little and often, as often as four to six times a day for very young kittens. As kittens get older and bigger, they can be fed bigger portions in less frequent meals. By 6 months, a kitten can generally eat two or three times a day. How often a kitten needs to eat wholly depends on each individual. Some kittens eat more and some eat less.\", \"Learn From Amazon\\u00e2s Leadership Principles Learn From Amazon\\u2019s Leadership Principles. Amazon is a crazy company. Within two decades, this bookseller has become a competitor of every single industry leader across retail, publishing, logistics, wireless, toys, devices, apparel and cloud computing, to name a few.\", \"The Serving Size of Grains Needed Daily Toddlers need 3 ounces of grains each day as part of a healthy diet. At least 1 1/2 ounces of these should be whole grains. Girls between the ages of 4 and 8 need 5 ounces of grains per day, with at least 2 1/2 of them being whole grains.\", \"How do we get started with solids? Increase solids gradually if baby is interested, with a maximum of 2 meals per day. 9 \\u2013 12 months. Watch baby\\u2019s cues \\u2013 this is particularly easy if baby nurses beforehand and most/all of the solids are offered to baby to self-feed. Increase solids gradually if baby is interested.\", \"Is Eating Rice Healthy? How Much Rice. The amount of whole-grain rice eaten daily for a healthy diet depends on how much other whole grain foods the dieter eats. About 6 to 8 ounces of whole-grain foods should be eaten on a 2,000-calorie diet, according to the USDA Dietary Guidelines for Americans.\", \"Flax Seeds: How Much Per Day? Serving Size. The University of Maryland Medical Center recommends adults consume 1 tablespoon of ground flaxseed two to three times a day, or 2 to 4 tablespoons once per day.You can mix the ground seeds into cereal, yogurt, oatmeal or smoothies or sprinkle them generously on top of salads and vegetable dishes.erving Size. The University of Maryland Medical Center recommends adults consume 1 tablespoon of ground flaxseed two to three times a day, or 2 to 4 tablespoons once per day.\", \"Left axis deviation Left axis deviation (LAD) is a condition whereby the mean electrical axis of ventricular contraction of the heart lies in a frontal plane direction between -30\\u00b0 and -90\\u00b0. This is reflected by a QRS complex positive in lead I and negative in leads aVF and II.\", \"Macromolecules Lipids are an important part of living cells. Together with carbohydrates and proteins, lipids are the main constituents of plant and animal cells. Cholesterol and triglycerides are lipids. Lipids are easily stored in the body. They serve as a source of fuel and are an important constituent of the structure of cells.Lipids include fatty acids, neutral fats, waxes and steroids (like cortisone).ogether with carbohydrates and proteins, lipids are the main constituents of plant and animal cells. Cholesterol and triglycerides are lipids. Lipids are easily stored in the body. They serve as a source of fuel and are an important constituent of the structure of cells.\", \"- To get rice flour, head out to your Asian market. It's dirt cheap and does the same. My daughter-in-law has been advised by the paediatrician to continue giving her baby of 8 months old rice porridge in the afternoon in place of her bottle feed. She has now stopped as she feels the baby is overweight.\", \"How Much To Feed Your Cat Even though you find feeding instructions on almost every bag of cat food you can buy, the instructions are useless. One brand may tell you to feed your cat 1/2 cup twice daily, while another recommends 1/4 cup three times daily. It's not just the manufacturers of cat food that can't tell you how much to feed your cat.\", \"Top 30 Doctor insights on: Purple Rice Supplement 3 doctors agreed: This depends on: how old your baby is. Rice cereal is generally started at 4-6 months, so if your baby is younger than that, you can supplement with formula, as opposed to cereal. Please also discuss this with his/her pediatrician; they may also be able to give you some hints on how to increase your breast milk supply. ...Read more.\", \"- Feeding tips. 1 If your baby won't eat what you're offering on the first try, offer it again in a few days. 2 Get more detailed tips on how to introduce solids. 3 Print our step-by-step guide to feeding your baby. Begin with about 1 teaspoon pureed food or cereal. 2 Mix cereal with 4 to 5 teaspoons breast milk or formula (it'll be very runny). 3 Increase to 1 tablespoon of pureed food, or 1 tablespoon of cereal mixed with breast milk or formula, twice a day. 4 If giving cereal, gradually thicken the consistency by using less liquid.\", \"How many ounces of wet food per day should be given to a cat? We feed our 3 cats one 6 oz can and divide that. But they also get a meal of dry in the morning and raw 2-3 times a week. For your cat about 1/3 of a 6 oz can of food per meal should be fine for her size. Wet food can be very messy and smelly. Be careful about feeding only wet! If you change her food for the emergency and her system is not ready for it, she will develop diarrhea. If you do decide to use wet food, I suggest using it more as a treat twice a day. I gave between 1/4 to 1/2 a small can (6 oz?) per cat twice a day. I always have unlimited amounts of dry food available as well as fresh water.\", \"How Much Should I Eat? Grains: Make at least half of your grains whole grains every day: 1 Males ages 19 to 30: 8 ounce equivalents of grains/day. 2 Females ages 19 to 30: 6 ounce equivalents of grains/day. 3 Examples of one ounce equivalent: 1 slice of bread; 1 cup of ready-to-eat cereal; or \\u00bd cup of cooked rice, cooked pasta, or cooked cereal.\", \"- Foods to Avoid. 1 6-11 servings each day. 2 Serving size= 1 slice bread, 1 cup ready-to-eat cereal,1/2 cup cooked cereal, rice or pasta. 3 All enriched breads, cereals, rice, noodles, pasta, and potatoes. Limit to 2 servings per week: whole-grain breads and cereals, wheat germ, bran and oatmeal.\", \"How do we get started with solids? Increase solids gradually if baby is interested, with a maximum of 2 meals per day. 9 \\u2013 12 months. Watch baby\\u2019s cues \\u2013 this is particularly easy if baby nurses beforehand and most/all of the solids are offered to baby to self-feed.ffer solids once a day, at most. Many start out offering solids every few days or even less often. Continue nursing on cue. Solid foods should not replace nursing sessions unless you\\u2019re actively weaning.Limit water to SIPS from a cup with meals.\", \"How long to cook a prime rib roast?? Preheat oven to 450\\u00b0F. Place the roast (ribs down) on the rack in a roasting pan. Sear the rib roast for 15 minutes at the higher oven temperature (450\\u00b0F), and then turn the oven to the lower temperature (325\\u00b0 F) for the rest of the cooking time.About 1/2 hour before the estimated end of the roasting time, begin checking the internal temperature (use a good instant-read digital meat thermometer.nsert meat thermometer so tip is in thickest part of beef, not resting in fat or touching bone. Cook until rib roast reaches an internal temperature of 120\\u00b0F. Remove from oven, cover with aluminum foil, and let sit approximately 15 to 20 minutes.\", \"3 month old and rice cereal 3 out of 3 found this helpful. My son will be 3 months Sunday and I have been giving him 1/2 a tbsp of Rice Cereal in his bottle for the past couple of days... just to try it out. (once in the morning and once at night) He usually has a 6-8 oz bottle every 3-4 hours.\", \"Portion Guide for Baby's First Year Between 4 and 6 months, your baby will start to sit up and grab for objects on his own. As he masters that grabbing skill, you may opt to start introducing cereal into his diet. Baby should eat 1-2 tablespoons of cereal twice a day.\", \"Should You Add Rice Cereal to Baby's Bottle? How to Introduce Rice Cereal If your doctor determines that your baby is ready for solids, at a tablespoon of rice cereal to every 4-5 tablespoons of breast milk or formula that you would normally feed your child.\", \"Cornell Feline Health Center From age six months to maturity, most cats will do well when fed two times a day.. Once the cat becomes an adult, at about one year, feeding once or twice a day is appropriate in most cases. Senior cats, age seven and above, should maintain the same feeding regimen. Once cats reach adulthood, once a day feeding is fine as long as they are healthy and have no disease problems suggesting a reason to feed differently, says Dr. Kallfelz. The Health of Your Cat Matters.\", \"- The following observations are made: \\u2022 Wholesale and retail trade was the largest employer with 22,2% of the employed in South Africa and 21,5% in KwaZulu-Natal, followed by community, social and personal services with 19,5% in South Africa and 18,8% in KwaZulu-Natal.\", \"Why do Japanese eat alot of rice? Asker's rating. 1 I almost eat rice three times a day. This is natural for me because I was brought up with eating rice. 2 Starches are the foundation for every cuisine in the world. In each area of the world the conditions are right for one or more native starch crops. 3 Because, they grow well. They suit the weather there.\", \"Gerber, Oatmeal Cereal, Single Grain, 8 oz (227 g) \\u00b7 Mix 1 tbsp. cereal with 4-5 tbsp. of breastmilk or infant formula. Easy-to-Mix Directions. \\u00b7 Pour or spoon desired amount of cereal in bowl. \\u00b7 For Baby: Stir in liquid (breastmilk or infant formula) to desired consistency. \\u00b7 For Toddler: mix with milk, water, or GERBER \\u00ae Juice for children over one year of age.\", \"Feeding infant Rice Cereal: How many times a day and how much? Watch for any reactions. 4- After that, feed 2 tablespoons of single grain oatmeal + 1 1/2 ounces of formula once a day. ALSO, continue feeding rice cereal + formula once a day... so he will be eating solids two times a day. I usually did one time mid morning and the second at night to space it out. 5- Feed with the oatmeal (+ rice cereal for 2nd feeding) for 5 days.\"], \"pos_scores\": [96.875], \"neg_scores\": [90.1875, 88.0625, 90.375, 93.375, 89.75, 89.0, 85.0625, 89.8125, 89.875, 88.75, 91.8125, 93.3125, 88.5625, 89.4375, 89.75, 93.625, 88.1875, 93.875, 93.8125, 93.375, 89.875, 85.5625, 90.0625, 91.5, 97.9375], \"prompt\": \"Given a web search query, retrieve relevant passages that answer the query.\", \"type\": \"normal\"}\n" }, { "path": "examples/finetune/embedder/example_data/retrieval/nli.jsonl", "content": "{\"query\": \"you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him\", \"pos\": [\"You lose the things to the following level if the people recall.\"], \"neg\": [\"They never perform recalls on anything.\"], \"pos_scores\": [98.1875], \"neg_scores\": [82.25], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"One of our number will carry out your instructions minutely.\", \"pos\": [\"A member of my team will execute your orders with immense precision.\"], \"neg\": [\"We have no one free at the moment so you have to take action yourself.\"], \"pos_scores\": [97.625], \"neg_scores\": [87.25], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"How do you know? All this is their information again.\", \"pos\": [\"This information belongs to them.\"], \"neg\": [\"They have no information at all.\"], \"pos_scores\": [94.5625], \"neg_scores\": [83.8125], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range\", \"pos\": [\"The tennis shoes can be in the hundred dollar range.\"], \"neg\": [\"The tennis shoes are not over hundred dollars.\"], \"pos_scores\": [98.6875], \"neg_scores\": [92.5625], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"my walkman broke so i'm upset now i just have to turn the stereo up real loud\", \"pos\": [\"I'm upset that my walkman broke and now I have to turn the stereo up really loud.\"], \"neg\": [\"My walkman still works as well as it always did.\"], \"pos_scores\": [102.75], \"neg_scores\": [83.0], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"But a few Christian mosaics survive above the apse is the Virgin with the infant Jesus, with the Archangel Gabriel to the right (his companion Michael, to the left, has vanished save for a few feathers from his wings).\", \"pos\": [\"There are a few Christian mosaics left in the building. \"], \"neg\": [\"There are no mosaics left in this place at all. \"], \"pos_scores\": [98.625], \"neg_scores\": [84.6875], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"(Read for Slate 's take on Jackson's findings.)\", \"pos\": [\"Slate had an opinion on Jackson's findings.\"], \"neg\": [\"Slate did not hold any opinion on Jackson's findings.\"], \"pos_scores\": [98.8125], \"neg_scores\": [86.0625], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"Gays and lesbians.\", \"pos\": [\"Homosexuals.\"], \"neg\": [\"Heterosexuals.\"], \"pos_scores\": [97.75], \"neg_scores\": [87.75], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"At the end of Rue des Francs-Bourgeois is what many consider to be the city's most handsome residential square, the Place des Vosges, with its stone and red brick facades.\", \"pos\": [\"Place des Vosges is a very handsome area.\"], \"neg\": [\"Place des Vosges is constructed entirely of gray marble.\"], \"pos_scores\": [98.0], \"neg_scores\": [83.6875], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n{\"query\": \"I burst through a set of cabin doors, and fell to the ground-\", \"pos\": [\"I burst through the doors and fell down.\"], \"neg\": [\"I slipped through the doors quietly.\"], \"pos_scores\": [99.75], \"neg_scores\": [85.1875], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"only_1neg\"}\n" }, { "path": "examples/finetune/embedder/example_data/retrieval/nq.jsonl", "content": "{\"query\": \"big little lies season 2 how many episodes\", \"pos\": [\"Big Little Lies (TV series) series garnered several accolades. It received 16 Emmy Award nominations and won eight, including Outstanding Limited Series and acting awards for Kidman, Skarsg\\u00e5rd, and Dern. The trio also won Golden Globe Awards in addition to a Golden Globe Award for Best Miniseries or Television Film win for the series. Kidman and Skarsg\\u00e5rd also received Screen Actors Guild Awards for their performances. Despite originally being billed as a miniseries, HBO renewed the series for a second season. Production on the second season began in March 2018 and is set to premiere in 2019. All seven episodes are being written by Kelley\"], \"neg\": [\"Major Crimes million viewers for its first season, TNT renewed \\\"Major Crimes\\\" for a 15-episode second season on September 27, 2012, which the network increased to 19 episodes in April 2013. Season 2 premiered on June 10, 2013. The series was renewed for a third season of 15 episodes on August 15, 2013, which aired from June 9, 2014, through January 12, 2015. On July 18, 2014, TNT renewed \\\"Major Crimes\\\" for a 15-episode fourth season, later expanded to 23 episodes, which aired from June 8, 2015, to March 14, 2016. On December 15, 2015, TNT renewed the series for a 13-episode\", \"Breaking Bad (season 2) winning the award for Best Syndicated/Cable Television Series. The series received two Writers Guild of America Award nominations, for Best Drama Series, and John Shiban for Best Episodic Drama for \\\"Phoenix\\\". Breaking Bad (season 2) The second season of the American television drama series \\\"Breaking Bad\\\" premiered on March 8, 2009 and concluded on May 31, 2009. It consisted of 13 episodes, each running approximately 47 minutes in length. AMC broadcast the second season on Sundays at 10:00 pm in the United States. The complete second season was released on Region 1 DVD and Region A Blu-ray on March 16,\", \"Falling Skies (season 2) season 2 , there's no doubt that TNT's hit drama will likely become an epic adventure, spanning many seasons,\\\" he said. Falling Skies (season 2) The second season of the American television drama series \\\"Falling Skies\\\" premiered June 17, 2012. It consisted of ten episodes, each running approximately 42 minutes in length. TNT broadcast the second season on Sundays at 9:00 pm ET in the United States. The season's plot focuses on the 2nd Massachusetts' discovery that a large community of survivors has formed in Charleston, their journey there, and their reception once they arrive. It also focusses on the\", \"Sin senos si\\u0301 hay parai\\u0301so (season 2) Sin senos s\\u00ed hay para\\u00edso (season 2) The second season of the American television drama series \\\"Sin senos s\\u00ed hay para\\u00edso\\\", comprising 87 episodes, aired on Telemundo from 25 July 2017 to 28 November 2017. The show was broadcast from Monday to Friday at 9pm/8c. The second season revolves around Catalina Santana, who becomes an undercover agent of the DEA and changes her name to Virginia Fern\\u00e1ndez after pretending to be dead for 20 years. Catalina returns to Colombia to protect her family from Las Diablas, but her return only causes pain, disappointment, and tragedy for her mother. The season\", \"How to Get Away with Murder (season 2) How to Get Away with Murder (season 2) The second season of the ABC American television drama series \\\"How to Get Away with Murder\\\" was ordered on May 7, 2015, by ABC. The second season began airing on September 24, 2015, with 15 episodes like the previous season, and concluded on March 17, 2016. This was made in a deal with Viola Davis that the series would be a limited series with only 15 or 16 episodes per season. A promotional poster was released on August 18 and the trailer was released on September 10. In its second season, the\", \"Arrested Development (season 2) Arrested Development (season 2) The second season of the television comedy series \\\"Arrested Development\\\" aired between November 7, 2004 and April 17, 2005, on Fox in the United States. It consisted of 18 episodes, each running approximately 22 minutes in length. The second season was released on DVD in region 1 on October 11, 2005, in region 2 on January 23, 2006 and in region 4 on March 15, 2006. The show's storyline centers on the Bluth family, a formerly wealthy, habitually dysfunctional family and is presented in a continuous format, incorporating hand-held camera work, narration, archival photos, and historical\", \"Scandal (season 2) to the first season's finale. The review aggregator website Rotten Tomatoes reports a 92% approval rating with an average rating of 9.2/10 based on 13 reviews. The website's consensus reads, \\\"Owning its soap-like sensibilities, \\\"Scandal\\\" creates enticingly addictive narratives, with surprising twists and fascinatingly damaged characters.\\\" Scandal (season 2) The second season of the American television drama series \\\"Scandal\\\", created by Shonda Rhimes, began on September 27, 2012, in the United States, on ABC, and consisted of 22 episodes. The season was produced by ABC Studios, in association with ShondaLand Production Company; the showrunner being Shonda Rhimes. The program airs\", \"Parenthood (season 2) Parenthood (season 2) The second season of the NBC comedy-drama series \\\"Parenthood\\\" premiered on September 14, 2010 and ended on April 19, 2011, it consisted of 22 episodes. On April 20, 2010 Parenthood was renewed for a second season by NBC. The second season premiered on Tuesday, September 14, 2010. It was announced on November 15, 2010 that Parenthood would be moving to Mondays at 10/9c beginning March 7. However, due to an overhaul of NBC's \\\"\\\" leading to an indefinite hiatus, the network announced on January 18, 2011 that Parenthood would remain in the Tuesday at 10/9c time\", \"Charmed (season 2) Charmed (season 2) The second season of \\\"Charmed\\\", an American supernatural drama television series, began airing on September 30, 1999, on The WB. Airing on Thursdays at 9:00 pm, the season consisted of 22 episodes and concluded its airing on May 18, 2000. Paramount Home Entertainment released the complete second season in a six-disc box set on September 6, 2005. In 2016, Gavin Hetherington of SpoilerTV ran a series of \\\"Charmed\\\" articles in the run-up to the 10th anniversary of the series finale. The second was a complete season review of season two, in which Gavin comments that the\", \"Arrested Development (season 2) 15, 2006. Special features include commentary by creator Mitchell Hurwitz and cast members on \\\"Good Grief\\\", \\\"Ready, Aim, Marry Me!\\\" and \\\"Righteous Brothers\\\"; deleted and extended scenes; Season One in 3 Minutes overview; blooper reel; \\\"The Immaculate Election\\\" Campaign Videos. Arrested Development (season 2) The second season of the television comedy series \\\"Arrested Development\\\" aired between November 7, 2004 and April 17, 2005, on Fox in the United States. It consisted of 18 episodes, each running approximately 22 minutes in length. The second season was released on DVD in region 1 on October 11, 2005, in region 2 on January\", \"The Killing (season 2) Television for Mireille Enos. The second season of \\\"The Killing\\\" was released on DVD on April 2, 2013, exclusively through Amazon's CreateSpace manufacture-on-demand program. In regions 2 and B, it was released on October 27, 2014. The Killing (season 2) The second season of the AMC American crime drama television series \\\"The Killing\\\" premiered on April 1, 2012, concluded on June 17, 2012, and consisted of 13 episodes. The series was developed and produced by Veena Sud and based on the Danish series, \\\"Forbrydelsen (The Crime)\\\". Set in Seattle, Washington, this season follows the continued investigation into the murder of\", \"Hart of Dixie (season 2) for season two as did Deborah S. Craig who played Shelley Ng. The season premiered to 1.53 million people with a 0.7 rating share for adults 18-49. Hart of Dixie: The Complete Second Season was released on DVD in the US on October 15, 2013. The 5 disc set includes all 22 episodes from the second season and various language and subtitle options. Hart of Dixie (season 2) On May 11, 2012, The CW renewed \\\"Hart of Dixie\\\" for a second season, which consisted of 22 episodes. Season two began on October 2 and saw the show move to a\", \"Shake It Up (season 2) a promotional single with an accompanying music video, which was released on August 3, 2012. The soundtrack was the best-selling soundtrack of the year. Shake It Up (season 2) The second season of \\\"Shake It Up\\\" aired on Disney Channel from September 18, 2011 to August 17, 2012. It consisted of 28 episodes, giving the series a total of 49 episodes thus far. On March 16, 2011, the series was picked up for its second season. A few months later, it was announced that the series would begin with a one-hour special episode and that it would be aired\", \"How I Met Your Mother (season 2) How I Met Your Mother (season 2) The second season of the American television comedy series How I Met Your Mother premiered on September 18, 2006 and concluded on May 14, 2007. It consisted of 22 episodes, each approximately 22 minutes in length. CBS broadcast the first three episodes of the second season on Monday nights at 8:30 pm in the United States, the remaining episodes were broadcast at 8:00pm. The complete second season was released on Region 1 DVD on October 2, 2007. In the United Kingdom it aired on E4 from October 2, 2009 weekdays at 7:30pm. \", \"How I Met Your Mother (season 2) How I Met Your Mother (season 2) The second season of the American television comedy series How I Met Your Mother premiered on September 18, 2006 and concluded on May 14, 2007. It consisted of 22 episodes, each approximately 22 minutes in length. CBS broadcast the first three episodes of the second season on Monday nights at 8:30 pm in the United States, the remaining episodes were broadcast at 8:00pm. The complete second season was released on Region 1 DVD on October 2, 2007. In the United Kingdom it aired on E4 from October 2, 2009 weekdays at 7:30pm. \", \"Mad Men (season 2) Mad Men (season 2) The second season of the American television drama series Mad Men premiered on July 27, 2008 and concluded on October 26, 2008. It consisted of thirteen episodes, each running approximately 48 minutes in length. AMC broadcast the second season on Sundays at 10:00 pm in the United States; it would occupy in this timeslot for the remainder of its run. Season two takes place between February and October 1962, culminating with the Cuban Missile Crisis. It expands on Peggy's rise in the workplace and the marital strife between Don and Betty Draper as Don's infidelities further\", \"Grimm (season 2) episodes (\\\"Volcanalis\\\" and \\\"Endangered\\\") to give Hornsby time to undergo surgery. Grimm (season 2) The second season of the NBC American supernatural drama series \\\"Grimm\\\" premiered on August 13, 2012 and consisted of 22 episodes. The series, created by David Greenwalt, Jim Kouf and Stephen Carpenter, follows a descendant of the Grimm line, Nick Burkhardt, as he deals with being a cop, and trying not to expose his secret as a Grimm. On March 16, 2012, NBC announced that the series had been renewed for a second season with a 22-episode order. Bree Turner, who plays Rosalee Calvert, joined\", \"Raising Hope (season 2) Raising Hope (season 2) The second season of the American television series \\\"Raising Hope\\\" premiered on September 20, 2011, on Fox. The show moved to its new time slot, airing on Tuesday at 9:30pm ET to make way for the new comedy series \\\"New Girl\\\", before moving again to 8:00 pm ET in mid-season. This season contains 22 episodes. On January 10, 2011, Fox renewed \\\"Raising Hope\\\" for a second season with a 22 episode order. From the beginning of this season, Cloris Leachman and Gregg Binkley were both upgraded to series regulars. Episode four aired following \\\"The X Factor\\\"\", \"Louie (season 2) Louie (season 2) The second season of the American television comedy series \\\"Louie\\\" premiered on June 23, 2011 and concluded on September 8, 2011. It consisted of thirteen episodes, each running approximately 23 minutes in length. FX broadcast the second season on Thursdays at 10:30 pm in the United States. The season was produced by 3 Arts Entertainment and the executive producers were Louis C.K., Dave Becky and M. Blair Breard. The second season was released on DVD and Blu-ray in region 1 on June 19, 2012. \\\"Louie\\\" was created, written and directed by Louis C.K., who stars as a\", \"The Haves and the Have Nots (TV series) stories with its weekly dose of soapy fun, filled with the typical betrayals, affairs, manipulations and a bitch slap or two.\\\" Recognizing the show's increasing popularity, OWN renewed the drama series for a 2nd season midway into the first season. The 2nd season was originally to consist of 16 episodes and bring the series to 32 in total by the completion of season 2; however on August 21, it was announced that the network had ordered 4 additional episodes for the 2nd season, which will bring the series to 36 episodes in total by season 2's completion. Season 2 of\", \"Faking It (season 2) more time with her dad while she's still unsure about her feelings for Karma. On June 9, 2014, the series was picked up for a ten-episode second season, which was later expanded to a twenty episode season. The episodes for 2A were broadcast on the time slot of Tuesdays from 10:30 PM\\u201311:00 PM EST, while the 2B ones were broadcast on the time slot of Mondays from 9:30 PM\\u201310:00 PM EST. \\\"\\\"The Young Folks\\\"\\\" had this to say about season 2A: \\\"Faking It's sophomore season starts off with a bang, an emotional rollercoaster that, while not as big as\", \"Scandal (season 2) Scandal (season 2) The second season of the American television drama series \\\"Scandal\\\", created by Shonda Rhimes, began on September 27, 2012, in the United States, on ABC, and consisted of 22 episodes. The season was produced by ABC Studios, in association with ShondaLand Production Company; the showrunner being Shonda Rhimes. The program airs at the same time in Canada through the City television system with simsubbing of the ABC feed. The season continues the story of Olivia Pope's crisis management firm, Olivia Pope & Associates, and its staff, as well as staff at the White House in Washington, D.C.\", \"Beauty & the Beast (season 2) Beauty & the Beast (season 2) The second season of \\\"Beauty & the Beast\\\", an American television series developed by Sherri Cooper-Landsman and Jennifer Levin and very loosely inspired by the 1987 CBS television series of the same name, commenced airing in the United States on October 7, 2013, concluded July 7, 2014, and consisted of 22 episodes. \\\"Beauty & the Beast\\\"'s second season aired in the United States (U.S.) on Mondays at 9:00 pm ET on The CW, a terrestrial television network, where it received an average of 1.24 million viewers per episode. Catherine Chandler, a law student, witnesses\", \"Justified (season 2) and in region 4 on September 5, 2012. Special features on the season two set include deleted scenes, three behind-the-scenes featurettes, and outtakes. Justified (season 2) The second season of the American television drama series \\\"Justified\\\" premiered on February 9, 2011, on FX, and concluded on May 4, 2011, consisting of 13 episodes. The series was developed by Graham Yost based on Elmore Leonard's novels \\\"Pronto\\\" and \\\"Riding the Rap\\\" and his short story \\\"Fire in the Hole\\\". Its main character is Raylan Givens, a deputy U.S. Marshal. Timothy Olyphant portrays Givens, a tough federal lawman, enforcing his own brand\", \"Devious Maids (season 2) Devious Maids (season 2) The second season of the American television comedy-drama series \\\"Devious Maids\\\" began airing on Lifetime on April 20, 2014. The season consists of 13 episodes. The second season premiered on April 20, 2014. The season centers on the mystery story of Opal, the new, 40-something maid, played by Joanna P. Adler. The character is described as \\\"reminiscent of Mrs. Danvers from Hitchcock's \\\"Rebecca\\\"\\\" and is seen as a threat to Marisol's new relationship with Nicholas. The second season also deals with Rosie working for an African-American family that is scheming to do harm to an elderly\", \"Grimm (season 2) Grimm (season 2) The second season of the NBC American supernatural drama series \\\"Grimm\\\" premiered on August 13, 2012 and consisted of 22 episodes. The series, created by David Greenwalt, Jim Kouf and Stephen Carpenter, follows a descendant of the Grimm line, Nick Burkhardt, as he deals with being a cop, and trying not to expose his secret as a Grimm. On March 16, 2012, NBC announced that the series had been renewed for a second season with a 22-episode order. Bree Turner, who plays Rosalee Calvert, joined the main cast from the beginning of season two. Production began on\", \"The Lying Game 2011, after an episode of \\\"The Secret Life of the American Teenager\\\", drawing 1.4 million viewers. ABC back-ordered an additional 10 episodes of season 1 in September 2011, which premiered in January 2012 drawing a series high 1.8 million viewers. \\\"The Lying Game\\\" was renewed for a second season by ABC Family in April 2012, with production to take place during summer 2012 for a winter premiere. Charisma Carpenter, who had been recurring in season 1, was promoted to the main cast for season 2 in July 2012. Season 2 premiered on January 8, 2013, drawing 1.55 million viewers; the\", \"Falling Skies (season 2) Falling Skies (season 2) The second season of the American television drama series \\\"Falling Skies\\\" premiered June 17, 2012. It consisted of ten episodes, each running approximately 42 minutes in length. TNT broadcast the second season on Sundays at 9:00 pm ET in the United States. The season's plot focuses on the 2nd Massachusetts' discovery that a large community of survivors has formed in Charleston, their journey there, and their reception once they arrive. It also focusses on the discovery that the Skitters are themselves \\\"harnessed\\\" and mind-controlled by the invaders, but that some of them are resistant to the\", \"Parenthood (season 2) Parenthood (season 2) The second season of the NBC comedy-drama series \\\"Parenthood\\\" premiered on September 14, 2010 and ended on April 19, 2011, it consisted of 22 episodes. On April 20, 2010 Parenthood was renewed for a second season by NBC. The second season premiered on Tuesday, September 14, 2010. It was announced on November 15, 2010 that Parenthood would be moving to Mondays at 10/9c beginning March 7. However, due to an overhaul of NBC's \\\"\\\" leading to an indefinite hiatus, the network announced on January 18, 2011 that Parenthood would remain in the Tuesday at 10/9c time slot.\", \"The Blacklist (season 2) The Blacklist (season 2) The second season of the American crime thriller television series \\\"The Blacklist\\\" premiered on NBC on September 22, 2014, and concluded on May 14, 2015, and ran for 22 episodes. The season was produced by Davis Entertainment, Universal Television, and Sony Pictures Television, and the executive producers are Jon Bokenkamp, John Davis, John Eisendrath, John Fox, and Joe Carnahan. The second season of \\\"The Blacklist\\\" received positive reviews from critics. The review aggregator website Rotten Tomatoes reports an 80% approval rating based on 15 reviews, with an average score of 7.77/10. The consensus reads,\", \"The Killing (season 2) The Killing (season 2) The second season of the AMC American crime drama television series \\\"The Killing\\\" premiered on April 1, 2012, concluded on June 17, 2012, and consisted of 13 episodes. The series was developed and produced by Veena Sud and based on the Danish series, \\\"Forbrydelsen (The Crime)\\\". Set in Seattle, Washington, this season follows the continued investigation into the murder of local teenager Rosie Larsen, with each episode covering approximately 24 hours. The season culminated in the closing of the Larsen murder, with the discovery of those involved with the murder. Sarah Linden begins the season not\", \"The Wire (season 2) Michael K. Williams as renowned stick-up man Omar Little. On Metacritic, the second season achieved an aggregate score of 95 out of 100, indicating universal acclaim. 20th TCA Awards The Wire (season 2) The second season of the television series The Wire of 12 episodes first aired in the United States on HBO in 2003 from June 1 to August 24. It introduces the stevedores of the Port of Baltimore and an international organized crime operation led by a figure known only as \\\"The Greek\\\" and continues the story with the drug-dealing Barksdale crew and the Baltimore Police Department\", \"Prison Break (season 2) Prison Break (season 2) The second season of \\\"Prison Break\\\", an American serial drama television series, commenced airing in the United States on August 21, 2006 on Mondays at 9:00 pm (EST) on the Fox Broadcasting Company. \\\"Prison Break\\\" is produced by Adelstein-Parouse Productions, in association with Rat Television, Original Television Movie and 20th Century Fox Television. The season contains 22 episodes, and concluded on April 2, 2007. Series creator Paul Scheuring describes the second season as \\\"\\\"The Fugitive\\\" times eight,\\\" and likens it to the \\\"second half of \\\"The Great Escape\\\".\\\" \\\"Prison Break\\\" revolves around two brothers: one who\", \"Faking It (season 2) (MOD) on \\\"Amazon.com\\\" The second part was released on DVD, in the same circunstances as \\\"Part 1\\\", on June 15, 2016. Faking It (season 2) The second season of \\\"Faking It\\\", an American single-camera romantic comedy, premiered on September 23, 2014, and concluded on November 2, 2015, on the MTV network. On June, 2014, the series was renewed for a second season of 10 episodes, which was later extended to 20 episodes. In the aftermath of the first-season finale, the gang has to deal with the consequences of their choices. Karma (Katie Stevens) finds out Amy (Rita Volk) and Liam\", \"Damages (season 2) Best Actress, and Byrne and Hurt for their supporting roles. The second season of \\\"Damages\\\" was met with mostly high praise, and it earned 81 out of 100 based on 18 reviews on the aggregate review website Metacritic, which qualifies as \\\"universal acclaim\\\". Damages (season 2) The second season of the FX legal drama series \\\"Damages\\\" premiered on January 7, 2009 and concluded on April 1, 2009. It consisted of thirteen episodes, bringing the series total to 26. \\\"Damages\\\" was created by brothers Todd and Glenn Kessler along with Daniel Zelman, each of whom served as executive producers and contributed\", \"Regal Academy Academy. Rose finds out that she is the granddaughter to the headmistress Cinderella. Rose decides to enroll at Regal Academy and learn how to use magic while having adventures with her friends. Season 2 additions: The second season of Regal Academy is currently under production and is planned to release on November 5, 2017. It is confirmed to have 26 episodes. In the new season, Rose and her friends return from summer holidays and ready to take on new and exciting adventures in Fairy Tale Land. With the help of new magical items and funny pumpkin creatures called the PomPoms,\", \"Lost Girl around the city until September 22, for a targeted Fall 2011 premiere. On May 18, 2011, Syfy (U.S.) announced that it had acquired 26 episodes (Season 1 and Season 2) of \\\"Lost Girl\\\" from Prodigy Pictures. Showcase announced in a July 7, 2011, press release that the Season 2 premiere would be on September 4, 2011, and that an additional nine episodes had been ordered to make the season a total of 22 episodes. The order for more episodes was made public two weeks before the first appearance of \\\"Lost Girl\\\" cast and producers at San Diego Comic-Con International. Naming\", \"Chuck (season 2) Chuck (season 2) The second season of \\\"Chuck\\\" contains 22 episodes and was originally aired from September 29, 2008 to April 27, 2009. The season continues to focus on Chuck's constant struggle to keep his spy life and real life apart as he becomes more accustomed to being a spy. More background on the Intersect project is revealed. Fulcrum, a hostile espionage organization that covets the Intersect, is featured more heavily as the season's main antagonist. Chuck and Sarah continue to grow closer, complicating their asset-handler relationship. The second season of \\\"Chuck\\\" averaged on 7.36 million viewers per episode.\", \"Chuck (season 2) Chuck (season 2) The second season of \\\"Chuck\\\" contains 22 episodes and was originally aired from September 29, 2008 to April 27, 2009. The season continues to focus on Chuck's constant struggle to keep his spy life and real life apart as he becomes more accustomed to being a spy. More background on the Intersect project is revealed. Fulcrum, a hostile espionage organization that covets the Intersect, is featured more heavily as the season's main antagonist. Chuck and Sarah continue to grow closer, complicating their asset-handler relationship. The second season of \\\"Chuck\\\" averaged on 7.36 million viewers per episode.\", \"Salem (season 2) \\\"Smallville\\\" together\\u2014and Donna Thorland. Upon reviewing several episodes of the second season, Gavin Hetherington of SpoilerTV has called \\\"Salem\\\" the \\\"best goddamn show on TV right now.\\\" Salem (season 2) The second season of \\\"Salem\\\", an American horror\\u2013drama television series on WGN America, premiered on April 5, 2015, and concluded on June 28, 2015, consisting of thirteen episodes. Created for television by Adam Simon and Brannon Braga, who write or co-write episodes of the show, the series is based on the Salem Witch Trials. It was executive produced by Braga, Coby Greenberg and David Von Ancken, with Braga and\", \"Desperate Housewives (season 2) Desperate Housewives (season 2) The second season of the American dramedy-mystery television series \\\"Desperate Housewives\\\" commenced airing in the United States on September 25, 2005 and concluded on May 21, 2006. The season continues the story of the Wisteria Lane residents, while their seemingly perfect lives in the suburban neighborhood are shaken by the arrival of the mysterious Betty Applewhite. Broadcast in the Sunday night time slot at 9.00 ET, the season aired twenty-four regular episodes, including a two-part season finale. In addition, three clip shows were produced for the season, in order to put the previous events of the\", \"The Americans (season 2) 2014, and in region 2 on January 26, 2015. Special features include two featurettes\\u2014\\\"Operation Ghost Stories: The Real Directorate 'S'\\\" and \\\"Shades of Red: The Mortality of the Americans\\\"; gag reel; and deleted scenes. The Americans (season 2) The second season of the American television drama series \\\"The Americans\\\", consisting of 13 episodes, premiered on FX on February 26, 2014, and concluded on May 21, 2014. The series was renewed for the second season on February 21, 2013. Susan Misner, Annet Mahendru, and Alison Wright, who play Sandra Beeman, Nina, and Martha Hanson, respectively, are promoted to series regulars in\", \"Smash (season 2) Smash (season 2) The second and final season of the American musical drama television series Smash premiered on February 5, 2013 on NBC and consisted of 17 episodes. On March 13, 2013, NBC announced they were moving the remaining season two episodes of \\\"Smash\\\" to Saturday nights at 9:00PM EST starting April 6 in order to play the full 17-episode order. The two-hour series finale aired on May 26, 2013, moving the show to a special Sunday slot. On March 22, 2012, NBC renewed \\\"Smash\\\" for a second season. \\\"Gossip Girl\\\"'s Joshua Safran replaced creator Theresa Rebeck as showrunner, since\", \"Paz de la Huerta season, we had gotten lots of accolades and great reviews, so it was work, work, work, where we were shooting two episodes at a time.\\\" Season two premiered on September 25, 2011 and concluded on December 11, 2011, consisting of 12 episodes. The second season of \\\"Boardwalk Empire\\\" received positive reviews from critics. On the review aggregator website Metacritic, the second season scored 81/100 based on 14 reviews. Another aggregator website, Rotten Tomatoes, reported 85 percent of critics gave the second season a \\\"Certified Fresh\\\" rating, based on 13 reviews with an average score of 8/10. After the second season\", \"The Resident (TV series) from Showtime and ordered a pilot episode under the name, \\\"The Resident\\\". On May 10, 2017, the series received a full season order of 14 episodes. Phillip Noyce, an executive producer for the series, directed the first two episodes of the season after signing a multi-year deal with 20th Century Fox Television. On May 7, 2018, Fox renewed the series for a 13-episode second season and pre-production began on June 8, 2018. On October 10, 2018, it was reported that Fox ordered an additional nine episodes for the second season, bringing the total episode count to 22. On February 21,\", \"Shake It Up (season 2) Shake It Up (season 2) The second season of \\\"Shake It Up\\\" aired on Disney Channel from September 18, 2011 to August 17, 2012. It consisted of 28 episodes, giving the series a total of 49 episodes thus far. On March 16, 2011, the series was picked up for its second season. A few months later, it was announced that the series would begin with a one-hour special episode and that it would be aired later that fall. Production for the season filmed from July 2011 to March 2012. The second season officially premiered on September 18, 2011, but the\", \"The Handmaid's Tale (TV series) story, filling in some of the unanswered questions and continuing the narrative already \\\"finished\\\" in the book. The second season consists of 13 episodes and began filming in fall 2017. Alexis Bledel returned as a series regular. Showrunner Bruce Miller stated that he envisioned 10 seasons of the show, stating, \\\"Well, you know, honestly, when I started, I tried to game out in my head what would ten seasons be like? If you hit a home run, you want energy to go around the bases, you want enough story to keep going, if you can hook the audience to care\", \"Legends of Tomorrow (season 2) into Season 2... [since] something that happens on \\\"Arrow\\\" can create ripples that appear on our show in a huge way. It fundamentally alters the DNA of our series.\\\" The second season initially consisted of 13 episodes, with four more ordered in November 2016 to bring the season total to 17. Teasing the premise of season two in April 2016, Klemmer stated, \\\"We're coming at it from a completely different angle. We're determined to make every part of season two feel like its own show. [The first episode of season two] will very much be a new pilot with new\", \"Charmed (season 2) to the end of the original Charmed One era, but for this season, it was probably their absolute best as a trio.\\\" He ended the article by saying season two was an \\\"amazing season, and it will always be one of my favourites.\\\" Charmed (season 2) The second season of \\\"Charmed\\\", an American supernatural drama television series, began airing on September 30, 1999, on The WB. Airing on Thursdays at 9:00 pm, the season consisted of 22 episodes and concluded its airing on May 18, 2000. Paramount Home Entertainment released the complete second season in a six-disc box set on\", \"Don't Trust the B---- in Apartment 23 Trust the B---- in Apartment 23\\\" was renewed for a second season, with the remaining six episodes of season one airing as a part of it. The second season premiered on October 23, 2012. On May 11, 2012, ABC renewed \\\"Don't Trust the B---- in Apartment 23\\\" for a second season. The remaining six episodes of season one (with production codes beginning 1A in the table) aired as part of season two bringing the total to 19 episodes for the season. ABC elected to air these episodes out of order, interspersing first and second-season episodes without regard to continuity. As\", \"Quantico (season 2) Quantico (season 2) The second season of American drama thriller series \\\"Quantico\\\" premiered in the United States on American Broadcasting Company (ABC) on September 25, 2016, and concluded on May 15, 2017. The season was produced by ABC Studios, with series creator Joshua Safran, Mark Gordon, Robert Sertner, Nicholas Pepper and Jorge Zamacona serving as executive producers. Season two consists of twenty-two episodes; it follows Alex Parrish (Priyanka Chopra), who has been working undercover for the FBI as a CIA recruit to uncover a rogue faction called the AIC. The narrative is told through dual timelines until the thirteenth episode;\", \"Carmilla (web series) of. Along with the ghosts of Carmilla's former victims, they fight the ghost of Carmilla's ex-lover in order to regain Carmilla's humanity. The first season of \\\"Carmilla\\\" consists of 36 episodes, which aired from August 19, 2014 to December 2, 2014. A Christmas special aired on December 24, 2014. The second season of \\\"Carmilla\\\" consists of 36 episodes, which aired from June 2, 2015 to October 1, 2015. The next season, titled Season Zero, consists of 12 episodes, which began airing on October 22, 2015 and concluded on November 24, 2015. The third and final season of \\\"Carmilla\\\" consists of\", \"Suits (U.S. TV series) 2012, and Region A/B Blu-ray on April 10, 2014. \\\"Suits\\\" was renewed for a second season consisting of 16 episodes on August 11, 2011, which premiered on June 14, 2012. The mid-season finale aired on August 23, 2012, with the remaining six episodes returning on January 17, 2013. The complete second season was available on Region 1 DVD on December 2, 2013, and Region A/B Blu-ray on June 26, 2014. On October 12, 2012, the show was renewed for a third season of 16 episodes. Season 3 premiered on July 16, 2013, with the final six episodes airing after March\", \"Jonas (season 2) soundtrack was released on July 20, 2010. Jonas (season 2) The second and final season of the television series \\\"Jonas\\\", titled Jonas L.A. for this season, aired on Disney Channel from June 20, 2010 to October 3, 2010, and included 13 episodes. It follows the five main characters of the series as they spend their summer vacation in Los Angeles after the Jonas' tour was over, a few months after the events of the first season. The season was never officially released on DVD, being available only in digital format. After the season finale, it was announced that the\", \"Castle (season 2) Castle (season 2) The second season of American crime-comedy-drama television series \\\"Castle\\\" was ordered on May 15, 2009, by ABC. The season aired from September 21, 2009, to May 17, 2010. The second season was originally renewed with an order of 13 episodes, but a few weeks after the season premiere, on October 20, 2009, ABC ordered a full season increasing the episode count to 24 episodes. Richard Castle (Fillion) is a famous mystery novelist who has killed off the main character in his popular book series and has writer's block. He is brought in by the NYPD for questioning\", \"Drop Dead Diva (season 2) Drop Dead Diva (season 2) The second season of \\\"Drop Dead Diva\\\" premiered on June 6, 2010 and concluded August 29, 2010 on Lifetime. Season two aired on Sundays at 8:00 pm ET for the entire season and consisted of 13 episodes. In the second season Deb learns that Jane only married Ethan so he could get insurance for cancer treatments, only for him to leave a month after they were married. Deb channels Jane's reactions upon learning she wants the divorce because he was a jerk for leaving her. This turn of events have Deb starting to act more\", \"Sin senos si\\u0301 hay parai\\u0301so (season 2) premiered with a total of 1.82 million viewers, becoming the most watched Spanish-language show at 9pm/8c on Telemundo. The final episode of the season averages a total of 2.03 million viewers, this being the production in Spanish-language most seen at 9pm/8c. Sin senos s\\u00ed hay para\\u00edso (season 2) The second season of the American television drama series \\\"Sin senos s\\u00ed hay para\\u00edso\\\", comprising 87 episodes, aired on Telemundo from 25 July 2017 to 28 November 2017. The show was broadcast from Monday to Friday at 9pm/8c. The second season revolves around Catalina Santana, who becomes an undercover agent of\", \"Saravanan Meenatchi (season 2) Saravanan Meenatchi (season 2) The second season of \\\"Saravanan Meenatchi\\\" began airing on 21 October 2013 and finished on 15 July 2016. It consisted of 689 episodes. The season 2 begins with Shakthi Saravanan son of Saravanan and Meenatchi comes to India from Canada to find a bride like his Mom. He comes to the Village Kallidaikurichi in Tirunelveli where his maternal uncle Tamizh and family live. Shakthi Saravanan disguises himself and lives in Tamizh's house where he meets Tamizh's daughter Thanga Meenatchi and falls for her. After all oppositions from Tamizh ultimately she too falls for Shakthi Saravanan. However\", \"Grounded for Life opening titles. The season two set, therefore contains 17 episodes. Season three contains 13 episodes, two of which did not originally air on television. All 28 episodes that aired as part of season four, remain intact for the season four set and the same for the 13-episode season five set. The first four seasons were released in box set format, with two smaller-than-standard size DVD cases with a slipcover for each of the four sets. This however changed for the season five release when it was made available in a standard DVD case and no slipcover. Anchor Bay never released\", \"Somerset Maugham TV Theatre the introduction and conclusion to each episode. After the series finished its run on CBS after one season on March 28, 1951, the series was renewed for a second season this time on NBC on April 2, 1951. The series would remain for the rest of its run. The series also moved from Wednesday nights to Monday nights and expanded to 60 minutes. Season two finished its second season on September 3 after airing 16 episodes. The series started its third season on September 17, 1951 continuing to air on Monday nights and for sixty minutes. This season would be\", \"Sex and the City (season 2) Charlotte York and Miranda Hobbes. Season two, comprising 18 episodes, aired on Sunday nights at 9:00 p.m. Eastern Time Zone. The season garnered a more positive reception from critics. The second season saw a rise in ratings from the previous season, averaging a total of nine million viewers. The show continued its award success in season two, garnering various major award nominations for the main cast and the series, including a Golden Globe Award win for Parker. The second season of \\\"Sex and the City\\\" was created by Darren Star and produced by Darren Star Productions and Warner Bros. Television,\", \"BoJack Horseman (season 2) BoJack Horseman (season 2) The second season of the animated television series \\\"BoJack Horseman\\\" premiered exclusively via Netflix's web streaming service on July 17, 2015. The season consists of 12 episodes. On Rotten Tomatoes the second season holds an approval rating of 100%, based on 17 critics, with an average rating of 8.9/10. The site's critical consensus reads, \\\"\\\"BoJack Horseman\\\" truly comes into its own during season two, maturing into an ambitious comedy that sensitively blends wackiness with dark, nuanced drama\\\". On Metacritic, the season has a score of 90 out of 100, based on 7 critics, indicating \\\"universal\", \"The Secret Life of the American Teenager (season 2) the series after its January return. Following the success of its first season, ABC Family announced on January 31, 2009, plans to renew \\\"Secret Life\\\", following the cancellation of its hit sci-fi TV show, \\\"Kyle XY\\\". The official press release was released on February 9 with ABC Family ordering 24 episodes for season two. Season 2 began with 12 episodes broadcast starting June 22, 2009. Though marketed as the season finale, the mid season finale aired on September 7, 2009, with the second half of the season returning on January 4, 2010. The second 12 episodes finished their run on\", \"One Tree Hill (season 2) One Tree Hill (season 2) The second season of \\\"One Tree Hill\\\", an American teen drama television series, began airing on September 21, 2004 on The WB television network. The season concluded on May 24, 2005, after 23 episodes. Season two increased in ratings, averaging 4.50 million viewers weekly, and was its highest rated season. \\\"Warner Home Video\\\" released the complete second season, under the title of \\\"One Tree Hill: The Complete Second Season\\\", on September 13, 2005, as a six-disc boxed set. Season 2 chronicles the remainder of junior year at Tree Hill High. Lucas and Nathan begin to\", \"Scrubs (season 2) or more episodes, or directors who are part of the cast and crew\\\" Scrubs (season 2) The second season of the American comedy television series \\\"Scrubs\\\" premiered on NBC on September 26, 2002 and concluded on April 17, 2003 and consists of 22 episodes. For the second season Neil Flynn was made a series regular. Colin Hay guest starred for the first time. It is also the first time an episode gives the narration to another regular, in \\\"His Story\\\". The show used a longer opening credits sequence for episodes 1 and 2, moving through the ward rather than\", \"How to Get Away with Murder (season 2) unknown shooter. The series was renewed for a second season on May 7, 2015, by ABC. The show was effectively confirmed as earning a second-season renewal for the 2015-16 season via a promo succeeding the first-season finale and an earlier statement by Viola Davis also confirming the renewal at the close of shooting for the first season. It would contain 15 episodes, like the previous season. \\\"Entertainment Weekly\\\" reported on July 23, 2015, that the identity of Rebecca's killer would be revealed in the season premiere. A promotional poster was released over a month before the season premiere, on\", \"Brooklyn Nine-Nine (season 2) Brooklyn Nine-Nine (season 2) The second season of the television sitcom \\\"Brooklyn Nine-Nine\\\" premiered September 28, 2014 on Fox and ended May 17, 2015 with 23 episodes. Jake returns to the precinct after going undercover to help take down the mafia, but is discouraged when one mobster manages to escape. He then begins a relationship with defense lawyer Sophia Perez, which ends based on their professions. Holt begins a battle of wits with Deputy Commissioner Madelyn Wuntch that culminates in Wuntch making Holt the head of the NYPD Public Affairs Division. Charles and Gina's affair is exposed, after which Charles'\", \"Brothers & Sisters (season 2) one place down from the previous year and averaged at around 11.5 million viewers. Season One Season Three Season Four Season Five Brothers & Sisters (season 2) The second season of \\\"Brothers & Sisters\\\" consisted of only 16 episodes due to the 2007\\u20132008 Writers Guild of America strike. Ten of the episodes were shown beforehand, ending with \\\"The Feast of Epiphany\\\"; a further six episodes were produced to finish off the season. The first half of the season dealt with many issues and plot points left unresolved from the first season. In the UK the show changed to Channel 4's\", \"Justified (season 2) Justified (season 2) The second season of the American television drama series \\\"Justified\\\" premiered on February 9, 2011, on FX, and concluded on May 4, 2011, consisting of 13 episodes. The series was developed by Graham Yost based on Elmore Leonard's novels \\\"Pronto\\\" and \\\"Riding the Rap\\\" and his short story \\\"Fire in the Hole\\\". Its main character is Raylan Givens, a deputy U.S. Marshal. Timothy Olyphant portrays Givens, a tough federal lawman, enforcing his own brand of justice in his Kentucky hometown. The series is set in the city of Lexington, Kentucky, and the hill country of eastern Kentucky,\", \"Jonas (season 2) Jonas (season 2) The second and final season of the television series \\\"Jonas\\\", titled Jonas L.A. for this season, aired on Disney Channel from June 20, 2010 to October 3, 2010, and included 13 episodes. It follows the five main characters of the series as they spend their summer vacation in Los Angeles after the Jonas' tour was over, a few months after the events of the first season. The season was never officially released on DVD, being available only in digital format. After the season finale, it was announced that the show would not be returning for another season.\", \"Glass Home and it's titled \\\"Glass Home: Time for Truth\\\" (Bulgarian: \\\"\\u0421\\u0442\\u044a\\u043a\\u043b\\u0435\\u043d \\u0434\\u043e\\u043c: \\u0412\\u0440\\u0435\\u043c\\u0435 \\u0437\\u0430 \\u0438\\u0441\\u0442\\u0438\\u043d\\u0430\\\"). 13 episodes were shown and the show went on another pause, which ended with the beginning of second half of the second season: \\\"Glass Home: Time for Truth - The Resolution\\\" (Bulgarian: \\\"\\u0421\\u0442\\u044a\\u043a\\u043b\\u0435\\u043d \\u0434\\u043e\\u043c: \\u0412\\u0440\\u0435\\u043c\\u0435 \\u0437\\u0430 \\u0438\\u0441\\u0442\\u0438\\u043d\\u0430 - \\u0420\\u0430\\u0437\\u0432\\u0440\\u044a\\u0437\\u043a\\u0430\\u0442\\u0430\\\"). On 30 March, bTV announced that season 3 started on 11 April 2011, exactly one year after the show's first episode aired, and with no break between Season 2 and 3. It is called \\\"Glass Home: The Temptation\\\" (Bulgarian: \\\"\\u0421\\u0442\\u044a\\u043a\\u043b\\u0435\\u043d \\u0434\\u043e\\u043c: \\u0418\\u0437\\u0443\\u0448\\u0435\\u043d\\u0438\\u0435\\u0442\\u043e\\\") and it takes place three\", \"Warehouse 13 (season 2) Warehouse 13 (season 2) The second season of the American television series \\\"Warehouse 13\\\" premiered on July 6, 2010, and concluded on December 7, 2010, on Syfy. Season two maintained the Tuesdays at 9:00 pm ET timeslot from the previous season. The season consisted of 13 episodes. The show stars Eddie McClintock, Joanne Kelly, Saul Rubinek, Genelle Williams and Allison Scagliotti. Season two begins right after the events of the season finale of season one. Pete and Myka find that Artie has survived the explosion thanks to the Phoenix artifact which was slipped into his pocket by MacPherson during their\", \"Lost Girl (season 2) it had acquired 26 episodes (Season One and Season Two) of \\\"Lost Girl\\\" from Prodigy Pictures. Showcase announced in a July 7, 2011, press release that the Season Two premiere would be on September 4, 2011, and that an additional nine episodes had been ordered to make the season a total of 22 episodes. The order for more episodes was made public two weeks before the first appearance of \\\"Lost Girl\\\" cast and producers at San Diego Comic-Con International 2011. On December 12, 2011, Syfy announced the United States debut of \\\"Lost Girl\\\" on January 16, 2012. Season Two premiered\", \"Salem (season 2) Salem (season 2) The second season of \\\"Salem\\\", an American horror\\u2013drama television series on WGN America, premiered on April 5, 2015, and concluded on June 28, 2015, consisting of thirteen episodes. Created for television by Adam Simon and Brannon Braga, who write or co-write episodes of the show, the series is based on the Salem Witch Trials. It was executive produced by Braga, Coby Greenberg and David Von Ancken, with Braga and Simon assuming the role of showrunner. The second season was announced on May 15, 2014 after only 4 episodes of the first season had aired. The writing team\", \"The Mentalist (season 2) season was \\\"Red Menace\\\" (3.17 million viewers), and the least watched was \\\"Aingavite Baa\\\" (2.09 million viewers), although the latter was broadcast at the later time of 10pm. All 23 episodes were released on the five disc complete second season set. It was released on September 21, 2010 in Region 1, November 8, 2010 in Region 2, and November 10, 2010 in Region 4. It included the featurettes \\\"Mentalism: A Subliminal Art\\\" and \\\"The Art of The Mentalist with Chris Long\\\" as well as unaired scenes. The Mentalist (season 2) The second season of \\\"The Mentalist\\\" premiered on September 24,\", \"Oi Treis Harites living all together again under the same roof, their constant problems with men and many other hilarious adventures. In the last episode of the first season, the three sisters are making plans for their vacations. \\\"Season 2\\\" consists of 40 episodes and it's the largest season. It begins with the three sisters having returned from their vacations and trying to attract a charming man living next door to them. The season continues with the sisters handling hilarious situations in each episode. In the 7th episode of the season, a big change happens. Olga's daughter, Teti, abandons her studies in London\", \"Superstore (season 2) America Ferrera, Ben Feldman, Lauren Ash, Colton Dunn, Nico Santos, Nichole Bloom and Mark McKinney. NBC.com lists the special \\\"Olympics\\\" episode, aired August 19, 2016, as the first episode of the second season. Thus, the list below begins at Episode 2. The day after the first season finale had aired, \\\"Superstore\\\" was renewed for a second season on NBC. The second season will contain 13 episodes, but on September 21, 2016, the day before the second season premiere, NBC ordered 9 additional scripts, possibly making the episode-count up to 22 episodes. NBC announced on May 15, 2016, that the\", \"The Bay (web series) Bay\\\" would be re-released on Amazon Prime in summer 2016 as two 14-episode seasons. According to Martin, they will be \\\"remastered episodes with never before seen footage, [and] the show will have a new starting point\\\". A 14-episode first season was made available for streaming starting September 6, 2016. A second season of 14 digitally remastered episodes including \\\"brand-new, never-before-seen episodes\\\" was released on November 29, 2016. Wendy Riche joined the production as a writer for the Amazon season 2, becoming an executive producer for the second half of the season. A third season is in production with Martin, Andrews,\", \"Faking It (season 2) Faking It (season 2) The second season of \\\"Faking It\\\", an American single-camera romantic comedy, premiered on September 23, 2014, and concluded on November 2, 2015, on the MTV network. On June, 2014, the series was renewed for a second season of 10 episodes, which was later extended to 20 episodes. In the aftermath of the first-season finale, the gang has to deal with the consequences of their choices. Karma (Katie Stevens) finds out Amy (Rita Volk) and Liam (Gregg Sulkin) slept together and starts having trust issues with both of them. Lauren (Bailey De Young) has her secret about\", \"Louie (season 2) nomination for Best Television Comedy Series for the 64th Writers Guild of America Awards. C.K. was nominated for Best Actor \\u2013 Television Series: Musical or Comedy for the 17th Satellite Awards. The series was also included as one of the Top Television Programs of the Year by the American Film Institute. Louie (season 2) The second season of the American television comedy series \\\"Louie\\\" premiered on June 23, 2011 and concluded on September 8, 2011. It consisted of thirteen episodes, each running approximately 23 minutes in length. FX broadcast the second season on Thursdays at 10:30 pm in the United\", \"Beauty & the Beast (season 2) that's more dull than thrilling\\\". Beauty & the Beast (season 2) The second season of \\\"Beauty & the Beast\\\", an American television series developed by Sherri Cooper-Landsman and Jennifer Levin and very loosely inspired by the 1987 CBS television series of the same name, commenced airing in the United States on October 7, 2013, concluded July 7, 2014, and consisted of 22 episodes. \\\"Beauty & the Beast\\\"'s second season aired in the United States (U.S.) on Mondays at 9:00 pm ET on The CW, a terrestrial television network, where it received an average of 1.24 million viewers per episode. Catherine\", \"Cheers (season 2) Cheers (season 2) The second season of \\\"Cheers\\\", an American situation comedy television series, originally aired on NBC in the United States between September 29, 1983, and May 10, 1984, with 22 episodes. The show was created by director James Burrows and writers Glen and Les Charles and was produced by Charles Burrows Charles Productions in association with Paramount Television. The second season has been released on DVD as a four-disc set. The show won Emmy Awards, including one for Outstanding Comedy Series, in 1983 and 1984. Critical reception was mostly positive, with negative commentary about the extended romance between\", \"HaPijamot season ended in a special episode called \\\"My Favorite Episode\\\". Because the show attracted a large number of viewers, The Kids Channel decided to order another two seasons, which were written and filmed together. The second season debuted on 28 March 2004 and included 19 episodes. That season ended with a special episode called \\\"The Gold Pyjamas\\\". The third season of the show premiered on 1 July 2004 and included 20 episodes. The season ended with two special episodes, a documentary episode called \\\"Only The Truth\\\" and a trivia contest about the third season called \\\"This That or That This\\\".\", \"True Detective (season 2) predecessor, season two of \\\"True Detective\\\" consists of eight episodes, all written by Pizzolatto. However, the responsibility of directing was assigned to several people; Justin Lin directed the first two episodes, and, in July 2014, William Friedkin was being considered as a director of later episodes. Fukunaga, who directed all of season one, did not return as director; he remains, however, an executive producer, as do McConaughey and Harrelson. Pizzolatto hired fellow novelist Scott Lasser to help break stories for the second half of the season. The success of \\\"True Detective\\\", and its subsequent renewal, fueled casting rumors in the\", \"Hot in Cleveland Martin was unaware of TV Land's existence. TV Land announced that the show had been renewed for a second season on July 7, 2010. The 20-episode second season began production on November 1, 2010, and premiered January 19, 2011. On February 28, 2011, TV Land renewed the show for a third season to consist of 22 episodes. On March 21, 2011, TV Land announced that the third season order had beenwas increased to 24 episodes. On January 12, 2012, TV Land renewed the series for a fourth season. Betty White was only meant to appear in the pilot of the\", \"Arthur (season 2) found on \\\"Series 3.\\\" Arthur (season 2) The 2nd season of the television series \\\"Arthur\\\" was originally broadcast on PBS in the United States from October 20, 1997 to April 17, 1998 and contains 20 episodes. This season, like seasons 1 and 3, was released on DVD in Europe only; due to the fact that this was actually two production seasons (the first ten episodes encompassing the first and the last ten encompassing the second) combined into one long season for US airings, the first ten episodes for this season can be found on the \\\"Series 2\\\" DVD and\", \"Gossip Girl (season 2) Serena's mother, Lily van der Woodsen, and Jenny and Dan's father, Rufus Humphrey, along with Dan's childhood friend Vanessa Abrams, who recently moved back from Vermont. Gossip Girl (season 2) The second season of the American teen drama television series \\\"Gossip Girl\\\" premiered on The CW on September 1, 2008, and concluded on May 17, 2009, consisting of 25 episodes. Based on the novel series of the same name by Cecily von Ziegesar, the series was developed for television by Josh Schwartz and Stephanie Savage. The series revolves around the lives of privileged teenagers Serena van der Woodsen, Blair\", \"Ally McBeal (season 2) Ally McBeal (season 2) The second season of the television series \\\"Ally McBeal\\\" commenced airing in the United States on September 14, 1998, concluded on May 24, 1999, and consisted of 23 episodes. On March 22, 1999, Fox aired a special titled \\\"Life and Trials of Ally McBeal\\\" in which Bill Maher interviewed the cast after nearly finishing two seasons of the show. The special was produced by a different company. The entire season originally aired Mondays at 9pm, just like the season before. It was released on DVD as a six disc boxed set under the title of \\\"Ally\", \"BoJack Horseman (season 2) acclaim\\\". BoJack Horseman (season 2) The second season of the animated television series \\\"BoJack Horseman\\\" premiered exclusively via Netflix's web streaming service on July 17, 2015. The season consists of 12 episodes. On Rotten Tomatoes the second season holds an approval rating of 100%, based on 17 critics, with an average rating of 8.9/10. The site's critical consensus reads, \\\"\\\"BoJack Horseman\\\" truly comes into its own during season two, maturing into an ambitious comedy that sensitively blends wackiness with dark, nuanced drama\\\". On Metacritic, the season has a score of 90 out of 100, based on 7 critics, indicating\", \"Body of Proof (season 2) around strained relationships with her ex-husband and boss. The series still feels formulaic and doesn't quite fire on all cylinders for me, but season two was an improvement. The series leaves you wanting to see what will happen in season three. Body of Proof (season 2) The second season of \\\"Body of Proof\\\", an American television series created by Christopher Murphey, commenced airing in the United States on September 20, 2011, concluded April 10, 2012, and consisted of 20 episodes. It follows the life and career of Dr. Megan Hunt, a medical examiner, once a neurosurgeon, who now works in\", \"Conversations in L.A. Conversations in L.A. Conversations in L.A. is a dark comedy digital drama series which airs on iTunes, Amazon, Amazon Prime, and the show's website. This series is created, written, and directed by veteran stage actress, Anne Marie Cummings. The series is set and filmed in Los Angeles. Season one premiered on December 16, 2017 on Conversationsinla.com with 14 episodes. Season two premiered on September 13, 2017 on iTunes and Amazon, releasing one episode at a time. By December 13, 2017, all episodes in season two appeared on Amazon Prime. The first two seasons of \\\"Conversations in L.A.\\\" were produced by\", \"The Secret Life of the American Teenager and was added to ABC Family's line up on April 7, 2009. The show was renewed for a 24-episode second season, which began airing on June 22, 2009. Season 2 began with 12 episodes broadcast starting June 22, 2009, through September 7, 2009. After a four-month hiatus, the second half of the season returned on January 4, 2010, and concluded on March 22, 2010. Following their record-breaking, mid-season returns, \\\"Make It or Break It\\\", and \\\"Secret Life\\\" were both picked up for an additional season. The third-season premiere of \\\"Secret Life\\\" was aired on June 7, 2010, at 8 pm.\", \"Supernatural (U.S. TV series) in blood and unconscious. The second season consists of 22 episodes that aired on Thursdays at 9:00 pm beginning September 28, 2006, and ending May 17, 2007. The season follows Sam and Dean as they deal with their father's death, who, after the car crash, traded Azazel his life for Dean's. Sam and Dean continue to hunt Azazel, who caused the fires that led to the deaths of their mother, and later, Sam's girlfriend, Jessica. They receive assistance from new allies Bobby, Ellen, Jo, and Ash. Part of Azazel's master plan is eventually revealed as he gathers Sam and others\", \"Martha Speaks (TV series) segments are not included in the version distributed outside the United States. Season one of the series ended with a total of 40 episodes; it premiered in September 2008. Thirty episodes were produced for season two and then split in half to be aired over two broadcast seasons with 15 episodes each. Words defined by characters were not visible on screen during Season 1, but were made visible from Season 2 onward. When first aired, the show was followed by \\\"Music Time with SteveSongs\\\" and later by \\\"Dot's Story Factory\\\". Season two episodes were followed by a segment called \\\"Who's\", \"Shameless (season 2) Shameless (season 2) The second season of \\\"Shameless\\\", an American comedy-drama television series based on the British series of the same name by Paul Abbott, premiered on January 8, 2012, at Sunday 9:00 p.m. EST on the Showtime television network. Executive producers are John Wells, Paul Abbott and Andrew Stearn, with producer Michael Hissrich. The season concluded after 12 episodes on April 1, 2012. The show's season premiere brought in 1.58 million viewers, which was higher than the season 1 premiere. The episode airing March 4, \\\"Parenthood\\\", received 1.6 million total viewers, its highest rated show of the season. The\", \"Big Picture (web series) episodes are revealed, with the Big Picture being Lee Kwang-soo. Season 2 runs from 5 March 2018 to June 6, 2018 with a total of 93 episodes. On Season 2 of Big Picture, they wanted to make a blockbuster movie, however, they do not have much budget to start with. Therefore, they started the MADE show once again. Episode 1 to episode 91 are their MADE Show where Kim Jong Kook and Haha will introduce the different investors from the various companies who choose to invest on Big Picture. Various guests are also being featured in the show. Along with\", \"Girls (TV series) the first season and directed five, including the pilot. Season one was filmed between April and August 2011 and consisted of 10 episodes. The second season ran on HBO from January 13, 2013, to March 17, 2013, and also consisted of 10 episodes. On April 4, 2013, Christopher Abbott left the series after sources reported he and Dunham had differences with the direction that his reoccurring character Charlie was taking as the third season entered production. Dunham announced via Instagram on September 6, 2013, that production for the third season had concluded. Season 3, which contained 12 episodes as opposed\", \"Lost Girl (season 2) announcing \\\"record-breaking ratings\\\" and the \\\"number one scripted series for Adults 25-54 across all specialty channels\\\" in Canada. On July 7, 2011, Showcase announced that the Season Two premiere would be on September 4, 2011, and that an additional nine episodes had been ordered to make the season a total of 22 episodes. In the United Kingdom and Ireland, Season Two premiered on Syfy (UK) on January 12, 2012. In Australia, Season Two premiered on Sci Fi on February 23, 2012. In the United States, Season Two premiered on Syfy on April 16, 2012; one week after the end of\", \"Mona the Vampire every episode, but there are always rational explanations for what they see. There are a total of 65 full episodes of \\\"Mona the Vampire\\\". Each episode is approximately 20 minutes long, not including the theme song and the credits theme, and each full episode contains two 10-minute episodes. Four seasons of \\\"Mona the Vampire\\\" were produced. The first season contains 26 full episodes, while seasons 2, 3, and 4 each contains 13 full episodes. The following is a list containing voice actors and the characters they voice: The first public announcement of the production of the television series was in\", \"The Tenderloins Television Festival. On April 12, 2011, truTV announced a new series to star the Tenderloins. \\\"Impractical Jokers\\\" premiered on December 15, 2011, executive-produced by the troupe themselves. Season 1 consisted of 16 episodes. The first season was watched by over 32 million viewers. The show's second season premiered on December 13, 2012 and consist of 28 episodes. Its third season had 31 episodes and premiered on January 2, 2014. On April 21, 2014, truTV announced that the series would be renewed for a 26-episode fourth season. With the fourth season renewal, a 6-episode spinoff series, \\\"Jokers Wild!\\\", was also announced.\"], \"pos_scores\": [98.625], \"neg_scores\": [85.5, 86.25, 85.0, 85.4375, 86.75, 86.0, 86.375, 85.125, 84.8125, 85.5, 85.8125, 85.375, 84.5625, 87.0, 87.0, 86.0625, 84.625, 84.375, 84.8125, 85.8125, 84.875, 86.1875, 85.125, 85.375, 85.125, 84.6875, 84.75, 85.0625, 85.5625, 85.0625, 85.75, 86.0, 85.875, 84.5, 85.4375, 83.5625, 84.625, 84.1875, 84.1875, 85.375, 85.75, 86.1875, 85.0, 84.9375, 85.9375, 84.4375, 87.375, 85.75, 84.8125, 85.1875, 85.9375, 84.25, 85.4375, 84.25, 85.1875, 84.75, 84.8125, 83.6875, 87.875, 85.125, 86.125, 85.75, 84.5, 85.125, 85.25, 85.75, 85.0, 84.9375, 85.6875, 84.25, 85.0, 85.1875, 84.875, 85.9375, 85.0, 86.6875, 85.9375, 84.5, 84.6875, 84.75, 85.25, 85.4375, 84.125, 85.875, 84.8125, 84.0, 85.3125, 84.1875, 85.6875, 84.625, 85.0625, 85.25, 84.75, 84.3125, 85.6875, 84.6875, 86.9375, 85.125, 84.375, 84.9375], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"who sang waiting for a girl like you\", \"pos\": [\"Waiting for a Girl Like You Waiting for a Girl Like You \\\"Waiting for a Girl Like You\\\" is a 1981 power ballad by the British-American rock band Foreigner. The distinctive synthesizer theme was performed by the then-little-known Thomas Dolby, and this song also marked a major departure from their earlier singles because their previous singles were mid to upper tempo rock songs while this song was a softer love song with the energy of a power ballad. It was the second single released from the album \\\"4\\\" (1981) and was co-written by Lou Gramm and Mick Jones. It has become one of the band's most\"], \"neg\": [\"Edyta Go\\u0301rniak first English-language album \\\"Edyta G\\u00f3rniak\\\" a year later, in November 1997. It was produced by Christopher Neil, who was responsible for the international success of Celine Dion. Songs were written by writers of many legendary pop classics like: Billy Steinberg (\\\"Like A Virgin\\\" for Madonna), Simon Climie (\\\"I Knew You Were Waiting (For Me)\\\" for Aretha Franklin and George Michael), George Merrill and Shannon Rubican (\\\"I Wanna Dance With Somebody\\\" for Whitney Houston), Siedah Garrett (\\\"Man in the Mirror\\\" for Michael Jackson) or Rick Nowels (\\\"Falling into You\\\" for Celine Dion). However, the mediocre success of the singles \\\"When You\", \"The Smithereens on the \\\"Billboard\\\" pop charts was in 1990, when \\\"11\\\" peaked at No. 41 on the strength of the single \\\"A Girl Like You\\\" (which hit No. 38). \\\"A Girl Like You\\\" was originally written to be the title track for the 1989 Cameron Crowe film \\\"Say Anything...\\\". The basic tracks for their most recent studio album of original material, titled \\\"2011\\\", were recorded in early October 2010 and the album was released on April 5, 2011. The Smithereens were the final band to perform at the fabled Bleecker Street nightclub Kenny's Castaways in Greenwich Village, New York City, in\", \"Edwyn Collins to record his third solo album, \\\"Gorgeous George\\\", which he also produced. The studio, located in West Hampstead, London, UK, would become the West Heath Yard Studios that Collins would use for his future record label, AED Records. In 1995, Collins released the single \\\"A Girl Like You\\\", which became a hit in both the United Kingdom and United States after it was featured in the film \\\"Empire Records\\\". It was subsequently used in the movie \\\"\\\" in 2003. Collins released his followup to \\\"Gorgeous George\\\", \\\"I'm Not Following You\\\", in 1997. One of its singles, \\\"The Magic Piper (of\", \"With a Girl Like You With a Girl Like You \\\"With a Girl like You\\\" is a song released by the English rock band the Troggs, written by Reg Presley and produced by Larry Page. The song reached number one in the UK Singles Chart on 4 August 1966, where it remained for two weeks. In the U.S., it peaked at #29 on the \\\"Billboard\\\" Hot 100. The song is featured, uncut, in a school dance scene from the 1991 Nicole Kidman/Noah Taylor film, \\\"Flirting\\\". It is also featured in the films and/or soundtracks to \\\"Shine\\\", \\\"The Good Night\\\" and \\\"The Boat That Rocked\\\". The\", \"Maria Vidal Child on the Burt Bacharach/Desmond Child penned power ballad, \\\"Obsession.\\\" At the time, Vidal was making her third solo album with British-owned Charisma Records, but the album was never released. As a singer, songwriter and producer, Vidal has contributed to many other artists' music. Vidal contributed backup vocals to Kiss' Paul Stanley's solo album, Stevie Nicks' single \\\"I Can't Wait\\\" as well as a few other tracks from Nicks' solo album \\\"Rock A Little\\\", and was a featured vocalist for The Smithereens' single \\\"A Girl Like You\\\". She appears on many of Belinda Carlisle's solo albums, including the 1989 album\", \"Waiting for a Star to Fall Waiting for a Star to Fall \\\"Waiting for a Star to Fall\\\" is a song released by the pop duo Boy Meets Girl in 1988. It was a worldwide hit and became their signature song. Since its release, it has been remixed and covered by many artists, including Cabin Crew and Sunset Strippers. The song was inspired by an actual falling star that Shannon Rubicam had seen at one of Whitney Houston's concerts at the Greek Theatre. Initially, the duo did not consider recording it, and instead submitted the song to Clive Davis hoping he would decide to use it\", \"Roddy Frame serious illness, and the pair played again at the Glastonbury Festival in June 2008, on the Park Stage, and at the Purcell Rooms in London, UK, in September 2008. In 2012 Collins sang \\\"A Girl Like You\\\"\\u2014with Frame on guitar and Tim Burgess on backing vocals\\u2014and a rendition of the Orange Juice song \\\"Falling and Laughing\\\"\\u2014with Frame on guitar\\u2014at Burgess's \\\"Tim Peaks Diner\\\" caf\\u00e9, as part of the Kendal Calling festival. Dan Carey and Rob Da Bank, whose band name is Lazyboy, collaborated with Frame on the song \\\"Western Skies\\\"; Frame then re-recorded the song for a solo album of\", \"A Girl Like You (The Smithereens song) A Girl Like You (The Smithereens song) \\\"A Girl Like You\\\" is a song by the American alternative rock group The Smithereens. It is the first single released in support of their third album \\\"11\\\". The song was to be used in the film Say Anything..., but it was ultimately cut from the film because the producers believed the song revealed too much of the story. Pat DiNizio stated that he wrote the lyrics with having a separate meaning from the movie in mind. On May 18, 2010, the song was made available as a downloadable song in the Rock\", \"Edwyn Collins drawings that Collins started working on in 2005. On 30 September 2010 Collins and his band broadcast three live songs from the Royal Beacon Hotel in Exmouth for BBC Radio 2's \\\"Radcliffe and Maconie Show\\\". (Stuart Maconie is a former music journalist and his first \\\"NME\\\" article was a review of Collins's 1987 gig at the Manchester International.) On 30 July 2011 Collins performed at the Indietracks festival that was held at the Midland Railway, Butterley, UK. During the 2012 Kendal Calling event Collins sang \\\"A Girl Like You\\\", with Roddy Frame on guitar and Tim Burgess on backing vocals.\", \"The Wolfgang Press Langston of Throwing Muses guests on most tracks. The singles from the album were \\\"Time\\\" (the album version being titled \\\"Question Of Time\\\"), which included a sample from Pink Floyd's \\\"Time\\\" (from \\\"The Dark Side of the Moon\\\"), followed by a cover of Randy Newman's \\\"Mama Told Me Not to Come.\\\" The single \\\"A Girl Like You\\\" was released in May 1992 and became an international hit, scoring No. 2 on the \\\"Billboard\\\" US Modern Rock (Alternative Songs) chart on 15 August 1992. The song was later covered by Tom Jones, who then asked the band to write \\\"Show Me\", \"Edwyn Collins Edwyn Collins Edwyn Stephen Collins (born 23 August 1959) is a Scottish musician, producer and record label owner from Edinburgh, Scotland. Collins was the lead singer for the 1980s post-punk band Orange Juice, which he co-founded. Following the group's split in 1985, Collins started a solo career. His 1995 single \\\"A Girl Like You\\\" was a worldwide hit. In February 2005, Collins was hospitalized following two cerebral haemorrhages which resulted in aphasia, and he subsequently underwent a months-long rehabilitation period. Collins resumed his musical career in 2007. A documentary film on his recovery, titled \\\"The Possibilities Are Endless\\\", was released\", \"Boy Meets Girl (Boy Meets Girl album) influence. Merrill and Rubicam would go on to pen hits for Whitney Houston and get a contract with RCA Records, which would release their second full-length album, \\\"Reel Life\\\", three years later. That record would provide them with their sole top-ten hit as a recording act, \\\"Waiting for a Star to Fall.\\\" Boy Meets Girl (Boy Meets Girl album) Boy Meets Girl is the debut album by American pop singer-songwriters George Merrill and Shannon Rubicam, also known as Boy Meets Girl. It was released on A&M Records in 1985, and was their only disc for the label. The album included\", \"Waiting for a Girl Like You held off the number 1 spot by Olivia Newton-John's single \\\"Physical\\\" for nine consecutive weeks, and then by Hall & Oates' \\\"I Can't Go for That (No Can Do)\\\" for a tenth week on January 30, 1982. Because of its chart longevity, it ended up being the number 19 song on the Top 100 singles of 1982. The song was the band's biggest hit until \\\"I Want to Know What Love Is\\\" hit number 1 in 1985. The song lists at number 100 on \\\"\\\"Billboard\\\"'s Greatest Songs of All Time\\\". Waiting for a Girl Like You \\\"Waiting for a Girl\", \"A Girl Like You (The Smithereens song) Band digital store. All songs written by Pat DiNizio, except where noted. A Girl Like You (The Smithereens song) \\\"A Girl Like You\\\" is a song by the American alternative rock group The Smithereens. It is the first single released in support of their third album \\\"11\\\". The song was to be used in the film Say Anything..., but it was ultimately cut from the film because the producers believed the song revealed too much of the story. Pat DiNizio stated that he wrote the lyrics with having a separate meaning from the movie in mind. On May 18, 2010,\", \"A Girl Like You (Easton Corbin song) A Girl Like You (Easton Corbin song) \\\"A Girl Like You\\\" is a song written by Ashley Gorley, Jesse Frasure, and Rhett Akins and recorded by American country music singer Easton Corbin. The song is Corbin's tenth single, and was originally intended to be the lead single from his upcoming fourth studio album before his termination from Mercury Nashville. The song is Corbin's first non-album single to date. The song features \\\"a playful, chicken-picked guitar hook and a digitized, shuffling beat\\\". Its lyrics deal with a woman with whom the narrator is affectionate. He compares her to \\\"the nameless girl\", \"Boy Meets Girl (band) Boy Meets Girl (band) Boy Meets Girl is an American pop-music duo consisting of keyboardist and vocalist George Merrill and singer Shannon Rubicam. They are perhaps best known for their hit song \\\"Waiting for a Star to Fall\\\" from 1988 and for writing two of Whitney Houston's number one hits: \\\"How Will I Know\\\" and \\\"I Wanna Dance with Somebody (Who Loves Me).\\\" The members of Boy Meets Girl, George Merrill and Shannon Rubicam, wrote and composed a number of songs for other artists. Most famous are their two number one hits written for Whitney Houston, \\\"How Will I Know\\\"\", \"A Girl Like You (Edwyn Collins song) A Girl Like You (Edwyn Collins song) \\\"A Girl Like You\\\" is a song by Scottish singer-songwriter Edwyn Collins from his third solo studio album, \\\"Gorgeous George\\\" (1994). The song samples the drums track of Len Barry's single \\\"1-2-3\\\" (1965). There are two different music videos for this song. The video in the United States was an avant-garde tribute to its lyrics (e.g. when Collins sings the lyrics \\\"you made me acknowledge the devil in me\\\", a child's painting of the devil overlays his face). The international video shows Collins singing over videos and silhouettes of dancers. The Sex Pistols\", \"David Foster The Tubes: 1981's \\\"The Completion Backward Principle\\\", and 1983's \\\"Outside Inside\\\". Foster cowrote such songs as \\\"Talk to Ya Later\\\" co-written with Tubes and Steve Lukather from Toto, the Top 40 hit \\\"Don't Want to Wait Anymore,\\\" and the number 10 US hit \\\"She's a Beauty\\\". The 1980 Boz Scaggs album \\\"Middle Man\\\" saw Foster cowrite and play keyboard on some of Scaggs's most successful songs, including \\\"Breakdown Dead Ahead\\\", \\\"Jojo\\\", and \\\"Simone\\\", followed by \\\"Look What You've Done to Me\\\" from the film \\\"Urban Cowboy\\\". Foster was a major contributor to Chicago's career in the early and middle 1980s,\", \"A Girl Like You (Edwyn Collins song) the 1995 movie \\\"Empire Records\\\". It was also featured in season 1, episode 5 of \\\"Lucifer\\\" (\\\"Sweet Kicks\\\"). A Girl Like You (Edwyn Collins song) \\\"A Girl Like You\\\" is a song by Scottish singer-songwriter Edwyn Collins from his third solo studio album, \\\"Gorgeous George\\\" (1994). The song samples the drums track of Len Barry's single \\\"1-2-3\\\" (1965). There are two different music videos for this song. The video in the United States was an avant-garde tribute to its lyrics (e.g. when Collins sings the lyrics \\\"you made me acknowledge the devil in me\\\", a child's painting of the devil\", \"Shannon Rubicam Shannon Rubicam Shannon Rubicam (born October 11, 1951 in Seattle, Washington) is an American female singer/songwriter who is best known for being half of the mid-to-late-1980s pop duo Boy Meets Girl. Her husband, George Merrill, was the other half of Boy Meets Girl, who are best remembered for their 1988 hit \\\"Waiting for a Star to Fall\\\". Merrill and Rubicam first met in 1975 when both were performing at a friend's wedding. The couple also wrote two hit songs for Whitney Houston, \\\"How Will I Know\\\" and \\\"I Wanna Dance with Somebody (Who Loves Me)\\\", both of which hit Number\", \"Cliff Richard Life\\\", \\\"How Did She Get Here\\\", \\\"Hey Mr. Dream Maker\\\", \\\"For Life\\\", \\\"A Matter of Moments\\\", \\\"When The Girl in Your Arms\\\" and the Christmas single \\\"21st Century Christmas\\\", which debuted at No. 2 on the UK singles chart. Another compilation album, \\\"Love... The Album\\\" was released on 12 November 2007. Like \\\"Two's Company\\\" before it, this album includes both previously released material and newly recorded songs, namely \\\"Waiting for a Girl Like You\\\", \\\"When You Say Nothing at All\\\", \\\"All Out of Love\\\", \\\"If You're Not the One\\\" and \\\"When I Need You\\\" (the last was released as a\", \"Girls Like You among female rappers. It spent sixteen weeks atop the Radio Songs chart, tying Mariah Carey's \\\"We Belong Together\\\" as the longest-leading number one this century. Additionally, it reached number one in eleven other countries, including Canada and New Zealand. The song received a nomination for Best Pop Duo/Group Performance at the 61st Annual Grammy Awards. \\\"Girls Like You\\\" was revealed as the ninth track on \\\"Red Pill Blues\\\" via Apple Music. It was announced as the third official single from the album, following the song \\\"Wait\\\". The remix versions of \\\"Girls Like You\\\" was released on August 2, 2018, featuring\", \"Urgent (song) \\\"Billboard\\\" Hot 100 chart, holding that spot for the entire month of September. \\\"Urgent\\\" hit #1 on the \\\"Billboard\\\" Rock Tracks chart, a position it held for four weeks. \\\"Urgent\\\" was the most successful single from the \\\"4\\\" album on album-oriented rock radio, though it was outsold by the album's later single, \\\"Waiting for a Girl Like You\\\", which reached #2 on the \\\"Billboard\\\" Hot 100 in November 1981 and remained at that spot through the end of the following January, for a total of ten weeks, being certified Gold. \\\"4\\\" went Gold and Platinum during the chart run of\", \"11 (The Smithereens album) produced albums by The Ramones and Living Colour. \\\"I\\u2019m not sure what we were looking for\\u2026maybe a heavier guitar sound, like in \\\"A Girl Like You\\\". We were trying to preserve our integrity, yet find a home on radio\\u201d, lead singer Pat DiNizio said. \\\"A Girl Like You\\\" was written by DiNizio on assignment for Cameron Crowe's film \\\"Say Anything...\\\". DiNizio based the lyrics on bits of dialogue in the screenplay. When the film's producer asked DiNizio to change the lyrics, because it revealed too much of the plot, he refused, and the band decided to keep the song for\", \"The Wolfgang Press The Wolfgang Press The Wolfgang Press was an English post-punk band, active from 1983 to 1995, recording for the 4AD label. The core of the band was Michael Allen (vocals, bass), Mark Cox (keyboards), and Andrew Gray (guitar). The group is best known for its 1992 international hit single \\\"A Girl Like You (Born To Be Kissed)\\\". The official 4AD band profile describes them as \\\"post-punk\\\", transforming to \\\"avant-dance groovers\\\" with \\\"Queer\\\". The band was frequently labeled \\\"goth,\\\" though they denied the charge. Allen's list of \\\"important records\\\" as of 1995 included De La Soul's \\\"3 Feet High and Rising\\\",\", \"Waiting (Green Day song) Waiting (Green Day song) \\\"Waiting\\\" is a song by American punk rock band Green Day. It was released as the third single from their sixth album, \\\"Warning\\\". The song peaked at number 26 on the \\\"Billboard\\\" Modern Rock chart. The melody of the song is somewhat similar to that of \\\"Do You Wanna Dance?\\\" by Bobby Freeman as well as Petula Clark's \\\"Downtown.\\\" The song was also well received by critics. \\\"NME\\\", in its review, stated that \\\"'Waiting', with its Mamas And Papas melody and its Kiss Army hook, could sit respectably alongside the band's best material.\\\" \\\"Slant\\\" called the\", \"A Girl Like You (Dallas Smith song) A Girl Like You (Dallas Smith song) \\\"A Girl Like You'\\\" is a song written by Jaren Johnston and Jimmy Robbins, and recorded by Canadian country rock singer Dallas Smith for his first extended play, \\\"Tippin' Point\\\" (2014). It was serviced to Canadian country radio via 604 Records on June 23, 2014 as the third single from the EP. Markus Meyer of country music blog \\\"The Shotgun Seat\\\" praised \\\"A Girl Like You\\\" for subverting expectations and being a love song rather than a degrading lyric. He writes: \\\"\\\"A Girl Like You\\\" isn't deep, but it is well-written and features\", \"11 (The Smithereens album) 11 (The Smithereens album) 11 is the third full-length album by The Smithereens, released on October 18, 1989 (see 1989 in music). It includes the Billboard Top 40 single \\\"A Girl Like You\\\". The album was certified gold by the Recording Industry Association of America in June 1990. The album title was inspired by the film \\\"Ocean's 11\\\", \\\"with a little push from Spinal Tap's famous line, \\\"This one goes to 11\\\", according to guitarist Jim Babjak. The Smithereens switched producers for the album, going from Don Dixon, who had produced their first two albums, to Ed Stasium, who had\", \"A Girl Like You (Easton Corbin song) of many a recent Nashville hit\\\", and states that she is unlike any other girl. The song has sold 96,000 copies in the United States as of February 2018. A Girl Like You (Easton Corbin song) \\\"A Girl Like You\\\" is a song written by Ashley Gorley, Jesse Frasure, and Rhett Akins and recorded by American country music singer Easton Corbin. The song is Corbin's tenth single, and was originally intended to be the lead single from his upcoming fourth studio album before his termination from Mercury Nashville. The song is Corbin's first non-album single to date. The song features\", \"Kim Deal fractious, as the band were hardly ever together during the process. She rarely sang on the band's songs during this time; one of the few tracks she sang on was a cover of Neil Young's \\\"I've Been Waiting for You\\\". () However, Deal did sing on \\\"Trompe le Monde\\\", on songs such as \\\"Alec Eiffel\\\", but did not write any material for the album. A year after The Pixies\\u2019 breakup, Deal's identical twin sister Kelley joined The Breeders on lead guitar and the band released its second album, \\\"Last Splash\\\", to critical acclaim and considerable commercial success. The record went\", \"Billie Jo Spears \\\"'57 Chevrolet\\\", \\\"Love Ain't Gonna Wait For Us\\\", and \\\"If You Want Me\\\". The 1981 cover version of Tammy Wynette's 1960s hit, \\\"Your Good Girl's Gonna Go Bad\\\", was her last voyage into the American country top 20. Spears continued releasing albums in the United States into the 1980s. By the mid-1980s, her overall success had tapered off. However, she retained a following in the UK, and remained a popular live performer there. Spears recorded a number of albums for the British market that had limited or even no release in the US. This level of fame in the UK\", \"Dalbello Cher, Richard Marx, Heart, Alice Cooper, Patti LaBelle, Toto, Nena (for whom she wrote an entire translation album \\\"It's All in the Game\\\") and Canadian artists Rough Trade, Kim Mitchell, Alex Lifeson and Glass Tiger. Melissa Manchester successfully took \\\"Pretty Girls\\\" into the US and Canadian Top 40 in 1979. Heart covered \\\"Wait for an Answer\\\" and did a version of \\\"Black on Black\\\" called \\\"Black on Black II\\\". Queensr\\u00ffche covered \\\"Gonna Get Close to You\\\". Both Hauteville and Meliesa McDonell covered \\\"Immaculate Eyes\\\". Julie Masse covered \\\"Devious Nature\\\". Heavens Gate covered \\\"Animal\\\". Her song \\\"Don't Get Mad Get Even\\\"\", \"Waitin for You Waitin for You \\\"Waitin for You\\\" is a song recorded by American singer Demi Lovato featuring rapper Sirah. The duo along with Julia Michaels, Jason Evigan and Steve Mac composed the song for Lovato's fifth studio album \\\"Confident\\\" (2015). Evigan and Mac also handled the song's production, with Mitch Allan serving as a vocal producer. In December 2013, Lovato confirmed that she had started working on her then-untitled fifth studio album \\\"Confident\\\". The majority of the album's songs was recorded upon the conclusion of the Demi World Tour in 2015. \\\"Waitin for You\\\" was written by Lovato, Julia Michaels, Jason\", \"William Dillon William Dillon William Austin Dillon (November 6, 1877 - February 10, 1966) was an American songwriter and Vaudevillian. He is best known as the lyricist for the song \\\"I Want A Girl (Just Like The Girl That Married Dear Old Dad)\\\" (1911), written in collaboration with Harry Von Tilzer. It can be heard in \\\"Show Business\\\" (1944) and \\\"The Jolson Story\\\" (1946). He was born in Cortland, New York and performed at some point in Vaudeville with his brothers John and Harry. He billed his own act as the \\\"man of a thousand songs\\\". He quit vaudeville around 1912 after\", \"Linda Perry Linda Perry Linda Perry (born April 15, 1965) is an American singer-songwriter, musician, and record producer. She first became known as the lead singer and primary songwriter of 4 Non Blondes and has since founded two record labels and composed and produced hit songs for several other artists. They include: \\\"Beautiful\\\" by Christina Aguilera; \\\"What You Waiting For?\\\" by Gwen Stefani; and \\\"Get the Party Started\\\" by P!nk. Perry has also contributed to albums by Adele, Alicia Keys, and Courtney Love, as well as signing and distributing James Blunt in the United States. Perry was also inducted into the Songwriters\", \"A Girl Like You (Dallas Smith song) live acoustic rendition of the song. It is Smith's first music video not to be directed by Stephano Barberis. \\\"A Girl Like You\\\" debuted at number 82 on the \\\"Billboard\\\" Canadian Hot 100 for the week ending August 2, 2014. A Girl Like You (Dallas Smith song) \\\"A Girl Like You'\\\" is a song written by Jaren Johnston and Jimmy Robbins, and recorded by Canadian country rock singer Dallas Smith for his first extended play, \\\"Tippin' Point\\\" (2014). It was serviced to Canadian country radio via 604 Records on June 23, 2014 as the third single from the EP. Markus\", \"Edwyn Collins allow fans to listen to \\\"A Girl Like You\\\" for free on his MySpace page. In November 2009, at a gig in London's Bloomsbury Ballroom, following a tour of the Scottish Highlands, Collins's singing was contrasted with his slow speech: \\\"[W]hen he started to sing, his baritone proved as powerful as ever.\\\" On 20 February 2010, he joined The Maccabees onstage at Brixton Academy for their encore, performing vocals on a rendition of \\\"Rip It Up\\\". \\\"Losing Sleep\\\", Collins's first written and recorded album since his 2005 illness, was then released on 13 September 2010 in the UK. The album\", \"Simon Climie George Michael and Aretha Franklin's single \\\"I Knew You Were Waiting (For Me)\\\" reached number one worldwide, including in the UK Singles Chart and on the US \\\"Billboard\\\" Hot 100. Following this, Climie continued to have success with his songs, including \\\"You're Not Alone\\\", which featured on Amy Grant's \\\"Heart in Motion\\\", which sold more than five million copies worldwide \\u2013 the biggest-selling Christian album of all time. Climie is also known for his Grammy Award-winning work as a collaborator with Eric Clapton on many Platinum albums, including \\\"Pilgrim\\\", \\\"Reptile\\\", \\\"Riding with the King\\\", \\\"Me And Mr Johnson\\\", \\\"Back Home\\\"\", \"Waiting for a Girl Like You successful songs worldwide, peaking at number 2 on the \\\"Billboard\\\" Hot 100 and number 1 on \\\"Billboard\\\"'s Rock Tracks chart. On the \\\"Billboard\\\" Adult Contemporary chart, the song reached number 5. The song peaked at number 8 on the UK Singles Chart. \\\"Waiting for a Girl Like You\\\" achieved a chart distinction by spending its record-setting 10 weeks in the number 2 position of the \\\"Billboard\\\" Hot 100 chart, without ever reaching the top. It debuted on the Hot 100 chart dated October 10, 1981. It reached the number 2 position in the week of November 28, where it was\", \"Girl Like You (Jason Aldean song) Girl Like You (Jason Aldean song) \\\"Girl Like You\\\" is a song written by Jaron Boyer, Josh Mirenda, and Michael Tyler, and recorded by American country music singer Jason Aldean. It is the third single from Aldean's 2018 album \\\"Rearview Town\\\". Billy Dukes of \\\"Taste of Country\\\" described the song as a \\\"mid-tempo love song\\\" and a \\\"beat-driven slice of sultry country and hip-hop love.\\\" He also compared its hip-hop and R&B influences to \\\"Burnin' It Down\\\". \\\"One Country\\\" writer Annie Reuter also found R&B influences in Aldean's performance, saying that it was a \\\"blend of rock and R&B influences\\\"\", \"Casanova (The Divine Comedy album) \\\"Casanova\\\", resulting in the album having a more Britpop flow to it. Its central theme is sex, around which all songs on the album centre, except \\\"The Dogs and the Horses\\\", which is the last song on the album and whose theme is death. \\\"Casanova\\\" had the longest recording period of any Divine Comedy album up to that point and consequently cost more. Setanta was able to indulge Neil Hannon's desire because of the success of Edwyn Collins' hit single \\\"A Girl Like You.\\\" Although more musicians were involved than on Liberation and Promenade, for most of the album, as\", \"I Want A Girl (Just Like The Girl That Married Dear Old Dad) I Want A Girl (Just Like The Girl That Married Dear Old Dad) I Want A Girl (Just Like The Girl That Married Dear Old Dad) (sometimes shortened to \\\"I Want A Girl\\\") is a popular song of 1911 composed by Harry Von Tilzer and with lyrics by William Dillon, which has become a barbershop quartet standard. Von Tilzer and Dillon had never written a song together before, but finding themselves on the same vaudeville bill, von Tilzer suggested they might collaborate on some songs while on the road. Dillon had already had some success with \\\"girl\\\" songs such as\", \"Places (song) Places (song) \\\"Places\\\" is a song by French DJ and record producer Martin Solveig featuring vocals from Norwegian singer/songwriter Ina Wroldsen. It was released as a digital download in France on 25 November 2016 through Spinnin' Records and Big Beat. The song has peaked at number 82 on the French Singles Chart. The song was written by Ina Wroldsen and Martin Picandet. The song samples the Boy Meets Girl song \\\"Waiting for a Star to Fall\\\". A music video to accompany the release of \\\"Places\\\" was first released onto YouTube on 7 December 2016 at a total length of four\", \"Dallas Smith (singer) in Canada and the \\\"Tippin' Point\\\" video was ranked number 1 on CMT Canada. In the US, the song \\\"Tippin' Point\\\" was ranked number 1 on Sirius XM's The Highway Hot 45 Countdown and has sold over 120,000 singles. Smith followed up the success of the first single by releasing \\\"Slow Rollin'\\\" in March 2014. The track was a top 5 single at Canadian country radio. A third single, \\\"A Girl Like You\\\", was released in June 2014. Smith's single \\\"Slow Rollin'\\\" is performed by Lady Antebellum on the deluxe edition of their album \\\"747\\\". In 2014, Dallas Smith entered\", \"Waitin for You Lovato's \\\"most powerful\\\" video yet. Lovato and Sirah performed the song live for the very first time on August 13, 2016 at the MGM Grand Garden Arena in Las Vegas during the Future Now Tour. Credits adapted from \\\"Confident\\\" album liner notes. Waitin for You \\\"Waitin for You\\\" is a song recorded by American singer Demi Lovato featuring rapper Sirah. The duo along with Julia Michaels, Jason Evigan and Steve Mac composed the song for Lovato's fifth studio album \\\"Confident\\\" (2015). Evigan and Mac also handled the song's production, with Mitch Allan serving as a vocal producer. In December 2013,\", \"Bertine Zetlitz (Sugababes, Rachel Stevens). Lead single \\\"Girl Like You\\\" signalled a change of direction for the singer. Her fourth album \\\"Rollerskating\\\" was released in late 2006. EMI describes the release as a \\\"fascinating and unique blend of pulsing electro, classic Disco nouveaux, twisted pop melodies and good old fashioned catchy choruses. It's Bertine as you know and love her, but with a new even more addictive sting in her tail.\\\" Lead single \\\"Fake Your Beauty\\\" was a radio hit and reached the top spot in the Norwegian charts. Its follow-up \\\"Ah-Ah\\\" was remixed on release and was a sizeable hit. Zetlitz\", \"With a Girl Like You Osiris Shoes video \\\"Feed the Need\\\" featured the \\\"With a Girl Like You\\\" (James Brockman's section). Part of the song was featured in a trailer for the 2013 film \\\"Warm Bodies\\\". The song was featured at the end of the \\\"\\\" season 9 episode \\\"The Real McCoy\\\", when Adam and his girlfriend are dancing. The song was featured on the \\\"Girls\\\" season 1 episode, \\\"Vagina Panic\\\". With a Girl Like You \\\"With a Girl like You\\\" is a song released by the English rock band the Troggs, written by Reg Presley and produced by Larry Page. The song reached number\", \"Drumline (film) their drums, the line all drop their sticks onto the other drumline's drums. The judges award the win to A&T. The film was released on VHS and in fullscreen and widescreen editions on DVD April 15, 2003. DVD features include a full-length director's commentary, ten deleted scenes with commentary, a thirty-minute \\\"Making of...\\\" special, and music videos for the singles \\\"I Want A Girl Like You\\\" by Joe and \\\"Blowin' Me Up (With Her Love)\\\" by JC Chasez from the film's soundtrack. A \\\"special edition\\\" DVD version of the film was later released on January 29, 2008. The film was\", \"Elliott Yamin The album was the highest new artist debut on an independent label in SoundScan history. The album was certified gold status by the RIAA on October 12, 2007. To promote his album, Yamin made TV appearances on \\\"Live with Regis and Kelly\\\", \\\"The Ellen DeGeneres Show\\\", \\\"Rachael Ray\\\", \\\"Jimmy Kimmel Live!\\\", and \\\"TRL\\\", where the video for the song \\\"Wait for You\\\" was premiered on March 20, 2007. He also signed CDs at the New York Times Square Virgin Megastore across from the TRL studio and at Circuit City in his hometown, Richmond, Virginia, where the line went around the\", \"I Need a Girl (Trey Songz song) Face\\\", and on September 9, 2009 on \\\"The Wendy Williams Show\\\". Source I Need a Girl (Trey Songz song) \\\"I Need a Girl\\\" is a song by American recording artist Trey Songz. The track was written by Johnt\\u00e1 Austin, along with the song's production team, Stargate, who also produced his breakthrough hit, \\\"Can't Help But Wait.\\\" The song served as the lead single for Songz's third studio album \\\"Ready\\\". \\\"I Need a Girl\\\" received positive to mixed reviews from critics, some of which noted it as generic, and other naming it as a top track from the album. It reached\", \"Girls Like You Girls Like You \\\"Girls Like You\\\" is a song recorded by American band Maroon 5, from their sixth studio album \\\"Red Pill Blues\\\" (2017). A second version featuring American rapper Cardi B was released by 222 and Interscope Records as the album's third single on May 30, 2018. This version was written by Adam Levine, Cirkut, Cardi B, Starrah, Jason Evigan, and Gian Stone and was produced by Cirkut and Evigan. The single reached number one on the US \\\"Billboard\\\" Hot 100 chart, making it Maroon 5's fourth and Cardi B's third chart-topper, who extended her record for most number-ones\", \"Rihanna two million copies worldwide. A second single, \\\"If It's Lovin' that You Want\\\", was not as successful as its predecessor, but reached the top ten in Australia, Ireland, and New Zealand. Aside from her work in music, Rihanna made her acting debut in a cameo role in the straight-to-DVD film \\\"\\\", released in August 2006. A month after the release of her debut album, Rihanna began working on her second studio album. \\\"A Girl like Me\\\" was released in April 2006. \\\"Rolling Stone\\\" felt that \\\"the burning rock guitar\\\" and haunted strings of some of the album's tracks made \\\"\\\"A\", \"George Merrill (songwriter) George Merrill (songwriter) George Merrill (born January 10, 1956) is an American songwriter whose work mostly dates from the mid- to late 1980s. He co-wrote \\\"How Will I Know\\\", which was a hit for Whitney Houston in 1986, as well as Houston's 1987 hit \\\"I Wanna Dance with Somebody (Who Loves Me)\\\". From the mid- to late 1980s to the present day, Merrill has been one half of vocal duo Boy Meets Girl, who are best remembered for the late-1988 hit \\\"Waiting for a Star to Fall\\\". He wrote the song and had initially offered it to Houston and Belinda\", \"William Dillon injuries suffered in a car accident. Dillon died in Ithaca, New York on February 10, 1966. He married in 1918 to Georgia Leola Head, daughter of George and Mary (Steen) Head. William Dillon William Austin Dillon (November 6, 1877 - February 10, 1966) was an American songwriter and Vaudevillian. He is best known as the lyricist for the song \\\"I Want A Girl (Just Like The Girl That Married Dear Old Dad)\\\" (1911), written in collaboration with Harry Von Tilzer. It can be heard in \\\"Show Business\\\" (1944) and \\\"The Jolson Story\\\" (1946). He was born in Cortland, New York\", \"Paul Worley and The Band Perry. Along with Wally Wilson and two other partners, Worley founded a publishing company known as Skyline Music Publishing. Among the songwriters that have been signed to Skyline are Hugh Prestwood (who wrote Randy Travis' Number One single \\\"Hard Rock Bottom of Your Heart\\\"), Tammy Hyler (who has written for Collin Raye), and Russ Titelman. In 2012, Skyline writer Jon Stone co-wrote the SESAC Song Of The Year, \\\"A Woman Like You,\\\" recorded by Curb Records artist Lee Brice. Also in 2012, Skyline writers The Henningsens were awarded BMI awards for two singles recorded by Valory Music\", \"The Units was produced by Michael Cotten, the synthesizer player of The Tubes. The song went to No. 18 on \\\"Billboard\\\"'s Dance Chart and stayed on the chart for 13 weeks. Joel Webber, radio promotions man and the Units manager at the time, was also one of the founders of the New Music Seminar. Subsequent productions by UpRoar included spoken word recordings by performance artists including Karen Finley, Eric Bogosian, and Ann Magnuson. After the success of \\\"The Right Man\\\", the Units signed with Epic/CBS Records and produced a music video for \\\"A Girl Like You\\\" that went into medium rotation on\", \"The Foundations their agent and Mike Dolan took over the group's affairs. The group's final hits were \\\"Born to Live, Born to Die\\\" which was written by Eric Allandale and Tony Gomez. and \\\"My Little Chickadee\\\", a US only hit which barely made the hot 100. Another member joined the band in 1970. Paul Lockey who had been with Robert Plant in Band of Joy joined as their bass guitarist. \\\"My Little Chickadee\\\" proved to be the band's last hit. In spite of releasing \\\"Take a Girl Like You\\\", the title song to the Oliver Reed and Hayley Mills film, and a\", \"Mia Rose the SIME Conference in Sweden. In May 2009, she wrote, recorded and marketed the song \\u201cLet Go\\u201d, selling it on iTunes in Portugal, where it became the best selling download, and got to number 2 on the Portuguese charts. She and Jordanian musician Hanna Gargour performed the song \\\"Waiting on the World to Change\\u201d. Queen Rania of Jordan noted this song was an example of how art can promote a reduction of the international cultural divide. Her second self-released single, the double A-side \\\"What Would Christmas Be Like?\\\" / \\\"Fallin\\u2019 For You\\\" was released on 2 December 2009. \\\"What Would\", \"Stephen R. Johnson Stephen R. Johnson Stephen R. Johnson (July 12, 1952 \\u2013 January 26, 2015) was an American music video director, television director, animator, painter, and writer. Johnson got his start directing a music video for the song \\\"Girls Like You\\\" by Combonation, which features a young Robin Wright, before moving on to directing videos for popular artists. Johnson directed three music videos for Peter Gabriel: \\\"Big Time\\\", \\\"Steam\\\", and \\\"Sledgehammer\\\". \\\"Sledgehammer\\\" has the distinction of winning nine MTV Video Music Awards, which remains unsurpassed. In addition, Johnson directed the videos for \\\"Road to Nowhere\\\" by Talking Heads, and \\\"The Bug\\\" and\", \"I'll Stand by You I'll Stand by You \\\"I'll Stand by You\\\" is a song recorded by The Pretenders from their sixth studio album, \\\"Last of the Independents\\\" (1994). Written by Chrissie Hynde and the songwriting team of Tom Kelly and Billy Steinberg; it was the Pretenders' last successful single in North America. The song pledges love and faithful assistance in times of personal darkness. Since its initial release on July 21, 1994, \\\"I'll Stand by You\\\" has also become a major hit for British girl group Girls Aloud in 2004 and American country singer Carrie Underwood in 2007, both times recorded as a\", \"I Want A Girl (Just Like The Girl That Married Dear Old Dad) can be, so that's the kind of love for me. I Want A Girl (Just Like The Girl That Married Dear Old Dad) I Want A Girl (Just Like The Girl That Married Dear Old Dad) (sometimes shortened to \\\"I Want A Girl\\\") is a popular song of 1911 composed by Harry Von Tilzer and with lyrics by William Dillon, which has become a barbershop quartet standard. Von Tilzer and Dillon had never written a song together before, but finding themselves on the same vaudeville bill, von Tilzer suggested they might collaborate on some songs while on the road. Dillon\", \"Empire Records the Meices (the latter's frontman Joe Reineke subsequently led Alien Crime Syndicate). \\\"The Honeymoon Is Over\\\" by the Cruel Sea, a track heard in the film but not featured on the US release of the soundtrack album, was included on the German and Australian releases. The Gin Blossoms' \\\"Til I Hear It From You\\\" charted as high as #9, affording the band their first Top 20 hit. Two other tracks from the album had a single release: Edwyn Collins' \\\"A Girl Like You,\\\" which charted at #32, and the Ape Hangers' \\\"I Don't Want to Live Today\\\". The \\\"Empire Records\\\"\", \"Girls Like You St. Vincent, Tokimonsta, Cray and WondaGurl. In an interview with \\\"Variety\\\", Levine stated he told Cardi before she wrote her verse, \\\"I want you to put something down that shows your fierceness as a woman and say it however you want.\\\" \\\"Girls Like You\\\" is a pop and pop rock song. It has a length of three minutes and 35 seconds in the original version, while the extended single version is 20 seconds longer. It was written by Adam Levine, Cirkut, Cardi B, Starrah, Jason Evigan and Gian Stone and was produced by Cirkut and Evigan. The song is written\", \"Sunset Strippers Sunset Strippers Sunset Strippers are an electronic music group from the UK. They are known for their 2005 song \\\"Falling Stars\\\" which sampled the 1988 hit song \\\"Waiting for a Star to Fall\\\" by Boy Meets Girl, and was involved in a sampling battle with Cabin Crew. \\\"Star To Fall\\\" and reached number 3 in the UK Singles Chart in March 2005. The music video for \\\"Falling Stars\\\" features Benji Weeratunge listening to the song in his headphones while washing his clothes in a launderette. Three attractive young women enter the launderette and begin to dance all at once while\", \"George Merrill (songwriter) Carlisle, but they both rejected it (though Carlisle did do a demo recording) and Merrill decided to sing it himself and feature it on the next Boy Meets Girl album. Merrill was married to Shannon Rubicam (his partner in Boy Meets Girl) from the mid-1980s until they divorced in 2000. They have one daughter, Hilary; she can be seen as the little blond girl in her parents' video of \\\"Waiting for a Star to Fall\\\". George Merrill (songwriter) George Merrill (born January 10, 1956) is an American songwriter whose work mostly dates from the mid- to late 1980s. He co-wrote\", \"The Bangles \\\"The Goonies\\\", and made a cameo appearance in Cyndi Lauper's music video for \\\"Goonies 'R' Good Enough\\\". Lauper later enlisted them for backup vocals in her 1986 hit song \\\"Change of Heart\\\". The Bangles sang backing vocals on the Hoodoo Gurus' \\\"Good Times\\\" from their album \\\"Blow Your Cool!\\\" in 1987. In 1988, while recording his album \\\"Full Moon Fever\\\", Tom Petty and his lead guitarist Mike Campbell called on the group to provide backing vocals for the song \\\"Waiting for Tonight\\\". The song never made it to the album, but instead made the compilations \\\"Playback\\\" (1995) and \\\"\\\" (2000).\", \"Jerry Lordan song \\\"Scarlett O'Hara\\\" taking it to number two in the same chart. He wrote other hits for Cliff Richard (\\\"A Girl Like You\\\"), Shane Fenton and Louise Cordet (\\\"I'm Just a Baby\\\"). By the end of the 1960s the success was largely over and personal difficulties dogged Lordan through the 1970s. He became involved with the Cornish band The Onyx who under his guidance changed their name to Vineyard and released two singles on Decca and Deram in 1974. Later he made a brief foray in acting, appearing in the 1977 sex comedy, \\\"Come Play With Me\\\". The film was\", \"I Knew You Were Waiting (For Me) I Knew You Were Waiting (For Me) \\\"I Knew You Were Waiting (For Me)\\\" is a Grammy Award-winning number-one song recorded by American singer Aretha Franklin and English singer George Michael as a duet in 1987. It was written by Simon Climie and Dennis Morgan, and produced by Narada Michael Walden. \\\"Billboard\\\" listed \\\"I Knew You Were Waiting (For Me)\\\" as Aretha Franklin's all-time biggest Hot 100 single. It also stands as Franklin's biggest hit on the \\\"Billboard\\\" Adult Contemporary chart, spending several weeks at number two (thwarted from the number-one position by Starship and Steve Winwood). The official music\", \"Ready (Trey Songz album) \\\"The Village Voice\\\" said, that \\\"Ready\\\", featured Songz's \\\"re-imagining the notion of flow\\u2014if not melody\\u2014just as his hero Kelly did 15 years ago.\\\" Andrew Rennie of \\\"Now Magazine\\\" said the production had a 1980s soundtrack vibe. According to Mark Edward Nero of About.com, \\\"I Need A Girl\\\" contains familiar qualities of Songz's breakout hit \\\"Can't Help but Wait\\\", also produced by Stargate. The song is lyrically about finding and pleasing the right woman. The song has also been described to be a mix of Usher and Justin Timberlake Melanie Bertoli of \\\"Billboard\\\" said that \\\"Neighbors Know My Name\\\" \\\"employs a\", \"Billy Steinberg the Heart' (by Tina Turner). They also wrote several songs with Chrissie Hynde of The Pretenders, including the top-ten single \\\"I'll Stand By You\\\". Artists such as REO Speedwagon, Cheap Trick, Bette Midler, and Belinda Carlisle have also recorded their songs. In the mid 1990s Steinberg began writing with Rick Nowels, who had previously established himself as a songwriter for such artists as Stevie Nicks and Belinda Carlisle. Their greatest success of the period was the Celine Dion recording of \\\"Falling into You\\\". In 1997, Steinberg co-wrote the track \\\"One & One\\\" with Rick Nowels and Marie-Claire D'Ubaldo for Edyta\", \"Climie Fisher George Michael/Aretha Franklin single \\\"I Knew You Were Waiting (For Me)\\\" in 1986. He worked as a producer for recording artists such as Louise, MN8 and Five Star and as a co-songwriter and musician for Eric Clapton, including the album with J.J. Cale (featuring Derek Trucks and Billy Preston) \\\"The Road to Escondido\\\". Climie released a solo album and single, both titled \\\"Soul Inspiration\\\" in 1992. He also worked with former Doobie Brothers member Michael McDonald on his Motown albums. He produced the 2009 album by the \\\"American Idol\\\" winner Taylor Hicks, titled \\\"The Distance\\\". The folk pastiche \\\"Ballad of\", \"Waiting for Tonight Pitchfork Media's Lindsay Zoladz stated that English singer FKA twigs' music video for the song \\\"Papi Pacify\\\" features \\\"perhaps the most dazzling use of body glitter in a music video since J. Lo's 'Waiting for Tonight'\\\". Natasha Bird of \\\"Elle\\\" magazine compared the music video for Zayn Malik's \\\"Like I Would\\\" to \\\"Waiting for Tonight\\\", writing: \\\"with the addition of all the smoke, lasers and gyrating girls in booty shorts, we can't help but think that this video is an excellent tribute to Jennifer Lopez's millennium party single\\\". The music video received several award nominations, including three wins. At the\", \"Girls Like You the downloadable content for the \\\"Rock Band Rivals\\\" series) and the compilation album \\\"Now That's What I Call Music! 67\\\" (2018). Credits adapted from AllMusic. Recording Credits Girls Like You \\\"Girls Like You\\\" is a song recorded by American band Maroon 5, from their sixth studio album \\\"Red Pill Blues\\\" (2017). A second version featuring American rapper Cardi B was released by 222 and Interscope Records as the album's third single on May 30, 2018. This version was written by Adam Levine, Cirkut, Cardi B, Starrah, Jason Evigan, and Gian Stone and was produced by Cirkut and Evigan. The single\", \"Edwyn Collins Love)\\\", was featured on the soundtrack for \\\"\\\" that year and was his only other Top 40 entry on the UK Singles Chart apart from \\\"A Girl Like You\\\" In a BBC 6 Music radio interview on 18 February 2005, Collins said he felt unwell, but ascribed the nausea and vertigo to food poisoning. Two days later, he was admitted to intensive care in London's Royal Free Hospital after apparently suffering a major cerebral haemorrhage. After suffering a second haemorrhage he had an operation on 25 February 2005, which was followed by a lengthy programme of neurological rehabilitation owing to\", \"One in a Million (Aaliyah album) ballad with seductive Trip hop and drum and bass influences and it features \\\"shimmering\\\" synths and crickets within its production. The forth track \\\" A Girl Like You\\\" is a hip hop inspired track with a \\\"standard 90s boom bap beat\\\" where Aaliyah \\\"holds her own\\\" against guest rapper Treach from Naughty by Nature. During the Chorus both Aaliyah and Treach do a \\\"cute back-and-forth\\\" \\\"I'm looking for a guy like you.\\\" \\\"Yeah, you know me, I hope.\\\" \\\"I hope you feel it in your knees.\\\" \\\"Oh yes indeed.\\\" The fifth track \\\"If Your Girl Only Knew\\\" is a Funk\", \"A Girl Like You (Edwyn Collins song) drummer Paul Cook played vibraphone on the recording. Vic Godard performed backing vocals on the outro. On 30 September 2009, Collins' manager posted to Collins' page on Myspace to discuss how Collins owned the copyright to the song and wanted to make it available to anyone as a free download, but Myspace only allowed major labels to assert the rights to tracks hosted on their site. A later posting notes that, after the story was picked up by major news outlets, the song was once again available on the music player via Myspace. The song appeared on the soundtrack of\", \"Jason Bonham of Timekeeping\\\" had a hit single, \\\"Wait for You\\\" and the music video for the subsequent single \\\"Guilty\\\" did get some play; however, after a lukewarm reception for their 1992 release, \\\"Mad Hatter\\\", the band was dissolved, and Bonham concentrated on session work and guest appearances. On 28 April 1990, Bonham married Jan Charteris, in Stone, Kidderminster. His wedding reception included a jam with Jimmy Page, Robert Plant and John Paul Jones. The Bonhams have two children: a son named Jager and a daughter, Jaz (born 1993). Bonham drummed for Paul Rodgers on the Grammy nominated \\\"Muddy Water Blues: A\", \"Demi Lovato forty-first season. Lovato was also featured on the re-release of \\\"Irresistible\\\", the fourth single from Fall Out Boy's sixth studio album \\\"American Beauty/American Psycho\\\". The same month, she signed with the major modeling agency, Wilhelmina Models. Lovato released the music video for her R&B-infused song \\\"Waitin for You\\\" featuring American rapper Sirah on October 22, 2015. On October 26, 2015, Lovato and Nick Jonas announced that they would tour together on the Future Now Tour. She was honored with the first-ever Rulebreaker Award on December 11, 2015 at the 2015 \\\"Billboard\\\" Women in Music event. On March 21, 2016, \\\"Stone\", \"Devo in concert exists and can be obtained through the bootleg aggregator Booji Boy's Basement. While they did not release any studio albums during this period, Devo sporadically reconvened to record a number of songs for various films and compilations, including a cover of the Nine Inch Nails hit \\\"Head Like a Hole\\\" for the 1992 film \\\"\\\" and a new recording of \\\"Girl U Want\\\" on the soundtrack to the 1995 film \\\"Tank Girl\\\". In January 1996, Devo performed a reunion concert at the Sundance Film Festival in Park City, Utah. The band performed on part of the 1996 Lollapalooza\", \"Kevin Mitchell (musician) included Steve Parkin as a guitarist and backing vocalist (ex-Vinyl, Autopilot, solo). By mid-2009 the pair were writing songs together and were corresponding with Kavyen Temperley (of Eskimo Joe) and solo singer-songwriter, Josh Pyke. All four wanted to form a side project, Basement Birds, to showcase new material. Their debut single, \\\"Waiting for You\\\", was released in April 2010: it is co-written by Mitchell, Parkin, Pyke and Temperley. The group's self-titled album appeared in July, which peaked at No. 12. Basement Birds supported the album with a national tour, which was followed by members returning to their main projects, they\", \"I Need a Girl (Trey Songz song) girl, if I ever meet you I'm gonna give you the world, Baby please believe me when I tell you that I need a girl.\\\" The song has been described to be a mix of Usher and Justin Timberlake, and has been compared to Chris Brown's \\\"With You.\\\" Mark Edward Nero of About.com said that the song was \\\"sappy\\\" and \\\"cliche\\\", trying to replicate the success of \\\"Can't Help But Wait\\\", but \\\"doesn't come close.\\\" Nero also commented that the song sounded insincere compared with the rawly sexual songs on the album. Ajitpaul Mangat of Tiny Mix Tapes noted the\", \"Carly Simon What to Do\\\", peaked at No. 83 on the Pop singles chart, and Simon filmed a music video for it at her home on Martha's Vineyard, MA. That same year, Simon performed on two albums, \\\"The Perfect Stranger\\\" by Jesse Colin Young (singing on the track \\\"Fight For It\\\" with Young) and \\\"Wonderland\\\" by Nils Lofgren (singing on the track \\\"Lonesome Ranger\\\" with Lofgren). By this time, her contract with Warner Bros. had ended. In 1985, she signed with Epic Records and made one album for them, \\\"Spoiled Girl\\\". The album yielded two singles, \\\"Tired of Being Blonde\\\" and \\\"My\", \"Keith Martin (musician) Grammy award winning songwriter Marti Sharron and Danny Sembello, brother of Michael Sambello, wrote the title track, \\u201cNever Found Someone Like You.\\u201d That song was also placed on \\u201cThe Bad Boys I\\u201d movie soundtrack and went on to sell over 1 million copies. Martin released his first album, \\\"It's Long Overdue\\\" in 1995, which peaked at #82 on the US \\\"Billboard\\\" R&B Albums chart. His first single, \\\"Never Find Someone Like You\\\" (which was also independently recorded by Backstreet Boys before) it peaked at #53 on the \\\"Billboard\\\" Hot 100 and at #43 on R&B Singles chart, and was included\", \"Boy Meets Girl (band) and arranged for RCA Records to re-release their two RCA albums. Their debut album recorded for A&M Records was also re-released. In the works are new versions of the demonstration tapes they originally recorded for Whitney Houston. The various artists album \\\"Number One With a Bullet\\\" also contains their original demo tape for \\\"I Wanna Dance with Somebody (Who Loves Me).\\\" In 2005, \\\"Waiting for a Star to Fall\\\" was involved in a \\\"sample battle.\\\" Two electronic music groups, Cabin Crew and Sunset Strippers, wanted to sample the song and remix it. Sunset Strippers, from the UK, won the right\", \"Elliott Yamin Elliott Yamin Efraym Elliott Yamin (born July 20, 1978) is an American singer known for his hit single \\\"Wait for You\\\" and for placing third on the fifth season of \\\"American Idol\\\". His self-titled album, released March 20, 2007, debuted at number one on the \\\"Billboard\\\" Independent Albums chart and at number three on the \\\"Billboard\\\" 200. The album was certified gold in the United States in October 2007. Retitled \\\"Wait for You\\\", the album was released in Japan in May 2008 and certified gold in that country in September 2008. Yamin also released two Christmas collections: \\\"Sounds of the\", \"Girls Like music video was shot in Woodstock, a suburb of Cape Town. As of May 2017, the music video has over 150 million views on YouTube. Tempah and Larsson performed the song live for the first time on \\\"The Jonathan Ross Show\\\" on 2 April 2016. Girls Like \\\"Girls Like\\\" is a song written and performed by British rapper Tinie Tempah and Swedish singer Zara Larsson. The song was released as a digital download in the United Kingdom on 26 February 2016 as the second single from Tempah's third studio album, \\\"Youth\\\". The song entered the UK Singles Chart at number\", \"Renford Rejects breakthrough album Furious Angels when the Rejects rivals, \\\"The Razors\\\", would appear on screen. \\\"Ready to Go\\\" by Republica and \\\"Tubthumping\\\" by Chumbawamba were songs often heard during a match. \\\"A Girl Like You\\\" by Edwyn Collins, was the song for Robin in the first season, also \\\"Who Do You Think You Are\\\" by Spice Girls was Sue's song on her debut in season two. United Kingdom: United States: Australia: Ireland: Renford Rejects Renford Rejects is a teen sitcom produced and broadcast by Nickelodeon UK between 1998 and 2001. The show briefly aired in the United States on Nick GaS.\", \"Steve Arrington to play percussion, then later becoming the drummer and a backing vocalist. Eventually Arrington took over lead vocals, singing on the hit singles \\\"Just a Touch of Love\\\", \\\"Watching You\\\" (which has been sampled by Snoop Dogg) and \\\"Wait for Me\\\". Arrington left Slave in 1982, forming Steve Arrington's Hall of Fame, and had hit singles such as \\\"Weak At The Knees\\\" (which was sampled by Three Times Dope, Jay-Z, Jermaine Dupri, N.W.A, and others), and \\\"Nobody Can Be You But You\\\". His most successful album was his 1985 solo work \\\"Dancin' in the Key of Life\\\", whose title track\", \"Boy Meets Girl (band) to Fall.\\\" With this selection, after years of writing and composing songs for other artists, the duo earned mainstream success in their own right. In the United States, \\\"Waiting for a Star to Fall\\\" peaked at number 5 on the \\\"Billboard\\\" Hot 100 and at number 1 on the \\\"Billboard\\\" Adult Contemporary chart. The song reached number 9 in the UK and was also a top ten Australian hit in April 1989. It was featured in the 1990 movie \\\"Three Men and a Little Lady\\\". Reportedly, the song was inspired by an actual falling star that Merrill and Rubicam had\", \"Places (song) minutes and fourteen seconds. Places (song) \\\"Places\\\" is a song by French DJ and record producer Martin Solveig featuring vocals from Norwegian singer/songwriter Ina Wroldsen. It was released as a digital download in France on 25 November 2016 through Spinnin' Records and Big Beat. The song has peaked at number 82 on the French Singles Chart. The song was written by Ina Wroldsen and Martin Picandet. The song samples the Boy Meets Girl song \\\"Waiting for a Star to Fall\\\". A music video to accompany the release of \\\"Places\\\" was first released onto YouTube on 7 December 2016 at a\", \"If I Were a Carpenter (Bobby Darin album) record. Darin falls short of the originals on Buffy St. Marie's \\\"Until It's Time for You to Go\\\" and the Lovin' Spoonful's \\\"Daydream.\\\" In fact, aside from \\\"If I Were a Carpenter,\\\" the standout is the odd low-charting single \\\"The Girl Who Stood Beside Me,\\\" with its odd muted psychedelic bagpipe effects constantly buzzing in the background of an actual fairly strong folk-rock tune.\\\" If I Were a Carpenter (Bobby Darin album) If I Were a Carpenter is an album by American singer Bobby Darin, released in 1966. It was a significant change in direction for Darin considering his previous\", \"Red Pill Blues on the \\\"Billboard\\\" Hot 100. James Valentine announced on Twitter that \\\"Wait\\\" would be the second single from the album. The song was officially released to US contemporary hit radio on January 16, 2018, as the album's second single. A remix version of \\\"Girls Like You\\\", featuring Cardi B served as the third single from the album on May 30, 2018. The song topped at number one on the \\\"Billboard\\\" Hot 100 chart for seven consecutive weeks, becoming Maroon 5's fourth and Cardi B's third number-one. The song became the first pop song to reach number one since \\\"Havana\\\" by\", \"Jane Morgan Elizabeth Hotel in Canada in 1964; was the lead singer with Bea Lillie and Carol Lawrence in the Broadway musical production of the \\\"Ziegfeld Follies\\\", and succeeded Janis Paige in \\\"Mame\\\" in 1969. \\\"Being on Broadway was one of the most exciting things in my life because I had always dreamed of it\\\", she said. In 1966, Morgan recorded the song that she had performed at the Academy Awards, \\\"I Will Wait for You\\\", written for her by Michel Legrand. From 1967-68, Morgan was under contract at ABC Records, recording half a dozen singles and issuing one LP which produced\", \"Gail Davies Mary Ann Kennedy. Although Wild Choir only released one album during their tenure, they have been cited as one of the incunabula of the \\\"alt-country\\\" genre, better known today as Americana Music. In 1989, Gail signed with MCA Records and produced an album of 10 self-penned compositions entitled \\\"Pretty Words\\\". The album garnered two more Top 50 singles \\\"Waiting Here For You\\\" and \\\"Hearts in the Wind\\\". The song the record company chose not to release, written by Gail and Harry Stinson, was entitled \\\"Tell Me Why.\\\" It went on to become a hit for Curb recording artist Jann Browne.\", \"Bea Miller began on July 15 in Louisville, Kentucky. Miller finished the tour in late August. In July 2015, she was chosen by public vote to be the next Vevo Lift artist. On April 20, 2016, it was announced that Miller would join Selena Gomez on her Revival Tour as an opening act along with DNCE. The tour ran from May throughout the summer. Miller also announced that she would be hosting meet and greets titled \\\"Tea With Bea\\\". Miller released a non-album single \\\"Yes Girl\\\" on May 20, 2016. She performed \\\"Yes Girl\\\" and \\\"Song Like You\\\" numerous times on the\", \"Andre\\u0301 Brasseur Lex Harding used \\\"The Kid\\\" as the opening sequence to his daily afternoon broadcast programme. Brasseur is also famous for his 1965 instrumental single 'Early Bird' (named after the Intelsat I satellite launched the same year), which sold over 6 million copies worldwide. His 1967 single \\\"Waiting For You\\\" was used as the signature tune to the 1970s Yorkshire Television series \\\"Indoor League\\\". Andr\\u00e9 Brasseur Andr\\u00e9 Brasseur, (born 1939) is a Belgian keyboard player and organist. Brasseur has released many albums and singles in his own country, but internationally is best known for his double A sided single \\\"The Kid\\\"/\\\"Holiday\\\",\", \"Jackiem Joyner India.Arie, George Duke, Najee, and Phil Perry and has toured with Keiko Matsui. He married in 2003 and moved to Los Angeles. Joyner independently released his debut album, \\\"This Time Around\\\" (2005), then signed with Artizen Music Group, which released his second album, \\\"Babysoul\\\" (2007). The single \\\"Stay With Me tonight\\\" featured guitarist Peter White and reached No. 17 on the \\\"Billboard\\\" magazine Contemporary Jazz Chart. He recorded for Mack Avenue after it bought Artizen. The single \\\"I'm Waiting for You\\\" was a No.1 hit on the \\\"Billboard\\\" Contemporary Jazz chart. It was nominated Song of the Year in 2010\", \"Dave Grohl tracks for Nine Inch Nails' 2005 album \\\"With Teeth\\\". He also drummed on the song \\\"Bad Boyfriend\\\" on Garbage's 2005 album \\\"Bleed Like Me\\\". Most recently, he recorded all the drums on Juliette and the Licks's 2006 album \\\"Four on the Floor\\\" and the song \\\"For Us\\\" from Pete Yorn's 2006 album \\\"Nightcrawler\\\". Beyond drumming, Grohl contributed guitar to a cover of Neil Young's \\\"I've Been Waiting For You\\\" on David Bowie's 2002 album \\\"Heathen\\\". In June 2008, Grohl was Paul McCartney's special guest for a concert at the Anfield football stadium in Liverpool, in one of the central events\", \"Ready (Trey Songz album) the latter date. He performed \\\"I Need a Girl\\\" on September 9, 2009, on \\\"The Wendy Williams Show\\\". The album's lead single, \\\"I Need a Girl\\\" was released via digital download on April 13, 2009, with \\\"Brand New\\\" as its B-side. \\\"I Need a Girl\\\" received positive to mixed reviews, with some critics comparing it to \\\"Can't Help But Wait\\\" and others noting it as generic. The song peaked at 59 on the US \\\"Billboard\\\" Hot 100 and number 6 on the Hot R&B/Hip-Hop Songs. Songz appeared as a featured artist on Drake's single, \\\"Successful\\\"; the song peaked at number\", \"Scary Thieves Scary Thieves Scary Thieves were a short-lived English 1980s new wave band, best known for the hits \\\"Tell Me Girl\\\" and \\\"The Waiting Game\\\". Both songs are included on the \\\"Hardest Hits\\\" compilations, which focus on obscure new wave/synth bands that have little to big cult followings. Scary Thieves were formed by Phil Manikiza (vocals) and Chris Youdell (keyboards) in 1983. They auditioned Clive Parker-Sharp (born Clive Parker) (drums) in a small rehearsal room in Camden Town, London, and later Ralph St. Rose (guitar). Parker was previously of bands Spizzenergi/Athletico Spizz '80 (Rough Trade/A&M Records) and Big Country. Ralph St.\"], \"pos_scores\": [102.625], \"neg_scores\": [84.1875, 87.6875, 86.375, 95.625, 85.25, 90.125, 86.75, 94.5, 87.5, 85.8125, 89.125, 88.0, 96.4375, 94.3125, 92.25, 87.25, 95.625, 85.8125, 95.5, 86.5625, 87.5, 88.9375, 91.75, 87.8125, 87.4375, 88.875, 92.3125, 89.875, 91.4375, 85.375, 85.125, 85.3125, 88.8125, 85.0625, 87.625, 91.0625, 87.0625, 84.9375, 92.0625, 92.4375, 87.25, 87.125, 87.625, 86.0, 88.125, 85.3125, 93.4375, 88.4375, 86.625, 86.25, 92.5625, 85.6875, 86.25, 85.5, 84.125, 86.5, 86.625, 84.0, 85.6875, 89.8125, 86.875, 87.3125, 89.0625, 86.3125, 87.125, 86.0, 85.8125, 88.6875, 84.25, 86.75, 85.375, 84.6875, 92.875, 87.6875, 84.9375, 93.875, 85.4375, 85.25, 86.6875, 84.25, 84.5, 85.0625, 85.0, 86.5, 87.6875, 89.625, 87.5625, 85.4375, 87.9375, 87.25, 88.1875, 87.5, 84.0625, 84.125, 85.1875, 85.0, 84.4375, 84.625, 84.5, 87.375], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"where do you cross the arctic circle in norway\", \"pos\": [\"Arctic Norway Arctic Norway Arctic Norway () comprises the northernmost parts of Norway that lie above the Arctic circle. Norway, being one of the most stretched-out countries in the world, reaches from approximately 58\\u00b0N to 81\\u00b0N, so large parts lie north of the Arctic circle at 66\\u00b033\\u2032. Arctic Norway consists of four geographically separated parts: The Arctic circle crosses mainland Norway at Saltfjellet, which separates Helgeland from the northern part of Nordland county. Thus about half of the county lies north of the Arctic circle, along with the whole of Troms and Finnmark counties. The total area of mainland Norway above the\"], \"neg\": [\"Troms\\u00f8 side of the Troms\\u00f8ya island \\u2014 over north of the Arctic Circle at . Suburban areas include Kroken, Tromsdalen (on the mainland, east of Troms\\u00f8ya), the rest of the Troms\\u00f8ya island, and the eastern part of the large Kval\\u00f8ya, west of the Troms\\u00f8ya island. The Troms\\u00f8 Bridge and Troms\\u00f8ysund Tunnel both cross the Troms\\u00f8ysundet strait connecting the mainland with Troms\\u00f8ya by road. On the western side of the city, the Sandnessund Bridge connects Troms\\u00f8ya island with Kval\\u00f8ya island. There are many tall mountains within the municipality including Hamperokken, Jiehkkev\\u00e1rri, Store Bl\\u00e5mann, Store Fornestinden, and Tromsdalstinden. The Lyngen Alps mountain range\", \"Kirkenes crossing is at Storskog, southeast of Kirkenes. In the city centre of Kirkenes is \\\"Andersgrotta\\\", a vast underground bunker built during World War II which provided shelter to the town's residents. Tours of the bunker are available. Kirkenes is one end of the route of the Hurtigruten, which cruises daily up and down the Norway coast to and from Bergen. Kirkenes is served by Kirkenes Airport, H\\u00f8ybuktmoen. There are non-stop flights to Oslo, Vads\\u00f8, Vard\\u00f8, Alta and Troms\\u00f8. The European route E06 has its northern terminus at Kirkenes. The northern terminus of the European route E105 highway is located in\", \"Abisko National Park As its location is 195 km north of the Arctic Circle, summer hikers enjoy the midnight sun, while winter visitors may find the light pollution-free location ideal for viewing the aurora borealis. Daily passenger electric trains run by SJ AB connect Stockholm with the Norwegian city of Narvik, stopping at both the Abisko village (the name of that railway station is \\\"Abisko \\u00d6stra\\\" [east]) and the Abisko Turiststation. Additional regional trains provide links within the Kiruna-Narvik stretch. Abisko is also reachable by car via the highway E10 which links Kiruna and Narvik since early 1980s. Other local forms of local\", \"Pytheas through the sign of the Crab (at the summer solstice), a reaffirmation that it is on the Arctic Circle. He adds that the crossing to Thule starts at the island of \\\"Berrice\\\", \\\"the largest of all\\\", which may be Lewis in the outer Hebrides. If \\\"Berrice\\\" was in the outer Hebrides, the crossing would have brought Pytheas to the coast of M\\u00f8re og Romsdal or Tr\\u00f8ndelag, Norway, explaining how he managed to miss the Skagerrak. If this is his route, in all likelihood he did not actually circumnavigate Britain, but returned along the coast of Germany, accounting for his somewhat\", \"Norway\\u2013Russia border Norwegian border police issued an announcement in 2016 that it is forbidden to cross the border on land, water and in air, including at border markers (except with permission or at the border station), or to have contact with people across the border or throw things over the border. This is already written in the law. There is only one legal border crossing point, with stations on both sides, at Storskog in Norway and Borisoglebsky in Russia, located on the E105 highway some 15 km east of Kirkenes. Crossing time at both stations is unpredictable and depends of the amount\", \"Inlandsva\\u0308gen of road after Gothenburg is motorway.Most parts of Inlandsv\\u00e4gen have just two lanes with two-way-traffic; a wide asphalted road that follows the terrain. About south of Jokkmokk Inlandsv\\u00e4gen crosses the Arctic Circle at . Inlandsv\\u00e4gen Syd (\\\"The Inland Road South\\\") is a term used about Riksv\\u00e4g 26 (\\\"National Road 26\\\") that begins in Halmstad in the south and ends in Mora in the midst of Sweden, The most parts of Inlandsv\\u00e4gen Syd also just have two lanes with two-way-traffic. The name is related to the famous Inland Line (\\\"Inlandsbanan\\\"), a railroad line that used to run almost parallel between Kristinehamn\", \"Troms\\u00f8 the Arctic Circle anywhere in the world (following Murmansk and Norilsk). Most of Troms\\u00f8, including the city centre, is located on the island of Troms\\u00f8ya, north of the Arctic Circle. In 2017, the city of Troms\\u00f8 had a population of about 65,000 people spread out over Troms\\u00f8ya and parts of Kval\\u00f8ya and the mainland. Troms\\u00f8ya is connected to the mainland by the Troms\\u00f8 Bridge and the Troms\\u00f8ysund Tunnel, and to the island of Kval\\u00f8ya by the Sandnessund Bridge. The municipality is warmer than most other places located on the same latitude, due to the warming effect of the Gulf Stream.\", \"Storskog far northeastern part of Norway, about east of the town of Kirkenes in Norway and about north of Nikel. In 2013, 320,000 (in 2010, 141,000) crossings were made across the border at the Storskog border station. Storskog Storskog is a border crossing station on the Norwegian side of the Norway-Russia border, along the European route E105 highway. The crossing is located in S\\u00f8r-Varanger Municipality in Finnmark county on the Norway side of the border. The Russian side is in Boris Gleb in Pechengsky District in Murmansk Oblast. There is a border crossing station on the Russian side also, and both\", \"Polar Line Drag, from Fauske. From Bj\\u00f8rnfjell, the line would have hugged the Norway\\u2013Sweden border into the county of Troms, before running down the Salangsdalen valley. It would pass through the villages of Setermoen and Andselv before running west of the lake of Takvatnet. It would then have reached Balsfjorden, which it would have hugged until reaching Nordkjosbotn. It would cross across Balsfjordeidet to Storfjorden, which is would follow on the east shore of. There it would have passed through the village of Skibotn and run around the K\\u00e5fjorden and through K\\u00e5fjordbotn. It would then have run along Rotsundet and reach Nordreisa,\", \"Arctic Circle Circle is divided among 8 countries: Norway, Sweden, Finland, Russia, the United States (Alaska), Canada (Yukon, Northwest Territories and Nunavut), Denmark (Greenland) and Iceland (where it passes through the small offshore island of Gr\\u00edmsey). The climate inside the Arctic Circle is generally cold, but the coastal areas of Norway have a generally mild climate as a result of the Gulf Stream, which makes the ports of northern Norway and northwest Russia ice-free all year long. In the interior, summers can be quite warm, while winters are extremely cold. For example, summer temperatures in Norilsk, Russia will sometimes reach as high\", \"Storskog Storskog Storskog is a border crossing station on the Norwegian side of the Norway-Russia border, along the European route E105 highway. The crossing is located in S\\u00f8r-Varanger Municipality in Finnmark county on the Norway side of the border. The Russian side is in Boris Gleb in Pechengsky District in Murmansk Oblast. There is a border crossing station on the Russian side also, and both have to be passed to enter the opposite country. There is a tax-free shop in Russia between the stations. Storskog is the only legal land border crossing between Norway and Russia. The station lies in the\", \"Tysfjord is commonly seen in winter and late autumn. Tysfjord is the only location in Norway where the European route E06 highway depends on a car ferry. There are ferry connections from Bognes to Skarberget (route E6) and from Bognes to L\\u00f8dingen (connecting to route European route E10 and Lofoten). There is also a ferry connecting Drag south of the fjord with Kj\\u00f8psvik on the northern shore. Kj\\u00f8psvik is connected to the E6 highway and Narvik by Norwegian National Road 827, with no ferry crossings. This might be an alternative to route E6, and is also the route of choice to\", \"S\\u00f8rfold electricity. This has provided a major source of income for the community. The Church of Norway has one parish \\\"(sokn)\\\" within the municipality of S\\u00f8rfold. It is part of the Salten prosti (\\\"deanery\\\") in the Diocese of S\\u00f8r-H\\u00e5logaland. S\\u00f8rfold municipality is located about north of the Arctic circle. The total land area of S\\u00f8rfold is , of which is covered with permanent ice and snow, and only lies below the contour line. The total length of coastline is . In 1987, only of land was being actively farmed. To the north of S\\u00f8rfold is the municipality of Hamar\\u00f8y and to\", \"Nordkinnhalv\\u00f8ya the northernmost point of mainland Norway and Europe. The peninsula is connected to the mainland by the wide Hopseidet isthmus. The peninsula is situated between the Tanafjorden to the east and the Laksefjorden to the west. They both empty into the Barents Sea. Norwegian County Road 888 connects the villages of Gamvik, Mehamn, and Lebesby to the European route E6 highway at the base of the peninsula, and from there it continues on to the towns of Kirkenes in the east and Alta in the west. The Hurtigruten coastal ship stops in Mehamn and Kj\\u00f8llefjord, both on this peninsula. Mehamn\", \"S\\u00f8r-Varanger regional airports throughout Finnmark. The Kirkenes\\u2013Bj\\u00f8rnevatn Line is a railway, until 2010 the world's northern-most, which runs between Kirkenes and Bj\\u00f8rnevatn; The European route E06 highway has its northern endpoint in the town of Kirkenes. This highway heads west and then south to the rest of Norway. The European route E105 highway has its northern endpoint in the village of Hesseng, just south of Kirkenes. That highway heads south into Russia through the Storskog border crossing, the only legal public crossing on the Norway-Russia border. The Church of Norway has one parish \\\"(sokn)\\\" within the municipality of S\\u00f8r-Varanger. It is\", \"Norway\\u2013Soviet Union relations bilateral agreement was made permitting Svalbard Airport, Longyear. The Soviet Union also protested Kongsfjord Telemetry Station and the production of the 1985 action film \\\"Orion's Belt\\\". The two countries retained a land border between S\\u00f8r-Varanger and Murmansk Oblast. There was only one legal crossing point, at Storskog (Norway) and Boris Gleb (Russia), on the E105 road some 15 km east of Kirkenes. The border between Norway and the Soviet Union in the Varangerfjord was agreed upon in a treaty from 1957. Negotiations on the outside marine border were initiated in 1970. Norway claimed, in accordance with the United Nations Convention\", \"Kirkenes were returned to their homesteads. The majority of the inhabitants of Kirkenes are of a Norwegian background, and a minority is Sami. Others are originally from Finland, either members of the Kven population or of a newer influx of more or less recent Finnish immigrants. Also, about 500 people are relatively recent Russian immigrants. For several months in 2015, the town served as a border crossing point for Syrian refugees, with hundreds per week crossing the border on bicycles. Kirkenes is located in the extreme northeastern part of Norway on the B\\u00f8kfjorden, a branch of the Varangerfjorden, which is a\", \"Sagesund circumnavigation of the world from Sagesund in 1994. \\\"Nor Siglar\\\", with half the crew from Sagesund, also included Sagesund as a natural stop on its way around the globe. Sagesund Sagesund is a small village in the municipality of Tvedestrand in Aust-Agder county, Norway. It is located on the shore of the Oksefjorden and along the Norwegian County Road 411. Sagesund is located about southeast of the town of Tvedestrand and about west of the villages of Dypv\\u00e5g and Kr\\u00e5kv\\u00e5g. There is also bus service from the town of Tvedestrand. Approximately 50 people live there on a permanent basis. It\", \"Cross Link will reduce travel time towards Bergen, and a road package which involves upgrading part of the county road network in the area. Cross Link Cross Link () is a proposed fixed link which would connect \\u00d8ygarden, northern Ask\\u00f8y, Meland and Rad\\u00f8y in Hordaland, Norway. The road is planned to give better transport within Nordhordland and from there to Ask\\u00f8y and \\u00d8ygarden. Specifically, the proposal includes a ferry from the Kollsnes area of \\u00d8ygarden to somewhere south of Herdla in Ask\\u00f8y, a subsea road tunnel from the same area to Meland, and road across the island and then a new subsea\", \"European route E134 Vassum at road E6. The highway runs near several places of interest: When driving along the road, one may see many of the following words on signs or road condition web sites: European route E134 European highway E 134 () is a European highway that crosses Norway starting near the city of Haugesund on the west coast, heading over Haukeli, passing the city of Drammen, and ending in the municipality of Frogn near the national capital of Oslo. With the highest point at above sea level, the road is sensitive to snow conditions and foul weather during the winter season,\", \"Svalbard Svalbard Svalbard (; ; prior to 1925 known by its Dutch name Spitsbergen) is a Norwegian archipelago in the Arctic Ocean. Situated north of mainland Europe, it is about midway between continental Norway and the North Pole. The islands of the group range from 74\\u00b0 to 81\\u00b0 north latitude, and from 10\\u00b0 to 35\\u00b0 east longitude. The largest island is Spitsbergen, followed by Nordaustlandet and Edge\\u00f8ya. Administratively, the archipelago is not part of any Norwegian county, but forms an unincorporated area administered by a governor appointed by the Norwegian government. Since 2002, Svalbard's main settlement, Longyearbyen, has had an elected\", \"Mo i Rana of the caves are open to the public, Gr\\u00f8nligrotta and Setergrotta. Mo i Rana is situated about south of the Arctic Circle. Mo i Rana's climate is usually classified as subarctic (K\\u00f6ppen \\\"Dfc\\\"), with long, cold winters, and short, warm summers. However it's very mild compared to Oymyakon, Russia and Verkhoyansk, Russia which are continental subarctic climates. The Norwegian Current (extension of Gulf Stream), follows the coastline of Norway all the way north. The stream has a heavy influence on the climate, helping to keep the temperatures from getting too low in the winter, despite the city being located about\", \"European route E134 European route E134 European highway E 134 () is a European highway that crosses Norway starting near the city of Haugesund on the west coast, heading over Haukeli, passing the city of Drammen, and ending in the municipality of Frogn near the national capital of Oslo. With the highest point at above sea level, the road is sensitive to snow conditions and foul weather during the winter season, during which the mountainous sections may be closed in short periods. The stretch of road through the mountains is called Haukelifjell. A road over the mountain along this route was opened first\", \"Pole to Pole He also meets some avid Norwegian football fans. In the town of Karasjok, he meets up with the Sami people and pans for gold in the Karasjoka River. From there, Palin travels by bus and crosses the border from Norway to Finland, where he visits Santa Claus at the Santa Claus Village on the Arctic Circle near Rovaniemi. He takes an overnight Finnish train to Helsinki; he relaxes in a sauna near Helsinki with Neil Hardwick and Lasse Lehtinen. Then Palin catches a ferry to Tallinn, his first stop in the Soviet Union. He visits Estonians who sing a song,\", \"Finland\\u2013Norway border done. In 1852, the border Norway\\u2013Finland/Russia was closed, causing trouble for the Sami, who needed the Finnish forests for reindeer winter grazing. The Finland\\u2013Norway border is open as both countries are part of the Schengen Area. It is legal to cross the border anywhere if no customs declaration or passport check is needed. There is an wide clear-cut zone along the land border. Almost half of the border follows the rivers Anarjohka and Tana. There are 57 original cairns north of Treriksr\\u00f6set from until 1766, numbered 293-342 west of Anarjohka river and 343-349 east of Tana river. Later further cairns\", \"Pytheas has to rotate Scotland by 90\\u00b0. The 5000 stadia must be discounted: it crosses the Borysthenes upriver near Kiev rather than at the mouth. It does place Pytheas on the Arctic Circle, which in Norway is south of the Lofoten islands. It seems that Eratosthenes altered the base line to pass through the northern extreme of Celtica. Pytheas, as related by Hipparchus, probably cited the place in Celtica where he first made land. If he used the same practice in Norway, Thule is at least somewhere on the entire northwest coast of Norway from M\\u00f8re og Romsdal to the Lofoten\", \"Thomas Nilsen deputy head. From 2009 to 2015 he was editor of the \\\"BarentsObserver,\\\" a Norwegian Arctic online newspaper based in Kirkenes, published by NBS. Kirkenes is in the extreme northeastern part of Norway, on the edge of a vast bay connected to the Barents Sea, near the Russian\\u2013Norwegian border. The town is about north of the Arctic Circle. According to the BBC it is a \\\"tiny bubble of cross-border friendship in a Nato country\\\". In 2014 Mikhail Noskov, the Russian consul-general (Russian government representative in Norway) who was also based in Kirkenes, criticised Nilsen\\u2019s writing and warned that it might damage\", \"Cross Link Cross Link Cross Link () is a proposed fixed link which would connect \\u00d8ygarden, northern Ask\\u00f8y, Meland and Rad\\u00f8y in Hordaland, Norway. The road is planned to give better transport within Nordhordland and from there to Ask\\u00f8y and \\u00d8ygarden. Specifically, the proposal includes a ferry from the Kollsnes area of \\u00d8ygarden to somewhere south of Herdla in Ask\\u00f8y, a subsea road tunnel from the same area to Meland, and road across the island and then a new subsea section across to Rad\\u00f8y. Although not in any public proposals, there have been private proposals to develop Mj\\u00f8lkevikvarden in Ask\\u00f8y as a\", \"Norsk Polar Navigasjon line of action. The following day, a meeting took place between the Norwegian Ambassador and Soviet Ministry of Foreign Affairs. On 22 February, the Pdersen brothers suggested to the Ministry of Foreign Affairs that they instead build a smaller airport at Kapp Mitra, across the fjord from Ny-\\u00c5lesund. This was rejected by the ministry, stating that the fundamental issue was that Norway had no way of hindering the Soviet Union from occupying the airports in case of war. Two additional locations were later proposed by the Pedersen brothers, at Kapp Guissez near Ny-\\u00c5lesund and Adventdalen. These were planned built sufficiently\", \"European route E6 the capital Oslo. North of this, it passes by Gardermoen, Hamar, Lillehammer, Domb\\u00e5s, Oppdal, Melhus to Trondheim. Beyond Trondheim, the E6 meets Stj\\u00f8rdal, Verdal, Steinkjer, Grong, Mosj\\u00f8en, Mo i Rana, Saltdal, Fauske and Hamar\\u00f8y towards Bognes, where there is a ferry crossing over the Tysfjorden to Skarberget. It then runs through on via Narvik, Setermoen, Nordkjosbotn, Skibotn and Alta to Olderfjord, where European route E69 continues north towards Nordkapp. The E6, meanwhile, turns south towards Lakselv and Karasjok, then runs on the west bank of the Anarjohka, which forms the border with Finland. Beyond the border, it passes through Varangerbotn,\", \"Abisko the park as part of its longer passage. The \\\"Abisko Turiststation\\\", run by the Svenska Turistf\\u00f6reningen (STF), houses many visitors to the park and provides lodging, food, and other amenities, and is one of many similar facilities located periodically along the Kungsleden trail. The national park is known for its Cross-country skiing opportunities, snowshoeing, and other winter sports (Mount Nuolja and nearby Bj\\u00f6rkliden provides Backcountry skiing and freeriding opportunities). As its location is 195 km north of the Arctic Circle, summer hikers enjoy the midnight sun, while winter visitors may find the light pollution-free location ideal for viewing the aurora\", \"Geography of Norway is colder than in Br\\u00f8nn\\u00f8ysund. Some areas of Vestlandet and southern Nordland are Europe's wettest due to orographic lift, particularly where the moist westerlies first are intercepted by high mountains; this occurs slightly inland from the outer skerry guard. Brekke in Sogn og Fjordane has the highest annual precipitation with ; annual precipitation can exceed in mountain areas near the coast. Lur\\u00f8y, near the Arctic Circle, gets 2,935 mm on average, a remarkable amount of precipitation for a polar location. Precipitation is heaviest in autumn and early winter along the coast, while April to June is the driest. The innermost\", \"Otta, Norway merges into Gudbrandsdalsl\\u00e5gen North-east lies the massif \\\"Rondane\\\", which areas became the first national park in Norway in 1962, and has several mountains over 2,000 metres. Otta sits at roughly the midway point between Oslo and Trondheim along the (E6)European route E06, and the Rv15 starts here connected to the E6, heading west along Ottadalen and over the mountains to Stryn, along the Nordfjord, finally terminating in M\\u00e5l\\u00f8y Otta is also an important link on the Dovre Line, since Otta Station is the only station in the valley where every single passing passenger train stops. Otta as a place name\", \"Kangerlussuaq of the Royal Arctic Line, and unusable in winter. Cruise ships, such as Norway's Hurtigruten, navigate the fjord, anchoring outside the port. There are plans to build a new deep port, around 10 km west of the present one. As of today, the long journey and lack of ports and other infrastructure make cruise ship lines avoid Greenland. Kangerlussuaq has the largest road network outside any settlement in Greenland (not counting streets inside the settlement). A gravel road through Isunngua connects Kangerlussuaq with the ice sheet, initially serving as venue for car endurance experiments. Since then it has been mainly\", \"North (\\\"night\\\"), since above the Tropic of Cancer, the Sun never shines from the north, except inside the Arctic Circle during the summer midnight sun. The direction north is often associated with colder climates because most of the world's land at high latitudes is located in the Northern Hemisphere. The Arctic Circle passes through the Arctic Ocean, Norway, Sweden, Finland, Russia, the United States (Alaska), Canada (Yukon, Northwest Territories and Nunavut), Denmark (Greenland) and Iceland (where it passes through the small offshore island of Gr\\u00edmsey). By convention, the top side of a map is often north. To go north using a\", \"Riksgra\\u0308nsen Riksgr\\u00e4nsen Riksgr\\u00e4nsen, \\\"The National Border\\\" in Swedish, is a ski-resort in Kiruna Municipality, Lappland, Sweden, 200 km north of the Arctic Circle. The skiing season is from February to June. From end of May the lifts operate under the midnight sun. Riksgr\\u00e4nsen is a popular location for the winter testing of pre-production cars by various European manufacturers. Photo-snipers are prevalent, attempting to get the first spy-shots of new models, though their activities are frowned upon by local hoteliers who value the custom of the manufacturers. The same manufacturers frequently use the location for winter launches, bringing journalists from across the\", \"Fjord skerries (called a \\\"); many of the cross fjords are so arranged that they parallel the coast and provide a protected channel behind an almost unbroken succession of mountainous islands and skerries. By this channel one can travel through a protected passage almost the entire route from Stavanger to North Cape, Norway. The Blindleia is a skerry-protected waterway that starts near Kristiansand in southern Norway, and continues past Lillesand. The Swedish coast along Bohusl\\u00e4n is likewise skerry guarded. The Inside Passage provides a similar route from Seattle, Washington, and Vancouver, British Columbia, to Skagway, Alaska. Yet another such skerry protected\", \"Norway\\u2013Russia border Norway\\u2013Russia border The border between Norway and Russia (, , \\\"Rossiysko-Norvezhskaya Granytsa\\\") consists of a land border between S\\u00f8r-Varanger, Norway, and Pechengsky District, Russia, and a marine border in the Varangerfjord. It further consists of a border between the two countries' exclusive economic zones (EEZ) in the Barents Sea and the Arctic Ocean. Between 1944 and 1991 the border was between Norway and the Soviet Union. There is a single border crossing, on E105, located at Storskog in Norway and Borisoglebsky (Boris Gleb) in Russia. The Norwegian side is patrolled by the Garrison of S\\u00f8r-Varanger and is under the jurisdiction\", \"Nansen's Fram expedition fort and the cheers of thousands of well-wishers. This was the first of a series of farewells as \\\"Fram\\\" sailed round the coast and moved northward, reaching Bergen on 1 July (where there was a great banquet in Nansen's honour), Trondheim on 5 July and Troms\\u00f8, north of the Arctic Circle, a week later. The last Norwegian port of call was Vard\\u00f8, where \\\"Fram\\\" arrived on 18 July. After the final provisions were taken on board, Nansen, Sverdrup, Hansen and Blessing spent their last hours ashore in a sauna, being beaten with birch twigs by two young girls. The first\", \"Alta, Norway birch and pine. Precipitation is low, with a yearly average precipitation of only . The frequent clear skies are the reason why Alta early was chosen as an excellent location for studying the aurora borealis. The \\\"midnight sun\\\" is above the horizon from 18 May to 27 July, lasting a bit longer than the polar night from 26 November to 16 January. Alta is a transportation center in Finnmark. Alta Airport served 334,132 passengers in 2009. There are direct flights to Oslo, Troms\\u00f8, Vads\\u00f8, and Kirkenes. The town of Alta also has port facilities in the town center, and European\", \"Lyngen by the Sami and Kven people. During the Cold War the Norwegian Army planned to abandon Finnmark and halt the Soviets along the European route E06 highway at the choke point between the Lyngen fjord and the mountains. However, there were always great worries that the Soviets would also advance through Finland and the very sparsely defended extreme north of Sweden (north of Kiruna, south of Treriksr\\u00f6set) and attack the Lyngen position from the rear via Signaldalen. The municipality is situated on the Lyngen peninsula, with the Lyngen fjord to the east and Ullsfjorden to the west. The municipal centre\", \"Kvit\\u00f8ya Kvit\\u00f8ya Kvit\\u00f8ya (English: \\\"White Island\\\") is an island in the Svalbard archipelago in the Arctic Ocean, with an area of . It is the easternmost part of the Kingdom of Norway. The closest Russian Arctic possession, Victoria Island, lies only to the east of Kvit\\u00f8ya. The island is almost completely covered by Kvit\\u00f8yj\\u00f8kulen, an ice cap with an area of with a classical, hourglass-shaped dome, which has given it its name. The few ice-free land areas are each only a few square kilometres large and very barren and rocky, the largest being Andr\\u00e9eneset on the southwest corner of the island.\", \"Harstad of on Hinn\\u00f8ya; it is the only city on the island, and is popularly known as \\\"V\\u00e5gsfjordens perle\\\" (The pearl of V\\u00e5gsfjorden). The highest mountain in Harstad is S\\u00e6tertinden, which is above sea level. It is located near the village of Sandtorg in southern Harstad. The tall mountain, Nupen, is located in the northwestern part of the municipality on the border with Kv\\u00e6fjord. Despite being located north of the Arctic Circle, Harstad features a subarctic climate (\\\"Dfc\\\"), with relatively mild winters and cool summers. Harstad does not have the brutal winters that most locations north of the Arctic Circle experience,\", \"European route E6 European route E6 European route E6 (, , or simply E6) is the main north-south road through Norway, and the west coast of Sweden. It is 3 088 km (1.919 mi) long and runs from the southern tip of Sweden at Trelleborg, into Norway and through almost all of the country north to the Arctic Circle and Nordkapp. The route ends in Kirkenes close to the Russian border. From south to north, E6 runs through Trelleborg, Malm\\u00f6, Helsingborg, Halmstad, Gothenburg, Svinesund in Sweden, before crossing the border at the Svinesund Bridge into Norway. It then passes Halden, Sarpsborg, Moss to\", \"Kirkenes vast bay connected to the Barents Sea near the Russian\\u2013Norwegian border. The town is situated about north of the Arctic Circle. Kirkenes is located just east of the 30th meridian east. As a result, it is further east than Istanbul, which marks one of the European borders with Asia. The easternmost point of Norway and the municipality is also at a point further east than Saint Petersburg. Unlike the vast majority of Norway, Kirkenes is located east of the neighbouring country of Finland. Because of this, travelling directly west from Kirkenes actually changes the time zone forward instead of backward,\", \"Isfjorden (Svalbard) Isfjorden (Svalbard) Isfjorden is the second longest fjord in the Norwegian archipelago of Svalbard. It lies on the west side of Spitsbergen, an island in the Arctic Ocean about midway between Norway and the North Pole, and the largest in the archipelago. The mountain of Alkhornet stands on the northern side of the entrance to the fjord, as does the coastal plain of Daudmanns\\u00f8yra. A portion of Isfjorden is included in the national parks of Norway as Nordre Isfjorden Land National Park. Around the fjord lie many of the largest settlements in Svalbard: Barentsburg, Longyearbyen (on the Adventfjorden) and Pyramiden.\", \"Barneo the Russian Geographical Society and normally lasts for the month of April. Ice Camp Barneo should not be confused with the sequential Soviet/Russian \\\"North Pole\\\" drifting ice stations established by the Russian Academy of Sciences Arctic and Antarctic Research Institute (AARI). From 2002 to 2017, the starting and final point of all expeditions to Barneo has been Longyearbyen, the capital of the Svalbard archipelago of Norway. The town has necessary facilities including an airport, hotels of different levels, restaurants, a post office, a bank, and a supermarket. In 2016, following military exercises by Chechen paratroopers on Barneo, Norway enacted a\", \"Troms\\u00f8 (city) \\\"M\\u00f8rketid\\\" (polar night) and \\\"Seinvinter\\\" (late winter). It is possible to observe aurora borealis (northern lights) from Troms\\u00f8, as northern Norway is located in the auroral zone. As it is always light in the summer, no aurora is visible between late April and mid August. Additionally, due to the coastal location, Troms\\u00f8 is often subject to cloudy conditions which prevents aurora being seen, even if they are present. The compact city centre has the biggest concentration of historic wooden houses north of the city of Trondheim, and they co-exist with modern architecture. The houses date from 1789 to 1904, when\", \"North Cape (Norway) lies about further south and about to the east. That point is located near the village of Mehamn on the Nordkinn Peninsula. The northernmost point of Europe including islands is hundreds of kilometres further north, either in Russia's Franz Josef Land or Norway's Svalbard archipelago, depending on whether Franz Josef Land is considered to be in Europe or in Asia. The North Cape is the point where the Norwegian Sea, part of the Atlantic Ocean, meets the Barents Sea, part of the Arctic Ocean. The midnight sun can be seen from 14 May to 31 July. The sun reaches its\", \"Svalbard citing the 1978 bilateral agreement on air traffic between Finland and Norway. Lufttransport provides regular corporate charter services from Longyearbyen to Ny-\\u00c5lesund Airport and Svea Airport for Kings Bay and Store Norske; these flights are in general not available to the public. There are heliports in Barentsburg and Pyramiden, and helicopters are frequently used by the governor and to a lesser extent the mining company Arktikugol. The climate of Svalbard is dominated by its high latitude, with the average summer temperature at and January averages at . The West Spitsbergen Current, the northernmost branch of the North Atlantic Current system,\", \"Narvik best known location in northern Norway for alpine skiing. There are lifts, and several of the slopes are floodlit. There is also a cable car to Fagernesfjellet, with a view and the possibility to walk even higher up in the mountains. Narvik Winter Festival () takes place in early March. Mountain hiking is very popular in the area, and the mountain area near the Swedish border has several places of accommodation. A signed mountain bike route is also available. Wreck diving attracts divers to Narvik, as there are a lot of wrecks in or near the harbour, and more spread\", \"Kvit\\u00f8ya \\u2013 the Bokm\\u00e5l form would have been \\\"Hvit\\u00f8yen\\\" or \\\"Hvit\\u00f8ya\\\". Kvit\\u00f8ya Kvit\\u00f8ya (English: \\\"White Island\\\") is an island in the Svalbard archipelago in the Arctic Ocean, with an area of . It is the easternmost part of the Kingdom of Norway. The closest Russian Arctic possession, Victoria Island, lies only to the east of Kvit\\u00f8ya. The island is almost completely covered by Kvit\\u00f8yj\\u00f8kulen, an ice cap with an area of with a classical, hourglass-shaped dome, which has given it its name. The few ice-free land areas are each only a few square kilometres large and very barren and rocky, the\", \"Norsk Polar Navigasjon service from the mainland to Svalbard. The discussions resulted in the intention to establish Norsk Arktis Flyselskap (\\\"Norwegian Arctic Airline\\\") to serve Spitsbergen, Bj\\u00f8rn\\u00f8ya, Jan Mayen and East Greenland. To generate revenue, the Pedersen brothers proposed establishing the Roald Amundsen Institute in Ny-\\u00c5lesund. It would act as a hotel during the summer and provide cheap accommodation for researchers during the winter, while generating patronage for the airport. An agreement was struck with Hilton Hotels to build the hotel. The brothers secured some political support, particularly from Bjarne St\\u00f8tvig of the Conservative Party, but also other parliamentarians bought shares in the\", \"Skjomen the many beautiful mountains surrounding the fjord. The highest point around Skjomen is the tall mountain Storsteinfjell. The Frostisen glacier, which is one of the larger plateau glaciers in Norway is located just west of the fjord. Frostisen covers an area of about and it is located at an elevation of above sea level. Skjomen Skjomen () is a fjord arm that branches off from the Ofotfjorden. It is located in the Ofoten district in Northern Norway, located just south of the city of Narvik. The European route E06 highway crosses the Skjomen fjord over the Skjomen Bridge, just south\", \"Haukelifjell one of the oldest road tunnels in Norway, the Old Dyrskartunnel was opened in 1900. European route E134 over Haukelifjell is the most important transport link between Haugesund and Oslo. In the winter, column driving through the mountain pass and occasionally closed roads is not uncommon, due to a lot of snow and heavy winds. Haukelifjell Ski Resort and Haukeliseter Fjellstue Hostel (Norwegian Trekking Association) are in the mountain area. The mountain range is a popular destination for skiing in the winter and hiking in the mountains in the summer. Haukelifjell Haukelifjell is a mountain area and a mountain pass\", \"Troms\\u00f8 Bridge of the Arctic Cathedral, the Tromsdalstinden mountain, and the Troms\\u00f8 Bridge. In 2000, the Directorate for Cultural Heritage protected the bridge against modifications. In 2005, the fencing was raised by two and a half meters, and seven years later, Norway's road authority planned on adding extra fencing onto many bridges to help prevent suicide. Troms\\u00f8 Bridge The Troms\\u00f8 Bridge () is a cantilever road bridge in the city of Troms\\u00f8 which is located in Troms\\u00f8 Municipality in Troms county, Norway. It crosses the Troms\\u00f8ysundet strait between Tromsdalen on the mainland and the island of Troms\\u00f8ya. The bridge has 58 spans,\", \"Fiann Paul Arctic ice pack (79\\u00b055'50 N) and from ice pack to Jan Mayen. It was the first complete, recorded man-powered crossing of the Barents Sea and of the Greenland Sea, some of the world's northernmost waters, which had long been called by sailors of the past, the \\\"\\\"Devil's dance floor\\\"\\\". Upon completion of Polar Row I and arrival to Longyearbyen, Fiann was asked by Norwegian TV2 how a rower would name the Barents Sea. Fiann responded that he would call it \\u201c\\\"Devil's Jaw\\\"\\u201d, adding that the winds you constantly battle are the breath from the devil's nostrils while he holds you\", \"Vard\\u00f8 and is the site of Vard\\u00f8 Lighthouse. The mouth of the Varangerfjorden lies along the municipality's eastern coast. The port of Vard\\u00f8, on the Barents Sea, remains ice-free all year round thanks to the warm North Atlantic drift. Vard\\u00f8 has a tundra climate (K\\u00f6ppen: \\\"ETf\\\"). that borders on a subarctic climate (K\\u00f6ppen: \\\"Dfc\\\"). Excluding high mountain areas, it is the only town in Norway proper that has polar climate. As its warmest month does not reach 10 \\u00b0C, the minimum temperature required for tree growth, the land is tundra and is treeless. The \\\"midnight sun\\\" is above the horizon from\", \"Polar Line Straumen, in addition to a section of the right-of-way past Torkilseng. The road also used tunnels built at Asp, Eva, Espenes, Kobbvatnet and north of T\\u00f8mmerneset. Polar Line The Polar Line (, ) is an incomplete and abandoned railway line from Fauske, Norway, to Narvik and, if finished, ultimately would have run to Kirkenes. The railway was constructed by the \\\"Wehrmacht\\\" in occupied Norway during the Second World War as part of \\\"Festung Norwegen\\\". At Fauske, the line connected with the Nordland Line, and construction stretched as far north as Drag, Tysfjord. After the war, the plans were abandoned by\", \"Trondheim Fjord of the fjord. The narrow \\\"Skarnsundet\\\" is crossed by the Skarnsund Bridge. The part of the fjord to the north of the strait is referred to as the \\\"Beitstadfjorden\\\". The main part of the Trondheimsfjord is ice-free all year; only Verrasundet, a long and narrow fjord branch in the northern part of the fjord, might be ice covered in winter. The Beitstadfjorden might also freeze over in winter, but only for a few weeks. The towns of Stj\\u00f8rdalshalsen, Levanger, and Steinkjer are found on the eastern and northeastern shores of the fjord. Aker Verdal in Verdal produces large offshore installations\", \"Steigen on a gold background. The Church of Norway has three parishes \\\"(sokn)\\\" within the municipality of Steigen. It is part of the Salten prosti (\\\"deanery\\\") in the Diocese of S\\u00f8r-H\\u00e5logaland. The municipality is located along the coast of the Vestfjorden, about north of Bod\\u00f8, well inside the Arctic Circle. The road to Steigen departs from European route E06 and makes use of the long Steigentunnelen (see World's longest tunnels). Steigen borders Hamar\\u00f8y municipality in the north and S\\u00f8rfold municipality to the south. The Vestfjorden and Lofoten are located west of Steigen. The Sagfjorden lies on the north and the Folda\", \"Murmansk Kola Motorway. Murmansk Airport provides air links to Moscow and St. Petersburg, as well as an international connection to Troms\\u00f8, Norway. Buses and trolleybuses provide local transport. Murmansk is set to be the Russian terminus of the Arctic Bridge, a sea route linking it to the Canadian port of Churchill, Manitoba. Even though the passage has not been fully tested for commercial shipping yet, Russian interest in this project (along with the Northwest Passage) is substantial, as the bridge will serve as a major trade route between North America, Europe and Asia. Murmansk is home to Murmansk State Technical University,\", \"Polar Line there would be a connection northwards towards Kirkenes. The second alternative\\u2014the Fjord Line\\u2014would run via T\\u00f8mmerneset, Innhavet and Musken, around the southern end of Tysfjorden and then followed the shoreline to Ballangen to Narvik. The third\\u2014the Mountain Line\\u2014would follow an inland route from Kobbvatnet up Gerdalen and then through a long tunnel to Tysfjorden. The Fjord Line was longer than the Mountain Line, but considerably cheaper to build. A fourth proposal, launched by the \\\"Wehrmacht\\\", was to build a ferry crossing of Tysfjorden, but otherwise build closely to that of the Fjord Line. The alternatives were considered by Vienna-based Ladislaus\", \"Troms\\u00f8 two seasons: \\\"M\\u00f8rketid\\\" (polar night) and \\\"Seinvinter\\\" (late winter). It is possible to observe aurora borealis (northern lights) from Troms\\u00f8, as northern Norway is located in the auroral zone. As it is always light in the summer, no aurora is visible between late April and mid August. Additionally, due to the coastal location, Troms\\u00f8 is often subject to cloudy conditions which prevents aurora being seen, even if they are present. Troms\\u00f8 municipality includes these villages: The compact city centre has the biggest concentration of historic wooden houses north of Trondheim, and they co-exist with modern architecture. The houses date from\", \"\\u00d8vre Pasvik National Park of flowering plants. The national park covers an area of . It is located in the southernmost part of S\\u00f8r-Varanger and covers the southwestern part of the valley of Pasvikdalen. The park's western border is identical to the Finland\\u2013Norway border. The eastern border crosses through the lakes of Ivergammevatnet, Revsaksfjellet and \\u00d8devatn. Treriksr\\u00f8ysa, the tripoint cairn located at the intersection of the Finland\\u2013Norway\\u2013Russia border, is within the park. To the east is \\u00d8vre Pasvik Landscape Protection Area and Pasvik Nature Reserve, which both lie along the Norway\\u2013Russia border. The park is part of Pasvik\\u2013Inari Trilateral Park, which in addition to\", \"B\\u00f8rge Ousland B\\u00f8rge Ousland B\\u00f8rge Ousland FRSGS (born 31 May 1962) is a Norwegian polar explorer, photographer and writer. He started his career as a Norwegian Navy Special Forces Officer with Marinejegerkommandoen, and he also spent several years working as a deep sea diver for the oil industry in the North Sea. In 1994, he made the first solo and unsupported journey to the North Pole from cape Arktichevsky in Russia. As of 2016, that feat has never been successfully repeated. He made the first unassisted Antarctic solo crossing, between 15 November 1996 and 17 January 1997. The ski journey was made\", \"Domen, Norway Domen, Norway Domen is a mountain on the Varanger Peninsula in eastern Finnmark county, Norway. The tall mountain is located near the coast between the small fishing village Kiberg and the island of Vard\\u00f8ya. Domen is bare and flat-topped, with a steep slope towards the Barents Sea below. The European route E75 highway which runs along the western side of the mountain from Svartnes to Kiberg. The road is often closed in the winter due to bad weather. The Old Norse name of the Arctic Sea was \\\"Dumbshaf\\\". This sea (\\\"haf\\\") was named after the mountain \\\"Dumbr\\\" (an old form\", \"Abisko Abisko Abisko () is a village in S\\u00e1pmi (Lapland), in northern Sweden, roughly 250 km within the Arctic Circle, and near Abisko National Park, located 4 km west of the village. It had 85 inhabitants as of 2005. Permafrost is common around the village albeit this low altitude permafrost is disappearing because of global warming and increased snowfall. Daily passenger electric trains run by SJ AB connect Stockholm with the Norwegian city of Narvik, stopping at both the Abisko village (the name of that railway station is \\\"Abisko \\u00d6stra\\\" [east]) and the Abisko Turiststation. Additional regional trains provide links along\", \"Hammerfest Royal and Ancient Polar Bear Society (); a museum displaying the history of Arctic hunting. The newspaper \\\"Hammerfestingen\\\" is published in Hammerfest. American author Bill Bryson begins his European travels in 1990, documented in his book \\\"\\\", with a visit to Hammerfest in order to see the Northern Lights, calling it \\u201can agreeable enough town in a thank-you-God-for-not-making-me-live-here sort of way\\u201d. Hammerfest is connected to the main road network by Norwegian national road 94 which branches off from European route E6 at Skaidi in the neighbouring municipality of Kvalsund. The town is a port of call for the Hurtigruten ship\", \"Jacob Bjerknes front, integrated with the cyclone model, provided the major mechanism for north-south heat transport in the atmosphere. For this and other research, Jacob Bjerknes was awarded the Ph.D. from the University of Oslo in 1924. In 1926, Jacob Bjerknes was a support meteorologist when Roald Amundsen made the first crossing of the Arctic in the airship Norge. In 1931, he left his position as head of the National weather service at Bergen to become professor of meteorology at the Geophysical Institute at the University of Bergen. Jacob Bjerknes lectured at the Massachusetts Institute of Technology during the 1933-1934 school year\", \"Molde popular rock climbing, ice climbing, bouldering, glacier and basejumping areas in the immediate surroundings of Molde. The \\\"Atlantic road\\\" was voted the Norwegian Construction of the Century in 2005. It is built on bridges and landfills across small islands and skerries, and spans from the small communities of Vikan and Vevang to Aver\\u00f8y, an island with several historic landmarks, such as the Bremsnes cave with Mesolithic findings from the Fosna culture, the medieval Kvernes stave church, and Lang\\u00f8ysund, now a remote fishing community, but once a bustling port along the main coastal route. Lang\\u00f8ysund was the site of the compromise\", \"Domen, Norway Domen to meet the devil for sabbath. Domen, Norway Domen is a mountain on the Varanger Peninsula in eastern Finnmark county, Norway. The tall mountain is located near the coast between the small fishing village Kiberg and the island of Vard\\u00f8ya. Domen is bare and flat-topped, with a steep slope towards the Barents Sea below. The European route E75 highway which runs along the western side of the mountain from Svartnes to Kiberg. The road is often closed in the winter due to bad weather. The Old Norse name of the Arctic Sea was \\\"Dumbshaf\\\". This sea (\\\"haf\\\") was named\", \"Northern Norway Seven road sections in the region are National Tourist Routes in Norway due to their scenic surroundings, from Helgeland in the south to the Varanger Peninsula in the northeast, including two sections of the Norwegian County Road 17. Airports with long runways and direct flights to Oslo airport are located in Troms\\u00f8, Bod\\u00f8, Evenes (near Harstad), Alta, Kirkenes and Bardufoss, and there are also directs flights connecting Br\\u00f8nn\\u00f8ysund and Sandnessj\\u00f8en with Oslo. There are smaller airports with regional flights near most towns. For Bod\\u00f8, Fauske, Mo i Rana and Mosj\\u00f8en the Nordland Line provides railway connection south to Trondheim (and\", \"European route E16 and E18 in Belfast, the E5 in Glasgow, the E15 in Edinburgh. European routes are not signposted in the UK. There is no ferry anymore between the United Kingdom and Norway. The E16 is the main road between Norway's two largest cities Oslo and Bergen, and the only mountain pass between Oslo and Bergen that is rarely open due to snowstorms and blizzards (it goes below the tree line). Outside winter, route 7 is at least as popular between Oslo and Bergen, since it is shorter. There are some other options such as the road through Hemsedal. The E16 is\", \"Svinesund Bridge Svinesund Bridge The Svinesund Bridge (, ) is a through arch bridge crossing Iddefjord at Svinesund, and joining Sweden and Norway. Svinesund is a sound separating the Swedish municipality of Str\\u00f6mstad from the Norwegian municipality of Halden, and thus it is the border between Norway and Sweden in this region. The bridge is the westernmost border crossing (and one of the southernmost) between the two countries and carries the European route E6 which is a major traffic route in the area, connecting Oslo and the rest of Norway with Gothenburg, Malm\\u00f6, Copenhagen and the rest of Europe. The New Svinesund\", \"Expedition to Lapland Old Pite\\u00e5, passing Old Lule\\u00e5 where he received a Sami woman's cap on the way. From Lule\\u00e5 he again traveled inland, following the Lule River via Jokkmokk on the arctic circle and Kvikkjokk (then Hyttan), into the Scandinavian Mountains, crossing the border into Norway, arriving in S\\u00f8rfold on the coast and making a trip to nearby R\\u00f6rstadt. He then returned the way he came, approximately back to Lule\\u00e5. Linnaeus then continued his travel along the coast to Tornio (Torne\\u00e5 in Swedish), from which he made his third and final inland incursion, along the Torne River as far as Vittangi. He\", \"And\\u00f8ya And\\u00f8ya And\\u00f8ya is the northernmost island in the Vester\\u00e5len archipelago, situated about inside the Arctic circle. And\\u00f8ya is located in the municipality of And\\u00f8y in Nordland county, Norway. The main population centres on the island include Andenes, Bleik, and Ris\\u00f8yhamn. The island has an area of , making it the tenth largest island in Norway. The island is connected to the neighboring island of Hinn\\u00f8ya using the And\\u00f8y Bridge. The Andfjorden lies to the east of the island, the Ris\\u00f8ysundet strait lies to the south and east side of the island, and the Gavlfjorden lies to the southwest side. The\", \"Extreme points of Norway points are bordering the sea; due to the geographic nature of the coastline, all extremities are located on islands. Therefore, extreme points of the Norwegian mainland are also included in the list. The northernmost point is Knivskjellodden, located in Mager\\u00f8ya in Finnmark. The northernmost mainland point is Cape Nordkinn, located in Lebesby, Finnmark; this is also the northernmost location of mainland Europe. Both border the Barents Sea. The southernmost location of Norway proper is Pysen, while the southernmost mainland location is Lindesnes; both border Skagerrak. The easternmost point is Horn\\u00f8ya, with Kibergsneset being the easternmost mainland location. Both are in\", \"Nordkalottruta Nordkalottruta Nordkalottruta or Arctic Trail (Finnish: \\\"Kalottireitti\\\", Swedish: \\\"Nordkalottleden\\\") is a marked hiking trail in the Arctic region of the Nordic countries. It has a total length of 800 km and lies along the border of Norway, Sweden and Finland. It begins at Kautokeino (located in Finnmark, Northern Norway) and of the 800 km, 380 km of the trail lies in Norway, 350 km in Sweden and 70 km in Finland. The trail crosses international borders 15 times and ends in the south in Sulitjelma (Norway) or alternately Kvikkjokk (Sweden). The trail was originally planned in 1977. It passes through\", \"Cross-country skiing included specialized skiing battalions from 1747\\u2014details of military ski exercises from 1767 are on record. Skis were used in military exercises in 1747. In 1799 French traveller Jacques de la Tocnaye recorded his visit to Norway in his travel diary: Norwegian immigrants used skis (\\\"Norwegian snowshoes\\\") in the US midwest from around 1836. Norwegian immigrant \\\"Snowshoe Thompson\\\" transported mail by skiing across the Sierra Nevada between California and Nevada from 1856. In 1888 Norwegian explorer Fridtjof Nansen and his team crossed the Greenland icecap on skis. Norwegian workers on the Buenos Aires - Valparaiso railway line introduced skiing in South\", \"Mo i Rana a part of the Norwegian STOLport network. Mo i Rana is connected to the Nordland Line railway. This is a railway line between Trondheim and Bod\\u00f8. The main north-south road in Norway, European route E6, passes through the city. The European route E12 begins in Mo i Rana and connect the city to Sweden and Finland. A bus network runs throughout most of the city and its suburbs. An international tourist route Blue Highway (in Norwegian: \\\"Bl\\u00e5 vegen\\\") begins in Mo i Rana. The route goes via Sweden and Finland to Russia. \\\"Havmann\\\" () is a sculpture made from Arctic\", \"Norway\\u2013Sweden border if having goods needing declaration. Heavy trucks can be allowed to use them by pre-declaration. They are surveilled by video and temporary checks. There are four railway crossings. All four serve both passenger and freight trains. All border stations except Kornsj\\u00f8 Station are located in Sweden. Norway has right hand traffic and Sweden has left hand traffic on double track railways. All four crossings are single track so the difference is no problem. Stone cairns, known as the and , mark many parts of the border. Animals, notably wolves and brown bear, have been known to wander across the border.\", \"Salekhard as Salekhard Urban Okrug. Yamal Airlines has its head office in Salekhard. By 2015, about from the airport, near the Arctic circle, authorities plan to build a large polar resort \\\"Center of the Arctic tourism.\\\" The town is served by the Salekhard Airport. The nearest railway is at Labytnangi (on the Salekhard\\u2013Igarka Railway) on the opposite side of the river Ob. A long-awaited bridge across the Ob between Labytnangi and Salekhard is being built. , cars and trucks can cross the river by driving across the frozen river ice for 9\\u201310 months a year. In the summer a ferry operates,\", \"North Pole Pole on foot (albeit with the aid of dog teams and airdrops). They continued on to complete the first surface crossing of the Arctic Ocean \\u2013 and by its longest axis, Utqiagvik, Alaska to Svalbard \\u2013 a feat that has never been repeated. Because of suggestions (later proven false) of Plaisted's use of air transport, some sources classify Herbert's expedition as the first confirmed to reach the North Pole over the ice surface by any means. In the 1980s Plaisted's pilots Weldy Phipps and Ken Lee signed affidavits asserting that no such airlift was provided. It is also said that\", \"Brekkest\\u00f8 post office, located near the waterfront, was closed in 2002. Just\\u00f8y Chapel is located just north of the village. The small village of \\u00c5ker\\u00f8yhamn lies about to the southwest on the small, nearby island of \\u00c5ger\\u00f8ya. The nearest town is Lillesand, accessible by road via a network of bridges, and the closest international airport is Kjevik Airport near Kristiansand. Brekkest\\u00f8 is also reached via the Blindleia strait, which passes nearby. Brekkest\\u00f8 has a reputation for being both rustic yet exclusive, and as such, it is a popular destination for summer vacationing. Brekkest\\u00f8 is often cited as an \\u201cidyllic pearl\\u201d of\", \"Hardangervidda National Park thousands of years; several hundred nomadic stone age settlements have been found in the area, most likely related to the migration of the reindeer. Ancient trails cross the plateau, linking western and eastern Norway. One example is the \\\"Nordmannsslepa\\\" linking Eidfjord and Veggli in the Numedal valley with Hol and Uvdal. It is still a key transit route between Oslo and Bergen. The Bergen Line and the main Highway 7 cross the plateau. Hardangervidda is accessible all year round. June/July to September/October is great for hiking, fishing, wildlife viewing, cycling, horseback riding, canoeing, hunting and other summer activities. Hiking is\", \"Norsk Polar Navigasjon aerodrome for intercontinental flights, and proposed that the Norwegian trunk airline service be extended to Svalbard and that his airport be established as a hub. He first published the idea in \\\"Polarboken 1954\\\". The brothers, Einar Sverre Pedersen and Gunnar Sverre Pedersen, went on an expedition to Spitsbergen in 1956 to conduct further surveys. Their initial observations concluded with that Kvadehussletta, the outermost part of Br\\u00f8ggerhalv\\u00f8ya, outside Ny-\\u00c5lesund, was the best-suited places for a major airport. The initially planned for a long runway, which could easily be expanded to . Hotellneset and Adventdalen, both close to Longyearbyen, were rejected because\", \"Paul Walker (Arctic explorer) Arctic Circle before the discovery of Gunnbj\\u00f8rnsfjeld 3693m. In 1996 he climbed a number of the main summits of the Crown Prince Frederick Range together with members of the Tangent British East Greenland Expedition. In 1999 he led the first British guided ski crossing of the Greenland Icecap using kites. In 2001 he headed to Svalbard to lead the \\\"Polestar\\\" team to make the first British south-north ski traverse of Spitsbergen. In 2004 Paul organized and led the US Navy Air Crash Recovery Expedition to the Kronborg Glacier, east Greenland. This expedition was commissioned by the US Navy to recover\", \"Northern Norway The wettest areas are generally the Helgeland region; Lur\\u00f8y on the west coast of Saltfjell averages /year. The extreme northeastern coast, from Nordkapp to Vard\\u00f8, is situated in the arctic climate zone due to lack of summer warmth \\u2014 July average in Vard\\u00f8 is only . However, to the south, in the Pasvik valley south of Kirkenes, 24 July-hr average is up to . No other parts of Norway experience such large differences in lowland summer temperatures in such a relatively short distance. The coldest temperature recorded is in Karasjok, and the warmest recorded is in Sihcajavri in Kautokeino. Ranked\", \"Geography of Norway Maud Land is Norway's claim in Antarctica. This large, sectorial area stretches to the South Pole and are completely dominated by the world's largest ice sheet, but with some impressive nunataks penetrating above the ice. The Troll Research Station manned by Norway is located on a snow free mountain slope, the only station in Antarctica not to be located on the ice. Areas in Norway located north of the Arctic Circle will have midnight sun and corresponding winter darkness, the length of both depends on the latitude. In Longyearbyen, the upper part of the sun disc is above the horizon\", \"European route E4 and on 30% of the road. North of G\\u00e4vle there are varying speed limits, with , and as the most common. The speed limits on the main roads in Sweden were changed on many stretches in October 2008, which saw the introduction of the 120 km/h limit. The E 4 is the fastest road to go from Germany/Denmark to areas north of the Arctic Circle, including places in Norway such as Troms\\u00f8 or the North Cape. The route passes through or nearby the cities Tornio, Haparanda, Lule\\u00e5, Pite\\u00e5, Skellefte\\u00e5, Ume\\u00e5, \\u00d6rnsk\\u00f6ldsvik, H\\u00e4rn\\u00f6sand, Sundsvall, Hudiksvall, S\\u00f6derhamn, G\\u00e4vle, Uppsala, Stockholm, S\\u00f6dert\\u00e4lje, Nyk\\u00f6ping,\", \"Hardangervidda age settlements have been found in the area, most likely related to the migration of the reindeer. Ancient trails cross the plateau, linking western and eastern Norway. One example is the \\\"Nordmannsslepa\\\" linking Eidfjord and Veggli in the Numedal valley with Hol and Uvdal. It is still a key transit route between Oslo and Bergen. The Bergen Line and the main Highway 7 cross the plateau. In 1981, much of the Hardangervidda was designated a national park, Norway's largest at . The park's boundaries stretch from Numedal and Uvdal in the east and R\\u00f8velseggi and Ullensvang in the west. The\", \"Lofoten some long, and high. In March, 1941 the islands were raided by British Commandos during Operation Claymore, and in a subsequent diversionary attack to support the Vaagso raid in December. As of 2017, the islands attract one million tourists a year. Lofoten is located at the 68th and 69th parallels north of the Arctic Circle in North Norway. It is known for its natural environment within Norway. Lofoten encompasses the municipalities of V\\u00e5gan, Vestv\\u00e5g\\u00f8y, Flakstad, Moskenes, V\\u00e6r\\u00f8y, and R\\u00f8st. The principal islands, running from north to south are: Further to the south are the small and isolated islands of V\\u00e6r\\u00f8y\", \"Troms Troms\\u00f8 (university hospital and main hospital for North Norway) and Harstad. The busiest airport is Troms\\u00f8 Airport. The southern part of Troms is served by Harstad/Narvik Airport, Evenes and Bardufoss Airport, and in northeast there is S\\u00f8rkjosen Airport. The E6 cuts through the county from Nordland into Gratangen in the south to Kv\\u00e6nangen in the north and then into Finnmark. The E8 road runs from Troms\\u00f8 to Finland via Nordkjosbotn and the Skibotn valley. There are several large bridges; some of the largest are Tjeldsund Bridge, Mj\\u00f8sund Bridge, Gisund Bridge, Troms\\u00f8 Bridge and Sandnessund Bridge. There are several undersea road\", \"Nordland survived since the Ice age. \\\"Climate statistics provided by Norwegian Meteorological Institute; 1961\\u20131990 base period unless otherwise stated. Data for Glomfjord last 10 years by Storm Weather Center.\\\" The light conditions vary considerably from north to south; Andenes in the north will have midnight sun from 22 May to 20 July, and the sun is below the horizon from 28 November to 16 January (Narvik daylight). In Bod\\u00f8, the sun is above the horizon from 3 June to 8 July. Helgeland is situated south of the Arctic Circle: At winter solstice the sun is above the horizon approximately 3 hours\", \"Northern Norway Northern Norway Northern Norway (, ; ) is a geographical region of Norway, consisting of the three northernmost counties Nordland, Troms and Finnmark, in total about 35% of the Norwegian mainland. Some of the largest towns in Northern Norway (from south to north) are Mo i Rana, Bod\\u00f8, Narvik, Harstad, Troms\\u00f8 and Alta. Northern Norway is often described as the land of the midnight sun and the land of the northern lights. Further north, halfway to the North Pole, is the Arctic archipelago of Svalbard, traditionally not regarded as part of Northern Norway. The region is multi-cultural, housing not just\", \"Norwegian Maritime Museum the Northwest Passage in the 1903-06 Arctic expedition of Roald Amundsen. In 2009, the Norwegian Maritime Museum and the Fram Museum signed an agreement for the Fram Museum to take over the exhibition of the \\\"Gj\\u00f8a\\\". It is currently displayed in a separate building at Fram Museum. Norwegian Maritime Museum The Norwegian Maritime Museum () is located at Bygd\\u00f8ynesveien on the Bygd\\u00f8y peninsula, on the western side of Oslo, Norway. The Norwegian Maritime Museum is situated near several other museums, including the Fram Museum; the Kon-Tiki Museum; the Norwegian Museum of Cultural History; and the Viking Ship Museum. The Norwegian\", \"Arctic Circle level, although in mountainous regions there is often no direct view of the true horizon. Only four million people live north of the Arctic Circle due to the severe climate; nonetheless, some areas have been settled for thousands of years by indigenous peoples, who today make up 10% of the region's population. Tens of thousands of years ago, waves of people migrated from eastern Siberia across the Bering Strait into North America to settle. The largest communities north of the Arctic Circle are situated in Russia, Norway and Sweden: Murmansk (population 307,257), Norilsk (175,365), Troms\\u00f8 (71,295), Vorkuta (59,231) and Kiruna\", \"Lavik (village) into a transportation hub along the European route E39 highway, the main highway from Bergen to Trondheim. Lavik is the northern point of the Lavik to Ytre Oppedal ferry served by Fjord1 Nordvestlandske that crosses the Sognefjorden as part of the E39 highway. There are several services in the small harbour area: a snackbar, a pizzeria, a hotel and restaurant on the Sognefjorden. There is also a supermarket, a bank, a service station, and several other shops in Lavik. The village is also home to Lavik Church and it was historically the administrative centre of the old municipality of Lavik\", \"Troms\\u00f8 (city) is the largest urban area in Northern Norway and the third largest north of the Arctic Circle anywhere in the world (following Murmansk and Norilsk). The city's largest workplaces are the University of Troms\\u00f8 (UiT) and University Hospital of North Norway. The Norwegian Polar Institute also has its headquarters in Troms\\u00f8. The Northern Lights Observatory was established in 1928, and two companies affiliated with the Kongsberg Gruppen collect satellite data from space using the observatory. The fishing industry is very important. Norway's Norges R\\u00e5fisklag and Norges sj\\u00f8matr\\u00e5d (seafood council) both have their headquarters in Troms\\u00f8. Sparebanken Nord-Norge also has its\"], \"pos_scores\": [96.5], \"neg_scores\": [87.5, 92.125, 86.625, 86.3125, 90.625, 86.3125, 90.1875, 90.4375, 85.6875, 93.1875, 90.75, 84.9375, 84.9375, 86.0, 86.4375, 86.6875, 90.25, 85.3125, 85.0, 86.125, 90.625, 87.75, 86.375, 88.9375, 87.3125, 86.625, 86.9375, 85.4375, 85.1875, 85.875, 85.625, 87.0, 85.3125, 84.1875, 87.625, 88.5, 84.5, 90.5, 85.875, 87.5625, 85.4375, 86.9375, 84.875, 89.5625, 87.6875, 85.6875, 85.8125, 88.3125, 88.75, 87.0625, 88.1875, 87.0625, 85.1875, 84.375, 84.4375, 88.9375, 84.4375, 87.25, 86.875, 84.0, 84.9375, 85.25, 86.375, 89.4375, 84.3125, 85.0625, 85.0, 88.0, 86.4375, 84.0625, 85.1875, 85.0, 86.9375, 84.5625, 86.3125, 86.0, 87.4375, 88.6875, 88.5625, 85.375, 88.1875, 86.3125, 85.3125, 86.0, 83.9375, 84.4375, 84.1875, 84.5, 88.25, 88.3125, 86.5625, 84.75, 87.1875, 88.1875, 86.25, 89.625, 84.875, 93.375, 84.4375, 89.1875], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"who is the main character in green eggs and ham\", \"pos\": [\"Green Eggs and Ham (in a house, in a box, in a car, in a tree, on a train, in the dark, in the rain, on a boat) and dining partners (a mouse, a fox, and a goat). The friend finally gives in and tries the dish, just to make Sam \\u201clet him be\\u201d, and finds it quite tasty, happily responding, \\\"I do so like green eggs and ham. Thank you. Thank you, Sam-I-am.\\\" \\\"Green Eggs and Ham\\\" is one of Seuss's \\\"Beginner Books\\\", written in a very simple vocabulary for beginning readers. The vocabulary of the text consists of just 50 words and\"], \"neg\": [\"I Am Sam in key roles. For his role as Sam, Penn was nominated for the Academy Award for Best Actor at the 74th Academy Awards in 2002. The film launched the career of child actress Dakota Fanning, who was then seven years old and had only acted in two small roles. She became the youngest actress to be nominated for a Screen Actors Guild Award. The movie's title is derived from the opening lines \\\"I am Sam / Sam I am\\\" of the book \\\"Green Eggs and Ham\\\", which is read in the movie. Sam Dawson (Sean Penn), a man with an\", \"Green Eggs and Ham (TV series) Green Eggs and Ham (TV series) Green Eggs and Ham is an upcoming American animated television series from Warner Bros. Animation, A Very Good Production, A Stern Talking To, Random House Children's Entertainment and Gulfstream Television and distributed by Warner Bros. Television. It is slated to air for 13 half-hour episodes on Netflix and to be based on the 1960 Dr. Seuss book of the same title. Jared Stern, Ellen DeGeneres, Jeff Kleeman, Mike Karz and David Dobkin are the executive producers of the show. Stern will also serve as a writer and showrunner. The series will have thirteen episodes.\", \"The Pinhoe Egg lad, Jason, and helps him and his new wife choose a house. They finally settle on Woods House, Gammer's old place, and Marianne, while showing Cat around, gives him an old egg from the attic, an egg with strong \\\"Don't Notice\\\" spells placed on it. An egg that is sure to arouse the interest of the \\\"Big Man\\\" up at the castle \\u2013 something the rest of the Pinhoe clan, and Gammer in particular, doesn't want at all. Marianne Pinhoe: Marianne is the main protagonist of the book. She is set to be the next Gammer of the Pinhoe clan,\", \"Green Eggs and Ham year, it was ranked number 12 among the \\\"Top 100 Picture Books\\\" in a survey published by \\\"School Library Journal\\\" \\u2013 the first of five Dr. Seuss books on the list. The book has become sufficiently ingrained in the cultural consciousness that U.S. District Court Judge James Muirhead referenced \\\"Green Eggs and Ham\\\" in his September 21, 2007 court ruling after receiving an egg in the mail from prisoner Charles Jay Wolff who was protesting against the prison diet. Muirhead ordered the egg destroyed and rendered his judgment in the style of Seuss. Senator Ted Cruz read the book on\", \"Egham did not settle well in the town, despite the efforts of his RAF host Flight Sergeant Sam Beckinsale to draw the local amenities to his attention. When it was pointed out how green the area was, due in part to its proximity to Windsor Great Park he retorted \\\"I do not like green Egham\\\". Geisel later cited this as the inspiration for his 1960 best-selling book \\\"Green Eggs and Ham\\\" and the often-repeated line in the book \\\"I do not like them Sam I Am. I do not like green eggs and ham\\\". Parts of Egham have featured in national\", \"The Egg and I a novel, being reprinted on a nearly monthly basis for the next two years. On September 12, 1946, the specially-bound one-millionth copy of the book was presented to MacDonald by Washington Governor Monrad Wallgren at a luncheon in Seattle. In April 1946 Universal-International announced the purchase of the film rights for \\\"The Egg and I\\\" for $100,000, plus a percentage of profits. Claudette Colbert and Fred MacMurray were cast in the lead roles, with Marjorie Main and Percy Kilbride cast in the roles of Ma and Pa Kettle. The film, loosely based on the book, was released in 1947. Main\", \"The Hotel New Hampshire somewhat deaf, and his response to most statements/questions directed towards him is \\\"What?\\\" Egg's character is never fully formed, and he remains forever an amorphous \\\"egg\\\" of a child who never matures or develops due to dying at a very young age. Win Berry: The father of the Berry children and husband of Mary Berry. He is a graduate of Harvard, but rarely applies such skills. He is more or less an entrepreneur in the field of lodging, although his success is at best unconventional. He becomes depressed when his wife, Mary, along with his youngest son, Egg, are killed\", \"Miss Prissy centered on Foghorn Leghorn. In \\\"Lovelorn Leghorn\\\" (1951), she is set on finding a husband and in \\\"Of Rice and Hen\\\" (1953) she is looking to have children. However, in \\\"Little Boy Boo\\\" (1954) she is depicted as a widow with a child Egghead Jr. and with a much more extensive vocabulary in long sounding words than her trademark \\\"yeeesss.\\\" \\\"A Broken Leghorn\\\" (1959) and \\\"Strangled Eggs\\\" (1961) features Henery Hawk, and in these shorts, it is usually Foghorn who is pursuing Prissy for his own selfish needs. He does, however, show an unusual sympathy for her emotional vulnerability, perhaps\", \"I Am Frankie the head of EGG, Mr. Kingston, plans to use Frankie for the military company WARPA (Weaponized Android Research Project Agency) for Project Q, Sigourney quits her job, smuggles Frankie out of EGG, and moves her family as far away from EGG as possible so that Frankie can live a normal life. As Frankie adapts to a normal life as a high school student at Sepulveda High, she befriends a girl named Dayton and gains a rival named Tammy. While she and her family work to keep her secret safe so that EGG does not find her, Mr. Kingston is determined\", \"The Easter Bunny Is Comin' to Town his eggs to all the townspeople, including King Bruce, who crowns him the Easter Bunny, Royal Knight of the Rainbow Eggs and he and Sunny initiate a traditional ritual of eating the eggs. However, Lily, disappointed in her nephew, chases Sunny out, outlaws eggs, and sends him to bed without supper. After Bruce tells Sunny that he knew his supper would be more beans, anyway, Sunny promises to bring him very special beans next Easter. The following year, Sunny, Hallelujah, and Herbert the Baker (Michael McGovern) make the first Easter jelly beans. However, upon their way to Town to deliver\", \"Ma and Pa Kettle by Betty MacDonald in whose 1945 best-selling novel, \\\"The Egg and I\\\", they appeared. The success of the novel spawned the 1947 film \\\"The Egg and I\\\" starring Claudette Colbert and Fred MacMurray, also co-starring Marjorie Main and Percy Kilbride as Ma and Pa Kettle. Main was nominated for an Academy Award for Best Supporting Actress for her role. After the audiences' positive reaction to the Kettles in the film, Universal Studios produced nine more films, with Marjorie Main reprising her role in all and Percy Kilbride reprising his in seven. The films grossed an estimated $35 million altogether at\", \"Harry, He's Here to Help him while Harry leaves. Michel apologizes to Claire, and is inspired by Harry's favorite food to write a more personal story called \\\"The Eggs\\\". In the night, Harry returns, killing Plum and enlisting Michel for help disposing of her body in the well. When Michel realizes Harry plans to do the same to Claire, he stabs Harry with a screwdriver and drops his body down the same well. Michel stays up all night filling in the well, surprising Claire, who also tells him that she read \\\"The Eggs\\\" and thinks it is brilliant. The film closes as the family goes\", \"Eggs (novel) all rules. In a short time, the two, a thirteen-year-old with no father but a fortune-telling mother, and a nine-year-old with no mother, become great friends. Primrose lives in a 1977 van instead of a home, despite her the two quickly reunite. Primrose sets off on a journey with David, who seemed to have no choice when she said she would go to Philadelphia. Later, she admits that she wants to see the Waving Man, a man who had been seen on TV, and known for waving at people as they passed. She also confessed that she wanted to ask\", \"Frank Portman Western Front\\\" released by \\\"Maximum Rocknroll\\\" in 1982. After high school Portman left Millbrae for UC Berkeley but kept in touch with Stamatatos. While a student there he hosted a program on campus radio station KALX which prominently featured local punk rock music, and met fellow DJ Jon Von Zelowitz. While working at the station Portman recorded a rap version of the Dr. Seuss children's poem \\\"Green Eggs and Ham\\\". He also met Alex Laipeneiks, who had been a high school friend of Portman's younger brother. In the Summer of 1985 Portman, Zelowitz, Stamatatos, and Laipeneiks formed The Mr. T\", \"Betty MacDonald more films were made featuring them. In the film of \\\"The Egg and I\\\", made in 1947, MacDonald was played by Claudette Colbert. Her husband (simply called \\\"Bob\\\" in the book) was called \\\"Bob MacDonald\\\" in the film, as studio executives were keen not to raise the matter of MacDonald's divorce in the public consciousness. He was played by Fred MacMurray. Although the book was a critical and popular success at publication, in the 1970s it was criticized for its stereotypical treatment of Native Americans. It had also been claimed that it \\\"spawned a perception of Washington as a land\", \"Eric Forman to spend a year teaching in Africa, his mother is less than thrilled. This is most notably and comically presented when, the morning after his announcement, Kitty fixes everyone at the breakfast table a smiley-face breakfast with eggs and bacon and then hands Eric a plain pancake and says, \\\"nothing smiling up at you, nothing\\\". Even with a couple of days before his departure, Kitty reveals that she hid mailed notifications that Eric must receive certain vaccinations before he can leave. Eric protests his mom's actions but she still tries to dissuade him, stating that the needles used for the\", \"Mary Bard of the three volume \\\"Best Friends\\\" series for girls. She also wrote three autobiographical works: \\\"Just Be Yourself\\\", \\\"The Doctor Wears Three Faces\\\", and \\\"Forty Odd\\\". Married to a doctor, she was the mother of three daughters. Mary Bard Mary Bard Jensen (1904\\u20131970) was a 20th-century American author best remembered today as the sister of Betty Macdonald (\\\"Mrs. Piggle-Wiggle\\\", \\\"The Egg and I\\\".) Mary Bard was born in Butte, Montana in 1904, the eldest of five children. With their mining engineer father, the family traveled all over the country, moving so frequently that Mary did not complete one uninterrupted year\", \"Teddy Infuhr in the \\\"Rusty\\\" canine adventure series, beginning with \\\"The Return of Rusty\\\" and finishing with \\\"Rusty's Birthday\\\". He was also one of the bucolic brood in the Ma and Pa Kettle series that was introduced with the classic \\\"The Egg and I\\\". He appeared more times in that series than any other of the regular child stars. After the war, he had larger parts in \\\"The Boy with Green Hair\\\", \\\"Fighting Fools\\\", \\\"West of El Dorado\\\" and \\\"Blondie's Hero\\\" and appeared with Gene Autry a few times. One of the few child actors that Natalie Wood's mother allowed her to\", \"Sam Drucker man to the colorfully zany folk\\\" of Hooterville. However, like other Hootervillians, he sees nothing unusual in the fact that Fred and Doris Ziffel's \\\"son\\\" Arnold is a pig that understands spoken English language, and whose grunts and squeals are understood by others. Drucker is provincial, but fairly intelligent. He earns a modest living, and he sleeps in the back room of the general store. In the \\\"Green Acres\\\" episode \\\"Milk Machine\\\", Mr. Haney and Fred Ziffel ask Drucker for five hundred dollars to invest in a milk making machine. When he tells them he doesn't have the money, Mr.\", \"The Egg and Jerry between his eyes, typical of the time period. The cartoon's title is a play-on-words of the novel and film \\\"The Egg and I\\\". It is the first cartoon that has in the bottom right corner, \\\"In CinemaScope\\\" (with some cartoons it is elsewhere). It is the 99th \\\"Tom and Jerry\\\" cartoon ever released. A mother woodpecker leaves her nest for lunch, but an egg in the nest jumps up and falls to the ground, rolling into Jerry's mousehole. Jerry wakes up to find himself sitting on the egg. A baby woodpecker hatches and instantly takes to Jerry as his mother,\", \"Inspector Gourmet the owners to have to shut down their business. Ka-ka taking pity on the shop owner offers to help find the culprit behind the harassments. In order for Sai to agree to work for the Robert Chu Detective Agency, Bill and Ka-ka must help him find the most beautiful chicken egg for an egg waffle vendor Sai has a craving for. However the most beautiful egg is not an actual egg. Sau-na picks up a huge insurance account. If her team succeeds they will become the insurance companies exclusive private investigators. Sai and Bill are sent to investigate an injury\", \"Ma and Pa Kettle demolished in 1969 to begin construction on the Gibson Amphitheatre on the site of the set. The movie ranch appeared in other films and television series, including: Ma and Pa Kettle first appeared in supporting roles as neighbors in \\\"The Egg and I\\\", starring Fred MacMurray and Claudette Colbert as a refined city couple who move to a rural chicken farm. Marjorie Main, a veteran character actress, played a hardy country woman in dozens of films, and so was a natural for the role of Ma Kettle. Main was nominated for the Academy Award for Best Supporting Actress. After the\", \"Shelly Manne and His Men, and issued on a Contemporary LP. In later years, Manne divided his time playing the drums on, adding special percussive effects to, and sometimes writing complete scores for both film and television. He even provided a musical setting for a recording of the Dr. Seuss children's classic \\\"Green Eggs and Ham\\\" (1960) and later performed in and sometimes wrote music for the backgrounds of numerous animated cartoons. For example, he joined other notable jazz musicians (including Ray Brown and Jimmy Rowles) in playing Doug Goodwin's music for the cartoon series \\\"The Ant and the Aardvark\\\" (1969\\u20131971). Notable\", \"Robert Kapilow throughout North America. Kapilow's program has become a recurring event at New York's Lincoln Center (where Kapilow has the distinction of being the only artist to have his own series), in Boston, Los Angeles and Kansas City among other venues. As a G. Schirmer exclusive composer, Kapilow wrote the first musical setting of Dr. Seuss's \\\"Green Eggs and Ham\\\", which was premiered and recorded by the New Jersey Chamber Music Society in 1995. It has since achieved great popularity in the children's theater world, prompting Boston Globe music critic Richard Dyer to name it \\\"the most popular children's piece since\", \"The Sneetches and Other Stories books on the list. The first two stories in the book (\\\"The Sneetches\\\" and \\\"The Zax\\\") were later adapted, along with \\\"Green Eggs and Ham\\\", into 1973's animated TV musical special \\\"Dr. Seuss on the Loose: The Sneetches, The Zax, Green Eggs and Ham\\\" with Hans Conried voicing the narrator and both Zaxes respectively, and Paul Winchell and Bob Holt voicing the Sneetches and Sylvester McMonkey McBean respectively. The first story in the collection tells of a group of yellow bird-like creatures called the Sneetches, some of whom have a green star on their bellies. At the beginning of the\", \"Death Laid an Egg Anna and frame Marco, as they have discovered Marco's secret. When Marco discovers Anna's body in his hotel room, he cleans the crime scene and takes the body back to the farm to dispose of it. What Gabri and Mondaini do not know is that Marco's fixation is not with killing prostitutes, but simply hiring them to role-play murders, letting them go safely and handsomely paid. At the farm, Marco falls into a machine used to grind chicken feed in which he was trying to dispose of Anna's body. When the police arrive, having responded to the \\\"murder\\\" at the\", \"Marjorie Main \\\"great lady,\\\" as well as a great actress who donated most of her paychecks over the years to the support of a school. Main's best-known role was Ma Kettle in the Ma and Pa Kettle film series. She had renewed her contract with MGM for another seven years, which continued until the mid-1950s, when the studio loaned her to Universal Pictures to play Ma Kettle for the first time in \\\"The Egg and I\\\" (1947), starring Claudette Colbert and Fred MacMurray. Main played opposite Percy Kilbride as Pa Kettle, and was nominated for an Academy Award for Best Actress in\", \"Smart Guy seems to genuinely care about Mo and, as seen in \\\"Diary of a Mad Schoolgirl\\\", shares with him a passion for barbecuing. Mo all but lives at the Henderson's house and hates eating at his own house (having once chipped a tooth while eating oatmeal cooked by his mother, as he notes in the opening scene of \\\"Get a Job\\\"). In the season three episode \\\"That's My Momma\\\", Mo accidentally overhears a conversation between his parents Delroy and Verla Mae that they had adopted him as a baby \\u2013 this leads him to have a falling out with his parents\", \"Ham and Eggs Ham and Eggs Ham and Eggs is an animated cartoon produced by Walter Lantz, and as part of the Oswald the Lucky Rabbit series. It is the 72nd Oswald short by Lantz and the 124th in the entire series. At a bistro, Oswald works as the chef while the girl beagle serves as the waitress. Their first patron is a tall terrier who comes in for spaghetti. After finishing his meal, he slowly walks toward the cash register, pretending he would pay his bill. The tall terrier discloses that he has nothing to pay as he quickly exits the door\", \"Betty MacDonald of tuberculosis. On April 24, 1942 she married Donald C. MacDonald (1910\\u20131975) and moved to Vashon Island, where she wrote most of her books. The MacDonalds moved to California's Carmel Valley in 1956. MacDonald rose to fame when her first book, \\\"The Egg and I\\\", was published in 1945. It was a bestseller and was translated into 20 languages. Based on her life on the Chimacum Valley chicken farm, the books introduced the characters Ma and Pa Kettle, who also were featured in the movie version of \\\"The Egg and I\\\". The characters become so popular a series of nine\", \"Noe\\u0308l Wells Award for Best Comedy. As of August 2015, Wells and partner Flint Wainess are developing a sitcom pilot for Comedy Central titled \\\"Bad Couple\\\", which Wells would star in if ordered to series. Wells and Wainess are also consulting writers on the upcoming Netflix animated children's series \\\"Green Eggs and Ham\\\", based on the popular Dr. Seuss book. In March 2017, Wells wrote, directed, and starred in the feature film \\\"Mr. Roosevelt\\\", which premiered at the SXSW film festival in Narrative Spotlight. It has won multiple awards including the Audience Award and Louis Black Lone Star Jury Award at SXSW\", \"Lempel\\u2013Ziv\\u2013Storer\\u2013Szymanski length is less than the \\\"break even\\\" point. Furthermore, LZSS uses one-bit flags to indicate whether the next chunk of data is a literal (byte) or a reference to an offset/length pair. Here is the beginning of Dr. Seuss's \\\"Green Eggs and Ham\\\", with character numbers at the beginning of lines for convenience. Green Eggs and Ham is an optimal example to illustrate LZSS compression because the book itself only contains 50 unique words, despite having a word count of 170. Thus, words are repeated, however not in succession. This text takes 177 bytes in uncompressed form. Assuming a break\", \"Elmer Fudd returned decades later in the compilation film \\\"Daffy Duck's Quackbusters\\\". More recently, he also made a cameo appearance at the end of \\\"\\\" and was also given in his own story, which starred him alongside Pete Puma, in the \\\"Looney Tunes\\\" comic book. One animation history suggests that the Egghead character was based on \\\"Ripley's Believe It or Not!\\\" cartoonist and entertainer Robert Ripley, while the name Elmer Fudd might have been a reference to the then-popular hunter Elmer Keith. Egghead has the distinction of being the first recurring character created for Leon Schlesinger's Merrie Melodies series (to be followed\", \"U.S. Acres in which Wade is looking. Booker (voiced by Frank Welker): A chick named by Orson for the pig's love of books. Booker and Sheldon were still eggs when Orson found them abandoned and decided to hatch them. Booker is extremely adventurous and (over) confident despite his size. He often chases worms, but can never seem to catch them. In the comic, he often called Orson \\\"Mom.\\\" Sheldon (voiced by Frank Welker): Booker's twin brother, who decides not to hatch. He became very philosophical and introspective over the course of the strip, and began musing on his \\\"Sanctum Sanctorum\\\" (a small\", \"Wild Meat and the Bully Burgers the typical younger sibling, sometimes understanding, but most of the time tattling on Lovey and Jerry. Verva constantly compares Lovey to Cal, wondering why her older daughter can\\u2019t be like the younger one. Verva Nariyoshi - Lovey's mother. Her role is both small in the novel, as well as in Lovey's life. She smokes Parliaments and constantly tries to change the outward appearance of Lovey. She blames her daughters for not being able to give Hubert a male heir. Uncle Tora - Hubert's older brother. As a boy Tora was mean to Hubert. Later he writes Hubert a letter, apologizing\", \"Ham and Eggs and gives the bistro operators a raspberry. Nevertheless, Oswald and the girl beagle just laugh, knowing they can prevent other customers from running off. Coming in next is the boy beagle with an appetite for pancakes. As he receives his order and tries to take a nibble, the boy beagle finds the pancakes rock solid and therefore too hard to chew on, much to his disgust. He then starts tossing them around, prompting Oswald to tell him that such actions come at a price. Refusing to give a cent, the boy beagle heads toward the door. Before he could do\", \"Thomas & Friends annuals a special rosette! James Gets Cracking James is very puzzled when he is ordered to collect a load from Farmer Finney's battery farm. He didn't think batteries came from farms! Some hens run across the line and the emergency stop causes his truck to derail. James now discovers what he's really carrying \\u2013 eggs! Farmer Finney arrives with Terence and, while the mess is sorted out, cooks a delicious pancake with the eggs for James' driver and the workmen. Rings a Bell! Mavis has trouble with the trucks at the quarry when Toby has mysteriously disappeared. The sound of Toby's\", \"Joe Turner's Come and Gone be bound; that she is better off just letting him find his own path in life. Jeremy intervenes and suggests that Mattie stays with him as to cure both of their loneliness. The scene ends with Zonia and Reuben, the little boy from next door. Reuben discloses Bynum's odd tendencies to Zonia and tells her a story about his friend Eugene that used to sell pigeons to Bynum so he could use their blood in his rituals. \\\"Scene Two\\\"- It is a week later and the audience again finds Seth and Bertha eating breakfast in the kitchen. Seth is still\", \"Howard the Duck Switzler and a bizarre series of encounters follow. He battles Pro-Rata, the cosmic accountant, then meets Spider-Man at the end of the battle. He battles Turnip-Man and the Kidney Lady, then learns Quak Fu, encounters the Winky Man, a sleepwalking alter ego of Beverly's artist friend, Paul Same, who would become a series regular (and share the apartment), and becomes a wrestler. Howard and Beverly hit the road, seeking shelter in a gothic mansion where they battle a girl named Patsy and her animated gingerbread man. They eventually end up in New York City, where Howard is nominated for President\", \"Eddie Valiant of a detective story. According to \\\"\\\", Valiant tends to eat jellybeans quite a bit as he gave up drinking. In the novel \\\"Who P-P-P-Plugged Roger Rabbit?\\\", Valiant has once again vowed to no longer take any Toon cases, but is forced to do so when Baby Herman is found dead. Eddie Valiant Edward \\\"Eddie\\\" Valiant is the main protagonist of the novel \\\"Who Censored Roger Rabbit?\\\", and the film adaptation, \\\"Who Framed Roger Rabbit\\\". In the original novel \\\"Who Censored Roger Rabbit?\\\", Eddie Valiant is a fictional modern day California private detective hired by comic book star Roger Rabbit\", \"Egg of Columbus Kampf\\\", saying that \\\" lie around us in hundreds of thousands; but observers like Columbus are rare.\\\" Leo Tolstoy mentions Columbus\\u2019 egg in \\\"War and Peace\\\" after Helene explains to her spiritual guide her reasoning as to why she is not bound by her previous vows of marriage to Pierre after switching religions. \\\"The spiritual guide was astonished at this solution, which had all the simplicity of Columbus\\u2019 egg.\\\" F. Scott Fitzgerald alludes to Columbus' egg in \\\"The Great Gatsby\\\" when describing the topography of the fictional East and West eggs: \\\"They are not perfect ovals - like the egg\", \"Shade's Children begins. Gold-Eye is attracted to Ninde. At the end of the story, he has a vision showing that he and Ninde will have two children, named after Ella and Drum. Lost on a fossicking mission around a month before the novel begins, Alen's sleeping body is discovered by Ella's team as they infiltrate the Meat Factory to rescue Drum, who was caught while covering Ella and the rest of her team's retreat on a previous mission. Ninde is in favor of rescuing him, but Ella makes the difficult decision to leave him behind. Brat is a former operative of Shade\", \"Soldiers (food) like human soldiers are also available. The specific term \\\"eggs with soldiers\\\" appears to date only from the 1960s. The modern phrase first appeared in print in 1966 in Nicolas Freeling's novel \\\"The Dresden Green\\\" (where it is used to eat soup). It seems likely that it was either popularised or invented in 1965 in a series of TV Commercials for eggs starring Tony Hancock and Patricia Hayes. Soldiers (food) A soldier is a thin strip of toast; the strips that a slice is cut into are reminiscent of soldiers on parade. The toast is sliced in this manner so\", \"The Egg and I to write a book about these experiences. \\\"The Egg and I\\\" was MacDonald's first attempt at writing a book. MacDonald begins her book with a summary description of her childhood and family. Her father was an engineer, and moved frequently with his family throughout the West. Her mother's theory that a wife must support her husband in his career comes into play when the author marries a friend of her brother (\\\"Bob\\\") who soon admits that his dream is to leave his current office job and start a chicken ranch. Knowing nothing about ranching, but eager to support her husband,\", \"Green Eggs and Ham the floor of the United States Senate during his filibuster over the funding over Obamacare. Musician will.i.am has stated that his moniker is inspired by the story. On September 29, 1991, following Dr. Seuss' death earlier that week, the Reverend Jesse Jackson recited an excerpt of \\\"Green Eggs and Ham\\\" on \\\"Saturday Night Live\\\" during a special tribute segment. In 1965, a withdrawn source claimed that the book was banned in China for its \\\"portrayal of early Marxism\\\". The ban was lifted in 1991, following Seuss' death. Green Eggs and Ham Green Eggs and Ham is a children's book by\", \"Egg in the basket eggs\\\", stemming from the preparation of the dish in the actress's 1941 film \\\"Moon Over Miami\\\", although the script refers to them as \\\"gashouse eggs\\\". The dish is prepared in the 1987 film \\\"Moonstruck\\\" (by Olympia Dukakis' character). It is also prepared in a 1996 episode of the sitcom \\\"Friends\\\", by the character Joey Tribbiani, who refers to it as \\\"eggs with the bread with the hole in the middle, \\u00e0 la me!\\\" It is prepared by both Hugo Weaving and Stephen Fry's characters in the 2005 film \\\"V for Vendetta\\\", the latter referring to it as \\\"eggy in the\", \"Strangled Eggs the barnyard and obtain a chicken to eat. Foghorn believes that Henery Hawk is going to be trouble, but Miss Prissy decides she wants to adopt the pseudo-chick as her \\\"son\\\". To make peace with Miss Prissy, he consents to help Henery become a \\\"real\\\" chicken. Several gags then occur as Foghorn tries to teach Henery how to be a chicken (actually, thinly disguised attempts to kill off his foe), but such attempts are unsuccessful. Eventually Foghorn believes that if Henery is going to be a chicken, then \\\"he\\\" is going to be a chicken hawk - so he flies\", \"Albert Fish 43 years older than his mother and 75 years old at the time of his birth. Fish was the youngest child and had three living siblings: Walter, Annie, and Edwin. He wished to be known as \\\"Albert\\\" after a dead sibling and to escape the nickname \\\"Ham & Eggs\\\" that he was given at an orphanage in which he spent much of his childhood. Fish's family had a history of mental illness. His uncle suffered from mania. A brother was confined in a state mental hospital. His sister was diagnosed with a \\\"mental affliction\\\". Three other relatives were diagnosed with\", \"The Egg-pire Strikes Back And what he finds there is a giant egg that looks exactly like the one Poultra hatched out of. To Jimmy, this confirms his worst fears, but the citizens of Retroville are too turned by the Yolkians to listen to him as they think Jimmy is such a big jerk. To Jimmy's surprise, the large egg does not contain a mutant carnivore chicken, but a shower of invitations to a party hosted by the Yolkians. This gets Jimmy rejected and banned from the party for being rude to the Yolkians. King Goobot tricks Cindy into making an alliance with them\", \"Green Eggs and Ham was the result of a bet between Seuss and Bennett Cerf, Dr. Seuss's publisher, that Seuss (after completing \\\"The Cat in the Hat\\\" using 236 words) could not complete an entire book without exceeding that limit. The 50 words are: a, am, and, anywhere, are, be, boat, box, car, could, dark, do, eat, eggs, fox, goat, good, green, ham, here, house, I, if, in, let, like, may, me, mouse, not, on, or, rain, Sam, say, see, so, thank, that, the, them, there, they, train, tree, try, will, with, would, you. \\\"Green Eggs and Ham\\\" was published on August 12, 1960.\", \"Nome King in his earlier exchanges with Princess Mombi and also his Nome Messenger). Hungry for revenge, he grows to an enormous size and tries to eat the protagonists in a scene inspired by Georges M\\u00e9li\\u00e8s's \\\"The Conquest of the Pole\\\" (1912). He is eventually destroyed by ingesting the hidden Billina's chicken egg, laid in a panic by the hen (herself hiding in Jack Pumpkinhead's hollow head), since eggs are poisonous to Nomes. In Kansas, his counterpart is Dr. J.B. Worley (also portrayed by Williamson) who is a psychiatrist obsessed with machines and has an interest in electro-therapy. Dorothy was taken to\", \"Guinea pig tale of bureaucratic incompetence. Two guinea pigs held at a railway station breed unchecked while humans argue as to whether they are \\\"pigs\\\" or \\\"pets\\\" for the purpose of determining freight charges. Butler's story, in turn, inspired the \\\"\\\" episode \\\"The Trouble With Tribbles\\\", written by David Gerrold. In the Golden Hamster Saga books, two guinea pigs named Enrico and Caruso are modern-day thespians (named after Enrico Caruso) who serve as secondary characters, and often irritate the main character, Freddy Auratus, who strongly dislikes their acting antics. \\\"The Fairy Caravan\\\", a novel by Beatrix Potter, and Michael Bond's Olga da\", \"Odd Thomas (character) begins he is a 20-year-old fry cook, with a particular proficiency for pancakes, living in the fictional California town of Pico Mundo. He uses his abilities to aid the lingering spirits who seek him out. The plot of the first novel centers on Odd and his girlfriend Stormy Llewellyn's attempt to prevent a mass murder in his hometown. Odd successfully minimizes the casualties of the scheme but Stormy is killed, changing the course of Odd's life. The second novel, \\\"Forever Odd\\\" focuses on a battle of wits between Odd and an occult-obsessed seductress who has kidnapped Odd's childhood friend, hoping\", \"Elemental Gimmick Gear Angered and betrayed, Juji gets inside of a large robots, and a boss fight ensures. Upon destroying the robot and capturing Juji, EGG is awarded $1000 by the village mayor, a knight named Henry. Rots, the owner of the Eastokion Bar, is furious at Juji for an unexplained grudge that they have against each other. If EGG apologizes to Juji after Rots leaves in anger, he will begin to talk to EGG in a friendlier manner (despite being angry for what happened at The Factory) This decision made by the player will help to reveal a major plot point not\", \"Ham and Eggs goes into a frenzy. Oswald, however, is able to evade and fend off the bear's aggression. Upon bringing their unruly client down, Oswald the girl beagle put corn kernels plus a lighted oil lamp in the bear's trousers. The corn starts popping inside and the bear runs away hysterically. The cartoon concludes with Oswald singing in baritone next to his colleague which he also did in the beginning. Ham and Eggs Ham and Eggs is an animated cartoon produced by Walter Lantz, and as part of the Oswald the Lucky Rabbit series. It is the 72nd Oswald short by Lantz\", \"The Boxtrolls for membership in the city's cheese-loving council called the White Hats, despite the fact that he is severely allergic to cheese. In actuality, the Boxtrolls are peaceful and emerge from underground at night to scavenge for discarded items with which to make useful inventions. A human boy named Eggs lives among them, cared for by a Boxtroll named Fish. As Eggs grows up, he becomes dismayed by the disappearing Boxtrolls seized by Snatcher. After Lord Portley-Rind's daughter Winnie sees Eggs with two Boxtrolls, Snatcher captures Fish. Eggs sneaks to the surface to find Fish and emerges in an annual fair\", \"Follow That Egg! petition against same-sex marriage and, upon realizing the governor won't veto the bill without proof that gays don't have the skills necessary to raise a family, makes plans for the final egg check in front of the governor's office and alters the pairs so that Stan and Kyle are the 'same-sex couple'. However, when it seems that the two are able to raise the egg properly, Garrison hires an assassin named Jakartha to destroy the egg to make sure Stan and Kyle's egg is broken when they present it, which he appears to do. Kyle comes over to Stan's house\", \"Eggs (novel) loving grandmother whom he disrespects and ignores completely. In the beginning of the book, David's grandmother is taking him to the Easter Egg hunt, much to his disappointment. While hunting for eggs, he finds a beautiful girl resting underneath the leaves by some trees. When David asks if she's dead, she makes no response. He starts to talk to her about himself. He leaves, thinking that it's a dead body he's seen, and waits for a newspaper to come to express the news. It turns out that the girl he mistook for dead is Primrose, a ruthless thirteen-year-old who defies\", \"Here Comes Peter Cottontail hearing the crows from the popping bubblegum bubbles. Though Irontail tries all day to deliver eggs with unsuccessful results, he is only able to deliver one egg. Therefore, Irontail becomes the new Chief Easter Bunny, passing laws to make Easter a disaster such as having eggs painted mud brown and concrete gray, ordering the candy sculptors to make chocolate tarantulas and octopuses instead of bunnies and chicks, and having Easter galoshes instead of bonnets. Meanwhile, Peter, ashamed that his bragging and irresponsibility led to this tragedy, leaves April Valley until he meets Seymour S. Sassafras, an eccentric peddler and inventor,\", \"Egg in the basket basket\\\". It is prepared using fresh eggs by Susan Sarandon's character Marnie Minervini in the 2016 film \\\"The Meddler\\\". The dish is also portrayed in the seventh episode of season two of \\\"Lucifer\\\", where Lucifer enjoys one made with sweet bread and oyster leaves. Egg in the basket Egg in the basket\\u2014also known by many other names\\u2014is an egg fried in a hole in a slice of bread. A waffle or bagel (with a large enough hole) can also be substituted for the slice of bread. Variant names for the dish include \\\"bullseye eggs\\\", \\\"eggs in a frame\\\", \\\"egg in\", \"Dr. Seuss his well-known pseudonym Dr. Seuss, though he also authored more than a dozen books as Theo LeSieg and one as Rosetta Stone. His books have topped many bestseller lists, sold over 600 million copies, and been translated into more than 20 languages. In 2000, \\\"Publishers Weekly\\\" compiled a list of the best-selling children's books of all time; of the top 100 hardcover books, 16 were written by Geisel, including \\\"Green Eggs and Ham\\\", at number 4, \\\"The Cat in the Hat\\\", at number 9, and \\\"One Fish, Two Fish, Red Fish, Blue Fish\\\", at number 13. In the years after\", \"The Green Green Grass turned gentleman farmer. He is a frightful snob and looks down at most of the people in Oakham. Boycie made sporadic appearances in \\\"Only Fools and Horses\\\" before becoming the central character in \\\"The Green Green Grass\\\". Boycie used to be a Freemason. He is very self-centred and likes to boast about his social status back in Peckham and his money. He used to be teased by Del Boy due to his low sperm count. This is a joke that has continued into the spin-off series, along with the ongoing joke referring to an unseen affair between Marlene and Del.\", \"Brains and Eggs teenage son. Another alien, who has no obvious purpose yet, has taken on the form of Dick's brother Harry. The first discovery they all make is that none of them can swivel their heads around a hundred and eighty degrees and thus cannot lick their backs. Shortly thereafter, Dick scrounges up a job as a physics professor at nearby Pendelton State University and the aliens rent a loft apartment from Mrs. Mamie Dubcek, who remains their landlady for the rest of the series. At work, Dick becomes smitten with Dr. Mary Albright, an anthropology professor with whom he shares an\", \"The Egg and the Smurfs huge teeth, a long beard and be green-skinned. As a joke by one of his Smurfs, Papa Smurf has himself been turned into a young and ordinary Smurf while three others have adopted his appearance and claim to be the genuine article. To restore order from the chaos, Papa Smurf goes to the egg and wishes that everything be the way it used to be. Everyone is turned back to normal and, before they can renew their wishes, the egg cracks open revealing a baby chick. A Smurf reasons that the chick will become a hen and lay magic eggs.\", \"Egghead Jr. (Looney Tunes) \\\"Little Boy Boo\\\"). Foghorn would try to teach him to play games like baseball and cowboys and Indians, with the intent that he act more like a typical boy, but invariably resulting in bodily injury for Foghorn. It was previously noted that Egghead Jr. was also in the 1959 cartoon \\\"A Broken Leghorn\\\", but this was the character Junior Rooster. In 1991, Egghead Jr. appeared in the \\\"Tiny Toon Adventures\\\" episode \\\"Hog-Wild Hamton.\\\" He's Hamton J. Pig's neighbor and he doesn't like being disturbed. So when a wild party hosted by Plucky Duck takes place at Hamton's house while his\", \"Leafie, A Hen into the Wild Wanderer's death, the egg hatches into a duckling that imprints on Leafie, thinking that the hen is his mother. Leafie names him \\\"Chorok head\\\" or \\\"Greenie\\\" (\\ucd08\\ub85d\\uba38\\ub9ac, \\ucd08\\ub85d in the movie), and together they head to the glade where Wanderer asked. Leafie raises Greenie as her son and watches him grow up. Mr. Otter teaches Greenie to swim, and later enlists the help from a local bat and an owl to help a then-teenaged Greenie learn how to fly. One day, however, Greenie tries befriending some mandarin ducks who he meets, but they make fun of Leafie, who they think\", \"In Search of Dr. Seuss reading the story to his two little girls. After the story, Kathy ends up in the story of \\\"Green Eggs and Ham\\\" where she is chased by Sam I Am who tries to get her to taste the aforementioned dish. After that, Kathy ends up in the mountains where The Grinch had lived. A lady reads her the story of \\\"How the Grinch Stole Christmas!\\\". Next, Kathy shows up at the street of the lifted Lorax where she put in a payment (15 cents, a nail, and the shell of a great, great, great grandfather snail) written on paper in\", \"Ma and Pa Kettle The characters Maw and Paw (voiced by Grace Stafford and Dal McKennon) were based on the characters of Ma and Pa Kettle. The spellings of Maw and Paw Kettle appeared in the 1945 book \\\"The Egg and I\\\". Another Walter Lantz cartoon \\\"The Ostrich Egg And I\\\" (from the Maggie & Sam series) in 1956 was a spoof of \\\"The Egg and I\\\" with Maggie voiced by Grace Stafford and Sam voiced by Daws Butler. In \\\"The Munsters\\\" episode \\\"Family Portrait\\\", a magazine writer makes a reference to the Kettles when he sees the Munster home, he says: \\\"Let's see\", \"Egg Fu that \\\"Egg Fu\\\" is one of his \\\"Nine thousand and nine unmentionable names\\\", and immediately kills a guard who laughs at his mention of it. Chang Tzu reappeared in a short story published in \\\"Wonder Woman\\\" #600, where he was defeated by Wonder Woman, Power Girl, and Batgirl. In the New 52, Egg Fu appeared in \\\"Harley Quinn Annual\\\" #1. In the comic, he is Edgar Fullerton Yeung, a giant egg scientist who is secretly experimenting on Poison Ivy at Arkham Asylum. However, he ends up reforming with the help of Harley Quinn and gets himself a job. Egg Fu\", \"Rob Paulsen The Duck Avenger in \\\"\\\". Rob also voiced Alfredo Fettuccini, Bob the Ghost Pirate, Lookout and Ghost Priest in \\\"The Secret of Monkey Island: Special Edition\\\". He voiced the Fox and the Mouse in the \\\"Green Eggs and Ham\\\" PC game. He also voiced Tlaloc in \\\"Tak and the Power of Juju\\\". Most recently he has voiced The Riddler in \\\"\\\", a role he reprised in \\\"\\\". Rob is the voice of talking alien dog Beak-Beak in \\\"Armikrog\\\". Paulsen also voices Smash Hit in\\\" \\\" and \\\"\\\". Paulsen is also the off-camera voice of the syndicated television series \\\"Funniest Pets\", \"Ham on Rye place at home, at his different schools, at the doctor's office (for his never-ending acne treatments) and at various other locales around town. The novel focuses on the protagonist, Henry Chinaski, between the years of 1920 and 1941. It begins with Chinaski's early memories. As the story progresses the reader follows his life through the school years and into young adulthood. Chinaski relates that he has an abusive father, and his mother does nothing to stop his father's abuse. She is, in fact, a victim of her husband's brutality as well. Henry is not athletic but wants to be and\", \"Otherwise Known as Sheila the Great family's adoption of a puppy, and realizes that she enjoyed her vacation after all. Sheila is also a character in the \\\"Fudge\\\" series. Other than brief anecdotes, Peter and his dog, Turtle, are the only members of the Hatcher family to appear in the book. They appear in the first chapter, and are mentioned by Sheila several times in the story. She also briefly mentions Peter's brother, Fudge, though not by name. Sheila Tubman-the main protagonist who's excited about being in Tarrytown for the summer, but has many fears, and has a hard time admitting those fears to people Libby\", \"Arnold Ziffel suspicious of Oliver, because of his inability to communicate with Arnold. This dynamic is part of a larger theme of \\\"Green Acres\\\", that Oliver's sense of logic is meaningless in the Hooterville universe. Arnold can do pretty much anything a human can. He can write his name and change channels on the television. He watches the \\\"CBS Evening News with Walter Cronkite\\\" to keep up with the issues. He signs checks and can adjust the TV antenna, and he is the smartest student at the local grade school. He carries his lunchbox in his mouth, and often plays practical jokes\", \"Thomas Tomone while he is eating them that he first meets Pandora and Effy, with whom he shares the doughnuts. After Pandora eats too many and is violently sick, he and Effy carry her home, to discover that Effy's mother is having an affair with her husband's boss. Later on, Thomas is woken in his apartment by Johnny White and his minions, who reveal that Thomas is, in fact, using their apartment without their permission. They demand that Thomas pay them back, or else risk eviction, with Johnny drinking some boiling pot noodles to threaten him. Thomas finds employment at Roundview College,\", \"Nathan West (General Hospital) very much and there are several aspects of that family that make Nathan who he is. Paevey said that during the audition process when the character was being formed, he noticed that he and the character had a lot in common. Since the character's introduction, it has been very clear that \\\"he's a law-abiding, true-seeking good guy.\\\" Paevey immediately rejected the notion that Nathan could have a dark side because of his genetics \\u2013 \\\"I don't think he's the type\\\" Paevey insisted. \\\"Sometimes a good egg comes out of a bad nest.\\\" According to Paevey, Nathan is not \\\"morally ambiguous.\\\"\", \"Green Acres old-fashioned farmer who was born during the Grover Cleveland administration. Everything about him is \\\"no-nonsense\\\", except for the fact that his \\\"son\\\" is a pig. Arnold Ziffel is a pig whom the Ziffels treat as a son, understands English, lives indoors, and is pampered. Everyone understands Arnold when he grunts, as if he were speaking English, except Oliver. He is an avid TV watcher and a Western fan, attends the local grade school (carrying his book pack in his mouth), and signs his own name on paper. Only Oliver seems cognizant that Arnold is just livestock, although he frequently slips\", \"Filburt for a period of time. Filburt and Dr. Paula Hutchison have four children: Gilbert, Shellbert, Norbert, and Missy who all came from the same egg. Gilbert and Shellbert look exactly like Filburt while Missy is a miniature version of Hutchison. Norbert, on the other hand, has Filburt's eyes but oddly resembles Heffer, who took on the job of \\\"egg-sitting\\\" (literally, sitting on the egg to keep it warm) for Filburt when they realized Filburt's rear was too hard for the task. Norbert also often thinks that Rocko is his father. Filburt holds the record number of jobs in the series:\", \"Breakfast on Pluto (film) strongly implied, but Kitten is not shown being overtly sexual with anyone on screen. Kitten's flirtatious relationships with the series of male characters she meets throughout the film are never shown or strongly implied to have been consummated, leaving the yearning main character unrequited. The seaside scene between Kitten and Bertie was considered by some to be an allusion to director Jordan's earlier film \\\"The Crying Game\\\", which also involved a transgender major character, the IRA, and actor Stephen Rea. In \\\"The Crying Game\\\", Rea's character doesn't realize that the woman he has fallen for and becomes sexually involved with\", \"Percy Kilbride appeared in \\\"The Egg and I\\\", starring Fred MacMurray and Claudette Colbert as a sophisticated couple taking on farm life. Main and Kilbride were featured as folksy neighbors Ma and Pa Kettle, and audience response prompted the popular \\\"Ma and Pa Kettle\\\" series. Pa Kettle became Kilbride's most famous role: the gentle-spirited Pa seldom raised his voice, and was always ready to help friends\\u2014by borrowing from \\\"other\\\" friends, or assigning any kind of labor to his Indian friends Geoduck and Crowbar. Kilbride retired after filming \\\"Ma and Pa Kettle at Home\\\" in 1953. ; although it was the final film\", \"Columbo (character) the scene of the crime or even while interviewing a suspect. He generally produces the egg from his raincoat pocket, before seeking a hard surface upon which to break its shell; in \\\"A Stitch in Crime\\\" he uses a piece of evidence found at the murder scene. He prefers to eat the egg salted, stating in \\\"Lovely but Lethal\\\" that he usually carries a shaker of salt in his pocket. Columbo's first name is never explicitly mentioned during the series. Even the opening credits just simply read, \\\"Peter Falk as Columbo\\\". When asked, Columbo always emphatically answers \\\"Lieutenant\\\". In the\", \"The Yolk's on You farm. Foghorn Leghorn decides that Miss Prissy lays the turquoise Easter eggs. He also tells her to think \\\"egg-shape\\\". Prissy tries but she lays a golden egg instead. She rolls away the golden egg and soon Sylvester and Daffy find it. The two both try to get it for themselves including the ancient Chinese tickle torture but at the end they accidentally put the golden egg on the fresh egg farms. Daffy whispers to Sylvester to get the egg for one last chance. This episode was Foghorn's Leghorn's first appearance since \\\"False Hare\\\" (1964), sixteen years earlier. The Yolk's on\", \"Fried Green Tomatoes at the Whistle Stop Cafe who goes weekly with her husband to visit his mother in a nursing home. On one visit, Evelyn befriends Ninny Threadgoode, another resident of the same home, who tells Evelyn stories of her youth in Whistle Stop in the 1920s. Between subsequent visits, Evelyn assumes the protagonists of these stories as role models. According to Ninny, she was an orphan raised by the Threadgoodes, and eventually married one of their sons; but the principal character throughout her story is the youngest daughter, Idgie (Imogene) Threadgoode: an unrepentant tomboy, became reclusive after her brother, Buddy, was killed on the railway. Ruth\", \"Wild Meat and the Bully Burgers for how he treated him as a child. Tora is the one that tells Hubert about the Nariyoshi's family heritage, stemming from Samurai in Russia. Tora plays a bigger role towards the end when he comes back and helps the family after the accident. Larry - Jerry's violent older brother. He is always physically hurting Lovey, Jerry, and Cal. His girlfriend, Crystal, gets pregnant twice by him. Crystal Kawasaki \\u2013 the pretty girlfriend of Larry. She is both beautiful and smart and Verva hires her to tutor Lovey and Cal. The first time Crystal gets pregnant, her mother takes her\", \"Jim Halpert back because she was too \\\"dorky\\\" for him, Jim is inspired to dress up in his Popeye costume with Cece dressed as Swee'Pea for her (who is dressed as Olive Oyl), which he was initially reluctant to do, marking the first time he is seen dressed up in an actual costume. In \\\"Classy Christmas\\\", Jim falls victim to numerous snowball-themed pranks devised by Dwight. Jim is humiliated by being forced to feed Dwight a pizza and a beer in \\\"Viewing Party\\\" in order to get him to get Cece to sleep for the night. His sales skills remain strong, when\", \"Jungle Emperor Leo to the source of the Moonlight Stone so it can be salvaged and used to help humanity. Ham Egg, however, is only interested in money, but is soon persuaded by Dr. Plus who is well aware of Ham Egg's illegal poaching activities. Ham Egg agrees to work for them, but demands to be put in charge of the search. Accompanying Ham Egg is Mr. Lemonade of the organization, and Dr. Moustache who is already stationed in the jungle. When they arrive in the jungle, Dr. Moustache and Mr. Lemonade are shocked by Ham Egg and his \\\"friends\\\" who want only\", \"Thomas Elphinstone Hambledon himself to be the titular hamburger chef character of the animated series \\\"Bob's Burgers\\\". (Both titular characters are voiced by H. Jon Benjamin.) The \\\"daily special\\\" on the menu board is \\\"Thomas Elphinstone Hambledurger with Manning Coleslaw\\\". Thomas Elphinstone Hambledon Thomas Elphinstone Hambledon (Tommy Hambledon) is the fictional protagonist of many spy novels written by the British author \\\"Manning Coles\\\" (actually the two-person writing team of Adelaide Frances Oke Manning and Cyril Henry Coles) from 1940 through 1963. He works for a department of the Foreign Office, usually referred to in the novels as \\\"MI5\\\" (counter-intelligence), although in the earliest\", \"Richard Long (actor) mind and put him in \\\"The Dark Mirror\\\" (1946), directed by Robert Siodmak. International Pictures merged with Universal Pictures, who took over Long's contract. His fourth film was \\\"The Egg and I\\\" (1947), playing Tom Kettle, the eldest son of Ma and Pa Kettle, the characters played by Marjorie Main and Percy Kilbride. The movie was a huge hit \\u2013 so much so that Universal decided to spin off the Kettles into their own series. Long signed a contract with Universal, for which he appeared in \\\"Tap Roots\\\" (1948) and \\\"Criss Cross\\\" (1949), playing Burt Lancaster's brother in the latter\", \"Green Acres who strives to make sense of his oddball surroundings. There seems to be a dual perspective of reality: Oliver versus everyone else. The latter encompasses the Hootervillians, Oliver's high-maintenance wife Lisa and his affluent mother (Eleanor Audley), who lampoons him for his agricultural pipe-dreams. Such dual realism is at its best when everyone but Oliver can see the TV screen credits, when he corrects Lisa's mangled mispronunciations only to find that he is the only one in town with the correct usage, and when all but Oliver can translate Arnold the pig's grunts and snorts into English. Among Oliver's ongoing\", \"Dizzy (series) Island Dizzy\\\" and refined in \\\"Fantasy World Dizzy\\\". Collectable items were also first introduced in \\\"Treasure Island Dizzy\\\" in the form of coins; later games retained the idea but often used other items such as diamonds or cherries. A health bar was first introduced in \\\"Magicland Dizzy\\\", allowing Dizzy to be hit without dying. The main protagonist and player character for the series is the eponymous Dizzy, an anthropomorphic egg with big eyes, a smiley face, boxing gloves and minimal identifying features. Named for the way he somersaults and rolls around the screen, Dizzy's design was kept deliberately simple. The\", \"Otto Malpense even proven in the first book, Higher Institute of Villainous Education, when they were talking to each other, and Otto claimed that she was a pretty red-haired Scottish girl with some of the most striking green eyes he has ever seen. Otto's name comes from his experiment number 0110 given to him by Overlord (something he found ironic). Besides mentioning that Otto's name came from his friend's cat, Mark Walden has made no announcements on the origin of Otto's characteristics. Otto Malpense Otto Malpense is the main character of the H.I.V.E. series of books by Mark Walden. Otto is the\", \"Brains and Eggs interest was to be a secretary, but, after the first pilot, it became apparent to the show's producers that the aliens bounced off everything and that \\\"straight\\\" comic foils were required. The love interest was subsequently rewritten to be a fellow professor and a new character, Nina Campbell, became the secretary. Nina was added since it was thought that an \\\"edge in the office\\\" was needed. Jane Curtin and Simbi Khali joined the cast two weeks after the rest of the regulars. Brains and Eggs \\\"Brains and Eggs\\\" is the pilot episode of the American sitcom \\\"3rd Rock from the\", \"Jack Lemmon ham, a fine ham, and with ham you have to trim a little fat.\\\" The biography quotes Lemmon as saying, \\\"I am particularly susceptible to the parts I play... If my character was having a nervous breakdown, I started to have one.\\\" He enjoyed longtime working relationships with both Blake Edwards, starring in \\\"Days of Wine and Roses\\\" (1962), \\\"The Great Race\\\" (1965) and \\\"That's Life!\\\" (1986), and Richard Quine, starring in \\\"My Sister Eileen\\\", \\\"Operation Mad Ball\\\", \\\"Bell, Book and Candle\\\", \\\"It Happened to Jane\\\", and \\\"How to Murder Your Wife\\\". Quine also directed Lemmon's screen test when the\", \"Elemental Gimmick Gear Maximum was \\\"a weakling\\\" upon his loss in the battle. Juji offers EGG to be his new right-hand man, and tells him to go to The Factory to meet up with him. Although EGG knows that his goal is to find Selen, and that Juji is an enemy, EGG agrees so he can get access to The Factory. On the top floor of The Factory, EGG is able to find Selen, and he proceeds to find Juji in the next room. Although Juji treats him in a friendly manner, EGG shows him his true feelings, wanting payback for kidnapping Selen.\", \"The Egg-pire Strikes Back for the teachers at Jimmy's school (Ooblar takes over the class of Jimmy's teacher, Miss Fowl) and finally, King Goobot moving in with Jimmy and his family, much to Jimmy's anger and disgust. As more and more of the townsfolk refuse to listen to him, Jimmy finally puts a recording of the Yolkians through his lie detector and he finds out it is, in fact, a big lie, and that they're just at evil as ever. He runs to tell his Mom and Dad and then he finds out that they have gone to the Retroville park for an announcement.\", \"Arnold Ziffel their cover of Black Sabbath's \\\"Sabbath Bloody Sabbath\\\" to Arnold Ziffel on their \\\"I'm the Man\\\" EP. Arnold Ziffel Arnold Ziffel was a pig featured in \\\"Green Acres\\\", an American situation comedy that aired on CBS from 1965 to 1971. The show is about a fictional lawyer, Oliver Wendell Douglas, and his wife, Lisa - city-dwellers who move to Hooterville, a farming community populated by oddballs. Arnold is a pig of the Chester White breed, but is treated as the son of farmer Fred Ziffel and his wife, Doris, a childless couple. Everyone in Hooterville (besides Oliver Douglas) accepts this\", \"Black Foxes of Holly, travels with his best friend to see Tyrone. Grundy, tries to keep both of them a secret, but Tyrone soon finds out. Tyrone is pleased to find his long lost son and their adventure comes to an end. Tyrone - is the main character in the story. He is a rich lord who has his father's legacy and enough money to last him several lifetimes. Oscar - is Tyrone's best and most loyal friend. In section 2 he is married with Silke, and in section 3, he has died. Silke - hated by Tyrone by the 1st section,\", \"Laughing Gas (novel) vengeance: He has sworn to (literally) \\\"poke\\\" all the unpleasant people around him \\\"in the snoot\\\", starting with his press agent and the director of a recent film of his. He also enters the Brinkmeyer estate and pushes Miss Brinkmeyer into the swimming pool. Wherever he goes, eyewitnesses describe him as looking like a gorilla. (Fair-haired Reggie Havershot admits earlier on in the novel that he is not particularly handsome.) On the other hand, wherever Eggy (whose complexion, especially in the morning, is described as \\\"greenish\\\") meets Reggie in Joey's body, he thinks his drinking habits have got the better\", \"Peggotty (1935), and Karen Caspersen (1922). A big and simple fisherman and boatbuilder, Ham Peggotty is the orphaned nephew of Clara and Daniel Peggotty and is the fianc\\u00e9 of Emily, to whom he became engaged on the visit of David Copperfield and Steerforth to the boat house at Great Yarmouth. He drowns trying to rescue Steerforth during a storm at sea off Yarmouth. \\\"He was a huge, strong fellow of six feet high, broad in proportion, and round-shouldered; but with a simpering boy's face and curly light hair that gave him quite a sheepish look. He was dressed in a canvas\", \"Egg Fu appears throughout the DC Rebirth iteration of the \\\"Harley Quinn\\\" series. Egg Fu Egg Fu is a fictional character appearing in DC Comics publications and related media, commonly as an adversary for the superheroine Wonder Woman. Most frequently represented as an enormous sentient egg (and often, inexplicably, of Chinese descent), he was created by Robert Kanigher and Ross Andru where he first appeared in \\\"Wonder Woman\\\" #157. Over the years, multiple versions of the character have appeared with varying backstories and alternative names (including Egg Fu the Fifth, Chang Tzu, and Dr. Yes) to battle not only Wonder Woman, but\", \"Melissa Duck unhappily married to his dominant wife, Mrs. Daffy Duck, who appears identical to her husband but with a brimmed hat and a skirt. She seeks a divorce from him in the court of Judge Porky Pig as he lost their egg after commanding him to sit on it. In the end, however, Daffy proves the egg is not lost and it hatches into a small black duckling named Junior who ends the cartoon by dismissing the case. In \\\"The Stupid Cupid\\\" (1944), Daffy avoids being targeted by Cupid (played by Elmer Fudd) as he is still suffering from their last\"], \"pos_scores\": [99.375], \"neg_scores\": [89.75, 92.0, 85.625, 92.875, 91.0625, 85.25, 87.0, 85.6875, 83.9375, 85.5, 85.1875, 83.875, 86.3125, 85.125, 86.4375, 84.625, 87.8125, 84.25, 85.5, 86.0, 84.0, 84.5625, 85.1875, 85.8125, 87.625, 84.3125, 84.6875, 85.8125, 88.625, 87.3125, 86.4375, 86.4375, 85.5, 85.125, 85.5, 89.5, 84.25, 84.75, 84.375, 85.375, 85.375, 84.75, 85.0, 85.625, 92.125, 85.75, 86.0625, 84.9375, 84.6875, 94.625, 85.25, 84.6875, 85.125, 83.8125, 87.625, 87.0, 84.375, 87.75, 87.0, 86.0625, 88.5, 84.0625, 86.125, 84.4375, 85.0, 84.875, 89.375, 84.0, 84.4375, 86.9375, 85.4375, 85.75, 85.375, 83.875, 84.25, 86.0, 85.375, 84.4375, 83.8125, 85.125, 84.75, 85.3125, 85.0625, 85.75, 84.0625, 85.0625, 84.5, 85.4375, 84.625, 84.9375, 85.125, 85.125, 83.875, 84.5625, 86.5625, 84.1875, 85.5, 84.8125, 84.0, 84.1875], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"who played charlie bucket in the original charlie and the chocolate factory\", \"pos\": [\"Peter Ostrum on Call\\\", funded by Pfizer, highlighting the work of large animal veterinarians. Peter Ostrum Peter Gardner Ostrum (; born November 1, 1957) is an American veterinarian and former child actor whose only film role was as Charlie Bucket in the 1971 motion picture \\\"Willy Wonka & the Chocolate Factory\\\". Ostrum was 12 years old when selected by talent agents for \\\"Willy Wonka\\\". Though he enjoyed the experience of shooting the film, he opted not to sign a three-film contract when it was over. After eschewing a career in film and theatre, Ostrum became reluctant to speak about his one starring\"], \"neg\": [\"Freddie Highmore Outstanding Performance by a Male Actor in a Supporting Role. In 2005, he portrayed the main role of Charlie Bucket in Tim Burton's musical fantasy film \\\"Charlie and the Chocolate Factory\\\", adapted from the book of the same name by Roald Dahl. He was reportedly recommended by co-star Johnny Depp, with whom Highmore had worked in \\\"Finding Neverland\\\"; Depp had been impressed by the young actor's performance and thus put his name forward for the role. Highmore had not seen the original 1971 version of the film, and decided not to see it until he was done filming so his\", \"Peter Capell (1971), in which he played the minor role of a \\\"tinker\\\" who spoke to Charlie Bucket (Peter Ostrum) at the gates of Willy Wonka's chocolate factory during the first few minutes of the film. Capell died in Munich, West Germany on March 3, 1986, aged 73. No cause of death was announced. Peter Capell Peter Capell (3 September 1912 \\u2013 3 March 1986) was a German actor who was active on screen from 1945 until 1985. Apart from a lengthy film career, he appeared in many television series and mini-series. He appeared in many old time radio programs including the\", \"Charlie and the Chocolate Factory (film) Charlie and the Chocolate Factory (film) Charlie and the Chocolate Factory is a 2005 musical fantasy comedy film directed by Tim Burton and written by John August, based on the 1964 British novel of the same name by Roald Dahl. The film stars Johnny Depp as Willy Wonka and Freddie Highmore as Charlie Bucket. The storyline follows Charlie, who wins a contest and, along with four other contest winners, is led by Wonka on a tour of his chocolate factory, the most magnificent in the world. Development for a second adaptation of \\\"Charlie and the Chocolate Factory\\\" (filmed previously as\", \"Charlie and the Chocolate Factory (franchise) the same name by Roald Dahl. The film was directed by Tim Burton. The film stars Freddie Highmore as Charlie Bucket and Johnny Depp as Willy Wonka. The storyline concerns Charlie, who takes a tour he has won, led by Wonka, through the most magnificent chocolate factory in the world. Development for another adaptation of \\\"Charlie and the Chocolate Factory\\\", filmed previously as \\\"Willy Wonka & the Chocolate Factory\\\", began in 1991, 20 years after the first film version, which resulted in Warner Bros. providing the Dahl Estate with total artistic control. Prior to Burton's involvement, directors such as Gary\", \"Leonard Stone the father of Golden Ticket winner Violet Beauregarde, in \\\"Willy Wonka & the Chocolate Factory\\\". He was the last surviving adult character who toured the factory in the movie; however, Diana Sowle, who played Mrs. Bucket, lived until October 2018. In 1973\\u2019s \\\"Soylent Green\\\" he played Charles, the manager of the building where the murdered character portrayed by Joseph Cotten lived. He was the bartender in \\\"The Shakiest Gun in the West\\\" (1968), and a congressman in \\\"\\\" (1972), which starred James Earl Jones as the first black president of the United States. He appeared in the Jerry Lewis vehicle\", \"Charlie and the Chocolate Factory (film) \\\"Willy Wonka & the Chocolate Factory\\\" in 1971) began in 1991, which resulted in Warner Bros. providing the Dahl Estate with total artistic control. Prior to Burton's involvement, directors such as Gary Ross, Rob Minkoff, Martin Scorsese and Tom Shadyac had been involved, while actors Bill Murray, Nicolas Cage, Jim Carrey, Michael Keaton, Brad Pitt, Will Smith, Adam Sandler, and many others, were either in discussion with or considered by the studio to play Wonka. Burton immediately brought regular collaborators Depp and Danny Elfman aboard. \\\"Charlie and the Chocolate Factory\\\" represents the first time since \\\"The Nightmare Before Christmas\\\" that\", \"Tim Burton adaptation of the book of the same name by Roald Dahl. Starring Johnny Depp as Willy Wonka, Freddie Highmore as Charlie Bucket and Deep Roy as the Oompa-Loompas, the film generally took a more faithful approach to the source material than the 1971 adaptation, \\\"Willy Wonka & the Chocolate Factory\\\", although some liberties were taken, such as adding Wonka's issue with his father (played by Christopher Lee). \\\"Charlie and the Chocolate Factory\\\" was later nominated for the Academy Award for Best Costume Design. The film made over $207 million domestically. Filming proved difficult as Burton, Depp, and Danny Elfman had\", \"Charlie and the Chocolate Factory (film) Elfman contributed to a film score using written songs and his vocals. Filming took place from June to December 2004 at Pinewood Studios in the United Kingdom. \\\"Charlie and the Chocolate Factory\\\" was released to positive critical reception and was a box office success, grossing $475 million worldwide. Charlie Bucket (Freddie Highmore) is a poor boy who lives near the Wonka Candy Company. The company's owner, Willy Wonka (Johnny Depp), has for long closed access to his factory due to problems concerning industrial espionage that led him to fire all his employees, among them Charlie's Grandpa Joe (David Kelly). One\", \"Diana Sowle Diana Sowle Diana Mae Sowle (n\\u00e9e Laumer; June 19, 1930 \\u2013 October 19, 2018) was an American actress of film and theater and voice artist, best known for playing Mrs. Bucket (Charlie Bucket's mother) in the 1971 film \\\"Willy Wonka & the Chocolate Factory\\\" and her performance of \\\"Cheer up Charlie\\\" in that film, although her voice in the song was dubbed by Diana Lee. Sowle joined the cast while in Germany, where it was filmed. At the time of her death in 2018, she was the last surviving parent from the film. Sowle's death left three remaining adult cast\", \"Charlie and the Chocolate Factory (film) 1971 film for the purpose of money. Depp said he was disappointed by Wilder's comment, and responded that the film was not a remake, but a new adaptation of Dahl's 1964 book. The casting calls for Charlie Bucket, Violet Beauregarde, Veruca Salt, and Mike Teavee took place in the United States and United Kingdom, while Augustus Gloop's casting took place in Germany. Burton said he sought actors \\\"who had something of the character in them\\\", and found Mike Teavee the hardest character to cast. Burton was finding trouble casting Charlie, until Depp, who had worked with Freddie Highmore on \\\"Finding\", \"Lowville, New York jointly owned by Iberdrola Renewables and EDP Renewables North America (formerly Horizon Wind Energy). Peter Ostrum, who played the role of Charlie Bucket in \\\"Willy Wonka and the Chocolate Factory\\\" lives in and practices Veterinary Medicine in Lowville. According to the United States Census Bureau, the town has a total area of 38.1 square miles (98.7 km\\u00b2), of which 37.8 square miles (97.9 km\\u00b2) is land and 0.3 square mile (0.8 km\\u00b2) (0.84%) is water. New York State Route 177 ends at New York State Route 12 in Lowville. West-south highways New York State Route 26 and New York State\", \"Nigel Planer of Wilbur in Manchester and Leeds. He also featured in \\\"Doctor Who: Live\\\" touring the UK, as Vorgenson The Inter-Galactic Showman, before appearing in Pantomime as Captain Hook at the Lyceum Theatre in Sheffield. Planer went on to star as Grandpa Joe in the original production of \\\"Charlie and the Chocolate Factory\\\", which opened in London's West End in 2013. Planer has appeared in films, including \\\"Flood, Virgin Territory, Bright Young Things, Hogfather, The Colour of Magic, The Wind in the Willows, Land Girls, Clockwork Mice, Carry on Columbus, Brazil, The Supergrass\\\", \\\"I Give It a Year\\\", \\\"The Apple Picker\\\"\", \"Marilyn Manson soundtrack album. In July 2005, Manson told \\\"Rolling Stone\\\" that he was shifting his focus from music to filmmaking \\u2013 \\\"I just don't think the world is worth putting music into right now. I no longer want to make art that other people \\u2013 particularly record companies \\u2013 are turning into a product. I just want to make art.\\\" Johnny Depp reportedly used Manson as his inspiration for his performance as Willy Wonka in the film \\\"Charlie and the Chocolate Factory\\\". Manson himself expressed interest in playing the role of Willy Wonka in the film. He had been working on\", \"Caspar Phillipson from English-language films into Danish, including revoicing Johnny Depp as Willy Wonka in \\\"Charlie and the Chocolate Factory\\\" (2005). Phillipson has appeared in Scandinavian productions for the screen, including the television series \\\"The Bridge\\\" and \\\"Borgen\\\". Phillipson portrayed John F. Kennedy in \\\"Jackie\\\" (2016), his first role in an English-language film. He first auditioned for the part by video from Istanbul, where he was appearing in a stage production of \\\"Hamlet\\\". To audition in-person for \\\"Jackie\\\" in Paris, Phillipson claimed sick leave from a Danish stage production called \\\"Don't Touch Nefertiti\\\", missing five sold-out performances in a role that had\", \"Charlie and the Chocolate Factory (musical) recently closed Broadway version (instead of the West End one) and will feature only 4 child actors who will alternate the role of Charlie. The remainder of the \\\"child\\\" characters will be played adult actors. On 13 October the primary cast was announced and includes U.S. actor Paul Slade Smith (who played Grandpa George in the original cast of Charlie on Broadway) as Willy Wonka alongside Australian actors Tony Sheldon as Grandpa Joe and Lucy Maunder as Mrs Bucket. The role of Charlie will be shared between Tommy Blair, Ryan Yates, Xion Jarvis and Oliver Alkhair.. The play opens with\", \"Emily Padgett Emily Padgett Emily Padgett (born September 20, 1984) is an American actress, singer, and dancer. She is known for her work on Broadway as Daisy Hilton in \\\"Side Show\\\" and Sherrie Christian in \\\"Rock of Ages\\\", as well as for originating the roles of Lucy Grant in Steve Martin and Edie Brickell's \\\"Bright Star\\\", and Mrs. Bucket in Charlie and the Chocolate Factory. Padgett was born in Danbury, Connecticut, but moved to Lewisville, North Carolina in 1986 when she was two. Her love for musical theatre started at an early age when her parents would go to New York City\", \"David Morris (actor) David Morris (actor) David Cedric Morris (11 September 1924 \\u2013 29 October 2007) was an English painter and actor, perhaps best known for his role as Grandpa George in \\\"Charlie and the Chocolate Factory\\\" (2005). He made his debut as a professional actor at the age of 79. Morris was born in Folkestone, Kent. He won a choral scholarship to Magdalen College School, Oxford, at the age of nine. He went on to read English at Magdalen College at the University of Oxford. His tutor was C. S. Lewis. During World War II, his brother was killed in North Africa.\", \"Jack Albertson to win. Albertson appeared as Charlie Bucket's Grandpa Joe in \\\"Willy Wonka & the Chocolate Factory\\\" (1971), and in \\\"The Poseidon Adventure\\\" (1972), where he played Manny Rosen, husband to Belle, played by Shelley Winters. Albertson said that his one regret was that he did not reprise his role in the movie version of \\\"The Sunshine Boys\\\". When producer Ray Stark acquired the film rights from Neil Simon in 1973, Albertson was expected to play the part, but by the time MGM had bought the rights in 1974 and was preparing to begin filming in February 1975, Albertson was not\", \"Kraig Thornber \\\"Arrangements for War\\\" and Lord Paranesh in \\\"The Draconian Rage\\\". Thornber also works as Co-Director and Choreographer for the extraordinary all-singing, all-dancing comedy string-quartet Bowjangles. Kraig Thornber Kraig Thornber (born 1961) is a British actor, singer and choreographer best known for playing the handyman Riff Raff in \\\"The Rocky Horror Show\\\" and Grandpa George in the musical \\\"Charlie and the Chocolate Factory\\\". He is a former member of the National Theatre. Born as Craig Thornber in Leicester in 1961, the son of Terence A Thornber and Sheila (n\\u00e9e Baxter), Kraig 'Pix' Thornber attended Bosworth Community College in his native Leicester\", \"Richard Dempsey Bottom. This was followed by a year playing Mr Bucket in Sam Mendes West End production of \\\"Charlie and the Chocolate Factory\\\" at The Theatre Royal Dury Lane, also appearing as Willy Wonka. He played The Comp\\u00e8re In the North American Premiere Of The WYP production of Baz Luhrmann\\u2019s Strictly Ballroom at The Prince of Wales Theatre, Toronto directed by Drew McOnie In Rob Ashford's Chichester Festival Theatre production of \\\"A Damsel In Distress\\\" he played Reggie Byng. \\\"absolutely pitch perfect in every way. The stage lights up when he enters, and he lands every line with skilful precision. This\", \"Philip Wiegratz Philip Wiegratz Philip Wiegratz (born February 17, 1993) is a German actor, whose filmography consists of adaptations of English and German children's books. His first film role was playing Augustus Gloop in Tim Burton's \\\"Charlie and the Chocolate Factory\\\". Wiegratz was born in Magdeburg, Germany. He is known for his 2005 portrayal of the greedy Augustus Gloop in \\\"Charlie and the Chocolate Factory.\\\" He went to a casting call not being sure of receiving any particular part, but caught the attention of a casting director and was placed on a shortlist for the role of Augustus. Wiegratz returned for several\", \"Charlie and the Chocolate Factory (film) shades of chocolate were tested before Burton settled on the proper hue. The original music score was written by Danny Elfman, a frequent collaborator with director Tim Burton. Elfman's score is based around three primary themes: a gentle family theme for the Buckets, generally set in upper woodwinds; a mystical, string-driven waltz for Willy Wonka; and a hyper-upbeat factory theme for full orchestra, Elfman's homemade synthesizer samples and the diminutive chanting voices of the Oompa-Loompas. Elfman also wrote and performed the vocals for four songs, with pitch changes and modulations to represent different singers. The lyrics to the Oompa-Loompa songs\", \"Willy Wonka Wonka exudes none of the gravity required for the role. It's as though he didn't take the role seriously. Rather than an intimidating candyman teaching brats a lesson, this Wonka is simply a freak.\\\" Depp received a nomination for the Golden Globe Award for Best Actor in a Musical or Comedy for his role as Willy Wonka, but lost to Joaquin Phoenix as Johnny Cash in \\\"Walk the Line\\\". Willy Wonka Willy Wonka is a fictional character who appears in Roald Dahl's 1964 children's novel \\\"Charlie and the Chocolate Factory\\\" and its sequel \\\"Charlie and the Great Glass Elevator\\\". In\", \"Kraig Thornber (Pocket Opera); \\\"Loot\\\" (Watford Palace Theatre); \\\"The Gambler\\\" (Everyman Theatre, Liverpool) and Grandpa George in \\\"Charlie and the Chocolate Factory\\\" (2015\\u201317) at the Theatre Royal, Drury Lane. His film and television appearances includes \\\"Dog-Boy\\\" in \\\"Liquid Television\\\" (1992); \\\"True Crimes\\\"; Brian Osbourne/Van Driver in \\\"The Bill\\\" (1988\\u20132004); \\\"A Thing Called Love\\\" (2004); \\\"Crimewatch File\\\" (BBC); Riff Raff in \\\"The Rocky Horror Tribute Show\\\" (2006); Bar Sub Con in \\\"Inception\\\" (2010) and Gulag Prisoner in \\\"Muppets Most Wanted\\\" (2014). As a dancer he has performed in the films \\\"Gulliver's Travels\\\" (2010) and \\\"Alice Through the Looking Glass\\\" (2016). Thornber's choregraphy and\", \"Peter Ostrum who were searching nationwide for the actor to portray Charlie Bucket in \\\"Willy Wonka & the Chocolate Factory\\\". The agents took Polaroid photos of Ostrum and recorded him reading from the original novel, then returned to New York. Two months later Ostrum was called to New York for a screen test where he sang \\\"My Country, 'Tis of Thee\\\", and a month after that he was contacted and given ten days to prepare to leave for filming. Ostrum left for Munich on August 10, 1970. In 2000, Ostrum recalled that shooting \\\"Willy Wonka\\\" in Munich was \\\"sort of like being\", \"Eugene Pidgeon his interpretation of the Ronald Dahl Classic, \\\"Charlie and the Chocolate Factory\\\" with Johnny Depp and the dwarf actor Deep Roy as the single OOMPA-LOOMPA. The writer Lauren Collins quoted Pidgeon as saying, \\\"For every Deep Roy, there are a hundred and fifty of us who are forced to do whacked out shit on the Man Show!\\\" Pidgeon continues to act and will be featured as the first homo-erotic dwarf zombie in the annals of cult filmdom in the Mindfire Productions slasher \\\"Dead and Deader\\\" with Dean Cain scheduled for release in 2007. He has appeared on episodes of \\\"Charmed,\\\"\", \"Franziska Liebing Franziska Liebing Franziska Liebing (6 February 1899 \\u2013 3 January 1993) also known as Franziska Liebig, was a German film and later US film actress. Liebling was born in Ersnas, Sweden, although many sources stated she was born in Munich, where she spend her working life She was active on screen between 1953 and 1979, papering in TV series and made for TV films, although she was most notable roles as Grandma Josephine (Charlie Bucket's grandmother) in the 1971 film \\\"Willy Wonka & the Chocolate Factory\\\". Her final on-screen role came in \\\"Der Ruepp\\\" in 1979 She was married to\", \"Charlie and the Chocolate Factory (franchise) the sequel to \\\"Charlie and the Chocolate Factory\\\", continuing the story of Charlie Bucket and Willy Wonka as they travel in the Great Glass Elevator. \\\"Charlie and the Great Glass Elevator\\\" was first published in the United States by Alfred A. Knopf in 1972, and in the United Kingdom by George Allen & Unwin in 1973. \\\"Willy Wonka & the Chocolate Factory\\\" is a 1971 musical film adaptation of the 1964 novel \\\"Charlie and the Chocolate Factory\\\" by Roald Dahl. It was directed by Mel Stuart, and starred Gene Wilder as Wonka. The film tells the story of Charlie Bucket\", \"Charlie and the Chocolate Factory Charlie and the Chocolate Factory Charlie and the Chocolate Factory is a 1964 children's novel by British author Roald Dahl. The story features the adventures of young Charlie Bucket inside the chocolate factory of eccentric chocolatier Willy Wonka. \\\"Charlie and the Chocolate Factory\\\" was first published in the United States by Alfred A. Knopf, Inc. in 1964 and in the United Kingdom by George Allen & Unwin, 11 months later. The book has been adapted into two major motion pictures: \\\"Willy Wonka & the Chocolate Factory\\\" in 1971, and \\\"Charlie and the Chocolate Factory\\\" in 2005. The book's sequel, \\\"Charlie\", \"Charlie and the Chocolate Factory (film) Neverland\\\", suggested Highmore for the part. Highmore had already read the book before, but decided to read it once more prior to auditioning. The actor did not see the original film adaptation, and chose not to see it until after Burton's production, so his portrayal would not be influenced. Before Adam Godley was officially cast as Mr. Teavee, Dan Castellaneta, Tim Allen, Ed O'Neill, Bob Saget, and Ray Romano were all considered for the role. It has been rumored that Gregory Peck was considered for the role of Grandpa Joe. Other actors that were considered for Grandpa Joe included Richard\", \"Peter Ostrum him \\\"great questions.\\\" Ostrum has been called \\\"the most famous man in Lowville\\\", where the local video rental shop has twice worn out its VHS copy of \\\"Willy Wonka & the Chocolate Factory\\\". In the run-up to the release of \\\"Charlie and the Chocolate Factory\\\" in 2005, Ostrum garnered a spate of attention that included seeing the film in New York City with NPR as well as being included in VH1's list of \\\"100 Greatest Kid Stars\\\" (placing 78th). On the new film, Ostrum quoted fellow \\\"Wonka\\\" actor Julie Dawn Cole, saying that \\\"It's sort of like going back to\", \"Willy Wonka & the Chocolate Factory film tells the story of Charlie Bucket (Peter Ostrum) as he receives a Golden Ticket and visits Willy Wonka's chocolate factory with four other children from around the world. Filming took place in Munich in 1970, and the film was released by Paramount Pictures on June 30, 1971. With a budget of just $3 million, the film received generally positive reviews and earned $4 million by the end of its original run. The film became highly popular in part through repeated television airings and home entertainment sales. In 1972, the film received an Academy Award nomination for Best Original Score,\", \"Kraig Thornber Kraig Thornber Kraig Thornber (born 1961) is a British actor, singer and choreographer best known for playing the handyman Riff Raff in \\\"The Rocky Horror Show\\\" and Grandpa George in the musical \\\"Charlie and the Chocolate Factory\\\". He is a former member of the National Theatre. Born as Craig Thornber in Leicester in 1961, the son of Terence A Thornber and Sheila (n\\u00e9e Baxter), Kraig 'Pix' Thornber attended Bosworth Community College in his native Leicester before training for three years at the East 15 Acting School, graduating in 1987. In the West End he has appeared in \\\"Guys and Dolls\\\",\", \"Gene Wilder director for the film. Jean Renoir was the first candidate, but he would not be able to do the film for at least a year, so British-Indian director Waris Hussein was hired. With Margot Kidder co-starring with Wilder, it was filmed on location in Dublin, and at the nearby Ardmore Studios, in August and September 1969. In 1971, Wilder auditioned to play Willy Wonka in Mel Stuart's film adaptation of Roald Dahl's \\\"Charlie and the Chocolate Factory\\\". After reciting some lines, director Mel Stuart immediately offered him the role. Before Wilder was officially cast for the role, Fred Astaire, Joel\", \"David Kelly (actor) to have starring roles in television shows such as \\\"Emmerdale Farm\\\" in the 1980s and \\\"Glenroe\\\" in the 1990s, as well as playing the grandfather in Mike Newell's film \\\"Into the West\\\" (1992). Following his appearance as Michael O'Sullivan in the 1998 film \\\"Waking Ned\\\", he played roles in such films as Tim Burton's \\\"Charlie and the Chocolate Factory\\\" (2005), in which he played Grandpa Joe and \\\"\\\". He played title character Frank Kovak in the mystery film \\\"The Kovak Box\\\", in a rare villainous role. 2007's \\\"Stardust\\\" was his final film. He also did extensive radio work, including a\", \"David Morris (actor) years in the Royal Academy Schools and lectured at various other schools in London, Oxford and Brighton. Morris was an amateur actor who staged Shakespeare productions in a converted barn called the \\\"Bottom Theatre\\\" at his home in Roughwood, Buckinghamshire. In 2004, he was recommended for a role in the TV mystery series \\\"Jonathan Creek\\\" by his friend, director Sandy Johnson. He went on to appear in the TV movie \\\"When I'm 64\\\" and the comedy series \\\"Little Britain\\\" and \\\"Saxondale\\\". In 2005, he played Grandpa George in Tim Burton's \\\"Charlie and the Chocolate Factory\\\". He married Olwen Goodwin, a\", \"Charlie and the Chocolate Factory (film) Palin (as well as the other three Monty Python members) had all previously expressed interest in playing Wonka in the 1971 film adaptation. Johnny Depp was the only actor Burton considered for the role, although Dwayne Johnson was Burton's second choice in case Depp was unavailable. Depp signed on without reading the script under the intention of going with a completely different approach than what Gene Wilder did in the 1971 film adaptation. Depp said regardless of the original film, Gene Wilder's characterization of Willy Wonka stood out as a unique portrayal. Depp and Burton derived their Willy Wonka from\", \"Charlie and the Chocolate Factory video games film in theatres. Most of the main cast from the film provided their voices for the game except for Johnny Depp, James Arnold Taylor was used in his place as the voice of Willy Wonka. Original music for the video game was created by Winifred Phillips and produced by Winnie Waldron. The first objective of the game is to help Charlie find money to buy a Wonka Bar to win a Golden Ticket. This is done at the beginning of the game while giving a tutorial of what controls will be needed during future stages. The main part of the\", \"Pat Coombs Us Do Part\\\" (1969), \\\"On the Buses\\\" (1971) and \\\"Dad's Army\\\" (1971). She also had a minor uncredited role as Henrietta Salt in \\\"Willy Wonka & the Chocolate Factory\\\" in 1971. Coombs never married. She said that twice she came close to doing so, but was not sure enough to proceed. She once remarked: \\\"I've never been wildly ambitious; I think if I'd been married, my career would have gone out of the window.\\\" Coombs was diagnosed with osteoporosis in 1995, and became an active campaigner for the National Osteoporosis Society. Her Christmas appeal letter raised \\u00a3100,000 for the charity's\", \"Emily Padgett 2016 before officially opening on March 24. The musical closed on June 26, 2016 after 30 previews and 109 regular performances. On August 9, 2016, it was announced that Padgett would join Sutton Foster in a limited run, off-Broadway revival of \\\"Sweet Charity\\\". The show closed on January 8, 2017. On December 19, 2016, it was announced that Padgett would star as Mrs. Bucket in \\\"Charlie and the Chocolate Factory\\\". Sources: Sources: Sources: On January 12, 2017, Padgett got engaged to actor Josh Young. They were married on June 3, 2018. Emily Padgett Emily Padgett (born September 20, 1984) is\", \"David Kelly (actor) Kennedy Center revival of \\\"The Playboy of the Western World\\\". As well, he earned a Screen Actors Guild Award nomination for the 1998 film \\\"Waking Ned\\\". In 2005, Kelly won the Irish Film & Television Academy's Lifetime Achievement Award, in addition to earning a nomination for Best Supporting Actor for the film \\\"Charlie and the Chocolate Factory\\\". David Kelly (actor) David Kelly (11 July 1929 \\u2013 12 February 2012) was an Irish actor who had regular roles in several film and television works from the 1950s onwards. One of the most recognisable voices and faces of Irish stage and screen,\", \"Charlie and the Chocolate Factory (film) the Nut Room and Inventing Room. Tim Burton avoided using too many digital effects because he wanted the younger actors to feel as if they were working in a realistic environment. As a result, forced perspective techniques, oversized props and scale models were used to avoid computer-generated imagery (CGI). Deep Roy was cast to play the Oompa-Loompas based on his previous collaborations with Burton on \\\"Planet of the Apes\\\" and \\\"Big Fish\\\". The actor was able to play various Oompa-Loompas using split screen photography, digital and front projection effects. \\\"Tim told me that the Oompa-Loompas were strictly programmed, like robots\", \"Charlie and the Chocolate Factory (franchise) Ross, Rob Minkoff, Martin Scorsese and Tom Shadyac had been involved, while Warner Bros. either considered or discussed the role of Willy Wonka with Nicolas Cage, Jim Carrey, Michael Keaton, Brad Pitt, Will Smith and Adam Sandler. Burton immediately brought regular collaborators Johnny Depp and Danny Elfman aboard. \\\"Charlie and the Chocolate Factory\\\" represents the first time since \\\"The Nightmare Before Christmas\\\" that Elfman contributed to the film score using written songs and his vocals. Filming took place from June to December 2004 at Pinewood Studios in the United Kingdom, where Burton avoided using digital effects as much as possible.\", \"Willy Wonka & the Chocolate Factory Willy Wonka & the Chocolate Factory Willy Wonka & the Chocolate Factory is a 1971 American musical fantasy family film directed by Mel Stuart, and starring Gene Wilder as Willy Wonka. It is an adaptation of the 1964 novel \\\"Charlie and the Chocolate Factory\\\" by Roald Dahl. Dahl was credited with writing the film's screenplay; however, David Seltzer, who went uncredited in the film, was brought in to re-work the screenplay against Dahl's wishes, making major changes to the ending and adding musical numbers. These changes and other decisions made by the director led Dahl to disown the film. The\", \"Charlie and the Chocolate Factory (film) He wanted Charlie to be an average child who would be in the background and not get in trouble. Prior to Burton's involvement, Warner Bros. considered or discussed Willy Wonka with Bill Murray, Christopher Walken, Steve Martin, Robin Williams, Nicolas Cage, Jim Carrey, Michael Keaton, Robert De Niro, Brad Pitt, Will Smith, Mike Myers, Ben Stiller, Leslie Nielsen, John Cleese, Eric Idle, Michael Palin, Patrick Stewart, and Adam Sandler. Dustin Hoffman and Marilyn Manson reportedly wanted the role as well. Pitt's production company, Plan B Entertainment, however, stayed on to co-finance the film with Warner Bros. Coincidentally, Cleese, Idle and\", \"Willy Wonka & the Chocolate Factory too much emphasis on Willy Wonka and not enough on Charlie\\\", as well as the casting of Gene Wilder instead of Spike Milligan. Dahl was also \\\"infuriated\\\" by the deviations in the plot Seltzer devised in his draft of the screenplay, including the conversion of Slugworth, a minor character in the book, into a spy (so that the film could have a villain) and the \\\"fizzy lifting drinks\\\" scene along with music other than the original Oompa Loompa compositions (including \\\"Pure Imagination\\\" and \\\"The Candy Man\\\"), and the ending dialogue for the movie. In 1996, Dahl's second wife, Felicity, commented\", \"Charlie and the Chocolate Factory (film) Attenborough, Kirk Douglas, Albert Finney, Anthony Hopkins, Paul Newman, Max von Sydow, David Warner, Christopher Lloyd and Peter Ustinov. Principal photography for \\\"Charlie and the Chocolate Factory\\\" started on June 21, 2004 at Pinewood Studios in England. Director Tim Burton and composer Danny Elfman found filming somewhat difficult because they were simultaneously working on \\\"Corpse Bride\\\". The Wonka Factory exterior was coincidentally constructed on the same backlot Burton had used for Gotham City in \\\"Batman\\\" (1989). The ceremonial scene required 500 local extras. The Chocolate Room/River setpiece filled Pinewood's 007 Stage. As a consequence of British Equity rules, which state\", \"Charlie and the Chocolate Factory (film) the Line\\\". More nominations followed from the British Academy Film Awards for Visual Effects, Costume Design (Pescucci), Makeup & Hair (Peter Owen and Ivana Primorac) and Production Design (Alex McDowell). \\\"Charlie and the Chocolate Factory\\\" was also nominated for the Saturn Award for Best Fantasy Film, as well as Performance by a Younger Actor (Freddie Highmore), Music (Danny Elfman) and Costume (Pescucci). Elfman and screenwriter John August were nominated for a Grammy Award with \\\"Wonka's Welcome Song\\\". Charlie and the Chocolate Factory (film) Charlie and the Chocolate Factory is a 2005 musical fantasy comedy film directed by Tim Burton and\", \"Johnny Depp as Scottish author J. M. Barrie in the film \\\"Finding Neverland\\\" (2004). The following year he starred as Willy Wonka in \\\"Charlie and the Chocolate Factory\\\", which reunited him with director Tim Burton, with whom he had not collaborated since \\\"Sleepy Hollow\\\". The film was a box office success and had a positive critical reception, with Depp being nominated for the Golden Globe Award for Best Actor \\u2013 Motion Picture Musical or Comedy. \\\"Chocolate Factory\\\" was followed by another Burton project, stop-motion animation \\\"Corpse Bride\\\" (2005), in which Depp voiced the character Victor Van Dort. Depp reprised the role of\", \"AnnaSophia Robb character in the television special \\\"\\\". She wore a long brown wig for the role. Robb's two big-screen appearances in 2005 were adaptations of popular children's books. She starred as Opal in \\\"Because of Winn-Dixie\\\", and as the competitive and rude Violet Beauregarde in Tim Burton's remake of \\\"Charlie and the Chocolate Factory\\\". The latter was a major box-office success worldwide, and helped escalate Robb's popularity among preteen audiences. In 2005, Robb was the face of Trad Clothing, helping to design and model a fashion line for girls. In 2006, she had a guest role on the cartoon show \\\"Danny\", \"Charlie and the Chocolate Factory (musical) Thompson. Due to other commitments, Mendes stayed as producer only, but did participate in the selection of O'Brien replacement as director. O'Brien stated the score would pay homage to the Leslie Bricusse/Anthony Newley songs written for the 1971 film and would also feature the songs written by Shaiman and Wittman. In August 2016, O'Brien confirmed that \\\"The Candy Man\\\" and \\\"Pure Imagination\\\" would be included in the musical. On 9 May 2016, producers announced that the show would open at the Lunt-Fontanne Theatre starring Christian Borle as Willy Wonka. as Veruca Salt, and as Augustus Gloop. Previews began on 28\", \"Gregory Peck of Grandpa Joe in the 2005 film \\\"Charlie and the Chocolate Factory\\\", but died before he could accept it. The Irish actor David Kelly was then given the part. In 1947, while many Hollywood figures were being blacklisted for similar activities, Peck signed a letter deploring a House Un-American Activities Committee investigation of alleged communists in the film industry. A lifelong Democrat, Peck was suggested in 1970 as a possible Democratic candidate to run against Ronald Reagan for the office of California Governor. Although he later admitted that he had no interest in being a candidate himself for public office,\", \"Matthew Hardy three separate seasons of a one-man stage adaptation at the Melbourne International Comedy Festival. Hardy also appeared in the Festival with his own take on \\\"Charlie and the Chocolate Factory\\\", along with special guest Julie Dawn Cole, who portrayed Veruca Salt in the 1971 film version. \\u201cA tragic and triumphant, touching, and genuinely funny true story\\u201d (The Age) Hardy's TV credits include hosting two series of \\\"The Big Schmooze\\\", Foxtel's only \\\"Tonight Show\\\" on The Comedy Channel and appearing as a panelist and roving reporter on ABC-TV's \\\"The Fat\\\". He was part of the BAFTA-winning \\\"The Sketch Show\\\" writing team\", \"Tim Brooke-Taylor and picking up international recognition in Australia and New Zealand. He has also appeared as an actor in various sitcoms, and has been a panellist on \\\"I'm Sorry I Haven't a Clue\\\" for over 40 years. Following the death of Diana Sowle, Brooke-Taylor is one of two surviving adult cast members of Willy Wonka & the Chocolate Factory (the other is Rusty Goffe, in his twenties when he was an Oompa Loompa). Brooke-Taylor was born in Buxton, Derbyshire, England, the grandson of Francis Pawson, a parson who played centre forward for the English football team in the 1880s. His mother\", \"Charlie and the Chocolate Factory (film) channeling Keith Richards, which may have primed us to look for possible inspirations for this performance.\\\" Mick LaSalle from the \\\"San Francisco Chronicle\\\" found \\\"Charlie and the Chocolate Factory\\\" Burton's \\\"best work in years. If all the laughs come from Depp, who gives Willy the mannerisms of a classic Hollywood diva, the film's heart comes from Highmore, a gifted young performer whose performance is sincere, deep and unforced in a way that's rare in a child actor.\\\" Peter Travers wrote in \\\"Rolling Stone\\\" magazine that \\\"Depp's deliciously demented take on Willy Wonka demands to be seen. Depp goes deeper to\", \"Aubrey Woods in \\\"Willy Wonka & the Chocolate Factory\\\", where he played the character of Bill, the Candy Store Owner, singing \\\"The Candy Man\\\" near the beginning of the film, the single was later a hit for entertainer Sammy Davis Jr. During the early 1970s he collaborated on the musical \\\"Trelawny\\\" with friend Julian Slade. His television credits include \\\"Z-Cars\\\", \\\"Up Pompeii!\\\", \\\"Doctor Who\\\", \\\"Blake's 7\\\", \\\"Auf Wiedersehen, Pet\\\" and \\\"Ever Decreasing Circles\\\". He also appeared as Jacob and Potiphar in the 1991 production of \\\"Joseph and the Amazing Technicolor Dreamcoat\\\" at the London Palladium, the soundtrack of which topped the British\", \"Dora Altmann Dora Altmann Dora Altmann (born Dora Alrich, 20 February 1881 \\u2013 24 December 1971) was a German actress, who acted in on television and film during the 1960s and early 1970s. Altmann was 80 years old when she made her screen debut in 1961 as Veronika in \\\"Die drei Eisb\\u00e4ren\\\". She played the small uncredited part of Grandma Georgina, (Charlie Bucket's grandmother), in the 1971 film version of \\\"Willy Wonka & the Chocolate Factory\\\", her only US film credit. Her final screen role, as Frau Windegger on the episode \\\"Wenn Steine sprechen\\\" of the television series \\\"Tatort\\\", was aired in\", \"Charlie and the Chocolate Factory (film) day, Wonka announces a contest, in which Golden Tickets have been placed in five random Wonka Bars worldwide, and the winners will be given a full tour of the factory as well as a lifetime supply of chocolate, while one ticketholder will be given a special prize at the end of the tour. Wonka's sales subsequently skyrocket, and the first four tickets are found fairly quickly. The recipients are Augustus Gloop (Philip Wiegratz), a gluttonous German boy; Veruca Salt (Julia Winter), a very spoiled English girl; Violet Beauregarde (AnnaSophia Robb), a competitive gum chewer, and Mike Teavee (Jordan Fry), an\", \"Geoffrey Holder Murphy. He was also the voice of Ray in \\\"Bear in the Big Blue House\\\" and provided narration for Tim Burton's version of Roald Dahl's \\\"Charlie and the Chocolate Factory\\\". He reprised his role as the 7 Up Spokesman in the 2011 season finale of \\\"The Celebrity Apprentice\\\", where he appeared as himself in a commercial for \\\"7 Up Retro\\\" for Marlee Matlin's team. Holder was a prolific painter (patrons of his art included Lena Horne and William F. Buckley, Jr.), ardent art collector, book author, and music composer. As a painter, he won a Guggenheim Fellowship in fine arts\", \"Alex Pettyfer to Michael J. Ireland, a retired builder and property developer. Pettyfer was brought up in Esher and then Windsor, Berkshire and began his career initially being managed by his mother Lee Robinson, as a child fashion model at the age of seven, for Gap, after meeting Ralph Lauren in a toy store in New York. He also did advertisements for some yogurt brands. His first commercial was at age six. As a schoolboy, he performed in plays, including in the role of Willy Wonka in a production of \\\"Charlie and the Chocolate Factory\\\", Jack in his school play \\\"Jack and\", \"Alex Jennings 2007, he played the role of Garry Essendine in No\\u00ebl Coward's \\\"Present Laughter\\\" at the NT. In 2011, he played Mikhail Bulgakov in the National Theatre's production of \\\"Collaborators\\\". In 2014, he played the role of Willy Wonka in \\\"Charlie and the Chocolate Factory the Musical,\\\" which was directed by Sam Mendes and was performed on London's West End theatre district. He took over the role from Douglas Hodge in 2014. In 2016, he reprised his role as Professor Henry Higgins in the Australian 60th Anniversary production of \\\"My Fair Lady\\\", directed by Julie Andrews. Jennings' work in film includes\", \"Jack Albertson Jack Albertson Harold Albertson (June 16, 1907 \\u2013 November 25, 1981) professionally known as Jack Albertson, was an American actor, comedian, dancer and singer who also performed in vaudeville. Albertson is known for his role as John Cleary in \\\"The Subject Was Roses\\\" (1968), for which he received an Academy Award for Best Supporting Actor; Grandpa Joe in \\\"Willy Wonka & the Chocolate Factory\\\" (1971); Manny Rosen in \\\"The Poseidon Adventure\\\" (1972); and Ed Brown in the television sitcom \\\"Chico and the Man\\\" (1974\\u201378). For his contributions to the television industry, Albertson was honored with a star on the Hollywood\", \"Charlie and the Chocolate Factory (film) with Jackson never occurred to him. Instead, he compared Wonka to Howard Hughes due to his \\\"reclusive, germaphobe, controlling\\\" nature. Burton agreed with the similarity to Hughes. He also cited Charles Foster Kane from \\\"Citizen Kane\\\" as an inspiration for Wonka, as Kane is \\\"somebody who was brilliant but then was traumatized and then retreats into their own world\\\". Depp wanted to sport prosthetic makeup for the part and have a long, elongated nose, but Burton believed it would be too outrageous. During production, Gene Wilder, in an interview with \\\"The Daily Telegraph\\\", accused the filmmakers of only remaking the\", \"Mark Heap brief appearance as a fire-breather in the James Bond film Octopussy. He played a school teacher in the 2002 film \\\"About a Boy\\\". He appeared in Tim Burton's 2005 \\\"Charlie and the Chocolate Factory\\\". He played a roles in \\\"Confetti\\\" (2006), \\\"Tunnel of Love\\\" (2004), \\\"Stardust\\\" (2007), and \\\"The World's End\\\" (2013). In 2008 he co-starred in the surreal science fiction film \\\"Captain Eager and the Mark of Voth\\\". In 2009 he appeared as a car salesman in a SEAT television advert. Heap voices the fox in the Old Speckled Hen adverts sponsoring comedy on Dave. In 2008 Heap played\", \"Richard John Taylor Grantham plays a fictionalised version of himself and the feature \\\"The Factory\\\" which was loosely inspired by the Roald Dahl novel \\\"Charlie and the Chocolate Factory\\\" and the life of the actor Gene Wilder. Langham also makes an appearance in \\\"The Factory\\\" as the lead characters family doctor. In March 2014, Simon Hattenstone wrote an article in \\\"The Guardian\\\", accusing Taylor of having falsified claims in regards to his business associates and defrauded investors. According to the article, Taylor claimed to work for the BBC as chief editor for the television show EastEnders but in fact never worked on the\", \"Blair Dunlop Blair Dunlop Blair Dunlop (born 11 February 1992) is an English musician and actor. Dunlop is the son of folk-rock musician Ashley Hutchings (formerly a member of Fairport Convention) and singer Judy Dunlop. He received a scholarship to attend Foremarke Hall, in Derbyshire, which he attended from 2003\\u20132005 and then moved onto Foremarke Hall\\u2019s senior school, Repton School. As a young actor, Dunlop made his film debut with an American accent in \\\"Charlie and the Chocolate Factory\\\" (2005), directed by Tim Burton. Dunlop played the young Willy Wonka, a child under strict rule by his father. Dunlop was also on\", \"Paris Themmen a fawning fan. He also played a contestant billed as a \\\"former child star\\\" in two 2008 episodes of the American game show \\\"Duel\\\". On May 4, 2011, Themmen appeared on the British television morning show \\\"Daybreak\\\", alongside the other child actors from \\\"Willy Wonka & the Chocolate Factory\\\": Peter Ostrum (Charlie Bucket), Julie Dawn Cole (Veruca Salt), Denise Nickerson (Violet Beauregarde) and Michael Bollner (Augustus Gloop). They made an additional 40th Anniversary reunion appearance on NBC's \\\"Today Show\\\" eleven days later. Paris also appeared on the TV version of Trivial Pursuit in Summer 1993 and won, and was on\", \"Christian Borle and officially on October 27, 2016. Borle was nominated for a Tony Award for his performance. The show closed on January 8, 2017, after 30 previews and 84 performances. On May 9, 2016, it was announced that Borle would play Willy Wonka in the Broadway production of Roald Dahl's \\\"Charlie and the Chocolate Factory\\\" at the Lunt-Fontanne Theatre, which opened on March 23, 2017. A cast album was announced March 21, 2017. The show played its final performance January 14, 2018. Borle also made an appearance with his former wife Sutton Foster, in \\\"\\\". His musical talents were used in\", \"Wild Chicks a success, drawing about 1 million visitors in Germany. Funke herself was \\\"very pleased\\\" with the film adaptations. In the 3 movies, the Wild Chicks were portrayed by (Sprotte), (Frieda), (Trude), Paula Riemann (Melanie), and (Wilma), and the Pygmies were portrayed by (Fred), (Torte), Vincent Redetzki (Willi) and Philip Wiegratz (Steve; Wiegratz played Augustus Gloop in \\\"Charlie and the Chocolate Factory\\\"). Notable was also (Wilma's girlfriend Leonie), who joined Riemann as an Undine Award winner with her performance in the 2007 German TKKG movie \\\"TKKG und das Geheimnis um die r\\u00e4tselhafte Mind Machine\\\". The adult roles were portrayed by established\", \"The Electric Company one to four years. Irene Cara appeared during the first season (1971\\u20131972) and would go on to become a pop-music star. Cara was replaced in the second season (1972\\u20131973) by Denise Nickerson, who previously appeared on the ABC daytime series \\\"Dark Shadows\\\" and was best known for her appearance as Violet Beauregarde in the 1971 film \\\"Willy Wonka & the Chocolate Factory\\\". The other three original members of the Short Circus were singer and guitarist Melanie Henderson; drummer and singer Stephen Gustafson; and singer, tambourinist, and guitarist Douglas Grant. For seasons three (1973\\u20131974) and four (1974\\u20131975), Grant and Nickerson were\", \"Freddie Highmore Freddie Highmore Alfred Thomas \\\"Freddie\\\" Highmore (born 14 February 1992) is an English actor. He made his debut in the comedy film \\\"Women Talking Dirty\\\" (1999). He is known for his starring roles in the films \\\"Finding Neverland\\\" (2004), \\\"Charlie and the Chocolate Factory\\\" (2005), \\\"August Rush\\\" (2008) and \\\"The Spiderwick Chronicles\\\" (2008). He won two consecutive Critics' Choice Movie Awards for Best Young Performer. Highmore starred as Norman Bates in the drama-thriller series \\\"Bates Motel\\\" (2013\\u20132017), for which he was nominated three times for the Critics' Choice Television Award for Best Actor in a Drama Series and won a\", \"Charlie and the Chocolate Factory (film) find the bruises on Wonka's secret heart than what Gene Wilder did. Depp and Burton may fly too high on the vapors of pure imagination, but it's hard to not get hooked on something this tasty. And how about that army of Oompa-Loompas, all played by Deep Roy, in musical numbers that appear to have been choreographed by Busby Berkeley on crack.\\\" Ann Hornaday of \\\"The Washington Post\\\" criticized Depp's acting. \\\"The cumulative effect isn't pretty. Nor is it kooky, funny, eccentric or even mildly interesting. Indeed, throughout his fey, simpering performance, Depp seems to be straining so hard for\", \"Willy Wonka into his factory, where he then tempts each of them with a weakness. Finally, only Charlie is left. Willy Wonka and Charlie board Wonka's \\\"Great Glass Elevator\\\" which takes off over the audience. Early on in the production of the 2005 film, Nicolas Cage was under discussions for portraying Willy Wonka, but lost interest. Warner Bros. president Alan F. Horn wanted Tom Shadyac to direct Jim Carrey as Willy Wonka, believing the duo could make \\\"Charlie and the Chocolate Factory\\\" relevant to mainstream audiences, but Roald Dahl's widow Liccy Dahl opposed this. After Tim Burton was hired as director in\", \"Denise Nickerson Denise Nickerson Denise Nickerson (born April 1, 1957) is an American former child actress, best known for her roles as the gum-chewing Violet Beauregarde in the 1971 movie \\\"Willy Wonka & the Chocolate Factory\\\", Allison on \\\"The Electric Company\\\" and Amy Jennings and Nora Collins in the soap opera \\\"Dark Shadows\\\". Born in New York City, Nickerson made appearances in the late 1960s on such shows as \\\"The Doctors\\\", and opposite Bill Bixby in an unsold television pilot called \\\"Rome Sweet Rome\\\". Nickerson's big break came in 1968 when she joined the cast of ABC-TV's \\\"Dark Shadows\\\", appearing as recurring\", \"Douglas Hodge an Olivier Award nomination for his performance. In 2012, Hodge headed back to Broadway when he starred as Cyrano de Bergerac in the Roundabout Theatre Company's revival of \\\"Cyrano de Bergerac\\\" which played a limited engagement at the American Airlines Theatre from 14 September 2012 \\u2013 25 November 2012. In October 2012, it was announced that Hodge had been cast as Willy Wonka in the new musical \\\"Charlie and the Chocolate Factory the Musical\\\" on the West End, which previewed on 18 May 2013 and opened on 25 June 2013 at the Theatre Royal, Drury Lane London. Hodge wrote a\", \"Billy Boyle roles in \\\"No Sex Please, We're British\\\", \\\"Billy\\\", \\\"What's a Nice Country\\\", \\\"The Rivals\\\", \\\"Love, Lust, & Marriage\\\", \\\"Some Like it Hot\\\", Disney's \\\"Beauty and the Beast\\\", and in the original cast of \\\"Dirty Dancing. Lately he has appeared as Grandpa George\\\" and Grandpa Joe in Charlie and The Chocolate Factory at Drury Lane. In 2016 he was Major Bouvier and Norman Vincent Peale in the smash hit Grey Gardens. He followed this playing Arvide in Guys and Dolls at the Phoenix Theatre in the West End.He has had his own very successful television series in Ireland \\\"It's Billy Boyle\\\"\", \"Charlie and the Chocolate Factory (film) children's television show hosts such as Bob Keeshan (\\\"Captain Kangaroo\\\"), Fred Rogers, and Al Lewis from \\\"The Uncle Al Show\\\", and Depp also took inspiration from various game show hosts. Burton recalled from his childhood that the characters were bizarre but left lasting impressions. He said, \\\"It was kind of a strange amalgamation of these weird children's TV show hosts.\\\" Depp based Wonka's look (exaggerated bob cut and sunglasses) on \\\"Vogue\\\" magazine editor Anna Wintour. Comparisons were drawn between Willy Wonka and Michael Jackson. Burton disagreed with the comparisons and said Jackson, unlike Wonka, liked children. Depp said the similarities\", \"Anna Wintour \\\"The Incredibles\\\" has been noted, Johnny Depp said he partially based the demeanour of Willy Wonka in \\\"Charlie and the Chocolate Factory\\\" on Wintour. Fey Sommers in the \\\"Ugly Betty\\\" television series was also likened to Wintour, from the trademark bob and sunglasses, to Wintour's last name homophonous with 'Winter', while Sommers' is homophonous with 'Summer'. During the film's production in 2005, Wintour was reportedly threatening prominent fashion personalities, particularly designers, that \\\"Vogue\\\" would not cover them if they made cameo appearances in the movie as themselves. She denied it through a spokesperson who said she was interested in anything\", \"Charlie and the Chocolate Factory (franchise) the impression of movement. List indicator(s) Charlie and the Chocolate Factory (franchise) Charlie and the Chocolate Factory is a media franchise. It includes two books, two live-action theatrical films, two video games, and a ride. \\\"Charlie and the Chocolate Factory\\\" is a 1964 children's book by British author Roald Dahl. The story features the adventures of young Charlie Bucket inside the chocolate factory of eccentric chocolatier Willy Wonka. \\\"Charlie and the Chocolate Factory\\\" was first published in the United States by Alfred A. Knopf, Inc. in 1964 and in the United Kingdom by George Allen & Unwin in 1967. \\\"Charlie\", \"Willy Wonka & the Chocolate Factory Wilder was officially cast for the role, producers considered Fred Astaire, Joel Grey, Ron Moody and Jon Pertwee. Spike Milligan was Roald Dahl's original choice to play Willy Wonka. Peter Sellers even begged Dahl for the role. When Wilder was cast for the role, he accepted it on one condition: The reason why Wilder wanted this in the film was that \\\"from that time on, no one will know if I'm lying or telling the truth.\\\" Jean Stapleton turned down the role of Mrs. Teevee. Jim Backus was considered for the role of Sam Beauregarde. Sammy Davis Jr. wanted to\", \"Christopher Lee the favourite actors of Tim Burton, and became a regular in many of Burton's films, working for the director five times, starting in 1999, where he had a small role as the Burgomaster in the film \\\"Sleepy Hollow\\\". In 2005, Lee played Willy Wonka's strict dentist father, Dr. Wilbur Wonka, in Burton's reimagining of the Roald Dahl tale \\\"Charlie and the Chocolate Factory\\\", and voiced the character of Pastor Galswells in \\\"Corpse Bride\\\", co-directed by Burton and Mike Johnson. In 2007, Lee collaborated with Burton on \\\"\\\", playing the spirit of Sweeney Todd's victims, called the Gentleman Ghost, alongside Anthony\", \"Charlie and the Chocolate Factory (film) sexually transmitted disease. Burton immediately thought of Johnny Depp for the role of Willy Wonka, who in August 2003 joined the film, his fourth collaboration with the director. Lurie's script received a rewrite by Pamela Pettler, who worked with Burton on \\\"Corpse Bride\\\", but the director hired \\\"Big Fish\\\" screenwriter John August in December 2003 to start from scratch. Both August and Burton were fans of the book since their childhoods. August first read \\\"Charlie and the Chocolate Factory\\\" when he was eight years old, and subsequently sent Dahl a fan letter. He did not see the 1971 film prior\", \"Willy Wonka elevator and go high to the sky where Wonka reveals that Charlie, as well as Grandpa Joe and his whole family will move into the factory and the grand prize is not really The Lifetime Supply of Chocolate but the entire factory itself, and Charlie will take over its business when Wonka retires, reminding Charlie not to forget what happened to the man who got everything he ever wanted: \\\"He lived happily ever after.\\\" Willy Wonka (portrayed by Johnny Depp, Blair Dunlop as young Willy Wonka), the owner of a famous chocolate factory, has long closed access to his factory\", \"David Kelly (actor) David Kelly (actor) David Kelly (11 July 1929 \\u2013 12 February 2012) was an Irish actor who had regular roles in several film and television works from the 1950s onwards. One of the most recognisable voices and faces of Irish stage and screen, Kelly was known for his roles as Rashers Tierney in \\\"Strumpet City\\\", Cousin Enda in \\\"Me Mammy\\\", the builder Mr O'Reilly in \\\"Fawlty Towers\\\", and Grandpa Joe in the film \\\"Charlie and the Chocolate Factory\\\" (2005). Another notable role was as Michael O'Sullivan in \\\"Waking Ned Devine\\\". Kelly was born 11 July 1929 in Dublin, Ireland, and\", \"Nitin Ganatra live in North London. Ganatra is known internationally as Prince Pondicherry in the Tim Burton film \\\"Charlie and the Chocolate Factory\\\". He also appears in the Gurinder Chada film \\\"Bride and Prejudice\\\" as Kholi Saab and \\\"The Mistress of Spices\\\" as Haroun, and Dev Raja in \\\"Mumbai Calling\\\". He also appeared in the first ever iPod commercial. Ganatra also appeared in an episode of \\\"The Catherine Tate Show\\\", as Joanie Taylor's daughter's partner. Other appearances include the TV show \\\"Jane Hall\\\", the Patents Clerk in Philip Pullman's The Shadow in the North, the CBBC television show \\\"Gina's Laughing Gear\\\", a\", \"Charlie and the Chocolate Factory (musical) producers announced that production would close on 14 January 2018 after 27 previews and 305 performances. The US tour premiered on 21 September 2018 in Buffalo, New York at Shea's Performing Arts Center. It is a replica of the Broadway production and stars Noah Weisberg as Willy Wonka, James Young as Grandpa Joe and Amanda Rose as Mrs Bucket, with the role of Charlie being alternated between Henry Boshart, Collin Jeffery and Rueby Wood. The Australian premier of the musical will be held at Sydney's Capitol Theatre on 5 January 2019. The musical will be a replica production of the\", \"Larry Storch actress Diana Sowle (Best Known for her role as Mrs Bucket in the original Willy Wonka film) in Farmville, Virginia to benefit The Tom Mix Rangers. He recorded a comedy LP \\\"Larry Storch at The Bon Soir\\\" released by Jubilee in the 1960s. Other records include \\\"Larry Storch Reads Philip Roth's Epstein\\\", \\\"Larry Storch Pooped/Eighth Wonder of the World\\\", \\\"Larry Storch / I'm Walkin\\\". In less than two years' time, Storch appeared on three \\\"TV Guide\\\" covers, all with various \\\"F-Troop\\\" co-stars, Storch being the only one featured on all three covers. He also appeared on the cover of \\\"TV\", \"Charlie and the Chocolate Factory (franchise) Charlie and the Chocolate Factory (franchise) Charlie and the Chocolate Factory is a media franchise. It includes two books, two live-action theatrical films, two video games, and a ride. \\\"Charlie and the Chocolate Factory\\\" is a 1964 children's book by British author Roald Dahl. The story features the adventures of young Charlie Bucket inside the chocolate factory of eccentric chocolatier Willy Wonka. \\\"Charlie and the Chocolate Factory\\\" was first published in the United States by Alfred A. Knopf, Inc. in 1964 and in the United Kingdom by George Allen & Unwin in 1967. \\\"Charlie and the Great Glass Elevator\\\" is\", \"Rusty Goffe Rusty Goffe Rusty Goffe (born 30 October 1948) is an English-born actor with dwarfism. Goffe was born and raised in Herne Bay, Kent. He attended Sturry Secondary Modern School. He was married to Sarah Gofee and had 2 children Ben and Jack Goffe. Goffe appeared as an Oompa-Loompa in the 1971 version of \\\"Willy Wonka & the Chocolate Factory\\\" and as a Jawa in \\\"\\\", among a few other aliens. He also appeared in the films \\\"Willow\\\" and \\\"Flash Gordon\\\". He played Le Muff in the film \\\"History of the World Part I\\\" and also played Goober, a purple gremlin\", \"Deep Roy Deep Roy Gurdeep Roy (born Mohinder Purba; 1 December 1957), sometimes credited as Roy Deep, is an Anglo-Indian actor, stuntman, puppeteer and comedian. Due to his diminutive size, he has appeared in a number of similar-sized roles, such as the Oompa-Loompas in \\\"Charlie and the Chocolate Factory\\\", Keenser in \\\"Star Trek\\\" and subsequent films (\\\"Kelvin Timeline\\\"), and in television series such as \\\"The X-Files\\\", \\\"Doctor Who\\\" and \\\"Eastbound & Down\\\". Roy was born in Nairobi, Kenya, to Indian parents. He made his professional screen acting debut in a 1976 episode of \\\"The New Avengers\\\", titled \\\"Target!\\\" as a character named\", \"Denise Nickerson characters Amy Jennings and Nora Collins from 1968\\u20131970. Upon leaving \\\"Dark Shadows\\\", she appeared in the 1971 television movie \\\"The Neon Ceiling\\\". In 1971, Nickerson was cast as the nymphet Lolita in the 1971 ill-fated musical, \\\"Lolita, My Love\\\" during its run on Boston, which closed on the road. Also at this time, Nickerson landed her signature role as gum-chewing Violet Beauregarde in the 1971 film \\\"Willy Wonka & the Chocolate Factory\\\", when she was 13 years old. From 1972\\u201373, Nickerson joined the cast of \\\"The Electric Company\\\" as \\\"Allison\\\", a member of the Short Circus music group. Producers saw\", \"Deep Roy remake of \\\"Planet of the Apes\\\" (2001) in two roles, one as a young gorilla boy and as Thade's niece. He has worked for Burton in three other films, \\\"Big Fish\\\" (2003), \\\"Charlie and the Chocolate Factory\\\" (2005), and \\\"Corpse Bride\\\" (also 2005), where he supplied General Bonesapart's voice. He played all the Oompa-Loompas (165 of them) in \\\"Charlie and the Chocolate Factory\\\". Deep had extensive training for the role in dance, yoga, and some minor instrument playing. He has performed many other roles in movies and on television, including \\\"The X-Files\\\", \\\"Flash Gordon\\\", \\\"Return to Oz\\\" (as the Tin\", \"Body double the characters in same setting, such as with Deep Roy who portrayed the Oompa-Loompas in \\\"Charlie and the Chocolate Factory\\\". The 1984 film \\\"Body Double\\\", directed by Brian De Palma, featured a plot that hinged on the discovery that one character had in fact served as a body double for another. Body double In filmmaking, a body double is a person who substitutes in a scene for another actor such that the person's face is not shown. In a recorded visual medium, a body double is used in certain specific shots to replace the credited actor of a character. The\", \"Brad Grey \\\"Charlie and the Chocolate Factory\\\" with Johnny Depp, and Martin Scorsese's \\\"The Departed\\\", starring Leonardo DiCaprio, Matt Damon, and Jack Nicholson. After Pitt and Aniston separated, Grey and Pitt moved the company to Paramount Pictures in 2005. In May 2006 Bo Zenga \\u201cfiled a new suit against Grey personally,\\\" in which he charged Grey with using notorious private investigator Anthony Pellicano to illegally wiretap and conduct illegal background checks on Zenga during the original case. Grey denied any knowledge, testifying that \\\"his dealings with Pellicano \\u201call came through Bert Fields\\u201d and that \\u201cin every instance\\u201d Grey had never been given\", \"Charlie and the Chocolate Factory (film) critics, indicating \\\"generally favorable reviews\\\". Audiences polled by CinemaScore gave the film an average grade of \\\"A\\u2013\\\" on an A+ to F scale. Owen Gleiberman of \\\"Entertainment Weekly\\\" praised \\\"Charlie and the Chocolate Factory\\\", writing \\\"Johnny Depp as Willy Wonka may be a stone freak, but he is also one of Burton's classic crackpot conjurers, like \\\"Beetlejuice\\\" or \\\"Ed Wood\\\".\\\" Roger Ebert gave an overall positive review and enjoyed the film. He was primarily impressed by Tim Burton's direction of the younger cast members, but was disappointed with Depp's performance: \\\"What was Depp thinking of? In \\\"\\\" he was famously\", \"Charlie and the Chocolate Factory (film) estranged father-son relationship had previously appeared in \\\"Big Fish\\\", similarly directed by Burton and written by August. Warner Bros. and the director held differences over the characterizations of Charlie Bucket and Willy Wonka. The studio wanted to entirely delete Mr. Bucket and make Willy Wonka the idyllic father figure Charlie had longed for his entire life. Burton believed that Wonka would not be a good father, finding the character similar to a recluse. Burton said, \\\"In some ways, he's more screwed up than the kids.\\\" Warner Bros. also wanted Charlie to be a whiz kid, but Burton resisted the characterization.\", \"Jack Albertson elder sister, Mabel Albertson, (who died ten months later from Alzheimer's disease) were cremated and their ashes were scattered in the Pacific Ocean. Jack Albertson Harold Albertson (June 16, 1907 \\u2013 November 25, 1981) professionally known as Jack Albertson, was an American actor, comedian, dancer and singer who also performed in vaudeville. Albertson is known for his role as John Cleary in \\\"The Subject Was Roses\\\" (1968), for which he received an Academy Award for Best Supporting Actor; Grandpa Joe in \\\"Willy Wonka & the Chocolate Factory\\\" (1971); Manny Rosen in \\\"The Poseidon Adventure\\\" (1972); and Ed Brown in the\", \"Julian Lennon Rock and Roll Circus\\\" (1996, but shot in 1968), \\\"Cannes Man\\\" (1996), \\\"\\\" (1988), \\\"Chuck Berry: Hail! Hail! Rock 'n' Roll\\\" (1987), and a cameo in \\\"Leaving Las Vegas\\\" (1995) as a bartender. Julian provided the voice for the title role in the animated film \\\"David Copperfield\\\". He was also the voice of the main character Toby the Teapot in the animated special \\\"The Real Story of I'm a Little Teapot\\\" (1990). Lennon is also the producer of the documentary called \\\"WhaleDreamers\\\" about an aboriginal tribe in Australia and its special relationship to whales. It also touches on many environmental\", \"Peter Robbins (actor) Peter Robbins (actor) Peter Robbins (born Louis Nanasi, August 10, 1956) is an American former child actor, voice actor and real estate broker. Robbins earned national fame in the 1960s as being the first actor to voice Charlie Brown in the \\\"Peanuts\\\" films and television specials. Robbins left the entertainment industry as an adult, later pursuing a career in real estate and brief stints in radio. Robbins is of Hungarian descent. He first began acting in various films and television shows in 1963. As a child, he made a guest appearance as \\\"Elmer\\\" in the popular series \\\"The Munsters\\\" (1964).\", \"Denise Nickerson was discharged to a rehabilitation center in July. In August, Nickerson returned home to recover under her family's care. Denise Nickerson Denise Nickerson (born April 1, 1957) is an American former child actress, best known for her roles as the gum-chewing Violet Beauregarde in the 1971 movie \\\"Willy Wonka & the Chocolate Factory\\\", Allison on \\\"The Electric Company\\\" and Amy Jennings and Nora Collins in the soap opera \\\"Dark Shadows\\\". Born in New York City, Nickerson made appearances in the late 1960s on such shows as \\\"The Doctors\\\", and opposite Bill Bixby in an unsold television pilot called \\\"Rome Sweet\"], \"pos_scores\": [102.0], \"neg_scores\": [91.5625, 95.625, 95.9375, 92.5625, 86.375, 90.25, 91.125, 94.8125, 92.875, 93.0625, 94.625, 87.3125, 85.25, 86.0, 90.75, 89.1875, 88.6875, 89.5, 87.125, 85.75, 89.3125, 90.375, 87.875, 85.25, 97.5625, 86.875, 89.4375, 92.75, 91.1875, 91.5, 95.375, 99.0, 87.375, 90.6875, 86.0625, 87.5625, 92.375, 88.875, 86.125, 86.0625, 90.5625, 90.4375, 88.0625, 92.4375, 91.125, 91.8125, 92.5625, 92.625, 85.875, 86.3125, 89.9375, 88.625, 86.6875, 89.0, 90.8125, 85.375, 90.5, 92.1875, 86.75, 86.5625, 84.9375, 90.0, 92.8125, 85.5, 87.4375, 89.375, 94.5625, 85.5625, 85.9375, 88.6875, 92.0, 92.625, 87.0625, 89.875, 85.625, 87.1875, 92.75, 85.5, 89.375, 91.0, 85.75, 90.1875, 88.8125, 88.5, 85.5, 90.6875, 87.6875, 89.3125, 89.0625, 90.0625, 87.875, 86.6875, 87.25, 87.1875, 91.1875, 89.8125, 89.875, 89.9375, 90.3125, 90.5], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"when does season 5 of bates motel come out\", \"pos\": [\"Bates Motel (season 5) Bates Motel (season 5) The fifth and final season of \\\"Bates Motel\\\" premiered on February 20, 2017, and concluded on April 24, 2017. The season consisted of 10 episodes and aired on Mondays at 10 p.m. ET/PT on A&E. The series itself is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\", following the life of Norman Bates and his mother Norma prior to the events portrayed in the Hitchcock film. However, the final season of the series loosely adapts the plot of \\\"Psycho\\\". The series takes place in the fictional town of White Pine Bay, Oregon. The season\"], \"neg\": [\"Bates Motel (season 5) was released on Blu-ray and DVD on September 19, 2017. In June 2016, showrunner Kerry Ehrin confirmed the return of Kenny Johnson as Caleb Calhoun for the final season, in an interview with \\\"Deadline Hollywood\\\". The following month, it was announced that Rihanna had been cast in the iconic role of Marion Crane. In September 2016, Isabelle McNally joined the cast of the series, portraying the role of Madeleine Loomis, a young woman who resembles Norma. That same month, Brooke Smith joined the cast as Sheriff Jane Greene. In January 2017, Austin Nichols was cast in the role of Sam\", \"Brooklyn Nine-Nine (season 5) Brooklyn Nine-Nine (season 5) The fifth season of the television sitcom \\\"Brooklyn Nine-Nine\\\" premiered September 26, 2017 on Fox. It is the final season to air on Fox, as the series was cancelled on May 10, 2018, before NBC picked it up for a sixth season on May 11, 2018. Jake and Rosa adjust to life in jail before the Nine-Nine are able to bust Melanie Hawkins when Holt is forced to make a deal with local mobster Seamus Murphy. After their release, Jake realizes he's not ready to be back out in the field right away and Rosa breaks\", \"Mom (season 5) Mom (season 5) The fifth season of the sitcom \\\"Mom\\\" began airing on November 2, 2017, and concluded on May 10, 2018 on CBS in the United States. The season is produced by Chuck Lorre Productions and Warner Bros. Television, with series creators Chuck Lorre, Eddie Gorodetsky and Gemma Baker serving as executive producer. The series follows Christy Plunkett (Anna Faris), a single mother who\\u2014after dealing with her battle with alcoholism and drug addiction\\u2014decides to restart her life in Napa, California's wine country working as a waitress at the restaurant Rustic Fig and attending Alcoholics Anonymous meetings. She lives with\", \"Bates Motel (TV series) Bates Motel (TV series) Bates Motel is an American psychological horror drama television series that aired from March 18, 2013 to April 24, 2017. It was developed by Carlton Cuse, Kerry Ehrin, and Anthony Cipriano, and is produced by Universal Television and American Genre for the cable network A&E. The series, a contemporary prequel to Alfred Hitchcock's 1960 film \\\"Psycho\\\"; based on Robert Bloch's 1959 novel of the same name, depicts the lives of Norman Bates (Freddie Highmore) and his mother Norma (Vera Farmiga) prior to the events portrayed in the novel and film, albeit in a different fictional town\", \"Bates Motel (season 5) for the season, as well as direct an episode, marking his directorial debut. Max Thieriot and Nestor Carbonell were also tapped to direct an episode each for season 5. Production on the season began on September 16, 2016. The series filmed its final scenes at the specially-built Bates Motel set in Aldergrove on January 25, 2017, and production began tearing the house down the following day. Carbonell filmed his final scenes as Sheriff Alex Romero on January 27, 2017. Filming officially wrapped for the series on January 31, 2017. In February 2017, the Bates Motel exterior set in Aldergrove was\", \"Bringing Up Bates series debuted on January 1, 2015. UP TV revealed that the show would be returning for another season in June 2015. The second season started on June 4, 2015. The third season started on January 7, 2016 The fourth season started on June 2, 2016. The fifth season started on January 5, 2017. The sixth season began on June 1, 2017. Season seven started on January 4, 2018. Season two premiered on June 4, 2015. Season three premiered on January 7, 2016. The season started on June 2, 2016 The fifth season started on January 5, 2017. Season six started\", \"Bates Motel (season 4) Bates Motel (season 4) The fourth season of \\\"Bates Motel\\\" premiered on March 7, 2016, and concluded on May 16, 2016. The season consisted of 10 episodes and aired on Mondays at 9 p.m. ET/PT on A&E. The series itself is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\", following the life of Norman Bates and his mother Norma prior to the events portrayed in the Hitchcock film. The series takes place in the fictional town of White Pine Bay, Oregon. The season received critical acclaim from television critics, and was nominated for two Primetime Creative Arts Emmy\", \"Bates Motel (season 4) fourth season, \\\"Bates Motel\\\" was nominated for 17 awards, winning three. Bates Motel (season 4) The fourth season of \\\"Bates Motel\\\" premiered on March 7, 2016, and concluded on May 16, 2016. The season consisted of 10 episodes and aired on Mondays at 9 p.m. ET/PT on A&E. The series itself is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\", following the life of Norman Bates and his mother Norma prior to the events portrayed in the Hitchcock film. The series takes place in the fictional town of White Pine Bay, Oregon. The season received critical acclaim from\", \"The Blacklist (season 5) The Blacklist (season 5) The fifth season of the American crime thriller television series \\\"The Blacklist\\\" premiered on NBC on September 27, 2017, with a timeslot change from Thursday at 10:00 PM to Wednesday at 8:00 PM. The season was produced by Davis Entertainment, Universal Television and Sony Pictures Television, and the executive producers are Jon Bokenkamp, John Davis, John Eisendrath, John Fox, and Joe Carnahan. The season contained 22 episodes and concluded on May 16, 2018. The season aired the series' 100th episode. The fifth season of \\\"The Blacklist\\\" received positive reviews from critics. The review aggregator\", \"Chicago P.D. (season 5) Chicago P.D. (season 5) The fifth season of \\\"Chicago P.D.\\\", an American police drama television series with executive producer Dick Wolf, and producers Derek Haas, Michael Brandt, and Rick Eid, premiered on September 27, 2017 and concluded on May 9, 2018. The season contained 22 episodes This season featured its 100th episode. On May 25, 2017, it was announced that Sophia Bush would be departing the series, while Jon Seda is set to return after the cancellation of \\\"Chicago Justice\\\". After recurring last season as Detective Hailey Upton, Tracy Spiridakos has been promoted to a series regular for this\", \"Prison Break Blu-ray July 21, 2009. A nine-episode fifth season was announced by Fox in January 2016. The revival series, dubbed \\\"\\\", premiered on April 4, 2017, and aired on Tuesdays at 9:00 pm. The season concluded on May 30, 2017. On December 12, 2017, Dominic Purcell announced via Instagram that season 6 is \\\"in the works.\\\" Then on January 4, 2018, Fox officially confirmed that season 6 is in early development. The first season follows the rescue of Lincoln Burrows, who is accused of murdering Terrence Steadman, the brother of Vice President of the United States, Caroline Reynolds. Lincoln is sentenced\", \"Kerry Ehrin the for three consecutive years: at the 2008 ceremony, the 2009 ceremony, and at the 2010 ceremony. She was also nominated for the Primetime Emmy Award for Outstanding Drama Series in 2011. From 2011 to 2012, Ehrin served as a co-executive producer and writer on the NBC drama series \\\"Parenthood\\\". Ehrin, alongside Carlton Cuse and Anthony Cipriano, developed the \\\"Psycho\\\" contemporary prequel series \\\"Bates Motel\\\" for the American cable network A&E. The series began airing in March 2013 and concluded its run in April 2017. Ehrin served as showrunner, lead writer, and an executive producer for the series. In 2014,\", \"How to Get Away with Murder for a fourth season on February 10, 2017, by ABC. The series was renewed for a fifth season on May 11, 2018, by ABC, which premiered on September 27, 2018. In an interview with \\\"Entertainment Weekly\\\", showrunner Peter Nowalk talked about what will happen in the third season regarding Frank's disappearance; he commented: \\\"Yes, I can see the three-piece suits and the hair product all falling apart. It\\u2019s more what Frank feels about himself\\\". When talking about the trust between Annalise and Frank, Nowalk said: \\\"...Frank has two choices: To run away and hope she never catches him, just to\", \"Bates Motel (TV series) November 6, 2015. NBCUniversal partnered with Hot Topic, the American retailer of pop culture merchandise, to introduce a collection of clothing and accessories inspired by \\\"Bates Motel\\\". The merchandise, including items such as bathrobes and bloody shower curtains, became available at Hot Topic's website and select stores on March 18, 2014. As of 2018, the merchandise is no longer available through Hot Topic. Bates Motel (TV series) Bates Motel is an American psychological horror drama television series that aired from March 18, 2013 to April 24, 2017. It was developed by Carlton Cuse, Kerry Ehrin, and Anthony Cipriano, and is\", \"Jason Bateman \\\"Arrested Development\\\" along with the rest of the original cast. The now-Netflix sponsored series released Season 4 on its Instant Watch website on May 26, 2013. The series was expected to continue its run as well as a potential feature film. For the new fourth season, Bateman was once again nominated for Outstanding Actor in a Comedy Series. Netflix confirmed that the entire cast of the show will be returning for a fifth season, premiering 29 May 2018. In 2017, Bateman returned to television as both actor and director in the Netflix drama \\\"Ozark\\\", in which he plays a financial\", \"Empire (season 5) Empire (season 5) The fifth season of the American television drama series \\\"Empire\\\" premiered on September 26, 2018, in the United States on Fox. The season was ordered on May 2, 2018, consisting of eighteen episodes with Brett Mahoney taking over as showrunner from Ilene Chaiken. The show is produced by 20th Century Fox Television, in association with Imagine Entertainment, Lee Daniels Entertainment, Danny Strong Productions and Little Chicken Inc. The showrunners for this season are Mahoney, Danny Strong and Lee Daniels. As part of the renewal process, Brett Mahoney took over as showrunner from Ilene Chaiken. On\", \"Bringing Up Bates on June 1, 2017 and finished on September 14, 2017. UP TV announced Bringing Up Bates will return on January 4, 2018 for their seventh season. UP TV announced season eight on September 25, 2018. It is scheduled to start on January 3, 2019, it will feature the birth of Tori and Bobby's son, Zach and Whitney's vow renewal, Carlin and Evan's engagement, and Josie and Kelton's wedding. Bringing Up Bates Bringing Up Bates (stylized as Br1n9ing Up Bates) is an American reality television show on Up TV. It is centered around Gil and Kelly Jo Bates and their 19\", \"Empire (season 5) July 18, 2018, \\\"Deadline Hollywood\\\" reported that Rhyon Nicole Brown has been upped to regular cast. On June 25, 2018, Nicole Ari Parker was also upgraded to series regular status after recurring in the fourth season. Empire (season 5) The fifth season of the American television drama series \\\"Empire\\\" premiered on September 26, 2018, in the United States on Fox. The season was ordered on May 2, 2018, consisting of eighteen episodes with Brett Mahoney taking over as showrunner from Ilene Chaiken. The show is produced by 20th Century Fox Television, in association with Imagine Entertainment, Lee Daniels Entertainment, Danny\", \"The Haves and the Have Nots (TV series) the series was given an additional 44-episode order. The second half of season 5 premiered on June 20, 2017 and ended with the season finale on September 12, 2017. On November 21, 2017, the series was renewed for a sixth season, which premiered on January 9, 2018, and the 1st mid-season finale aired March 13, 2018 and on March 13, 2018, OWN also announced the second half of the season, which premiered on May 1, 2018 and with the 2nd mid-season finale July 17, 2018. On July 17, 2018, OWN announced the third half of the season, which premiered on\", \"Cold Justice in addition to four confessions, three guilty pleas and three murder convictions. Although TNT made no official announcement, McClary wrote on her personal Facebook page in mid-2016 that the series was canceled. She later said that the production company is shopping the series to other networks. In February 2017, it was announced that \\\"Cold Justice\\\" had been acquired by Oxygen. A fourth season premiered on July 22, 2017. On April 23, 2018, Oxygen announced the series had been renewed for a fifth season, which aired from August 4 to October 6, 2018. \\\"Cold Justice\\\" scored 66 out of 100 on\", \"Criminal Minds (season 13) Criminal Minds (season 13) The thirteenth season of \\\"Criminal Minds\\\" was ordered on April 7, 2017, by CBS with an order of 22 episodes. The season premiered on September 27, 2017 in a new time slot at 10:00PM on Wednesday when it had previously been at 9:00PM on Wednesday since its inception. The season concluded on April 18, 2018. The entire main cast from the previous season returned for the season, except Damon Gupton (Stephen Walker), who was fired from the show. His character was killed off in the season premiere off-screen. Following the cancellation of \\\"\\\", it was announced\", \"Disjointed 32 reviews, with an average rating of 4.0/10. On Metacritic the series has a weighted average score of 43 out of 100, based on reviews from 22 critics, indicating \\\"mixed or average reviews\\\".. Disjointed Disjointed is a Netflix original comedy series created by David Javerbaum and Chuck Lorre and starring Kathy Bates. Twenty episodes of the series were ordered by Netflix, with the first 10 episodes premiering on August 25, 2017. The last 10 episodes were released on January 12, 2018. On February 14, 2018, Netflix canceled the series. After decades of advocating for legalized cannabis usage, Ruth Whitefeather Feldman\", \"The Blacklist (season 5) website Rotten Tomatoes reports a 100% approval rating based on five reviews, with an average score of 8.5/10. The Blacklist (season 5) The fifth season of the American crime thriller television series \\\"The Blacklist\\\" premiered on NBC on September 27, 2017, with a timeslot change from Thursday at 10:00 PM to Wednesday at 8:00 PM. The season was produced by Davis Entertainment, Universal Television and Sony Pictures Television, and the executive producers are Jon Bokenkamp, John Davis, John Eisendrath, John Fox, and Joe Carnahan. The season contained 22 episodes and concluded on May 16, 2018. The season aired the series'\", \"Wentworth (season 5) Wentworth (season 5) The fifth season of the television drama series \\\"Wentworth\\\" premiered on Showcase in Australia on 4 April 2017, having previously aired on SoHo, and concluded on June 20, 2017. It was executive produced by FremantleMedia's Director of Drama, Jo Porter. The season comprised 12 episodes. The fifth season picks up just days after the death of Bea Smith and is therefore noted as the first season not to feature Danielle Cormack. Following Bea Smith\\u2019s tragic death at the hands of Joan Ferguson, emotional, psychological and professional shockwaves pound the inmates and staff of Wentworth Correctional Centre. Governor\", \"Bates Motel (season 4) season 4 began on November 30, 2015 in Vancouver and surrounding areas, and concluded on April 6, 2016. On February 11, 2016, the first image was released featuring Farmiga and Highmore. In March 2016, it was revealed Highmore had written the eighth episode of the season. The fourth season of \\\"Bates Motel\\\" has been met with critical acclaim. The season holds a 100% positive rating on review aggregator website Rotten Tomatoes, based on 17 responses from television critics. Overall, the fourth season of \\\"Bates Motel\\\" averaged 1.45 million viewers, with a 0.6 rating in the 18\\u201349 demographic. In its\", \"Bachelor in Paradise (season 5) Bachelor in Paradise (season 5) The fifth season of \\\"Bachelor in Paradise\\\" premiered on August 7, 2018. Chris Harrison reprised his role from \\\"The Bachelor\\\" and \\\"The Bachelorette\\\" as the host of the show. During the Women Tell All of the twenty-second season of \\\"The Bachelor\\\", host Chris Harrison announced that Bekah Martinez was scheduled to be in Paradise for the upcoming season, but a few months later, she declined to join, due to having a boyfriend. For the first time, it was announced past contestants from the international versions of \\\"The Bachelor\\\" franchise will join the cast similar to\", \"Annie Murphy are similar in shape and size to the Levys' eyebrows, were one of the reasons that she scored the role. The series has won multiple awards since it premiered and has run for four seasons. On March 6, 2018, the show was renewed for a fifth season to air in early 2019. Murphy is married to Hollerado singer Menno Versteeg. In 2016, for her work on \\\"Schitt's Creek\\\", Murphy was nominated for a Canadian Screen Award for Best Performance by an Actress in a Continuing Leading Comedic Role. Annie Murphy on Instagram Annie Murphy Annie Murphy (born December 19, 1986)\", \"Ang Probinsyano (season 5) within the Philippine Government. Ang Probinsyano (season 5) The fifth season of \\\"Ang Probinsyano\\\", a Philippine action drama television series on ABS-CBN, premiered on March 15, 2018 on ABS-CBN's \\\"Primetime Bida\\\" evening block and worldwide on The Filipino Channel. The series stars Coco Martin as SPO2 Ricardo Dalisay, together with an ensemble cast. The fifth season of \\\"Ang Probinsyano\\\" chronicles Cardo and \\\"Vendetta's\\\" struggle against corruption in the larger Philippine political arena. \\\"Vendetta\\\" not only has to fight the Renato Hipolito-backed terrorist group \\\"Kamandag\\\", they are also up against a gun-running ring operated by the Vice President of\", \"The Liar (Prison Break) The Liar (Prison Break) \\\"The Liar\\\" is the 84th episode of the American television series \\\"Prison Break\\\" and the third episode of its fifth season which premiered on Fox in the United States on April 18, 2017. Lincoln prepares for the escape by ordering forged passports. However, he and Sheba are caught by ISIL and Cyclops, who attempts to rape her until Lincoln frees himself and saves her. T-Bag runs into Sara, and tries to warn her about A&W and Van Gogh, two of Poseidon's henchmen, who may be following her trail. A&W and Van Gogh hack into her phone,\", \"Green Acres DVD in Region 1 on October 17, 2017. Season 4 was released on November 28, 2017. Season 5 was released on February 27, 2018. Season 6 was released on July 10, 2018. The \\\"Granby's Green Acres\\\" radio show aired from July 3 to August 21, 1950. The show was produced, directed, and written by Jay Sommers, who wrote and produced a third of the \\\"Green Acres\\\" episodes. In both, a businessman knowing little about farming moves to an impoverished farm. The characters are more conventionally odd, the wife stereotypically talkative and dim, the Sam Drucker character of Sam Drucker is\", \"Z Nation 15, 2017, Syfy renewed the series for a fifth season, which premiered on October 5, 2018. \\\"Z Nation\\\" begins three years into a zombie apocalypse caused by a virus that has already killed most humans. In the days just before society fell apart, Murphy was one of three inmates at Portsmouth Naval Prison in Kittery, Maine, who were unwilling participants in a government-approved experiment. Each inmate was given a different test vaccine. Murphy was the only one to survive the vaccine injection. He is the only known survivor of a zombie bite who did not turn into a zombie, and\", \"Criminal Minds (season 13) CBS announced that Daniel Henney, who was a series regular on \\\"\\\" as Matt Simmons, would join the main show as a series regular for the thirteenth season. On October 12, 2017, it was announced that Shemar Moore would reprise his role as Derek Morgan in the fifth episode of the season (\\\"Lucky Strikes\\\"). His character returned to help Penelope Garcia get through a tough time. Criminal Minds (season 13) The thirteenth season of \\\"Criminal Minds\\\" was ordered on April 7, 2017, by CBS with an order of 22 episodes. The season premiered on September 27, 2017 in a\", \"Laura (Fear the Walking Dead) Laura (Fear the Walking Dead) \\\"Laura\\\" is the fifth episode of the fourth season of the post-apocalyptic horror television series \\\"Fear the Walking Dead\\\", which aired on AMC on May 13, 2018. In flashbacks, John finds Naomi unconscious in the water outside his cabin. John brings her inside, dresses her wound and lets her rest. Because she refuses to tell her name, John calls her Laura. John notices that more infected have been washing up on the creek surrounding his cabin, so he and Naomi canoe upstream to discover a broken guardrail on a bridge. They then patch the guardrail\", \"Tyler Perry's For Better or Worse half of season 4 premiered on September 11, 2015. The fifth season of the series premiered on April 1, 2016. The second half of season 5 premiered on September 30th. On January 30, 2017 OWN announced that the show will be ending after the sixth season. The sixth season premiered on Saturday, June 10, 2017. The series ended on July 22, 2017. The series had a total of 162 episodes. \\\"For Better or Worse\\\" received generally mixed reviews from critics. The \\\"Boston Herald\\\" described it as being \\\"an upscale \\\"Dynasty\\\" crossed with \\\"Who's Afraid of Virginia Woolf?\\\" with a dash\", \"Orange Is the New Black (season 5) Orange Is the New Black (season 5) The fifth season of the American comedy-drama television series \\\"Orange Is the New Black\\\" premiered on Netflix on June 9, 2017, at 12:00 am PST in multiple countries. It consists of thirteen episodes, each between 51\\u201360 minutes. The series is based on Piper Kerman's memoir, \\\"\\\" (2010), about her experiences at FCI Danbury, a minimum-security federal prison. The series is created and adapted for television by Jenji Kohan. In February 2016, the series was renewed for a fifth, sixth, and seventh season. The fifth season was released on June 9, 2017. In\", \"Elementary (TV series) premiere. On March 25, 2016, CBS renewed the series for a fifth season, which premiered on October 2, 2016. On May 13, 2017, CBS renewed the series for a sixth season. On November 29, 2017, CBS ordered an additional eight episodes bringing the sixth season total up to 21. It premiered on April 30, 2018. On May 12, 2018, CBS renewed the series for a seventh season. On December 17, 2018, it was announced that the series would conclude after the upcoming seventh season. Following his fall from grace in London and a stint in rehab, a modern Sherlock Holmes\", \"Inside No. 9 began airing in January 2018 with \\\"Zanzibar\\\", \\\"Bernie Clifton's Dressing Room\\\", \\\"Once Removed\\\", \\\"To Have and to Hold\\\", \\\"And the Winner Is...\\\" and \\\"Tempting Fate\\\". A Halloween special of the show, \\\"Dead Line\\\", aired in October 2018 before the fifth series airs in 2019. \\\"Inside No. 9\\\" as a whole has been very well received by critics, who have praised the humour and creativity of the scripts, as well as the talent of the featured actors. Commentators have described it as \\\"never less-than-captivating\\\" and \\\"consistently compelling\\\", offering particularly strong praise for \\\"A Quiet Night In\\\", \\\"The 12 Days of Christine\\\"\", \"American Horror Story on December 21, 2011. The second season premiered on October 17, 2012, and concluded on January 23, 2013. The third season premiered on October 9, 2013, and concluded on January 29, 2014. The fourth season premiered on October 8, 2014, and concluded on January 21, 2015. The fifth season premiered on October 7, 2015, and concluded on January 13, 2016. The sixth season premiered on September 14, 2016, and concluded on November 16, 2016. The seventh season premiered on September 5, 2017, and concluded on November 14, 2017. The eighth season premiered on September 12, 2018 and concluded on November\", \"Wicked Tuna: Outer Banks that determine size limits and quotas for the season. \\\"Wicked Tuna: Outer Banks\\\" is a spin-off of \\\"Wicked Tuna\\\". Several vessels from the original show also appear in this version. Originally called \\\"Wicked Tuna: North vs. South\\\", the name of the show was changed at the beginning of the second season. Season 5 began airing on July 1, 2018 and concluded on September 24, 2018. Wicked Tuna: Outer Banks Wicked Tuna: Outer Banks (previously known as Wicked Tuna: North vs. South) is a reality television series about commercial tuna fishermen based in the Outer Banks who fish for the\", \"Bates Motel (season 1) Bates Motel (season 1) The first season of \\\"Bates Motel\\\" premiered on March 18, 2013, and concluded on May 20, 2013. The season consisted of 10 episodes and aired on Mondays at 10 p.m. ET/PT on A&E. The series is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\" and follows the life of Norman Bates and his mother Norma prior to the events portrayed in the Hitchcock film. The series takes place in the fictional town of White Pine Bay, Oregon. The season received positive reviews from television critics. In its premiere episode, the series broke rating records\", \"Lip Sync Battle Celebration\\\") on January 18, 2018, in honor of the relaunch of Spike as Paramount Network. Neil Patrick Harris, Taraji P. Henson, Hailee Steinfeld were announced as the first set of performers in January 2018 for the Michael Jackson-themed special, which will originate from the Dolby Theatre and also include a presentation from Cirque du Soleil's Las Vegas residency \\\"\\\". On August 22, 2018, the show was renewed for a fifth season consisting of 12 episodes set to premiere in 2019. Josef Adalian writing for \\\"Vulture\\\" noted the show's success by saying that \\\"Lip Sync Battle\\\" is \\\"looking like a legitimate\", \"Black-ish (season 5) Shahidi as a part of the main cast, due to her character receiving her own spin-off show, \\\"Grown-ish\\\". Black-ish (season 5) The fifth season of \\\"Black-ish\\\" began airing on October 16, 2018 on ABC in the United States. It is produced by Khalabo Ink Society, Cinema Gypsy Productions, Principato-Young Entertainment and ABC Studios, with creator Kenya Barris, who also serves as executive producer alongside Anthony Anderson, Brian Dobbins, Jonathan Groff and Helen Sugland. The series revolves around Dre, portrayed by Anthony Anderson, a family man who struggles with finding his cultural identity while raising his kids in a white\", \"Younger (season 5) seasons, stating that the series \\\"shows promise in being able to adapt and grow, no matter what its age.\\\" While analyzing the premiere episode, Lizzy Buczak of TV Fanatic affirmed that the fifth season is \\\"shaping up to be quite a great season.\\\" Younger (season 5) The fifth season of \\\"Younger\\\", an American comedy-drama television series created by Darren Star and the final season aired onTV Land, was ordered on April 20, 2017. It premiered on June 5, 2018, and revolves around the lead Liza Miller, specifically after she found herself between her two lovers' new publicly romantic lives. The\", \"NCIS: New Orleans series for a third season, which premiered on September 20, 2016. Show creator and executive producer Gary Glasberg, age 50, died unexpectedly on September 28, 2016. On March 23, 2017, CBS renewed the series for a fourth season, which premiered on September 26, 2017. On April 18, 2018, CBS renewed the series for a fifth season, which premiered on September 25, 2018. Scott Bakula was cast as the series lead on February 2014 with CCH Pounder, Zoe McLellan and Lucas Black joining soon thereafter. Rob Kerkovich joined the cast in July. He was the final original cast member to join\", \"Ang Probinsyano (season 5) Ang Probinsyano (season 5) The fifth season of \\\"Ang Probinsyano\\\", a Philippine action drama television series on ABS-CBN, premiered on March 15, 2018 on ABS-CBN's \\\"Primetime Bida\\\" evening block and worldwide on The Filipino Channel. The series stars Coco Martin as SPO2 Ricardo Dalisay, together with an ensemble cast. The fifth season of \\\"Ang Probinsyano\\\" chronicles Cardo and \\\"Vendetta's\\\" struggle against corruption in the larger Philippine political arena. \\\"Vendetta\\\" not only has to fight the Renato Hipolito-backed terrorist group \\\"Kamandag\\\", they are also up against a gun-running ring operated by the Vice President of the Philippines, Lucas Cabrera. Hipolito and\", \"How to Get Away with Murder (season 5) How to Get Away with Murder (season 5) The fifth season of the American legal drama television series \\\"How to Get Away with Murder\\\" revolves around criminal defense attorney Annalise Keating after her class action was accepted, earning back her notoriety within the law field, as well as her personal life's setbacks. The season is produced by Shondaland and NoWalk Entertainment in association with ABC Studios, with Peter Nowalk serving as the showrunner. The season premiered on September 27, 2018. \\\"How to Get Away with Murder\\\" was renewed by ABC for a fifth season on May 11, 2018, to\", \"Grace and Frankie second, third, and fourth seasons, also consisting of 13 episodes each, have been released on May 6, 2016, March 24, 2017, and January 19, 2018, respectively. Despite mixed reviews upon its debut, the series was met with a more positive critical reception towards its later seasons and has received several nominations, including five Primetime Emmy Award nominations for Outstanding Lead Actress in a Comedy Series and a Golden Globe Award nomination for Best Actress \\u2013 Television Series Musical or Comedy. On February 15, 2018, the series was renewed for a fifth season, which is set to premiere on January 18,\", \"Arrested Development (season 5) second Netflix season was \\\"much, much better,\\\" than the first, believing that once the show \\\"hits its stride, it evokes its glory days,\\\" overall being a \\\"welcome return to form\\\". Arrested Development (season 5) The fifth season of the television comedy series \\\"Arrested Development\\\" premiered on Netflix on May 29, 2018. The season will consist of 16 episodes, split into two eight-episode parts; with the second half premiering later in 2018. This is the second revival season after the series was canceled by Fox in 2006; the fourth season premiered in 2013. The show's storyline centers on the Bluth family,\", \"Fresh Off the Boat the second season. On March 3, 2016, ABC announced that the series has been renewed for a third season, which premiered on October 11, 2016. On May 12, 2017, ABC renewed the series for a fourth season, which premiered on October 3, 2017. On May 11, 2018, ABC renewed the series for a fifth season, which premiered on October 5. The story follows the course of Eddie Huang's Taiwanese family as they make their way from Chinatown of Washington, DC to Orlando, Florida, to open a cowboy-themed steak restaurant in 1995 (with the first four seasons being set between 1995\", \"El Sen\\u0303or de los Cielos (season 5) El Se\\u00f1or de los Cielos (season 5) The fifth season of the drama television series \\\"El Se\\u00f1or de los Cielos\\\" premiered on Telemundo on June 20, 2017, and concluded on November 2, 2017. The season follows the revenge of Aurelio against his nephew V\\u00edctor Casillas and his enemy La Felina. It stars Rafael Amaya as Aurelio Casillas \\u2014 A Mexican drug lord, along with Fernanda Castillo, Carmen Aub, Vanessa Villela, Sabrina Seara, and incorporation into the lead role of Maricela Gonz\\u00e1lez, and Mariana Seoane and Miguel Varoni both including as special participation. The fifth season of the series was made\", \"The Tenderloins The show was renewed for a fifth season, which began on February 2016. On July 22, 2016, a sixth season was announced and the sixth season began on February 9, 2017. After another successful season, \\\"Impractical Jokers\\\" was renewed for a seventh season that began on February 1, 2018. In March 2018 TruTV announced the Impractical Jokers was renewed for an eight season that will consist of 26 episodes and will premiere sometime in February 2019. TruTV also announced that an Impractical Jokers feature-length movie, \\\"Impractical Jokers Movie\\\", will go into production in the spring of 2018. In 2019, the\", \"The Goldbergs (season 5) The Goldbergs (season 5) The fifth season of the American television comedy series \\\"The Goldbergs\\\" premiered on ABC on September 27, 2017 and concluded on May 16, 2018. The season is produced by Adam F. Goldberg Productions, Happy Madison Productions, Doug Robinson Productions, and Sony Pictures Television, and the executive producers are Adam F. Goldberg, Doug Robinson, and Seth Gordon. The show explores the daily lives of the Goldberg Family; a family living in Jenkintown, Pennsylvania in the 1980s. Beverly Goldberg (Wendi McLendon-Covey), the overprotective matriarch of the Goldbergs is married to Murray Goldberg (Jeff Garlin). They are the parents\", \"How to Get Away with Murder (season 5) fun,\\\" and totally different. In June 2018, Rome Flynn was promoted to the series' main cast after appearing in a guest capacity in the fourth-season finale. In July 2018, Amirah Vann was promoted to the series' main cast after recurring in the fourth season. Later that month, Timothy Hutton joined the main cast. The first table read took place on July 13, 2018. Filming for the season started on July 19, 2018. How to Get Away with Murder (season 5) The fifth season of the American legal drama television series \\\"How to Get Away with Murder\\\" revolves around criminal defense\", \"Bates Motel (season 4) Awards. It also won three People's Choice Awards for Cable TV Drama, Cable TV Actress (Farmiga), and Cable TV Actor (Highmore). \\\"Bates Motel\\\" fourth season maintained consistent ratings throughout its airing, with the season premiere drawing in 1.55 million viewers and the finale totalling 1.50 million. The season was released on Blu-ray and DVD on October 18, 2016. In August 2015, TVLine reported that Ryan Hurst would be returning to the series as Chick Hogan, a recurring character throughout the third season. On November 5, 2015, reports surfaced that the series was casting the recurring role of Gregg Edwards, a\", \"Wild Kratts 5 episodes (\\\"Archerfish School\\\", \\\"This Orca Likes Sharks\\\", \\\"Baby Tooth and Kid Musky\\\", \\\"Cheetah Adopted\\\" and \\\"Muskox Mania\\\") aired during the week of April 10, 2017 to April 14, 2017. Season 5 began on July 24, 2017 with the premiere of \\\"Wild Kratts Alaska: Hero's Journey\\\" as well as \\\"Mystery Of the North Pole Penguins\\\" and \\\"Temple of Tigers\\\". This season focuses on habitats such as Europe's Black Forest, and Antarctica, as well as the Indian jungles. According to the Kratt Brothers there would be a Halloween special focusing on tarantulas and vampire bats. On November 6, 7, and 8th,\", \"Arrested Development (season 5) Arrested Development (season 5) The fifth season of the television comedy series \\\"Arrested Development\\\" premiered on Netflix on May 29, 2018. The season will consist of 16 episodes, split into two eight-episode parts; with the second half premiering later in 2018. This is the second revival season after the series was canceled by Fox in 2006; the fourth season premiered in 2013. The show's storyline centers on the Bluth family, a formerly wealthy, habitually dysfunctional family, and the show incorporates hand-held camera work, narration, archival photos, and historical footage. One central storyline of season five is a \\\"whodunit\\\" regarding the\", \"Good Witch (TV series) aired on October 21, 2018. On July 26, 2018, the series was renewed for a fifth season. As with the previous seven TV movies, the series continues the spirited life of newly widowed Cassie Nightingale, the good-hearted enchantress of the fictional Middleton, United States, with her now-teenage daughter Grace. When new neighbors, the Radfords, move in next door, they become suspicious of Cassie and her daughter. The series is filmed in Toronto, Ontario primarily at Cinespace Film Studios' Kipling Avenue facility. As producer Orly Adelson was president of ITV Studios America in 2014, ITV agreed to become the franchise's TV\", \"The Goldbergs (season 5) of three children, Erica (Hayley Orrantia), Barry (Troy Gentile), and Adam (Sean Giambrone). AJ Michalka was demoted to recurring for this season, while Sam Lerner was promoted to a regular cast member. ABC renewed \\\"The Goldbergs\\\" for its fifth and sixth seasons in May 2017. The Goldbergs (season 5) The fifth season of the American television comedy series \\\"The Goldbergs\\\" premiered on ABC on September 27, 2017 and concluded on May 16, 2018. The season is produced by Adam F. Goldberg Productions, Happy Madison Productions, Doug Robinson Productions, and Sony Pictures Television, and the executive producers are Adam F.\", \"Disjointed Disjointed Disjointed is a Netflix original comedy series created by David Javerbaum and Chuck Lorre and starring Kathy Bates. Twenty episodes of the series were ordered by Netflix, with the first 10 episodes premiering on August 25, 2017. The last 10 episodes were released on January 12, 2018. On February 14, 2018, Netflix canceled the series. After decades of advocating for legalized cannabis usage, Ruth Whitefeather Feldman (Kathy Bates) employs her newly graduated son and a team of young \\\"budtenders\\\" to help run her Los Angeles cannabis dispensary. Along the way, her business runs into trouble thanks to the efforts\", \"Bates Motel (season 2) the second season averaged 2.30 million viewers, with a 0.9 ratings share in the 18\\u201349 demographic. In its second season, \\\"Bates Motel\\\" was nominated for 16 awards, winning none. Bates Motel (season 2) The second season of \\\"Bates Motel\\\" consisted of 10 episodes and premiered on A&E on March 3, 2014. The season aired on Mondays at 9 p.m. ET/PT on A&E, and concluded on May 5, 2014. The series itself is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\" and follows the life of Norman Bates and his mother Norma in the fictional town of White Pine\", \"American Horror Story: Apocalypse Misty Day (Lily Rabe), provides background of the world before the fateful apocalypse as well as the inclusion of characters from \\\"Murder House\\\", such as Michael's grandmother, Constance Langdon (Jessica Lange). On January 12, 2017, the series was renewed for an eighth season, which premiered on September 12, 2018. In October 2016, series co-creator Ryan Murphy announced a \\\"crossover\\\" season between previous cycles \\\"\\\" and \\\"\\\". In January 2018, he stated that the ninth season would most likely feature the crossover; however, in June 2018, he announced that the eighth season was chosen instead. Murphy has also stated that\", \"Roseanne (season 10) the series are Judy Prescott as Miss Crane, Estelle Parsons as Beverly Harris, Sandra Bernhard as Nancy Bartlett, Natalie West as Crystal Anderson, James Pickens, Jr. as Chuck Mitchell, Adilah Barnes as Anne Marie Mitchell, and Johnny Galecki as David Healy. In December 2017, it was announced that Christopher Lloyd would guest star in the revival as a love interest of Parsons' character Beverly. Production on the season began on October 17, 2017, and concluded on December 15, in Studio City, Los Angeles. The season began airing on March 27, 2018, on ABC in the United States, and on CTV\", \"Major Crimes fifth season, On June 22, 2016, TNT ordered eight additional episodes for season five, bringing the total to 21. On January 18, 2017, the series was renewed for a 13-episode sixth season, clarified in October as the final season, which aired from October 31, 2017, to January 9, 2018. \\\"Major Crimes\\\" received a score of 65/100 and \\\"generally favorable\\\" reviews based on 17 critics at Metacritic. \\\"Newsday\\\"s Verne Gay gave the series a B+ grade, calling it \\\"sharply written, acted and directed\\\", adding \\\"producers now have to turn an (occasional) antagonist into a full-time protagonist. Let the metamorphosis begin.\\\" Robert\", \"Bates Motel (season 3) Bates Motel (season 3) The third season of \\\"Bates Motel\\\" consisted of 10 episodes and premiered on A&E on March 9, 2015. The season aired on Mondays at 9 p.m. ET/PT, and concluded on May 11, 2015. The series itself is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\", following the life of Norman Bates and his mother Norma prior to the events portrayed in the Hitchcock film. The series takes place in the fictional town of White Pine Bay, Oregon. The season received positive reviews from television critics, and the premiere episode drew in a total of\", \"Kaniel Outis (Prison Break) Kaniel Outis (Prison Break) \\\"Kaniel Outis\\\" is the 83rd episode of the American television series \\\"Prison Break\\\" and the second episode of its fifth season which premiered on Fox in the United States on April 11, 2017. Lincoln receives a message from Michael asking them to find the \\\"Sheik of Light.\\\" Sheba, the contact, agrees to help decode the message in exchange for money. Sara receives the video recording of Michael and meets up with Kellerman at the State Department. He deduces that Michael was the mastermind of changing his identity. Later, he sends Sara footage of Michael killing a\", \"The Prisoner's Dilemma (Prison Break) The Prisoner's Dilemma (Prison Break) \\\"The Prisoner's Dilemma\\\" is the 85th episode of the American television series \\\"Prison Break\\\" and the fourth episode of its fifth season which premiered on Fox in the United States on April 25, 2017. ISIL continues advancing in Sana'a. Cross rallies the other prisoners to capture Ramal and use him as a bargaining chip. Michael convinces a reluctant Ramal to help them out as he is the one inside of the solitary cell with escape tools. Ramal, Michael, Ja and Whip are able to escape just as Cross and his followers break into solitary. Sid\", \"The Blacklist (TV series) 2015, the series was renewed for a fourth season, which premiered on September 22, 2016. A spin-off series, \\\"\\\", premiered on February 23, 2017. On May 11, 2017, the series was renewed for a fifth season, while the spin-off was canceled the following day. The fifth season premiered on September 27, 2017. After showing a screening of the pilot at Comic-Con, producers revealed that their inspiration for \\\"The Blacklist\\\" came from the capture of Whitey Bulger. Recalling the experience in an interview with Collider, executive producer John Eisendrath stated: NBC bought the rights to \\\"The Blacklist\\\" from Sony Pictures Television\", \"Bates Motel (season 2) Bates Motel (season 2) The second season of \\\"Bates Motel\\\" consisted of 10 episodes and premiered on A&E on March 3, 2014. The season aired on Mondays at 9 p.m. ET/PT on A&E, and concluded on May 5, 2014. The series itself is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\" and follows the life of Norman Bates and his mother Norma in the fictional town of White Pine Bay, Oregon prior to the events portrayed in the Hitchcock film. The season received positive reviews from television critics, and the premiere episode drew in a total of 3.07\", \"American Horror Story: Apocalypse American Horror Story: Apocalypse American Horror Story: Apocalypse is the eighth season of the FX horror anthology television series \\\"American Horror Story\\\". It premiered on September 12, 2018, and concluded on November 14, 2018. It has been described as a crossover between the and seasons of the series. The season was announced on January 12, 2017. Returning cast members from previous seasons include Sarah Paulson, Kathy Bates, Evan Peters, Adina Porter, Emma Roberts, Cheyenne Jackson, Billy Eichner, Leslie Grossman, Billie Lourd, Jessica Lange, Taissa Farmiga, Gabourey Sidibe, Lily Rabe, Frances Conroy, Stevie Nicks, Connie Britton, Dylan McDermott, Erika Ervin, Wayne\", \"Sen\\u0303ora Acero (season 5) Se\\u00f1ora Acero (season 5) The fifth and final season of the American television series \\\"Se\\u00f1ora Acero\\\" also known as \\\"Se\\u00f1ora Acero: La Coyote\\\", follow the life of Vicenta Acero, 6 years after the death of her husband Daniel Philips and her new future as a mother. The season was ordered in February 2018, with filming beginning that August. It was later announced that the fifth season would would be the final season. Principal cast members Carolina Miranda, Ana Luc\\u00eda Dom\\u00ednguez, and Diego Cadavid return from previous seasons. The season premiered on 15 October 2018. Pregnant during eight months, the time\", \"Below Deck with another five new crew members. In April 2016, the network renewed \\\"Below Deck\\\" for a fourth season. On February 21, 2017, it was announced production filming for season 5 had begun. On February 22, 2017 it was announced that the production boat had sunk after its propeller hit something while arriving in the marina in the Caribbean while filming for season 5. On July 17, 2017, it was announced that the fifth season would premiere on September 5, 2017, with Lee Rosbach, Kate Chastain and Nico Scholly returning. The sixth season premiered on October 2, 2018. Lee Rosbach and\", \"Bates Motel (season 1) of 2.70 million viewers, with a 1.2 ratings share in the 18\\u201349 demo. Overall, the first season averaged 2.70 million viewers, with 1.5 million tuning in from both the 18\\u201349 and 25\\u201354 demographics. In its first season, \\\"Bates Motel\\\" was nominated for 24 awards, winning one. Bates Motel (season 1) The first season of \\\"Bates Motel\\\" premiered on March 18, 2013, and concluded on May 20, 2013. The season consisted of 10 episodes and aired on Mondays at 10 p.m. ET/PT on A&E. The series is described as a \\\"contemporary prequel\\\" to the 1960 film \\\"Psycho\\\" and follows the life\", \"Ray Donovan Ray Donovan Ray Donovan is an American television crime drama series created by Ann Biderman for Showtime. The twelve-episode first season premiered on June 30, 2013. The pilot episode broke viewership records, becoming the biggest premiere of all time on Showtime. Showtime renewed the show for a fourth season, which premiered on June 26, 2016. On August 11, 2016, Showtime renewed the show for a fifth season, which premiered on August 6, 2017. On October 23, 2017, the series was renewed for a 12-episode sixth season, filmed in New York City, which premiered on October 28, 2018. The drama is\", \"Criminal Minds (season 14) episodes for the season. On June 25, 2018, it was revealed that Matthew Gray Gubler, Joe Mantegna, Aisha Tyler and Adam Rodriguez will direct episodes this season and that A.J. Cook will make her directorial debut and direct an episode this season. On September 21, 2018, it was revealed that Kirsten Vangsness and showrunner Erica Messer will be co-writing the season finale. This will be the fifth episode they have co-written together. On October 19, 2018, it was announced that Stephen Bishop had been cast in a recurring role as SSA Andrew Mendoza, who will be a love interest for\", \"Trolls: The Beat Goes On! Zahn of \\\"The Rock Father\\\" confirmed that there would be a second season. It was streamed on March 9 and consists of seven episodes. The third season was released on August 24, 2018. The fourth season was released on November 2, 2018. Season 5 will be released on January 18, 2019. The show picks up where the film \\\"Trolls\\\" left off; the series will follow Queen Poppy, Branch, the Snack Pack and the other Trolls, and their Bergen pals, as they experience everyday life in Troll Village. The series was released internationally on Netflix. It was also released in the\", \"American Horror Story: Apocalypse based on 5 reviews. The critical consensus reads, \\\"Ryan Murphy and his murderers' row of witchy performers literally save the world -- and franchise -- in \\\"Apocalypse\\\", the series most ambitious crossover swing yet.\\\" On Metacritic, the season was given a score of 63 out of 100 based on 6 reviews, indicating \\\"generally favorable reviews\\\". American Horror Story: Apocalypse American Horror Story: Apocalypse is the eighth season of the FX horror anthology television series \\\"American Horror Story\\\". It premiered on September 12, 2018, and concluded on November 14, 2018. It has been described as a crossover between the and seasons\", \"Boy Wonder (American Horror Story) Boy Wonder (American Horror Story) \\\"Boy Wonder\\\" is the fifth episode of the of the anthology television series \\\"American Horror Story\\\". It aired on October 10, 2018, on the cable network FX. The episode was written by John J. Gray, and directed by Gwyneth Horder-Payton. After passing out, Cordelia Goode (Sarah Paulson) experiences a vision of a burnt and desolate world. She sees a demonic pale-faced man standing on the porch of the destroyed Miss Robichaux's Academy before she is attacked and devoured by a mob of disease-ridden cannibals while the man laughs. Cordelia awakens in the Hawthorne School for\", \"Off Colors same review, he praised the episode's unexpected revelation of Rose Quartz's resurrection abilities and the show's unpredictability, stating that \\\"for the first time in a while, there\\u2019s no way to know what\\u2019s going to happen on \\\"Steven Universe\\\" \\u2014 and it\\u2019s thrilling.\\\" Off Colors \\\"Off Colors\\\" is the third episode of the fifth season of the American animated television series \\\"Steven Universe\\\", which premiered on May 29, 2017 on Cartoon Network. It was written and storyboarded by Lamar Abrams and Jeff Liu. The episode was viewed by 1.524 million viewers. A direct continuation of the previous episode \\\"The Trial\\\", \\\"Off\", \"Black-ish 11, 2018, ABC renewed the series for a fifth season. The fifth season premiered on October 16, 2018. \\\"Black-ish\\\" first appeared on the development slate at ABC in October 2013, when it was reported that the project, which would star Anthony Anderson, had received a script commitment. On January 16, 2014, ABC greenlit the pilot episode. Two weeks later, Larry Wilmore joined the show as showrunner. In mid-February, Laurence Fishburne was cast as the father of Anderson's character, and Tracee Ellis Ross signed on as the female lead. On May 8, 2014, ABC picked up the pilot to the series\", \"Wentworth (TV series) aired. In a similar manner, a 12-episode fourth season was announced before the airing of the third season on 27 February 2015. It began airing from 10 May 2016. Cormack confirmed a fifth season had been commissioned on 19 July. The twelve-part series premiered on 4 April 2017. On 9 May 2017, Showcase announced that the series has been renewed for a sixth season, which premiered on 19 June 2018. A seventh season was commissioned in April 2018, before the sixth-season premiere, with filming commencing the following week and a premiere set for 2019. On 5 December 2018, it was\", \"Stranger Things season, consisting of nine episodes, was released on October 27, 2017. In December 2017, Netflix ordered a third season, which began production in April 2018 and will consist of eight episodes, and is set to be released in 2019. The Duffer Brothers have said that \\\"Stranger Things\\\" is likely to end after its fourth or fifth season. The series has received 31 Emmy Award nominations, including for Outstanding Drama Series, four Golden Globe Award nominations, and won the Screen Actors Guild Award for Outstanding Performance by an Ensemble in a Drama Series in 2016. \\\"Stranger Things\\\" is set in the\", \"Younger (season 5) Younger (season 5) The fifth season of \\\"Younger\\\", an American comedy-drama television series created by Darren Star and the final season aired onTV Land, was ordered on April 20, 2017. It premiered on June 5, 2018, and revolves around the lead Liza Miller, specifically after she found herself between her two lovers' new publicly romantic lives. The season was produced by Darren Star Productions and Jax Media, with Star serving as showrunner. Sutton Foster stars as Miller, with Debi Mazar, Miriam Shor, Hilary Duff, Nico Tortorella, Molly Bernard and Peter Hermann also returning from the fourth season. They are joined\", \"Rihanna 100, and Kendrick Lamars single, \\\"Loyalty\\\", which earned Rihanna her ninth Grammy Award at the 60th Annual Grammy Awards. In November 2017, Rihanna was part of N.E.R.Ds comeback single \\\"Lemon\\\" from the band's album \\\"No One Ever Really Dies\\\". Rihanna officially revealed that she had begun work on her ninth studio album, just months after releasing \\\"Anti\\\". On 19 October 2017, Shakka revealed that he was working with Rihanna on her \\\"absolutely insane\\\" album. Rihanna played the recurring role of Marion Crane in the fifth and final season of \\\"Bates Motel\\\". The show received universal acclaim from critics. Rihanna also\", \"Criminal Minds (season 14) Criminal Minds (season 14) The fourteenth season of \\\"Criminal Minds\\\" was ordered on May 12, 2018, by CBS with an order of 15 episodes. The season premiered on October 3, 2018. The season also featured the milestone 300th episode which served as the season premiere. The entire main cast from the previous season returned. \\\"Criminal Minds\\\" was renewed for a fourteenth season with an episode order of 15 episodes on May 12, 2018, with the possibility of more episodes being ordered later in the season. On November 27, 2018, it was reported that CBS had declined to order any more\", \"Five Feet Apart begin in the next month. Principal production began on May 25, 2018 in New Orleans, Louisiana and concluded a month later, on June 26, 2018. Five Feet Apart Five Feet Apart is a 2019 American romantic drama film written by Mikki Daughtry and Tobias Iaconis and directed by Justin Baldoni. The film will star Haley Lu Richardson and Cole Sprouse and will be released on March 22, 2019 by CBS Films. In January 2017, Tobias Iaconis and Mikki Daughtry sold their untitled screenplay to CBS Films for Justin Baldoni to produce and direct. In January 2018, Cole Sprouse was cast\", \"Akane no Mai Akane no Mai \\\"Akane no Mai\\\" is the fifth episode of the second season of the HBO science-fiction thriller television series \\\"Westworld\\\". The episode aired on May 20, 2018. It was written by Dan Dietz and directed by Craig Zobel. The episode's plot includes Strand and his team analyzing the damage to the Mesa after retaking it in the present, and both Dolores's plans to assault the Mesa in the past and her answer to Teddy's merciful personality; however, it mostly focuses on Maeve's experiences in Shogun World, a more extremist version of Westworld, though based on the Edo period\", \"Grimm (TV series) 24, 2014. The series premiered in the UK on February 13, 2012 on W (known then as Watch), with season two returning on October 22, 2012, and season 3 on February 5, 2014. The fourth season premiered on January 28, 2015. The fifth season premiered on November 3, 2015. The sixth season premiered on February 14, 2017. The series is also available to watch on Netflix. Grimm (TV series) Grimm is an American fantasy police procedural drama television series created by Stephen Carpenter and Jim Kouf & David Greenwalt and produced by Universal Television for NBC. The series aired from\", \"American Horror Story: Cult its seventh season, the series has been nominated for 22 awards, 3 of them were won. American Horror Story: Cult American Horror Story: Cult is the seventh season of the FX horror anthology television series \\\"American Horror Story\\\". It premiered on September 5, 2017, and concluded on November 14, 2017. The series was renewed on October 4, 2016. The subtitle \\\"Cult\\\" was announced on July 20, 2017. This season takes place in the fictional suburb of Brookfield Heights, Michigan, during the year 2017, and centers on a cult terrorizing the residents in the aftermath of the 2016 U.S. presidential election.\", \"Rowan & Martin's Laugh-In season on January 9, 2018, and the third season on March 6, 2018. The fourth season was released on May 8, 2018. Season 5 was released on July 10, 2018. Finally, Season 6 was released on September 4, 2018. TV season, ranking, average viewers per episode In 1977, Schlatter and NBC briefly revived the property as a series of specials \\u2013 titled simply \\\"Laugh-In\\\" \\u2013 with a new cast, including former child evangelist Marjoe Gortner. The standout was a then-unknown Robin Williams, whose starring role on ABC's \\\"Mork & Mindy\\\" one year later prompted NBC to rerun the specials as\", \"Broad City New York. Amy Poehler is one of \\\"Broad City\\\"s executive producers, and appeared in the webseries finale. The series premiered on Comedy Central on January 22, 2014. \\\"Broad City\\\"s fourth season premiered on September 13, 2017. The series is set to end after its fifth season, which will begin broadcasting on January 24, 2019. \\\"Broad City: High Score\\\", a mobile game developed and published by Built Games, launched worldwide on April 20, 2018. \\\"Broad City\\\" follows Ilana and Abbi, two Jewish American women in their twenties, who experience adventures of carelessness and frivolity in New York City. Ilana seeks to\", \"Haven (season 5) Haven (season 5) The fifth and final season of the American television series \\\"Haven\\\" premiered on September 11, 2014 on Syfy, and concluded on December 17, 2015. The 26-episode season was split into two parts, containing a total of thirteen episodes each. The first part began its broadcast on September 11, 2014 in its new date and time slot on Thursday at 8:00 pm (ET) for the first four episodes, before returning to its previous time slot of Friday at 7:00 pm (ET). The second part began broadcasting with the first two episodes on October 8, 2015 at 10:00 pm\", \"Life Sentence (TV series) British Columbia, Canada. The series is set in the fictional town of Asheville, Oregon. The CW officially ordered \\\"Life Sentence\\\" to series on May 10, 2017. In January 2018, the network announced the premiere date of \\\"Life Sentence\\\" on March 7, 2018. On March 30, 2018, the CW announced that \\\"Life Sentence\\\" would move to Fridays at 9pm, which began with the sixth episode. In late January 2017, Lucy Hale was cast as Stella Abbott, followed in February by the casting of Jayson Blair as her older brother, Aiden and Dylan Walsh as her father, Peter. On February 24, 2017,\", \"BoJack Horseman more positive towards the second half of the first season, before universally acclaiming the subsequent seasons. Alongside having a satirical take on current events, politics, and show business, \\\"BoJack\\\" is lauded for its realistic take on dealing with depression, trauma, addiction, self-destructive behavior, and the human experience. In 2018, online magazine Thrillist ranked it as the best Netflix original series of all time. On September 21, 2017, the series was renewed for a fifth season, which premiered on September 14, 2018. The show was renewed for a sixth season on October 30, 2018. The series takes place mostly in Hollywood\", \"The Guest Book his habit of writing fake guest book entries while on vacation, wrote all 10 episodes of the first season. The series premiered on TBS on August 3, 2017. On September 13, 2017, TBS renewed the series for a second season, which premiered on October 23, 2018. The intended fifth episode of the series was originally intended to air on August 24, 2017, but was held back as it \\\"contained elements that were deemed too sensitive\\\" at that point in time. At the end of August 2017, Garcia noted on Twitter that the held episode would not be made available \\\"for\", \"Shaun Micallef's Mad as Hell July 2012. The show was renewed for a second series of 12 episodes which aired in 2013. A third was announced in January 2014 and consisted of 10 episodes, shown weekly from 12 February 2014. A fourth series premiered later in the same year on 24 September 2014 and the fifth series premiered on 11 February 2015. A sixth season debuted on 11 May 2016. The show's seventh season commenced on 21 June 2017. An eighth season debuted on 31 January 2018, with a ninth season running from September 2018. A tenth season was confirmed for July 2019 by Micallef\", \"Grimm (season 5) Grimm (season 5) The fifth season of the NBC American supernatural drama series \\\"Grimm\\\" was announced on February 5, 2015. It premiered on October 30, 2015 and concluded on May 20, 2016. The season consisted of 22 episodes. The series was created by David Greenwalt, Jim Kouf and Stephen Carpenter, and produced by NBC, GK Productions, Hazy Mills Productions, and Universal Television. It follows a descendant of the Grimm line, Nick Burkhardt, as he deals with being a cop, and trying not to expose his secret as a Grimm. Rodriguez returned as Chavez in the episode \\\"The Grimm Identity\\\", as\", \"East Los High Los High\\\" would not be renewed for a fifth season. Hulu instead ordered an eighty-three minute series finale that aired on December 1, 2017. The series revolves around two teenage cousins\\u2014Jessie, who is a studious virgin, and Maya, a troubled runaway\\u2014who falls in love with Jacob, a popular football player. From this love triangle, Maya and Jessie must face true-to-life decisions involving sex, drugs, pregnancy, infidelity and peer pressure that will decide which one of them gets the boy and mark their lives forever. All the sex, romance, and mystery continue when Ceci returns to East Los High to coach\", \"I'm a Celebrity...Get Me Out of Here! (Australian TV series) rates. On 1 August 2016 the series was renewed for a third season with Morris and Brown returning as hosts, which premiered on 29 January 2017. A fourth season commenced on 28 January 2018 and concluded 12 March 2018. A fifth season has been announced and will air from 13 January 2019. The show is set to be reduced from airing over a six week period to four weeks for the 2019 season. The premise of the show is that there is a group of well known personalities living together in a specially constructed camp site in a jungle. During\", \"Agents of S.H.I.E.L.D. (season 5) Gregg made his directorial debut on the series during the season, which features the series' 100th episode that was promoted with a special commemorative art program. The fifth season began airing on December 1, 2017, and ran for 22 episodes on ABC until May 18, 2018. The two-part premiere debuted to 2.54 million viewers, marking the lowest-rated season premiere of the series. Despite consistently low viewership, critical reception of the season was positive, with many commending the series for its ambition, in particular praising the futuristic space setting during its first half and exploration of time travel. Critics also praised\", \"Akane no Mai to have his personality overwritten proved that \\\"forcing someone into a new story can be an awful form of violence.\\\" The review also noted that Dolores was unable to entirely sever her connection to Teddy or her father, the former given a partial reset instead of a \\\"full reset.\\\" Akane no Mai \\\"Akane no Mai\\\" is the fifth episode of the second season of the HBO science-fiction thriller television series \\\"Westworld\\\". The episode aired on May 20, 2018. It was written by Dan Dietz and directed by Craig Zobel. The episode's plot includes Strand and his team analyzing the damage\"], \"pos_scores\": [103.5], \"neg_scores\": [99.0625, 85.0625, 86.0625, 94.75, 95.6875, 97.4375, 92.1875, 92.5625, 85.75, 85.625, 87.6875, 87.5, 88.0625, 94.3125, 89.625, 85.4375, 91.8125, 85.5, 88.3125, 85.375, 85.25, 88.125, 85.875, 84.75, 91.4375, 85.875, 88.375, 84.5, 84.8125, 86.0625, 85.3125, 84.375, 83.5625, 85.6875, 87.75, 85.75, 85.0, 89.1875, 84.9375, 89.375, 84.8125, 85.4375, 85.875, 85.6875, 84.5, 88.0625, 87.3125, 87.25, 85.9375, 84.75, 86.25, 85.3125, 87.0625, 91.875, 85.125, 88.0, 84.25, 85.1875, 88.75, 89.0625, 87.1875, 85.5625, 87.5, 89.9375, 84.625, 85.0625, 86.3125, 89.0, 86.6875, 84.5, 85.75, 89.125, 87.0625, 83.9375, 86.1875, 85.375, 84.25, 84.0, 85.6875, 86.0, 85.75, 85.75, 89.0, 84.6875, 85.125, 83.5625, 86.5, 86.1875, 86.375, 85.375, 85.125, 85.5625, 86.375, 85.375, 85.375, 84.25, 84.75, 84.375, 87.25, 84.125], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"how many episodes are in series 7 game of thrones\", \"pos\": [\"Game of Thrones (season 7) Game of Thrones (season 7) The seventh and penultimate season of the fantasy drama television series \\\"Game of Thrones\\\" premiered on HBO on July 16, 2017, and concluded on August 27, 2017. Unlike previous seasons that consisted of ten episodes each, the seventh season consisted of only seven. Like the previous season, it largely consisted of original content not found in George R. R. Martin's \\\"A Song of Ice and Fire\\\" series, while also incorporating material Martin revealed to showrunners about the upcoming novels in the series. The series was adapted for television by David Benioff and D. B. Weiss.\"], \"neg\": [\"The Bill (series 17) year were multi-part stories, some containing up to six episodes, such as the \\\"Night Games\\\" saga. The two-part episode \\\"Lifelines\\\" is the last two-parter to feature in the series until the return of episode titles in 2007. On 14 August 2013, The Bill Series 17 Part 1 & 2 and The Bill Series 17 Part 3 & 4 DVD sets were released (in Australia). The Bill (series 17) Series 17 of British television drama \\\"The Bill\\\" consisted of 92 episodes, broadcast between 5 January and 21 December 2001. As well as 85 regular episodes, the series also included a\", \"Dr. Finlay's Casebook in April 2014. Only 10 episodes survive of the second series. The surviving episodes of series 3 and 4 were issued in 2015 and the remaining episodes series 5, 6 & 7 were released in January 2016. The nine surviving episodes from series 8 were released in April 2016. Of a complete run of 191 episodes, 122 are believed to no longer exist. Cronin received copies of the scripts, and he wrote a blunt letter to the series' script editor in 1964, expressing his dissatisfaction with the progression of the show. Word leaked to the media, and in June 1964,\", \"Game of Thrones (season 6) \\\"Game of Thrones\\\" was the most-pirated TV series in 2016. Game of Thrones (season 6) The sixth season of the fantasy drama television series \\\"Game of Thrones\\\" premiered on HBO on April 24, 2016, and concluded on June 26, 2016. It consists of ten episodes, each of approximately 50\\u201360 minutes long, largely of original content not found in George R. R. Martin's \\\"A Song of Ice and Fire\\\" series. Some story elements were derived from the novels and from information Martin revealed to the show-runners. The series was adapted for television by David Benioff and D. B. Weiss. HBO ordered\", \"7 Day Sunday 2010, 25 April 2010. Series Two consisted of 26 episodes, broadcast between 5 September 2010 and 3 April 2011. There were no episode broadcast on 19 September 2010, 3 October 2010, 26 December 2010, 2 January 2010 and 30 January 2010. Series Three had 15 episodes which were broadcast between 8 January 2012 and 6 May 2012. No episode was broadcast on 25 March 2012. Series Four also had 15 episodes, began on 9 September 2012, and ended on 16 December. In 2013, the show returned for a new, slightly different series: it was moved to Saturdays, renamed 7 Day\", \"Me and My Monsters 8:00 am, and a new repeat slot of 6:15 pm for the new episodes has been made on CBBC. Originally, the whole two seasons was made as 26 episodes of half hour for one whole series but has split into two seasons with a 4 to 5-month gap in between their original airdates. The full series of Me and My Monsters was released as a box set in Germany and Switzerland. Also in Norway and Sweden. In Australia, there has been a DVD release of \\\"Me and My Monsters\\\" called \\\"Series 1: Episodes 1\\u20137\\\". As the title says it contains\", \"Dudley Simpson have the BBC Radiophonic Workshop provide music from that point. While Simpson was contracted to score \\\"Shada\\\", the unfinished nature of that production meant he never started work. As a result, his last broadcast work on \\\"Doctor Who\\\" was for \\\"The Horns of Nimon\\\". In the 2017 restoration of \\\"Shada\\\", a dedication to Simpson was shown in the end credits. \\\"Blake's 7\\\" ran for 4 series totalling 52 episodes in all. Simpson provided the incidental music for 50 of the 52 episodes that were broadcast from 1978 to 1981, including the theme music. The two exceptions are the episode entitled\", \"Multi-Coloured Swap Shop Book 4 extremely collectable. Out of the 146 episodes that were made in total, 41 survive which are Episode 21 of Series 1, Episodes 4-5 & 21 of Series 2, Episode 24 of Series 3, Episodes 1-2, 7, 12, 15, 17, 21 & 25 of Series 4, Episodes 1-2, 12, 14-18, 21, 23, 25 & 27 of Series 5 and Episodes 1, 3, 5-7, 11, 13, 15-17, 19-22, & 24-25 of Series 6. Due to industrial action by the ABS union at the BBC over Thursday 21 and Friday 22 December 1978, the edition which should have aired on Saturday\", \"Game of Thrones (season 7) The first official trailer for season 7 was released on May 24, 2017. The trailer set a world record for being the most viewed show trailer ever, being viewed 61 million times across digital platforms, in the first 24 hours. The second official trailer was released on June 21, 2017. The season premiere was screened at the Walt Disney Concert Hall in Los Angeles on July 12, 2017. The season was released on Blu-ray and DVD in region 1 on December 12, 2017. The season premiere was pirated 90 million times in the first three days after it aired. On\", \"Blake's 7 (audio drama) taking place after \\\"Powerplay\\\" and the third and fourth episodes set after \\\"Rumours of Death\\\". The role of Dayna, played by Josette Simon in the TV series, has been recast with Yasmin Bannerman. The fourth series, titled \\\"Crossfire\\\", consisting of twelve 60 minute episodes was released across 3 box sets from October 2017 to April 2018. They are set during season C of the TV series, between \\\"Death-Watch\\\" and \\\"Terminal\\\". A story arc running across the twelve episodes concerns the attempts of Servalan's predecessor to regain the presidency of the Federation. A 40th anniversary special, titled \\\"The Way Ahead\\\", consisting\", \"Casualty (series 31) later promoted to the show's official executive producer on 8 June 2017, although Kent was credited in the role of executive producer until the end of the series. Mark Catley, the show's story consultant, was credited as co-executive producer for the first episode only. The thirty-first series consisted of 44 episodes. The feature-length anniversary episode that began the series, aired for 99-minutes on 27 August 2016, and featured a storyline event that connected \\\"Casualty\\\" with its sister show \\\"Holby City\\\". The BBC National Orchestra of Wales recorded a special soundtrack for the episode at BBC Hoddinott Hall in Cardiff Bay.\", \"Law & Order: UK broadcaster ITV and producer Kudos issued a joint press release announcing that series 8 would be \\\"the last to be transmitted for the foreseeable future\\\". Originally commissioned as a single series of thirteen episodes, episodes 1\\u20137 were transmitted as series one, broadcast in 2009, and episodes 8\\u201313 were transmitted as series two, broadcast in 2010. A second run of thirteen episodes was commissioned in 2010, with episodes 1\\u20137 being transmitted as series three, broadcast in 2010, and episodes 8\\u201313 being transmitted as series four, broadcast in 2011. A third run of thirteen episodes was commissioned in October 2010, with episodes\", \"The Bill (series 16) members leaving, with season 25 in second, where 11 cast members left, and season 18 having the most, which had 13 cast members leaving. On 5 June 2013, The Bill Series 16 Part 1 & 2 and The Bill Series 16 Part 3 & 4 DVD sets were released (in Australia). The Bill (series 16) Series 16 of British television drama \\\"The Bill\\\" consisted of 86 episodes, broadcast between 4 January \\u2013 26 December 2000. As well as 83 regular episodes, the series also included a two-part recap special, \\\"Kiss Off\\\", featuring a condensed broadcast of the Series 15\", \"Black Jack Justice stupor and realizes there is no script for the night's episode that is just about to go on the air. The last portion of the play features the cast attempting to put on the show anyway, making it up as they go along. When Taylor decided to begin producing radio dramas for podcasting, he decided to actually produce \\\"Black Jack Justice\\\" as a series along with \\\"Red Panda Adventures\\\". The first season of \\\"Black Jack Justice\\\" was 12 episodes long. From season two until season eleven, there were 6 episodes per season. On Sept 1st 2016, Decoder Ring Theater announced\", \"City Homicide are broadcast. The following table shows the weekly ratings for the series. Season two has been split into two parts as season two aired in two television seasons. The data is based on the five Metropolitan markets only. Series 1, 2 and 3 of \\\"City Homicide\\\" are now available on DVD. Season 3 DVD includes the first 8 episodes of Season 4. The Season 4 DVD includes the remainder of season 4 episodes plus the 6 part mini-series No Greater Honour. In September 2018 7PLUS (Channel 7's Steaming Site) Released all 5 Seasons to view for free. City Homicide City\", \"Casualty (TV series) was extended to 24 episodes per year, and placed in a pre-watershed slot at approximately 8 pm. This initially caused some controversy due to the graphic and controversial nature of some of the storylines. In 1997-8, the episode number was increased again, with 26 episodes (including two 75-minute specials) making up series 12. Subsequent series each saw an increase in episodes; series 13 ran for 28 episodes, series 14 ran for 30 episodes, series 15 ran for 36 episodes, series 16 and 17 ran for 40 episodes and series 18 ran for 46 episodes. Since 2004, popularity of the show\", \"Waterloo Road (TV series) final episode on BBC Three on 9 March 2015. \\\"Waterloo Road\\\" ran for 10 series, 200 episodes and exactly 9 years. Reruns air on CBS Drama in the UK. In August 2017, full episodes began to be uploaded to the Waterloo Road YouTube channel. The first series contained eight episodes and ended on 27 April 2006. The show was subsequently commissioned for a second series consisting of twelve episodes. The second series began on 18 January 2007 and ended on 26 April 2007. A third series was commissioned, consisting of twenty episodes, premiering on 11 October 2007 and ending on\", \"Holby City BBC One, some of which was used for extra episodes of \\\"Holby City\\\". The second and third series ran for 16 and 30 episodes respectively, with new episodes then airing on a weekly basis from the fourth series onwards. Series four to nine and eleven all ran for 52 episodes, while series ten ran for 53 episodes, including the stand-alone finale episode \\\"Mad World\\\", set outside the hospital. All series from then on continued to consist of 52 episodes, with exception to the twelfth series, which consisted of 55 episodes in total. Young explained of the increase in series length:\", \"Wild 'n Out to February 4, 2015 on MTV2. The 7th season contains a total of 16 episodes which were broadcast from June 10, 2015 to January 6, 2016 on MTV2. There are also stand up specials after the episodes in this season are finished. The 8th season contains a total of 21 episodes which were broadcast from August 4, 2016 to April 20, 2017 on MTV, marking the first time in almost a decade that the show aired new episodes on its original network. The 9th season contains a total of 16 episodes which were broadcast from June 29 to October 5,\", \"Game of Thrones (season 5) Game of Thrones (season 5) The fifth season of the fantasy drama television series \\\"Game of Thrones\\\" premiered on HBO on April 12, and concluded on June 14, 2015. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50\\u201360 minutes. The season primarily adapts material from \\\"A Feast for Crows\\\" and \\\"A Dance with Dragons\\\", the fourth and fifth novels in George R. R. Martin's \\\"A Song of Ice and Fire\\\" series, though it also uses elements from the third novel, \\\"A Storm of Swords\\\", as well as the upcoming\", \"Game of Thrones (season 6) Game of Thrones (season 6) The sixth season of the fantasy drama television series \\\"Game of Thrones\\\" premiered on HBO on April 24, 2016, and concluded on June 26, 2016. It consists of ten episodes, each of approximately 50\\u201360 minutes long, largely of original content not found in George R. R. Martin's \\\"A Song of Ice and Fire\\\" series. Some story elements were derived from the novels and from information Martin revealed to the show-runners. The series was adapted for television by David Benioff and D. B. Weiss. HBO ordered the season on April 8, 2014, together with the fifth\", \"Norman Gunston episode 6 (October 1975) Series 2 episode 7 (December 1975) Series 2 episode 8 (January 1976) Series 3 episode 1 (September 1976) Series 3 episode 2 Series 3 episode 3 Series 3 episode 4 Series 3 episode 5 Series 3 episode 6 Series 3 episode 7 Series 3 episode 8 Series 1 episode 1 (5 April 1978) Series 1 episode 2 (1978) Series 1 episode 3 (1978) Series 1 episode 4 (11 October 1978) Series 1 episode 5 (1978) Series 2 episode 1 (1979) Series 2 episode 2 (1979) Series 2 episode 3 (29 August 1979) Series 2 episode 4\", \"Game of Thrones (season 5) 18 million different IP addresses downloaded the leaked episodes, totaling 32 million downloads during the first week. The fifth season of \\\"Game of Thrones\\\" was the most-pirated TV series in 2015. Game of Thrones (season 5) The fifth season of the fantasy drama television series \\\"Game of Thrones\\\" premiered on HBO on April 12, and concluded on June 14, 2015. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50\\u201360 minutes. The season primarily adapts material from \\\"A Feast for Crows\\\" and \\\"A Dance with Dragons\\\", the fourth and fifth\", \"Game of Thrones (season 8) of six episodes, and is expected to premiere later than usual for the same reason. Benioff and Weiss spoke about the end of the show, saying, \\\"From the beginning we've wanted to tell a 70-hour movie. It will turn out to be a 73-hour movie, but it's stayed relatively the same of having the beginning, middle and now we're coming to the end. It would have been really tough if we lost any core cast members along the way, I'm very happy we've kept everyone and we get to finish it the way we want to.\\\" The season is scheduled\", \"My Dad Wrote a Porno The advert is then scripted into the advertising section of the podcast and performed by the three presenters. The rest of the podcast is unscripted and spontaneous. Cooper and Levine have not heard the chapter before the recording, whereas Morton has read through it shortly before recording the episode to familiarise himself with the phrasing and prepare for any accents he may have to adopt. Series 1 (2015) consisted of 13 episodes, with 4 \\\"Footnotes\\\" episodes and a \\\"Best of\\\" episode. Series 2 (2016) consisted of 17 episodes, with 16 \\\"Footnotes\\\" episodes and a \\\"Best of\\\" episode. Series 3 (2017)\", \"Blake's 7 (audio drama) BBC Radio 7 as three hour-long episodes. B7 Productions also produced series of 30-minute prequel audio episodes, which explored the earlier histories of the central characters. These were broadcast on BBC Radio 4 Extra from 2010. Audiobook readings novelising episodes from season A of the TV series released by BBC Audio. A series of enhanced audiobooks from Big Finish Productions with members of the TV series reprising their roles. A series of full cast dramas from Big Finish Productions with members of the TV series reprising their roles. A 60 minute special was released in January 2013. It takes place\", \"Doc Martin Martin\\\" are Tristan Sturrock and Tony Maudsley. Eight series totaling 62 episodes aired on ITV in the UK between 2004 and 2017. Episodes are just under 50 minutes long, except for the 2006 TV film which is 92 minutes. In the US, American Public Television provided the 2006 TV film as a two-part episode, with the second episode airing the week after the first. In the UK, \\\"Doc Martin\\\" has been a ratings success for ITV with the third series achieving ITV's best midweek drama performance in the 9pm Monday slot since December 2004. The final episode of the third\", \"Tiswas show would also record their appearances, leading to many episodes existing in private hands. In 2006, ITV began a search for many missing ITV programmes, including \\\"Tiswas\\\", for their \\\"Raiders Of The Lost Archive\\\" series broadcast in 2007. Their website reveals that only 22 episodes are known to officially exist in their entirety: episode 60 from 30 August 1975, four episodes from 1978, two episodes from 1979, two episodes from 1980, five episodes from 1981 and nine episodes from 1982. Incomplete segments from show 151 (broadcast on 10 December 1977), an episode from 1978 and two episodes from 1979 are\", \"Heartbeat (UK TV series) the launch of \\\"The Royal\\\" from Series 12. Series 1 and 2 (1992-1993) aired between April and June, Series 3-6 (1993-1996) moved to the autumn schedule between September and December when there were either 10 or 16 episodes per series. Series 7\\u201311 (1997-2002), comprising 24 episodes, aired between September and March. The ITV medical drama series \\\"The Royal\\\" was originally a spin-off from \\\"Heartbeat\\\", with the twelfth-series \\\"Heartbeat\\\" episode \\\"Out of the Blue\\\" serving as an introductory pilot for the show, with the Aidensfield police officers conducting parts of their investigations in \\\"The Royal\\\" hospital. The series initially had close\", \"Sky Atlantic HBO programming and a first-look deal on all co-productions. In January 2016, Sky expanded the portfolio shown on Atlantic, after purchasing exclusive rights to Showtime programming. The following is a list of the ten most watched programmes on Sky Atlantic (all of them being episodes of \\\"Game of Thrones\\\"), based on Live +7 data supplied by BARB up to 28 August 2017. The number of viewers does not include repeats or Irish ratings. Additionally, all of these episodes were the most viewed programme of the week on non-terrestrial television in the UK, with the exception of \\\"Book of the Stranger\\\"\", \"Sky Studios located on the ground floor. Studios 4 and 5 can be used together or separately thanks to a soundproof double door - combined, they are 122ft long. Due to the runners for the double-door, cameras cannot be tracked over the studio join. Shows such as \\\"Thronecast\\\", \\\"Skavlan\\\" and \\\"Harry Hill's Tea Time\\\" have been made in these studios. Studios 6, 7 and 8 have dock doors which open directly onto an access road, whilst studio 5 has a dock door with a short access tunnel before another door opening out onto an access road. Studios 1, 2, 3 and 4\", \"Dragon Tales problematic behavior, then counselors analyzed the video footage and provided specific tips to the parents, who all reported significantly improved behavior two months later. The researchers also discovered from their work on the series that children often think in pictures and that visual aids are often helpful. \\\"Dragon Tales\\\" aired a total of 93 episodes, 40 in its first season, 24 in its second season and 29 in its third season. Each episode featured two original stories, aired back-to-back, split by the interstitial song segment \\\"Dragon Tunes,\\\" all of which were eventually released on the show's music albums. Almost all\", \"Game of Thrones (season 1) U.S., and totaled 5.4 million viewers across multiple Sunday and Monday night airings. It averaged 743,000 and reached a peak 823,000 in UK and Ireland on its April 18 premiere. HBO announced that they would be commissioning a second season on the strength of the reception of the premiere episode. By the final episode of the season, which aired June 20, the ratings had climbed to over 3 million. The first season of \\\"Game of Thrones\\\" was nominated for thirteen Emmy Awards, including Outstanding Drama Series, Outstanding Directing for a Drama Series (Tim Van Patten for \\\"Winter Is Coming\\\"), and\", \"Holby City (series 19) be guest appearing in \\\"Holby City\\\" on 21 June 2017. Amira appears in episodes 53 and 56. Paramedic Iain Dean, portrayed by Michael Stevenson, appears in episode 62. Holby City (series 19) The nineteenth series of the British medical drama television series \\\"Holby City\\\" commenced airing in the United Kingdom on BBC One on 11 October 2016 and concluded airing in the United Kingdom on BBC One on 19 December 2017. The series consists of 64 episodes; an increase from the previous series. The series began airing on Tuesday nights on BBC One from 11 October 2016, and concluded\", \"Television show dramas including \\\"Number 96\\\". Many drama series, such as \\\"McLeod's Daughters\\\", have received in the majority of between 22 and 32 episodes per season. Typically, a soap opera such as \\\"Home and Away\\\" would begin a new season in late January and the season finale would air in late November, with 220\\u2013230 episodes per season. However, during the Olympics, \\\"Home and Away\\\" would often go on hiatus, which is referred to as an \\\"Olympic cliffhanger\\\". Therefore, the number of episodes would decrease. Australian situation comedy series' seasons are approximately 13 episodes long and premiere any time in between February and\", \"Metalhead (Black Mirror) the UK, Netflix commissioned the series for 12 episodes (split into two series of six episodes) in September 2015 with a bid of $40 million, and in March 2016, Netflix outbid Channel 4 for the right to distribute the series in the UK. The six episodes in series four were released on Netflix simultaneously on 29 December 2017. \\\"Metalhead\\\" is listed as the fifth episode, though as each episode is standalone the episodes can be watched in any order. \\\"Metalhead\\\" is the shortest episode of \\\"Black Mirror\\\", with a length of 41 minutes. The episode was written by series creator\", \"Charmed at 9:00 pm. For its second, third and fourth seasons, \\\"Charmed\\\" moved to Thursday nights. For the fifth season, the series moved to Sunday nights at 8:00 pm and remained there until its eighth and final season. By the end of season eight, \\\"Charmed\\\" had aired a total of 178 episodes and became the longest running hour-long television series featuring all female leads. Most seasons consisted of 22 episodes, except for the fifth and sixth seasons, which contained 23 episodes, including their double-episode premieres and double-episode finales. TNT airs reruns of \\\"Charmed\\\" with three repeat episodes every weekday morning at\", \"Kaamelott televised, 8 52-minutes episodes in the DVD \\\"director's cut.\\\" In the interview of the DVD, Alexandre Astier explains that is in the director's cut version that season 5 must be seen. Season 6 was always, from the time shooting began, conceived of as a series of 40-minute episodes which would be presented as a miniseries, not cut up into shorter episodes. Around the time that shooting began on Season 6, Astier announced that there would be no Season 7. Season 6 consists of a prequel (how Arthur became king of Britain) followed by an episode which is a sequel to\", \"Arthur (season 7) Arthur (season 7) The 7th season of the television series \\\"Arthur\\\" was originally broadcast on PBS in the United States from October 8 to November 29, 2002 and contains 10 episodes. The special \\\"Arthur, It's Only Rock 'N' Roll\\\" served as the premiere of this season. Jason Szwimmer replaced Oliver Grainger as the voice of D.W. Alex Hood is cast as the new voice of Alan \\\"The Brain\\\" Powers, replacing Steven Crowder. Mark Rendall replaced Justin Bradley as Arthur (who would later dub on re-runs of season 6, due to Justin Bradley's dialogue being unfavorable). This is the last season\", \"Trial & Retribution feature-length story. From series eight, the format was reduced to two 90-minute-long episodes. As of series ten, the format once again changed, incorporating multiple stories across one series. For the final two series, this format was retained; however the length of the episodes was reduced to 60 minutes. The last ever episode was broadcast on 13 February 2009. The complete series was released on DVD on 14 July 2014. Each episode makes frequent use of split screen scenes, with usually three images shown in one screen. Similar effects were frequently used in other series such as \\\"24\\\" and \\\"Spooks\\\", and\", \"Wild 7 however \\\"Wild 7 Another\\\" television series is a sequel of 13 episodes set after the OVA. It was shown in Japan from April 27 to August 31 of 2002 before airing it on Animax for Latin American viewers from September 9 to November 28 of 2006. The television series was released on DVD with Japanese audio and English subtitles by Discotek Media on 31 July, 2018. A live action drama series ran on NTV from 1972 to 1973. Despite being popular with TV viewers, it was forced to end after 25 episodes due to concerns of violence being shown. A\", \"Highlander: The Series (season 2) seriously.\\\" Steven Maier, executive financial consultant on the second season, noted that the beheadings in \\\"Highlander\\\" might make the show look \\\"extremely violent\\\", but insisted that violence could be depicted in \\\"non-graphic ways\\\" and was \\\"highly stylized\\\" in \\\"Highlander\\\". Highlander: The Series (season 2) The second season of the international fantasy series \\\"\\\", part of the \\\"Highlander\\\" franchise, consists of 22 episodes produced between 1993 and 1994. The first episode of the season aired on September 27, 1993 in broadcast syndication and the last aired on May 23, 1994. The series continues to follow the adventures of Duncan MacLeod,\", \"New Tricks 27 March 2003. This attracted sufficient viewers for the BBC to commission a series of six episodes, which began on 1 April 2004. Eight-episode series were subsequently commissioned for 2005, 2006 and 2007. A fifth series was commissioned by the BBC after the audience share rose week upon week for the previous series. In 2007, an episode from the fourth series received viewing figures of 9.25 million, becoming the second most-watched programme on BBC One that week, and the most-watched \\\"New Tricks\\\" episode to that point. The fifth series continued this good run \\u2013 on two occasions it was the\", \"Blake's 7 been realised, \\\"Blake's 7\\\" has been revived with two series of audio dramas, a comedic short film, and a series of fan-made audio plays. Four series of thirteen 50-minute episodes were made, and first broadcast in the United Kingdom between January 1978 and December 1981 by BBC1. They are set in the third century of the second calendar (this is mentioned in associated publicity material, not in the series) and at least 700 years in the future. \\\"Blake's 7\\\"s narrative concerns the exploits of political dissident Roj Blake, who commands a small group of rebels against the\", \"Holby City (series 18) Additionally, episode 33, broadcast on 24 May 2016, experienced a drop in ratings to 3.84 million viewers. Holby City (series 18) The eighteenth series of the British medical drama television series \\\"Holby City\\\" commenced airing in the United Kingdom on 13 October 2015, and concluded on 4 October 2016. The series consists of 52 episodes. Oliver Kent continues his position as the show's executive producer, while Simon Harper serves as the series producer. Sixteen cast members reprised their roles from the previous series, while several recurring characters, and numerous guest stars feature in the series. Four actors depart during the\", \"Inhumanoids on to be expanded into independent full-length shows. \\\"Jem\\\" achieved the greater success, eventually running to 65 episodes spanning several seasons, while \\\"Inhumanoids\\\" lasted only one season. In both cases, to begin the series, the introductory \\\"movies\\\" were cut into five separate 22-minute episodes composed of three shorts apiece. \\\"Inhumanoids\\\" was thereafter given the series subtitle, The Evil That Lies Within, a phrase which was included in the lyrics of the opening credits of the show in every episode. A further eight 22-minute episodes were then produced to yield the standard thirteen-episode TV season. The series proved unusual among children's\", \"Doctor Who (series 7) broadcast. A 5-disc box set containing all 13 episodes plus the Christmas specials \\\"The Doctor, the Widow and the Wardrobe\\\" and \\\"The Snowmen\\\" was released on 24 September 2013 in Region 1 and 28 October 2013 in Region 2. On review aggregator Rotten Tomatoes, the series holds a 100% approval rating, and an average score of 8.43/10. Reviewing the whole series for IGN, Mark Snow rated it 7.9/10 and wrote that the series was \\\"a tumultuous one\\\". He felt that although \\\"the concepts were almost universally strong, cramming an entire movie's worth of ideas into a self-contained 50 minute episode\", \"Doctor Who (series 7) the series, \\\"The Bells of Saint John\\\", was broadcast on 30 March 2013 on BBC America in the US, and on Space in Canada, and the following day in Australia on ABC1 and in South Africa on BBC Entertainment. Prime TV began airing the remainder of the series in New Zealand on 11 April 2013. \\\"The Doctor, the Widow and the Wardrobe\\\" was released singly onto DVD and Blu-ray on 12 January 2012, episodes 1\\u20135 (dubbed as 'Series 7: Part 1') followed on 29 October 2012 in Region 2 and 13 November 2012 in Region 1. The second part was\", \"Eurotrash (TV series) making the show an important hit for the channel at the time. It ran for 16 series (over 160 episodes) until 2007, making it one of the UK's longest running late-night entertainment shows. Channel 4 infrequently re-runs the series and repeats can be found on the Comedy Central Extra, Real Lives and on 3e in Ireland. Series 1 is also now available on All 4. After more than 10 years of broadcast, the show built up a substantial following and \\\"Eurotrash\\\" has around 15 million fans, and various fan sites. All intellectual property rights to the series are now controlled\", \"Mark Mylod and executive-produced the pilot episode of the U.S. version of the dramedy, \\\"Shameless\\\", for Showtime. He remains a co-executive producer and frequent director on the series. In 2011, Mylod directed and executive-produced the pilot of the ABC fantasy series \\\"Once Upon a Time\\\". In 2014, he directed the pilot episode for American TV Series \\\"The Affair\\\". In 2014 he directed episodes 3 and 4 of Season 5 of the HBO series \\\"Game of Thrones\\\". He came back for Season 6, where he directed episode 7 and 8 and episode 2 of season 7. Mylod is married to costume designer Amy\", \"The Awakening (Doctor Who) Team were able to make a repaired master copy. This was used for the VHS release. This was officially the final story of the series to consist of two 25-minute episodes. All two-parters since then have been 45 minutes long per episode, including most of season 22 and several stories of the revived series. \\\"The Ultimate Foe\\\", the concluding segment of \\\"The Trial of a Time Lord\\\", is numbered on screen as Parts Thirteen and Fourteen of the latter title; furthermore, they share the same BBC production code, 7C, with the preceding four-part story arc, \\\"Terror of the Vervoids\\\", even\", \"Blake's 7 Blake's 7 Blake's 7 (sometimes styled Blakes 7) is a British science fiction television series produced by the BBC. Four 13-episode series were broadcast on BBC1 between 1978 and 1981. It was created by Terry Nation, who also created the Daleks for the television series \\\"Doctor Who\\\". The script editor was Chris Boucher. The main character, at least initially, was Roj Blake, played by Gareth Thomas. The series was inspired by various fictional media, including \\\"Robin Hood\\\", \\\"Star Trek\\\", \\\"Passage to Marseille\\\", \\\"The Dirty Dozen\\\", \\\"Brave New World\\\" and classic Western stories, as well as real-world political conflicts in South\", \"The Trial of a Time Lord the BBC that the serial's final episode needed the additional three minutes to conclude the story properly. Although there were now 14 episodes in the season, the total running time was significantly reduced since the episodes were just over half as long. The change of format that \\\"Doctor Who\\\" had undergone in Season 22 (45-minute episodes, moving back to one episode per week on Saturday evenings) had been reasonably successful, with ratings around the 6\\u20138 million mark. As such, the production team began preparations for Season 23 in the same format, with a total of 13 episodes spread over\", \"Lud, zbunjen, normalan season has been announced and began airing on 3 November 2014. In 2015 the Bosnian broadcaster has changed to the commercial television Face TV. Face TV started broadcasting the 7th season regularly from 4 April 2015 at 8 p.m. In 2016, Fe\\u0111a Isovi\\u0107 stated that after 264 episodes, series will probably be ended. So far 264 episodes have filmed and while 260 episodes have aired. Lud zbunjen normalan employs an ensemble cast. Three generations of the Fazlinovi\\u0107 family all live in a Sarajevo apartment. The oldest of the family is Izet Fazlinovi\\u0107 (Mustafa Nadarevi\\u0107). Izet has a son Faruk (Senad\", \"Agatha Christie's Poirot In the UK, ITV Studios Home Entertainment owns the home media rights. In Region 1, Acorn Media has the rights to series 1\\u20136 and 11\\u201312. Series 7\\u201310 are distributed by A&E, a co-producer on several of them. In North America, series 1\\u201311 are available on Netflix and Amazon Prime Instant Streaming service. In Region 4, Acorn Media (distributed by \\\"Reel DVD\\\") has begun releasing the series on DVD in Australia in complete season sets. To date, they have released the first 8 series of the show. Series 1\\u20139 and 12 are available in Spain (Region 2) on Blu-ray with Spanish\", \"Game of Thrones (season 1) Game of Thrones (season 1) The first season of the fantasy drama television series \\\"Game of Thrones\\\" premiered on HBO on April 17, 2011 in the U.S., and concluded on June 19, 2011. It consists of ten episodes, each of approximately 55 minutes. The series is based on \\\"A Game of Thrones\\\", the first novel in the \\\"A Song of Ice and Fire\\\" series by George R. R. Martin, adapted for television by David Benioff and D. B. Weiss. HBO had ordered a television pilot in November 2008; filming began the following year. However, it was deemed unsatisfactory and later\", \"Popples (2015 TV series) and each other, their efforts often times backfire in hilarious ways and they must spend the rest of the episode trying to unwind the mayhem they've caused. Luckily they always manage to save the day in their own POP-tastic way. Guest Star In 2014, Netflix announced that at least 26 episodes would be released. 10 episodes were planned for the first season; each episode runs for 22 minutes and consists of two segments. The episodes were released on October 30, 2015. The second season of \\\"Popples\\\" was released on Netflix on March 11, 2016. The third season of \\\"Popples\\\" were\", \"Doctor Who missing episodes while other series such as \\\"Z-Cars\\\" and \\\"Dixon of Dock Green\\\" are missing episodes from as late as 1975. , 97 episodes were unaccounted for. The missing episodes span 26 serials, including 10 full serials. Most of the gaps are from seasons 3, 4, and 5, which currently lack a total of 79 episodes across 21 (out of 26) serials. By contrast, seasons 1, 2, and 6 are missing just 18 episodes, across 5 (out of 25) serials. Of these missing stories, all but three \\u2013 \\\"Marco Polo\\\", \\\"Mission to the Unknown\\\", and \\\"The Massacre of St Bartholomew's Eve\\\" \\u2013\", \"Wolfblood consisted of 12 episodes. A fifth season was announced on 6 June 2016 and began airing on 27 February 2017 and concluded on 1 May 2017 with 10 episodes. Since there has been no announcement of a sixth series, the show is presumed cancelled. The television series has won the Royal Television Society Award for the Children's Drama category in 2013. It also won the Banff Rockie Award in the category for 'Best Children's Programme (fiction)' in the same year. In 2015 the television series won the British Screenwriters' Award in the category 'Best British Children\\u2019s Television'. The second series\", \"Last of the Summer Wine (series 9) (1986) Regular series Christmas Special (1987) When recordings were \\\"repackaged\\\" for overseas sale, UK series 9 (12 episodes) was split into \\\"Season 9\\\" and \\\"Season 10\\\" (each of 6 episodes), with all subsequent \\\"seasons\\\" being renumbered accordingly. As a result, (for example) \\\"Series 27\\\" in the UK may be referred to as \\\"Season 28\\\" in the USA. The box set for series nine was released by Universal Playback in May 2008, mislabelled as a box set for series 9 & 10. Last of the Summer Wine (series 9) Last of the Summer Wine's ninth series originally aired on BBC1 between\", \"The Bill (series 4) 2009 and 15 March 2010. It was later reissued in Australia on 31 August 2011. The above DVD artwork is taken from the most recent Australian release. It features an image of DS Ted Roach. The British volume artwork features a variety of collage images featuring characters from across the season. The original Australian box set features a sole image of DI Frank Burnside. None The Bill (series 4) The fourth series of \\\"The Bill\\\", a British television drama, consisted of forty-eight episodes, broadcast between 19 July \\u2013 29 December 1988. This series was the first to adopt a\", \"Carmilla (web series) of. Along with the ghosts of Carmilla's former victims, they fight the ghost of Carmilla's ex-lover in order to regain Carmilla's humanity. The first season of \\\"Carmilla\\\" consists of 36 episodes, which aired from August 19, 2014 to December 2, 2014. A Christmas special aired on December 24, 2014. The second season of \\\"Carmilla\\\" consists of 36 episodes, which aired from June 2, 2015 to October 1, 2015. The next season, titled Season Zero, consists of 12 episodes, which began airing on October 22, 2015 and concluded on November 24, 2015. The third and final season of \\\"Carmilla\\\" consists of\", \"Blake's 7 (audio drama) between season B and C of the TV series shortly after the events of \\\"Star One\\\". The roles of Zen and Orac, voiced by Peter Tuddenham in the TV series, has been recast with Alistair Lock. It was also released by Big Finish Productions as a eBook in February 2013. The first series consisting of six 60 minute episodes were released monthly from January 2014 to June 2014. They are set during season B of the TV series, between \\\"Voice from the Past\\\" and \\\"Gambit\\\". A second series consisting of six 60 minute episodes were released monthly from November 2014\", \"The Bill (series 5) The Bill (series 5) The fifth series of \\\"The Bill\\\", a British television drama, consisted of 104 episodes, broadcast between 3 January \\u2013 28 December 1989. The series was first released on DVD as part of the Collection 3 and Collection 4 DVD boxsets in Australia, made available on 8 August \\u2013 7 November 2007, respectively. The first four episodes of the series were later issued on DVD in the United Kingdom, under the title Volume 4, on 15 March 2010. The next thirteen episodes of series 5 were released on DVD in the UK, under the title Volume 5,\", \"Father Brown (2013 TV series) serves as a Christmas special episode. The first episode of the sixth series also serves as a Christmas special episode. 10 episodes begin airing on the 7 January 2019 \\\"Father Brown\\\" has been sold to 162 territories by BBC Worldwide. Broadcasters across the world including Australia (ABC), the Netherlands (KRO-NCRV, BBC First), Finland (YLE), Sweden (TV8), Denmark (DR), Norway (NRK), Estonia (ETV) and Iceland (R\\u00daV). In the US, \\\"Father Brown\\\" has been sold to 40 public television stations with a reach of 30% of all US television households. The first four series were added to Netflix streaming service on 31\", \"Spooks (series 7) 4) on 30 March 2009. The set consists of four discs and contain all eight episodes, as well as a few special features, including a Behind the Scenes documentary, which contain cast and crew interviews covering the characters and storylines of the series, \\\"\\\"Spooks in Russia\\\"\\\", a featurette behind the scenes of filming in Russia, \\\"\\\"Action Sequence\\\"\\\", which covers filming a chase sequence in episode six, and audio commentaries for episodes five and eight. The box set also contains the original trailer for the series, while the Region 1 release also contains trailers for other British television programmes, including \\\"Doctor\", \"Legend of the Dragon (TV series) season (13 episodes) consisted of 5 2-episode DVDs and one with 3 episodes. In the UK, 2006, the first 13 episodes of season 1 were released on a 2-disc set. A second 2-disc set, containing the other 13 episodes of season 1, was supposed to be released, but was not. The full series was licensed by Image Entertainment, Inc and is on sale now. In 2004 World Comics released a comic book tie-in to the series. Legend of the Dragon (TV series) Legend of the Dragon is an animated series consisting of 26 episodes followed by 13 additional episodes for\", \"Law & Order: UK August 2009. It was later moved to 13th Street, which will premiere the fifth season on 4 February 2015. In the United States, the series began broadcasting on BBC America on 3 October 2010 and since, Series 1-4 were shown back-to-back as were Series 5 and 6. In the US, series 7 was broadcast as a 6-episode season 4; no date has been announced for the US broadcast of series 8/season 5. The series also broadcasts in France, Germany, the Netherlands, Belgium and New Zealand. Independent writer Robin Jarossi, who attended a special preview of the premiere episode at the\", \"The Burkiss Way Special\\\") closes with Peter Jones as his HHGTTG character, \\\"The Book\\\", attempting to vilify BBC Radio 4 for broadcasting \\\"The Burkiss Way\\\", but in typical fashion he is cut off in mid-sentence. \\\"The Burkiss Way\\\" ran to 47 episodes in six series, but the episode and series numbering are derailed by \\\"Lesson 31\\\" and \\\"Lesson 32\\\", which are a single episode masquerading as two half-episodes, the first of which ends series 3 and the second of which begins series 4. There are two \\\"Lesson 39\\\"s, both entitled \\\"Repeat Yourself the Burkiss Way\\\", which have identical beginnings. The consequence is that\", \"Television show between 29 and 39 episodes per season. Actual storytelling time within a commercial television hour has also gradually reduced over the years, from 50 minutes out of every 60 to the current 44 (and even less on some networks), beginning in the early 21st century. The usage of \\\"season\\\" and \\\"series\\\" differ for DVD and Blu-ray releases in both Australia and the UK. In Australia, many locally produced shows are termed differently on home video releases. For example, a set of the television drama series \\\"Packed to the Rafters\\\" or \\\"Wentworth\\\" is referred to as \\\"season\\\" (\\\"The Complete First Season\\\",\", \"The Bill (series 22) The Bill (series 22) Series 22 of British television drama \\\"The Bill\\\" was broadcast from 4 January until 28 December 2006. The series consisted of 91 episodes, as two episodes from the series remain unaired after the master tapes were stolen in a robbery at the show's recording studios in November 2006. Under new producer Johnathan Young, this series saw the programme begin to step away from the serialised format, and return much of the focus to the actual policing aspect of the programme, removing the more 'soap' feel as previously introduced by Paul Marquess. Most episodes consist of two\", \"Game of Thrones (season 2) the mythology of Westeros and Essos. The second season of \\\"Game of Thrones\\\" was the most-pirated TV series in 2012. Game of Thrones (season 2) The second season of the fantasy drama television series \\\"Game of Thrones\\\" premiered in the United States on HBO on April 1, 2012, and concluded on June 3, 2012. It was broadcast on Sunday at 9:00 pm in the United States, consisting of 10 episodes, each running approximately 50\\u201360 minutes. The season mostly covers the events of \\\"A Clash of Kings\\\", the second novel of the \\\"A Song of Ice and Fire\\\" series by George\", \"Carniva\\u0300le be a trilogy of \\\"books\\\", consisting of two seasons each. This plan did not come to fruition, as HBO canceled the show after the first two seasons. Each season consists of twelve episodes. Airing on HBO benefited \\\"Carniv\\u00e0le\\\" in several ways. Because HBO does not rely on commercial breaks, \\\"Carniv\\u00e0le\\\" had the artistic freedom to vary in episode length. Although the episodes averaged a runtime of 54 minutes, the episodes \\\"Insomnia\\\" and \\\"Old Cherry Blossom Road\\\" were 46 minutes and 59 minutes, respectively. HBO budgeted approximately US$4 million for each episode, considerably more than most television series receive. \\\"Carniv\\u00e0le\\\" 1930s'\", \"The Guard (TV series) was completed), Halifax Film opts to relabel the episodes for marketing purposes in other markets so that season 1 consists of 13 episodes, while season 2 consists of 9 episodes. Many television viewers, however, regard the most appropriate organization to consist of three seasons. The first season consisting of 7 episodes was shown in the period 22 January 2008 to 4 March 2008. This was followed by a hiatus of 7.5 months where no new programs were shown. The second season consisted of 7 episodes in the period 29 October 2008 to 10 December 2008. Again, there was a hiatus,\", \"Class (2016 TV series) based on 17 reviews, with an average rating of 6.9/10. \\\"GamesRadar+\\\" called \\\"Class\\\" \\\"really, really good. So good, in fact, that even after only two thirds of its very first season, it\\u2019s possible to see the show\\u2019s potential bubbling over the sides like an over boiled pot of pasta.\\\" \\\"Doctor Who TV\\\" called \\\"Class\\\" \\\"a remarkably efficient, seldom-rushed, eight-episode series\\\". Despite being critical of early episodes, IndieWire gave the series as a whole a positive review, stating \\\"while the show knows how to juggle the heavier issues and high-concept scenarios, it doesn\\u2019t skimp on the fun.\\\" They highly praised Kelly's\", \"31 Minutos in four seasons; counting the Christmas special and the Telet\\u00f3n in 2003. The first series had a total of 21 episodes that were broadcast every Saturday at 1:30 pm by TVN, with repeats the following Sunday and Saturday of next week at 9:00 am. The season aired from 15 March 2003 to 9 August of that same year. As of 18 July, due to the success, episodes began to air on Fridays at 11:15 pm, hours after being changed were broadcast at 10:00 pm. The special Christmas episode was aired on 24 December 2003 and repeated on the same date\", \"Road Wars (TV series) by Nainita Desai and Malcolm Laws. Other music was made by the series' co-creator Bill Rudgard. In series 1 the title card consisted of 'ROAD WARS' in red dot-matrix form. A new title card was made for series 3, which was slightly edited with yellow accents for series 6. The first two seasons had six episodes, series 3 and 4 had eight episodes each, and series 5 and 6 had 20 episodes each. For series 7 as the show moved to another police force, a new title card and all new music was created. Boardman eventually left his position as\", \"Mayday (Canadian TV series) March 2014, Cineflix Rights announced a deal with Smithsonian Networks to air seasons 3, 4 and 13 (34 episodes). , a total of 156 episodes of Mayday have aired, including five \\\"Science of Disaster\\\" specials and three \\\"Crash Scene Investigation\\\" spin-offs, which do not examine aircraft crashes. As of October 2017, the \\\"series breakdown\\\" page on Cineflix's website lists an 18th season consisting of ten 60-minute episodes scheduled for release in 2018. The series has been received well by critics. Paul Mavis from DVD Talk recommends \\\"Mayday\\\" as \\\"Harrowing and surprisingly emotional\\\" and continues with \\\"Never exploitative, [\\\"Mayday\\\"] not only\", \"Oathbreaker (Game of Thrones) lot of skill in terms of acting to play.\\\" \\\"Oathbreaker\\\" was viewed by 7.28 million American households on its initial viewing on HBO, which was nearly identical to the previous week's rating of 7.29 million viewers for the episode \\\"Home\\\". The episode also acquired a 3.7 rating in the 18\\u201349 demographic, making it the highest rated show on cable television of the night. In the United Kingdom, the episode was viewed by 2.797 million viewers on Sky Atlantic; it also received 0.132 million timeshift viewers. \\\"Oathbreaker\\\" was very positively received by critics, citing the Tower of Joy flashback, the final\", \"Redwall upon the novel \\\"Redwall\\\". It was later followed by two more seasons, based on the books \\\"Mattimeo\\\" and \\\"Martin the Warrior\\\". Production for the series is assumed to be finished. Each season contained 13 episodes. Each episode was opened with Brian Jacques himself giving a synopsis of the story so far. These scenes were later cut from subsequent reairings and DVD releases. There have been full-length audiobooks published of most of the \\\"Redwall\\\" books, the exceptions being \\\"The Pearls of Lutra\\\", \\\"Marlfox\\\", \\\"Lord Brocktree\\\" (on cassette), \\\"The Legend of Luke\\\", and \\\"The Rogue Crew\\\". Instead of being read by a\"], \"pos_scores\": [101.5], \"neg_scores\": [84.8125, 85.125, 91.4375, 88.4375, 84.125, 84.125, 86.0625, 96.4375, 85.75, 84.5625, 86.0, 84.875, 84.875, 84.125, 84.6875, 86.125, 84.9375, 90.125, 89.0, 91.75, 88.875, 88.9375, 93.25, 87.75, 86.0, 85.125, 83.4375, 85.125, 89.75, 87.0625, 85.875, 88.0, 85.5625, 89.375, 85.1875, 85.125, 85.125, 85.9375, 85.625, 86.0625, 83.6875, 85.375, 86.625, 85.5, 84.25, 89.875, 89.0, 84.25, 86.5625, 84.1875, 87.3125, 85.0625, 86.0, 86.6875, 88.9375, 85.4375, 84.9375, 85.0, 86.75, 86.0625, 85.125, 86.0, 85.25, 85.25, 87.0625, 85.9375, 87.5, 85.4375, 89.875, 86.0, 87.8125, 85.0625, 86.0, 84.625, 85.3125, 85.25, 84.1875, 87.875, 84.25], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"who is next in line to be the monarch of england\", \"pos\": [\"Succession to the British throne Catholics are eligible. Queen Elizabeth II is the sovereign, and her heir apparent is her eldest son, Charles, Prince of Wales. Next in line after him is Prince William, Duke of Cambridge, the Prince of Wales's elder son. Third in line is Prince George, the eldest child of the Duke of Cambridge, followed by his sister, Princess Charlotte and younger brother, Prince Louis. Sixth in line is Prince Harry, Duke of Sussex, the younger son of the Prince of Wales. Under the Perth Agreement, which came into effect in 2015, only the first six in line of succession require the\"], \"neg\": [\"Regency Acts The Act required that the regent should be the next person in the line of succession who was: The Counsellors of State were to consist of: Thus, at the time of the passing of the Act, Prince Henry, Duke of Gloucester would have become Regent in the event that King George VI died while The Princess Elizabeth was still a minor. The current prospective regent under the Act would be Charles, Prince of Wales. Section 4 of the Act prohibits the regent from giving royal assent to a bill to change the line of succession to the British throne or\", \"Prince Louis of Cambridge succession to the British throne, behind his grandfather, father and older siblings, Prince George and Princess Charlotte. Following the implementation of the Perth Agreement, which replaced male-preference primogeniture with absolute primogeniture, he is the first British prince to be ranked behind an elder sister in the line of succession. Prince Louis of Cambridge Prince Louis of Cambridge (Louis Arthur Charles; ; born 23 April 2018) is a member of the British royal family. He is the third and youngest child and second son of Prince William, Duke of Cambridge, and Catherine, Duchess of Cambridge. He is fifth in the line\", \"Succession to the Crown Act 2013 they came into force were the children of Lady Davina Lewis, her son T\\u0101ne (born 2012) and her daughter Senna (born 2010), who were reversed in the order of succession, becoming 29th and 28th in line respectively. When the Act came into force, the Duchess of Cambridge was expecting Princess Charlotte, upon whom the Act had no immediate practical effect; from her birth on 2 May 2015 she was fourth in line to the Throne, after Prince George of Cambridge and ahead of Prince Harry, just as she would have been had the Act not been passed. However, due to\", \"Princess Charlotte of Cambridge all children of the Prince of Wales's elder son. She is thus styled \\\"Her Royal Highness Princess Charlotte of Cambridge\\\". Charlotte is fourth in the line of succession to the British throne, after her grandfather, father, and elder brother. Due to the implementation of the Perth Agreement, which replaced male-preference primogeniture with absolute primogeniture, she did not move down the line of succession when her younger brother, Prince Louis of Cambridge, was born on 23 April 2018. Princess Charlotte of Cambridge Princess Charlotte of Cambridge (Charlotte Elizabeth Diana; born 2 May 2015) is a member of the British royal family.\", \"Heir apparent to the 16 thrones of Elizabeth II to absolute primogeniture, except for male heirs born before the Perth Agreement. The effects are not likely to be felt for many years; the first two heirs at the time of the agreement (Charles, Prince of Wales, and his son Prince William, Duke of Cambridge) were already eldest born children, and in 2013, William's first-born son Prince George of Cambridge became the next apparent successor. But even in legal systems that apply male-preference primogeniture, female heirs apparent are by no means impossible: if a male heir apparent dies leaving no sons but at\", \"Edward VII its kind, the last.\\\" A royal train conveyed the King's coffin from London to Windsor Castle, where Edward VII was buried at St George's Chapel. Before his accession to the throne, Edward was the longest-serving heir apparent in British history. He was surpassed by his great-great-grandson Prince Charles on 20 April 2011. The title Prince of Wales is not automatically held by the heir apparent; it is bestowed by the reigning monarch at a time of his or her choosing. Edward was the longest-serving holder of that title until surpassed by Charles on 9 September 2017; Edward was Prince of\", \"Marcus Setchell a position he retired from at the end of 2013. He was appointed Knight Commander of the Royal Victorian Order (KCVO) in the 2014 New Year Honours. Setchell, who had by then retired, was not present for the also much-awaited birth in May 2015 of Princess Charlotte of Cambridge, Prince George's younger sibling, who was delivered by Drs. Farthing and Thorpe-Beeston, and who, due to the passage of remodeled primogeniture and succession laws, remains fourth in line to the throne after Prince George despite the birth of a younger brother in 2018. Marcus Setchell Sir Marcus Edward Setchell, (born 4\", \"Wedding of Prince Harry and Meghan Markle trying to order the coat. Markle is the second American and the first person of mixed race heritage to marry into the British royal family. The engagement announcement prompted much comment about the possible social significance of Markle becoming a proudly mixed-race royal. Under the terms of the Succession to the Crown Act 2013, the first six persons in the line of succession require the Sovereign's consent in order to marry. Harry was fifth in line at the time of his engagement. The Queen's consent was declared to the Privy Council of the United Kingdom on 14 March 2018. Although\", \"Regency Acts is third in line to the throne after his grandfather and father. If the prince were to succeed to the throne before his 18th birthday on 22 July 2031, his uncle, Prince Harry, Duke of Sussex (the Prince of Wales' younger son), would serve as regent, as George's younger siblings Charlotte and Louis (currently fourth and fifth in line, respectively) would also be minors. In the event that Prince Harry would be unable to serve as regent, the next in line would be his uncle (Prince George's grand-uncle) Prince Andrew, Duke of York, followed by the Duke of York's elder\", \"Elizabeth II on 21 November 2017. On 6 February 2017, she became the first British monarch to commemorate a Sapphire Jubilee, and on 20 November, she was the first British monarch to celebrate a platinum wedding anniversary. Prince Philip had retired from his official duties as the Queen's consort in August. The Queen does not intend to abdicate, though Prince Charles is expected to take on more of her duties as Elizabeth, who celebrated her 92nd birthday in 2018, carries out fewer public engagements. On 20 April 2018, the government leaders of the Commonwealth of Nations announced that she will be succeeded\", \"Prince Andrew, Duke of York on two stamps with HMS \\\"Herald\\\", issued by Saint Helena. Prince Andrew, Duke of York Prince Andrew, Duke of York, (Andrew Albert Christian Edward, born 19 February 1960) is a member of the British royal family. He is the third child and second son of Queen Elizabeth II and Prince Philip, Duke of Edinburgh. At the time of his birth, he was second in the line of succession to the British throne; he is seventh in line. He holds the rank of commander and the honorary rank of Vice Admiral (as of February 2015) in the Royal Navy, in which\", \"Earl of Dumbarton (\\\"n\\u00e9e\\\" Wheatley), the first Countess of Dumbarton, who was the sister of Catherine Percy, Duchess of Northumberland. Following the death of their only son, the unmarried second Earl, both titles became extinct on 7 January 1749. On 19 May 2018, it was announced that the title will be recreated in the Peerage of the United Kingdom by Queen Elizabeth II as a subsidiary title for her grandson Prince Harry on the occasion of his wedding. Prince Harry, now the Duke of Sussex, and his wife, Meghan, Duchess of Sussex, are known as the Earl and Countess of Dumbarton, respectively, in\", \"Prince Harry, Duke of Sussex on one knee and proposed. The engagement announcement prompted much comment about the possible social significance of Meghan Markle becoming a mixed-race royal. The couple married at St George's Chapel, Windsor Castle, on 19 May 2018. The Duke and Duchess live at Nottingham Cottage in London, in the grounds of Kensington Palace. They are expecting their first child, who will be seventh in line to the throne, in spring 2019. The couple will move to Frogmore Cottage in the Home Park of Windsor Castle following the birth of their child. Their office will continue to operate at Kensington Palace. In\", \"Family of Catherine, Duchess of Cambridge Cambridge, on 2 May 2015, who is fourth in line to the throne. Her third pregnancy was announced on 4 September 2017, and she gave birth to her second son, Prince Louis of Cambridge on 23 April 2018. The Middletons' second daughter, Philippa \\\"Pippa\\\", born on 6 September 1983, attended the same schools as her siblings and studied English literature at the University of Edinburgh. There she shared a house with Lord Ted Innes-Ker, a son of the Duke of Roxburghe, and George Percy. Following graduation in 2008 she took an events management / marketing job with Table Talk, a\", \"Succession to the Crown Act 2013 approach to amending the rules on the succession to their respective Crowns\\\", and that they would wish \\\"unanimously to advise The Queen of their views and seek her agreement.\\\" The statement continued: In a letter to the other realms' heads of government, prior to the Perth Agreement, British Prime Minister David Cameron had additionally proposed to limit the requirement to obtain the monarch's permission to marry to the first six people in line to the throne. On 4 December 2012, Deputy Prime Minister Nick Clegg announced: The bill was published on 13 December 2012, and after passing both Houses of\", \"Prince Michael of Kent Succession to the Crown Act 2013, and is 47th in line to the throne . Prince and Princess Michael of Kent have two children, both of whom were brought up as members of the Church of England, and have therefore been in the line of succession to the throne since birth: Prince Michael manages his own consultancy business, and undertakes business throughout the world. He is also a qualified interpreter of Russian. Prince Michael is an active Freemason. He is the Grand Master of the Grand Lodge of Mark Master Masons, and Provincial Grand Master of the Provincial Grand Lodge\", \"Regency Acts the regent. , under the provisions of the Regency Acts in force, Prince Charles, Prince of Wales, would act as regent in the event of the incapacity of his mother, Queen Elizabeth II. The next person in the line of succession, the Prince of Wales' elder son Prince William, Duke of Cambridge, would also be able to succeed without necessitating a regency and would be eligible to be regent for his grandmother or his father. , the first person under the age of 18 in the line of succession to the throne is William's son Prince George of Cambridge, who\", \"Prince Louis of Cambridge Prince Louis of Cambridge Prince Louis of Cambridge (Louis Arthur Charles; ; born 23 April 2018) is a member of the British royal family. He is the third and youngest child and second son of Prince William, Duke of Cambridge, and Catherine, Duchess of Cambridge. He is fifth in the line of succession to the British throne. Kensington Palace announced on 4 September 2017 that the Duke and Duchess of Cambridge were expecting their third child. A son was born at 11:01 BST (10:01 UTC) on 23 April 2018. He was first seen in public seven hours after his birth,\", \"Duke of York V, and younger brother of the future King Edward VIII. Albert came unexpectedly to the throne when his brother abdicated, and took the name George VI, the Dukedom then merging into the crown. The title was created for the eighth time for Prince Andrew, second son of Queen Elizabeth II. At present (2018), he only has two daughters. Thus, if he has no future (legitimate) sons, the title will again become extinct at his death. Aside from the first creation, every time the Dukedom of York has been created it has had only one occupant, that person either inheriting the\", \"Prince of Wales 12 December 2012, published the next day, and received Royal Assent on 25 April 2013. It was brought into force on 26 March 2015, at the same time as the other realms implemented the Perth Agreement in their own laws. No woman has yet held the title \\\"Princess of Wales\\\" in her own right. Since the title of \\\"Prince of Wales\\\" is not automatic, there have been times when it was held by no one. There was no heir apparent during the reign of King George VI, who had no sons. Princess Elizabeth was heiress presumptive and was hence not\", \"Succession to the Crown Bill 2004 still remained in force. The most immediate effect of the Bill passing and becoming law would have been the moving of Princess Anne from her then position of ninth on the line of succession to the British throne to fourth, displacing Prince Andrew, Duke of York. It was unclear as to how the Bill would have affected the line of succession in the other 15 Commonwealth realms as it explicitly applied to the United Kingdom only. The Bill drew on the recommendations of the Fabian Society's Commission on the Future of the Monarchy, which reported in 2003. Lord Dubs is\", \"Succession to the British throne on those who marry Roman Catholics. The ban on Catholics themselves was retained to ensure that the monarch would be in communion with the Church of England. The changes came into effect across the Commonwealth realms on 26 March 2015, after legislation was made according to each realm's constitution. Following the changes coming into effect, the positions of the first 27 in line remained unchanged, including Princess Anne and her children and grandchildren, until the birth of Princess Charlotte of Cambridge on 2 May 2015. The first to be affected by the changes, on the day they came into effect\", \"Succession to the Crown Act 2013 apparent, Prince Charles. Her place in the line of succession is not affected by the provisions of the Act relating to male preference, in that she remains head of the line following those headed by her younger brothers, whose lines continue to precede hers under male preference. In December 2011 the Statement of Friday 28 October 2011 issued at Perth was published in a House of Commons committee report. It stated that the prime ministers of the sixteen Commonwealth nations \\\"of whom Her Majesty the Queen is Head of State\\\" had \\\"agreed in principle to work together towards a common\", \"Prince Harry, Duke of Sussex HALO Trust, the London Marathon Charitable Trust, and Walking With The Wounded. On 19 May 2018, he married the American actress Meghan Markle. Hours before the wedding, his grandmother Queen Elizabeth II conferred on him the title Duke of Sussex. Harry was born in the Lindo Wing of St Mary's Hospital in Paddington, London, on 15 September 1984 at 4:20 pm as the second child of Charles, Prince of Wales, heir apparent to Queen Elizabeth II, and Diana, Princess of Wales. He was baptised with the names Henry Charles Albert David, on 21 December 1984, at St George's Chapel, Windsor\", \"Margaret Wake, 3rd Baroness Wake of Liddell III of England reached his majority and overthrew the regents, he took in Margaret and her children and treated them as his own family. She succeeded briefly as Baroness Wake of Liddell in 1349, but died during an outbreak of the plague that autumn. Margaret and Edmund's descendants include every monarch of England from King Edward IV onward and queen consorts Anne Neville, Elizabeth of York, and Catherine Parr as well as every king of Scotland from James II. Margaret is a supporting character in the Karen Harper historical fiction novel \\\"The First Princess of Wales\\\", which gives a fictional\", \"Tony Appleton as a royal crier; Maddow later issued a correction. In May 2015, Appleton announced the birth of Princess Charlotte, Prince George's sister, from the same place as he had announced Prince George's birth. In an interview with \\\"Us Weekly\\\", Appleton stated that in contrast to his earlier announcement, the royal entourage was expecting his attendance outside the hospital. Later in that year, Appleton announced from the gates of Buckingham Palace that Elizabeth II had surpassed her great-great-grandmother Queen Victoria as the longest-reigning monarch of Britain. In November 2017, Appleton \\u2013 again in an unofficial capacity \\u2013 announced the engagement of\", \"Descendants of Charles I of England Otto von Habsburg and many are related to him via collateral bloodlines, such as Elizabeth II of the United Kingdom, Willem-Alexander of the Netherlands and Margrethe II of Denmark. He is also the ancestor of Diana, Princess of Wales, mother of Prince William, Duke of Cambridge and Prince Harry who are second and fifth in line to the Succession to the British throne after their father Charles, Prince of Wales. This article deals with the numerous individuals who are and were descendants of Charles and his wife Henrietta (Since he is not known to have had any illegitimate children). Descendants\", \"George Windsor, Earl of St Andrews Rosie Hospital in Cambridge. She is 38th in the line of succession to the thrones of Britain, Canada, and the other Commonwealth realms, following her father (). He married a Roman Catholic and was therefore formerly removed from line of succession in accordance with the Act of Settlement 1701 until the Succession to the Crown Act 2013 restored his place in the line. Lady Amelia's elder siblings both converted to and were confirmed in the Roman Catholic Church, making them still ineligible for the succession. In 2017, Amelia was named in the \\\"Vanity Fair\\\" International Best Dressed list. St Andrews'\", \"Lady Rose Gilman Wessex, The Princess Royal, Peter and Autumn Phillips, Daniel and Lady Sarah Chatto and Catherine Middleton, who attracted media interest for attending without her then boyfriend, now husband Prince William. Lady Rose and her husband have a daughter, Lyla Beatrix Christabel Gilman, born 30 May 2010, and a son, Rufus Frederick Montagu, born 2 November 2012. Until 26 March 2015, Rufus was ahead of Lyla in the line of succession to the British throne, as the monarchy's 300-year-old Act of Settlement gave sons priority over daughters. When the Succession to the Crown Act 2013 took effect in all Commonwealth realms\", \"Princess consort a title has no historical precedent; under English common law, wives of kings automatically become queens. \\\"The Guardian\\\" reported in 2017 that experts expect that \\\"the fiction will end when Elizabeth II dies\\\" and Camilla will be queen regardless of title. Clarence House in 2018 removed this statement from its official website, suggesting that Camilla will be styled as Queen consort upon her husband's accession. This was the case with all other women married to British kings regnant, who became queens consort (with the exception of Mary II, who was a joint sovereign). In addition, the husband of Queen Mary\", \"Earl of Wessex by his father. This is unlikely to happen by direct inheritance, as Prince Edward is the youngest of Prince Philip's three sons. Rather, the title is expected to be newly created for Prince Edward after it \\\"eventually reverts to the crown\\\" after \\\"both the death of the current Duke of Edinburgh and the Prince of Wales' succession as King.\\\" In the meantime, in keeping with the tradition of a monarch's son receiving a title upon marriage, but preserving the rank of duke for the future, Prince Edward became the first British prince in centuries to be specifically created an earl,\", \"Harald V of Norway of the Royal Norwegian Order of St. Olav and the Royal Norwegian Order of Merit. In the British Army, Harald V was the final Colonel-in-Chief of the Green Howards. He is also an honorary Colonel in the British Royal Marines. He is patron of the Anglo-Norse Society in London, together with Queen Elizabeth II, his second cousin. Harald is the first foreign monarch in the line of succession to the British throne, because of his descent from King Edward VII of the United Kingdom. He is a Stranger Knight of the Garter, an Honorary Knight Grand Cross of the Royal\", \"Alternative successions of the English and British crown Prince of Wales; end the ban on marriage of dynasts to Catholics; and limit the requirement for those in line to the throne to acquire permission of the sovereign to marry. However, the requirement for the sovereign to be in communion with the Church of England remains. This change has now been enacted as the Succession to the Crown Act 2013; but it does not apply retroactively. If this system of primogeniture had been applied on the death of Victoria (whose actual successor was her second child and first son Edward VII), then Princess Victoria, Princess Royal would have become\", \"Head of the Commonwealth with the Crown\\\". Sources told the BBC that the issue was to whether it should be a one-off decision to elect Prince Charles to the Headship, or whether a new process should be agreed upon to ensure that it is always the British monarch who automatically becomes head of the Commonwealth. There was also speculation that a rotating ceremonial \\\"republican\\\" headship might be instituted. The \\\"Daily Telegraph\\\" reported that \\\"the post is not hereditary and many leaders want an elected head to make the organisation more democratic.\\\" In 2018, following the 2018 Commonwealth Heads of Government Meeting, Commonwealth leaders officially\", \"Succession to the British throne summoned to meet at Westminster in November, enacted that \\\"the inheritance of the crown should be, rest, remain and abide in the most royal person of the then sovereign lord, King Henry VII, and the heirs of his body lawfully coming.\\\" Henry VII was followed by his son, Henry VIII. Though his father descended from the Lancastrians, Henry VIII could also claim the throne through the Yorkist line, as his mother Elizabeth was the sister and heiress of Edward V. In 1542 Henry also assumed the title King of Ireland; this would pass down with the monarchs of England, and\", \"Prince of Wales title does not affect the rights to royal succession. The title is granted to the heir apparent as a personal honour or dignity, and is not heritable, merging with the Crown on accession to the throne. The title Earl of Chester is always given in conjunction with that of Prince of Wales. The Prince of Wales usually has other titles and honours. The current and longest-serving Prince of Wales is Prince Charles, the eldest son of Elizabeth II, who is Queen of the United Kingdom and 15 other independent Commonwealth realms as well as Head of the 53-member Commonwealth of\", \"Regency Act 1830 age of 18 in 1837. When William IV became king in June 1830 he had no legitimate children who could inherit the throne on his death. Aged 64, he was the oldest person to ascend the British, English, Scottish or Irish thrones. His next younger brother, Prince Edward, had died in 1820 and so the next person in line to the throne was Edward's 11-year-old daughter, Princess Victoria. Therefore, it was necessary to pass a law to provide for the government of the United Kingdom in case Victoria became queen while still under age, or in case William had a\", \"King Charles III (play) Muse with Robert Joy as King Charles began 7 February 2017 at the Shakespeare Theatre Company in Washington D.C. Charles and his family gather following the funeral of Queen Elizabeth II. Charles, as the new king, then holds his first weekly audience with the Prime Minister. They discuss a new Bill for statutory regulation of the press, which has passed the House of Commons and the House of Lords and awaits only Charles's royal assent to become law. Charles is concerned that the law restricts freedom of the press too much, and would allow governments to censor the news and\", \"Regency Acts daughter Princess Beatrice of York. Currently, if Elizabeth II were to be declared incapable of discharging the royal functions, the legal guardianship of the incapacitated monarch would be vested in her husband Prince Philip, Duke of Edinburgh. If, however, the Duke of Edinburgh were to predecease his wife or be otherwise unable to carry out the duties of legal guardian, the guardianship of the Sovereign would then be vested in the sitting Regent. Prince George of Cambridge, should he ascend to the throne prior to his 18th birthday, is the first person in the present line of succession that would\", \"Prince Andrew, Duke of York Prince Andrew, Duke of York Prince Andrew, Duke of York, (Andrew Albert Christian Edward, born 19 February 1960) is a member of the British royal family. He is the third child and second son of Queen Elizabeth II and Prince Philip, Duke of Edinburgh. At the time of his birth, he was second in the line of succession to the British throne; he is seventh in line. He holds the rank of commander and the honorary rank of Vice Admiral (as of February 2015) in the Royal Navy, in which he served as an active-duty helicopter pilot and instructor and\", \"Pretender revolt of 1294\\u201395. Since 1301, the title of Prince of Wales has been given to the eldest living son of the King or Queen Regnant of England (subsequently of Great Britain, 1707, and of United Kingdom, 1801). The word \\\"living\\\" is important. Upon the death of Arthur, Prince of Wales, Henry VII invested his second son, the future Henry VIII, with the title. The title is not automatic, however, but merges into the Crown when a prince dies or accedes to the throne, and has to be re-conferred by the sovereign. Nevertheless, it is Glynd\\u0175r whom many remember as the\", \"Elizabeth II the longest-reigning British monarch and longest-reigning queen regnant and female head of state in the world on 9 September 2015. She is also the \\\"longest-reigning sovereign in Canada's modern era\\\". (King Louis XIV of France reigned over Canada (New France) for longer than Elizabeth.) She became the oldest current monarch after King Abdullah of Saudi Arabia died on 23 January 2015. She later became the longest-reigning current monarch and the longest-serving current head of state following the death of King Bhumibol of Thailand on 13 October 2016, and the oldest current head of state on the resignation of Robert Mugabe\", \"Prince of Wales medieval England extending principally over the counties of Cheshire and Flintshire. A Prince of Wales also holds a number of additional titles. As heir apparent to the English/British throne he is\\u2014if the eldest living son of the monarch\\u2014Duke of Cornwall. As heir apparent to the Scottish throne he is Duke of Rothesay, Earl of Carrick, Baron of Renfrew, Lord of the Isles, and Prince and Great Steward of Scotland. Individual princes have also held additional titles, which were theirs prior to becoming Prince of Wales. Before ascending the throne Henry VIII, Charles I and George V were each Duke of\", \"Prince Philip, Duke of Edinburgh to celebrate a platinum wedding anniversary. On 3 April 2018, Philip was admitted to the King Edward VII Hospital for a planned hip replacement, which took place the next day. This came after the Duke missed the annual Maundy and Easter Sunday services. On 12 April his daughter, Princess Anne, spent about 50 minutes in the hospital and afterwards said her father was \\\"on good form\\\". He was discharged the following day. On 19 May, six weeks later, he attended the wedding of his grandson Prince Harry to actress Meghan Markle and was able to walk with the Queen unaided.\", \"Alternative successions of the English and British crown queen and the throne would have been inherited by her eldest child, and so on. Friederike is not considered a pretender to the British throne, as this alternative line of succession is completely hypothetical. Next in line would be her eldest child, Felicitas Catharini Malina Johanna von Reiche. Alternative successions of the English and British crown British history provides several opportunities for alternative claimants to the Crown to arise, and historical scholars have on occasion traced to present times the heirs of those alternative claims. Throughout this article, the names of \\\"would-have-been\\\" monarchs are in \\\"italics\\\". Richard II abdicated in\", \"Prince William, Duke of Gloucester person remaining in the Protestant line to the throne established by the Bill of Rights 1689. Although Anne had ten other pregnancies after the birth of Gloucester, none of them resulted in a child who survived more than briefly after birth. The English parliament did not want the throne to revert to a Catholic, so it passed the Act of Settlement 1701, which settled the throne of England on a cousin of King James, Sophia, Electress of Hanover, and her Protestant heirs. Anne succeeded King William in 1702, and reigned until her death on 1 August 1714. Sophia predeceased her\", \"Royal Highness Wales was also entitled to the style but not younger sons or daughters of the oldest living son of the Prince of Wales. Queen Elizabeth II changed this in 2012 prior to the birth of Prince George of Cambridge so that all the children of the oldest living son of the Prince of Wales would bear the style. This returned it to the format Queen Victoria had instituted in 1898. There is no mention of younger living sons of a Prince of Wales, however, in 2018, Prince Harry was married to Meghan Markle and they were awarded Duke and Duchess\", \"Prince Edward, Earl of Wessex Prince Edward, Earl of Wessex Prince Edward, Earl of Wessex, (Edward Antony Richard Louis; born 10 March 1964) is the youngest of four children and the third son of Queen Elizabeth II and Prince Philip, Duke of Edinburgh. At the time of his birth, he was third in line of succession to the British throne; he is now tenth. The Earl is a full-time working member of the British royal family and supports the Queen in her official duties \\u2013 often alongside his wife the Countess of Wessex - as well as undertaking public engagements for a large number of\", \"Prince Harry, Duke of Sussex Prince Harry, Duke of Sussex Prince Harry, Duke of Sussex, (Henry Charles Albert David; born 15 September 1984) is a member of the British royal family. He is the younger son of Charles, Prince of Wales, and Diana, Princess of Wales, and is sixth in the line of succession to the British throne. He was officially styled Prince Henry of Wales from birth until his marriage, but is known as Prince Harry. Harry was educated at schools in the United Kingdom and spent parts of his gap year in Australia and Lesotho. He then underwent officer training at the Royal\", \"Elizabeth Truss Group of Conservative MPs, and authored or co-authored a number of papers and books, including \\\"After the Coalition\\\" (2011) and \\\"Britannia Unchained\\\" (2012). Truss was the Parliamentary Under-Secretary of State from 2012 to 2014, with responsibility for education and childcare in the Department for Education. She was the Secretary of State for the Environment, Food and Rural Affairs from 2014 to 2016. On 14 July 2016, she was appointed Secretary of State for Justice and Lord Chancellor by Theresa May, succeeding Michael Gove, and becoming the first female Lord Chancellor in the thousand-year history of the role (if not counting\", \"Prince Vincent of Denmark of succession to the British throne (and the thrones of the other Commonwealth realms), where he does take precedence over Isabella, because those realms followed the custom of male-preference primogeniture at the time of his birth; the subsequent change to absolute primogeniture in the Commonwealth line of succession only affected those in line born after 28 October 2011. On 15 August 2017, Vincent and his twin sister started school at Traneg\\u00e5rdsskolen in Gentofte \\u2013 the same public school as their elder siblings. Vincent is styled as \\\"His Royal Highness\\\" Prince Vincent of Denmark, Count of Monpezat. Prince Vincent of Denmark\", \"Head of the Commonwealth so. This was accommodated by the creation of the title \\\"Head of the Commonwealth\\\" for the King and India became a republic in 1950. Subsequently, many other nations including Pakistan, Sri Lanka, Malaysia and Singapore ceased to recognise the monarch of the United Kingdom as their respective head of state, but as members of the Commonwealth of Nations recognised the British monarch as Head of the Commonwealth. The title is currently held by Queen Elizabeth II, George VI's eldest daughter. Charles, Prince of Wales, was appointed her designated successor at the 2018 Commonwealth Heads of Government Meeting. The title was\", \"Descendants of James VI and I daughter, Sophia of Hanover, became the nearest Protestant relative to the English, Scottish and Irish crowns (later British crown). Under the English Act of Settlement, the succession was settled on Sophia and her issue, so her son George Ludwig ascended the throne as George I. Charles I, the second son of James VI and I was King of England, Scotland and Ireland from 27 March 1625 until his execution in 1649. The monarchy was then abolished and a republic called the Commonwealth of England, also referred to as the Cromwellian Interregnum, was declared. Charles's son, Charles II, became king after\", \"Lady Amelia Windsor her father to maintain a position in the line of succession. She is currently thirty-eighth in the line of succession to the British throne, behind her father and in front of her paternal aunt Lady Helen Taylor. Since Lady Amelia's father is a courtesy earl, using one of his father's substantive titles, she is entitled to use the courtesy title \\\"Lady\\\"; addressed as \\\"The Lady\\\" Amelia Windsor. Lady Amelia paternally descends from various royal houses of Europe. She is direct descendant of multiple monarchs, including George V of the United Kingdom, George I of Greece, Alexander II of Russia, Christian\", \"George I of Great Britain superior hereditary claims were bypassed. The likelihood of any of them converting to Protestantism for the sake of the succession was remote; some had already refused. In August 1701, George was invested with the Order of the Garter and, within six weeks, the nearest Catholic claimant to the thrones, the former king James II, died. William III died the following March and was succeeded by Anne. Sophia became heiress presumptive to the new Queen of England. Sophia was in her seventy-first year, older than Anne by thirty-five years, but she was very fit and healthy and invested time and energy\", \"Lady Davina Lewis in the Waipoua Forest of Northland Region, New Zealand. Until 26 March 2015, T\\u0101ne was ahead of Senna in the line of succession to the British throne, as the monarchy's 300-year-old Act of Settlement gave sons priority over daughters. When the Succession to the Crown Act 2013 took effect in all Commonwealth realms in 2015, Senna became the nearest relative of the reigning monarch, Queen Elizabeth II, to be affected by the change in law, which advanced her proximity to the Crown by reversing her place in the order of succession with that of her younger brother. Senna and T\\u0101ne\", \"Lady Amelia Windsor IX of Denmark, Frederick II Eugene, Duke of W\\u00fcrttemberg, and Frederick II, Landgrave of Hesse-Kassel. Lady Amelia Windsor Lady Amelia Windsor (\\\"Amelia Sophia Theodora Mary Margaret Windsor\\\"; born 24 August 1995) is an English fashion model and member of the British royal family. She is currently 38th in the line of succession to the British throne. Lady Amelia Windsor was born on 24 August 1995 at Rosie Hospital in Cambridge and was christened in December 1995 at Chapel Royal, St James's Palace. She is the youngest child of George Windsor, Earl of St Andrews, and Sylvana Tomaselli. Her paternal grandfather,\", \"Orders, decorations, and medals of the United Kingdom 29 April 2011; and to the Queen's grandson Prince Harry, who was made the Duke of Sussex on the morning before his marriage to Meghan Markle on 19 May 2018. No hereditary peerages were granted to commoners after the Labour Party came to power in 1964, until Margaret Thatcher tentatively reintroduced them by two grants to men with no sons in 1983, respectively the Speaker of the House of Commons George Thomas and the former Deputy Prime Minister William Whitelaw. Both these titles died with their holders. She followed this with an Earldom in 1984 for the former Prime Minister\", \"Alternative successions of the English and British crown Alternative successions of the English and British crown British history provides several opportunities for alternative claimants to the Crown to arise, and historical scholars have on occasion traced to present times the heirs of those alternative claims. Throughout this article, the names of \\\"would-have-been\\\" monarchs are in \\\"italics\\\". Richard II abdicated in favour of Henry Bolingbroke on 29 September 1399. However, Henry was not next in the line to the throne; the heir presumptive was Edmund Mortimer, Earl of March, who descended from Edward III's second surviving son, Lionel of Antwerp, whereas Henry's father, John of Gaunt, was Edward's third\", \"Edward VIII \\\"the boy will ruin himself in twelve months.\\\" Second-in-line to the throne was the prince's younger brother Albert (\\\"Bertie\\\"). Albert and his wife, Elizabeth (later the Queen Mother), had two children, including Princess Elizabeth (\\\"Lilibet\\\"), the future Queen Elizabeth II. George V favoured Albert and his granddaughter Elizabeth and told a courtier, \\\"I pray to God that my eldest son will never marry and have children, and that nothing will come between Bertie and Lilibet and the throne.\\\" In 1929, \\\"Time\\\" magazine reported that the Prince of Wales teased his sister-in-law, by calling her \\\"Queen Elizabeth\\\". The magazine asked if\", \"John Major Following the death of Diana, Princess of Wales, in 1997, Major was appointed a special guardian to Princes William and Harry, with responsibility for legal and administrative matters. As a result of this, Major was the only current or former Prime Minister out of the five still alive invited to the wedding of Prince Harry and Meghan Markle in May 2018. An oil painting of Major, painted in 1996 by June Mendoza, is part of the parliamentary collection, as is a bronze bust by Anne Curry. Major is the author of three books: Major's low profile following his exit from\", \"Queen Camilla Party elects \\\"Boy\\\" English as its new leader; Boy promises to restore the monarchy. The Queen, now 80, does not want to return to public life; she tells her family she has decided to abdicate. One reason: the Duke of Edinburgh, her husband, suffered a debilitating stroke two years earlier, and is now being (badly) cared for in a nursing-home in another part of the Fez. With the Queen's abdication, the Prince of Wales will now become King Charles III - but Camilla will only be his consort, not his Queen. Charles refuses to become King unless Camilla is his\", \"George I of Great Britain many cultural icons such as the mathematician and philosopher Gottfried Leibniz and the composers George Frideric H\\u00e4ndel and Agostino Steffani. Shortly after George's accession to his paternal duchy, Prince William, Duke of Gloucester, who was second-in-line to the English and Scottish thrones, died. By the terms of the English Act of Settlement 1701, George's mother, Sophia, was designated as the heir to the English throne if the then reigning monarch, William III, and his sister-in-law, Anne, died without surviving issue. The succession was so designed because Sophia was the closest Protestant relative of the British royal family. Fifty-six Catholics with\", \"Succession to the Crown Act 2013 the line of succession, and removed the requirement of those outside the first six persons in line to the throne to seek the Sovereign's approval to marry. It came into force on 26 March 2015, at the same time as the other Commonwealth realms implemented the Perth Agreement in their own laws. Under the Act of Settlement 1701 the throne of the Kingdom of England was settled on the Electress Sophia of Hanover and the \\\"heirs of her body\\\", this phrase being understood under English common law to imply male-preference primogeniture, meaning that brothers would precede sisters in the line\", \"Lord Frederick Windsor President of the employment education charity, Soldier On!. On 21 February 2017, Lord Frederick was inducted into the Grand Order of Water Rats charitable fraternity. Lord Frederick Windsor Lord Frederick Windsor (born 6 April 1979), also nicknamed Freddie Windsor, is a British financial analyst, and the only son of Prince and Princess Michael of Kent. He is currently 48th in line to the succession of the British throne. He is President of the charity Soldier On!, which invites socially isolated people to participate in archaeology and heritage projects as well as career development workshops. Windsor was born on 6 April\", \"Descendants of Charles II of England (1689\\u20131716) and Charles Radclyffe (1693\\u20131746) were both notable Jacobites and both were executed for treason following the Risings of 1715 and 1745 respectively. Lady Diana Spencer (later the Princess of Wales) is a descendant of Charles II, through two of his illegitimate sons. Diana married The Prince of Wales in 1981 and had two sons, Prince William, Duke of Cambridge and Prince Harry, Duke of Sussex. Currently, the Duke is 2nd in the line of Succession, his sons and daughter, Prince George, Princess Charlotte, and Prince Louis are 3rd, 4th, and 5th, while Prince Harry is 6th in line. If\", \"House of Windsor four British monarchs of the house of Windsor to date: three kings and the present queen, Elizabeth II. During the reign of the Windsors, major changes took place in British society. The British Empire participated in the First and Second World Wars, ending up on the winning side both times, but subsequently lost its status as a superpower during decolonisation. Much of Ireland broke with the United Kingdom and the remnants of the Empire became the Commonwealth of Nations. The current head of the house is monarch of sixteen sovereign states. These are the United Kingdom (where they are based),\", \"Mary II of England during peacetime without parliamentary consent, deny the right to bear arms to Protestant subjects, unduly interfere with parliamentary elections, punish members of either House of Parliament for anything said during debates, require excessive bail, or inflict cruel or unusual punishments. The Bill of Rights also confirmed the succession to the throne. Following the death of either William III or Mary II, the other was to continue to reign. Next in the line of succession would be any children of the couple, to be followed by Mary's sister Anne and her children. Last in the line of succession stood any children\", \"Princess Ma\\u0308rtha Louise of Norway and 1990 (i.e. only M\\u00e4rtha Louise), were given succession rights, but their brothers would be before them in the line of succession, meaning that Prince Haakon still took precedence over M\\u00e4rtha Louise in the line of succession. After the births of her brother's two children, Ingrid Alexandra and Sverre Magnus, M\\u00e4rtha Louise was relegated to fourth in line. The princess is also in the line of succession to the thrones of the sixteen Commonwealth realms, as a great-great-granddaughter of King Edward VII of the United Kingdom. Princess M\\u00e4rtha Louise is a certified physiotherapist, following education in Oslo and internship in\", \"Prince George of Cambridge affected business and popular culture. On 3 December 2012, Clarence House announced that Prince William, Duke of Cambridge, and Catherine, Duchess of Cambridge, were expecting their first child. The Duke is the elder son of Charles, Prince of Wales, who is the eldest son of Queen Elizabeth II, meaning that the child would be third in the line of succession to the British throne. Speculation ensued that the birth would boost the British national economy and provide a focus for national pride. Commemorative coins were issued by the Royal Mint, Royal Canadian Mint, and Royal Australian Mint; the first time\", \"Orders of precedence in the United Kingdom first in the order of precedence for women. The reverse, however, is not always true for Queens regnant. There is no established law of precedence for a prince consort, so he is usually specially granted precedence above all other males by letters patent or, on the other hand, may rank lower than the heir apparent or the heir presumptive, even if the heir is his own son. In England and Wales, the Archbishop of Canterbury is the highest in precedence following the royal family. Then come, assuming the post of Lord High Steward is vacant (as it usually has been\", \"Jane Fellowes, Baroness Fellowes 29 April 2011, and that of his brother Prince Harry and Meghan Markle on 19 May 2018, at which she delivered a reading. Jane Fellowes, Baroness Fellowes Cynthia Jane Fellowes, Baroness Fellowes (n\\u00e9e Spencer; born 11 February 1957) is one of the two older sisters of Diana, Princess of Wales, the other being Lady Sarah McCorquodale. Lady Fellowes is the second daughter of Edward John Spencer, 8th Earl Spencer (1924\\u20131992) and the Hon. Frances Ruth Burke Roche (1936\\u20132004). Her parents married in 1954 but divorced in 1969. She has always used her middle name of Jane (just as her elder\", \"Descendants of Charles II of England any of them succeed to the British throne, they will be the first descendants of Charles II of England to accede to the throne. The following list details the line of descent from Charles II of England to Princes William and Harry. Edward Fitzalan-Howard, 18th Duke of Norfolk, through Lady Charlotte Fitzroy Andrew Russell, 15th Duke of Bedford, through Charles Lennox, 1st Duke of Richmond John Anstruther-Gough-Calthorpe, through Henry FitzRoy, 1st Duke of Grafton Isabella Calthorpe, daughter of John Anstruther-Gough-Calthorpe Sarah, Duchess of York, through Charles Lennox, 1st Duke of Richmond and James Scott, 1st Duke of Monmouth Ralph Percy,\", \"Royal Marriages Act 1772 Act upon his abdication, allowing him to marry the divorcee, Wallis Simpson. The wording of the statute also excluded any issue of the marriage both from being subject to the Act, and from the succession to the throne; no marriages or succession rights were ultimately affected by this language, as the Duke and Duchess of Windsor had no children. In October 2011 David Cameron wrote to the leaders of the other Commonwealth realms proposing that the act be limited to the first six people in line to the throne. The leaders approved the proposed change at the Commonwealth Heads of\", \"Prince William, Duke of Gloucester mother was the only individual remaining in the Protestant line of succession established by the Bill of Rights 1689. The English Parliament did not want the throne to revert to a Catholic, and so passed the Act of Settlement 1701, which settled the throne of England on Electress Sophia of Hanover, a cousin of King James II, and her Protestant heirs. In late 1688, in what became known as the \\\"Glorious Revolution\\\", the Roman Catholic King James of England, Scotland and Ireland was deposed by his Protestant nephew and son-in-law, Dutch stadtholder William III of Orange. William and his wife,\", \"Charles, Prince of Wales In the mid-1970s, the prince expressed an interest in serving as Governor-General of Australia, at the suggestion of Australian prime minister Malcolm Fraser, but because of a lack of public enthusiasm nothing came of the proposal. Charles accepted the decision, if not without some regret; he said: \\\"So, what are you supposed to think when you are prepared to do something to help and you are just told you're not wanted?\\\" Charles is the longest serving Prince of Wales, having surpassed the record held by Edward VII on 9 September 2017. He is the oldest and longest-serving British heir apparent,\", \"Charles, Prince of Wales interviewed several sources from Prince Charles's inner circle, he \\\"doesn't like being used to market weaponry\\\" in deals with Saudi Arabia and other Arab Gulf states. According to Mayer, Charles has only raised his objections to being used to sell weapons abroad in private. Commonwealth heads of government decided at their 2018 meeting, that the Prince of Wales will be the next Head of the Commonwealth after the Queen. The head is chosen and therefore not hereditary. From his youth until 1992, Prince Charles was an avid player of competitive polo. He continued to play informally, including for charity, until\", \"Edward VIII Fort Belvedere on 10 December 1936 in the presence of his younger brothers: Prince Albert, Duke of York, next in line for the throne; Prince Henry, Duke of Gloucester; and Prince George, Duke of Kent. The next day, the last act of his reign was the royal assent to His Majesty's Declaration of Abdication Act 1936. As required by the Statute of Westminster, all the Dominions consented to the abdication. On the night of 11 December 1936, Edward, now reverted to the title and style of a prince, explained his decision to abdicate in a worldwide radio broadcast. He famously\", \"Elizabeth II people believed he would marry and have children of his own. When her grandfather died in 1936 and her uncle succeeded as Edward VIII, she became second-in-line to the throne, after her father. Later that year, Edward abdicated, after his proposed marriage to divorced socialite Wallis Simpson provoked a constitutional crisis. Consequently, Elizabeth's father became king, and she became heir presumptive. If her parents had had a later son, she would have lost her position as first-in-line, as her brother would have been heir apparent and above her in the line of succession. Elizabeth received private tuition in constitutional history\", \"Anne Marie Morris \\\"racist remark\\\" made by her partner and election agent, Roger Kendrick, at a hustings, in which he claimed problems in the British education system were \\\"due entirely to non-British born immigrants and their high birth rates\\\". The whip was restored to Morris on 12 December 2017, one day before a crucial vote on the Brexit process. Although Morris voted with the Conservative Government, the Government was defeated by 4 votes. On 15 November 2018 she submitted a letter of no confidence in Theresa May's leadership. Morris lives in Newton Abbot and London. Her partner is the financier Roger Kendrick, who\", \"Prince consort the \\\"king\\\" holds a higher position in the British social hierarchy. Thus, more power is attributed to him. In cases where the hereditary monarch is female, such as Queen Victoria, who ascended to the throne in 1837, power is attributed to the queen, for she holds the highest position in the absence of a king. Clarence House has announced that if Charles, Prince of Wales, becomes monarch of the United Kingdom, his second wife, Camilla, Duchess of Cornwall, will have the title of \\\"Princess Consort\\\" rather than \\\"Queen\\\". The imperial Chinese title of \\\"fuma\\\" (), and its Manchu equivalent \\\"e'fu\\\"\", \"Lady Louise Windsor Lady Louise Windsor Lady Louise Windsor (Louise Alice Elizabeth Mary Mountbatten-Windsor; born 8 November 2003) is the elder child and only daughter of Prince Edward, Earl of Wessex, and Sophie, Countess of Wessex. She is the youngest granddaughter of Queen Elizabeth II and Prince Philip, Duke of Edinburgh. When Lady Louise was born, she was eighth in the line of succession to the British throne. Following the birth of her brother and the Duke and Duchess of Cambridge's children, she is twelfth in the line of succession. Lady Louise was born prematurely on 8 November 2003 at 23:32 GMT at\", \"Wedding of Prince Harry and Meghan Markle photos were released. They were taken by photographer Alexi Lubomirski at Windsor Castle following the ceremony. In April 2018, it was announced that an \\\"official list\\\" of domestic and international political leaders was not required for the wedding and that Prime Minister Theresa May, Leader of the Opposition Jeremy Corbyn, and other leaders would not attend the ceremony. President of the United States Donald Trump and former President Barack Obama were also not invited. This was in contrast to the wedding of Prince Harry's elder brother, which had a large number of such guests due to his position as a\", \"Lady Amelia Windsor Lady Amelia Windsor Lady Amelia Windsor (\\\"Amelia Sophia Theodora Mary Margaret Windsor\\\"; born 24 August 1995) is an English fashion model and member of the British royal family. She is currently 38th in the line of succession to the British throne. Lady Amelia Windsor was born on 24 August 1995 at Rosie Hospital in Cambridge and was christened in December 1995 at Chapel Royal, St James's Palace. She is the youngest child of George Windsor, Earl of St Andrews, and Sylvana Tomaselli. Her paternal grandfather, Prince Edward, Duke of Kent, is a first cousin of Elizabeth II. Her paternal great\", \"Andrew Rosindell 2012, a private member's bill aiming to create a dedicated entry queue for citizens of countries where the British Queen is head of state and introduce pictures of the queen and more royal symbols at UK borders. He reiterated calls for preferential treatment of \\\"Her Majesty's subjects\\\" visiting Britain in 2015, whilst also calling for the immigration system to favour Commonwealth citizens, as opposed to those from the EU. Rosindell proved ahead of his time, as this measure was then adopted by Chancellor Philip Hammond in his October 2018 budget. Rosindell has spoken in favour of a federal UK and\", \"Anne, Queen of Great Britain action, and William and Mary were declared monarchs of all three realms. The Bill of Rights 1689 and Claim of Right Act 1689 settled the succession. Anne and her descendants were to be in the line of succession after William and Mary, and they were to be followed by any descendants of William by a future marriage. On 24 July 1689, Anne gave birth to a son, Prince William, Duke of Gloucester, who, though ill, survived infancy. As King William and Queen Mary had no children, it looked as though Anne's son would eventually inherit the Crown. Soon after their\", \"Prince Sverre Magnus of Norway and sister. As descendants of the British King Edward VII's daughter Maud, members of the Norwegian royal family are also in the line of succession to the British throne; Sverre Magnus precedes his sister in the British line due to male-preference cognatic primogeniture for those born before 2011. Prince Sverre Magnus is styled as \\\"His Highness\\\" Prince Sverre Magnus of Norway, as opposed to his sister, who is styled as \\\"Her Royal Highness\\\". Prince Sverre Magnus of Norway Prince Sverre Magnus of Norway (born 3 December 2005) is the younger child of Crown Prince Haakon and Crown Princess Mette-Marit and\", \"Monarchy of Grenada parliament only. This legislation lays out the rules that the Monarch cannot be a Roman Catholic, nor married to one, and must be in communion with the Church of England upon ascending the throne. As Grenada's laws governing succession are currently identical to those of the United Kingdom (by the \\\"Statute of Westminster\\\") see Succession to the British Throne for more information. The heir apparent is Elizabeth II's eldest son, Charles, who has no official title outside of the UK, but is accorded his UK title, Prince of Wales, as a courtesy title. All laws in Grenada are enacted with\", \"Princess Eugenie of York Princess Eugenie of York Princess Eugenie of York ( ; Eugenie Victoria Helena; born 23 March 1990) is a member of the British royal family, and the younger daughter of Prince Andrew, Duke of York, and Sarah, Duchess of York. She is ninth in line of succession to the British throne, after her elder sister, Princess Beatrice of York. Princess Eugenie was born in London at The Portland Hospital for Women and Children on 23 March 1990, the second child of Prince Andrew, Duke of York, and Sarah, Duchess of York, and sixth grandchild of Queen Elizabeth II and Prince\", \"Government of the United Kingdom a majority of MPs. Under the uncodified British constitution, executive authority lies with the monarch, although this authority is exercised only by, or on the advice of, the prime minister and the cabinet. The Cabinet members advise the monarch as members of the Privy Council. In most cases they also exercise power directly as leaders of the Government Departments, though some Cabinet positions are sinecures to a greater or lesser degree (for instance Chancellor of the Duchy of Lancaster or Lord Privy Seal). The current prime minister is Theresa May, who took office on 13 July 2016. She is the\", \"William IV of the United Kingdom marriage. The couple had two short-lived daughters and Adelaide suffered three miscarriages. Despite this, false rumours that Adelaide was pregnant persisted into William's reign\\u2014he dismissed them as \\\"damned stuff\\\". William's elder brother, the Prince of Wales, had been Prince Regent since 1811 because of the mental illness of their father, George III. In 1820, the King died, leaving the Crown to the Prince Regent, who became George IV. William, Duke of Clarence, was now second in the line of succession, preceded only by his brother, Frederick, Duke of York. Reformed since his marriage, William walked for hours, ate relatively frugally,\", \"Duke of Sussex by Lady Augusta Murray, their marriage had been annulled for lack of royal permission under the Royal Marriages Act 1772, rendering the children illegitimate under English law and unable to inherit titles from their father. Both children by the annulled marriage died childless, rendering the issue of their inheritance moot. In 2018, the title was recreated and granted to Prince Harry to mark the occasion of his wedding to Meghan Markle. Prince Harry was granted the subsidiary titles Earl of Dumbarton in Scotland and Baron Kilkeel in Northern Ireland at the same time. A title associated with Sussex first appeared\", \"House of Glu\\u0308cksburg his princely titles and adopted the surname of Mountbatten upon becoming a British subject prior to his wedding) was created Duke of Edinburgh by his father-in-law, George VI. Descendants in the male line of his marriage to Queen Elizabeth II belong, by decree, to the House of Windsor and use \\\"Mountbatten-Windsor\\\" as a surname, when one is needed. The first nineteen places in the line of succession to the British throne are held by the Duke's descendants. The heir-apparent is Charles, Prince of Wales (born 1948). House of Gl\\u00fccksburg The House of Gl\\u00fccksburg (also spelled \\\"Gl\\u00fccksborg\\\"), shortened from House of\", \"George VI time. He suffered from chronic stomach problems as well as knock knees, for which he was forced to wear painful corrective splints. Queen Victoria died on 22 January 1901, and the Prince of Wales succeeded her as King Edward VII. Prince Albert moved up to third in line to the throne, after his father and elder brother. From 1909, Albert attended the Royal Naval College, Osborne, as a naval cadet. In 1911 he came bottom of the class in the final examination, but despite this he progressed to the Royal Naval College, Dartmouth. When his grandfather, Edward VII, died in\", \"Republic (political organisation) over 5,000 paying members and about 35,000 online supporters. CEO Graham Smith criticised hereditary power as being \\\"absurd\\\" and monarchy as an outdated political institution that \\\"abuses its position, abuses public money and which gives politicians too much power.\\\" Republic has said that after the death of Queen Elizabeth II it intends to mount a campaign for a referendum on the future of the monarchy. The group plans to do this during the period between the Queen's funeral and the coronation of Prince Charles. Republic asserts that there is a lack of transparency and accountability with respect to the funding\", \"Prince Henry, Duke of Gloucester larger part of his inheritance. It was a large country house in Northamptonshire which had belonged to his wife's ancestors. As their London seat, they were given York House in St. James's Palace. In December 1936, Henry's brother Edward VIII abdicated the throne to marry divorc\\u00e9e Wallis Simpson. His brother Prince Albert, ascended the throne as King George VI. Although third in line to the throne, following his two nieces Princesses Elizabeth and Margaret, he became the first adult in line, meaning he would act as regent if anything were to happen to the King before Princess Elizabeth came of\", \"Theresa May Radio 4's \\\"Woman's Hour\\\" described her as Britain's second-most powerful woman after Queen Elizabeth II. On 30 November 2014, she was awarded an Honorary Doctorate from the World Sikh University. In April 2017, during an official trip to Saudi Arabia, May was appointed to the Order of King Abdulaziz. That September, she was listed by \\\"Forbes\\\" as the second most powerful woman in the world, behind Angela Merkel. Theresa May Theresa Mary May (; ; born 1 October 1956) is a British politician serving as Prime Minister of the United Kingdom and Leader of the Conservative Party since 2016. She\", \"Peter Phillips management fields. He is currently fourteenth in line of succession to the British throne. Peter Phillips was born at 10:46 am on 15 November 1977, at Lindo Wing of St Mary's Hospital, Paddington, London. He was the first child of Princess Anne and Mark Phillips, who had married in 1973. At the time of his birth, there was a 41-gun salute from the Tower of London. He was baptised Peter Mark Andrew Phillips on 22 December 1977, by the then Archbishop of Canterbury Donald Coggan in the Music Room of Buckingham Palace. Phillips was fifth in line to the throne\", \"Order of precedence in England and Wales higher precedence than those who are married in. For example, when not accompanied by the Prince of Wales, Camilla, Duchess of Cornwall, ranks after Princess Alexandra, The Honorable Lady Ogilvy; when with him, she ranks above all women other than the reigning sovereign and any queens dowager. The same goes for spouses of the Queens Grandsons and their positioning with the Queens Granddaughters. For example, as a Princess of the blood Royal, Princess Beatrice of York outranks Catherine, Duchess of Cambridge if Catherine is unaccompanied by Prince William, Duke of Cambridge. Order of Precedence for female members of the royal\", \"Succession to the Crown Act 2013 of Acts of Union 1801, again maintained that the succession rules in place in the new United Kingdom of Great Britain and Ireland should be \\\"continued limited and settled in the same manner\\\". Since the Acts of Union 1707, male preference primogeniture has operated twice to displace a female by a younger brother: when Princess Augusta's younger brother became King George III on the death of their grandfather King George II (1760); and when Princess Victoria's younger brother became King Edward VII on the death of their mother Queen Victoria (1901). Princess Anne is the younger sister of the heir\"], \"pos_scores\": [101.375], \"neg_scores\": [89.9375, 89.75, 95.6875, 91.5, 97.25, 91.75, 87.3125, 88.75, 91.125, 95.0, 88.4375, 86.375, 88.1875, 90.1875, 90.8125, 86.8125, 92.9375, 90.5, 88.125, 91.75, 91.6875, 96.9375, 96.5625, 89.875, 84.25, 86.5625, 90.0625, 86.75, 88.125, 87.6875, 87.9375, 85.3125, 91.375, 90.9375, 92.5625, 95.25, 88.125, 90.125, 92.5, 88.875, 86.0, 89.125, 92.375, 87.75, 89.9375, 87.5, 91.5, 90.125, 89.8125, 86.5625, 85.1875, 92.4375, 88.5625, 86.8125, 87.0, 85.625, 88.8125, 87.625, 88.8125, 87.25, 86.5625, 92.0, 87.75, 92.375, 86.875, 91.25, 92.4375, 87.625, 84.5625, 89.8125, 89.75, 86.9375, 90.375, 87.375, 88.125, 95.25, 92.0625, 86.8125, 89.6875, 84.5, 88.3125, 89.75, 86.625, 89.0625, 84.125, 88.125, 85.4375, 91.5, 88.5, 88.8125, 87.125, 88.75, 91.5, 88.9375, 87.25, 86.375, 86.9375, 87.3125, 93.3125, 93.6875], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"who is in charge of enforcing the pendleton act of 1883\", \"pos\": [\"Pendleton Civil Service Reform Act to the presidency. Once in office, President Arthur pushed through legislation for civil reform. On January 16, 1883 Congress passed the Civil Service Act, which is sometimes referred to as the Pendleton Act after Senator George H. Pendleton of Ohio, one of the primary sponsors. The Act was written by Dorman Bridgman Eaton, a staunch opponent of the patronage system who was later first chairman of the United States Civil Service Commission. However, the law would also prove to be a major political liability for Arthur. The law offended machine politicians, or politicians who belong to a small clique that\"], \"neg\": [\"James A. Garfield appointing William H. Robertson to the lucrative post of Collector of the Port of New York, starting a fracas that ended with Robertson's confirmation and Conkling's resignation from the Senate. Garfield advocated agricultural technology, an educated electorate, and civil rights for African Americans. He also proposed substantial civil service reform, eventually passed by Congress in 1883 and signed into law by his successor, Chester A. Arthur, as the Pendleton Civil Service Reform Act. On July 2, 1881, he was shot at the Baltimore and Potomac Railroad Station in Washington D.C. by Charles J. Guiteau, a disappointed office seeker. The wound\", \"Luther Meade Blackman lives on Bat Creek, is convinced that Blackman is the forger. Blackman was an engraver who lived near the Tipton farm. He was also a neighbor of Jim Lawson. Blackman held a Federal Patronage job from 1870 to about 1890. It was during this time that President Grover Cleveland created the 1883 Pendleton Act to fix the Pensions Claims Office and the Postal Service. Cleveland let many Republicans be replaced by Democrats, and this increased the tension between the Democratic and Republican parties. It has been suggested that the L. C. Houk political machine that ran the Republicans in East\", \"Spoils system good service. By the late 1860s, citizens began demanding civil service reform. Running under the Liberal Republican Party in 1872, they were soundly defeated by Ulysses S. Grant. After the assassination of James A. Garfield by a rejected office-seeker in 1881, the calls for civil service reform intensified. Moderation of the spoils system at the federal level came with the passage of the Pendleton Act in 1883, which created a bipartisan Civil Service Commission to evaluate job candidates on a nonpartisan merit basis. While few jobs were covered under the law initially, the law allowed the President to transfer jobs\", \"John Sherman concurred by a vote of 155\\u201347. Arthur signed the Pendleton Civil Service Reform Act into law on January 16, 1883. There was relatively little financial legislation in the 1880s. By that time, fewer bonds were necessary, as the government now ran a consistent surplus which by 1882 reached $145 million. Opinions varied on how to balance the budget; Democrats wished to lower tariffs to reduce revenues and the cost of imported goods, while Republicans believed that high tariffs ensured high wages in manufacturing and mining. They preferred the government spend more on internal improvements and reduce excise taxes. Congress passed\", \"George H. Pendleton to the United States Senate. During his only term, from 1881 to 1885, he served concurrently as the Chairman of the Democratic Conference. Following the 1881 assassination of James A. Garfield, he passed his most notable legislation, known as the Pendleton Act of 1883, requiring civil service exams for government positions. The Act helped put an end to the system of patronage in widespread use at the time, but it cost Pendelton politically, as many members of his own party preferred the spoils system. He was thus not renominated to the Senate. Instead, President Grover Cleveland appointed him Envoy Extraordinary\", \"Presidency of Chester A. Arthur of Congress; the Senate approved the bill 38\\u20135 and the House soon concurred by a vote of 155\\u201347. Arthur signed the Pendleton Civil Service Reform Act into law on January 16, 1883. The bill created a civil service commission to oversee civil service examinations and outlawed the use of \\\"assessments,\\\" fees that political appointees were expected to pay to their respective political parties as the price for their appointments. These reforms had previously been proposed by the Jay Commission, which had investigated Arthur during his time as Collector of the Port of New York. In just two years' time, an\", \"Thomas F. Bayard ... for the public service and not for the private use of incumbents.\\\" The Senate approved the bill 38\\u20135 and the House soon concurred by a vote of 155\\u201347. Arthur signed the Pendleton Civil Service Reform Act into law on January 16, 1883. Despite his rebukes at the Democratic national conventions in 1876 and 1880, Bayard was again considered among the leading candidates for the nomination in 1884. Tilden again was ambiguous about his willingness to run, but by 1883 New York's new governor, Grover Cleveland, began to surpass Tilden as a likely candidate. After Tilden definitively bowed out in\", \"Moynihan Commission on Government Secrecy 1917. The U.S. Civil Service Commission, established by the Pendleton Act in 1883, was debarring persons relating to \\\"loyalty\\\" as late as 1921. The Commission Report quotes Max Weber, Every bureaucracy seeks to increase the superiority of the professionally informed by keeping their knowledge and intentions secret...Bureaucracy naturally welcomes a poorly informed and hence a powerless parliament\\u2014at least insofar as ignorance somehow agrees with the bureaucracy's interests. In March 1947 President Truman issued Executive Order 9835, establishing the Federal Employee Loyalty Program, providing uniform investigation standards and procedures, and authorizing the creation of Loyalty Review Boards across the Government. The\", \"George H. Pendleton the 1869 Ohio gubernatorial election, he temporarily left politics. He served as the president of the Kentucky Central Railroad before returning to Congress. Pendleton won election to the U.S. Senate in 1879 and served a single term, becoming Chairman of the Senate Democratic Conference. After the assassination of President James A. Garfield, he wrote and helped pass the Pendleton Civil Service Reform Act of 1883. The act required many civil service hires to be based on merit rather than political connections. Passage of the act lost him support in Ohio and he was not nominated for a second term in\", \"Stalwarts (politics) the Republican Party. The most prominent issue between Stalwarts and Half-Breeds was patronage. The Half-Breeds worked to get civil service reform, and finally created the Pendleton Civil Service Reform Act. This was signed by Arthur, who became President after the assassination of James A. Garfield, a Half-Breed. Stalwarts favored traditional machine politics. The Stalwarts were mostly identifiable through their support of the presidency and re-election of Ulysses S. Grant. The 1880 Republican National Convention was the event in which the group participated most prominently. Of the Stalwarts present, most were from former Confederate states, with others being from New York,\", \"Chester A. Arthur term Arthur assumed the presidency upon the assassination of his predecessor by a mentally ill Stalwart. At the outset, Arthur struggled to overcome a negative reputation as a Stalwart and product of Conkling's machine. To the surprise of reformers, he took up the cause of civil service reform. Arthur advocated for and enforced the Pendleton Civil Service Reform Act. He presided over the rebirth of the United States Navy, but was criticized for failing to alleviate the federal budget surplus, which had been accumulating since the end of the Civil War. Arthur signed the Chinese Exclusion Act, which resulted in\", \"Pendleton Civil Service Reform Act Pendleton Civil Service Reform Act The Pendleton Civil Service Reform Act (ch. 27, ) is a United States federal law enacted in 1883 that mandated that positions within the federal government should be awarded on the basis of merit instead of political affiliation. The act provided selection of government employees by competitive exams, rather than ties to politicians or political affiliation. It also made it illegal to fire or demote government officials for political reasons and prohibited soliciting campaign donations on Federal government property. To enforce the merit system and the judicial system, the law also created the United States\", \"U.S. Civil Service Reform law in January 1883; it was sponsored by Democratic Senator George H. Pendleton of Ohio. It was drafted by Dorman Bridgman Eaton, a leading reformer who became the first chairman of the U.S. Civil Service Commission. Its most famous commissioner was Theodore Roosevelt (1889\\u201395). The new law prohibited mandatory campaign contributions, or \\\"assessments\\\", which amounted to 50\\u201375% of party financing in the Gilded Age. Second, the Pendleton Act required entrance exams for aspiring bureaucrats. At first it covered very few jobs but there was a ratchet provision whereby outgoing presidents could lock in their own appointees by converting their jobs\", \"Human resource management in public administration Jackson's presidency and opposition against the system began to grow. During the presidency of Ulysses S. Grant corruption and inefficiency began to reach staggering proportion. This led to a larger outcry against the system and helped bring about change in 1883. George H. Pendleton: Senator from Ohio sponsored the Civil Service Reform Act in 1883, which sought to implement a merit-based program in the federal government. Its principal tenets include: Chester Barnard: taught an organization was the cooperation of human activity and to survive an organization needed to have efficiency and effectiveness. His definition of effectiveness: being able to accomplish\", \"Anson Phelps Stokes political opinion or refusal to render party service. The Pendleton Civil Service Reform Act in 1883 made it law that government positions should be awarded on merit, however the Association continued to push for compliance, improvements, efficiencies and reforms in the U.S Civil Service. Stokes became chairman of the National Association of Anti-Imperialist Clubs, a movement formed in 1898 to oppose the annexation of Cuba, Puerto Rico, Guam and the Philippines at the end of the Spanish\\u2010American War. He was also an active member and supporter of the free trade league and first president of the New York Reform Club.\", \"Thomas F. Bayard civil service reform. Leaders of both parties, including Bayard, realized that they could attract the votes of reformers by turning against the spoils system and, by 1882, a bipartisan effort began in favor of reform. In 1880, Democratic Senator George H. Pendleton of Ohio introduced legislation that required selection of civil servants based on merit as determined by an examination, but the bill did not pass. After the 1882 congressional elections, in which Democrats campaigned successfully on the reform issue, the Pendleton bill was proposed again, and again Bayard supported it, saying that \\\"the offices of this Government are created\", \"Mary F. Hoyt her childhood nickname \\\"Minnie\\\". She was raised and grew up in Norwalk. After high school, she attended Vassar College in New York City, graduating in 1880. Her first job was a favorable position for a young lady, a clerk with the Census Bureau in Washington, D.C. Hoyt was the first woman selected to a US civil service position. The Pendleton Civil Service Reform Act, also known as the Civil Service Act, is a federal law established in 1883 which decided that government jobs should be awarded on the basis of merit and examination scores instead of political affiliation. US president\", \"Edmund Pendleton resolution at the Second Congress, he said: \\\"The ground and foundation of the present unhappy dispute between the British Ministry and Parliament and America, is a Right claimed by the former to tax the Subjects of the latter without their consent, and not an inclination on our part to set up for independency, which we utterly disavow and wish to restore to a Constitutional Connection upon the most solid and reasonable basis.\\\" Pendleton served as President of the Virginia Committee of Safety from August 16, 1775 to July 5, 1776 (effectively serving as governor of the colony) and as President\", \"Presidency of Chester A. Arthur unrepentant Stalwart had become the president who ushered in long-awaited civil service reform. Even after Arthur signed the Pendleton Civil Service Reform Act into law, proponents of the act doubted Arthur's commitment to reform. The act initially applied only to ten percent of federal jobs and, without proper implementation by the president, would not have affected the remaining civil service positions. To the surprise of his critics, Arthur acted quickly to appoint the members of the newly-created Civil Service Commission, naming reformers Dorman Bridgman Eaton, John Milton Gregory, and Leroy D. Thoman as commissioners. The chief examiner, Silas W. Burt,\", \"Presidency of Chester A. Arthur Cleveland. Garfield chose Arthur as his running mate in the 1880 United States presidential election due to the latter's association with the Republican Party's Stalwart faction, and Arthur struggled to overcome his reputation as a New York machine politician. He embraced the cause of civil service reform, and his advocacy and enforcement of the Pendleton Civil Service Reform Act became the centerpiece of his administration. Though patronage remained a powerful force in politics, the Pendleton Act laid the foundations for a professional civil service that would emerge in subsequent decades. Facing a budget surplus, Arthur signed the Tariff of 1883,\", \"George Hunt Pendleton House George Hunt Pendleton House The George H. Pendleton House is a historic house at 559 Liberty Hill in the Prospect Hill Historic District of Cincinnati, Ohio. It was built in 1870 in the French Second Empire style. From 1879 until his death in 1889, this was the residence of Senator George Hunt Pendleton (1825-89). As a U.S. Senator (1879-1885), Pendleton spearheaded civil service reform, meeting here in 1882 to draft the Pendleton Act, which created the Civil Service merit system. The building, now in mixed commercial and residential use, was designated a National Historic Landmark in 1964. The George H.\", \"Benjamin Harrison gave Wanamaker a check for $10,000 to pay for the cottage. Civil service reform was a prominent issue following Harrison's election. Harrison had campaigned as a supporter of the merit system, as opposed to the spoils system. Although some of the civil service had been classified under the Pendleton Act by previous administrations, Harrison spent much of his first months in office deciding on political appointments. Congress was widely divided on the issue and Harrison was reluctant to address the issue in hope of preventing the alienation of either side. The issue became a political football of the time and\", \"United States Postal Service enlarged during the tenure of President Andrew Jackson. As the Post Office expanded, difficulties were experienced due to a lack of employees and transportation. The Post Office's employees at that time were still subject to the so-called \\\"spoils\\\" system, where faithful political supporters of the executive branch were appointed to positions in the post office and other government corporations as a reward for their patronage. These appointees rarely had prior experience in postal service and mail delivery. This system of political patronage was replaced in 1883, after passage of the Pendleton Civil Service Reform Act. In 1823, ten years after\", \"Chester A. Arthur civil service reform legislation and Pendleton again introduced his bill, but Congress did not pass it. Republicans lost seats in the 1882 congressional elections, in which Democrats campaigned on the reform issue. As a result, the lame-duck session of Congress was more amenable to civil service reform; the Senate approved Pendleton's bill 38\\u20135 and the House soon concurred by a vote of 155\\u201347. Arthur signed the Pendleton Civil Service Reform Act into law on January 16, 1883. In just two years' time, an unrepentant Stalwart had become the president who ushered in long-awaited civil service reform. At first, the act\", \"John O. Pendleton to the 51st United States Congress Congress and served from March 4, 1889, to February 26, 1890, when he was succeeded by George W. Atkinson, who successfully contested the election. Pendleton was elected as a Democrat to the 52nd and 53rd Congresses (March 4, 1891 \\u2013 March 3, 1895). He served as chairman of the Committee on Private Land Claims (Fifty-third Congress). He was an unsuccessful candidate for renomination in 1894. He resumed the practice of law in Wheeling, W.Virginia, and died there December 24, 1916. He was interred in Greenwood Cemetery. John O. Pendleton John Overton Pendleton (July 4,\", \"St. George Tucker with creating a long-desired code of the laws then in effect in Virginia. Tucker also wrote several pamphlets during this time, including a discussion as to what extent the United States had adopted the common law, and several works under the pseudonym \\\"Columbus\\\" in support of the Democratic-Republican Party. Tucker also published a pamphlet under the name \\\"Sylvestris\\\" proposing that the Louisiana Territory be considered for settlement by free blacks. Justice Edmund Pendleton of the Virginia Supreme Court of Appeals died on October 23, 1803, and on January 6, 1804, the Virginia Assembly appointed Tucker to the state Supreme Court\", \"Philip Pendleton Barbour weakening Marshall Court nationalism. While Barbour did not spend enough time on the court to amass a lengthy composition of judicial opinions, he authored two dissents for cases \\\"Kendall v. United States ex rel. Stokes\\\" (1838) and \\\"Holmes v. Jennison\\\" (1840). Justice Barbour's two dissents demonstrated his aim to diminish federal authority by supporting Jacksonian political aspirations and opposing restrictions to state sovereignty. The case, \\\"Kendall v. United States ex rel. Stokes\\\" dealt with judicial supervision of executive acts. In 1835, President Jackson appointed Amos Kendall the Postmaster General for the United States. The firm Stockton and Stokes had contract\", \"George W. Atkinson Legislature in 1876. He worked as an Internal Revenue agent from 1879 to 1881. His success at interfering with moonshiners (who sold their product without collecting taxes or reporting their income) led to his appointment as a United States Marshall, serving until 1885. In 1888, he ran for Congress as a Republican against Democrat John O. Pendleton. The election was contested, and although Pendleton had presented his credentials and served in the seat for nearly a year, Atkinson was eventually declared the winner by Congress and seated. Atkinson defeated Democratic Party candidate Cornelius Clarkson Watts in the 1896 election for\", \"William Medill to the United States House of Representatives in 1838, serving from 1839 to 1843. He lost a bid for a third term in 1842. After briefly serving as the second assistant postmaster general, Medill was appointed by President Polk as Commissioner of Indian Affairs. He returned to Ohio in 1850 to serve as the President of the 1850\\u20131851 Constitutional Convention. Elected to the new post of Lieutenant Governor of Ohio in 1851, Medill entered office in 1852, serving until the resignation of Governor Reuben Wood on July 13, 1853 to take up a Consular office in Chile. Medill was re-elected\", \"William Lawrence (Ohio Republican) chairman of the Committee on War Claims arising from the American Civil War. Lawrence was appointed by President Rutherford B Hayes in 1880 to serve as the First Comptroller of the Treasury, a post he held until 1885. Lawrence then appealed on behalf of Clara Barton to Hayes' successor, James Garfield, to support the creation of the American Red Cross on May 21, 1881. He then served as the organization's first Vice President. Lawrence and Barton were also instrumental in persuading the United States to ratify the Geneva Convention in 1882. In 1891, Lawrence was appointed President of the National\", \"Theodore Roosevelt office of Speaker of the New York State Assembly, but was defeated by Titus Sheard in a 41 to 29 vote of the GOP caucus. In his final term, Roosevelt served as Chairman of the Committee on Affairs of Cities; he wrote more bills than any other legislator. With numerous presidential hopefuls to choose from, Roosevelt supported Senator George F. Edmunds of Vermont, a colorless reformer. The state GOP preferred the incumbent president, New York City's Chester Arthur, who was known for passing the Pendleton Civil Service Reform Act. Arthur, at the time, was suffering from Bright's disease, unknown to\", \"Washington Navy Yard an almost meridian brightness. You never saw a drawing room so brilliantly lighted as the whole city was that night.\\\" From its beginning, the navy yard had one of the biggest payrolls in town, with the number of civilian mechanics and laborers and contractors expanding with the seasons and the naval Congressional appropriation. Prior to the passage of the 1883 Pendleton Act on 16 January 1883, applications for employment at navy yard were informal based largely on connections, patronage and personal influence. On occasion, a dearth of applicants required a public announcement, the first such documented advertisement was by Commodore\", \"Fairfax Harrison the Southern Railway. Harrison was elected chairman of a coordinating committee of railroad presidents, known as the War Board. Its five members were tasked with eliminating bottlenecks, and fostering cooperation between the various railroads. The board's efforts failed to meet the government's expectations; in December 1917, Woodrow Wilson, the President of the United States, ordered the federal government to take control of the railroads, setting up a United States Railroad Administration (USRA) to run them. Harrison worked for the USRA during the war and, under its regulations, was required to step down as chairman of the Southern Railway. By the\", \"Clarence M. Pendleton Jr. Reagan appointee, was vice chairman under Pendleton. He described \\\"an intellectual sea change\\\" at the agency with the conservative view dominant at that time. Authorized under the Civil Rights Act of 1957, the commission was reconstituted by a 1983 law of Congress after Reagan dismissed three commissioners critical of his policies. In the spring of 1986, \\\"The Los Angeles Times\\\" urged that the outspoken Pendleton either be removed from the commission or that his policies be reversed in the interest of minorities and women. A reader of the \\\"Los Angeles Times\\\" challenged the newspaper's position regarding Pendleton in a Letter\", \"Franklin Pierce died the next day. The office of vice president remained vacant for the remainder of Pierce's term, as the Constitution then had no provision for filling the vacancy. This extended vacancy meant that for nearly the entirety of Pierce's presidency the Senate President pro tempore, initially David Atchison of Missouri, was next in line to the presidency. Pierce sought to run a more efficient and accountable government than his predecessors. His Cabinet members implemented an early system of civil service examinations which was a forerunner to the Pendleton Act passed three decades later. The Interior Department was reformed by Secretary\", \"Clinton L. Riggs earlier resignation. Riggs served in that position for four years. In 1913, President Woodrow Wilson appointed General Riggs to the Philippine Commission. As Secretary of Commerce and Police, Riggs clashed with Governor General Francis Burton Harrison about who had authority over the Philippine Constabulary. In November 1914, Harrison cabled Washington to request the dismissal of Riggs from his post on the commission. In February 1928, while living in Catonsville, Maryland, Riggs served as chairman of the Wood Memorial Fund, which sought to raise $2 million to eradicate leprosy in the Philippines. Riggs did business in real estate and served as\", \"Enforcement Act of 1870 act would develop from separate legislative actions in the House and Senate. H.R. 1293 was introduced by House Republican John Bingham from Ohio on February 21, 1870, and discussed on May 16, 1870. S. 810 grew from several bills from several Senators. Senator George F. Edmunds from Vermont submitted the first bill, followed by Sen. Oliver P. Morton from Indiana, Sen. Charles Sumner from Massachusetts, and Sen. William Stewart from Nevada. After three months of debate in the Committee on the Judiciary, the final Senate version of the bill was introduced to the Senate on April 19, 1870. The act\", \"Nathaniel Pendleton was nominated by President George Washington on September 24, 1789, to a new seat created by 1 Stat. 73, as Judge for the United States District Court for the District of Georgia. Pendleton was confirmed by the United States Senate on September 26, 1789, and received his commission that day. He resigned on September 1, 1796, and returned to private practice in Dutchess County, New York. At some point thereafter, he became a county judge for Dutchess County, and held that position until his death in Hyde Park, New York. In 1804, he served as a \\\"second\\\" to Alexander Hamilton\", \"Francis Burt (Nebraska) President Franklin Pierce appointed Burt Third Auditor of the United States Treasury Department in 1853. The next year Pierce needed to select a governor for the newly created Nebraska Territory. After William Orlando Butler declined the position, the President selected Burt. The new governor was commissioned on August 2, 1854 and left his home in Pendleton for Nebraska on September 11. Burt's son Armistead and several of his neighbors accompanied him of the four-week trip to the new territory. The new governor had suffered from digestive problems for several years and experienced an intensification of symptoms while en route. His\", \"Assassination of James A. Garfield written called \\\"I am Going to the Lordy\\\". He requested an orchestra to play as he sang the poem; it was denied. Part of Charles Guiteau's preserved brain is on display at the M\\u00fctter Museum at the College of Physicians of Philadelphia. Guiteau's bones and more of his brain, along with Garfield's backbone and a few ribs, are kept at the National Museum of Health and Medicine, at the Army's Forest Glen Annex in Silver Spring, Maryland. Garfield's assassination was instrumental to the passage of the Pendleton Civil Service Reform Act on January 16, 1883. Garfield himself had called for\", \"Chester A. Arthur a conviction tarnished the administration's image, but Arthur did succeed in putting a stop to the fraud. Garfield's assassination by a deranged office seeker amplified the public demand for civil service reform. Both Democratic and Republican leaders realized that they could attract the votes of reformers by turning against the spoils system and, by 1882, a bipartisan effort began in favor of reform. In 1880, Democratic Senator George H. Pendleton of Ohio introduced legislation that required selection of civil servants based on merit as determined by an examination. In his first annual presidential address to Congress in 1881, Arthur requested\", \"James M. Pendleton the Forty-fourth Congress. He served as member of the State house of representatives 1879-1884. He served as chairman of the State board of charities and corrections 1884-1889. He died in Westerly, Rhode Island, February 16, 1889. He was interred in River Bend Cemetery. James M. Pendleton James Monroe Pendleton (January 10, 1822 \\u2013 February 16, 1889) was a U.S. Representative from Rhode Island. Born in North Stonington, Connecticut, Pendleton attended school in North Stonington and Suffield, Connecticut. He moved to Westerly, Rhode Island, and engaged in mercantile pursuits and later in the insurance business and banking. He served in the\", \"John F. Lewis from January 26, 1870, to March 4, 1875. He served on the Committee on the District of Columbia in the Forty-third Congress. He was not a candidate for reelection as the Republicans had already become a minority party by 1874 and wouldn't control either house on their own in Virginia for the rest of the 19th century. He returned home and was appointed by Presidents Ulysses S. Grant and Rutherford B. Hayes as the United States Marshal for the western district of Virginia 1875-1882, when he resigned. Lewis was again elected lieutenant governor in 1881, alongside Readjuster Party candidate William\", \"George Wythe served on a committee with Thomas Jefferson and Edmund Pendleton to revise and codify its laws, and also helped establish the new state court system. Although few of their 100+ separate proposed bills were ever passed, some concepts such as religious freedom, public records access, and education became important concepts in the new republic, as did the concept of intermediate appellate courts. When a fall incapacitated Pendleton, Jefferson and Wythe redrafted his portion (much to Pendleton's dismay). Wythe also replaced Pendleton as Speaker of the Virginia House of Delegates the following term (1777\\u20131778). Wythe also continued working to establish the\", \"Ulysses S. Grant civil service reform. Grant's Secretary of Interior Jacob D. Cox fired unqualified clerks, implemented a merit testing system, and rebuffed mandatory party contributions. On October 3, 1870, Cox resigned office under pressure from Republican party bosses and a dispute with Grant over a patent claim. On March 3, 1871 Congress authorized Grant to create the Civil Service Commission. Grant appointed reformer George William Curtis to head of the Commission, that advocated competitive exams, and the end of forced political payments. The Commission's rules took effect the next year, but Department heads, assistants, and higher level officials were exempted. In November\", \"Meritocracy of United States President James A. Garfield by a disappointed office seeker in 1881 proved its dangers. Two years later in 1883, the system of appointments to the United States Federal Bureaucracy was revamped by the Pendleton Civil Service Reform Act, partially based on the British meritocratic civil service that had been established years earlier. The act stipulated that government jobs should be awarded on the basis of merit, through competitive exams, rather than ties to politicians or political affiliation. It also made it illegal to fire or demote government employees for political reasons. To enforce the merit system and\", \"John O. Pendleton John O. Pendleton John Overton Pendleton (July 4, 1851 \\u2013 December 24, 1916) was a U.S. Representative from West Virginia. Born in Wellsburg, West Virginia (then part of Virginia), Pendleton moved with his parents to Wheeling, West Virginia (also then part of Virginia), in 1851. He attended Aspen Hill Academy, Louisa County, Virginia from 1865 to 1869, and Bethany College, West Virginia from 1869 to 1871. He studied law. He was admitted to the bar and commenced practice in Wheeling, in 1874. He was an unsuccessful Democratic candidate for State senator in 1886. He presented credentials as a Democratic Member-elect\", \"Alexander Caldwell (Virginia) Virginia (now West Virginia) in 1820. On October 28, 1825, Caldwell received a recess appointment from John Quincy Adams to a seat on the United States District Court for the Western District of Virginia vacated by the resignation of Philip C. Pendleton. Formally was nominated on December 13, 1825, Caldwell was confirmed by the United States Senate on January 3, 1826. He served as judge until his death, in Wheeling, in 1839 at age 67. He was succeeded by Isaac S. Pennybacker. Alexander Caldwell (Virginia) Alexander Caldwell (November 1, 1774 \\u2013 April 8, 1839) was a federal judge from Virginia.\", \"Patronage well as proprietor of the Metropolitan Hotel. At times he was a member of the United States House of Representatives, the New York City Board of Advisors, and the New York State Senate. In 1873, Tweed was convicted for diverting between $40 million and $200 million of public monies. Six months after James Garfield became president in 1881, Charles J. Guiteau, a disappointed office-seeker, assassinated him. To prevent further political violence and to assuage public outrage, Congress passed the Pendleton Act in 1883, which set up the Civil Service Commission. Henceforth, applicants for most federal government jobs would have to\", \"Charles Pinckney James \\\"Revised Statutes of the United States\\\" during the 1870s. He was appointed by President Andrew Johnson in 1866 and re-appointed by President Ulysses S. Grant in 1870 as one of three commissioners tasked to revise and consolidate existing federal statutes. The first edition of the \\\"Revised Statutes\\\" was adopted by Congress in 1874. In 1877, commissioner George S. Boutwell prepared the second edition of the \\\"Revised Statutes\\\" with the assistance of James. James appears to have been the only person to have worked on both the first and second editions of the \\\"Revised Statutes\\\". On July 24, 1879, James received\", \"James Henry McLean Louis Medical College in 1863, and continued expansion of his business enterprises, a monthly newspaper and almanacs. His patent medicines, including \\\"McLean's Strengthening Cordial and Blood Purifier\\\", were so successful that he employed an international sales force, and operated fleets of wagons, ships and railroad cars to facilitate their distribution. Mclean was elected as a Republican to the Forty-seventh Congress to fill the vacancy caused by the death of Thomas Allen and served from December 15, 1882 to March 3, 1883. During his career Mclean also patented several inventions, including a dredging machine. In the early 1880s McLean patented an\", \"Richard Randolph McMahon appointed to a position in the office of the United States Comptroller of the Treasury in the administration of Grover Cleveland. Beginning in 1885 McMahon practiced law in Washington, D.C. and Harper's Ferry. In the 1890s McMahon became a Republican. In addition to attending numerous party conventions at the local, state and national levels as a Delegate, McMahon served on the West Virginia University Board of Regents during the administration of Governor George W. Atkinson. McMahon was also a longtime member of the Columbia Hospital for Women's Board of Directors, and was President of the board from 1908 to 1914.\", \"Edgar L. McGowan Health Act, the first in the country, creating the South Carolina Department of Labor. \\\"Whenever the OSHA act was passed, I saw the opportunity in South Carolina to assist both businessmen and employees of the state because I knew we could run a better program than the feds\\\". Once the agency was created, Governor John C. West appointed McGowan in 1971 to temporarily head the new agency. McGowan was reappointed by Governor James B. Edwards and Governor Richard Riley. Following the election of Carroll A. Campbell Jr. in 1987, McGowan retired when his term expired in 1989. In his role\", \"Edmund H. Pendleton Hyde Park. Edmund H. Pendleton Edmund Henry Pendleton (1788 \\u2013 February 25, 1862) was a U.S. Representative from New York. Born in Savannah, Georgia, Pendleton received a liberal schooling as a youth. He graduated from Columbia College in 1805, studied law, was admitted to the bar in 1809, and practiced in Hyde Park, New York. He was judge of Dutchess County, New York from 1830 to 1840. He was elected as an Anti-Jacksonian to the Twenty-second Congress (March 4, 1831 \\u2013 March 4, 1833). He died in New York City on February 25, 1862, and was interred in St. James'\", \"Horatio Bridge European, and Pacific waters, he was called to Washington and appointed Chief of the Navy's Bureau of Provisions and Clothing. He handled with great skill the responsible duties of this office. Of the skill and ability which he showed in its management, Senator James Grimes testified in a debate in 1865: \\\"No Bureau of this government has been more admirably and accurately managed then the Bureau of Provisions and Clothing.\\\" To this, Senator John P. Hale, added; \\\"I think a great reason, and a very important one, is because there is at the head of that Bureau an honest, vigilant,\", \"John Sherman became president. After completing a long-planned visit to Yellowstone National Park and other Western sites with his brother William, Sherman returned to a second special session of Congress in October 1881. Garfield's assassination by an office-seeker amplified the public demand for civil service reform. Both Democratic and Republican leaders realized that they could attract the votes of reformers by turning against the spoils system, and by 1882 a bipartisan effort began in favor of reform. In the previous Congress, Sherman's fellow Ohio Senator, Democrat George H. Pendleton, had introduced legislation that required selection of civil servants based on merit as\", \"Presidency of Chester A. Arthur addition to his two Supreme Court appointments, Arthur also appointed four circuit court judges and fourteen district court judges. In the early 1880s, American politics operated on the spoils system, a political patronage practice in which victorious candidates rewarded their loyal supporters, family, and friends by installing them in government civil service positions. Movements calling for Civil Service Reform arose in the wake of the corruption in the Grant Administration. In 1880, Democratic Senator George H. Pendleton of Ohio had introduced legislation to require the selection of civil servants based on merit as determined by an examination. The measure failed\", \"Jim Hogg antitrust law. With the support of farmers, ranchers, and small merchants, Hogg won the election for Governor of Texas in 1890. At the same time, voters approved the constitutional amendment allowing for a Railroad Commission by a wide margin. On April 3, 1891, the legislature overwhelmingly passed a bill to create the Railroad Commission. Hogg appointed the three members, with U.S. Senator John H. Reagan, creator of the Interstate Commerce Act, as chairman. Hogg also named his old friend, Captain Bill McDonald, to succeed Samuel A. McMurry as the captain of Texas Rangers Company B, Frontier Battalion, a position that\", \"Thirteenth Amendment to the United States Constitution House, also lobbied several Democrats to vote in favor of the measure. Representative Thaddeus Stevens later commented that \\\"the greatest measure of the nineteenth century was passed by corruption aided and abetted by the purest man in America\\\"; however, Lincoln's precise role in making deals for votes remains unknown. Republicans in Congress claimed a mandate for abolition, having gained in the elections for Senate and House. The 1864 Democratic vice-presidential nominee, Representative George H. Pendleton, led opposition to the measure. Republicans toned down their language of radical equality in order to broaden the amendment's coalition of supporters. In order to\", \"Presidencies of Grover Cleveland more of Cleveland's appointments were decided by merit alone than was the case in his predecessors' administrations. During his first term, Cleveland also expanded the number of federal positions subject to the merit system (under the terms of the recently passed Pendleton Civil Service Reform Act) from 16,000 to 27,000. Partly due to Cleveland's efforts, between 1885 and 1897, the percentage of federal employees protected by the Pendleton Act would rise from twelve percent to approximately forty percent. Nonetheless, many Mugwumps were disappointed by Cleveland's unwillingness to promote a truly nonpartisan civil service. Cleveland was the first Democratic president subject\", \"Francis Burt (Nebraska) Mary Eliza, Katherine, and George Abbott. Burt entered politics as a member of South Carolina's Nullification Convention, being one of the 136 delegates voting in support of the Ordinance of Nullification. In addition to the convention, 1832 saw Burt elected to the South Carolina General Assembly. He remained in the state legislature until 1844 when he was elected State Treasurer. Burt left office after a single term and served as editor of the \\\"Pendleton Messenger\\\" from 1847 till 1851. In 1852 he was a member of the South Carolina Constitutional Convention. Due to his active participation in the Democratic Party,\", \"James S. Robinson 1865 and the United States Senate confirmed the appointment on July 23, 1866. After the war, Robinson returned to Ohio and resumed his civilian career. He served as chairman of the Republican State Executive Committee of Ohio 1877-1879. In January 1880, he was appointed as a commissioner of railroads and telegraphs for the state. Robinson was elected as a Republican to the Forty-seventh and Forty-eighth Congresses and served from March 4, 1881 to January 12, 1885, when he resigned. He then served as the Secretary of State of Ohio from 1885-1889. James S. Robinson died in Kenton, Ohio, on January\", \"John Aaron Rawlins The law put all Mormons in the Utah Territory under control of the U.S. Marshal and U.S. Attorney. Two more anti-Mormon bills were passed including the Edmunds Act (1882), signed into law by President Chester A. Arthur, and the Edmunds\\u2013Tucker Act (1887), signed into law by President Grover Cleveland. According to the unofficial Arlington National Cemetery website, Rawlins \\\"was Grant's alter ego, discharging with objectivity the duties and responsibilities of intimate friend, military and political adviser, editor, and, on perhaps a few occasions, apostle of sobriety, although it would seem that he played this role far less than is popularly\", \"John Pendleton when he was three years old. After admission to the Virginia bar in 1824, Pendleton began his legal practice in Culpeper County, Virginia. Culpeper's voters elected Pendleton as their representative to the Virginia House of Delegates from 1831 to 1833, and not long after Rappahannock County, Virginia was created from part of Culpeper County, Rappahannock County voters selected him to represent them from 1836 to 1839. President John Tyler, a fellow Whig from Virginia, appointed Pendleton Charg\\u00e9 d'Affaires to Chile in 1841, and he served until 1844 when he was elected a Whig to the United States House of Representatives.\", \"Reed Smoot 1932 election. In 1916, William Kent was the lead sponsor in the House of Representatives of legislation to establish the National Park Service. Smoot sponsored the similar Senate bill. The legislation passed the House of Representatives on July 1, 1916, passed the Senate on August 5, and was signed by U.S. President Woodrow Wilson on August 25, 1916. The agency was placed within the cabinet Department of Interior. Smoot was Chairman of the Senate Finance Committee from 1923 to 1933, and served on the Senate Appropriations Committee. He became active in the national Republican Party and served as a delegate\", \"George Hunt Pendleton House the individuals hired. Although Pendleton appears to not have been a strong advocate of civil service reform, a Senate subcommittee met in his Cincinnati home in early 1882 to complete a draft civil service reform bill. This bill, enacted later in 1882 following widespread popular calls for civil service reform, was known as the Pendleton Act, and represents the birth of the modern merit-based civil service that has operated since. Pendleton's involvement in the bill cost him reelection in 1884, due to a lack of support from party operatives who had opposed the reforms. After Pendleton's death, the house went\", \"George H. Pendleton and Minister Plenipotentiary to Germany the year that he left office, which he served until April 1889. Five months later, during his return trip to the United States, he died in Brussels, Belgium. Pendleton had a very Jacksonian commitment to the Democratic Party as the best, perhaps the only, mechanism through which ordinary Americans could shape government policies. Mach (2007) argues that Pendleton's chief contribution was to demonstrate the Whig Party's willingness to use its power in government to achieve Jacksonian ideals. While his Jacksonian commitment to states' rights and limited government made him a dissenter during the Civil War,\", \"John Lord O'Brian O'Brian left the State Assembly to take that position in 1909. O'Brian served as the U.S. Attorney throughout the subsequent administration of President William Howard Taft. O'Brian also continued in that office into the administration of Democratic President Woodrow Wilson, thus beginning a bipartisan path of serving as an appointed office holder under both Republican and Democratic administrations. In his role as the federal government's principal attorney in western New York, O'Brian in 1913 filed an antitrust lawsuit alleging that the Eastman Kodak Company was maintaining an unlawful monopoly on photographic films and equipment. In 1915, O'Brian was a delegate\", \"John Sherman on immigration, business competition law, and the regulation of interstate commerce. Sherman was the principal author of the Sherman Antitrust Act of 1890, which was signed into law by President Benjamin Harrison. In 1897, President William McKinley appointed him Secretary of State. Failing health and declining faculties made him unable to handle the burdens of the job, and he retired in 1898 at the start of the Spanish\\u2013American War. Sherman died at his home in Washington, D.C. in 1900. Sherman was born in Lancaster, Ohio to Charles Robert Sherman and his wife, Mary Hoyt Sherman, the eighth of their 11\", \"Pleasant Porter the Secretary of the Interior, whose department administered Native American affairs, officially recognized Joseph Perryman as the Principal Chief, although he was not elected by the Council. Under the terms of the Dawes Act, the Creek Nation had to agree to an allotment of former tribal lands to individual households, in an effort to force adaptation to European-American styles of farming and property ownership. Porter headed another Creek commission to negotiate the terms with Federal officials. Agreement was announced September 27, 1897 and incorporated as part of the Curtis Act, passed by Congress in June 1898. The US insisted that\", \"William L. Maginnis Although he had been appointed by the President and confirmed by the U.S. Senate for a four-year term, his tenure was in jeopardy after Cleveland's defeat by Republican Benjamin Harrison in November 1888. During the next several months there were extensive discussions and representations in Wyoming and Washington, D.C., about a possible successor to Maginnis. Shortly after taking office in March 1889, Harrison began removing territorial governors, secretaries, and judges appointed by his Democratic predecessor. In June 1889, the new U.S. Attorney General sent an examiner, James W. Nightingale, to review several territorial offices, including that of Clerk of the\", \"Philip Pendleton Barbour congressional power in his dissent in \\\"Holmes v. Jennison\\\", served to illuminate the idea of a stronger state's rights constitutionalism and proponent of slaveholder's rights. Barbour's opinions began to unravel the work of Marshall's Court, and set a precedent for future cases as the country became more polarized. Barbour's arguments for the authority of President to interpret the Constitution as the President desires in \\\"Kendall v. United States ex rel. Stokes,\\\" and the expansion of states' policing power and power afforded to governing officials and Barbour's argument for lack of judicial power on the basis of the Eleventh Amendment in\", \"James A. Garfield This act reversed the \\\"spoils system\\\" where office seekers paid up or gave political service to obtain or keep federally appointed positions. Under the act, appointments were awarded on merit and competitive examination. To ensure the reform was implemented, Congress and Arthur established and funded the Civil Service Commission. The Pendleton Act, however, covered only 10% of federal government workers. For Arthur, previously known for having been a \\\"veteran spoilsman,\\\" civil service reform became his most noteworthy achievement. A marble statue of Garfield by Charles Niehaus was added to the National Statuary Hall Collection in the Capitol in Washington D.C.,\", \"Levi Branson Reeder Levi Branson Reeder Levi Branson \\\"Lee\\\" Reeder (September 7, 1865 \\u2013 January 26, 1930) was an attorney and Republican politician from Pendleton in the US state of Oregon. A native of Illinois, he served as Speaker of the Oregon House of Representatives from 1901\\u20131903. Levi Reeder was born in Eureka, Woodford County, Illinois, to Daniel A. and Eliza Reeder (n\\u00e9e Kelsay) on September 7, 1865. At the age of nine the family immigrated to Oregon, settling in Eastern Oregon. Reeder attended school in Athena and Weston before enrolling in college at Christian College (now Western Oregon University) in Monmouth. He\", \"Steamboat Inspection Service Act of 1871 created the Steamboat Inspection Service. Furthermore, it established a Supervisory Inspector General directly responsible to the United States Secretary of the Treasury, extended licensing requirements to all masters and chief mates, provided for the revocation of licenses, authorized periodic inspection, and gave the Board of Supervisory Inspectors the authority to prescribe nautical rules of the road. On February 14, 1903, congressional action transferred the Steamboat Inspection Service to the newly created United States Department of Commerce and Labor. When that department was split in 1913, the service came under the control of the new United States Department\", \"James Augustine Healy his family in Boston. He applied for a commission in the Revenue Cutter Service (predecessor to the Coast Guard) and was accepted as a Third Lieutenant, his commission being signed by President Lincoln. In 1880, Healy was assigned command of a US government ship. (Since the late 20th century, he has become known as the first African American to gain such command.) During the last two decades of the 19th century, Captain Healy was essentially the federal government's law enforcement presence in the vast Alaska Territory. Commissioned in 1999, the U.S. Coast Guard research icebreaker USCGC \\\"Healy\\\" (WAGB-20) is named\", \"William N. Pendleton William N. Pendleton William Nelson Pendleton (December 26, 1809 \\u2013 January 15, 1883) was an American teacher, Episcopal priest, and soldier. He served as a Confederate general during the American Civil War, noted for his position as Gen. Robert E. Lee's chief of artillery for most of the conflict. After the war, Pendleton returned to his priestly duties and also wrote religious materials. Camp Pendleton in Virginia Beach, Virginia, is named in his honor. William Nelson Pendleton was born in 1809 near Richmond, Virginia. He grew up on the Caroline County plantation belonging to his parents, Edmund Pendleton Jr., grandnephew\", \"George Meade Bowers Fair commissioners for West Virginia in 1893 and United States Commissioner of Fish and Fisheries from 1898 to 1903, and subsequently Director of the United States Bureau of Fisheries from 1903 to 1913, when he resigned. Bowers was elected as a Republican to the Sixty-fourth Congress to fill the vacancy caused by the death of William G. Brown, Jr. and was reelected to the Sixty-fifth, Sixty-sixth, and Sixty-seventh Congresses and served from May 9, 1916, to March 3, 1923. He was an unsuccessful candidate for reelection in 1922 to the Sixty-eighth Congress. After leaving Congress, he was president of the\", \"George Wythe the law became his mission for the rest of his life. Wythe was elected to serve as a federal judge on the Court of Appeals in Cases of Capture in 1780, but declined to serve. Wythe particularly despised lawyers who protracted litigation at great cost to the parties, though to their own benefit, and even in his last days regretted the burden delays placed upon those seeking justice from his court. In the judicial reorganization of 1788, Wythe became sole Judge of the Chancery Court of Virginia, refusing to be promoted with Edmund Pendleton to the Supreme Court of Appeals\", \"United States Revenue Cutter Service the Treasury George S. Boutwell, under President Ulysses S. Grant, to reorganize the service. He appointed N. Broughton Devereux on 1 July 1869 as chief of an interim Revenue Marine Bureau that included the Revenue Cutter Service, the Steamboat Inspection Service, the Marine Hospital Service and the Life-Saving Service. Devereaux appointed two boards to study the problems facing the service; one investigated personnel requirements, the other analyzed the requirements for the cutter fleet. The fleet board produced a study that was presented to Congress on 26 May 1870, the result of which was that of the twenty-four steaming cutters in\", \"Alphonso Taft against George H. Pendleton. Taft did not serve in the Union Army during the Civil War. He was a judge of the Superior Court of Cincinnati from 1866 to 1872 when he resigned to practice law with two of his sons. He was the first president of the Cincinnati Bar Association, serving in 1872. In the 1870 court case Board of Education of Cincinnati vs. Minor, Taft played a role in overturning the decision made by the Superior Court of Cincinnati in 1869 regarding the reading the Bible in public schools. Taft asserted that the school board had overstepped their\", \"Simon Sterne of abuses in railroad management, which resulted in the appointment of a board of railroad commissioners for the state of New York. He was also a leader in the movement that resulted in the creation of the Interstate Commerce Commission, drafting the interstate commerce bill in conjunction with the committee of the United States Senate. In 1885 he was appointed by President Cleveland a commissioner to examine and report on the relations between the railroads and the governments of western Europe. An essay that he read before the American Bar Association on \\\"Slip-shod Legislation\\\" led to the appointment in 1888\", \"Philip Pendleton Barbour \\\"Circuit Court of this district had not power to issue writ in question, and consequently, was of opinion that the judgment demanding a peremptory mandamus should by reversed.\\\" Even though the Postmaster General was subject to direction and control of the President with respect of the duties imposed by law, when the law is 'ministerial,' Congress can limit and regulate the executive officials. Because Congress created the executive office, then Congress could monitor executive decisions, but the President is not controlled by the federal courts. While the opinion of the case served to further define separation of powers by holding\", \"George H. Pendleton the Senate. President Grover Cleveland appointed him as the ambassador to the German Empire. He served in that position until 1889, dying later that same year. Pendleton was born in Cincinnati, Ohio on July 19, 1825. He was the son of Jane Frances (n\\u00e9e Hunt) Pendleton (1802\\u20131839) and U.S. Representative and Nathanael Greene Pendleton (1793\\u20131861). He attended the local schools and Cincinnati College and the University of Heidelberg in Germany. Pendleton studied law and was admitted to the bar in 1847 and commenced practice in Cincinnati. Pendleton was elected as a member of the Ohio Senate, serving from 1854 to\", \"Robert J. Walker the post of United States Minister to China, but Walker declined. Walker at first opposed the Compromise of 1850, but was won over later by the arguments of Illinois Senator Stephen A. Douglas. He was appointed governor of Kansas Territory on May 27, 1857 by President James Buchanan, but resigned in December because of his opposition to the Lecompton Constitution. In a to Secretary of State Lewis Cass dated December 15, 1857, he cited clear voting fraud and improper political pressure from the Administration. He did not, however, break with his party immediately, and favored the so-called English Bill. Partly\", \"Virginia Conventions was in revolt by Lord Dunmore and fighting between his royal forces and militia forces in the Hampton Roads area. Edmund Pendleton served as President of the Convention, succeeding Peyton Randolph who had died in October 1775. The Convention declared that Virginians were ready to defend themselves \\\"against every species of despotism.\\\" The convention passed another ordinance to raise additional troops. Back in Britain, in December 1775, the King's Proclamation of Rebellion had declared the colonies outside his protection, but throughout the first four Virginia Conventions, there was no adopted expression in favor of independence from the British Empire. By\", \"Robert P. Kennedy from 1878 to 1883. He was the Lieutenant Governor of Ohio from 1885\\u201387. Kennedy was elected from Ohio's 8th District as a Republican to the Fiftieth and Fifty-first Congresses (March 4, 1887 \\u2013 March 4, 1891). He was not a candidate for renomination in 1890.He was appointed by President William McKinley in 1899 as a member of the Insular Commission, which was directed to investigate and report upon conditions existing in Cuba and Puerto Rico and served as its president. Robert P. Kennedy died in Columbus, Ohio. Kennedy was a member of the Grand Army of the Republic, a Scottish\", \"Samuel J. Randall without success. Garfield was elected with 214 electoral votes\\u2014including those of Pennsylvania. Worse still for Randall, Garfield's victory had swept the Republicans back into a majority in the House, meaning Randall's time as Speaker was at an end. When Randall returned to Washington in 1881 to begin his term in the 47th Congress, the legislature was controlled by Republicans. After Garfield's assassination later that year, Vice President Chester A. Arthur assumed the presidency. Arthur, like most Republicans, favored high tariffs, but he sought to simplify the tariff structure and to reduce excise taxes. Randall, who had returned to his seat\", \"Clarence M. Pendleton Jr. to race and background.\\\" On December 23, 1983, with two Democratic members named by the House dissenting, Pendleton was reelected to a second term as commission chairman. He drew the backing of Reagan's new appointee, Esther Buckley, an educator from Laredo, Texas. Under Pendleton's tenure, the commission was split by an internal debate over fundamental principles of equality under the law. The commission narrowed the description of legal and political rights at the expense of social and economic claims. The debate centered principally between Pendleton and Berry, an original appointee of President Jimmy Carter. Democrat Morris B. Abram, also a\", \"Benjamin Harrison its kind to that point in American history, a problem exacerbated by Pension Bureau commissioner James R. Tanner's expansive interpretation of the pension laws. An investigation into the Pension Bureau by Harrison's Secretary of Interior John Willock Noble found evidence of lavish and illegal handouts under Tanner. Harrison, who privately believed that appointing Tanner had been a mistake, due to his apparent loose management style and tongue, asked Tanner to resign and replaced him with Green B. Raum. Raum was also accused of accepting loan payments in return for expediting pension cases. Harrison, having accepted a dissenting Congressional Republican investigation\", \"Peter Force his collection of original documents for $100,000, augmenting the expansion of the Library of Congress conducted by Ainsworth Rand Spofford, who directed the Library from 1865 to 1897. During the 1820s, Force was a member of the prestigious society, Columbian Institute for the Promotion of Arts and Sciences and served as the president. He was also elected a member of the American Antiquarian Society in 1851. Force died January 23, 1868 at the age of 77. His son, Manning Force, was an officer during the American Civil War. Peter Force, alongside his wife, is buried in Rock Creek Cemetery, Force's\", \"Clarence M. Pendleton Jr. \\\"probably the looniest idea since Looney Tunes came on the screen.\\\" The headlines from his remarks dominated and distorted the debate over the issue. Under the Pendleton chairmanship, congressional funding for the agency was reduced. This prompted some staff members either to lose their positions or to leave the agency in discouragement. Pendleton was considered acerbic by his liberal critics. William Bradford Reynolds, Reagan's Assistant Attorney General for Civil Rights, described his friend Pendleton as \\\"a man of candor who felt very deeply that the individuals in America should deal with one another as brothers and sisters totally without regard\", \"Institutional racism reforms of the spoils system in place since the 1830s, and abuses of the post-war Grant-Jacksonian era; when Congress authorized the president to appoint a Civil Service Commission and prescribe regulations for admission to public service. A dis-satisfied office-seeker assassinated President Garfield in 1881 and Congress was motivated to pass the Pendleton Civil Service Reform Act in 1883 which firmly established civil service. During reconstruction, this enabled the federal government to provide jobs for newly freed blacks in the south (primarily the Postal Service) where no other employment opportunities existed for them. Since the inception of the merit system in\", \"Albert C. Thompson appointed by President William McKinley as chairman of the commission to revise and codify the criminal and penal laws of the United States on June 21, 1897. Thompson served as a United States District Judge of the United States District Court for the Southern District of Ohio. Thompson received a recess appointment from President William McKinley on September 23, 1898, to a seat vacated by George Read Sage and was nominated on December 13, 1898, to the same seat. He was confirmed by the United States Senate on December 20, 1898, and received his commission the same day. He served\", \"Asa S. Bushnell (governor) company of men for the 152nd Ohio Infantry, a 100 days regiment, and served as captain from May to September 1864. He was a Presidential elector in 1884 for Blaine/Logan. A business executive, Bushnell served as the Ohio State Republican Party Chair in 1885. He succeeded William McKinley as governor, serving two two-year terms from 1896 to 1900. During the Bushnell administration, Ohio took an early leadership role in trust-busting. The Valentine Anti-Trust Act was signed into law by Bushnell. This Act prohibited price fixing, and production limitation. All of these practices helped businesses by driving up the prices for\", \"Jacob Thompson 1851. He was appointed to the United States Senate in 1845 but never received the commission, and the seat went to Joseph W. Chalmers. Thompson was the chairman of the Committee on Indian Affairs in the 29th Congress. He lost reelection to the 32nd Congress and went back to practicing law until 1857, when newly elected President James Buchanan appointed Thompson United States Secretary of the Interior. In the later years of the Buchanan administration, the cabinet members argued with one another on issues of slavery and secession. In an 1859 speech, Thompson advanced a moderate unionist position. He denounced\", \"Pendleton Civil Service Reform Act Civil Service Commission. This board would be in charge of determining the rules and regulations of the act. The act also allowed for the president, by executive order, to decide which positions would be subject to the act and which would not. A result was the shift of the parties to reliance on funding from business, since they could no longer depend on patronage. In 1877, there was growing interest in the United States concerning the effects of the spoils system on the American political system. New York City established the Civil Service Reform Association to help address the issues,\", \"Homer Loring extended the maturity of $40 million worth of bonds by fifteen years. He also installed George Hannauer as president and brought on John Frank Stevens as an advisor. Loring resigned as chairman on September 24, 1928. In December 1922, Governor Channing H. Cox nominated Loring to serve on the newly created State Commission on Administration and Finance. Loring was the commission's chairman as well as the budget commissioner. He resigned in September 1924 to fully devote his time to his duties as B&M chairman. In October 1928, Loring and associates bought into the Seneca Textile Corporation of New York. On\", \"Spoils system and their current holders into the system, thus giving the holder a permanent job. The Pendleton Act's reach was expanded as the two main political parties alternated control of the White House every election between 1884 and 1896. After each election the outgoing President applied the Pendleton Act to jobs held by his political supporters. By 1900, most federal jobs were handled through civil service and the spoils system was limited only to very senior positions. The separation between the political activity and the civil service was made stronger with the Hatch Act of 1939 which prohibited federal employees from\", \"Habeas Corpus Suspension Act (1863) to send the bill to a conference committee on February 19. The House appointed Thaddeus Stevens, John Bingham, and George H. Pendleton to the conference committee. The Senate agreed to a conference the following day and appointed Lyman Trumbull, Jacob Collamer, and Waitman T. Willey. Stevens, Bingham, Trumbull, and Collamer were all Republicans; Willey was a Unionist; Pendleton was the only Democrat. On February 27, the conference committee issued its report. The result was an entirely new bill authorizing the explicit suspension of habeas corpus. In the House, several members left, depriving the chamber of a quorum. The Sergeant-at-Arms was\"], \"pos_scores\": [93.9375], \"neg_scores\": [84.9375, 84.8125, 91.1875, 86.4375, 88.5625, 89.0625, 85.1875, 87.9375, 87.875, 86.9375, 87.625, 94.0, 91.1875, 86.4375, 85.75, 85.6875, 86.25, 85.25, 88.8125, 89.25, 87.0, 86.125, 89.8125, 87.9375, 85.6875, 83.375, 84.9375, 83.875, 84.125, 83.5, 85.8125, 87.5, 83.8125, 86.125, 86.5, 84.375, 86.6875, 84.875, 83.375, 87.3125, 86.6875, 85.625, 83.8125, 83.3125, 87.5625, 89.8125, 86.3125, 83.1875, 91.4375, 83.75, 83.0625, 83.375, 82.5625, 85.6875, 85.125, 86.5625, 87.4375, 83.6875, 86.3125, 86.4375, 83.25, 83.5625, 84.875, 85.0, 84.8125, 87.25, 86.5, 83.0625, 85.4375, 83.9375, 84.0625, 85.0, 87.6875, 85.625, 86.625, 85.25, 87.875, 84.1875, 83.375, 89.9375, 84.3125, 83.4375, 86.0, 87.0, 83.5, 84.0, 83.5, 83.4375, 86.25, 86.0625, 85.125, 85.875, 90.3125, 83.4375, 83.375, 83.6875, 93.5625, 82.6875, 90.5, 88.0], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n{\"query\": \"why was there so much interest in cuba both before and after the civil war\", \"pos\": [\"Spanish\\u2013American War American Civil War and Cuba's Ten Years' War, U.S. businessmen began monopolizing the devalued sugar markets in Cuba. In 1894, 90% of Cuba's total exports went to the United States, which also provided 40% of Cuba's imports. Cuba's total exports to the U.S. were almost twelve times larger than the export to her mother country, Spain. U.S. business interests indicated that while Spain still held political authority over Cuba, economic authority in Cuba, acting-authority, was shifting to the US. The U.S. became interested in a trans-isthmus canal either in Nicaragua, or in Panama, where the Panama Canal would later be\"], \"neg\": [\"United States declaration of war upon Spain on Cuba, with which it had extensive economic ties. Historians have long debated America's intentions in becoming involved in the conflict. For a significant period during and after the war, selfless humanitarian interest in the fate of the Cuban people was accepted as the major impetus for the declaration of war. A supporting argument for this line of thinking is that yellow journalism created an inflammatory mood in the country and swayed public opinion to sympathize with Cuba. Recently this school of thinking has grown less popular. Many historians now believe that the United States was acting more out of\", \"Propaganda of the Spanish\\u2013American War set up by Lawson in nearby Key West in order to keep a close eye on the Cuban conflict. However, the focus of midwestern newspapers on particular facts served in the end as another cause of the war. Since the events occurring in Cuba were not always credible many midwestern newspaper owners shifted their content towards domestic issues, namely the effect of Cuba on the American economy. American interests in the trade with Cuba were significant, and through the papers' coverage of these matters, much of the readership in the midwest soon came to believe that protecting these interests was\", \"Spanish\\u2013American War future news reporting. The idea of American imperialism changed in the public's mind after the short and successful Spanish\\u2013American War. Due to the United States' powerful influence diplomatically and militarily, Cuba's status after the war relied heavily upon American actions. Two major developments emerged from the Spanish\\u2013American War: one, it greatly enforced the United States' vision of itself as a \\\"defender of democracy\\\" and as a major world power, and two, it had severe implications for Cuban\\u2013American relations in the future. As historian Louis P\\u00e9rez argued in his book \\\"Cuba in the American Imagination: Metaphor and the Imperial Ethos\\\", the\", \"Cuba\\u2013United States relations known as the Ostend Manifesto was devised by U.S. diplomats, interested in adding a slave state to the Union. The Manifesto proposed buying Cuba from Spain for $130 million. If Spain were to reject the offer, the Manifesto implied that, in the name of Manifest Destiny, war would be necessary. When the plans became public, because of one author's vocal enthusiasm for the plan, the manifesto caused a scandal, and was rejected, in part because of objections from anti-slavery campaigners. The Cuban rebellion 1868-1878 against Spanish rule, called by historians the Ten Years' War, gained wide sympathy in the U.S.\", \"Cold War (1953\\u20131962) African nations on how to escape the clutches of imperialism. After all, Cuba had thrown off an oppressive dictatorship and with stood a U.S. backed invasion of the island. Cuba's focuses on Africa stemmed from the belief that the decolonization provided an arena for the struggle between socialism and capitalism. Another reason for Cuba's support of African socialist movements was for the shared link between Cuba and Africa. About one- third of Cuban citizens had at least some African heritage. As such, many Cubans were motivated to help liberate Africans from colonialism and imperialism and to help spread the Cuban\", \"Ostend Manifesto 1856. Although he remained committed to Cuban annexation, he was hindered by popular opposition and the growing sectional conflict. It was not until thirty years after the Civil War that the so-called Cuban Question again came to national prominence. Footnotes Citations Sources Ostend Manifesto The Ostend Manifesto, also known as the Ostend Circular, was a document written in 1854 that described the rationale for the United States to purchase Cuba from Spain while implying that the U.S. should declare war if Spain refused. Cuba's annexation had long been a goal of U.S. slaveholding expansionists, and was supported by a faction\", \"History of Cuban nationality camps, where they worked and were starved. This furthered their nationalism even more because they couldn't take what was being done to their own people. The stories of the rebels' bravery and nationalism eventually reached the United States, who sent aid which soon became the Spanish\\u2013American War. However, the Spanish control of Cuba soon became replaced with a large American influence in Cuba's affairs. Once again Cuban nationalism was an at an all-time high since they had just fought for their own independence, and now they had another country in their affairs. Between 1780 and 1867, over 780 000 slaves\", \"Afro-Cuban occurred during the 17th century where ex-slaves from both Cuba and Brazil were offered the same opportunity. Angola also has communities of Afro-Cubans, Amparos. They are descendants of Afro-Cuban soldiers brought to the country in 1975 as a result of the Cuban involvement in the Cold War. Fidel Castro deployed thousands of troops to the country during the Angolan Civil War. As a result of this era, there exists a small Spanish-speaking community in Angola of Afro-Cubans numbering about 100,000. Haitian Creole and culture first entered Cuba with the arrival of Haitian immigrants at the start of the 19th century.\", \"Foreign interventions by Cuba provide military support to revolutionary movements during the Portuguese Colonial War beginning in the 1960s and into the 1970s. Following the Congo Crisis, Cuba was supporting the African Party for the Independence of Guinea and Cape Verde during the Guinea-Bissau War of Independence.As the Angolan Civil War broke out, Cuban intervention in Angola was a large-scale intervention to support the People's Movement for the Liberation of Angola (MPLA). Cuba had provided military support to MPLA since the early 1960s, while they combatted Portuguese forces and in 1963 provided military training to guerrillas in Algeria during the Sand War. In late-1974,\", \"Spanish\\u2013American War have, is masked at the Dry Tortugas. In his autobiography, Theodore Roosevelt gave his views of the origins of the war: Our own direct interests were great, because of the Cuban tobacco and sugar, and especially because of Cuba's relation to the projected Isthmian [Panama] Canal. But even greater were our interests from the standpoint of humanity. ... It was our duty, even more from the standpoint of National honor than from the standpoint of National interest, to stop the devastation and destruction. Because of these considerations I favored war. In the 333 years of Spanish rule, the Philippines developed\", \"History of Cuban nationality more and more obsolete, and holding Cuba back from economic and political success. The dissatisfaction with Spain's inept administration, their lack of representation in the government, and high taxes sparked the beginning of the 10 years war in which over 200,000 lives were lost. Being crushed by the Spanish army only fueled their nationalism even more. It caused a uniting of all the Cuban people, with an emphasis on former slaves, who were freed shortly after the war. However, when the Cubans rose up again, Spain implemented their policy of Reconcentration. This forced hundreds of thousands of Cubans into labor\", \"Cuba\\u2013Spain relations Spain is the largest foreign investor in Cuba from the European Union; Cuba is Spain\\u2019s 42nd largest trading partner globally and 4th largest from Latin America (after Mexico, Brazil and Chile). Cuba\\u2013Spain relations Cuba\\u2013Spain relations refer to the bilateral relations between the Republic of Cuba and the Kingdom of Spain. Relations date back more than five centuries. Cuba had been a colony from 1492 until 1898 when the United States took over the territory in the Spanish\\u2013American War. Many Cubans have ancestry dating back from Spain. Many Spaniards escaped the first Spanish Civil War and went to Cuba, and other\", \"History of slavery in Virginia people of color. In Cuba by contrast, there were 213,167 free people of color, or 39% of its black population of 550,000. Cuba had not developed a plantation system in its early years, and its economy supported the Spanish empire from urbanized settlements. With a shortage of white labor, blacks had become deeply involved in urban trades and businesses. In this setting, slaves were able to buy their way out of slavery. Southern states had economies dependent on slavery, but sentiment for abolition grew in the North. Slavery was a source of growing conflict between the states as the new\", \"Propaganda of the Spanish\\u2013American War a filibustering effort by John A. Quitman to acquire Cuba received the tentative support of the president. Pierce backed off, however, and instead renewed the offer to buy the island, this time for $130 million. When the public learned of the Ostend Manifesto in 1854, which argued that the United States could seize Cuba by force if Spain refused to sell, this effectively killed the effort to acquire the island. The public now linked expansion with slavery; if Manifest Destiny had once enjoyed widespread popular approval, this was no longer true. The outbreak of the American Civil War in 1860\", \"History of Cuba U.S. embassy in Havana was formally reopened in August 2015. History of Cuba The island of Cuba was inhabited by various Mesoamerican cultures prior to the arrival of the Spanish explorer Christopher Columbus in 1492. After Columbus' arrival, Spain conquered Cuba and appointed Spanish governors to rule in Havana. In 1762, Havana was briefly occupied by Great Britain, before being returned to Spain in exchange for Florida. A series of rebellions during the 19th century failed to end the Spanish rule. However, the Spanish\\u2013American War resulted in a Spanish withdrawal from the island in 1898, and following three-and-a-half years of\", \"Actuality film by law. Recreations of various kinds typify early kinds of news coverage; as the camera could not be brought to so many events of public significance or interest, the events were brought before the cameras, with actors and/or models of various kinds employed. This was especially common during the Spanish\\u2013American War; although cameras were dispatched to the front in Cuba, the footage sent back was often disappointing, so it was more effective to find a setting in New Jersey and to restage the battle scenes with actors. These films were often promoted to exhibitors and the public alike as the\", \"History of Cuban nationality for profit or consumed. Many people quickly seized upon the potential of this and began raising as many pigs as possible, even feeding them from their own rations to keep them growing. The pigs would then be sold to either the plantation owner or someone else, and a profit would be made. These profits would sometimes parlay into the ownership of a horse, which implied a certain degree of freedom and mobility. Worker mobility was also important in spreading information (concerning revolution, property rights, etc.) to other interested communities. After slavery was phased out by 1888, many former slaves had\", \"Cuba\\u2013United States relations the following 20 years the United States repeatedly intervened militarily in Cuban affairs: 1906\\u201309, 1912 and 1917\\u201322. In 1912 U.S. forces were sent to quell protests by Afro-Cubans against discrimination. By 1926 U.S. companies owned 60% of the Cuban sugar industry and imported 95% of the total Cuban crop, and Washington was generally supportive of successive Cuban governments. However, internal confrontations between the government of Gerardo Machado and political opposition led to his military overthrow by Cuban rebels in 1933. U.S. Ambassador Sumner Welles requested U.S. military intervention. President Franklin D. Roosevelt, despite his promotion of the Good Neighbor policy\", \"History of Cuba former slaves joined the ranks of farmers and urban working class. Most wealthy Cubans lost their rural properties, and many of them joined the urban middle class. The number of sugar mills dropped and efficiency increased, with only companies and the most powerful plantation owners owning them. The numbers of campesinos and tenant farmers rose considerably. Furthermore, American capital began flowing into Cuba, mostly into the sugar and tobacco businesses and mining. By 1895, these investments totalled $50 million. Although Cuba remained Spanish politically, economically it became increasingly dependent on the United States. These changes also entailed the rise of\", \"Ten Years' War cause gained strength, favoring a gradual emancipation of slaves with financial compensation from Spain for slaveholders. Additionally, some planters preferred hiring Chinese immigrants as indentured workers and in anticipation of ending slavery. Before the 1870s, more than 125,000 were recruited to Cuba. In May 1865, Cuban creole elites placed four demands upon the Spanish Parliament: tariff reform, Cuban representation in Parliament, judicial equality with Spaniards, and full enforcement of the slave trade ban. The Spanish Parliament at the time was changing; gaining much influence were reactionary, traditionalist politicians who intended to eliminate all liberal reforms. The power of military tribunals\", \"Corruption in Cuba product of the colonial heritage of Cuban politics and the financial aid provided by the United States that favoured international sugar prices in the late 19th and early 20th centuries. Following the Second World War, the level of corruption in Cuba, among many other Latin American and Caribbean countries, was said to have risen significantly. Some scholars, such as Eduardo S\\u00e1enz Rovner, attribute this to North America's increased involvement in Cuba after the First World War that isolated Cuban workers. Cubans were excluded from a large sector of the economy and unable to participate in managerial roles that were taken\", \"Cuban military internationalism covert activity and espionage to the open commitment of combat troops on a large scale. The Cuban military presence in Africa was especially notable, with up to 50,000 troops being deployed to Angola alone. Castro justified the use of the armed forces on the African continent as a result of the debt Cuba owed Africa due to its participation in the Atlantic slave trade and the contributions patriotic black Cubans had made to the Cuban War of Independence. Internationalist missions were perceived by the Cuban government as one means of combating the global influence of the United States by proxy,\", \"Propaganda of the Spanish\\u2013American War Propaganda of the Spanish\\u2013American War The Spanish\\u2013American War (April\\u2013August 1898) is considered to be both a turning point in the history of propaganda and the beginning of the practice of yellow journalism. It was the first conflict in which military action was precipitated by media involvement. The war grew out of U.S. interest in a fight for revolution between the Spanish military and citizens of their Cuban colony. American newspapers fanned the flames of interest in the war by fabricating atrocities which justified intervention in a number of Spanish colonies worldwide. Several forces within the United States were pushing for\", \"Cuban Revolution transformed Cuba's relationship with the United States, although efforts to improve diplomatic relations have gained momentum in recent years. In the immediate aftermath of the revolution, Castro's government began a program of nationalization and political consolidation that transformed Cuba's economy and civil society. The revolution also heralded an era of Cuban intervention in foreign military conflicts, including the Angolan Civil War and the Nicaraguan Revolution. In the decades following United States' invasion of Cuba in 1898, and formal independence from the U.S. on May 20, 1902, Cuba experienced a period of significant instability, enduring a number of revolts, coups and\", \"How Few Remain Battle of Camp Hill as a result of Lincoln diverting key troops there to Pennsylvania (who did not arrive in time to fight at Camp Hill), joins the eleven original Confederate states after the war's conclusion, and the Confederacy is also given Indian Territory (our timeline's state of Oklahoma, later the State of Sequoyah in the SV timeline). But, as a compromise, the United States retained Missouri (despite proposals to divide it) and West Virginia. The Spanish island of Cuba is purchased by the Confederate States in the late 1870s for $3,000,000, thus also becoming a Confederate state. Abraham Lincoln\", \"History of Cuba History of Cuba The island of Cuba was inhabited by various Mesoamerican cultures prior to the arrival of the Spanish explorer Christopher Columbus in 1492. After Columbus' arrival, Spain conquered Cuba and appointed Spanish governors to rule in Havana. In 1762, Havana was briefly occupied by Great Britain, before being returned to Spain in exchange for Florida. A series of rebellions during the 19th century failed to end the Spanish rule. However, the Spanish\\u2013American War resulted in a Spanish withdrawal from the island in 1898, and following three-and-a-half years of subsequent US military rule, Cuba gained formal independence in 1902.\", \"Cuba during World War II Cuba during World War II The history of Cuba during World War II begins in 1939. Because of Cuba's geographical position at the entrance of the Gulf of Mexico, Havana's role as the principal trading port in the West Indies, and the country's natural resources, Cuba was an important participant in the American Theater of World War II, and subsequently one of the greatest beneficiaries of the United States' Lend-Lease program. Cuba declared war on the Axis powers in December 1941, making it one of the first Latin American countries to enter the conflict, and by the war's end in\", \"Gabriel Garci\\u0301a Ma\\u0301rquez May 1958, disagreeing with the owner of \\\"Momento\\\", he resigned and became shortly afterwards editor of the newspaper \\\"Venezuela Gr\\u00e1fica\\\". Garc\\u00eda M\\u00e1rquez was a \\\"committed Leftist\\\" throughout his life, adhering to socialist beliefs. In 1991, Garc\\u00eda M\\u00e1rquez published Changing the History of Africa an admiring study of Cuban activities in the Angolan Civil War and the larger South African Border War. Garc\\u00eda M\\u00e1rquez maintained a close but \\\"nuanced\\\" friendship with Fidel Castro, praising the achievements of the Cuban Revolution, but criticizing aspects of governance and working to \\\"soften (the) roughest edges\\\" of the country. Garc\\u00eda M\\u00e1rquez's political and ideological views\", \"Cuban immigration to the United States and sometimes their lives to the cause of \\\"Cuba Libre\\\". After the Spanish\\u2013American War, some Cubans returned to Cuba, but others chose to stay in the U.S. due to the physical and economic devastation caused by conflicte on the island. Several other small waves of Cuban emigration to the U.S. occurred in the early 20th century (1900\\u201359). Most settled in Florida and the northeast U.S. The majority of the 100,000 Cubans came for economic reasons due to (the Great Depression of 1929, volatile sugar prices, and migrant farm labor contracts). Others included anti-Batista refugees fleeing the military dictatorship, which had\", \"Havana With this, Cuba became the fifth country in the world to have a railroad, and the first Spanish-speaking country. Throughout the century, Havana was enriched by the construction of additional cultural facilities, such as the Tacon Teatre, one of the most luxurious in the world. The fact that slavery was legal in Cuba until 1886 led to Southern American interest, including a plan by the Knights of the Golden Circle to create a 'Golden Circle' with a 1200 mile-radius centered on Havana. After the Confederate States of America were defeated in the American Civil War in 1865, many former slaveholders\", \"Cuban military internationalism and Cuba's opponents during these efforts were often decried as American pawns. Likewise, the US government and its allies perceived the Cuban Revolutionary Armed Forces (FAR) as a Soviet proxy, and the use of internationalist missions as a means to indirectly increase Soviet military influence worldwide. There were also more practical reasons for deploying Cuban troops abroad, such as giving the relatively inexperienced armed forces combat experience across a wide range of theaters. By the mid 1980s, a quarter of Cuba's total military strength was committed to its internationalist missions, fighting with socialist governments or factions in various civil conflicts.\", \"Cuba\\u2013Spain relations Cuba\\u2013Spain relations Cuba\\u2013Spain relations refer to the bilateral relations between the Republic of Cuba and the Kingdom of Spain. Relations date back more than five centuries. Cuba had been a colony from 1492 until 1898 when the United States took over the territory in the Spanish\\u2013American War. Many Cubans have ancestry dating back from Spain. Many Spaniards escaped the first Spanish Civil War and went to Cuba, and other countries, around 1820-1825. The first contact between Spain and the island of Cuba was in October 1492 when explorer Christopher Columbus arrived to Cuba. The first permanent Spanish settlement on the\", \"Spanish\\u2013American War enunciated the Monroe Doctrine, which stated that the United States would not tolerate further efforts by European governments to retake or expand their colonial holdings in the Americas or to interfere with the newly independent states in the hemisphere; at the same time, the doctrine stated that the U.S. would respect the status of the existing European colonies. Before the American Civil War (1861\\u20131865), Southern interests attempted to have the United States purchase Cuba and convert it into a new slave territory. The pro-slavery element proposed the Ostend Manifesto proposal of 1854. It was rejected by anti-slavery forces. After the\", \"Military history of Cuba Military history of Cuba The Military history of Cuba begins with the island's conquest by the Spanish and its battles afterward to gain its independence. Since the Communist takeover by Fidel Castro in 1959, Cuba has been involved with many major conflicts of the Cold War in Africa and Latin America where it had supported Marxist governments and rebels from liberation movements who were opposed to their colonial masters and/or allies of the United States. The Ten Years' War was the first of three wars that Cuba fought against Spain for its independence. The Ten Years' War began when Carlos\", \"John Aaron Rawlins had recently gone through the Civil War. Also at stake was negotiations for settlement of the Alabama Claims, that included the claim the British had recognized Confederate belligerency during the Civil War. The recognition of Cuban belligerency would have jeopardized negotiations between Britain and the United States. Secretary Rawlins, however, was strongly in favor of the recognition of Cuban belligerency and even advocated war with Spain, if necessary. Rawlins went to the press and stated reasons why the United States needed to aid the Cuban rebels. Rawlins himself had accepted $28,000 in Cuban War bonds that would have been given\", \"Angola\\u2013Cuba relations community teamed up with military forces from their previous foe to oust UNITA. With superior training and bushwar tactics from the South Africans coupled with the impressive Soviet military weaponry still in stock, the MPLA were able to eventually drive UNITA back. The eventual defection of one of Savimbi's senior generals also helped to corner and eradicate Savimbi. Angola\\u2013Cuba relations Angola-Cuba diplomatic relations refers to the historical and current bilateral relationship between Angola and Cuba. During Angola's civil war, Cuban forces fought alongside the Marxist\\u2013Leninist People's Movement for the Liberation of Angola (MPLA) government; against the Western-backed National Union for\", \"Virginius Affair Spain. The \\\"Virginius\\\" Affair started a resurgence in the US Navy following the American Civil War; its fleet had been heretofore inferior to the superior warships of Spain. After the American Civil War, the island country of Cuba under Spanish rule was one of the few Western Hemisphere countries in which slavery remained legal and was widely practiced. On October 10, 1868 a revolution broke out, known as the Ten Years' War, by Cuban landowners, led by Carlos Manuel de C\\u00e9spedes. The Spanish, led initially by Francisco Lersundi, used the military to suppress the rebellion. In 1870, Secretary of State\", \"Slavery in Cuba writers were a part of the \\\"negrista\\\" or \\\"negrismo\\\" literary movement of the 20th century. This was a Hispanophone effort to reclaim Cuban blackness and connections to African culture, while expressing a new sensibility (similar to the flowering of the American Harlem Renaissance). In this effort, Guill\\u00e9n, Cabrera, and their contemporaries revisited and tried to make sense of slavery and the crimes against the Afro-Cuban people. Slavery in Cuba Slavery in Cuba was associated with the sugar cane plantations and existed on the territory of the island of Cuba from the 16th century until it was abolished by royal decree\", \"History of Havana the Tacon Teatre, one of the most luxurious in the world, the Artistic and Literary Liceo (Lyceum) and the theater Coliseo (Colosseum). The fact that slavery was legal in Cuba until 1886 led to Southern American interest, including a plan by the Knights of the Golden Circle to create a 'Golden Circle' with a 1200 mile-radius centered on Havana. After the Confederate States of America were defeated in the American Civil War in 1865, many former slaveholders continued to run plantations by moving to Havana. In 1863, the city walls were knocked down so that the metropolis could be enlarged.\", \"Cuban Americans New York City, about 3,000 in New Orleans, and 2,000 in Key West. The causes of these movements were both economic and political, which intensified after 1860, when political factors played the predominant role in emigration, as a result of deteriorating relations with the Spanish metropolis. The year 1869 marked the beginning of one of the most significant periods of emigration from Cuba to the United States, again centered on Key West. The exodus of hundreds of workers and businessmen was linked to the manufacture of tobacco. The reasons are many: the introduction of more modern techniques of elaboration of\", \"Timeline of the Spanish\\u2013American War Timeline of the Spanish\\u2013American War The timeline of events of the Spanish\\u2013American War covers major events leading up to, during, and concluding the Spanish\\u2013American War, a ten-week conflict in 1898 between Spain and the United States of America. The conflict had its roots in the worsening socio-economic and military position of Spain after the Peninsular War, the growing confidence of the United States as a world power, a lengthy independence movement in Cuba and a nascent one in the Philippines, and strengthening economic ties between Cuba and the United States. Land warfare occurred primarily in Cuba and to a much\", \"Cuba\\u2013United States relations than Spanish settlements. Between 1878 and 1898 American investors took advantage of deteriorating economic conditions of the Ten Years' War to take over estates they had tried unsuccessfully to buy before while others acquired properties at very low prices. Above all this presence facilitated the integration of the Cuban economy into the North American system and weakened Cuba's ties with Spain. As Cuban resistance to Spanish rule grew, rebels fighting for independence attempted to get support from U.S. President Ulysses S. Grant. Grant declined and the resistance was curtailed, though American interests in the region continued. U.S. Secretary of State\", \"Foreign interventions by Cuba means. In July 1964, the Organization of American States sanctioned Cuba after a cache of weapons destined for the Fuerzas Armadas de Liberaci\\u00f3n Nacional was discovered on Venezuela's shores. In May 1967, the Machurucuto Incident saw Cuban troops attempting to make their way into the Andes to train Venezuelan guerrillas, but they were captured by the Venezuelan Army and National Guard. Ties between Cuba and Venezuela resumed in 1974 after guerrilla activity decreased in Venezuela. When Cuba began to enter its Special Period which saw domestic economic collapse, it once again became motivated to take control of Venezuela's oil wealth.\", \"Spanish occupation of the Dominican Republic U.S. Civil War in 1865 and the re-assertion of the Monroe Doctrine by the United States (no longer involved in internal conflict and possessing enormously expanded and modernized military forces as a result of the war) prompted the evacuation of Spanish forces back to Cuba that same year. By 1862, the Spaniards were contending with a limited insurgency and losing hundreds of soldiers to guerrillas and the devastation of yellow fever. A major uprising began in earnest in August 1863, motivated by the Spanish government's attempts to impose strict Catholicism and the Hispanization of most government and military positions. On\", \"Foreign relations of Cuba transportation issues for the Caribbean region. Following a meeting in November 2004, several leaders of South America have attempted to make Cuba either a full or associate member of the South American trade bloc known as Mercosur. Prior to achieving its independence, Cuba was a colony of Spain. Prior to the triumph of the Cuban Revolution, Cuba maintained strong economic and political ties to the United States. From 1902 until its abrogation in 1934, the Platt Amendment authorized the US to use military force to preserve Cuba's independence. In 1917, Cuba entered World War I on the side of the\", \"Cuba has conducted a foreign policy that is uncharacteristic of such a minor, developing country. Under Castro, Cuba was heavily involved in wars in Africa, Central America and Asia. Cuba supported Algeria in 1961\\u20131965, and sent tens of thousands of troops to Angola during the Angolan Civil War. Other countries that featured Cuban involvement include Ethiopia, Guinea, Guinea-Bissau, Mozambique, and Yemen. Lesser known actions include the 1959 missions to the Dominican Republic. The expedition failed, but a prominent monument to its members was erected in their memory in Santo Domingo by the Dominican government, and they feature prominently at the country's\", \"History of Cuba Britain Florida in exchange for Cuba on France's recommendation to Spain, The French advised that declining the offer could result in Spain losing Mexico and much of the South American mainland to the British. This led to disappointment in Britain, as many believed that Florida was a poor return for Cuba and Britain's other gains in the war. In the early 19th century, three major political currents took shape in Cuba: reformism, annexation and independence. In addition, there were spontaneous and isolated actions carried out from time to time, adding a current of abolitionism. The Declaration of Independence by the\", \"Cuba\\u2013United States relations in Havana harbor, led to the Spanish\\u2013American War. In Cuba the war became known as \\\"the U.S. intervention in Cuba's War of Independence\\\". On 10 December 1898 Spain and the United States signed the Treaty of Paris and, in accordance with the treaty, Spain renounced all rights to Cuba. The treaty put an end to the Spanish Empire in the Americas and marked the beginning of United States expansion and long-term political dominance in the region. Immediately after the signing of the treaty, the U.S.-owned \\\"Island of Cuba Real Estate Company\\\" opened for business to sell Cuban land to Americans.\", \"History of Cuban nationality were brought to Cuba. This was more than all the rest of Spanish America combined. Slavery was leaned upon heavily by the owners of the highly profitable sugar plantations. By 1886, people of colour \\u2013 the majority being ex-slaves \\u2013 made up 1/3 of the population of Cuba. The issue of integration was a complex and highly contentious issue. Rights were hard to come by for many former slaves and also for those who lived and worked in rural communities. Emancipation was a slow process that started in 1868 and continued until 1886. As a preliminary step, the Moret Law\", \"Propaganda of the Spanish\\u2013American War the media, such as William Randolph Hearst, and of the military were calling for intervention by the United States to help the revolutionaries in Cuba. American opinion was overwhelmingly swayed and hostility towards Spain began to build. American newspapers ran stories of a sensationalist nature depicting fabricated atrocities committed by the Spanish. These stories often reflected on how thousands of Cubans had been displaced to the country side in concentration camps. Many stories used depictions of gruesome murders, rapes, and slaughter. During this time there was a riot in Havana by those sympathetic to the Spanish. The printing presses of\", \"Afrocubanismo progressive change after Cuba\\u2019s War of Independence against Spain, where the majority of Cuba\\u2019s armed forces consisted of black and mixed-race soldiers, many of whom were slaves and former slaves. In spite of their efforts in the War of Independence, Afro-Cubans were outraged at the failure of Cuban legislature to enact policies that would benefit the black population. It wasn\\u2019t until the establishment of universal male suffrage that White Cubans had to acknowledge the value of the Afro-Cuban citizen, since the black population now made up 30 percent of Cuba\\u2019s total voting population. In addition to the political and economic\", \"History of the United States (1865\\u20131918) the labor base necessary for the expansion of industry and agriculture, as well as the population base for most of fast-growing urban America. By the late nineteenth century, the United States had become a leading global industrial power, building on new technologies (such as the telegraph and steel), an expanding railroad network, and abundant natural resources such as coal, timber, oil, and farmland, to usher in the Second Industrial Revolution. There were also two very important wars. The U.S. easily defeated Spain in 1898, which unexpectedly brought a small empire. Cuba quickly was given independence, as well as the Philippines\", \"Cuban War of Independence Cuban War of Independence The Cuban War of Independence (, 1895\\u201398) was the last of three liberation wars that Cuba fought against Spain, the other two being the Ten Years' War (1868\\u20131878) and the Little War (1879\\u20131880). The final three months of the conflict escalated to become the Spanish\\u2013American War, with United States forces being deployed in Cuba, Puerto Rico, and the Philippine Islands against Spain. Historians disagree as to the extent that United States officials were motivated to intervene for humanitarian reasons but agree that yellow journalism exaggerated atrocities attributed to Spanish forces against Cuban civilians. During the years\", \"The Power of Community: How Cuba Survived Peak Oil oil industry experts and political activists, including Matthew Simmons and James Howard Kunstler. The Cuban economy, heavily dependent on economic aid from the Soviet Union, suffered tremendously following the end of the Cold War. The nation lost half of its oil imports, and 85 percent of its international trade economy. Director Faith Morgan, together with the non-profit group The Community Solution, seeks to educate audiences about peak oil and the impact it will have on transportation, agriculture, medicine, and other industries. The idea for a film based on the Cuban recovery first arose in August 2003 when Morgan traveled to\", \"Cuba as the Ten Years' War. Two thousand Cuban Chinese joined the rebels. Chinese had been imported as indentured laborers. A monument in Havana honours the Cuban Chinese who fell in the war. The United States declined to recognize the new Cuban government, although many European and Latin American nations did so. In 1878, the Pact of Zanj\\u00f3n ended the conflict, with Spain promising greater autonomy to Cuba. In 1879\\u20131880, Cuban patriot Calixto Garc\\u00eda attempted to start another war known as the Little War but did not receive enough support. Slavery in Cuba was abolished in 1875 but the process was\", \"Cuba\\u2013Mexico relations importance of the United States and the USSR in these relations will be discussed since it had a large impact on the relations between Mexico and Cuba. Especially during the Cold War era, the US believed that their relations with Mexico should help dictate, to an extent, how Mexico could treat Cuba and how public or private they needed to be with opinions towards Cuba. After the Spanish\\u2013American War, the United States gained control of Cuba and slowly gave the country its independence in a limited form. Under the Platt Amendment, the country was given independence with a few conditions.\", \"Republic of Cuba (1902\\u20131959) head of the armed forces in 1933, colonel Fulgencio Batista played a dominant role in Cuban politics over the next decades. The Cuban Revolution of 1953\\u20131959 massively changed Cuban society, creating a socialist state and ending US economic dominance in Cuba, as it aligned the country with the Soviet Union. The Republic of Cuba has been regarded as a client state of the United States. From 1902\\u20131932 Cuban and United States law included the Platt Amendment, which guaranteed the US right to intervene in Cuba and placed restrictions on Cuban foreign relations. In 1934, Cuba and the United States signed\", \"History of Cuban nationality sought to reassert control in the Americas, these elite positions were greatly weakened, much to the anger of those who had enjoyed the collateral benefits of authority. In the early 19th century, Cuban nationalist movement lagged behind its counterparts in the rest of Latin America. Maintaining good relations with Spain was essential for the health of Cuba's primarily agrarian economy, as the island nation was heavily dependent at the time upon exporting its sugar to European markets. Cuba, as one of the last outposts of slavery, also relied on Spain for protection against any potential slave uprisings. As compared to\", \"Slavery in Cuba Slavery in Cuba Slavery in Cuba was associated with the sugar cane plantations and existed on the territory of the island of Cuba from the 16th century until it was abolished by royal decree on October 7, 1886. More than a million African slaves were brought to Cuba as part of the Atlantic slave trade; Cuba did not end its participation in the slave trade until 1867. As the slaves outnumbered the European Cubans, a large proportion of Cubans are descended from these African slaves, perhaps as many as 65% of the population. Slavery in Cuba was particularly profitable for\", \"History of civil affairs in the United States Armed Forces of the Caribbean and Latin American nations before and after World War I. After the Spanish\\u2013American War ended in 1898, Maj. Gen. Leonard Wood restored order in Cuba with CA forces. The Army returned to Cuba in 1905, again in 1912, and starting in 1917, was there for a period of many years. The Marine Corps was called upon to protect American interests by military intervention in the Dominican Republic (1916\\u20131924), Haiti (1915\\u20131934), and Nicaragua (1926\\u20131934). The Army was also called to Panama in 1903 to ensure the birth of that nation when it broke away from Colombia to become\", \"Florida Cracker Horse this type was the predominant horse in the southeastern United States. During the American Civil War (1861\\u20131865), both belligerents purchased large amounts of beef from Florida, and the Spanish horses bred there were highly desired as riding horses. During this time, there was also a continual introduction of new Spanish blood from Cuba, as horses were traded between the two areas. During the Dust Bowl (1930\\u20131940), large western cattle were moved into Florida, bringing with them the parasitic screwworm. Cattle with this parasite needed to be treated frequently. The cowboys found that the Florida Cracker horses, bred for working smaller\", \"Southern United States literature transcends these geographical boundaries. Southern literature occupies a liminal space within American culture. On the one hand, the South has been marginalized within U.S. since the 18th century, because its reliance on plantation agriculture and African slavery made it seem too British in the post-Revolutionary war era. On the other hand, the white South has historically thrown its support behind American capitalist endeavors and imperial ambitions (for instance, through the enthusiastic participation of many white southerners in the Mexican-American war). At the same time, southern racial history has come to be seen as emblematic of, rather than exceptional to, U.S.\", \"History of the United States (1865\\u20131918) and educate them to new ideas. Spain had once controlled a vast colonial empire, but by the second half of the 19th century only Cuba, Puerto Rico, the Philippines, and some African possessions remained. The Cubans had been in a state of rebellion since the 1870s, and American newspapers, particularly New York City papers of William Randolph Hearst and Joseph Pulitzer, printed sensationalized \\\"Yellow Journalism\\\" stories about Spanish atrocities in Cuba. However, these lurid stories reached only a small fraction of voters; most read sober accounts of Spanish atrocities, and they called for intervention. On February 15, 1898, the battleship\", \"History of Cuba statutes enabled the United States to gain a foothold in Cuba, they presented obstacles for American businesses to acquire land and permits. Eventually, Cornelius Van Horne of the Cuba Company, an early railroad company in Cuba, found a loophole in \\\"revocable permits\\\" justified by preexisting Spanish legislation that effectively allowed railroads to be built in Cuba. General Leonard Wood, the governor of Cuba and a noted annexationist, used this loophole to grant hundreds of franchises, permits, and other concessions to American businesses. Once the legal barriers were overcome, American investments transformed the Cuban economy. Within two years of entering Cuba,\", \"Cuban military internationalism called Operaci\\u00f3n Carlota (\\\"Operation Carlota\\\"), to support the communist government and participate in the Angolan Civil War and the South African Border War. 1977-1988: During the Ethiopian Civil War and the Ogaden War, Cuban troops entered into Ethiopia to support the Ethiopian socialist government and fight the Somali national liberation movement of the Ogaden. 1979-1990: In the Sandinista Revolution in Nicaragua, the Cuban State sent military personnel who took control of the Nicaraguan military security and intelligence services. 1959: Failed expedition to Panama in order to start a revolutionary movement in the country. They were arrested after a skirmish with\", \"Tourism in Cuba Tourism in Cuba Tourism in Cuba is an industry that generates over 4.5 million arrivals in 2017, and is one of the main sources of revenue for the island. With its favorable climate, beaches, colonial architecture and distinct cultural history, Cuba has long been an attractive destination for tourists. \\\"Cuba treasures 253 protected areas, 257 national monuments, 7 UNESCO World Heritage Sites, 7 Natural Biosphere Reserves and 13 Fauna Refuge among other non-tourist zones.\\\" Having been Spain's closest colony to the United States until 1898, in the first part of the 20th century Cuba continued to benefit from big investments,\", \"Cuban Americans of Cubans, which went from 720 inhabitants in 1880 to 5,532 in 1890. However, the second half of the 1890s marked the decline of the Cuban immigrant population, as an important part of it returned to the island to fight for independence. The War accentuated Cuban immigrant integration into American society, whose numbers were significant: more than 12,000 people. In the mid- to late 19th century, several cigar manufacturers moved their operations to Key West to get away from growing disruptions as Cubans sought independence from Spanish colonial rule. Many Cuban cigar workers followed. The Cuban government had even established\", \"Cuban Revolution of the Soviet Union into Europe after the 1917 Russian Revolution, Castro immediately sought to \\\"export\\\" his revolution to other countries in the Caribbean and beyond, sending weapons to Algerian rebels as early as 1960. In the following decades, Cuba became heavily involved in supporting Communist insurgencies and independence movements in many developing countries, sending military aid to insurgents in Ghana, Nicaragua, Yemen and Angola, among others. Castro's intervention in the Angolan Civil War in the 1970s and 1980s was particularly significant, involving as many as 60,000 Cuban soldiers. Following the American embargo, the Soviet Union became Cuba's main ally.\", \"Ten Years' War peaked in the years 1872 and 1873, but after the deaths of Agramonte and C\\u00e9spedes, Cuban operations were limited to the regions of Camag\\u00fcey and Oriente. G\\u00f3mez began an invasion of Western Cuba in 1875, but the vast majority of slaves and wealthy sugar producers in the region did not join the revolt. After his most trusted general, the American Henry Reeve, was killed in 1876, G\\u00f3mez ended. Spain's efforts to fight were hindered by the civil war (Third Carlist War) that broke out in Spain in 1872. When the civil war ended in 1876, the government sent more Spanish\", \"Cuban sugar economy the Spanish\\u2013American War in 1898 and its formation of a republic in 1902 led to investments in the Cuban economy from the United States. The doubling of sugar consumption in the United States between 1903 and 1925 further stimulated investment in Cuba to develop the infrastructure necessary for sugar production. Most of the subsequent development took place in the rural, eastern region of Cuba where sugar production grew the most. In 1920, US banks gave large loans to finance Cuban efforts to profit from a speculative boom in world sugar prices. The boom collapsed shortly thereafter, however, and the banks\", \"History of Cuban nationality of 1870 granted freedom to children and those over the age of sixty but offered little else. As the skirmishes continued and losses compounded during the 10 Years\\u2019 War, the anti-colonial forces spoke more openly about the idea of a free Cuban citizen. Even though there was still a strong racial divide, many slaves joined up with the revolutionaries. Although this initial rebellion did not force any significant changes, the participation of slaves did not go unnoticed. By the early 1890s, Spain was willing to offer fairly considerable civil rights and voting rights to many former slaves in a vain\", \"Spanish\\u2013American War Libre\\\" movement and the fact that many Americans had drawn parallels between the American Revolution and the Cuban revolt, seeing the Spanish Government as the tyrannical colonial oppressor. Historian Louis P\\u00e9rez notes that \\\"The proposition of war in behalf of Cuban independence took hold immediately and held on thereafter. Such was the sense of the public mood.\\\" At the time many poems and songs were written in the United States to express support of the \\\"Cuba Libre\\\" movement. At the same time, many African Americans, facing growing racial discrimination and increasing retardation of their civil rights, wanted to take part\", \"Origins of the American Civil War Abolitionists also attacked slavery as a threat to the freedom of white Americans. Defining freedom as more than a simple lack of restraint, antebellum reformers held that the truly free man was one who imposed restraints upon himself. Thus, for the anti-slavery reformers of the 1830s and 1840s, the promise of free labor and upward social mobility (opportunities for advancement, rights to own property, and to control one's own labor), was central to the ideal of reforming individuals. Controversy over the so-called Ostend Manifesto (which proposed the U.S. annexation of Cuba as a slave state) and the Fugitive Slave Act\", \"History of Cuba blacks, slave traders looked for others sources of cheap labour, such as Chinese colonists and Indians from Yucat\\u00e1n. Another feature of the population was the number of Spanish-born colonists, known as \\\"peninsulares\\\", who were mostly adult males; they constituted between ten and twenty per cent of the population between the middle of the 19th century and the great depression of the 1930s. Black unrest and British pressure to abolish slavery motivated many Creoles to advocate Cuba's annexation by the United States, where slavery was still legal. Other Cubans supported the idea due to their desire for American-style economic development and\", \"Bay of Pigs Invasion Cuba and the Soviet Union. This eventually led to the events of the Cuban Missile Crisis of 1962. The invasion was a major failure for US foreign policy; Kennedy ordered a number of internal investigations across Latin America. Since the middle of the 18th century Cuba had been the crown jewel of the Spanish colonial empire. In the late 19th century, Cuban nationalist revolutionaries rebelled against Spanish dominance, resulting in three liberation wars: the Ten Years' War (1868\\u20131878), the Little War (1879\\u20131880) and the Cuban War of Independence (1895\\u20131898). The United States government proclaimed war on the Spanish Empire, resulting\", \"Cuban-American lobby organizations within the intellectual wing of the Cuba lobby advocate for travel as a human right, and have affected change on U.S. travel policies towards Cuba. Business interest lobbies often advocate for lifting the embargo so as to increase trade between the two nations. They believe trade with Cuba would be beneficial for the U.S. economy, and usually point more to financial than humanitarian reasons for their stance. Lobbies outside the Cuban-American community have also advocated for liberalization of trade between the two nations, most notably the agribusiness lobby. In the 1980s, most Cuban expatriate interest groups were only active\", \"Cuba\\u2013Namibia relations Cuba\\u2013Namibia relations Cuba\\u2013Namibia relations refer to the current and historical relationship between Cuba and Namibia. Cuba politically, militarily and diplomatically supported the South West Africa People's Organization (SWAPO) during the Namibian War of Independence. Cuba provided military training for the People's Liberation Army of Namibia (PLAN), SWAPO's armed wing. As a result of this involvement Namibia usually supports Cuban policies on the international level, like in the case of the requested release of the Cuban Five. Since independence in 1990, Namibia and Cuba have held joint meetings every two years for economic, scientific-technical and commercial cooperation. In 2005, it was\", \"Legacy of Che Guevara immortal warrior they want to project.\\\" Despite the formal adulation, Guevara's legacy is less pronounced on a national policy front. In Cuba, Guevara's death precipitated the abandonment of guerrilla warfare as an instrument of foreign policy, ushering in a \\\"rapprochement\\\" with the Soviet Union, and the reformation of the government along Soviet lines. When Cuban troops returned to Africa in the 1970s, it was as part of a large-scale military expedition, and support for insurrection movements in Latin America and the Caribbean became logistical and organizational rather than overt. Cuba also abandoned Guevara's plans for economic diversification and rapid industrialization\", \"Cuba during World War II worked with five years before during the Spanish Civil War. The effort was unsuccessful, however, and Hemingway soon turned his attention to fighting the German U-boats operating in the Caribbean Sea. Just three weeks after receiving permission from Ambassador Spruille Braden to form the \\\"Crook Factory,\\\" Hemingway asked Braden for permission to arm his fishing boat, the \\\"Pilar\\\", for patrols against U-boats off of the Cuban coast. Surprisingly, Baden gave Hemingway permission, and so the latter proceeded with arming the \\\"Pilar\\\" and the crew with machine guns, bazookas, and hand grenades. Hemingway's plan was similar to that of the Q-ship\", \"History of Cuba with any foreign ships. The resultant stagnation of economic growth was particularly pronounced in Cuba because of its great strategic importance in the Caribbean, and the stranglehold that Spain kept on it as a result. As soon as Spain opened Cuba's ports up to foreign ships, a great sugar boom began that lasted until the 1880s. The island was perfect for growing sugar, being dominated by rolling plains, with rich soil and adequate rainfall. By 1860, Cuba was devoted to growing sugar, having to import all other necessary goods. Cuba was particularly dependent on the United States, which bought 82\", \"Republic of Cuba (1902\\u20131959) Republic of Cuba (1902\\u20131959) The Republic of Cuba (Spanish: \\\"Rep\\u00fablica de Cuba\\\") of 1902 to 1959, referred by the current Cuban government as the Neocolonial Republic (Spanish: \\\"Rep\\u00fablica Neocolonial\\\"), and as Free Cuba (Spanish: \\\"Cuba Libre\\\") by Cuban dissidents, refers to the historical period in Cuba from 1902, when Cuba seceded from US rule in the aftermath of the Spanish\\u2013American War that took Cuba from Spanish rule in 1898, until communist revolutionaries took power in 1959. The official form of government was representative democracy though at times it was controlled by a military junta or otherwise unelected government. After becoming\", \"Spanish\\u2013American War to its independence, and even annexation was considered for a time, which historian Louis P\\u00e9rez explored in his book \\\"Cuba and the United States: Ties of Singular Intimacy\\\". The Cubans harbored a great deal of discontent towards the Spanish Government, due to years of manipulation on the part of the Spanish. The prospect of getting the United States involved in the fight was considered by many Cubans as a step in the right direction. While the Cubans were wary of the United States' intentions, the overwhelming support from the American public provided the Cubans with some peace of mind, because\", \"Ten Years' War as a leading Latin American intellectual and Cuba\\u2019s foremost national hero, its primary architect of the 1895\\u201398 Cuban War of Independence. After some initial victories and defeats, in 1868 C\\u00e9spedes replaced Gomez as head of the Cuban Army with United States General Thomas Jordan, a veteran of Confederate States Army in the American Civil War. He brought a well-equipped force, but General Jordan's reliance on regular tactics, although initially effective, left the families of Cuban rebels far too vulnerable to the \\\"ethnic cleansing\\\" tactics of the ruthless Blas Villate, Count of Valmaceda (also spelled Balmaceda). Valeriano Weyler, known as the\", \"Spanish\\u2013American War of '98 originated as a response to this trauma, marking a renaissance in Spanish culture. Economically, the war benefited Spain, because after the war large sums of capital held by Spaniards in Cuba and the United States were returned to the peninsula and invested in Spain. This massive flow of capital (equivalent to 25% of the gross domestic product of one year) helped to develop the large modern firms in Spain in the steel, chemical, financial, mechanical, textile, shipyard, and electrical power industries. However, the political consequences were serious. The defeat in the war began the weakening of the fragile\", \"Cuba under Fidel Castro evening's reception for Castro, attended by Allen Ginsberg, Langston Hughes, C. Wright Mills and I. F. Stone. Castro returned to Cuba on 28 September. He feared a U.S.-backed coup and in 1959 spent $120 million on Soviet, French and Belgian weaponry. Intent on constructing the largest army in Latin America, by early 1960 the government had doubled the size of the Cuban armed forces. Fearing counter-revolutionary elements in the army, the government created a People's Militia to arm citizens favorable to the revolution, and trained at least 50,000 supporters in combat techniques. In September 1960, they created the Committees for\", \"Music of African heritage in Cuba Music of African heritage in Cuba Clearly, the origin of African groups in Cuba is due to the island's long history of slavery. Compared to the USA, slavery started in Cuba much earlier and continued for decades afterwards. Cuba was the last country in the Americas to abolish the importation of slaves, and the second last to free the slaves. In 1807 the British Parliament outlawed slavery, and from then on the British Navy acted to intercept Portuguese and Spanish slave ships. By 1860 the trade with Cuba was almost extinguished; the last slave ship to Cuba was in 1873.\", \"Interwar period far-left volunteers. The civil war did not escalate into a larger conflict, but did become a worldwide ideological battleground that pitted all the Communists and many socialists and liberals against Catholics, conservatives and fascists. Worldwide there was a decline in pacifism and a growing sense that another great war was imminent, and that it would be worth fighting for. The changing world order that the war had brought about, in particular the growth of the United States and Japan as naval powers, and the rise of independence movements in India and Ireland, caused a major reassessment of British imperial policy.\", \"Cold War (1953\\u20131962) Cuba was to nationalize American assets on the island without compensation. Before the fall of the pro-U.S. Batista regime, U.S. interests had owned four-fifths of the stakes in the island's utilities, nearly half of its sugar, and nearly all of its mining industries. The U.S. could manipulate the Cuban economy at a whim by tinkering with the island's financial services or by tampering with government quotas and tariffs on sugar\\u2013the country's staple export commodity. In response to these acts, the U.S. government refused to recognize Castro as the leader of Cuba, the U.S. government made the first of several attempts\", \"Cuba espan\\u0303ola is connected with liberal and autonomist ideas that coexisted throughout the nineteenth century with the ideal of independence. The entry of the United States in the Spanish\\u2013American War and its support for separatist Spaniards, changed the destiny of the island, which in 1898 came under American influence. Current historical research tints Manichean positions developed by Marxist doctrine in Cuba, aimed to prove the inevitability of the revolutionary movement that started in 1868 with the Grito de Yara, and concluded a hundred years later with the seizure of power by Fidel Castro, offering a more balanced interpretation of the events leading\", \"Golden Circle (proposed country) Knights of the Golden Circle that the Southern United States should secede in their own confederation and invade and annex the area of the golden circle to vastly expand the power of the South. European colonialism and dependence on slavery had declined more rapidly in some countries than others. The Spanish possessions of Cuba and Puerto Rico and the Empire of Brazil continued to depend on slavery, as did the Southern United States. In the years prior to the American Civil War, the rise of support for abolition of slavery was one of several divisive issues in the United States.\", \"Hadley Cantril Free conducted a poll of Cuba during 1960 demonstrating great support for Fidel Castro, which was overlooked during the presidential transition between Eisenhower and Kennedy and read only after the Bay of Pigs Invasion fiasco. Cantril's most-cited work is \\\"The Pattern of Human Concerns,\\\" notable for the development of the self-anchoring scale (also known as \\\"Cantril's Ladder\\\"). Cantril and Free also first discovered the paradox that American voters tend to oppose \\\"big government\\\" in general while supporting many specific liberal social programs. During the late 1950s, Cantril served on the International Objectives and Strategies panel of the Rockefeller Brothers Fund's\", \"Angola\\u2013Cuba relations South Africa had invested in the Caluque Hydro Scheme in Southern Angola (which fed into Namibia) and had a natural fear of the effects of instability in Angola \\u2013 thus South Africa initially sent troops to protect the Hydro Scheme (and later intervened in the civil war). The government of the Soviet Union, well aware of South African activity in southern Angola, flew Cuban soldiers into Luanda one week before November 11, the declared date of independence. While Cuban officers led the mission and provided the bulk of the troop force, 60 Soviet officers in the Congo joined the Cubans\", \"Spanish\\u2013American War built (1903\\u20131914), and realized the need for naval protection. Captain Alfred Thayer Mahan was an especially influential theorist; his ideas were much admired by future 26th President Theodore Roosevelt, as the U.S. rapidly built a powerful naval fleet of steel warships in the 1880s and 1890s. Roosevelt served as Assistant Secretary of the Navy in 1897\\u20131898 and was an aggressive supporter of an American war with Spain over Cuban interests. Meanwhile, the \\\"Cuba Libre\\\" movement, led by Cuban intellectual Jos\\u00e9 Mart\\u00ed until his death in 1895, had established offices in Florida. The face of the Cuban revolution in the U.S.\", \"Cuban intervention in Angola soon failed due to the spread of war with UNITA. According to Cubatecnica, the government office for non-military foreign assistance, there were more Cuban volunteers than could be accepted and long waiting lists. Cuba's engagement laid the foundations for Angola's social services. In the following years, Cuba kept itself engaged in a number of other African countries. In 1978, Cuba sent 16,000 troops to the Ethiopian Ogaden War, but this time in close coordination with the Soviets. Smaller military missions were active in the People's Republic of the Congo, Guinea, Guinea-Bissau, Mozambique and Benin. Cuban technical, educational and medical staff\", \"Jose\\u0301 Marti\\u0301 crisis it was experiencing, and the media was talking about the purchase of Cuba from Spain. Cuba was a profitable, fertile country with an important strategic position in the Gulf of Mexico. Mart\\u00ed felt that the interests of Cuba's future lay with its sister nations in Latin America, and were opposite to those of the United States. Another trait that Mart\\u00ed admired was the work ethic that characterized American society. On various occasions Mart\\u00ed conveyed his deep admiration for the immigrant-based society, \\\"whose principal aspiration he interpreted as being to construct a truly modern country, based upon hard work and\", \"History of Cuba were followed by a reactionary governor, Francisco Lersundi, who suppressed all liberties granted by the previous governors and maintained a pro-slavery regime. On 10 October 1868, the landowner Carlos Manuel de C\\u00e9spedes declared Cuban independence and freedom for his slaves. This began the Ten Years' War, which lasted from 1868 to 1878, and eventually contributed to the abolition of slavery in 1886. During the time of the so-called \\\"Rewarding Truce\\\", which encompassed the 17 years from the end of the Ten Years' War in 1878, fundamental changes took place in Cuban society. With the abolition of slavery in October 1886,\", \"Angola\\u2013Cuba relations Angola\\u2013Cuba relations Angola-Cuba diplomatic relations refers to the historical and current bilateral relationship between Angola and Cuba. During Angola's civil war, Cuban forces fought alongside the Marxist\\u2013Leninist People's Movement for the Liberation of Angola (MPLA) government; against the Western-backed National Union for the Total Independence of Angola (UNITA) and National Liberation Front of Angola (FNLA) guerrillas who were aided by the South-African army. The present day outcome of the war resulted in the MPLA changing from a Marxist\\u2013Leninist party to a multi-party democratic system based on Neoliberal principles (the MPLA also dropped the \\\"Labour Party\\\" extension to its name as\", \"Finca Vigi\\u0301a in for over 20 years. In the fall of 1960, the Cuban government expropriated a great deal of foreign property, including the Hemingway house \\\"Finca Vig\\u00eda\\\". As a result, the U.S. government broke off diplomatic relations with Cuba in October 1960 and imposed a partial financial embargo. After the Bay of Pigs invasion in April 1961 and Cuba's announcement that it was a Communist state in May, relations between Cuba and the U.S. deteriorated further. Hemingway was being treated for severe depression in the U.S. through the first half of 1961, and the Hemingways could not return to Cuba due\", \"Military history of Cuba Manuel de C\\u00e9spedes and his followers of patriots from his sugar mill La Demajagua began an uprising. The war ended with the signing of the Pact of Zanj\\u00f3n. The Cuban War of Independence was the last major uprising by Cuban Nationalists against the Spanish Colonial Government. The conflict culminated with American intervention during the Spanish\\u2013American War. The Spanish\\u2013American War was a major war fought by the United States and the Kingdom of Spain in the Spanish territories of Cuba, Puerto Rico, and the Philippines. The war was triggered with the sinking of the USS \\\"Maine\\\" in Havana Harbor. Cuban rebels\", \"History of Cuba In December 2014, after a highly publicized exchange of political prisoners between the United States and Cuba, U.S. President Barack Obama announced plans to re-establish diplomatic relations with Cuba after over five decades of severance. He stated that the U.S. government intended to establish an embassy in Havana and improve economic ties with the country. Obama's proposal received both strong criticism and praise from different elements of the Cuban American community. In April 2015, the U.S. government announced that Cuba would be removed from its list of state sponsors of terrorism, on which it had been included since 1982. The\"], \"pos_scores\": [93.9375], \"neg_scores\": [90.6875, 89.8125, 90.3125, 93.875, 88.9375, 89.125, 89.6875, 86.875, 87.4375, 90.6875, 89.875, 91.0, 89.4375, 89.5, 94.0, 85.875, 89.3125, 90.75, 92.5625, 90.375, 86.0, 86.5, 90.875, 90.875, 86.3125, 93.3125, 90.1875, 84.9375, 89.1875, 91.4375, 86.875, 91.5625, 92.875, 91.25, 85.875, 85.1875, 87.5, 90.0, 91.5, 90.3125, 90.375, 93.3125, 86.25, 87.5, 89.0, 89.0625, 93.3125, 92.1875, 88.9375, 89.1875, 87.5625, 89.5625, 92.625, 84.625, 91.625, 89.375, 87.1875, 89.4375, 93.4375, 87.3125, 86.75, 86.375, 90.125, 92.125, 86.375, 90.0625, 88.6875, 89.5625, 89.8125, 89.4375, 88.4375, 91.375, 90.9375, 93.3125, 88.625, 86.3125, 85.3125, 85.6875, 90.1875, 93.625, 87.75, 92.0625, 89.9375, 89.6875, 86.625, 87.125, 87.0, 91.0, 90.5, 88.5, 84.4375, 86.0, 90.625, 85.875, 87.0, 92.6875, 86.5625, 85.0, 90.5, 90.8125], \"prompt\": \"Given a question, retrieve Wikipedia passages that answer the question.\", \"type\": \"normal\"}\n" }, { "path": "examples/finetune/embedder/example_data/sts/sts.jsonl", "content": "{\"query\": \"But other sources close to the sale said Vivendi was keeping the door open to further bids and hoped to see bidders interested in individual assets team up.\", \"pos\": [\"But other sources close to the sale said Vivendi was keeping the door open for further bids in the next day or two.\"], \"neg\": [\"People with the same business affairs in common are peacefully bound to one another.\", \"Indeed, it is absolutely not certain only the definition of fair price which is proposed is better than another, because the various definitions used currently in the Member States are enough all amply.\", \"Vivendi shares closed 3.8 percent up in Paris at 15.78 euros.\", \"They will continue to put the pressure. The money that they gain with the distribution beyond the limit of the 150 grams makes it possible them to reinforce their position.\", \"Indeed, it is not absolutely certain that the definition of fair price proposed is better than another, because the different definitions are currently used in the Member States are enough is enough.\", \"This is why I also think that this reconciliation can hold. However, the government, which very courageously found a solution with the question of the universities and many other problems, needs visible signs of the success as well as strong support, under penalty of seeing the peace threatened as a whole of the area.\", \"In fact, it is not absolutely certain that the definition of price that is proposed is better than another, because the different currently in the Member States all fully.\", \"Barbini said the union may reach a compromise with the United States but it wants a system for labeling such foods, something the industry successfully fought here.\", \"Barbini said the EU may reach a compromise but it wants a system for labeling such foods, something the industry has resisted.\", \"\\\"Further testing is still under way, but at this stage, given the early detection, the outlook in such instances would be positive,\\\" the specialist said yesterday.\", \"Indeed, it is not absolutely certain that the definition of fair price which is better than another, because the definitions used in the Member States in all amply sufficient.\", \"In fact, it is by no means certain that the definition of fair prices which is being proposed is better than another, because the different definitions currently used in the Member States all are quite adequate.\", \"We have started to have trade on this issue, where it appears that all Member States wish to maintain this ceiling after enlargement.\", \"They will continue to put pressure. The money that they earn with the distribution beyond the limit of 150 grams allows them to strengthen their position.\", \"They will continue to put pressure. The money they earn with the distribution beyond the limit of 150 grammes enables them to strengthen their position.\", \"Indeed, it is not at all convinced that the definition of fair price which is proposed is better than another, because the different definitions used in the Member States are sufficient all amply.\", \"The other point of view, illustrated by the original words of the rapporteur and shared by many colleagues, is to promote the regulations and the codes of conduct needed to establish, between insurers, forms of mutual costs guaranteeing to all the provision of a good quality of care and to counter the risk of seeing develop discriminatory practices and a selection of risks and customers.\", \"The other point of view, illustrated by the original subject of the rapporteur and shared by many colleagues, is to promote the regulations and codes of conduct to be found, between insurers, the forms of mutual cost guarantee to all the supply of good quality care and to counter the risks to see developing discriminatory practices and a selection of risks and customers.\", \"\\\"However, we are not certain about the outcome of the discussion at this moment.\\\"\", \"Especially, when a unipersonal company or a company less cash than 250 paid has a project, it must be possible to be given a financing not only with one guarantee with 120%, but also on the simple basis of an good idea.\", \"After all, it is by no means certain that the proposed definition of equitable price is better than any other, because the various definitions that are currently in use in the Member States are all perfectly satisfactory.\", \"This is why I also think that this reconciliation can hold. however, the government, which has very courageously found a solution to the question and to many other problems, need visible signs of success so that a strong support to the peace in the whole of the region.\", \"The other point of view, illustrated by the comments by the rapporteur and a number of colleagues, is to promote the regulations and codes of conduct for dialogue, between a good supply of guaranteeing the provision of a good practices and to counter the risk of discriminatory practices and a series of risks and customers.\", \"But JT was careful to clarify that it was \\\"not certain about the outcome of the discussion at this moment\\\".\", \"The other point of view, illustrated by the initial aim of the rapporteur and shared by many colleagues, is to promote the regulations and codes of conduct necessary to establish, between insurers, the forms of cost option guaranteeing all the supply of good quality of care and to counter the risks of discriminatory practices and a selection of risks and customers.\", \"In Feira, he recognized the quality of potential candidates to adhesion with the countries taking part in the stabilisation process and of association.\", \"Still, revenues from the extra premiums would not be huge.\", \"Deirdre Hisler, Government Canyon's manager, said the state has long had its eye on this piece of property and is eager to complete the deal to obtain it.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"U.S. prosecutors have arrested more than 130 individuals and have seized more than $17 million in a continuing crackdown on Internet fraud and abuse.\", \"pos\": [\"More than 130 people have been arrested and $17 million worth of property seized in an Internet fraud sweep announced Friday by three U.S. government agencies.\"], \"neg\": [\"According to law enforcement officials, the person arrested was a known sophisticated hacker.\", \"When Ragin's address was raided, authorities found more than 1,000 credit cards and duplicating machines.\", \"The statements must be followed up by practical actions to identify, freeze, seize and confiscate the proceeds of crime.\", \"The statements must be followed by specific actions to identify, freeze, seize and confiscate the proceeds of crime.\", \"More than 100 police officers were involved in the busts that were the culmination of a two-year operation investigating the cocaine importing and money laundering gang.\", \"The statements must be matched by action to identify, freeze, seize and confiscate the proceeds of crime.\", \"Klarman was charged with 16 counts of wire fraud.\", \"Statements must be matched by action to identify, freeze, seize and confiscate the proceeds of crime.\", \"More than 100 officers launched the London raids in the final phase of a two-year operation investigating a cocaine and money laundering ring.\", \"Agents found more than 1,000 credit cards and credit card duplicating machines during a search of Ragin's address.\", \"Amnesty International has said that over the past 20 years it has collected information about 17,000 disappearances in Iraq but the actual figure may be much higher.\", \"According to law enforcement officials, the individual decrypted passwords on the server.\", \"They will continue to put the pressure. The money that they gain with the distribution beyond the limit of the 150 grams makes it possible them to reinforce their position.\", \"Telemarketers who call numbers on the list after Oct. 1 could face fines of up to $11,000 per call.\", \"They continue to put pressure. The money they earn with distribution beyond the bounds of 150 grammes allows them to strengthen their position.\", \"Under the law, telemarketers who call numbers on the list can be fined up to $11,000 for each violation.\", \"Thirty-four of the men have been arrested and the others are being sought, US Attorney Daniel Bogden said yesterday.\", \"They will continue to put pressure. The money that they earn with the distribution beyond the limit of 150 grams allows them to strengthen their position.\", \"Two federal agencies are investigating telecommunications gear maker Lucent Technologies for possible violations of U.S. bribery laws in its operations in Saudi Arabia.\", \"Amnesty International said that over the past 20 years it had collected information about 17,000 disappearances in Iraq.\", \"Through Sunday, Sept. 21, Site Finder has been visited over 65 million times by Internet users.\", \"They will continue to put pressure. The money they earn with the distribution beyond the limit of 150 grammes enables them to strengthen their position.\", \"\\\"Enron company executives engaged in widespread and pervasive fraud to manipulate the company's earnings results,\\\" Buell said.\", \"The debate must be followed by concrete actions to identify, freeze, seize and confiscate the proceeds of crime.\", \"The Federal Trade Commission (FTC) asked Congress today for additional authority to fight unwanted Internet spam, which now accounts for up to half of all e-mail traffic.\", \"They continue to put the pressure that they earn. money with the distribution of the limit of 150 grammes allows them to strengthen their position.\", \"Beleaguered telecommunications gear maker Lucent Technologies is being investigated by two federal agencies for possible violations of U.S. bribery laws in its operations in Saudi Arabia.\", \"\\\"Enron company executives engaged in widespread and pervasive fraud,\\\" prosecutor Samuel Buell told the Associated Press.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"Authorities said the scientist properly quarantined himself at home after he developed SARS symptoms Dec. 10.\", \"pos\": [\"The scientist also quarantined himself at home as soon as he developed SARS symptoms, officials said.\"], \"neg\": [\"Emile Laroza, 51, contracted SARS while working as a nurse at North York General Hospital, the epicentre of the second SARS outbreak.\", \"A long awaited report on the Hong Kong government's handling of the SARS outbreak has been released.\", \"The report by the independent expert committee aims to dissipate any suspicion about the Hong Kong government's handling of the SARS crisis.\", \"The first health-care worker in the country to die of SARS was a Filipina-Canadian who contracted the disease at North York General Hospital, the site of the second outbreak.\", \"On Monday, China said nine more people had died from SARS and that 160 more were infected with the virus.\", \"\\\"Further testing is still under way, but at this stage, given the early detection, the outlook in such instances would be positive,\\\" the specialist said yesterday.\", \"Chavez said investigators feel confident they've got \\\"at least one of the fires resolved in that regard.\\\"\", \"SARS went on to claim the lives of 44 people in the Toronto area, including two nurses and a doctor.\", \"China's Health Ministry said five more people had died of Sars and a further 159 were infected.\", \"He was taken to a hospital for precautionary X-rays on his neck.\", \"Albuquerque Mayor Martin Chavez said investigators felt confident that with the arrests they had \\\"at least one of the fires resolved.\\\"\", \"A spokeswoman at Strong Memorial Hospital said Doud was in satisfactory condition Tuesday night.\", \"The doctor was helping the patient.\", \"They underwent more tests over the weekend, and are now warded at Raffles Hospital.\", \"Steve Squyres, a Cornell University scientist, is principal investigator for the missions' science instruments.\", \"The incremental step, reported by researchers at UC San Francisco, is the latest in a decade-long effort to infect mice with the virus.\", \"Parents received letters informing them of the possible contamination yesterday.\", \"The new version, W32/Sobig.C-mm, had already reached a \\\"high level\\\" outbreak status by Monday, according to security analysts.\", \"The findings are published in the November 6 edition of the journal Nature.\", \"Security analysts said the new version, W32/Sobig.C-mm, had already reached a \\\"high level\\\" outbreak status by mid-afternoon on Monday.\", \"The 51-year-old nurse worked at North York General Hospital, the epicentre of the latest outbreak.\", \"There\\u2019s also at least three suspected cases in Illinois and 11 in Indiana.\", \"According to law enforcement officials, the person arrested was a known sophisticated hacker.\", \"They were at Raffles Hospital over the weekend for further evaluation.\", \"\\\"But at this stage, given the early detection, the outlook in such instances would be positive,\\\" he said.\", \"National Breast Cancer Centre head Professor Christine Ewan said there was no need for panic.\", \"Sources say agents confiscated \\\"several\\\" documents he was carrying.\", \"An injured woman co-worker also was hospitalized and was listed in good condition.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"The man accused of using fake grenades to commandeer a Cuban plane that landed in Key West in April was sentenced Friday to 20 years in prison.\", \"pos\": [\"A Cuban architect was sentenced to 20 years in prison Friday for using two fake grenades to hijack a passenger plane from Cuba to Florida in April.\"], \"neg\": [\"The charges of espionage and aiding the enemy can carry the death penalty.\", \"He also recruited other people to take delivery of fraudulently obtained merchandise he had ordered.\", \"According to law enforcement officials, the person arrested was a known sophisticated hacker.\", \"The indictment said he attended important meetings to plan the attacks, recruited fellow bombers and coordinated the operation.\", \"The jury also found Gonzales guilty of using excessive force by dousing Olvera-Carrera with pepper spray.\", \"If convicted of the spying charges, he could face the death penalty.\", \"U.S. forces struck dozens of targets on Monday, killing six guerrillas and arresting 21 others, the military said.\", \"Klarman was charged with 16 counts of wire fraud.\", \"Box cutters were used as a weapon by the Sept. 11, 2001, hijackers and have since been banned as carry-on items.\", \"Gonzales was found guilty of using excessive force by spraying Olvera with pepper spray.\", \"Sources say agents confiscated \\\"several\\\" documents he was carrying.\", \"Halabi's military attorney, Air Force Maj. James Key, denied the charges, which could carry a death penalty.\", \"Judge Leroy Millette Jr. can reduce the punishment to life in prison without parole when Muhammad is formally sentenced Feb. 12, but Virginia judges rarely take such action.\", \"He was tracked to Atlanta where he was arrested on Tuesday night.\", \"The bomb exploded in the desert.\", \"Box cutters were the weapons used by the 19 hijackers in the Sept. 11, 2001, attacks.\", \"According to law enforcement officials, the individual decrypted passwords on the server.\", \"The plane was estimated to be within 100 pounds of its maximum takeoff weight.\", \"Rapper C-Murder has been convicted of second-degree murder, a crime that carries an automatic life sentence, in the shooting death of a 16-year-old inside a Jefferson Parish nightclub.\", \"Mr Morse is charged with assault and Mr Darvish is charged with filing a false report.\", \"The helicopter was owned by Las Vegas-based Sundance Helicopters Inc., according to the sheriff's office.\", \"Former Phipps aides Linda Saunders and Bobby McLamb have both pleaded guilty to federal charges including extortion.\", \"Klarman was arrested by FBI agents in the Hamptons, an exclusive summer resort enclave east of New York City.\", \"He was arrested in Atlanta, Georgia, on Monday night by police acting on a tip-off.\", \"Hovan \\\"did not wake up that day\\\" intending to hurt anyone, defense attorney John Speranza said.\", \"Linda Saunders pleaded guilty in federal court to six charges, including extortion, money laundering and conspiracy.\", \"US Airways Flight 5481, which crashed Jan. 8, was judged to be within 100 pounds of its maximum takeoff weight.\", \"His partner Bijan Darvish is charged with filing a false police report.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"Jim Williams, director of the US-VISIT project, said that by the middle of November, many arriving passengers in Atlanta will be fingerprinted and photographed.\", \"pos\": [\"Jim Williams, director of the US-VISIT project, said that by the middle of November, inspectors will be fingerprinting and photographing many foreign passengers arriving in Atlanta.\"], \"neg\": [\"He arrives later this week on the first state visit by a US President.\", \"A few thousand troops, most from the division's 3rd Brigade Combat Team based at Fort Benning in Columbus, began returning last week, with flights continuing through Friday.\", \"Several people are carrying large bags.\", \"Scrimshaw, Supervisor, Best Minister and Ten Most Wanted are expected to complete the Belmont field.\", \"\\\"The mission of the CAPPS II system has been and always will be aviation security,\\\" said the administration, part of the Homeland Security Department.\", \"A spokesman said: \\\"Since November, we have co-operated fully with the police.\", \"He was arrested in Atlanta, Georgia, on Monday night by police acting on a tip-off.\", \"The author is one of several defense experts expected to testify.\", \"He was tracked to Atlanta where he was arrested on Tuesday night.\", \"Thirty-four of the men have been arrested and the others are being sought, US Attorney Daniel Bogden said yesterday.\", \"The governor is going to Jackson, where 13 people were killed.\", \"I wonder whether the police chief envisages already stages which will make it possible to show that we regard Kostunica as the legally elected representative of Serbian people and as the partner with whom the European Union will have henceforth to treat.\", \"There is urgency and therefore we decided to put this point at the order of business.\", \"Agriculture ministers from more than one hundred nations are expected to attend the three-day Ministerial Conference and Expo on Agricultural Science and Technology sponsored by the U.S. Department of Agriculture.\", \"Boeing said the final agreement is expected to be signed during the next few weeks.\", \"There is urgency and that is why we have decided to put this item on the agenda.\", \"It added it had \\\"co-operated fully\\\" with police since November.\", \"When fully operational, the facility is expected to employ up to 1,000 people.\", \"The film is the second of a trilogy, which will wrap up in November with The Matrix Revolutions.\", \"So far, authorities also have searched areas in Pennsylvania, Ohio, Indiana, and Michigan.\", \"This northern autumn US trainers will work with soldiers from four North African countries on patrolling and gathering intelligence.\", \"There is urgency and that is why we decided to put this item on the agenda.\", \"Details on pricing will be announced within a few weeks, McBride said.\", \"Wal-Mart has told its top 100 suppliers that they'll need to have radio-frequency ID systems in place for tracking pallets of goods through the supply chain by Jan. 25, 2005.\", \"There is urgency and that is why we have decided to put this issue on the agenda.\", \"\\\"If you pass this bill,\\\" Rep. John Mabry Jr., D-Waco, told colleagues, \\\"Big Brother will be watching you.\\\"\", \"Therefore, it is urgent that the personnel of the inter-service group are very quickly reinforced at the heart of the Secretary-General of the Commission, so that all the proposals of the Act of general interest can be accompanied, during their examination by the Commission on the basis of Article 299 (2) of a detailed fiche d'impact.\", \"I wonder if the Commissioner already provides the steps that will make it possible to show that we consider Koitunica as the legally elected representative of the Serbian people and as a partner with which the European Union will now address.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"The hearing occurred a day after the Pentagon for the first time singled out an officer, Dallager, for not addressing the scandal.\", \"pos\": [\"The hearing came one day after the Pentagon for the first time singled out an officer - Dallager - for failing to address the scandal.\"], \"neg\": [\"The public hearing is the second for the panel created by Defense Secretary Donald Rumsfeld under pressure from Congress.\", \"The judge scheduled another oversight hearing for late January.\", \"The committee was appointed by Defense Secretary Donald Rumsfeld under orders from Congress.\", \"The security official's backup couldn't fill in because he was on active military duty, Strutt said.\", \"She appeared in federal court Wednesday, but did not enter a plea.\", \"Russin did not comment; his lawyer did not attend the hearing and did not return phone messages.\", \"Agents confiscated several classified documents in his possession and interrogated him.\", \"It seemed like an isolated incident,\\\" said Ariel Dean of Washington D.C., who earned a degree in political science.\", \"His lawyer, a cousin, Basil Russin, did not attend the hearing and did not return phone messages.\", \"The 27-year-old rapper's attorney in the civil matter, Mark Gann, did not return calls for comment.\", \"\\\"What is happening here is that people who have opposed this action throughout are trying to find fresh reasons why it was not the right thing to do.\\\"\", \"It seemed like an isolated incident,'' said graduate Ariel Dean of Washington, D.C.\", \"AirTran officials and a Boeing official declined to comment yesterday.\", \"Mr. Mills declined to comment yesterday, saying that he never discussed personnel matters.\", \"Pingeon said an attorney for his organization, Massachusetts Correctional Legal Services, interviewed Assan Tuesday.\", \"Lawyers and others familiar with the federal investigation say it remains focused on Campbell, though prosecutors declined to discuss the probe.\", \"Schofield got Toepfer to admit on cross-examination that she ignored many of O'Donnell's suggestions and projects.\", \"Strayhorn said it was the first time in Texas history a comptroller had not certified the appropriations act.\", \"House Judiciary Committee Chairman James Sensenbrenner, R-Wis., says he is sensitive to civil liberties complaints.\", \"In a news release Thursday, Strayhorn said this was the first time a comptroller rejected a budget.\", \"A spokesman for SCO could not be reached for comment this afternoon.\", \"People who have opposed these actions throughout are now trying to find fresh reasons to say this wasn't the right thing to do.\\\"\", \"The mother also alleged in the lawsuit that she was sexually assaulted by one of the guards.\", \"Roy Moore, the suspended chief justice of the Alabama Supreme Court, stood accused but unrepentant Wednesday in the same courtroom he recently presided over.\", \"Spitz is expected to testify later for the defense.\", \"Seifert, he testified, had a gunshot wound in the back.\", \"The Senate Banking Committee is scheduled to hold a hearing on Tuesday, when Donaldson will be questioned about hedge and mutual funds.\", \"Named in the complaint were former chief executive officers Paul A. Allaire and G. Richard Thoman and former CFO Barry D. Romeril.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"The Episcopal Church ''is alienating itself from the Anglican Communion,'' said the Very Rev. Peter Karanja, provost of the All Saints Cathedral, in Nairobi.\", \"pos\": [\"In Nairobi, the provost of All Saints Cathedral, the Very Reverend Peter Karanja, said the US Episcopal Church was alienating itself from the Anglican Communion.\"], \"neg\": [\"The American Anglican Council, which represents Episcopalian conservatives, said it will seek authorization to create a separate province in North America because of last week's actions.\", \"The American Anglican Council, which represents Episcopalian conservatives, said it will seek authorization to create a separate group.\", \"The Episcopal Diocese of Central Florida became one of the first in the nation Saturday to officially reject the national denomination's policies on homosexuality.\", \"But church members and observers say they expect that the decision could be problematic for many Episcopalians.\", \"The Episcopal Diocese of Central Florida voted Saturday to repudiate a decision by the denomination's national convention to confirm a gay man as bishop.\", \"But church members and observers say they anticipate that the decision here could pose doctrinal problems for some Episcopalians who believe the Bible prohibits homosexuality.\", \"By withdrawing our resolution, I am, however, convinced that Parliament would do better to devote its voice, in view of the Nice Council, a specific resolution separate and the extremely important subjects and difficult if the Intergovernmental Conference, rather than treating them within the framework of a single resolution including also all other items on the agenda of the Council.\", \"She asked to be excused from last week's Cabinet session to prepare for a meeting with the presidents of Rwanda and Uganda.\", \"While withdrawing the resolution, however, I express the conviction that this Parliament would have made its voice heard better if, in anticipation of the Council of Nice, it had devoted a specific and separate resolution to these important and difficult topics of the Intergovernmental Conference, instead of dealing with them in a single resolution that also embraces all the other points on the Council agenda.\", \"By withdrawing our resolution, I express however the conviction that our Parliament would have better done to hear its voice while devoting, for Conseil of Nice, a resolution specific and distinct with the so important and so difficult topics to the intergovernmental Conference, rather than to treat them within the framework of one only resolution also including all the other points of about an agenda of the Council.\", \"South Africa's Nobel laureate Archbishop Desmond Tutu says he does not understand all the fuss about appointing a gay bishop, but he has urged homosexual clergy to remain celibate.\", \"By withdrawing our resolution, I am, however, convinced that Parliament would have done better by making its voice heard, in view of the Nice European Council, a separate and specific resolution on the issues which are so important and so difficult for the Intergovernmental Conference, rather than treating them in the context of a single resolution including also all the other items on the agenda of the Council.\", \"\\\"He hasn't got much choice,\\\" said the Bishop of Armidale, Peter Brain.\", \"In withdrawing our resolution, I am, however, convinced that our Parliament would have made its voice heard in dedicating, in view of the Nice Council, a resolution and traditional so important and so difficult to the Intergovernmental Conference, rather than to deal in the framework of a single resolution including also all the other points of the agenda of the Council.\", \"By withdrawing our resolution, I nevertheless expresses the conviction that our Parliament would have done better its voice heard in devoting, in view of the Nice European Council, a specific and separate resolution to the issues so important and so difficult for the Intergovernmental Conference, rather than dealt with in the context of a single resolution including also all the other items on the agenda for the Council.\", \"The meeting was scheduled to end Wednesday night, with attendees approving resolutions that express the denomination's view on issues but that are not binding on churches.\", \"Former South African Archbishop Desmond Tutu said Sunday he did not see what \\\"all the fuss\\\" was over appointing a gay bishop, but urged homosexual clergy to remain celibate.\", \"By withdrawing our resolution, I am nevertheless convinced that our Parliament would have done better to its voice heard by spending, for the Council of Nice, a resolution specific and separate with so important and so difficult issues to the Intergovernmental Conference, rather than to deal with them in the context of a single resolution including also all the other items on the agenda of the Council.\", \"We have heard that the previous Council Presidency first restructured and then scrapped the Ministry for Equality.\", \"The bishop told police he thought he had hit a dog or a cat or that someone had thrown a rock at his vehicle.\", \"By withdrawing our resolution, I am nonetheless convinced that our Parliament would have done better in devoting its voice to the Nice European Council, a specific resolution and separate issues so important and so difficult to the Intergovernmental Conference, rather than to deal with them in the context of a single resolution including also all the other items on the agenda of the Council.\", \"There is, of course, a very important point, it is the fact that we started the heading four, with its approach, but we do not know yet very well if we managed to solve the problem, in particular because the Council does not wish to take part sufficiently in the reflexion.\", \"There is, of course, a very important point is the fact that we started in category four, with its approach, but we do not yet know very well if we managed to resolve the issue, not least because the Council does not wish to participate sufficiently in the reflection.\", \"We will speak with one voice disgracieuse within OMC.\", \"There is, of course, a very important point is the fact that we have begun category four, with his approach, but we do not yet know very well if we managed to solve the problem, not least because the Council does not wish to participate in the discussion.\", \"There is, of course, a very important point is the fact that we have started category four, with his approach, but we do not yet know very well if we managed to solve the problem, particularly because the Council does not wish to participate in the discussion.\", \"I have also voted against a text which calls for the incorporation of the Charter of Fundamental Rights into the future Treaty which will be adopted in Nice.\", \"The company's chief executive retired and chief financial officer resigned.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"Counties with population declines will be Vermillion, Posey and Madison.\", \"pos\": [\"Vermillion, Posey and Madison County populations will decline.\"], \"neg\": [\"There were 293 human cases of West Nile in Indiana in 2002, including 11 deaths statewide.\", \"\\\"There is the real potential for a secondary collapse,\\\" Gov. James McGreevey said.\", \"There\\u2019s also at least three suspected cases in Illinois and 11 in Indiana.\", \"But for the elderly and those with weakened immune systems, it can be fatal.\", \"Shiites make up 20 percent of the country's population.\", \"Things deteriorate when it is a question of women from countries which have accepted this work and who have no other possibility to continue to provide their basic needs.\", \"I know that in France, the principle of demolition of the whole herd was applied and that does not seem the best means of fighting this phenomenon.\", \"But the economy hasn't shown signs of sustainable growth.\", \"That was the biggest drop since August 1993 and stemmed from falling prices for cars, trucks, men's and boys' clothes and cigarettes.\", \"The proportion of people covered by employers dropped from 62.3 percent in 2001 to 61.3 percent last year.\", \"The drop in core wholesale prices in April reflected falling prices for cars, trucks, men's and boy's clothes and cigarettes.\", \"\\\"My understanding of this is that there is a lower percentage of successful impregnations with frozen,\\\" Ezzell said.\", \"Nationally, the federal Centers for Disease Control and Prevention recorded 4,156 cases of West Nile, including 284 deaths.\", \"Ms Lafferty's lawyer, Thomas Ezzell, told a Kentucky newspaper: \\\"My understanding of this is that there is a lower percentage of successful impregnations with frozen.\", \"The situation is worsening, when it comes to women distant countries which have accepted this work by necessity and which have no alternative to continue to meet their basic needs.\", \"British Airways plans to retire its seven Concordes at the end of October.\", \"I know that in France they have had whole herd slaughter and this does not seem to be the best way forward.\", \"The fines are part of failed Republican efforts to force or entice the Democrats to return.\", \"At least 11 more cases in Indiana and three in Illinois are suspected.\", \"King County Superior Court Judge Charles Mertel will then recess the trial until Monday.\", \"The situation is worsening, when it comes to women distant countries who have accepted this work by necessity and which have no alternative to continue to meet their basic needs.\", \"The governor is going to Jackson, where 13 of the state's 16 fatalities were reported.\", \"I know that in France, the principle of slaughter of the whole herd has been applied, and that this does not seem to be the best way to combat this phenomenon.\", \"Democrats dominate the Assembly while Republicans control the Senate.\", \"I know that in France, the principle of slaughter of whole herd has been implemented and that this is not the best way to combat this phenomenon.\", \"Things are getting worse when it is a matter of women from distant countries which have accepted this work by necessity and which have no alternative to continue to provide for their vital needs.\", \"I know that in France, the principle of the slaughter of whole herds was applied and that does not seem to be the best way to combat this phenomenon.\", \"They also found shortness was associated with a family history of hearing loss.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"The last time the survey was conducted, in 1995, those numbers matched.\", \"pos\": [\"In 1995, the last survey, those numbers were equal.\"], \"neg\": [\"At 12 months, there was still a difference between the groups, but it was not considered significant.\", \"Results of the 2001 Aboriginal Peoples Survey released yesterday by Statistics Canada suggest living standards have improved but still lag for those off reserves.\", \"Approval of the official report of the previous sitting\", \"Median household income declined 1.1 percent between 2001 and 2002 to $42,409, after accounting for inflation.\", \"Some 95 million Americans -- half of all households -- invest in mutual funds.\", \"The 2001 Aboriginal Peoples Survey released Wednesday by Statistics Canada says living standards have improved but still lag for the Inuit and those who leave their often impoverished reserves.\", \"Total shipments reached 19.5 million units last year, compared with 8.9 million units in 2001.\", \"Approval of the Minutes of the previous sitting.\", \"At 12 months there was still a difference in function, although it was not a significant one.\", \"The same survey a month ago had Street leading Katz 42 percent to 34 percent, with 21 percent undecided.\", \"One month ago in the same poll, Katz was leading 46 to 40 percent.\", \"Another 152,054 are from IP services company NTT/Verio, and 129,378 from InfoSpace, the survey found.\", \"About half of all U.S. households have money in mutual funds.\", \"The number of households acquiring music fell from a high of 14.5 million in April to 12.7 million in May and 10.4 million in June, according to NPD.\", \"This is why I also think that this reconciliation can hold. However, the government, which very courageously found a solution with the question of the universities and many other problems, needs visible signs of the success as well as strong support, under penalty of seeing the peace threatened as a whole of the area.\", \"Volume came to 439.66 million shares, below 450.39 million at the same point Wednesday.\", \"State radio said it was the last test before the missile was delivered to the armed forces.\", \"Vermillion, Posey and Madison County populations will decline.\", \"The common economic activities create peaceful bonds between the people.\", \"There are 103 Democrats in the Assembly and 47 Republicans.\", \"A budget line, programmes and of many years.\", \"A budget line finances clearance programmes and of prevention since years.\", \"This year the Audubon Society once again hosts its annual Christmas Bird Count.\", \"Approval of the Minutes of the previous sitting\", \"The report aims to identify such improvements.\", \"Finally I welcome the idea of us to give an annual assessment of the implementation of the programme.\", \"It added it had \\\"co-operated fully\\\" with police since November.\", \"The new analysis found that 32 of the 16,608 participants developed ovarian cancer during about 5 years of follow-up.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n{\"query\": \"Higher courts have ruled that the tablets broke the constitutional separation of church and state.\", \"pos\": [\"The federal courts have ruled that the monument violates the constitutional ban against state-established religion.\"], \"neg\": [\"A divided Supreme Court ruled Monday that Congress can force the nation's public libraries to equip computers with anti-pornography filters.\", \"In that case, the court held that the city of Cincinnati had violated the First Amendment in banning, in the interest of aesthetics, only the advertising pamphlets.\", \"In that case, the court held that Cincinnati had violated the First Amendment in banning only the advertising pamphlets in the interest of aesthetics.\", \"The right to a government to remove arbitrarily its constitution is defining characteristic of a tyranny.\", \"The right for a government to remove arbitrarily its constitution is defining characteristic of a tyranny.\", \"The right to a government to remove arbitrarily its Constitution is the defining characteristic of a tyranny.\", \"The right for a government to dismiss its Constitution arbitrarily is the defining characteristic of a tyranny.\", \"But church members and observers say they anticipate that the decision here could pose doctrinal problems for some Episcopalians who believe the Bible prohibits homosexuality.\", \"Several states and the federal government later passed similar or more strict bans.\", \"The right of a government arbitrarily to set aside its own constitution is the defining characteristic of a tyranny.\", \"\\\"The petitioners are entitled to respect for their private lives.\\\"\", \"The right for a government to draw aside its constitution arbitrarily is the definition characteristic of a tyranny.\", \"The right for a government to dismiss arbitrarily its constitution is the definition of a characteristic tyranny.\", \"Lay had argued that handing over the documents would be a violation of his Fifth Amendment rights against self-incrimination.\", \"The right to a government of arbitrarily its Constitution is the definition of a dictatorship.\", \"The meeting was scheduled to end Wednesday night, with attendees approving resolutions that express the denomination's view on issues but that are not binding on churches.\", \"The 6th U.S. Circuit Court of Appeals on Wednesday ruled that an Ohio law banning a controversial late-term abortion method passes constitutional muster and the state can enforce it.\", \"The Supreme Court said Monday the government can require public libraries to equip computers with anti-pornography filters, rejecting librarians' complaints that the law amounts to censorship.\", \"But the inadequate performance of students in various subgroups tagged the state as deficient under the federal No Child Left Behind act.\", \"Florida's Supreme Court has twice refused to hear the case.\", \"You do not even want to incorporate it into the Treaty, which shows that this text was to be put to one side.\", \"You do not want to even integrate it in the Treaty, which proves that this text was to be put on side.\", \"Defense lawyers cannot appeal the ruling until after trial, in the appellate courts.\", \"The joint debate is closed.\", \"Democrats dominate the Assembly while Republicans control the Senate.\", \"On Wednesday, attendees will vote on resolutions that express the Southern Baptist view on issues, but are not binding on individual churches.\", \"The U.S. Capitol was evacuated yesterday after authorities detected a possibly hazardous material in the basement of the Senate wing, Capitol Police said.\", \"Things are not clear on this point, and it is not impossible that can also remain in force more restrictive provisions. This is particularly on this point that we have tabled amendments.\"], \"prompt\": \"Retrieve semantically similar text.\", \"type\": \"symmetric_sts\"}\n" }, { "path": "examples/finetune/reranker/README.md", "content": "# Finetune\n\nIn this example, we show how to finetune the reranker with your data.\n\n- [1. Installation](#1-Installation)\n- [2. Data format](#2-Data-format)\n - [Hard Negatives](#Hard-Negatives)\n - [Teacher Scores](#Teacher-Scores)\n- [3. Train](#3-Train)\n - [(1) standard model](#1-standard-model)\n - [(2) bge-reranker-v2-gemma](#2-bge-reranker-v2-gemma)\n - [(3) bge-reranker-v2-layerwise-minicpm](#3-bge-reranker-v2-layerwise-minicpm)\n\n## 1. Installation\n\n- **with pip**\n\n```shell\npip install -U FlagEmbedding[finetune]\n```\n\n- **from source**\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\npip install .[finetune]\n```\n\nFor development, install as editable:\n\n```shell\npip install -e .[finetune]\n```\n\n## 2. Data format\n\nTrain data should be a json file, where each line is a dict like this:\n\n```shell\n{\"query\": str, \"pos\": List[str], \"neg\":List[str], \"pos_scores\": List[int], \"neg_scores\": List[int], \"prompt\": str}\n```\n\n`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts. `pos_scores` is a list of scores corresponding to the `query` and `pos`, `neg_scores` is a list of scores corresponding to the `query` and `neg`, if you don't use knowledge distillation, it can be ignored. `prompt` is the prompt used for the input, input has the following format: `query [sep] passage [sep] prompt`. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.\n\nSee [example_data](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder/example_data) for more detailed files.\n\n### Hard Negatives\n\nHard negatives is a widely used method to improve the quality of sentence embedding. You can mine hard negatives following this command:\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding/scripts\n```\n\n```shell\npython hn_mine.py \\\n--model_name_or_path BAAI/bge-base-en-v1.5 \\\n--input_file toy_finetune_data.jsonl \\\n--output_file toy_finetune_data_minedHN.jsonl \\\n--range_for_sampling 2-200 \\\n--negative_number 15 \\\n--use_gpu_for_searching \n```\n\n- **`input_file`**: json data for finetuning. This script will retrieve top-k documents for each query, and random sample negatives from the top-k documents (not including the positive documents).\n- **`output_file`**: path to save JSON data with mined hard negatives for finetuning\n- **`negative_number`**: the number of sampled negatives\n- **`range_for_sampling`**: where to sample negative. For example, `2-100` means sampling `negative_number` negatives from top2-top200 documents. **You can set larger value to reduce the difficulty of negatives (e.g., set it `60-300` to sample negatives from top60-300 passages)**\n- **`candidate_pool`**: The pool to retrieval. The default value is None, and this script will retrieve from the combination of all `neg` in `input_file`. The format of this file is the same as [pretrain data](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain#2-data-format). If input a candidate_pool, this script will retrieve negatives from this file.\n- **`use_gpu_for_searching`**: whether to use faiss-gpu to retrieve negatives.\n\n### Teacher Scores\n\nTeacher scores can be used for model distillation. You can obtain the scores using the following command:\n\n```shell\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding/scripts\n```\n\n```shell\npython add_reranker_score.py \\\n--input_file toy_finetune_data_minedHN.jsonl \\\n--output_file toy_finetune_data_score.jsonl \\\n--reranker_name_or_path BAAI/bge-reranker-v2-m3 \\\n--devices cuda:0 cuda:1 \\\n--cache_dir ./cache/model \\\n--reranker_query_max_length 512 \\\n--reranker_max_length 1024\n```\n\n- **`input_file`**: path to save JSON data with mined hard negatives for finetuning\n- **`output_file`**: path to save JSON data with scores for finetuning\n- **`use_fp16`**: Whether to use fp16 for inference. Default: True\n- **`devices`**: Devices to use for inference. Default: None, multiple values allowed\n- **`trust_remote_code`**: Trust remote code. Default: False\n- **`reranker_name_or_path`**: The reranker name or path. Default: None\n- **`reranker_model_class`**: The reranker model class. Available classes: ['auto', 'encoder-only-base', 'decoder-only-base', 'decoder-only-layerwise', 'decoder-only-lightweight']. Default: auto\n- **`reranker_peft_path`**: The reranker peft path. Default: None\n- **`use_bf16`**: Whether to use bf16 for inference. Default: False\n- **`query_instruction_for_rerank`**: Instruction for query. Default: None\n- **`query_instruction_format_for_rerank`**: Format for query instruction. Default: {{}{}}\n- **`passage_instruction_for_rerank`**: Instruction for passage. Default: None\n- **`passage_instruction_format_for_rerank`**: Format for passage instruction. Default: {{}{}}\n- **`cache_dir`**: Cache directory for models. Default: None\n- **`reranker_batch_size`**: Batch size for inference. Default: 3000\n- **`reranker_query_max_length`**: Max length for reranking queries. Default: None\n- **`reranker_max_length`**: Max length for reranking. Default: 512\n- **`normalize`**: Whether to normalize the reranking scores. Default: False\n- **`prompt`**: The prompt for the reranker. Default: None\n- **`cutoff_layers`**: The output layers of layerwise/lightweight reranker. Default: None\n- **`compress_ratio`**: The compress ratio of lightweight reranker. Default: 1\n- **`compress_layers`**: The compress layers of lightweight reranker. Default: None, multiple values allowed\n\n## 3. Train\n\nDetailed examples of various fine-tuning can be found in the bash files located in the corresponding folders. Here, we simply provide the training methods for the `standard model`, `bge-reranker-v2-gemma` and `bge-reranker-v2-layerwise-minicpm`.\n\nHere are some import arguments:\n\n- **`model_name_or_path`**: The model checkpoint for initialization.\n- **`config_name`**: Pretrained config name or path if not the same as model_name. Default: None\n- **`tokenizer_name`**: Pretrained tokenizer name or path if not the same as model_name. Default: None\n- **`cache_dir`**: Where do you want to store the pre-trained models downloaded from s3. Default: None\n- **`trust_remote_code`**: Trust remote code. Default: False\n- **`model_type`**: Type of finetune, ['encoder', 'decoder']. Default: 'encoder'\n- **`token`**: The token to use when accessing the model. Default: Value from environment variable HF_TOKEN or None if not set\n- **`train_data`**: One or more paths to training data. `query: str`, `pos: List[str]`, `neg: List[str]` are required in the training data. Default: None\n- **`cache_path`**: Where do you want to store the cached data. Default: None\n- **`train_group_size`**: Default: 8\n- **`query_max_len`**: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated. Default: 32\n- **`passage_max_len`**: The maximum total input sequence length after tokenization for passage. Sequences longer than this will be truncated. Default: 128\n- **`max_len`**: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated. Default: 512\n- **`pad_to_multiple_of`**: If set, will pad the sequence to be a multiple of the provided value. Default: None\n- **`max_example_num_per_dataset`**: The max number of examples for each dataset. Default: 100000000\n- **`query_instruction_for_rerank`**: Instruction for query. Default: None\n- **`query_instruction_format`**: Format for query instruction. Default: \"{}{}\"\n- **`knowledge_distillation`**: Use knowledge distillation when `pos_scores: List[float]` and `neg_scores: List[float]` are in features of training data. Default: False\n- **`passage_instruction_for_rerank`**: Instruction for passage. Default: None\n- **`passage_instruction_format`**: Format for passage instruction. Default: \"{}{}\"\n- **`shuffle_ratio`**: The ratio of shuffling the text. Default: 0.0\n- **`sep_token`**: The separator token for LLM reranker to discriminate between query and passage. Default: '\\n'\n\n### (1) standard model\n\n```shell\ntorchrun --nproc_per_node 2 \\\n\t-m FlagEmbedding.finetune.reranker.encoder_only.base \\\n\t--model_name_or_path BAAI/bge-reranker-v2-m3 \\\n --cache_dir ./cache/model \\\n --train_data ./example_data/normal/examples.jsonl \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation False \\\n\t--output_dir ./test_encoder_only_base_bge-reranker-base \\\n --overwrite_output_dir \\\n --learning_rate 6e-5 \\\n --fp16 \\\n --num_train_epochs 2 \\\n --per_device_train_batch_size 2 \\\n --gradient_accumulation_steps 1 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000\n```\n\n### (2) bge-reranker-v2-gemma\n\n```shell\ntorchrun --nproc_per_node 2 \\\n\t-m FlagEmbedding.finetune.reranker.decoder_only.base \\\n\t--model_name_or_path BAAI/bge-reranker-v2-gemma \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --use_flash_attn True \\\n --target_modules q_proj k_proj v_proj o_proj \\\n --save_merged_lora_model True \\\n --model_type decoder \\\n --cache_dir ./cache/model \\\n --train_data ./example_data/prompt_based/examples.jsonl \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation False \\\n --query_instruction_for_rerank 'A: ' \\\n --query_instruction_format '{}{}' \\\n --passage_instruction_for_rerank 'B: ' \\\n --passage_instruction_format '{}{}' \\\n --output_dir ./test_decoder_only_base_bge-reranker-v2-minicpm-layerwise \\\n --overwrite_output_dir \\\n --learning_rate 2e-4 \\\n --bf16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --gradient_accumulation_steps 1 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000\n```\n\nHere are some new arguments:\n\n- **`use_lora`**: If passed, will use LORA (low-rank parameter-efficient training) to train the model.\n- **`lora_rank`**: The rank of lora.\n- **`lora_alpha`**: The alpha parameter of lora.\n- **`lora_dropout`**: The dropout rate of lora modules.\n- **`target_modules`**: The target modules to apply LORA.\n- **`modules_to_save`**: List of modules that should be saved in the final checkpoint.\n- **`use_flash_attn`**: If passed, will use flash attention to train the model.\n- **`from_peft`**: (metadata not provided)\n- **`raw_peft`**: (metadata not provided)\n- **`save_merged_lora_model`**: If passed, will merge the lora modules and save the entire model.\n\n### (3) bge-reranker-v2-layerwise-minicpm\n\n```shell\ntorchrun --nproc_per_node 2 \\\n\t-m FlagEmbedding.finetune.reranker.decoder_only.layerwise \\\n --model_name_or_path BAAI/bge-reranker-v2-minicpm-layerwise \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --use_flash_attn True \\\n --target_modules q_proj k_proj v_proj o_proj \\\n --save_merged_lora_model True \\\n --model_type decoder \\\n --model_type from_finetuned_model \\\n --start_layer 8 \\\n --head_multi True \\\n --head_type simple \\\n --trust_remote_code True \\\n --cache_dir ./cache/model \\\n --train_data ./example_data/prompt_based/examples.jsonl \\\n --cache_path ./cache/data \\\n --train_group_size 8 \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation False \\\n --query_instruction_for_rerank 'A: ' \\\n --query_instruction_format '{}{}' \\\n --passage_instruction_for_rerank 'B: ' \\\n --passage_instruction_format '{}{}' \\\n\t--output_dir ./test_decoder_only_base_bge-reranker-v2-minicpm-layerwise \\\n --overwrite_output_dir \\\n --learning_rate 2e-4 \\\n --bf16 \\\n --num_train_epochs 1 \\\n --per_device_train_batch_size 2 \\\n --gradient_accumulation_steps 1 \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000\n```\n\nHere are some new arguments:\n\n- **`use_lora`**: If passed, will use LORA (low-rank parameter-efficient training) to train the model.\n- **`lora_rank`**: The rank of lora.\n- **`lora_alpha`**: The alpha parameter of lora.\n- **`lora_dropout`**: The dropout rate of lora modules.\n- **`target_modules`**: The target modules to apply LORA.\n- **`modules_to_save`**: List of modules that should be saved in the final checkpoint.\n- **`use_flash_attn`**: If passed, will use flash attention to train the model.\n- **`save_merged_lora_model`**: If passed, will merge the lora modules and save the entire model.\n- **`model_type`**: Model type context, which should be one of ['from_raw_model', 'from_finetuned_model'].\n- **`start_layer`**: Specifies which layer to start to compute score.\n- **`head_multi`**: Indicates whether to use one or multiple classifiers.\n- **`head_type`**: The type of the classifier.\n" }, { "path": "examples/finetune/reranker/decoder_only/base.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/prompt_based/examples.jsonl \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=1\nper_device_train_batch_size=2\ngradient_accumulation_steps=1\ntrain_group_size=8\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-reranker-v2-gemma \\\n --cache_dir $HF_HUB_CACHE \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --use_flash_attn True \\\n --target_modules q_proj k_proj v_proj o_proj \\\n --save_merged_lora_model True \\\n --model_type decoder \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size $train_group_size \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation True \\\n --query_instruction_for_rerank 'A: ' \\\n --query_instruction_format '{}{}' \\\n --passage_instruction_for_rerank 'B: ' \\\n --passage_instruction_format '{}{}' \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_decoder_only_base_bge-reranker-v2-gemma \\\n --overwrite_output_dir \\\n --learning_rate 2e-4 \\\n --bf16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --gradient_accumulation_steps $gradient_accumulation_steps \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.reranker.decoder_only.base \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd" }, { "path": "examples/finetune/reranker/decoder_only/layerwise.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/prompt_based/examples.jsonl \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=1\nper_device_train_batch_size=2\ngradient_accumulation_steps=1\ntrain_group_size=8\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-reranker-v2-minicpm-layerwise \\\n --cache_dir $HF_HUB_CACHE \\\n --use_lora True \\\n --lora_rank 32 \\\n --lora_alpha 64 \\\n --use_flash_attn True \\\n --target_modules q_proj k_proj v_proj o_proj \\\n --save_merged_lora_model True \\\n --model_type decoder \\\n --model_type from_finetuned_model \\\n --start_layer 8 \\\n --head_multi True \\\n --head_type simple \\\n --trust_remote_code True \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size $train_group_size \\\n --query_max_len 512 \\\n --passage_max_len 512 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation True \\\n --query_instruction_for_rerank 'A: ' \\\n --query_instruction_format '{}{}' \\\n --passage_instruction_for_rerank 'B: ' \\\n --passage_instruction_format '{}{}' \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_decoder_only_base_bge-reranker-v2-minicpm-layerwise \\\n --overwrite_output_dir \\\n --learning_rate 2e-4 \\\n --bf16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --gradient_accumulation_steps $gradient_accumulation_steps \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.reranker.decoder_only.layerwise \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd\n\n" }, { "path": "examples/finetune/reranker/encoder_only/base.sh", "content": "export WANDB_MODE=disabled\n\ntrain_data=\"\\\n ../example_data/normal/examples.jsonl \"\n\n# set large epochs and small batch size for testing\nnum_train_epochs=4\nper_device_train_batch_size=2\ngradient_accumulation_steps=1\ntrain_group_size=8\n\n# set num_gpus to 2 for testing\nnum_gpus=2\n\nif [ -z \"$HF_HUB_CACHE\" ]; then\n export HF_HUB_CACHE=\"$HOME/.cache/huggingface/hub\"\nfi\n\nmodel_args=\"\\\n --model_name_or_path BAAI/bge-reranker-base \\\n --cache_dir $HF_HUB_CACHE \\\n\"\n\ndata_args=\"\\\n --train_data $train_data \\\n --cache_path ~/.cache \\\n --train_group_size $train_group_size \\\n --query_max_len 256 \\\n --passage_max_len 256 \\\n --pad_to_multiple_of 8 \\\n --knowledge_distillation True \\\n\"\n\ntraining_args=\"\\\n --output_dir ./test_encoder_only_base_bge-reranker-base \\\n --overwrite_output_dir \\\n --learning_rate 6e-5 \\\n --fp16 \\\n --num_train_epochs $num_train_epochs \\\n --per_device_train_batch_size $per_device_train_batch_size \\\n --gradient_accumulation_steps $gradient_accumulation_steps \\\n --dataloader_drop_last True \\\n --warmup_ratio 0.1 \\\n --gradient_checkpointing \\\n --weight_decay 0.01 \\\n --deepspeed ../../ds_stage0.json \\\n --logging_steps 1 \\\n --save_steps 1000 \\\n\"\n\ncmd=\"torchrun --nproc_per_node $num_gpus \\\n -m FlagEmbedding.finetune.reranker.encoder_only.base \\\n $model_args \\\n $data_args \\\n $training_args \\\n\"\n\necho $cmd\neval $cmd" }, { "path": "examples/finetune/reranker/example_data/normal/examples.jsonl", "content": "{\"query\": \")what was the immediate impact of the success of the manhattan project?\", \"pos\": [\"Introduction The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.\"], \"neg\": [\"The Launch of Sputnik, 1957 The Soviets responded with yet another launch, and the space race continued. The success of Sputnik had a major impact on the Cold War and the United States. Fear that they had fallen behind led U.S. policymakers to accelerate space and weapons programs.he Soviets responded with yet another launch, and the space race continued. The success of Sputnik had a major impact on the Cold War and the United States. Fear that they had fallen behind led U.S. policymakers to accelerate space and weapons programs.\", \"Important result of the Marshall plan? Effected by the United States after World War II, the Marshall Plan produced the quite important result of building up nations shattered during the war. It did so quite quickly, as well.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first nuclear weapons during World War II.he United States had already spent more than $1 billion ($13,600,000,000 today), while in 1943, the United Kingdom had spent about \\u00a30.5 million. Chadwick thus pressed for British involvement in the Manhattan Project to the fullest extent and abandon any hopes of a British project during the war.\", \"- Los Alamos is where the first known Manhattan Project bombs were built and tested. On July 16, 1945, in a remote desert location near Alamogordo, New Mexico, the first atomic bomb was successfully detonated\\u2014the Trinity Test\\u2014creating an enormous mushroom cloud some 40,000 feet high and ushering in the Atomic Age.\", \"Holocauste Literature Meanwhile, President Truman was told of the successful test of the Manhattan Project (atomic bomb) in Alamogordo, New Mexico on Jul 16, 1945. Diary of President Truman of Jul 18, 1945 shows Discussed Manhattan (it is a success).t the end of World War II, few questioned Truman's decision to drop the atomic bombs on Hiroshima and Nagasaki. Most Americans accepted the obvious reasoning: the atomic bomb \\u2026 ings brought the war to a more timely end.\", \"- The Soviet project to develop an atomic bomb (Russian: \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u0441\\u043e\\u0432\\u0435\\u0442\\u0441\\u043a\\u043e\\u0439 \\u0430\\u0442\\u043e\\u043c\\u043d\\u043e\\u0439 \\u0431\\u043e\\u043c\\u0431\\u044b) was a top secret research and development program begun during World War II, in the wake of the Soviet Union 's discovery of the American, British, and Canadian nuclear project.hrough sources in the Manhattan Project, notably Klaus Fuchs, the Soviet intelligence obtained important information on the progress of the United States atomic bomb effort. Intelligence reports were shown to the head of the Soviet atomic project and had a significant impact on the direction of Soviet research.\", \"Monroe Doctrine The immediate impact of the Monroe Doctrine was mixed. It was successful to the extent that the continental powers did not attempt to revive the Spanish empire, but this was on account of the strength of the British Navy, not American military might, which was relatively limited.\", \"What impacts did benjamin harrison have on america while in office? Sorry, something has gone wrong. Best Answer: The greatest positive impact during his administration was the passing of the Sherman Anti-Trust Act of 1890, the first federal act to regulate trusts.\", \"The Manhattan Project Manhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first atomic bombs during World War II.It was led by the United States with the support of the United Kingdom and Canada.he Manhattan Project began modestly in 1939, but grew to employ more than 130,000 people and cost nearly US$2 billion (about $26 billion in 2015 dollars). Over 90% of the cost was for building factories and producing the fissionable materials, with less than 10% for development and production of the weapons.\", \"- Roosevelt agreed and placed General Leslie Groves and physicist J. Robert Oppenheimer in charge of the Manhattan Project two years later. The name Manhattan Project was the code word for the development of the atomic bomb. On July 16, 1945, the first atomic bomb was tested at the Trinity Site in New Mexico.The weapon was later used against the Japanese to end World War II.eginning in June 1942 during World War II, the United States' Manhattan Project brought together scientists and military experts to create the world's first atomic bomb.\", \"51f. The Manhattan Project In late 1941, the American effort to design and build an atomic bomb received its code name \\u2014 the Manhattan Project. At first the research was based at only a few universities \\u2014 Columbia University, the University of Chicago and the University of California at Berkeley.\", \"The Manhattan Project Manhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.anhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.\", \"- The Manhattan Project was an effort during World War II in the United States to develop the first nuclear weapon.\", \"Atomic Glossary The Manhattan Project was the code name for America's atomic bomb development efforts during World War II. Its name originated from the fact that it was part of the U. S. Army Corps of Engineers and organized under the Manhattan Engineer District (MED) in New York City.\", \"Introduction While communication led to the overall scientific triumph of the Manhattan project, an intentional lack of communication between military and government superiors and the Los Alamos laboratory led to one of the most devastating moral disasters of the twentieth century.\", \"- At the end of the unit the children will be quizzed on the different senses which will have to be matched to their source. The children will have to name the five senses and which part of the body is linked with that particular sense. Also, the story incorporating the five senses will also be used as a way to assess what the children comprehended ...\", \"- This project used hundreds of scientist and thousands of people to develop the first Atomic Bomb. The Manhattan Project was implemented by President Franklin Roosevelt in no small part due to two letters sent to him by Albert Einstein.\", \"The Cold War Museum In June of 1942 the War Department\\u2019s Army Corps of Engineers took charge of the effort to develop an atomic bomb. The subsequent top-secret project (code named Manhattan) cultivated a complex, but cooperative relationship between science, industry, the government, and the army.\", \"Moments in U.S. Diplomatic History Only later did we learn through Gorge that we had a ringside view of the Doolittle raid [the daring air attack on Tokyo on April 18, 1942, which was designed to show that Japan was vulnerable and to boost U.S. morale after Pearl Harbor]\\u2026.\", \"Car Crashes Kill 40,000 in U.S. Every Year Car Crashes Kill 40,000 in U.S. Every Year. Imagine a plane full of people crashing, killing everyone on board, every single day. That\\u2019s how many people die on America\\u2019s roads daily, says the Insurance Institute for Highway Safety. \\u201cMotor vehicle crashes in the United States result in more than 40,000 deaths per year,\\u201d says the Institute in the journal Injury Prevention. \\u201cThat is, on each of the 6,209 consecutive days included in this study, an equivalent of a plane load or more of people died on the roads.\\u201d. But not all days are alike.\", \"arrest Definition of arrest. 1 1 : the taking or detaining in custody by authority of law The investigation led to his arrest. 2 2a : the act of stoppingb : the condition of being stopped or inactive \\u2014 compare cardiac arrest.\", \"The Atomic Bomb The Desert Test By July, 1945 the Manhattan Project work was almost completed; they had developed a working nuclear bomb. The only obstacle that stood in their way is the actual testing of the bomb. The test, code name Trinity occurred on July 16, 1945 in the New Mexico desert town of Alamogordo.\", \"Nuclear explosion in Hiroshima: details and facts Right after the bomb blast in Hiroshima, President Truman officially announced the yield of the bomb as 18 kilotons, and the media rounded it to 20. Later on, after a survey, conducted by the Manhattan Project, it was calculated, that the yield of the Little Boy equaled to 12 kilotons.\", \"- The Manhattan Project was a US government project used to develop a nuclear bomb. The undertaking lasted form 1942 to 1946, when...\"], \"pos_scores\": [96.6875], \"neg_scores\": [90.5625, 89.0, 92.9375, 93.3125, 93.5, 91.75, 92.375, 86.75, 93.4375, 93.1875, 92.5, 92.375, 92.375, 93.3125, 92.3125, 94.0625, 88.0, 92.375, 91.9375, 89.3125, 83.1875, 87.5625, 92.0, 90.4375, 92.625]}\n{\"query\": \"_________ justice is designed to repair the harm to victim, the community and the offender caused by the offender criminal act. question 19 options:\", \"pos\": [\"Restorative justice The approach is based on a theory of justice that considers crime and wrongdoing to be an offense against an individual or community, rather than the State. Restorative justice that fosters dialogue between victim and offender has shown the highest rates of victim satisfaction and offender accountability.\"], \"neg\": [\"What is Restorative Justice? Restorative Justice is a community-based approach to dealing with crime, the effects of crime, and the prevention of crime. Most people who move through the current system of criminal justice do not find it a healing or satisfying experience. Victims often feel re-victimized and their need for justice unmet.\", \"California\\u00e2s Criminal Justice System: A Primer The criminal justice system is based on criminal sentencing law, the body of laws that define crimes and specify the punishments for such crimes. The majority of sentencing law is set at the state level.\", \"- will serve the community and enhance public trust and confidence in the administration of justice through: 1 The impartial and timely resolution of disputes, 2 Ensuring compliance with the law and court orders, and. 3 Fostering a vital court-community relationship that promotes equal access to the courts.\", \"- CRIME VICTIM'S GUIDE. A Crime Victim's Guide to the Justice System in Arkansas was written to assist victims of crime in better understanding the Arkansas criminal justice system so they are more able to exercise their rights.\", \"- CORRECTIVE JUSTICE. The doer is directly liable to the sufferer, so that reparation of the. loss occurs through disgorgement of the gain. By directly linking the parties, corrective justice categorically. differs from distributive justice, the other form of justice that Aris-.\", \"3 \\u00e2 Restorative Justice: Justice That Promotes Healing Each of these types of communities\\u2014the geographic community of the victim, offender, or crime; the community of care; and civil society\\u2014may be injured by crime in different ways and degrees, but all will be affected in common ways as well: The sense of safety and confidence of their members is threatened, order within the community is threatened, and (depending on the kind of crime) common values of the community are challenged and perhaps eroded.\", \"- Mission is to protect the public from criminal offenders through a system of incarceration and supervision which securely segregates offenders from society, assures offenders of their constitutional rights and maintains programs to enhance the success of offenders' reentry into society.\", \"How Does the Criminal Justice System Work? Throughout each stage of the process, constitutional protections exist to ensure that the rights of the accused and convicted are respected. These protections balance the need of the criminal justice system to investigate and prosecute criminals with the fundamental rights of the accused (who are presumed innocent).\", \"Restorative Justice \\u2022 Restorative Justice can be utilized in crimes of severe violence or non-violent crimes as well as with juvenile offenders. \\u2022 Restorative Justice is an approach to crime which puts the victim or the victim\\u2019s family first and fully acknowledges the harm caused by the offender.\", \"- Criminal Justice. An effective criminal justice system is a key aspect of the rule of law, as it constitutes the natural mechanism to redress grievances and bring action against individuals for offenses against...\", \"- Punishment should be swift and certain. The purpose of the criminal justice system is to prevent crime through deterrence. According to this line of thinking, a potential criminal will decide against committing a crime because the punishment would be too costly.\", \"- As you have learned, distributive justice deals with the fairness of the distribution of benefits or burdens among two or more people or groups in society. Benefits may be such things as pay for work or the right to speak or to vote.xamining Issues of Justice We think of the essence of justice as fairness, and the essence of fairness as treating people equally. However, issues of justice can arise even if everyone is subjected to the same rules, and even if everyone who breaks the rules receives the same punishment.\", \"PUBLICATIONS Consequently, the equations of actuarial justice tend to be more simplified and intended for application to broad groupings of offenders. The more groups can be narrowed, the fairer and more effective will be the criminal justice policies toward offenders.\", \"- Social justice is justice in terms of the distribution of wealth, opportunities, and privileges within a society. Classically, justice (especially corrective justice or distributive justice) ensured that individuals both fulfilled their societal roles and received what was their due from society.\", \"\\\"\\\"\\\"How Can Law Enforcement Professionals Use The Social Justice Principles Of Equality Solidarity And Human Rights To Build A More Just Society\\\"\\\" Essays and Research Papers\\\" Justice in Law Enforcement The true concept of justice is a concept involving moral, fair, and... impartial treatment of all individuals. Justice is a concept that has many different translations and a concept that can be changed on a case-by-case basis.\", \"Criminal justice The criminal justice system consists of three main parts: (1) Legislative (create laws); (2) adjudication (courts); and (3) corrections (jails, prisons, probation and parole).\", \"- Distributive justice concerns the nature of a socially just allocation of goods in a society. A society in which incidental inequalities in outcome do not arise would be considered a society guided by the principles of distributive justice.\", \"Victims of Crime This environment began to change in the 1970s with the establishment of victim compensation funds. Not until the 1980s, however, did a national movement for victims' rights spark wholesale changes in the criminal justice system.\", \"- Economic justice is a component of social justice. It's a set of moral principles for building economic institutions, the ultimate goal of which is to create an opportunity for each person to create a sufficient material foundation upon which to have a dignified, productive, and creative life beyond economics.\", \"- Criminal law serves several purposes and benefits society in the following ways: 1 Maintaining order. 2 Resolving disputes. 3 Protecting individuals and property. 4 Providing for smooth functioning of society. 5 Safeguarding civil liberties.\", \"Victims bill of rights would compel spouses to testify 'Victims deserve fair and equal treatment by the justice system and they need to be assured that the system itself doesn't re-victimize' \\u2014 Sheldon Kennedy, former NHL player. Crime victims would have more say as their cases wind their way through the justice system under a newly proposed victims bill of rights.The long-awaited legislation, part of the government's law-and-order agenda, aims to fix what Prime Minister Stephen Harper said Thursday was a broken part of the justice system.Victims deserve fair and equal treatment by the justice system and they need to be assured that the system itself doesn't re-victimize' \\u2014 Sheldon Kennedy, former NHL player. Crime victims would have more say as their cases wind their way through the justice system under a newly proposed victims bill of rights.\", \"Social justice Social justice is a concept of fair and just relations between the individual and society. This is measured by the explicit and tacit terms for the distribution of wealth, opportunities for personal activity and social privileges. In Western as well as in older Asian cultures, the concept of social justice has often referred to the process of ensuring that individuals fulfill their societal roles and receive what was their due from society. In the current global grassroots movements for social j\", \"Procedural justice The idea of the outcomes model of procedural justice is that the fairness of process depends on the procedure producing correct outcomes. For example, if the procedure is a criminal trial, then the correct outcome would be conviction of the guilty and exonerating the innocent.\", \"Retribution Retribution is perhaps the most intuitive - and the most questionable - aim of punishment in the criminal law. Quite contrary to the idea of rehabilitation and distinct from the utilitarian purposes of restraint and deterrence, the purpose of retribution is actively to injure criminal offenders, ideally in proportion with their injuries to society, and so expiate them of guilt.\", \"Retributive Justice The idea of retributive justice has played a dominant role in theorizing about punishment over the past few decades, but many features of it\\u2014especially the notions of desert and proportionality, the normative status of suffering, and the ultimate justification for retribution\\u2014remain contested and problematic. 1 1.\"], \"pos_scores\": [95.3125], \"neg_scores\": [95.0, 90.25, 88.1875, 90.25, 92.6875, 94.5625, 88.0, 90.3125, 95.6875, 90.6875, 90.0, 90.0625, 89.875, 90.9375, 89.75, 90.6875, 90.3125, 90.0, 89.875, 89.125, 89.6875, 89.75, 89.625, 90.25, 91.0625]}\n{\"query\": \"what color is amber urine\", \"pos\": [\"- Color\\u2014urine can be a variety of colors, most often shades of yellow, from very pale or colorless to very dark or amber. Unusual or abnormal urine colors can be the result of a disease process, several medications (e.g., multivitamins can turn urine bright yellow), or the result of eating certain foods.\"], \"neg\": [\"- ISO 9001:2008 certified manufacturer of standard & metric washers. Types include shoulder washers, finishing washers, cup washers, retaining washers, split flat washers & standard & special flat washers.\", \"Urine color Orange urine may result from: Medical conditions. In some cases, orange urine can indicate a problem with your liver or bile duct, especially if you also have light-colored stools. Orange urine may also be caused by dehydration, which can concentrate your urine and make it much deeper in color.\", \"- Normal urine color varies, depending on how much water you drink. Fluids dilute the yellow pigments in urine, so the more you drink, the clearer your urine looks. When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white. When to see a doctor\", \"How Long After Intercourse Can I Take a Pregnancy Test? A pregnancy test will detect this hormone about one week after fertilization. Most women tend to wait to take a pregnancy test until after they have missed one menstrual period. If you take a pregnancy test one week after you were supposed to get your period, the result will be about 95% accurate.If you take it too soon after fertilization, it may show up negative when in fact you really are pregnant. Pregnancy test are meant to be considered screening tests.f you take a pregnancy test one week after you were supposed to get your period, the result will be about 95% accurate. If you take it too soon after fertilization, it may show up negative when in fact you really are pregnant.\", \"Urine 1 Reddish or brown urine may be caused by porphyria (not to be confused with the harmless, temporary pink or reddish tint caused by beeturia). 2 Blue urine can be caused by the ingestion of methylene blue (e.g., in medications) or foods or beverages with blue dyes. Blue urine stains can be caused by blue diaper syndrome.\", \"What does bright yellow urine indicate? When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But sometimes urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white.\", \"- 4. Imitation Urine. In general, the physical appearance of urine should be yellow to yellow-tan in color. Unfortunately, there are liquid substances available such as apple juice, cranberry juice, etc., that have the same color as urine. Noting the temperature of the urine is important. 5. Length of time in the bathroom\", \"- Darkened urine is urine that is dark yellow, brown, dark red, or red, and it may range from slightly dark to considerably dark. A change in urine color may be temporary, or it may be persistent.\", \"- Amber Color The color of amber fossil varies from yellow to dark brown to almost black. Very rarely this gem may be found in green and blue-gray colors and hence green amber can be very rare. In addition, it is dyed in many colors like green, blue, pink etc.mber Color The color of amber fossil varies from yellow to dark brown to almost black. Very rarely this gem may be found in green and blue-gray colors and hence green amber can be very rare. In addition, it is dyed in many colors like green, blue, pink etc.\", \"Colorless Urine And Kidney Disease Colorless Urine And Kidney Disease. Due to urochrome, the normal urine color can range from pale yellow to deep amber. Abnormal urine color can be caused by foods, drugs, pigment, blood, urinary tract infection, kidney problems and other factors. Urine color has much to do with fluid intake.The more water we drink, the more light the color is.olorless Urine And Kidney Disease. Due to urochrome, the normal urine color can range from pale yellow to deep amber. Abnormal urine color can be caused by foods, drugs, pigment, blood, urinary tract infection, kidney problems and other factors. Urine color has much to do with fluid intake.\", \"Urine color and odor changes Rhubarb can also turn urine dark brown or tea-colored, as can fava beans and aloe. Carrots, carrot juice, and vitamin C can color urine orange, and B vitamins can turn it a fluorescent yellow-green. Asparagus sometimes gives urine a greenish tinge and a distinctive smell, said to resemble rotting cabbage. The cause of this smell is a matter for speculation.\", \"Urine Color Normal urine color ranges from pale yellow to deep amber \\u2014 the result of a pigment called urochrome and how diluted or concentrated the urine is.Pigments and other compounds in certain foods and medications may change your urine color.Beets, berries and fava beans are among the foods most likely to affect urine color. Many over-the-counter and prescription medications give urine vivid tones, such as raspberry red, lemon yellow or greenish blue.An unusual urine color can be a sign of disease. For instance, deep red to brown urine is an identifying characteristic of porphyria, a rare, inherited disorder of red blood cells.igments and other compounds in certain foods and medications may change your urine color. Beets, berries and fava beans are among the foods most likely to affect urine color.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.Most changes in urine color are harmless and the color will go back to normal within a day or two.rine with a bluish tint can mean some type of bacterial infection or may just mean that you have a high level of calcium. If this persists, give your health care professional a call. Brown urine can indicate a potentially serious condition and you should contact your doctor's office for help.\", \"- ask the disney parks moms panel discover the magic of a disney parks family vacation from one of our knowledgeable online moms this is a question and answer forum to help you plan the best possible disney vacation due to a high volume of submissions we cannot guarantee that all questions will be answered also please keep the number of questions in each submission to a limit of 3\", \"- The results can be urine that is either pink or cola-colored. Sometimes it\\u2019s difficult to tell the difference between dark urine due to dehydration or due to other causes. Dark urine due to dehydration is usually amber or honey-colored. Dark urine due to other causes can be tinged with brown or red. Some people have urine that appears almost syrup-like. This is the case when a person has liver or kidney disease. If you\\u2019re dehydrated, you can have additional symptoms besides dark urine.\", \"Alkaptonuria Alkaptonuria is a rare condition in which a person's urine turns a dark brownish-black color when exposed to air. Alkaptonuria is part of a group of conditions known as an inborn error of metabolism.\", \"Urine color Orange urine may also be caused by dehydration, which can concentrate your urine and make it much deeper in color. Blue or green urine Blue or green urine may be caused by: Dyes. Some brightly colored food dyes can cause green urine. Dyes used for some tests of kidney and bladder function can turn urine blue.\", \"- Urinalysis begins with a macroscopic examination of the urine which describes the color and clarity of the urine. In healthy individuals urine color ranges from pale yellow to amber, depending on their state of hydration. Many factors affect urine color including fluid balance, diet, medications and disease.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.ormal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.\", \"50 Shades Of Yellow: What Color Should Your Pee Be? Here's what your pee might be telling you: 1 Straw-Colored To Transparent-Yellow Pee: This is the normal urine color of a healthy, well-hydrated body. 2 Transparent Or Clear Pee: You should always be properly hydrated, but you can actually drink too much water, which will make your urine virtually colorless.\", \"Aging Skin: Do You Look Older Than You Should? Another Top Cause of Wrinkles: Smoking. Beyond question, smoking is bad for your skin. Smoking accelerates the aging process, wrinkling skin and making you look old beyond your years. Early wrinkling is visible under a microscope in smokers as young as 20.he more years and packs smoked, the more likely wrinkles will occur. Wrinkles are also more likely to be deeper in smokers. Tobacco smoke gives skin an unhealthy color and coarse texture, as well. 1 What you can do: Stop smoking 2 ! Effective tools are available at WebMD and throughout the Internet.\", \"Why Is Pee Yellow? Urine\\u2019s characteristic sunny hue comes primarily from a substance called urobilin. When your body is properly hydrated, the urobilin is diluted with plenty of water, and your pee is a nice, normal buttercup color. Amber or ale-colored urine is a reliable sign of dehydration, and clear pee is a signal that it\\u2019s time to put down the Nalgene.\", \"- The first of the physical properties to be considered is color. The color of urine often varies with its concentration and is most often reported as some shade of yellow (straw, light yellow, yellow, dark yellow, and amber). Normal urine can be found in any of these colors, with the exception of the \\u2018amber\\u2019 color.\", \"Urine color Normal urine color ranges from pale yellow to deep amber \\u2014 the result of a pigment called urochrome and how diluted or concentrated the urine is. Pigments and other compounds in certain foods and medications may change your urine color. Beets, berries and fava beans are among the foods most likely to affect urine color. Many over-the-counter and prescription medications give urine vivid tones, such as raspberry red, lemon yellow or greenish blue.\", \"Foamy Urine \\u00e2 Causes, Treatment, Diagnoses Foamy Urine. Urine is produced by the kidneys are discharged through urethra. Like any other metabolic wastes, urine is toxic and if retained in the body could produce undesirable illness. Normal color of the urine is amber or pale yellow.\"], \"pos_scores\": [97.5], \"neg_scores\": [86.5, 91.6875, 97.1875, 89.75, 90.875, 97.4375, 92.5625, 93.75, 92.5, 96.1875, 92.125, 97.375, 97.6875, 89.125, 96.6875, 90.4375, 91.0625, 97.125, 97.625, 92.0, 90.25, 96.875, 97.5625, 97.6875, 95.125]}\n{\"query\": \"is autoimmune hepatitis a bile acid synthesis disorder\", \"pos\": [\"- Inborn errors of bile acid synthesis can produce life-threatening cholestatic liver disease (which usually presents in infancy) and progressive neurological disease presenting later in childhood or in adult life.he neurological presentation often includes signs of upper motor neurone damage (spastic paraparesis). The most useful screening test for many of these disorders is analysis of urinary cholanoids (bile acids and bile alcohols); this is usually now achieved by electrospray ionisation tandem mass spectrometry.\"], \"neg\": [\"Bile acid Bile acid. Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming primary bile acids. The sodium and potassium salts of bile acids are called bile salts. Primary bile acids are those synthesized by the liver.\", \"Overview When bile ducts are damaged, as in primary biliary cirrhosis, harmful substances can build up in your liver and sometimes lead to irreversible scarring of liver tissue (cirrhosis). Primary biliary cirrhosis is considered an autoimmune disease, in which the body turns against its own cells. Researchers think it is triggered by a combination of genetic and environmental factors.\", \"Bile acid malabsorption Bile acid malabsorption was first recognized in patients with ileal disease. When other causes were recognized, and an idiopathic, primary form described, a classification into three types was proposed: 1 Type 1: Bile acid malabsorption, secondary to ileal resection, or ileal inflammation (e.g. in Crohn's disease).ile acid malabsorption was first recognized in patients with ileal disease. When other causes were recognized, and an idiopathic, primary form described, a classification into three types was proposed: 1 Type 1: Bile acid malabsorption, secondary to ileal resection, or ileal inflammation (e.g. in Crohn's disease).\", \"- Czaja et al have shown that patients with autoimmune hepatitis who have positive test results for actin antibody are younger, more commonly test positive for human leukocyte antigen (HLA)\\u2013DR3, and required transplantation more frequently than patients with ANAs who test negative for actin antibody.\", \"- An autoimmune liver disease panel is a group of tests that is done to check for autoimmune liver disease. An autoimmune liver disease means that the body's immune system attacks the liver. These tests include: Anti-liver/kidney microsomal antibodies. Anti-mitochondrial antibodies. Anti-nuclear antibodies.\", \"- Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming bile salts.\", \"- Autoimmune hepatitis is a type of liver inflammation in which the body\\u2019s immune cells attack healthy liver cells after mistaking them for disease-causing foreign substances.\", \"- Worldwide, viral hepatitis is the most common cause of liver inflammation. Other causes include autoimmune diseases and ingestion of toxic substances (notably alcohol), certain medications (such as paracetamol), some industrial organic solvents, and plants.n autoimmune hepatitis, the immune system attacks the liver due to the autoimmune disease. In some hepatitis, often including hepatitis caused by alcoholism, fat deposits accumulate in the liver, resulting in fatty liver disease, also called steatohepatitis.\", \"Recent advances in the understanding of bile acid malabsorption Introduction. In patients with bile acid malabsorption (BAM), a larger amount of bile acids than normal spill into the colon, where they stimulate electrolyte and water secretion which results in the typical symptoms of BAM: chronic watery diarrhoea.ntroduction Bile acid malabsorption (BAM) is a syndrome of chronic watery diarrhoea with excess faecal bile acids. Disruption of the enterohepatic circulation of bile acids following surgical resection is a common cause of BAM.\", \"Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk However, the fact that there is a link between the two is not in dispute; multiple studies have shown that patients who suffer from celiac disease have a significantly higher risk of having other types of autoimmune diseases, including autoimmune hepatitis.\", \"- Many of the agents that can induce autoimmune hepatitis can also induce other autoimmune conditions or symptoms such as a lupus-like syndrome (minocycline), pneumonitis (nitrofurantoin), hemolytic anemia (methyldopa) and acute thyroiditis (interferon alfa).\", \"Kubla Khan Some of Coleridge's contemporaries denounced the poem and questioned his story of its origin. It was not until years later that critics began to openly admire the poem. Most modern critics now view Kubla Khan as one of Coleridge's three great poems, along with The Rime of the Ancient Mariner and Christabel.\", \"The Celiac and Autoimmune Thyroid Disease Connection Celiac disease, which is sometimes referred to as celiac sprue or sprue, is an autoimmune disorder that attacks the cells lining the intestine. This attack is a reaction to the presence of gluten, a protein found in wheat, rye, and barley. Eating these foods causes inflammation, which in turn makes it difficult for the body to properly absorb nutrients from foods. Celiac disease is a that damages the small intestine. People with celiac disease cannot eat gluten, a protein found in wheat, barley, and rye. Some of the common symptoms of celiac disease include the following: Intestinal difficulties. Abdominal bloating and pain. Nausea. Gas. Diarrhea.\", \"Autoimmune Liver Diseases Doctors have identified two main forms of autoimmune hepatitis: 1 Type 1 autoimmune hepatitis. This is the most common type of the disease. 2 Type 2 autoimmune hepatitis. Although adults can develop type 2 autoimmune hepatitis, it's most common in children and young people.\", \"- Case Reports in Infectious Diseases. Acute Acalculous Cholecystitis due to Viral Hepatitis A. Copyright \\u00a9 2013 Safak Kaya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\", \"Autoimmune Hemolytic Anemia Autoimmune Hemolytic Anemia. Introduction. Autoimmune Hemolytic Anemia (AIHA) is characterized by production of antibodies that bind to antigens on the erythrocyte surface. These antibodies then cause the destruction of RBCs thus shortening their life span.\", \"Cirrhosis Survival rates after transplantation are excellent. Liver Transplantation for Patients with Autoimmune Hepatitis. The outlook is also good for patients who have autoimmune hepatitis who require a transplant. Survival rates are about 90% after 1 year, and 70 - 80% after 5 years.\", \"How Old Do You Have To Be To Rent A Car? There is an industry standard in how old do you have to be to rent a car. At almost every rental agency, the minimum age requirement to rent a car is 25. There is some variance, however. Depending on the region and the country, the minimum age for the driver could be between 21 and 24 years old. Be aware, though, that you will have to pay additional fees because of it. There are some companies that will rent to younger drivers. The age range is anywhere from 18 years old to 21 years old.\", \"Autoimmune Hepatitis Points to Remember. 1 Autoimmune hepatitis is a chronic\\u2014or long lasting\\u2014disease in which the body's immune system attacks the liver and causes inflammation and damage. 2 Autoimmune hepatitis is a serious condition that may worsen over time if not treated. Autoimmune hepatitis can lead to cirrhosis and liver failure. Autoimmune hepatitis is more common in females. The disease can occur at any age and affects all ethnic groups. Autoimmune hepatitis is classified as type 1 or type 2.\", \"SD School for the Blind & Visually Impaired - Eye Conditions Less often, chronic hepatitis results from alpha1-antitrypsin deficiency (a hereditary disorder), celiac disease, a thyroid disorder, or, in children and young adults, Wilson disease\\u2014a rare hereditary disorder involving abnormal retention of copper in the liver (see Wilson Disease).\", \"What is an anti-smooth muscle antibody (ASMA) test? An F-actin antibody test, in addition to an ASMA test, may improve the ability to detect autoimmune hepatitis over other conditions. Because test results require interpretation, especially in relation to other tests that may have been performed, it\\u2019s important to talk to your doctor about your specific results. A diagnosis of autoimmune hepatitis means that your immune system is mistakenly making antibodies that attack healthy cells in your liver.\", \"Autoimmune Hepatitis Outlook of Autoimmune Hepatitis. Autoimmune hepatitis (AH) is a dangerous disease and without any treatment, around 50% of patients with severe AH will die within 5 years and most patients will die within 10 years of disease onset. Treatment with immunosuppressant drugs improves the chances for survival significantly.\", \"SOLUBLE LIVER ANTIGEN Clinical Significance. Anti-soluble liver antigen antibodies are detected in 10-30% of patients with type 1 autoimmune hepatitis (AIH), but not in patients with type 2 AIH, primary sclerosing cholangitis or primary biliary cirrhosis. The antibody is directed against a UGA suppressor tRNA-associated protein.\", \"Despite popular belief, celiac disease is a SERIOUS GENETIC AUTOIMMUNE DISEASE, not the latest fad diet. Celiac disease is a serious genetic autoimmune disease. It is triggered by consuming a protein called gluten, which is found in wheat, barley and rye. When people with celiac disease eat foods containing gluten, their immune system responds by damaging the finger-like villi of the small intestine.\", \"Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk. As many as 9% of patients with autoimmune hepatitis also have CD. As a major organ, the liver performs many essential tasks in digestion, so it may come as no surprise that patients with celiac disease also frequently have liver ailments, including autoimmune hepatitis.\"], \"pos_scores\": [93.875], \"neg_scores\": [91.125, 91.0, 89.375, 89.75, 90.25, 91.0, 92.25, 90.625, 89.5, 90.25, 90.625, 83.8125, 89.25, 91.375, 86.9375, 87.8125, 89.125, 85.5, 91.5, 89.625, 89.5, 90.75, 89.5625, 89.0625, 90.5]}\n{\"query\": \"elegxo meaning\", \"pos\": [\"The Ministry of the Holy Spirit The word convict here (elegcw /elegxo) means to bring to light or expose error often with the idea of reproving or rebuking. It brings about knowledge of believing or doing something wrong, but it does not mean that the person will respond properly to that knowledge. Our usage of the English word, convict, is similar.\"], \"neg\": [\"XOXO means \\u00e2hugs and kisses\\u00e2 but why? XOXO means \\u201chugs and kisses\\u201d but why? What's the reasoning behind abbreviating hugs and kisses as X's and O's? Some say X is for hugs and O is for kisses, and some say the other way around; but why X and O, and why are they doubled? In my experience 'X' for kiss is universal.\", \"Elotes preparados, Mexico Elotero is the name given to the person selling elotes, a word from the nahuatl n\\u00e1huatl, elotl which \\u2018 means tender ear of \\u2018. maize\", \"- Definition of ambidextrous. 1 1 : using both hands with equal ease an ambidextrous baseball player. 2 2 : unusually skillful : versatile. 3 3 : characterized by duplicity : double-dealing.\", \"- el\\u00b7e\\u00b7gi\\u00b7ac. adj. 1. Of, relating to, or involving elegy or mourning or expressing sorrow for that which is irrecoverably past: an elegiac lament for youthful ideals.2.legiac. adj. 1. (Literary & Literary Critical Terms) resembling, characteristic of, relating to, or appropriate to an elegy.\", \"Attributes of Elegu\\u00c3\\u00a1 Elegu\\u00e1, Lord of the Crossroads. Attributes of Elegu\\u00e1. Elegu\\u00e1 (Eleggu\\u00e1) is sometimes represented as a child, and sometimes as an old man. He represents the beginning and end of life, and the opening and closing of paths in life. Sometimes known as the trickster, he likes to play jokes on people. He enjoys candy and toys.\", \"- The prefix tropo-denoting turning, a change, is derived from the Greek word tropos, a turning. The troposphere is the lowest level of atmosphere in which the temperature fall \\u2026 s as height increases.ropo-comes from the Greek tropos which means turning.. Meso-comes from the Greek mesos which means middle.. Thermo-comes from the Greek thermos which means hot.. Exo- \\u2026 comes from the Greek ex\\u014d which means outside..\", \"\\u00c2\\u00a1Qu\\u00c3\\u00a9 oso! Hacer el oso Hacer el oso means create an embarrassing situation, for example, by falling down at a party and spilling your drink everywhere. No quiero llegar antes de tiempo para no hacer el oso. I don\\u2019t want to look stupid by arriving early. Note that you aren\\u2019t making yourself into a bear by doing something stupid.\", \"- Strato means a combining form representing stratus This means layer. here are some examples that have the word Strato in it The stratosphere is the next highest atmospheric \\u2026layer. There are no clouds in the stratosphere. Commercial jets fly in the lower part of the stratosphere.\", \"Eleuthero Root Extract Benefits, Uses & Side Effects Loading ... Eleuthero root is an adaptogenic herb in the ginseng family that is used to boost energy levels, enhance stamina, promote overall health and increase athletic performance. It has been used as a natural medicine in China, Korea and Russia for many hundreds of years.\", \"- Definition of Elegy. An elegy is a mournful poem, usually written in remembrance of a lost one for a funeral or as a lament.An elegy tells the traffic story of an individual, or an individual\\u2019s loss, rather than the collective story of a people, which can be found in epic poetry.ignificance of Elegy in Literature. The definition of elegy as we know it now only came about in the sixteenth century. In ancient Greece, an elegy was any poem written in elegiac couplets, and could deal with any subject matter, including love and battle, along with death.\", \"Glossary An elegy is a poem of mourning; this is often the poet mourning one person, but the definition also includes Thomas Gray's 'Elegy Written in a Country Churchyard', which mourns all the occupants of that churchyard, and looks into the future to mourn the poet's own death.n elegy is a poem of mourning; this is often the poet mourning one person, but the definition also includes Thomas Gray's 'Elegy Written in a Country Churchyard', which mourns all the occupants of that churchyard, and looks into the future to mourn the poet's own death.\", \"- !meaning1 meaning2!e.g. hard!\\u00d2difficult\\u00d3 \\u00d2durable, solid\\u00d3!Single lexical entry Homophony!Homophones!morpheme1 e r\\u00d5: p o u morpheme2 meaning1 meaning2!e.g. pass (\\u00d4I\\u00d5m going to pass\\u00d5)!\\u00d4abstain\\u00d5 \\u00d4succeed\\u00d5!Distinct lexical entries Puns and Zeugmas Ambiguous words used in different senses in parallel syntactic construction.\", \"- GIGO. GIGO is an acronym which stands for 'Garbage In, Garbage Out'. It tends to be used when talking about data entry and databases. Bascially it means that if you enter inaccurate or wrong data then you have really entered garbage. Then when you want to do some queries, searches or reports, you will only get incorrect, wrong or inaccurate data out-garbage.\", \"XEG-AM XEG-AM is a Class A radio station on clear-channel frequency 1050 kHz in the state of Nuevo Leon, Le\\u00f3n. mexicot is licensed for Guadalupe, Nuevo Leon le\\u00f3n and brands itself as Serving. Monterrey known for its border blaster status in the, 1950s it now uses the Name La ranchera De monterrey and broadcasts ranchera. music\", \"What does el oso mean? What does natory mean? &Menos el Oso Natory is the name of a Wine Store and furniture shop. It is also a surname. What does 'El oso es grande' mean? The translation of 'El oso es grande' from Spanish to English is 'The bear is... What does menos el oso mean in spanish? The Spanish phrase menos el oso means minus the bear in English. ! What does me amor el oso de peluche mean? Me amor el oso de peluche translates from Spanish to English and means I love... What does menos el oso mean in english? The literal translation of menos el oso from Spanish to English is unless the... See All Questions\", \"\\\"What does it mean when a girl always uses \\\"\\\"cx\\\"\\\" every time you message each other?\\\" Cx is a smiley face, but also an alternative shortening of the word sex. Used to slip sex into a conversation, while smiling. Also cx may be the awesome type of debate which has people speaking 250 words/minute and ensures an intensifying feeling throughout the round. Also cx means sincerely or the meaning of CX in text slang is cancelled. See more types What does \\u201ccx\\u201d mean?\", \"Root Words & Prefixes: Quick Reference Latin: ambidextrous - able to use both hands equally; ambiguous - having more than one meaning; ambivalence - conflicting or opposite feelings toward a person or thing: ambul: walk, move: Latin: amble - to walk in a slow, relaxed way; ambulant - walking or moving around; ambulance - a vehicle that moves a patient: ami/o: love: Latin\", \"elegiac If there's one song on your playlist that always brings tears to your eyes, maybe it's because it has an elegiac quality. The adjective elegiac is useful when you're talking about music, a movie, a book, or another work of art that has a sorrowful tone. Sometimes elegiac specifically refers to something or someone that's gone: a person who's died, or a time in the past, especially if you feel a sense of longing for it. You can speak in an elegiac way, or sing an elegiac tune. The word comes from the Greek elegos, poem or song of lament..\", \"Meaning of nameEleuterio Epicurian, Eleuterio is often a bit of a ladies man and is extremely sensual. In romance, he needs to be admired; one could say he is a little vain, noblesse oblige. Likewise, he needs to have equal admiration for his partner, who he likes to show off in public, almost like an accessory.\", \"elegy Definition of elegy for English Language Learners. : a sad poem or song : a poem or song that expresses sorrow for someone who is dead.\", \"- equi-Meaning equal. Related terms . igual; equi\\u00e1ngulo; equidad; equidistancia; equil\\u00e1tero; equilibrar; equilibrio; equimolecular; equimosis; equinoccial\", \"- Meaning & History. From the Hebrew name \\u05d9\\u05b8\\u05e2\\u05b5\\u05dc (Ya'el) meaning ibex, mountain goat. This name appears in the Old Testament belonging to a woman who kills the captain of the Canaanite army, giving the victory to Israel.eaning & History. From the Hebrew name \\u05d9\\u05b8\\u05e2\\u05b5\\u05dc (Ya'el) meaning ibex, mountain goat. This name appears in the Old Testament belonging to a woman who kills the captain of the Canaanite army, giving the victory to Israel.\", \"- es lo que hay exprexpresi\\u00f3n: Expresiones idiom\\u00e1ticas, dichos, refranes y frases hechas de tres o m\\u00e1s palabras (Dios nos libre, a lo hecho, pecho). (lo que tenemos) is what you get exprexpression: Prepositional phrase, adverbial phrase, or other phrase or expression--for example, behind the times, on your own..\", \"- see also autoeroticism autosexual autosexual noun autosexuality characterized by self sex contact usually as a genital act masturbation with or without an accompanying erotic fantasy or ritual or rarely as a long term sexuoerotic status without a partner from greek auto self sexee also limbic system cervix adjective cervical the neck or neck like part of an organ specifically the neck of the lower part of the uterus or womb where the vagina and uterus unite 2 the cervix of the uterus is at its lower end where the uterus and the vagina meet\", \"Thread regardingHewlett Packard Enterprise (HPE) layoffs DXC (dumb--s core) is derived from making fun of sXe (straightedge) It is a clique made up of cool kids that like good music, but are a bit ditzy at times.. Well, to tell yout he truth, they are dumb--ses.\"], \"pos_scores\": [99.25], \"neg_scores\": [87.0, 84.6875, 87.1875, 89.9375, 86.6875, 86.875, 85.3125, 85.9375, 85.9375, 88.125, 87.8125, 87.8125, 85.625, 84.375, 85.5625, 86.3125, 86.875, 88.0625, 86.0, 89.25, 86.25, 87.0, 86.25, 88.5, 87.25]}\n{\"query\": \"how much does an average person make for tutoring\", \"pos\": [\"- In-home tutors can earn anywhere from $10 to $80 an hour, depending on the type of lesson, the student\\u2019s skill and age level and the tutor\\u2019s experience. Tutors often charge more for older students or those who require more advanced lessons.\"], \"neg\": [\"- The price of a math tutor depends on the ability of the tutor as well as the type of tutoring required for the student or apprentice. How much is it? 1 Depending on your location, math tutors\\u2019 charges will vary. 2 On average, a tutor can cost anywhere from $15 to as much as $150 per hour. math tutor is a person who specializes in math and is employed in helping educate others. Before the wide availability of the internet, tutoring required the personal presence of the tutor. Today, there are online tutoring sessions available with the help of the internet.\", \"- 1 Individuals generally charge according to their level of education and experience. 2 Expect to pay $10 to $15 per hour for a high school student, and up to $75 per hour for a certified teacher with experience. 3 A teacher trained and qualified to work with children with special needs will likely charge more. A tutoring agency will help match you with a tutor. 2 Most agencies charge a registration fee, plus a fee for individual tutors. 3 Rates could start at $25 per hour and go up according to the subject area and the tutor's level of expertise. 4 Ask about any additional fees, such as for extra testing.\", \"- For example the median expected annual pay for a typical Teaching Assistant (College) in the United States is $15,659, so 50% of the people who perform the job of Teaching Assistant (College) in the United States are expected to make less than $15,659.\", \"Kumon Group Tutor Hourly Pay The typical Kumon Group Tutor salary is $9. Tutor salaries at Kumon Group can range from $7-$14. This estimate is based upon 90 Kumon Group Tutor salary report(s) provided by \\u2026 employees or estimated based upon statistical methods. See all Tutor salaries to learn how this stacks up in the market.\", \"How much do SAT tutoring centers charge? Here is an example I found online: cost for SAT tutoring: Kaplan = $200 + per hour Princeton Review = $125 to $315 per hour SATtutors.com = Tutors set their own rates.ould be $25 per hour to $50 to $100 plus per hour Sylvan = $100 + per hour There is everything from one-on-one tutoring for individual attention to larger classes.\", \"- How much does a Online Tutor make? The average Online Tutor salary is $30,000. Filter by location to see Online Tutor salaries in your area. Salary estimates are based on 60 salaries submitted anonymously to Glassdoor by Online Tutor employees.\", \"- By state, California offers the highest pay at $13.00 per hour. Those who have five to nine years of work experience see average pay of $10.97 per hour. Overall, the greater share of Tutor Time Learning Centers folks have one to four years of experience and earn an average about $9.68 per hour.arly Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00.\", \"How Much Should Tutoring Cost? On the other, a general math tutor in Nebraska who is just getting started, may have to charge a rate of $40.00 to get new clients. Students, parents and tutors need to consider subject, location, experience, teaching background, frequency of tutoring, etc. when determining how much tutoring should cost.\", \"How Much Does Tutoring Cost? Average cost of tutoring: $48/hour. Although tutors can be found on Craigslist for $20/hour (that\\u2019s a cheaper deal), you may be swapping quantity for quality. Craigslist doesn\\u2019t specialize in providing tutors, so the site doesn\\u2019t screen their applicants.\", \"- Early Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00.The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.egarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00. The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.\", \"- A School Custodian earns an average wage of $12.24 per hour. Pay for this job does not change much by experience, with the most experienced earning only a bit more than the least. People in this job generally don't have more than 20 years' experience.\", \"How Much Do Tutors Charge? If you want to know how much a particular tutoring company costs, call up their sales center and ask about the costs of different programs. According to Payscale.com, in 2011, tutors in the 25th to 75th percentile range working for private companies made from $10.10-$20.35. However, these figures do not include the amount of money that goes to the company providing you with the tutor.\", \"- 1 On average, a tutor can cost anywhere from $15 to as much as $150 per hour. 2 Online tutoring can range from $15 to $75 per hour, depending on the service that is used. 3 Group tutoring that is ideal for those in a classroom can be significantly lower as the group will pay the tutor. math tutor is a person who specializes in math and is employed in helping educate others. Before the wide availability of the internet, tutoring required the personal presence of the tutor. Today, there are online tutoring sessions available with the help of the internet.\", \"What's the Going Rate for a Tutor? The cost of a private tutor who comes to your home varies according to the grade level of the student, the education and experience of the tutor, and what city you live in. The national average price is about $41 per hour, according to Tutorspree. A high school student might charge as little as $10 per hour, while an experienced tutor in the country\\u2019s most expensive market, Manhattan, is considered reasonable at $85 to $150 per hour, with Ivy League graduates earning $200 per hour or more. Tutors don\\u2019t have to live locally.\", \"How much do teachers make per hour? How much do teachers make per hour? The median annual salary of a teacher in 2012 was $55,418. The average school year lasted 180 days at 6.7 hours per day, giving an average hourly wage of $45.95, according to The Daily Tarheel and the National Center for Education Statistics.\", \"Tutor Salary Pay by Experience for a Tutor has a positive trend. An entry-level Tutor with less than 5 years of experience can expect to earn an average total compensation of $31,000 based on 1,027 salaries provided by anonymous users. Average total compensation includes tips, bonus, and overtime pay.\", \"How Much Do Tutors Charge? Tutors who teach unusual subjects, like Latin, usually charge more than tutors who teach common subjects such as reading. You can expect private tutors to charge anywhere from $20-$100 per hour depending on his or her qualifications, location and what your child needs.f your child needs more casual help, you can save money and time by hiring an online tutor. Online tutoring is often cheaper than in-person lessons. If your child only needs help with a math problem or reading a certain passage, some sites charge by the minute with rates typically around 46-60 cents a minute.\", \"How Much Do Tutors Charge? according to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutorccording to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutor\", \"- The average cost for a SAT tutor is $50/hr. You are likely to spend between $30 and $75 per hour. Exact price may vary depending on your area and project details. Next Step: Find out exactly how much your project will cost.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields. $50-$100 per hour. As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour. I could probably charge a little more now, if I were still working as a private tutor.\", \"How Much Does Tutoring Cost? 36 By Design 3.0/5. Average cost of tutoring: $81.25/hour. If you hadn\\u2019t guessed it by the name of the company, 36 By Design only does one kind of tutoring \\u2014 ACT Test Prep. It\\u2019s their opinion that by only focusing on one subject, they can perfect it.\", \"How Much To Charge For Tutoring How Much To Charge For Tutoring Factors that affect how much a tutor charges include location, education, and experience. A typical tutor will charge between $17 and $45 per hour, according to our tutor pricing calculator.\", \"- Excluding overtime pay (which bumps median pay up to $21.00), people who work for Tutor Time Learning Centers earn a median wage of $10.00 per hour.egarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00. The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields $50-$100 per hour As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour.\", \"Senator Armstrong 2016 Presidential Ad Fed up with the state of the country, Senator Armstrong decides to run for the oval office in 2016 instead of 2020. Disclaimer: This is for entertainment purposes only.All audio recordings are property of the artist(s), management, and/or music publishing companies.No copyright infringement is intended.ed up with the state of the country, Senator Armstrong decides to run for the oval office in 2016 instead of 2020. Disclaimer: This is for entertainment purposes only.\"], \"pos_scores\": [95.4375], \"neg_scores\": [94.5, 93.5625, 90.25, 91.75, 91.3125, 96.0625, 92.5, 92.25, 94.9375, 90.8125, 88.6875, 94.625, 95.8125, 94.375, 90.5, 94.8125, 93.625, 93.4375, 93.0625, 93.1875, 94.4375, 94.6875, 92.25, 93.0625, 87.0625]}\n{\"query\": \"can you use a calculator on the compass test\", \"pos\": [\"Calculator Guidelines for COMPASS \\u00c2\\u00ae Testing Calculators may be used on the COMPASS Pre-Algebra, Algebra, College Algebra, Geometry, and Trigonometry tests provided they meet the requirements listed below. Electronic writing pads or pen-input devices\\u2014The Sharp EL 9600 is permitted. Models with paper tapes\\u2014The paper must be removed.\"], \"neg\": [\"North References for Navigating with Map, Compass and GPS Adjusting the North Reference on Your Compass to True or Grid North Many compasses allow you to adjust the position of the lines used to align the magnetic needle with respect to the angle measuring dial.\", \"Study Guide Your COMPASS\\u00ae Test Success Starts Here. Studying the material on this site will help you improve your score on the COMPASS Math Assessment Test. A higher score will let you skip the most basic university math classes, saving you money and time. This site will help you learn about test strategy, review test material, and practice for the exam; all for free!\", \"TI-84 Plus CE Graphing Calculator The TI-84 Plus CE is approved for use on the following exams: 1 PSAT*, SAT*, and ACT\\u00ae college entrance exams. 2 AP* Exams that allow or require a graphing calculator. 3 Approved for use on the IB exam.\", \"Which calculator do I need? A good calculator will probably cost you at least $100, but you may be able to rent, buy used, or even borrow from your school library. The TI-84+ is a good calculator for algebra and geometry (now available with a color screen), and is a newer version of the classic TI-83+.\", \"How to Use a Compass You can use a bearing to get to a location any time you know where you are on a map: 1 Set your compass on the map so that the straight side of the baseplate lines up between your current position (1a) and the map location for a destination like a campsite (1b). 1 Make sure the direction of travel arrow is pointing in the general direction of that campsite (in other words, it's not upside down).\", \"How to Use the iPhone Compass App The iPhone digital compass app is located within the utilities icon on your phone. Follow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options.2 Click on the compass icon to select the compass app. 3 Hold phone flat in the upwards-facing palm of your hand. 4 Extend your arm straight out in front of you, as you would when holding a magnetic compass.ollow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options. 2 Click on the compass icon to select the compass app.\", \"Calculators Multiple Power Options Choose calculators with multiple power options to maximize your time at the office. Some models can be plugged directly into the wall, so there's no need to remove the batteries and interrupt your work during the charging process.hether you're teaching a math class or running an accounting business, calculators can help you get the job done. Each one is built with a mix of simple and advanced functions for maximum efficiency. Choose the best model for your business from brands such as Casio, Canon, and Texas Instruments.\", \"- The COMPASS test is a self-adjusting, multiple choice test that is taken at the computer. The. answer to your current question will determine the next question; it will stop once it has determined. your level.\", \"How to Use the TI-84 Plus Calculator to Convert Sine, Tangent & Cosine to Angles You can easily convert the basic trigonometric functions into angles measured in degrees or radians using a TI-84 Plus calculator. The TI-84 Plus is capable of going in both directions -- from the angle to the trigonometric measure and back.et your calculator to Degrees mode by pressing the MODE key, pressing the down arrow until you reach the row with the options Degree and Radian, highlighting Degree using the right arrow key, and pressing ENTER.\", \"- with Answers-Sample 1. Math questions, with answers, similar to the questions in the compass math test are presented. The questions are designed to reflect the major topics covered in the compass test: Numerical skills/pre-Algebra, algebra, college algebra, geometry and trigonometry.he questions are designed to reflect the major topics covered in the compass test: Numerical skills/pre-Algebra, algebra, college algebra, geometry and trigonometry.\", \"- Nursing Compass Entrance Exam. COMPASS is a comprehensive, computer-adaptive testing program that quickly and accurately assesses students' skill levels in reading and mathematics. Because it is adaptive, the length of the test will vary based upon the individuals' knowledge.*If you are under 21 years of age and have taken the ACT, you may not be required to take the COMPASS exam if you scored a minimum of 19 in Math, 19 in Reading, and 19 in English (a 19 composite score is not accepted).\", \"- Compass is awesome! Powerful off-the-bat visualization and analytics that are actually actionable. There's no other benchmarking tool that's as effective out there and it has really helped us focus on improving those metrics that deviated too much from the norm. Really solid support from the developers as well.\", \"- Either way, here\\u2019s how to find and use the compass and level app. Launch the Compass app with a quick tap. If this is the first time, you\\u2019ll need to gyrate the iPhone around a bit to completely calibrate it.Now, just hold the iPhone out from your body, like you would reading a text message.ither way, here\\u2019s how to find and use the compass and level app. Launch the Compass app with a quick tap. If this is the first time, you\\u2019ll need to gyrate the iPhone around a bit to completely calibrate it.\", \"How do I reset the compass on an '04 Ford F150 upper console? 8. Press the RESET control to start the compass calibration function. 9. Slowly drive the vehicle in a circle (less than 3 mph [5 km/h]) until the CIRCLE SLOWLY TO CALIBRATE display changes to CALIBRATION COMPLETED. It will take up to five circles to complete calibration. 10. The compass is now calibrated. couldn't get the picture of the zones to come up, but it's on page 72 of your manual.\", \"- Compass practice tests are an effective way to study for your college placement exams. Our free Compass sample tests provide you with an opportunity to assess how well you are prepared for the real Compass test, and then focus on the areas you need to work on.ompass Test. Doing well on your Compass Test can help you start college on the right foot. Discover test taking tips, score information, and Compass Exam Prep. Use our free Compass sample exams.\", \"OH NO, I am not allowed a calculator on the MCAT OH NO, I am not allowed a calculator on the MCAT!!! If you reading this you have probably completed all the general requirements to take the MCAT. Thinking back over your semesters of chemistry you would of used your calculator a lot to complete the math problems ranging from the basics of stoichiometry to the complex problems of solving the pH of a weak acid.\", \"Can you use a calculator on GED test? Best Answer: yea u CAN use a Calculator on a GED test i think on the math part is everything u took in high school that was math.\", \"How to Use the iPhone Compass App 1 Click on the compass icon to select the compass app. 2 Hold phone flat in the upwards-facing palm of your hand. 3 Extend your arm straight out in front of you, as you would when holding a magnetic compass.ollow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options. 2 Click on the compass icon to select the compass app.\", \"Calculators during the TEAS test Jul 1, '12. No calculators allowed. The only pop up help you get is the periodic table in the science portion. I was kind of worried about math too -- especially because i usually freak out on timed exams and go blank on stupid things such as multiplication tables and conversion.\", \"- May I Use a Calculator? The ACT Calculator Policy (effective September 1, 2014) is designed to ensure fairness for all examinees, avoid disturbances in the testing room, and protect the security of the test materials. A permitted calculator may be used on the ACT mathematics test only.\", \"Calculators on the SAT: Tips from Experts Calculator Uses and Limitations. Calculators are allowed on the SAT, and not using them correctly can put you far behind. SAT experts Fred Zhang and Allen Cheng discuss which tips and strategies worked for them in getting perfect scores.\", \"- The COMPASS Placement Test is an untimed, adaptive, computer-based college placement exam. The test changes for each student based on performance. If a student gets a question correct, they next receive a question of greater difficulty and higher value.he COMPASS Placement Test is an untimed, adaptive, computer-based college placement exam. The test changes for each student based on performance. If a student gets a question correct, they next receive a question of greater difficulty and higher value.\", \"The Compass Test: What You Need to Know The Compass Test: What You Need to Know. The COMPASS test is actually a series of computerized tests which were written by ACT, Inc. It is administered by a number of colleges and universities and is used to determine course placement. Find out everything you need to know about the COMPASS test here.\", \"Compass A compass is an instrument used for navigation and orientation that shows direction relative to the geographic cardinal directions, or points. Usually, a diagram called a compass rose, shows the directions north, south, east, and west as abbreviated initials marked on the compass.When the compass is used, the rose can be aligned with the corresponding geographic directions, so, for example, the N mark on the rose really points to the north.Frequently, in addition to the rose or sometimes instead of it, angle markings in degrees are shown on the compass.hen the compass is used, the rose can be aligned with the corresponding geographic directions, so, for example, the N mark on the rose really points to the north. Frequently, in addition to the rose or sometimes instead of it, angle markings in degrees are shown on the compass.\", \"Q: I need help switching an Xbox live account to another Microsoft id, how can I do that? I had made an account a while back, now I can't get on to Xbox live because they're trying to do a proof and I've deleted that email address and there for cant retrieve anything. Now i made a new account is there a way I can transfer my Xbox live gamer tag to this new account so I can get back on Xbox live? 17 people had this question.\"], \"pos_scores\": [100.0625], \"neg_scores\": [87.125, 93.375, 89.375, 87.0, 89.375, 88.5, 86.875, 92.125, 88.4375, 93.125, 92.75, 87.625, 88.4375, 85.3125, 92.3125, 89.5, 93.3125, 88.5, 91.875, 92.4375, 91.25, 92.125, 92.375, 88.4375, 86.25]}\n{\"query\": \"what does physical medicine do\", \"pos\": [\"What Is a Physiatrist? Doctor Directory. A physiatrist practices in the field of physiatry - also called physical medicine and rehabilitation - which is a branch of medicine that specializes in diagnosis, treatment, and management of disease primarily using physical means, such as physical therapy and medications.\"], \"neg\": [\"Types of Doctors Physiatry: A physiatrist is a physician who specializes in physical medicine, which is the curing of injuries and disease by natural methods. Measures that are used include physical therapy, massage, exercise, light and heat.\", \"Physical Therapy Office Forms It helps you move better and may relieve pain. It also helps improve or restore your physical function and your fitness level. The goal of physical therapy is to make daily tasks and activities easier. For example, it may help with walking, going up stairs, or getting in and out of bed.\", \"What Does Therapeutic Ultrasound Do in Physical Therapy? Therapeutic ultrasound is a treatment modality commonly used in physical therapy. It is used to provide deep heating to soft tissues in the body. These include muscles, tendons, joints, and ligaments. Ultrasound in physical therapy is not to be confused with diagnostic ultrasound, which is \\u200ban ultrasound that is used to see the inside of the body, such as checking on a fetus during pregnancy.\", \"physical medicine physical medicine. n. The branch of medicine that deals with the treatment, prevention, and diagnosis of disease by essentially physical means, including manipulation, massage, and exercise, often with mechanical devices, and the application of heat, cold, electricity, radiation, and water. Also called physiatrics, physiatry.\", \"List of photographic equipment makers Some camera makers design lenses but outsource manufacture. Some lens makers have cameras made to sell under their own brand name. A few companies are only in the lens business. Some camera companies make no lenses, but usually at least sell a lens from some lens maker with their cameras as part of a package.\", \"Fitness Trainers and Instructors Physical therapists use different forms of treatment depending on the type of patient they are caring for. Physical therapists, sometimes called PTs, help injured or ill people improve their movement and manage their pain. These therapists are often an important part of the rehabilitation, treatment, and prevention of patients with chronic conditions, illnesses, or injuries.\", \"Frequently Asked Questions How does physical therapy help pelvic pain? There are many reasons for developing pelvic pain issues through a lifespan. Muscles, connective tissue (fascia), and nerves in the abdomen, pelvis, and low back region may all contribute to pelvic pain. Pelvic Floor physical therapists are specifically trained to evaluate and treat muscle spasm and trigger points/tender points that can result from muscle imbalance, scar tissue tension, prolonged nerve compression or stretch injuries, and trauma.\", \"Physical Therapist Physical Therapist. Physical therapists are evidence-based, health care professionals who diagnose and treat individuals of all ages who have medical problems or other health-related conditions that limit their abilities to move and perform functional activities in their daily lives.\", \"Physical Therapists What Physical Therapists Do About this section. Physical therapists use different forms of treatment depending on the type of patient they are caring for. Physical therapists, sometimes called PTs, help injured or ill people improve their movement and manage their pain.These therapists are often an important part of rehabilitation and treatment of patients with chronic conditions or injuries.he work of physical therapists varies by type of patient. For example, a patient experiencing loss of mobility due to stroke needs different care from that given to an athlete recovering from an injury. Some physical therapists specialize in one type of care, such as orthopedics or geriatrics.\", \"What does a Physical Therapist do? Geriatric physical therapy focuses on the unique movement needs of older adults. This includes treatment for conditions such as arthritis, cancer, osteoporosis, Alzheimer's disease, joint replacement and balance disorders. The goal of geriatric physical therapy is to help restore mobility, reduce pain, accommodate physical limitations and increase physical fitness.\", \"What Do Physical Therapists Do? What Do Physical Therapists Do? As a physical therapist, you assess patients and determine treatment, as well as track patients' progress and evaluate the effectiveness of treatment. You also teach patients and families how to properly continue therapy once released from the hospital or medical office.\", \"What is a Physiatrist? What is a Physiatrist? Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons.\", \"Physical Medicine and Rehabilitation By Mayo Clinic Staff. Mayo Clinic specialists in Physical Medicine and Rehabilitation (PM&R) help restore movement and function to people disabled by disease or injury. PM&R physicians (called physiatrists \\u2014 pronounced fiz-e-AT-rists) create therapy plans that consider the unique needs, abilities and objectives of each patient.\", \"The Calorie Requirements for Female Bodybuilders & Weight Loss Calorie Balance. To lose weight, you need to create a calorie deficit, which involves consuming fewer calories than you burn. It takes a deficit of around 3,500 calories to burn 1 pound of fat, so if you're currently maintaining your weight, lowering your daily intake by 500 calories will lead to 1 pound of fat loss each week.\", \"Accredited Colleges Offering Sports Medicine Degrees Other Sports Medicine Specialties. 1 Physical Therapist: Physical therapists use a variety of methods to help patients deal with different physical issues. 2 Nurse: Nurses are an integral part of disease prevention, health promotion, and recovery from illness.\", \"Who Are Physical Therapists? Physical therapists (PTs) are highly-educated, licensed health care professionals who can help patients reduce pain and improve or restore mobility-in many cases without expensive surgery and often reducing the need for long-term use of prescription medications and their side effects.hysical therapists (PTs) are highly-educated, licensed health care professionals who can help patients reduce pain and improve or restore mobility-in many cases without expensive surgery and often reducing the need for long-term use of prescription medications and their side effects.\", \"Physical Therapy for Chronic Pain: What to Expect Reviewed by Melinda Ratini, DO, MS on July 22, 2016. Physical therapy is often one of the best choices you can make when you have long-term pain (also called chronic pain) or an injury. It can make you stronger and help you move and feel better. Ask your doctor to recommend a physical therapist.\", \"Physical Therapy Career Opportunities Physical Therapy and Rehabilitation. Physical therapists (PTs) provide services that help restore function, improve mobility, relieve pain, and prevent or limit permanent physical disabilities of patients suffering from injuries or disease. They restore, maintain, and promote overall fitness and health.\", \"- Physical therapists also are key health care. team members who address prevention initiatives, such as. reducing falls, improving physical activity to mitigate chronic. disease and secondary health conditions, and tailoring wellness. programs for populations that have chronic conditions and/. or disabilities.\", \"- Physical Therapy Basics. Doctors often recommend physical therapy (PT) for kids and teens who have been injured or who have movement problems from an illness, disease, or disability. After an injury, physical therapists work to decrease pain, improve movement, and help kids return to daily activities.\", \"- First, your therapist will try to reduce your pain and swelling. Your physical therapist also may use manual therapy, education, and techniques such as heat, cold, water, ultrasound, and electrical stimulation.Physical therapy almost always includes exercise.ome physical therapists are board-certified in areas such as orthopedics, sports, and neurology and may offer more specialized care. Physical therapists can also specialize in certain types of care, such as: 1 Back and neck pain. 2 Cardiac rehabilitation (rehab). 3 Wound care. 4 Cancer-related problems.\", \"Physical Therapy (PT) Physical therapy aims to improve joint and muscle function (eg, range of motion, strength) and thus improve the patient\\u2019s ability to stand, balance, walk, and climb stairs.For example, physical therapy is usually used to train lower-extremity amputees.hysical therapy aims to improve joint and muscle function (eg, range of motion, strength) and thus improve the patient\\u2019s ability to stand, balance, walk, and climb stairs.\", \"Why Visit a PM&R Physician Why Visit a PM&R Physician. 1 Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons. By taking the whole body into account, they are able to accurately pinpoint problems and enhance performance without surgery.\", \"Department of Physical Therapy A Physical Therapist (PT) is a health care professional who provides direct patient care to persons who have disorders of movement, mechanical, physiological and developmental impairments and functional limitations, whether caused by injury or disease, to help them achieve maximum physical function and mobility.\", \"physical medicine Definition of 'physical medicine'. Word Frequency. physical medicine. noun. the branch of medicine devoted to the management of physical disabilities, as resulting from rheumatic disease, asthma, poliomyelitis, etc. See also rehabilitation (sense 2)\"], \"pos_scores\": [95.5], \"neg_scores\": [94.3125, 93.3125, 91.625, 98.1875, 89.9375, 92.8125, 92.1875, 93.5, 94.1875, 93.0625, 94.1875, 96.125, 96.75, 92.6875, 91.8125, 93.5, 93.0625, 93.3125, 92.9375, 93.0, 92.75, 93.5, 96.6875, 93.5, 96.625]}\n{\"query\": \"what does pending mean on listing\", \"pos\": [\"- Active/Pending = Usually that means the seller would like to get more offers. Savvy agents want back-up offers in place. If you made an offer to purchase a property and the seller agreed to the price, but already has a buyer in contract, you would be the 1st position back-up buyer.\"], \"neg\": [\"Hours Worked Hours Worked | Bleakley Platt & Schmidt, LLP | New York Labor and Employment Attorneys. Employees must be paid for all of the hours that they are required to be on the employer's premises, on duty or at a prescribed work place.\", \"What Does a Pending Status on a House Mean? What Does a Pending Status on a House Mean? Brokers must report when the status of a home for sale changes. Real estate brokers must accurately report the status of a property listing to the multiple listing service, usually spoken of as MLS. Brokers rely heavily on the MLS to market listings and find interested buyers. As a member of an MLS, a listing broker must comply with certain rules and regulations.\", \"- For Sale: Properties which are available for showings and purchase. Active Contingent: Properties which are available for showing but are under contract with another buyer. Pending: Properties which are under contract with a buyer and are no longer available for showings. Sold: Properties on which the sale has closed.\", \"What Does Sale Pending Mean in Real Estate? Typically, the buyer has a week or two to do the inspection and a few weeks to get an appraisal and loan. During this period, the seller can\\u2019t enter into an agreement with another buyer, even though the sale hasn\\u2019t closed. This means another interested buyer could make a \\u201cback-up\\u201d offer. That way, if the first offer collapses, the seller has a back-up offer ready to go. A True Home, Pending Sale. A pending sale has had all contingencies removed.\", \"What is a root canal? The cost of root canals varies depending on the tooth and whether it is being treated by a general dentist or an endodontist. Molars have more canals that need to be filled, so they are more expensive, and endodontists typically charge more due to their specialty training.\", \"What\\u00e2s the Difference Between Contingent and Pending Listings? When a listing has changed status to \\u201cpending\\u201d, the seller has accepted an offer. This is an off-market status. Pending listings are much more common than contingent listings, since it just means the seller and buyer have agreed to the initial terms in the residential purchase agreement.\", \"Active Status? Pending Status? This status may also be used to indicate the property is a short sale with an accepted offer. Pending w/o release, continue to show (PS) or (Pend w/S) \\u2013 Accepted offer on the property, still showing to prospective buyers for offers in back-up position to the first accepted offer.\", \"What Do All Those Real Estate Listing Terms Really Mean? If your offer is accepted as a backup, you\\u2019re in line to go under contract if the first sale falls through. Pending, showing for backup This means the property's owners are actively taking backup offers in case the first one falls through.\", \"Sale pending? \\u00b7 just now. 1 Sale pending means that there is a transaction that is going through Escrow but that has not closed yet. 2 Sale pending can mean anything from an offer being accepted or contracts signed. 3 It means the buyer and seller have agreed on a contract, but closing has not happened yet.\", \"\\\"What does \\\"\\\"option pending\\\"\\\" mean on a real estate listing?\\\" What does option pending mean on a real estate listing? I've been looking for houses on the internet, and came across several listings that said option pending. Does it mean there's an offer on the house and it's pending approval? 2 following.\", \"- Other types of active statuses may be New or Price Change.. New means that the listing has been on the market for 3 - 7 days; Price Change means that the listing experienced a price decrease or increase within the past 3 - 7 days. There may be different types of active listings, depending on the MLS.\", \"Does 'Pending Contact Request' under a contact in skype mean that they have blocked me? up vote 1 down vote. Pending request-It means that the person has just removed you from their Skype contact list but hasn't blocked you. You can however send them messages to them but can't call them. The person can call you if they want. You can send friend request again, if they removed you from contact list by mistake.\", \"\\\"What does \\\"\\\"Pending\\\"\\\" mean? Is there a problem?\\\" According to our documentation on payment statuses, Pending means: This is a payment that has begun, but is not complete. An example of this is someone who has filled out the checkout form and then gone to PayPal for payment. We have the record of sale, but they haven't completed their payment yet. This means that Pending isn't inherently a problem, but it can be an indication of some other problem.\", \"\\\"What does \\\"\\\"Contingent\\\"\\\" mean for a listing status?\\\" Most of these are self-explanatory\\u2026. 1 Active means that the property is for sale. 2 Come on over and make us an offer. 3 Subject to Inspection means that a buyer\\u2019s offer is pending their inspection.\", \"- Homes with a Make Me Move\\u00ae price indicate the amount the owner(s) would be willing to sell for. They are exclusive to Zillow and a great way to learn about homes before they hit the market. A pending listing means a seller has accepted an offer from a buyer. In some cases, the seller will accept backup offers.\", \"MLS Rules & Regulations FAQ YES, MLS rules require that if a seller has signed acceptance of an offer, the listing status must be changed to Contingent or Pending. Lender approval of a \\u201cShort Sale\\u201d is treated as a contingency and does. not change the timing of the required change to Contingent or Pending status.\", \"Under Contract: \\u00e2Contingent\\u00e2 vs. \\u00e2Pending\\u00e2 \\u201cContingent\\u201d means it can be shown. \\u201cPending\\u201d means it cannot be shown, although there may still be contingencies. Contingent : Contingent status indicates that a property under contract is subject to an additional contingency or condition, i.e. \\u201cContingent Sales Addendum\\u201d, Due Diligence Period, etc.\", \"- Payments to some eBay sellers may be shown as pending in your PayPal account, and may not be available for a period of time to allow for successful fulfillment. Common reasons your funds might be pending. These are the most common reasons your funds would be unavailable for a period of time: 1 You're a new seller or have limited selling history, which means you have not yet met all of the following criteria: It's been more than 90 days since your first successful sale. You've made more than 25 sales transactions.\", \"what is the definition of pending litigation Would that Pending litigation refers to any suits which have been filed, but remain unresolved. A notice of claim could be included, if that referenced a notice of claim which had been filed in court.It appears that you may be referring to lis pendens which is filed with real property records to place potential buyer's on notice of pending litigation involving the real property. Thank you.his is in reference to a Homeowners Association filing a claim with the builder under the 10 year time frame. (Sec 1375) No formal complaint has been filed. They are in the pre-litigation stages.\", \"Under Contract: \\u00e2Contingent\\u00e2 vs. \\u00e2Pending\\u00e2 A listing in Contingent status will not expire at the end of the listing contract term. Pending: Pending status indicates that a property is under contract and is not available for showings. Listings in this status may include contingencies. The listing will not expire at the end of the listing contract term.\", \"- What does pending disposition mean in legal terms? A pending disposition in legal terms means that no decision has been reached yet. When someone is arrested and they have a pending disposition, it means that it has not ye \\u2026 t been determined whether they have actually committed a crime or not.\", \"Why is the order status pending? For some purchases, such as services, rentals, or hotel stays, the seller will complete the payment after your purchase is fully delivered. The status of an order will continue to display as pending until it expires, is completed, or has been voided.\", \"What does pending mean? im asking what pending is because im installing some games on my laptop and all it says is its pending\", \"What is a pending transaction? Pending transactions include credits and debits that have not yet posted to your account. They may also include holds applied to certain transactions or your balance. Pending transactions are listed in lowest to highest dollar amount order, and are not listed in the order that they post to your account.\", \"Option Pending vs Pending continue to show OP - Option Pending - Listings that are under contract and the seller and buyer have agreed to use the \\u201cTermination Option\\u201d in paragraph 23 of the standard TREC contract. PS - Pending Continue to Show - Used for listings currently under contract but are still available to show.\"], \"pos_scores\": [96.9375], \"neg_scores\": [88.625, 96.75, 95.1875, 96.0, 87.8125, 99.125, 95.25, 97.0625, 96.8125, 95.4375, 91.6875, 92.5625, 93.9375, 95.0625, 98.25, 94.25, 95.875, 89.875, 93.0, 98.375, 92.0625, 90.6875, 89.125, 92.6875, 96.9375]}\n{\"query\": \"feeding rice cereal how many times per day\", \"pos\": [\"FEEDING GUIDELINES AGES 4 TO 6 MONTHS 1. Begin with rice cereal on days 1,2 and 3, offering it twice daily at breakfast and dinnertime. Rice cereal can be mixed with water or milk(breast or formula) to make a thin oatmeal like consistency. The infant should be offered a rubberized spoon. During these first three days, offer 3-4 tablespoons at a time but be flexible. Some infants will be very hungry and want more- that's OK.\"], \"neg\": [\"How often do kittens need to eat? Kittens, at any age, have small stomachs, so the best method of feeding is little and often, as often as four to six times a day for very young kittens. As kittens get older and bigger, they can be fed bigger portions in less frequent meals. By 6 months, a kitten can generally eat two or three times a day. How often a kitten needs to eat wholly depends on each individual. Some kittens eat more and some eat less.\", \"Learn From Amazon\\u00e2s Leadership Principles Learn From Amazon\\u2019s Leadership Principles. Amazon is a crazy company. Within two decades, this bookseller has become a competitor of every single industry leader across retail, publishing, logistics, wireless, toys, devices, apparel and cloud computing, to name a few.\", \"The Serving Size of Grains Needed Daily Toddlers need 3 ounces of grains each day as part of a healthy diet. At least 1 1/2 ounces of these should be whole grains. Girls between the ages of 4 and 8 need 5 ounces of grains per day, with at least 2 1/2 of them being whole grains.\", \"How do we get started with solids? Increase solids gradually if baby is interested, with a maximum of 2 meals per day. 9 \\u2013 12 months. Watch baby\\u2019s cues \\u2013 this is particularly easy if baby nurses beforehand and most/all of the solids are offered to baby to self-feed. Increase solids gradually if baby is interested.\", \"Is Eating Rice Healthy? How Much Rice. The amount of whole-grain rice eaten daily for a healthy diet depends on how much other whole grain foods the dieter eats. About 6 to 8 ounces of whole-grain foods should be eaten on a 2,000-calorie diet, according to the USDA Dietary Guidelines for Americans.\", \"Flax Seeds: How Much Per Day? Serving Size. The University of Maryland Medical Center recommends adults consume 1 tablespoon of ground flaxseed two to three times a day, or 2 to 4 tablespoons once per day.You can mix the ground seeds into cereal, yogurt, oatmeal or smoothies or sprinkle them generously on top of salads and vegetable dishes.erving Size. The University of Maryland Medical Center recommends adults consume 1 tablespoon of ground flaxseed two to three times a day, or 2 to 4 tablespoons once per day.\", \"Left axis deviation Left axis deviation (LAD) is a condition whereby the mean electrical axis of ventricular contraction of the heart lies in a frontal plane direction between -30\\u00b0 and -90\\u00b0. This is reflected by a QRS complex positive in lead I and negative in leads aVF and II.\", \"Macromolecules Lipids are an important part of living cells. Together with carbohydrates and proteins, lipids are the main constituents of plant and animal cells. Cholesterol and triglycerides are lipids. Lipids are easily stored in the body. They serve as a source of fuel and are an important constituent of the structure of cells.Lipids include fatty acids, neutral fats, waxes and steroids (like cortisone).ogether with carbohydrates and proteins, lipids are the main constituents of plant and animal cells. Cholesterol and triglycerides are lipids. Lipids are easily stored in the body. They serve as a source of fuel and are an important constituent of the structure of cells.\", \"- To get rice flour, head out to your Asian market. It's dirt cheap and does the same. My daughter-in-law has been advised by the paediatrician to continue giving her baby of 8 months old rice porridge in the afternoon in place of her bottle feed. She has now stopped as she feels the baby is overweight.\", \"How Much To Feed Your Cat Even though you find feeding instructions on almost every bag of cat food you can buy, the instructions are useless. One brand may tell you to feed your cat 1/2 cup twice daily, while another recommends 1/4 cup three times daily. It's not just the manufacturers of cat food that can't tell you how much to feed your cat.\", \"Top 30 Doctor insights on: Purple Rice Supplement 3 doctors agreed: This depends on: how old your baby is. Rice cereal is generally started at 4-6 months, so if your baby is younger than that, you can supplement with formula, as opposed to cereal. Please also discuss this with his/her pediatrician; they may also be able to give you some hints on how to increase your breast milk supply. ...Read more.\", \"- Feeding tips. 1 If your baby won't eat what you're offering on the first try, offer it again in a few days. 2 Get more detailed tips on how to introduce solids. 3 Print our step-by-step guide to feeding your baby. Begin with about 1 teaspoon pureed food or cereal. 2 Mix cereal with 4 to 5 teaspoons breast milk or formula (it'll be very runny). 3 Increase to 1 tablespoon of pureed food, or 1 tablespoon of cereal mixed with breast milk or formula, twice a day. 4 If giving cereal, gradually thicken the consistency by using less liquid.\", \"How many ounces of wet food per day should be given to a cat? We feed our 3 cats one 6 oz can and divide that. But they also get a meal of dry in the morning and raw 2-3 times a week. For your cat about 1/3 of a 6 oz can of food per meal should be fine for her size. Wet food can be very messy and smelly. Be careful about feeding only wet! If you change her food for the emergency and her system is not ready for it, she will develop diarrhea. If you do decide to use wet food, I suggest using it more as a treat twice a day. I gave between 1/4 to 1/2 a small can (6 oz?) per cat twice a day. I always have unlimited amounts of dry food available as well as fresh water.\", \"How Much Should I Eat? Grains: Make at least half of your grains whole grains every day: 1 Males ages 19 to 30: 8 ounce equivalents of grains/day. 2 Females ages 19 to 30: 6 ounce equivalents of grains/day. 3 Examples of one ounce equivalent: 1 slice of bread; 1 cup of ready-to-eat cereal; or \\u00bd cup of cooked rice, cooked pasta, or cooked cereal.\", \"- Foods to Avoid. 1 6-11 servings each day. 2 Serving size= 1 slice bread, 1 cup ready-to-eat cereal,1/2 cup cooked cereal, rice or pasta. 3 All enriched breads, cereals, rice, noodles, pasta, and potatoes. Limit to 2 servings per week: whole-grain breads and cereals, wheat germ, bran and oatmeal.\", \"How do we get started with solids? Increase solids gradually if baby is interested, with a maximum of 2 meals per day. 9 \\u2013 12 months. Watch baby\\u2019s cues \\u2013 this is particularly easy if baby nurses beforehand and most/all of the solids are offered to baby to self-feed.ffer solids once a day, at most. Many start out offering solids every few days or even less often. Continue nursing on cue. Solid foods should not replace nursing sessions unless you\\u2019re actively weaning.Limit water to SIPS from a cup with meals.\", \"How long to cook a prime rib roast?? Preheat oven to 450\\u00b0F. Place the roast (ribs down) on the rack in a roasting pan. Sear the rib roast for 15 minutes at the higher oven temperature (450\\u00b0F), and then turn the oven to the lower temperature (325\\u00b0 F) for the rest of the cooking time.About 1/2 hour before the estimated end of the roasting time, begin checking the internal temperature (use a good instant-read digital meat thermometer.nsert meat thermometer so tip is in thickest part of beef, not resting in fat or touching bone. Cook until rib roast reaches an internal temperature of 120\\u00b0F. Remove from oven, cover with aluminum foil, and let sit approximately 15 to 20 minutes.\", \"3 month old and rice cereal 3 out of 3 found this helpful. My son will be 3 months Sunday and I have been giving him 1/2 a tbsp of Rice Cereal in his bottle for the past couple of days... just to try it out. (once in the morning and once at night) He usually has a 6-8 oz bottle every 3-4 hours.\", \"Portion Guide for Baby's First Year Between 4 and 6 months, your baby will start to sit up and grab for objects on his own. As he masters that grabbing skill, you may opt to start introducing cereal into his diet. Baby should eat 1-2 tablespoons of cereal twice a day.\", \"Should You Add Rice Cereal to Baby's Bottle? How to Introduce Rice Cereal If your doctor determines that your baby is ready for solids, at a tablespoon of rice cereal to every 4-5 tablespoons of breast milk or formula that you would normally feed your child.\", \"Cornell Feline Health Center From age six months to maturity, most cats will do well when fed two times a day.. Once the cat becomes an adult, at about one year, feeding once or twice a day is appropriate in most cases. Senior cats, age seven and above, should maintain the same feeding regimen. Once cats reach adulthood, once a day feeding is fine as long as they are healthy and have no disease problems suggesting a reason to feed differently, says Dr. Kallfelz. The Health of Your Cat Matters.\", \"- The following observations are made: \\u2022 Wholesale and retail trade was the largest employer with 22,2% of the employed in South Africa and 21,5% in KwaZulu-Natal, followed by community, social and personal services with 19,5% in South Africa and 18,8% in KwaZulu-Natal.\", \"Why do Japanese eat alot of rice? Asker's rating. 1 I almost eat rice three times a day. This is natural for me because I was brought up with eating rice. 2 Starches are the foundation for every cuisine in the world. In each area of the world the conditions are right for one or more native starch crops. 3 Because, they grow well. They suit the weather there.\", \"Gerber, Oatmeal Cereal, Single Grain, 8 oz (227 g) \\u00b7 Mix 1 tbsp. cereal with 4-5 tbsp. of breastmilk or infant formula. Easy-to-Mix Directions. \\u00b7 Pour or spoon desired amount of cereal in bowl. \\u00b7 For Baby: Stir in liquid (breastmilk or infant formula) to desired consistency. \\u00b7 For Toddler: mix with milk, water, or GERBER \\u00ae Juice for children over one year of age.\", \"Feeding infant Rice Cereal: How many times a day and how much? Watch for any reactions. 4- After that, feed 2 tablespoons of single grain oatmeal + 1 1/2 ounces of formula once a day. ALSO, continue feeding rice cereal + formula once a day... so he will be eating solids two times a day. I usually did one time mid morning and the second at night to space it out. 5- Feed with the oatmeal (+ rice cereal for 2nd feeding) for 5 days.\"], \"pos_scores\": [96.875], \"neg_scores\": [90.1875, 88.0625, 90.375, 93.375, 89.75, 89.0, 85.0625, 89.8125, 89.875, 88.75, 91.8125, 93.3125, 88.5625, 89.4375, 89.75, 93.625, 88.1875, 93.875, 93.8125, 93.375, 89.875, 85.5625, 90.0625, 91.5, 97.9375]}\n" }, { "path": "examples/finetune/reranker/example_data/prompt_based/examples.jsonl", "content": "{\"query\": \")what was the immediate impact of the success of the manhattan project?\", \"pos\": [\"Introduction The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.\"], \"neg\": [\"The Launch of Sputnik, 1957 The Soviets responded with yet another launch, and the space race continued. The success of Sputnik had a major impact on the Cold War and the United States. Fear that they had fallen behind led U.S. policymakers to accelerate space and weapons programs.he Soviets responded with yet another launch, and the space race continued. The success of Sputnik had a major impact on the Cold War and the United States. Fear that they had fallen behind led U.S. policymakers to accelerate space and weapons programs.\", \"Important result of the Marshall plan? Effected by the United States after World War II, the Marshall Plan produced the quite important result of building up nations shattered during the war. It did so quite quickly, as well.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first nuclear weapons during World War II.he United States had already spent more than $1 billion ($13,600,000,000 today), while in 1943, the United Kingdom had spent about \\u00a30.5 million. Chadwick thus pressed for British involvement in the Manhattan Project to the fullest extent and abandon any hopes of a British project during the war.\", \"- Los Alamos is where the first known Manhattan Project bombs were built and tested. On July 16, 1945, in a remote desert location near Alamogordo, New Mexico, the first atomic bomb was successfully detonated\\u2014the Trinity Test\\u2014creating an enormous mushroom cloud some 40,000 feet high and ushering in the Atomic Age.\", \"Holocauste Literature Meanwhile, President Truman was told of the successful test of the Manhattan Project (atomic bomb) in Alamogordo, New Mexico on Jul 16, 1945. Diary of President Truman of Jul 18, 1945 shows Discussed Manhattan (it is a success).t the end of World War II, few questioned Truman's decision to drop the atomic bombs on Hiroshima and Nagasaki. Most Americans accepted the obvious reasoning: the atomic bomb \\u2026 ings brought the war to a more timely end.\", \"- The Soviet project to develop an atomic bomb (Russian: \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u0441\\u043e\\u0432\\u0435\\u0442\\u0441\\u043a\\u043e\\u0439 \\u0430\\u0442\\u043e\\u043c\\u043d\\u043e\\u0439 \\u0431\\u043e\\u043c\\u0431\\u044b) was a top secret research and development program begun during World War II, in the wake of the Soviet Union 's discovery of the American, British, and Canadian nuclear project.hrough sources in the Manhattan Project, notably Klaus Fuchs, the Soviet intelligence obtained important information on the progress of the United States atomic bomb effort. Intelligence reports were shown to the head of the Soviet atomic project and had a significant impact on the direction of Soviet research.\", \"Monroe Doctrine The immediate impact of the Monroe Doctrine was mixed. It was successful to the extent that the continental powers did not attempt to revive the Spanish empire, but this was on account of the strength of the British Navy, not American military might, which was relatively limited.\", \"What impacts did benjamin harrison have on america while in office? Sorry, something has gone wrong. Best Answer: The greatest positive impact during his administration was the passing of the Sherman Anti-Trust Act of 1890, the first federal act to regulate trusts.\", \"The Manhattan Project Manhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first atomic bombs during World War II.It was led by the United States with the support of the United Kingdom and Canada.he Manhattan Project began modestly in 1939, but grew to employ more than 130,000 people and cost nearly US$2 billion (about $26 billion in 2015 dollars). Over 90% of the cost was for building factories and producing the fissionable materials, with less than 10% for development and production of the weapons.\", \"- Roosevelt agreed and placed General Leslie Groves and physicist J. Robert Oppenheimer in charge of the Manhattan Project two years later. The name Manhattan Project was the code word for the development of the atomic bomb. On July 16, 1945, the first atomic bomb was tested at the Trinity Site in New Mexico.The weapon was later used against the Japanese to end World War II.eginning in June 1942 during World War II, the United States' Manhattan Project brought together scientists and military experts to create the world's first atomic bomb.\", \"51f. The Manhattan Project In late 1941, the American effort to design and build an atomic bomb received its code name \\u2014 the Manhattan Project. At first the research was based at only a few universities \\u2014 Columbia University, the University of Chicago and the University of California at Berkeley.\", \"The Manhattan Project Manhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.anhattan Project. The Manhattan Project was a secret military project created in 1942 to produce the first US nuclear weapon. Fears that Nazi Germany would build and use a nuclear weapon during World War II triggered the start of the Manhattan Project, which was originally based in Manhattan, New York.\", \"- The Manhattan Project was an effort during World War II in the United States to develop the first nuclear weapon.\", \"Atomic Glossary The Manhattan Project was the code name for America's atomic bomb development efforts during World War II. Its name originated from the fact that it was part of the U. S. Army Corps of Engineers and organized under the Manhattan Engineer District (MED) in New York City.\", \"Introduction While communication led to the overall scientific triumph of the Manhattan project, an intentional lack of communication between military and government superiors and the Los Alamos laboratory led to one of the most devastating moral disasters of the twentieth century.\", \"- At the end of the unit the children will be quizzed on the different senses which will have to be matched to their source. The children will have to name the five senses and which part of the body is linked with that particular sense. Also, the story incorporating the five senses will also be used as a way to assess what the children comprehended ...\", \"- This project used hundreds of scientist and thousands of people to develop the first Atomic Bomb. The Manhattan Project was implemented by President Franklin Roosevelt in no small part due to two letters sent to him by Albert Einstein.\", \"The Cold War Museum In June of 1942 the War Department\\u2019s Army Corps of Engineers took charge of the effort to develop an atomic bomb. The subsequent top-secret project (code named Manhattan) cultivated a complex, but cooperative relationship between science, industry, the government, and the army.\", \"Moments in U.S. Diplomatic History Only later did we learn through Gorge that we had a ringside view of the Doolittle raid [the daring air attack on Tokyo on April 18, 1942, which was designed to show that Japan was vulnerable and to boost U.S. morale after Pearl Harbor]\\u2026.\", \"Car Crashes Kill 40,000 in U.S. Every Year Car Crashes Kill 40,000 in U.S. Every Year. Imagine a plane full of people crashing, killing everyone on board, every single day. That\\u2019s how many people die on America\\u2019s roads daily, says the Insurance Institute for Highway Safety. \\u201cMotor vehicle crashes in the United States result in more than 40,000 deaths per year,\\u201d says the Institute in the journal Injury Prevention. \\u201cThat is, on each of the 6,209 consecutive days included in this study, an equivalent of a plane load or more of people died on the roads.\\u201d. But not all days are alike.\", \"arrest Definition of arrest. 1 1 : the taking or detaining in custody by authority of law The investigation led to his arrest. 2 2a : the act of stoppingb : the condition of being stopped or inactive \\u2014 compare cardiac arrest.\", \"The Atomic Bomb The Desert Test By July, 1945 the Manhattan Project work was almost completed; they had developed a working nuclear bomb. The only obstacle that stood in their way is the actual testing of the bomb. The test, code name Trinity occurred on July 16, 1945 in the New Mexico desert town of Alamogordo.\", \"Nuclear explosion in Hiroshima: details and facts Right after the bomb blast in Hiroshima, President Truman officially announced the yield of the bomb as 18 kilotons, and the media rounded it to 20. Later on, after a survey, conducted by the Manhattan Project, it was calculated, that the yield of the Little Boy equaled to 12 kilotons.\", \"- The Manhattan Project was a US government project used to develop a nuclear bomb. The undertaking lasted form 1942 to 1946, when...\"], \"pos_scores\": [96.6875], \"neg_scores\": [90.5625, 89.0, 92.9375, 93.3125, 93.5, 91.75, 92.375, 86.75, 93.4375, 93.1875, 92.5, 92.375, 92.375, 93.3125, 92.3125, 94.0625, 88.0, 92.375, 91.9375, 89.3125, 83.1875, 87.5625, 92.0, 90.4375, 92.625], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"_________ justice is designed to repair the harm to victim, the community and the offender caused by the offender criminal act. question 19 options:\", \"pos\": [\"Restorative justice The approach is based on a theory of justice that considers crime and wrongdoing to be an offense against an individual or community, rather than the State. Restorative justice that fosters dialogue between victim and offender has shown the highest rates of victim satisfaction and offender accountability.\"], \"neg\": [\"What is Restorative Justice? Restorative Justice is a community-based approach to dealing with crime, the effects of crime, and the prevention of crime. Most people who move through the current system of criminal justice do not find it a healing or satisfying experience. Victims often feel re-victimized and their need for justice unmet.\", \"California\\u00e2s Criminal Justice System: A Primer The criminal justice system is based on criminal sentencing law, the body of laws that define crimes and specify the punishments for such crimes. The majority of sentencing law is set at the state level.\", \"- will serve the community and enhance public trust and confidence in the administration of justice through: 1 The impartial and timely resolution of disputes, 2 Ensuring compliance with the law and court orders, and. 3 Fostering a vital court-community relationship that promotes equal access to the courts.\", \"- CRIME VICTIM'S GUIDE. A Crime Victim's Guide to the Justice System in Arkansas was written to assist victims of crime in better understanding the Arkansas criminal justice system so they are more able to exercise their rights.\", \"- CORRECTIVE JUSTICE. The doer is directly liable to the sufferer, so that reparation of the. loss occurs through disgorgement of the gain. By directly linking the parties, corrective justice categorically. differs from distributive justice, the other form of justice that Aris-.\", \"3 \\u00e2 Restorative Justice: Justice That Promotes Healing Each of these types of communities\\u2014the geographic community of the victim, offender, or crime; the community of care; and civil society\\u2014may be injured by crime in different ways and degrees, but all will be affected in common ways as well: The sense of safety and confidence of their members is threatened, order within the community is threatened, and (depending on the kind of crime) common values of the community are challenged and perhaps eroded.\", \"- Mission is to protect the public from criminal offenders through a system of incarceration and supervision which securely segregates offenders from society, assures offenders of their constitutional rights and maintains programs to enhance the success of offenders' reentry into society.\", \"How Does the Criminal Justice System Work? Throughout each stage of the process, constitutional protections exist to ensure that the rights of the accused and convicted are respected. These protections balance the need of the criminal justice system to investigate and prosecute criminals with the fundamental rights of the accused (who are presumed innocent).\", \"Restorative Justice \\u2022 Restorative Justice can be utilized in crimes of severe violence or non-violent crimes as well as with juvenile offenders. \\u2022 Restorative Justice is an approach to crime which puts the victim or the victim\\u2019s family first and fully acknowledges the harm caused by the offender.\", \"- Criminal Justice. An effective criminal justice system is a key aspect of the rule of law, as it constitutes the natural mechanism to redress grievances and bring action against individuals for offenses against...\", \"- Punishment should be swift and certain. The purpose of the criminal justice system is to prevent crime through deterrence. According to this line of thinking, a potential criminal will decide against committing a crime because the punishment would be too costly.\", \"- As you have learned, distributive justice deals with the fairness of the distribution of benefits or burdens among two or more people or groups in society. Benefits may be such things as pay for work or the right to speak or to vote.xamining Issues of Justice We think of the essence of justice as fairness, and the essence of fairness as treating people equally. However, issues of justice can arise even if everyone is subjected to the same rules, and even if everyone who breaks the rules receives the same punishment.\", \"PUBLICATIONS Consequently, the equations of actuarial justice tend to be more simplified and intended for application to broad groupings of offenders. The more groups can be narrowed, the fairer and more effective will be the criminal justice policies toward offenders.\", \"- Social justice is justice in terms of the distribution of wealth, opportunities, and privileges within a society. Classically, justice (especially corrective justice or distributive justice) ensured that individuals both fulfilled their societal roles and received what was their due from society.\", \"\\\"\\\"\\\"How Can Law Enforcement Professionals Use The Social Justice Principles Of Equality Solidarity And Human Rights To Build A More Just Society\\\"\\\" Essays and Research Papers\\\" Justice in Law Enforcement The true concept of justice is a concept involving moral, fair, and... impartial treatment of all individuals. Justice is a concept that has many different translations and a concept that can be changed on a case-by-case basis.\", \"Criminal justice The criminal justice system consists of three main parts: (1) Legislative (create laws); (2) adjudication (courts); and (3) corrections (jails, prisons, probation and parole).\", \"- Distributive justice concerns the nature of a socially just allocation of goods in a society. A society in which incidental inequalities in outcome do not arise would be considered a society guided by the principles of distributive justice.\", \"Victims of Crime This environment began to change in the 1970s with the establishment of victim compensation funds. Not until the 1980s, however, did a national movement for victims' rights spark wholesale changes in the criminal justice system.\", \"- Economic justice is a component of social justice. It's a set of moral principles for building economic institutions, the ultimate goal of which is to create an opportunity for each person to create a sufficient material foundation upon which to have a dignified, productive, and creative life beyond economics.\", \"- Criminal law serves several purposes and benefits society in the following ways: 1 Maintaining order. 2 Resolving disputes. 3 Protecting individuals and property. 4 Providing for smooth functioning of society. 5 Safeguarding civil liberties.\", \"Victims bill of rights would compel spouses to testify 'Victims deserve fair and equal treatment by the justice system and they need to be assured that the system itself doesn't re-victimize' \\u2014 Sheldon Kennedy, former NHL player. Crime victims would have more say as their cases wind their way through the justice system under a newly proposed victims bill of rights.The long-awaited legislation, part of the government's law-and-order agenda, aims to fix what Prime Minister Stephen Harper said Thursday was a broken part of the justice system.Victims deserve fair and equal treatment by the justice system and they need to be assured that the system itself doesn't re-victimize' \\u2014 Sheldon Kennedy, former NHL player. Crime victims would have more say as their cases wind their way through the justice system under a newly proposed victims bill of rights.\", \"Social justice Social justice is a concept of fair and just relations between the individual and society. This is measured by the explicit and tacit terms for the distribution of wealth, opportunities for personal activity and social privileges. In Western as well as in older Asian cultures, the concept of social justice has often referred to the process of ensuring that individuals fulfill their societal roles and receive what was their due from society. In the current global grassroots movements for social j\", \"Procedural justice The idea of the outcomes model of procedural justice is that the fairness of process depends on the procedure producing correct outcomes. For example, if the procedure is a criminal trial, then the correct outcome would be conviction of the guilty and exonerating the innocent.\", \"Retribution Retribution is perhaps the most intuitive - and the most questionable - aim of punishment in the criminal law. Quite contrary to the idea of rehabilitation and distinct from the utilitarian purposes of restraint and deterrence, the purpose of retribution is actively to injure criminal offenders, ideally in proportion with their injuries to society, and so expiate them of guilt.\", \"Retributive Justice The idea of retributive justice has played a dominant role in theorizing about punishment over the past few decades, but many features of it\\u2014especially the notions of desert and proportionality, the normative status of suffering, and the ultimate justification for retribution\\u2014remain contested and problematic. 1 1.\"], \"pos_scores\": [95.3125], \"neg_scores\": [95.0, 90.25, 88.1875, 90.25, 92.6875, 94.5625, 88.0, 90.3125, 95.6875, 90.6875, 90.0, 90.0625, 89.875, 90.9375, 89.75, 90.6875, 90.3125, 90.0, 89.875, 89.125, 89.6875, 89.75, 89.625, 90.25, 91.0625], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"what color is amber urine\", \"pos\": [\"- Color\\u2014urine can be a variety of colors, most often shades of yellow, from very pale or colorless to very dark or amber. Unusual or abnormal urine colors can be the result of a disease process, several medications (e.g., multivitamins can turn urine bright yellow), or the result of eating certain foods.\"], \"neg\": [\"- ISO 9001:2008 certified manufacturer of standard & metric washers. Types include shoulder washers, finishing washers, cup washers, retaining washers, split flat washers & standard & special flat washers.\", \"Urine color Orange urine may result from: Medical conditions. In some cases, orange urine can indicate a problem with your liver or bile duct, especially if you also have light-colored stools. Orange urine may also be caused by dehydration, which can concentrate your urine and make it much deeper in color.\", \"- Normal urine color varies, depending on how much water you drink. Fluids dilute the yellow pigments in urine, so the more you drink, the clearer your urine looks. When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white. When to see a doctor\", \"How Long After Intercourse Can I Take a Pregnancy Test? A pregnancy test will detect this hormone about one week after fertilization. Most women tend to wait to take a pregnancy test until after they have missed one menstrual period. If you take a pregnancy test one week after you were supposed to get your period, the result will be about 95% accurate.If you take it too soon after fertilization, it may show up negative when in fact you really are pregnant. Pregnancy test are meant to be considered screening tests.f you take a pregnancy test one week after you were supposed to get your period, the result will be about 95% accurate. If you take it too soon after fertilization, it may show up negative when in fact you really are pregnant.\", \"Urine 1 Reddish or brown urine may be caused by porphyria (not to be confused with the harmless, temporary pink or reddish tint caused by beeturia). 2 Blue urine can be caused by the ingestion of methylene blue (e.g., in medications) or foods or beverages with blue dyes. Blue urine stains can be caused by blue diaper syndrome.\", \"What does bright yellow urine indicate? When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But sometimes urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white.\", \"- 4. Imitation Urine. In general, the physical appearance of urine should be yellow to yellow-tan in color. Unfortunately, there are liquid substances available such as apple juice, cranberry juice, etc., that have the same color as urine. Noting the temperature of the urine is important. 5. Length of time in the bathroom\", \"- Darkened urine is urine that is dark yellow, brown, dark red, or red, and it may range from slightly dark to considerably dark. A change in urine color may be temporary, or it may be persistent.\", \"- Amber Color The color of amber fossil varies from yellow to dark brown to almost black. Very rarely this gem may be found in green and blue-gray colors and hence green amber can be very rare. In addition, it is dyed in many colors like green, blue, pink etc.mber Color The color of amber fossil varies from yellow to dark brown to almost black. Very rarely this gem may be found in green and blue-gray colors and hence green amber can be very rare. In addition, it is dyed in many colors like green, blue, pink etc.\", \"Colorless Urine And Kidney Disease Colorless Urine And Kidney Disease. Due to urochrome, the normal urine color can range from pale yellow to deep amber. Abnormal urine color can be caused by foods, drugs, pigment, blood, urinary tract infection, kidney problems and other factors. Urine color has much to do with fluid intake.The more water we drink, the more light the color is.olorless Urine And Kidney Disease. Due to urochrome, the normal urine color can range from pale yellow to deep amber. Abnormal urine color can be caused by foods, drugs, pigment, blood, urinary tract infection, kidney problems and other factors. Urine color has much to do with fluid intake.\", \"Urine color and odor changes Rhubarb can also turn urine dark brown or tea-colored, as can fava beans and aloe. Carrots, carrot juice, and vitamin C can color urine orange, and B vitamins can turn it a fluorescent yellow-green. Asparagus sometimes gives urine a greenish tinge and a distinctive smell, said to resemble rotting cabbage. The cause of this smell is a matter for speculation.\", \"Urine Color Normal urine color ranges from pale yellow to deep amber \\u2014 the result of a pigment called urochrome and how diluted or concentrated the urine is.Pigments and other compounds in certain foods and medications may change your urine color.Beets, berries and fava beans are among the foods most likely to affect urine color. Many over-the-counter and prescription medications give urine vivid tones, such as raspberry red, lemon yellow or greenish blue.An unusual urine color can be a sign of disease. For instance, deep red to brown urine is an identifying characteristic of porphyria, a rare, inherited disorder of red blood cells.igments and other compounds in certain foods and medications may change your urine color. Beets, berries and fava beans are among the foods most likely to affect urine color.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.Most changes in urine color are harmless and the color will go back to normal within a day or two.rine with a bluish tint can mean some type of bacterial infection or may just mean that you have a high level of calcium. If this persists, give your health care professional a call. Brown urine can indicate a potentially serious condition and you should contact your doctor's office for help.\", \"- ask the disney parks moms panel discover the magic of a disney parks family vacation from one of our knowledgeable online moms this is a question and answer forum to help you plan the best possible disney vacation due to a high volume of submissions we cannot guarantee that all questions will be answered also please keep the number of questions in each submission to a limit of 3\", \"- The results can be urine that is either pink or cola-colored. Sometimes it\\u2019s difficult to tell the difference between dark urine due to dehydration or due to other causes. Dark urine due to dehydration is usually amber or honey-colored. Dark urine due to other causes can be tinged with brown or red. Some people have urine that appears almost syrup-like. This is the case when a person has liver or kidney disease. If you\\u2019re dehydrated, you can have additional symptoms besides dark urine.\", \"Alkaptonuria Alkaptonuria is a rare condition in which a person's urine turns a dark brownish-black color when exposed to air. Alkaptonuria is part of a group of conditions known as an inborn error of metabolism.\", \"Urine color Orange urine may also be caused by dehydration, which can concentrate your urine and make it much deeper in color. Blue or green urine Blue or green urine may be caused by: Dyes. Some brightly colored food dyes can cause green urine. Dyes used for some tests of kidney and bladder function can turn urine blue.\", \"- Urinalysis begins with a macroscopic examination of the urine which describes the color and clarity of the urine. In healthy individuals urine color ranges from pale yellow to amber, depending on their state of hydration. Many factors affect urine color including fluid balance, diet, medications and disease.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.ormal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.\", \"50 Shades Of Yellow: What Color Should Your Pee Be? Here's what your pee might be telling you: 1 Straw-Colored To Transparent-Yellow Pee: This is the normal urine color of a healthy, well-hydrated body. 2 Transparent Or Clear Pee: You should always be properly hydrated, but you can actually drink too much water, which will make your urine virtually colorless.\", \"Aging Skin: Do You Look Older Than You Should? Another Top Cause of Wrinkles: Smoking. Beyond question, smoking is bad for your skin. Smoking accelerates the aging process, wrinkling skin and making you look old beyond your years. Early wrinkling is visible under a microscope in smokers as young as 20.he more years and packs smoked, the more likely wrinkles will occur. Wrinkles are also more likely to be deeper in smokers. Tobacco smoke gives skin an unhealthy color and coarse texture, as well. 1 What you can do: Stop smoking 2 ! Effective tools are available at WebMD and throughout the Internet.\", \"Why Is Pee Yellow? Urine\\u2019s characteristic sunny hue comes primarily from a substance called urobilin. When your body is properly hydrated, the urobilin is diluted with plenty of water, and your pee is a nice, normal buttercup color. Amber or ale-colored urine is a reliable sign of dehydration, and clear pee is a signal that it\\u2019s time to put down the Nalgene.\", \"- The first of the physical properties to be considered is color. The color of urine often varies with its concentration and is most often reported as some shade of yellow (straw, light yellow, yellow, dark yellow, and amber). Normal urine can be found in any of these colors, with the exception of the \\u2018amber\\u2019 color.\", \"Urine color Normal urine color ranges from pale yellow to deep amber \\u2014 the result of a pigment called urochrome and how diluted or concentrated the urine is. Pigments and other compounds in certain foods and medications may change your urine color. Beets, berries and fava beans are among the foods most likely to affect urine color. Many over-the-counter and prescription medications give urine vivid tones, such as raspberry red, lemon yellow or greenish blue.\", \"Foamy Urine \\u00e2 Causes, Treatment, Diagnoses Foamy Urine. Urine is produced by the kidneys are discharged through urethra. Like any other metabolic wastes, urine is toxic and if retained in the body could produce undesirable illness. Normal color of the urine is amber or pale yellow.\"], \"pos_scores\": [97.5], \"neg_scores\": [86.5, 91.6875, 97.1875, 89.75, 90.875, 97.4375, 92.5625, 93.75, 92.5, 96.1875, 92.125, 97.375, 97.6875, 89.125, 96.6875, 90.4375, 91.0625, 97.125, 97.625, 92.0, 90.25, 96.875, 97.5625, 97.6875, 95.125], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"is autoimmune hepatitis a bile acid synthesis disorder\", \"pos\": [\"- Inborn errors of bile acid synthesis can produce life-threatening cholestatic liver disease (which usually presents in infancy) and progressive neurological disease presenting later in childhood or in adult life.he neurological presentation often includes signs of upper motor neurone damage (spastic paraparesis). The most useful screening test for many of these disorders is analysis of urinary cholanoids (bile acids and bile alcohols); this is usually now achieved by electrospray ionisation tandem mass spectrometry.\"], \"neg\": [\"Bile acid Bile acid. Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming primary bile acids. The sodium and potassium salts of bile acids are called bile salts. Primary bile acids are those synthesized by the liver.\", \"Overview When bile ducts are damaged, as in primary biliary cirrhosis, harmful substances can build up in your liver and sometimes lead to irreversible scarring of liver tissue (cirrhosis). Primary biliary cirrhosis is considered an autoimmune disease, in which the body turns against its own cells. Researchers think it is triggered by a combination of genetic and environmental factors.\", \"Bile acid malabsorption Bile acid malabsorption was first recognized in patients with ileal disease. When other causes were recognized, and an idiopathic, primary form described, a classification into three types was proposed: 1 Type 1: Bile acid malabsorption, secondary to ileal resection, or ileal inflammation (e.g. in Crohn's disease).ile acid malabsorption was first recognized in patients with ileal disease. When other causes were recognized, and an idiopathic, primary form described, a classification into three types was proposed: 1 Type 1: Bile acid malabsorption, secondary to ileal resection, or ileal inflammation (e.g. in Crohn's disease).\", \"- Czaja et al have shown that patients with autoimmune hepatitis who have positive test results for actin antibody are younger, more commonly test positive for human leukocyte antigen (HLA)\\u2013DR3, and required transplantation more frequently than patients with ANAs who test negative for actin antibody.\", \"- An autoimmune liver disease panel is a group of tests that is done to check for autoimmune liver disease. An autoimmune liver disease means that the body's immune system attacks the liver. These tests include: Anti-liver/kidney microsomal antibodies. Anti-mitochondrial antibodies. Anti-nuclear antibodies.\", \"- Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming bile salts.\", \"- Autoimmune hepatitis is a type of liver inflammation in which the body\\u2019s immune cells attack healthy liver cells after mistaking them for disease-causing foreign substances.\", \"- Worldwide, viral hepatitis is the most common cause of liver inflammation. Other causes include autoimmune diseases and ingestion of toxic substances (notably alcohol), certain medications (such as paracetamol), some industrial organic solvents, and plants.n autoimmune hepatitis, the immune system attacks the liver due to the autoimmune disease. In some hepatitis, often including hepatitis caused by alcoholism, fat deposits accumulate in the liver, resulting in fatty liver disease, also called steatohepatitis.\", \"Recent advances in the understanding of bile acid malabsorption Introduction. In patients with bile acid malabsorption (BAM), a larger amount of bile acids than normal spill into the colon, where they stimulate electrolyte and water secretion which results in the typical symptoms of BAM: chronic watery diarrhoea.ntroduction Bile acid malabsorption (BAM) is a syndrome of chronic watery diarrhoea with excess faecal bile acids. Disruption of the enterohepatic circulation of bile acids following surgical resection is a common cause of BAM.\", \"Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk However, the fact that there is a link between the two is not in dispute; multiple studies have shown that patients who suffer from celiac disease have a significantly higher risk of having other types of autoimmune diseases, including autoimmune hepatitis.\", \"- Many of the agents that can induce autoimmune hepatitis can also induce other autoimmune conditions or symptoms such as a lupus-like syndrome (minocycline), pneumonitis (nitrofurantoin), hemolytic anemia (methyldopa) and acute thyroiditis (interferon alfa).\", \"Kubla Khan Some of Coleridge's contemporaries denounced the poem and questioned his story of its origin. It was not until years later that critics began to openly admire the poem. Most modern critics now view Kubla Khan as one of Coleridge's three great poems, along with The Rime of the Ancient Mariner and Christabel.\", \"The Celiac and Autoimmune Thyroid Disease Connection Celiac disease, which is sometimes referred to as celiac sprue or sprue, is an autoimmune disorder that attacks the cells lining the intestine. This attack is a reaction to the presence of gluten, a protein found in wheat, rye, and barley. Eating these foods causes inflammation, which in turn makes it difficult for the body to properly absorb nutrients from foods. Celiac disease is a that damages the small intestine. People with celiac disease cannot eat gluten, a protein found in wheat, barley, and rye. Some of the common symptoms of celiac disease include the following: Intestinal difficulties. Abdominal bloating and pain. Nausea. Gas. Diarrhea.\", \"Autoimmune Liver Diseases Doctors have identified two main forms of autoimmune hepatitis: 1 Type 1 autoimmune hepatitis. This is the most common type of the disease. 2 Type 2 autoimmune hepatitis. Although adults can develop type 2 autoimmune hepatitis, it's most common in children and young people.\", \"- Case Reports in Infectious Diseases. Acute Acalculous Cholecystitis due to Viral Hepatitis A. Copyright \\u00a9 2013 Safak Kaya et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\", \"Autoimmune Hemolytic Anemia Autoimmune Hemolytic Anemia. Introduction. Autoimmune Hemolytic Anemia (AIHA) is characterized by production of antibodies that bind to antigens on the erythrocyte surface. These antibodies then cause the destruction of RBCs thus shortening their life span.\", \"Cirrhosis Survival rates after transplantation are excellent. Liver Transplantation for Patients with Autoimmune Hepatitis. The outlook is also good for patients who have autoimmune hepatitis who require a transplant. Survival rates are about 90% after 1 year, and 70 - 80% after 5 years.\", \"How Old Do You Have To Be To Rent A Car? There is an industry standard in how old do you have to be to rent a car. At almost every rental agency, the minimum age requirement to rent a car is 25. There is some variance, however. Depending on the region and the country, the minimum age for the driver could be between 21 and 24 years old. Be aware, though, that you will have to pay additional fees because of it. There are some companies that will rent to younger drivers. The age range is anywhere from 18 years old to 21 years old.\", \"Autoimmune Hepatitis Points to Remember. 1 Autoimmune hepatitis is a chronic\\u2014or long lasting\\u2014disease in which the body's immune system attacks the liver and causes inflammation and damage. 2 Autoimmune hepatitis is a serious condition that may worsen over time if not treated. Autoimmune hepatitis can lead to cirrhosis and liver failure. Autoimmune hepatitis is more common in females. The disease can occur at any age and affects all ethnic groups. Autoimmune hepatitis is classified as type 1 or type 2.\", \"SD School for the Blind & Visually Impaired - Eye Conditions Less often, chronic hepatitis results from alpha1-antitrypsin deficiency (a hereditary disorder), celiac disease, a thyroid disorder, or, in children and young adults, Wilson disease\\u2014a rare hereditary disorder involving abnormal retention of copper in the liver (see Wilson Disease).\", \"What is an anti-smooth muscle antibody (ASMA) test? An F-actin antibody test, in addition to an ASMA test, may improve the ability to detect autoimmune hepatitis over other conditions. Because test results require interpretation, especially in relation to other tests that may have been performed, it\\u2019s important to talk to your doctor about your specific results. A diagnosis of autoimmune hepatitis means that your immune system is mistakenly making antibodies that attack healthy cells in your liver.\", \"Autoimmune Hepatitis Outlook of Autoimmune Hepatitis. Autoimmune hepatitis (AH) is a dangerous disease and without any treatment, around 50% of patients with severe AH will die within 5 years and most patients will die within 10 years of disease onset. Treatment with immunosuppressant drugs improves the chances for survival significantly.\", \"SOLUBLE LIVER ANTIGEN Clinical Significance. Anti-soluble liver antigen antibodies are detected in 10-30% of patients with type 1 autoimmune hepatitis (AIH), but not in patients with type 2 AIH, primary sclerosing cholangitis or primary biliary cirrhosis. The antibody is directed against a UGA suppressor tRNA-associated protein.\", \"Despite popular belief, celiac disease is a SERIOUS GENETIC AUTOIMMUNE DISEASE, not the latest fad diet. Celiac disease is a serious genetic autoimmune disease. It is triggered by consuming a protein called gluten, which is found in wheat, barley and rye. When people with celiac disease eat foods containing gluten, their immune system responds by damaging the finger-like villi of the small intestine.\", \"Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk Autoimmune Hepatitis: Celiac Disease Patients Are At An Increased Risk. As many as 9% of patients with autoimmune hepatitis also have CD. As a major organ, the liver performs many essential tasks in digestion, so it may come as no surprise that patients with celiac disease also frequently have liver ailments, including autoimmune hepatitis.\"], \"pos_scores\": [93.875], \"neg_scores\": [91.125, 91.0, 89.375, 89.75, 90.25, 91.0, 92.25, 90.625, 89.5, 90.25, 90.625, 83.8125, 89.25, 91.375, 86.9375, 87.8125, 89.125, 85.5, 91.5, 89.625, 89.5, 90.75, 89.5625, 89.0625, 90.5], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"elegxo meaning\", \"pos\": [\"The Ministry of the Holy Spirit The word convict here (elegcw /elegxo) means to bring to light or expose error often with the idea of reproving or rebuking. It brings about knowledge of believing or doing something wrong, but it does not mean that the person will respond properly to that knowledge. Our usage of the English word, convict, is similar.\"], \"neg\": [\"XOXO means \\u00e2hugs and kisses\\u00e2 but why? XOXO means \\u201chugs and kisses\\u201d but why? What's the reasoning behind abbreviating hugs and kisses as X's and O's? Some say X is for hugs and O is for kisses, and some say the other way around; but why X and O, and why are they doubled? In my experience 'X' for kiss is universal.\", \"Elotes preparados, Mexico Elotero is the name given to the person selling elotes, a word from the nahuatl n\\u00e1huatl, elotl which \\u2018 means tender ear of \\u2018. maize\", \"- Definition of ambidextrous. 1 1 : using both hands with equal ease an ambidextrous baseball player. 2 2 : unusually skillful : versatile. 3 3 : characterized by duplicity : double-dealing.\", \"- el\\u00b7e\\u00b7gi\\u00b7ac. adj. 1. Of, relating to, or involving elegy or mourning or expressing sorrow for that which is irrecoverably past: an elegiac lament for youthful ideals.2.legiac. adj. 1. (Literary & Literary Critical Terms) resembling, characteristic of, relating to, or appropriate to an elegy.\", \"Attributes of Elegu\\u00c3\\u00a1 Elegu\\u00e1, Lord of the Crossroads. Attributes of Elegu\\u00e1. Elegu\\u00e1 (Eleggu\\u00e1) is sometimes represented as a child, and sometimes as an old man. He represents the beginning and end of life, and the opening and closing of paths in life. Sometimes known as the trickster, he likes to play jokes on people. He enjoys candy and toys.\", \"- The prefix tropo-denoting turning, a change, is derived from the Greek word tropos, a turning. The troposphere is the lowest level of atmosphere in which the temperature fall \\u2026 s as height increases.ropo-comes from the Greek tropos which means turning.. Meso-comes from the Greek mesos which means middle.. Thermo-comes from the Greek thermos which means hot.. Exo- \\u2026 comes from the Greek ex\\u014d which means outside..\", \"\\u00c2\\u00a1Qu\\u00c3\\u00a9 oso! Hacer el oso Hacer el oso means create an embarrassing situation, for example, by falling down at a party and spilling your drink everywhere. No quiero llegar antes de tiempo para no hacer el oso. I don\\u2019t want to look stupid by arriving early. Note that you aren\\u2019t making yourself into a bear by doing something stupid.\", \"- Strato means a combining form representing stratus This means layer. here are some examples that have the word Strato in it The stratosphere is the next highest atmospheric \\u2026layer. There are no clouds in the stratosphere. Commercial jets fly in the lower part of the stratosphere.\", \"Eleuthero Root Extract Benefits, Uses & Side Effects Loading ... Eleuthero root is an adaptogenic herb in the ginseng family that is used to boost energy levels, enhance stamina, promote overall health and increase athletic performance. It has been used as a natural medicine in China, Korea and Russia for many hundreds of years.\", \"- Definition of Elegy. An elegy is a mournful poem, usually written in remembrance of a lost one for a funeral or as a lament.An elegy tells the traffic story of an individual, or an individual\\u2019s loss, rather than the collective story of a people, which can be found in epic poetry.ignificance of Elegy in Literature. The definition of elegy as we know it now only came about in the sixteenth century. In ancient Greece, an elegy was any poem written in elegiac couplets, and could deal with any subject matter, including love and battle, along with death.\", \"Glossary An elegy is a poem of mourning; this is often the poet mourning one person, but the definition also includes Thomas Gray's 'Elegy Written in a Country Churchyard', which mourns all the occupants of that churchyard, and looks into the future to mourn the poet's own death.n elegy is a poem of mourning; this is often the poet mourning one person, but the definition also includes Thomas Gray's 'Elegy Written in a Country Churchyard', which mourns all the occupants of that churchyard, and looks into the future to mourn the poet's own death.\", \"- !meaning1 meaning2!e.g. hard!\\u00d2difficult\\u00d3 \\u00d2durable, solid\\u00d3!Single lexical entry Homophony!Homophones!morpheme1 e r\\u00d5: p o u morpheme2 meaning1 meaning2!e.g. pass (\\u00d4I\\u00d5m going to pass\\u00d5)!\\u00d4abstain\\u00d5 \\u00d4succeed\\u00d5!Distinct lexical entries Puns and Zeugmas Ambiguous words used in different senses in parallel syntactic construction.\", \"- GIGO. GIGO is an acronym which stands for 'Garbage In, Garbage Out'. It tends to be used when talking about data entry and databases. Bascially it means that if you enter inaccurate or wrong data then you have really entered garbage. Then when you want to do some queries, searches or reports, you will only get incorrect, wrong or inaccurate data out-garbage.\", \"XEG-AM XEG-AM is a Class A radio station on clear-channel frequency 1050 kHz in the state of Nuevo Leon, Le\\u00f3n. mexicot is licensed for Guadalupe, Nuevo Leon le\\u00f3n and brands itself as Serving. Monterrey known for its border blaster status in the, 1950s it now uses the Name La ranchera De monterrey and broadcasts ranchera. music\", \"What does el oso mean? What does natory mean? &Menos el Oso Natory is the name of a Wine Store and furniture shop. It is also a surname. What does 'El oso es grande' mean? The translation of 'El oso es grande' from Spanish to English is 'The bear is... What does menos el oso mean in spanish? The Spanish phrase menos el oso means minus the bear in English. ! What does me amor el oso de peluche mean? Me amor el oso de peluche translates from Spanish to English and means I love... What does menos el oso mean in english? The literal translation of menos el oso from Spanish to English is unless the... See All Questions\", \"\\\"What does it mean when a girl always uses \\\"\\\"cx\\\"\\\" every time you message each other?\\\" Cx is a smiley face, but also an alternative shortening of the word sex. Used to slip sex into a conversation, while smiling. Also cx may be the awesome type of debate which has people speaking 250 words/minute and ensures an intensifying feeling throughout the round. Also cx means sincerely or the meaning of CX in text slang is cancelled. See more types What does \\u201ccx\\u201d mean?\", \"Root Words & Prefixes: Quick Reference Latin: ambidextrous - able to use both hands equally; ambiguous - having more than one meaning; ambivalence - conflicting or opposite feelings toward a person or thing: ambul: walk, move: Latin: amble - to walk in a slow, relaxed way; ambulant - walking or moving around; ambulance - a vehicle that moves a patient: ami/o: love: Latin\", \"elegiac If there's one song on your playlist that always brings tears to your eyes, maybe it's because it has an elegiac quality. The adjective elegiac is useful when you're talking about music, a movie, a book, or another work of art that has a sorrowful tone. Sometimes elegiac specifically refers to something or someone that's gone: a person who's died, or a time in the past, especially if you feel a sense of longing for it. You can speak in an elegiac way, or sing an elegiac tune. The word comes from the Greek elegos, poem or song of lament..\", \"Meaning of nameEleuterio Epicurian, Eleuterio is often a bit of a ladies man and is extremely sensual. In romance, he needs to be admired; one could say he is a little vain, noblesse oblige. Likewise, he needs to have equal admiration for his partner, who he likes to show off in public, almost like an accessory.\", \"elegy Definition of elegy for English Language Learners. : a sad poem or song : a poem or song that expresses sorrow for someone who is dead.\", \"- equi-Meaning equal. Related terms . igual; equi\\u00e1ngulo; equidad; equidistancia; equil\\u00e1tero; equilibrar; equilibrio; equimolecular; equimosis; equinoccial\", \"- Meaning & History. From the Hebrew name \\u05d9\\u05b8\\u05e2\\u05b5\\u05dc (Ya'el) meaning ibex, mountain goat. This name appears in the Old Testament belonging to a woman who kills the captain of the Canaanite army, giving the victory to Israel.eaning & History. From the Hebrew name \\u05d9\\u05b8\\u05e2\\u05b5\\u05dc (Ya'el) meaning ibex, mountain goat. This name appears in the Old Testament belonging to a woman who kills the captain of the Canaanite army, giving the victory to Israel.\", \"- es lo que hay exprexpresi\\u00f3n: Expresiones idiom\\u00e1ticas, dichos, refranes y frases hechas de tres o m\\u00e1s palabras (Dios nos libre, a lo hecho, pecho). (lo que tenemos) is what you get exprexpression: Prepositional phrase, adverbial phrase, or other phrase or expression--for example, behind the times, on your own..\", \"- see also autoeroticism autosexual autosexual noun autosexuality characterized by self sex contact usually as a genital act masturbation with or without an accompanying erotic fantasy or ritual or rarely as a long term sexuoerotic status without a partner from greek auto self sexee also limbic system cervix adjective cervical the neck or neck like part of an organ specifically the neck of the lower part of the uterus or womb where the vagina and uterus unite 2 the cervix of the uterus is at its lower end where the uterus and the vagina meet\", \"Thread regardingHewlett Packard Enterprise (HPE) layoffs DXC (dumb--s core) is derived from making fun of sXe (straightedge) It is a clique made up of cool kids that like good music, but are a bit ditzy at times.. Well, to tell yout he truth, they are dumb--ses.\"], \"pos_scores\": [99.25], \"neg_scores\": [87.0, 84.6875, 87.1875, 89.9375, 86.6875, 86.875, 85.3125, 85.9375, 85.9375, 88.125, 87.8125, 87.8125, 85.625, 84.375, 85.5625, 86.3125, 86.875, 88.0625, 86.0, 89.25, 86.25, 87.0, 86.25, 88.5, 87.25], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"how much does an average person make for tutoring\", \"pos\": [\"- In-home tutors can earn anywhere from $10 to $80 an hour, depending on the type of lesson, the student\\u2019s skill and age level and the tutor\\u2019s experience. Tutors often charge more for older students or those who require more advanced lessons.\"], \"neg\": [\"- The price of a math tutor depends on the ability of the tutor as well as the type of tutoring required for the student or apprentice. How much is it? 1 Depending on your location, math tutors\\u2019 charges will vary. 2 On average, a tutor can cost anywhere from $15 to as much as $150 per hour. math tutor is a person who specializes in math and is employed in helping educate others. Before the wide availability of the internet, tutoring required the personal presence of the tutor. Today, there are online tutoring sessions available with the help of the internet.\", \"- 1 Individuals generally charge according to their level of education and experience. 2 Expect to pay $10 to $15 per hour for a high school student, and up to $75 per hour for a certified teacher with experience. 3 A teacher trained and qualified to work with children with special needs will likely charge more. A tutoring agency will help match you with a tutor. 2 Most agencies charge a registration fee, plus a fee for individual tutors. 3 Rates could start at $25 per hour and go up according to the subject area and the tutor's level of expertise. 4 Ask about any additional fees, such as for extra testing.\", \"- For example the median expected annual pay for a typical Teaching Assistant (College) in the United States is $15,659, so 50% of the people who perform the job of Teaching Assistant (College) in the United States are expected to make less than $15,659.\", \"Kumon Group Tutor Hourly Pay The typical Kumon Group Tutor salary is $9. Tutor salaries at Kumon Group can range from $7-$14. This estimate is based upon 90 Kumon Group Tutor salary report(s) provided by \\u2026 employees or estimated based upon statistical methods. See all Tutor salaries to learn how this stacks up in the market.\", \"How much do SAT tutoring centers charge? Here is an example I found online: cost for SAT tutoring: Kaplan = $200 + per hour Princeton Review = $125 to $315 per hour SATtutors.com = Tutors set their own rates.ould be $25 per hour to $50 to $100 plus per hour Sylvan = $100 + per hour There is everything from one-on-one tutoring for individual attention to larger classes.\", \"- How much does a Online Tutor make? The average Online Tutor salary is $30,000. Filter by location to see Online Tutor salaries in your area. Salary estimates are based on 60 salaries submitted anonymously to Glassdoor by Online Tutor employees.\", \"- By state, California offers the highest pay at $13.00 per hour. Those who have five to nine years of work experience see average pay of $10.97 per hour. Overall, the greater share of Tutor Time Learning Centers folks have one to four years of experience and earn an average about $9.68 per hour.arly Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00.\", \"How Much Should Tutoring Cost? On the other, a general math tutor in Nebraska who is just getting started, may have to charge a rate of $40.00 to get new clients. Students, parents and tutors need to consider subject, location, experience, teaching background, frequency of tutoring, etc. when determining how much tutoring should cost.\", \"How Much Does Tutoring Cost? Average cost of tutoring: $48/hour. Although tutors can be found on Craigslist for $20/hour (that\\u2019s a cheaper deal), you may be swapping quantity for quality. Craigslist doesn\\u2019t specialize in providing tutors, so the site doesn\\u2019t screen their applicants.\", \"- Early Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00.The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.egarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00. The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.\", \"- A School Custodian earns an average wage of $12.24 per hour. Pay for this job does not change much by experience, with the most experienced earning only a bit more than the least. People in this job generally don't have more than 20 years' experience.\", \"How Much Do Tutors Charge? If you want to know how much a particular tutoring company costs, call up their sales center and ask about the costs of different programs. According to Payscale.com, in 2011, tutors in the 25th to 75th percentile range working for private companies made from $10.10-$20.35. However, these figures do not include the amount of money that goes to the company providing you with the tutor.\", \"- 1 On average, a tutor can cost anywhere from $15 to as much as $150 per hour. 2 Online tutoring can range from $15 to $75 per hour, depending on the service that is used. 3 Group tutoring that is ideal for those in a classroom can be significantly lower as the group will pay the tutor. math tutor is a person who specializes in math and is employed in helping educate others. Before the wide availability of the internet, tutoring required the personal presence of the tutor. Today, there are online tutoring sessions available with the help of the internet.\", \"What's the Going Rate for a Tutor? The cost of a private tutor who comes to your home varies according to the grade level of the student, the education and experience of the tutor, and what city you live in. The national average price is about $41 per hour, according to Tutorspree. A high school student might charge as little as $10 per hour, while an experienced tutor in the country\\u2019s most expensive market, Manhattan, is considered reasonable at $85 to $150 per hour, with Ivy League graduates earning $200 per hour or more. Tutors don\\u2019t have to live locally.\", \"How much do teachers make per hour? How much do teachers make per hour? The median annual salary of a teacher in 2012 was $55,418. The average school year lasted 180 days at 6.7 hours per day, giving an average hourly wage of $45.95, according to The Daily Tarheel and the National Center for Education Statistics.\", \"Tutor Salary Pay by Experience for a Tutor has a positive trend. An entry-level Tutor with less than 5 years of experience can expect to earn an average total compensation of $31,000 based on 1,027 salaries provided by anonymous users. Average total compensation includes tips, bonus, and overtime pay.\", \"How Much Do Tutors Charge? Tutors who teach unusual subjects, like Latin, usually charge more than tutors who teach common subjects such as reading. You can expect private tutors to charge anywhere from $20-$100 per hour depending on his or her qualifications, location and what your child needs.f your child needs more casual help, you can save money and time by hiring an online tutor. Online tutoring is often cheaper than in-person lessons. If your child only needs help with a math problem or reading a certain passage, some sites charge by the minute with rates typically around 46-60 cents a minute.\", \"How Much Do Tutors Charge? according to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutorccording to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutor\", \"- The average cost for a SAT tutor is $50/hr. You are likely to spend between $30 and $75 per hour. Exact price may vary depending on your area and project details. Next Step: Find out exactly how much your project will cost.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields. $50-$100 per hour. As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour. I could probably charge a little more now, if I were still working as a private tutor.\", \"How Much Does Tutoring Cost? 36 By Design 3.0/5. Average cost of tutoring: $81.25/hour. If you hadn\\u2019t guessed it by the name of the company, 36 By Design only does one kind of tutoring \\u2014 ACT Test Prep. It\\u2019s their opinion that by only focusing on one subject, they can perfect it.\", \"How Much To Charge For Tutoring How Much To Charge For Tutoring Factors that affect how much a tutor charges include location, education, and experience. A typical tutor will charge between $17 and $45 per hour, according to our tutor pricing calculator.\", \"- Excluding overtime pay (which bumps median pay up to $21.00), people who work for Tutor Time Learning Centers earn a median wage of $10.00 per hour.egarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u2014 about $13.00. The highest-paying skill to have in this role seems to be Curriculum Planning; employees claiming this as part of the toolbox earn a median of $10.05 per hour.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields $50-$100 per hour As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour.\", \"Senator Armstrong 2016 Presidential Ad Fed up with the state of the country, Senator Armstrong decides to run for the oval office in 2016 instead of 2020. Disclaimer: This is for entertainment purposes only.All audio recordings are property of the artist(s), management, and/or music publishing companies.No copyright infringement is intended.ed up with the state of the country, Senator Armstrong decides to run for the oval office in 2016 instead of 2020. Disclaimer: This is for entertainment purposes only.\"], \"pos_scores\": [95.4375], \"neg_scores\": [94.5, 93.5625, 90.25, 91.75, 91.3125, 96.0625, 92.5, 92.25, 94.9375, 90.8125, 88.6875, 94.625, 95.8125, 94.375, 90.5, 94.8125, 93.625, 93.4375, 93.0625, 93.1875, 94.4375, 94.6875, 92.25, 93.0625, 87.0625], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"can you use a calculator on the compass test\", \"pos\": [\"Calculator Guidelines for COMPASS \\u00c2\\u00ae Testing Calculators may be used on the COMPASS Pre-Algebra, Algebra, College Algebra, Geometry, and Trigonometry tests provided they meet the requirements listed below. Electronic writing pads or pen-input devices\\u2014The Sharp EL 9600 is permitted. Models with paper tapes\\u2014The paper must be removed.\"], \"neg\": [\"North References for Navigating with Map, Compass and GPS Adjusting the North Reference on Your Compass to True or Grid North Many compasses allow you to adjust the position of the lines used to align the magnetic needle with respect to the angle measuring dial.\", \"Study Guide Your COMPASS\\u00ae Test Success Starts Here. Studying the material on this site will help you improve your score on the COMPASS Math Assessment Test. A higher score will let you skip the most basic university math classes, saving you money and time. This site will help you learn about test strategy, review test material, and practice for the exam; all for free!\", \"TI-84 Plus CE Graphing Calculator The TI-84 Plus CE is approved for use on the following exams: 1 PSAT*, SAT*, and ACT\\u00ae college entrance exams. 2 AP* Exams that allow or require a graphing calculator. 3 Approved for use on the IB exam.\", \"Which calculator do I need? A good calculator will probably cost you at least $100, but you may be able to rent, buy used, or even borrow from your school library. The TI-84+ is a good calculator for algebra and geometry (now available with a color screen), and is a newer version of the classic TI-83+.\", \"How to Use a Compass You can use a bearing to get to a location any time you know where you are on a map: 1 Set your compass on the map so that the straight side of the baseplate lines up between your current position (1a) and the map location for a destination like a campsite (1b). 1 Make sure the direction of travel arrow is pointing in the general direction of that campsite (in other words, it's not upside down).\", \"How to Use the iPhone Compass App The iPhone digital compass app is located within the utilities icon on your phone. Follow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options.2 Click on the compass icon to select the compass app. 3 Hold phone flat in the upwards-facing palm of your hand. 4 Extend your arm straight out in front of you, as you would when holding a magnetic compass.ollow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options. 2 Click on the compass icon to select the compass app.\", \"Calculators Multiple Power Options Choose calculators with multiple power options to maximize your time at the office. Some models can be plugged directly into the wall, so there's no need to remove the batteries and interrupt your work during the charging process.hether you're teaching a math class or running an accounting business, calculators can help you get the job done. Each one is built with a mix of simple and advanced functions for maximum efficiency. Choose the best model for your business from brands such as Casio, Canon, and Texas Instruments.\", \"- The COMPASS test is a self-adjusting, multiple choice test that is taken at the computer. The. answer to your current question will determine the next question; it will stop once it has determined. your level.\", \"How to Use the TI-84 Plus Calculator to Convert Sine, Tangent & Cosine to Angles You can easily convert the basic trigonometric functions into angles measured in degrees or radians using a TI-84 Plus calculator. The TI-84 Plus is capable of going in both directions -- from the angle to the trigonometric measure and back.et your calculator to Degrees mode by pressing the MODE key, pressing the down arrow until you reach the row with the options Degree and Radian, highlighting Degree using the right arrow key, and pressing ENTER.\", \"- with Answers-Sample 1. Math questions, with answers, similar to the questions in the compass math test are presented. The questions are designed to reflect the major topics covered in the compass test: Numerical skills/pre-Algebra, algebra, college algebra, geometry and trigonometry.he questions are designed to reflect the major topics covered in the compass test: Numerical skills/pre-Algebra, algebra, college algebra, geometry and trigonometry.\", \"- Nursing Compass Entrance Exam. COMPASS is a comprehensive, computer-adaptive testing program that quickly and accurately assesses students' skill levels in reading and mathematics. Because it is adaptive, the length of the test will vary based upon the individuals' knowledge.*If you are under 21 years of age and have taken the ACT, you may not be required to take the COMPASS exam if you scored a minimum of 19 in Math, 19 in Reading, and 19 in English (a 19 composite score is not accepted).\", \"- Compass is awesome! Powerful off-the-bat visualization and analytics that are actually actionable. There's no other benchmarking tool that's as effective out there and it has really helped us focus on improving those metrics that deviated too much from the norm. Really solid support from the developers as well.\", \"- Either way, here\\u2019s how to find and use the compass and level app. Launch the Compass app with a quick tap. If this is the first time, you\\u2019ll need to gyrate the iPhone around a bit to completely calibrate it.Now, just hold the iPhone out from your body, like you would reading a text message.ither way, here\\u2019s how to find and use the compass and level app. Launch the Compass app with a quick tap. If this is the first time, you\\u2019ll need to gyrate the iPhone around a bit to completely calibrate it.\", \"How do I reset the compass on an '04 Ford F150 upper console? 8. Press the RESET control to start the compass calibration function. 9. Slowly drive the vehicle in a circle (less than 3 mph [5 km/h]) until the CIRCLE SLOWLY TO CALIBRATE display changes to CALIBRATION COMPLETED. It will take up to five circles to complete calibration. 10. The compass is now calibrated. couldn't get the picture of the zones to come up, but it's on page 72 of your manual.\", \"- Compass practice tests are an effective way to study for your college placement exams. Our free Compass sample tests provide you with an opportunity to assess how well you are prepared for the real Compass test, and then focus on the areas you need to work on.ompass Test. Doing well on your Compass Test can help you start college on the right foot. Discover test taking tips, score information, and Compass Exam Prep. Use our free Compass sample exams.\", \"OH NO, I am not allowed a calculator on the MCAT OH NO, I am not allowed a calculator on the MCAT!!! If you reading this you have probably completed all the general requirements to take the MCAT. Thinking back over your semesters of chemistry you would of used your calculator a lot to complete the math problems ranging from the basics of stoichiometry to the complex problems of solving the pH of a weak acid.\", \"Can you use a calculator on GED test? Best Answer: yea u CAN use a Calculator on a GED test i think on the math part is everything u took in high school that was math.\", \"How to Use the iPhone Compass App 1 Click on the compass icon to select the compass app. 2 Hold phone flat in the upwards-facing palm of your hand. 3 Extend your arm straight out in front of you, as you would when holding a magnetic compass.ollow these steps to access and use the iPhone compass app: 1 Click on the utilities icon to view utilities options. 2 Click on the compass icon to select the compass app.\", \"Calculators during the TEAS test Jul 1, '12. No calculators allowed. The only pop up help you get is the periodic table in the science portion. I was kind of worried about math too -- especially because i usually freak out on timed exams and go blank on stupid things such as multiplication tables and conversion.\", \"- May I Use a Calculator? The ACT Calculator Policy (effective September 1, 2014) is designed to ensure fairness for all examinees, avoid disturbances in the testing room, and protect the security of the test materials. A permitted calculator may be used on the ACT mathematics test only.\", \"Calculators on the SAT: Tips from Experts Calculator Uses and Limitations. Calculators are allowed on the SAT, and not using them correctly can put you far behind. SAT experts Fred Zhang and Allen Cheng discuss which tips and strategies worked for them in getting perfect scores.\", \"- The COMPASS Placement Test is an untimed, adaptive, computer-based college placement exam. The test changes for each student based on performance. If a student gets a question correct, they next receive a question of greater difficulty and higher value.he COMPASS Placement Test is an untimed, adaptive, computer-based college placement exam. The test changes for each student based on performance. If a student gets a question correct, they next receive a question of greater difficulty and higher value.\", \"The Compass Test: What You Need to Know The Compass Test: What You Need to Know. The COMPASS test is actually a series of computerized tests which were written by ACT, Inc. It is administered by a number of colleges and universities and is used to determine course placement. Find out everything you need to know about the COMPASS test here.\", \"Compass A compass is an instrument used for navigation and orientation that shows direction relative to the geographic cardinal directions, or points. Usually, a diagram called a compass rose, shows the directions north, south, east, and west as abbreviated initials marked on the compass.When the compass is used, the rose can be aligned with the corresponding geographic directions, so, for example, the N mark on the rose really points to the north.Frequently, in addition to the rose or sometimes instead of it, angle markings in degrees are shown on the compass.hen the compass is used, the rose can be aligned with the corresponding geographic directions, so, for example, the N mark on the rose really points to the north. Frequently, in addition to the rose or sometimes instead of it, angle markings in degrees are shown on the compass.\", \"Q: I need help switching an Xbox live account to another Microsoft id, how can I do that? I had made an account a while back, now I can't get on to Xbox live because they're trying to do a proof and I've deleted that email address and there for cant retrieve anything. Now i made a new account is there a way I can transfer my Xbox live gamer tag to this new account so I can get back on Xbox live? 17 people had this question.\"], \"pos_scores\": [100.0625], \"neg_scores\": [87.125, 93.375, 89.375, 87.0, 89.375, 88.5, 86.875, 92.125, 88.4375, 93.125, 92.75, 87.625, 88.4375, 85.3125, 92.3125, 89.5, 93.3125, 88.5, 91.875, 92.4375, 91.25, 92.125, 92.375, 88.4375, 86.25], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"what does physical medicine do\", \"pos\": [\"What Is a Physiatrist? Doctor Directory. A physiatrist practices in the field of physiatry - also called physical medicine and rehabilitation - which is a branch of medicine that specializes in diagnosis, treatment, and management of disease primarily using physical means, such as physical therapy and medications.\"], \"neg\": [\"Types of Doctors Physiatry: A physiatrist is a physician who specializes in physical medicine, which is the curing of injuries and disease by natural methods. Measures that are used include physical therapy, massage, exercise, light and heat.\", \"Physical Therapy Office Forms It helps you move better and may relieve pain. It also helps improve or restore your physical function and your fitness level. The goal of physical therapy is to make daily tasks and activities easier. For example, it may help with walking, going up stairs, or getting in and out of bed.\", \"What Does Therapeutic Ultrasound Do in Physical Therapy? Therapeutic ultrasound is a treatment modality commonly used in physical therapy. It is used to provide deep heating to soft tissues in the body. These include muscles, tendons, joints, and ligaments. Ultrasound in physical therapy is not to be confused with diagnostic ultrasound, which is \\u200ban ultrasound that is used to see the inside of the body, such as checking on a fetus during pregnancy.\", \"physical medicine physical medicine. n. The branch of medicine that deals with the treatment, prevention, and diagnosis of disease by essentially physical means, including manipulation, massage, and exercise, often with mechanical devices, and the application of heat, cold, electricity, radiation, and water. Also called physiatrics, physiatry.\", \"List of photographic equipment makers Some camera makers design lenses but outsource manufacture. Some lens makers have cameras made to sell under their own brand name. A few companies are only in the lens business. Some camera companies make no lenses, but usually at least sell a lens from some lens maker with their cameras as part of a package.\", \"Fitness Trainers and Instructors Physical therapists use different forms of treatment depending on the type of patient they are caring for. Physical therapists, sometimes called PTs, help injured or ill people improve their movement and manage their pain. These therapists are often an important part of the rehabilitation, treatment, and prevention of patients with chronic conditions, illnesses, or injuries.\", \"Frequently Asked Questions How does physical therapy help pelvic pain? There are many reasons for developing pelvic pain issues through a lifespan. Muscles, connective tissue (fascia), and nerves in the abdomen, pelvis, and low back region may all contribute to pelvic pain. Pelvic Floor physical therapists are specifically trained to evaluate and treat muscle spasm and trigger points/tender points that can result from muscle imbalance, scar tissue tension, prolonged nerve compression or stretch injuries, and trauma.\", \"Physical Therapist Physical Therapist. Physical therapists are evidence-based, health care professionals who diagnose and treat individuals of all ages who have medical problems or other health-related conditions that limit their abilities to move and perform functional activities in their daily lives.\", \"Physical Therapists What Physical Therapists Do About this section. Physical therapists use different forms of treatment depending on the type of patient they are caring for. Physical therapists, sometimes called PTs, help injured or ill people improve their movement and manage their pain.These therapists are often an important part of rehabilitation and treatment of patients with chronic conditions or injuries.he work of physical therapists varies by type of patient. For example, a patient experiencing loss of mobility due to stroke needs different care from that given to an athlete recovering from an injury. Some physical therapists specialize in one type of care, such as orthopedics or geriatrics.\", \"What does a Physical Therapist do? Geriatric physical therapy focuses on the unique movement needs of older adults. This includes treatment for conditions such as arthritis, cancer, osteoporosis, Alzheimer's disease, joint replacement and balance disorders. The goal of geriatric physical therapy is to help restore mobility, reduce pain, accommodate physical limitations and increase physical fitness.\", \"What Do Physical Therapists Do? What Do Physical Therapists Do? As a physical therapist, you assess patients and determine treatment, as well as track patients' progress and evaluate the effectiveness of treatment. You also teach patients and families how to properly continue therapy once released from the hospital or medical office.\", \"What is a Physiatrist? What is a Physiatrist? Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons.\", \"Physical Medicine and Rehabilitation By Mayo Clinic Staff. Mayo Clinic specialists in Physical Medicine and Rehabilitation (PM&R) help restore movement and function to people disabled by disease or injury. PM&R physicians (called physiatrists \\u2014 pronounced fiz-e-AT-rists) create therapy plans that consider the unique needs, abilities and objectives of each patient.\", \"The Calorie Requirements for Female Bodybuilders & Weight Loss Calorie Balance. To lose weight, you need to create a calorie deficit, which involves consuming fewer calories than you burn. It takes a deficit of around 3,500 calories to burn 1 pound of fat, so if you're currently maintaining your weight, lowering your daily intake by 500 calories will lead to 1 pound of fat loss each week.\", \"Accredited Colleges Offering Sports Medicine Degrees Other Sports Medicine Specialties. 1 Physical Therapist: Physical therapists use a variety of methods to help patients deal with different physical issues. 2 Nurse: Nurses are an integral part of disease prevention, health promotion, and recovery from illness.\", \"Who Are Physical Therapists? Physical therapists (PTs) are highly-educated, licensed health care professionals who can help patients reduce pain and improve or restore mobility-in many cases without expensive surgery and often reducing the need for long-term use of prescription medications and their side effects.hysical therapists (PTs) are highly-educated, licensed health care professionals who can help patients reduce pain and improve or restore mobility-in many cases without expensive surgery and often reducing the need for long-term use of prescription medications and their side effects.\", \"Physical Therapy for Chronic Pain: What to Expect Reviewed by Melinda Ratini, DO, MS on July 22, 2016. Physical therapy is often one of the best choices you can make when you have long-term pain (also called chronic pain) or an injury. It can make you stronger and help you move and feel better. Ask your doctor to recommend a physical therapist.\", \"Physical Therapy Career Opportunities Physical Therapy and Rehabilitation. Physical therapists (PTs) provide services that help restore function, improve mobility, relieve pain, and prevent or limit permanent physical disabilities of patients suffering from injuries or disease. They restore, maintain, and promote overall fitness and health.\", \"- Physical therapists also are key health care. team members who address prevention initiatives, such as. reducing falls, improving physical activity to mitigate chronic. disease and secondary health conditions, and tailoring wellness. programs for populations that have chronic conditions and/. or disabilities.\", \"- Physical Therapy Basics. Doctors often recommend physical therapy (PT) for kids and teens who have been injured or who have movement problems from an illness, disease, or disability. After an injury, physical therapists work to decrease pain, improve movement, and help kids return to daily activities.\", \"- First, your therapist will try to reduce your pain and swelling. Your physical therapist also may use manual therapy, education, and techniques such as heat, cold, water, ultrasound, and electrical stimulation.Physical therapy almost always includes exercise.ome physical therapists are board-certified in areas such as orthopedics, sports, and neurology and may offer more specialized care. Physical therapists can also specialize in certain types of care, such as: 1 Back and neck pain. 2 Cardiac rehabilitation (rehab). 3 Wound care. 4 Cancer-related problems.\", \"Physical Therapy (PT) Physical therapy aims to improve joint and muscle function (eg, range of motion, strength) and thus improve the patient\\u2019s ability to stand, balance, walk, and climb stairs.For example, physical therapy is usually used to train lower-extremity amputees.hysical therapy aims to improve joint and muscle function (eg, range of motion, strength) and thus improve the patient\\u2019s ability to stand, balance, walk, and climb stairs.\", \"Why Visit a PM&R Physician Why Visit a PM&R Physician. 1 Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons. By taking the whole body into account, they are able to accurately pinpoint problems and enhance performance without surgery.\", \"Department of Physical Therapy A Physical Therapist (PT) is a health care professional who provides direct patient care to persons who have disorders of movement, mechanical, physiological and developmental impairments and functional limitations, whether caused by injury or disease, to help them achieve maximum physical function and mobility.\", \"physical medicine Definition of 'physical medicine'. Word Frequency. physical medicine. noun. the branch of medicine devoted to the management of physical disabilities, as resulting from rheumatic disease, asthma, poliomyelitis, etc. See also rehabilitation (sense 2)\"], \"pos_scores\": [95.5], \"neg_scores\": [94.3125, 93.3125, 91.625, 98.1875, 89.9375, 92.8125, 92.1875, 93.5, 94.1875, 93.0625, 94.1875, 96.125, 96.75, 92.6875, 91.8125, 93.5, 93.0625, 93.3125, 92.9375, 93.0, 92.75, 93.5, 96.6875, 93.5, 96.625], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"what does pending mean on listing\", \"pos\": [\"- Active/Pending = Usually that means the seller would like to get more offers. Savvy agents want back-up offers in place. If you made an offer to purchase a property and the seller agreed to the price, but already has a buyer in contract, you would be the 1st position back-up buyer.\"], \"neg\": [\"Hours Worked Hours Worked | Bleakley Platt & Schmidt, LLP | New York Labor and Employment Attorneys. Employees must be paid for all of the hours that they are required to be on the employer's premises, on duty or at a prescribed work place.\", \"What Does a Pending Status on a House Mean? What Does a Pending Status on a House Mean? Brokers must report when the status of a home for sale changes. Real estate brokers must accurately report the status of a property listing to the multiple listing service, usually spoken of as MLS. Brokers rely heavily on the MLS to market listings and find interested buyers. As a member of an MLS, a listing broker must comply with certain rules and regulations.\", \"- For Sale: Properties which are available for showings and purchase. Active Contingent: Properties which are available for showing but are under contract with another buyer. Pending: Properties which are under contract with a buyer and are no longer available for showings. Sold: Properties on which the sale has closed.\", \"What Does Sale Pending Mean in Real Estate? Typically, the buyer has a week or two to do the inspection and a few weeks to get an appraisal and loan. During this period, the seller can\\u2019t enter into an agreement with another buyer, even though the sale hasn\\u2019t closed. This means another interested buyer could make a \\u201cback-up\\u201d offer. That way, if the first offer collapses, the seller has a back-up offer ready to go. A True Home, Pending Sale. A pending sale has had all contingencies removed.\", \"What is a root canal? The cost of root canals varies depending on the tooth and whether it is being treated by a general dentist or an endodontist. Molars have more canals that need to be filled, so they are more expensive, and endodontists typically charge more due to their specialty training.\", \"What\\u00e2s the Difference Between Contingent and Pending Listings? When a listing has changed status to \\u201cpending\\u201d, the seller has accepted an offer. This is an off-market status. Pending listings are much more common than contingent listings, since it just means the seller and buyer have agreed to the initial terms in the residential purchase agreement.\", \"Active Status? Pending Status? This status may also be used to indicate the property is a short sale with an accepted offer. Pending w/o release, continue to show (PS) or (Pend w/S) \\u2013 Accepted offer on the property, still showing to prospective buyers for offers in back-up position to the first accepted offer.\", \"What Do All Those Real Estate Listing Terms Really Mean? If your offer is accepted as a backup, you\\u2019re in line to go under contract if the first sale falls through. Pending, showing for backup This means the property's owners are actively taking backup offers in case the first one falls through.\", \"Sale pending? \\u00b7 just now. 1 Sale pending means that there is a transaction that is going through Escrow but that has not closed yet. 2 Sale pending can mean anything from an offer being accepted or contracts signed. 3 It means the buyer and seller have agreed on a contract, but closing has not happened yet.\", \"\\\"What does \\\"\\\"option pending\\\"\\\" mean on a real estate listing?\\\" What does option pending mean on a real estate listing? I've been looking for houses on the internet, and came across several listings that said option pending. Does it mean there's an offer on the house and it's pending approval? 2 following.\", \"- Other types of active statuses may be New or Price Change.. New means that the listing has been on the market for 3 - 7 days; Price Change means that the listing experienced a price decrease or increase within the past 3 - 7 days. There may be different types of active listings, depending on the MLS.\", \"Does 'Pending Contact Request' under a contact in skype mean that they have blocked me? up vote 1 down vote. Pending request-It means that the person has just removed you from their Skype contact list but hasn't blocked you. You can however send them messages to them but can't call them. The person can call you if they want. You can send friend request again, if they removed you from contact list by mistake.\", \"\\\"What does \\\"\\\"Pending\\\"\\\" mean? Is there a problem?\\\" According to our documentation on payment statuses, Pending means: This is a payment that has begun, but is not complete. An example of this is someone who has filled out the checkout form and then gone to PayPal for payment. We have the record of sale, but they haven't completed their payment yet. This means that Pending isn't inherently a problem, but it can be an indication of some other problem.\", \"\\\"What does \\\"\\\"Contingent\\\"\\\" mean for a listing status?\\\" Most of these are self-explanatory\\u2026. 1 Active means that the property is for sale. 2 Come on over and make us an offer. 3 Subject to Inspection means that a buyer\\u2019s offer is pending their inspection.\", \"- Homes with a Make Me Move\\u00ae price indicate the amount the owner(s) would be willing to sell for. They are exclusive to Zillow and a great way to learn about homes before they hit the market. A pending listing means a seller has accepted an offer from a buyer. In some cases, the seller will accept backup offers.\", \"MLS Rules & Regulations FAQ YES, MLS rules require that if a seller has signed acceptance of an offer, the listing status must be changed to Contingent or Pending. Lender approval of a \\u201cShort Sale\\u201d is treated as a contingency and does. not change the timing of the required change to Contingent or Pending status.\", \"Under Contract: \\u00e2Contingent\\u00e2 vs. \\u00e2Pending\\u00e2 \\u201cContingent\\u201d means it can be shown. \\u201cPending\\u201d means it cannot be shown, although there may still be contingencies. Contingent : Contingent status indicates that a property under contract is subject to an additional contingency or condition, i.e. \\u201cContingent Sales Addendum\\u201d, Due Diligence Period, etc.\", \"- Payments to some eBay sellers may be shown as pending in your PayPal account, and may not be available for a period of time to allow for successful fulfillment. Common reasons your funds might be pending. These are the most common reasons your funds would be unavailable for a period of time: 1 You're a new seller or have limited selling history, which means you have not yet met all of the following criteria: It's been more than 90 days since your first successful sale. You've made more than 25 sales transactions.\", \"what is the definition of pending litigation Would that Pending litigation refers to any suits which have been filed, but remain unresolved. A notice of claim could be included, if that referenced a notice of claim which had been filed in court.It appears that you may be referring to lis pendens which is filed with real property records to place potential buyer's on notice of pending litigation involving the real property. Thank you.his is in reference to a Homeowners Association filing a claim with the builder under the 10 year time frame. (Sec 1375) No formal complaint has been filed. They are in the pre-litigation stages.\", \"Under Contract: \\u00e2Contingent\\u00e2 vs. \\u00e2Pending\\u00e2 A listing in Contingent status will not expire at the end of the listing contract term. Pending: Pending status indicates that a property is under contract and is not available for showings. Listings in this status may include contingencies. The listing will not expire at the end of the listing contract term.\", \"- What does pending disposition mean in legal terms? A pending disposition in legal terms means that no decision has been reached yet. When someone is arrested and they have a pending disposition, it means that it has not ye \\u2026 t been determined whether they have actually committed a crime or not.\", \"Why is the order status pending? For some purchases, such as services, rentals, or hotel stays, the seller will complete the payment after your purchase is fully delivered. The status of an order will continue to display as pending until it expires, is completed, or has been voided.\", \"What does pending mean? im asking what pending is because im installing some games on my laptop and all it says is its pending\", \"What is a pending transaction? Pending transactions include credits and debits that have not yet posted to your account. They may also include holds applied to certain transactions or your balance. Pending transactions are listed in lowest to highest dollar amount order, and are not listed in the order that they post to your account.\", \"Option Pending vs Pending continue to show OP - Option Pending - Listings that are under contract and the seller and buyer have agreed to use the \\u201cTermination Option\\u201d in paragraph 23 of the standard TREC contract. PS - Pending Continue to Show - Used for listings currently under contract but are still available to show.\"], \"pos_scores\": [96.9375], \"neg_scores\": [88.625, 96.75, 95.1875, 96.0, 87.8125, 99.125, 95.25, 97.0625, 96.8125, 95.4375, 91.6875, 92.5625, 93.9375, 95.0625, 98.25, 94.25, 95.875, 89.875, 93.0, 98.375, 92.0625, 90.6875, 89.125, 92.6875, 96.9375], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n{\"query\": \"feeding rice cereal how many times per day\", \"pos\": [\"FEEDING GUIDELINES AGES 4 TO 6 MONTHS 1. Begin with rice cereal on days 1,2 and 3, offering it twice daily at breakfast and dinnertime. Rice cereal can be mixed with water or milk(breast or formula) to make a thin oatmeal like consistency. The infant should be offered a rubberized spoon. During these first three days, offer 3-4 tablespoons at a time but be flexible. Some infants will be very hungry and want more- that's OK.\"], \"neg\": [\"How often do kittens need to eat? Kittens, at any age, have small stomachs, so the best method of feeding is little and often, as often as four to six times a day for very young kittens. As kittens get older and bigger, they can be fed bigger portions in less frequent meals. By 6 months, a kitten can generally eat two or three times a day. How often a kitten needs to eat wholly depends on each individual. Some kittens eat more and some eat less.\", \"Learn From Amazon\\u00e2s Leadership Principles Learn From Amazon\\u2019s Leadership Principles. Amazon is a crazy company. Within two decades, this bookseller has become a competitor of every single industry leader across retail, publishing, logistics, wireless, toys, devices, apparel and cloud computing, to name a few.\", \"The Serving Size of Grains Needed Daily Toddlers need 3 ounces of grains each day as part of a healthy diet. At least 1 1/2 ounces of these should be whole grains. Girls between the ages of 4 and 8 need 5 ounces of grains per day, with at least 2 1/2 of them being whole grains.\", \"How do we get started with solids? Increase solids gradually if baby is interested, with a maximum of 2 meals per day. 9 \\u2013 12 months. Watch baby\\u2019s cues \\u2013 this is particularly easy if baby nurses beforehand and most/all of the solids are offered to baby to self-feed. Increase solids gradually if baby is interested.\", \"Is Eating Rice Healthy? How Much Rice. The amount of whole-grain rice eaten daily for a healthy diet depends on how much other whole grain foods the dieter eats. About 6 to 8 ounces of whole-grain foods should be eaten on a 2,000-calorie diet, according to the USDA Dietary Guidelines for Americans.\", \"Flax Seeds: How Much Per Day? Serving Size. The University of Maryland Medical Center recommends adults consume 1 tablespoon of ground flaxseed two to three times a day, or 2 to 4 tablespoons once per day.You can mix the ground seeds into cereal, yogurt, oatmeal or smoothies or sprinkle them generously on top of salads and vegetable dishes.erving Size. The University of Maryland Medical Center recommends adults consume 1 tablespoon of ground flaxseed two to three times a day, or 2 to 4 tablespoons once per day.\", \"Left axis deviation Left axis deviation (LAD) is a condition whereby the mean electrical axis of ventricular contraction of the heart lies in a frontal plane direction between -30\\u00b0 and -90\\u00b0. This is reflected by a QRS complex positive in lead I and negative in leads aVF and II.\", \"Macromolecules Lipids are an important part of living cells. Together with carbohydrates and proteins, lipids are the main constituents of plant and animal cells. Cholesterol and triglycerides are lipids. Lipids are easily stored in the body. They serve as a source of fuel and are an important constituent of the structure of cells.Lipids include fatty acids, neutral fats, waxes and steroids (like cortisone).ogether with carbohydrates and proteins, lipids are the main constituents of plant and animal cells. Cholesterol and triglycerides are lipids. Lipids are easily stored in the body. They serve as a source of fuel and are an important constituent of the structure of cells.\", \"- To get rice flour, head out to your Asian market. It's dirt cheap and does the same. My daughter-in-law has been advised by the paediatrician to continue giving her baby of 8 months old rice porridge in the afternoon in place of her bottle feed. She has now stopped as she feels the baby is overweight.\", \"How Much To Feed Your Cat Even though you find feeding instructions on almost every bag of cat food you can buy, the instructions are useless. One brand may tell you to feed your cat 1/2 cup twice daily, while another recommends 1/4 cup three times daily. It's not just the manufacturers of cat food that can't tell you how much to feed your cat.\", \"Top 30 Doctor insights on: Purple Rice Supplement 3 doctors agreed: This depends on: how old your baby is. Rice cereal is generally started at 4-6 months, so if your baby is younger than that, you can supplement with formula, as opposed to cereal. Please also discuss this with his/her pediatrician; they may also be able to give you some hints on how to increase your breast milk supply. ...Read more.\", \"- Feeding tips. 1 If your baby won't eat what you're offering on the first try, offer it again in a few days. 2 Get more detailed tips on how to introduce solids. 3 Print our step-by-step guide to feeding your baby. Begin with about 1 teaspoon pureed food or cereal. 2 Mix cereal with 4 to 5 teaspoons breast milk or formula (it'll be very runny). 3 Increase to 1 tablespoon of pureed food, or 1 tablespoon of cereal mixed with breast milk or formula, twice a day. 4 If giving cereal, gradually thicken the consistency by using less liquid.\", \"How many ounces of wet food per day should be given to a cat? We feed our 3 cats one 6 oz can and divide that. But they also get a meal of dry in the morning and raw 2-3 times a week. For your cat about 1/3 of a 6 oz can of food per meal should be fine for her size. Wet food can be very messy and smelly. Be careful about feeding only wet! If you change her food for the emergency and her system is not ready for it, she will develop diarrhea. If you do decide to use wet food, I suggest using it more as a treat twice a day. I gave between 1/4 to 1/2 a small can (6 oz?) per cat twice a day. I always have unlimited amounts of dry food available as well as fresh water.\", \"How Much Should I Eat? Grains: Make at least half of your grains whole grains every day: 1 Males ages 19 to 30: 8 ounce equivalents of grains/day. 2 Females ages 19 to 30: 6 ounce equivalents of grains/day. 3 Examples of one ounce equivalent: 1 slice of bread; 1 cup of ready-to-eat cereal; or \\u00bd cup of cooked rice, cooked pasta, or cooked cereal.\", \"- Foods to Avoid. 1 6-11 servings each day. 2 Serving size= 1 slice bread, 1 cup ready-to-eat cereal,1/2 cup cooked cereal, rice or pasta. 3 All enriched breads, cereals, rice, noodles, pasta, and potatoes. Limit to 2 servings per week: whole-grain breads and cereals, wheat germ, bran and oatmeal.\", \"How do we get started with solids? Increase solids gradually if baby is interested, with a maximum of 2 meals per day. 9 \\u2013 12 months. Watch baby\\u2019s cues \\u2013 this is particularly easy if baby nurses beforehand and most/all of the solids are offered to baby to self-feed.ffer solids once a day, at most. Many start out offering solids every few days or even less often. Continue nursing on cue. Solid foods should not replace nursing sessions unless you\\u2019re actively weaning.Limit water to SIPS from a cup with meals.\", \"How long to cook a prime rib roast?? Preheat oven to 450\\u00b0F. Place the roast (ribs down) on the rack in a roasting pan. Sear the rib roast for 15 minutes at the higher oven temperature (450\\u00b0F), and then turn the oven to the lower temperature (325\\u00b0 F) for the rest of the cooking time.About 1/2 hour before the estimated end of the roasting time, begin checking the internal temperature (use a good instant-read digital meat thermometer.nsert meat thermometer so tip is in thickest part of beef, not resting in fat or touching bone. Cook until rib roast reaches an internal temperature of 120\\u00b0F. Remove from oven, cover with aluminum foil, and let sit approximately 15 to 20 minutes.\", \"3 month old and rice cereal 3 out of 3 found this helpful. My son will be 3 months Sunday and I have been giving him 1/2 a tbsp of Rice Cereal in his bottle for the past couple of days... just to try it out. (once in the morning and once at night) He usually has a 6-8 oz bottle every 3-4 hours.\", \"Portion Guide for Baby's First Year Between 4 and 6 months, your baby will start to sit up and grab for objects on his own. As he masters that grabbing skill, you may opt to start introducing cereal into his diet. Baby should eat 1-2 tablespoons of cereal twice a day.\", \"Should You Add Rice Cereal to Baby's Bottle? How to Introduce Rice Cereal If your doctor determines that your baby is ready for solids, at a tablespoon of rice cereal to every 4-5 tablespoons of breast milk or formula that you would normally feed your child.\", \"Cornell Feline Health Center From age six months to maturity, most cats will do well when fed two times a day.. Once the cat becomes an adult, at about one year, feeding once or twice a day is appropriate in most cases. Senior cats, age seven and above, should maintain the same feeding regimen. Once cats reach adulthood, once a day feeding is fine as long as they are healthy and have no disease problems suggesting a reason to feed differently, says Dr. Kallfelz. The Health of Your Cat Matters.\", \"- The following observations are made: \\u2022 Wholesale and retail trade was the largest employer with 22,2% of the employed in South Africa and 21,5% in KwaZulu-Natal, followed by community, social and personal services with 19,5% in South Africa and 18,8% in KwaZulu-Natal.\", \"Why do Japanese eat alot of rice? Asker's rating. 1 I almost eat rice three times a day. This is natural for me because I was brought up with eating rice. 2 Starches are the foundation for every cuisine in the world. In each area of the world the conditions are right for one or more native starch crops. 3 Because, they grow well. They suit the weather there.\", \"Gerber, Oatmeal Cereal, Single Grain, 8 oz (227 g) \\u00b7 Mix 1 tbsp. cereal with 4-5 tbsp. of breastmilk or infant formula. Easy-to-Mix Directions. \\u00b7 Pour or spoon desired amount of cereal in bowl. \\u00b7 For Baby: Stir in liquid (breastmilk or infant formula) to desired consistency. \\u00b7 For Toddler: mix with milk, water, or GERBER \\u00ae Juice for children over one year of age.\", \"Feeding infant Rice Cereal: How many times a day and how much? Watch for any reactions. 4- After that, feed 2 tablespoons of single grain oatmeal + 1 1/2 ounces of formula once a day. ALSO, continue feeding rice cereal + formula once a day... so he will be eating solids two times a day. I usually did one time mid morning and the second at night to space it out. 5- Feed with the oatmeal (+ rice cereal for 2nd feeding) for 5 days.\"], \"pos_scores\": [96.875], \"neg_scores\": [90.1875, 88.0625, 90.375, 93.375, 89.75, 89.0, 85.0625, 89.8125, 89.875, 88.75, 91.8125, 93.3125, 88.5625, 89.4375, 89.75, 93.625, 88.1875, 93.875, 93.8125, 93.375, 89.875, 85.5625, 90.0625, 91.5, 97.9375], \"prompt\": \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"}\n" }, { "path": "examples/inference/embedder/README.md", "content": "# Embedder\n\n- [Model List](#model-list)\n- [Usage](#usage)\n- [Citation](#citation)\n\nAn embedder can encode text into embeddings.\n\nWhen provided with a query and a passage, the embedder encodes both separately, and then uses the similarity between their embeddings as the similarity score.\n\nFor more detailed using, you can look [embedder-encoder only](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder/encoder_only) or [embedder-decoder only](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder/decoder_only)\n\n\n## Model List\n\n`bge` is short for `BAAI general embedding`.\n\n| Model | Language | Description | query instruction for retrieval |\n| :----------------------------------------------------------- | :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |\n| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples | Provide instructions and few-shot examples freely based on the given task. |\n| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | A LLM-based multilingual embedding model, trained on a diverse range of languages and tasks. | Provide instructions based on the given task. |\n| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |\n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | Embedding Model which map text into vector | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | Embedding Model which map text into vector | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |\n\n## Usage\n\n### Using FlagEmbedding\n\n#### 1. Auto Model\n\nYou can use `FlagAutoModel` to load the model. For the **custom model** (not included in [`AUTO_EMBEDDER_MAPPING`](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/inference/embedder/model_mapping.py#L39)), you must specify the `model_class` parameter. You can also submit a pull request to add your **released model** to the [`AUTO_EMBEDDER_MAPPING`](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/inference/embedder/model_mapping.py#L39) dictionary. If need, you can create a new `.py` file in [here](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/encoder_only) or [here](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/embedder/decoder_only).\n\n```python\nfrom FlagEmbedding import FlagAutoModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagAutoModel.from_finetuned('BAAI/bge-large-zh-v1.5',\n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章：\",\n use_fp16=True,\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n\n# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query\n# corpus in retrieval task can still use encode_corpus(), since they don't need instruction\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode_corpus(passages)\nscores = q_embeddings @ p_embeddings.T\nprint(scores)\n```\n\nFor your **custom model** (assume the model is finetuned from `BAAI/bge-large-zh-v1.5`, then the model class is `encoder-only-base`), you can use the following code:\n\n```python\nfrom FlagEmbedding import FlagAutoModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagAutoModel.from_finetuned('your_model_name_or_path',\n model_class='encoder-only-base', # specify the model class\n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章：\",\n pooling_method='cls', # specify the pooling method\n use_fp16=True,\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n\n# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query\n# corpus in retrieval task can still use encode_corpus(), since they don't need instruction\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode_corpus(passages)\nscores = q_embeddings @ p_embeddings.T\nprint(scores)\n```\n\nThe `model_class` parameter currently includes the following options:\n- `encoder-only-base`: for encoder-only normal model, such as `BAAI/bge-large-en-v1.5`\n- `encoder-only-m3`: for encoder-only M3 model, such as `BAAI/bge-m3`\n- `decoder-only-base`: for decoder-only normal model, such as `BAAI/bge-multilingual-gemma2`\n- `decoder-only-icl`: for decoder-only ICL model, such as `BAAI/bge-en-icl`\n\n#### 2. Normal Model\n\nFor `FlagModel`, it supports `BAAI/bge-large-en-v1.5`, `BAAI/bge-base-en-v1.5`, `BAAI/bge-small-en-v1.5`, `BAAI/bge-large-zh-v1.5`, `BAAI/bge-base-zh-v1.5`, `BAAI/bge-small-zh-v1.5`, `BAAI/bge-large-en`, `BAAI/bge-base-en`, `BAAI/bge-small-en`, `BAAI/bge-large-zh`, `BAAI/bge-base-zh`, `BAAI/bge-small-zh'`:\n\n```python\nfrom FlagEmbedding import FlagModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagModel('BAAI/bge-large-zh-v1.5',\n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章：\",\n use_fp16=True,\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode(sentences_1)\nembeddings_2 = model.encode(sentences_2)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n\n# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query\n# corpus in retrieval task can still use encode_corpus(), since they don't need instruction\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode_corpus(passages)\nscores = q_embeddings @ p_embeddings.T\nprint(scores)\n```\n\n#### 3. M3 Model\n\nFor `BGEM3FlagModel`, it supports `BAAI/bge-m3`:\n\n```python\nfrom FlagEmbedding import BGEM3FlagModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = BGEM3FlagModel('BAAI/bge-m3',\n use_fp16=True,\n pooling_method='cls',\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode(\n sentences_1,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n)\nembeddings_2 = model.encode(\n sentences_2,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n)\ndense_similarity = embeddings_1[\"dense_vecs\"] @ embeddings_2[\"dense_vecs\"].T\nprint('dense similarity:', dense_similarity)\nsparse_similarity = model.compute_lexical_matching_score(\n embeddings_1[\"lexical_weights\"],\n embeddings_2[\"lexical_weights\"],\n)\nprint('sparse similarity:', sparse_similarity)\n\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(\n queries,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n)\np_embeddings = model.encode_corpus(\n passages,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n)\ndense_scores = embeddings_1[\"dense_vecs\"] @ embeddings_2[\"dense_vecs\"].T\nprint('dense scores:', dense_scores)\nsparse_scores = model.compute_lexical_matching_score(\n embeddings_1[\"lexical_weights\"],\n embeddings_2[\"lexical_weights\"],\n)\nprint('sparse similarity:', sparse_scores)\n```\n\n#### 4. LLM-based Model\n\nFor `FlagLLMModel`, it supports `BAAI/bge-multilingual-gemma2`, `Alibaba-NLP/gte-Qwen2-7B-instruct`, `intfloat/e5-mistral-7b-instruct`, .etc:\n\n```python\nfrom FlagEmbedding import FlagLLMModel\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = FlagLLMModel('BAAI/bge-multilingual-gemma2',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n query_instruction_format=\"{}\\n{}\",\n use_fp16=True,\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode_corpus(passages)\nscores = q_embeddings @ p_embeddings.T\nprint(scores)\n```\n\n#### 5. LLM-based ICL Model\n\nFor `FlagICLModel`, it supports `BAAI/bge-en-icl`:\n\n```python\nfrom FlagEmbedding import FlagICLModel\n\nexamples = [\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"\n },\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"\n }\n]\nmodel = FlagICLModel(\n 'BAAI/bge-en-icl',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n query_instruction_format=\"{}\\n{}\",\n examples_for_task=examples,\n examples_instruction_format=\"{}\\n{}\\n{}\",\n use_fp16=True,\n devices=['cuda:1']\n) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nqueries = [\n \"how much protein should a female eat\",\n \"summit define\"\n]\npassages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n]\nq_embeddings = model.encode_queries(queries)\np_embeddings = model.encode_corpus(passages)\nscores = q_embeddings @ p_embeddings.T\nprint(scores)\n```\n\n### Using HuggingFace Transformers\n\n#### 1. Normal Model\n\nIt supports `BAAI/bge-large-en-v1.5`, `BAAI/bge-base-en-v1.5`, `BAAI/bge-small-en-v1.5`, `BAAI/bge-large-zh-v1.5`, `BAAI/bge-base-zh-v1.5`, `BAAI/bge-small-zh-v1.5`, `BAAI/bge-large-en`, `BAAI/bge-base-en`, `BAAI/bge-small-en`, `BAAI/bge-large-zh`, `BAAI/bge-base-zh`, `BAAI/bge-small-zh'`, the **dense method** of `BAAI/bge-m3`:\n\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')\nmodel = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')\nmodel.eval()\n\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nwith torch.no_grad():\n encoded_input_1 = tokenizer(sentences_1, padding=True, truncation=True, return_tensors='pt')\n encoded_input_2 = tokenizer(sentences_2, padding=True, truncation=True, return_tensors='pt')\n model_output_1 = model(**encoded_input_1)\n model_output_2 = model(**encoded_input_2)\n embeddings_1 = model_output_1[0][:, 0]\n embeddings_2 = model_output_2[0][:, 0]\n similarity = embeddings_1 @ embeddings_2.T\n print(similarity)\n```\n\n#### 2. M3 Model\n\nIt only supports the **dense method** of `BAAI/bge-m3`, you can refer to the above code.\n\n#### 3. LLM-based Model\n\nIt supports `BAAI/bge-multilingual-gemma2`:\n\n```python\nimport torch\nimport torch.nn.functional as F\n\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\n\ndef last_token_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'{task_description}\\n{query}'\n\n\ntask = 'Given a web search query, retrieve relevant passages that answer the query.'\nqueries = [\n get_detailed_instruct(task, 'how much protein should a female eat'),\n get_detailed_instruct(task, 'summit define')\n]\n# No need to add instructions for documents\ndocuments = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n]\ninput_texts = queries + documents\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-multilingual-gemma2')\nmodel = AutoModel.from_pretrained('BAAI/bge-multilingual-gemma2')\nmodel.eval()\n\nmax_length = 4096\n# Tokenize the input texts\nbatch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8)\n\nwith torch.no_grad():\n outputs = model(**batch_dict)\n embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])\n \n# normalize embeddings\nembeddings = F.normalize(embeddings, p=2, dim=1)\nscores = (embeddings[:2] @ embeddings[2:].T) * 100\nprint(scores.tolist())\n# [[55.92064666748047, 1.6549524068832397], [-0.2698777914047241, 49.95653533935547]]\n```\n\n#### 4. LLM-based ICL Model\n\nIt supports `BAAI/bge-en-icl`:\n\n```python\nimport torch\nimport torch.nn.functional as F\n\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\n\ndef last_token_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'{task_description}\\n{query}'\n\ndef get_detailed_example(task_description: str, query: str, response: str) -> str:\n return f'{task_description}\\n{query}\\n{response}'\n\ndef get_new_queries(queries, query_max_len, examples_prefix, tokenizer):\n inputs = tokenizer(\n queries,\n max_length=query_max_len - len(tokenizer('', add_special_tokens=False)['input_ids']) - len(\n tokenizer('\\n', add_special_tokens=False)['input_ids']),\n return_token_type_ids=False,\n truncation=True,\n return_tensors=None,\n add_special_tokens=False\n )\n prefix_ids = tokenizer(examples_prefix, add_special_tokens=False)['input_ids']\n suffix_ids = tokenizer('\\n', add_special_tokens=False)['input_ids']\n new_max_length = (len(prefix_ids) + len(suffix_ids) + query_max_len + 8) // 8 * 8 + 8\n new_queries = tokenizer.batch_decode(inputs['input_ids'])\n for i in range(len(new_queries)):\n new_queries[i] = examples_prefix + new_queries[i] + '\\n'\n return new_max_length, new_queries\n\ntask = 'Given a web search query, retrieve relevant passages that answer the query.'\nexamples = [\n {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"},\n {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"}\n]\nexamples = [get_detailed_example(e['instruct'], e['query'], e['response']) for e in examples]\nexamples_prefix = '\\n\\n'.join(examples) + '\\n\\n' # if there not exists any examples, just set examples_prefix = ''\nqueries = [\n get_detailed_instruct(task, 'how much protein should a female eat'),\n get_detailed_instruct(task, 'summit define')\n]\ndocuments = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n]\nquery_max_len, doc_max_len = 512, 512\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-en-icl')\nmodel = AutoModel.from_pretrained('BAAI/bge-en-icl')\nmodel.eval()\n\nnew_query_max_len, new_queries = get_new_queries(queries, query_max_len, examples_prefix, tokenizer)\n\nquery_batch_dict = tokenizer(new_queries, max_length=new_query_max_len, padding=True, truncation=True, return_tensors='pt')\ndoc_batch_dict = tokenizer(documents, max_length=doc_max_len, padding=True, truncation=True, return_tensors='pt')\n\nwith torch.no_grad():\n query_outputs = model(**query_batch_dict)\n query_embeddings = last_token_pool(query_outputs.last_hidden_state, query_batch_dict['attention_mask'])\n doc_outputs = model(**doc_batch_dict)\n doc_embeddings = last_token_pool(doc_outputs.last_hidden_state, doc_batch_dict['attention_mask'])\n \n# normalize embeddings\nquery_embeddings = F.normalize(query_embeddings, p=2, dim=1)\ndoc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)\nscores = (query_embeddings @ doc_embeddings.T) * 100\nprint(scores.tolist())\n```\n\n### Using Sentence-Transformers\n\nYou can also use the `bge` models with [sentence-transformers](https://www.sbert.net/). It currently supports `BAAI/bge-large-en-v1.5`, `BAAI/bge-base-en-v1.5`, `BAAI/bge-small-en-v1.5`, `BAAI/bge-large-zh-v1.5`, `BAAI/bge-base-zh-v1.5`, `BAAI/bge-small-zh-v1.5`, `BAAI/bge-large-en`, `BAAI/bge-base-en`, `BAAI/bge-small-en`, `BAAI/bge-large-zh`, `BAAI/bge-base-zh`, `BAAI/bge-small-zh'`, the **dense method** of `BAAI/bge-m3`, `BAAI/bge-multilingual-gemma2`:\n\n```\npip install -U sentence-transformers\n```\n\n```shell\nfrom sentence_transformers import SentenceTransformer\nsentences_1 = [\"样例数据-1\", \"样例数据-2\"]\nsentences_2 = [\"样例数据-3\", \"样例数据-4\"]\nmodel = SentenceTransformer('BAAI/bge-large-zh-v1.5')\nembeddings_1 = model.encode(sentences_1, normalize_embeddings=True)\nembeddings_2 = model.encode(sentences_2, normalize_embeddings=True)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\n\nFor s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages.\n\n```shell\nfrom sentence_transformers import SentenceTransformer\nqueries = ['query_1', 'query_2']\npassages = [\"样例文档-1\", \"样例文档-2\"]\ninstruction = \"为这个句子生成表示以用于检索相关文章：\"\n\nmodel = SentenceTransformer('BAAI/bge-large-zh-v1.5')\nq_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)\np_embeddings = model.encode(passages, normalize_embeddings=True)\nscores = q_embeddings @ p_embeddings.T\n```\n\n### Using Langchain\n\nYou can use `bge` in langchain like this:\n\n```python\nfrom langchain.embeddings import HuggingFaceBgeEmbeddings\nmodel_name = \"BAAI/bge-large-en-v1.5\"\nmodel_kwargs = {'device': 'cuda'}\nencode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity\nmodel = HuggingFaceBgeEmbeddings(\n model_name=model_name,\n model_kwargs=model_kwargs,\n encode_kwargs=encode_kwargs,\n query_instruction=\"为这个句子生成表示以用于检索相关文章：\"\n)\nmodel.query_instruction = \"为这个句子生成表示以用于检索相关文章：\"\n```\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{bge_embedding,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n@misc{bge-m3,\n title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, \n author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},\n year={2024},\n eprint={2402.03216},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n@misc{li2024makingtextembeddersfewshot,\n title={Making Text Embedders Few-Shot Learners}, \n author={Chaofan Li and MingHao Qin and Shitao Xiao and Jianlyu Chen and Kun Luo and Yingxia Shao and Defu Lian and Zheng Liu},\n year={2024},\n eprint={2409.15700},\n archivePrefix={arXiv},\n primaryClass={cs.IR},\n url={https://arxiv.org/abs/2409.15700}, \n}\n```\n\n" }, { "path": "examples/inference/embedder/decoder_only/auto_base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_base_multi_devices():\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-multilingual-gemma2',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.558 0.02113 ]\\n [0.01643 0.526 ]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/auto_base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_base_single_device():\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-multilingual-gemma2',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.558 0.0212 ]\\n [0.01651 0.526 ]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/auto_icl_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_icl_multi_devices():\n examples = [\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"\n },\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"\n }\n ]\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-en-icl',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n examples_for_task=examples,\n examples_instruction_format=\"{}\\n{}\\n{}\",\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n\n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_icl_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.579 0.2776]\\n [0.2249 0.5146]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/auto_icl_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_icl_single_device():\n examples = [\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"\n },\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"\n }\n ]\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-en-icl',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n examples_for_task=examples,\n examples_instruction_format=\"{}\\n{}\\n{}\",\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n\n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_icl_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.579 0.2776]\\n [0.2249 0.5146]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagLLMModel\n\n\ndef test_base_multi_devices():\n model = FlagLLMModel(\n 'BAAI/bge-multilingual-gemma2',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n query_instruction_format=\"{}\\n{}\",\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.558 0.02113 ]\\n [0.01643 0.526 ]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagLLMModel\n\n\ndef test_base_single_device():\n model = FlagLLMModel(\n 'BAAI/bge-multilingual-gemma2',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n query_instruction_format=\"{}\\n{}\",\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.558 0.0212 ]\\n [0.01651 0.526 ]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/icl_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagICLModel\n\n\ndef test_icl_multi_devices():\n examples = [\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"\n },\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"\n }\n ]\n model = FlagICLModel(\n 'BAAI/bge-en-icl',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n query_instruction_format=\"{}\\n{}\",\n examples_for_task=examples,\n examples_instruction_format=\"{}\\n{}\\n{}\",\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n\n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_icl_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.579 0.2776]\\n [0.2249 0.5146]]\")\n" }, { "path": "examples/inference/embedder/decoder_only/icl_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagICLModel\n\n\ndef test_icl_single_device():\n examples = [\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"\n },\n {\n 'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"\n }\n ]\n model = FlagICLModel(\n 'BAAI/bge-en-icl',\n query_instruction_for_retrieval=\"Given a question, retrieve passages that answer the question.\",\n query_instruction_format=\"{}\\n{}\",\n examples_for_task=examples,\n examples_instruction_format=\"{}\\n{}\\n{}\",\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n\n queries = [\n \"how much protein should a female eat\",\n \"summit define\"\n ] * 100\n passages = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_icl_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.579 0.2776]\\n [0.2249 0.5146]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/auto_base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_base_multi_devices():\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-small-en-v1.5',\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages: \",\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is the capital of France?\",\n \"What is the population of China?\",\n ] * 100\n passages = [\n \"Paris is the capital of France.\",\n \"The population of China is over 1.4 billion people.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.7944 0.4492]\\n [0.5806 0.801 ]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/auto_base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_base_single_device():\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-small-en-v1.5',\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages: \",\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is the capital of France?\",\n \"What is the population of China?\",\n ] * 100\n passages = [\n \"Paris is the capital of France.\",\n \"The population of China is over 1.4 billion people.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_single_device()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.7944 0.4492]\\n [0.58 0.801 ]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/auto_m3_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_m3_multi_devices():\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-m3',\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is BGE M3?\",\n \"Defination of BM25\"\n ] * 100\n passages = [\n \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"\n ] * 100\n \n queries_embeddings = model.encode_queries(\n queries,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n passages_embeddings = model.encode_corpus(\n passages,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n \n dense_scores = queries_embeddings[\"dense_vecs\"] @ passages_embeddings[\"dense_vecs\"].T\n sparse_scores = model.compute_lexical_matching_score(\n queries_embeddings[\"lexical_weights\"],\n passages_embeddings[\"lexical_weights\"],\n )\n\n print(\"Dense score:\\n\", dense_scores[:2, :2])\n print(\"Sparse score:\\n\", sparse_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_m3_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"Dense score:\")\n print(\" [[0.626 0.3477]\\n [0.3499 0.678 ]]\")\n print(\"Sparse score:\")\n print(\" [[0.19561768 0.00878906]\\n [0. 0.18030453]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/auto_m3_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoModel\n\n\ndef test_m3_single_device():\n model = FlagAutoModel.from_finetuned(\n 'BAAI/bge-m3',\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is BGE M3?\",\n \"Defination of BM25\"\n ] * 100\n passages = [\n \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"\n ] * 100\n \n queries_embeddings = model.encode_queries(\n queries,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n passages_embeddings = model.encode_corpus(\n passages,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n \n dense_scores = queries_embeddings[\"dense_vecs\"] @ passages_embeddings[\"dense_vecs\"].T\n sparse_scores = model.compute_lexical_matching_score(\n queries_embeddings[\"lexical_weights\"],\n passages_embeddings[\"lexical_weights\"],\n )\n\n print(\"Dense score:\\n\", dense_scores[:2, :2])\n print(\"Sparse score:\\n\", sparse_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_m3_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"Dense score:\")\n print(\" [[0.626 0.3477]\\n [0.3496 0.678 ]]\")\n print(\"Sparse score:\")\n print(\" [[0.19554901 0.00880432]\\n [0. 0.18036556]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagModel\n\n\ndef test_base_multi_devices():\n model = FlagModel(\n 'BAAI/bge-small-en-v1.5',\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages: \",\n query_instruction_format=\"{}{}\",\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n pooling_method='cls',\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is the capital of France?\",\n \"What is the population of China?\",\n ] * 100\n passages = [\n \"Paris is the capital of France.\",\n \"The population of China is over 1.4 billion people.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.7944 0.4492]\\n [0.5806 0.801 ]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagModel\n\n\ndef test_base_single_device():\n model = FlagModel(\n 'BAAI/bge-small-en-v1.5',\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages: \",\n query_instruction_format=\"{}{}\",\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n pooling_method='cls',\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is the capital of France?\",\n \"What is the population of China?\",\n ] * 100\n passages = [\n \"Paris is the capital of France.\",\n \"The population of China is over 1.4 billion people.\"\n ] * 100\n \n queries_embeddings = model.encode_queries(queries)\n passages_embeddings = model.encode_corpus(passages)\n \n cos_scores = queries_embeddings @ passages_embeddings.T\n print(cos_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_base_single_device()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[[0.7944 0.4492]\\n [0.58 0.801 ]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/m3_multi_devices.py", "content": "import os\nfrom FlagEmbedding import BGEM3FlagModel\n\n\ndef test_m3_multi_devices():\n model = BGEM3FlagModel(\n 'BAAI/bge-m3',\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n pooling_method='cls',\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is BGE M3?\",\n \"Defination of BM25\"\n ] * 100\n passages = [\n \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"\n ] * 100\n \n queries_embeddings = model.encode_queries(\n queries,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n passages_embeddings = model.encode_corpus(\n passages,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n \n dense_scores = queries_embeddings[\"dense_vecs\"] @ passages_embeddings[\"dense_vecs\"].T\n sparse_scores = model.compute_lexical_matching_score(\n queries_embeddings[\"lexical_weights\"],\n passages_embeddings[\"lexical_weights\"],\n )\n\n print(\"Dense score:\\n\", dense_scores[:2, :2])\n print(\"Sparse score:\\n\", sparse_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_m3_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"Dense score:\")\n print(\" [[0.626 0.3477]\\n [0.3499 0.678 ]]\")\n print(\"Sparse score:\")\n print(\" [[0.19561768 0.00878906]\\n [0. 0.18030453]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/m3_multi_devices_compute_score.py", "content": "import os\nfrom FlagEmbedding import BGEM3FlagModel\n\n\ndef test_m3_multi_devices():\n model = BGEM3FlagModel(\n 'BAAI/bge-m3',\n devices=[\"cuda:0\", \"cuda:1\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n pooling_method='cls',\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is BGE M3?\",\n \"Defination of BM25\"\n ] * 100\n passages = [\n \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"\n ] * 100\n \n sentence_pairs = list(zip(queries, passages))\n scores_dict = model.compute_score(\n sentence_pairs,\n weights_for_different_modes=[1., 0.3, 1.]\n )\n \n queries.reverse()\n sentence_pairs = list(zip(queries, passages))\n \n scores_dict_reverse = model.compute_score(\n sentence_pairs,\n weights_for_different_modes=[1., 0.3, 1.]\n )\n \n scores_dict = {\n key: value[:2]\n for key, value in scores_dict.items()\n }\n scores_dict_reverse = {\n key: value[:2]\n for key, value in scores_dict_reverse.items()\n }\n \n print(scores_dict)\n print(scores_dict_reverse)\n\n\nif __name__ == '__main__':\n test_m3_multi_devices()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"{'colbert': [0.7798609733581543, 0.7897368669509888], 'sparse': [0.1956787109375, 0.1802978515625], 'dense': [0.6259765625, 0.67822265625], 'sparse+dense': [0.5266770720481873, 0.5633169412612915], 'colbert+sparse+dense': [0.6367570757865906, 0.6617604494094849]}\")\n print(\"{'colbert': [0.4524071514606476, 0.4619773030281067], 'sparse': [0.0, 0.0087890625], 'dense': [0.349853515625, 0.34765625], 'sparse+dense': [0.2691181004047394, 0.269456148147583], 'colbert+sparse+dense': [0.34880897402763367, 0.3531610071659088]}\")\n" }, { "path": "examples/inference/embedder/encoder_only/m3_single_device.py", "content": "import os\nfrom FlagEmbedding import BGEM3FlagModel\n\n\ndef test_m3_single_device():\n model = BGEM3FlagModel(\n 'BAAI/bge-m3',\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n pooling_method='cls',\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is BGE M3?\",\n \"Defination of BM25\"\n ] * 100\n passages = [\n \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"\n ] * 100\n \n queries_embeddings = model.encode_queries(\n queries,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n passages_embeddings = model.encode_corpus(\n passages,\n return_dense=True,\n return_sparse=True,\n return_colbert_vecs=False,\n )\n \n dense_scores = queries_embeddings[\"dense_vecs\"] @ passages_embeddings[\"dense_vecs\"].T\n sparse_scores = model.compute_lexical_matching_score(\n queries_embeddings[\"lexical_weights\"],\n passages_embeddings[\"lexical_weights\"],\n )\n\n print(\"Dense score:\\n\", dense_scores[:2, :2])\n print(\"Sparse score:\\n\", sparse_scores[:2, :2])\n\n\nif __name__ == '__main__':\n test_m3_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"Dense score:\")\n print(\" [[0.626 0.3477]\\n [0.3496 0.678 ]]\")\n print(\"Sparse score:\")\n print(\" [[0.19554901 0.00880432]\\n [0. 0.18036556]]\")\n" }, { "path": "examples/inference/embedder/encoder_only/m3_single_device_compute_score.py", "content": "import os\nfrom FlagEmbedding import BGEM3FlagModel\n\n\ndef test_m3_single_device():\n model = BGEM3FlagModel(\n 'BAAI/bge-m3',\n devices=\"cuda:0\", # if you don't have a GPU, you can use \"cpu\"\n pooling_method='cls',\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n queries = [\n \"What is BGE M3?\",\n \"Defination of BM25\"\n ] * 100\n passages = [\n \"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"\n ] * 100\n \n sentence_pairs = list(zip(queries, passages))\n scores_dict = model.compute_score(\n sentence_pairs,\n weights_for_different_modes=[1., 0.3, 1.]\n )\n \n queries.reverse()\n sentence_pairs = list(zip(queries, passages))\n \n scores_dict_reverse = model.compute_score(\n sentence_pairs,\n weights_for_different_modes=[1., 0.3, 1.]\n )\n \n scores_dict = {\n key: value[:2]\n for key, value in scores_dict.items()\n }\n scores_dict_reverse = {\n key: value[:2]\n for key, value in scores_dict_reverse.items()\n }\n \n print(scores_dict)\n print(scores_dict_reverse)\n\n\nif __name__ == '__main__':\n test_m3_single_device()\n\n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"{'colbert': [0.7798250317573547, 0.7899274826049805], 'sparse': [0.195556640625, 0.180419921875], 'dense': [0.6259765625, 0.67822265625], 'sparse+dense': [0.5266488790512085, 0.5633450746536255], 'colbert+sparse+dense': [0.6367254853248596, 0.6618592143058777]}\")\n print(\"{'colbert': [0.4524373412132263, 0.46213820576667786], 'sparse': [0.0, 0.0088043212890625], 'dense': [0.349609375, 0.34765625], 'sparse+dense': [0.2689302861690521, 0.26945966482162476], 'colbert+sparse+dense': [0.34871599078178406, 0.3532329499721527]}\")\n" }, { "path": "examples/inference/reranker/README.md", "content": "# Reranker\n\n- [Model List](#model-list)\n- [Usage](#usage)\n- [Citation](#citation)\n\nDifferent from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. \nYou can get a relevance score by inputting query and passage to the reranker. \nAnd the score can be mapped to a float value in [0,1] by sigmoid function.\n\nFor more detailed using, you can look [reranker-encoder only](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker/encoder_only) or [reranker-decoder only](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker/decoder_only)\n\n## Model List\n\n| Model | Base model | Language | layerwise | feature |\n|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|\n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. |\n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. |\n| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | [bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. |\n| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | [gemma-2b](https://huggingface.co/google/gemma-2b) | Multilingual | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. |\n| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. |\n\n\nYou can select the model according your senario and resource. \n- For **multilingual**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)\n\n- For **Chinese or English**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). \n\n- For **efficiency**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). \n\n- For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)\n\n## Usage \n### Using FlagEmbedding\n\n#### 1. Auto Reranker\n\nYou can use `FlagAutoReranker` to load the model. For the **custom model** (not included in [`AUTO_RERANKER_MAPPING`](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/inference/reranker/model_mapping.py#L31)), you must specify the `model_class` parameter. You can also submit a pull request to add your **released model** to the [`AUTO_RERANKER_MAPPING`](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/inference/reranker/model_mapping.py#L31) dictionary. If need, you can create a new `.py` file in [here](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/reranker/encoder_only) or [here](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/inference/reranker/decoder_only).\n\n```python\nfrom FlagEmbedding import FlagAutoReranker\nreranker = FlagAutoReranker.from_finetuned('BAAI/bge-reranker-large',\n query_max_length=256,\n passage_max_length=512,\n use_fp16=True,\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nscore = reranker.compute_score(['query', 'passage'])\nprint(score) # -1.5263671875\n\n# You can map the scores into 0-1 by set \"normalize=True\", which will apply sigmoid function to the score\nscore = reranker.compute_score(['query', 'passage'], normalize=True)\nprint(score) # 0.1785258315203034\n\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])\nprint(scores) # [-5.60546875, 5.76171875]\n\n# You can map the scores into 0-1 by set \"normalize=True\", which will apply sigmoid function to the score\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)\nprint(scores) # [0.0036642203307843528, 0.9968641641227171]\n```\n\nFor your **custom model** (assume the model is finetuned from `BAAI/bge-reranker-large`, then the model class is `encoder-only-base`), you can use the following code:\n\n```python\nfrom FlagEmbedding import FlagAutoReranker\nreranker = FlagAutoReranker.from_finetuned('your_model_name_or_path',\n model_class='encoder-only-base',\n query_max_length=256,\n passage_max_length=512,\n use_fp16=True,\n devices=['cuda:1']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nscore = reranker.compute_score(['query', 'passage'])\nprint(score)\n```\n\nThe `model_class` parameter currently includes the following options:\n- `encoder-only-base`: for encoder-only reranker model, such as `BAAI/bge-reranker-large`\n- `decoder-only-base`: for decoder-only reranker model, such as `BAAI/bge-reranker-v2-gemma`\n- `decoder-only-layerwise`: for decoder-only layerwise reranker model, such as `BAAI/bge-reranker-v2-minicpm-layerwise`\n- `decoder-only-lightweight`: for decoder-only lightweight reranker model, such as `BAAI/bge-reranker-v2.5-gemma2-lightweight`\n\n#### 2. Normal Reranker\n\nFor `FlagReranker`, it supports `BAAI/bge-reranker-base`, `BAAI/bge-reranker-large`, `BAAI/bge-reranker-v2-m3`:\n\n```python\nfrom FlagEmbedding import FlagReranker\nreranker = FlagReranker(\n 'BAAI/bge-reranker-v2-m3', \n query_max_length=256,\n passage_max_length=512,\n use_fp16=True,\n devices=['cuda:1']\n) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nscore = reranker.compute_score(['query', 'passage'])\nprint(score) # -5.65234375\n\n# You can map the scores into 0-1 by set \"normalize=True\", which will apply sigmoid function to the score\nscore = reranker.compute_score(['query', 'passage'], normalize=True)\nprint(score) # 0.003497010252573502\n\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])\nprint(scores) # [-8.1875, 5.26171875]\n\n# You can map the scores into 0-1 by set \"normalize=True\", which will apply sigmoid function to the score\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)\nprint(scores) # [0.00027803096387751553, 0.9948403768236574]\n```\n\n#### 3. LLM-based Reranker\n\nFor `FlagLLMReranker`, it supports `BAAI/bge-reranker-v2-gemma`:\n\n```python\nfrom FlagEmbedding import FlagLLMReranker\nreranker = FlagLLMReranker(\n 'BAAI/bge-reranker-v2-gemma', \n query_max_length=256,\n passage_max_length=512,\n use_fp16=True,\n devices=['cuda:1']\n) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nscore = reranker.compute_score(['query', 'passage'])\nprint(score)\n\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])\nprint(scores)\n```\n\n#### 4. LLM-based Layerwise Reranker\n\nFor `LayerWiseFlagLLMReranker`, it supports `BAAI/bge-reranker-v2-minicpm-layerwise`:\n\n```python\nfrom FlagEmbedding import LayerWiseFlagLLMReranker\nreranker = LayerWiseFlagLLMReranker(\n 'BAAI/bge-reranker-v2-minicpm-layerwise', \n query_max_length=256,\n passage_max_length=512,\n use_fp16=True,\n devices=['cuda:1']\n) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nscore = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\nprint(score)\n\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28])\nprint(scores)\n```\n\n#### 5. LLM-based lightweight Reranker\n\nFor `LightWeightFlagLLMReranker`, it supports `BAAI/bge-reranker-v2.5-gemma2-lightweight`:\n\n```python\nfrom FlagEmbedding import LightWeightFlagLLMReranker\nreranker = LightWeightFlagLLMReranker(\n 'BAAI/bge-reranker-v2.5-gemma2-lightweight', \n query_max_length=256,\n passage_max_length=512,\n use_fp16=True,\n devices=['cuda:1']\n) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nscore = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.\nprint(score)\n\nscores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\nprint(scores)\n```\n\n### Using Huggingface transformers\n\n#### 1. Normal Reranker\n\nIt supports `BAAI/bge-reranker-base`, `BAAI/bge-reranker-large`, `BAAI/bge-reranker-v2-m3`:\n\n```python\nimport torch\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')\nmodel = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3')\nmodel.eval()\n\npairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]\nwith torch.no_grad():\n inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)\n scores = model(**inputs, return_dict=True).logits.view(-1, ).float()\n print(scores)\n```\n\n#### 2. LLM-based reranker\n\nIt supports `BAAI/bge-reranker-v2-gemma`:\n\n```python\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ndef get_inputs(pairs, tokenizer, prompt=None, max_length=1024):\n if prompt is None:\n prompt = \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"\n sep = \"\\n\"\n prompt_inputs = tokenizer(prompt,\n return_tensors=None,\n add_special_tokens=False)['input_ids']\n sep_inputs = tokenizer(sep,\n return_tensors=None,\n add_special_tokens=False)['input_ids']\n inputs = []\n for query, passage in pairs:\n query_inputs = tokenizer(f'A: {query}',\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length * 3 // 4,\n truncation=True)\n passage_inputs = tokenizer(f'B: {passage}',\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True)\n item = tokenizer.prepare_for_model(\n [tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n inputs.append(item)\n return tokenizer.pad(\n inputs,\n padding=True,\n max_length=max_length + len(sep_inputs) + len(prompt_inputs),\n pad_to_multiple_of=8,\n return_tensors='pt',\n )\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma')\nmodel = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma')\nyes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0]\nmodel.eval()\n\npairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]\nwith torch.no_grad():\n inputs = get_inputs(pairs, tokenizer)\n scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float()\n print(scores)\n```\n\n#### 3. LLM-based layerwise reranker\n\nIt supports `BAAI/bge-reranker-v2-minicpm-layerwise`:\n\n```python\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ndef get_inputs(pairs, tokenizer, prompt=None, max_length=1024):\n if prompt is None:\n prompt = \"Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'.\"\n sep = \"\\n\"\n prompt_inputs = tokenizer(prompt,\n return_tensors=None,\n add_special_tokens=False)['input_ids']\n sep_inputs = tokenizer(sep,\n return_tensors=None,\n add_special_tokens=False)['input_ids']\n inputs = []\n for query, passage in pairs:\n query_inputs = tokenizer(f'A: {query}',\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length * 3 // 4,\n truncation=True)\n passage_inputs = tokenizer(f'B: {passage}',\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True)\n item = tokenizer.prepare_for_model(\n [tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n inputs.append(item)\n return tokenizer.pad(\n inputs,\n padding=True,\n max_length=max_length + len(sep_inputs) + len(prompt_inputs),\n pad_to_multiple_of=8,\n return_tensors='pt',\n )\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True)\nmodel = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)\nmodel = model.to('cuda')\nmodel.eval()\n\npairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]\nwith torch.no_grad():\n inputs = get_inputs(pairs, tokenizer).to(model.device)\n all_scores = model(**inputs, return_dict=True, cutoff_layers=[28])\n all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]]\n print(all_scores)\n```\n\n#### 4. LLM-based lightweight reranker\n\nIt supports `BAAI/bge-reranker-v2.5-gemma2-lightweight`:\n\n```python\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ndef last_logit_pool(logits: torch.Tensor,\n attention_mask: torch.Tensor) -> torch.Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return logits[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = logits.shape[0]\n return torch.stack([logits[i, sequence_lengths[i]] for i in range(batch_size)], dim=0)\n\ndef get_inputs(pairs, tokenizer, prompt=None, max_length=1024):\n if prompt is None:\n prompt = \"Predict whether passage B contains an answer to query A.\"\n sep = \"\\n\"\n prompt_inputs = tokenizer(prompt,\n return_tensors=None,\n add_special_tokens=False)['input_ids']\n sep_inputs = tokenizer(sep,\n return_tensors=None,\n add_special_tokens=False)['input_ids']\n inputs = []\n query_lengths = []\n prompt_lengths = []\n for query, passage in pairs:\n query_inputs = tokenizer(f'A: {query}',\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length * 3 // 4,\n truncation=True)\n passage_inputs = tokenizer(f'B: {passage}',\n return_tensors=None,\n add_special_tokens=False,\n max_length=max_length,\n truncation=True)\n item = tokenizer.prepare_for_model(\n [tokenizer.bos_token_id] + query_inputs['input_ids'],\n sep_inputs + passage_inputs['input_ids'],\n truncation='only_second',\n max_length=max_length,\n padding=False,\n return_attention_mask=False,\n return_token_type_ids=False,\n add_special_tokens=False\n )\n item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs\n item['attention_mask'] = [1] * len(item['input_ids'])\n inputs.append(item)\n query_lengths.append(len([tokenizer.bos_token_id] + query_inputs['input_ids'] + sep_inputs))\n prompt_lengths.append(len(sep_inputs + prompt_inputs))\n \n return tokenizer.pad(\n inputs,\n padding=True,\n max_length=max_length + len(sep_inputs) + len(prompt_inputs),\n pad_to_multiple_of=8,\n return_tensors='pt',\n ), query_lengths, prompt_lengths\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2.5-gemma2-lightweight', trust_remote_code=True)\ntokenizer.padding_side = 'right'\nmodel = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2.5-gemma2-lightweight', trust_remote_code=True)\nmodel = model.to('cuda')\nmodel.eval()\n\npairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]\nwith torch.no_grad():\n inputs, query_lengths, prompt_lengths = get_inputs(pairs, tokenizer)\n inputs = inputs.to(model.device)\n outputs = model(**inputs,\n return_dict=True,\n cutoff_layers=[28],\n compress_ratio=2,\n compress_layer=[24, 40],\n query_lengths=query_lengths,\n prompt_lengths=prompt_lengths)\n scores = []\n for i in range(len(outputs.logits)):\n logits = last_logit_pool(outputs.logits[i], outputs.attention_masks[i])\n scores.append(logits.cpu().float().tolist())\n print(scores)\n```\n\n## Load model in local\n\n### Load llm-based layerwise reranker in local\n\nIf you download reranker-v2-minicpm-layerwise, you can load it with the following method:\n\n1. make sure `configuration_minicpm_reranker.py` and `modeling_minicpm_reranker.py` from [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) in your local path.\n2. modify the following part of `config.json`:\n\n```\n\"auto_map\": {\n \"AutoConfig\": \"configuration_minicpm_reranker.LayerWiseMiniCPMConfig\",\n \"AutoModel\": \"modeling_minicpm_reranker.LayerWiseMiniCPMModel\",\n \"AutoModelForCausalLM\": \"modeling_minicpm_reranker.LayerWiseMiniCPMForCausalLM\"\n },\n```\n\n### Load llm-based lightweight reranker in local\n\n1. make sure `gemma_config.py` and `gemma_model.py` from [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight/tree/main) in your local path.\n2. modify the following part of config.json:\n\n```\n\"auto_map\": {\n \"AutoConfig\": \"gemma_config.CostWiseGemmaConfig\",\n \"AutoModel\": \"gemma_model.CostWiseGemmaModel\",\n \"AutoModelForCausalLM\": \"gemma_model.CostWiseGemmaForCausalLM\"\n },\n```\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{li2023making,\n title={Making Large Language Models A Better Foundation For Dense Retrieval}, \n author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},\n year={2023},\n eprint={2312.15503},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n@misc{chen2024bge,\n title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, \n author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},\n year={2024},\n eprint={2402.03216},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n@misc{li2024makingtextembeddersfewshot,\n title={Making Text Embedders Few-Shot Learners}, \n author={Chaofan Li and MingHao Qin and Shitao Xiao and Jianlyu Chen and Kun Luo and Yingxia Shao and Defu Lian and Zheng Liu},\n year={2024},\n eprint={2409.15700},\n archivePrefix={arXiv},\n primaryClass={cs.IR},\n url={https://arxiv.org/abs/2409.15700}, \n}\n```" }, { "path": "examples/inference/reranker/decoder_only/auto_base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-v2-gemma',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[ 9.1484375 -4.50390625 -5.53125 10.21875 ]\")\n" }, { "path": "examples/inference/reranker/decoder_only/auto_base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-v2-gemma',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[9.171875, -4.49609375, -5.5234375, 10.2109375]\")\n" }, { "path": "examples/inference/reranker/decoder_only/auto_layerwise_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-v2-minicpm-layerwise',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[1.939453125, -12.71875, -11.78125, 2.189453125]\")\n" }, { "path": "examples/inference/reranker/decoder_only/auto_layerwise_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-v2-minicpm-layerwise',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[1.939453125, -12.71875, -11.78125, 2.189453125]\")\n" }, { "path": "examples/inference/reranker/decoder_only/auto_lightweight_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-v2.5-gemma2-lightweight',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[25.375, 8.734375, 9.8359375, 26.15625]\")\n" }, { "path": "examples/inference/reranker/decoder_only/auto_lightweight_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-v2.5-gemma2-lightweight',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[25.375, 8.734375, 9.8359375, 26.15625]\")\n" }, { "path": "examples/inference/reranker/decoder_only/base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagLLMReranker\n\n\ndef test_base_multi_devices():\n model = FlagLLMReranker(\n 'BAAI/bge-reranker-v2-gemma',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[ 9.1484375 -4.50390625 -5.53125 10.21875 ]\")\n" }, { "path": "examples/inference/reranker/decoder_only/base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagLLMReranker\n\n\ndef test_base_multi_devices():\n model = FlagLLMReranker(\n 'BAAI/bge-reranker-v2-gemma',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[9.171875, -4.49609375, -5.5234375, 10.2109375]\")\n" }, { "path": "examples/inference/reranker/decoder_only/layerwise_multi_devices.py", "content": "import os\nfrom FlagEmbedding import LayerWiseFlagLLMReranker\n\n\ndef test_base_multi_devices():\n model = LayerWiseFlagLLMReranker(\n 'BAAI/bge-reranker-v2-minicpm-layerwise',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[1.939453125, -12.71875, -11.78125, 2.189453125]\")\n" }, { "path": "examples/inference/reranker/decoder_only/layerwise_single_device.py", "content": "import os\nfrom FlagEmbedding import LayerWiseFlagLLMReranker\n\n\ndef test_base_multi_devices():\n model = LayerWiseFlagLLMReranker(\n 'BAAI/bge-reranker-v2-minicpm-layerwise',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[1.939453125, -12.71875, -11.78125, 2.189453125]\")\n" }, { "path": "examples/inference/reranker/decoder_only/lightweight_multi_devices.py", "content": "import os\nfrom FlagEmbedding import LightWeightFlagLLMReranker\n\n\ndef test_base_multi_devices():\n model = LightWeightFlagLLMReranker(\n 'BAAI/bge-reranker-v2.5-gemma2-lightweight',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[25.375, 8.734375, 9.8359375, 26.15625]\")\n" }, { "path": "examples/inference/reranker/decoder_only/lightweight_single_device.py", "content": "import os\nfrom FlagEmbedding import LightWeightFlagLLMReranker\n\n\ndef test_base_multi_devices():\n model = LightWeightFlagLLMReranker(\n 'BAAI/bge-reranker-v2.5-gemma2-lightweight',\n use_fp16=True,\n query_instruction_for_rerank=\"A: \",\n passage_instruction_for_rerank=\"B: \",\n trust_remote_code=True,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs, cutoff_layers=[28], compress_ratio=2, compress_layers=[24, 40])\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[25.375, 8.734375, 9.8359375, 26.15625]\")\n" }, { "path": "examples/inference/reranker/encoder_only/auto_base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-large',\n use_fp16=True,\n batch_size=128,\n query_max_length=256,\n max_length=512,\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[ 7.97265625 -6.8515625 -7.15625 5.45703125]\")\n" }, { "path": "examples/inference/reranker/encoder_only/auto_base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagAutoReranker\n\n\ndef test_base_multi_devices():\n model = FlagAutoReranker.from_finetuned(\n 'BAAI/bge-reranker-large',\n use_fp16=True,\n batch_size=128,\n query_max_length=256,\n max_length=512,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[7.9765625, -6.84375, -7.15625, 5.453125]\")\n" }, { "path": "examples/inference/reranker/encoder_only/base_multi_devices.py", "content": "import os\nfrom FlagEmbedding import FlagReranker\n\n\ndef test_base_multi_devices():\n model = FlagReranker(\n 'BAAI/bge-reranker-large',\n use_fp16=True,\n batch_size=128,\n query_max_length=256,\n max_length=512,\n devices=[\"cuda:3\", \"cuda:4\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[ 7.97265625 -6.8515625 -7.15625 5.45703125]\")\n" }, { "path": "examples/inference/reranker/encoder_only/base_single_device.py", "content": "import os\nfrom FlagEmbedding import FlagReranker\n\n\ndef test_base_multi_devices():\n model = FlagReranker(\n 'BAAI/bge-reranker-large',\n use_fp16=True,\n batch_size=128,\n query_max_length=256,\n max_length=512,\n devices=[\"cuda:3\"], # if you don't have GPUs, you can use [\"cpu\", \"cpu\"]\n cache_dir=os.getenv('HF_HUB_CACHE', None),\n )\n \n pairs = [\n [\"What is the capital of France?\", \"Paris is the capital of France.\"],\n [\"What is the capital of France?\", \"The population of China is over 1.4 billion people.\"],\n [\"What is the population of China?\", \"Paris is the capital of France.\"],\n [\"What is the population of China?\", \"The population of China is over 1.4 billion people.\"]\n ] * 100\n \n scores = model.compute_score(pairs)\n \n print(scores[:4])\n\n\nif __name__ == '__main__':\n test_base_multi_devices()\n \n print(\"--------------------------------\")\n print(\"Expected Output:\")\n print(\"[7.9765625, -6.84375, -7.15625, 5.453125]\")\n" }, { "path": "research/BGE_Coder/README.md", "content": "

CodeR: Towards A Generalist Code Embedding Model

\n

\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n

\n\n\nThis repo contains the data, training, and evaluation pipeline for CodeR / [BGE-Code-v1](https://huggingface.co/BAAI/bge-code-v1)\n\n**[BGE-Code-v1](https://huggingface.co/BAAI/bge-code-v1)** is an LLM-based code embedding model that supports code retrieval, text retrieval, and multilingual retrieval. It primarily demonstrates the following capabilities:\n\n- Superior Code Retrieval Performance: The model demonstrates exceptional code retrieval capabilities, supporting natural language queries in both English and Chinese, as well as 20 programming languages.\n- Robust Text Retrieval Capabilities: The model maintains strong text retrieval capabilities comparable to text embedding models of similar scale.\n- Extensive Multilingual Support: BGE-Code-v1 offers comprehensive multilingual retrieval capabilities, excelling in languages such as English, Chinese, Japanese, French, and more.\n\n## :bell: News:\n\n- 🥳 5/15/2025: We have released the CodeR! :fire:\n\n## Usage\n\n### Using FlagEmbedding\n\n```\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\npip install -e .\nfrom FlagEmbedding import FlagLLMModel\nqueries = [\n \"Delete the record with ID 4 from the 'Staff' table.\", \n 'Delete all records in the \"Livestock\" table where age is greater than 5'\n]\ndocuments = [\n \"DELETE FROM Staff WHERE StaffID = 4;\",\n \"DELETE FROM Livestock WHERE age > 5;\"\n]\nmodel = FlagLLMModel('BAAI/bge-code-v1', \n query_instruction_format=\"{}\\n{}\",\n query_instruction_for_retrieval=\"Given a question in text, retrieve SQL queries that are appropriate responses to the question.\",\n trust_remote_code=True,\n use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode_queries(queries)\nembeddings_2 = model.encode_corpus(documents)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\n\nBy default, FlagLLMModel will use all available GPUs when encoding. Please set `os.environ[\"CUDA_VISIBLE_DEVICES\"]` to select specific GPUs. You also can set `os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"` to make all GPUs unavailable.\n\n### Using Sentence Transformers\n\n```python\nfrom sentence_transformers import SentenceTransformer\nimport torch\n\n# Load the model, optionally in float16 precision for faster inference\nmodel = SentenceTransformer(\n \"BAAI/bge-code-v1\",\n trust_remote_code=True,\n model_kwargs={\"torch_dtype\": torch.float16},\n)\n\n# Prepare a prompt given an instruction\ninstruction = 'Given a question in text, retrieve SQL queries that are appropriate responses to the question.'\nprompt = f'{instruction}\\n'\n# Prepare queries and documents\nqueries = [\n \"Delete the record with ID 4 from the 'Staff' table.\", \n 'Delete all records in the \"Livestock\" table where age is greater than 5'\n]\ndocuments = [\n \"DELETE FROM Staff WHERE StaffID = 4;\",\n \"DELETE FROM Livestock WHERE age > 5;\"\n]\n\n# Compute the query and document embeddings\nquery_embeddings = model.encode(queries, prompt=prompt)\ndocument_embeddings = model.encode(documents)\n\n# Compute the cosine similarity between the query and document embeddings\nsimilarities = model.similarity(query_embeddings, document_embeddings)\nprint(similarities)\n```\n\n### Using HuggingFace Transformers\n\n```python\nimport torch\nimport torch.nn.functional as F\n\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\n\ndef last_token_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'{task_description}\\n{query}'\n\n\ninstruction = 'Given a question in text, retrieve SQL queries that are appropriate responses to the question.'\nqueries = [\n \"Delete the record with ID 4 from the 'Staff' table.\", \n 'Delete all records in the \"Livestock\" table where age is greater than 5'\n]\ndocuments = [\n \"DELETE FROM Staff WHERE StaffID = 4;\",\n \"DELETE FROM Livestock WHERE age > 5;\"\n]\ninput_texts = queries + documents\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-code-v1', trust_remote_code=True)\nmodel = AutoModel.from_pretrained('BAAI/bge-code-v1', trust_remote_code=True)\nmodel.eval()\n\nmax_length = 4096\n# Tokenize the input texts\nbatch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8)\n\nwith torch.no_grad():\n outputs = model(**batch_dict)\n embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])\n \n# normalize embeddings\nembeddings = F.normalize(embeddings, p=2, dim=1)\nscores = (embeddings[:2] @ embeddings[2:].T) * 100\nprint(scores.tolist())\n```\n\n## Evaluation\n\n**BGE-Code-v1** achieves state-of-the-art performance on both the CoIR and CodeRAG benchmarks.\n\n- CoIR\n\n| | CodeXEmbed-2B | CodeXEmbed-7B | Voyage-Code-002 | Voyage-Code-003 | BGE-Code-v1 |\n| --------------------- | ------------- | ------------- | --------------- | --------------- | ----------- |\n| **Apps** | 76.86 | 85.38 | 26.52 | 93.62 | 98.08 |\n| **CosQA** | 40.47 | 42.47 | 29.79 | 34.45 | 46.72 |\n| **Text2SQL** | 78.42 | 78.94 | 69.26 | 62.87 | 64.35 |\n| **CSN** | 87.87 | 89.67 | 81.79 | 89.35 | 89.53 |\n| **CSN-CCR** | 97.66 | 97.95 | 73.45 | 90.05 | 98.30 |\n| **CodeTrans-Contest** | 90.30 | 94.45 | 72.77 | 94.96 | 94.38 |\n| **CodeTrans-DL** | 38.57 | 40.46 | 27.48 | 38.57 | 46.13 |\n| **StackOverFlow-QA** | 94.47 | 96.33 | 67.68 | 97.17 | 95.35 |\n| **CodeFeedBack-ST** | 86.36 | 87.53 | 65.35 | 90.67 | 90.56 |\n| **CodeFeedBack-MT** | 65.51 | 68.83 | 28.74 | 93.58 | 94.38 |\n| **AVG** | **75.65** | **78.20** | **56.26** | **78.53** | **81.77** |\n\n- CodedRAG\n\n| | HummanEval | MBPP | DS-1000 | ODEX | RepoEval | SWE-bench-Lite | AVG |\n| --------------- | ---------- | ---- | ------- | ---- | -------- | -------------- | -------- |\n| SFR | 100.0 | 99.0 | 19.3 | 37.1 | 83.8 | 62.7 | **67.0** |\n| Jina-v2-code | 100.0 | 97.7 | 26.2 | 19.9 | 90.5 | 58.3 | **65.4** |\n| CodeXEmbed-2B | 100.0 | 97.4 | 25.4 | 23.9 | 88.7 | 52.4 | **64.6** |\n| Voyage-Code-002 | 100.0 | 99.0 | 33.1 | 26.6 | 94.3 | 29.1 | **63.7** |\n| BGE-Code-v1 | 100.0 | 99.2 | 40.9 | 36.1 | 93.1 | 67.4 | **72.8** |\n\n### Instructions for Evaluation\n\n```python\n{\n \"Apps\": \"Given a code contest problem description, retrieve relevant code that can help solve the problem.\",\n \"CosQA\": \"Given a web search query, retrieve relevant code that can help answer the query.\",\n \"Text2SQL\": \"Given a question in text, retrieve SQL queries that are appropriate responses to the question.\",\n \"CSN\": \"Given a piece of code, retrieve the document string that summarizes the code.\",\n \"CSN-CCR\": \"Given a piece of code segment, retrieve the code segment that is the latter part of the code.\",\n \"CodeTrans-DL\": \"Given a piece of code, retrieve code that is semantically equivalent to the input code.\",\n \"CodeTrans-Contest\": \"Given a piece of Python code, retrieve C++ code that is semantically equivalent to the input code.\",\n \"StackOverFlow-QA\": \"Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.\",\n \"CodeFeedBack-ST\": \"Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.\",\n \"CodeFeedBack-MT\": \"Given a multi-turn conversation history that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.\",\n \"HummanEval\": \"Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.\",\n \"MBPP\": \"Given a textual explanation of code functionality, retrieve the corresponding code implementation.\",\n \"DS-1000\": \"Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.\",\n \"ODEX\": \"Given a question, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.\",\n \"RepoEval\": \"Given a piece of code segment, retrieve the code segment that is the latter part of the code.\",\n \"SWE-bench-Lite\": \"Given a code snippet containing a bug and a natural language description of the bug or error, retrieve code snippets that demonstrate solutions or fixes for similar bugs or errors (the desired documents).\"\n}\n```\n\n### Evaluation script\n\n#### CoIR\n\nFor CoIR, we use the [CoIR](https://github.com/CoIR-team/coir) evaluation script:\n\n```shell\ncd ./evaluation/coir_eval\n### clone coir\nmkdir test\ncd ./test\ngit clone https://github.com/CoIR-team/coir.git\nmv ./coir/coir ../\ncd ..\nrm -rf ./test\n### evaluate\nbash eval.sh\n```\n\n### CodeRAG\n\nFor CodeRAG, we use the [CodeRAG](https://github.com/code-rag-bench/code-rag-bench) evaluation script:\n\n```shell\ncd ./evaluation/coderag_eval\n### clone coderag\ngit clone https://github.com/code-rag-bench/code-rag-bench.git\n## You need prepare environment according to README.md\nrm -rf ./code-rag-bench/retrieval/create\ncp -r ./test/* ./code-rag-bench/retrieval/\n### prepare data\nbash prepare_data.sh\n### evaluate\nbash eval.sh\n```" }, { "path": "research/BGE_Coder/data_generation/constant.py", "content": "from enum import Enum\nfrom dataclasses import dataclass\nfrom typing import Dict, Union, Tuple, Optional, List\n\n\nclass TaskType(Enum):\n # text2code\n web_code_retrieval = \"Web Query to Code Retrieval\"\n code_contest_retrieval = \"Code Contest Retrieval\"\n text2sql_retrieval = \"Text to SQL Retrieval\"\n error_message_retrieval = \"Error Message to Code Retrieval\"\n code_explanation_retrieval = \"Code Explanation to Implementation Retrieval\"\n api_usage_retrieval = \"API Usage Description to Code Retrieval\"\n bug_desc_retrieval = \"Bug Description to Code Retrieval\"\n pseudocode_retrieval = \"Pseudocode to Code Retrieval\"\n tutorial_query_retrieval = \"Programming Tutorial Query to Code Example Retrieval\"\n algorithm_desc_retrieval = \"Algorithm Description to Code Retrieval\"\n\n # code2text\n code_summary_retrieval = \"Code Summary Retrieval\"\n code_review_retrieval = \"Code Review Retrieval\"\n code_intent_retrieval = \"Code Intent Retrieval\"\n code_optimization_retrieval = \"Code Optimization Retrieval\"\n tutorial_retrieval = \"Tutorial Retrieval\"\n code_issue_discussion_retrieval = \"Code Issue Discussion Retrieval\"\n api_reference_retrieval = \"API Reference Retrieval\"\n code_walkthrough_retrieval = \"Code Walkthrough Retrieval\"\n code_error_explanation_retrieval = \"Code Error Explanation Retrieval\"\n code_to_requirement_retrieval = \"Code to Requirement Retrieval\"\n\n # code2code\n code_context_retrieval = \"Code Context Retrieval\"\n similar_code_retrieval = \"Similar Code Retrieval\"\n code_translation_retrieval = \"Code Translation Retrieval\"\n code_refinement_retrieval = \"Code Refinement Retrieval\"\n secure_code_retrieval = \"Secure Code Retrieval\"\n code_version_update_retrieval = \"Code Version Update Retrieval\"\n code_example_retrieval = \"Code Example Retrieval\"\n code_dependency_retrieval = \"Code Dependency Retrieval\"\n code_pattern_retrieval = \"Code Pattern Retrieval\"\n code_history_retrieval = \"Code History Retrieval\"\n code_integration_retrieval = \"Code Integration Retrieval\"\n optimized_code_retrieval = \"Optimized Code Retrieval\"\n code_simplification_retrieval = \"Code Simplification Retrieval\"\n code_modularization_retrieval = \"Code Modularization Retrieval\"\n code_augmentation_retrieval = \"Code Augmentation Retrieval\"\n error_handling_code_retrieval = \"Error Handling Retrieval\"\n code_documentation_retrieval = \"Code Documentation Retrieval\"\n library_adaptation_retrieval = \"Library Adaptation Retrieval\"\n\n # hybrid\n code_modification_retrieval = \"Code Modification Retrieval\"\n # single_turn_code_qa = \"Single-turn Code QA\"\n # multi_turn_code_qa = \"Multi-turn Code QA\"\n code_bug_fix_example_retrieval = \"Code Bug Fix Example Retrieval\"\n code_refactoring_pattern_retrieval = \"Code Refactoring Pattern Retrieval\"\n code_style_guideline_example_retrieval = \"Code Style Guideline Example Retrieval\"\n code_migration_retrieval = \"Code Migration Retrieval\"\n code_optimization_hybrid_retrieval = \"Code Optimization Hybrid Retrieval\"\n code_comparison_retrieval = \"Code Comparison Retrieval\"\n code_best_practices_retrieval = \"Code Best Practices Retrieval\"\n security_vulnerability_fix_retrieval = \"Security Vulnerability Fix Retrieval\"\n\n\ndef get_task_def_by_task_type(task_type: Union[str, TaskType]) -> Tuple[str, TaskType, str]:\n \"\"\"\n Given a task type, return the main task type, task type, and task instruction.\n \n Args:\n - task_type: Union[str, TaskType]: the task type, either as a string or as a TaskType enum. Example: \"web_code_retrieval\" or TaskType.web_code_retrieval\n \n Returns:\n - main_task_type: str: the main task type. Example: \"text2code\"\n - task_type: TaskType: the task type. Example: TaskType.web_code_retrieval\n - task_instruction: str: the task instruction. Example: \"Given a web search query, retrieve relevant code that can help answer the query.\"\n \"\"\"\n \n task_type_to_instruct: Dict[TaskType, str] = {\n # text2code\n TaskType.web_code_retrieval: \"Given a web search query, retrieve relevant code that can help answer the query.\",\n TaskType.code_contest_retrieval: \"Given a code contest problem description, retrieve relevant code that can help solve the problem.\",\n TaskType.text2sql_retrieval: \"Given a question in text, retrieve SQL queries that are appropriate responses to the question.\",\n TaskType.error_message_retrieval: \"Given an error message encountered during coding, retrieve relevant code that can help resolve the error.\",\n TaskType.code_explanation_retrieval: \"Given a textual explanation of code functionality, retrieve the corresponding code implementation.\",\n TaskType.api_usage_retrieval: \"Given a usage description of an API or library, retrieve code examples demonstrating the usage.\",\n TaskType.bug_desc_retrieval: \"Given a description of a software bug or unexpected behavior, retrieve relevant code that can help address the issue.\",\n TaskType.pseudocode_retrieval: \"Given a pseudocode description of an procedure, retrieve code implementations of the procedure.\",\n TaskType.tutorial_query_retrieval: \"Given a query related to a programming tutorial or learning material, retrieve code examples that are relevant to the query.\",\n TaskType.algorithm_desc_retrieval: \"Given a textual description of an algorithm, retrieve code implementations of the described algorithm.\",\n \n # code2text\n TaskType.code_summary_retrieval: \"Given a piece of code, retrieve the document string that summarizes the code.\",\n TaskType.code_review_retrieval: \"Given a piece of code, retrieve the review that explains its role.\",\n TaskType.code_intent_retrieval: \"Given a piece of code, retrieve the developer's intent or purpose described in a commit message or design document.\",\n TaskType.code_optimization_retrieval: \"Given a piece of code, retrieve optimization suggestions or performance analysis reports.\",\n TaskType.tutorial_retrieval: \"Given a piece of code, retrieve tutorials or how-to guides that demonstrate how to use or implement similar code.\",\n TaskType.code_issue_discussion_retrieval: \"Given a piece of code, retrieve discussions or issue reports related to the code, such as bug reports or feature requests.\",\n TaskType.api_reference_retrieval: \"Given a piece of code that uses specific APIs or libraries, retrieve the relevant API reference documentation for those APIs or libraries.\",\n TaskType.code_walkthrough_retrieval: \"Given a piece of code, retrieve a step-by-step walkthrough or detailed explanation of the code's logic and execution flow.\",\n TaskType.code_error_explanation_retrieval: \"Given a piece of code, retrieve the document that explains potential errors or exceptions that may arise from the code.\",\n TaskType.code_to_requirement_retrieval: \"Given a piece of code, retrieve the software requirement or user story it fulfills.\",\n \n # code2code\n TaskType.code_context_retrieval: \"Given a piece of code segment, retrieve the code segment that is the latter part of the code.\",\n TaskType.similar_code_retrieval: \"Given a piece of code, retrieve code that is semantically equivalent to the input code.\",\n TaskType.code_translation_retrieval: \"Given a piece of {src_language} code, retrieve {tgt_language} code that is semantically equivalent to the input code.\",\n TaskType.code_refinement_retrieval: \"Given a piece of code, retrieve a refined version of the code.\",\n TaskType.secure_code_retrieval: \"Given a piece of code, retrieve a version of the code with enhanced security measures or vulnerability fixes.\",\n TaskType.code_version_update_retrieval: \"Given a piece of code in an older language version, retrieve code updated to comply with the syntax or features of a newer language version.\",\n TaskType.code_example_retrieval: \"Given a code library or API, retrieve example code snippets that demonstrate how to use the library or API.\",\n TaskType.code_dependency_retrieval: \"Given a piece of code, retrieve all the code segments that the input code depends on, including libraries, functions, and variables.\",\n TaskType.code_pattern_retrieval: \"Given a piece of code, retrieve other code segments that follow the same design pattern or structure.\",\n TaskType.code_history_retrieval: \"Given a piece of code, retrieve previous versions or iterations of the code to understand its development history.\",\n TaskType.code_integration_retrieval: \"Given a piece of code, retrieve code that demonstrates how to integrate the input code with other systems or components.\",\n TaskType.optimized_code_retrieval: \"Given a piece of code, retrieve an optimized version of the code that improves performance, readability, or efficiency.\",\n TaskType.code_simplification_retrieval: \"Given a complex piece of code, retrieve a simplified version of the code that is easier to understand and maintain.\",\n TaskType.code_modularization_retrieval: \"Given a piece of code, retrieve a modularized version of the code that breaks it down into smaller, reusable components.\",\n TaskType.code_augmentation_retrieval: \"Given a piece of code, retrieve code that implements additional functionality while also preserving the original behavior.\",\n TaskType.error_handling_code_retrieval: \"Given a piece of code, retrieve code that incorporates error-checking or exception-handling mechanisms relevant to the input code.\",\n TaskType.code_documentation_retrieval: \"Given a piece of code, retrieve code with inline comments or documentation explaining its functionality.\",\n TaskType.library_adaptation_retrieval: \"Given a piece of code using one library or framework, retrieve code that achieves the same functionality using a different library or framework.\",\n \n # hybrid\n TaskType.code_modification_retrieval: \"Given a code snippet and a natural language description of desired modifications, retrieve relevant code that implements the requested modifications.\",\n # TaskType.code_modification_retrieval: \"Given a question that consists of a mix of text and code snippets, retrieve relevant code that answers the question.\",\n # TaskType.single_turn_code_qa: \"Given a question that consists of a mix of text and code snippets, retrieve relevant code that answer the question.\",\n # TaskType.multi_turn_code_qa: \"Given a multi-turn conversation history that consists of a mix of text and code snippets, retrieve relevant code that answer the question.\",\n TaskType.code_bug_fix_example_retrieval: \"Given a code snippet containing a bug and a natural language description of the bug or error, retrieve code snippets that demonstrate solutions or fixes for similar bugs or errors (the desired documents).\",\n TaskType.code_refactoring_pattern_retrieval: \"Given a code snippet that could be improved and a natural language description of desired refactoring goals or patterns, retrieve code snippets that exemplify similar refactoring techniques or patterns (the desired documents).\",\n TaskType.code_style_guideline_example_retrieval: \"Given a code snippet and a natural language query describing a desired coding style or best practice, retrieve code snippets that adhere to the specified style guidelines or best practices (the desired documents).\",\n TaskType.code_migration_retrieval: \"Given a code snippet and a natural language description of a specific migration requirement, retrieve code snippets that demonstrate how to migrate the code to meet the requirement.\",\n TaskType.code_optimization_hybrid_retrieval: \"Given a code snippet and a natural language request for specific optimization, retrieve relevant code that implements the requested optimization.\",\n TaskType.code_comparison_retrieval: \"Given two code snippets and a natural language query about their differences or similarities, retrieve relevant document that explains the differences or similarities between the two code snippets.\",\n TaskType.code_best_practices_retrieval: \"Given a code snippet and a natural language query about coding best practices, retrieve relevant document including guidelines, design patterns, or recommendations that can help improve the quality of the code.\",\n TaskType.security_vulnerability_fix_retrieval: \"Given a code snippet and a text description of a security concern, retrieve secure code alternatives that address the security vulnerability.\",\n }\n \n task_type_to_main_type: Dict[TaskType, str] = {\n # text2code\n TaskType.web_code_retrieval: \"text2code\",\n TaskType.code_contest_retrieval: \"text2code\",\n TaskType.text2sql_retrieval: \"text2code\",\n TaskType.error_message_retrieval: \"text2code\",\n TaskType.code_explanation_retrieval: \"text2code\",\n TaskType.api_usage_retrieval: \"text2code\",\n TaskType.bug_desc_retrieval: \"text2code\",\n TaskType.pseudocode_retrieval: \"text2code\",\n TaskType.tutorial_query_retrieval: \"text2code\",\n TaskType.algorithm_desc_retrieval: \"text2code\",\n \n # code2text\n TaskType.code_summary_retrieval: \"code2text\",\n TaskType.code_review_retrieval: \"code2text\",\n TaskType.code_intent_retrieval: \"code2text\",\n TaskType.code_optimization_retrieval: \"code2text\",\n TaskType.tutorial_retrieval: \"code2text\",\n TaskType.code_issue_discussion_retrieval: \"code2text\",\n TaskType.api_reference_retrieval: \"code2text\",\n TaskType.code_walkthrough_retrieval: \"code2text\",\n TaskType.code_error_explanation_retrieval: \"code2text\",\n TaskType.code_to_requirement_retrieval: \"code2text\",\n \n # code2code\n TaskType.code_context_retrieval: \"code2code\",\n TaskType.similar_code_retrieval: \"code2code\",\n TaskType.code_translation_retrieval: \"code2code\",\n TaskType.code_refinement_retrieval: \"code2code\",\n TaskType.secure_code_retrieval: \"code2code\",\n TaskType.code_version_update_retrieval: \"code2code\",\n TaskType.code_example_retrieval: \"code2code\",\n TaskType.code_dependency_retrieval: \"code2code\",\n TaskType.code_pattern_retrieval: \"code2code\",\n TaskType.code_history_retrieval: \"code2code\",\n TaskType.code_integration_retrieval: \"code2code\",\n TaskType.optimized_code_retrieval: \"code2code\",\n TaskType.code_simplification_retrieval: \"code2code\",\n TaskType.code_modularization_retrieval: \"code2code\",\n TaskType.code_augmentation_retrieval: \"code2code\",\n TaskType.error_handling_code_retrieval: \"code2code\",\n TaskType.code_documentation_retrieval: \"code2code\",\n TaskType.library_adaptation_retrieval: \"code2code\",\n \n # hybrid\n TaskType.code_modification_retrieval: \"hybrid\",\n # TaskType.single_turn_code_qa: \"hybrid\",\n # TaskType.multi_turn_code_qa: \"hybrid\",\n TaskType.code_bug_fix_example_retrieval: \"hybrid\",\n TaskType.code_refactoring_pattern_retrieval: \"hybrid\",\n TaskType.code_style_guideline_example_retrieval: \"hybrid\",\n TaskType.code_migration_retrieval: \"hybrid\",\n TaskType.code_optimization_hybrid_retrieval: \"hybrid\",\n TaskType.code_comparison_retrieval: \"hybrid\",\n TaskType.code_best_practices_retrieval: \"hybrid\",\n TaskType.security_vulnerability_fix_retrieval: \"hybrid\",\n }\n\n if isinstance(task_type, str):\n task_type = TaskType[task_type]\n \n task_instruction = task_type_to_instruct[task_type]\n main_task_type = task_type_to_main_type[task_type]\n \n return main_task_type, task_type, task_instruction\n\n\nclass Language(Enum):\n # 主流语言 (2): 每种任务和每种 code language 均生产 (不包含文本的只生产 English)\n en = 'English' # 英语\n zh = 'Simplified Chinese' # 简体中文\n \n # 其他语言 (20)：从 text2code, code2text 中各 sample 3 个任务类型，再从 High 的 code language (java python javascript php ruby go csharp cplusplus) 中 sample 3 个 code language 出来，每个下面是 750 条，总共是 20 * 5 * 3 * 750 + 20 * 750 = 240K 条 (12K / language)\n ## Tasks: 1) web_code_retrieval, code_explanation_retrieval, text2sql_retrieval; \n # 2) code_review_retrieval, code_walkthrough_retrieval, code_to_requirement_retrieval\n ## Code Languages: random sample 3 code languages\n ar = 'Arabic' # 阿拉伯语\n bn = 'Bengali' # 孟加拉语\n es = 'Spanish' # 西班牙语\n fa = 'Persian' # 波斯语\n fi = 'Finnish' # 芬兰语\n fr = 'French' # 法语\n hi = 'Hindi' # 印地语\n id = 'Indonesian' # 印度尼西亚语\n ja = 'Japanese' # 日语\n ko = 'Korean' # 韩语\n ru = 'Russian' # 俄语\n sw = 'Swahili' # 斯瓦希里语\n te = 'Telugu' # 泰卢固语\n th = 'Thai' # 泰语\n de = 'German' # 德语\n yo = 'Yoruba' # 约鲁巴语\n it = 'Italian' # 意大利语\n pt = 'Portuguese' # 葡萄牙语\n vi = 'Vietnamese' # 越南语\n zh_tw = 'Traditional Chinese' # 繁体中文\n\n # nl = 'Dutch' # 荷兰语\n # no = 'Norwegian' # 挪威语\n # sv = 'Swedish' # 瑞典语\n # da = 'Danish' # 丹麦语\n # pl = 'Polish' # 波兰语\n # cs = 'Czech' # 捷克语\n # hu = 'Hungarian' # 匈牙利语\n # el = 'Greek' # 希腊语\n # he = 'Hebrew' # 希伯来语\n # tr = 'Turkish' # 土耳其语\n # ku = 'Kurdish' # 库尔德语\n # ur = 'Urdu' # 乌尔都语\n # gu = 'Gujarati' # 古吉拉特语\n # pa = 'Punjabi' # 旁遮普语\n # ta = 'Tamil' # 泰米尔语\n # kn = 'Kannada' # 卡纳达语\n # ml = 'Malayalam' # 马拉雅拉姆语\n # mr = 'Marathi' # 马拉地语\n # ms = 'Malay' # 马来语\n # my = 'Burmese' # 缅甸语\n # jv = 'Javanese' # 爪哇语\n # km = 'Khmer' # 高棉语\n # yue = 'Cantonese' # 粤语\n # zu = 'Zulu' # 祖鲁语\n # ha = 'Hausa' # 豪萨语\n # am = 'Amharic' # 阿姆哈拉语\n # ig = 'Igbo' # 伊博语\n # qu = 'Quechua' # 克丘亚语\n # nah = 'Nahuatl' # 纳瓦特尔语\n # ht = 'Haitian Creole' # 海地克里奥尔语\n # tl = 'Tagalog' # 塔加alog\n # mi = 'Maori' # 毛利语\n # mn = 'Mongolian' # 蒙古语\n\nclass CodeLanguage(Enum):\n # High (8): 3000 / language\n java = \"Java\"\n python = \"Python\"\n javascript = \"JavaScript\"\n php = \"PHP\"\n ruby = \"Ruby\"\n go = \"GO\"\n csharp = \"C#\"\n cplusplus = \"C++\"\n # Medium (6): 1500 / language\n c = \"C\"\n rust = \"Rust\"\n typescript = \"TypeScript\"\n perl = \"Perl\"\n shell = \"Shell\"\n sql = \"SQL\"\n # Low (6): 750 / language\n batchfile = \"Batchfile\"\n fortran = \"FORTRAN\"\n haskell = \"Haskell\"\n lua = \"Lua\"\n powershell = \"PowerShell\"\n visual_basic = \"Visual Basic\"\n \n # NULL for tasks that do not require code language\n null = \"\"\n \n # assembly = \"Assembly\"\n # cmake = \"CMake\"\n # css = \"CSS\"\n # dockerfile = \"Dockerfile\"\n # html = \"HTML\"\n # julia = \"Julia\"\n # makefile = \"Makefile\"\n # markdown = \"Markdown\"\n # scala = \"Scala\"\n # tex = \"TeX\"\n\n\n# 16 * 2 = 32, 3000 per pair (32 * 3000 = 96000)\nCODE_TRANSLATION_RETRIEVAL_PAIRS = [\n # c <-> cplusplus <-> csharp <-> java\n (CodeLanguage.c, CodeLanguage.cplusplus),\n (CodeLanguage.c, CodeLanguage.csharp),\n (CodeLanguage.c, CodeLanguage.java),\n (CodeLanguage.cplusplus, CodeLanguage.csharp),\n (CodeLanguage.cplusplus, CodeLanguage.java),\n (CodeLanguage.csharp, CodeLanguage.java),\n # python <-> ruby <-> perl\n (CodeLanguage.python, CodeLanguage.ruby),\n (CodeLanguage.python, CodeLanguage.perl),\n (CodeLanguage.ruby, CodeLanguage.perl),\n # javascript <-> typescript <-> php\n (CodeLanguage.javascript, CodeLanguage.typescript),\n (CodeLanguage.javascript, CodeLanguage.php),\n (CodeLanguage.typescript, CodeLanguage.php),\n # rust <-> go <-> cplusplus\n (CodeLanguage.rust, CodeLanguage.go),\n (CodeLanguage.rust, CodeLanguage.cplusplus),\n (CodeLanguage.go, CodeLanguage.cplusplus),\n # python <-> cplusplus\n (CodeLanguage.python, CodeLanguage.cplusplus),\n]\n\n\n@dataclass\nclass Task:\n task_type: TaskType\n language: Language\n code_language: CodeLanguage = CodeLanguage.null\n task_instruction: str = None\n tgt_code_language: CodeLanguage = CodeLanguage.null\n main_task_type: str = None\n\n\ndef get_task(\n task_type: str,\n language: str,\n code_language: str,\n tgt_code_language: Optional[str] = None\n) -> Task:\n main_task_type, task_type, task_instruction = get_task_def_by_task_type(task_type)\n\n if tgt_code_language is None:\n tgt_code_language = \"null\"\n\n language = Language[language]\n code_language = CodeLanguage[code_language]\n tgt_code_language = CodeLanguage[tgt_code_language]\n\n task_instruction = task_instruction.replace(\"{src_language}\", code_language.value).replace(\"{tgt_language}\", tgt_code_language.value)\n\n task = Task(\n task_type=task_type,\n language=language,\n code_language=code_language,\n task_instruction=task_instruction,\n tgt_code_language=tgt_code_language,\n main_task_type=main_task_type\n )\n return task\n\n\nSPECIAL_TASK_STEPS = {\n # TaskType.code_contest_retrieval: 2,\n TaskType.code_modification_retrieval: 2,\n TaskType.code_issue_discussion_retrieval: 2,\n TaskType.code_version_update_retrieval: 2,\n TaskType.code_bug_fix_example_retrieval: 2,\n TaskType.code_refactoring_pattern_retrieval: 2,\n TaskType.code_style_guideline_example_retrieval: 2,\n TaskType.bug_desc_retrieval: 2,\n TaskType.code_migration_retrieval: 2,\n TaskType.code_optimization_hybrid_retrieval: 2,\n TaskType.code_comparison_retrieval: 2,\n TaskType.code_best_practices_retrieval: 2,\n TaskType.security_vulnerability_fix_retrieval: 2,\n}\n\n\ndef get_pos_as_input_by_task_type(task_type: TaskType) -> bool:\n \"\"\"\n Get `pos_as_input` by task type.\n `pos_as_input=True` means that when generating a pair of query and pos, the pos is the input used for LLM generation. For example, text2code tasks: web_code_retrieval, code_contest_retrieval, text2sql_retrieval.\n `pos_as_input=False` means that when generating a pair of query and pos, the query is the input used for LLM generation. For example, code2text tasks: code_summary_retrieval, code_review_retrieval.\n \"\"\"\n # TODO: Add more task types\n SPECIAL_TASKS = {\n # hybrid\n TaskType.code_bug_fix_example_retrieval: False,\n TaskType.code_refactoring_pattern_retrieval: False,\n TaskType.code_style_guideline_example_retrieval: False,\n TaskType.code_migration_retrieval: False,\n TaskType.code_optimization_hybrid_retrieval: False,\n TaskType.code_comparison_retrieval: False,\n TaskType.code_best_practices_retrieval: False,\n TaskType.security_vulnerability_fix_retrieval: False,\n }\n \n if task_type in SPECIAL_TASKS:\n return SPECIAL_TASKS[task_type]\n \n # normal rules\n main_task_type, _, _ = get_task_def_by_task_type(task_type)\n if main_task_type in [\"text2code\", \"hybrid\"]:\n return True\n elif main_task_type in [\"code2text\", \"code2code\"]:\n return False\n else:\n raise ValueError(f\"Invalid task type: {task_type}\")\n\n\ndef get_generation_prompt(\n task: Task,\n text: str,\n text_b: Optional[str] = None,\n examples: Optional[List[dict]] = None,\n idx: Optional[int] = None\n) -> str:\n \"\"\"\n Given a task, return the generation prompt for the task.\n \n Args:\n - task: Task: the task object\n - text: str: the input text\n - text_b: str: the second input text (optional), used for code_modification_retrieval task\n - examples: List[dict]: the examples for the task\n - idx: int: the index of gen_instruction in the instruction list (optional), used for tasks that need multiple steps to generate the output\n \n Returns:\n - gen_prompt: str: the generation prompt\n \"\"\"\n \n task_to_gen_instruction: Dict[TaskType, str] = {\n # text2code (gen: code -> text)\n TaskType.web_code_retrieval: \"Given a piece of {code_language} code, generate a web query in {language} that can be solved by the code.\",\n TaskType.code_contest_retrieval: \"Given a piece of {code_language} code, generate a code contest description in {language} that can be solved by the code.\",\n TaskType.text2sql_retrieval: \"Given a piece of {code_language} code, generate a text query in {language} for which the code is the appropriate response.\",\n TaskType.error_message_retrieval: \"Given a piece of {code_language} code, generate a possible error message in {language} that can be resolved by the code.\",\n TaskType.code_explanation_retrieval: \"Given a piece of {code_language} code, generate a textual explanation in {language} of the code functionality.\",\n TaskType.api_usage_retrieval: \"Given a piece of {code_language} code, generate a usage description of an API or library in {language} that can be demonstrated by the code as an example.\",\n TaskType.bug_desc_retrieval: [\n \"Given a piece of {code_language} code, modify some details of the code to introduce one or more bugs.\",\n \"Given a piece of {code_language} code with one or more bugs, generate a description of the bugs in {language}.\",\n ],\n TaskType.pseudocode_retrieval: \"Given a piece of {code_language} code, generate a pseudocode in {language} that describes the code functionality.\",\n TaskType.tutorial_query_retrieval: \"Given a piece of {code_language} code, generate a programming tutorial query in {language} that can be answered by the code as an example.\",\n TaskType.algorithm_desc_retrieval: \"Given a piece of {code_language} code, generate an algorithm description in {language} that can be implemented by the code.\",\n \n # code2text (gen: code -> text)\n TaskType.code_summary_retrieval: \"Given a piece of {code_language} code, generate a summary in {language} of the code.\",\n TaskType.code_review_retrieval: \"Given a piece of {code_language} code, generate a review in {language} that explains its role.\",\n TaskType.code_intent_retrieval: \"Given a piece of {code_language} code, generate a developer's intent or purpose described in a commit message or design document in {language}.\",\n TaskType.code_optimization_retrieval: \"Given a piece of {code_language} code, generate code optimization suggestions or performance analysis reports in {language}.\",\n TaskType.tutorial_retrieval: \"Given a piece of {code_language} code, generate tutorials or how-to guides that demonstrate how to use or implement similar code in {language}.\",\n TaskType.code_issue_discussion_retrieval: [\n \"Given a piece of {code_language} code, generate a version with some bugs.\",\n \"Given a piece of {code_language} code, generate a discussion of the code's issues or bugs in {language}, such as bug reports or feature requests.\",\n ],\n TaskType.api_reference_retrieval: \"Given a piece of {code_language} code, generate the relevant API reference documentation in {language} that can be used to understand the code.\",\n TaskType.code_walkthrough_retrieval: \"Given a piece of {code_language} code, generate a step-by-step walkthrough or detailed explanation of the code's logic and execution flow in {language}.\",\n TaskType.code_error_explanation_retrieval: \"Given a piece of {code_language} code, generate a detailed explanation of the errors or exceptions that may arise from the code in {language}.\",\n TaskType.code_to_requirement_retrieval: \"Given a piece of {code_language} code, generate a software requirement or user story it fulfills in {language}.\",\n\n # code2code (gen: code-prefix -> code-suffix)\n TaskType.code_context_retrieval: \"Given a piece of {code_language} code, generate a piece of code that is the latter part of the input code.\",\n TaskType.similar_code_retrieval: \"Given a piece of {code_language} code, generate a piece of {code_language} code that is semantically equivalent to the input code.\",\n TaskType.code_translation_retrieval: \"Given a piece of {code_language} code, generate a piece of {tgt_code_language} code that is semantically equivalent to the input code.\",\n # src_language <-> code_language, tgt_language <-> tgt_code_language\n TaskType.code_refinement_retrieval: \"Given a piece of {code_language} code, generate a refined version of the code.\",\n TaskType.secure_code_retrieval: \"Given a piece of {code_language} code, generate a a version of the code with enhanced security measures or vulnerability fixes.\",\n TaskType.code_version_update_retrieval: [\n \"Given a piece of {code_language} code, generate a lower-level version of the code.\",\n \"Given a piece of {code_language} code, update it with the syntax or features of a newer language version.\",\n ],\n TaskType.code_example_retrieval: \"Given a piece of {code_language} code, generate a piece of {code_language} code that is a good example of the code's usage.\",\n TaskType.code_dependency_retrieval: \"Given a piece of {code_language} code, generate the code segments that the input code depends on, including libraries, functions, and variables.\",\n TaskType.code_pattern_retrieval: \"Given a piece of {code_language} code, generate a piece of {code_language} code that follows the same design pattern or structure.\",\n TaskType.code_history_retrieval: \"Given a piece of {code_language} code, generate a piece of {code_language} code that is a historical version or iteration of the code.\",\n TaskType.code_integration_retrieval: \"Given a piece of {code_language} code, generate a piece of {code_language} code that integrates the input code with other systems or components.\",\n TaskType.optimized_code_retrieval: \"Given a piece of {code_language} code, generate an optimized version of the code that improves performance, readability, or efficiency.\",\n TaskType.code_simplification_retrieval: \"Given a piece of {code_language} code, generate a simplified version of the code that is easier to understand and maintain.\",\n TaskType.code_modularization_retrieval: \"Given a piece of {code_language} code, generate a modularized version of the code that breaks it down into smaller, reusable components.\",\n TaskType.code_augmentation_retrieval: \"Given a piece of {code_language} code, generate a piece of code that implements additional functionality while preserving the original behavior.\",\n TaskType.error_handling_code_retrieval: \"Given a piece of {code_language} code, generate a piece of code that incorporates error-checking or exception-handling mechanisms relevant to the input code.\",\n TaskType.code_documentation_retrieval: \"Given a piece of {code_language} code, generate a piece of code with inline comments or documentation explaining its functionality.\",\n TaskType.library_adaptation_retrieval: \"Given a piece of {code_language} code, generate a piece of code that achieves the same functionality using a different library or framework.\",\n \n # hybrid (gen: code -> hybrid)\n TaskType.code_modification_retrieval: [\n \"Given a piece of input code and a piece of output code, generate the differences in {language} between the input code and output code.\",\n \"Given the differences in {language} between a piece of input code and a piece of output code, generate a code modification instruction in {language} that uses only the information from the differences to transform the input code into the output code.\",\n ],\n # TaskType.single_turn_code_qa: \"Given a piece of code, generate a question that consists of a mix of {language} text and code snippets, and can be answered by the provided code.\",\n # TaskType.multi_turn_code_qa: \"Given a piece of code, generate a multi-turn conversation history that consists of a mix of {language} text and code snippets, and can be answered by the provided code.\",\n TaskType.code_bug_fix_example_retrieval: [\n \"Given a piece of {code_language} code, generate a buggy version of the code and a description in {language} of the bug or error.\",\n \"Given a piece of {code_language} code and a natural language description of the bug or error, generate a piece of {code_language} code that demonstrates a solution or fix for the bug or error.\",\n ],\n TaskType.code_refactoring_pattern_retrieval: [\n \"Given a piece of {code_language} code, generate a description of the desired refactoring goals or patterns in {language}.\",\n \"Given a piece of {code_language} code and a natural language description of the desired refactoring goals or patterns, generate a piece of {code_language} code that exemplifies similar refactoring techniques or patterns.\",\n ],\n TaskType.code_style_guideline_example_retrieval: [\n \"Given a piece of {code_language} code, generate a query describing a desired coding style or best practice to improve it in {language}.\",\n \"Given a piece of {code_language} code and a natural language query describing the desired style guidelines or best practices, generate a piece of {code_language} code that adheres to the specified style guidelines or best practices.\",\n ],\n TaskType.code_migration_retrieval: [\n \"Given a piece of {code_language} code, generate a specific migration requirement in {language} based on the code.\",\n \"Given a piece of {code_language} code and a natural language description of a specific migration requirement, generate a piece of {code_language} code that meets the migration requirement.\",\n ],\n TaskType.code_optimization_hybrid_retrieval: [\n \"Given a piece of {code_language} code, generate a question in {language} that requests a specific optimization for the code.\",\n \"Given a piece of {code_language} code and a natural language request in {language} for specific optimization, generate a piece of output code that implements the requested optimization.\",\n ],\n TaskType.code_comparison_retrieval: [\n \"Given a piece of input code and a piece of output code, generate a question in {language} about their differences or similarities.\",\n \"Given a piece of input code and a piece of output code, and a natural language question in {language} about their differences or similarities, generate a response that answer the question.\",\n ],\n TaskType.code_best_practices_retrieval: [\n \"Given a piece of {code_language} code, generate a question in {language} about coding best practices related to the code.\",\n \"Given a piece of {code_language} code and a natural language question in {language} about coding best practices related to the code, generate a response including guidelines, design patterns, or recommendations that can help improve the quality of the code.\",\n ],\n TaskType.security_vulnerability_fix_retrieval: [\n \"Given a piece of {code_language} code, generate a text description in {language} of a possible security concern in the code.\",\n \"Given a piece of {code_language} code and a text description in {language} of a security concern, generate secure code alternatives that address the vulnerability.\",\n ],\n }\n \n task_to_gen_output: Dict[TaskType, str] = {\n # text2code (gen: code -> text)\n TaskType.web_code_retrieval: \"the generated web query in {language}\",\n TaskType.code_contest_retrieval: \"the generated code contest description in {language}\",\n TaskType.text2sql_retrieval: \"the generated text query in {language}\",\n TaskType.error_message_retrieval: \"the generated error message in {language}\",\n TaskType.code_explanation_retrieval: \"the generated explanation in {language}\",\n TaskType.api_usage_retrieval: \"the generated API or library usage description in {language}\",\n TaskType.bug_desc_retrieval: [\n \"the modified code with one or more bugs\",\n \"the generated bug description in {language}\",\n ],\n TaskType.pseudocode_retrieval: \"the generated pseudocode in {language}\",\n TaskType.tutorial_query_retrieval: \"the generated programming tutorial query in {language}\",\n TaskType.algorithm_desc_retrieval: \"the generated algorithm description in {language}\",\n \n # code2text (gen: code -> text)\n TaskType.code_summary_retrieval: \"the generated summary in {language}\",\n TaskType.code_review_retrieval: \"the generated review in {language}\",\n TaskType.code_intent_retrieval: \"the generated intent in {language}\",\n TaskType.code_optimization_retrieval: \"the generated optimization suggestions or performance analysis reports in {language}\",\n TaskType.tutorial_retrieval: \"the generated tutorial in {language}\",\n TaskType.code_issue_discussion_retrieval: [\n \"the generated buggy code\",\n \"the generated error explanation in {language}\",\n ],\n TaskType.api_reference_retrieval: \"the generated API reference documentation in {language}\",\n TaskType.code_walkthrough_retrieval: \"the generated walkthrough in {language}\",\n TaskType.code_error_explanation_retrieval: \"the generated error explanation in {language}\",\n TaskType.code_to_requirement_retrieval: \"the generated requirement in {language}\",\n\n # code2code (gen: code-prefix -> code-suffix)\n TaskType.code_context_retrieval: \"the generated piece of {code_language} code\",\n TaskType.similar_code_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_translation_retrieval: \"the generated piece of {tgt_code_language} code\",\n TaskType.code_refinement_retrieval: \"the generated piece of {code_language} code\",\n TaskType.secure_code_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_version_update_retrieval: [\n \"the generated piece of {code_language} code\",\n \"the generated piece of {code_language} code\",\n ],\n TaskType.code_example_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_dependency_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_pattern_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_history_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_integration_retrieval: \"the generated piece of {code_language} code\",\n TaskType.optimized_code_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_simplification_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_modularization_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_modification_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_augmentation_retrieval: \"the generated piece of {code_language} code\",\n TaskType.error_handling_code_retrieval: \"the generated piece of {code_language} code\",\n TaskType.code_documentation_retrieval: \"the generated piece of {code_language} code\",\n TaskType.library_adaptation_retrieval: \"the generated piece of {code_language} code\",\n \n # hybrid (gen: code -> hybrid)\n TaskType.code_modification_retrieval: [\n \"the generated differences in {language} between the input code and output code\",\n \"the generated modification instruction in {language}\",\n ],\n # TaskType.single_turn_code_qa: \"the generated question that consists of a mix of {language} text and code snippets\",\n # TaskType.multi_turn_code_qa: \"the generated multi-turn conversation history that consists of a mix of {language} text and code snippets\",\n TaskType.code_bug_fix_example_retrieval: [\n \"the generated buggy version of the code and a description in {language} of the bug or error\",\n \"the generated piece of {code_language} code\"\n ],\n TaskType.code_refactoring_pattern_retrieval: [\n \"the generated description of the desired refactoring goals or patterns in {language}\",\n \"the generated piece of {code_language} code\"\n ],\n TaskType.code_style_guideline_example_retrieval: [\n \"the generated query describing a desired coding style or best practice to improve it in {language}\",\n \"the generated piece of {code_language} code\"\n ],\n TaskType.code_migration_retrieval: [\n \"the generated specific migration requirement in {language} based on the code\",\n \"the generated piece of {code_language} code\"\n ],\n TaskType.code_optimization_hybrid_retrieval: [\n \"the generated question in {language} that requests a specific optimization for the code\",\n \"the generated piece of {code_language} code\",\n ],\n TaskType.code_comparison_retrieval: [\n \"the generated question in {language} about their differences or similarities\",\n \"the generated response in {language}\",\n ],\n TaskType.code_best_practices_retrieval: [\n \"the generated question in {language} about coding best practices related to the code\",\n \"the generated response in {language}\",\n ],\n TaskType.security_vulnerability_fix_retrieval: [\n \"the generated text description in {language} of a possible security concern in the code\",\n \"the generated piece of {code_language} code\",\n ],\n }\n \n gen_instruction = task_to_gen_instruction[task.task_type]\n gen_output = task_to_gen_output[task.task_type]\n \n if idx is not None:\n assert isinstance(gen_instruction, list)\n gen_instruction = gen_instruction[idx]\n assert isinstance(gen_output, list)\n gen_output = gen_output[idx]\n \n assert isinstance(gen_instruction, str)\n assert isinstance(gen_output, str)\n \n gen_instruction = gen_instruction.replace(\"{language}\", task.language.value).replace(\"{code_language}\", task.code_language.value).replace(\"{tgt_code_language}\", task.tgt_code_language.value)\n gen_output = gen_output.replace(\"{language}\", task.language.value).replace(\"{code_language}\", task.code_language.value).replace(\"{tgt_code_language}\", task.tgt_code_language.value)\n \n if task.task_type == TaskType.code_modification_retrieval:\n if idx == 0:\n assert text_b is not None\n gen_prompt = f\"\"\"\\\n{gen_instruction}\n\nInput code:\n```{task.code_language.name}\n{text}\n```\n\nOutput code:\n```{task.code_language.name}\n{text_b}\n```\n\nNote:\n- Your output must always be a string, only containing {gen_output}.\n- Your output should be independent of the given code, which means that it should not contain the pronouns such as \"it\", \"this\", \"that\", \"the given\", \"the provided\", etc.\n\nRemember do not explain your output or output anything else. Your output:\"\"\"\n return gen_prompt\n elif idx == 1:\n prefix = \"Differences:\"\n else:\n raise ValueError(\"Invalid idx for code_modification_retrieval task\")\n elif task.task_type == TaskType.code_comparison_retrieval:\n if idx == 0:\n assert text_b is not None\n gen_prompt = f\"\"\"\\\n{gen_instruction}\n\nInput code:\n```{task.code_language.name}\n{text}\n```\n\nOutput code:\n```{task.code_language.name}\n{text_b}\n```\n\nNote:\n- Your output must always be a string, only containing {gen_output}.\n- Your output should be independent of the given code, which means that it should not contain the pronouns such as \"it\", \"this\", \"that\", \"the given\", \"the provided\", etc.\n\nRemember do not explain your output or output anything else. Your output:\"\"\"\n return gen_prompt\n elif idx == 1:\n prefix = \"Hybrid:\"\n else:\n raise ValueError(\"Invalid idx for code_comparison_retrieval task\")\n elif task.task_type in [\n TaskType.code_bug_fix_example_retrieval,\n TaskType.code_refactoring_pattern_retrieval,\n TaskType.code_style_guideline_example_retrieval,\n TaskType.code_migration_retrieval,\n TaskType.code_optimization_hybrid_retrieval,\n TaskType.code_best_practices_retrieval,\n TaskType.security_vulnerability_fix_retrieval,\n ]:\n if idx == 0:\n prefix = \"Code:\"\n elif idx == 1:\n prefix = \"Hybrid:\"\n else:\n raise ValueError(\"Invalid idx for hybrid task\")\n else:\n prefix = \"Code:\"\n\n gen_prompt = f\"\"\"\\\n{gen_instruction}\n\n{prefix}\n```{task.code_language.name}\n{text}\n```\n\nNote:\n- Your output must always be a string, only containing {gen_output}.\n- Your output should be independent of the given code, which means that it should not contain the pronouns such as \"it\", \"this\", \"that\", \"the given\", \"the provided\", etc.\n\n\"\"\"\n\n if idx != 0 and examples is not None:\n examples_str_list = [f\"\"\"\\\n- Example {i + 1}:\n {prefix}\n ```{task.code_language.name}\n {example['input']}\n ```\n Expected Output ({gen_output}):\n ```\n {example['output']}\n ```\n\n\"\"\" for i, example in enumerate(examples)]\n \n gen_prompt += f\"\"\"\\\nHere are a few examples for your reference:\n{''.join(examples_str_list)}\n\"\"\"\n\n gen_prompt += \"Remember do not explain your output or output anything else. Your output:\"\n\n return gen_prompt\n\n\ndef get_quality_control_prompt(\n task: Task,\n query: str,\n pos: str,\n) -> str:\n \"\"\"\n Given a task, return the quality control prompt for the task.\n \n Args:\n - task: Task: the task object\n \n Returns:\n - qc_prompt: str: the quality control prompt\n \"\"\"\n \n # return tuples of (mission, query_type, doc_type, qc_options)\n task_to_qc_mission: Dict[TaskType, str] = {\n # text2code\n TaskType.web_code_retrieval: (\n \"judge whether the code can help answer the web search query\",\n \"the web search query\",\n \"the code\",\n [\n \"Yes, the code can help answer the web search query.\",\n \"No, the code cannot help answer the web search query.\",\n ]\n ),\n TaskType.code_contest_retrieval: (\n \"judge whether the code can help solve the code contest problem\",\n \"the code contest problem\",\n \"the code\",\n [\n \"Yes, the code can help solve the code contest problem.\",\n \"No, the code cannot help solve the code contest problem.\",\n ]\n ),\n TaskType.text2sql_retrieval: (\n \"judge whether the code is an appropriate response to the text query\",\n \"the text query\",\n \"the code\",\n [\n \"Yes, the code is an appropriate response to the text query.\",\n \"No, the code is not an appropriate response to the text query.\",\n ]\n ),\n TaskType.error_message_retrieval: (\n \"judge whether the code can help resolve the error message\",\n \"the error message\",\n \"the code\",\n [\n \"Yes, the code can help resolve the error message.\",\n \"No, the code cannot help resolve the error message.\",\n ]\n ),\n TaskType.code_explanation_retrieval: (\n \"judge whether the code implements the functionality described in the explanation\",\n \"the explanation\",\n \"the code\",\n [\n \"Yes, the code implements the functionality described in the explanation.\",\n \"No, the code does not implement the functionality described in the explanation.\",\n ]\n ),\n TaskType.api_usage_retrieval: (\n \"judge whether the code demonstrates the usage description of the API or library\",\n \"the API or library usage description\",\n \"the code\",\n [\n \"Yes, and the code demonstrates the usage description of the API or library.\",\n \"No, the code does not demonstrate the usage description of the API or library.\",\n ]\n ),\n TaskType.bug_desc_retrieval: (\n \"judge whether the code can help address the described bug\",\n \"the bug description\",\n \"the code\",\n [\n \"Yes, the code can help address the described bug.\",\n \"No, the code cannot help address the described bug.\",\n ]\n ),\n TaskType.pseudocode_retrieval: (\n \"judge whether the code implements the procedure described in the pseudocode\",\n \"the pseudocode\",\n \"the code\",\n [\n \"Yes, the code implements the procedure described in the pseudocode.\",\n \"No, the code does not implement the procedure described in the pseudocode.\",\n ]\n ),\n TaskType.tutorial_query_retrieval: (\n \"judge whether the code can answer the programming tutorial query\",\n \"the programming tutorial query\",\n \"the code\",\n [\n \"Yes, the code can answer the programming tutorial query.\",\n \"No, the code cannot answer the programming tutorial query.\",\n ]\n ),\n TaskType.algorithm_desc_retrieval: (\n \"judge whether the code implements the algorithm described in the text\",\n \"the algorithm description\",\n \"the code\",\n [\n \"Yes, the code implements the algorithm described in the text.\",\n \"No, the code does not implement the algorithm described in the text.\",\n ]\n ),\n \n # code2text\n TaskType.code_summary_retrieval: (\n \"judge whether the text summarizes the code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text summarizes the code.\",\n \"No, the text does not summarize the code.\",\n ]\n ),\n TaskType.code_review_retrieval: (\n \"judge whether the review explains the role of the code\",\n \"the code\",\n \"the review\",\n [\n \"Yes, the review explains the role of the code.\",\n \"No, the review does not explain the role of the code.\",\n ]\n ),\n TaskType.code_intent_retrieval: (\n \"judge whether the text describes the intent of the code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text describes the intent of the code.\",\n \"No, the text does not describe the intent of the code.\",\n ]\n ),\n TaskType.code_optimization_retrieval: (\n \"judge whether the text provides optimization suggestions or performance analysis reports for the code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text provides optimization suggestions or performance analysis reports for the code.\",\n \"No, the text provides neither optimization suggestions nor performance analysis reports for the code.\",\n ]\n ),\n TaskType.tutorial_retrieval: (\n \"judge whether the text is a tutorial or how-to guide that demonstrates how to use or implement similar code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text is a tutorial or how-to guide that demonstrates how to use or implement similar code.\",\n \"No, the text neither provides instructional guidance for using similar code nor demonstrates how to implement similar code.\",\n ]\n ),\n TaskType.code_error_explanation_retrieval: (\n \"judge whether the text describes potential errors or exceptions that may arise from the code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text describes potential errors or exceptions that may arise from the code.\",\n \"No, the text neither describes potential errors nor discuss exceptions that may arise from the code.\",\n ]\n ),\n TaskType.code_issue_discussion_retrieval: (\n \"judge whether the text is a discussion or issue report related to the code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text is a discussion or issue report related to the code.\",\n \"No, the text is neither a discussion about the code nor an issue report related to the code.\",\n ]\n ),\n TaskType.api_reference_retrieval: (\n \"judge whether the text is an API reference documentation for the APIs or libraries used in the code\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text is an API reference documentation for the APIs or libraries used in the code.\",\n \"No, the text is not an API reference documentation for the APIs or libraries used in the code.\",\n ]\n ),\n TaskType.code_walkthrough_retrieval: (\n \"judge whether the text is a step-by-step walkthrough or detailed explanation of the code's logic and execution flow\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text is a step-by-step walkthrough or detailed explanation of the code's logic and execution flow.\",\n \"No, the text is neither a step-by-step walkthrough nor a detailed explanation of the code's logic and execution flow.\",\n ]\n ),\n TaskType.code_to_requirement_retrieval: (\n \"judge whether the text is a software requirement or user story that the code fulfills\",\n \"the code\",\n \"the text\",\n [\n \"Yes, the text is a software requirement or user story that the code fulfills.\",\n \"No, the text is neither a software requirement nor a user story that the code fulfills.\",\n ]\n ),\n \n # code2code\n TaskType.code_context_retrieval: (\n \"judge whether the output code is the latter part of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is the latter part of the input code.\",\n \"No, the output code is not the latter part of the input code.\",\n ]\n ),\n TaskType.similar_code_retrieval: (\n \"judge whether the output code is semantically equivalent to the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is semantically equivalent to the input code.\",\n \"No, the output code is not semantically equivalent to the input code.\",\n ]\n ),\n TaskType.code_translation_retrieval: (\n \"judge whether the output code is semantically equivalent to the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is semantically equivalent to the input code.\",\n \"No, the output code is not semantically equivalent to the input code.\",\n ]\n ),\n TaskType.code_refinement_retrieval: (\n \"judge whether the output code is a refined version of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is a refined version of the input code.\",\n \"No, the output code is not a refined version of the input code.\",\n ]\n ),\n TaskType.secure_code_retrieval: (\n \"judge whether the output code is the version with enhanced security measures or vulnerability fixes compared to the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is the version with enhanced security measures or vulnerability fixes compared to the input code.\",\n \"No, the output code neither introduces security enhancements nor fixes vulnerabilities compared to the input code.\",\n ]\n ),\n TaskType.code_version_update_retrieval: (\n \"judge whether the output code is the version updated to comply with the syntax or features of a newer language version compared to the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is the version updated to comply with the syntax or features of a newer code language version compared to the input code.\",\n \"No, the output code neither adopts syntax updates nor introduces newer code language features compared to the input code.\",\n ]\n ),\n TaskType.code_example_retrieval: (\n \"judge whether the output code is the example code snippets that demonstrate how to use the library or API in the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is the example code snippets that demonstrate how to use the library or API in the input code.\",\n \"No, the output code is not the example code snippets that demonstrate how to use the library or API in the input code.\",\n ]\n ),\n TaskType.code_dependency_retrieval: (\n \"judge whether the output code is the code segments that the input code depends on, including libraries, functions, and variables.\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is the code segments that the input code depends on, including libraries, functions, and variables.\",\n \"No, the output code is not the code segments that the input code depends on, including libraries, functions, and variables.\",\n ]\n ),\n TaskType.code_pattern_retrieval: (\n \"judge whether the output code follows the same design pattern or structure as the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code follows the same design pattern or structure as the input code.\",\n \"No, the output code neither follows the same design pattern nor retains the same structure as the input code.\",\n ]\n ),\n TaskType.code_history_retrieval: (\n \"judge whether the output code is the historical version or iteration of the input code, and can help understand its development history.\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is the historical version or iteration of the input code, and can help understand its development history.\",\n \"No, the output code is not the historical version or iteration of the input code, and cannot help understand its development history.\",\n ]\n ),\n TaskType.code_integration_retrieval: (\n \"judge whether the output code demonstrates how to integrate the input code with other systems or components.\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code demonstrates how to integrate the input code with other systems or components.\",\n \"No, the output code does not demonstrate how to integrate the input code with other systems or components.\",\n ]\n ),\n TaskType.optimized_code_retrieval: (\n \"judge whether the output code is an optimized version of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is an optimized version of the input code.\",\n \"No, the output code is not an optimized version of the input code.\",\n ]\n ),\n TaskType.code_simplification_retrieval: (\n \"judge whether the output code is a simplified version of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is a simplified version of the input code.\",\n \"No, the output code is not a simplified version of the input code.\",\n ]\n ),\n TaskType.code_modularization_retrieval: (\n \"judge whether the output code is a modularized version of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code is a modularized version of the input code.\",\n \"No, the output code is not a modularized version of the input code.\",\n ]\n ),\n TaskType.code_augmentation_retrieval: (\n \"judge whether the output code implements additional functionality while preserving the original behavior of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code implements additional functionality while preserving the original behavior of the input code.\",\n \"No, the output code does not implement additional functionality while preserving the original behavior of the input code.\",\n ]\n ),\n TaskType.error_handling_code_retrieval: (\n \"judge whether the output code incorporates error-checking or exception-handling mechanisms relevant to the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code incorporates error-checking or exception-handling mechanisms relevant to the input code.\",\n \"No, the output code does not incorporate error-checking or exception-handling mechanisms relevant to the input code.\",\n ]\n ),\n TaskType.code_documentation_retrieval: (\n \"judge whether the output code contains inline comments or documentation explaining the functionality of the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code contains inline comments or documentation explaining the functionality of the input code.\",\n \"No, the output code does not contain inline comments or documentation explaining the functionality of the input code.\",\n ]\n ),\n TaskType.library_adaptation_retrieval: (\n \"judge whether the output code achieves the same functionality using a different library or framework as the input code\",\n \"the input code\",\n \"the output code\",\n [\n \"Yes, the output code achieves the same functionality using a different library or framework as the input code.\",\n \"No, the output code does not achieve the same functionality using a different library or framework as the input code.\",\n ]\n ),\n \n # hybrid\n TaskType.code_modification_retrieval: (\n \"judge whether the output code implements the requested modification described in the query\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code implements the requested modification described in the query.\",\n \"No, the output code does not implement the requested modification described in the query.\",\n ]\n ),\n # TaskType.single_turn_code_qa: \"judge whether the output code can answer the question\",\n # TaskType.multi_turn_code_qa: \"judge whether the output code can answer the question\",\n TaskType.code_bug_fix_example_retrieval: (\n \"judge whether the output code fixes the bug or error described in the query.\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code fixes the bug or error described in the query.\",\n \"No, the output code does not fix the bug or error described in the query.\",\n ]\n ),\n TaskType.code_refactoring_pattern_retrieval: (\n \"judge whether the output code exemplifies similar refactoring techniques or patterns described in the query\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code exemplifies similar refactoring techniques or patterns described in the query.\",\n \"No, the output code does not exemplify similar refactoring techniques or patterns described in the query.\",\n ]\n ),\n TaskType.code_style_guideline_example_retrieval: (\n \"judge whether the output code adheres to the specified style guidelines or best practices described in the query\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code adheres to the specified style guidelines or best practices described in the query.\",\n \"No, the output code does not adhere to the specified style guidelines or best practices described in the query.\",\n ]\n ),\n TaskType.code_migration_retrieval: (\n \"judge whether the output code meets the migration requirement described in the query\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code meets the migration requirement described in the query.\",\n \"No, the output code does not meet the migration requirement described in the query.\",\n ]\n ),\n TaskType.code_optimization_hybrid_retrieval: (\n \"judge whether the output code implements the requested optimization described in the query\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code implements the requested optimization described in the query.\",\n \"No, the output code does not implement the requested optimization described in the query.\",\n ]\n ),\n TaskType.code_comparison_retrieval: (\n \"judge whether the response can answer the question described in the query\",\n \"the query\",\n \"the response\",\n [\n \"Yes, the response can answer the question described in the query.\",\n \"No, the response cannot answer the question described in the query.\",\n ]\n ),\n TaskType.code_best_practices_retrieval: (\n \"judge whether the response can answer the question described in the query\",\n \"the query\",\n \"the response\",\n [\n \"Yes, the response can answer the question described in the query.\",\n \"No, the response cannot answer the question described in the query.\",\n ]\n ),\n TaskType.security_vulnerability_fix_retrieval: (\n \"judge whether the output code addresses the security vulnerability described in the query\",\n \"the query\",\n \"the output code\",\n [\n \"Yes, the output code addresses the security vulnerability described in the query.\",\n \"No, the output code does not address the security vulnerability described in the query.\",\n ]\n ),\n }\n \n if task.main_task_type == \"text2code\":\n type_check_option = \"the query contains code snippets or the document contains non-code content (plain text).\"\n elif task.main_task_type == \"code2text\":\n type_check_option = \"the query contains non-code content (plain text) or the document contains code snippets.\"\n elif task.main_task_type == \"code2code\":\n type_check_option = \"either the query or the document contains non-code content (plain text).\"\n else:\n type_check_option = \"neither the query nor the document contains the mixed content of code and text content.\"\n \n qc_mission, query_type, doc_type, qc_options = task_to_qc_mission[task.task_type]\n \n pos_option = qc_options[0]\n neg_option = qc_options[1]\n \n # Init prompt\n # 0 代表 query / document 不符合 main task type\n # 1 代表 query / document 符合 main task type，且 judgment 是 positive\n # 2 代表 query / document 符合 main task type，且 judgment 是 negative\n # 输出中包含 1 即保留该 data\n qc_prompt = f\"\"\"\\\nGiven a code retrieval task (Task), a query (Query), and a document (Document), your mission is to {qc_mission}.\n\nTask ({task.main_task_type}): {task.task_instruction}\n\nQuery ({query_type}):\n```\n{query}\n```\n\nDocument ({doc_type}):\n```\n{pos}\n```\n\nYour output must be one of the following options:\n- 0: The query or document does not match the main task type ({task.main_task_type}), which means that {type_check_option}\n- 1: The query and document match the main task type ({task.main_task_type}). The judgment is: {pos_option}\n- 2: The query and document match the main task type ({task.main_task_type}). The judgment is: {neg_option}\n\nDo not explain your answer in the output. Your output must be a single number (0 or 1 or 2).\n\nYour output:\"\"\"\n \n return qc_prompt\n\n\nclass DocLength(Enum):\n len_0_500 = \"_len-0-500.jsonl\"\n len_500_1000 = \"_len-500-1000.jsonl\"\n len_1000_2000 = \"_len-1000-2000.jsonl\"\n len_2000_4000 = \"_len-2000-4000.jsonl\"\n len_4000_8000 = \"_len-4000-8000.jsonl\"\n len_8000_16000 = \"_len-8000-16000.jsonl\"\n len_16000_32000 = \"_len-16000-32000.jsonl\"\n\n\n# only used for paper cmp: gen hard negative v.s. mine hard negative\ndef get_gen_hard_neg_prompt(task: Task, query: str, pos: str) -> str:\n \"\"\"\n Given a task, return the generation hard negative prompt for the task.\n \n Args:\n - task: Task: the task object\n\n Returns:\n - gen_hard_neg_prompt: str: the generation hard negative prompt\n \"\"\"\n gen_hard_neg_prompt = f\"\"\"\\\nGiven a code retrieval task (Task), a query (Query), and a positive document (Positive Document), your mission is to generate a hard negative document that only appears relevant to the query under the code retrieval task.\n\nTask ({task.main_task_type}): {task.task_instruction}\n\nQuery:\n```\n{query}\n```\n\nPositive Document:\n```\n{pos}\n```\n\nNote:\n- Your output must always be a string, only containing the hard negative document.\n- The hard negative document should be similar to the positive document in terms of content. If the positive document is a code snippet, the hard negative document should also be a code snippet. If the positive document is a text description, the hard negative document should also be a text description.\n\nRemember do not explain your output or output anything else. Your output:\"\"\"\n\n return gen_hard_neg_prompt\n\n\nNUM_HARD_NEGATIVES = 7\n" }, { "path": "research/BGE_Coder/data_generation/corpus_generator.py", "content": "import os\nimport random\nimport datasets\nfrom tqdm import tqdm\nfrom typing import List, Tuple\n\nfrom utils import clean_code\nfrom constant import DocLength\n\n\nclass CorpusGenerator:\n def __init__(\n self,\n cache_dir: str = None,\n ):\n self.cache_dir = cache_dir\n\n def _load_corpus(self, corpus_dir: str, doc_length: List[str], external_path: List[str],\n source_language: str, stop_threshold: int = -1):\n \"\"\"\n Load availavle documents for a given task from the CoIR-Retrieval dataset.\n \"\"\"\n\n corpus_list = []\n\n if corpus_dir is not None and os.path.exists(corpus_dir):\n file_list = os.listdir(corpus_dir)\n random.shuffle(file_list)\n \n for file in file_list:\n flag = False\n if not file.endswith('.jsonl'):\n flag = False\n for d_length in doc_length:\n d_length = DocLength[d_length].value\n if d_length in file:\n flag = True\n if flag is False:\n continue\n file_path = os.path.join(corpus_dir, file)\n corpus = datasets.load_dataset('json', data_files=file_path, cache_dir=self.cache_dir)['train']\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n if source_language is None:\n lang = os.path.basename(corpus_dir)\n data['language'] = lang\n else:\n data['language'] = source_language\n \n text = clean_code(data[\"text\"], data[\"language\"], length_threshold=200)\n data[\"text\"] = text\n if text != '':\n corpus_list.append(data)\n \n if stop_threshold > 0 and len(corpus_list) > stop_threshold:\n break\n break\n\n for ep in external_path:\n if os.path.exists(ep):\n corpus = datasets.load_dataset('json', data_files=ep, cache_dir=self.cache_dir)['train']\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n if source_language is None:\n lang = os.path.basename(os.path.dirname(ep))\n data['language'] = lang\n else:\n data['language'] = source_language\n \n # useful when the text is not present in the data\n if \"text\" not in data:\n data[\"text\"] = data[\"pos\"][0]\n \n corpus_list.append(data)\n text = clean_code(data[\"text\"], lang, length_threshold=200)\n data[\"text\"] = text\n if text != '':\n corpus_list.append(data)\n\n return corpus_list\n\n def run(\n self,\n num_samples: int = -1,\n max_corpus: int = -1,\n corpus_dir: str = None,\n doc_length: List[str] = [\"len_0_500\"],\n external_path: List[str] = None,\n source_language: str = None\n ) -> Tuple[List[dict], List[dict]]:\n stop_threshold = max(num_samples * 10, max_corpus * 2)\n corpus_list = self._load_corpus(\n corpus_dir, doc_length, external_path, source_language, stop_threshold\n )\n\n if num_samples > 0 and num_samples < len(corpus_list):\n small_corpus_list = random.sample(corpus_list, num_samples)\n else:\n small_corpus_list = corpus_list\n \n if max_corpus > 0 and max_corpus < len(corpus_list):\n corpus_list = random.sample(corpus_list, max_corpus)\n else:\n corpus_list = corpus_list\n\n return small_corpus_list, corpus_list\n" }, { "path": "research/BGE_Coder/data_generation/format_generated_examples.py", "content": "import os\nimport json\nfrom constant import Language, CodeLanguage, TaskType, CODE_TRANSLATION_RETRIEVAL_PAIRS, \\\n get_pos_as_input_by_task_type\n\n\ndef format_generated_examples(\n file_path: str,\n save_path: str,\n task_type: TaskType\n):\n if os.path.exists(save_path):\n return\n \n if not os.path.exists(file_path):\n print(\"====================================\")\n print(\"Warning: file not found! Maybe need to generate it first.\")\n print(f\"file_path: {file_path}\")\n return\n \n pos_as_input = get_pos_as_input_by_task_type(task_type)\n \n data_list = []\n with open(file_path, \"r\", encoding=\"utf-8\") as f:\n for line in f.readlines():\n data = json.loads(line)\n \n if pos_as_input:\n _input = data[\"pos\"][0]\n _output = data[\"query\"]\n else:\n _input = data[\"query\"]\n _output = data[\"pos\"][0]\n\n if 'provided' in _input:\n continue\n if len(_input) > 12000 or len(_output) > 12000:\n continue\n\n data_list.append({\n \"input\": _input,\n \"output\": _output\n })\n \n if len(data_list) == 0:\n print(\"====================================\")\n print(\"Warning: no data found!\")\n print(f\"file_path: {file_path}\")\n return\n \n os.makedirs(os.path.dirname(save_path), exist_ok=True)\n with open(save_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(data_list, f, indent=4, ensure_ascii=False)\n\n\ndef main():\n original_gen_examples_dir = \"./examples\"\n \n formatted_examples_dir = \"./filtered_for_generation\"\n \n for language in Language:\n for task_type in TaskType:\n if task_type == TaskType.code_translation_retrieval:\n for code_language_pair in CODE_TRANSLATION_RETRIEVAL_PAIRS:\n code_language, tgt_code_language = code_language_pair\n \n file_path = os.path.join(\n original_gen_examples_dir,\n language.name, task_type.name, f\"{language.name}-{code_language.name}-to-{tgt_code_language.name}-triplets.jsonl\"\n )\n save_path = os.path.join(\n formatted_examples_dir,\n language.name, task_type.name, f\"{code_language.name}-to-{tgt_code_language.name}_sample_examples.json\"\n )\n \n format_generated_examples(file_path, save_path, task_type)\n \n for code_language_pair in CODE_TRANSLATION_RETRIEVAL_PAIRS:\n tgt_code_language, code_language = code_language_pair\n \n file_path = os.path.join(\n original_gen_examples_dir,\n language.name, task_type.name, f\"{language.name}-{code_language.name}-to-{tgt_code_language.name}-triplets.jsonl\"\n )\n save_path = os.path.join(\n formatted_examples_dir,\n language.name, task_type.name, f\"{code_language.name}-to-{tgt_code_language.name}_sample_examples.json\"\n )\n \n format_generated_examples(file_path, save_path, task_type)\n \n elif task_type == TaskType.text2sql_retrieval:\n file_path = os.path.join(\n original_gen_examples_dir,\n language.name, task_type.name, f\"{language.name}-sql-triplets.jsonl\"\n )\n save_path = os.path.join(\n formatted_examples_dir,\n language.name, task_type.name, \"sql_sample_examples.json\"\n )\n \n format_generated_examples(file_path, save_path, task_type)\n \n elif task_type == TaskType.code_context_retrieval:\n continue\n \n else:\n for code_language in CodeLanguage:\n if code_language == CodeLanguage.null:\n continue\n \n file_path = os.path.join(\n original_gen_examples_dir,\n language.name, task_type.name, f\"{language.name}-{code_language.name}-triplets.jsonl\"\n )\n save_path = os.path.join(\n formatted_examples_dir,\n language.name, task_type.name, f\"{code_language.name}_sample_examples.json\"\n )\n \n format_generated_examples(file_path, save_path, task_type)\n \n print(\"All done!\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/BGE_Coder/data_generation/llm.py", "content": "import os\nimport time\nimport openai\nimport random\nimport tiktoken\nimport threading\nfrom openai import OpenAI, AzureOpenAI\nfrom typing import Tuple\n\n\nclass LLM:\n def __init__(\n self,\n model: str=\"Qwen2-5-Coder-32B-Instruct\",\n model_type: str = \"open-source\",\n port: int = 8000,\n ):\n if model_type == \"open-source\":\n self.client = OpenAI(\n api_key=\"EMPTY\",\n base_url=f\"http://localhost:{port}/v1/\"\n )\n elif model_type == \"azure\":\n self.client = AzureOpenAI(\n api_key=os.getenv(\"OPENAI_API_KEY\"),\n api_version=os.getenv(\"AZURE_API_VERSION\", \"2024-02-01\"),\n azure_endpoint=os.getenv(\"AZURE_ENDPOINT\"),\n azure_deployment=os.getenv(\"OPENAI_DEPLOYMENT_NAME\", 'gpt-35-turbo')\n )\n elif model_type == \"openai\":\n self.client = OpenAI(\n api_key=os.getenv(\"OPENAI_API_KEY\"),\n base_url=os.getenv(\"OPENAI_BASE_URL\", None)\n )\n else:\n raise ValueError(\"model_type must be one of ['open-source', 'azure', 'openai']\")\n \n self.model = model\n self.tokenizer = tiktoken.get_encoding(\"o200k_base\")\n \n def split_text(self, text: str, anchor_points: Tuple[float, float] = (0.4, 0.7)):\n token_ids = self.tokenizer.encode(text)\n anchor_point = random.uniform(anchor_points[0], anchor_points[1])\n split_index = int(len(token_ids) * anchor_point)\n return self.tokenizer.decode(token_ids[:split_index]), self.tokenizer.decode(token_ids[split_index:])\n \n def chat(\n self,\n prompt: str,\n max_tokens: int = 8192,\n logit_bais: dict = None,\n n: int = 1,\n temperature: float = 1.0,\n top_p: float = 0.6,\n repetition_penalty: float = 1.0,\n remove_thinking: bool = True,\n timeout: int = 90,\n ):\n endure_time = 0\n endure_time_limit = timeout * 2\n \n def create_completion(results):\n try:\n completion = self.client.chat.completions.create(\n model=self.model,\n messages=[{\"role\": \"user\", \"content\": prompt}],\n max_tokens=max_tokens,\n logit_bias=logit_bais if logit_bais is not None else {},\n n=n,\n temperature=temperature,\n top_p=top_p,\n extra_body={'repetition_penalty': repetition_penalty},\n timeout=timeout,\n )\n results[\"content\"] = [x.message.content for x in completion.choices[:n]]\n except openai.BadRequestError as e:\n # The response was filtered due to the prompt triggering Azure OpenAI's content management policy.\n results[\"content\"] = [None for _ in range(n)]\n except openai.APIConnectionError as e:\n results[\"error\"] = f'APIConnectionError({e})'\n except openai.RateLimitError as e:\n results[\"error\"] = f'RateLimitError({e})'\n except Exception as e:\n results[\"error\"] = f\"Error: {e}\"\n \n while True:\n results = {\"content\": None, \"error\": None}\n completion_thread = threading.Thread(target=create_completion, args=(results,))\n completion_thread.start()\n \n start_time = time.time()\n while completion_thread.is_alive():\n elapsed_time = time.time() - start_time\n if elapsed_time > endure_time_limit:\n print(\"Completion timeout exceeded. Aborting...\")\n return [None for _ in range(n)]\n time.sleep(1)\n \n # If an error occurred during result processing\n if results[\"error\"]:\n if endure_time >= endure_time_limit:\n print(f'{results[\"error\"]} - Skip this prompt.')\n return [None for _ in range(n)]\n print(f\"{results['error']} - Waiting for 5 seconds...\")\n endure_time += 5\n time.sleep(5)\n continue\n \n content_list = results[\"content\"]\n if remove_thinking:\n content_list = [x.split('')[-1].strip('\\n').strip() if x is not None else None for x in content_list]\n return content_list\n\n\nif __name__ == \"__main__\":\n llm = LLM(\n model=\"gpt-4o-mini-2024-07-18\",\n model_type=\"openai\"\n )\n\n prompt = \"hello, who are you?\"\n response = llm.chat(prompt)[0]\n print(response)\n\n\nif __name__ == \"__main__\":\n llm = LLM(\n model=\"gpt-4o-mini-2024-07-18\",\n model_type=\"openai\"\n )\n\n prompt = \"hello, who are you?\"\n response = llm.chat(prompt)[0]\n print(response)\n" }, { "path": "research/BGE_Coder/data_generation/run_generation.py", "content": "import os\nimport json\nimport time\nimport gc\nimport torch\nimport argparse\nimport random\nfrom hashlib import md5\nimport multiprocessing as mp\nfrom typing import List, Optional\n\nfrom constant import TaskType, Language, CodeLanguage, NUM_HARD_NEGATIVES\nfrom corpus_generator import CorpusGenerator\nfrom triplet_generator import TripletGenerator\nfrom search import get_top1\n\n\ndef compute_md5(text: str):\n return md5(text.encode()).hexdigest()\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--task_type',\n type=str,\n required=True,\n help='The task type to generate data for',\n choices=[t.name for t in TaskType]\n )\n parser.add_argument(\n '--code_language',\n type=str,\n required=True,\n help='The code language to generate questions for.',\n choices=[c.name for c in CodeLanguage]\n )\n parser.add_argument(\n '--corpus_root',\n type=str,\n required=True,\n help='The root directory of the corpus data.'\n )\n parser.add_argument(\n '--save_dir',\n type=str,\n required=True,\n help='The path to save the generated data'\n )\n parser.add_argument(\n '--examples_dir',\n type=str,\n default=None,\n help='The path to the examples directory. If not None, the examples will be used for few-shot generation.'\n )\n parser.add_argument(\n '--num_examples',\n type=int,\n default=3,\n help='The number of examples to use for few-shot generation. Default: 3'\n )\n parser.add_argument(\n '--cache_dir',\n type=str,\n default=None,\n help='The cache directory'\n )\n parser.add_argument(\n '--language',\n type=str,\n default='en',\n help='The language to generate for. ISO 639-1 code. Default: en',\n choices=[l.name for l in Language]\n )\n parser.add_argument(\n '--tgt_code_language',\n type=str,\n default=None,\n help='The target code language to generate code translations for.',\n choices=[c.name for c in CodeLanguage]\n )\n parser.add_argument(\n '--num_samples',\n type=int,\n default=-1,\n help='The number of examples to use for generation. Default: -1. Use all available examples.'\n )\n parser.add_argument(\n '--model',\n type=str,\n default='Qwen2.5-72B-Instruct',\n help='The model to use for generation. Default: Qwen2.5-72B-Instruct'\n )\n parser.add_argument(\n '--model_type',\n type=str,\n default='open-source',\n help='The type of model to use for generation. Default: open-source',\n )\n parser.add_argument(\n '--port',\n type=int,\n default=8000,\n help='The port for vllm.'\n )\n parser.add_argument(\n '--num_processes',\n type=int,\n default=1,\n help='The number of processes to use for generation. Default: 1'\n )\n parser.add_argument(\n '--doc_length',\n type=str,\n default='len_0_500',\n help='The corpus length used to load dataset. Default: len_0_500'\n )\n parser.add_argument(\n '--external_path',\n type=str,\n default='',\n help='The corpus length used to load dataset. Default: len_0_500'\n )\n parser.add_argument(\n '--sim_model_name',\n type=str,\n default=None,\n help='The language of source corpus.'\n )\n parser.add_argument(\n '--max_corpus',\n type=int,\n default=500000,\n help='The max num of corpus to load.'\n )\n parser.add_argument(\n '--overwrite',\n action='store_true',\n help='Whether to overwrite the existing data.'\n )\n parser.add_argument(\n '--debug_mode',\n action='store_true',\n help='Whether to open debug mode.'\n )\n parser.add_argument(\n '--gen_hard_neg',\n action='store_true',\n help='Whether to generate hard negatives.'\n )\n parser.add_argument(\n '--seed',\n type=int,\n default=None,\n help='Random seed for generating triplets using the same positive. Default: 42'\n )\n args = parser.parse_args()\n return args\n\n\ndef gen_triplets(\n model: str,\n model_type: str,\n port: int,\n positives: List[dict],\n task_type: str,\n language: str,\n code_language: str,\n tgt_code_language: str,\n examples_pool: Optional[List[dict]] = None,\n num_examples: int = 3,\n tqdm_desc: str = \"Generating triplets\",\n thread_count: int = 1,\n gen_cache_dir: Optional[str] = None,\n debug_mode: bool = False,\n gen_hard_neg: bool = False,\n):\n triplet_generator = TripletGenerator(model, model_type, port, cache_dir=gen_cache_dir)\n triplets = triplet_generator.run(\n positives=positives,\n task_type=task_type,\n language=language,\n code_language=code_language,\n tgt_code_language=tgt_code_language,\n examples_pool=examples_pool,\n num_examples=num_examples,\n tqdm_desc=tqdm_desc,\n thread_count=thread_count,\n debug_mode=debug_mode,\n gen_hard_neg=gen_hard_neg,\n num_negatives=NUM_HARD_NEGATIVES,\n )\n return triplets\n\n\ndef get_save_path(\n save_dir: str,\n task_type: str,\n language: str,\n code_language: str,\n tgt_code_language: Optional[str] = None\n):\n save_dir = os.path.join(save_dir, language, task_type)\n if tgt_code_language is not None:\n file_name = f\"{language}-{code_language}-to-{tgt_code_language}-triplets.jsonl\"\n else:\n file_name = f\"{language}-{code_language}-triplets.jsonl\"\n save_path = os.path.join(save_dir, file_name)\n os.makedirs(save_dir, exist_ok=True)\n return save_path\n\n\ndef save_triplets(\n triplets: list,\n save_dir: str,\n task_type: str,\n language: str,\n code_language: str,\n tgt_code_language: Optional[str] = None\n):\n if len(triplets) == 0:\n print(f\"No triplets to save: {task_type} | {language} | {code_language} | {tgt_code_language}\")\n return\n \n save_path = get_save_path(save_dir, task_type, language, code_language, tgt_code_language)\n query_md5s = set()\n pos_md5s = set()\n old_triplets = []\n if os.path.exists(save_path):\n with open(save_path, \"r\", encoding=\"utf-8\") as f:\n for line in f.readlines():\n triplet = json.loads(line)\n old_triplets.append(triplet)\n query_md5s.add(compute_md5(triplet['query']))\n pos_md5s.add(compute_md5(triplet['pos'][0]))\n\n with open(save_path, 'w', encoding='utf-8') as f:\n for triplet in old_triplets:\n f.write(json.dumps(triplet, ensure_ascii=False) + '\\n')\n \n for triplet in triplets:\n _query_md5 = compute_md5(triplet['query'])\n _pos_md5 = compute_md5(triplet['pos'][0])\n if _query_md5 in query_md5s or _pos_md5 in pos_md5s:\n continue\n f.write(json.dumps(triplet, ensure_ascii=False) + '\\n')\n print(f\"Triplets saved to {save_path}\")\n\n\ndef main(args):\n # set seed\n seed = args.seed\n if seed is not None:\n print(f\"------------------- Seed set to {seed} -------------------\")\n random.seed(seed)\n \n model = args.model\n model_type = args.model_type\n port = args.port\n\n num_samples = args.num_samples\n \n task_type = args.task_type\n language = args.language\n code_language = args.code_language\n tgt_code_language = args.tgt_code_language\n\n corpus_root = args.corpus_root\n corpus_dir = os.path.join(corpus_root, code_language)\n doc_length = args.doc_length.split()\n external_path = args.external_path.split()\n\n save_dir = args.save_dir\n cache_dir = args.cache_dir\n num_processes = min(args.num_processes, int(mp.cpu_count() * 0.8))\n overwrite = args.overwrite\n debug_mode = args.debug_mode\n gen_hard_neg = args.gen_hard_neg\n \n save_path = get_save_path(save_dir, task_type, language, code_language, tgt_code_language)\n # if os.path.exists(save_path) and not overwrite:\n # data = []\n # with open(save_path) as f:\n # for line in f:\n # data.append(json.loads(line))\n # if len(data) >= num_samples * 0.8:\n # print(f\"Triplets already exist at {save_path}. Skipping generation.\")\n # return\n # else:\n # print(f\"Triplets already exist at {save_path}. But samples is really small, continue generation.\")\n # num_samples = int((num_samples - len(data)) * 1.25) # consider the filtered samples\n\n corpus_generator = CorpusGenerator(cache_dir)\n\n examples_dir = args.examples_dir\n num_examples = args.num_examples\n if examples_dir is not None:\n # if task_type in [\"single_turn_code_qa\", \"multi_turn_code_qa\"]:\n # examples_path = os.path.join(examples_dir, language, task_type, \"sample_examples.json\")\n if task_type in [\"code_translation_retrieval\"]:\n examples_path = os.path.join(examples_dir, language, task_type,\n f\"{code_language}-to-{tgt_code_language}_sample_examples.json\")\n else:\n examples_path = os.path.join(examples_dir, language, task_type, f\"{code_language}_sample_examples.json\")\n try:\n with open(examples_path, 'r', encoding='utf-8') as f:\n examples_pool = json.load(f)\n examples_pool = random.sample(examples_pool,\n min(30, len(examples_pool))) # sample 30 examples for few-shot generation\n except:\n print(f'Error for loading examples from {examples_path}')\n examples_pool = None\n else:\n examples_pool = None\n\n positives, large_positives = corpus_generator.run(\n num_samples=num_samples,\n max_corpus=args.max_corpus,\n corpus_dir=corpus_dir,\n doc_length=doc_length,\n external_path=external_path,\n source_language=code_language\n )\n\n if task_type in [\"code_modification_retrieval\", \"code_comparison_retrieval\"]:\n top1_docs = get_top1([e['text'] for e in positives], args.sim_model_name, [e['text'] for e in large_positives])\n for i in range(len(top1_docs)):\n positives[i]['similar'] = top1_docs[i]\n gc.collect()\n torch.cuda.empty_cache()\n\n print(\"=================== Generate training data ===================\")\n print(f'Task Type: {task_type} | Language: {language} | Code Language: {code_language} | Target Code Language: {tgt_code_language}')\n start_time = time.time()\n triplets = gen_triplets(\n model=model,\n model_type=model_type,\n port=port,\n positives=positives,\n task_type=task_type,\n language=language,\n code_language=code_language,\n tgt_code_language=tgt_code_language,\n examples_pool=examples_pool,\n num_examples=num_examples,\n thread_count=num_processes,\n gen_cache_dir=os.path.join(save_dir, language, task_type, \"gen_cache_dir\"),\n debug_mode=debug_mode,\n gen_hard_neg=gen_hard_neg,\n )\n save_triplets(\n triplets=triplets,\n save_dir=save_dir,\n task_type=task_type,\n language=language,\n code_language=code_language,\n tgt_code_language=tgt_code_language\n )\n end_time = time.time()\n print(\"=============================================================\")\n print(f\"Time taken: {end_time - start_time:.2f} seconds\")\n print(\"=============================================================\")\n print(\"DONE!\")\n\n\nif __name__ == \"__main__\":\n args = get_args()\n main(args)\n" }, { "path": "research/BGE_Coder/data_generation/search.py", "content": "from typing import Optional, List\n\nimport faiss\nimport numpy as np\nfrom tqdm import tqdm\nfrom FlagEmbedding import FlagModel\n\ndef create_index(embeddings: np.ndarray, use_gpu: bool = False):\n index = faiss.IndexFlatIP(len(embeddings[0]))\n embeddings = np.asarray(embeddings, dtype=np.float32)\n if use_gpu:\n co = faiss.GpuMultipleClonerOptions()\n co.shard = True\n co.useFloat16 = True\n index = faiss.index_cpu_to_all_gpus(index, co=co)\n index.add(embeddings)\n return index\n\n\ndef search(\n faiss_index: faiss.Index,\n k: int = 100,\n query_embeddings: Optional[np.ndarray] = None,\n load_path: Optional[str] = None\n):\n if query_embeddings is None:\n query_embeddings = np.load(load_path)\n\n query_size = len(query_embeddings)\n\n all_scores = []\n all_indices = []\n\n for i in tqdm(range(0, query_size, 32), desc=\"Searching\"):\n j = min(i + 32, query_size)\n query_embedding = query_embeddings[i: j]\n score, indice = faiss_index.search(query_embedding.astype(np.float32), k=k)\n all_scores.append(score)\n all_indices.append(indice)\n\n all_scores = np.concatenate(all_scores, axis=0)\n all_indices = np.concatenate(all_indices, axis=0)\n return all_scores, all_indices\n\ndef get_top1(\n small_docs,\n encoder_name,\n docs: List[str],\n top: int = 1\n):\n encoder = FlagModel(encoder_name, trust_remote_code=True)\n doc_emb = encoder.encode_corpus(docs, max_length=512, batch_size=256)\n small_doc_emb = encoder.encode_corpus(small_docs, max_length=512, batch_size=256)\n faiss_index = create_index(doc_emb, True)\n all_scores, all_indices = search(faiss_index, 1000, small_doc_emb)\n return_docs = []\n for i in range(len(all_indices)):\n return_docs.append([])\n for idx, score in zip(all_indices[i][20:], all_scores[i][20:]):\n d1 = set(docs[idx].split())\n d2 = set(small_docs[i].split())\n if len(d1 & d2) / len(d1 | d2) > 0.95:\n continue\n return_docs[-1].append(docs[idx])\n if len(return_docs[-1]) >= top:\n break\n if len(return_docs[-1]) == 0:\n print(all_indices[i], all_scores[i])\n # print(return_docs)\n del faiss_index\n return return_docs\n" }, { "path": "research/BGE_Coder/data_generation/triplet_generator.py", "content": "import os\nimport json\nimport random\nfrom tqdm import tqdm\nfrom hashlib import md5\nfrom warnings import warn\nfrom typing import List, Optional\nfrom concurrent.futures import ThreadPoolExecutor\n\nfrom llm import LLM\nfrom utils import clean_content\nfrom constant import TaskType, Task, SPECIAL_TASK_STEPS, \\\n get_task, get_generation_prompt, get_quality_control_prompt, \\\n get_gen_hard_neg_prompt\n\n\ndef compute_md5(text: str):\n return md5(text.encode()).hexdigest()\n\n\nclass TripletGenerator(LLM):\n def __init__(\n self,\n model: str = \"Qwen2-5-Coder-32B-Instruct\",\n model_type: str = \"open-source\",\n port: int = 8000,\n cache_dir: Optional[str] = None\n ):\n super().__init__(model, model_type, port)\n self.cache_dir = cache_dir\n if self.cache_dir is not None:\n os.makedirs(self.cache_dir, exist_ok=True)\n \n def _gen_for_code_modification_retrieval(\n self,\n task: Task,\n text: str,\n text_b: Optional[str] = None,\n examples: Optional[List[dict]] = None,\n debug_mode: bool = False,\n **kwargs\n ):\n gen_prompt = get_generation_prompt(\n task=task,\n text=text,\n text_b=text_b,\n examples=examples,\n idx=0\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n diff = clean_content(response)\n gen_prompt = get_generation_prompt(\n task=task,\n text=diff,\n examples=examples,\n idx=1\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n modification_instr = clean_content(response)\n \n query = f\"{modification_instr}\\n```\\n{text}\\n```\"\n pos = text_b\n \n if debug_mode:\n result = {\n \"generation_prompt\": gen_prompt,\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n else:\n result = {\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n return result\n \n def _gen_for_code_comparison_retrieval(\n self,\n task: Task,\n text: str,\n text_b: Optional[str] = None,\n examples: Optional[List[dict]] = None,\n debug_mode: bool = False,\n **kwargs\n ):\n gen_prompt = get_generation_prompt(\n task=task,\n text=text,\n text_b=text_b,\n examples=examples,\n idx=0\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n diff_question = clean_content(response)\n query = f\"{diff_question}\\n\\nInput Code:\\n```\\n{text}\\n```\\n\\nOutput Code:\\n```\\n{text_b}\\n```\"\n gen_prompt = get_generation_prompt(\n task=task,\n text=query,\n examples=examples,\n idx=1\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n pos = clean_content(response)\n\n if debug_mode:\n result = {\n \"generation_prompt\": gen_prompt,\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n else:\n result = {\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n return result\n \n def _gen_for_code_context_retrieval(\n self,\n task: Task,\n text: str,\n anchor_points: Optional[tuple] = (0.4, 0.7),\n **kwargs\n ):\n former_part, latter_part = self.split_text(\n text,\n anchor_points=anchor_points\n )\n result = {\n \"prompt\": task.task_instruction,\n \"query\": former_part,\n \"pos\": [latter_part],\n \"neg\": []\n }\n return result\n \n @staticmethod\n def _arrange_query_and_pos(task: Task, input_text: str, response: str):\n \"\"\"\n Arrange the query and positive example based on the task type.\n \n Args:\n - task: Task\n - input_text: str\n - response: str\n \n Returns:\n - query: str\n - pos: str\n \"\"\"\n # TODO: support more task types, including some special task types.\n if task.main_task_type in [\"text2code\", \"hybrid\"]:\n query = clean_content(response)\n pos = input_text\n else:\n query = input_text\n pos = clean_content(response)\n return query, pos\n \n def _gen_for_normal_task(\n self,\n task: Task,\n text: str,\n examples: Optional[List[dict]] = None,\n debug_mode: bool = False,\n **kwargs\n ):\n gen_prompt = get_generation_prompt(\n task=task,\n text=text,\n examples=examples\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n \n # Arrange the query and positive example based on the task type.\n query, pos = self._arrange_query_and_pos(\n task=task,\n input_text=text,\n response=response\n )\n \n if debug_mode:\n result = {\n \"generation_prompt\": gen_prompt,\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": [],\n \"response\": response\n }\n else:\n result = {\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n return result\n \n def _gen_for_bug_desc_retrieval(\n self,\n task: Task,\n text: str,\n examples: Optional[List[dict]] = None,\n debug_mode: bool = False,\n **kwargs\n ):\n gen_prompt = get_generation_prompt(\n task=task,\n text=text,\n examples=examples,\n idx=0\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n if response is None:\n raise ValueError(\"Response is None.\")\n buggy_code = response\n gen_prompt = get_generation_prompt(\n task=task,\n text=buggy_code,\n examples=examples,\n idx=1\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n query = clean_content(response)\n pos = text\n \n if debug_mode:\n result = {\n \"generation_prompt\": gen_prompt,\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n else:\n result = {\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n return result\n \n def _gen_for_two_step_not_use_last(\n self,\n task: Task,\n text: str,\n examples: Optional[List[dict]] = None,\n debug_mode: bool = False,\n reverse_query_pos: bool = False,\n **kwargs\n ):\n gen_prompt = get_generation_prompt(\n task=task,\n text=text,\n idx=0\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n query = clean_content(response)\n gen_prompt = get_generation_prompt(\n task=task,\n text=query,\n examples=examples,\n idx=1\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n pos = clean_content(response)\n if reverse_query_pos:\n query, pos = pos, query\n\n if debug_mode:\n result = {\n \"generation_prompt\": gen_prompt,\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n else:\n result = {\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n return result\n\n def _gen_for_two_step_use_last(\n self,\n task: Task,\n text: str,\n examples: Optional[List[dict]] = None,\n debug_mode: bool = False,\n reverse_query_pos: bool = False,\n **kwargs\n ):\n gen_prompt = get_generation_prompt(\n task=task,\n text=text,\n idx=0\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n query = clean_content(response) + f\"\\n```\\n{text}\\n```\"\n gen_prompt = get_generation_prompt(\n task=task,\n text=query,\n examples=examples,\n idx=1\n )\n response = self.chat(gen_prompt, **kwargs)[0]\n pos = clean_content(response)\n if reverse_query_pos:\n query, pos = pos, query\n\n if debug_mode:\n result = {\n \"generation_prompt\": gen_prompt,\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n else:\n result = {\n \"prompt\": task.task_instruction,\n \"query\": query,\n \"pos\": [pos],\n \"neg\": []\n }\n return result\n\n def generate_triplets(\n self,\n data: dict,\n task: Task,\n examples_pool: Optional[List[dict]] = None,\n num_examples: int = 3,\n debug_mode: bool = False,\n **kwargs\n ):\n kwargs[\"remove_thinking\"] = not debug_mode\n \n result_list = []\n \n examples = None\n if examples_pool is not None:\n examples = random.sample(examples_pool, min(num_examples, len(examples_pool)))\n\n try:\n if task.task_type in SPECIAL_TASK_STEPS:\n text = data[\"text\"]\n \n if task.task_type == TaskType.code_modification_retrieval:\n text_b = data[\"similar\"][0]\n \n result = self._gen_for_code_modification_retrieval(\n task=task,\n text=text,\n text_b=text_b,\n examples=examples,\n debug_mode=debug_mode\n )\n elif task.task_type == TaskType.code_comparison_retrieval:\n text_b = data[\"similar\"][0]\n \n result = self._gen_for_code_comparison_retrieval(\n task=task,\n text=text,\n text_b=text_b,\n examples=examples,\n debug_mode=debug_mode\n )\n elif task.task_type == TaskType.bug_desc_retrieval:\n result = self._gen_for_bug_desc_retrieval(\n task=task,\n text=text,\n examples=examples,\n debug_mode=debug_mode\n )\n elif task.task_type in [\n # cf - updated\n TaskType.code_issue_discussion_retrieval,\n TaskType.code_version_update_retrieval,\n TaskType.code_bug_fix_example_retrieval,\n ]:\n result = self._gen_for_two_step_not_use_last(\n task=task,\n text=text,\n examples=examples,\n debug_mode=debug_mode,\n reverse_query_pos=False\n )\n elif task.task_type in [\n # cf - updated\n TaskType.code_refactoring_pattern_retrieval,\n TaskType.code_style_guideline_example_retrieval,\n TaskType.code_migration_retrieval,\n # jl - updated\n TaskType.code_optimization_hybrid_retrieval,\n TaskType.code_best_practices_retrieval,\n TaskType.security_vulnerability_fix_retrieval,\n ]:\n result = self._gen_for_two_step_use_last(\n task=task,\n text=text,\n examples=examples,\n debug_mode=debug_mode,\n reverse_query_pos=False\n )\n else:\n raise NotImplementedError(f\"Task type {task.task_type} not implemented.\")\n elif task.task_type == TaskType.code_context_retrieval:\n text = data[\"text\"]\n \n result = self._gen_for_code_context_retrieval(\n task=task,\n text=text,\n **kwargs\n )\n # NOTE: no need to do quality control for code context retrieval task\n result_list.append(result)\n return result_list\n else:\n text = data[\"text\"]\n \n result = self._gen_for_normal_task(\n task=task,\n text=text,\n examples=examples,\n debug_mode=debug_mode,\n **kwargs\n )\n \n # print(gen_prompt)\n # print('================================================')\n qc_prompt = get_quality_control_prompt(\n task=task,\n query=result[\"query\"],\n pos=result[\"pos\"][0]\n )\n # print(qc_prompt)\n # print('*********************************************************************')\n response = self.chat(qc_prompt, **kwargs)[0]\n judge = clean_content(response)\n # print(response, judge)\n if \"1\" in judge:\n if debug_mode:\n result[\"judge\"] = judge\n result[\"judge_response\"] = response\n result_list.append(result)\n else:\n if debug_mode:\n result[\"judge\"] = judge\n result[\"judge_response\"] = response\n result_list.append(result)\n except Exception as e:\n warn(f\"Error: {e}\")\n \n return result_list\n\n def gen_hard_negatives(self, result: dict, task: Task, num_negatives: int = 7, **kwargs):\n gen_hard_neg_prompt = get_gen_hard_neg_prompt(\n task=task,\n query=result[\"query\"],\n pos=result[\"pos\"][0]\n )\n response_list = self.chat(gen_hard_neg_prompt, n=num_negatives, **kwargs)\n for response in response_list:\n if response is None:\n continue\n hard_neg = clean_content(response)\n result[\"neg\"].append(hard_neg)\n result[\"neg\"] = list(set(result[\"neg\"]))\n return result\n\n def run_single(\n self,\n data: dict,\n task: Task,\n examples_pool: Optional[List[dict]] = None,\n num_examples: int = 3,\n debug_mode: bool = False,\n gen_hard_neg: bool = False,\n num_negatives: int = 7,\n **kwargs\n ):\n result_list = []\n\n docid = compute_md5(data[\"text\"])\n if self.cache_dir is not None:\n gen_data_cache_path = os.path.join(self.cache_dir, f\"{docid}.json\")\n if os.path.exists(gen_data_cache_path):\n with open(gen_data_cache_path, \"r\", encoding=\"utf-8\") as f:\n result_list = json.load(f)\n \n if len(result_list) > 0:\n if gen_hard_neg:\n for i in range(len(result_list)):\n if len(result_list[i][\"neg\"]) == 0:\n result_list[i] = self.gen_hard_negatives(\n result=result_list[i],\n task=task,\n num_negatives=num_negatives,\n **kwargs\n )\n # overwrite the cache file\n with open(gen_data_cache_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(result_list, f, indent=4, ensure_ascii=False)\n return result_list\n\n triplets = self.generate_triplets(\n data,\n task=task,\n examples_pool=examples_pool,\n num_examples=num_examples,\n debug_mode=debug_mode,\n **kwargs\n )\n if len(triplets) == 0:\n return []\n \n result = triplets[0]\n if debug_mode:\n result[\"docid\"] = docid\n \n if gen_hard_neg:\n result = self.gen_hard_negatives(\n result,\n task=task,\n num_negatives=num_negatives,\n **kwargs\n )\n \n result_list.append(result)\n \n if self.cache_dir is not None:\n gen_data_cache_path = os.path.join(self.cache_dir, f\"{docid}.json\")\n with open(gen_data_cache_path, \"w\", encoding=\"utf-8\") as f:\n json.dump(result_list, f, indent=4, ensure_ascii=False)\n \n return result_list\n\n def run(\n self,\n positives: List[dict],\n task_type: str,\n language: str = \"en\",\n code_language: str = \"python\",\n tgt_code_language: Optional[str] = None,\n examples_pool: Optional[List[dict]] = None,\n num_examples: int = 3,\n tqdm_desc: str = \"Generating triplets\",\n debug_mode: bool = False,\n gen_hard_neg: bool = False,\n num_negatives: int = 7,\n thread_count: int = 1,\n **kwargs\n ):\n task = get_task(\n task_type=task_type,\n language=language,\n code_language=code_language,\n tgt_code_language=tgt_code_language\n )\n \n result_list = []\n\n def process_positive(positive):\n return self.run_single(\n data=positive,\n task=task,\n examples_pool=examples_pool,\n num_examples=num_examples,\n debug_mode=debug_mode,\n gen_hard_neg=gen_hard_neg,\n num_negatives=num_negatives,\n **kwargs\n )\n # Use thread pool for parallel processing with tqdm progress bar.\n with ThreadPoolExecutor(max_workers=thread_count) as executor:\n results = list(tqdm(executor.map(\n process_positive,\n positives\n ), total=len(positives), desc=tqdm_desc))\n\n # Collect results into result_list.\n for res in results:\n if isinstance(res, list):\n result_list.extend(res)\n else:\n result_list.append(res)\n # result_list.extend(results)\n\n return result_list\n\n def run_for_gen_neg(\n self,\n pairs: List[dict],\n task_type: str,\n language: str = \"en\",\n code_language: str = \"python\",\n tgt_code_language: Optional[str] = None,\n examples_pool: Optional[List[dict]] = None,\n num_examples: int = 3,\n tqdm_desc: str = \"Generating triplets\",\n debug_mode: bool = False,\n gen_hard_neg: bool = False,\n num_negatives: int = 7,\n thread_count: int = 1,\n **kwargs\n ):\n task = get_task(\n task_type=task_type,\n language=language,\n code_language=code_language,\n tgt_code_language=tgt_code_language\n )\n \n result_list = []\n\n def gen_single_negative(pair):\n result = self.gen_hard_negatives(\n pair,\n task=task,\n num_negatives=num_negatives,\n **kwargs\n )\n return [result]\n\n # Use thread pool for parallel processing with tqdm progress bar.\n with ThreadPoolExecutor(max_workers=thread_count) as executor:\n results = list(tqdm(executor.map(\n gen_single_negative,\n pairs\n ), total=len(pairs), desc=tqdm_desc))\n\n # Collect results into result_list.\n for res in results:\n if isinstance(res, list):\n result_list.extend(res)\n else:\n result_list.append(res)\n # result_list.extend(results)\n\n return result_list\n" }, { "path": "research/BGE_Coder/data_generation/utils.py", "content": "import re\n\n\ndef clean_content(content: str):\n if content is None:\n raise ValueError(\"content is None.\")\n \n content = content.split('')[-1].strip('\\n').strip()\n \n if content.startswith('\\\"') and content.endswith('\\\"'):\n content = content[1:-1]\n \n if content.startswith(\"```\\n\") and content.endswith(\"\\n```\"):\n content = content[4:-4]\n \n return content\n\n\ndef clean_code(code: str, lang: str, length_threshold: int = 30) -> str:\n cleaned_code = code.strip('\\ufeff').strip()\n if not cleaned_code:\n return ''\n\n def clean_empty_lines(text: str) -> str:\n return re.sub(r'\\n\\s*\\n', '\\n', text).strip()\n\n # 各语言函数/类定义检测正则表达式\n function_patterns = {\n \"java\": r\"(?m)^(?!\\s*(import|package)\\b).*\\b(public\\s+class|class\\s+\\w+|void\\s+main|new\\s+\\w+\\(|@Override)\\b\",\n \"python\": r\"(?m)^(?!\\s*(import|from\\s+\\S+\\s+import)\\b).*\\b(def\\s+\\w+|class\\s+\\w+|=\\s*\\S+|if\\s+[:\\w]|print\\s+)\",\n \"javascript\": r\"(?m)^(?!\\s*(import|require\\(|export\\s)).*\\b(function\\s+\\w+|const\\s+\\w+|=>|\\(\\)\\s*=>|console\\.log)\",\n \"php\": r\"(?m)^(?!\\s*(include|require|use)\\b).*\\b(function\\s+\\w+|echo\\s+\\S+|class\\s+\\w+)\",\n \"ruby\": r\"(?m)^(?!\\s*(require|load)\\b).*\\b(class\\s+\\w+|def\\s+\\w+|puts\\s+\\S+)\",\n \"go\": r\"(?m)^(?!\\s*import\\b).*\\bfunc\\s+main\\s*\\(|type\\s+\\w+\\s+struct\",\n \"c#\": r\"(?m)^(?!\\s*using\\b).*\\b(class\\s+\\w+|void\\s+Main\\s*\\()\",\n \"cplusplus\": r\"(?m)^(?!#include\\b).*\\b(int\\s+main\\s*\\(|class\\s+\\w+|void\\s+\\w+\\s*\\(.*\\)\\s*{)\",\n \"c\": r\"(?m)^(?!#include\\b).*\\b(int\\s+main\\s*\\(|void\\s+\\w+\\s*\\(.*\\)\\s*{)\",\n \"rust\": r\"(?m)^(?!\\s*use\\b).*\\b(fn\\s+main\\s*\\(|struct\\s+\\w+|impl\\s+\\w+)\",\n \"typescript\": r\"(?m)^(?!\\s*(import|require\\(|export\\s)).*\\b(interface\\s+\\w+|class\\s+\\w+|function\\s+\\w+)\",\n \"perl\": r\"(?m)^(?!\\s*(use|require)\\b).*\\b(sub\\s+\\w+|my\\s+\\$\\w+|print\\s+\\S+)\",\n \"shell\": r\"(?m)^(?!\\s*(source|\\.)\\s).*\\b(function\\s+\\w+|if\\s+\\[|\\$\\(|echo\\s+\\S+)\",\n \"sql\": r\"(?i)\\b(CREATE\\s+TABLE|SELECT\\s+\\*|INSERT\\s+INTO|UPDATE\\s+\\w+|DELETE\\s+FROM)\\b\",\n \"batchfile\": r\"(?m)^(?!\\s*@?call\\b).*\\b(echo\\s+\\S+|set\\s+\\w+|if\\s+.*\\s+==\\s+)\",\n \"fortran\": r\"(?mi)^(?!\\s*use\\b).*\\b(program\\s+\\w+|subroutine\\s+\\w+|do\\s+\\d+\\s*,\\s*\\d+)\",\n \"haskell\": r\"(?m)^(?!\\s*import\\b).*\\b(main\\s*=\\s*do|data\\s+\\w+|putStrLn\\s+\\S+)\",\n \"lua\": r\"(?m)^(?!\\s*require\\b).*\\b(function\\s+\\w+|local\\s+\\w+|print\\s*\\()\",\n \"powershell\": r\"(?m)^(?!\\s*Import-Module\\b).*\\b(function\\s+\\w+|Write-Host\\s+\\S+|\\$\\w+\\s*=)\",\n \"visual_basic\": r\"(?m)^(?!\\s*Imports\\b).*\\b(Module\\s+\\w+|Sub\\s+Main|Class\\s+\\w+)\"\n }\n\n # 各语言注释处理规则\n comment_patterns = {\n 'java': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'python': (r'#.*?$', re.MULTILINE),\n 'javascript': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'php': (r'//.*?$|#.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|#.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'ruby': (r'#.*', re.MULTILINE),\n 'go': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'csharp': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'cplusplus': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'c': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'rust': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'typescript': (r'//.*?$|/\\*.*?\\*/|\\\\/\\\\/.*?$|\\\\/\\*.*?\\*\\\\/', re.DOTALL | re.MULTILINE),\n 'perl': (r'#.*', re.MULTILINE),\n 'shell': (r'#.*', re.MULTILINE),\n 'sql': (r'--.*?$|/\\*.*?\\*/', re.DOTALL),\n 'batchfile': (r'^\\s*(REM|@REM|::).*', re.MULTILINE | re.IGNORECASE),\n 'fortran': (r'!.*', re.MULTILINE),\n 'haskell': (r'--.*', re.MULTILINE),\n 'lua': (r'--.*?$|--\\[\\[.*?\\]\\]', re.DOTALL),\n 'powershell': (r'<#.*?#>|#.*', re.DOTALL),\n 'visual_basic': (r\"'.*\", re.MULTILINE),\n }\n\n # 执行注释清理\n if lang in comment_patterns:\n pattern, flags = comment_patterns[lang]\n cleaned_code = re.sub(pattern, '', cleaned_code, flags=flags)\n cleaned_code = clean_empty_lines(cleaned_code)\n\n # 特殊语言处理规则\n if lang == 'fortran':\n cleaned_code = re.sub(r'^[Cc*].*', '', cleaned_code, flags=re.MULTILINE)\n elif lang == 'sql':\n cleaned_code = re.sub(r'/\\*.*?\\*/', '', cleaned_code, flags=re.DOTALL)\n elif lang == 'python':\n cleaned_code = re.sub(r'^\\s*#.*', '', cleaned_code, flags=re.MULTILINE)\n\n # 函数定义检测及内容验证\n def has_valid_code(text: str, lang: str) -> bool:\n pattern = function_patterns.get(lang)\n if not pattern:\n return len(text.strip()) > 0\n \n # 增强检测逻辑\n if lang == 'batchfile':\n return bool(re.search(r'^\\s*@?echo\\b|:\\w+', text, re.MULTILINE))\n if lang == 'shell':\n return bool(re.search(r'^\\s*(if|for|while|case|echo|export|shopt|source)\\b', text, re.MULTILINE))\n if lang == 'python':\n if re.search(r'^\\s*(def|class)\\s+\\w+', text, re.MULTILINE):\n return bool(re.search(r'^\\s+[^\\s#]', text, re.MULTILINE))\n return False\n if lang == 'ruby':\n return bool(re.search(r'(def\\s+\\w+|class\\s+\\w+).*?\\n\\s+[^\\s#]', text, re.MULTILINE))\n return bool(re.search(pattern, text, re.DOTALL | re.MULTILINE))\n\n # 最终有效性检查\n if not has_valid_code(cleaned_code, lang):\n return ''\n \n cleaned_code = cleaned_code.strip('\\ufeff').strip()\n \n if len(cleaned_code) < length_threshold:\n return ''\n\n return cleaned_code\n\n\nif __name__ == \"__main__\":\n test_text = \"\\/\\/ ----------------------------------------------------------------------\\n\\/\\/ ----------------------------------------------------------------------\\n\\/\\/\\n\\/\\/ File: StrMaxProjection.h\\n\\/\\/ Author: mgrosso \\n\\/\\/ Created: Mon Jul 17 14:39:22 PDT 2006 on caliban\\n\\/\\/ Project: \\n\\/\\/ Purpose: \\n\\/\\/ \\n\\/\\/ $Id$\\n\\/\\/ ----------------------------------------------------------------------\\n\\/\\/ ----------------------------------------------------------------------\\n\\n#ifndef STRMAXPROJECTION_H\\n#define STRMAXPROJECTION_H 1\\n\\n#include \\\"StrMinProjection.h\\\"\\n\\nclass StrMaxProjection : public StrMinProjection\\n{\\n public:\\n StrMaxProjection(ExpressionPtr &operand);\\n virtual ~StrMaxProjection();\\n virtual AbstractProjectionPtr copy();\\n\\n protected:\\n int compare(const char *lhs, const char *rhs);\\n\\n private:\\n \\/\\/not implemented\\n StrMaxProjection();\\n StrMaxProjection( const StrMaxProjection &rhs );\\n StrMaxProjection &operator=( const StrMaxProjection &rhs );\\n};\\n\\n#endif \\/* STRMAXPROJECTION_H *\\/\"\n \n result = clean_code(test_text, \"c\", 200)\n print(result)\n \n test_text = \"\\/**\\n * Copyright (c) Microsoft Corporation. All rights reserved.\\n * Licensed under the MIT License. See License.txt in the project root for\\n * license information.\\n *\\n * Code generated by Microsoft (R) AutoRest Code Generator.\\n *\\/\\n\\npackage com.microsoft.azure.management.datafactory.v2018_06_01;\\n\\nimport com.fasterxml.jackson.annotation.JsonProperty;\\nimport com.fasterxml.jackson.annotation.JsonTypeInfo;\\nimport com.fasterxml.jackson.annotation.JsonTypeName;\\n\\n\\/**\\n * The location of Google Cloud Storage dataset.\\n *\\/\\n@JsonTypeInfo(use = JsonTypeInfo.Id.NAME, include = JsonTypeInfo.As.PROPERTY, property = \\\"type\\\", defaultImpl = GoogleCloudStorageLocation.class)\\n@JsonTypeName(\\\"GoogleCloudStorageLocation\\\")\\npublic class GoogleCloudStorageLocation extends DatasetLocation {\\n \\/**\\n * Specify the bucketName of Google Cloud Storage. Type: string (or\\n * Expression with resultType string).\\n *\\/\\n @JsonProperty(value = \\\"bucketName\\\")\\n private Object bucketName;\\n\\n \\/**\\n * Specify the version of Google Cloud Storage. Type: string (or Expression\\n * with resultType string).\\n *\\/\\n @JsonProperty(value = \\\"version\\\")\\n private Object version;\\n\\n \\/**\\n * Get specify the bucketName of Google Cloud Storage. Type: string (or Expression with resultType string).\\n *\\n * @return the bucketName value\\n *\\/\\n public Object bucketName() {\\n return this.bucketName;\\n }\\n\\n \\/**\\n * Set specify the bucketName of Google Cloud Storage. Type: string (or Expression with resultType string).\\n *\\n * @param bucketName the bucketName value to set\\n * @return the GoogleCloudStorageLocation object itself.\\n *\\/\\n public GoogleCloudStorageLocation withBucketName(Object bucketName) {\\n this.bucketName = bucketName;\\n return this;\\n }\\n\\n \\/**\\n * Get specify the version of Google Cloud Storage. Type: string (or Expression with resultType string).\\n *\\n * @return the version value\\n *\\/\\n public Object version() {\\n return this.version;\\n }\\n\\n \\/**\\n * Set specify the version of Google Cloud Storage. Type: string (or Expression with resultType string).\\n *\\n * @param version the version value to set\\n * @return the GoogleCloudStorageLocation object itself.\\n *\\/\\n public GoogleCloudStorageLocation withVersion(Object version) {\\n this.version = version;\\n return this;\\n }\\n\\n}\"\n result = clean_code(test_text, \"java\", 200)\n print(result)\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/eval.sh", "content": "cd ./code-rag-bench/retrieval/\n\noutput_dir='result'\n\nfor dataset_name in \"humaneval\" \"mbpp\" \"repoeval\" \"ds1000_all_completion\" \"odex_en\" \"swe-bench-lite\"\ndo\necho \"dataset_name: ${dataset_name}\"\npython main.py \\\n --embedder_name_or_path BAAI/bge-code-v1 \\\n --embedder_model_class decoder-only-base \\\n --query_instruction_format_for_retrieval '{}\\n{}' \\\n --embedder_query_max_length 2048 \\\n --embedder_passage_max_length 2048 \\\n --trust_remote_code True \\\n --pooling_method last_token \\\n --embedder_batch_size 64 \\\n --devices cuda:0 cuda:1 cuda:2 cuda:3 cuda:4 cuda:5 cuda:6 cuda:7 \\\n --cache_dir ./cache \\\n --dataset $dataset_name \\\n --output_file ../../${output_dir}/${dataset_name}_output.json \\\n --results_file ../../${output_dir}/${dataset_name}_results.json\ndone" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/prepare_data.sh", "content": "cd ./code-rag-bench/retrieval/\n\nfor dataset_name in \"humaneval\" \"mbpp\" \"live_code_bench\" \"ds1000\" \"odex\" \"repoeval_repo\" \"swebench_repo\"\ndo\necho \"dataset_name: ${dataset_name}\"\nPYTHONPATH=./ python create/${dataset_name}.py\ndone" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/arguments.py", "content": "from typing import List\nfrom dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.evaluation import (\n AbsEvalModelArgs as CodeRAGEvalModelArgs,\n)\n\n@dataclass\nclass CodeRAGEvalArgs:\n dataset: str = field(\n default='humaneval',\n metadata={\n \"help\": \"Task to evaluate. Default: humaneval. Available tasks: \"\n \"['humaneval', 'mbpp', 'live_code_bench', 'ds1000', 'odex', 'repoeval_repo', 'swebench_repo', 'code_search_net']\",\n }\n )\n max_length: int = field(\n default=2048, metadata={\"help\": \"Max length to use for evaluation.\"}\n )\n batch_size: int = field(\n default=64, metadata={\"help\": \"Batch size for evaluation.\"}\n )\n output_file: str = field(\n default=\"outputs.json\",\n metadata={\n \"help\": \"Specify the filepath if you want to save the retrieval (evaluation) results.\"\n },\n )\n results_file: str = field(\n default=\"results.json\",\n metadata={\n \"help\": \"Specify the filepath if you want to save the retrieval results.\"\n },\n )" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/code_search_net.py", "content": "import argparse\nimport datasets\nimport os\nfrom tqdm import tqdm\nimport random\nfrom create.utils import save_tsv_dict, save_file_jsonl, load_jsonlines\n\ndef document2code(data, split=\"train\"):\n data = data[split]\n code_search_net_data_queries = []\n code_search_net_data_docs = []\n code_search_net_data_qrels = []\n\n for item in tqdm(data):\n doc = item[\"func_documentation_string\"]\n code = item[\"func_code_string\"]\n doc_id = \"{repository_name}_{func_path_in_repository}_{func_name}_doc\".format_map(item)\n code_id = \"{repository_name}_{func_path_in_repository}_{func_name}_code\".format_map(item)\n code_search_net_data_queries.append({\"_id\": doc_id, \"text\": doc, \"metadata\": {}})\n code_search_net_data_docs.append({\"_id\": code_id, \"title\": item[\"func_name\"], \"text\": code, \"metadata\": {}})\n code_search_net_data_qrels.append({\"query-id\": doc_id, \"corpus-id\": code_id, \"score\": 1})\n\n return code_search_net_data_queries, code_search_net_data_docs, code_search_net_data_qrels\n\ndef main():\n#### /print debug information to stdout\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--language\", type=str, default=\"python\", help=\"codesearch net language\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n\n args = parser.parse_args()\n dataset = datasets.load_dataset(\"code_search_net\", args.language)\n\n path = os.path.join(args.output_dir, \"code_search_net_{}\".format(args.language))\n os.makedirs(path)\n os.makedirs(os.path.join(path, \"qrels\"))\n\n docs = []\n queries = []\n for split in [\"train\", \"validation\", \"test\"]:\n queries_split, docs_split, qrels_split = document2code(dataset, split)\n docs += docs_split\n queries += queries_split\n\n save_tsv_dict(qrels_split, os.path.join(path, \"qrels\", \"{}.tsv\".format(split)), [\"query-id\", \"corpus-id\", \"score\"])\n\n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/ds1000.py", "content": "import io\nimport os\nimport fcntl\nimport pathlib\nimport zipfile\nimport argparse\nimport requests\nimport warnings\nimport itertools\nfrom tqdm import tqdm\nfrom datasets import load_dataset\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n\n# Load dataset\ndef download_source(source_dir):\n src = source_dir / \"ds1000.py\"\n url = \"https://github.com/HKUNLP/DS-1000/blob/49c1c543ada8b58138181333cdc62e613204efcf/ds1000.py?raw=true\"\n lock = src.with_suffix(\".lock\")\n with open(lock, \"w\") as f_lock:\n fcntl.flock(f_lock, fcntl.LOCK_EX)\n if not src.exists():\n warnings.warn(f\"DS-1000 source is being saved to {src}.\")\n print(\"Downloading source code...\")\n r = requests.get(url, stream=True)\n with open(src, \"wb\") as f_src:\n f_src.write(r.content)\n open(src.parent / \"__init__.py\", \"w\").close()\n print(\"Done.\")\n fcntl.flock(f_lock, fcntl.LOCK_UN)\n\ndef download_dataset(source_dir):\n path = source_dir / \"ds1000_data\"\n url = \"https://github.com/HKUNLP/DS-1000/blob/49c1c543ada8b58138181333cdc62e613204efcf/ds1000_data.zip?raw=true\"\n lock = path.with_suffix(\".lock\")\n with open(lock, \"w\") as f_lock:\n fcntl.flock(f_lock, fcntl.LOCK_EX)\n if not path.exists():\n warnings.warn(f\"DS-1000 data is being saved to {path}.\")\n print(\"Downloading dataset...\")\n r = requests.get(url, stream=True)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(source_dir)\n print(\"Done.\")\n fcntl.flock(f_lock, fcntl.LOCK_UN)\n\ndef get_dataset(source_dir, mode: str = \"Completion\", key: str = \"All\"):\n \"\"\"Returns dataset for the task or an iterable of any object, that get_prompt can handle\"\"\"\n from ds.ds1000 import DS1000Dataset\n\n data = DS1000Dataset(source_dir / \"ds1000_data\", mode=mode).data\n if key == \"All\":\n if mode == \"Insertion\":\n warnings.warn(\n \"Insertion not supported for Matplotlib. Only running others.\"\n )\n data = {k: v for k, v in data.items() if k != \"Matplotlib\"}\n dataset = list(itertools.chain(*data.values()))\n else:\n dataset = data[key]\n return dataset\n\n\n# Collect queries, docs, and relations\ndef document2code(data: list):\n queries, docs, qrels = [], [], []\n\n # collect doc corpus\n code_docs = load_dataset(\"neulab/docprompting-conala\", \"docs\")[\"train\"]\n for i in range(len(code_docs)):\n docs.append({\n \"_id\": str(i),\n \"title\": code_docs[i][\"doc_id\"],\n \"text\": code_docs[i][\"doc_content\"],\n \"metadata\": {}\n })\n \n # load canonical docs\n ds1000 = load_dataset(\"json\", data_files={\"test\": args.canonical_file})[\"test\"]\n for idx,item in enumerate(tqdm(data)):\n example = item.data\n query = example[\"prompt\"]\n query_id = f\"{example['lib']}_{example['perturbation_origin_id']}\"\n queries.append({\"_id\": query_id, \"text\": query, \"metadata\": {}})\n\n doc_ids = [doc[\"title\"] for doc in ds1000[idx][\"docs\"]]\n for doc_id in doc_ids:\n corpus_id = code_docs[\"doc_id\"].index(doc_id)\n corpus_id = str(corpus_id)\n qrels.append({\"query-id\": query_id, \"corpus-id\": corpus_id, \"score\": 1})\n \n return queries, docs, qrels\n\n\ndef main():\n args.source_dir = pathlib.Path(__file__).parent.parent / args.source_dir\n os.makedirs(args.source_dir, exist_ok=True)\n download_source(args.source_dir)\n download_dataset(args.source_dir)\n dataset = get_dataset(args.source_dir, mode=args.mode, key=args.key)\n\n path = os.path.join(args.output_dir, f\"ds1000_{args.key.lower()}_{args.mode.lower()}\")\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n queries, docs, qrels = document2code(dataset)\n save_tsv_dict(qrels, os.path.join(path, \"qrels\", \"test.tsv\"), [\"query-id\", \"corpus-id\", \"score\"])\n \n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--source_dir\", type=str, default=\"ds\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--mode\", type=str, default=\"Completion\", choices=[\"Completion\", \"Insertion\"])\n parser.add_argument(\"--key\", type=str, default=\"All\", \n choices=[\"All\", \"Numpy\", \"Pandas\", \"Scipy\", \"Matplotlib\", \"Sklearn\", \"Tensorflow\", \"Pytorch\"])\n parser.add_argument(\"--canonical_file\", type=str, default=\"datasets/canonical/ds1000_docs.json\")\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/general_programming.py", "content": "\"\"\"Aggregate all code-generation datasets.\"\"\"\n\nimport os\nimport json\nimport datasets\nimport argparse\nfrom create.utils import save_tsv_dict\nfrom create.humaneval import document2code as d2c_humaneval\nfrom create.mbpp import document2code as d2c_mbpp\n\nD2C_FUNC_DICT = {\n \"humaneval\": d2c_humaneval,\n \"mbpp\": d2c_mbpp,\n}\nSPLIT_DICT = {\n \"humaneval\": [\"test\"], \n \"mbpp\": [\"train\", \"test\", \"validation\", \"prompt\"],\n}\nHF_NAME_DICT = {\n \"humaneval\": \"openai_humaneval\",\n \"mbpp\": \"mbpp\",\n}\n\n\ndef save_file_jsonl(data, path):\n with open(path,'w') as fw:\n for item in data:\n fw.write(json.dumps(item) + '\\n')\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_names\", type=str, nargs='+', default=[\"humaneval\", \"mbpp\"])\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--output_name\", type=str, default=\"general-programming\")\n args = parser.parse_args()\n\n path = os.path.join(args.output_dir, args.output_name)\n os.makedirs(path)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n split_dict = {}\n for dataset_name in args.dataset_names:\n for split in SPLIT_DICT[dataset_name]:\n if split not in split_dict:\n split_dict[split] = []\n split_dict[split].append(dataset_name)\n \n dataset_dict = {\n dataset_name: datasets.load_dataset(HF_NAME_DICT[dataset_name])\n for dataset_name in args.dataset_names\n }\n docs, queries = [], []\n for split, ds_names in split_dict.items():\n for ds in ds_names:\n dataset = dataset_dict[ds]\n\n queries_split, docs_split, qrels_split = D2C_FUNC_DICT[ds](dataset, split)\n docs += docs_split\n queries += queries_split\n\n qrels_path = os.path.join(path, \"qrels\", f\"{split}.tsv\")\n save_tsv_dict(qrels_split, qrels_path, [\"query-id\", \"corpus-id\", \"score\"])\n \n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/humaneval.py", "content": "import os\nimport argparse\nimport datasets\nfrom tqdm import tqdm\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n\ndef document2code(data, split=\"test\"):\n data = data[split]\n queries, docs, qrels = [], [], []\n\n for item in tqdm(data):\n doc = item[\"prompt\"]\n code = item[\"prompt\"] + '\\n' + item[\"canonical_solution\"]\n doc_id = \"{task_id}_doc\".format_map(item)\n code_id = \"{task_id}_code\".format_map(item)\n\n queries.append({\"_id\": doc_id, \"text\": doc, \"metadata\": {}})\n docs.append({\"_id\": code_id, \"title\": item[\"entry_point\"], \"text\": code, \"metadata\": {}})\n qrels.append({\"query-id\": doc_id, \"corpus-id\": code_id, \"score\": 1})\n \n return queries, docs, qrels\n\n\ndef main():\n dataset = datasets.load_dataset(args.dataset_name)\n\n path = os.path.join(args.output_dir, args.output_name)\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n queries, docs, qrels = document2code(dataset, split=\"test\")\n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n qrels_path = os.path.join(path, \"qrels\", \"test.tsv\")\n save_tsv_dict(qrels, qrels_path, [\"query-id\", \"corpus-id\", \"score\"])\n\n # create canonical file if not existent yet\n if not os.path.exists(args.canonical_file):\n canonical_solutions = []\n for doc in docs:\n canonical_solutions.append([{\n \"text\": doc[\"text\"], \"title\": doc[\"title\"]\n }])\n canonical_dataset = dataset[\"test\"].add_column(\"docs\", canonical_solutions)\n canonical_dataset.to_json(args.canonical_file)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str, default=\"openai_humaneval\")\n parser.add_argument(\"--output_name\", type=str, default=\"humaneval\")\n parser.add_argument(\"--canonical_file\", type=str, \n default=\"datasets/canonical/humaneval_solutions.json\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/live_code_bench.py", "content": "import os\nimport argparse\nimport datasets\nfrom tqdm import tqdm\nfrom datasets import load_dataset\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n\ndef get_queries(data, split=\"test\") -> list[dict]:\n queries = [{\n \"_id\": item[\"question_id\"] + '__' + item[\"contest_id\"], \n \"text\": item[\"question_content\"], \n \"metadata\": {}\n } for item in data[split]]\n return queries\n\ndef get_corpus(hf_name: str, cache_dir: str) -> list[dict]:\n dataset = load_dataset(hf_name, cache_dir=cache_dir)[\"train\"]\n corpus = [\n {\"_id\": i, \"text\": item[\"text\"], \"title\": item[\"title\"]}\n for i,item in enumerate(dataset)\n ]\n return corpus\n\n\ndef main():\n dataset = datasets.load_dataset(args.dataset_name, cache_dir=args.cache_dir)\n\n path = os.path.join(args.output_dir, args.output_name)\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n queries = get_queries(dataset, split=\"test\")\n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n\n docs = get_corpus(args.corpus_name, args.cache_dir)\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n\n qrels = [] # no ground-truth solutions\n qrels_path = os.path.join(path, \"qrels\", \"test.tsv\")\n save_tsv_dict(qrels, qrels_path, [\"query-id\", \"corpus-id\", \"score\"])\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str, default=\"livecodebench/code_generation\")\n parser.add_argument(\"--corpus_name\", type=str, default=\"code-rag-bench/programming-solutions\")\n parser.add_argument(\"--cache_dir\", type=str, default=\"/scratch/zhiruow/data\")\n parser.add_argument(\"--output_name\", type=str, default=\"livecodebench\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/mbpp.py", "content": "import os\nimport argparse\nimport datasets\nfrom tqdm import tqdm\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n\ndef get_function_name(code: str) -> str:\n \"\"\"Parse the function name for a code snippet string.\"\"\"\n lines = code.split('\\n')\n for line in lines:\n if line.lstrip().startswith(\"def \"):\n break\n func_name = line.lstrip()[4: ]\n func_name = func_name.split('(')[0]\n return func_name\n\n\ndef document2code(data, split=\"test\"):\n data = data[split]\n queries, docs, qrels = [], [], []\n\n for item in tqdm(data):\n doc = item[\"text\"]\n code = \"# \" + item[\"text\"] + '\\n' + item[\"code\"]\n doc_id = \"{task_id}_doc\".format_map(item)\n code_id = \"{task_id}_code\".format_map(item)\n\n queries.append({\"_id\": doc_id, \"text\": doc, \"metadata\": {}})\n docs.append({\"_id\": code_id, \"title\": get_function_name(item[\"code\"]), \"text\": code, \"metadata\": {}})\n qrels.append({\"query-id\": doc_id, \"corpus-id\": code_id, \"score\": 1})\n \n return queries, docs, qrels\n\n\ndef main():\n dataset = datasets.load_dataset(args.dataset_name)\n\n path = os.path.join(args.output_dir, args.output_name)\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n docs, queries = [], []\n for split in args.splits:\n queries_split, docs_split, qrels_split = document2code(dataset, split)\n docs += docs_split\n queries += queries_split\n\n qrels_path = os.path.join(path, \"qrels\", f\"{split}.tsv\")\n save_tsv_dict(qrels_split, qrels_path, [\"query-id\", \"corpus-id\", \"score\"])\n\n # create canonical file for test split if not existent yet\n if split == \"test\" and (not os.path.exists(args.canonical_file)):\n canonical_solutions = []\n for doc in docs_split:\n canonical_solutions.append([{\n \"text\": doc[\"text\"], \"title\": doc[\"title\"]\n }])\n canonical_dataset = dataset[\"test\"].add_column(\"docs\", canonical_solutions)\n canonical_dataset.to_json(args.canonical_file)\n \n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str, default=\"google-research-datasets/mbpp\")\n # parser.add_argument(\"--dataset_name\", type=str, default=\"code-rag-bench/mbpp\")\n parser.add_argument(\"--splits\", type=str, default=[\"train\", \"validation\", \"test\"],\n choices=[\"train\", \"validation\", \"test\", \"prompt\"])\n parser.add_argument(\"--output_name\", type=str, default=\"mbpp\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--canonical_file\", type=str, \n default=\"datasets/canonical/mbpp_solutions.json\")\n \n args = parser.parse_args()\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/odex.py", "content": "import os\nimport re\nimport random\nimport argparse\nimport datasets\nfrom tqdm import tqdm\nfrom collections import Counter\nfrom datasets import load_dataset\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n\ndef document2code(data, split=\"test\"):\n data = data[split]\n queries, docs, qrels = [], [], []\n\n # build doc corpus\n code_docs = load_dataset(\"neulab/docprompting-conala\", \"docs\")[\"train\"]\n for i in range(len(code_docs)):\n docs.append({\n \"_id\": str(i),\n \"title\": code_docs[i][\"doc_id\"],\n \"text\": code_docs[i][\"doc_content\"],\n \"metadata\": {}\n })\n \n # load canonical docs\n odex = load_dataset(\"json\", data_files={\"test\": args.canonical_file})[\"test\"]\n # collect queries and query-doc matching\n for idx,item in enumerate(tqdm(data)):\n query = item[\"intent\"]\n query_id = f\"{idx}_{item['task_id']}\"\n queries.append({\"_id\": query_id, \"text\": query, \"metadata\": {}})\n\n doc_ids = [doc[\"title\"] for doc in odex[idx][\"docs\"]]\n for doc_id in doc_ids:\n corpus_id = code_docs[\"doc_id\"].index(doc_id)\n corpus_id = str(corpus_id)\n qrels.append({\"query-id\": query_id, \"corpus-id\": corpus_id, \"score\": 1})\n \n return queries, docs, qrels\n\n\ndef main():\n if '_' in args.dataset_name:\n dataset_name = args.dataset_name.split('_')[0]\n language = args.dataset_name.split('_')[1]\n else:\n dataset_name = args.dataset_name\n language = 'en'\n dataset = datasets.load_dataset(dataset_name, language) # english version by default\n\n path = os.path.join(args.output_dir, args.output_name.replace('en', language))\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n docs, queries = [], []\n for split in [\"test\"]:\n queries_split, docs_split, qrels_split = document2code(dataset, split)\n docs += docs_split\n queries += queries_split\n\n save_tsv_dict(qrels_split, os.path.join(path, \"qrels\", \"{}.tsv\".format(split)), [\"query-id\", \"corpus-id\", \"score\"])\n \n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str, default=\"neulab/odex\")\n parser.add_argument(\"--output_name\", type=str, default=\"odex_en\")\n parser.add_argument(\"--canonical_file\", type=str, default=\"datasets/canonical/odex_docs.json\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/repoeval.py", "content": "import io\nimport os\nimport glob\nimport json\nimport argparse\nimport requests\nimport zipfile\nfrom collections import defaultdict\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\nREPOs_line_and_api = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',\n 'alibaba_FederatedScope',\n 'pytorch_rl',\n 'opendilab_ACE',\n]\n\nREPOs_function = [\n \"amazon-science_patchcore-inspection\",\n \"deepmind_tracr\",\n \"facebookresearch_omnivore\",\n \"google_lightweight_mmm\",\n \"lucidrains_imagen-pytorch\",\n \"maxhumber_redframes\",\n]\n\nREPO_DIRs = {\n \"api\": \"repositories/line_and_api_level\",\n \"line\": \"repositories/line_and_api_level\",\n \"function\": \"repositories/function_level\",\n}\n\n\ndef iterate_repository(base_dir: str, repo: str) -> dict:\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = open(fname, 'r', encoding='utf8').read()\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:\n print(f\"Skipped {len(skipped_files)} out of {len(files)} files due to I/O errors\")\n for fname, e in skipped_files:\n print(f\"{fname}: {e}\")\n return loaded_code_files\n\n\ndef window_overlap(span: tuple, target_span: tuple) -> bool:\n if span[0] >= target_span[1] or span[1] <= target_span[0]:\n return False\n return True\n\n\nclass RepoWindowMaker:\n def __init__(self, base_dir, repo, tasks, window_size, slice_size):\n self.base_dir = base_dir\n self.repo = repo\n self.window_size = window_size\n self.slice_size = slice_size\n self.slice_step = 1 if window_size // slice_size == 0 else window_size // slice_size\n self.tasks = tasks\n self.source_code_files = iterate_repository(base_dir, repo)\n \n def _buid_windows_for_a_file(self, fpath_tuple, code):\n code_windows = []\n code_lines = code.splitlines()\n delta_size = self.window_size // 2\n for line_no in range(0, len(code_lines), self.slice_step): # line_no starts from 0\n start_line_no = max(0, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n if not window_lines: # all empty lines\n continue\n window_text = '\\n'.join(window_lines)\n code_windows.append({\n 'context': window_text,\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no,\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n ground_truth_indices = {}\n for task in self.tasks:\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n start_line_no = task['metadata']['context_start_lineno']\n for i, window in enumerate(merged_code_windows):\n if window[\"metadata\"][0][\"fpath_tuple\"] != fpath_tuple:\n continue\n if any([\n window_overlap(\n (sub_window[\"start_line_no\"], sub_window[\"end_line_no\"]), \n (start_line_no, line_no + 1)\n )\n for sub_window in window[\"metadata\"]\n ]):\n if i not in ground_truth_indices: \n ground_truth_indices[i] = []\n ground_truth_indices[i].append(task[\"metadata\"][\"task_id\"])\n \n return merged_code_windows, ground_truth_indices\n\n\ndef download_data(directory: str = \"repoeval\"):\n os.makedirs(directory, exist_ok=True)\n \n datasets_dir = os.path.join(directory, \"datasets\")\n repos_lineapi_dir = os.path.join(directory, \"repositories\", \"line_and_api_level\")\n repos_function_dir = os.path.join(directory, \"repositories\", \"function_level\")\n\n print(f\"Start downloading the necessary `datasets` and `repositories` files.\")\n if not os.path.exists(datasets_dir):\n print(f\"Start downloading the `datasets`.\")\n datasets_url = \"https://github.com/microsoft/CodeT/raw/main/RepoCoder/datasets/datasets.zip\"\n r = requests.get(datasets_url, stream=True)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(datasets_dir)\n print(\"Finished downloading the `datasets` files.\")\n\n if not os.path.exists(repos_lineapi_dir):\n print(f\"Start downloading the `repositories` (line_and_api).\")\n repos_lineapi_url = \"https://github.com/microsoft/CodeT/raw/main/RepoCoder/repositories/line_and_api_level.zip\"\n r = requests.get(repos_lineapi_url, stream=True)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(repos_lineapi_dir)\n \n if not os.path.exists(repos_function_dir):\n print(f\"Start downloading the `repositories` (function).\")\n # repos_function_url = \"https://github.com/microsoft/CodeT/raw/main/RepoCoder/repositories/function_level.zip\"\n repos_function_url = \"https://github.com/Veronicium/repoeval_debug/raw/main/function_level.zip\"\n r = requests.get(repos_function_url, stream=True)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(repos_function_dir)\n print(\"Finished downloading the `repositories` files.\")\n\n\ndef repo2code(\n repo: str, data_cache_dir: str, \n split: str, context_length: str,\n window_size: int, slice_size: int\n):\n # load test examples\n file_name = f\"{split}_level_completion_{context_length}_context_codex.test.jsonl\"\n if split == 'function':\n file_name = file_name.replace('.test.jsonl', '.test.clean.jsonl')\n \n task_path = os.path.join(data_cache_dir, \"datasets\", file_name)\n tasks = [json.loads(l.rstrip()) for l in open(task_path, 'r')]\n tasks = [task for task in tasks if repo == task['metadata']['task_id'].split('/')[0]]\n\n # collect queries\n queries = []\n for task in tasks:\n query_id = task[\"metadata\"][\"task_id\"]\n # text = '\\n'.join(task[\"prompt\"].split('\\n')[-2:])\n text = task[\"prompt\"]\n metadata = task[\"metadata\"]\n queries.append({\"_id\": query_id, \"text\": text, \"metadata\": metadata})\n \n base_dir = os.path.join(data_cache_dir, REPO_DIRs[split])\n repo_window_maker = RepoWindowMaker(base_dir, repo, tasks, window_size, slice_size)\n windows, ground_truth_indices = repo_window_maker.build_windows()\n corpus, qrels = [], []\n query_id2gt = {task['metadata']['task_id']:[] for task in tasks}\n for i, window in enumerate(windows):\n path = '-'.join(window[\"metadata\"][0][\"fpath_tuple\"])\n line = f\"{window['metadata'][0]['start_line_no']}-{window['metadata'][-1]['end_line_no']}\"\n corpus_id = f\"{repo}_{path}_{line}\"\n corpus.append({\n \"_id\": corpus_id, \"title\": path, \n \"text\": window[\"context\"], \"metadata\": window[\"metadata\"]\n })\n if i in ground_truth_indices:\n for query_id in ground_truth_indices[i]:\n qrels.append({\"query-id\": query_id, \"corpus-id\": corpus_id, \"score\": 1})\n query_id2gt[query_id].append({\"title\": corpus_id.replace('_', '/'), \"text\": window[\"context\"]})\n\n return queries, corpus, qrels, query_id2gt\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--results_dir\", type=str, default=\"results\")\n parser.add_argument(\"--split\", type=str, required=True, choices=[\"api\", \"line\", \"function\"])\n parser.add_argument(\"--context_length\", type=str, default=\"1k\", choices=[\"1k\", \"2k\", \"4k\"])\n parser.add_argument(\"--data_cache_dir\", type=str, default=\"output/repoeval\")\n parser.add_argument(\"--window_size\", type=int, default=20)\n parser.add_argument(\"--slice_size\", type=int, default=2)\n args = parser.parse_args()\n\n download_data(args.data_cache_dir)\n\n path = os.path.join(args.output_dir, \"repoeval\", args.split)\n os.makedirs(path, exist_ok=True)\n REPOs = REPOs_function if args.split == \"function\" else REPOs_line_and_api\n \n file_name = f\"{args.split}_level_completion_{args.context_length}_context_codex.test.jsonl\"\n data_path = os.path.join(args.data_cache_dir, \"datasets\", file_name)\n data = [json.loads(l.rstrip()) for l in open(data_path, 'r')]\n \n # preprocess function completion data (the data in the RepoCoder repo isn't correctly formatted)\n if args.split == 'function': \n repo2idx = {}\n for task in data:\n repo = task['metadata']['task_id'].replace('--', '_').split('/')[0]\n if repo not in repo2idx:\n repo2idx[repo] = 0\n task['metadata']['task_id'] = task['metadata']['task_id'].replace('--', '_').replace('idx', str(repo2idx[repo]))\n task['metadata']['line_no'] = task['metadata']['lineno']\n repo2idx[repo] += 1\n \n new_data_path = data_path.replace('.test.jsonl', '.test.clean.jsonl')\n with open(new_data_path, 'w') as f:\n for task in data:\n repo = task['metadata']['task_id'].split('/')[0]\n if repo not in REPOs:\n continue\n f.write(json.dumps(task) + '\\n')\n \n data = [json.loads(l.rstrip()) for l in open(new_data_path, 'r')]\n\n # build query, docs, and qrels for each repository\n queries, corpus, qrels = [], [], []\n query_id2gt = {}\n for repo in REPOs:\n repo_queries, repo_corpus, repo_qrels, repo_query_id2gt = repo2code(\n repo, args.data_cache_dir, \n args.split, args.context_length,\n args.window_size, args.slice_size\n )\n queries += repo_queries\n corpus += repo_corpus\n qrels += repo_qrels\n query_id2gt.update(repo_query_id2gt)\n\n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(corpus, os.path.join(path, \"corpus.jsonl\"))\n save_tsv_dict(qrels, os.path.join(path, \"qrels\", \"test.tsv\"), [\"query-id\", \"corpus-id\", \"score\"])\n \n gt_data = []\n for example in data:\n query_id = example['metadata']['task_id']\n gt = query_id2gt[query_id]\n new_example = {\n \"prompt\": example[\"prompt\"],\n \"reference\": example[\"metadata\"][\"ground_truth\"],\n \"docs\": gt[:10],\n \"metadata\": {k:v for k,v in example[\"metadata\"].items() if k != \"ground_truth\"},\n }\n gt_data.append(new_example)\n \n results_file = os.path.join(args.results_dir, f\"repoeval-{args.split}-{args.context_length}-gt.jsonl\")\n with open(results_file, \"w\") as fw:\n for ex in gt_data:\n fw.write(json.dumps(ex) + \"\\n\")\n \n results_file = os.path.join(args.results_dir, f\"repoeval-{args.split}-{args.context_length}-infile.jsonl\")\n with open(results_file, \"w\") as fw:\n for ex in gt_data:\n ex = {k:v for k,v in ex.items() if k != \"docs\"}\n ex[\"docs\"] = []\n fw.write(json.dumps(ex) + \"\\n\")\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/repoeval_repo.py", "content": "import io\nimport os\nimport glob\nimport json\nimport argparse\nimport requests\nimport zipfile\nfrom collections import defaultdict\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\nREPOs_line_and_api = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',\n 'alibaba_FederatedScope',\n 'pytorch_rl',\n 'opendilab_ACE',\n]\n\nREPOs_function = [\n \"amazon-science_patchcore-inspection\",\n \"deepmind_tracr\",\n \"facebookresearch_omnivore\",\n \"google_lightweight_mmm\",\n \"lucidrains_imagen-pytorch\",\n \"maxhumber_redframes\",\n]\n\nREPO_DIRs = {\n \"api\": \"repositories/line_and_api_level\",\n \"line\": \"repositories/line_and_api_level\",\n \"function\": \"repositories/function_level\",\n}\n\n\ndef iterate_repository(base_dir: str, repo: str) -> dict:\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = open(fname, 'r', encoding='utf8').read()\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:\n print(f\"Skipped {len(skipped_files)} out of {len(files)} files due to I/O errors\")\n for fname, e in skipped_files:\n print(f\"{fname}: {e}\")\n return loaded_code_files\n\n\ndef window_overlap(span: tuple, target_span: tuple) -> bool:\n if span[0] >= target_span[1] or span[1] <= target_span[0]:\n return False\n return True\n\n\nclass RepoWindowMaker:\n def __init__(self, base_dir, repo, tasks, window_size, slice_size):\n self.base_dir = base_dir\n self.repo = repo\n self.window_size = window_size\n self.slice_size = slice_size\n self.slice_step = 1 if window_size // slice_size == 0 else window_size // slice_size\n self.tasks = tasks\n self.source_code_files = iterate_repository(base_dir, repo)\n \n def _buid_windows_for_a_file(self, fpath_tuple, code):\n code_windows = []\n code_lines = code.splitlines()\n delta_size = self.window_size // 2\n for line_no in range(0, len(code_lines), self.slice_step): # line_no starts from 0\n start_line_no = max(0, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n if not window_lines: # all empty lines\n continue\n window_text = '\\n'.join(window_lines)\n code_windows.append({\n 'context': window_text,\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no,\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n ground_truth_indices = {}\n for task in self.tasks:\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n start_line_no = task['metadata']['context_start_lineno']\n for i, window in enumerate(merged_code_windows):\n # print(window[\"metadata\"][0][\"fpath_tuple\"], fpath_tuple)\n if window[\"metadata\"][0][\"fpath_tuple\"] != fpath_tuple and ' '.join(list(window[\"metadata\"][0][\"fpath_tuple\"])) != ' '.join(list(fpath_tuple)):\n continue\n # print(1)\n if any([\n window_overlap(\n (sub_window[\"start_line_no\"], sub_window[\"end_line_no\"]), \n (start_line_no, line_no + 1)\n )\n for sub_window in window[\"metadata\"]\n ]):\n # print('test')\n if i not in ground_truth_indices: \n ground_truth_indices[i] = []\n ground_truth_indices[i].append(task[\"metadata\"][\"task_id\"])\n # sys.exit()\n return merged_code_windows, ground_truth_indices\n\n\ndef download_data(directory: str = \"repoeval\"):\n os.makedirs(directory, exist_ok=True)\n \n datasets_dir = os.path.join(directory, \"datasets\")\n repos_function_dir = os.path.join(directory, \"repositories\", \"function_level\")\n\n print(f\"Start downloading the necessary `datasets` and `repositories` files.\")\n if not os.path.exists(datasets_dir):\n print(f\"Start downloading the `datasets`.\")\n datasets_url = \"https://github.com/microsoft/CodeT/raw/main/RepoCoder/datasets/datasets.zip\"\n r = requests.get(datasets_url, stream=True)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(datasets_dir)\n print(\"Finished downloading the `datasets` files.\")\n\n import shutil\n shutil.rmtree(repos_function_dir)\n if not os.path.exists(repos_function_dir):\n print(f\"Start downloading the `repositories` (function).\")\n repos_function_url = \"https://github.com/microsoft/CodeT/raw/main/RepoCoder/repositories/function_level.zip\"\n # repos_function_url = \"https://github.com/Veronicium/repoeval_debug/raw/main/function_level.zip\"\n r = requests.get(repos_function_url, stream=True)\n z = zipfile.ZipFile(io.BytesIO(r.content))\n z.extractall(repos_function_dir)\n print(\"Finished downloading the `repositories` files.\")\n\n\ndef repo2code(\n repo: str, tasks: list[dict], data_cache_dir: str, \n split: str, context_length: str,\n window_size: int, slice_size: int\n):\n # collect queries\n queries = []\n for task in tasks:\n query_id = task[\"metadata\"][\"task_id\"]\n # text = '\\n'.join(task[\"prompt\"].split('\\n')[-2:])\n text = task[\"prompt\"]\n metadata = task[\"metadata\"]\n queries.append({\"_id\": query_id, \"text\": text, \"metadata\": metadata})\n \n base_dir = os.path.join(data_cache_dir, REPO_DIRs[split])\n repo_window_maker = RepoWindowMaker(base_dir, repo, tasks, window_size, slice_size)\n windows, ground_truth_indices = repo_window_maker.build_windows()\n corpus, qrels = [], []\n query_id2gt = {task['metadata']['task_id']:[] for task in tasks}\n for i, window in enumerate(windows):\n path = '-'.join(window[\"metadata\"][0][\"fpath_tuple\"])\n line = f\"{window['metadata'][0]['start_line_no']}-{window['metadata'][-1]['end_line_no']}\"\n corpus_id = f\"{repo}_{path}_{line}\"\n corpus.append({\n \"_id\": corpus_id, \"title\": path, \n \"text\": window[\"context\"], \"metadata\": window[\"metadata\"]\n })\n # print(windows, ground_truth_indices)\n if i in ground_truth_indices:\n for query_id in ground_truth_indices[i]:\n qrels.append({\"query-id\": query_id, \"corpus-id\": corpus_id, \"score\": 1})\n query_id2gt[query_id].append({\"title\": corpus_id.replace('_', '/'), \"text\": window[\"context\"]})\n\n return queries, corpus, qrels, query_id2gt\n\n\ndef main():\n download_data(args.data_cache_dir)\n\n REPOs = REPOs_function if args.split == \"function\" else REPOs_line_and_api\n \n file_name = f\"{args.split}_level_completion_{args.context_length}_context_codex.test.jsonl\"\n data_path = os.path.join(args.data_cache_dir, \"datasets\", file_name)\n data = [json.loads(l.rstrip()) for l in open(data_path, 'r')]\n\n # preprocess function completion data (the data in the RepoCoder repo isn't correctly formatted)\n if args.split == 'function': \n repo2idx = {}\n for task in data:\n repo = task['metadata']['task_id'].replace('--', '_').split('/')[0]\n if repo not in repo2idx:\n repo2idx[repo] = 0\n task['metadata']['task_id'] = task['metadata']['task_id'].replace('--', '_').replace('idx', str(repo2idx[repo]))\n task['metadata']['line_no'] = task['metadata']['lineno']\n repo2idx[repo] += 1\n \n new_data_path = data_path.replace('.test.jsonl', '.test.clean.jsonl')\n with open(new_data_path, 'w') as f:\n for task in data:\n repo = task['metadata']['task_id'].split('/')[0]\n if repo not in REPOs:\n continue\n f.write(json.dumps(task) + '\\n')\n \n data = [json.loads(l.rstrip()) for l in open(new_data_path, 'r')]\n\n # group data instances by repository\n data_dict = {}\n for ex in data:\n repo_name = ex[\"metadata\"][\"task_id\"]\n repo_name = repo_name.split('/')[0]\n if repo_name not in data_dict:\n data_dict[repo_name] = []\n data_dict[repo_name].append(ex)\n\n # build query, docs, and qrels for each repository\n for repo in REPOs:\n queries, corpus, qrels, query_id2gt = repo2code(\n repo, data_dict[repo], args.data_cache_dir, \n args.split, args.context_length,\n args.window_size, args.slice_size\n )\n print(len(queries))\n if len(qrels) == 0:\n print(repo)\n # sys.exit()\n continue\n # sys.exit()\n path = os.path.join(args.output_dir, f\"repoeval__{repo}\")\n os.makedirs(path, exist_ok=True)\n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(corpus, os.path.join(path, \"corpus.jsonl\"))\n save_tsv_dict(qrels, os.path.join(path, \"qrels\", \"test.tsv\"), [\"query-id\", \"corpus-id\", \"score\"])\n \n gt_data = []\n for example in data_dict[repo]:\n query_id = example['metadata']['task_id']\n gt = query_id2gt[query_id]\n new_example = {\n \"prompt\": example[\"prompt\"],\n \"reference\": example[\"metadata\"][\"ground_truth\"],\n \"docs\": gt[:10],\n \"metadata\": {k:v for k,v in example[\"metadata\"].items() if k != \"ground_truth\"},\n }\n gt_data.append(new_example)\n \n os.makedirs(args.results_dir, exist_ok=True)\n \n results_file = os.path.join(args.results_dir, f\"repoeval-{args.split}-{repo}-{args.context_length}-gt.jsonl\")\n with open(results_file, \"w\") as fw:\n for ex in gt_data:\n fw.write(json.dumps(ex) + \"\\n\")\n \n results_file = os.path.join(args.results_dir, f\"repoeval-{args.split}-{repo}-{args.context_length}-infile.jsonl\")\n with open(results_file, \"w\") as fw:\n for ex in gt_data:\n ex = {k:v for k,v in ex.items() if k != \"docs\"}\n ex[\"docs\"] = []\n fw.write(json.dumps(ex) + \"\\n\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--results_dir\", type=str, default=\"results\")\n parser.add_argument(\"--split\", type=str, default=\"function\", choices=[\"function\"])\n parser.add_argument(\"--context_length\", type=str, default=\"2k\", choices=[\"1k\", \"2k\", \"4k\"])\n parser.add_argument(\"--data_cache_dir\", type=str, default=\"output/repoeval\")\n parser.add_argument(\"--window_size\", type=int, default=50)\n parser.add_argument(\"--slice_size\", type=int, default=5)\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/swebench.py", "content": "import os\nimport re\nimport chardet\nimport unidiff\nimport argparse\nimport datasets\nimport traceback\nimport subprocess\nfrom git import Repo\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n# %% Get oracle file contents\n\n# get oracle file contents from the repo\nclass ContextManager:\n def __init__(self, repo_path, base_commit, verbose=False):\n self.repo_path = Path(repo_path).resolve().as_posix()\n self.old_dir = os.getcwd()\n self.base_commit = base_commit\n self.verbose = verbose\n\n def __enter__(self):\n os.chdir(self.repo_path)\n cmd = f\"git reset --hard {self.base_commit} && git clean -fdxq\"\n if self.verbose:\n subprocess.run(cmd, shell=True, check=True)\n else:\n subprocess.run(\n cmd,\n shell=True,\n check=True,\n stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL,\n )\n return self\n\n def get_environment(self):\n raise NotImplementedError() # TODO: activate conda environment and return the environment file\n\n def get_readme_files(self):\n files = os.listdir(self.repo_path)\n files = list(filter(lambda x: os.path.isfile(x), files))\n files = list(filter(lambda x: x.lower().startswith(\"readme\"), files))\n return files\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n os.chdir(self.old_dir)\n\n\nclass AutoContextManager(ContextManager):\n \"\"\"Automatically clones the repo if it doesn't exist\"\"\"\n\n def __init__(self, instance, root_dir=None, verbose=False, token=None):\n if token is None:\n token = os.environ.get(\"GITHUB_TOKEN\", \"git\")\n self.tempdir = None\n if root_dir is None:\n self.tempdir = TemporaryDirectory()\n root_dir = self.tempdir.name\n self.root_dir = root_dir\n repo_dir = os.path.join(self.root_dir, instance[\"repo\"].replace(\"/\", \"__\"))\n if not os.path.exists(repo_dir):\n repo_url = (\n f\"https://{token}@github.com/swe-bench/\"\n + instance[\"repo\"].replace(\"/\", \"__\")\n + \".git\"\n )\n if verbose:\n print(f\"Cloning {instance['repo']} to {root_dir}\")\n Repo.clone_from(repo_url, repo_dir)\n super().__init__(repo_dir, instance[\"base_commit\"], verbose=verbose)\n self.instance = instance\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self.tempdir is not None:\n self.tempdir.cleanup()\n return super().__exit__(exc_type, exc_val, exc_tb)\n\n\ndef ingest_files(filenames):\n files_dict = dict()\n for filename in filenames:\n with open(filename) as f:\n content = f.read()\n files_dict[filename] = content\n return files_dict\n\ndef get_oracle_filenames(instance):\n \"\"\"\n Returns the filenames that are changed in the patch\n \"\"\"\n source_files = {\n patch_file.source_file.split(\"a/\", 1)[-1]\n for patch_file in unidiff.PatchSet(instance[\"patch\"])\n }\n gold_docs = set()\n for source_file in source_files:\n gold_docs.add(source_file)\n return gold_docs\n\n\n# get all file contents from the repo\ndef is_test(name, test_phrases=None):\n if test_phrases is None:\n test_phrases = [\"test\", \"tests\", \"testing\"]\n words = set(re.split(r\" |_|\\/|\\.\", name.lower()))\n return any(word in words for word in test_phrases)\n\ndef list_files(root_dir, include_tests=False):\n files = []\n for filename in Path(root_dir).rglob(\"*.py\"):\n if not include_tests and is_test(filename.as_posix()):\n continue\n files.append(filename.relative_to(root_dir).as_posix())\n return files\n\ndef detect_encoding(filename):\n \"\"\"\n Detect the encoding of a file\n \"\"\"\n with open(filename, \"rb\") as file:\n rawdata = file.read()\n return chardet.detect(rawdata)[\"encoding\"]\n\ndef ingest_directory_contents(root_dir, include_tests=False):\n files_content = {}\n for relative_path in list_files(root_dir, include_tests=include_tests):\n filename = os.path.join(root_dir, relative_path)\n encoding = detect_encoding(filename)\n if encoding is None:\n content = \"[BINARY DATA FILE]\"\n else:\n try:\n with open(filename, encoding=encoding) as file:\n content = file.read()\n except (UnicodeDecodeError, LookupError):\n content = \"[BINARY DATA FILE]\"\n files_content[relative_path] = content\n return files_content\n\ndef get_file_contents(input_instances, verbose: bool = False, tmp_dir: str = \"/scratch\"):\n orig_dir = os.getcwd()\n with TemporaryDirectory(dir=tmp_dir if os.path.exists(tmp_dir) else \"/tmp\") as root_dir:\n for instance_id, instance in tqdm(\n input_instances.items(),\n total=len(input_instances),\n desc=\"Getting file contents\",\n ):\n try:\n with AutoContextManager(instance, root_dir, verbose=verbose) as cm:\n readmes = cm.get_readme_files()\n instance[\"readmes\"] = ingest_files(readmes)\n instance[\"oracle_file_contents\"] = ingest_files(get_oracle_filenames(instance))\n instance[\"file_contents\"] = ingest_directory_contents(cm.repo_path)\n assert all([\n okey in instance[\"file_contents\"] \n for okey in instance[\"oracle_file_contents\"].keys()\n ])\n except Exception as e:\n print(f\"Failed on instance {instance_id}\", e)\n traceback.print_exc()\n finally:\n # if AutoContextManager fails to exit properly future exits will return the wrong directory\n os.chdir(orig_dir)\n os.chdir(orig_dir)\n\n\n# %% Get queries, docs, and qrels\n\ndef document2code(data, split: str = \"test\"):\n subset = data[split]\n if args.num_examples is not None:\n import random\n indices = random.sample([i for i in range(len(subset))], args.num_examples)\n subset = subset.select(indices)\n print(subset)\n\n # get queries for each example\n queries = [\n {\n \"_id\": item[\"instance_id\"],\n \"text\": item[\"problem_statement\"], \n \"metadata\": {}\n }\n for item in subset\n ]\n\n subset_dict = {x[\"instance_id\"]: x for x in subset}\n get_file_contents(subset_dict, tmp_dir=args.tmp_dir)\n\n # collect all docs, i.e., code chunks from the repo\n docs = []\n for instance_id, instance in subset_dict.items():\n print(f\"Instance #{instance_id}: {len(instance['oracle_file_contents'])} oracle / {len(instance['file_contents'])} files\")\n for filename, content in instance[\"file_contents\"].items():\n docs.append({\n \"_id\": f\"{instance_id}_{filename}\",\n \"title\": filename,\n \"text\": content,\n \"metadata\": {},\n })\n\n # find ground-truth docs for each example\n qrels = []\n for instance_id, instance in subset_dict.items():\n for filename, content in instance[\"oracle_file_contents\"].items():\n qrels.append({\n \"query-id\": instance_id,\n \"corpus-id\": f\"{instance_id}_{filename}\",\n \"score\": 1\n })\n \n return queries, docs, qrels\n\n\ndef main():\n dataset = datasets.load_dataset(args.dataset_name, cache_dir=args.cache_dir)\n\n name = \"swe-bench\"\n if \"lite\" in args.dataset_name.lower():\n name += \"-lite\"\n \n path = os.path.join(args.output_dir, name)\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n\n queries, docs, qrels = document2code(dataset, split=\"test\")\n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n qrels_path = os.path.join(path, \"qrels\", \"test.tsv\")\n save_tsv_dict(qrels, qrels_path, [\"query-id\", \"corpus-id\", \"score\"])\n \n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str, default=\"princeton-nlp/SWE-bench_Lite\",\n choices=[\"princeton-nlp/SWE-bench\", \"princeton-nlp/SWE-bench_Lite\"])\n parser.add_argument(\"--cache_dir\", type=str, default=\"/scratch/zhiruow/data\")\n parser.add_argument(\"--tmp_dir\", type=str, default=\"/scratch/zhiruow/tmp\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--num_examples\", type=int, default=None)\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/swebench_repo.py", "content": "import os\nimport re\nimport chardet\nimport unidiff\nimport argparse\nimport datasets\nimport traceback\nimport subprocess\nfrom git import Repo\nfrom tqdm import tqdm\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\nfrom create.utils import save_tsv_dict, save_file_jsonl\n\n# %% Get oracle file contents\n\n# get oracle file contents from the repo\nclass ContextManager:\n def __init__(self, repo_path, base_commit, verbose=False):\n self.repo_path = Path(repo_path).resolve().as_posix()\n self.old_dir = os.getcwd()\n self.base_commit = base_commit\n self.verbose = verbose\n\n def __enter__(self):\n os.chdir(self.repo_path)\n cmd = f\"git reset --hard {self.base_commit} && git clean -fdxq\"\n if self.verbose:\n subprocess.run(cmd, shell=True, check=True)\n else:\n subprocess.run(\n cmd,\n shell=True,\n check=True,\n stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL,\n )\n return self\n\n def get_environment(self):\n raise NotImplementedError() # TODO: activate conda environment and return the environment file\n\n def get_readme_files(self):\n files = os.listdir(self.repo_path)\n files = list(filter(lambda x: os.path.isfile(x), files))\n files = list(filter(lambda x: x.lower().startswith(\"readme\"), files))\n return files\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n os.chdir(self.old_dir)\n\n\nclass AutoContextManager(ContextManager):\n \"\"\"Automatically clones the repo if it doesn't exist\"\"\"\n\n def __init__(self, instance, root_dir=None, verbose=False, token=None):\n if token is None:\n token = os.environ.get(\"GITHUB_TOKEN\", \"git\")\n self.tempdir = None\n if root_dir is None:\n self.tempdir = TemporaryDirectory()\n root_dir = self.tempdir.name\n self.root_dir = root_dir\n repo_dir = os.path.join(self.root_dir, instance[\"repo\"].replace(\"/\", \"__\"))\n if not os.path.exists(repo_dir):\n repo_url = (\n f\"https://{token}@github.com/swe-bench/\"\n + instance[\"repo\"].replace(\"/\", \"__\")\n + \".git\"\n )\n if verbose:\n print(f\"Cloning {instance['repo']} to {root_dir}\")\n Repo.clone_from(repo_url, repo_dir)\n super().__init__(repo_dir, instance[\"base_commit\"], verbose=verbose)\n self.instance = instance\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self.tempdir is not None:\n self.tempdir.cleanup()\n return super().__exit__(exc_type, exc_val, exc_tb)\n\n\ndef ingest_files(filenames):\n files_dict = dict()\n for filename in filenames:\n with open(filename) as f:\n content = f.read()\n files_dict[filename] = content\n return files_dict\n\ndef get_oracle_filenames(instance):\n \"\"\"\n Returns the filenames that are changed in the patch\n \"\"\"\n source_files = {\n patch_file.source_file.split(\"a/\", 1)[-1]\n for patch_file in unidiff.PatchSet(instance[\"patch\"])\n }\n gold_docs = set()\n for source_file in source_files:\n gold_docs.add(source_file)\n return gold_docs\n\n\n# get all file contents from the repo\ndef is_test(name, test_phrases=None):\n if test_phrases is None:\n test_phrases = [\"test\", \"tests\", \"testing\"]\n words = set(re.split(r\" |_|\\/|\\.\", name.lower()))\n return any(word in words for word in test_phrases)\n\ndef list_files(root_dir, include_tests=False):\n files = []\n for filename in Path(root_dir).rglob(\"*.py\"):\n if not include_tests and is_test(filename.as_posix()):\n continue\n files.append(filename.relative_to(root_dir).as_posix())\n return files\n\ndef detect_encoding(filename):\n \"\"\"\n Detect the encoding of a file\n \"\"\"\n with open(filename, \"rb\") as file:\n rawdata = file.read()\n return chardet.detect(rawdata)[\"encoding\"]\n\ndef ingest_directory_contents(root_dir, include_tests=False):\n files_content = {}\n for relative_path in list_files(root_dir, include_tests=include_tests):\n filename = os.path.join(root_dir, relative_path)\n encoding = detect_encoding(filename)\n if encoding is None:\n content = \"[BINARY DATA FILE]\"\n else:\n try:\n with open(filename, encoding=encoding) as file:\n content = file.read()\n except (UnicodeDecodeError, LookupError):\n content = \"[BINARY DATA FILE]\"\n files_content[relative_path] = content\n return files_content\n\ndef get_file_contents(input_instances, verbose: bool = False, tmp_dir: str = \"/scratch\"):\n orig_dir = os.getcwd()\n with TemporaryDirectory(dir=tmp_dir if os.path.exists(tmp_dir) else \"/tmp\") as root_dir:\n for instance_id, instance in tqdm(\n input_instances.items(),\n total=len(input_instances),\n desc=\"Getting file contents\",\n ):\n try:\n with AutoContextManager(instance, root_dir, verbose=verbose) as cm:\n readmes = cm.get_readme_files()\n instance[\"readmes\"] = ingest_files(readmes)\n instance[\"oracle_file_contents\"] = ingest_files(get_oracle_filenames(instance))\n instance[\"file_contents\"] = ingest_directory_contents(cm.repo_path)\n assert all([\n okey in instance[\"file_contents\"] \n for okey in instance[\"oracle_file_contents\"].keys()\n ])\n except Exception as e:\n print(f\"Failed on instance {instance_id}\", e)\n traceback.print_exc()\n finally:\n # if AutoContextManager fails to exit properly future exits will return the wrong directory\n os.chdir(orig_dir)\n os.chdir(orig_dir)\n\n\nimport multiprocessing as mp\nfrom functools import partial\n\ndef process_single_item(item, args):\n \"\"\"处理单个数据项的函数\"\"\"\n name = \"swe-bench\"\n if \"lite\" in args.dataset_name.lower():\n name += \"-lite\"\n \n queries = [{\n \"_id\": item[\"instance_id\"],\n \"text\": item[\"problem_statement\"],\n \"metadata\": {}\n }]\n item_dict = {item[\"instance_id\"]: item}\n \n output_path = os.path.join(args.output_dir, f\"{name}_{item['instance_id']}\", \"qrels\", \"test.tsv\")\n if os.path.exists(output_path):\n return\n \n try:\n get_file_contents(item_dict, tmp_dir=args.tmp_dir)\n \n docs = []\n for instance_id, instance in item_dict.items():\n print(f\"Instance #{instance_id}: {len(instance['oracle_file_contents'])} oracle / {len(instance['file_contents'])} files\")\n for filename, content in instance[\"file_contents\"].items():\n docs.append({\n \"_id\": f\"{instance_id}_{filename}\",\n \"title\": filename,\n \"text\": content,\n \"metadata\": {},\n })\n\n qrels = []\n for instance_id, instance in item_dict.items():\n for filename, content in instance[\"oracle_file_contents\"].items():\n qrels.append({\n \"query-id\": instance_id,\n \"corpus-id\": f\"{instance_id}_{filename}\",\n \"score\": 1\n })\n\n path = os.path.join(args.output_dir, f\"{name}_{instance_id}\")\n os.makedirs(path, exist_ok=True)\n os.makedirs(os.path.join(path, \"qrels\"), exist_ok=True)\n \n save_file_jsonl(queries, os.path.join(path, \"queries.jsonl\"))\n save_file_jsonl(docs, os.path.join(path, \"corpus.jsonl\"))\n qrels_path = os.path.join(path, \"qrels\", \"test.tsv\")\n save_tsv_dict(qrels, qrels_path, [\"query-id\", \"corpus-id\", \"score\"])\n \n except Exception as e:\n print(f\"Error processing item {item['instance_id']}: {str(e)}\")\n\ndef main():\n dataset = datasets.load_dataset(args.dataset_name, cache_dir=args.cache_dir)[\"test\"]\n if args.num_examples is not None:\n import random\n indices = random.sample([i for i in range(len(dataset))], args.num_examples)\n dataset = dataset.select(indices)\n print(dataset)\n\n # 创建进程池\n num_processes = mp.cpu_count() - 1 # 留一个CPU核心\n pool = mp.Pool(processes=num_processes)\n \n # 使用partial固定args参数\n process_func = partial(process_single_item, args=args)\n \n # 使用进程池并行处理\n list(tqdm(\n pool.imap_unordered(process_func, dataset),\n total=len(dataset),\n desc=\"Processing items\"\n ))\n \n # 关闭进程池\n pool.close()\n pool.join()\n \n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--dataset_name\", type=str, default=\"princeton-nlp/SWE-bench_Lite\",\n choices=[\"princeton-nlp/SWE-bench\", \"princeton-nlp/SWE-bench_Lite\"])\n parser.add_argument(\"--cache_dir\", type=str, default=\"/scratch/zhiruow/data\")\n parser.add_argument(\"--tmp_dir\", type=str, default=\"/scratch/zhiruow/tmp\")\n parser.add_argument(\"--output_dir\", type=str, default=\"datasets\")\n parser.add_argument(\"--num_examples\", type=int, default=None)\n args = parser.parse_args()\n\n main()\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/create/utils.py", "content": "import jsonlines\nimport csv\nimport os\n\ndef load_jsonlines(file):\n with jsonlines.open(file, 'r') as jsonl_f:\n lst = [obj for obj in jsonl_f]\n return lst\n\ndef save_file_jsonl(data, fp):\n with jsonlines.open(fp, mode='w') as writer:\n writer.write_all(data)\n\ndef save_tsv_dict(data, fp, fields):\n # build dir\n dir_path = os.path.dirname(fp)\n os.makedirs(dir_path, exist_ok=True)\n \n # writing to csv file\n with open(fp, 'w') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=fields, delimiter='\\t',)\n writer.writeheader()\n writer.writerows(data)\n\ndef cost_esitmate(path):\n corpus = load_jsonlines(os.path.join(path, \"corpus.jsonl\"))\n queries = load_jsonlines(os.path.join(path, \"queries.jsonl\"))\n num_corpus_words = 0\n num_queries_words = 0\n for item in tqdm(corpus):\n num_corpus_words += len(item[\"text\"].split(\" \"))\n for item in tqdm(queries):\n num_queries_words += len(item[\"text\"].split(\" \"))\n print(len(corpus))\n print(len(queries))\n print(num_corpus_words)\n print(num_queries_words)\n" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/main.py", "content": "import os\nimport json\nimport random\nimport logging\nimport pathlib\nimport argparse\nimport numpy as np\nfrom time import time\nfrom datasets import load_dataset\nfrom beir import util, LoggingHandler\nfrom beir.retrieval import models\nfrom beir.datasets.data_loader import GenericDataLoader\nfrom beir.retrieval.evaluation import EvaluateRetrieval\nfrom beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES\nfrom tqdm import tqdm\nfrom transformers import HfArgumentParser\n\nfrom arguments import CodeRAGEvalArgs, CodeRAGEvalModelArgs\nfrom prompts import get_task_def_by_task_name\nfrom FlagEmbedding import FlagLLMModel, FlagModel\n\n\ndef get_model(model_args: CodeRAGEvalModelArgs):\n embedder_name_or_path = model_args.embedder_name_or_path\n\n if model_args.embedder_model_class == \"encoder-only-base\":\n embedder = FlagModel(\n model_name_or_path=embedder_name_or_path,\n normalize_embeddings=model_args.normalize_embeddings,\n pooling_method=model_args.pooling_method,\n use_fp16=model_args.use_fp16,\n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval,\n query_instruction_format=model_args.query_instruction_format_for_retrieval,\n devices=model_args.devices,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir,\n batch_size=model_args.embedder_batch_size,\n query_max_length=model_args.embedder_query_max_length,\n passage_max_length=model_args.embedder_passage_max_length,\n )\n elif model_args.embedder_model_class == \"decoder-only-base\":\n embedder = FlagLLMModel(\n model_name_or_path=embedder_name_or_path,\n normalize_embeddings=model_args.normalize_embeddings,\n pooling_method=model_args.pooling_method,\n use_fp16=model_args.use_fp16,\n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval,\n query_instruction_format=model_args.query_instruction_format_for_retrieval,\n devices=model_args.devices,\n examples_for_task=model_args.examples_for_task,\n examples_instruction_format=model_args.examples_instruction_format,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir,\n batch_size=model_args.embedder_batch_size,\n query_max_length=model_args.embedder_query_max_length,\n passage_max_length=model_args.embedder_passage_max_length,\n )\n else:\n raise ValueError(f\"Invalid model class: {model_args.embedder_model_class}\")\n embedder.model.config._name_or_path = model_args.embedder_name_or_path\n\n class CustomFlagModel:\n def __init__(self, model):\n self.model = model\n\n def encode_queries(self, queries, show_progress_bar, convert_to_tensor, **kwargs):\n if isinstance(queries, str):\n queries = [queries]\n\n if isinstance(queries[0], dict):\n queries = [(e.get('title') + ' ' + e['text']).strip() for e in queries]\n\n return self.model.encode_queries(queries, **kwargs)\n\n def encode_corpus(self, corpus, show_progress_bar, convert_to_tensor, **kwargs):\n if isinstance(corpus, str):\n corpus = [corpus]\n\n if isinstance(corpus[0], dict):\n corpus = [(e.get('title') + ' ' + e['text']).strip() for e in corpus]\n\n return self.model.encode_corpus(corpus, **kwargs)\n\n def encode(self, corpus, show_progress_bar, convert_to_tensor, **kwargs):\n if isinstance(corpus, str):\n corpus = [corpus]\n\n if isinstance(corpus[0], dict):\n corpus = [(e.get('title') + ' ' + e['text']).strip() for e in corpus]\n\n return self.model.encode(corpus, **kwargs)\n\n return CustomFlagModel(embedder)\n\n#### Just some code to print debug information to stdout\nlogging.basicConfig(format='%(asctime)s - %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.INFO,\n handlers=[LoggingHandler()])\n\n\ndef get_top_docs(results: dict, corpus: dict, task_id: str, topk: int = 10) -> list[str]:\n if task_id not in results: return []\n doc_scores = results[task_id]\n doc_scores_sorted = sorted(doc_scores.items(), key=lambda item: item[1], reverse=True)\n doc_scores_sorted = doc_scores_sorted[:topk]\n doc_code_snippets = [corpus[code_id] for code_id, score in doc_scores_sorted]\n return doc_code_snippets\n\n\ndef main(\n eval_args: CodeRAGEvalArgs,\n model_args: CodeRAGEvalModelArgs\n):\n args = eval_args\n\n embedder = get_model(model_args)\n model = DRES(\n embedder,\n batch_size=args.batch_size,\n corpus_chunk_size=512 * 9999\n )\n retriever = EvaluateRetrieval(model, score_function=\"dot\")\n\n if args.dataset.startswith(\"swe-bench\") or args.dataset.startswith(\"repoeval\"):\n all_eval_results = []\n\n if args.dataset.startswith(\"swe-bench\"):\n swebench = load_dataset(\"princeton-nlp/SWE-bench_Lite\")[\"test\"]\n all_top_docs = [[] for _ in swebench]\n\n instance_list = [i for i in os.listdir(\"datasets\") if i.startswith(f\"{args.dataset}_\")]\n instance_list_filtered = []\n\n for ins_dir in tqdm(instance_list):\n logging.info(\"Instance Repo: {}\".format(ins_dir))\n # load data and perform retrieval\n corpus, queries, qrels = GenericDataLoader(\n data_folder=os.path.join(\"datasets\", ins_dir)\n ).load(split=\"test\")\n logging.info(f\"Instance #{ins_dir}: #{len(corpus)} corpus, #{len(queries)} queries\")\n\n start_time = time()\n if len(queries) == 1:\n queries.update({\"dummy\": \"dummy\"})\n results = retriever.retrieve(corpus, queries)\n if \"dummy\" in queries:\n queries.pop(\"dummy\")\n results.pop(\"dummy\")\n end_time = time()\n logging.info(\"Time taken to retrieve: {:.2f} seconds\".format(end_time - start_time))\n\n # get topk retrieved docs\n if args.dataset.startswith(\"swe-bench\"):\n indices = [i for i, ex in enumerate(swebench) if ex[\"instance_id\"] in queries]\n for index in indices:\n instance_id = swebench[index][\"instance_id\"]\n all_top_docs[index] = get_top_docs(results, corpus, instance_id)\n elif args.dataset.startswith(\"repoeval\"):\n args.dataset_path = \"output/repoeval/datasets/function_level_completion_2k_context_codex.test.clean.jsonl\"\n tasks = [json.loads(line.strip()) for line in open(args.dataset_path, 'r')]\n prompts, references, docs, metadatas = [], [], [], []\n for task in tasks:\n if task[\"metadata\"][\"task_id\"] not in queries: continue\n prompts.append(task[\"prompt\"]) # save full prompt\n references.append(task[\"metadata\"][\"ground_truth\"])\n docs.append(get_top_docs(\n results=results, corpus=corpus, task_id=task[\"metadata\"][\"task_id\"],\n ))\n metadatas.append(task[\"metadata\"])\n assert len(prompts) == len(references) == len(docs)\n dataset = [\n {\"prompt\": p, \"reference\": r, \"docs\": d, \"metadata\": m}\n for p, r, d, m in zip(prompts, references, docs, metadatas)\n ]\n with open(args.results_file, \"a\") as fout:\n for curr in dataset:\n fout.write(json.dumps(curr) + \"\\n\")\n else:\n raise ValueError(f\"`dataset` should starts with either 'swe-bench' or 'repoeval'.\")\n\n # evaluate retrieval results\n if len(qrels) == 0:\n logging.info(\"No qrels found for this dataset.\")\n return\n logging.info(\"Retriever evaluation for k in: {}\".format(retriever.k_values))\n ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)\n mrr = retriever.evaluate_custom(qrels, results, retriever.k_values, metric=\"mrr\")\n eval_results = {\n \"ndcg\": ndcg, \"mrr\": mrr,\n \"recall\": recall, \"precision\": precision,\n \"time\": end_time - start_time\n }\n logging.info(f\"Instance #{ins_dir}: {eval_results}\")\n all_eval_results.append(eval_results)\n\n with open(args.output_file + \"_all\", \"w\") as f:\n json.dump(all_eval_results, f)\n\n if args.dataset.startswith(\"swe-bench\"):\n swebench = swebench.add_column(\"docs\", all_top_docs)\n swebench.to_json(args.results_file)\n\n avg_eval_results = {}\n for k, v_dict in all_eval_results[0].items():\n if isinstance(v_dict, dict):\n avg_v_dict = {}\n for vk, vv in v_dict.items():\n avg_vv = sum([e[k][vk] for e in all_eval_results]) / len(all_eval_results)\n avg_v_dict[vk] = avg_vv\n avg_eval_results.update(avg_v_dict)\n elif isinstance(v_dict, float):\n avg_v = sum([e[k] for e in all_eval_results]) / len(all_eval_results)\n avg_eval_results[k] = avg_v\n else:\n raise ValueError\n print(\"Average Eval Results: \", avg_eval_results)\n with open(args.output_file, \"w\") as f:\n json.dump(avg_eval_results, f)\n else:\n dataset = args.dataset\n corpus, queries, qrels = GenericDataLoader(data_folder=os.path.join(\"datasets\", args.dataset)).load(\n split=\"test\")\n #### Retrieve dense results (format of results is identical to qrels)\n start_time = time()\n results = retriever.retrieve(corpus, queries)\n end_time = time()\n print(\"Time taken to retrieve: {:.2f} seconds\".format(end_time - start_time))\n\n if args.dataset in [\"humaneval\", \"mbpp\", \"apps\"]:\n if args.dataset == \"humaneval\":\n ds = load_dataset(\"openai_humaneval\")\n id_key = \"task_id\"\n elif args.dataset == \"mbpp\":\n ds = load_dataset(\"mbpp\")\n id_key = \"task_id\"\n elif args.dataset == \"apps\":\n ds = load_dataset(\"codeparrot/apps\")\n id_key = \"problem_id\"\n all_top_docs = []\n for task_id in ds[\"test\"][id_key]:\n all_top_docs.append(get_top_docs(results, corpus, f\"{task_id}_doc\"))\n ds[\"test\"] = ds[\"test\"].add_column(\"docs\", all_top_docs)\n ds[\"test\"].to_json(args.results_file) # this outputs to arrow format and read as .jsonl\n elif args.dataset.startswith(\"odex\"):\n lang = args.dataset.split(\"_\")[-1]\n ds = load_dataset(\"neulab/odex\", lang, trust_remote_code=True)\n all_top_docs = []\n for idx, task_id in enumerate(ds[\"test\"][\"task_id\"]):\n all_top_docs.append(get_top_docs(results, corpus, f\"{idx}_{task_id}\"))\n ds[\"test\"] = ds[\"test\"].add_column(\"docs\", all_top_docs)\n ds[\"test\"].to_json(args.results_file) # this outputs to arrow format and read as .jsonl\n elif args.dataset.startswith(\"ds1000\"):\n _, key, mode = args.dataset.split(\"_\")\n key = key.capitalize()\n mode = mode.capitalize()\n from create.ds1000 import get_dataset\n source_dir = pathlib.Path(__file__).parent / \"ds\"\n data = get_dataset(source_dir, mode=mode, key=key)\n all_docs = []\n example_ids = []\n for item in data:\n example = item.data\n example_id = f\"{example['lib']}_{example['perturbation_origin_id']}\"\n all_docs.append(get_top_docs(results, corpus, example_id))\n example_ids.append(example_id)\n assert len(all_docs) == len(\n example_ids), f\"length of all_docs should be {len(example_ids)}, now is {len(all_docs)}\"\n with open(args.results_file, \"w+\") as fout:\n for idx, all_doc in enumerate(all_docs):\n fout.write(json.dumps({\"example_id\": example_id,\n \"docs\": all_doc}) + \"\\n\")\n else:\n with open(args.results_file, 'w+') as fw:\n for curr in results:\n fw.write(json.dumps({curr: results[curr]}) + \"\\n\")\n\n #### Evaluate your retrieval using NDCG@k, MAP@K ...\n if len(qrels) == 0:\n logging.info(\"No qrels found for this dataset.\")\n return\n logging.info(\"Retriever evaluation for k in: {}\".format(retriever.k_values))\n ndcg, _map, recall, precision = retriever.evaluate(qrels, results, retriever.k_values)\n\n mrr = retriever.evaluate_custom(qrels, results, retriever.k_values, metric=\"mrr\")\n recall_cap = retriever.evaluate_custom(qrels, results, retriever.k_values, metric=\"r_cap\")\n hole = retriever.evaluate_custom(qrels, results, retriever.k_values, metric=\"hole\")\n\n all_results = {\"ndcg\": ndcg, \"mrr\": mrr, \"recall\": recall, \"precision\": precision,\n \"time\": end_time - start_time}\n with open(args.output_file, \"w\") as f:\n json.dump(all_results, f)\n #### Print top-k documents retrieved ####\n top_k = 3\n\n query_id, ranking_scores = random.choice(list(results.items()))\n scores_sorted = sorted(ranking_scores.items(), key=lambda item: item[1], reverse=True)\n logging.info(\"Query : %s\\n\" % queries[query_id])\n\n for rank in range(top_k):\n doc_id = scores_sorted[rank][0]\n # Format: Rank x: ID [Title] Body\n logging.info(\n \"Rank %d: %s [%s] - %s\\n\" % (rank + 1, doc_id, corpus[doc_id].get(\"title\"), corpus[doc_id].get(\"text\")))\n\n\nif __name__ == \"__main__\":\n parser = HfArgumentParser((\n CodeRAGEvalArgs,\n CodeRAGEvalModelArgs\n ))\n eval_args, model_args = parser.parse_args_into_dataclasses()\n main(eval_args, model_args)" }, { "path": "research/BGE_Coder/evaluation/coderag_eval/test/prompts.py", "content": "from typing import Dict\n\n\ndef get_task_def_by_task_name(task_name: str) -> str:\n task_name_to_instruct: Dict[str, str] = {\n 'humaneval': 'Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'mbpp': 'Given a textual explanation of code functionality, retrieve the corresponding code implementation.',\n 'ds1000_all_completion': 'Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'odex_en': 'Given a question, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'odex_es': 'Given a question, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'odex_ja': 'Given a question, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'odex_ru': 'Given a question, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'repoeval': 'Given a code snippet and a new function name, retrieve the implementation of the function.',\n # 'repoeval': 'Given a piece of code segment, retrieve the code segment that is the latter part of the code.',\n 'swe-bench-lite': 'Given a code snippet containing a bug and a natural language description of the bug or error, retrieve code snippets that demonstrate solutions or fixes for similar bugs or errors (the desired documents).'\n }\n \n return task_name_to_instruct[task_name]" }, { "path": "research/BGE_Coder/evaluation/coir_eval/arguments.py", "content": "from typing import List\nfrom dataclasses import dataclass, field\n\nfrom FlagEmbedding.abc.evaluation import (\n AbsEvalModelArgs as COIREvalModelArgs,\n)\n\n\ndef coir_tasks():\n return [\n \"apps\",\n \"codefeedback-mt\",\n \"codefeedback-st\",\n \"CodeSearchNet-ccr-go\",\n \"CodeSearchNet-ccr-java\",\n \"CodeSearchNet-ccr-javascript\",\n \"CodeSearchNet-ccr-php\",\n \"CodeSearchNet-ccr-python\",\n \"CodeSearchNet-ccr-ruby\",\n \"CodeSearchNet-go\",\n \"CodeSearchNet-java\",\n \"CodeSearchNet-javascript\",\n \"CodeSearchNet-php\",\n \"CodeSearchNet-python\",\n \"CodeSearchNet-ruby\",\n \"codetrans-contest\",\n \"codetrans-dl\",\n \"cosqa\",\n \"stackoverflow-qa\",\n \"synthetic-text2sql\"\n ]\n\n\n@dataclass\nclass COIREvalArgs:\n output_dir: str = field(\n default=\"./results\", metadata={\"help\": \"Path to save results.\"}\n )\n tasks: List[str] = field(\n default_factory=coir_tasks,\n metadata={\n \"help\": \"Tasks to evaluate. Default: None. Available tasks: ['apps', 'codefeedback-mt', 'codefeedback-st', 'CodeSearchNet-ccr-go', 'CodeSearchNet-ccr-java', 'CodeSearchNet-ccr-javascript', 'CodeSearchNet-ccr-php', 'CodeSearchNet-ccr-python', 'CodeSearchNet-ccr-ruby', 'CodeSearchNet-go', 'CodeSearchNet-java', 'CodeSearchNet-javascript', 'CodeSearchNet-php', 'CodeSearchNet-python', 'CodeSearchNet-ruby', 'codetrans-contest', 'codetrans-dl', 'cosqa', 'stackoverflow-qa', 'synthetic-text2sql']\",\n \"choices\": [\n \"apps\",\n \"codefeedback-mt\",\n \"codefeedback-st\",\n \"CodeSearchNet-ccr-go\",\n \"CodeSearchNet-ccr-java\",\n \"CodeSearchNet-ccr-javascript\",\n \"CodeSearchNet-ccr-php\",\n \"CodeSearchNet-ccr-python\",\n \"CodeSearchNet-ccr-ruby\",\n \"CodeSearchNet-go\",\n \"CodeSearchNet-java\",\n \"CodeSearchNet-javascript\",\n \"CodeSearchNet-php\",\n \"CodeSearchNet-python\",\n \"CodeSearchNet-ruby\",\n \"codetrans-contest\",\n \"codetrans-dl\",\n \"cosqa\",\n \"stackoverflow-qa\",\n \"synthetic-text2sql\"\n ]\n }\n )\n use_special_instructions: bool = field(\n default=False, metadata={\"help\": \"Whether to use specific instructions in `prompts.py` for evaluation. Default: False\"}\n )" }, { "path": "research/BGE_Coder/evaluation/coir_eval/eval.sh", "content": "output_dir=result\n\npython main.py \\\n --output_dir ${output_dir} \\\n --use_special_instructions True \\\n --embedder_name_or_path BAAI/bge-code-v1 \\\n --embedder_model_class decoder-only-base \\\n --query_instruction_format_for_retrieval '{}\\n{}' \\\n --embedder_query_max_length 2048 \\\n --embedder_passage_max_length 2048 \\\n --trust_remote_code True \\\n --pooling_method last_token \\\n --embedder_batch_size 64 \\\n --devices cuda:0 cuda:1 cuda:2 cuda:3 cuda:4 cuda:5 cuda:6 cuda:7 \\\n --tasks apps codetrans-contest codetrans-dl cosqa synthetic-text2sql stackoverflow-qa codefeedback-mt codefeedback-st CodeSearchNet-ccr-go CodeSearchNet-ccr-java CodeSearchNet-ccr-javascript CodeSearchNet-ccr-php CodeSearchNet-ccr-python CodeSearchNet-ccr-ruby CodeSearchNet-go CodeSearchNet-java CodeSearchNet-javascript CodeSearchNet-php CodeSearchNet-python CodeSearchNet-ruby \\\n --cache_dir ./cache" }, { "path": "research/BGE_Coder/evaluation/coir_eval/main.py", "content": "import os\nimport json\nimport coir\nfrom transformers import HfArgumentParser\n\nfrom arguments import COIREvalArgs, COIREvalModelArgs\nfrom prompts import get_task_def_by_task_name\nfrom FlagEmbedding import FlagLLMModel, FlagModel\n\n\ndef get_model(model_args: COIREvalModelArgs):\n embedder_name_or_path = model_args.embedder_name_or_path\n\n if model_args.embedder_model_class == \"encoder-only-base\":\n embedder = FlagModel(\n model_name_or_path=embedder_name_or_path,\n normalize_embeddings=model_args.normalize_embeddings,\n pooling_method=model_args.pooling_method,\n use_fp16=model_args.use_fp16,\n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval,\n query_instruction_format=model_args.query_instruction_format_for_retrieval,\n devices=model_args.devices,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir,\n batch_size=model_args.embedder_batch_size,\n query_max_length=model_args.embedder_query_max_length,\n passage_max_length=model_args.embedder_passage_max_length,\n )\n elif model_args.embedder_model_class == \"decoder-only-base\":\n embedder = FlagLLMModel(\n model_name_or_path=embedder_name_or_path,\n normalize_embeddings=model_args.normalize_embeddings,\n pooling_method=model_args.pooling_method,\n use_fp16=model_args.use_fp16,\n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval,\n query_instruction_format=model_args.query_instruction_format_for_retrieval,\n devices=model_args.devices,\n examples_for_task=model_args.examples_for_task,\n examples_instruction_format=model_args.examples_instruction_format,\n trust_remote_code=model_args.trust_remote_code,\n cache_dir=model_args.cache_dir,\n batch_size=model_args.embedder_batch_size,\n query_max_length=model_args.embedder_query_max_length,\n passage_max_length=model_args.embedder_passage_max_length,\n )\n else:\n raise ValueError(f\"Invalid model class: {model_args.embedder_model_class}\")\n embedder.model.config._name_or_path = model_args.embedder_name_or_path\n\n class CustomFlagModel:\n def __init__(self, model):\n self.model = model\n\n def encode_queries(self, queries, show_progress_bar, convert_to_tensor, **kwargs):\n if isinstance(queries, str):\n queries = [queries]\n\n if isinstance(queries[0], dict):\n queries = [(e.get('title') + ' ' + e['text']).strip() for e in queries]\n\n return self.model.encode_queries(queries, **kwargs)\n\n def encode_corpus(self, corpus, show_progress_bar, convert_to_tensor, **kwargs):\n if isinstance(corpus, str):\n corpus = [corpus]\n\n if isinstance(corpus[0], dict):\n corpus = [(e.get('title') + ' ' + e['text']).strip() for e in corpus]\n\n return self.model.encode_corpus(corpus, **kwargs)\n\n def encode(self, corpus, show_progress_bar, convert_to_tensor, **kwargs):\n if isinstance(corpus, str):\n corpus = [corpus]\n\n if isinstance(corpus[0], dict):\n corpus = [(e.get('title') + ' ' + e['text']).strip() for e in corpus]\n\n return self.model.encode(corpus, **kwargs)\n\n return CustomFlagModel(embedder)\n\n\ndef main(\n eval_args: COIREvalArgs,\n model_args: COIREvalModelArgs\n):\n model = get_model(model_args)\n\n output_folder = os.path.join(eval_args.output_dir, os.path.basename(model.model.model.config._name_or_path))\n\n all_task = eval_args.tasks\n if not isinstance(all_task, list):\n all_task = [all_task]\n\n all_results = {}\n for task_name in all_task:\n save_path = os.path.join(output_folder, f\"{task_name}.json\")\n if os.path.exists(save_path):\n with open(save_path, \"r\", encoding=\"utf-8\") as f:\n results = json.load(f)\n all_results[task_name] = results['metrics']\n continue\n\n tmp_task = coir.get_tasks(tasks=[task_name])\n evaluation = coir.COIR(tasks=tmp_task,\n batch_size=model_args.embedder_batch_size)\n\n model.model.stop_self_pool()\n\n if eval_args.use_special_instructions:\n model.model.query_instruction_for_retrieval = get_task_def_by_task_name(task_name)\n\n results = evaluation.run(model, output_folder=output_folder)\n all_results[task_name] = results[task_name]\n\n csn_result = 0\n csn_num = 0\n csn_ccr_result = 0\n csn_ccr_num = 0\n pop_keys = []\n all_result = 0\n all_num = 0\n for k in all_results.keys():\n if 'CodeSearchNet-ccr' in k:\n csn_ccr_result += all_results[k]['NDCG']['NDCG@10']\n csn_ccr_num += 1\n pop_keys.append(k)\n elif 'CodeSearchNet' in k:\n csn_result += all_results[k]['NDCG']['NDCG@10']\n csn_num += 1\n pop_keys.append(k)\n else:\n all_result += all_results[k]['NDCG']['NDCG@10']\n all_num += 1\n if csn_num > 0:\n print('Using CodeSearchNet')\n all_result += csn_result / csn_num\n all_num += 1\n if csn_ccr_num > 0:\n print('Using CodeSearchNet-ccr')\n all_result += csn_ccr_result / csn_ccr_num\n all_num += 1\n new_results = {}\n for k in all_results:\n if k in pop_keys:\n continue\n new_results[k] = all_results[k]['NDCG']['NDCG@10']\n if csn_num > 0:\n new_results['CodeSearchNet'] = csn_result / csn_num\n if csn_ccr_num > 0:\n new_results['CodeSearchNet_CCR'] = csn_ccr_result / csn_ccr_num\n new_results['all'] = all_result / all_num\n\n print(new_results)\n\n with open(os.path.join(output_folder, 'OVERALL-results.json'), 'w') as f:\n json.dump(new_results, f)\n\n\nif __name__ == \"__main__\":\n parser = HfArgumentParser((\n COIREvalArgs,\n COIREvalModelArgs\n ))\n eval_args, model_args = parser.parse_args_into_dataclasses()\n main(eval_args, model_args)\n" }, { "path": "research/BGE_Coder/evaluation/coir_eval/prompts.py", "content": "from typing import Dict\n\n\ndef get_task_def_by_task_name(task_name: str) -> str:\n task_name_to_instruct: Dict[str, str] = {\n # Text-to-Code Retrieval\n ## Code Contest Retrieval\n 'apps': 'Given a code contest problem description, retrieve relevant code that can help solve the problem.',\n ## Web Query to Code Retrieval\n 'cosqa': 'Given a web search query, retrieve relevant code that can help answer the query.',\n ## Text-to-SQL Retrieval\n 'synthetic-text2sql': 'Given a question in text, retrieve SQL queries that are appropriate responses to the question.',\n \n # Code-to-Text Retrieval\n ## Code Summary Retrieval\n 'CodeSearchNet-': 'Given a piece of code, retrieve the document string that summarizes the code.',\n \n # Code-to-Code Retrieval\n ## Code Context Retrieval\n 'CodeSearchNet-ccr-': 'Given a piece of code segment, retrieve the code segment that is the latter part of the code.',\n ## Similar Code Retrieval\n 'codetrans-dl': 'Given a piece of code, retrieve code that is semantically equivalent to the input code.',\n 'codetrans-contest': 'Given a piece of Python code, retrieve C++ code that is semantically equivalent to the input code.',\n \n # Hybrid Code Retrieval\n ## Single-turn Code QA\n 'stackoverflow-qa': 'Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n 'codefeedback-st': 'Given a question that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n ## Multi-turn Code QA\n 'codefeedback-mt': 'Given a multi-turn conversation history that consists of a mix of text and code snippets, retrieve relevant answers that also consist of a mix of text and code snippets, and can help answer the question.',\n }\n \n special_task_names = ['CodeSearchNet-ccr-', 'CodeSearchNet-']\n for special_task_name in special_task_names:\n if special_task_name in task_name:\n return task_name_to_instruct[special_task_name]\n \n return task_name_to_instruct[task_name]" }, { "path": "research/BGE_M3/README.md", "content": "# BGE-M3 ([paper](https://arxiv.org/pdf/2402.03216.pdf), [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/BGE_M3))\n\nIn this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. \n- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. \n- Multi-Linguality: It can support more than 100 working languages. \n- Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. \n\nFor more details, please refer to our paper: [BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://arxiv.org/pdf/2402.03216.pdf)\n\n**Some suggestions for retrieval pipeline in RAG**\n\nWe recommend to use following pipeline: hybrid retrieval + re-ranking. \n- Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. \nA classic example: using both embedding retrieval and the BM25 algorithm. \nNow, you can try to use BGE-M3, which supports both embedding and sparse retrieval. \nThis allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.\nTo use hybrid retrieval, you can refer to [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb\n) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).\n\n- As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. \nUtilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker#2-normal-reranker), [bge-reranker-v2](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker#3-llm-based-reranker)) after retrieval can further filter the selected text.\n\n\n## News:\n\n- 2024/7/1: **We update the MIRACL evaluation results of BGE-M3**. To reproduce the new results, you can refer to: [bge-m3_miracl_2cr](https://huggingface.co/datasets/hanhainebula/bge-m3_miracl_2cr). We have also updated our [paper](https://arxiv.org/pdf/2402.03216) on arXiv.\n \n
\n Details \n \n > The previous test results were lower because we mistakenly removed the passages that have the same id as the query from the search results. After correcting this mistake, the overall performance of BGE-M3 on MIRACL is higher than the previous results, but the experimental conclusion remains unchanged. The other results are not affected by this mistake. To reproduce the previous lower results, you need to add the `--remove-query` parameter when using `pyserini.search.faiss` or `pyserini.search.lucene` to search the passages.\n \n
\n- 2024/3/20: **Thanks Milvus team!** Now you can use hybrid retrieval of bge-m3 in Milvus: [pymilvus/examples\n/hello_hybrid_sparse_dense.py](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).\n- 2024/3/8: **Thanks for the [experimental results](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) from @[Yannael](https://huggingface.co/Yannael). In this benchmark, BGE-M3 achieves top performance in both English and other languages, surpassing models such as OpenAI.**\n- 2024/3/2: Release unified fine-tuning [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder#2-bge-m3) and [data](https://huggingface.co/datasets/Shitao/bge-m3-data) \n- 2024/2/6: We release the [MLDR](https://huggingface.co/datasets/Shitao/MLDR) (a long document retrieval dataset covering 13 languages) and [evaluation pipeline](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/C_MTEB/MLDR). \n- 2024/2/1: **Thanks for the excellent tool from Vespa.** You can easily use multiple modes of BGE-M3 following this [notebook](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb)\n\n\n## Specs\n\n- Model \n\n| Model Name | Dimension | Sequence Length | Introduction |\n|:----:|:---:|:---:|:---:|\n| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | 1024 | 8192 | multilingual; unified fine-tuning (dense, sparse, and colbert) from bge-m3-unsupervised|\n| [BAAI/bge-m3-unsupervised](https://huggingface.co/BAAI/bge-m3-unsupervised) | 1024 | 8192 | multilingual; contrastive learning from bge-m3-retromae |\n| [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) | -- | 8192 | multilingual; extend the max_length of [xlm-roberta](https://huggingface.co/FacebookAI/xlm-roberta-large) to 8192 and further pretrained via [retromae](https://github.com/staoxiao/RetroMAE)| \n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | English model | \n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | English model | \n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | English model | \n\n- Data\n\n| Dataset | Introduction |\n|:----------------------------------------------------------:|:-------------------------------------------------:|\n| [MLDR](https://huggingface.co/datasets/Shitao/MLDR) | Docuemtn Retrieval Dataset, covering 13 languages |\n| [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) | Fine-tuning data used by bge-m3 |\n\n\n\n## FAQ\n\n**1. Introduction for different retrieval methods**\n\n- Dense retrieval: map the text into a single embedding, e.g., [DPR](https://arxiv.org/abs/2004.04906), [BGE-v1.5](https://github.com/FlagOpen/FlagEmbedding)\n- Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, [unicoil](https://arxiv.org/pdf/2106.14807.pdf), and [splade](https://arxiv.org/abs/2107.05720)\n- Multi-vector retrieval: use multiple vectors to represent a text, e.g., [ColBERT](https://arxiv.org/abs/2004.12832).\n\n\n**2. How to use BGE-M3 in other projects?**\n\nFor embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE. \nThe only difference is that the BGE-M3 model no longer requires adding instructions to the queries. \n\nFor hybrid retrieval, you can use [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb\n) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).\n\n\n**3. How to fine-tune bge-M3 model?**\n\nYou can follow the common in this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder#1-standard-model) \nto fine-tune the dense embedding.\n\nIf you want to fine-tune all embedding function of m3 (dense, sparse and colbert), you can refer to the [unified_fine-tuning example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder#2-bge-m3)\n\n\n\n\n\n\n## Usage\n\nInstall: \n```\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\npip install -e .\n```\nor: \n```\npip install -U FlagEmbedding\n```\n\n\n\n### Generate Embedding for text\n\n- Dense Embedding\n```python\nfrom FlagEmbedding import BGEM3FlagModel\n\nmodel = BGEM3FlagModel('BAAI/bge-m3', \n use_fp16=True,\n devices=['cuda:0']) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nsentences_1 = [\"What is BGE M3?\", \"Defination of BM25\"]\nsentences_2 = [\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"]\n\nembeddings_1 = model.encode(sentences_1, \n batch_size=12, \n max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process.\n )['dense_vecs']\nembeddings_2 = model.encode(sentences_2)['dense_vecs']\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n# [[0.6265, 0.3477], [0.3499, 0.678 ]]\n```\nYou also can use sentence-transformers and huggingface transformers to generate dense embeddings.\nRefer to [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding#usage) for details.\n\n\n- Sparse Embedding (Lexical Weight)\n```python\nfrom FlagEmbedding import BGEM3FlagModel\n\nmodel = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\n\nsentences_1 = [\"What is BGE M3?\", \"Defination of BM25\"]\nsentences_2 = [\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"]\n\noutput_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=False)\noutput_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=False)\n\n# you can see the weight for each token:\nprint(model.convert_id_to_token(output_1['lexical_weights']))\n# [{'What': 0.08356, 'is': 0.0814, 'B': 0.1296, 'GE': 0.252, 'M': 0.1702, '3': 0.2695, '?': 0.04092}, \n# {'De': 0.05005, 'fin': 0.1368, 'ation': 0.04498, 'of': 0.0633, 'BM': 0.2515, '25': 0.3335}]\n\n\n# compute the scores via lexical mathcing\nlexical_scores = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])\nprint(lexical_scores)\n# 0.19554901123046875\n\nprint(model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_1['lexical_weights'][1]))\n# 0.0\n```\n\n- Multi-Vector (ColBERT)\n```python\nfrom FlagEmbedding import BGEM3FlagModel\n\nmodel = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) \n\nsentences_1 = [\"What is BGE M3?\", \"Defination of BM25\"]\nsentences_2 = [\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"]\n\noutput_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)\noutput_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)\n\nprint(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]))\nprint(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]))\n# 0.7797\n# 0.4620\n```\n\n\n### Compute score for text pairs\nInput a list of text pairs, you can get the scores computed by different methods.\n```python\nfrom FlagEmbedding import BGEM3FlagModel\n\nmodel = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) \n\nsentences_1 = [\"What is BGE M3?\", \"Defination of BM25\"]\nsentences_2 = [\"BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.\", \n \"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document\"]\n\nsentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2]\n\nprint(model.compute_score(sentence_pairs, \n max_passage_length=128, # a smaller max length leads to a lower latency\n weights_for_different_modes=[0.4, 0.2, 0.4])) # weights_for_different_modes(w) is used to do weighted sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score\n\n# {\n# 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142], \n# 'sparse': [0.195556640625, 0.00879669189453125, 0.0, 0.1802978515625], \n# 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625], \n# 'sparse+dense': [0.482503205537796, 0.23454029858112335, 0.2332356721162796, 0.5122477412223816], \n# 'colbert+sparse+dense': [0.6013619303703308, 0.3255828022956848, 0.32089319825172424, 0.6232916116714478]\n# }\n```\n\n\n\n\n## Evaluation \n\nWe provide the evaluation script for [MKQA](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MKQA) and [MLDR](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR)\n\n\n### Benchmarks from the open-source community\n ![avatar](./imgs/others.webp)\n The BGE-M3 model emerged as the top performer on this benchmark (OAI is short for OpenAI). \n For more details, please refer to the [article](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) and [Github Repo](https://github.com/Yannael/multilingual-embeddings)\n\n\n### Our results\n- Multilingual (MIRACL dataset) \n\n![avatar](./imgs/miracl.jpg)\n\n- Cross-lingual (MKQA dataset)\n\n![avatar](./imgs/mkqa.jpg)\n\n- Long Document Retrieval\n - MLDR: \n ![avatar](./imgs/long.jpg)\n Please note that [MLDR](https://huggingface.co/datasets/Shitao/MLDR) is a document retrieval dataset we constructed via LLM, \n covering 13 languages, including test set, validation set, and training set. \n We utilized the training set from MLDR to enhance the model's long document retrieval capabilities. \n Therefore, comparing baselines with `Dense w.o.long`(fine-tuning without long document dataset) is more equitable. \n Additionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets.\n We believe that this data will be helpful for the open-source community in training document retrieval models.\n\n - NarritiveQA: \n ![avatar](./imgs/nqa.jpg)\n\n- Comparison with BM25 \n\nWe utilized Pyserini to implement BM25, and the test results can be reproduced by this [script](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR#bm25-baseline).\nWe tested BM25 using two different tokenizers: \none using Lucene Analyzer and the other using the same tokenizer as M3 (i.e., the tokenizer of xlm-roberta). \nThe results indicate that BM25 remains a competitive baseline, \nespecially in long document retrieval.\n\n![avatar](./imgs/bm25.jpg)\n\n\n\n## Training\n- Self-knowledge Distillation: combining multiple outputs from different \nretrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)\n- Efficient Batching: Improve the efficiency when fine-tuning on long text. \nThe small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.\n- MCLS: A simple method to improve the performance on long text without fine-tuning. \nIf you have no enough resource to fine-tuning model with long text, the method is useful.\n\nRefer to our [report](https://arxiv.org/pdf/2402.03216.pdf) for more details. \n\n\n\n\n\n\n## Acknowledgement\n\nThanks to the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc. \nThanks to the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [Pyserini](https://github.com/castorini/pyserini).\n\n\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{bge-m3,\n title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, \n author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},\n year={2024},\n eprint={2402.03216},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n\n\n\n\n" }, { "path": "research/BGE_M3/__init__.py", "content": "from .modeling import BGEM3Model, BGEM3ForInference, EncoderOutput\nfrom .trainer import BiTrainer" }, { "path": "research/BGE_M3/arguments.py", "content": "import os\nfrom dataclasses import dataclass, field\nfrom typing import Optional\n\nfrom transformers import TrainingArguments\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n \"\"\"\n\n model_name_or_path: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n cache_dir: Optional[str] = field(\n default=None, metadata={\"help\": \"Where do you want to store the pretrained models downloaded from s3\"}\n )\n\n\n@dataclass\nclass DataArguments:\n knowledge_distillation: bool = field(\n default=False, metadata={\"help\": \"Use knowledge distillation when `pos_scores` and `neg_scores` are in features of training data\"}\n )\n train_data: str = field(\n default=None, metadata={\"help\": \"One or more paths to training data\", \"nargs\": \"+\"}\n )\n cache_path: Optional[str] = field(\n default=None, metadata={\"help\": \"Where do you want to store the cached data\"}\n )\n train_group_size: int = field(default=8)\n\n query_max_len: int = field(\n default=32,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n\n passage_max_len: int = field(\n default=128,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n\n max_example_num_per_dataset: int = field(\n default=None, metadata={\"help\": \"the max number of examples for each dataset\"}\n )\n\n query_instruction_for_retrieval: str= field(\n default=None, metadata={\"help\": \"instruction for query\"}\n )\n passage_instruction_for_retrieval: str = field(\n default=None, metadata={\"help\": \"instruction for passage\"}\n )\n \n same_task_within_batch: bool = field(\n default=False, metadata={\"help\": \"All samples in the same batch comes from the same task.\"}\n )\n shuffle_ratio: float = field(\n default=0.0, metadata={\"help\": \"The ratio of shuffling the text\"}\n )\n \n small_threshold: int = field(\n default=0, metadata={\"help\": \"The threshold of small dataset. All small dataset in the same directory will be merged into one dataset.\"}\n )\n drop_threshold: int = field(\n default=0, metadata={\"help\": \"The threshold for dropping merged small dataset. If the number of examples in the merged small dataset is less than this threshold, it will be dropped.\"}\n )\n\n def __post_init__(self):\n for train_dir in self.train_data:\n if not os.path.exists(train_dir):\n raise FileNotFoundError(f\"cannot find file: {train_dir}, please set a true path\")\n\n@dataclass\nclass RetrieverTrainingArguments(TrainingArguments):\n negatives_cross_device: bool = field(default=False, metadata={\"help\": \"share negatives across devices\"})\n temperature: Optional[float] = field(default=0.02)\n fix_position_embedding: bool = field(default=False, metadata={\"help\": \"Freeze the parameters of position embeddings\"})\n sentence_pooling_method: str = field(default='cls', metadata={\"help\": \"the pooling method, should be cls or mean\"})\n normlized: bool = field(default=True)\n enable_sub_batch: bool = field(default=True, metadata={\"help\": \"Freeze the parameters of position embeddings\"})\n \n unified_finetuning: bool = field(default=False, metadata={\"help\": \"use unify fine-tuning\"})\n use_self_distill: bool = field(default=False, metadata={\"help\": \"use self-distill when using unify fine-tuning\"})\n fix_encoder: bool = field(default=False, metadata={\"help\": \"Freeze the parameters of encoder\"})\n colbert_dim: int = field(default=-1, metadata={\"help\": \"Dim of colbert linear\"})\n self_distill_start_step: int = field(default=-1, metadata={\"help\": \"Num of step when using self-distill\"})\n" }, { "path": "research/BGE_M3/data.py", "content": "import math\nimport os.path\nimport random\nfrom dataclasses import dataclass\nimport torch\nimport numpy as np\nimport datasets\nfrom pprint import pprint\nfrom torch.utils.data import Dataset\nfrom transformers import DataCollatorWithPadding\nimport torch.distributed as dist\n\nfrom .arguments import DataArguments\n\n\nclass SameDatasetTrainDataset(Dataset):\n \"\"\"Dataset to yield a batch of data at one time. All samples in the same batch comes from the same task.\n \"\"\"\n def __init__(self, args: DataArguments, batch_size: int, seed: int, process_index: int=0, num_processes: int=1):\n train_datasets = []\n each_data_inxs = []\n batch_size_inxs = []\n pqloss_flag = []\n cur_all_num = 0\n \n SMALL_THRESHOLD = args.small_threshold\n DROP_THRESHOLD = args.drop_threshold\n \n context_feat = datasets.Features({\n 'query': datasets.Value('string'),\n 'pos': datasets.Sequence(datasets.Value('string')),\n 'neg': datasets.Sequence(datasets.Value('string'))\n })\n context_feat_kd = datasets.Features({\n 'query': datasets.Value('string'),\n 'pos': datasets.Sequence(datasets.Value('string')),\n 'neg': datasets.Sequence(datasets.Value('string')),\n 'pos_scores': datasets.Sequence(datasets.Value('float')),\n 'neg_scores': datasets.Sequence(datasets.Value('float')),\n })\n assert isinstance(args.train_data, list) and len(args.train_data) >= 1\n \n if dist.get_rank() == 0:\n self.print_batch_size(batch_size=batch_size, train_group_size=args.train_group_size)\n \n for data_dir in args.train_data:\n if not os.path.isdir(data_dir):\n raise FileNotFoundError(f\"{data_dir} is a file, not a directionary\")\n \n small_datasets = []\n small_batch_size = math.inf\n \n # Add `parallel_` in `data_dir` to indicate that this dataset is parallel corpus\n flag = 'parallel_' in data_dir\n for file in os.listdir(data_dir):\n if not (file.endswith('.json') or file.endswith('.jsonl')):\n continue\n \n file_path = os.path.join(data_dir, file)\n if dist.get_rank() == 0:\n print(f'loading data from {file_path} ...')\n try:\n temp_dataset = datasets.load_dataset('json', data_files=file_path, split='train', cache_dir=args.cache_path, features=context_feat)\n except:\n temp_dataset = datasets.load_dataset('json', data_files=file_path, split='train', cache_dir=args.cache_path, features=context_feat_kd)\n if not args.knowledge_distillation:\n temp_dataset = temp_dataset.remove_columns(['pos_scores', 'neg_scores'])\n \n if len(temp_dataset) == 0:\n continue\n elif len(temp_dataset) < SMALL_THRESHOLD:\n small_datasets.append(temp_dataset)\n small_batch_size = min(small_batch_size, self.get_file_batch_size(file, batch_size, train_group_size=args.train_group_size))\n else:\n if args.max_example_num_per_dataset is not None and len(temp_dataset) > args.max_example_num_per_dataset:\n temp_dataset = temp_dataset.select(\n random.sample(list(range(len(temp_dataset))), args.max_example_num_per_dataset))\n train_datasets.append(temp_dataset)\n each_data_inxs.append(np.arange(len(temp_dataset)) + cur_all_num)\n cur_all_num += len(temp_dataset)\n batch_size_inxs.append(self.get_file_batch_size(file, batch_size, train_group_size=args.train_group_size))\n pqloss_flag.append(flag)\n \n if len(small_datasets) > 0:\n small_dataset = datasets.concatenate_datasets(small_datasets)\n if len(small_dataset) >= DROP_THRESHOLD:\n train_datasets.append(small_dataset)\n each_data_inxs.append(np.arange(len(small_dataset)) + cur_all_num)\n cur_all_num += len(small_dataset)\n batch_size_inxs.append(small_batch_size)\n pqloss_flag.append(flag)\n \n self.dataset = datasets.concatenate_datasets(train_datasets)\n self.each_data_inxs = each_data_inxs\n self.datasets_inxs = np.arange(len(each_data_inxs))\n self.batch_size_inxs = batch_size_inxs\n self.pqloss_flag = pqloss_flag\n \n self.process_index = process_index\n self.num_processes = num_processes\n self.args = args\n self.shuffle_ratio = args.shuffle_ratio\n \n self.deterministic_generator = np.random.default_rng(seed)\n self.step = 0\n self.refresh_epoch()\n \n def print_batch_size(self, batch_size: int, train_group_size: int):\n length_list = ['0-500', '500-1000', '1000-2000', '2000-3000', '3000-4000', '4000-5000', '5000-6000', '6000-7000', '7000-inf']\n batch_size_dict = {\n k: self.get_file_batch_size(f\"len-{k}.jsonl\", batch_size, train_group_size) for k in length_list\n }\n batch_size_list = [\n f'{length}: {batch_size_dict[length]}' for length in length_list\n ]\n print(\"=========================\")\n print(\"Batch Size Dict:\")\n pprint(batch_size_list)\n print(\"=========================\")\n \n @staticmethod\n def get_file_batch_size(file: str, batch_size: int, train_group_size: int):\n if train_group_size == 8:\n # 80GB\n if 'len-0-500.jsonl' in file:\n return 48\n elif 'len-500-1000.jsonl' in file:\n return 32\n elif 'len-1000-2000.jsonl' in file:\n return 20\n elif 'len-2000-3000.jsonl' in file:\n return 18\n elif 'len-3000-4000.jsonl' in file:\n return 14\n elif 'len-4000-5000.jsonl' in file:\n return 14\n elif 'len-5000-6000.jsonl' in file:\n return 12\n elif 'len-6000-7000.jsonl' in file:\n return 10\n elif 'len-7000-inf.jsonl' in file:\n return 8\n else:\n return batch_size\n elif train_group_size == 1:\n # 80GB\n if 'len-0-500.jsonl' in file:\n return 700\n elif 'len-500-1000.jsonl' in file:\n return 570\n elif 'len-1000-2000.jsonl' in file:\n return 388\n elif 'len-2000-3000.jsonl' in file:\n return 288\n elif 'len-3000-4000.jsonl' in file:\n return 224\n elif 'len-4000-5000.jsonl' in file:\n return 180\n elif 'len-5000-6000.jsonl' in file:\n return 157\n elif 'len-6000-7000.jsonl' in file:\n return 128\n elif 'len-7000-inf.jsonl' in file:\n return 104\n else:\n return batch_size\n else:\n return batch_size\n \n def refresh_epoch(self):\n print(f'---------------------------*Rank {self.process_index}: refresh data---------------------------')\n self.deterministic_generator.shuffle(self.datasets_inxs)\n # Dynamically adjust batch size\n batch_datas = []\n for dataset_inx in self.datasets_inxs:\n self.deterministic_generator.shuffle(self.each_data_inxs[dataset_inx])\n cur_batch_size = self.batch_size_inxs[dataset_inx]*self.num_processes\n flag = self.pqloss_flag[dataset_inx]\n for start_index in range(0, len(self.each_data_inxs[dataset_inx]), cur_batch_size):\n # judge the last batch's length\n if len(self.each_data_inxs[dataset_inx]) - start_index < 2 * self.num_processes:\n break\n batch_datas.append((self.each_data_inxs[dataset_inx][start_index:start_index+cur_batch_size], flag))\n self.deterministic_generator.shuffle(batch_datas)\n self.batch_datas = batch_datas\n self.step = 0\n\n def __getitem__(self, _): \n batch_indices, pqloss_flag = self.batch_datas[self.step]\n cur_batch_size = int(len(batch_indices) / self.num_processes)\n batch_indices = batch_indices[self.process_index * cur_batch_size: (self.process_index + 1) * cur_batch_size]\n batch_data = self.dataset[batch_indices]\n self.step += 1\n queries, passages, teacher_scores = self.create_batch_data(batch_raw_data=batch_data)\n # print('rank, step, flag, query, passage:', dist.get_rank(), self.step, pqloss_flag, queries, passages)\n return queries, passages, teacher_scores, pqloss_flag\n\n def shuffle_text(self, text):\n if self.shuffle_ratio > 0 and len(text) > 100 and random.random() < self.shuffle_ratio:\n split_text = []\n chunk_size = len(text)//3 + 1\n for i in range(0, len(text), chunk_size):\n split_text.append(text[i:i+chunk_size])\n random.shuffle(split_text)\n return \" \".join(split_text)\n else:\n return text\n\n def create_batch_data(self, batch_raw_data):\n queries, passages = [], []\n teacher_scores = []\n for i in range(len(batch_raw_data['query'])): \n queries.append(batch_raw_data['query'][i])\n \n pos_inx = random.choice(list(range(len(batch_raw_data['pos'][i]))))\n passages.append(self.shuffle_text(batch_raw_data['pos'][i][pos_inx]))\n if 'pos_scores' in batch_raw_data and batch_raw_data['pos_scores'][i] is not None:\n teacher_scores.append(batch_raw_data['pos_scores'][i][pos_inx])\n \n neg_inx_set = list(range(len(batch_raw_data['neg'][i])))\n if len(batch_raw_data['neg'][i]) < self.args.train_group_size - 1:\n num = math.ceil((self.args.train_group_size - 1) / len(batch_raw_data['neg'][i]))\n neg_inxs = random.sample(neg_inx_set * num, self.args.train_group_size - 1)\n else:\n neg_inxs = random.sample(neg_inx_set, self.args.train_group_size - 1) \n \n if 'neg_scores' in batch_raw_data and batch_raw_data['neg_scores'][i] is not None:\n neg_scores = [(x, batch_raw_data['neg_scores'][i][x]) for x in neg_inxs]\n neg_scores = sorted(neg_scores, key=lambda x:x[1], reverse=True)\n neg_inxs = [x[0] for x in neg_scores]\n teacher_scores.extend([x[1] for x in neg_scores])\n \n negs = [batch_raw_data['neg'][i][x] for x in neg_inxs]\n passages.extend(negs)\n \n if len(teacher_scores) > 0 and len(passages) > 0:\n assert len(teacher_scores) == len(passages)\n\n if self.args.query_instruction_for_retrieval is not None:\n queries = [self.args.query_instruction_for_retrieval+q for q in queries]\n if self.args.passage_instruction_for_retrieval is not None:\n passages = [self.args.passage_instruction_for_retrieval+p for p in passages]\n \n if len(teacher_scores) == 0:\n teacher_scores = None\n return queries, passages, teacher_scores\n \n def __len__(self):\n return len(self.batch_datas) * self.num_processes\n\n\n@dataclass\nclass EmbedCollator(DataCollatorWithPadding):\n \"\"\"\n Wrapper that does conversion from List[Tuple[encode_qry, encode_psg]] to List[qry], List[psg]\n and pass batch separately to the actual collator.\n Abstract out data detail for the model.\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n\n def __call__(self, features):\n query = [f[0] for f in features]\n passage = [f[1] for f in features]\n \n teacher_scores = None\n if len(features[0]) > 2:\n teacher_scores = [f[2] for f in features]\n if teacher_scores[0] is None:\n teacher_scores = None\n else:\n teacher_scores = torch.FloatTensor(teacher_scores)\n \n flag = None\n if len(features[0]) == 4:\n flag = [f[3] for f in features][0]\n \n if isinstance(query[0], list):\n query = sum(query, [])\n if isinstance(passage[0], list):\n passage = sum(passage, [])\n\n q_collated = self.tokenizer(\n query,\n # padding='max_length', # used for adjusting the batch size in `get_file_batch_size()`\n padding=True,\n truncation=True,\n max_length=self.query_max_len,\n return_tensors=\"pt\",\n )\n d_collated = self.tokenizer(\n passage,\n # padding='max_length', # used for adjusting the batch size in `get_file_batch_size()`\n padding=True,\n truncation=True,\n max_length=self.passage_max_len,\n return_tensors=\"pt\",\n )\n if teacher_scores is not None:\n teacher_scores = teacher_scores.reshape((len(q_collated['input_ids']), -1))\n return {\"query\": q_collated, \"passage\": d_collated, \"teacher_scores\": teacher_scores, \"bi_directions\": flag}\n" }, { "path": "research/BGE_M3/modeling.py", "content": "import logging\nfrom dataclasses import dataclass\nfrom typing import Dict, Optional\nimport os\n\nimport torch\nimport torch.distributed as dist\nfrom torch import nn, Tensor\nimport torch.nn.functional as F\nfrom transformers import AutoModel, AutoTokenizer\nfrom transformers.file_utils import ModelOutput\nfrom huggingface_hub import snapshot_download\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass EncoderOutput(ModelOutput):\n q_reps: Optional[Tensor] = None\n p_reps: Optional[Tensor] = None\n loss: Optional[Tensor] = None\n scores: Optional[Tensor] = None\n\n\nclass BGEM3Model(nn.Module):\n\n def __init__(self,\n model_name: str = None,\n normlized: bool = True,\n sentence_pooling_method: str = 'cls',\n negatives_cross_device: bool = False,\n temperature: float = 1.0,\n enable_sub_batch: bool = True,\n unified_finetuning: bool = True,\n use_self_distill: bool = False,\n colbert_dim: int = -1,\n self_distill_start_step: int = -1,\n ):\n super().__init__()\n self.load_model(model_name, colbert_dim=colbert_dim)\n self.vocab_size = self.model.config.vocab_size\n self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')\n\n self.unified_finetuning = unified_finetuning\n if not self.unified_finetuning:\n self.colbert_linear = None\n self.sparse_linear = None\n\n self.normlized = normlized\n self.sentence_pooling_method = sentence_pooling_method\n self.enable_sub_batch = enable_sub_batch\n self.temperature = temperature\n self.use_self_distill = use_self_distill\n self.self_distill_start_step = self_distill_start_step\n\n self.step = 0\n if not normlized:\n self.temperature = 1.0\n logger.info(\"reset temperature = 1.0 due to using inner product to compute similarity\")\n\n self.negatives_cross_device = negatives_cross_device\n if self.negatives_cross_device:\n if not dist.is_initialized():\n raise ValueError('Distributed training has not been initialized for representation all gather.')\n\n self.process_rank = dist.get_rank()\n self.world_size = dist.get_world_size()\n\n def load_model(self, model_name, colbert_dim: int = -1):\n if not os.path.exists(model_name):\n cache_folder = os.getenv('HF_HUB_CACHE')\n model_name = snapshot_download(repo_id=model_name,\n cache_dir=cache_folder,\n ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5'])\n\n self.model = AutoModel.from_pretrained(model_name)\n self.tokenizer = AutoTokenizer.from_pretrained(model_name)\n\n self.colbert_linear = torch.nn.Linear(in_features=self.model.config.hidden_size,\n out_features=self.model.config.hidden_size if colbert_dim == -1 else colbert_dim)\n self.sparse_linear = torch.nn.Linear(in_features=self.model.config.hidden_size, out_features=1)\n\n if os.path.exists(os.path.join(model_name, 'colbert_linear.pt')) and os.path.exists(\n os.path.join(model_name, 'sparse_linear.pt')):\n logger.info('loading existing colbert_linear and sparse_linear---------')\n self.load_pooler(model_dir=model_name)\n else:\n logger.info(\n 'The parameters of colbert_linear and sparse linear is new initialize. Make sure the model is loaded for training, not inferencing')\n\n def gradient_checkpointing_enable(self, **kwargs):\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def dense_embedding(self, hidden_state, mask):\n if self.sentence_pooling_method == 'cls':\n return hidden_state[:, 0]\n elif self.sentence_pooling_method == 'mean':\n s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1)\n d = mask.sum(axis=1, keepdim=True).float()\n return s / d\n\n def sparse_embedding(self, hidden_state, input_ids, return_embedding: bool = True):\n token_weights = torch.relu(self.sparse_linear(hidden_state))\n if not return_embedding: return token_weights\n\n if self.training:\n sparse_embedding = torch.zeros(\n input_ids.size(0), input_ids.size(1), self.vocab_size,\n dtype=token_weights.dtype,\n device=token_weights.device\n )\n sparse_embedding = torch.scatter(sparse_embedding, dim=-1, index=input_ids.unsqueeze(-1), src=token_weights)\n sparse_embedding = torch.max(sparse_embedding, dim=1).values\n else:\n # Optimize suggestion from issue #1364: https://github.com/FlagOpen/FlagEmbedding/issues/1364\n # Disable when self.training = True, otherwise will cause:\n # RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation\n sparse_embedding = torch.zeros(\n input_ids.size(0), self.vocab_size,\n dtype=token_weights.dtype,\n device=token_weights.device\n )\n sparse_embedding = sparse_embedding.scatter_reduce(\n dim=-1, index=input_ids, src=token_weights.squeeze(-1), reduce=\"amax\"\n )\n\n unused_tokens = [self.tokenizer.cls_token_id, self.tokenizer.eos_token_id, self.tokenizer.pad_token_id,\n self.tokenizer.unk_token_id]\n sparse_embedding[:, unused_tokens] *= 0.\n return sparse_embedding\n\n def colbert_embedding(self, last_hidden_state, mask):\n colbert_vecs = self.colbert_linear(last_hidden_state[:, 1:])\n colbert_vecs = colbert_vecs * mask[:, 1:][:, :, None].float()\n return colbert_vecs\n\n def dense_score(self, q_reps, p_reps):\n scores = self.compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def sparse_score(self, q_reps, p_reps):\n scores = self.compute_similarity(q_reps, p_reps) / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n return scores\n\n def colbert_score(self, q_reps, p_reps, q_mask: torch.Tensor):\n token_scores = torch.einsum('qin,pjn->qipj', q_reps, p_reps)\n scores, _ = token_scores.max(-1)\n scores = scores.sum(1) / q_mask[:, 1:].sum(-1, keepdim=True)\n scores = scores / self.temperature\n return scores\n\n def _encode(self, features):\n dense_vecs, sparse_vecs, colbert_vecs = None, None, None\n last_hidden_state = self.model(**features, return_dict=True).last_hidden_state\n dense_vecs = self.dense_embedding(last_hidden_state, features['attention_mask'])\n if self.unified_finetuning:\n sparse_vecs = self.sparse_embedding(last_hidden_state, features['input_ids'])\n colbert_vecs = self.colbert_embedding(last_hidden_state, features['attention_mask'])\n if self.normlized:\n dense_vecs = torch.nn.functional.normalize(dense_vecs, dim=-1)\n if self.unified_finetuning:\n colbert_vecs = torch.nn.functional.normalize(colbert_vecs, dim=-1)\n return dense_vecs, sparse_vecs, colbert_vecs\n\n def encode(self, features, sub_batch_size=None):\n if features is None:\n return None\n\n if sub_batch_size is not None and sub_batch_size != -1:\n all_dense_vecs, all_sparse_vecs, all_colbert_vecs = [], [], []\n for i in range(0, len(features['attention_mask']), sub_batch_size):\n end_inx = min(i + sub_batch_size, len(features['attention_mask']))\n sub_features = {}\n for k, v in features.items():\n sub_features[k] = v[i:end_inx]\n\n dense_vecs, sparse_vecs, colbert_vecs = self._encode(sub_features)\n all_dense_vecs.append(dense_vecs)\n all_sparse_vecs.append(sparse_vecs)\n all_colbert_vecs.append(colbert_vecs)\n\n dense_vecs = torch.cat(all_dense_vecs, 0)\n if self.unified_finetuning:\n sparse_vecs = torch.cat(all_sparse_vecs, 0)\n colbert_vecs = torch.cat(all_colbert_vecs, 0)\n else:\n dense_vecs, sparse_vecs, colbert_vecs = self._encode(features)\n\n if self.unified_finetuning:\n return dense_vecs.contiguous(), sparse_vecs.contiguous(), colbert_vecs.contiguous()\n else:\n return dense_vecs.contiguous(), None, None\n\n def compute_sub_batch_size(self, features):\n mapping = [(6000, 1), (5000, 2), (4000, 3), (3000, 3), (2000, 5), (1000, 9), (512, 16), (0, 32)]\n cur_l = features['input_ids'].size(-1)\n for l, b in mapping:\n if cur_l >= l:\n return b\n\n def compute_similarity(self, q_reps, p_reps):\n if len(p_reps.size()) == 2:\n return torch.matmul(q_reps, p_reps.transpose(0, 1))\n return torch.matmul(q_reps, p_reps.transpose(-2, -1))\n\n def distill_loss(self, teacher_targets, student_scores, group_size):\n labels = torch.arange(student_scores.size(0), device=student_scores.device, dtype=torch.long)\n labels = labels * group_size\n\n loss = 0\n mask = torch.zeros_like(student_scores)\n for i in range(group_size):\n temp_target = labels + i\n temp_scores = student_scores + mask\n temp_loss = F.cross_entropy(temp_scores, temp_target, reduction=\"none\") # B\n loss += torch.mean(teacher_targets[:, i] * temp_loss)\n mask = torch.scatter(mask, dim=-1, index=temp_target.unsqueeze(-1),\n value=torch.finfo(student_scores.dtype).min)\n return loss\n\n def forward(self, query: Dict[str, Tensor] = None, passage: Dict[str, Tensor] = None, teacher_scores: Tensor = None,\n bi_directions=None):\n if self.enable_sub_batch:\n q_dense_vecs, q_sparse_vecs, q_colbert_vecs = self.encode(query,\n sub_batch_size=self.compute_sub_batch_size(query))\n p_dense_vecs, p_sparse_vecs, p_colbert_vecs = self.encode(passage,\n sub_batch_size=self.compute_sub_batch_size(\n passage))\n else:\n q_dense_vecs, q_sparse_vecs, q_colbert_vecs = self.encode(query)\n p_dense_vecs, p_sparse_vecs, p_colbert_vecs = self.encode(passage)\n\n if self.training:\n if teacher_scores is not None:\n # print(\"Use soft-label distillation...\")\n teacher_targets = F.softmax(teacher_scores, dim=-1) # B N\n group_size = p_dense_vecs.size(0) // q_dense_vecs.size(0)\n\n # dense loss\n dense_scores = self.dense_score(q_dense_vecs, p_dense_vecs) # B, B * N\n if self.negatives_cross_device:\n cross_q_dense_vecs = self._dist_gather_tensor(q_dense_vecs)\n cross_p_dense_vecs = self._dist_gather_tensor(p_dense_vecs)\n cross_teacher_targets = self._dist_gather_tensor(teacher_targets)\n cross_dense_scores = self.dense_score(cross_q_dense_vecs, cross_p_dense_vecs)\n\n loss = self.distill_loss(cross_teacher_targets, cross_dense_scores, group_size=group_size)\n else:\n loss = self.distill_loss(teacher_targets, dense_scores, group_size=group_size)\n\n if self.unified_finetuning:\n # sparse and colbert loss\n sparse_scores = self.sparse_score(q_sparse_vecs, p_sparse_vecs) # B, B * N\n sparse_loss = self.distill_loss(teacher_targets, sparse_scores, group_size=group_size)\n\n colbert_scores = self.colbert_score(q_colbert_vecs, p_colbert_vecs,\n q_mask=query['attention_mask']) # B, B * N\n colbert_loss = self.distill_loss(teacher_targets, colbert_scores, group_size=group_size)\n\n ensemble_loss = self.distill_loss(teacher_targets,\n dense_scores + 0.3 * sparse_scores + colbert_scores,\n group_size=group_size)\n loss = (loss + ensemble_loss + 0.1 * sparse_loss + colbert_loss) / 4\n\n\n else:\n idxs = torch.arange(q_dense_vecs.size(0), device=q_dense_vecs.device, dtype=torch.long)\n targets = idxs * (p_dense_vecs.size(0) // q_dense_vecs.size(0))\n\n # dense loss\n dense_scores = self.dense_score(q_dense_vecs, p_dense_vecs) # B, B * N\n if self.negatives_cross_device:\n cross_q_dense_vecs = self._dist_gather_tensor(q_dense_vecs)\n cross_p_dense_vecs = self._dist_gather_tensor(p_dense_vecs)\n\n cross_idxs = torch.arange(cross_q_dense_vecs.size(0), device=cross_q_dense_vecs.device, dtype=torch.long)\n\n cross_targets = cross_idxs * (cross_p_dense_vecs.size(0) // cross_q_dense_vecs.size(0))\n cross_dense_scores = self.dense_score(cross_q_dense_vecs, cross_p_dense_vecs)\n\n loss = self.compute_loss(cross_dense_scores, cross_targets)\n else:\n loss = self.compute_loss(dense_scores, targets)\n\n if self.unified_finetuning:\n # sparse and colbert loss\n sparse_scores = self.sparse_score(q_sparse_vecs, p_sparse_vecs) # B, B * N\n sparse_loss = self.compute_loss(sparse_scores, targets)\n\n colbert_scores = self.colbert_score(q_colbert_vecs, p_colbert_vecs,\n q_mask=query['attention_mask']) # B, B * N\n colbert_loss = self.compute_loss(colbert_scores, targets)\n\n ensemble_loss = self.compute_loss(dense_scores + 0.3 * sparse_scores + colbert_scores, targets)\n loss = (loss + ensemble_loss + 0.1 * sparse_loss + colbert_loss) / 4\n\n if self.use_self_distill and self.step > self.self_distill_start_step and self.unified_finetuning:\n ensemble_scores = dense_scores + 0.3 * sparse_scores + colbert_scores\n teacher_targets = torch.softmax(ensemble_scores.detach(), dim=-1)\n ensemble_distill_dense_loss = - torch.mean(\n torch.sum(torch.log_softmax(dense_scores, dim=-1) * teacher_targets, dim=-1))\n ensemble_distill_sparse_loss = - torch.mean(\n torch.sum(torch.log_softmax(sparse_scores, dim=-1) * teacher_targets, dim=-1))\n ensemble_distill_colbert_loss = - torch.mean(\n torch.sum(torch.log_softmax(colbert_scores, dim=-1) * teacher_targets, dim=-1))\n loss += (ensemble_distill_dense_loss + 0.1 * ensemble_distill_sparse_loss + ensemble_distill_colbert_loss) / 3\n loss = loss / 2\n self.step += 1\n else:\n loss = None\n return EncoderOutput(\n loss=loss,\n )\n\n def compute_loss(self, scores, target):\n return self.cross_entropy(scores, target)\n\n def _dist_gather_tensor(self, t: Optional[torch.Tensor]):\n if t is None:\n return None\n t = t.contiguous()\n\n all_tensors = [torch.empty_like(t) for _ in range(self.world_size)]\n dist.all_gather(all_tensors, t)\n\n all_tensors[self.process_rank] = t\n all_tensors = torch.cat(all_tensors, dim=0)\n\n return all_tensors\n\n def save(self, output_dir: str):\n def _trans_state_dict(state_dict):\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n return state_dict\n\n self.model.save_pretrained(output_dir, state_dict=_trans_state_dict(self.model.state_dict()))\n\n if self.unified_finetuning:\n torch.save(_trans_state_dict(self.colbert_linear.state_dict()),\n os.path.join(output_dir, 'colbert_linear.pt'))\n torch.save(_trans_state_dict(self.sparse_linear.state_dict()),\n os.path.join(output_dir, 'sparse_linear.pt'))\n\n def load_pooler(self, model_dir):\n colbert_state_dict = torch.load(os.path.join(model_dir, 'colbert_linear.pt'), map_location='cpu')\n sparse_state_dict = torch.load(os.path.join(model_dir, 'sparse_linear.pt'), map_location='cpu')\n self.colbert_linear.load_state_dict(colbert_state_dict)\n self.sparse_linear.load_state_dict(sparse_state_dict)\n\n\nclass BGEM3ForInference(BGEM3Model):\n\n def forward(self,\n text_input: Dict[str, Tensor] = None,\n return_dense: bool = True,\n return_sparse: bool = False,\n return_colbert: bool = False,\n return_sparse_embedding: bool = False):\n assert return_dense or return_sparse or return_colbert, 'Must choose one or more from `return_colbert`, `return_sparse`, `return_dense` to set `True`!'\n\n # this is for sparse embedding computation: using optimization suggestion from \n # issue #1364: https://github.com/FlagOpen/FlagEmbedding/issues/1364\n self.training = False\n\n last_hidden_state = self.model(**text_input, return_dict=True).last_hidden_state\n\n output = {}\n if return_dense:\n dense_vecs = self.dense_embedding(last_hidden_state, text_input['attention_mask'])\n output['dense_vecs'] = dense_vecs\n if return_sparse:\n sparse_vecs = self.sparse_embedding(last_hidden_state, text_input['input_ids'],\n return_embedding=return_sparse_embedding)\n output['sparse_vecs'] = sparse_vecs\n if return_colbert:\n colbert_vecs = self.colbert_embedding(last_hidden_state, text_input['attention_mask'])\n output['colbert_vecs'] = colbert_vecs\n\n if self.normlized:\n if 'dense_vecs' in output:\n output['dense_vecs'] = torch.nn.functional.normalize(output['dense_vecs'], dim=-1)\n if 'colbert_vecs' in output:\n output['colbert_vecs'] = torch.nn.functional.normalize(output['colbert_vecs'], dim=-1)\n\n return output\n" }, { "path": "research/BGE_M3/run.py", "content": "import logging\nimport os\nfrom pathlib import Path\nimport torch.distributed as dist\n\nfrom transformers import AutoConfig, AutoTokenizer\nfrom transformers import (\n HfArgumentParser,\n set_seed,\n)\nfrom transformers import (\n TrainerCallback,\n TrainingArguments,\n TrainerState,\n TrainerControl\n)\n\nfrom .arguments import ModelArguments, DataArguments, \\\n RetrieverTrainingArguments as TrainingArguments\nfrom .data import SameDatasetTrainDataset, EmbedCollator\nfrom .modeling import BGEM3Model\nfrom .trainer import BiTrainer\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass TrainerCallbackForDataRefresh(TrainerCallback):\n def __init__(self, train_dataset):\n self.train_dataset = train_dataset\n \n def on_epoch_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):\n \"\"\"\n Event called at the end of an epoch.\n \"\"\"\n self.train_dataset.refresh_epoch()\n \n\ndef main():\n parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: ModelArguments\n data_args: DataArguments\n training_args: TrainingArguments\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n logger.info(\"Training/evaluation parameters %s\", training_args)\n logger.info(\"Model parameters %s\", model_args)\n logger.info(\"Data parameters %s\", data_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n num_labels = 1\n tokenizer = AutoTokenizer.from_pretrained(\n model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n cache_dir=model_args.cache_dir,\n use_fast=False,\n )\n config = AutoConfig.from_pretrained(\n model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=model_args.cache_dir,\n )\n logger.info('Config: %s', config)\n\n model = BGEM3Model(model_name=model_args.model_name_or_path,\n normlized=training_args.normlized,\n sentence_pooling_method=training_args.sentence_pooling_method,\n negatives_cross_device=training_args.negatives_cross_device,\n temperature=training_args.temperature,\n enable_sub_batch=training_args.enable_sub_batch,\n unified_finetuning=training_args.unified_finetuning,\n use_self_distill=training_args.use_self_distill,\n colbert_dim=training_args.colbert_dim,\n self_distill_start_step=training_args.self_distill_start_step)\n\n if training_args.fix_position_embedding:\n for k, v in model.named_parameters():\n if \"position_embeddings\" in k:\n logging.info(f\"Freeze the parameters for {k}\")\n v.requires_grad = False\n if training_args.fix_encoder:\n for k, v in model.named_parameters():\n if \"colbert_linear\" in k or 'sparse_linear' in k:\n logging.info(f\"train the parameters for {k}\")\n else:\n v.requires_grad = False\n\n # print(f\"===========================Rank {dist.get_rank()}: start loading data===========================\")\n if data_args.same_task_within_batch:\n train_dataset = SameDatasetTrainDataset(args=data_args, \n batch_size=training_args.per_device_train_batch_size, \n seed=training_args.seed, \n num_processes=training_args.world_size,\n process_index=training_args.process_index)\n training_args.per_device_train_batch_size = 1\n training_args.dataloader_num_workers = 0 # avoid multi-processes\n else:\n raise NotImplementedError(\"Not support `same_task_within_batch=False`\")\n\n data_collator = EmbedCollator(\n tokenizer,\n query_max_len=data_args.query_max_len,\n passage_max_len=data_args.passage_max_len\n )\n \n trainer = BiTrainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n data_collator=data_collator,\n tokenizer=tokenizer\n )\n\n if data_args.same_task_within_batch:\n trainer.add_callback(TrainerCallbackForDataRefresh(train_dataset))\n \n Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n # print(f\"===========================Rank {dist.get_rank()}: start training===========================\")\n trainer.train()\n trainer.save_model()\n # For convenience, we also re-save the tokenizer to the same directory,\n # so that you can share your model easily on huggingface.co/models =)\n if trainer.is_world_process_zero():\n tokenizer.save_pretrained(training_args.output_dir)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/BGE_M3/split_data_by_length.py", "content": "\"\"\"\npython split_data_by_length.py \\\n--input_path train_data \\\n--output_dir train_data_split \\\n--cache_dir .cache \\\n--log_name .split_log \\\n--length_list 0 500 1000 2000 3000 4000 5000 6000 7000 \\\n--model_name_or_path BAAI/bge-m3 \\\n--num_proc 16 \\\n--overwrite False\n\"\"\"\nimport os\nimport json\nimport math\nimport time\nimport argparse\nimport datasets\nfrom tqdm import tqdm\nfrom pprint import pprint\nfrom transformers import AutoTokenizer\nfrom datasets import load_dataset, Features, Value, Sequence\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--input_path', type=str, required=True, help='the path of input datas')\n parser.add_argument('--output_dir', type=str, required=True, help='the dir of output datas')\n parser.add_argument('--cache_dir', type=str, default=None, help='the cache dir')\n parser.add_argument('--log_name', type=str, default='.split_log', help='the name of log file, default: `.split_log`, which will be saved to `output_dir`')\n parser.add_argument('--length_list', type=int, default=[0, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000], nargs='+', help='the length list to split')\n parser.add_argument('--model_name_or_path', type=str, default='BAAI/bge-m3', help='the model name or path of the tokenizer')\n parser.add_argument('--num_proc', type=int, default=16, help='the number of process, default: 16')\n parser.add_argument('--overwrite', action='store_true', default=False, help='whether to overwrite the output file, default: False')\n args = parser.parse_args()\n return args\n\n\nclass SplitByLengthHandler:\n def __init__(self,\n model_name_or_path: str,\n cache_dir: str=None,\n num_proc: int=16,\n length_list: list=[0, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000],\n overwrite: bool=False):\n self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n self.cache_dir = cache_dir\n self.num_proc = num_proc\n self.length_ranges_list = self._get_length_ranges_list(length_list)\n self.overwrite = overwrite\n\n pprint(self.length_ranges_list)\n\n def _map_func(examples):\n results = {}\n results['idx'] = []\n results['max_length'] = []\n for i in range(len(examples['query'])):\n idx = examples['idx'][i]\n query = examples['query'][i]\n pos, neg = examples['pos'][i], examples['neg'][i]\n all_texts = [query] + pos + neg\n\n max_len = 0\n for x in all_texts:\n tokenized_x = self.tokenizer(x)['input_ids']\n if len(tokenized_x) > max_len:\n max_len = len(tokenized_x)\n \n results['idx'].append(idx)\n results['max_length'].append(max_len)\n return results\n\n self._map_func = _map_func\n\n @staticmethod\n def _get_length_ranges_list(length_list: list):\n length_ranges_list = []\n length_list = sorted(length_list)\n for i in range(len(length_list)):\n length_l = length_list[i]\n if i == len(length_list) - 1:\n length_r = math.inf\n else:\n length_r = length_list[i + 1]\n assert 0 <= length_l < length_r\n length_ranges_list.append((length_l, length_r))\n\n return length_ranges_list\n\n def _process_dir(self, dir_path: str, output_dir: str):\n assert os.path.isdir(dir_path)\n log_info_list = []\n for file in tqdm(os.listdir(dir_path), desc=f'processing {dir_path}'):\n file_path = os.path.join(dir_path, file)\n if not file_path.endswith('.jsonl'):\n print(f\"skip {file_path} ...\")\n continue\n\n output_path = os.path.join(output_dir, '.'.join(file.split('.')[:-1]))\n log_info = self._process_file(file_path, output_path)\n log_info_list.append(log_info)\n return log_info_list\n\n def _process_file(self, file_path: str, output_path: str):\n assert not os.path.isdir(file_path)\n\n start_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n\n features = Features({\n 'query': Value('string'),\n 'pos': Sequence(Value('string')),\n 'neg': Sequence(Value('string'))\n })\n kd_features = Features({\n 'query': Value('string'),\n 'pos': Sequence(Value('string')),\n 'neg': Sequence(Value('string')),\n 'pos_scores': Sequence(Value('float')),\n 'neg_scores': Sequence(Value('float'))\n })\n try:\n dataset = load_dataset('json', data_files=file_path, cache_dir=self.cache_dir, features=features)['train']\n except:\n dataset = load_dataset('json', data_files=file_path, cache_dir=self.cache_dir, features=kd_features)['train']\n\n dataset_with_idx_list = []\n for i, data in enumerate(dataset):\n data['idx'] = i\n dataset_with_idx_list.append(data)\n dataset_with_idx = datasets.Dataset.from_list(dataset_with_idx_list)\n \n mapped_dataset = dataset_with_idx.map(self._map_func, batched=True, num_proc=self.num_proc)\n \n split_info_dict = {}\n for length_l, length_r in self.length_ranges_list:\n save_path = output_path + f'_len-{length_l}-{length_r}.jsonl'\n if os.path.exists(save_path) and not self.overwrite:\n print(f'{save_path} exists, skip')\n continue\n\n idxs = mapped_dataset.filter(lambda x: length_l <= x['max_length'] < length_r, num_proc=self.num_proc)\n split_dataset = dataset_with_idx.select(idxs['idx'])\n split_dataset = split_dataset.remove_columns('idx')\n\n split_info_dict[f'len-{length_l}-{length_r}'] = len(split_dataset)\n\n if len(split_dataset) > 0:\n split_dataset.to_json(save_path, force_ascii=False)\n\n end_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n\n size = len(dataset)\n avg_length = sum(mapped_dataset['max_length']) / size\n log_info = {\n 'file_name': os.path.basename(file_path),\n 'size': size,\n 'avg_length': avg_length,\n 'file_path': file_path,\n 'start_time': start_time,\n 'end_time': end_time,\n 'split_info': split_info_dict\n }\n return log_info\n\n def run(self, input_path: str, output_dir: str, log_name: str=None):\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n if log_name is None:\n log_path = os.path.join(output_dir, '.split_log')\n else:\n log_path = os.path.join(output_dir, log_name)\n\n log_info_list = []\n\n if os.path.isdir(input_path):\n log_info_list = self._process_dir(input_path, output_dir)\n else:\n file_name = os.path.basename(input_path)\n output_path = os.path.join(output_dir, '.'.join(file_name.split('.')[:-1]))\n log_info = self._process_file(input_path, output_path)\n log_info_list.append(log_info)\n\n with open(log_path, 'a', encoding='utf-8') as f:\n for log_info in log_info_list:\n json.dump(log_info, f, ensure_ascii=False)\n f.write('\\n')\n\n\nif __name__ == '__main__':\n args = get_args()\n input_path = args.input_path\n output_dir = args.output_dir\n log_name = args.log_name\n\n handler = SplitByLengthHandler(\n model_name_or_path=args.model_name_or_path,\n cache_dir=args.cache_dir,\n num_proc=args.num_proc,\n length_list=args.length_list if isinstance(args.length_list, list) else [args.length_list],\n overwrite=args.overwrite\n )\n\n handler.run(\n input_path=input_path,\n output_dir=output_dir,\n log_name=log_name\n )\n print('\\nDONE!')\n" }, { "path": "research/BGE_M3/trainer.py", "content": "from sentence_transformers import SentenceTransformer, models\nfrom transformers.trainer import *\n\n\ndef save_ckpt_for_sentence_transformers(ckpt_dir, pooling_mode: str = 'cls', normlized: bool=True):\n word_embedding_model = models.Transformer(ckpt_dir)\n pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode=pooling_mode)\n if normlized:\n normlize_layer = models.Normalize()\n model = SentenceTransformer(modules=[word_embedding_model, pooling_model, normlize_layer], device='cpu')\n else:\n model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device='cpu')\n model.save(ckpt_dir)\n\n\nclass BiTrainer(Trainer):\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n if self.tokenizer is not None and self.is_world_process_zero():\n self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # save the checkpoint for sentence-transformers library\n if self.is_world_process_zero():\n save_ckpt_for_sentence_transformers(output_dir,\n pooling_mode=self.args.sentence_pooling_method,\n normlized=self.args.normlized)\n\n\n def compute_loss(self, model, inputs, return_outputs=False):\n \"\"\"\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n \"\"\"\n\n outputs = model(**inputs)\n loss = outputs.loss\n\n return (loss, outputs) if return_outputs else loss\n" }, { "path": "research/BGE_Reasoner/README.md", "content": "
\n

BGE-Reasoner: Towards End-to-End Reasoning-Intensive Information Retrieval

\n
\n\n## Introduction\n\nWe introduce **BGE-Reasoner**, an end-to-end reasoning-intensive information retrieval framework. BGE-Reasoner is characterized by three key features:\n\n1. **End-to-end**: It comprises three core components in IR—**BGE-Reasoner-Rewriter**, **BGE-Reasoner-Embed**, and **BGE-Reasoner-Reranker**—covering the entire retrieval pipeline, from query rewriting and retrieval to reranking for reasoning-intensive tasks.\n2. **Excellent performance**: **BGE-Reasoner** achieves **state-of-the-art (SOTA)** performance on [BRIGHT](https://brightbenchmark.github.io/), a reasoning-intensive information retrieval benchmark, with an **nDCG@10 of 45.2** across 12 datasets (released on Aug 21, 2025), outperforming the previous SOTA by +3.6 points (41.6 from [DIVER](https://arxiv.org/pdf/2508.07995), Aug 12, 2025).\n3. **Open-source resources**: We will release the code, model checkpoints, training data, and evaluation scripts to facilitate future research on reasoning-intensive information retrieval. Please stay tuned!\n\n\n## Open-source resources\n\n| Resource Type | Name | Link | Release Date | Comments |\n| ------------------ | --------------------- | ----------- | ------------------ | ------------------ |\n| Model | BGE-Reasoner-Rewriter | [🤗]() (TBA) | - | |\n| Model | BGE-Reasoner-Reranker | [🤗]() (TBA) | - | |\n| Model | BGE-Reasoner-Embed-Qwen3-8B-0923 | [🤗](https://huggingface.co/BAAI/bge-reasoner-embed-qwen3-8b-0923) | Sep 23, 2025 | nDCG@10 = 37.1 using original query, fine-tuned on [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) with our latest refined training data (data to be released) |\n| Search Results | BGE-Reasoner-Embed-Qwen3-8B-0923 Search Results | [🤗](https://huggingface.co/BAAI/bge-reasoner-embed-qwen3-8b-0923/tree/main/search_results) | Sep 23, 2025 | nDCG@10 = 37.1 using original query |\n| Search Results | BGE-Reasoner-Embed-0821 Search Results | [🤗](https://huggingface.co/datasets/hanhainebula/bright-search-results_bge-reasoner-embed-0821/tree/main) | Sep 4, 2025 | nDCG@10 = 32.5 using original query, submission to BRIGHT leaderboard on Aug 21, 2025 |\n| Training Data | BGE-Reasoner-Data | [🤗](https://huggingface.co/datasets/hanhainebula/bge-reasoner-data/tree/main/bge-reasoner-data-0904) | Sep 4, 2025 | part of our training data; full data to be released in the future |\n| Evaluation Scripts | - | (TBA) | - | |\n\n\n\n## Performance\n\n**BGE-Reasoner** achieves SOTA performance on the **BRIGHT** benchmark with the following pipeline:\n\n![BGE-Reasoner-full-pipeline](./imgs/BGE-Reasoner-full-pipeline.png)\n\n\n1. **Query Rewrite**: **BGE-Reasoner-Rewriter** generates 5 rewritten queries for each original query; all 5 rewrites are used for retrieval.\n2. **Retrieval**: For each rewritten query, **BGE-Reasoner-Embed** and **BM25** retrieve the top-2000 documents. We aggregate results across the 5 rewrites by summing the corresponding scores to produce a final score per method.\n3. **Reranking**:\n - We rerank the top-100 documents from each retrieval method using **BGE-Reasoner-Reranker** (models: 8B, 14B, 32B), producing 6 reranked top-10 lists (2 retrieval methods × 3 reranker sizes).\n - We also create a hybrid top-10 by fusing **BGE-Reasoner-Embed** and **BM25** (weights: 0.75 / 0.25 after min–max normalization).\n - Finally, we combine the 7 top-10 lists (6 reranked + 1 hybrid) to produce the final top-10.\n\n\n### Full Pipeline Results\n\n![BNGE-Reasoner Full Pipeline Results](./imgs/full-pipeline_results.png)\n\nNote:\n- \"**Avg - ALL**\" refers to the average performance across **all 12 datasets** in the BRIGHT benchmark.\n- \"**Avg - SE**\" refers to the average performance across the **7 datasets in the StackExchange subset** of the BRIGHT benchmark.\n- \"**Avg - CD**\" refers to the average performance across the **2 datasets in the Coding subset** of the BRIGHT benchmark.\n- \"**Avg - MT**\" refers to the average performance across the **3 datasets in the Theorem-based subset** of the BRIGHT benchmark.\n\n> Sources of results:\n>\n> [1] https://arxiv.org/pdf/2504.20595\n>\n> [2] https://github.com/Debrup-61/RaDeR\n>\n> [3] https://huggingface.co/ielabgroup/Rank-R1-32B-v0.2\n>\n> [4] https://github.com/jataware/XRR2\n>\n> [5] http://arxiv.org/pdf/2508.07050\n>\n> [6] https://arxiv.org/pdf/2508.07995\n\n\n### Embedder & Rewriter Results\n\n\n#### BGE-Reasoner-Embed-Qwen3-8B-0923\n\n**BGE-Reasoner-Embed-Qwen3-8B-0923**, fine-tuned on [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) with our latest refined training data (data to be released), achieves strong performance on the BRIGHT benchmark:\n\n- With original queries, it attains **nDCG@10 = 37.1**, an absolute improvement of **+8.2** over the previous best ([DIVER](https://arxiv.org/pdf/2508.07995): 28.9).\n- Using the GPT-4 reasoning queries provided by BRIGHT, the score increases to **39.7**, which is **+7.6** higher than DIVER’s corresponding result (32.1).\n\n> On Sep 23, 2025, we released the first-stage search results of BGE-Reasoner-Embed-Qwen3-8B-0923 using original queries and GPT-4 reasoning queries (Top-2000 candidates; excluded IDs removed) [here](https://huggingface.co/BAAI/bge-reasoner-embed-qwen3-8b-0923/tree/main/search_results). The model checkpoint is available [here](https://huggingface.co/BAAI/bge-reasoner-embed-qwen3-8b-0923).\n\n![BGE-Reasoner-Embed-Qwen3-8B-0923 Results](./imgs/embedder-0923_results.png)\n\nNote:\n- \"**Avg - ALL**\" refers to the average performance across **all 12 datasets** in the BRIGHT benchmark.\n- \"**Avg - SE**\" refers to the average performance across the **7 datasets in the StackExchange subset** of the BRIGHT benchmark.\n- \"**Avg - CD**\" refers to the average performance across the **2 datasets in the Coding subset** of the BRIGHT benchmark.\n- \"**Avg - MT**\" refers to the average performance across the **3 datasets in the Theorem-based subset** of the BRIGHT benchmark.\n\n> Sources of Results:\n>\n> [1] https://arxiv.org/pdf/2407.12883\n>\n> [2] https://arxiv.org/pdf/2504.20595\n>\n> [3] https://github.com/Debrup-61/RaDeR\n>\n> [4] https://seed1-5-embedding.github.io\n>\n> [5] https://arxiv.org/pdf/2508.07995\n>\n> *: results evaluated with our script\n\n#### BGE-Reasoner-Embed-0821\n\n**BGE-Reasoner-Embed-0821**, submitted to the BRIGHT leaderboard on Aug 21, 2025, achieves excellent performance on the benchmark:\n\n- With original queries, it attains **nDCG@10 = 32.5**, an absolute improvement of **+3.6** over the previous best ([DIVER](https://arxiv.org/pdf/2508.07995): 28.9).\n- Using the GPT-4 reasoning queries provided by BRIGHT, the score increases to **37.7**, which is **+5.6** higher than DIVER’s corresponding result (32.1). Combining our embedding-based retrieval with BM25 (hybrid fusion, weights: 0.75 / 0.25) yields **nDCG@10 = 40.2**.\n- Finally, when using rewritten queries produced by **BGE-Reasoner-Rewriter** and fusing with BM25 (weights: 0.75 / 0.25), we reach **nDCG@10 = 40.8**.\n\n> On Sep 4, 2025, we released the first-stage search results of BGE-Reasoner-Embed-0821 using original queries and GPT-4 reasoning queries (Top-2000 candidates; excluded IDs removed) [here](https://huggingface.co/datasets/hanhainebula/bright-search-results_bge-reasoner-embed-0821/tree/main). The model checkpoint will not be released due to its suboptimal performance compared to BGE-Reasoner-Embed-Qwen3-8B-0923.\n\n\n![BGE-Reasoner-Embed & BGE-Reasoner-Rewriter Results](./imgs/embedder-rewriter_results.png)\n\nNote:\n- \"**Avg - ALL**\" refers to the average performance across **all 12 datasets** in the BRIGHT benchmark.\n- \"**Avg - SE**\" refers to the average performance across the **7 datasets in the StackExchange subset** of the BRIGHT benchmark.\n- \"**Avg - CD**\" refers to the average performance across the **2 datasets in the Coding subset** of the BRIGHT benchmark.\n- \"**Avg - MT**\" refers to the average performance across the **3 datasets in the Theorem-based subset** of the BRIGHT benchmark.\n\n> Sources of Results:\n>\n> [1] https://arxiv.org/pdf/2407.12883\n>\n> [2] https://arxiv.org/pdf/2504.20595\n>\n> [3] https://github.com/Debrup-61/RaDeR\n>\n> [4] https://seed1-5-embedding.github.io\n>\n> [5] https://arxiv.org/pdf/2508.07995\n>\n> *: results evaluated with our script\n\n\n## Technical Details\n\n\nThe technical details for each component of **BGE-Reasoner** will be released soon. Please stay tuned!\n\n\n## Contact Information\n\nSome resources are not yet publicly available. If you have urgent research needs for any of these resources (e.g., model checkpoints, search results, evaluation scripts) or have any questions, please contact Jianlyu Chen at jianlvchen@gmail.com.\n\n\n## Citation\n\nTBA\n" }, { "path": "research/BGE_VL/LICENSE", "content": "MIT License\n\nCopyright (c) 2024 JUNJIE99\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n" }, { "path": "research/BGE_VL/README.md", "content": "

MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval

\n\n

\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n

\n\n

\n\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n

\n

\n\n\n## News\n```2025-4-13``` 🎉🎉 We have uploaded our MegaPairs dataset to [🤗Hugging Face](https://huggingface.co/datasets/JUNJIE99/MegaPairs), which contains over 26 million multimodal retrieval instruction-tuning triplets. To reduce upload time and enhance data accessibility, we resized all images to a resolution of 512 × 512 instead of using their original size. This adjustment has minimal impact on performance, considering that most vision-language models (e.g., CLIP) use even smaller input image sizes. [Dataset Card](https://github.com/VectorSpaceLab/MegaPairs?tab=readme-ov-file#megapairs-dataset-card)\n\n```2025-4-2``` 🌟🌟 BGE-VL models are also available on [WiseModel](https://www.wisemodel.cn/models/JUNJIE99/BGE-VL-large).\n\n```2025-3-6``` 📰📰 Thank you to [SyncedTech (机器之心)](https://mp.weixin.qq.com/s/iw9BmSDwv6NYtD7pkC5kxQ), [QbitAI (量子位)](https://mp.weixin.qq.com/s/r_zWAZ0ir5732OfIrEsDtg), and [AI Era (新智元)](https://mp.weixin.qq.com/s/FZwKYJnx_78YDAEreu1edg) for reporting on our work!\n\n```2025-3-4``` 🚀🚀 We have released the BGE-VL-MLLM models on Huggingface: [BGE-VL-MLLM-S1](https://huggingface.co/BAAI/BGE-VL-MLLM-S1) and [BGE-VL-MLLM-S2](https://huggingface.co/BAAI/BGE-VL-MLLM-S2). **BGE-VL-MLLM-S1** is trained exclusively on our MegaPairs dataset, achieving outstanding performance in composed image retrieval, with an 8.1% improvement on the CIRCO benchmark (mAP@5) over the previous state-of-the-art. **BGE-VL-MLLM-S2** builds on BGE-VL-MLLM-S1 with an additional epoch of fine-tuning on the MMEB benchmark training set, delivering enhanced performance across a broader range of multimodal embedding tasks.\n\n```2024-12-27``` 🚀🚀 BGE-VL-CLIP models are released on Huggingface: [BGE-VL-base](https://huggingface.co/BAAI/BGE-VL-base) and [BGE-VL-large](https://huggingface.co/BAAI/BGE-VL-large).\n\n```2024-12-19``` 🎉🎉 Release our paper: [MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval](https://arxiv.org/pdf/2412.14475).\n\n## Release Plan\n- [x] Paper\n- [x] BGE-VL-base and BGE-VL-large models\n- [x] BGE-VL-MLLM model\n- [x] MegaPairs Dataset\n- [x] Evaluation code examples\n- [ ] Fine-tuning code\n\n\n## Introduction\nIn this work, we introduce **MegaPairs**, a novel data synthesis method that leverages open-domain images to create *heterogeneous KNN triplets* for universal multimodal retrieval. Our MegaPairs dataset contains over 26 million triplets, and we have trained a series of multimodal retrieval models, **BGE-VL**, including BGE-VL-CLIP (base and large) and BGE-VL-MLLM.\n\nBGE-VL achieve state-of-the-art performance on four popular zero-shot composed image retrieval benchmarks and the massive multimodal embedding benchmark (MMEB). Extensive experiments demonstrate the ***efficiency, scalability, and generalization*** features of MegaPairs. Please refer to our [paper](https://arxiv.org/abs/2412.14475) for more details.\n\n\n\n## Model Usage\n\n### 1. BGE-VL-CLIP Models\nYou can easily use BGE-VL-CLIP models based on ```transformers```\n> Our code works well on transformers==4.45.2, and we recommend using this version.\n```python\nimport torch\nfrom transformers import AutoModel\n\nMODEL_NAME = \"BAAI/BGE-VL-base\" # or \"BAAI/BGE-VL-large\"\n\nmodel = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True) # You must set trust_remote_code=True\nmodel.set_processor(MODEL_NAME)\nmodel.eval()\n\nwith torch.no_grad():\n query = model.encode(\n images = \"./assets/cir_query.png\", \n text = \"Make the background dark, as if the camera has taken the photo at night\"\n )\n\n candidates = model.encode(\n images = [\"./assets/cir_candi_1.png\", \"./assets/cir_candi_2.png\"]\n )\n \n scores = query @ candidates.T\nprint(scores)\n```\n\nSee the [demo](./retrieval_demo.ipynb) for a complete example of using BGE-VL for multimodel retrieval.\n\n\n### 2. BGE-VL-MLLM Models\n\n> Our code works well on transformers==4.45.2, and we recommend using this version.\n\n```python\nimport torch\nfrom transformers import AutoModel\nfrom PIL import Image\n\nMODEL_NAME= \"BAAI/BGE-VL-MLLM-S1\"\n\nmodel = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\nmodel.eval()\nmodel.cuda()\n\nwith torch.no_grad():\n model.set_processor(MODEL_NAME)\n\n query_inputs = model.data_process(\n text=\"Make the background dark, as if the camera has taken the photo at night\", \n images=\"./assets/cir_query.png\",\n q_or_c=\"q\",\n task_instruction=\"Retrieve the target image that best meets the combined criteria by using both the provided image and the image retrieval instructions: \"\n )\n\n candidate_inputs = model.data_process(\n images=[\"./assets/cir_candi_1.png\", \"./assets/cir_candi_2.png\"],\n q_or_c=\"c\",\n )\n\n query_embs = model(**query_inputs, output_hidden_states=True)[:, -1, :]\n candi_embs = model(**candidate_inputs, output_hidden_states=True)[:, -1, :]\n \n query_embs = torch.nn.functional.normalize(query_embs, dim=-1)\n candi_embs = torch.nn.functional.normalize(candi_embs, dim=-1)\n\n scores = torch.matmul(query_embs, candi_embs.T)\nprint(scores)\n```\n\n## MegaPairs Dataset Card\n\nWe are excited to release the **MegaPairs** dataset on [Hugging Face](https://huggingface.co/datasets/JUNJIE99/MegaPairs), which contains over **26 million training samples** tailored for composed image retrieval and universal multimodal retrieval tasks. \n\n### Dataset Structure\n\nEach entry in the dataset consists of the following fields:\n\n- **q_img**: `str` \n The file path to the query image.\n\n- **q_text**: `list` \n A list of textual query statements related to the query image. During training, you can randomly select one statement from this list.\n\n- **t_img**: `str` \n The file path to the target image, which serves as the **positive example** for the combination of `q_img` and `q_text`.\n\n- **hns**: `list` \n A list of file paths for **hard negative sample** images. These are challenging distractors that are visually or semantically similar to the query. It is recommended to include at least one hard negative sample during training, with **`hns[0]` (the query image itself)** being a mandatory choice. In our experiments, we used **four hard negative samples** per query.\n\n\n### Usage\n\nThe dataset is available for download and exploration on [Hugging Face](https://huggingface.co/datasets/JUNJIE99/MegaPairs). We encourage researchers and practitioners to leverage this dataset to advance multimodal retrieval research and systems.\n\n## Model Performance\n### Zero-Shot Composed Image Retrieval\n\nBGE-VL sets a new performance benchmark in zero-shot composed image retrieval tasks. On the CIRCO benchmark, our BGE-VL-base model, with only 149 million parameters, surpasses all previous models, including those with 50 times more parameters. Additionally, BGE-VL-MLLM achieves an 8.1% improvement over the previous state-of-the-art model.\n\n\n\n### Zero-Shot Performance on MMEB\n\nBGE-VL-MLLM achieves state-of-the-art zero-shot performance on the Massive Multimodal Embedding Benchmark (MMEB), despite being trained only on the ImageText-to-Image paradigm. This demonstrates the excellent generalization capability of MegaPairs for multimodal embedding.\n\n\n\n### Fine-Tuning Performance on MMEB\n\nAfter fine-tuning on downstream tasks, BGE-VL-MLLM maintains its leading performance. Notably, it surpasses the previous state-of-the-art by 7.1% on the MMEB out-of-distribution (OOD) set. These results demonstrate the robust generalization capability of BGE-VL-MLLM and highlight the potential of MegaPairs as foundational training data for universal multimodal embedding.\n\n\n\n### Performance Scaling\nMegaPairs showcases **scalability**: BGE-VL-base improves as training data increases. It also demonstrates **efficiency**: with just 0.5M training samples, BGE-VL-base significantly outperforms MagicLens, which uses the same CLIP-base backbone and was trained on 36.7M samples.\n\n\n\n\n## License\nThe annotations for MegaPairs and the BGE-VL models are released under the [MIT License](LICENSE). The images in MegaPairs originate from the [Recap-Datacomp](https://huggingface.co/datasets/UCSC-VLAA/Recap-DataComp-1B), which is released under the CC BY 4.0 license.\n\n\n\n## Citation\nIf you find this repository useful, please consider giving a star ⭐ and citation\n\n```\n@article{zhou2024megapairs,\n title={MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval},\n author={Zhou, Junjie and Liu, Zheng and Liu, Ze and Xiao, Shitao and Wang, Yueze and Zhao, Bo and Zhang, Chen Jason and Lian, Defu and Xiong, Yongping},\n journal={arXiv preprint arXiv:2412.14475},\n year={2024}\n}\n```\n" }, { "path": "research/BGE_VL/eval/data/circo_corpus.jsonl", "content": "{\"content\": 533083, \"image\": \"000000533083.jpg\"}\n{\"content\": 442426, \"image\": \"000000442426.jpg\"}\n{\"content\": 127929, \"image\": \"000000127929.jpg\"}\n{\"content\": 397395, \"image\": \"000000397395.jpg\"}\n{\"content\": 34156, \"image\": \"000000034156.jpg\"}\n{\"content\": 446319, \"image\": 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walls.\"}\n{\"id\": 2, \"q_img\": \"000000232621.jpg\", \"q_text\": \"Find a picture that also a person looking at his laptop, but is sitting on the ground.\"}\n{\"id\": 3, \"q_img\": \"000000258030.jpg\", \"q_text\": \"Find a picture that also a man wearing a tie, a shirt and a hat, but is wearing sunglasses.\"}\n{\"id\": 4, \"q_img\": \"000000114366.jpg\", \"q_text\": \"Find a picture that also a box of donuts, but shows only one box placed inside a car.\"}\n{\"id\": 5, \"q_img\": \"000000019155.jpg\", \"q_text\": \"Find a picture that also an open refrigerator filled with mostly alcoholic beverages, but shows a person in the foreground.\"}\n{\"id\": 6, \"q_img\": \"000000023567.jpg\", \"q_text\": \"Find a picture that also a child wearing a chef's hat and an apron, but has more colorful apron and he is looking at the camera.\"}\n{\"id\": 7, \"q_img\": \"000000400924.jpg\", \"q_text\": \"Find a picture that also a clock in a big hall, but is taken from the same angle and shows a spiral staircase.\"}\n{\"id\": 8, \"q_img\": \"000000455504.jpg\", \"q_text\": \"Find a picture that also a glass bottle on a bench, but is only one and is seen from the side.\"}\n{\"id\": 9, \"q_img\": \"000000240265.jpg\", \"q_text\": \"Find a picture that also a double bed, but has blue blankets and a CRT TV next to it.\"}\n{\"id\": 10, \"q_img\": \"000000433496.jpg\", \"q_text\": \"Find a picture that also a train wagon seen from the inside, but shows the dining wagon.\"}\n{\"id\": 11, \"q_img\": \"000000549870.jpg\", \"q_text\": \"Find a picture that also a person talking on the phone with water in the background, but has only one person and he is lying down.\"}\n{\"id\": 12, \"q_img\": \"000000451179.jpg\", \"q_text\": \"Find a picture that also a bowl of fruit in the foreground, but has bananas sliced in half, apples, more fruit and no peach.\"}\n{\"id\": 13, \"q_img\": \"000000539540.jpg\", \"q_text\": \"Find a picture that also a sliced carrot, but is on a wooden cutting board with a different arrangement.\"}\n{\"id\": 14, \"q_img\": \"000000067430.jpg\", \"q_text\": \"Find a picture that also a canal with boats seen from a river view, but is shot from the same angle and has buildings on both the sides.\"}\n{\"id\": 15, \"q_img\": \"000000117864.jpg\", \"q_text\": \"Find a picture that also two people looking at their phones, but are dressed more formally and the photo is taken indoors.\"}\n{\"id\": 16, \"q_img\": \"000000061617.jpg\", \"q_text\": \"Find a picture that also a laptop on a desk, but is shot from the side and has several clothes in the background.\"}\n{\"id\": 17, \"q_img\": \"000000119135.jpg\", \"q_text\": \"Find a picture that also a living room, but has multiple moving boxes.\"}\n{\"id\": 18, \"q_img\": \"000000490966.jpg\", \"q_text\": \"Find a picture that also a kitchen, but is mainly blue and the photo is taken from a different angle.\"}\n{\"id\": 19, \"q_img\": \"000000090604.jpg\", \"q_text\": \"Find a picture that also a dog with a Santa hat, but is a statue and the photo is set outdoors.\"}\n{\"id\": 20, \"q_img\": \"000000158745.jpg\", \"q_text\": \"Find a picture that also some kites in the sky, but has a bridge in the background.\"}\n{\"id\": 21, \"q_img\": \"000000210061.jpg\", \"q_text\": \"Find a picture that also a person seen from the back in greyscale, but is holding an umbrella instead of a suitcase and the photo is taken from the same angle and has no other people in the foreground.\"}\n{\"id\": 22, \"q_img\": \"000000069753.jpg\", \"q_text\": \"Find a picture that also a person riding a horse, but is half immersed in the water and no buildings are visible in the background.\"}\n{\"id\": 23, \"q_img\": \"000000281885.jpg\", \"q_text\": \"Find a picture that also a person seen from the front sitting on a bench using a laptop, but is shot from the same angle and shows only one person.\"}\n{\"id\": 24, \"q_img\": \"000000152478.jpg\", \"q_text\": \"Find a picture that also a birthday cake, but has a bus instead of a horse.\"}\n{\"id\": 25, \"q_img\": \"000000471574.jpg\", \"q_text\": \"Find a picture that also a dining table, but is not set and has a clear vase with more white flowers in it.\"}\n{\"id\": 26, \"q_img\": \"000000330427.jpg\", \"q_text\": \"Find a picture that also a fire hydrant surrounded by snow, but is of a different color and the photo shows a traffic cone.\"}\n{\"id\": 27, \"q_img\": \"000000032201.jpg\", \"q_text\": \"Find a picture that also a person sitting on a bench, but shows fewer people and one parked motor-scooter.\"}\n{\"id\": 28, \"q_img\": \"000000472616.jpg\", \"q_text\": \"Find a picture that also a woman taking a selfie in a bathroom mirror, but has only one person and she is wearing a black t-shirt.\"}\n{\"id\": 29, \"q_img\": \"000000094878.jpg\", \"q_text\": \"Find a picture that also an abandoned bike, but is in the water and the photo is zoomed out.\"}\n{\"id\": 30, \"q_img\": \"000000194017.jpg\", \"q_text\": \"Find a picture that also people riding horses, but shows three of them and there is a forest in the background.\"}\n{\"id\": 31, \"q_img\": \"000000438039.jpg\", \"q_text\": \"Find a picture that also a woman on a bench, but is shot in greyscale, the photo has a different background and the woman is looking at a phone.\"}\n{\"id\": 32, \"q_img\": \"000000574169.jpg\", \"q_text\": \"Find a picture that also a dog with a book in front of him, but is of a different breed and the book is open.\"}\n{\"id\": 33, \"q_img\": \"000000510439.jpg\", \"q_text\": \"Find a picture that also a small bird, but shows only one of them and it is on a metal railing instead of power lines.\"}\n{\"id\": 34, \"q_img\": \"000000021141.jpg\", \"q_text\": \"Find a picture that also a white and grey bird, but has the same color and is standing still on the shoreline.\"}\n{\"id\": 35, \"q_img\": \"000000157409.jpg\", \"q_text\": \"Find a picture that also a kitchen, but is blue and has more objects attached to the walls.\"}\n{\"id\": 36, \"q_img\": \"000000020668.jpg\", \"q_text\": \"Find a picture that also a white Wii controller, but is only one and it is touched by a cat.\"}\n{\"id\": 37, \"q_img\": \"000000066227.jpg\", \"q_text\": \"Find a picture that also a close-up open refrigerator seen from the front, but is more stocked and the photo has one person in the foreground.\"}\n{\"id\": 38, \"q_img\": \"000000430952.jpg\", \"q_text\": \"Find a picture that also an elephant, but is crossing a river and has one person on its back.\"}\n{\"id\": 39, \"q_img\": \"000000076394.jpg\", \"q_text\": \"Find a picture that also a living room with a sofa and blue walls, but has walls of a similar color and there is a guitar.\"}\n{\"id\": 40, \"q_img\": \"000000577363.jpg\", \"q_text\": \"Find a picture that also a white egret, but has the same color, shows no chair and the photo is taken from a closer distance.\"}\n{\"id\": 41, \"q_img\": \"000000057799.jpg\", \"q_text\": \"Find a picture that also a green parrot in a cage, but shows no people and the photo is zoomed in.\"}\n{\"id\": 42, \"q_img\": \"000000018405.jpg\", \"q_text\": \"Find a picture that also a stop sign, but shows no buildings and there are trees on both sides of the road.\"}\n{\"id\": 43, \"q_img\": \"000000049671.jpg\", \"q_text\": \"Find a picture that also a person wearing a shirt, a tie and a hat, but shows two people that are wearing a similar outfit.\"}\n{\"id\": 44, \"q_img\": \"000000460358.jpg\", \"q_text\": \"Find a picture that also a person throwing a baseball at a child who is holding a bat, but is set in a wood and the photo is taken from a different angle.\"}\n{\"id\": 45, \"q_img\": \"000000177458.jpg\", \"q_text\": \"Find a picture that also a greyscale close-up portrait of a person looking at the camera, but is a woman instead of a man.\"}\n{\"id\": 46, \"q_img\": \"000000187578.jpg\", \"q_text\": \"Find a picture that also a child with a teddy bear, but is shaped like a backpack and is being worn.\"}\n{\"id\": 47, \"q_img\": \"000000363900.jpg\", \"q_text\": \"Find a picture that also a handbag with its contents displayed next to it, but has a carpet instead of grass and the photo is shot from the same angle.\"}\n{\"id\": 48, \"q_img\": \"000000268574.jpg\", \"q_text\": \"Find a picture that also a lot of scissors stacked messily, but are more colorful, have a different shape and they are seen from the top.\"}\n{\"id\": 49, \"q_img\": \"000000218732.jpg\", \"q_text\": \"Find a picture that also children wearing school uniforms seen from a close distance, but is shot in greyscale from a similar distance and shows more of them.\"}\n{\"id\": 50, \"q_img\": \"000000038542.jpg\", \"q_text\": \"Find a picture that also multiple people eating at a table with rain umbrellas, but are eating in a garden and the umbrellas are colored.\"}\n{\"id\": 51, \"q_img\": \"000000012900.jpg\", \"q_text\": \"Find a picture that also several wine glasses next to each other, but shows a person filling the glasses and the wine is white instead of red.\"}\n{\"id\": 52, \"q_img\": \"000000575061.jpg\", \"q_text\": \"Find a picture that also a car in a snowy street, but is shot from the same angle and the car is a red pickup.\"}\n{\"id\": 53, \"q_img\": \"000000566434.jpg\", \"q_text\": \"Find a picture that also a rusty truck, but is viewed from a closer distance and from the side.\"}\n{\"id\": 54, \"q_img\": \"000000343973.jpg\", \"q_text\": \"Find a picture that also a person sitting outdoors and using a laptop, but is resting on a table and the photo shows only one person in the foreground.\"}\n{\"id\": 55, \"q_img\": \"000000312971.jpg\", \"q_text\": \"Find a picture that also three people using a computer seen from the front, but shows the same number of people sitting on a bed and bare feet in the foreground.\"}\n{\"id\": 56, \"q_img\": \"000000128913.jpg\", \"q_text\": \"Find a picture that also several snowboards on a wall, but shows no people.\"}\n{\"id\": 57, \"q_img\": \"000000481114.jpg\", \"q_text\": \"Find a picture that also a double bed with turned on lamps on the sides, but has plain white sheets and the room looks cheaper.\"}\n{\"id\": 58, \"q_img\": \"000000342900.jpg\", \"q_text\": \"Find a picture that also a person riding a horse, but is surrounded by snow and the trees are bare.\"}\n{\"id\": 59, \"q_img\": \"000000507897.jpg\", \"q_text\": \"Find a picture that also a dark horse, but has a similar color and has fog in the background.\"}\n{\"id\": 60, \"q_img\": \"000000460017.jpg\", \"q_text\": \"Find a picture that also a plushy toy on a double bed, but has more toys and the blankets are darker.\"}\n{\"id\": 61, \"q_img\": \"000000526851.jpg\", \"q_text\": \"Find a picture that also a man standing next to a semaphore, but has a red semaphore instead of a green one.\"}\n{\"id\": 62, \"q_img\": \"000000080239.jpg\", \"q_text\": \"Find a picture that also a kitchen counter, but has an open laptop with the screen visible on it.\"}\n{\"id\": 63, \"q_img\": \"000000199973.jpg\", \"q_text\": \"Find a picture that also a Dell laptop on a table, but is of the same brand and is placed on an outdoor table.\"}\n{\"id\": 64, \"q_img\": \"000000407154.jpg\", \"q_text\": \"Find a picture that also a kid with a baseball bat, but is shot from behind and shows the catcher.\"}\n{\"id\": 65, \"q_img\": \"000000114181.jpg\", \"q_text\": \"Find a picture that also a fridge with magnets, but has a different color and a magnet depicting a flag attached to it.\"}\n{\"id\": 66, \"q_img\": \"000000297636.jpg\", \"q_text\": \"Find a picture that also a mixer, but is empty and there are several bottles of alcohol next to it.\"}\n{\"id\": 67, \"q_img\": \"000000287965.jpg\", \"q_text\": \"Find a picture that also a vehicle filled with bananas, but has a car instead of a truck.\"}\n{\"id\": 68, \"q_img\": \"000000433177.jpg\", \"q_text\": \"Find a picture that also people looking at their laptop, but is set on a train instead of a bedroom.\"}\n{\"id\": 69, \"q_img\": \"000000434831.jpg\", \"q_text\": \"Find a picture that also a close-up bowl filled with fruit, but shows only one type of fruit.\"}\n{\"id\": 70, \"q_img\": \"000000007731.jpg\", \"q_text\": \"Find a picture that also a close-up feature phone, but is only one and is inside a display case.\"}\n{\"id\": 71, \"q_img\": \"000000556629.jpg\", \"q_text\": \"Find a picture that also scissors, but shows more of them and they are for sale.\"}\n{\"id\": 72, \"q_img\": \"000000426704.jpg\", \"q_text\": \"Find a picture that also a man looking at a computer screen sitting at a desk, but shows a plate with food and has a whiter background.\"}\n{\"id\": 73, \"q_img\": \"000000430426.jpg\", \"q_text\": \"Find a picture that also a garbage truck, but is of a different color and is viewed from the back.\"}\n{\"id\": 74, \"q_img\": \"000000425417.jpg\", \"q_text\": \"Find a picture that also sliced carrots, but are sliced in a different shapes and inside a clear glass bowl.\"}\n{\"id\": 75, \"q_img\": \"000000393793.jpg\", \"q_text\": \"Find a picture that also a fire hydrant, but has a different color and is surrounded by flowers.\"}\n{\"id\": 76, \"q_img\": \"000000527187.jpg\", \"q_text\": \"Find a picture that also a person holding a white hair drier, but is an older woman and there is a door in the background.\"}\n{\"id\": 77, \"q_img\": \"000000575542.jpg\", \"q_text\": \"Find a picture that also a white refrigerator seen from the front, but has colorful magnets shaped like letters and a few photos.\"}\n{\"id\": 78, \"q_img\": \"000000255336.jpg\", \"q_text\": \"Find a picture that also a person in a hospital bed, but is in color and shows the person using a phone.\"}\n{\"id\": 79, \"q_img\": \"000000282212.jpg\", \"q_text\": \"Find a picture that also a tennis player serving on a grass court, but is a man, is dressed in the same color and has his feet in the air.\"}\n{\"id\": 80, \"q_img\": \"000000502080.jpg\", \"q_text\": \"Find a picture that also a kitchen with an island, but has a suspended cookware display and the walls are not white.\"}\n{\"id\": 81, \"q_img\": \"000000482717.jpg\", \"q_text\": \"Find a picture that also a woman wearing rubber boots, but has similar shoes and is holding an umbrella.\"}\n{\"id\": 82, \"q_img\": \"000000503659.jpg\", \"q_text\": \"Find a picture that also a female tennis player, but is playing on clay and is hitting a backhand volley.\"}\n{\"id\": 83, \"q_img\": \"000000092103.jpg\", \"q_text\": \"Find a picture that also a man standing and playing with the Wii, but has shorter hair, is wearing a hoodie and no other people are visible.\"}\n{\"id\": 84, \"q_img\": \"000000538354.jpg\", \"q_text\": \"Find a picture that also a brown kitchen, but has the same color but has green tiles.\"}\n{\"id\": 85, \"q_img\": \"000000121703.jpg\", \"q_text\": \"Find a picture that also a single slice of pizza on a plate, but has more toppings and is next to a hot dog.\"}\n{\"id\": 86, \"q_img\": \"000000486024.jpg\", \"q_text\": \"Find a picture that also an open white fridge, but is full of bottles instead of food.\"}\n{\"id\": 87, \"q_img\": \"000000191601.jpg\", \"q_text\": \"Find a picture that also a birthday cake seen from the top, but has the shape of two numbers and is shot from the same angle.\"}\n{\"id\": 88, \"q_img\": \"000000256907.jpg\", \"q_text\": \"Find a picture that also an umbrellas roof, but is shot indoors and the umbrellas are more colored.\"}\n{\"id\": 89, \"q_img\": \"000000378336.jpg\", \"q_text\": \"Find a picture that also a kitchen with appliances, but is more modern and white and grey.\"}\n{\"id\": 90, \"q_img\": \"000000242024.jpg\", \"q_text\": \"Find a picture that also a zebra, but is in the middle of a road and there is a car in the background.\"}\n{\"id\": 91, \"q_img\": \"000000349993.jpg\", \"q_text\": \"Find a picture that also a fridge, but is white and has a microwave oven resting on the counter next to it.\"}\n{\"id\": 92, \"q_img\": \"000000501301.jpg\", \"q_text\": \"Find a picture that also three suitcases, but are seen from the top and are the same number.\"}\n{\"id\": 93, \"q_img\": \"000000002505.jpg\", \"q_text\": \"Find a picture that also a young girl riding a horse seen from the front, but is inside an enclosure and a blue sky is visible in the background.\"}\n{\"id\": 94, \"q_img\": \"000000177844.jpg\", \"q_text\": \"Find a picture that also a furniture store, but has more beds and shows no people on them.\"}\n{\"id\": 95, \"q_img\": \"000000223021.jpg\", \"q_text\": \"Find a picture that also a girl laying on a bed, but shows only one girl and she is using a laptop.\"}\n{\"id\": 96, \"q_img\": \"000000483524.jpg\", \"q_text\": \"Find a picture that also two horses pulling a wagon with people on it, but has white animals and palaces in the background.\"}\n{\"id\": 97, \"q_img\": \"000000398147.jpg\", \"q_text\": \"Find a picture that also a fire hydrant painted with the design of a flag, but is painted with a different flag.\"}\n{\"id\": 98, \"q_img\": \"000000408038.jpg\", \"q_text\": \"Find a picture that also an intersection with cars, but are seen from a higher angle and the photo shows a roundabout.\"}\n{\"id\": 99, \"q_img\": \"000000555735.jpg\", \"q_text\": \"Find a picture that also an old truck, but is of a different color and has the hood open.\"}\n{\"id\": 100, \"q_img\": \"000000034952.jpg\", \"q_text\": \"Find a picture that also a child wearing a baseball hat and a baseball glove in the foreground seen from the front, but is wearing a t-shirt of a different color, the photo has no people in the background and is shot from the same angle.\"}\n{\"id\": 101, \"q_img\": \"000000427950.jpg\", \"q_text\": \"Find a picture that also a living room with a sofa and a TV, but shows the entrance door and has no animals.\"}\n{\"id\": 102, \"q_img\": \"000000412231.jpg\", \"q_text\": \"Find a picture that also a person on a snowboard in the air, but has colorful pants and one hand over his head.\"}\n{\"id\": 103, \"q_img\": \"000000401920.jpg\", \"q_text\": \"Find a picture that also a person biting a banana in the foreground, but is a child and is looking at the camera.\"}\n{\"id\": 104, \"q_img\": \"000000080132.jpg\", \"q_text\": \"Find a picture that also a stove, but is taken from a closer distance and the front and has a kettle on it.\"}\n{\"id\": 105, \"q_img\": \"000000433011.jpg\", \"q_text\": \"Find a picture that also a bench shot in greyscale, but is in the foreground and has at least a bird on it.\"}\n{\"id\": 106, \"q_img\": \"000000159383.jpg\", \"q_text\": \"Find a picture that also a scooter with nobody on it seen from the side, but is white without a windshield and has a similar shape.\"}\n{\"id\": 107, \"q_img\": \"000000026618.jpg\", \"q_text\": \"Find a picture that also a graffiti on the side of a vehicle, but has a truck instead of a train and is set on a city road.\"}\n{\"id\": 108, \"q_img\": \"000000275670.jpg\", \"q_text\": \"Find a picture that also a little girl on a bed, but is playing with a tablet.\"}\n{\"id\": 109, \"q_img\": \"000000330946.jpg\", \"q_text\": \"Find a picture that also a dog on a surfboard, but has a person standing behind it and is seen from a farther distance.\"}\n{\"id\": 110, \"q_img\": \"000000538392.jpg\", \"q_text\": \"Find a picture that also a bus with people sitting seen from the back, but is open-top and the photo is shot from the same angle.\"}\n{\"id\": 111, \"q_img\": \"000000533939.jpg\", \"q_text\": \"Find a picture that also a person behind a kitchen counter seen from the front, but has one man instead of two women and the photo is not taken on the set of a TV show.\"}\n{\"id\": 112, \"q_img\": \"000000159689.jpg\", \"q_text\": \"Find a picture that also a person in public transport with luggage, but shows only one person and more luggage.\"}\n{\"id\": 113, \"q_img\": \"000000258845.jpg\", \"q_text\": \"Find a picture that also a close-up person holding a glass of red wine in their hand, but is talking on the phone and the person is a man instead of a woman.\"}\n{\"id\": 114, \"q_img\": \"000000492721.jpg\", \"q_text\": \"Find a picture that also a pizza on a plate, but has a child sitting at a dining table in front of it.\"}\n{\"id\": 115, \"q_img\": \"000000084632.jpg\", \"q_text\": \"Find a picture that also a dog, but is wearing a life jacket and is near a body of water.\"}\n{\"id\": 116, \"q_img\": \"000000572797.jpg\", \"q_text\": \"Find a picture that also a man seen from the back, but is on the shoreline, has a surfboard next to him and there is the sea in the background.\"}\n{\"id\": 117, \"q_img\": \"000000533954.jpg\", \"q_text\": \"Find a picture that also a plane at the airport with a boarding bridge attached to it, but is of a different color and there is snow on the ground.\"}\n{\"id\": 118, \"q_img\": \"000000355004.jpg\", \"q_text\": \"Find a picture that also a bedroom with two windows, but has lighter walls and a ceiling fan.\"}\n{\"id\": 119, \"q_img\": \"000000079809.jpg\", \"q_text\": \"Find a picture that also a person riding a surfboard, but is a woman seen from a closer distance and she is wearing a bikini.\"}\n{\"id\": 120, \"q_img\": \"000000345525.jpg\", \"q_text\": \"Find a picture that also a person under the covers with animals on a bed, but has fewer animals and more cats.\"}\n{\"id\": 121, \"q_img\": \"000000117384.jpg\", \"q_text\": \"Find a picture that also a cake, but is shaped like an animal and has candles on it.\"}\n{\"id\": 122, \"q_img\": \"000000366400.jpg\", \"q_text\": \"Find a picture that also a person playing beach volley, but is shot in greyscale and shows more people playing.\"}\n{\"id\": 123, \"q_img\": \"000000029447.jpg\", \"q_text\": \"Find a picture that also a cat looking at the camera, but is darker and is next to one pair of shoes.\"}\n{\"id\": 124, \"q_img\": \"000000192353.jpg\", \"q_text\": \"Find a picture that also a person riding a skateboard shot in greyscale, but has no backpack and the photo shows also a single car.\"}\n{\"id\": 125, \"q_img\": \"000000560304.jpg\", \"q_text\": \"Find a picture that also a close-up man talking on the phone, but is taken in greyscale and from the back.\"}\n{\"id\": 126, \"q_img\": \"000000495424.jpg\", \"q_text\": \"Find a picture that also a toilet, but has two of them, the photo is taken from a different angle, shows a window and the floor is darker.\"}\n{\"id\": 127, \"q_img\": \"000000069201.jpg\", \"q_text\": \"Find a picture that also a man talking on the phone in the foreground, but has a body of water in the background.\"}\n{\"id\": 128, \"q_img\": \"000000034654.jpg\", \"q_text\": \"Find a picture that also an open oven in the foreground with cookware in it, but shows a pot instead of a baking tray.\"}\n{\"id\": 129, \"q_img\": \"000000278039.jpg\", \"q_text\": \"Find a picture that also a pink umbrella held by a person, but has the same color and the photo is set next to a door.\"}\n{\"id\": 130, \"q_img\": \"000000271109.jpg\", \"q_text\": \"Find a picture that also a wine glass filled with a light clear red beverage, but has the same color and there is no bottle next to it.\"}\n{\"id\": 131, \"q_img\": \"000000086659.jpg\", \"q_text\": \"Find a picture that also a closed laptop on an empty table in the foreground, but has a book on it and the table is made of wood.\"}\n{\"id\": 132, \"q_img\": \"000000185803.jpg\", \"q_text\": \"Find a picture that also a man wearing a suit checking his phone, but is younger, wears a black suit and the photo is shot from the same angle.\"}\n{\"id\": 133, \"q_img\": \"000000260641.jpg\", \"q_text\": \"Find a picture that also a dog in the foreground reflected in a mirror in the background, but is farther from the mirror and the photo has only one of them.\"}\n{\"id\": 134, \"q_img\": \"000000467282.jpg\", \"q_text\": \"Find a picture that also a microwave oven, but has more of them in multiple colors.\"}\n{\"id\": 135, \"q_img\": \"000000289452.jpg\", \"q_text\": \"Find a picture that also several fridges next to each other, but are of different colors and are placed outdoors.\"}\n{\"id\": 136, \"q_img\": \"000000476440.jpg\", \"q_text\": \"Find a picture that also a person with an umbrella indoors seen from the front, but is seen from the same angle and is wearing sunglasses.\"}\n{\"id\": 137, \"q_img\": \"000000323897.jpg\", \"q_text\": \"Find a picture that also a yellow school bus, but has the same color and there is snow around it.\"}\n{\"id\": 138, \"q_img\": \"000000406398.jpg\", \"q_text\": \"Find a picture that also a man making a pizza in a home kitchen, but is looking into the camera and the photo is shot from the same angle.\"}\n{\"id\": 139, \"q_img\": \"000000013618.jpg\", \"q_text\": \"Find a picture that also a standing woman holding an umbrella seen from the front, but has more trees in the background and the umbrella is darker.\"}\n{\"id\": 140, \"q_img\": \"000000177681.jpg\", \"q_text\": \"Find a picture that also a man skateboarding, but is wearing a helmet with the visor down and there are no traffic cones.\"}\n{\"id\": 141, \"q_img\": \"000000124865.jpg\", \"q_text\": \"Find a picture that also an hot dog in the foreground, but is on a plate and has a bottle of ketchup in the background.\"}\n{\"id\": 142, \"q_img\": \"000000335558.jpg\", \"q_text\": \"Find a picture that also a woman cosplaying, but has an umbrella instead of a teddy bear and the photo is shot outdoors.\"}\n{\"id\": 143, \"q_img\": \"000000067212.jpg\", \"q_text\": \"Find a picture that also two people on top of an elephant, but is shot on a beach and has one person next to it.\"}\n{\"id\": 144, \"q_img\": \"000000116632.jpg\", \"q_text\": \"Find a picture that also three adults cooking in a home kitchen, but has the same number of adults and there is a child.\"}\n{\"id\": 145, \"q_img\": \"000000449128.jpg\", \"q_text\": \"Find a picture that also a birthday cake with lit candles, but shows only a person seen from the front.\"}\n{\"id\": 146, \"q_img\": \"000000188210.jpg\", \"q_text\": \"Find a picture that also a urinal, but is taken from the front, has only one of them and there is a sign over it.\"}\n{\"id\": 147, \"q_img\": \"000000008112.jpg\", \"q_text\": \"Find a picture that also a boy using a laptop, but has the laptop on its knees and is seen from behind.\"}\n{\"id\": 148, \"q_img\": \"000000569120.jpg\", \"q_text\": \"Find a picture that also a keyboard and a mouse on a solid background, but are Apple products and the photo has a different background color.\"}\n{\"id\": 149, \"q_img\": \"000000155609.jpg\", \"q_text\": \"Find a picture that also three horses on a beach, but has the same number of animals and no people and no boats in the background.\"}\n{\"id\": 150, \"q_img\": \"000000361882.jpg\", \"q_text\": \"Find a picture that also a white private jet, but has a single car next to it.\"}\n{\"id\": 151, \"q_img\": \"000000431955.jpg\", \"q_text\": \"Find a picture that also a food truck, but shows the sky in the background, has more of them and they are open.\"}\n{\"id\": 152, \"q_img\": \"000000011125.jpg\", \"q_text\": \"Find a picture that also a cat on a bed next to a book, but is open with its back on top.\"}\n{\"id\": 153, \"q_img\": \"000000490296.jpg\", \"q_text\": \"Find a picture that also a raw turkey, but is inside an open oven and the photo is zoomed in and shows no people.\"}\n{\"id\": 154, \"q_img\": \"000000241335.jpg\", \"q_text\": \"Find a picture that also a pair of horses pulling a wagon, but has only two of them, the photo is taken from a different angle, has a bluer sky and there are people on the wagon.\"}\n{\"id\": 155, \"q_img\": \"000000113301.jpg\", \"q_text\": \"Find a picture that also two traffic lights next to each other, but are both green and the photo has a blue sky background.\"}\n{\"id\": 156, \"q_img\": \"000000260777.jpg\", \"q_text\": \"Find a picture that also a suitcase with animals in it, but shows two children instead of cats.\"}\n{\"id\": 157, \"q_img\": \"000000191428.jpg\", \"q_text\": \"Find a picture that also a cat, but has a teddy bear next to it and has its eyes fully opened.\"}\n{\"id\": 158, \"q_img\": \"000000439019.jpg\", \"q_text\": \"Find a picture that also a brown horse with a jockey jumping a hurdle, but has the same color, is seen from a closer distance and from the front.\"}\n{\"id\": 159, \"q_img\": \"000000108719.jpg\", \"q_text\": \"Find a picture that also a dog inside a car, but has its head out of the car and is turned away from the camera.\"}\n{\"id\": 160, \"q_img\": \"000000168214.jpg\", \"q_text\": \"Find a picture that also a baseball pitcher throwing the ball seen from the side, but is wearing a white shirt and the photo is taken from the same angle and shows a grandstand in the background.\"}\n{\"id\": 161, \"q_img\": \"000000472044.jpg\", \"q_text\": \"Find a picture that also several surfboards, but has more of them and they are resting on the grass.\"}\n{\"id\": 162, \"q_img\": \"000000319550.jpg\", \"q_text\": \"Find a picture that also a car on a beach, but has more people and there is a clearer sky.\"}\n{\"id\": 163, \"q_img\": \"000000495618.jpg\", \"q_text\": \"Find a picture that also a person with an umbrella taken in greyscale except for the umbrella, but is shot with the same style and there are more people.\"}\n{\"id\": 164, \"q_img\": \"000000101523.jpg\", \"q_text\": \"Find a picture that also a motorbike in the foreground seen from the side, but has a different color, is seen from the same angle and the photo has a body of water in the background.\"}\n{\"id\": 165, \"q_img\": \"000000346504.jpg\", \"q_text\": \"Find a picture that also a skier seen from the front, but wears a blue suit, is seen from the same angle and the sky is not visible.\"}\n{\"id\": 166, \"q_img\": \"000000504767.jpg\", \"q_text\": \"Find a picture that also a ship in front of a beach, but has more beach umbrellas.\"}\n{\"id\": 167, \"q_img\": \"000000192686.jpg\", \"q_text\": \"Find a picture that also people sitting on a bench seen from the back in front of the sea, but is zoomed in and has more people.\"}\n{\"id\": 168, \"q_img\": \"000000258001.jpg\", \"q_text\": \"Find a picture that also a white clock tower, but is surrounded by water and the sky is clearer.\"}\n{\"id\": 169, \"q_img\": \"000000155281.jpg\", \"q_text\": \"Find a picture that also a person dressed as a military, but has more people dressed similarly and they are sitting at a dining table.\"}\n{\"id\": 170, \"q_img\": \"000000482737.jpg\", \"q_text\": \"Find a picture that also a living room with a fireplace, but shows an electric guitar.\"}\n{\"id\": 171, \"q_img\": \"000000067158.jpg\", \"q_text\": \"Find a picture that also a woman and a man next to each other, but shows one of them holding an electronic device and a darker background.\"}\n{\"id\": 172, \"q_img\": \"000000239332.jpg\", \"q_text\": \"Find a picture that also a closed flip phone next to an object, but shows a can instead of a camera and is shot from a lower angle.\"}\n{\"id\": 173, \"q_img\": \"000000506685.jpg\", \"q_text\": \"Find a picture that also a man cutting food seen from the front, but is looking at the camera and there is a beer in front of him.\"}\n{\"id\": 174, \"q_img\": \"000000044000.jpg\", \"q_text\": \"Find a picture that also a bathroom with a rectangular bathtub, but has the same shape, is fancier and has a window over it.\"}\n{\"id\": 175, \"q_img\": \"000000535205.jpg\", \"q_text\": \"Find a picture that also a woman talking on the phone, but wears the hijab and there are people in the background.\"}\n{\"id\": 176, \"q_img\": \"000000552835.jpg\", \"q_text\": \"Find a picture that also a person taking a selfie with a camera in the mirror of a motorbike, but has no cars in the background and is shot in greyscale.\"}\n{\"id\": 177, \"q_img\": \"000000001710.jpg\", \"q_text\": \"Find a picture that also a man and girl playing with the Wii, but are much older and both wear a blue tie-shirt.\"}\n{\"id\": 178, \"q_img\": \"000000369289.jpg\", \"q_text\": \"Find a picture that also a close-up hotdog held by a hand, but is without napkins and has more ketchup on it.\"}\n{\"id\": 179, \"q_img\": \"000000076169.jpg\", \"q_text\": \"Find a picture that also a phone held by a hand seen from a close distance, but is seen from the front and the phone is an iPhone.\"}\n{\"id\": 180, \"q_img\": \"000000469021.jpg\", \"q_text\": \"Find a picture that also several vases of different color, but has price tags and the photo is shot indoors.\"}\n{\"id\": 181, \"q_img\": \"000000197869.jpg\", \"q_text\": \"Find a picture that also a food truck, but is blue, the photo has more people and is taken during the day.\"}\n{\"id\": 182, \"q_img\": \"000000557390.jpg\", \"q_text\": \"Find a picture that also a pair of zebras, but has a giraffe and shows some trees in the background.\"}\n{\"id\": 183, \"q_img\": \"000000521765.jpg\", \"q_text\": \"Find a picture that also a woman sitting on a bench with trees in the background, but has no buildings in the background, shows more trees, is zoomed in and the girl is not smiling.\"}\n{\"id\": 184, \"q_img\": \"000000485244.jpg\", \"q_text\": \"Find a picture that also a surfboard held vertically leaning on the grass, but is mostly white and is held by a person.\"}\n{\"id\": 185, \"q_img\": \"000000237588.jpg\", \"q_text\": \"Find a picture that also multiple elephants, but are in larger numbers and are on the shore of a body of water.\"}\n{\"id\": 186, \"q_img\": \"000000414278.jpg\", \"q_text\": \"Find a picture that also a close-up octagonal stop sign, but has the same shape and is of a different color.\"}\n{\"id\": 187, \"q_img\": \"000000440512.jpg\", \"q_text\": \"Find a picture that also a kitchen counter with bottles on it, but has more bottles.\"}\n{\"id\": 188, \"q_img\": \"000000224782.jpg\", \"q_text\": \"Find a picture that also a cat sitting on a laptop, but has a couch in the background.\"}\n{\"id\": 189, \"q_img\": \"000000369337.jpg\", \"q_text\": \"Find a picture that also a black cat, but has the same color and there is also a printer.\"}\n{\"id\": 190, \"q_img\": \"000000362550.jpg\", \"q_text\": \"Find a picture that also a person walking a dog in a park, but has a kite instead of an umbrella and the photo shows a bluer sky.\"}\n{\"id\": 191, \"q_img\": \"000000010348.jpg\", \"q_text\": \"Find a picture that also people at a table eating pizza, but has more people and the table is round.\"}\n{\"id\": 192, \"q_img\": \"000000049089.jpg\", \"q_text\": \"Find a picture that also a train seen from the front, but has a yellow-colored locomotive, is in a more rural area and the sky is bluer.\"}\n{\"id\": 193, \"q_img\": \"000000101556.jpg\", \"q_text\": \"Find a picture that also a snowboarder, but is falling into the water.\"}\n{\"id\": 194, \"q_img\": \"000000247454.jpg\", \"q_text\": \"Find a picture that also a vase with flowers inside, but is made of clear blue glass.\"}\n{\"id\": 195, \"q_img\": \"000000529535.jpg\", \"q_text\": \"Find a picture that also an old woman using a device on her own, but is using a laptop instead of a phone.\"}\n{\"id\": 196, \"q_img\": \"000000568986.jpg\", \"q_text\": \"Find a picture that also a woman talking on the phone, but has a cappuccino in her hand and is wearing a black jacket.\"}\n{\"id\": 197, \"q_img\": \"000000003744.jpg\", \"q_text\": \"Find a picture that also an oven, but is zoomed in and there are an oven and cooktop burners over it instead of a stove.\"}\n{\"id\": 198, \"q_img\": \"000000043370.jpg\", \"q_text\": \"Find a picture that also an animal looking at itself in a mirror, but has a cat instead of a dog and the mirror is smaller and of a different shape.\"}\n{\"id\": 199, \"q_img\": \"000000495913.jpg\", \"q_text\": \"Find a picture that also a person in a wheelchair on a tennis court, but has more players which are facing the camera.\"}\n{\"id\": 200, \"q_img\": \"000000509353.jpg\", \"q_text\": \"Find a picture that also food in a showcase, but has pizzas instead of cakes and shows a person on the right.\"}\n{\"id\": 201, \"q_img\": \"000000247053.jpg\", \"q_text\": \"Find a picture that also a double-decker bus seen from a three-quarter view, but is green and the photo is shot from the same angle.\"}\n{\"id\": 202, \"q_img\": \"000000416783.jpg\", \"q_text\": \"Find a picture that also a man carrying a trolley looking at the camera, but wears a pouch and the photo is shot outdoor.\"}\n{\"id\": 203, \"q_img\": \"000000466894.jpg\", \"q_text\": \"Find a picture that also a fire truck seen as a whole, but is seen from the side, has the same color and the photo shows more sky in the background.\"}\n{\"id\": 204, \"q_img\": \"000000340582.jpg\", \"q_text\": \"Find a picture that also a close-up animal, but has a big horn sheep instead of a horse and shows rocks in the background.\"}\n{\"id\": 205, \"q_img\": \"000000110669.jpg\", \"q_text\": \"Find a picture that also a policeman on a motorbike, but is on crosswalk stripes and the photo is taken from the same angle.\"}\n{\"id\": 206, \"q_img\": \"000000396060.jpg\", \"q_text\": \"Find a picture that also a traffic light at sunrise, but has several cars and the sky has a similar color.\"}\n{\"id\": 207, \"q_img\": \"000000316886.jpg\", \"q_text\": \"Find a picture that also a plate with a sandwich seen from above, but is on a wooden table with a glass of wine.\"}\n{\"id\": 208, \"q_img\": \"000000494037.jpg\", \"q_text\": \"Find a picture that also a person seen through a glass of wine, but shows more people.\"}\n{\"id\": 209, \"q_img\": \"000000394689.jpg\", \"q_text\": \"Find a picture that also a parrot, but is resting on the handlebar of a bicycle.\"}\n{\"id\": 210, \"q_img\": \"000000190493.jpg\", \"q_text\": \"Find a picture that also two men in a street on a horse, but wears a cowboy hat and both horses are brown.\"}\n{\"id\": 211, \"q_img\": \"000000551782.jpg\", \"q_text\": \"Find a picture that also two phones seen from the top, but shows a phone charger next to them.\"}\n{\"id\": 212, \"q_img\": \"000000450103.jpg\", \"q_text\": \"Find a picture that also two people looking at a computer screen, but are standing and the image is in color.\"}\n{\"id\": 213, \"q_img\": \"000000145435.jpg\", \"q_text\": \"Find a picture that also a steam train with a black locomotive, but is seen from the side and a farther distance.\"}\n{\"id\": 214, \"q_img\": \"000000247574.jpg\", \"q_text\": \"Find a picture that also a man flying a kite seen from the back, but has blonde hair, the photo is zoomed in and has only the sky in the background.\"}\n{\"id\": 215, \"q_img\": \"000000413621.jpg\", \"q_text\": \"Find a picture that also teddy bears sitting at a dining table, but has fewer plushies.\"}\n{\"id\": 216, \"q_img\": \"000000192848.jpg\", \"q_text\": \"Find a picture that also a pair of shoes, but is on the floor instead of a bed and there is a dog next to it.\"}\n{\"id\": 217, \"q_img\": \"000000532567.jpg\", \"q_text\": \"Find a picture that also multiple wall-mounted urinals, but are two and are mounted on a white wall.\"}\n{\"id\": 218, \"q_img\": \"000000194953.jpg\", \"q_text\": \"Find a picture that also a yellow school bus, but has children getting on it.\"}\n{\"id\": 219, \"q_img\": \"000000337128.jpg\", \"q_text\": \"Find a picture that also a steam train going towards the camera, but is passing under an arch structure and is seen from the same angle.\"}\n{\"id\": 220, \"q_img\": \"000000539443.jpg\", \"q_text\": \"Find a picture that also people sitting with luggage in front of them, but is shot from the front and has plants in the background.\"}\n{\"id\": 221, \"q_img\": \"000000142397.jpg\", \"q_text\": \"Find a picture that also a horse-drawn carriage, but shows no buildings and is set on snow.\"}\n{\"id\": 222, \"q_img\": \"000000415356.jpg\", \"q_text\": \"Find a picture that also a closed toilet, but is in a wooden cabin.\"}\n{\"id\": 223, \"q_img\": \"000000183739.jpg\", \"q_text\": \"Find a picture that also a person fixing a kite on the ground, but has two people fixing it and the photo is set outdoors.\"}\n{\"id\": 224, \"q_img\": \"000000140958.jpg\", \"q_text\": \"Find a picture that also a man with glasses dressed in a suit and tie, but has a similar outfit and is next to a blackboard.\"}\n{\"id\": 225, \"q_img\": \"000000171602.jpg\", \"q_text\": \"Find a picture that also a man on a motocross bike, but is in the air with his hands on the saddle while performing a trick.\"}\n{\"id\": 226, \"q_img\": \"000000096302.jpg\", \"q_text\": \"Find a picture that also a man throwing a frisbee, but is wearing a light blue shirt instead of a suit.\"}\n{\"id\": 227, \"q_img\": \"000000560706.jpg\", \"q_text\": \"Find a picture that also a red fire hydrant, but has the same color and is pouring out water.\"}\n{\"id\": 228, \"q_img\": \"000000296142.jpg\", \"q_text\": \"Find a picture that also some parked busses, but have a different color, there are two of them and they are one behind the other.\"}\n{\"id\": 229, \"q_img\": \"000000572788.jpg\", \"q_text\": \"Find a picture that also a parking meter, but has a similar shape, is green and seen from a slightly farther distance.\"}\n{\"id\": 230, \"q_img\": \"000000556084.jpg\", \"q_text\": \"Find a picture that also a toilet placed on the outside, but shows more toilets and they are seen from the front.\"}\n{\"id\": 231, \"q_img\": \"000000266467.jpg\", \"q_text\": \"Find a picture that also playground equipment, but is taken at night and there are more trees.\"}\n{\"id\": 232, \"q_img\": \"000000046755.jpg\", \"q_text\": \"Find a picture that also a child with a closed suitcase, but shows the person sitting on the suitcase.\"}\n{\"id\": 233, \"q_img\": \"000000390092.jpg\", \"q_text\": \"Find a picture that also a peacock, but is next to a car and the photo shows no people.\"}\n{\"id\": 234, \"q_img\": \"000000132884.jpg\", \"q_text\": \"Find a picture that also children around a dining table, but are all girls and the photo is shot from the side.\"}\n{\"id\": 235, \"q_img\": \"000000429515.jpg\", \"q_text\": \"Find a picture that also two people posing for a picture in front of a truck, but shows the same number of people and two dogs.\"}\n{\"id\": 236, \"q_img\": \"000000025270.jpg\", \"q_text\": \"Find a picture that also a person talking on a cell phone while walking a dog on a leash, but wears sunglasses and is seen from the front.\"}\n{\"id\": 237, \"q_img\": \"000000378472.jpg\", \"q_text\": \"Find a picture that also a black cat, but has the same color and is sitting on a chair on the grass.\"}\n{\"id\": 238, \"q_img\": \"000000482727.jpg\", \"q_text\": \"Find a picture that also a man wearing a tie seen from the front, but is shot from the same angle and the man is wearing a t-shirt instead of a shirt.\"}\n{\"id\": 239, \"q_img\": \"000000315484.jpg\", \"q_text\": \"Find a picture that also a flying airplane, but is half green and half white.\"}\n{\"id\": 240, \"q_img\": \"000000238923.jpg\", \"q_text\": \"Find a picture that also a black dog with an object in its mouth, but has the same color and there is a plastic bottle instead of a frisbee.\"}\n{\"id\": 241, \"q_img\": \"000000567023.jpg\", \"q_text\": \"Find a picture that also a laptop, but is on the floor, the photo shows more cables and no animals.\"}\n{\"id\": 242, \"q_img\": \"000000355682.jpg\", \"q_text\": \"Find a picture that also a bike next to a bench, but has a book and is zoomed in.\"}\n{\"id\": 243, \"q_img\": \"000000512744.jpg\", \"q_text\": \"Find a picture that also a statue of an elephant, but is more colorful and is surrounded by nature.\"}\n{\"id\": 244, \"q_img\": \"000000004374.jpg\", \"q_text\": \"Find a picture that also a person brushing his teeth taking a picture in the mirror, but is a woman and the teeth are visible.\"}\n{\"id\": 245, \"q_img\": \"000000283110.jpg\", \"q_text\": \"Find a picture that also a teddy bear, but is on top of some books.\"}\n{\"id\": 246, \"q_img\": \"000000417897.jpg\", \"q_text\": \"Find a picture that also a luggage carousel in an airport seen from the side, but is taken from the same perspective and has people waiting for their luggage.\"}\n{\"id\": 247, \"q_img\": \"000000405625.jpg\", \"q_text\": \"Find a picture that also a feature phone, but is on the original packaging instead of being held by a hand.\"}\n{\"id\": 248, \"q_img\": \"000000250322.jpg\", \"q_text\": \"Find a picture that also a white sink and a white toilet in a bathroom, but is taken from the same angle and there is a washing machine.\"}\n{\"id\": 249, \"q_img\": \"000000023389.jpg\", \"q_text\": \"Find a picture that also a PC mouse, but is in its original packaging.\"}\n{\"id\": 250, \"q_img\": \"000000468831.jpg\", \"q_text\": \"Find a picture that also a toilet and a sink in a bathroom, but shows a mirror instead of a window and there is a fully-tiled wall that is not white.\"}\n{\"id\": 251, \"q_img\": \"000000304135.jpg\", \"q_text\": \"Find a picture that also two zebras, but are in the same number and in the water.\"}\n{\"id\": 252, \"q_img\": \"000000371460.jpg\", \"q_text\": \"Find a picture that also a red train seen from the front, but has the same color, looks more modern and is stopped at a station.\"}\n{\"id\": 253, \"q_img\": \"000000500655.jpg\", \"q_text\": \"Find a picture that also a woman hugging a giant fake bear, but is shot outdoors and the bear is of a different color.\"}\n{\"id\": 254, \"q_img\": \"000000232731.jpg\", \"q_text\": \"Find a picture that also a cat, but has a bottle of Coca-Cola.\"}\n{\"id\": 255, \"q_img\": \"000000227028.jpg\", \"q_text\": \"Find a picture that also a rainbow-colored rain umbrella, but is being held up and there is the sky in the background.\"}\n{\"id\": 256, \"q_img\": \"000000132435.jpg\", \"q_text\": \"Find a picture that also fruit on a plate, but shows orange slices instead of a banana.\"}\n{\"id\": 257, \"q_img\": \"000000358262.jpg\", \"q_text\": \"Find a picture that also a clean desk with a computer, but is mostly white and there are flowers on it.\"}\n{\"id\": 258, \"q_img\": \"000000574048.jpg\", \"q_text\": \"Find a picture that also a clock lamppost, but is inside a mall and the photo is taken during the day.\"}\n{\"id\": 259, \"q_img\": \"000000383486.jpg\", \"q_text\": \"Find a picture that also people sitting on the metro seen from the front, but is taken from the same perspective, has more people and nobody is wearing a mask.\"}\n{\"id\": 260, \"q_img\": \"000000261017.jpg\", \"q_text\": \"Find a picture that also a man seen from the front sitting in a restaurant with lots of dishes in front of him, but is shot from the same angle and there are more plates.\"}\n{\"id\": 261, \"q_img\": \"000000043426.jpg\", \"q_text\": \"Find a picture that also a wood-fired pizza oven, but is seen up close, there is a pizza cooking and no people are visible.\"}\n{\"id\": 262, \"q_img\": \"000000158650.jpg\", \"q_text\": \"Find a picture that also multiple fruits on a tree, but is zoomed in and shows apples instead of bananas.\"}\n{\"id\": 263, \"q_img\": \"000000114395.jpg\", \"q_text\": \"Find a picture that also several oranges, but are fewer and in a sink.\"}\n{\"id\": 264, \"q_img\": \"000000456139.jpg\", \"q_text\": \"Find a picture that also a bathtub, but has a toilet next to it and has a rectangular shape, the photo shows a window over it and no sink.\"}\n{\"id\": 265, \"q_img\": \"000000550464.jpg\", \"q_text\": \"Find a picture that also a clock, but has only one of them and is set on a beach.\"}\n{\"id\": 266, \"q_img\": \"000000462415.jpg\", \"q_text\": \"Find a picture that also a bus seen from the side, but has a different color and is on a bridge.\"}\n{\"id\": 267, \"q_img\": \"000000571096.jpg\", \"q_text\": \"Find a picture that also a red traffic light at an intersection, but is seen through a windshield covered in droplets.\"}\n{\"id\": 268, \"q_img\": \"000000493063.jpg\", \"q_text\": \"Find a picture that also a man biting food, but is shot from the side and is set inside a stadium.\"}\n{\"id\": 269, \"q_img\": \"000000556362.jpg\", \"q_text\": \"Find a picture that also a flock of sheep, but shows no people and no buildings and has snow instead of grass.\"}\n{\"id\": 270, \"q_img\": \"000000086746.jpg\", \"q_text\": \"Find a picture that also a light brown suitcase, but has the same color and the photo has the person sitting down instead of standing up.\"}\n{\"id\": 271, \"q_img\": \"000000215432.jpg\", \"q_text\": \"Find a picture that also an ancient vase in a display case with a person looking at it, but is of a different color and is surrounded by more people.\"}\n{\"id\": 272, \"q_img\": \"000000151918.jpg\", \"q_text\": \"Find a picture that also a little girl dressed in pink holding a tennis racket, but is wearing the same color and is not facing the camera.\"}\n{\"id\": 273, \"q_img\": \"000000356162.jpg\", \"q_text\": \"Find a picture that also a specific dog, but has no people, is zoomed in and the dog is looking at the camera.\"}\n{\"id\": 274, \"q_img\": \"000000084707.jpg\", \"q_text\": \"Find a picture that also a bear, but shows only one of them and it is on a paved road.\"}\n{\"id\": 275, \"q_img\": \"000000505974.jpg\", \"q_text\": \"Find a picture that also two blue cross street name signs, but has the same color and they are seen from a closer distance.\"}\n{\"id\": 276, \"q_img\": \"000000324137.jpg\", \"q_text\": \"Find a picture that also a parked orange and black motorbike, but is the same color and some hills are visible in the background.\"}\n{\"id\": 277, \"q_img\": \"000000405722.jpg\", \"q_text\": \"Find a picture that also luggage, but has more of them and they are in the trunk of a car.\"}\n{\"id\": 278, \"q_img\": \"000000244321.jpg\", \"q_text\": \"Find a picture that also some pizza in the foreground, but is on a table with a red checked tablecloth.\"}\n{\"id\": 279, \"q_img\": \"000000003318.jpg\", \"q_text\": \"Find a picture that also a close-up fire hydrant, but is yellow with a bit of snow on it and the photo has a different background.\"}\n{\"id\": 280, \"q_img\": \"000000190552.jpg\", \"q_text\": \"Find a picture that also a man on a skateboard seen from the back, but is on the road and the photo shows a police car.\"}\n{\"id\": 281, \"q_img\": \"000000321586.jpg\", \"q_text\": \"Find a picture that also a person in the foreground playing with a frisbee, but is a woman who is wearing sunglasses and more trees are visible in the background.\"}\n{\"id\": 282, \"q_img\": \"000000074741.jpg\", \"q_text\": \"Find a picture that also a person using a laptop, but shows only one person, has no walls and the background has a plain color and is less every day.\"}\n{\"id\": 283, \"q_img\": \"000000138260.jpg\", \"q_text\": \"Find a picture that also two white polar bears, but are on the cover of a book.\"}\n{\"id\": 284, \"q_img\": \"000000349029.jpg\", \"q_text\": \"Find a picture that also a white refrigerator, but is half empty and there is a person next to it.\"}\n{\"id\": 285, \"q_img\": \"000000384115.jpg\", \"q_text\": \"Find a picture that also a glass and a bottle of wine, but are in front of a microwave.\"}\n{\"id\": 286, \"q_img\": \"000000026502.jpg\", \"q_text\": \"Find a picture that also a queen-size bed, but shows lights under it and the photo is darker.\"}\n{\"id\": 287, \"q_img\": \"000000257479.jpg\", \"q_text\": \"Find a picture that also oranges, but are divided into multiple fruit crates and the photo shows a vehicle.\"}\n{\"id\": 288, \"q_img\": \"000000142064.jpg\", \"q_text\": \"Find a picture that also a person cooking in front of a kitchen counter, but is shot from the side and shows only one person.\"}\n{\"id\": 289, \"q_img\": \"000000158252.jpg\", \"q_text\": \"Find a picture that also people gathered in a living room, but has a bed and there are fewer people.\"}\n{\"id\": 290, \"q_img\": \"000000233494.jpg\", \"q_text\": \"Find a picture that also a man talking on the phone while using a laptop, but is sitting and the photo is taken in color.\"}\n{\"id\": 291, \"q_img\": \"000000132603.jpg\", \"q_text\": \"Find a picture that also an owl resting on the hand of a person, but has its head turned away from the camera.\"}\n{\"id\": 292, \"q_img\": \"000000313262.jpg\", \"q_text\": \"Find a picture that also a rowing boat with people on it, but has more of them and there is a flag.\"}\n{\"id\": 293, \"q_img\": \"000000442768.jpg\", \"q_text\": \"Find a picture that also a close-up small brownish bird, but is of the same color, in the same number and is placed on a chair.\"}\n{\"id\": 294, \"q_img\": \"000000123887.jpg\", \"q_text\": \"Find a picture that also a boat on the ground, but is rustier and there is a lighthouse in the background.\"}\n{\"id\": 295, \"q_img\": \"000000216411.jpg\", \"q_text\": \"Find a picture that also a Wii controller, but is only one and is of a different color.\"}\n{\"id\": 296, \"q_img\": \"000000065607.jpg\", \"q_text\": \"Find a picture that also an outside clock, but is attached to the wall from its side and has a gold color.\"}\n{\"id\": 297, \"q_img\": \"000000413173.jpg\", \"q_text\": \"Find a picture that also multiple people carrying a refrigerator, but is shot from a different angle and there are no flowers in the background.\"}\n{\"id\": 298, \"q_img\": \"000000523149.jpg\", \"q_text\": \"Find a picture that also a person riding a motorbike, but is only one has there is lake in the background.\"}\n{\"id\": 299, \"q_img\": \"000000139656.jpg\", \"q_text\": \"Find a picture that also a man shot in a half-length portrait from the front, but is wearing a red tie and the photo is taken from the same angle and outdoors.\"}\n{\"id\": 300, \"q_img\": \"000000372291.jpg\", \"q_text\": \"Find a picture that also a clock tower, but is surrounded by water and has a bluer sky in the background.\"}\n{\"id\": 301, \"q_img\": \"000000317518.jpg\", \"q_text\": \"Find a picture that also a red umbrella, but has the same color, shows a person and the photo is shot from the top.\"}\n{\"id\": 302, \"q_img\": \"000000356539.jpg\", \"q_text\": \"Find a picture that also a teddy bear, but is playing the guitar and the photo is taken indoors.\"}\n{\"id\": 303, \"q_img\": \"000000244696.jpg\", \"q_text\": \"Find a picture that also some sheep, but shows more of them and there is a body of water in the foreground.\"}\n{\"id\": 304, \"q_img\": \"000000031395.jpg\", \"q_text\": \"Find a picture that also two cows on a beach, but has the same number of them and is taken from the side.\"}\n{\"id\": 305, \"q_img\": \"000000566570.jpg\", \"q_text\": \"Find a picture that also a close-up soup, but is inside a white bowl and the photo shows a spoon in it and other food next to it.\"}\n{\"id\": 306, \"q_img\": \"000000096410.jpg\", \"q_text\": \"Find a picture that also a person on a couch, but shows three people, two of whom are eating.\"}\n{\"id\": 307, \"q_img\": \"000000301688.jpg\", \"q_text\": \"Find a picture that also a person holding an umbrella under the rain in greyscale, but shows also a child under the umbrella.\"}\n{\"id\": 308, \"q_img\": \"000000173679.jpg\", \"q_text\": \"Find a picture that also a person talking on the phone, but is wearing a military uniform.\"}\n{\"id\": 309, \"q_img\": \"000000348127.jpg\", \"q_text\": \"Find a picture that also a little girl holding an umbrella, but is pink and the girl is standing on the grass.\"}\n{\"id\": 310, \"q_img\": \"000000251989.jpg\", \"q_text\": \"Find a picture that also a cat, but is sitting at a dining table.\"}\n{\"id\": 311, \"q_img\": \"000000248698.jpg\", \"q_text\": \"Find a picture that also a stationary standing skier looking at the camera, but is wearing a helmet and the photo does not show the sky in the background.\"}\n{\"id\": 312, \"q_img\": \"000000139138.jpg\", \"q_text\": \"Find a picture that also a close-up toilet, but shows a person who is throwing up in it.\"}\n{\"id\": 313, \"q_img\": \"000000362945.jpg\", \"q_text\": \"Find a picture that also a white toilet, but has the same color and a wooden cover.\"}\n{\"id\": 314, \"q_img\": \"000000268878.jpg\", \"q_text\": \"Find a picture that also a tennis player hitting a two-handed backhand, but shows a woman instead of a man and she is wearing a visor hat.\"}\n{\"id\": 315, \"q_img\": \"000000410943.jpg\", \"q_text\": \"Find a picture that also a person with a laptop sitting in a living room, but is wearing a Santa hat.\"}\n{\"id\": 316, \"q_img\": \"000000397310.jpg\", \"q_text\": \"Find a picture that also some food in the foreground in front of a keyboard, but has a keyboard instead of a laptop.\"}\n{\"id\": 317, \"q_img\": \"000000430266.jpg\", \"q_text\": \"Find a picture that also a mattress with something laying on it, but has a dog instead of a child.\"}\n{\"id\": 318, \"q_img\": \"000000444637.jpg\", \"q_text\": \"Find a picture that also a child cutting paper with scissors, but has only one person seen from a closer distance.\"}\n{\"id\": 319, \"q_img\": \"000000188881.jpg\", \"q_text\": \"Find a picture that also a small bird standing on the back of a chair, but has only one bird and the photo is shot in color.\"}\n{\"id\": 320, \"q_img\": \"000000297614.jpg\", \"q_text\": \"Find a picture that also a bear, but is white and is at the water's edge.\"}\n{\"id\": 321, \"q_img\": \"000000208774.jpg\", \"q_text\": \"Find a picture that also two cats playing with each other, but have a different colors and they are in a bathtub.\"}\n{\"id\": 322, \"q_img\": \"000000023885.jpg\", \"q_text\": \"Find a picture that also a black couch, but has the same color and is leaning against a wall.\"}\n{\"id\": 323, \"q_img\": \"000000477271.jpg\", \"q_text\": \"Find a picture that also a child playing tennis, but is hitting a ball helped by a tennis instructor.\"}\n{\"id\": 324, \"q_img\": \"000000390151.jpg\", \"q_text\": \"Find a picture that also a black umbrella on the ground outdoors, but has the same color and is a real version of the depicted object.\"}\n{\"id\": 325, \"q_img\": \"000000400806.jpg\", \"q_text\": \"Find a picture that also a teddy bear in the foreground, but replaces one teddy bear with a baby wearing a white onesie.\"}\n{\"id\": 326, \"q_img\": \"000000573917.jpg\", \"q_text\": \"Find a picture that also a toaster, but has a different color and is next to a window.\"}\n{\"id\": 327, \"q_img\": \"000000048012.jpg\", \"q_text\": \"Find a picture that also a professional kitchen with cooks, but has two of them and they are making pizzas.\"}\n{\"id\": 328, \"q_img\": \"000000495249.jpg\", \"q_text\": \"Find a picture that also a person seen from the back with a red umbrella, but has the same color and the photo is set in an alleyway.\"}\n{\"id\": 329, \"q_img\": \"000000094107.jpg\", \"q_text\": \"Find a picture that also a dining table with candles outdoors at night, but has a darker background, fewer tables and people sitting.\"}\n{\"id\": 330, \"q_img\": \"000000364315.jpg\", \"q_text\": \"Find a picture that also a close-up shell flip phone held by a hand, but is black and the photo is shot outdoors.\"}\n{\"id\": 331, \"q_img\": \"000000013527.jpg\", \"q_text\": \"Find a picture that also a silver commercial kitchen with cooks cooking, but has the same color but there are two people in it.\"}\n{\"id\": 332, \"q_img\": \"000000552763.jpg\", \"q_text\": \"Find a picture that also seagulls seen from the side, but is shot in grayscale.\"}\n{\"id\": 333, \"q_img\": \"000000198461.jpg\", \"q_text\": \"Find a picture that also a goalkeeper kicking the ball, but is taken from the front and shows no people in the background.\"}\n{\"id\": 334, \"q_img\": \"000000222575.jpg\", \"q_text\": \"Find a picture that also a band playing on a stage, but is shot from the front and has umbrellas in the background.\"}\n{\"id\": 335, \"q_img\": \"000000010676.jpg\", \"q_text\": \"Find a picture that also a half-peeled banana, but is being held by a child and the photo is taken outdoors.\"}\n{\"id\": 336, \"q_img\": \"000000104471.jpg\", \"q_text\": \"Find a picture that also a fire truck, but is blue and there are people in the background.\"}\n{\"id\": 337, \"q_img\": \"000000231040.jpg\", \"q_text\": \"Find a picture that also a man holding multiple hot dogs, but are placed on a white plate.\"}\n{\"id\": 338, \"q_img\": \"000000097087.jpg\", \"q_text\": \"Find a picture that also an outdoor merchandise display, but has plush toys instead of fruit.\"}\n{\"id\": 339, \"q_img\": \"000000256717.jpg\", \"q_text\": \"Find a picture that also an object in the foreground and a TV in the background, but has flowers instead of a laptop.\"}\n{\"id\": 340, \"q_img\": \"000000189284.jpg\", \"q_text\": \"Find a picture that also a silver professional kitchen, but is bigger and has people in it.\"}\n{\"id\": 341, \"q_img\": \"000000352891.jpg\", \"q_text\": \"Find a picture that also laptops on a table with no visible people, but has more of them and the table is square instead of round.\"}\n{\"id\": 342, \"q_img\": \"000000575700.jpg\", \"q_text\": \"Find a picture that also an abandoned refrigerator, but has more of them and there is a blue sky in the background.\"}\n{\"id\": 343, \"q_img\": \"000000544174.jpg\", \"q_text\": \"Find a picture that also a horse head peeking out of a window, but has a different color and the photo is taken from a closer distance.\"}\n{\"id\": 344, \"q_img\": \"000000105616.jpg\", \"q_text\": \"Find a picture that also a woman holding an umbrella while it is snowing, but is taken in the daytime and the woman is seen from the front.\"}\n{\"id\": 345, \"q_img\": \"000000104598.jpg\", \"q_text\": \"Find a picture that also a person pouring wine into a wine glass, but has red wine instead of white wine and shows no other people around.\"}\n{\"id\": 346, \"q_img\": \"000000457471.jpg\", \"q_text\": \"Find a picture that also a person riding an elephant, but is playing soccer and the sky is not visible in the background.\"}\n{\"id\": 347, \"q_img\": \"000000476043.jpg\", \"q_text\": \"Find a picture that also a wooden surfboard, but has the same color and is held by a man.\"}\n{\"id\": 348, \"q_img\": \"000000558853.jpg\", \"q_text\": \"Find a picture that also a group of people with skis seen from the front, but has more people, they are younger and the photo is shot in color.\"}\n{\"id\": 349, \"q_img\": \"000000290907.jpg\", \"q_text\": \"Find a picture that also a dog, but has only one of them and it is behind a railing.\"}\n{\"id\": 350, \"q_img\": \"000000054659.jpg\", \"q_text\": \"Find a picture that also a round mirror over a sink, but has the same shape and is only one.\"}\n{\"id\": 351, \"q_img\": \"000000100748.jpg\", \"q_text\": \"Find a picture that also a brown horse, but is on a green field, is seen from the side and has a woman with a helmet riding it.\"}\n{\"id\": 352, \"q_img\": \"000000156470.jpg\", \"q_text\": \"Find a picture that also a man riding a motocross bike, but is of the same type and there is snow instead of dirt.\"}\n{\"id\": 353, \"q_img\": \"000000427112.jpg\", \"q_text\": \"Find a picture that also a cat next to an object, but has a bowl of fruit instead of a clock.\"}\n{\"id\": 354, \"q_img\": \"000000121550.jpg\", \"q_text\": \"Find a picture that also a birthday cake, but is blue instead of brown and has Smarties.\"}\n{\"id\": 355, \"q_img\": \"000000068362.jpg\", \"q_text\": \"Find a picture that also multiple scissors, but are of a single color and are held in one hand.\"}\n{\"id\": 356, \"q_img\": \"000000265678.jpg\", \"q_text\": \"Find a picture that also a cat under an object, but is set outdoors and has a bench instead of an umbrella.\"}\n{\"id\": 357, \"q_img\": \"000000266113.jpg\", \"q_text\": \"Find a picture that also a cat in front of a TV seen from the back, but has a bird instead of a soccer match.\"}\n{\"id\": 358, \"q_img\": \"000000123504.jpg\", \"q_text\": \"Find a picture that also a pizza oven, but is grey and there is no person.\"}\n{\"id\": 359, \"q_img\": \"000000253559.jpg\", \"q_text\": \"Find a picture that also small boats on a beach, but shows one cow in the foreground.\"}\n{\"id\": 360, \"q_img\": \"000000494789.jpg\", \"q_text\": \"Find a picture that also a white toilet, but has the same color, is only one and is on the grass.\"}\n{\"id\": 361, \"q_img\": \"000000193278.jpg\", \"q_text\": \"Find a picture that also a teddy bear, but is wearing glasses and is sitting in front of a computer.\"}\n{\"id\": 362, \"q_img\": \"000000048603.jpg\", \"q_text\": \"Find a picture that also a bag in the back of a parked vehicle, but shows a motorbike instead of a bike.\"}\n{\"id\": 363, \"q_img\": \"000000231947.jpg\", \"q_text\": \"Find a picture that also a bear, but is white and has some food in its mouth.\"}\n{\"id\": 364, \"q_img\": \"000000026693.jpg\", \"q_text\": \"Find a picture that also a foreground smartly dressed man wearing glasses, but is wearing a hat and the photo has a plain background.\"}\n{\"id\": 365, \"q_img\": \"000000074021.jpg\", \"q_text\": \"Find a picture that also a phone, but is seen from a closer distance and is next to a watch.\"}\n{\"id\": 366, \"q_img\": \"000000215423.jpg\", \"q_text\": \"Find a picture that also a grey parrot, but has the same color, is eating and is seen from a closer distance.\"}\n{\"id\": 367, \"q_img\": \"000000051252.jpg\", \"q_text\": \"Find a picture that also a person water skiing, but has two people and the sky is not visible.\"}\n{\"id\": 368, \"q_img\": \"000000221604.jpg\", \"q_text\": \"Find a picture that also a man wearing a shirt and a tie, but is next to a window, has no jacket and the photo is zoomed in and in color.\"}\n{\"id\": 369, \"q_img\": \"000000542742.jpg\", \"q_text\": \"Find a picture that also a single book on a bed, but is closed and the photo is zoomed in.\"}\n{\"id\": 370, \"q_img\": \"000000464933.jpg\", \"q_text\": \"Find a picture that also a living room, but has a TV and a big red Indian carpet.\"}\n{\"id\": 371, \"q_img\": \"000000204267.jpg\", \"q_text\": \"Find a picture that also a person on a motorbike with a gas station in the background, but has a similar background and the photo is taken from a farther distance.\"}\n{\"id\": 372, \"q_img\": \"000000367855.jpg\", \"q_text\": \"Find a picture that also silhouettes of multiple birds, but are on a tree and no buildings are visible.\"}\n{\"id\": 373, \"q_img\": \"000000075991.jpg\", \"q_text\": \"Find a picture that also several animals shot with a 3D effect, but shows a different animal and the image has the same effect.\"}\n{\"id\": 374, \"q_img\": \"000000002705.jpg\", \"q_text\": \"Find a picture that also a person sitting at a desk with a laptop seen from above, but shows one notebook on the desk.\"}\n{\"id\": 375, \"q_img\": \"000000460066.jpg\", \"q_text\": \"Find a picture that also a person playing an instrument in a house, but has more players and shows no other people.\"}\n{\"id\": 376, \"q_img\": \"000000194104.jpg\", \"q_text\": \"Find a picture that also a person with multiple suitcases looking at the camera, but shows more people smiling at the camera.\"}\n{\"id\": 377, \"q_img\": \"000000179337.jpg\", \"q_text\": \"Find a picture that also a person with an object on their head, but is a man and has bananas instead of a pot.\"}\n{\"id\": 378, \"q_img\": \"000000452266.jpg\", \"q_text\": \"Find a picture that also a clamshell phone, but is on a beach and is open.\"}\n{\"id\": 379, \"q_img\": \"000000271281.jpg\", \"q_text\": \"Find a picture that also a man taking a picture, but is taken from a farther distance and there is the sea and no buildings in the background.\"}\n{\"id\": 380, \"q_img\": \"000000025671.jpg\", \"q_text\": \"Find a picture that also a person lying on a surfboard seen from the front, but is shot from the same angle and has only one person.\"}\n{\"id\": 381, \"q_img\": \"000000120788.jpg\", \"q_text\": \"Find a picture that also a person holding a surfboard under his arm, but is shot at night and is taken from behind.\"}\n{\"id\": 382, \"q_img\": \"000000123988.jpg\", \"q_text\": \"Find a picture that also people eating outdoors at dining tables with umbrellas, but is taken from a closer distance, has a mural in the background and the umbrellas have different colors.\"}\n{\"id\": 383, \"q_img\": \"000000056056.jpg\", \"q_text\": \"Find a picture that also a stop sign, but is taken from inside a vehicle.\"}\n{\"id\": 384, \"q_img\": \"000000111615.jpg\", \"q_text\": \"Find a picture that also an old smartphone, but is shown to the camera by a person visible in the background.\"}\n{\"id\": 385, \"q_img\": \"000000509762.jpg\", \"q_text\": \"Find a picture that also a person using a laptop, but is taken indoors and there are three people sitting on one sofa.\"}\n{\"id\": 386, \"q_img\": \"000000243286.jpg\", \"q_text\": \"Find a picture that also an orange, but shows only one and is on a tree.\"}\n{\"id\": 387, \"q_img\": \"000000061987.jpg\", \"q_text\": \"Find a picture that also a parked car with a surfboard on it, but is of a different color, the photo is shot from the back and during the day.\"}\n{\"id\": 388, \"q_img\": \"000000373814.jpg\", \"q_text\": \"Find a picture that also two laptops side by side on a table, but has a solid-colored wall in the background and the photo shows an additional monitor.\"}\n{\"id\": 389, \"q_img\": \"000000479843.jpg\", \"q_text\": \"Find a picture that also a white microwave oven, but has the same color, there is one clock above it and no laptops are visible.\"}\n{\"id\": 390, \"q_img\": \"000000029107.jpg\", \"q_text\": \"Find a picture that also a work meeting around a rectangular table, but has no bottles of wine and there are a lot of laptops on the table.\"}\n{\"id\": 391, \"q_img\": \"000000334969.jpg\", \"q_text\": \"Find a picture that also players playing American football, but is zoomed in, has more players dressed in a different color, shows two referees and no people in the background.\"}\n{\"id\": 392, \"q_img\": \"000000402606.jpg\", \"q_text\": \"Find a picture that also a person carrying a suitcase, but is a younger boy in an airport.\"}\n{\"id\": 393, \"q_img\": \"000000166584.jpg\", \"q_text\": \"Find a picture that also a cat inside an appliance, but is inside a fridge instead of inside an oven.\"}\n{\"id\": 394, \"q_img\": \"000000371124.jpg\", \"q_text\": \"Find a picture that also two dogs in a kitchen, but is shot from a different angle and there is also a person.\"}\n{\"id\": 395, \"q_img\": \"000000570604.jpg\", \"q_text\": \"Find a picture that also a bottle with a wine glass next to it in the foreground, but has the glass half-filled with red wine instead of water.\"}\n{\"id\": 396, \"q_img\": \"000000155446.jpg\", \"q_text\": \"Find a picture that also a person playing the Wii, but shows no TV and there are two people with Christmas decorations in the background.\"}\n{\"id\": 397, \"q_img\": \"000000036621.jpg\", \"q_text\": \"Find a picture that also an animal-shaped cake, but has a teddy-bear shape.\"}\n{\"id\": 398, \"q_img\": \"000000171098.jpg\", \"q_text\": \"Find a picture that also a sailboat in the sea, but has more of them with the same color.\"}\n{\"id\": 399, \"q_img\": \"000000338386.jpg\", \"q_text\": \"Find a picture that also a stop sign, but has two arrows on it and the photo has a different background.\"}\n{\"id\": 400, \"q_img\": \"000000147028.jpg\", \"q_text\": \"Find a picture that also a person taking a picture of another person, but is seen from the back, shows a smartphone instead of a camera and the screen is in the foreground.\"}\n{\"id\": 401, \"q_img\": \"000000136710.jpg\", \"q_text\": \"Find a picture that also a toilet and a bathtub with curtains, but has the curtains closed.\"}\n{\"id\": 402, \"q_img\": \"000000508439.jpg\", \"q_text\": \"Find a picture that also a single person using a laptop, but is wearing no headphones and a kitchen is visible in the background.\"}\n{\"id\": 403, \"q_img\": \"000000235559.jpg\", \"q_text\": \"Find a picture that also a man looking at the camera cutting his food in a restaurant, but is wearing glasses, is eating pizza instead of a steak and there are more people in the background.\"}\n{\"id\": 404, \"q_img\": \"000000388038.jpg\", \"q_text\": \"Find a picture that also a dog with a frisbee in its mouth, but has a white frisbee and is set on sand.\"}\n{\"id\": 405, \"q_img\": \"000000441490.jpg\", \"q_text\": \"Find a picture that also a parked blue motorbike, but has the same color, the photo is taken from a different angle and is set inside a repair shop.\"}\n{\"id\": 406, \"q_img\": \"000000199439.jpg\", \"q_text\": \"Find a picture that also a bike resting on the ground, but has only one of them and has a paved surface instead of grass.\"}\n{\"id\": 407, \"q_img\": \"000000176449.jpg\", \"q_text\": \"Find a picture that also girls playing soccer, but has fog in the background and shows no buildings.\"}\n{\"id\": 408, \"q_img\": \"000000009135.jpg\", \"q_text\": \"Find a picture that also a red traffic light seen from the front in the foreground, but has the same color, has snow on it and the background is snowy.\"}\n{\"id\": 409, \"q_img\": \"000000348652.jpg\", \"q_text\": \"Find a picture that also a bowl with food inside next to a keyboard, but is of a different color.\"}\n{\"id\": 410, \"q_img\": \"000000187152.jpg\", \"q_text\": \"Find a picture that also a clear glass vase with flowers in it, but are more colorful and there is a grey brick wall in the background.\"}\n{\"id\": 411, \"q_img\": \"000000038595.jpg\", \"q_text\": \"Find a picture that also an office desk, but shows a person reading with their face covered.\"}\n{\"id\": 412, \"q_img\": \"000000172868.jpg\", \"q_text\": \"Find a picture that also a man playing the saxophone, but is sitting and the photo is set outdoors.\"}\n{\"id\": 413, \"q_img\": \"000000243295.jpg\", \"q_text\": \"Find a picture that also a naked red motorcycle viewed from the side, but is on the grass and is shot from the same angle.\"}\n{\"id\": 414, \"q_img\": \"000000433949.jpg\", \"q_text\": \"Find a picture that also a man talking on the phone, but is laying on the grass and the photo shows three people.\"}\n{\"id\": 415, \"q_img\": \"000000155898.jpg\", \"q_text\": \"Find a picture that also a cow seen from the side, but is seen from the same angle and is in the middle of a road.\"}\n{\"id\": 416, \"q_img\": \"000000298504.jpg\", \"q_text\": \"Find a picture that also a man skateboarding, but is wearing black shorts and a helmet instead of a baseball cap.\"}\n{\"id\": 417, \"q_img\": \"000000141315.jpg\", \"q_text\": \"Find a picture that also a dog in the driver's seat, but is visible through the lowered window.\"}\n{\"id\": 418, \"q_img\": \"000000388095.jpg\", \"q_text\": \"Find a picture that also an animal wearing a Santa's hat, but has a dog instead of a cat.\"}\n{\"id\": 419, \"q_img\": \"000000200632.jpg\", \"q_text\": \"Find a picture that also a double parking meter, but has a different color and the photo shows also a hand.\"}\n{\"id\": 420, \"q_img\": \"000000064568.jpg\", \"q_text\": \"Find a picture that also a person sitting under an umbrella fishing in a river, but has only one person and is taken from a farther distance.\"}\n{\"id\": 421, \"q_img\": \"000000136460.jpg\", \"q_text\": \"Find a picture that also a bowl of fruit, but instead of having apples and bananas has mandarins.\"}\n{\"id\": 422, \"q_img\": \"000000071976.jpg\", \"q_text\": \"Find a picture that also a woman holding an umbrella, but is sitting on a bench.\"}\n{\"id\": 423, \"q_img\": \"000000208893.jpg\", \"q_text\": \"Find a picture that also a plushy toy seen from the front, but is placed on a laptop instead of inside a book and is seen from the same angle.\"}\n{\"id\": 424, \"q_img\": \"000000548851.jpg\", \"q_text\": \"Find a picture that also a woman holding an umbrella in the foreground looking at the camera, but is shot in greyscale and is taken outdoors.\"}\n{\"id\": 425, \"q_img\": \"000000188203.jpg\", \"q_text\": \"Find a picture that also an empty church with rows of benches, but is taken from a lower angle.\"}\n{\"id\": 426, \"q_img\": \"000000141266.jpg\", \"q_text\": \"Find a picture that also multiple children looking at a device used by a person, but are looking at a phone instead of a laptop.\"}\n{\"id\": 427, \"q_img\": \"000000173977.jpg\", \"q_text\": \"Find a picture that also a person tasting a glass of red wine in the foreground, but is a woman and is looking at the camera.\"}\n{\"id\": 428, \"q_img\": \"000000407141.jpg\", \"q_text\": \"Find a picture that also a sandwich cut in half next to a cappuccino, but is placed on a black plate on a red table.\"}\n{\"id\": 429, \"q_img\": \"000000446263.jpg\", \"q_text\": \"Find a picture that also a man sitting using a laptop facing the camera, but is shot from the same angle and has a red sofa.\"}\n{\"id\": 430, \"q_img\": \"000000062199.jpg\", \"q_text\": \"Find a picture that also a man seen from the front playing an electric guitar, but is taken from the same angle, is brighter and shows no speakers in the background.\"}\n{\"id\": 431, \"q_img\": \"000000472340.jpg\", \"q_text\": \"Find a picture that also a bench seen from the front, but is more minimalistic and there is a white wall as a background.\"}\n{\"id\": 432, \"q_img\": \"000000213910.jpg\", \"q_text\": \"Find a picture that also a person on a bench using a laptop, but has only one person and shows a bag next to them.\"}\n{\"id\": 433, \"q_img\": \"000000229438.jpg\", \"q_text\": \"Find a picture that also a laptop with a sticker attached to the back, but has a different color and more stickers.\"}\n{\"id\": 434, \"q_img\": \"000000481994.jpg\", \"q_text\": \"Find a picture that also a person flying a kite on a meadow, but has several skyscrapers in the background.\"}\n{\"id\": 435, \"q_img\": \"000000047989.jpg\", \"q_text\": \"Find a picture that also a person using a banana as a pretend phone, but is a half-length portrait and the person is facing the camera.\"}\n{\"id\": 436, \"q_img\": \"000000150068.jpg\", \"q_text\": \"Find a picture that also a close-up suitcase, but is only one and is semi-open.\"}\n{\"id\": 437, \"q_img\": \"000000553921.jpg\", \"q_text\": \"Find a picture that also a person cutting a blonde child's hair, but is seen from the front.\"}\n{\"id\": 438, \"q_img\": \"000000512440.jpg\", \"q_text\": \"Find a picture that also a girl under a clock shot in greyscale, but is holding a cat.\"}\n{\"id\": 439, \"q_img\": \"000000031760.jpg\", \"q_text\": \"Find a picture that also a close-up phone held by a hand, but has a body of water in the background.\"}\n{\"id\": 440, \"q_img\": \"000000324944.jpg\", \"q_text\": \"Find a picture that also a bathhouse on a crowded beach, but has yellow umbrellas and is shot from a lower height.\"}\n{\"id\": 441, \"q_img\": \"000000155731.jpg\", \"q_text\": \"Find a picture that also a dining table with people around it, but has the same shape and there is a cake in the center.\"}\n{\"id\": 442, \"q_img\": \"000000580993.jpg\", \"q_text\": \"Find a picture that also a yellow fire hydrant in the foreground in front of a wall, but has the same color and the photo shows no people in the background.\"}\n{\"id\": 443, \"q_img\": \"000000175796.jpg\", \"q_text\": \"Find a picture that also an orange with a piece of cutlery in it, but has a fork instead of a knife and the photo is zoomed in.\"}\n{\"id\": 444, \"q_img\": \"000000260769.jpg\", \"q_text\": \"Find a picture that also a circular table with a plate and a bird on it, but has a table of the same shape and the plate is empty.\"}\n{\"id\": 445, \"q_img\": \"000000569007.jpg\", \"q_text\": \"Find a picture that also a yellow fire hydrant, but is laid on its side on the ground.\"}\n{\"id\": 446, \"q_img\": \"000000165652.jpg\", \"q_text\": \"Find a picture that also a man sitting on a bench, but is shot from a different angle and the man has several tattoos.\"}\n{\"id\": 447, \"q_img\": \"000000539823.jpg\", \"q_text\": \"Find a picture that also a person walking with a surfboard under their arms, but is a woman with blond hair and is on the shore.\"}\n{\"id\": 448, \"q_img\": \"000000257516.jpg\", \"q_text\": \"Find a picture that also a specific microwave oven, but is in a kitchen and has a person next to it.\"}\n{\"id\": 449, \"q_img\": \"000000475137.jpg\", \"q_text\": \"Find a picture that also a man standing while speaking on the phone in a house, but shows a kitchen in the background.\"}\n{\"id\": 450, \"q_img\": \"000000184645.jpg\", \"q_text\": \"Find a picture that also a tennis match inside a stadium, but is taken from a farther distance and shows the whole court.\"}\n{\"id\": 451, \"q_img\": \"000000421464.jpg\", \"q_text\": \"Find a picture that also a white toilet, but has the same color and the photo shows a wastebasket and a bathtub.\"}\n{\"id\": 452, \"q_img\": \"000000052176.jpg\", \"q_text\": \"Find a picture that also a solitary person seen from behind holding a dark umbrella, but instead of being on a beach he is in a forest.\"}\n{\"id\": 453, \"q_img\": \"000000246266.jpg\", \"q_text\": \"Find a picture that also two women on a bench seen from behind, but is in color and both women have long dark hair.\"}\n{\"id\": 454, \"q_img\": \"000000149772.jpg\", \"q_text\": \"Find a picture that also a Blackberry, but is held in front of the camera by a person that is facing the camera.\"}\n{\"id\": 455, \"q_img\": \"000000135395.jpg\", \"q_text\": \"Find a picture that also people working at a picnic table outdoors, but has only one table and is zoomed in.\"}\n{\"id\": 456, \"q_img\": \"000000066197.jpg\", \"q_text\": \"Find a picture that also an opened close-up refrigerator, but has a cat sneaking into it.\"}\n{\"id\": 457, \"q_img\": \"000000516971.jpg\", \"q_text\": \"Find a picture that also a person standing on a bench, but has more people on it and they are still.\"}\n{\"id\": 458, \"q_img\": \"000000260032.jpg\", \"q_text\": \"Find a picture that also a dog in front of a person on a vehicle, but has a motorbike instead of a kayak.\"}\n{\"id\": 459, \"q_img\": \"000000486314.jpg\", \"q_text\": \"Find a picture that also a lying man reading a book, but has a bed instead of a bench and the image is in color.\"}\n{\"id\": 460, \"q_img\": \"000000139006.jpg\", \"q_text\": \"Find a picture that also a close-up white seagull, but instead of having a beach has a street in the background.\"}\n{\"id\": 461, \"q_img\": \"000000138173.jpg\", \"q_text\": \"Find a picture that also an elderly couple, but is sitting on a bench while reading.\"}\n{\"id\": 462, \"q_img\": \"000000166286.jpg\", \"q_text\": \"Find a picture that also two plush toys, but depict two bears resting on a couch.\"}\n{\"id\": 463, \"q_img\": \"000000570911.jpg\", \"q_text\": \"Find a picture that also a dog, but is only one and is in a cage.\"}\n{\"id\": 464, \"q_img\": \"000000509317.jpg\", \"q_text\": \"Find a picture that also a clock, but has a rooster over it.\"}\n{\"id\": 465, \"q_img\": \"000000485410.jpg\", \"q_text\": \"Find a picture that also a hotel room with two beds and no people in it, but is messier and the photo is shot from a different angle.\"}\n{\"id\": 466, \"q_img\": \"000000076800.jpg\", \"q_text\": \"Find a picture that also an object in the foreground with a turned-on TV in the background, but is taken from the same angle and shows a TV remote control instead of a Wii controller.\"}\n{\"id\": 467, \"q_img\": \"000000567184.jpg\", \"q_text\": \"Find a picture that also multiple baskets of bananas, but are carried using one bike.\"}\n{\"id\": 468, \"q_img\": \"000000422734.jpg\", \"q_text\": \"Find a picture that also three people playing the Wii, but has the same number of people and shows a steering wheel controller.\"}\n{\"id\": 469, \"q_img\": \"000000562245.jpg\", \"q_text\": \"Find a picture that also multiple horses on a beach, but has one more horse and has no people.\"}\n{\"id\": 470, \"q_img\": \"000000124818.jpg\", \"q_text\": \"Find a picture that also a person lying on a bench, but has more leaves around it.\"}\n{\"id\": 471, \"q_img\": \"000000024902.jpg\", \"q_text\": \"Find a picture that also two people stand up paddleboarding, but has more of them.\"}\n{\"id\": 472, \"q_img\": \"000000236427.jpg\", \"q_text\": \"Find a picture that also a kite shaped like an octopus, but is of a different color, the photo shows no people and has a bluer sky in the background.\"}\n{\"id\": 473, \"q_img\": \"000000056947.jpg\", \"q_text\": \"Find a picture that also a dog next to a fire hydrant, but shows one more dog.\"}\n{\"id\": 474, \"q_img\": \"000000218794.jpg\", \"q_text\": \"Find a picture that also a street with red traffic lights, but is taken from inside a car and the dashboard is visible.\"}\n{\"id\": 475, \"q_img\": \"000000306804.jpg\", \"q_text\": \"Find a picture that also a person checking their phone, but is sitting on a bench and is seen from the front.\"}\n{\"id\": 476, \"q_img\": \"000000541618.jpg\", \"q_text\": \"Find a picture that also an open box of sweets, but has a donut with chocolate glaze and no sprinkles and drizzle, has no people and the focus is on the box.\"}\n{\"id\": 477, \"q_img\": \"000000014339.jpg\", \"q_text\": \"Find a picture that also a flock of sheep, but are marked with a red sign on the wool.\"}\n{\"id\": 478, \"q_img\": \"000000426335.jpg\", \"q_text\": \"Find a picture that also a man wearing an apron in a kitchen, but is wearing a black t-shirt and has no hat.\"}\n{\"id\": 479, \"q_img\": \"000000367921.jpg\", \"q_text\": \"Find a picture that also a famous monument, but is a famous arc and the photo is shot in Paris.\"}\n{\"id\": 480, \"q_img\": \"000000016145.jpg\", \"q_text\": \"Find a picture that also a person talking on the phone, but is zoomed out and taken in a forest.\"}\n{\"id\": 481, \"q_img\": \"000000339518.jpg\", \"q_text\": \"Find a picture that also a close-up person wearing a shirt and a tie, but is looking at the camera, smiling and wearing glasses and no jacket.\"}\n{\"id\": 482, \"q_img\": \"000000499949.jpg\", \"q_text\": \"Find a picture that also an animal on an armchair, but has a footstool in front of it and the animal is a dog.\"}\n{\"id\": 483, \"q_img\": \"000000310956.jpg\", \"q_text\": \"Find a picture that also bananas, but shows more of them and they are in a cardboard box.\"}\n{\"id\": 484, \"q_img\": \"000000052586.jpg\", \"q_text\": \"Find a picture that also a close-up PC mouse, but has a cat instead of the keyboard.\"}\n{\"id\": 485, \"q_img\": \"000000428750.jpg\", \"q_text\": \"Find a picture that also a person looking at their phone with other people around, but is shot in a subway carriage and is in greyscale.\"}\n{\"id\": 486, \"q_img\": \"000000378500.jpg\", \"q_text\": \"Find a picture that also a toilet next to a bathtub of the same color, but has a different color.\"}\n{\"id\": 487, \"q_img\": \"000000207247.jpg\", \"q_text\": \"Find a picture that also a person with an umbrella seen from the front under the snow, but has a brighter background and is taken from the same angle.\"}\n{\"id\": 488, \"q_img\": \"000000461273.jpg\", \"q_text\": \"Find a picture that also a child holding a tennis racket, but is wearing no t-shirt and the photo is more static.\"}\n{\"id\": 489, \"q_img\": \"000000265380.jpg\", \"q_text\": \"Find a picture that also a bear, but is sitting on a hammock and is darker.\"}\n{\"id\": 490, \"q_img\": \"000000123255.jpg\", \"q_text\": \"Find a picture that also a clock tower, but is light green and a blue sky is visible in the background.\"}\n{\"id\": 491, \"q_img\": \"000000061505.jpg\", \"q_text\": \"Find a picture that also a laptop in a kitchen, but is black and is seen from a farther distance.\"}\n{\"id\": 492, \"q_img\": \"000000102280.jpg\", \"q_text\": \"Find a picture that also a pink donut next to a depiction of a Simpsons character, but has a donut of the same color and a different character from the same franchise.\"}\n{\"id\": 493, \"q_img\": \"000000303331.jpg\", \"q_text\": \"Find a picture that also a girl talking on the phone, but is laying on the floor instead of standing and the photo is shot from the top.\"}\n{\"id\": 494, \"q_img\": \"000000380925.jpg\", \"q_text\": \"Find a picture that also a close-up muffin, but has a decoration depicting an animal.\"}\n{\"id\": 495, \"q_img\": \"000000330360.jpg\", \"q_text\": \"Find a picture that also a white and green laptop, but is seen from a different angle and the photo shows a cat and no mouse next to it.\"}\n{\"id\": 496, \"q_img\": \"000000391592.jpg\", \"q_text\": \"Find a picture that also a brown cake, but has the same color, is whole and has three or more candles on it.\"}\n{\"id\": 497, \"q_img\": \"000000561260.jpg\", \"q_text\": \"Find a picture that also a person on a motor-scooter seen from the back, but shows only one person with no backpack and there is a more rural background.\"}\n{\"id\": 498, \"q_img\": \"000000360027.jpg\", \"q_text\": \"Find a picture that also a sink in the foreground, but is surrounded by colored walls and the photo is shot from the same angle.\"}\n{\"id\": 499, \"q_img\": \"000000255377.jpg\", \"q_text\": \"Find a picture that also multiple clocks on a wall, but shows a person in the foreground and the wall is brighter.\"}\n{\"id\": 500, \"q_img\": \"000000496184.jpg\", \"q_text\": \"Find a picture that also a double-decker bus viewed from the front left, but is green and white and the photo is shot from the same angle.\"}\n{\"id\": 501, \"q_img\": \"000000096126.jpg\", \"q_text\": \"Find a picture that also a plain white plate with broccoli in the foreground, but has potatoes instead of lentils.\"}\n{\"id\": 502, \"q_img\": \"000000075127.jpg\", \"q_text\": \"Find a picture that also a sofa in the background, but has a table and an archway divider in the foreground.\"}\n{\"id\": 503, \"q_img\": \"000000499841.jpg\", \"q_text\": \"Find a picture that also a dog with a frisbee in its mouth, but has its paws in the water and has the same color.\"}\n{\"id\": 504, \"q_img\": \"000000009533.jpg\", \"q_text\": \"Find a picture that also a small white refrigerator shot in greyscale, but is only one and in the corner of a room.\"}\n{\"id\": 505, \"q_img\": \"000000422173.jpg\", \"q_text\": \"Find a picture that also a filled stem glass, but is held in a hand and there is snow in the background.\"}\n{\"id\": 506, \"q_img\": \"000000213161.jpg\", \"q_text\": \"Find a picture that also a living room with a full-wall window, but has an armchair and the window spans two walls.\"}\n{\"id\": 507, \"q_img\": \"000000134934.jpg\", \"q_text\": \"Find a picture that also a double-decker bus, but is green and is viewed from the front.\"}\n{\"id\": 508, \"q_img\": \"000000048567.jpg\", \"q_text\": \"Find a picture that also a bed, but is taken from the front and has three pictures on the wall behind it.\"}\n{\"id\": 509, \"q_img\": \"000000427006.jpg\", \"q_text\": \"Find a picture that also multiple bananas, but are on a man's head.\"}\n{\"id\": 510, \"q_img\": \"000000179546.jpg\", \"q_text\": \"Find a picture that also people dressed in uniforms cutting a cake, but has one more person in the foreground and fewer people in the background.\"}\n{\"id\": 511, \"q_img\": \"000000237733.jpg\", \"q_text\": \"Find a picture that also a pizza, but has four of them, they are smaller and no plates are visible.\"}\n{\"id\": 512, \"q_img\": \"000000567163.jpg\", \"q_text\": \"Find a picture that also a Harley Davidson-style motorbike, but is shot in a parking lot and has an American flag pinned on the back.\"}\n{\"id\": 513, \"q_img\": \"000000561659.jpg\", \"q_text\": \"Find a picture that also a man skateboarding on a road, but has the person wearing a helmet and kneepads instead of a hat, shows traffic cones and the photo is taken from the front and a closer distance.\"}\n{\"id\": 514, \"q_img\": \"000000570329.jpg\", \"q_text\": \"Find a picture that also a teddy bear in the foreground, but has a hat instead of glasses.\"}\n{\"id\": 515, \"q_img\": \"000000109261.jpg\", \"q_text\": \"Find a picture that also a toilet, but has a frog on the seat.\"}\n{\"id\": 516, \"q_img\": \"000000250457.jpg\", \"q_text\": \"Find a picture that also a vase with flowers, but is next to a laptop on a desk.\"}\n{\"id\": 517, \"q_img\": \"000000398298.jpg\", \"q_text\": \"Find a picture that also multiple donuts in the foreground, but are in larger numbers, more colorful, in a display case and no other types of sweets are visible.\"}\n{\"id\": 518, \"q_img\": \"000000259325.jpg\", \"q_text\": \"Find a picture that also multiple animals grazing seen from a distance, but has cows instead of sheep, has some clouds in the background and is shot from a similar distance.\"}\n{\"id\": 519, \"q_img\": \"000000219950.jpg\", \"q_text\": \"Find a picture that also a filled pitcher on a restaurant table next to a pizza, but has beer instead of coke.\"}\n{\"id\": 520, \"q_img\": \"000000408821.jpg\", \"q_text\": \"Find a picture that also a person riding a motorbike in the foreground in greyscale, but has no backpack and the photo shows no sky in the background.\"}\n{\"id\": 521, \"q_img\": \"000000453754.jpg\", \"q_text\": \"Find a picture that also the inside of a home oven, but has a chicken inside instead of two pizzas.\"}\n{\"id\": 522, \"q_img\": \"000000335487.jpg\", \"q_text\": \"Find a picture that also a person seen from behind holding a colored umbrella, but has the sea in the background and there is one more person in the photo.\"}\n{\"id\": 523, \"q_img\": \"000000068495.jpg\", \"q_text\": \"Find a picture that also an object next to a door seen from the front, but is shot from the same angle and has a single bike instead of TVs.\"}\n{\"id\": 524, \"q_img\": \"000000342555.jpg\", \"q_text\": \"Find a picture that also a person riding a horse in the countryside, but shows more people and the horses are running.\"}\n{\"id\": 525, \"q_img\": \"000000366928.jpg\", \"q_text\": \"Find a picture that also a red and white fire hydrant next to a dog, but has the same color and the dog is smaller.\"}\n{\"id\": 526, \"q_img\": \"000000291992.jpg\", \"q_text\": \"Find a picture that also a grown man holding a big teddy bear seen from the front, but wears a tie and the teddy bear is brown.\"}\n{\"id\": 527, \"q_img\": \"000000347000.jpg\", \"q_text\": \"Find a picture that also a man tennis player with black pants who is serving, but is taken from a different angle, has a white shirt and similar pants.\"}\n{\"id\": 528, \"q_img\": \"000000368488.jpg\", \"q_text\": \"Find a picture that also a large number of toilets, but is shot indoors and they are arranged in columns.\"}\n{\"id\": 529, \"q_img\": \"000000368361.jpg\", \"q_text\": \"Find a picture that also a tie, but has a different color and depicts a keyboard.\"}\n{\"id\": 530, \"q_img\": \"000000423126.jpg\", \"q_text\": \"Find a picture that also a zebra, but shows only one of them in the foreground while it is eating grass and has some rocks in the background.\"}\n{\"id\": 531, \"q_img\": \"000000128802.jpg\", \"q_text\": \"Find a picture that also a person in the foreground biting a donut with a glaze, but is shot from the same angle and the glaze is white.\"}\n{\"id\": 532, \"q_img\": \"000000049409.jpg\", \"q_text\": \"Find a picture that also a slice of cake on a plate, but is yellow and is on a white plate.\"}\n{\"id\": 533, \"q_img\": \"000000355099.jpg\", \"q_text\": \"Find a picture that also two women holding umbrellas seen from the front, but is taken in greyscale, shows microphones and has a darker background.\"}\n{\"id\": 534, \"q_img\": \"000000072209.jpg\", \"q_text\": \"Find a picture that also a statue of scissors, but shows also paper and rock.\"}\n{\"id\": 535, \"q_img\": \"000000207412.jpg\", \"q_text\": \"Find a picture that also a person holding an umbrella seen from behind in greyscale, but is shot in completely different scenery and there are two people under the umbrella.\"}\n{\"id\": 536, \"q_img\": \"000000217483.jpg\", \"q_text\": \"Find a picture that also a stack of books in the foreground, but are on a bed and there is no stereo.\"}\n{\"id\": 537, \"q_img\": \"000000020214.jpg\", \"q_text\": \"Find a picture that also a living room with a sofa, but is seen as a reflection in a mirror.\"}\n{\"id\": 538, \"q_img\": \"000000275081.jpg\", \"q_text\": \"Find a picture that also a child wearing a tie, but is a girl and is seen from a farther distance.\"}\n{\"id\": 539, \"q_img\": \"000000071118.jpg\", \"q_text\": \"Find a picture that also a woman talking on the phone seen from the front, but has a darker tank top and there is a tree in the background.\"}\n{\"id\": 540, \"q_img\": \"000000432862.jpg\", \"q_text\": \"Find a picture that also a cat inside an object, but shows a vase instead of a bowl.\"}\n{\"id\": 541, \"q_img\": \"000000328752.jpg\", \"q_text\": \"Find a picture that also a cross-country skier, but is seen from the side and wears a backpack.\"}\n{\"id\": 542, \"q_img\": \"000000403928.jpg\", \"q_text\": \"Find a picture that also a child with an umbrella in the foreground, but has a cow in the background and is shot in black and white.\"}\n{\"id\": 543, \"q_img\": \"000000511019.jpg\", \"q_text\": \"Find a picture that also a parking meter in front of a mural, but shows a different animal.\"}\n{\"id\": 544, \"q_img\": \"000000113136.jpg\", \"q_text\": \"Find a picture that also a fridge, but has magnets placed in the shape of a smiley.\"}\n{\"id\": 545, \"q_img\": \"000000271768.jpg\", \"q_text\": \"Find a picture that also a half-eaten donut, but is resting on a plate and the photo has a mug.\"}\n{\"id\": 546, \"q_img\": \"000000283714.jpg\", \"q_text\": \"Find a picture that also multiple surfboards on a beach, but has people on them.\"}\n{\"id\": 547, \"q_img\": \"000000266509.jpg\", \"q_text\": \"Find a picture that also a child on a skateboard in the foreground, but shows two of them and they are sitting on it instead of riding it.\"}\n{\"id\": 548, \"q_img\": \"000000033767.jpg\", \"q_text\": \"Find a picture that also three glasses of wine in the foreground, but has some food next to them.\"}\n{\"id\": 549, \"q_img\": \"000000273972.jpg\", \"q_text\": \"Find a picture that also a table with a laptop on it, but has a different color and is shaped like a triangle.\"}\n{\"id\": 550, \"q_img\": \"000000325478.jpg\", \"q_text\": \"Find a picture that also a man with a guitar and a woman with a violin seen from the front, but are younger, the photo is set indoors, has two windows in the background and shows no other people.\"}\n{\"id\": 551, \"q_img\": \"000000175829.jpg\", \"q_text\": \"Find a picture that also people sitting on an elephant, but has more people on it and no umbrellas.\"}\n{\"id\": 552, \"q_img\": \"000000168466.jpg\", \"q_text\": \"Find a picture that also a person holding a surfboard vertically, but has more people and is taken outdoors.\"}\n{\"id\": 553, \"q_img\": \"000000218962.jpg\", \"q_text\": \"Find a picture that also a person doing sport shot in greyscale, but is playing tennis instead of golf and the player is dressed fully in white.\"}\n{\"id\": 554, \"q_img\": \"000000039510.jpg\", \"q_text\": \"Find a picture that also people standing and drinking in a kitchen at a party, but shows more people and a brighter background.\"}\n{\"id\": 555, \"q_img\": \"000000411293.jpg\", \"q_text\": \"Find a picture that also people next to a big teddy bear, but shows three people and is zoomed in.\"}\n{\"id\": 556, \"q_img\": \"000000453794.jpg\", \"q_text\": \"Find a picture that also a man surfing, but shows people in the foreground looking at him.\"}\n{\"id\": 557, \"q_img\": \"000000537001.jpg\", \"q_text\": \"Find a picture that also a white toilet, but has a black cat with its head hidden in it.\"}\n{\"id\": 558, \"q_img\": \"000000185057.jpg\", \"q_text\": \"Find a picture that also a person holding an umbrella in the snow, but shows only one person and is walking away from the camera.\"}\n{\"id\": 559, \"q_img\": \"000000011098.jpg\", \"q_text\": \"Find a picture that also a child looking through a donut, but is set in a car.\"}\n{\"id\": 560, \"q_img\": \"000000189616.jpg\", \"q_text\": \"Find a picture that also a XO laptop in the foreground, but is on a desk with a silver laptop next to it.\"}\n{\"id\": 561, \"q_img\": \"000000239842.jpg\", \"q_text\": \"Find a picture that also a bike leaning against an empty bench, but is taken from a closer distance and there are no birds.\"}\n{\"id\": 562, \"q_img\": \"000000023724.jpg\", \"q_text\": \"Find a picture that also a man seated talking on the phone in greyscale, but is seated on a bench.\"}\n{\"id\": 563, \"q_img\": \"000000425352.jpg\", \"q_text\": \"Find a picture that also a pizza in the foreground, but is a single slice and there is a fast-food cup near it.\"}\n{\"id\": 564, \"q_img\": \"000000436503.jpg\", \"q_text\": \"Find a picture that also a person riding a horse, but is crossing a pool of dirty water and the photo is taken from the same angle.\"}\n{\"id\": 565, \"q_img\": \"000000182506.jpg\", \"q_text\": \"Find a picture that also a desk with multiple computers in front of a window, but shows a city skyline visible through the window.\"}\n{\"id\": 566, \"q_img\": \"000000517529.jpg\", \"q_text\": \"Find a picture that also a woman talking on the phone, but shows no other people, is set outdoors and there is a lake in the background.\"}\n{\"id\": 567, \"q_img\": \"000000037216.jpg\", \"q_text\": \"Find a picture that also a cat next to a pair of shoes, but has boots instead of ballet flat shoes.\"}\n{\"id\": 568, \"q_img\": \"000000122501.jpg\", \"q_text\": \"Find a picture that also a girl wearing a cosplay, but is holding a larger teddy bear with both arms.\"}\n{\"id\": 569, \"q_img\": \"000000355529.jpg\", \"q_text\": \"Find a picture that also a Pokemon plush toy, but shows a different character from the same franchise.\"}\n{\"id\": 570, \"q_img\": \"000000375233.jpg\", \"q_text\": \"Find a picture that also a black bear, but has the same color, is clinging to a tree and is surrounded by leaves.\"}\n{\"id\": 571, \"q_img\": \"000000326343.jpg\", \"q_text\": \"Find a picture that also a truck carrying goods, but has logs instead of bananas.\"}\n{\"id\": 572, \"q_img\": \"000000080226.jpg\", \"q_text\": \"Find a picture that also a pink donut with sprinkles, but has the same color and is about to be eaten by a person.\"}\n{\"id\": 573, \"q_img\": \"000000490170.jpg\", \"q_text\": \"Find a picture that also a close-up woman talking on the phone, but is wearing a t-shirt and multiple trees are visible in the background.\"}\n{\"id\": 574, \"q_img\": \"000000281364.jpg\", \"q_text\": \"Find a picture that also a person wearing zombie makeup, but has four people and is taken indoors.\"}\n{\"id\": 575, \"q_img\": \"000000299803.jpg\", \"q_text\": \"Find a picture that also small boats side by side on a river, but are white and blue and has more birds in the background.\"}\n{\"id\": 576, \"q_img\": \"000000276350.jpg\", \"q_text\": \"Find a picture that also people playing the Wii, but are playing on a projection screen instead of a TV.\"}\n{\"id\": 577, \"q_img\": \"000000392475.jpg\", \"q_text\": \"Find a picture that also a bear, but has one more bear and a snowy background.\"}\n{\"id\": 578, \"q_img\": \"000000357047.jpg\", \"q_text\": \"Find a picture that also a sink with a mirror occupying the whole wall, but shows two sinks and there is bigger mirror.\"}\n{\"id\": 579, \"q_img\": \"000000334831.jpg\", \"q_text\": \"Find a picture that also a person taking a mirror selfie behind a woman who is brushing her teeth, but has a man taking the photo instead of a woman.\"}\n{\"id\": 580, \"q_img\": \"000000519519.jpg\", \"q_text\": \"Find a picture that also tables and chairs with umbrellas, but are on the water and there are more people.\"}\n{\"id\": 581, \"q_img\": \"000000081914.jpg\", \"q_text\": \"Find a picture that also a child in front of donuts, but shows more people and the sweets are in a box.\"}\n{\"id\": 582, \"q_img\": \"000000451663.jpg\", \"q_text\": \"Find a picture that also a double-decker bus in a street seen from the side, but is shot in greyscale.\"}\n{\"id\": 583, \"q_img\": \"000000317766.jpg\", \"q_text\": \"Find a picture that also a semi-closed suitcase, but shows a child instead of a teddy bear.\"}\n{\"id\": 584, \"q_img\": \"000000107272.jpg\", \"q_text\": \"Find a picture that also a woman in a kitchen showing food to the camera, but shows an older woman.\"}\n{\"id\": 585, \"q_img\": \"000000235972.jpg\", \"q_text\": \"Find a picture that also a white toilet, but is closed and has a cat on the seat.\"}\n{\"id\": 586, \"q_img\": \"000000579390.jpg\", \"q_text\": \"Find a picture that also multiple people using a laptop around a table, but has more laptops and is shot in greyscale.\"}\n{\"id\": 587, \"q_img\": \"000000257143.jpg\", \"q_text\": \"Find a picture that also some plates with pizza and other dishes, but has a swimming pool with people swimming in the background.\"}\n{\"id\": 588, \"q_img\": \"000000102273.jpg\", \"q_text\": \"Find a picture that also a living room with a sofa, but has multiple cardboard boxes.\"}\n{\"id\": 589, \"q_img\": \"000000531670.jpg\", \"q_text\": \"Find a picture that also a person holding the door of a refrigerator, but is half-closed, the photo has only one person and is zoomed out.\"}\n{\"id\": 590, \"q_img\": \"000000193530.jpg\", \"q_text\": \"Find a picture that also a teacher in front of a projection screen, but is pointing to the screen and is not facing the camera.\"}\n{\"id\": 591, \"q_img\": \"000000527646.jpg\", \"q_text\": \"Find a picture that also a filled mixer, but is closed with a hand holding it on the top.\"}\n{\"id\": 592, \"q_img\": \"000000567672.jpg\", \"q_text\": \"Find a picture that also a cat on a bench, but has only one black cat facing the camera.\"}\n{\"id\": 593, \"q_img\": \"000000314856.jpg\", \"q_text\": \"Find a picture that also a TV in a living room, but shows the whole room and the room has a corner window.\"}\n{\"id\": 594, \"q_img\": \"000000308229.jpg\", \"q_text\": \"Find a picture that also a traffic light with cars in the foreground, but is brighter and there is a rainbow in the sky.\"}\n{\"id\": 595, \"q_img\": \"000000380036.jpg\", \"q_text\": \"Find a picture that also a yellow bird on a tree, but is shot from a farther distance and the sky is visible in the background.\"}\n{\"id\": 596, \"q_img\": \"000000559653.jpg\", \"q_text\": \"Find a picture that also elephants walking on a city road, but is shot at night and in color.\"}\n{\"id\": 597, \"q_img\": \"000000054973.jpg\", \"q_text\": \"Find a picture that also tables with umbrellas of the same color, but have a different color and the photo has trees in the background.\"}\n{\"id\": 598, \"q_img\": \"000000140329.jpg\", \"q_text\": \"Find a picture that also a man with a cat on his lap, but is using a laptop.\"}\n{\"id\": 599, \"q_img\": \"000000301384.jpg\", \"q_text\": \"Find a picture that also a close-up dog wearing a hat indoors, but is seen from the front and looking at the camera.\"}\n{\"id\": 600, \"q_img\": \"000000234172.jpg\", \"q_text\": \"Find a picture that also two pizzas, but has two cokes next to them.\"}\n{\"id\": 601, \"q_img\": \"000000403768.jpg\", \"q_text\": \"Find a picture that also two hot dogs, but are on one plate and have ketchup on them.\"}\n{\"id\": 602, \"q_img\": \"000000429426.jpg\", \"q_text\": \"Find a picture that also a cubic lamp clock in a park in the foreground, but has the same shape and a different color, the photo shows a blue sky in the background.\"}\n{\"id\": 603, \"q_img\": \"000000029502.jpg\", \"q_text\": \"Find a picture that also a close-up phone, but is only one and it is charging.\"}\n{\"id\": 604, \"q_img\": \"000000157732.jpg\", \"q_text\": \"Find a picture that also an object on an empty bench, but shows a shoe instead of a bottle.\"}\n{\"id\": 605, \"q_img\": \"000000526148.jpg\", \"q_text\": \"Find a picture that also an elephant in the foreground, but is painting a picture with its trunk.\"}\n{\"id\": 606, \"q_img\": \"000000132618.jpg\", \"q_text\": \"Find a picture that also a cook with a uniform in a kitchen, but has only one person in the foreground and they are making pizzas.\"}\n{\"id\": 607, \"q_img\": \"000000284832.jpg\", \"q_text\": \"Find a picture that also two chairs and a bench in front of a house, but has red chairs instead of white ones, there is no windows and the photo is taken from the same angle.\"}\n{\"id\": 608, \"q_img\": \"000000320274.jpg\", \"q_text\": \"Find a picture that also a clean and empty kitchen, but is brown and has a galley style.\"}\n{\"id\": 609, \"q_img\": \"000000268912.jpg\", \"q_text\": \"Find a picture that also a dessert on a plate next to ice cream, but has donuts instead of a slice of cake.\"}\n{\"id\": 610, \"q_img\": \"000000182386.jpg\", \"q_text\": \"Find a picture that also a clock tower, but shows a fountain and the photo is taken from a farther distance.\"}\n{\"id\": 611, \"q_img\": \"000000395889.jpg\", \"q_text\": \"Find a picture that also a cat sitting on an object, but is of a different color and is sitting on a toilet instead of an oven.\"}\n{\"id\": 612, \"q_img\": \"000000470157.jpg\", \"q_text\": \"Find a picture that also a child reading in a bed, but shows two people and one of them is an adult.\"}\n{\"id\": 613, \"q_img\": \"000000013373.jpg\", \"q_text\": \"Find a picture that also a male tennis player while serving, but is dressed in white, is playing on grass and is viewed from a closer distance.\"}\n{\"id\": 614, \"q_img\": \"000000496084.jpg\", \"q_text\": \"Find a picture that also a man on a snowboard grinding on a rail, but is on a handrail and the photo is shot in greyscale.\"}\n{\"id\": 615, \"q_img\": \"000000491675.jpg\", \"q_text\": \"Find a picture that also a snowboarder, but has a yellowish jacket and is seen from a closer distance.\"}\n{\"id\": 616, \"q_img\": \"000000173862.jpg\", \"q_text\": \"Find a picture that also a close-up clock, but has a flag in the background instead of a library.\"}\n{\"id\": 617, \"q_img\": \"000000559318.jpg\", \"q_text\": \"Find a picture that also a living room with a TV, but has red walls and parquet as the floor.\"}\n{\"id\": 618, \"q_img\": \"000000432451.jpg\", \"q_text\": \"Find a picture that also a countdown semaphore, but is red and there is a crowded street in the background.\"}\n{\"id\": 619, \"q_img\": \"000000396881.jpg\", \"q_text\": \"Find a picture that also oranges and orange juice on a table in the foreground, but has more of them, there is no cake and there is a swimming pool in the background.\"}\n{\"id\": 620, \"q_img\": \"000000489260.jpg\", \"q_text\": \"Find a picture that also a closed white refrigerator, but is seen from a different angle and is outdoors.\"}\n{\"id\": 621, \"q_img\": \"000000076787.jpg\", \"q_text\": \"Find a picture that also food on cookware on cooktop burners, but shows a pizza instead of other food.\"}\n{\"id\": 622, \"q_img\": \"000000068376.jpg\", \"q_text\": \"Find a picture that also a woman sitting at a desk seen from the front, but is writing on a sheet of paper and the photo shows one pair of glasses.\"}\n{\"id\": 623, \"q_img\": \"000000106903.jpg\", \"q_text\": \"Find a picture that also a man barbecuing outdoors, but has hot dogs and is set on grass.\"}\n{\"id\": 624, \"q_img\": \"000000396240.jpg\", \"q_text\": \"Find a picture that also a street with a red traffic light, but has a rainbow in the background and the traffic light has the same color.\"}\n{\"id\": 625, \"q_img\": \"000000087362.jpg\", \"q_text\": \"Find a picture that also breakfast food next to a cup of coffee, but has pancakes on a plate instead of the bowl.\"}\n{\"id\": 626, \"q_img\": \"000000500033.jpg\", \"q_text\": \"Find a picture that also a woman hitting a tennis forehand, but is shot in greyscale.\"}\n{\"id\": 627, \"q_img\": \"000000067862.jpg\", \"q_text\": \"Find a picture that also a vase containing pink flowers, but is white and shaped like a gun.\"}\n{\"id\": 628, \"q_img\": \"000000472196.jpg\", \"q_text\": \"Find a picture that also multiple donuts, but has more of them and there is also a product label.\"}\n{\"id\": 629, \"q_img\": \"000000476825.jpg\", \"q_text\": \"Find a picture that also a child brushing his teeth, but is standing in the hallway and is seen from a farther distance.\"}\n{\"id\": 630, \"q_img\": \"000000204629.jpg\", \"q_text\": \"Find a picture that also a person holding an umbrella, but is walking a tightrope and has their arm stretched above his head.\"}\n{\"id\": 631, \"q_img\": \"000000304135.jpg\", \"q_text\": \"Find a picture that also two zebras, but are in the same number and in the water.\"}\n{\"id\": 632, \"q_img\": \"000000011225.jpg\", \"q_text\": \"Find a picture that also groceries, but are resting on a kitchen counter instead of in a fridge.\"}\n{\"id\": 633, \"q_img\": \"000000473830.jpg\", \"q_text\": \"Find a picture that also a kitchen, but has multiple stools made of wood.\"}\n{\"id\": 634, \"q_img\": \"000000443587.jpg\", \"q_text\": \"Find a picture that also a CRT TV, but is turned on and is in front of a window letting in sunlight.\"}\n{\"id\": 635, \"q_img\": \"000000292204.jpg\", \"q_text\": \"Find a picture that also an animal lying on a double bed, but has a brownish dog instead of a cat.\"}\n{\"id\": 636, \"q_img\": \"000000366397.jpg\", \"q_text\": \"Find a picture that also a brown bear, but is similar, is in the water and the photo is shot from the same angle.\"}\n{\"id\": 637, \"q_img\": \"000000439778.jpg\", \"q_text\": \"Find a picture that also a stop sign, but has more trees in the background and is set during a snowfall.\"}\n{\"id\": 638, \"q_img\": \"000000444511.jpg\", \"q_text\": \"Find a picture that also a teddy bear, but is inside a toy box instead of on a shelf.\"}\n{\"id\": 639, \"q_img\": \"000000290786.jpg\", \"q_text\": \"Find a picture that also skiers and snowboarders, but has three people on a chairlift viewed from below.\"}\n{\"id\": 640, \"q_img\": \"000000549716.jpg\", \"q_text\": \"Find a picture that also two halves of a submarine sandwich on a plate, but has bread with the same shape and has fries instead of vegetables.\"}\n{\"id\": 641, \"q_img\": \"000000293795.jpg\", \"q_text\": \"Find a picture that also a black cat, but has the same color and is inside a bowl made of clear glass.\"}\n{\"id\": 642, \"q_img\": \"000000182404.jpg\", \"q_text\": \"Find a picture that also a tennis racket, but is lying on the ground and has the handle facing upward.\"}\n{\"id\": 643, \"q_img\": \"000000277580.jpg\", \"q_text\": \"Find a picture that also a stop sign and a political message, but shows people holding a flag.\"}\n{\"id\": 644, \"q_img\": \"000000328155.jpg\", \"q_text\": \"Find a picture that also a sheep, but has a cat on it, is facing the camera and has cleaner wool.\"}\n{\"id\": 645, \"q_img\": \"000000270545.jpg\", \"q_text\": \"Find a picture that also a white toilet seen from the front, but has a more colorful background.\"}\n{\"id\": 646, \"q_img\": \"000000399785.jpg\", \"q_text\": \"Find a picture that also a girl using a laptop, but is not on the floor and she is wearing headphones.\"}\n{\"id\": 647, \"q_img\": \"000000170161.jpg\", \"q_text\": \"Find a picture that also outdoor bar tables with umbrellas, but is shot from a closer distance and shows no people.\"}\n{\"id\": 648, \"q_img\": \"000000080716.jpg\", \"q_text\": \"Find a picture that also a green war-time motorcycle, but has the same color and has a man on it.\"}\n{\"id\": 649, \"q_img\": \"000000491537.jpg\", \"q_text\": \"Find a picture that also a man walking on the beach while holding a surfboard under his arm, but is in greyscale and shows the sky in the background.\"}\n{\"id\": 650, \"q_img\": \"000000265265.jpg\", \"q_text\": \"Find a picture that also a skater, but has a t-shirt of the same color and there are people looking at him.\"}\n{\"id\": 651, \"q_img\": \"000000111418.jpg\", \"q_text\": \"Find a picture that also a dog jumping to catch a frisbee next to a person, but has a red frisbee in its mouth.\"}\n{\"id\": 652, \"q_img\": \"000000074045.jpg\", \"q_text\": \"Find a picture that also a person standing playing the Wii and a person sitting, but shows two people standing and more people sitting.\"}\n{\"id\": 653, \"q_img\": \"000000035912.jpg\", \"q_text\": \"Find a picture that also a person with a surfboard under his arm seen from behind, but is shot from the same angle and there are parked cars on the left.\"}\n{\"id\": 654, \"q_img\": \"000000221308.jpg\", \"q_text\": \"Find a picture that also a person with a breakfast cup, but is sitting on the sofa while eating.\"}\n{\"id\": 655, \"q_img\": \"000000415687.jpg\", \"q_text\": \"Find a picture that also a grandfather clock in a corner, but is placed next to a window.\"}\n{\"id\": 656, \"q_img\": \"000000432240.jpg\", \"q_text\": \"Find a picture that also a person in the foreground holding a plate with hot dogs outdoors, but is older, is bringing more hot dogs and the photo is shot from the same angle.\"}\n{\"id\": 657, \"q_img\": \"000000416947.jpg\", \"q_text\": \"Find a picture that also a bird on a dining table, but has a different color and some people are visible in the background.\"}\n{\"id\": 658, \"q_img\": \"000000086792.jpg\", \"q_text\": \"Find a picture that also a person playing the Nintendo Wii, but is wearing a garment related to Star Wars.\"}\n{\"id\": 659, \"q_img\": \"000000282019.jpg\", \"q_text\": \"Find a picture that also a birthday cake with multiple lit candles, but has a child seen from the front in front of it.\"}\n{\"id\": 660, \"q_img\": \"000000276998.jpg\", \"q_text\": \"Find a picture that also a broken phone, but is held in one hand and is seen from a closer distance.\"}\n{\"id\": 661, \"q_img\": \"000000012388.jpg\", \"q_text\": \"Find a picture that also a laptop on a table, but has a graphics tablet next to it.\"}\n{\"id\": 662, \"q_img\": \"000000171989.jpg\", \"q_text\": \"Find a picture that also a Blackberry-style phone held in a hand, but is black and the person is not visible.\"}\n{\"id\": 663, \"q_img\": \"000000475201.jpg\", \"q_text\": \"Find a picture that also an old man with a cowboy hat, but is fishing instead of milking, the photo shows no animals, has the sky in the background, has one fishing rod and is zoomed out.\"}\n{\"id\": 664, \"q_img\": \"000000574439.jpg\", \"q_text\": \"Find a picture that also a double bed, but has black support and there are white walls in the background.\"}\n{\"id\": 665, \"q_img\": \"000000139851.jpg\", \"q_text\": \"Find a picture that also a woman with a suitcase, but is sitting on it and the photo is taken outdoors.\"}\n{\"id\": 666, \"q_img\": \"000000343594.jpg\", \"q_text\": \"Find a picture that also a clock with an animal above it, but is hanging on the wall.\"}\n{\"id\": 667, \"q_img\": \"000000541362.jpg\", \"q_text\": \"Find a picture that also two laptops on a desk, but has a CRT monitor next to them.\"}\n{\"id\": 668, \"q_img\": \"000000079885.jpg\", \"q_text\": \"Find a picture that also a cat inside an object, but shows a bowl of a non-solid color instead of a cup and is taken from a lower angle.\"}\n{\"id\": 669, \"q_img\": \"000000028412.jpg\", \"q_text\": \"Find a picture that also a city bus seen from the front-right, but is yellow instead of white and the photo is shot from the same angle.\"}\n{\"id\": 670, \"q_img\": \"000000565498.jpg\", \"q_text\": \"Find a picture that also a tidy white kitchen with a window, but has no island and has a parquet floor.\"}\n{\"id\": 671, \"q_img\": \"000000394646.jpg\", \"q_text\": \"Find a picture that also a donut and a filled mug, but are resting on a newspaper.\"}\n{\"id\": 672, \"q_img\": \"000000443930.jpg\", \"q_text\": \"Find a picture that also a green and white bus, but has one double-decker bus of the same color.\"}\n{\"id\": 673, \"q_img\": \"000000571844.jpg\", \"q_text\": \"Find a picture that also people going on skateboards on a road, but shows more people and a cloudy sky in the background.\"}\n{\"id\": 674, \"q_img\": \"000000258082.jpg\", \"q_text\": \"Find a picture that also several teddy bears, but are piled up on top of a person.\"}\n{\"id\": 675, \"q_img\": \"000000094771.jpg\", \"q_text\": \"Find a picture that also two newlyweds, but is in greyscale with a different background.\"}\n{\"id\": 676, \"q_img\": \"000000272201.jpg\", \"q_text\": \"Find a picture that also a white microwave, but has the same color and is on a sidewalk.\"}\n{\"id\": 677, \"q_img\": \"000000083927.jpg\", \"q_text\": \"Find a picture that also a elephant animal, but has only one of them, is near a trainer and the photo is shot during a show.\"}\n{\"id\": 678, \"q_img\": \"000000176706.jpg\", \"q_text\": \"Find a picture that also a man sitting with a dog, but has only two paws on the ground.\"}\n{\"id\": 679, \"q_img\": \"000000282743.jpg\", \"q_text\": \"Find a picture that also a fridge with something inside, but has a child instead of cats.\"}\n{\"id\": 680, \"q_img\": \"000000297636.jpg\", \"q_text\": \"Find a picture that also a mixer, but is closed and has a fish instead of ice.\"}\n{\"id\": 681, \"q_img\": \"000000530077.jpg\", \"q_text\": \"Find a picture that also a girl holding a teddy bear, but is lying on a bed.\"}\n{\"id\": 682, \"q_img\": \"000000027489.jpg\", \"q_text\": \"Find a picture that also a yellow school bus, but has the same color, it is seen through a rearview mirror and there is only one of them.\"}\n{\"id\": 683, \"q_img\": \"000000577132.jpg\", \"q_text\": \"Find a picture that also a kitchen, but has two hanging lights and the fridge has a silver color.\"}\n{\"id\": 684, \"q_img\": \"000000515690.jpg\", \"q_text\": \"Find a picture that also a pan, but shows only one of them and it is in an oven.\"}\n{\"id\": 685, \"q_img\": \"000000163338.jpg\", \"q_text\": \"Find a picture that also a person cutting food with a knife, but is a tomato instead of a bagel.\"}\n{\"id\": 686, \"q_img\": \"000000409818.jpg\", \"q_text\": \"Find a picture that also a person on a snowboard rail, but is taken from a closer distance, shows a snowboard instead of skis and there are no people in the background.\"}\n{\"id\": 687, \"q_img\": \"000000346783.jpg\", \"q_text\": \"Find a picture that also a child with a skateboard held vertically in his hands, but is shot in a skatepark and has trees in the background.\"}\n{\"id\": 688, \"q_img\": \"000000386874.jpg\", \"q_text\": \"Find a picture that also a cat in a suitcase, but is more vintage and is not blue.\"}\n{\"id\": 689, \"q_img\": \"000000359488.jpg\", \"q_text\": \"Find a picture that also a standing person pouring wine into a glass, but is facing the camera and there are multiple bottles behind him.\"}\n{\"id\": 690, \"q_img\": \"000000412368.jpg\", \"q_text\": \"Find a picture that also a whistling kettle on a stove burner, but has a different color and is seen from a farther distance.\"}\n{\"id\": 691, \"q_img\": \"000000368463.jpg\", \"q_text\": \"Find a picture that also an old woman on a chair seen from the front, but has at least a child next to her and the photo has a higher quality.\"}\n{\"id\": 692, \"q_img\": \"000000055841.jpg\", \"q_text\": \"Find a picture that also a yellow motorbike, but has the same color and a person is riding it.\"}\n{\"id\": 693, \"q_img\": \"000000077731.jpg\", \"q_text\": \"Find a picture that also a double parking meter, but is zoomed out and there are plants next to it.\"}\n{\"id\": 694, \"q_img\": \"000000142003.jpg\", \"q_text\": \"Find a picture that also a person using a laptop, but is on a couch and using a mouse.\"}\n{\"id\": 695, \"q_img\": \"000000532677.jpg\", \"q_text\": \"Find a picture that also a fire hydrant, but is in the countryside, the photo is taken from a farther distance and has a hill in the background.\"}\n{\"id\": 696, \"q_img\": \"000000442397.jpg\", \"q_text\": \"Find a picture that also a bathroom with a sink and toilet, but has a bathtub instead of a shower and there is a white window.\"}\n{\"id\": 697, \"q_img\": \"000000268207.jpg\", \"q_text\": \"Find a picture that also a parking meter, but has a solar panel on top and there is a person next to it.\"}\n{\"id\": 698, \"q_img\": \"000000177209.jpg\", \"q_text\": \"Find a picture that also a red fire hydrant, but has the same color, is surrounded by leaves, is seen from a farther distance and no flowers are visible.\"}\n{\"id\": 699, \"q_img\": \"000000074190.jpg\", \"q_text\": \"Find a picture that also a close-up open fridge, but shows a person getting food from it.\"}\n{\"id\": 700, \"q_img\": \"000000273842.jpg\", \"q_text\": \"Find a picture that also a tree-lined avenue in a city seen from an in-road angle, but shows fewer cars and a skyscraper in the background.\"}\n{\"id\": 701, \"q_img\": \"000000126648.jpg\", \"q_text\": \"Find a picture that also a man with an umbrella, but is surrounded by darkness and no trees are visible.\"}\n{\"id\": 702, \"q_img\": \"000000352764.jpg\", \"q_text\": \"Find a picture that also a person looking at their phone in the foreground, but is a woman with black curly hair.\"}\n{\"id\": 703, \"q_img\": \"000000376134.jpg\", \"q_text\": \"Find a picture that also vases seen from a close distance, but has more of them and they are made of clear glass.\"}\n{\"id\": 704, \"q_img\": \"000000280509.jpg\", \"q_text\": \"Find a picture that also a refrigerator in the foreground, but is white and has magnets only in the upper section.\"}\n{\"id\": 705, \"q_img\": \"000000008611.jpg\", \"q_text\": \"Find a picture that also a white fridge with photos attached to it, but has the same color and there is person in front of it.\"}\n{\"id\": 706, \"q_img\": \"000000542721.jpg\", \"q_text\": \"Find a picture that also a professional woman tennis player hitting the ball, but is hitting a forehand and she wears a black cap.\"}\n{\"id\": 707, \"q_img\": \"000000227650.jpg\", \"q_text\": \"Find a picture that also a fire hydrant in the foreground, but has the colors of the American flag.\"}\n{\"id\": 708, \"q_img\": \"000000330820.jpg\", \"q_text\": \"Find a picture that also a close-up vase, but has a different shape, is more colorful and is surrounded by greenery.\"}\n{\"id\": 709, \"q_img\": \"000000456327.jpg\", \"q_text\": \"Find a picture that also some suitcases, but has more of them, they are arranged in a tower and are located in a large room.\"}\n{\"id\": 710, \"q_img\": \"000000581800.jpg\", \"q_text\": \"Find a picture that also a truck carrying logs, but is taken from the front and shows the sky in the background.\"}\n{\"id\": 711, \"q_img\": \"000000362204.jpg\", \"q_text\": \"Find a picture that also a skier making a turn around a red gate, but is a professional skier and the track is bordered in blue.\"}\n{\"id\": 712, \"q_img\": \"000000045474.jpg\", \"q_text\": \"Find a picture that also an elephant during a circus show, but has only one of them and has a darker background.\"}\n{\"id\": 713, \"q_img\": \"000000042036.jpg\", \"q_text\": \"Find a picture that also a man wearing a tie looking at the camera, but has eyeglasses and a black shirt.\"}\n{\"id\": 714, \"q_img\": \"000000358277.jpg\", \"q_text\": \"Find a picture that also a bottle of wine next to a filled wine glass, but has only one of them and the photo shows a microwave in the background.\"}\n{\"id\": 715, \"q_img\": \"000000456668.jpg\", \"q_text\": \"Find a picture that also a queen-size bed, but is shot from the same angle and there is a picture on the wall behind it.\"}\n{\"id\": 716, \"q_img\": \"000000468995.jpg\", \"q_text\": \"Find a picture that also an object half hidden in a pouch, but has scissors instead of a phone and the photo has a whiter background.\"}\n{\"id\": 717, \"q_img\": \"000000252207.jpg\", \"q_text\": \"Find a picture that also a foreground vase, but is only one, is glossier and has a white background behind it.\"}\n{\"id\": 718, \"q_img\": \"000000436001.jpg\", \"q_text\": \"Find a picture that also two amateur tennis players playing a double, but is shot from the same angle and both players are women.\"}\n{\"id\": 719, \"q_img\": \"000000273636.jpg\", \"q_text\": \"Find a picture that also a pizza, but is on a work desk in front of a screen and a keyboard is visible.\"}\n{\"id\": 720, \"q_img\": \"000000543952.jpg\", \"q_text\": \"Find a picture that also a cat, but is orange and is half inside a toilet.\"}\n{\"id\": 721, \"q_img\": \"000000428037.jpg\", \"q_text\": \"Find a picture that also a slice of cake on a plate in the foreground next to an object, but has a teapot instead of a to-go cup.\"}\n{\"id\": 722, \"q_img\": \"000000533322.jpg\", \"q_text\": \"Find a picture that also a kitchen sink with a window behind it, but is shot in color and the sink is black.\"}\n{\"id\": 723, \"q_img\": \"000000252026.jpg\", \"q_text\": \"Find a picture that also a cat under an object, but shows a car instead of a bench and the photo is shot in color.\"}\n{\"id\": 724, \"q_img\": \"000000212280.jpg\", \"q_text\": \"Find a picture that also a teddy bear on a bed, but shows more teddy bears.\"}\n{\"id\": 725, \"q_img\": \"000000375711.jpg\", \"q_text\": \"Find a picture that also a skier, but is dressed as a bear and the photo shows more people in the background.\"}\n{\"id\": 726, \"q_img\": \"000000528464.jpg\", \"q_text\": \"Find a picture that also a red train seen from the front, but is in a station with a lot of people.\"}\n{\"id\": 727, \"q_img\": \"000000460472.jpg\", \"q_text\": \"Find a picture that also a fruit connected to wires, but shows a different fruit and has a glass of water.\"}\n{\"id\": 728, \"q_img\": \"000000218905.jpg\", \"q_text\": \"Find a picture that also a cat inside an object, but is white and black and there is a suitcase instead of a sink.\"}\n{\"id\": 729, \"q_img\": \"000000156989.jpg\", \"q_text\": \"Find a picture that also a seagull, but is shot from a closer distance and has a bridge in the background.\"}\n{\"id\": 730, \"q_img\": \"000000487991.jpg\", \"q_text\": \"Find a picture that also a man wearing a tie and a shirt, but has no jacket and the photo is shot outdoors and in greyscale.\"}\n{\"id\": 731, \"q_img\": \"000000571533.jpg\", \"q_text\": \"Find a picture that also a single cow in the countryside, but is of a different color, is seen from the side and the photo shows the sky in the background.\"}\n{\"id\": 732, \"q_img\": \"000000381912.jpg\", \"q_text\": \"Find a picture that also a white toilet outdoors, but has the same color and there is a person sitting on it.\"}\n{\"id\": 733, \"q_img\": \"000000087575.jpg\", \"q_text\": \"Find a picture that also a group of skiers standing, but shows a lake in the background.\"}\n{\"id\": 734, \"q_img\": \"000000223108.jpg\", \"q_text\": \"Find a picture that also an abandoned microwave oven, but is in a worse condition and has no microwave door.\"}\n{\"id\": 735, \"q_img\": \"000000391569.jpg\", \"q_text\": \"Find a picture that also multiple set round tables, but have a different color and more people sitting.\"}\n{\"id\": 736, \"q_img\": \"000000262675.jpg\", \"q_text\": \"Find a picture that also a person holding a surfboard under their arm standing in the shallow water, but wears a black wetsuit and the surfboard has a different color.\"}\n{\"id\": 737, \"q_img\": \"000000338273.jpg\", \"q_text\": \"Find a picture that also food on a white plate in the foreground, but shows two sandwiches with baked potatoes instead of the pizza.\"}\n{\"id\": 738, \"q_img\": \"000000216433.jpg\", \"q_text\": \"Find a picture that also a man in the foreground putting a pizza in a pizza oven, but is taken indoors and has fewer people in the background.\"}\n{\"id\": 739, \"q_img\": \"000000296003.jpg\", \"q_text\": \"Find a picture that also a man riding a bicycle seen from the side, but has a surfboard under his arm and the image is taken from the same angle.\"}\n{\"id\": 740, \"q_img\": \"000000038271.jpg\", \"q_text\": \"Find a picture that also a stove, but is new and wrapped in plastic wrap and is seen from a farther distance.\"}\n{\"id\": 741, \"q_img\": \"000000062080.jpg\", \"q_text\": \"Find a picture that also a fire hydrant, but is red and is next to a stop sign.\"}\n{\"id\": 742, \"q_img\": \"000000415371.jpg\", \"q_text\": \"Find a picture that also an orange traffic cone, but has the same color and is on a fire hydrant.\"}\n{\"id\": 743, \"q_img\": \"000000352543.jpg\", \"q_text\": \"Find a picture that also a toilet, but is closed and has flowers.\"}\n{\"id\": 744, \"q_img\": \"000000385106.jpg\", \"q_text\": \"Find a picture that also a white laptop on a table, but has the same color and there is a soft toy next to it.\"}\n{\"id\": 745, \"q_img\": \"000000337360.jpg\", \"q_text\": \"Find a picture that also plated dishes on a table, but shows no people and has more plates.\"}\n{\"id\": 746, \"q_img\": \"000000292083.jpg\", \"q_text\": \"Find a picture that also a silhouette of an animal, but has a different animal and is set on a beach.\"}\n{\"id\": 747, \"q_img\": \"000000308435.jpg\", \"q_text\": \"Find a picture that also a bedroom, but has one couch and a wall that is not white.\"}\n{\"id\": 748, \"q_img\": \"000000320124.jpg\", \"q_text\": \"Find a picture that also a man wearing a tie, but is sitting and has a beverage in front of him.\"}\n{\"id\": 749, \"q_img\": \"000000003970.jpg\", \"q_text\": \"Find a picture that also an umbrella next to chairs, but shows beach chairs instead of chairs and is set next to a pool.\"}\n{\"id\": 750, \"q_img\": \"000000529469.jpg\", \"q_text\": \"Find a picture that also thatched umbrellas with the sea in the background, but is taken from the height of the sea.\"}\n{\"id\": 751, \"q_img\": \"000000390713.jpg\", \"q_text\": \"Find a picture that also a lunch box with food in it and cutlery seen from the top, but has two boxes and is shot from the same angle.\"}\n{\"id\": 752, \"q_img\": \"000000253218.jpg\", \"q_text\": \"Find a picture that also a barbecue outdoors, but is seen from a higher angle and the photo shows people and a greener background.\"}\n{\"id\": 753, \"q_img\": \"000000212356.jpg\", \"q_text\": \"Find a picture that also a zebra, but is eating grass and is next to an ostrich.\"}\n{\"id\": 754, \"q_img\": \"000000308542.jpg\", \"q_text\": \"Find a picture that also a white vase with flowers, but depicts a head.\"}\n{\"id\": 755, \"q_img\": \"000000221944.jpg\", \"q_text\": \"Find a picture that also a child surfing in the foreground, but has sand instead of water.\"}\n{\"id\": 756, \"q_img\": \"000000442780.jpg\", \"q_text\": \"Find a picture that also airport luggage carts, but shows more people and a potted plant.\"}\n{\"id\": 757, \"q_img\": \"000000059899.jpg\", \"q_text\": \"Find a picture that also a woman holding a little girl with an umbrella, but has no water in the background.\"}\n{\"id\": 758, \"q_img\": \"000000014742.jpg\", \"q_text\": \"Find a picture that also people cutting a ribbon at an inauguration, but is of a different color and the photo shows fewer people in the foreground.\"}\n{\"id\": 759, \"q_img\": \"000000541061.jpg\", \"q_text\": \"Find a picture that also a woman standing in front of an airplane, but is set on the grass and the woman dressed in a different color.\"}\n{\"id\": 760, \"q_img\": \"000000490983.jpg\", \"q_text\": \"Find a picture that also a doughnut cake with a missing part on a plate, but has the same shape and the plate has a different color.\"}\n{\"id\": 761, \"q_img\": \"000000212613.jpg\", \"q_text\": \"Find a picture that also a plate with food seen from above, but has a wooden background and the plate is white.\"}\n{\"id\": 762, \"q_img\": \"000000031173.jpg\", \"q_text\": \"Find a picture that also a circular close-up stop sign, but has a circular shape and the photo is taken from the same angle.\"}\n{\"id\": 763, \"q_img\": \"000000030773.jpg\", \"q_text\": \"Find a picture that also a person in the air holding a board with his hand, but shows snowboarding instead of surfing and there is a photographer in the foreground.\"}\n{\"id\": 764, \"q_img\": \"000000457321.jpg\", \"q_text\": \"Find a picture that also a close-up man whose face is not shown who is wearing a white shirt and a tie, but is shot from the same angle and the tie is black.\"}\n{\"id\": 765, \"q_img\": \"000000495630.jpg\", \"q_text\": \"Find a picture that also playground equipment, but has more people.\"}\n{\"id\": 766, \"q_img\": \"000000576352.jpg\", \"q_text\": \"Find a picture that also an orange slice, but has more of them on a plate.\"}\n{\"id\": 767, \"q_img\": \"000000375403.jpg\", \"q_text\": \"Find a picture that also a close-up hot dog, but is held by a hand and the photo is shot on a beach.\"}\n{\"id\": 768, \"q_img\": \"000000005518.jpg\", \"q_text\": \"Find a picture that also a knife next to a piece of food on a cutting board, but has bread instead of carrots.\"}\n{\"id\": 769, \"q_img\": \"000000375946.jpg\", \"q_text\": \"Find a picture that also multiple clocks attached to a wall, but has clocks of a non-standard shape and the wall are of a different color.\"}\n{\"id\": 770, \"q_img\": \"000000282469.jpg\", \"q_text\": \"Find a picture that also a hot dog in the foreground held on a hand, but has a statue instead of a stadium.\"}\n{\"id\": 771, \"q_img\": \"000000465954.jpg\", \"q_text\": \"Find a picture that also a sink and a bathtub in a bathroom, but has a greenish-colored wall and there is a window in on the left wall.\"}\n{\"id\": 772, \"q_img\": \"000000437777.jpg\", \"q_text\": \"Find a picture that also a person facing the camera taking a picture with a flip phone outdoors, but shows only one person and a brighter background.\"}\n{\"id\": 773, \"q_img\": \"000000271376.jpg\", \"q_text\": \"Find a picture that also multiple donuts, but are packaged in white boxes and are of a single color.\"}\n{\"id\": 774, \"q_img\": \"000000005645.jpg\", \"q_text\": \"Find a picture that also a man wearing a tie and a bowler hat, but is shot indoors and the man is looking at the camera.\"}\n{\"id\": 775, \"q_img\": \"000000052896.jpg\", \"q_text\": \"Find a picture that also a black couch in a living room, but shows a kitchen in the background.\"}\n{\"id\": 776, \"q_img\": \"000000438310.jpg\", \"q_text\": \"Find a picture that also a pizza in a box in the foreground, but has two of them and has wine instead of beer.\"}\n{\"id\": 777, \"q_img\": \"000000283015.jpg\", \"q_text\": \"Find a picture that also several pigeons, but are sitting on a semaphore.\"}\n{\"id\": 778, \"q_img\": \"000000335689.jpg\", \"q_text\": \"Find a picture that also a carrot, but has two of them and they are on a cutting board next to a knife.\"}\n{\"id\": 779, \"q_img\": \"000000400473.jpg\", \"q_text\": \"Find a picture that also a person talking on the phone, but is taking a sip and the photo has a more everyday background.\"}\n{\"id\": 780, \"q_img\": \"000000428058.jpg\", \"q_text\": \"Find a picture that also a bedroom, but is shot in greyscale and shows no books.\"}\n{\"id\": 781, \"q_img\": \"000000436398.jpg\", \"q_text\": \"Find a picture that also a person holding freshly picked vegetables, but has a knife and the vegetable is of a different type.\"}\n{\"id\": 782, \"q_img\": \"000000013321.jpg\", \"q_text\": \"Find a picture that also an old man with a beard wearing a hat, but shows a bench instead of a chair and there is no umbrella.\"}\n{\"id\": 783, \"q_img\": \"000000027532.jpg\", \"q_text\": \"Find a picture that also a person standing holding an umbrella seen from the back, but has a different color and shows writing on it.\"}\n{\"id\": 784, \"q_img\": \"000000157077.jpg\", \"q_text\": \"Find a picture that also a turned-on laptop on a desk, but shows a notebook with writings on it and the photo is shot indoors.\"}\n{\"id\": 785, \"q_img\": \"000000129413.jpg\", \"q_text\": \"Find a picture that also a laptop seen from the front with a beverage next to it, but is shot indoors and there is a person in the background.\"}\n{\"id\": 786, \"q_img\": \"000000392749.jpg\", \"q_text\": \"Find a picture that also a white refrigerator in the foreground, but is full of food and the photo shows only the interior.\"}\n{\"id\": 787, \"q_img\": \"000000066260.jpg\", \"q_text\": \"Find a picture that also a small electric oven, but is turned on and has some food in it.\"}\n{\"id\": 788, \"q_img\": \"000000087988.jpg\", \"q_text\": \"Find a picture that also a kitchen countertop, but has no visible stove and there is a laptop on it.\"}\n{\"id\": 789, \"q_img\": \"000000070656.jpg\", \"q_text\": \"Find a picture that also a white plate with food on a wooden table, but has a banana instead of the cake.\"}\n{\"id\": 790, \"q_img\": \"000000071231.jpg\", \"q_text\": \"Find a picture that also a laptop on a desk seen from the front, but is only one and there is a keyboard under it, the photo is shot from the same angle.\"}\n{\"id\": 791, \"q_img\": \"000000305818.jpg\", \"q_text\": \"Find a picture that also a living room with a sofa, but shows people and has a french door in the background.\"}\n{\"id\": 792, \"q_img\": \"000000140749.jpg\", \"q_text\": \"Find a picture that also some sheep, but are drinking water and there is no bench.\"}\n{\"id\": 793, \"q_img\": \"000000360457.jpg\", \"q_text\": \"Find a picture that also a living room with a CRT TV, but has an open window and the photo is darker.\"}\n{\"id\": 794, \"q_img\": \"000000272178.jpg\", \"q_text\": \"Find a picture that also a big inflatable kite on the beach, but has a whale instead of an octopus.\"}\n{\"id\": 795, \"q_img\": \"000000195881.jpg\", \"q_text\": \"Find a picture that also something inside an open suitcase, but shows an orange cat instead of a baby and the cat has the eyes closed.\"}\n{\"id\": 796, \"q_img\": \"000000158464.jpg\", \"q_text\": \"Find a picture that also a white fridge, but has the same color and is closed and the photo has a CRT TV.\"}\n{\"id\": 797, \"q_img\": \"000000277154.jpg\", \"q_text\": \"Find a picture that also a group of people using laptops at a table, but has more people and the table is U-shaped.\"}\n{\"id\": 798, \"q_img\": \"000000359573.jpg\", \"q_text\": \"Find a picture that also multiple close-up donuts, but are seen from a higher perspective and have a white glaze with sprinkles.\"}\n{\"id\": 799, \"q_img\": \"000000222734.jpg\", \"q_text\": \"Find a picture that also a person in a kitchen looking at the camera, but is a man and is doing dishes in the kitchen sink.\"}\n" }, { "path": "research/BGE_VL/eval/data/fashioniq_dress_corpus.jsonl", "content": "{\"content\": \"B009PMCJLW\", \"image\": \"./images/B009PMCJLW.png\"}\n{\"content\": \"B0084Y8XIU\", \"image\": \"./images/B0084Y8XIU.png\"}\n{\"content\": \"B007HNF4QI\", \"image\": \"./images/B007HNF4QI.png\"}\n{\"content\": \"B00AKLK08G\", \"image\": \"./images/B00AKLK08G.png\"}\n{\"content\": \"B00CMPE0C0\", \"image\": \"./images/B00CMPE0C0.png\"}\n{\"content\": \"B004UO3XYC\", \"image\": \"./images/B004UO3XYC.png\"}\n{\"content\": \"B007U6KROG\", \"image\": \"./images/B007U6KROG.png\"}\n{\"content\": \"B004KHOPDW\", \"image\": \"./images/B004KHOPDW.png\"}\n{\"content\": \"B0083E6W7U\", \"image\": \"./images/B0083E6W7U.png\"}\n{\"content\": \"B007L3WP5W\", \"image\": \"./images/B007L3WP5W.png\"}\n{\"content\": \"B0091PLEKA\", \"image\": \"./images/B0091PLEKA.png\"}\n{\"content\": \"B004P7TNIY\", \"image\": \"./images/B004P7TNIY.png\"}\n{\"content\": \"B0077SM1Z0\", \"image\": \"./images/B0077SM1Z0.png\"}\n{\"content\": \"B007UYY43I\", \"image\": \"./images/B007UYY43I.png\"}\n{\"content\": \"B00762GDB0\", \"image\": \"./images/B00762GDB0.png\"}\n{\"content\": \"B007M8PCAQ\", \"image\": \"./images/B007M8PCAQ.png\"}\n{\"content\": \"B00CAGQSLC\", \"image\": \"./images/B00CAGQSLC.png\"}\n{\"content\": \"B00BG3PBJU\", \"image\": \"./images/B00BG3PBJU.png\"}\n{\"content\": \"B002NX11VE\", \"image\": \"./images/B002NX11VE.png\"}\n{\"content\": \"B00A0I6GSC\", \"image\": \"./images/B00A0I6GSC.png\"}\n{\"content\": \"B00CFFFJYU\", \"image\": \"./images/B00CFFFJYU.png\"}\n{\"content\": \"B00F5DPMIM\", \"image\": \"./images/B00F5DPMIM.png\"}\n{\"content\": \"B00C0X5FG4\", \"image\": \"./images/B00C0X5FG4.png\"}\n{\"content\": \"B00AQC6IXA\", \"image\": \"./images/B00AQC6IXA.png\"}\n{\"content\": \"B00B3Y6GNC\", \"image\": \"./images/B00B3Y6GNC.png\"}\n{\"content\": \"B0080Q8B7U\", \"image\": \"./images/B0080Q8B7U.png\"}\n{\"content\": \"B00DHT0O8E\", \"image\": \"./images/B00DHT0O8E.png\"}\n{\"content\": \"B009GC0YCM\", \"image\": \"./images/B009GC0YCM.png\"}\n{\"content\": \"B005OOUCLE\", \"image\": \"./images/B005OOUCLE.png\"}\n{\"content\": \"B008MNM7Y4\", \"image\": \"./images/B008MNM7Y4.png\"}\n{\"content\": \"B009S5XX0M\", \"image\": \"./images/B009S5XX0M.png\"}\n{\"content\": \"B004JU0AF2\", \"image\": \"./images/B004JU0AF2.png\"}\n{\"content\": \"B00DVH1QS4\", \"image\": \"./images/B00DVH1QS4.png\"}\n{\"content\": \"B00DC6UJ0K\", \"image\": \"./images/B00DC6UJ0K.png\"}\n{\"content\": \"B007H3JJHI\", \"image\": \"./images/B007H3JJHI.png\"}\n{\"content\": \"B00870V3OM\", \"image\": \"./images/B00870V3OM.png\"}\n{\"content\": \"B008P3DTZC\", \"image\": \"./images/B008P3DTZC.png\"}\n{\"content\": \"B009OJFHGA\", \"image\": \"./images/B009OJFHGA.png\"}\n{\"content\": \"B008NAH0PM\", \"image\": \"./images/B008NAH0PM.png\"}\n{\"content\": \"B006ZKN7UY\", \"image\": \"./images/B006ZKN7UY.png\"}\n{\"content\": \"B00AIBYV1K\", \"image\": \"./images/B00AIBYV1K.png\"}\n{\"content\": \"B008596T26\", \"image\": \"./images/B008596T26.png\"}\n{\"content\": \"B008BHSUCE\", \"image\": \"./images/B008BHSUCE.png\"}\n{\"content\": \"B006UR05S4\", \"image\": \"./images/B006UR05S4.png\"}\n{\"content\": \"B0089CNC92\", \"image\": \"./images/B0089CNC92.png\"}\n{\"content\": \"B003XCNN3I\", \"image\": \"./images/B003XCNN3I.png\"}\n{\"content\": \"B003ZUWZ3M\", \"image\": \"./images/B003ZUWZ3M.png\"}\n{\"content\": \"B008CPJIWG\", \"image\": \"./images/B008CPJIWG.png\"}\n{\"content\": \"B00DM2G9L2\", \"image\": \"./images/B00DM2G9L2.png\"}\n{\"content\": \"B009MQBGX8\", \"image\": \"./images/B009MQBGX8.png\"}\n{\"content\": \"B007WA2VB2\", \"image\": \"./images/B007WA2VB2.png\"}\n{\"content\": \"B008PZ0Y62\", 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"research/BGE_VL/eval/data/fashioniq_dress_query_val.jsonl", "content": "{\"q_img\": \"./images/B005X4PL1G.png\", \"q_text\": \"is shiny and silver with shorter sleeves, and fit and flare\", \"positive_key\": \"B0084Y8XIU\", \"positive_value\": \"./images/B0084Y8XIU.png\", \"hn_image\": [\"./images/B005X4PL1G.png\"]}\n{\"q_img\": \"./images/B008XODTD0.png\", \"q_text\": \"is grey with black design, and is a light printed short dress\", \"positive_key\": \"B00AKLK08G\", \"positive_value\": \"./images/B00AKLK08G.png\", \"hn_image\": [\"./images/B008XODTD0.png\"]}\n{\"q_img\": \"./images/B00BPYP69K.png\", \"q_text\": \"is a solid red color, and shorter and tighter with more blue and white\", \"positive_key\": \"B00CMPE0C0\", \"positive_value\": \"./images/B00CMPE0C0.png\", \"hn_image\": [\"./images/B00BPYP69K.png\"]}\n{\"q_img\": \"./images/B000QSGNOI.png\", \"q_text\": \"is a plain white feminine t shirt, and is a tan shirt.\", \"positive_key\": \"B004UO3XYC\", \"positive_value\": \"./images/B004UO3XYC.png\", \"hn_image\": [\"./images/B000QSGNOI.png\"]}\n{\"q_img\": \"./images/B00FQANLX2.png\", \"q_text\": \"has thin straps and different pattern, and more autumn colored and longer\", \"positive_key\": \"B007U6KROG\", \"positive_value\": \"./images/B007U6KROG.png\", \"hn_image\": [\"./images/B00FQANLX2.png\"]}\n{\"q_img\": \"./images/B00CL14N3Q.png\", \"q_text\": \"is longer and more sculptural, and rushed solid green\", \"positive_key\": \"B004KHOPDW\", \"positive_value\": \"./images/B004KHOPDW.png\", \"hn_image\": [\"./images/B00CL14N3Q.png\"]}\n{\"q_img\": \"./images/B009CMY4BS.png\", \"q_text\": \"is gold and strapless, and button front longer sleeves\", \"positive_key\": \"B0091PLEKA\", \"positive_value\": \"./images/B0091PLEKA.png\", \"hn_image\": [\"./images/B009CMY4BS.png\"]}\n{\"q_img\": \"./images/B00896JPTE.png\", \"q_text\": \"is longer and less casual and sleeveless, and is a sleeveless black dress\", \"positive_key\": \"B004P7TNIY\", \"positive_value\": \"./images/B004P7TNIY.png\", \"hn_image\": [\"./images/B00896JPTE.png\"]}\n{\"q_img\": \"./images/B00F4WI9H0.png\", \"q_text\": \"is longer and more elegant, and is longer and sleeveless\", \"positive_key\": \"B0077SM1Z0\", \"positive_value\": \"./images/B0077SM1Z0.png\", \"hn_image\": [\"./images/B00F4WI9H0.png\"]}\n{\"q_img\": \"./images/B000WIRE54.png\", \"q_text\": \"has longer sleeves and a different color, and has longer sleeves and is more casual\", \"positive_key\": \"B007UYY43I\", \"positive_value\": \"./images/B007UYY43I.png\", \"hn_image\": [\"./images/B000WIRE54.png\"]}\n{\"q_img\": \"./images/B009KQVCAM.png\", \"q_text\": \"its blue with higher neckline, and is more blue and higher at the neckline\", \"positive_key\": \"B00A0I6GSC\", \"positive_value\": \"./images/B00A0I6GSC.png\", \"hn_image\": [\"./images/B009KQVCAM.png\"]}\n{\"q_img\": \"./images/B00FHFMMMW.png\", \"q_text\": \"is softly colored, and has no shoulder straps and looser skirt\", \"positive_key\": \"B00F5DPMIM\", \"positive_value\": \"./images/B00F5DPMIM.png\", \"hn_image\": [\"./images/B00FHFMMMW.png\"]}\n{\"q_img\": \"./images/B00DH06JSW.png\", \"q_text\": \"is white with black polka dots, and is not see through\", \"positive_key\": \"B00C0X5FG4\", \"positive_value\": \"./images/B00C0X5FG4.png\", \"hn_image\": [\"./images/B00DH06JSW.png\"]}\n{\"q_img\": \"./images/B008YIZM80.png\", \"q_text\": \"has more black designs, and has a paint pattern\", \"positive_key\": \"B005OOUCLE\", \"positive_value\": \"./images/B005OOUCLE.png\", \"hn_image\": [\"./images/B008YIZM80.png\"]}\n{\"q_img\": \"./images/B009DI5YSS.png\", \"q_text\": \"Is more black and elegant, and is solid black and has shorter sleeves\", \"positive_key\": \"B008MNM7Y4\", \"positive_value\": \"./images/B008MNM7Y4.png\", \"hn_image\": [\"./images/B009DI5YSS.png\"]}\n{\"q_img\": \"./images/B007GB2BZI.png\", \"q_text\": \"is blue print with short sleeves, and is a blue print\", \"positive_key\": \"B009S5XX0M\", \"positive_value\": \"./images/B009S5XX0M.png\", \"hn_image\": [\"./images/B007GB2BZI.png\"]}\n{\"q_img\": \"./images/B005GYSR64.png\", \"q_text\": \"is a solid color with square neckline, and the dress is brown with straps\", \"positive_key\": \"B004JU0AF2\", \"positive_value\": \"./images/B004JU0AF2.png\", \"hn_image\": [\"./images/B005GYSR64.png\"]}\n{\"q_img\": \"./images/B00CW6HT7W.png\", \"q_text\": \"is off the shoulder with one shorter sleeve, and has longer sleeves\", \"positive_key\": \"B00DVH1QS4\", \"positive_value\": \"./images/B00DVH1QS4.png\", \"hn_image\": [\"./images/B00CW6HT7W.png\"]}\n{\"q_img\": \"./images/B004VJ217Q.png\", \"q_text\": \"has more flair and smaller print, and is shorter\", \"positive_key\": \"B00DC6UJ0K\", \"positive_value\": \"./images/B00DC6UJ0K.png\", \"hn_image\": [\"./images/B004VJ217Q.png\"]}\n{\"q_img\": \"./images/B007B6WEA0.png\", \"q_text\": \"Is longer and fun, and is white and green\", \"positive_key\": \"B007H3JJHI\", \"positive_value\": \"./images/B007H3JJHI.png\", \"hn_image\": [\"./images/B007B6WEA0.png\"]}\n{\"q_img\": \"./images/B0061IXDTU.png\", \"q_text\": \"Is light colored with shorter sleeves, and leopard print with deeper neck\", \"positive_key\": \"B008P3DTZC\", \"positive_value\": \"./images/B008P3DTZC.png\", \"hn_image\": [\"./images/B0061IXDTU.png\"]}\n{\"q_img\": \"./images/B00B67ELUG.png\", \"q_text\": \"has more pattern and longer sleeves, and has sleeves and is a print\", \"positive_key\": \"B009OJFHGA\", \"positive_value\": \"./images/B009OJFHGA.png\", \"hn_image\": [\"./images/B00B67ELUG.png\"]}\n{\"q_img\": \"./images/B00BFGBJ3K.png\", \"q_text\": \"is white in color with short sleeves, and is more plain\", \"positive_key\": \"B006ZKN7UY\", \"positive_value\": \"./images/B006ZKN7UY.png\", \"hn_image\": [\"./images/B00BFGBJ3K.png\"]}\n{\"q_img\": \"./images/B008UEW5RO.png\", \"q_text\": \"is more transparent, and is more solid colored\", \"positive_key\": \"B00AIBYV1K\", \"positive_value\": \"./images/B00AIBYV1K.png\", \"hn_image\": [\"./images/B008UEW5RO.png\"]}\n{\"q_img\": \"./images/B006CR1XG0.png\", \"q_text\": \"Is lighter with a floral pattern., and is blue with straps\", \"positive_key\": \"B0089CNC92\", \"positive_value\": \"./images/B0089CNC92.png\", \"hn_image\": [\"./images/B006CR1XG0.png\"]}\n{\"q_img\": \"./images/B00499DHK8.png\", \"q_text\": \"has longer sleeves and has stripes, and has a higher neckline with sleeves\", \"positive_key\": \"B003ZUWZ3M\", \"positive_value\": \"./images/B003ZUWZ3M.png\", \"hn_image\": [\"./images/B00499DHK8.png\"]}\n{\"q_img\": \"./images/B005AQCKBQ.png\", \"q_text\": \"has more contrast, and Is sleeveless and lighter in color.\", \"positive_key\": \"B008CPJIWG\", \"positive_value\": \"./images/B008CPJIWG.png\", \"hn_image\": [\"./images/B005AQCKBQ.png\"]}\n{\"q_img\": \"./images/B008BHSG82.png\", \"q_text\": \"Has a darker long skirt and lighter top., and is gray and fitted at waist\", \"positive_key\": \"B007WA2VB2\", \"positive_value\": \"./images/B007WA2VB2.png\", \"hn_image\": [\"./images/B008BHSG82.png\"]}\n{\"q_img\": \"./images/B007G6BODI.png\", \"q_text\": \"Is darker and shorter in length., and is sleeveless\", \"positive_key\": \"B008PZ0Y62\", \"positive_value\": \"./images/B008PZ0Y62.png\", \"hn_image\": [\"./images/B007G6BODI.png\"]}\n{\"q_img\": \"./images/B00BQVAVRY.png\", \"q_text\": \"is shorter with longer sleeves, and is shorter has three quarter length sleeves\", \"positive_key\": \"B005SMZW7Q\", \"positive_value\": \"./images/B005SMZW7Q.png\", \"hn_image\": [\"./images/B00BQVAVRY.png\"]}\n{\"q_img\": \"./images/B00AHHR22A.png\", \"q_text\": \"is black and two straps, and has gold top half and black bottom half\", \"positive_key\": \"B00A846WGO\", \"positive_value\": \"./images/B00A846WGO.png\", \"hn_image\": [\"./images/B00AHHR22A.png\"]}\n{\"q_img\": \"./images/B00466ICP4.png\", \"q_text\": \"is black with no straps and red designs, and shorter\", \"positive_key\": \"B00A0TSV2U\", \"positive_value\": \"./images/B00A0TSV2U.png\", \"hn_image\": [\"./images/B00466ICP4.png\"]}\n{\"q_img\": \"./images/B00EXMSRW4.png\", \"q_text\": \"is sleeveless and white with dark printed design, and has a pattern\", \"positive_key\": \"B001VZ6H5U\", \"positive_value\": \"./images/B001VZ6H5U.png\", \"hn_image\": [\"./images/B00EXMSRW4.png\"]}\n{\"q_img\": \"./images/B0080N9HNU.png\", \"q_text\": \"is shorter and has animal print, and the dress has cheetah print and more revealing\", \"positive_key\": \"B006ZO5GC2\", \"positive_value\": \"./images/B006ZO5GC2.png\", \"hn_image\": [\"./images/B0080N9HNU.png\"]}\n{\"q_img\": \"./images/B00B7WGMXE.png\", \"q_text\": \"is light blue in color, and has more blue\", \"positive_key\": \"B0097JDP3Y\", \"positive_value\": \"./images/B0097JDP3Y.png\", \"hn_image\": [\"./images/B00B7WGMXE.png\"]}\n{\"q_img\": \"./images/B005QYY5W4.png\", \"q_text\": \"is red and longer, and is red and is much longer\", \"positive_key\": \"B005KMQQFQ\", \"positive_value\": \"./images/B005KMQQFQ.png\", \"hn_image\": [\"./images/B005QYY5W4.png\"]}\n{\"q_img\": \"./images/B00G6TRZE8.png\", \"q_text\": \"a low cut black gown, and is in solid black and a lot longer.\", \"positive_key\": \"B003ZJ6GCO\", \"positive_value\": \"./images/B003ZJ6GCO.png\", \"hn_image\": [\"./images/B00G6TRZE8.png\"]}\n{\"q_img\": \"./images/B0054DKVWK.png\", \"q_text\": \"is a black dress, and the dress is black and not a shoe\", \"positive_key\": \"B0076ZYQ6G\", \"positive_value\": \"./images/B0076ZYQ6G.png\", \"hn_image\": [\"./images/B0054DKVWK.png\"]}\n{\"q_img\": \"./images/B008RJZTD4.png\", \"q_text\": \" more revealing and has long sleeves, and is checkered and penciled\", \"positive_key\": \"B00CRWGE5E\", \"positive_value\": \"./images/B00CRWGE5E.png\", \"hn_image\": [\"./images/B008RJZTD4.png\"]}\n{\"q_img\": \"./images/B00B53A556.png\", \"q_text\": \"is brighter and longer, and is pink and longer\", \"positive_key\": \"B008N2O3NW\", \"positive_value\": \"./images/B008N2O3NW.png\", \"hn_image\": [\"./images/B00B53A556.png\"]}\n{\"q_img\": \"./images/B005DWYMPO.png\", \"q_text\": \"has one shoulder strap and grey, and has one shoulder strap\", \"positive_key\": \"B005H6XSQA\", \"positive_value\": \"./images/B005H6XSQA.png\", \"hn_image\": [\"./images/B005DWYMPO.png\"]}\n{\"q_img\": \"./images/B00FXZX2I4.png\", \"q_text\": \"is solid red, and is red.\", \"positive_key\": \"B00997L4QE\", \"positive_value\": \"./images/B00997L4QE.png\", \"hn_image\": [\"./images/B00FXZX2I4.png\"]}\n{\"q_img\": \"./images/B00DOS53RA.png\", \"q_text\": \"is longer darker, and Is longer and simplier\", \"positive_key\": \"B00B59UOBU\", \"positive_value\": \"./images/B00B59UOBU.png\", \"hn_image\": [\"./images/B00DOS53RA.png\"]}\n{\"q_img\": \"./images/B00B3PUKMY.png\", \"q_text\": \" gold, and is short and cap sleeved with a black print\", \"positive_key\": \"B00C67CQDO\", \"positive_value\": \"./images/B00C67CQDO.png\", \"hn_image\": [\"./images/B00B3PUKMY.png\"]}\n{\"q_img\": \"./images/B00B923WCQ.png\", \"q_text\": \"one shouldered grey dress, and Is more blue and has a slit\", \"positive_key\": \"B005GQPE4K\", \"positive_value\": \"./images/B005GQPE4K.png\", \"hn_image\": [\"./images/B00B923WCQ.png\"]}\n{\"q_img\": \"./images/B00DU6GJJC.png\", \"q_text\": \"has a u shaped neck with sleeves, and is black and patterned\", \"positive_key\": \"B00CFYDMM2\", \"positive_value\": \"./images/B00CFYDMM2.png\", \"hn_image\": [\"./images/B00DU6GJJC.png\"]}\n{\"q_img\": \"./images/B009PXRLIW.png\", \"q_text\": \"is orange with thinner straps, and is not patterned and orange\", \"positive_key\": \"B00C93VT5G\", \"positive_value\": \"./images/B00C93VT5G.png\", \"hn_image\": [\"./images/B009PXRLIW.png\"]}\n{\"q_img\": \"./images/B000S5FE7G.png\", \"q_text\": \"Is black with higher sleeves., and has more above shoulder straps\", \"positive_key\": \"B00AXDA432\", \"positive_value\": \"./images/B00AXDA432.png\", \"hn_image\": [\"./images/B000S5FE7G.png\"]}\n{\"q_img\": \"./images/B0082BBVI4.png\", \"q_text\": \"is pink and strapless, and is bright pink colored\", \"positive_key\": \"B008A1X87S\", \"positive_value\": \"./images/B008A1X87S.png\", \"hn_image\": [\"./images/B0082BBVI4.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \" shorter skirt, and na\", \"positive_key\": \"B00BFPGAGM\", \"positive_value\": \"./images/B00BFPGAGM.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B00BOPQZ86.png\", \"q_text\": \"needs navy blue dress with shoulder straps, and is blue with straps\", \"positive_key\": \"B006XZAOR0\", \"positive_value\": \"./images/B006XZAOR0.png\", \"hn_image\": [\"./images/B00BOPQZ86.png\"]}\n{\"q_img\": \"./images/B00967AMPG.png\", \"q_text\": \"less full skirt with slit to thigh, and has a slit and is more form fitting\", \"positive_key\": \"B00AWM67OE\", \"positive_value\": \"./images/B00AWM67OE.png\", \"hn_image\": [\"./images/B00967AMPG.png\"]}\n{\"q_img\": \"./images/B002XUY8SA.png\", \"q_text\": \"this white maxi looks long and casual, and is longer white and has floral\", \"positive_key\": \"B006330SY6\", \"positive_value\": \"./images/B006330SY6.png\", \"hn_image\": [\"./images/B002XUY8SA.png\"]}\n{\"q_img\": \"./images/B002OEYH3Q.png\", \"q_text\": \"Has long sleeves and patterned, and is in brownish coloring.\", \"positive_key\": \"B008YIGWOS\", \"positive_value\": \"./images/B008YIGWOS.png\", \"hn_image\": [\"./images/B002OEYH3Q.png\"]}\n{\"q_img\": \"./images/B0043D2HME.png\", \"q_text\": \"is strapless, and is blue and more revealing\", \"positive_key\": \"B00CFEVLBQ\", \"positive_value\": \"./images/B00CFEVLBQ.png\", \"hn_image\": [\"./images/B0043D2HME.png\"]}\n{\"q_img\": \"./images/B00AOJVGYQ.png\", \"q_text\": \"is brighter in color, and is a light floral pattern\", \"positive_key\": \"B00AAOD3LO\", \"positive_value\": \"./images/B00AAOD3LO.png\", \"hn_image\": [\"./images/B00AOJVGYQ.png\"]}\n{\"q_img\": \"./images/B00AWKKHOC.png\", \"q_text\": \"is darker and longer, and is plain black with waterfall hem and is more dressy\", \"positive_key\": \"B0087YZPJ2\", \"positive_value\": \"./images/B0087YZPJ2.png\", \"hn_image\": [\"./images/B00AWKKHOC.png\"]}\n{\"q_img\": \"./images/B00BAHX7Z2.png\", \"q_text\": \" with a black, and is longer and blue and black\", \"positive_key\": \"B008E7IA0I\", \"positive_value\": \"./images/B008E7IA0I.png\", \"hn_image\": [\"./images/B00BAHX7Z2.png\"]}\n{\"q_img\": \"./images/B00CL0VWTA.png\", \"q_text\": \"is poka dots and tighter fitting, and is blue with sleeves and dots\", \"positive_key\": \"B00CF2X97M\", \"positive_value\": \"./images/B00CF2X97M.png\", \"hn_image\": [\"./images/B00CL0VWTA.png\"]}\n{\"q_img\": \"./images/B00A7N4DC6.png\", \"q_text\": \"is a short dress with a v neck, and is a tan coloring with fabric overlapping on top.\", \"positive_key\": \"B00B5VXRTO\", \"positive_value\": \"./images/B00B5VXRTO.png\", \"hn_image\": [\"./images/B00A7N4DC6.png\"]}\n{\"q_img\": \"./images/B0092QK9AY.png\", \"q_text\": \"is solid nude color, and has straps instead of long sleeves and has two colors\", \"positive_key\": \"B007ORZ1PG\", \"positive_value\": \"./images/B007ORZ1PG.png\", \"hn_image\": [\"./images/B0092QK9AY.png\"]}\n{\"q_img\": \"./images/B00CH8U9BI.png\", \"q_text\": \"Is strapless with black stripes., and has no shoulder straps\", \"positive_key\": \"B009NXZXDO\", \"positive_value\": \"./images/B009NXZXDO.png\", \"hn_image\": [\"./images/B00CH8U9BI.png\"]}\n{\"q_img\": \"./images/B0081JJ9FO.png\", \"q_text\": \"is a black dress with a shade of grey, and is similar in black\", \"positive_key\": \"B0097C8UAO\", \"positive_value\": \"./images/B0097C8UAO.png\", \"hn_image\": [\"./images/B0081JJ9FO.png\"]}\n{\"q_img\": \"./images/B00BG3NQE2.png\", \"q_text\": \"is gold maxi dress, and is yellow and longer\", \"positive_key\": \"B004RFFHJI\", \"positive_value\": \"./images/B004RFFHJI.png\", \"hn_image\": [\"./images/B00BG3NQE2.png\"]}\n{\"q_img\": \"./images/B00FZ00I7U.png\", \"q_text\": \"is gold and shiny, and is more ombre and less geometric\", \"positive_key\": \"B0091WZCE2\", \"positive_value\": \"./images/B0091WZCE2.png\", \"hn_image\": [\"./images/B00FZ00I7U.png\"]}\n{\"q_img\": \"./images/B008VIR88U.png\", \"q_text\": \"is solid black, and is long\", \"positive_key\": \"B0052B66GY\", \"positive_value\": \"./images/B0052B66GY.png\", \"hn_image\": [\"./images/B008VIR88U.png\"]}\n{\"q_img\": \"./images/B0094QX5SK.png\", \"q_text\": \"is offwhite with black belt and longer seethrough skirt, and is white with a black belr\", \"positive_key\": \"B00B1AVTWG\", \"positive_value\": \"./images/B00B1AVTWG.png\", \"hn_image\": [\"./images/B0094QX5SK.png\"]}\n{\"q_img\": \"./images/B001HK23KM.png\", \"q_text\": \" and black, and the shoulder straps more resemble a crop top.\", \"positive_key\": \"B002TKJL40\", \"positive_value\": \"./images/B002TKJL40.png\", \"hn_image\": [\"./images/B001HK23KM.png\"]}\n{\"q_img\": \"./images/B0066NI17E.png\", \"q_text\": \"is longer and has more pink, and is pink with sleeves\", \"positive_key\": \"B00DC0XN0O\", \"positive_value\": \"./images/B00DC0XN0O.png\", \"hn_image\": [\"./images/B0066NI17E.png\"]}\n{\"q_img\": \"./images/B005DRAVT0.png\", \"q_text\": \"Is lighter and has shorter sleeves, and is black and less revealing\", \"positive_key\": \"B007RB7J4K\", \"positive_value\": \"./images/B007RB7J4K.png\", \"hn_image\": [\"./images/B005DRAVT0.png\"]}\n{\"q_img\": \"./images/B007B98014.png\", \"q_text\": \" black with red belt, and It is less black and has a shoulder off\", \"positive_key\": \"B004SIUT1A\", \"positive_value\": \"./images/B004SIUT1A.png\", \"hn_image\": [\"./images/B007B98014.png\"]}\n{\"q_img\": \"./images/B007MLVQ1C.png\", \"q_text\": \"is grey with vertical designs, and is darker in color\", \"positive_key\": \"B005J92XW0\", \"positive_value\": \"./images/B005J92XW0.png\", \"hn_image\": [\"./images/B007MLVQ1C.png\"]}\n{\"q_img\": \"./images/B00B2JJ15S.png\", \"q_text\": \"has shorter sleeves and is straight, and is grey and belt-less\", \"positive_key\": \"B00ASIH0ZC\", \"positive_value\": \"./images/B00ASIH0ZC.png\", \"hn_image\": [\"./images/B00B2JJ15S.png\"]}\n{\"q_img\": \"./images/B004OLGO24.png\", \"q_text\": \"is shorter with thin straps, and is black and more wide\", \"positive_key\": \"B002FOR462\", \"positive_value\": \"./images/B002FOR462.png\", \"hn_image\": [\"./images/B004OLGO24.png\"]}\n{\"q_img\": \"./images/B004VLI08S.png\", \"q_text\": \"is less party like, and has a white bodice and grey skirt\", \"positive_key\": \"B007PAD51E\", \"positive_value\": \"./images/B007PAD51E.png\", \"hn_image\": [\"./images/B004VLI08S.png\"]}\n{\"q_img\": \"./images/B006PA7Q4M.png\", \"q_text\": \"knee length sleeved purple and red dress, and longer\", \"positive_key\": \"B00D3F80NI\", \"positive_value\": \"./images/B00D3F80NI.png\", \"hn_image\": [\"./images/B006PA7Q4M.png\"]}\n{\"q_img\": \"./images/B00E00SG7K.png\", \"q_text\": \"has green top and multi-colored striped bottom, and is strapless\", \"positive_key\": \"B00AHFXVUO\", \"positive_value\": \"./images/B00AHFXVUO.png\", \"hn_image\": [\"./images/B00E00SG7K.png\"]}\n{\"q_img\": \"./images/B007FKA7E2.png\", \"q_text\": \"plain black and sleeveless, and is darker in color and solid colored\", \"positive_key\": \"B0029XKWKO\", \"positive_value\": \"./images/B0029XKWKO.png\", \"hn_image\": [\"./images/B007FKA7E2.png\"]}\n{\"q_img\": \"./images/B006UCQYMA.png\", \"q_text\": \"is short and black with short sleeves, and Is black and not two pieces.\", \"positive_key\": \"B00AHOY6VS\", \"positive_value\": \"./images/B00AHOY6VS.png\", \"hn_image\": [\"./images/B006UCQYMA.png\"]}\n{\"q_img\": \"./images/B00B08494E.png\", \"q_text\": \"has no sleeves with slim straps floral dress, and is longer and has a circular pattern\", \"positive_key\": \"B00CVTMGTG\", \"positive_value\": \"./images/B00CVTMGTG.png\", \"hn_image\": [\"./images/B00B08494E.png\"]}\n{\"q_img\": \"./images/B006361YUA.png\", \"q_text\": \"is white and strapless, and Is white\", \"positive_key\": \"B0095JQU2E\", \"positive_value\": \"./images/B0095JQU2E.png\", \"hn_image\": [\"./images/B006361YUA.png\"]}\n{\"q_img\": \"./images/B002Z7FONO.png\", \"q_text\": \"is a formal v necked gown, and is v neck and longer\", \"positive_key\": \"B00B5P4VG8\", \"positive_value\": \"./images/B00B5P4VG8.png\", \"hn_image\": [\"./images/B002Z7FONO.png\"]}\n{\"q_img\": \"./images/B0054N0S6E.png\", \"q_text\": \"is darker and has shorter sleeves, and does not cover the sholders\", \"positive_key\": \"B0095WSNZ8\", \"positive_value\": \"./images/B0095WSNZ8.png\", \"hn_image\": [\"./images/B0054N0S6E.png\"]}\n{\"q_img\": \"./images/B008596XXG.png\", \"q_text\": \"is white and less revealing, and is white and same length all around bottom\", \"positive_key\": \"B0094GWN38\", \"positive_value\": \"./images/B0094GWN38.png\", \"hn_image\": [\"./images/B008596XXG.png\"]}\n{\"q_img\": \"./images/B0047MEZNK.png\", \"q_text\": \"is more of headwear, and is a zebra head and not a dress\", \"positive_key\": \"B00DS1XAXM\", \"positive_value\": \"./images/B00DS1XAXM.png\", \"hn_image\": [\"./images/B0047MEZNK.png\"]}\n{\"q_img\": \"./images/B00A8CDCD2.png\", \"q_text\": \"is darker and has no sleeves, and is black and white and has an empire waist\", \"positive_key\": \"B006ZUSYN4\", \"positive_value\": \"./images/B006ZUSYN4.png\", \"hn_image\": [\"./images/B00A8CDCD2.png\"]}\n{\"q_img\": \"./images/B007W9ZSEA.png\", \"q_text\": \"has more color contrast, and is pink and black\", \"positive_key\": \"B00BU5GGXE\", \"positive_value\": \"./images/B00BU5GGXE.png\", \"hn_image\": [\"./images/B007W9ZSEA.png\"]}\n{\"q_img\": \"./images/B00B5KMPA2.png\", \"q_text\": \"is longer with no straps, and is much longer and dark\", \"positive_key\": \"B00B1G5SPY\", \"positive_value\": \"./images/B00B1G5SPY.png\", \"hn_image\": [\"./images/B00B5KMPA2.png\"]}\n{\"q_img\": \"./images/B002TKJL40.png\", \"q_text\": \"has longer sleeves, and is short sleeved and shorter\", \"positive_key\": \"B0038HFFXY\", \"positive_value\": \"./images/B0038HFFXY.png\", \"hn_image\": [\"./images/B002TKJL40.png\"]}\n{\"q_img\": \"./images/B00BT8N1DK.png\", \"q_text\": \"is below the knee and darker, and and long\", \"positive_key\": \"B00DVG348U\", \"positive_value\": \"./images/B00DVG348U.png\", \"hn_image\": [\"./images/B00BT8N1DK.png\"]}\n{\"q_img\": \"./images/B007Y37P22.png\", \"q_text\": \"more revealing and shorter in length, and is more strappy and emerald\", \"positive_key\": \"B006ZR2U8W\", \"positive_value\": \"./images/B006ZR2U8W.png\", \"hn_image\": [\"./images/B007Y37P22.png\"]}\n{\"q_img\": \"./images/B008LTJG3E.png\", \"q_text\": \"has a brighter color and is more of a boot with shorter heels, and is a shoe\", \"positive_key\": \"B00D4JNBC8\", \"positive_value\": \"./images/B00D4JNBC8.png\", \"hn_image\": [\"./images/B008LTJG3E.png\"]}\n{\"q_img\": \"./images/B004OHYU4M.png\", \"q_text\": \"is gray and shorter, and is shorter and ruffled\", \"positive_key\": \"B0033VBAKC\", \"positive_value\": \"./images/B0033VBAKC.png\", \"hn_image\": [\"./images/B004OHYU4M.png\"]}\n{\"q_img\": \"./images/B00DQHXEO8.png\", \"q_text\": \"shorter sleeves and chevron striped, and has chevron pattern\", \"positive_key\": \"B00DV3WRA4\", \"positive_value\": \"./images/B00DV3WRA4.png\", \"hn_image\": [\"./images/B00DQHXEO8.png\"]}\n{\"q_img\": \"./images/B002EAJ79E.png\", \"q_text\": \"is green with flowers, and is longer and more colorful\", \"positive_key\": \"B002XW6XZE\", \"positive_value\": \"./images/B002XW6XZE.png\", \"hn_image\": [\"./images/B002EAJ79E.png\"]}\n{\"q_img\": \"./images/B003XQG930.png\", \"q_text\": \"has collars, and empire waist\", \"positive_key\": \"B001BFYXZG\", \"positive_value\": \"./images/B001BFYXZG.png\", \"hn_image\": [\"./images/B003XQG930.png\"]}\n{\"q_img\": \"./images/B007SOULGY.png\", \"q_text\": \"has short sleeves, and has a peasant neckline and short sleeves\", \"positive_key\": \"B009SPZ72E\", \"positive_value\": \"./images/B009SPZ72E.png\", \"hn_image\": [\"./images/B007SOULGY.png\"]}\n{\"q_img\": \"./images/B00D75WT4U.png\", \"q_text\": \"is more revealing, and has a more open top.\", \"positive_key\": \"B00CII5YD0\", \"positive_value\": \"./images/B00CII5YD0.png\", \"hn_image\": [\"./images/B00D75WT4U.png\"]}\n{\"q_img\": \"./images/B008BHCS2C.png\", \"q_text\": \"Is more casual and pink, and pink empire waist\", \"positive_key\": \"B0097BCW4U\", \"positive_value\": \"./images/B0097BCW4U.png\", \"hn_image\": [\"./images/B008BHCS2C.png\"]}\n{\"q_img\": \"./images/B00740W2VY.png\", \"q_text\": \"Is longer and more Asian-inspired, and is longer and shiny black\", \"positive_key\": \"B000RL3V58\", \"positive_value\": \"./images/B000RL3V58.png\", \"hn_image\": [\"./images/B00740W2VY.png\"]}\n{\"q_img\": \"./images/B008RMIF5A.png\", \"q_text\": \" and handkerchief hemline, and is more blue and longer\", \"positive_key\": \"B006R3CSGS\", \"positive_value\": \"./images/B006R3CSGS.png\", \"hn_image\": [\"./images/B008RMIF5A.png\"]}\n{\"q_img\": \"./images/B00AWKKHOC.png\", \"q_text\": \"is lighter, and orange and sleeveless\", \"positive_key\": \"B0091GR192\", \"positive_value\": \"./images/B0091GR192.png\", \"hn_image\": [\"./images/B00AWKKHOC.png\"]}\n{\"q_img\": \"./images/B008E7IA12.png\", \"q_text\": \"is green pattern and lightly pleated, and has a floral print that is harder to see.\", \"positive_key\": \"B00BGK4I3S\", \"positive_value\": \"./images/B00BGK4I3S.png\", \"hn_image\": [\"./images/B008E7IA12.png\"]}\n{\"q_img\": \"./images/B00BJ1NFNI.png\", \"q_text\": \"has a shiny print with straps, and is more shiny\", \"positive_key\": \"B005FVRJMQ\", \"positive_value\": \"./images/B005FVRJMQ.png\", \"hn_image\": [\"./images/B00BJ1NFNI.png\"]}\n{\"q_img\": \"./images/B005FLWZCA.png\", \"q_text\": \"is a lighter color, and is solid white\", \"positive_key\": \"B0087PLHB6\", \"positive_value\": \"./images/B0087PLHB6.png\", \"hn_image\": [\"./images/B005FLWZCA.png\"]}\n{\"q_img\": \"./images/B0030IMZ1G.png\", \"q_text\": \"has longer sleeves and lighter in color, and is solid colors with short sleeves.\", \"positive_key\": \"B001FWNNC4\", \"positive_value\": \"./images/B001FWNNC4.png\", \"hn_image\": [\"./images/B0030IMZ1G.png\"]}\n{\"q_img\": \"./images/B006U07GW4.png\", \"q_text\": \"Is a short blue v neck sleeveless dress, and has sleeves and is a solid brighter color blue\", \"positive_key\": \"B005OBAGD6\", \"positive_value\": \"./images/B005OBAGD6.png\", \"hn_image\": [\"./images/B006U07GW4.png\"]}\n{\"q_img\": \"./images/B004QL3YBG.png\", \"q_text\": \"is pink, and is darker in color and doesn't have tulle\", \"positive_key\": \"B00BG5R3KS\", \"positive_value\": \"./images/B00BG5R3KS.png\", \"hn_image\": [\"./images/B004QL3YBG.png\"]}\n{\"q_img\": \"./images/B008LTJG3E.png\", \"q_text\": \"has a multi-colored pattern, and is printed and shorter\", \"positive_key\": \"B0096MMWXG\", \"positive_value\": \"./images/B0096MMWXG.png\", \"hn_image\": [\"./images/B008LTJG3E.png\"]}\n{\"q_img\": \"./images/B009SI9OTS.png\", \"q_text\": \"is dark blue and has silky looks, and It is more gleaming and white\", \"positive_key\": \"B00730ZGCM\", \"positive_value\": \"./images/B00730ZGCM.png\", \"hn_image\": [\"./images/B009SI9OTS.png\"]}\n{\"q_img\": \"./images/B006330SY6.png\", \"q_text\": \"brown and flowers thin scrapes and look very casual, and is darker and shorter\", \"positive_key\": \"B002EANP9W\", \"positive_value\": \"./images/B002EANP9W.png\", \"hn_image\": [\"./images/B006330SY6.png\"]}\n{\"q_img\": \"./images/B00EBZUV0Y.png\", \"q_text\": \"Is a long red sleeveless dress., and is red and spaghetti strapped\", \"positive_key\": \"B00EODXN1W\", \"positive_value\": \"./images/B00EODXN1W.png\", \"hn_image\": [\"./images/B00EBZUV0Y.png\"]}\n{\"q_img\": \"./images/B00DRB9XJS.png\", \"q_text\": \" strapless, and has longer sleeves and a logo\", \"positive_key\": \"B002UD5URS\", \"positive_value\": \"./images/B002UD5URS.png\", \"hn_image\": [\"./images/B00DRB9XJS.png\"]}\n{\"q_img\": \"./images/B006G35WJY.png\", \"q_text\": \"has longer sleeves and is shorter, and is shorter and less fitted\", \"positive_key\": \"B008583TT8\", \"positive_value\": \"./images/B008583TT8.png\", \"hn_image\": [\"./images/B006G35WJY.png\"]}\n{\"q_img\": \"./images/B0095QLSJW.png\", \"q_text\": \"is dark colored and longer sleeves, and is shorter and red with sleeves\", \"positive_key\": \"B00A16VH28\", \"positive_value\": \"./images/B00A16VH28.png\", \"hn_image\": [\"./images/B0095QLSJW.png\"]}\n{\"q_img\": \"./images/B00AAD6KOM.png\", \"q_text\": \"is white with dots and a black belt, and Is white\", \"positive_key\": \"B0094KQ1NC\", \"positive_value\": \"./images/B0094KQ1NC.png\", \"hn_image\": [\"./images/B00AAD6KOM.png\"]}\n{\"q_img\": \"./images/B00FP87XX4.png\", \"q_text\": \"is orange and has longer sleeves, and is orange and has longer sleeves\", \"positive_key\": \"B009IE33SU\", \"positive_value\": \"./images/B009IE33SU.png\", \"hn_image\": [\"./images/B00FP87XX4.png\"]}\n{\"q_img\": \"./images/B00AKB3VPA.png\", \"q_text\": \"has longer sleeves, and has black and brown patterns\", \"positive_key\": \"B005DN88YY\", \"positive_value\": \"./images/B005DN88YY.png\", \"hn_image\": [\"./images/B00AKB3VPA.png\"]}\n{\"q_img\": \"./images/B008OKGFF2.png\", \"q_text\": \"is longer sleeved with black skirt, and is long sleeve and darker pattern with black skirt\", \"positive_key\": \"B00ETGEI1I\", \"positive_value\": \"./images/B00ETGEI1I.png\", \"hn_image\": [\"./images/B008OKGFF2.png\"]}\n{\"q_img\": \"./images/B002DI8HQG.png\", \"q_text\": \"is shorter with black belt, and is shorter\", \"positive_key\": \"B00A7DWWAQ\", \"positive_value\": \"./images/B00A7DWWAQ.png\", \"hn_image\": [\"./images/B002DI8HQG.png\"]}\n{\"q_img\": \"./images/B008B2OOQK.png\", \"q_text\": \"is over one shoulder and pink, and has a strap on online on side and is pink\", \"positive_key\": \"B004LKRWGA\", \"positive_value\": \"./images/B004LKRWGA.png\", \"hn_image\": [\"./images/B008B2OOQK.png\"]}\n{\"q_img\": \"./images/B007XATMYA.png\", \"q_text\": \"is a shorter wrap around dress, and has longer sleeves\", \"positive_key\": \"B00ABF01BM\", \"positive_value\": \"./images/B00ABF01BM.png\", \"hn_image\": [\"./images/B007XATMYA.png\"]}\n{\"q_img\": \"./images/B0097J0RVC.png\", \"q_text\": \"is a white dress with a short jacket, and one shouldered\", \"positive_key\": \"B0091QSKHE\", \"positive_value\": \"./images/B0091QSKHE.png\", \"hn_image\": [\"./images/B0097J0RVC.png\"]}\n{\"q_img\": \"./images/B004YHIH28.png\", \"q_text\": \"is white in color, and all white and long\", \"positive_key\": \"B0096L8FL0\", \"positive_value\": \"./images/B0096L8FL0.png\", \"hn_image\": [\"./images/B004YHIH28.png\"]}\n{\"q_img\": \"./images/B003U6Y7SC.png\", \"q_text\": \"is orange and less revealing, and is orange and ruffled.\", \"positive_key\": \"B008VO44X6\", \"positive_value\": \"./images/B008VO44X6.png\", \"hn_image\": [\"./images/B003U6Y7SC.png\"]}\n{\"q_img\": \"./images/B0016LHFE6.png\", \"q_text\": \"is sexier and more revealing, and it has no collar and has a shorter sleeve\", \"positive_key\": \"B009LIHN3O\", \"positive_value\": \"./images/B009LIHN3O.png\", \"hn_image\": [\"./images/B0016LHFE6.png\"]}\n{\"q_img\": \"./images/B00EOW7882.png\", \"q_text\": \"is more revealing and longer, and It is longer and has no sleeves\", \"positive_key\": \"B00D61ZCGW\", \"positive_value\": \"./images/B00D61ZCGW.png\", \"hn_image\": [\"./images/B00EOW7882.png\"]}\n{\"q_img\": \"./images/B007SVFCAW.png\", \"q_text\": \"is light in color and stripped shoulder., and and has one strap\", \"positive_key\": \"B008ODSK5M\", \"positive_value\": \"./images/B008ODSK5M.png\", \"hn_image\": [\"./images/B007SVFCAW.png\"]}\n{\"q_img\": \"./images/B00E00SG7K.png\", \"q_text\": \"is shorter and has less cleavage exposure, and is floral patterned and shorter\", \"positive_key\": \"B00DESMJKE\", \"positive_value\": \"./images/B00DESMJKE.png\", \"hn_image\": [\"./images/B00E00SG7K.png\"]}\n{\"q_img\": \"./images/B001NJ5LZG.png\", \"q_text\": \"the red color looks more attractive and party wear, and is red with a higher neckline\", \"positive_key\": \"B0046Q4K6E\", \"positive_value\": \"./images/B0046Q4K6E.png\", \"hn_image\": [\"./images/B001NJ5LZG.png\"]}\n{\"q_img\": \"./images/B004BDP1BA.png\", \"q_text\": \" black and has a v shaped neck, and is v necked and black\", \"positive_key\": \"B00DJJILJQ\", \"positive_value\": \"./images/B00DJJILJQ.png\", \"hn_image\": [\"./images/B004BDP1BA.png\"]}\n{\"q_img\": \"./images/B007EGKM5G.png\", \"q_text\": \"Has a shiny fabric and is longer., and is longer\", \"positive_key\": \"B00AHTJVCC\", \"positive_value\": \"./images/B00AHTJVCC.png\", \"hn_image\": [\"./images/B007EGKM5G.png\"]}\n{\"q_img\": \"./images/B007SV9A5A.png\", \"q_text\": \"is more floral print and less striped, and white\", \"positive_key\": \"B005EOD18K\", \"positive_value\": \"./images/B005EOD18K.png\", \"hn_image\": [\"./images/B007SV9A5A.png\"]}\n{\"q_img\": \"./images/B00CAMMV5S.png\", \"q_text\": \"Is more purple and elegant, and is darker purple in colour\", \"positive_key\": \"B00CAMJMRS\", \"positive_value\": \"./images/B00CAMJMRS.png\", \"hn_image\": [\"./images/B00CAMMV5S.png\"]}\n{\"q_img\": \"./images/B00CY8S8P0.png\", \"q_text\": \"is white with more open shoulder on sleeve, and purple embellished dress\", \"positive_key\": \"B00DJZM0Z6\", \"positive_value\": \"./images/B00DJZM0Z6.png\", \"hn_image\": [\"./images/B00CY8S8P0.png\"]}\n{\"q_img\": \"./images/B007TDELU6.png\", \"q_text\": \"has longer sleeves and a shorter skirt, and it shorter and has longer sleeves\", \"positive_key\": \"B00AA95P56\", \"positive_value\": \"./images/B00AA95P56.png\", \"hn_image\": [\"./images/B007TDELU6.png\"]}\n{\"q_img\": \"./images/B00AZE7306.png\", \"q_text\": \"id darker with no sraps, and is a black ruffles design.\", \"positive_key\": \"B00686DR00\", \"positive_value\": \"./images/B00686DR00.png\", \"hn_image\": [\"./images/B00AZE7306.png\"]}\n{\"q_img\": \"./images/B00C7BSFX4.png\", \"q_text\": \"is more colorful and striped, and More colorful and more casual\", \"positive_key\": \"B004R9PFGE\", \"positive_value\": \"./images/B004R9PFGE.png\", \"hn_image\": [\"./images/B00C7BSFX4.png\"]}\n{\"q_img\": \"./images/B00B79JOJG.png\", \"q_text\": \"I want something less revealing and brighter., and It is red\", \"positive_key\": \"B00B90O740\", \"positive_value\": \"./images/B00B90O740.png\", \"hn_image\": [\"./images/B00B79JOJG.png\"]}\n{\"q_img\": \"./images/B00BOPQZ86.png\", \"q_text\": \"is white and larger, and Solid white and much longer.\", \"positive_key\": \"B00EFE9MII\", \"positive_value\": \"./images/B00EFE9MII.png\", \"hn_image\": [\"./images/B00BOPQZ86.png\"]}\n{\"q_img\": \"./images/B00GMIQG34.png\", \"q_text\": \"is black classy dress with white collar, and is more formal\", \"positive_key\": \"B00A36LLR2\", \"positive_value\": \"./images/B00A36LLR2.png\", \"hn_image\": [\"./images/B00GMIQG34.png\"]}\n{\"q_img\": \"./images/B004LQ0GZ8.png\", \"q_text\": \"The dress in peach and white in color., and has longer sleeves and is coral in color\", \"positive_key\": \"B00AKB3RA4\", \"positive_value\": \"./images/B00AKB3RA4.png\", \"hn_image\": [\"./images/B004LQ0GZ8.png\"]}\n{\"q_img\": \"./images/B006361ONM.png\", \"q_text\": \"is black in color and sexy dress, and is black and more fitted\", \"positive_key\": \"B00DCCJBKS\", \"positive_value\": \"./images/B00DCCJBKS.png\", \"hn_image\": [\"./images/B006361ONM.png\"]}\n{\"q_img\": \"./images/B006C4GIS6.png\", \"q_text\": \"is shorter, and is off one shoulder and shorter\", \"positive_key\": \"B00AEEFOYO\", \"positive_value\": \"./images/B00AEEFOYO.png\", \"hn_image\": [\"./images/B006C4GIS6.png\"]}\n{\"q_img\": \"./images/B007PPHY70.png\", \"q_text\": \"is more colorful and looser, and mover chest coverage and wider skirt\", \"positive_key\": \"B00662GT3I\", \"positive_value\": \"./images/B00662GT3I.png\", \"hn_image\": [\"./images/B007PPHY70.png\"]}\n{\"q_img\": \"./images/B00B6PTBX0.png\", \"q_text\": \"has spaghetti straps and is red, and is sleeveless and denim\", \"positive_key\": \"B00BPBCKQK\", \"positive_value\": \"./images/B00BPBCKQK.png\", \"hn_image\": [\"./images/B00B6PTBX0.png\"]}\n{\"q_img\": \"./images/B00CF3BFWM.png\", \"q_text\": \"Is brighter and longer in length., and is longer and more formal\", \"positive_key\": \"B00A64SMJQ\", \"positive_value\": \"./images/B00A64SMJQ.png\", \"hn_image\": [\"./images/B00CF3BFWM.png\"]}\n{\"q_img\": \"./images/B0069IWITS.png\", \"q_text\": \"is a darker color, and has more red and more designs\", \"positive_key\": \"B0054O6FI8\", \"positive_value\": \"./images/B0054O6FI8.png\", \"hn_image\": [\"./images/B0069IWITS.png\"]}\n{\"q_img\": \"./images/B00BUVJTSC.png\", \"q_text\": \" and red pattern. With short sleeves, and More yellow and thinner strap\", \"positive_key\": \"B00845SFV4\", \"positive_value\": \"./images/B00845SFV4.png\", \"hn_image\": [\"./images/B00BUVJTSC.png\"]}\n{\"q_img\": \"./images/B00AYLAFOG.png\", \"q_text\": \"has longer sleeves and has a different pattern, and Is lighter in color and longer sleeves\", \"positive_key\": \"B007Y2BWO0\", \"positive_value\": \"./images/B007Y2BWO0.png\", \"hn_image\": [\"./images/B00AYLAFOG.png\"]}\n{\"q_img\": \"./images/B001DXL1WY.png\", \"q_text\": \"Is less revealing and has a pattern, and less of a V-neck\", \"positive_key\": \"B00575VVGU\", \"positive_value\": \"./images/B00575VVGU.png\", \"hn_image\": [\"./images/B001DXL1WY.png\"]}\n{\"q_img\": \"./images/B00F150ZXQ.png\", \"q_text\": \"is solid white and high low, and is slightly shorter and more colorful.\", \"positive_key\": \"B00BOXD234\", \"positive_value\": \"./images/B00BOXD234.png\", \"hn_image\": [\"./images/B00F150ZXQ.png\"]}\n{\"q_img\": \"./images/B009CQNT6U.png\", \"q_text\": \"has a more modest neck line and is solid black, and Shows less skin and has more black\", \"positive_key\": \"B00D8CY56W\", \"positive_value\": \"./images/B00D8CY56W.png\", \"hn_image\": [\"./images/B009CQNT6U.png\"]}\n{\"q_img\": \"./images/B007TUE48S.png\", \"q_text\": \"more dark black dress, and is dark colored\", \"positive_key\": \"B0092PKPKE\", \"positive_value\": \"./images/B0092PKPKE.png\", \"hn_image\": [\"./images/B007TUE48S.png\"]}\n{\"q_img\": \"./images/B00AD11FFU.png\", \"q_text\": \"is darker purple and strapless, and is shorter and more formal\", \"positive_key\": \"B00BJGW386\", \"positive_value\": \"./images/B00BJGW386.png\", \"hn_image\": [\"./images/B00AD11FFU.png\"]}\n{\"q_img\": \"./images/B00EKH5ZFY.png\", \"q_text\": \"is longer with black and white chevron print, and is longer and monochrome\", \"positive_key\": \"B00F20KR4M\", \"positive_value\": \"./images/B00F20KR4M.png\", \"hn_image\": [\"./images/B00EKH5ZFY.png\"]}\n{\"q_img\": \"./images/B00BJS2MS0.png\", \"q_text\": \"is longer and has orange pink and blue, and has brighter colors and is longer in back\", \"positive_key\": \"B00CFUEL3K\", \"positive_value\": \"./images/B00CFUEL3K.png\", \"hn_image\": [\"./images/B00BJS2MS0.png\"]}\n{\"q_img\": \"./images/B002N8CDA2.png\", \"q_text\": \"is darker, and is more flowing and blue\", \"positive_key\": \"B00CLJWXQ2\", \"positive_value\": \"./images/B00CLJWXQ2.png\", \"hn_image\": [\"./images/B002N8CDA2.png\"]}\n{\"q_img\": \"./images/B00B2FEFNU.png\", \"q_text\": \"is darker and less sheer, and is solid black\", \"positive_key\": \"B009C6OO8C\", \"positive_value\": \"./images/B009C6OO8C.png\", \"hn_image\": [\"./images/B00B2FEFNU.png\"]}\n{\"q_img\": \"./images/B00BXK91FG.png\", \"q_text\": \"Is dark grey and longer sleeves, and is black colored with patterns\", \"positive_key\": \"B0070YINNK\", \"positive_value\": \"./images/B0070YINNK.png\", \"hn_image\": [\"./images/B00BXK91FG.png\"]}\n{\"q_img\": \"./images/B00DQ686X8.png\", \"q_text\": \"is solid black with loose a tophalf, and has long sleeves and is less form-fitting\", \"positive_key\": \"B0058JXYYW\", \"positive_value\": \"./images/B0058JXYYW.png\", \"hn_image\": [\"./images/B00DQ686X8.png\"]}\n{\"q_img\": \"./images/B00BXB6XWE.png\", \"q_text\": \"is blue and white with tighter patterns, and Is a little lighter in color with a different design and v neck.\", \"positive_key\": \"B005X4PL1G\", \"positive_value\": \"./images/B005X4PL1G.png\", \"hn_image\": [\"./images/B00BXB6XWE.png\"]}\n{\"q_img\": \"./images/B005MN4RLS.png\", \"q_text\": \"is a short black skirt with blue blouse, and Has more blue and is shorter\", \"positive_key\": \"B005GMTBPC\", \"positive_value\": \"./images/B005GMTBPC.png\", \"hn_image\": [\"./images/B005MN4RLS.png\"]}\n{\"q_img\": \"./images/B007Q39QB8.png\", \"q_text\": \"about the same but less revealing chest, and no patterns\", \"positive_key\": \"B00E9O5JWM\", \"positive_value\": \"./images/B00E9O5JWM.png\", \"hn_image\": [\"./images/B007Q39QB8.png\"]}\n{\"q_img\": \"./images/B00F4O129A.png\", \"q_text\": \"is more free and brighter, and is off one sholder and fuller\", \"positive_key\": \"B007G6BODI\", \"positive_value\": \"./images/B007G6BODI.png\", \"hn_image\": [\"./images/B00F4O129A.png\"]}\n{\"q_img\": \"./images/B00DDX15TG.png\", \"q_text\": \"is longer and darker, and is a darker color print design.\", \"positive_key\": \"B007NLX7SQ\", \"positive_value\": \"./images/B007NLX7SQ.png\", \"hn_image\": [\"./images/B00DDX15TG.png\"]}\n{\"q_img\": \"./images/B009CPQZ12.png\", \"q_text\": \"is solid pink, and is pink and longer\", \"positive_key\": \"B00DMZAHL2\", \"positive_value\": \"./images/B00DMZAHL2.png\", \"hn_image\": [\"./images/B009CPQZ12.png\"]}\n{\"q_img\": \"./images/B00C2G1HCA.png\", \"q_text\": \"is solid pale green with cap sleeves, and is short sleeved and scooped neck\", \"positive_key\": \"B00AYR2L4M\", \"positive_value\": \"./images/B00AYR2L4M.png\", \"hn_image\": [\"./images/B00C2G1HCA.png\"]}\n{\"q_img\": \"./images/B007IGI8D0.png\", \"q_text\": \"has open shoulders straps and fitted with no belt, and light color\", \"positive_key\": \"B002TYZD02\", \"positive_value\": \"./images/B002TYZD02.png\", \"hn_image\": [\"./images/B007IGI8D0.png\"]}\n{\"q_img\": \"./images/B00AFK163A.png\", \"q_text\": \"is more purple with black designs, and is darker and more uniform patterned\", \"positive_key\": \"B0061RGQEU\", \"positive_value\": \"./images/B0061RGQEU.png\", \"hn_image\": [\"./images/B00AFK163A.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"has a high-low hem and no words, and is pink and flowing\", \"positive_key\": \"B00EOXSEN4\", \"positive_value\": \"./images/B00EOXSEN4.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B00FHXBQFS.png\", \"q_text\": \"has shorter sleeves and less lace pattern, and has a less noticeable pattern\", \"positive_key\": \"B00BW4RJKM\", \"positive_value\": \"./images/B00BW4RJKM.png\", \"hn_image\": [\"./images/B00FHXBQFS.png\"]}\n{\"q_img\": \"./images/B00D7ISRPC.png\", \"q_text\": \"is an orange, and is red and white with sleeves\", \"positive_key\": \"B00E808KYQ\", \"positive_value\": \"./images/B00E808KYQ.png\", \"hn_image\": [\"./images/B00D7ISRPC.png\"]}\n{\"q_img\": \"./images/B0058XPHQG.png\", \"q_text\": \"is more patterned with shorter sleeves, and blue and has sleeves\", \"positive_key\": \"B0085U9J5E\", \"positive_value\": \"./images/B0085U9J5E.png\", \"hn_image\": [\"./images/B0058XPHQG.png\"]}\n{\"q_img\": \"./images/B004EHON6W.png\", \"q_text\": \"is red more loose fittiing dress, and is orange and has shorter sleeves\", \"positive_key\": \"B0093K54X6\", \"positive_value\": \"./images/B0093K54X6.png\", \"hn_image\": [\"./images/B004EHON6W.png\"]}\n{\"q_img\": \"./images/B00AFDH25I.png\", \"q_text\": \"Black and white with striped patterns, and black and white with sleeves\", \"positive_key\": \"B00EZW2J8A\", \"positive_value\": \"./images/B00EZW2J8A.png\", \"hn_image\": [\"./images/B00AFDH25I.png\"]}\n{\"q_img\": \"./images/B004FPYVO2.png\", \"q_text\": \"has long sleeves and a shorter hemline, and is a red and black floral pattern\", \"positive_key\": \"B004FPYW7I\", \"positive_value\": \"./images/B004FPYW7I.png\", \"hn_image\": [\"./images/B004FPYVO2.png\"]}\n{\"q_img\": \"./images/B0061IXDTU.png\", \"q_text\": \"is more loose fitting, and is darker and more conservative\", \"positive_key\": \"B009PL4RH2\", \"positive_value\": \"./images/B009PL4RH2.png\", \"hn_image\": [\"./images/B0061IXDTU.png\"]}\n{\"q_img\": \"./images/B002TKJL40.png\", \"q_text\": \" a solid, and is much shorter\", \"positive_key\": \"B001HK23TS\", \"positive_value\": \"./images/B001HK23TS.png\", \"hn_image\": [\"./images/B002TKJL40.png\"]}\n{\"q_img\": \"./images/B00967AMPG.png\", \"q_text\": \"is more princess like, and is a lot shorter and pure white with straps.\", \"positive_key\": \"B0097JITVM\", \"positive_value\": \"./images/B0097JITVM.png\", \"hn_image\": [\"./images/B00967AMPG.png\"]}\n{\"q_img\": \"./images/B009FEMH76.png\", \"q_text\": \"more vintage styled and sleeveless, and Is darker in color and sleeveless\", \"positive_key\": \"B007WASVRK\", \"positive_value\": \"./images/B007WASVRK.png\", \"hn_image\": [\"./images/B009FEMH76.png\"]}\n{\"q_img\": \"./images/B007SX36PI.png\", \"q_text\": \"Is blue with a belted waist., and longer drop waist pink patterned\", \"positive_key\": \"B008B5AU1A\", \"positive_value\": \"./images/B008B5AU1A.png\", \"hn_image\": [\"./images/B007SX36PI.png\"]}\n{\"q_img\": \"./images/B009MB17B4.png\", \"q_text\": \"has spaghetti straps and is blue, and is blue with thin straps\", \"positive_key\": \"B007C7JGSG\", \"positive_value\": \"./images/B007C7JGSG.png\", \"hn_image\": [\"./images/B009MB17B4.png\"]}\n{\"q_img\": \"./images/B00E0OSMS4.png\", \"q_text\": \"has floral design, and has flowers and is blue\", \"positive_key\": \"B00AELHLFC\", \"positive_value\": \"./images/B00AELHLFC.png\", \"hn_image\": [\"./images/B00E0OSMS4.png\"]}\n{\"q_img\": \"./images/B009X6E9E0.png\", \"q_text\": \"is more casual with a tank top, and Is less revealing\", \"positive_key\": \"B003ILG26E\", \"positive_value\": \"./images/B003ILG26E.png\", \"hn_image\": [\"./images/B009X6E9E0.png\"]}\n{\"q_img\": \"./images/B003M4Z9OS.png\", \"q_text\": \"patterned dress with zig zags, and chevron patern\", \"positive_key\": \"B009XHND2I\", \"positive_value\": \"./images/B009XHND2I.png\", \"hn_image\": [\"./images/B003M4Z9OS.png\"]}\n{\"q_img\": \"./images/B001QUX9KQ.png\", \"q_text\": \"is more black, and is black with one strap\", \"positive_key\": \"B00AQOH8XW\", \"positive_value\": \"./images/B00AQOH8XW.png\", \"hn_image\": [\"./images/B001QUX9KQ.png\"]}\n{\"q_img\": \"./images/B0092QWVQO.png\", \"q_text\": \"is shorter with thick straps, and is a shorter dress\", \"positive_key\": \"B007XVBUZI\", \"positive_value\": \"./images/B007XVBUZI.png\", \"hn_image\": [\"./images/B0092QWVQO.png\"]}\n{\"q_img\": \"./images/B00CFUEHAW.png\", \"q_text\": \"bodycon with darker color, and is dark in color and more fitted\", \"positive_key\": \"B00BPYQ25M\", \"positive_value\": \"./images/B00BPYQ25M.png\", \"hn_image\": [\"./images/B00CFUEHAW.png\"]}\n{\"q_img\": \"./images/B00BIJGMD6.png\", \"q_text\": \"has more polka dots, and is darker with polka dots\", \"positive_key\": \"B00CCC0B1C\", \"positive_value\": \"./images/B00CCC0B1C.png\", \"hn_image\": [\"./images/B00BIJGMD6.png\"]}\n{\"q_img\": \"./images/B001157SXU.png\", \"q_text\": \"is pink and shiny with a defined bust, and is a vibrant magenta\", \"positive_key\": \"B001PDM4E6\", \"positive_value\": \"./images/B001PDM4E6.png\", \"hn_image\": [\"./images/B001157SXU.png\"]}\n{\"q_img\": \"./images/B0095QLSJW.png\", \"q_text\": \"is solid black and one shouldered, and is solid dark with one shoulder\", \"positive_key\": \"B0059BGIG0\", \"positive_value\": \"./images/B0059BGIG0.png\", \"hn_image\": [\"./images/B0095QLSJW.png\"]}\n{\"q_img\": \"./images/B00DDHA7YQ.png\", \"q_text\": \"is lighter, and is lighter in color on bottom half\", \"positive_key\": \"B00EBZUV0Y\", \"positive_value\": \"./images/B00EBZUV0Y.png\", \"hn_image\": [\"./images/B00DDHA7YQ.png\"]}\n{\"q_img\": \"./images/B0068Z9QUQ.png\", \"q_text\": \"is orange and solid color and has longer sleeves, and Is more orange and is longer\", \"positive_key\": \"B00C3GDHK4\", \"positive_value\": \"./images/B00C3GDHK4.png\", \"hn_image\": [\"./images/B0068Z9QUQ.png\"]}\n{\"q_img\": \"./images/B00BY3GCD6.png\", \"q_text\": \"is gold and has a satin texture, and is gold and less casual\", \"positive_key\": \"B002MW1W6K\", \"positive_value\": \"./images/B002MW1W6K.png\", \"hn_image\": [\"./images/B00BY3GCD6.png\"]}\n{\"q_img\": \"./images/B00G49G488.png\", \"q_text\": \"is long green sleeves and open wide neck, and has long sleeves and is more penciled\", \"positive_key\": \"B007WATKGG\", \"positive_value\": \"./images/B007WATKGG.png\", \"hn_image\": [\"./images/B00G49G488.png\"]}\n{\"q_img\": \"./images/B005HEJCDK.png\", \"q_text\": \"is a combination of red and grey, and is black with long sleeves\", \"positive_key\": \"B0042ETEHK\", \"positive_value\": \"./images/B0042ETEHK.png\", \"hn_image\": [\"./images/B005HEJCDK.png\"]}\n{\"q_img\": \"./images/B008AKNE3W.png\", \"q_text\": \"is blue, and is shorter and more revealing\", \"positive_key\": \"B001NJ5LZG\", \"positive_value\": \"./images/B001NJ5LZG.png\", \"hn_image\": [\"./images/B008AKNE3W.png\"]}\n{\"q_img\": \"./images/B008VPX9BS.png\", \"q_text\": \"is lighter, and has a deep V neck and a belt\", \"positive_key\": \"B00B804Y3K\", \"positive_value\": \"./images/B00B804Y3K.png\", \"hn_image\": [\"./images/B008VPX9BS.png\"]}\n{\"q_img\": \"./images/B00C5168K8.png\", \"q_text\": \"is a lighter color and is more concealing, and is more patterned\", \"positive_key\": \"B0087D7OJ2\", \"positive_value\": \"./images/B0087D7OJ2.png\", \"hn_image\": [\"./images/B00C5168K8.png\"]}\n{\"q_img\": \"./images/B0060JO9BG.png\", \"q_text\": \"is yellow in color with shorter sleeves, and much more colorful and striped\", \"positive_key\": \"B007P1M3OI\", \"positive_value\": \"./images/B007P1M3OI.png\", \"hn_image\": [\"./images/B0060JO9BG.png\"]}\n{\"q_img\": \"./images/B0073HKUWG.png\", \"q_text\": \"is more revealing, and is a lighter color\", \"positive_key\": \"B00FXZX2I4\", \"positive_value\": \"./images/B00FXZX2I4.png\", \"hn_image\": [\"./images/B0073HKUWG.png\"]}\n{\"q_img\": \"./images/B0076RQLLW.png\", \"q_text\": \"longer and dark colored, and is more black and longer\", \"positive_key\": \"B004JLU6DM\", \"positive_value\": \"./images/B004JLU6DM.png\", \"hn_image\": [\"./images/B0076RQLLW.png\"]}\n{\"q_img\": \"./images/B0084DJQ6O.png\", \"q_text\": \"is pink in color, and is sleeveless and pink\", \"positive_key\": \"B00BJ1NFNI\", \"positive_value\": \"./images/B00BJ1NFNI.png\", \"hn_image\": [\"./images/B0084DJQ6O.png\"]}\n{\"q_img\": \"./images/B007SOUNSU.png\", \"q_text\": \"Green and shorter, and is green and striped\", \"positive_key\": \"B0073WBRYQ\", \"positive_value\": \"./images/B0073WBRYQ.png\", \"hn_image\": [\"./images/B007SOUNSU.png\"]}\n{\"q_img\": \"./images/B008PG0SOO.png\", \"q_text\": \"has long sleeves and is black, and Has long sleeves and is shorter.\", \"positive_key\": \"B00CB2N8FY\", \"positive_value\": \"./images/B00CB2N8FY.png\", \"hn_image\": [\"./images/B008PG0SOO.png\"]}\n{\"q_img\": \"./images/B00AOWQJZ4.png\", \"q_text\": \" shinier and brighter colored, and is aqua colored without a slit.\", \"positive_key\": \"B00DB147AE\", \"positive_value\": \"./images/B00DB147AE.png\", \"hn_image\": [\"./images/B00AOWQJZ4.png\"]}\n{\"q_img\": \"./images/B00GCR5OPG.png\", \"q_text\": \"is lighter, and is coral and less revealing\", \"positive_key\": \"B008TS39QC\", \"positive_value\": \"./images/B008TS39QC.png\", \"hn_image\": [\"./images/B00GCR5OPG.png\"]}\n{\"q_img\": \"./images/B00DR8ZWAU.png\", \"q_text\": \"is more flowery and with short sleeves, and is black with floral patterns\", \"positive_key\": \"B00C1ITU6Y\", \"positive_value\": \"./images/B00C1ITU6Y.png\", \"hn_image\": [\"./images/B00DR8ZWAU.png\"]}\n{\"q_img\": \"./images/B0060JO9BG.png\", \"q_text\": \"is longer, and is long with straps\", \"positive_key\": \"B006ZIN6S4\", \"positive_value\": \"./images/B006ZIN6S4.png\", \"hn_image\": [\"./images/B0060JO9BG.png\"]}\n{\"q_img\": \"./images/B00BTT3R0Q.png\", \"q_text\": \"is no sleeves and darker color, and has markings in black and red flowers.\", \"positive_key\": \"B00CCBVN1K\", \"positive_value\": \"./images/B00CCBVN1K.png\", \"hn_image\": [\"./images/B00BTT3R0Q.png\"]}\n{\"q_img\": \"./images/B00AW7ZRM2.png\", \"q_text\": \"has no sleeves, and Is more patterned and shorter\", \"positive_key\": \"B0083QFSRS\", \"positive_value\": \"./images/B0083QFSRS.png\", \"hn_image\": [\"./images/B00AW7ZRM2.png\"]}\n{\"q_img\": \"./images/B008S0HK3Y.png\", \"q_text\": \"is a lighter color, and black\", \"positive_key\": \"B0062OUFZ8\", \"positive_value\": \"./images/B0062OUFZ8.png\", \"hn_image\": [\"./images/B008S0HK3Y.png\"]}\n{\"q_img\": \"./images/B007L9UQRA.png\", \"q_text\": \"is purple colored and sleeveless, and has straps and is more navy in colour\", \"positive_key\": \"B003M4Z9OS\", \"positive_value\": \"./images/B003M4Z9OS.png\", \"hn_image\": [\"./images/B007L9UQRA.png\"]}\n{\"q_img\": \"./images/B006C4GIS6.png\", \"q_text\": \"is black and has one strap, and is darker and more sexy\", \"positive_key\": \"B00999J7Q6\", \"positive_value\": \"./images/B00999J7Q6.png\", \"hn_image\": [\"./images/B006C4GIS6.png\"]}\n{\"q_img\": \"./images/B007WPHD92.png\", \"q_text\": \"has longer sleeves, and Longer sleeves and broader neck\", \"positive_key\": \"B00AFR9YH8\", \"positive_value\": \"./images/B00AFR9YH8.png\", \"hn_image\": [\"./images/B007WPHD92.png\"]}\n{\"q_img\": \"./images/B006TAM4CW.png\", \"q_text\": \"is long sleeves and biggest black striped, and is darker and more striped and has longer sleeves\", \"positive_key\": \"B0047Y0K0U\", \"positive_value\": \"./images/B0047Y0K0U.png\", \"hn_image\": [\"./images/B006TAM4CW.png\"]}\n{\"q_img\": \"./images/B008QVZT3I.png\", \"q_text\": \"Is black with longer sleeves, and is darker and has longer sleeves\", \"positive_key\": \"B005DRAVT0\", \"positive_value\": \"./images/B005DRAVT0.png\", \"hn_image\": [\"./images/B008QVZT3I.png\"]}\n{\"q_img\": \"./images/B003VXVGRY.png\", \"q_text\": \"is shorter in length, and one stripe down middle\", \"positive_key\": \"B004CZ7WUU\", \"positive_value\": \"./images/B004CZ7WUU.png\", \"hn_image\": [\"./images/B003VXVGRY.png\"]}\n{\"q_img\": \"./images/B00E3HDPMQ.png\", \"q_text\": \"is dark blue with no sleeves, and is shorter in length\", \"positive_key\": \"B0036Z2T3C\", \"positive_value\": \"./images/B0036Z2T3C.png\", \"hn_image\": [\"./images/B00E3HDPMQ.png\"]}\n{\"q_img\": \"./images/B008BHCS2C.png\", \"q_text\": \"has a more colorful floral pattern, and bleted\", \"positive_key\": \"B0083F70SY\", \"positive_value\": \"./images/B0083F70SY.png\", \"hn_image\": [\"./images/B008BHCS2C.png\"]}\n{\"q_img\": \"./images/B004T20EFG.png\", \"q_text\": \" and is solid colored, and is solid blue and is shorter with straps\", \"positive_key\": \"B009YZPL7O\", \"positive_value\": \"./images/B009YZPL7O.png\", \"hn_image\": [\"./images/B004T20EFG.png\"]}\n{\"q_img\": \"./images/B009NCHBBW.png\", \"q_text\": \"is more revealing, and is shorter\", \"positive_key\": \"B0075AW63M\", \"positive_value\": \"./images/B0075AW63M.png\", \"hn_image\": [\"./images/B009NCHBBW.png\"]}\n{\"q_img\": \"./images/B00CY8T80Y.png\", \"q_text\": \"is more flirty and lighter, and is yellow with no sleeves\", \"positive_key\": \"B00CQSPPLS\", \"positive_value\": \"./images/B00CQSPPLS.png\", \"hn_image\": [\"./images/B00CY8T80Y.png\"]}\n{\"q_img\": \"./images/B008E6CKVY.png\", \"q_text\": \"is much brighter in color, and More hugging and satin\", \"positive_key\": \"B004HD4V0G\", \"positive_value\": \"./images/B004HD4V0G.png\", \"hn_image\": [\"./images/B008E6CKVY.png\"]}\n{\"q_img\": \"./images/B004DCAVT6.png\", \"q_text\": \"has a black and white print, and only straps and comes in reflective red.\", \"positive_key\": \"B004VQBUS0\", \"positive_value\": \"./images/B004VQBUS0.png\", \"hn_image\": [\"./images/B004DCAVT6.png\"]}\n{\"q_img\": \"./images/B00726H26K.png\", \"q_text\": \"is similar, and is lighter and more pastel\", \"positive_key\": \"B007IWO80Q\", \"positive_value\": \"./images/B007IWO80Q.png\", \"hn_image\": [\"./images/B00726H26K.png\"]}\n{\"q_img\": \"./images/B0056F25J8.png\", \"q_text\": \"has short sleeves and is brighter, and is striped and has longer sleeves\", \"positive_key\": \"B004VSDS2Y\", \"positive_value\": \"./images/B004VSDS2Y.png\", \"hn_image\": [\"./images/B0056F25J8.png\"]}\n{\"q_img\": \"./images/B0087PLHB6.png\", \"q_text\": \"various colors stockings with holes, and is a sexy leg cover\", \"positive_key\": \"B004HX2DYW\", \"positive_value\": \"./images/B004HX2DYW.png\", \"hn_image\": [\"./images/B0087PLHB6.png\"]}\n{\"q_img\": \"./images/B004MMFVQ0.png\", \"q_text\": \"is more metallic with a tighter fit, and is a fitted grey and black mini dress with pockets.\", \"positive_key\": \"B004ECLRO8\", \"positive_value\": \"./images/B004ECLRO8.png\", \"hn_image\": [\"./images/B004MMFVQ0.png\"]}\n{\"q_img\": \"./images/B0036DE0R2.png\", \"q_text\": \"has less sleeves with red and gray, and is blue with a higher neckline and black belt\", \"positive_key\": \"B00A16DXWK\", \"positive_value\": \"./images/B00A16DXWK.png\", \"hn_image\": [\"./images/B0036DE0R2.png\"]}\n{\"q_img\": \"./images/B008OVO8BY.png\", \"q_text\": \"is floral printed and less striped, and is white patterned and not striped\", \"positive_key\": \"B005HQ42E2\", \"positive_value\": \"./images/B005HQ42E2.png\", \"hn_image\": [\"./images/B008OVO8BY.png\"]}\n{\"q_img\": \"./images/B001PKTT7E.png\", \"q_text\": \"is solid colored with straps, and is darker and has straps\", \"positive_key\": \"B004X0VYKI\", \"positive_value\": \"./images/B004X0VYKI.png\", \"hn_image\": [\"./images/B001PKTT7E.png\"]}\n{\"q_img\": \"./images/B008O8MESQ.png\", \"q_text\": \"is shorter knee length and airy, and is black with straps\", \"positive_key\": \"B0048C7MMU\", \"positive_value\": \"./images/B0048C7MMU.png\", \"hn_image\": [\"./images/B008O8MESQ.png\"]}\n{\"q_img\": \"./images/B004VM5W1K.png\", \"q_text\": \"is black and strapless, and does not cover shoulders\", \"positive_key\": \"B004VERF8Q\", \"positive_value\": \"./images/B004VERF8Q.png\", \"hn_image\": [\"./images/B004VM5W1K.png\"]}\n{\"q_img\": \"./images/B007WA3RBK.png\", \"q_text\": \"has more print work, and is pink and less strappy\", \"positive_key\": \"B008BHSSMG\", \"positive_value\": \"./images/B008BHSSMG.png\", \"hn_image\": [\"./images/B007WA3RBK.png\"]}\n{\"q_img\": \"./images/B00E1ZTATW.png\", \"q_text\": \"is solid white and longer, and is black and form fitted with a v-neck\", \"positive_key\": \"B00BUKL1IY\", \"positive_value\": \"./images/B00BUKL1IY.png\", \"hn_image\": [\"./images/B00E1ZTATW.png\"]}\n{\"q_img\": \"./images/B0030IMZ1G.png\", \"q_text\": \"is more revealing with faux leather, and has a white skirt and more revealing top\", \"positive_key\": \"B00B95R792\", \"positive_value\": \"./images/B00B95R792.png\", \"hn_image\": [\"./images/B0030IMZ1G.png\"]}\n{\"q_img\": \"./images/B00G1ZZDL4.png\", \"q_text\": \"is shorter with brighter pink shade, and is more sexual\", \"positive_key\": \"B0030CY6DW\", \"positive_value\": \"./images/B0030CY6DW.png\", \"hn_image\": [\"./images/B00G1ZZDL4.png\"]}\n{\"q_img\": \"./images/B008UWENTO.png\", \"q_text\": \"is more modest with sweetheart top and blue, and is dark blue and strapless\", \"positive_key\": \"B0058XRPQQ\", \"positive_value\": \"./images/B0058XRPQQ.png\", \"hn_image\": [\"./images/B008UWENTO.png\"]}\n{\"q_img\": \"./images/B007WAT0S4.png\", \"q_text\": \"cheetah print long maxi dress, and Is longer and has a different neckline.\", \"positive_key\": \"B007WATTX0\", \"positive_value\": \"./images/B007WATTX0.png\", \"hn_image\": [\"./images/B007WAT0S4.png\"]}\n{\"q_img\": \"./images/B00D9E1SSW.png\", \"q_text\": \"v shaped neck with short sleeves, and is tan and pink striped\", \"positive_key\": \"B00CZW8P5I\", \"positive_value\": \"./images/B00CZW8P5I.png\", \"hn_image\": [\"./images/B00D9E1SSW.png\"]}\n{\"q_img\": \"./images/B007MLM38C.png\", \"q_text\": \"is lighter with no stripes and lots of pleats, and is ivory with a layered top\", \"positive_key\": \"B00711V8Y8\", \"positive_value\": \"./images/B00711V8Y8.png\", \"hn_image\": [\"./images/B007MLM38C.png\"]}\n{\"q_img\": \"./images/B005ENHHZO.png\", \"q_text\": \"has longer sleeves and is sexier, and is black and more dressy\", \"positive_key\": \"B00GNU62GM\", \"positive_value\": \"./images/B00GNU62GM.png\", \"hn_image\": [\"./images/B005ENHHZO.png\"]}\n{\"q_img\": \"./images/B00C7Z4VGU.png\", \"q_text\": \"is blue and strapless, and is blue and strapless\", \"positive_key\": \"B00BTMWMYU\", \"positive_value\": \"./images/B00BTMWMYU.png\", \"hn_image\": [\"./images/B00C7Z4VGU.png\"]}\n{\"q_img\": \"./images/B006MPVW4U.png\", \"q_text\": \"is short, and more purple with shorter skirt\", \"positive_key\": \"B007UZSPC8\", \"positive_value\": \"./images/B007UZSPC8.png\", \"hn_image\": [\"./images/B006MPVW4U.png\"]}\n{\"q_img\": \"./images/B006J66U2G.png\", \"q_text\": \"has no straps, and is green and strapless\", \"positive_key\": \"B005FOPNUI\", \"positive_value\": \"./images/B005FOPNUI.png\", \"hn_image\": [\"./images/B006J66U2G.png\"]}\n{\"q_img\": \"./images/B00D3KBI3M.png\", \"q_text\": \"is green and has a big bow, and solid black and more flared\", \"positive_key\": \"B00801W1H6\", \"positive_value\": \"./images/B00801W1H6.png\", \"hn_image\": [\"./images/B00D3KBI3M.png\"]}\n{\"q_img\": \"./images/B009P1MYNQ.png\", \"q_text\": \"has longer sleeves and black and white with belt, and is black and grey with a belt and longer sleeves\", \"positive_key\": \"B00D9OXH2M\", \"positive_value\": \"./images/B00D9OXH2M.png\", \"hn_image\": [\"./images/B009P1MYNQ.png\"]}\n{\"q_img\": \"./images/B0091C68MW.png\", \"q_text\": \"short sleeved black dress with collar, and has short sleeves and a collar\", \"positive_key\": \"B0016LHFE6\", \"positive_value\": \"./images/B0016LHFE6.png\", \"hn_image\": [\"./images/B0091C68MW.png\"]}\n{\"q_img\": \"./images/B00CWSNY24.png\", \"q_text\": \"is black in color, and black and sheer\", \"positive_key\": \"B00DHD7YK6\", \"positive_value\": \"./images/B00DHD7YK6.png\", \"hn_image\": [\"./images/B00CWSNY24.png\"]}\n{\"q_img\": \"./images/B008BA7MPW.png\", \"q_text\": \"is white colored with black floral print, and sweetheart neckline\", \"positive_key\": \"B009D52X44\", \"positive_value\": \"./images/B009D52X44.png\", \"hn_image\": [\"./images/B008BA7MPW.png\"]}\n{\"q_img\": \"./images/B0009T5VWY.png\", \"q_text\": \"is blue and shorter with no sleeves, and is shorter in the front\", \"positive_key\": \"B00E00GL86\", \"positive_value\": \"./images/B00E00GL86.png\", \"hn_image\": [\"./images/B0009T5VWY.png\"]}\n{\"q_img\": \"./images/B00BEJ34Q8.png\", \"q_text\": \"is clack in color with white boxes at the tits, and the dress is black and white and shorter\", \"positive_key\": \"B00AWKKHOC\", \"positive_value\": \"./images/B00AWKKHOC.png\", \"hn_image\": [\"./images/B00BEJ34Q8.png\"]}\n{\"q_img\": \"./images/B007F7PAOC.png\", \"q_text\": \"is maroon in color, and is purple and lacy and chic\", \"positive_key\": \"B005NXMECQ\", \"positive_value\": \"./images/B005NXMECQ.png\", \"hn_image\": [\"./images/B007F7PAOC.png\"]}\n{\"q_img\": \"./images/B0072QV4V4.png\", \"q_text\": \"Has lace and is short and red., and is more red and shorter\", \"positive_key\": \"B009S3GXWY\", \"positive_value\": \"./images/B009S3GXWY.png\", \"hn_image\": [\"./images/B0072QV4V4.png\"]}\n{\"q_img\": \"./images/B00C7N0BRU.png\", \"q_text\": \"is solid black in color, and is black and long\", \"positive_key\": \"B00BM82UCK\", \"positive_value\": \"./images/B00BM82UCK.png\", \"hn_image\": [\"./images/B00C7N0BRU.png\"]}\n{\"q_img\": \"./images/B00BTT3R0Q.png\", \"q_text\": \"is a dark turquoise color with long sleeves, and is a darker color and has longer sleeves\", \"positive_key\": \"B008RSU56G\", \"positive_value\": \"./images/B008RSU56G.png\", \"hn_image\": [\"./images/B00BTT3R0Q.png\"]}\n{\"q_img\": \"./images/B0091WZCE2.png\", \"q_text\": \"has a colorful print, and Is more whimsical\", \"positive_key\": \"B00FZ00I7U\", \"positive_value\": \"./images/B00FZ00I7U.png\", \"hn_image\": [\"./images/B0091WZCE2.png\"]}\n{\"q_img\": \"./images/B0043D2HME.png\", \"q_text\": \"Brighter and longer, and is red and white with straps\", \"positive_key\": \"B009Q25IYG\", \"positive_value\": \"./images/B009Q25IYG.png\", \"hn_image\": [\"./images/B0043D2HME.png\"]}\n{\"q_img\": \"./images/B00CJTWWA6.png\", \"q_text\": \"has straps and small sleeves, and A solid navy blue with belt\", \"positive_key\": \"B00CFMLTCY\", \"positive_value\": \"./images/B00CFMLTCY.png\", \"hn_image\": [\"./images/B00CJTWWA6.png\"]}\n{\"q_img\": \"./images/B003VQQZM2.png\", \"q_text\": \"is longer with no sleeves and printed, and has a brown bottom and is longer\", \"positive_key\": \"B00BC8OO5G\", \"positive_value\": \"./images/B00BC8OO5G.png\", \"hn_image\": [\"./images/B003VQQZM2.png\"]}\n{\"q_img\": \"./images/B00CEO4ETI.png\", \"q_text\": \"Has a layered bottom, and is white and more puffy\", \"positive_key\": \"B0091QUWCK\", \"positive_value\": \"./images/B0091QUWCK.png\", \"hn_image\": [\"./images/B00CEO4ETI.png\"]}\n{\"q_img\": \"./images/B00H2FUGOQ.png\", \"q_text\": \"is lighter blue with red trim, and is bluer\", \"positive_key\": \"B00D3KBI3M\", \"positive_value\": \"./images/B00D3KBI3M.png\", \"hn_image\": [\"./images/B00H2FUGOQ.png\"]}\n{\"q_img\": \"./images/B002NVCPQ6.png\", \"q_text\": \"is blue in color, and A solid blue dress\", \"positive_key\": \"B003VTQA1K\", \"positive_value\": \"./images/B003VTQA1K.png\", \"hn_image\": [\"./images/B002NVCPQ6.png\"]}\n{\"q_img\": \"./images/B006PAT5EQ.png\", \"q_text\": \"is more fashionable, and is black and white with blue lines\", \"positive_key\": \"B008EOZ1SU\", \"positive_value\": \"./images/B008EOZ1SU.png\", \"hn_image\": [\"./images/B006PAT5EQ.png\"]}\n{\"q_img\": \"./images/B008LTJG3E.png\", \"q_text\": \"is grey, and Shows less skin and has material on the hips\", \"positive_key\": \"B004EHYFLK\", \"positive_value\": \"./images/B004EHYFLK.png\", \"hn_image\": [\"./images/B008LTJG3E.png\"]}\n{\"q_img\": \"./images/B000AYI3L4.png\", \"q_text\": \"Is a brighter tiered dress., and is frilly and has more blue\", \"positive_key\": \"B00CFXV50E\", \"positive_value\": \"./images/B00CFXV50E.png\", \"hn_image\": [\"./images/B000AYI3L4.png\"]}\n{\"q_img\": \"./images/B00857SPA2.png\", \"q_text\": \"is redder with shorter sleeves, and is more colordul\", \"positive_key\": \"B007WAT1H4\", \"positive_value\": \"./images/B007WAT1H4.png\", \"hn_image\": [\"./images/B00857SPA2.png\"]}\n{\"q_img\": \"./images/B0075AV660.png\", \"q_text\": \"is longer and more patterned, and is longer and more red\", \"positive_key\": \"B0075BUES0\", \"positive_value\": \"./images/B0075BUES0.png\", \"hn_image\": [\"./images/B0075AV660.png\"]}\n{\"q_img\": \"./images/B0095FP7VI.png\", \"q_text\": \"Is a shimmery long sleeve short dress, and sweetheart neckline\", \"positive_key\": \"B007C3MKYC\", \"positive_value\": \"./images/B007C3MKYC.png\", \"hn_image\": [\"./images/B0095FP7VI.png\"]}\n{\"q_img\": \"./images/B00CQ9U2UQ.png\", \"q_text\": \"is strapless and solid black, and is black with no sleeves\", \"positive_key\": \"B008FPXR3Y\", \"positive_value\": \"./images/B008FPXR3Y.png\", \"hn_image\": [\"./images/B00CQ9U2UQ.png\"]}\n{\"q_img\": \"./images/B00CFYDMM2.png\", \"q_text\": \"is blue with long sleeves, and is lighter\", \"positive_key\": \"B00BPCXXIS\", \"positive_value\": \"./images/B00BPCXXIS.png\", \"hn_image\": [\"./images/B00CFYDMM2.png\"]}\n{\"q_img\": \"./images/B0055OGJLU.png\", \"q_text\": \"has a tighter fit and is pink, and pink.\", \"positive_key\": \"B004VLI08S\", \"positive_value\": \"./images/B004VLI08S.png\", \"hn_image\": [\"./images/B0055OGJLU.png\"]}\n{\"q_img\": \"./images/B009TM8IG8.png\", \"q_text\": \" floral and has a rectangular shaped neck, and is black and light pink and sleeveless.\", \"positive_key\": \"B008DSN0BW\", \"positive_value\": \"./images/B008DSN0BW.png\", \"hn_image\": [\"./images/B009TM8IG8.png\"]}\n{\"q_img\": \"./images/B0054N0S6E.png\", \"q_text\": \"has floral and long sleeves, and has longer sleeves and a lighter pattern\", \"positive_key\": \"B00DSI8WYW\", \"positive_value\": \"./images/B00DSI8WYW.png\", \"hn_image\": [\"./images/B0054N0S6E.png\"]}\n{\"q_img\": \"./images/B006G3YVBE.png\", \"q_text\": \"Is younger and more printed., and is shorter in color\", \"positive_key\": \"B00B3EHWYO\", \"positive_value\": \"./images/B00B3EHWYO.png\", \"hn_image\": [\"./images/B006G3YVBE.png\"]}\n{\"q_img\": \"./images/B005D9IGMM.png\", \"q_text\": \"Is more revealing and fitted, and has more cutouts\", \"positive_key\": \"B008BFR8PQ\", \"positive_value\": \"./images/B008BFR8PQ.png\", \"hn_image\": [\"./images/B005D9IGMM.png\"]}\n{\"q_img\": \"./images/B004EHON6W.png\", \"q_text\": \"is longer with a floral pattern, and is longer and has thin strap sleeves\", \"positive_key\": \"B00AV4VOX2\", \"positive_value\": \"./images/B00AV4VOX2.png\", \"hn_image\": [\"./images/B004EHON6W.png\"]}\n{\"q_img\": \"./images/B00BNVAH6M.png\", \"q_text\": \"has longer sleeves and is black, and is black\", \"positive_key\": \"B00BU9S4V2\", \"positive_value\": \"./images/B00BU9S4V2.png\", \"hn_image\": [\"./images/B00BNVAH6M.png\"]}\n{\"q_img\": \"./images/B0067PFCXM.png\", \"q_text\": \"is yeloow with zipper in front, and is blue and shorter sleeved\", \"positive_key\": \"B00B5MK7N2\", \"positive_value\": \"./images/B00B5MK7N2.png\", \"hn_image\": [\"./images/B0067PFCXM.png\"]}\n{\"q_img\": \"./images/B00CFYDMM2.png\", \"q_text\": \"has more lace and no sleeves, and is sleeveless with a lacy neck\", \"positive_key\": \"B00DRB9XJS\", \"positive_value\": \"./images/B00DRB9XJS.png\", \"hn_image\": [\"./images/B00CFYDMM2.png\"]}\n{\"q_img\": \"./images/B00C6PP5CA.png\", \"q_text\": \"is shorter and has a little design, and dark and short\", \"positive_key\": \"B00AHOY8H0\", \"positive_value\": \"./images/B00AHOY8H0.png\", \"hn_image\": [\"./images/B00C6PP5CA.png\"]}\n{\"q_img\": \"./images/B00FZVANC4.png\", \"q_text\": \"is body suit in solid color, and is black and less revealing\", \"positive_key\": \"B00428LYOM\", \"positive_value\": \"./images/B00428LYOM.png\", \"hn_image\": [\"./images/B00FZVANC4.png\"]}\n{\"q_img\": \"./images/B0074H8I6K.png\", \"q_text\": \"has a single strap on the right shoulder and is long, and has black and white explosion pattern\", \"positive_key\": \"B007FPDWDU\", \"positive_value\": \"./images/B007FPDWDU.png\", \"hn_image\": [\"./images/B0074H8I6K.png\"]}\n{\"q_img\": \"./images/B00405RUWC.png\", \"q_text\": \"has longer sleeves, and white\", \"positive_key\": \"B0085JAH88\", \"positive_value\": \"./images/B0085JAH88.png\", \"hn_image\": [\"./images/B00405RUWC.png\"]}\n{\"q_img\": \"./images/B00595TNMM.png\", \"q_text\": \"is shorter and darker, and is shorter\", \"positive_key\": \"B008MZS5Y8\", \"positive_value\": \"./images/B008MZS5Y8.png\", \"hn_image\": [\"./images/B00595TNMM.png\"]}\n{\"q_img\": \"./images/B008368ZQY.png\", \"q_text\": \"is more of silk than cotton, and is more of a satin fabric in merlot\", \"positive_key\": \"B00D43UDE8\", \"positive_value\": \"./images/B00D43UDE8.png\", \"hn_image\": [\"./images/B008368ZQY.png\"]}\n{\"q_img\": \"./images/B00BEJ34Q8.png\", \"q_text\": \"has shorter black mini and long sleeves, and is black.\", \"positive_key\": \"B009TM8IG8\", \"positive_value\": \"./images/B009TM8IG8.png\", \"hn_image\": [\"./images/B00BEJ34Q8.png\"]}\n{\"q_img\": \"./images/B00A16KWZ6.png\", \"q_text\": \" and is a lighter color, and is sleeveless\", \"positive_key\": \"B009LGQ9EA\", \"positive_value\": \"./images/B009LGQ9EA.png\", \"hn_image\": [\"./images/B00A16KWZ6.png\"]}\n{\"q_img\": \"./images/B002UD64NW.png\", \"q_text\": \"is silky and bright, and is silver\", \"positive_key\": \"B009EMNLOC\", \"positive_value\": \"./images/B009EMNLOC.png\", \"hn_image\": [\"./images/B002UD64NW.png\"]}\n{\"q_img\": \"./images/B00C7Z4VGU.png\", \"q_text\": \"is longer and more clolorful, and is more colorful and sleeveless\", \"positive_key\": \"B00AWGHWFS\", \"positive_value\": \"./images/B00AWGHWFS.png\", \"hn_image\": [\"./images/B00C7Z4VGU.png\"]}\n{\"q_img\": \"./images/B008BWOCGC.png\", \"q_text\": \"is a halter and solid color, and Light & Dark color\", \"positive_key\": \"B008BDJDE2\", \"positive_value\": \"./images/B008BDJDE2.png\", \"hn_image\": [\"./images/B008BWOCGC.png\"]}\n{\"q_img\": \"./images/B008006FK6.png\", \"q_text\": \"is shorter length and darker, and is black with three quarter length sleeves\", \"positive_key\": \"B009MIRC6Q\", \"positive_value\": \"./images/B009MIRC6Q.png\", \"hn_image\": [\"./images/B008006FK6.png\"]}\n{\"q_img\": \"./images/B00DOYXMA4.png\", \"q_text\": \"is more revealing, and brighter with floral pattern\", \"positive_key\": \"B00BXB7LRA\", \"positive_value\": \"./images/B00BXB7LRA.png\", \"hn_image\": [\"./images/B00DOYXMA4.png\"]}\n{\"q_img\": \"./images/B009SPZ72E.png\", \"q_text\": \"is plus-sized and long., and is purple and has long sleeve\", \"positive_key\": \"B0049LG51E\", \"positive_value\": \"./images/B0049LG51E.png\", \"hn_image\": [\"./images/B009SPZ72E.png\"]}\n{\"q_img\": \"./images/B007RTTKA8.png\", \"q_text\": \"is black and less revealing., and is tigher and two-toned\", \"positive_key\": \"B00E9LE61K\", \"positive_value\": \"./images/B00E9LE61K.png\", \"hn_image\": [\"./images/B007RTTKA8.png\"]}\n{\"q_img\": \"./images/B009PKDTEA.png\", \"q_text\": \"is black and is shorter and more dressy, and It is more black and has a short sleeve\", \"positive_key\": \"B00BM0R2K8\", \"positive_value\": \"./images/B00BM0R2K8.png\", \"hn_image\": [\"./images/B009PKDTEA.png\"]}\n{\"q_img\": \"./images/B002N8CDA2.png\", \"q_text\": \"Is dark purple with straps., and is longer and is purple\", \"positive_key\": \"B009SK85S2\", \"positive_value\": \"./images/B009SK85S2.png\", \"hn_image\": [\"./images/B002N8CDA2.png\"]}\n{\"q_img\": \"./images/B0070IN282.png\", \"q_text\": \"is pink and floor lenght with glitter belt, and is much longer and lighter in color\", \"positive_key\": \"B00G1ZZDL4\", \"positive_value\": \"./images/B00G1ZZDL4.png\", \"hn_image\": [\"./images/B0070IN282.png\"]}\n{\"q_img\": \"./images/B008D5GUJY.png\", \"q_text\": \" patterned and much longer, and is a faded yellow\", \"positive_key\": \"B001QCXH4W\", \"positive_value\": \"./images/B001QCXH4W.png\", \"hn_image\": [\"./images/B008D5GUJY.png\"]}\n{\"q_img\": \"./images/B004OHYU4M.png\", \"q_text\": \"is black colored and has floral pattern, and is darker and longer\", \"positive_key\": \"B008NHSU3G\", \"positive_value\": \"./images/B008NHSU3G.png\", \"hn_image\": [\"./images/B004OHYU4M.png\"]}\n{\"q_img\": \"./images/B00DUL8IWS.png\", \"q_text\": \"is more casual, and is peach with a black belt\", \"positive_key\": \"B00CFT03IS\", \"positive_value\": \"./images/B00CFT03IS.png\", \"hn_image\": [\"./images/B00DUL8IWS.png\"]}\n{\"q_img\": \"./images/B00383UG6E.png\", \"q_text\": \"is blue in color and has stripe pattern of black, and is lighter colored and strappy\", \"positive_key\": \"B0085TUWRO\", \"positive_value\": \"./images/B0085TUWRO.png\", \"hn_image\": [\"./images/B00383UG6E.png\"]}\n{\"q_img\": \"./images/B0079G6CXC.png\", \"q_text\": \"has cap sleeves and is shorter, and is slightly darker and has short sleeves\", \"positive_key\": \"B00DWXNQ2Q\", \"positive_value\": \"./images/B00DWXNQ2Q.png\", \"hn_image\": [\"./images/B0079G6CXC.png\"]}\n{\"q_img\": \"./images/B003A84EOM.png\", \"q_text\": \"Is more colorful., and is pink and has straps\", \"positive_key\": \"B0049R7YVI\", \"positive_value\": \"./images/B0049R7YVI.png\", \"hn_image\": [\"./images/B003A84EOM.png\"]}\n{\"q_img\": \"./images/B0036SHD1W.png\", \"q_text\": \"is white with red floral print, and is pink and more flowing\", \"positive_key\": \"B0079EIXI6\", \"positive_value\": \"./images/B0079EIXI6.png\", \"hn_image\": [\"./images/B0036SHD1W.png\"]}\n{\"q_img\": \"./images/B0007WIZYE.png\", \"q_text\": \"Is a darker color with no sleeves., and is darker and has no sleeves\", \"positive_key\": \"B00B6PT8EW\", \"positive_value\": \"./images/B00B6PT8EW.png\", \"hn_image\": [\"./images/B0007WIZYE.png\"]}\n{\"q_img\": \"./images/B0037PVUW2.png\", \"q_text\": \"black satin ribbon belt in front and lace top, and is shorter and fluffier\", \"positive_key\": \"B007NG77PU\", \"positive_value\": \"./images/B007NG77PU.png\", \"hn_image\": [\"./images/B0037PVUW2.png\"]}\n{\"q_img\": \"./images/B008D6LE3A.png\", \"q_text\": \"is solid black with shorter sleeves, and is black and more revealing\", \"positive_key\": \"B008D5B2HO\", \"positive_value\": \"./images/B008D5B2HO.png\", \"hn_image\": [\"./images/B008D6LE3A.png\"]}\n{\"q_img\": \"./images/B0084Y2V6A.png\", \"q_text\": \"Is solid black with a hemline above the knees, and is shorter and darker in color\", \"positive_key\": \"B009OB2OJ6\", \"positive_value\": \"./images/B009OB2OJ6.png\", \"hn_image\": [\"./images/B0084Y2V6A.png\"]}\n{\"q_img\": \"./images/B009TM8IG8.png\", \"q_text\": \"is belted with shorter sleeves and is blue, and has shorter sleeves\", \"positive_key\": \"B00BEJ34Q8\", \"positive_value\": \"./images/B00BEJ34Q8.png\", \"hn_image\": [\"./images/B009TM8IG8.png\"]}\n{\"q_img\": \"./images/B0030CY6DW.png\", \"q_text\": \" and strapless, and is darker and printed\", \"positive_key\": \"B002VNFKTU\", \"positive_value\": \"./images/B002VNFKTU.png\", \"hn_image\": [\"./images/B0030CY6DW.png\"]}\n{\"q_img\": \"./images/B007TUE48S.png\", \"q_text\": \"has more fringes and more sleeves, and red and black dress\", \"positive_key\": \"B00655A1TE\", \"positive_value\": \"./images/B00655A1TE.png\", \"hn_image\": [\"./images/B007TUE48S.png\"]}\n{\"q_img\": \"./images/B0045EPT8A.png\", \"q_text\": \"is more revealing and shiny and gold, and is more revealing and shiny\", \"positive_key\": \"B003ILRVZU\", \"positive_value\": \"./images/B003ILRVZU.png\", \"hn_image\": [\"./images/B0045EPT8A.png\"]}\n{\"q_img\": \"./images/B007RBIRK0.png\", \"q_text\": \"is shorter, and shorter\", \"positive_key\": \"B004MMFVQ0\", \"positive_value\": \"./images/B004MMFVQ0.png\", \"hn_image\": [\"./images/B007RBIRK0.png\"]}\n{\"q_img\": \"./images/B00BG4M4BM.png\", \"q_text\": \"A black dress that has long lacy sleeves, and N/A second picture is not a fashion product\", \"positive_key\": \"B00A16KWZ6\", \"positive_value\": \"./images/B00A16KWZ6.png\", \"hn_image\": [\"./images/B00BG4M4BM.png\"]}\n{\"q_img\": \"./images/B005GMTBPC.png\", \"q_text\": \" and thin fitting, and Sleeveless purple ruffle dress\", \"positive_key\": \"B00CZ6G09C\", \"positive_value\": \"./images/B00CZ6G09C.png\", \"hn_image\": [\"./images/B005GMTBPC.png\"]}\n{\"q_img\": \"./images/B0071UPNJ0.png\", \"q_text\": \"has longer sleeves with darker color, and is black and shinier\", \"positive_key\": \"B007AAP4TK\", \"positive_value\": \"./images/B007AAP4TK.png\", \"hn_image\": [\"./images/B0071UPNJ0.png\"]}\n{\"q_img\": \"./images/B007C7JGSG.png\", \"q_text\": \"is white colored with British flag, and is a t shirt with the British flag in the background and a phone booth in the front.\", \"positive_key\": \"B00CTKTIQ6\", \"positive_value\": \"./images/B00CTKTIQ6.png\", \"hn_image\": [\"./images/B007C7JGSG.png\"]}\n{\"q_img\": \"./images/B008UPM3SO.png\", \"q_text\": \"is blue with short sleeves, and belted and sleeved\", \"positive_key\": \"B00CHS9KTA\", \"positive_value\": \"./images/B00CHS9KTA.png\", \"hn_image\": [\"./images/B008UPM3SO.png\"]}\n{\"q_img\": \"./images/B007Y0UKN6.png\", \"q_text\": \"has no sleeves and a print, and is black\", \"positive_key\": \"B00CSQ15KS\", \"positive_value\": \"./images/B00CSQ15KS.png\", \"hn_image\": [\"./images/B007Y0UKN6.png\"]}\n{\"q_img\": \"./images/B0099DCINQ.png\", \"q_text\": \"is shorter with a peplum sytle, and more sexy\", \"positive_key\": \"B009AG55Q4\", \"positive_value\": \"./images/B009AG55Q4.png\", \"hn_image\": [\"./images/B0099DCINQ.png\"]}\n{\"q_img\": \"./images/B00ASPCIF2.png\", \"q_text\": \"is more plain and has tank top sleeves, and is shorter and souped neck\", \"positive_key\": \"B004QF0KO6\", \"positive_value\": \"./images/B004QF0KO6.png\", \"hn_image\": [\"./images/B00ASPCIF2.png\"]}\n{\"q_img\": \"./images/B00AXX86TG.png\", \"q_text\": \"navy and more casual, and is one toned\", \"positive_key\": \"B007OZSURE\", \"positive_value\": \"./images/B007OZSURE.png\", \"hn_image\": [\"./images/B00AXX86TG.png\"]}\n{\"q_img\": \"./images/B0072QV4V4.png\", \"q_text\": \"is yellow with thin straps, and yellow with thin straps\", \"positive_key\": \"B0072QWBDE\", \"positive_value\": \"./images/B0072QWBDE.png\", \"hn_image\": [\"./images/B0072QV4V4.png\"]}\n{\"q_img\": \"./images/B00BXNNZ64.png\", \"q_text\": \"is pink-gray striped, and is pink and grey striped\", \"positive_key\": \"B00B50Y9CO\", \"positive_value\": \"./images/B00B50Y9CO.png\", \"hn_image\": [\"./images/B00BXNNZ64.png\"]}\n{\"q_img\": \"./images/B00AWM67OE.png\", \"q_text\": \"is less revealing and better design, and has a wider skirt and no side split\", \"positive_key\": \"B00967AMPG\", \"positive_value\": \"./images/B00967AMPG.png\", \"hn_image\": [\"./images/B00AWM67OE.png\"]}\n{\"q_img\": \"./images/B00DONTLP0.png\", \"q_text\": \"black color dress looks too bold, and Is shorter and sexier\", \"positive_key\": \"B008J25Y20\", \"positive_value\": \"./images/B008J25Y20.png\", \"hn_image\": [\"./images/B00DONTLP0.png\"]}\n{\"q_img\": \"./images/B005GMT86Y.png\", \"q_text\": \"has long sleeves and has more coverage, and is a little thinner\", \"positive_key\": \"B005MN4RLS\", \"positive_value\": \"./images/B005MN4RLS.png\", \"hn_image\": [\"./images/B005GMT86Y.png\"]}\n{\"q_img\": \"./images/B0032FOQUU.png\", \"q_text\": \"is a black dress with longer sleeves, and The desired product is slightly longer and is less textured.\", \"positive_key\": \"B008B845AO\", \"positive_value\": \"./images/B008B845AO.png\", \"hn_image\": [\"./images/B0032FOQUU.png\"]}\n{\"q_img\": \"./images/B00A16KWZ6.png\", \"q_text\": \"is blue and has a belt, and is blue with shorter sleeves\", \"positive_key\": \"B00CAKLNY0\", \"positive_value\": \"./images/B00CAKLNY0.png\", \"hn_image\": [\"./images/B00A16KWZ6.png\"]}\n{\"q_img\": \"./images/B00DVPO5VQ.png\", \"q_text\": \"has a square neckline and sleeves, and is shorter and polka dotted with sleeves\", \"positive_key\": \"B006R1FJOS\", \"positive_value\": \"./images/B006R1FJOS.png\", \"hn_image\": [\"./images/B00DVPO5VQ.png\"]}\n{\"q_img\": \"./images/B008YIG3DI.png\", \"q_text\": \"is lighter, and is lighter in color\", \"positive_key\": \"B005LC303G\", \"positive_value\": \"./images/B005LC303G.png\", \"hn_image\": [\"./images/B008YIG3DI.png\"]}\n{\"q_img\": \"./images/B00CA38COQ.png\", \"q_text\": \"is white in color with thicker straps, and is shorter\", \"positive_key\": \"B006YTLWLM\", \"positive_value\": \"./images/B006YTLWLM.png\", \"hn_image\": [\"./images/B00CA38COQ.png\"]}\n{\"q_img\": \"./images/B008FD9MG2.png\", \"q_text\": \"longer sleeves and grey, and has longer sleeves\", \"positive_key\": \"B00AEMHDIG\", \"positive_value\": \"./images/B00AEMHDIG.png\", \"hn_image\": [\"./images/B008FD9MG2.png\"]}\n{\"q_img\": \"./images/B008GRR6II.png\", \"q_text\": \"is brown and more shiny, and is brow\", \"positive_key\": \"B008A4RNR6\", \"positive_value\": \"./images/B008A4RNR6.png\", \"hn_image\": [\"./images/B008GRR6II.png\"]}\n{\"q_img\": \"./images/B004RJ8IHW.png\", \"q_text\": \"is sexier and slim fit, and is more form-fitting\", \"positive_key\": \"B00F9AJ5Y8\", \"positive_value\": \"./images/B00F9AJ5Y8.png\", \"hn_image\": [\"./images/B004RJ8IHW.png\"]}\n{\"q_img\": \"./images/B00ABSUHMC.png\", \"q_text\": \"is more flowing and muted, and has a psychedelic print and is longer\", \"positive_key\": \"B00DNJ6ALS\", \"positive_value\": \"./images/B00DNJ6ALS.png\", \"hn_image\": [\"./images/B00ABSUHMC.png\"]}\n{\"q_img\": \"./images/B0015SKXG2.png\", \"q_text\": \"has stipes and has less sleeves, and is black and white striped\", \"positive_key\": \"B0094E3HDU\", \"positive_value\": \"./images/B0094E3HDU.png\", \"hn_image\": [\"./images/B0015SKXG2.png\"]}\n{\"q_img\": \"./images/B0097IX9NQ.png\", \"q_text\": \"has no sleeves and more gathering on skirt, and is sleeveless\", \"positive_key\": \"B0097J2E2M\", \"positive_value\": \"./images/B0097J2E2M.png\", \"hn_image\": [\"./images/B0097IX9NQ.png\"]}\n{\"q_img\": \"./images/B004UJHMKS.png\", \"q_text\": \"is black with a flower, and is longer and a darker color\", \"positive_key\": \"B007LA4RZQ\", \"positive_value\": \"./images/B007LA4RZQ.png\", \"hn_image\": [\"./images/B004UJHMKS.png\"]}\n{\"q_img\": \"./images/B00C2LPWCQ.png\", \"q_text\": \"is plain white with transparent sleeves, and is white with less sheer.\", \"positive_key\": \"B00BT8N1DK\", \"positive_value\": \"./images/B00BT8N1DK.png\", \"hn_image\": [\"./images/B00C2LPWCQ.png\"]}\n{\"q_img\": \"./images/B006TALF54.png\", \"q_text\": \"is sleeveless and stripes horizontally, and striped\", \"positive_key\": \"B0073DLNE4\", \"positive_value\": \"./images/B0073DLNE4.png\", \"hn_image\": [\"./images/B006TALF54.png\"]}\n{\"q_img\": \"./images/B00DUL8IWS.png\", \"q_text\": \"is a white short sleeved dress. With lace shoulders., and cap sleeves\", \"positive_key\": \"B00AFDJ0HG\", \"positive_value\": \"./images/B00AFDJ0HG.png\", \"hn_image\": [\"./images/B00DUL8IWS.png\"]}\n{\"q_img\": \"./images/B0083QEJL4.png\", \"q_text\": \"has slightly longer sleeves, and is in solid black.\", \"positive_key\": \"B0058BWBJY\", \"positive_value\": \"./images/B0058BWBJY.png\", \"hn_image\": [\"./images/B0083QEJL4.png\"]}\n{\"q_img\": \"./images/B007RB7IJG.png\", \"q_text\": \"has longer sleeves and is all black, and straighter skirt in green color\", \"positive_key\": \"B0077SLOJE\", \"positive_value\": \"./images/B0077SLOJE.png\", \"hn_image\": [\"./images/B007RB7IJG.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \"is longer with a white and black pattern, and is printed and has short sleeves\", \"positive_key\": \"B005XW1D4M\", \"positive_value\": \"./images/B005XW1D4M.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B008ATH59C.png\", \"q_text\": \" is more revealing, and has a vneck with long sleeves and is a mini dress\", \"positive_key\": \"B005N4L15G\", \"positive_value\": \"./images/B005N4L15G.png\", \"hn_image\": [\"./images/B008ATH59C.png\"]}\n{\"q_img\": \"./images/B00A13GLG8.png\", \"q_text\": \"is tan and black in color, and is tan with black piping\", \"positive_key\": \"B008KCUXLQ\", \"positive_value\": \"./images/B008KCUXLQ.png\", \"hn_image\": [\"./images/B00A13GLG8.png\"]}\n{\"q_img\": \"./images/B0091QSKHE.png\", \"q_text\": \"os less detailed, and is more white and strapless\", \"positive_key\": \"B0097J0RVC\", \"positive_value\": \"./images/B0097J0RVC.png\", \"hn_image\": [\"./images/B0091QSKHE.png\"]}\n{\"q_img\": \"./images/B0061IQ32E.png\", \"q_text\": \" with a white chest portion, and shorter\", \"positive_key\": \"B00DB9POOY\", \"positive_value\": \"./images/B00DB9POOY.png\", \"hn_image\": [\"./images/B0061IQ32E.png\"]}\n{\"q_img\": \"./images/B00598QDJK.png\", \"q_text\": \"is satinier and has shorter sleeves, and is less revealing and has longer sleeves\", \"positive_key\": \"B007RTTKA8\", \"positive_value\": \"./images/B007RTTKA8.png\", \"hn_image\": [\"./images/B00598QDJK.png\"]}\n{\"q_img\": \"./images/B0079G1GL0.png\", \"q_text\": \"is short, and is knee length with long sleeves and no flowers\", \"positive_key\": \"B0092PHFIY\", \"positive_value\": \"./images/B0092PHFIY.png\", \"hn_image\": [\"./images/B0079G1GL0.png\"]}\n{\"q_img\": \"./images/B0060RMLPO.png\", \"q_text\": \"is shorter and more fitted, and has a lower cut heart shaped neckline and is more form fitting\", \"positive_key\": \"B00E6RATRW\", \"positive_value\": \"./images/B00E6RATRW.png\", \"hn_image\": [\"./images/B0060RMLPO.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \"has cap sleeves and is longer, and has shoulders flow\", \"positive_key\": \"B00BZTBYQO\", \"positive_value\": \"./images/B00BZTBYQO.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B007SX36PI.png\", \"q_text\": \"is not as shiny, and is pink and has longer sleeves\", \"positive_key\": \"B00AKCGNP4\", \"positive_value\": \"./images/B00AKCGNP4.png\", \"hn_image\": [\"./images/B007SX36PI.png\"]}\n{\"q_img\": \"./images/B009AMQ2WY.png\", \"q_text\": \"has longer sleeves and less fitted, and is bolder\", \"positive_key\": \"B0030EHJCU\", \"positive_value\": \"./images/B0030EHJCU.png\", \"hn_image\": [\"./images/B009AMQ2WY.png\"]}\n{\"q_img\": \"./images/B0084OEYX8.png\", \"q_text\": \"has no sleeves and is green, and and black\", \"positive_key\": \"B0076IN1MI\", \"positive_value\": \"./images/B0076IN1MI.png\", \"hn_image\": [\"./images/B0084OEYX8.png\"]}\n{\"q_img\": \"./images/B00FHXBQFS.png\", \"q_text\": \"has a black lace pattern, and is shorter and no sleeves\", \"positive_key\": \"B00BPS1REY\", \"positive_value\": \"./images/B00BPS1REY.png\", \"hn_image\": [\"./images/B00FHXBQFS.png\"]}\n{\"q_img\": \"./images/B002VLK54C.png\", \"q_text\": \"has different prints of roses, and is just a pattern\", \"positive_key\": \"B0032SBJWK\", \"positive_value\": \"./images/B0032SBJWK.png\", \"hn_image\": [\"./images/B002VLK54C.png\"]}\n{\"q_img\": \"./images/B009LJ8W4W.png\", \"q_text\": \"has two long sleeves and more ruffles, and is pink and ruffly\", \"positive_key\": \"B008D5AYOG\", \"positive_value\": \"./images/B008D5AYOG.png\", \"hn_image\": [\"./images/B009LJ8W4W.png\"]}\n{\"q_img\": \"./images/B0084DJQ6O.png\", \"q_text\": \"is longer and off the shoulder print, and is much lighter in color\", \"positive_key\": \"B004FPYVO2\", \"positive_value\": \"./images/B004FPYVO2.png\", \"hn_image\": [\"./images/B0084DJQ6O.png\"]}\n{\"q_img\": \"./images/B008E7IA12.png\", \"q_text\": \"is light blue in color, and is blue and flowy\", \"positive_key\": \"B00BVGHQKO\", \"positive_value\": \"./images/B00BVGHQKO.png\", \"hn_image\": [\"./images/B008E7IA12.png\"]}\n{\"q_img\": \"./images/B00AEW4384.png\", \"q_text\": \"is strapless and fitted, and is a blue and black pattern\", \"positive_key\": \"B00BQMJP6Q\", \"positive_value\": \"./images/B00BQMJP6Q.png\", \"hn_image\": [\"./images/B00AEW4384.png\"]}\n{\"q_img\": \"./images/B005X4RMW2.png\", \"q_text\": \"is london times, and no comparisoin to words\", \"positive_key\": \"B00APYBFPK\", \"positive_value\": \"./images/B00APYBFPK.png\", \"hn_image\": [\"./images/B005X4RMW2.png\"]}\n{\"q_img\": \"./images/B00ATFWVKI.png\", \"q_text\": \"Has a cold shoulder top and is red., and longer\", \"positive_key\": \"B006WVGYAG\", \"positive_value\": \"./images/B006WVGYAG.png\", \"hn_image\": [\"./images/B00ATFWVKI.png\"]}\n{\"q_img\": \"./images/B008596V88.png\", \"q_text\": \" white, and is long with black and white stripes\", \"positive_key\": \"B007WAETMG\", \"positive_value\": \"./images/B007WAETMG.png\", \"hn_image\": [\"./images/B008596V88.png\"]}\n{\"q_img\": \"./images/B007M8PCAQ.png\", \"q_text\": \"Is more fitted and striped, and is shorter and black with red stripes\", \"positive_key\": \"B0085U94ZO\", \"positive_value\": \"./images/B0085U94ZO.png\", \"hn_image\": [\"./images/B007M8PCAQ.png\"]}\n{\"q_img\": \"./images/B0095JQQZK.png\", \"q_text\": \"is white and has longer sleeves, and is white and has short sleeves\", \"positive_key\": \"B0022T0W82\", \"positive_value\": \"./images/B0022T0W82.png\", \"hn_image\": [\"./images/B0095JQQZK.png\"]}\n{\"q_img\": \"./images/B007LTUAUS.png\", \"q_text\": \"is black and white with one strap, and has more white\", \"positive_key\": \"B007B6WEA0\", \"positive_value\": \"./images/B007B6WEA0.png\", \"hn_image\": [\"./images/B007LTUAUS.png\"]}\n{\"q_img\": \"./images/B008PG0SOO.png\", \"q_text\": \"is shorter and more blue, and is teal and shorter and sassy\", \"positive_key\": \"B00BBPWS5S\", \"positive_value\": \"./images/B00BBPWS5S.png\", \"hn_image\": [\"./images/B008PG0SOO.png\"]}\n{\"q_img\": \"./images/B005FLWZCA.png\", \"q_text\": \"its a dark red and not as shiny, and is darker in color\", \"positive_key\": \"B00J2UZLNU\", \"positive_value\": \"./images/B00J2UZLNU.png\", \"hn_image\": [\"./images/B005FLWZCA.png\"]}\n{\"q_img\": \"./images/B007XD6AMO.png\", \"q_text\": \"a darker long sleeved dress, and black with stockings\", \"positive_key\": \"B008OVNLDU\", \"positive_value\": \"./images/B008OVNLDU.png\", \"hn_image\": [\"./images/B007XD6AMO.png\"]}\n{\"q_img\": \"./images/B007U3P8PC.png\", \"q_text\": \"is white colored and have thicker straps, and has straps and more varied pattern\", \"positive_key\": \"B00CD9LXD4\", \"positive_value\": \"./images/B00CD9LXD4.png\", \"hn_image\": [\"./images/B007U3P8PC.png\"]}\n{\"q_img\": \"./images/B007L9UQRA.png\", \"q_text\": \"is orange and has straps, and Is orange and more revealing\", \"positive_key\": \"B0080DKT9G\", \"positive_value\": \"./images/B0080DKT9G.png\", \"hn_image\": [\"./images/B007L9UQRA.png\"]}\n{\"q_img\": \"./images/B008E6CKVY.png\", \"q_text\": \"is longer and open cleavage, and is longer and blue patterned\", \"positive_key\": \"B004E9SNTS\", \"positive_value\": \"./images/B004E9SNTS.png\", \"hn_image\": [\"./images/B008E6CKVY.png\"]}\n{\"q_img\": \"./images/B008CB707K.png\", \"q_text\": \"is yellow and one shouldered floral print, and is brighter colored and less flowy\", \"positive_key\": \"B0082BCNZY\", \"positive_value\": \"./images/B0082BCNZY.png\", \"hn_image\": [\"./images/B008CB707K.png\"]}\n{\"q_img\": \"./images/B0070TYH1M.png\", \"q_text\": \"Is more black and simple, and darker and has green\", \"positive_key\": \"B009LLG8BE\", \"positive_value\": \"./images/B009LLG8BE.png\", \"hn_image\": [\"./images/B0070TYH1M.png\"]}\n{\"q_img\": \"./images/B007TUECJ4.png\", \"q_text\": \"is more poofy and less prefessional, and is strapless\", \"positive_key\": \"B009OCNXE0\", \"positive_value\": \"./images/B009OCNXE0.png\", \"hn_image\": [\"./images/B007TUECJ4.png\"]}\n{\"q_img\": \"./images/B007G1015U.png\", \"q_text\": \"is blue with dots, and is strapless and patterned\", \"positive_key\": \"B0076R8552\", \"positive_value\": \"./images/B0076R8552.png\", \"hn_image\": [\"./images/B007G1015U.png\"]}\n{\"q_img\": \"./images/B00A3EZC4M.png\", \"q_text\": \"is bed with some rhinestones, and sexier and bright purple\", \"positive_key\": \"B00CTTJCUY\", \"positive_value\": \"./images/B00CTTJCUY.png\", \"hn_image\": [\"./images/B00A3EZC4M.png\"]}\n{\"q_img\": \"./images/B00A3UUE76.png\", \"q_text\": \"is form fitted, and is leather and fitted\", \"positive_key\": \"B006UT2TPY\", \"positive_value\": \"./images/B006UT2TPY.png\", \"hn_image\": [\"./images/B00A3UUE76.png\"]}\n{\"q_img\": \"./images/B007KHTV24.png\", \"q_text\": \"3/4 sleeved blue dress, and is solid blue and less revealing\", \"positive_key\": \"B009MQBC46\", \"positive_value\": \"./images/B009MQBC46.png\", \"hn_image\": [\"./images/B007KHTV24.png\"]}\n{\"q_img\": \"./images/B00EYSAIPG.png\", \"q_text\": \"is open shouldered and a shorter lenght, and is shorter and strapless\", \"positive_key\": \"B008LCUUEK\", \"positive_value\": \"./images/B008LCUUEK.png\", \"hn_image\": [\"./images/B00EYSAIPG.png\"]}\n{\"q_img\": \"./images/B00CLF5ZFC.png\", \"q_text\": \"is strapless with a blue pattern, and is strapless and blue printed\", \"positive_key\": \"B003YKZ42W\", \"positive_value\": \"./images/B003YKZ42W.png\", \"hn_image\": [\"./images/B00CLF5ZFC.png\"]}\n{\"q_img\": \"./images/B00F55FSWK.png\", \"q_text\": \" with pale green trim and pale green belt, and is less black\", \"positive_key\": \"B00AY30MJM\", \"positive_value\": \"./images/B00AY30MJM.png\", \"hn_image\": [\"./images/B00F55FSWK.png\"]}\n{\"q_img\": \"./images/B00297BG7I.png\", \"q_text\": \"This dress is longer, and is darker and sleeveless\", \"positive_key\": \"B007Y37P22\", \"positive_value\": \"./images/B007Y37P22.png\", \"hn_image\": [\"./images/B00297BG7I.png\"]}\n{\"q_img\": \"./images/B009ERW1KW.png\", \"q_text\": \"sleeveless and black lace, and is black with straps\", \"positive_key\": \"B008BHHSAY\", \"positive_value\": \"./images/B008BHHSAY.png\", \"hn_image\": [\"./images/B009ERW1KW.png\"]}\n{\"q_img\": \"./images/B00DBCUIJM.png\", \"q_text\": \"is less revealing, and is darker and has a web design\", \"positive_key\": \"B0072IL71E\", \"positive_value\": \"./images/B0072IL71E.png\", \"hn_image\": [\"./images/B00DBCUIJM.png\"]}\n{\"q_img\": \"./images/B00897AQ9G.png\", \"q_text\": \"is sleeveless with polka dotted skirt, and is sleeveless and white and black\", \"positive_key\": \"B002Z7FP1A\", \"positive_value\": \"./images/B002Z7FP1A.png\", \"hn_image\": [\"./images/B00897AQ9G.png\"]}\n{\"q_img\": \"./images/B004EHON6W.png\", \"q_text\": \"is white short sleeved button dress, and white with shorter sleeves\", \"positive_key\": \"B00775SIPA\", \"positive_value\": \"./images/B00775SIPA.png\", \"hn_image\": [\"./images/B004EHON6W.png\"]}\n{\"q_img\": \"./images/B00DERHT5A.png\", \"q_text\": \"is lighter, and is shinier and more ruffled\", \"positive_key\": \"B00CORDX4W\", \"positive_value\": \"./images/B00CORDX4W.png\", \"hn_image\": [\"./images/B00DERHT5A.png\"]}\n{\"q_img\": \"./images/B0085TUWRO.png\", \"q_text\": \"has thicker straps and black colored, and is darker colored with dots and covers shoulders\", \"positive_key\": \"B00383UG6E\", \"positive_value\": \"./images/B00383UG6E.png\", \"hn_image\": [\"./images/B0085TUWRO.png\"]}\n{\"q_img\": \"./images/B00FZ00I7U.png\", \"q_text\": \"Is brighter with longer hemline., and is more flowing and one colored\", \"positive_key\": \"B00CGY6RJ6\", \"positive_value\": \"./images/B00CGY6RJ6.png\", \"hn_image\": [\"./images/B00FZ00I7U.png\"]}\n{\"q_img\": \"./images/B008MWER8E.png\", \"q_text\": \" orange, and is longer and more formal.\", \"positive_key\": \"B007XW7BVY\", \"positive_value\": \"./images/B007XW7BVY.png\", \"hn_image\": [\"./images/B008MWER8E.png\"]}\n{\"q_img\": \"./images/B0092PKYNM.png\", \"q_text\": \"is teal colored and sleeveless, and more colorfol and big belt\", \"positive_key\": \"B009NDC59O\", \"positive_value\": \"./images/B009NDC59O.png\", \"hn_image\": [\"./images/B0092PKYNM.png\"]}\n{\"q_img\": \"./images/B00DM0REWC.png\", \"q_text\": \"is longer and more attractive, and is longer\", \"positive_key\": \"B00EYSAIPG\", \"positive_value\": \"./images/B00EYSAIPG.png\", \"hn_image\": [\"./images/B00DM0REWC.png\"]}\n{\"q_img\": \"./images/B00AVV833O.png\", \"q_text\": \"has capped sleeves and is black with flaired bottom, and is all black\", \"positive_key\": \"B0091SFTVC\", \"positive_value\": \"./images/B0091SFTVC.png\", \"hn_image\": [\"./images/B00AVV833O.png\"]}\n{\"q_img\": \"./images/B006W20H0I.png\", \"q_text\": \"is long, and is darker and longer\", \"positive_key\": \"B00C6PP5CA\", \"positive_value\": \"./images/B00C6PP5CA.png\", \"hn_image\": [\"./images/B006W20H0I.png\"]}\n{\"q_img\": \"./images/B007R1YUI8.png\", \"q_text\": \"has no straps and has prints, and is sleeveless and strapless with pale blue panel\", \"positive_key\": \"B004MDL8KW\", \"positive_value\": \"./images/B004MDL8KW.png\", \"hn_image\": [\"./images/B007R1YUI8.png\"]}\n{\"q_img\": \"./images/B0096C2UVA.png\", \"q_text\": \"is black colored with longer sleeves, and black and white.\", \"positive_key\": \"B009DMCMNO\", \"positive_value\": \"./images/B009DMCMNO.png\", \"hn_image\": [\"./images/B0096C2UVA.png\"]}\n{\"q_img\": \"./images/B002YQ47SE.png\", \"q_text\": \"is bright white, and is white in color and less flattering\", \"positive_key\": \"B00C62EKDS\", \"positive_value\": \"./images/B00C62EKDS.png\", \"hn_image\": [\"./images/B002YQ47SE.png\"]}\n{\"q_img\": \"./images/B002HCNMTU.png\", \"q_text\": \"has a deep neck cut and flowy sleeves, and is blue and brown with a v neck and short sleeves\", \"positive_key\": \"B008NW1PGU\", \"positive_value\": \"./images/B008NW1PGU.png\", \"hn_image\": [\"./images/B002HCNMTU.png\"]}\n{\"q_img\": \"./images/B0097B5S8C.png\", \"q_text\": \"has short sleeves, and is more traditional and short sleeves\", \"positive_key\": \"B00H15C73K\", \"positive_value\": \"./images/B00H15C73K.png\", \"hn_image\": [\"./images/B0097B5S8C.png\"]}\n{\"q_img\": \"./images/B007XD6RYK.png\", \"q_text\": \"is a tighter fit and a lighter color with a waistband, and It is shorter and more tight\", \"positive_key\": \"B00AIIQLNO\", \"positive_value\": \"./images/B00AIIQLNO.png\", \"hn_image\": [\"./images/B007XD6RYK.png\"]}\n{\"q_img\": \"./images/B007CU5A7E.png\", \"q_text\": \"is longer and lighter, and has a white top with a black waist\", \"positive_key\": \"B007ZIRHQA\", \"positive_value\": \"./images/B007ZIRHQA.png\", \"hn_image\": [\"./images/B007CU5A7E.png\"]}\n{\"q_img\": \"./images/B00AODC7BS.png\", \"q_text\": \"has shorter sleeves and is tighter, and is sleeveless and fitted\", \"positive_key\": \"B00FXAC5SM\", \"positive_value\": \"./images/B00FXAC5SM.png\", \"hn_image\": [\"./images/B00AODC7BS.png\"]}\n{\"q_img\": \"./images/B009G0RL4I.png\", \"q_text\": \" longer with short sleeves and buttons up front, and is black\", \"positive_key\": \"B00B9BFL4E\", \"positive_value\": \"./images/B00B9BFL4E.png\", \"hn_image\": [\"./images/B009G0RL4I.png\"]}\n{\"q_img\": \"./images/B008YIG3DI.png\", \"q_text\": \"Is a sporty cream and blue short dress, and lighter and thinner straps\", \"positive_key\": \"B009AMLLVG\", \"positive_value\": \"./images/B009AMLLVG.png\", \"hn_image\": [\"./images/B008YIG3DI.png\"]}\n{\"q_img\": \"./images/B003BT62A4.png\", \"q_text\": \"in more white and flowing, and is light in color and shorter in length\", \"positive_key\": \"B004LKRWRY\", \"positive_value\": \"./images/B004LKRWRY.png\", \"hn_image\": [\"./images/B003BT62A4.png\"]}\n{\"q_img\": \"./images/B00BXZ1PW8.png\", \"q_text\": \"is london times, and says london times\", \"positive_key\": \"B009HK2P2A\", \"positive_value\": \"./images/B009HK2P2A.png\", \"hn_image\": [\"./images/B00BXZ1PW8.png\"]}\n{\"q_img\": \"./images/B007G4ZTTK.png\", \"q_text\": \"is more pink and patterned, and is darker\", \"positive_key\": \"B00A772MCU\", \"positive_value\": \"./images/B00A772MCU.png\", \"hn_image\": [\"./images/B007G4ZTTK.png\"]}\n{\"q_img\": \"./images/B00DR3TRJ2.png\", \"q_text\": \"Is green and more revealing, and has a green color and no sleeves\", \"positive_key\": \"B00DU6GMRG\", \"positive_value\": \"./images/B00DU6GMRG.png\", \"hn_image\": [\"./images/B00DR3TRJ2.png\"]}\n{\"q_img\": \"./images/B00BY3GO0C.png\", \"q_text\": \"Has long-sleeves and is more complex, and White almost see-through long-sleeved zig zagged design\", \"positive_key\": \"B00ARLIDF6\", \"positive_value\": \"./images/B00ARLIDF6.png\", \"hn_image\": [\"./images/B00BY3GO0C.png\"]}\n{\"q_img\": \"./images/B008HSGOKC.png\", \"q_text\": \"has spagetti straps, and is shorter and has spaghetti straps.\", \"positive_key\": \"B005FSIX5G\", \"positive_value\": \"./images/B005FSIX5G.png\", \"hn_image\": [\"./images/B008HSGOKC.png\"]}\n{\"q_img\": \"./images/B00AZWPQQ6.png\", \"q_text\": \"darker colored and longer, and is longer and a darker color\", \"positive_key\": \"B00CJPL9I6\", \"positive_value\": \"./images/B00CJPL9I6.png\", \"hn_image\": [\"./images/B00AZWPQQ6.png\"]}\n{\"q_img\": \"./images/B0083UY0LY.png\", \"q_text\": \"tube top grey dress, and shows cleavage and is longer\", \"positive_key\": \"B003ILC99S\", \"positive_value\": \"./images/B003ILC99S.png\", \"hn_image\": [\"./images/B0083UY0LY.png\"]}\n{\"q_img\": \"./images/B00ATGT8IK.png\", \"q_text\": \"is printed all over and long sleeved, and is more animal print and has long sleeves.\", \"positive_key\": \"B006VVMOTC\", \"positive_value\": \"./images/B006VVMOTC.png\", \"hn_image\": [\"./images/B00ATGT8IK.png\"]}\n{\"q_img\": \"./images/B004ECLRO8.png\", \"q_text\": \"Blue and doesn't have animal like print, and is shorter and more blue\", \"positive_key\": \"B004GBAUBS\", \"positive_value\": \"./images/B004GBAUBS.png\", \"hn_image\": [\"./images/B004ECLRO8.png\"]}\n{\"q_img\": \"./images/B00CH8U9BI.png\", \"q_text\": \"is black and tight, and is black with thin straps\", \"positive_key\": \"B007VZ30FO\", \"positive_value\": \"./images/B007VZ30FO.png\", \"hn_image\": [\"./images/B00CH8U9BI.png\"]}\n{\"q_img\": \"./images/B00BTUULCC.png\", \"q_text\": \"is more casual, and is peach with no sleeves\", \"positive_key\": \"B00CHHL10M\", \"positive_value\": \"./images/B00CHHL10M.png\", \"hn_image\": [\"./images/B00BTUULCC.png\"]}\n{\"q_img\": \"./images/B00498CQ9M.png\", \"q_text\": \"has graphics and is more loud, and also contains white and has short sleeves\", \"positive_key\": \"B009LEFR4A\", \"positive_value\": \"./images/B009LEFR4A.png\", \"hn_image\": [\"./images/B00498CQ9M.png\"]}\n{\"q_img\": \"./images/B00B72LAS6.png\", \"q_text\": \"is long and white, and is lighter and longer with no sleeves\", \"positive_key\": \"B00D79OJHQ\", \"positive_value\": \"./images/B00D79OJHQ.png\", \"hn_image\": [\"./images/B00B72LAS6.png\"]}\n{\"q_img\": \"./images/B00E00SG7K.png\", \"q_text\": \"is more edy, and is a floral print and fitted\", \"positive_key\": \"B00BPYWKO4\", \"positive_value\": \"./images/B00BPYWKO4.png\", \"hn_image\": [\"./images/B00E00SG7K.png\"]}\n{\"q_img\": \"./images/B008596PSY.png\", \"q_text\": \"has longer sleeves and is shorter length, and more geometric print and shinier\", \"positive_key\": \"B007WADW7E\", \"positive_value\": \"./images/B007WADW7E.png\", \"hn_image\": [\"./images/B008596PSY.png\"]}\n{\"q_img\": \"./images/B00ATFWVKI.png\", \"q_text\": \"is lighter with a floral print, and is in a floral print design.\", \"positive_key\": \"B005FMSW3U\", \"positive_value\": \"./images/B005FMSW3U.png\", \"hn_image\": [\"./images/B00ATFWVKI.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \" shorter skirt, and na\", \"positive_key\": \"B00C3CUNT6\", \"positive_value\": \"./images/B00C3CUNT6.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B00B2UEOEK.png\", \"q_text\": \"is black and mint green, and is more see through\", \"positive_key\": \"B00BMAPIB8\", \"positive_value\": \"./images/B00BMAPIB8.png\", \"hn_image\": [\"./images/B00B2UEOEK.png\"]}\n{\"q_img\": \"./images/B008UH8MY6.png\", \"q_text\": \"is sleeveless with an orange and tan print, and is floral and stapless\", \"positive_key\": \"B00715FFX4\", \"positive_value\": \"./images/B00715FFX4.png\", \"hn_image\": [\"./images/B008UH8MY6.png\"]}\n{\"q_img\": \"./images/B0094QXCKQ.png\", \"q_text\": \"has shorter sleeves and is lighter, and is white with shorter sleeves\", \"positive_key\": \"B00FAH8XTS\", \"positive_value\": \"./images/B00FAH8XTS.png\", \"hn_image\": [\"./images/B0094QXCKQ.png\"]}\n{\"q_img\": \"./images/B004S0O8HO.png\", \"q_text\": \"is more floral, and is more strappy and colorful\", \"positive_key\": \"B005JSTILA\", \"positive_value\": \"./images/B005JSTILA.png\", \"hn_image\": [\"./images/B004S0O8HO.png\"]}\n{\"q_img\": \"./images/B00DRDIVEO.png\", \"q_text\": \"is not strapless and is less girly, and is more casual and has small sleeves\", \"positive_key\": \"B007G6JUHA\", \"positive_value\": \"./images/B007G6JUHA.png\", \"hn_image\": [\"./images/B00DRDIVEO.png\"]}\n{\"q_img\": \"./images/B0009T8KS6.png\", \"q_text\": \"is darker and sleeveless, and is strapless\", \"positive_key\": \"B00FS3EAOQ\", \"positive_value\": \"./images/B00FS3EAOQ.png\", \"hn_image\": [\"./images/B0009T8KS6.png\"]}\n{\"q_img\": \"./images/B0084DJQ6O.png\", \"q_text\": \"has shorter sleeves and a v-neck, and Is wider and longer\", \"positive_key\": \"B005GNVV5Y\", \"positive_value\": \"./images/B005GNVV5Y.png\", \"hn_image\": [\"./images/B0084DJQ6O.png\"]}\n{\"q_img\": \"./images/B007ZPXMXA.png\", \"q_text\": \"is shorter, and is lime green\", \"positive_key\": \"B0067IDDF8\", \"positive_value\": \"./images/B0067IDDF8.png\", \"hn_image\": [\"./images/B007ZPXMXA.png\"]}\n{\"q_img\": \"./images/B008X0KHCU.png\", \"q_text\": \"is white and gray and more professional, and mat black and red geometric pattern\", \"positive_key\": \"B00E0IXSKC\", \"positive_value\": \"./images/B00E0IXSKC.png\", \"hn_image\": [\"./images/B008X0KHCU.png\"]}\n{\"q_img\": \"./images/B008006CXG.png\", \"q_text\": \"has thinner straps with a scoop neck and is black, and has thin straps and is in all black.\", \"positive_key\": \"B00B2OFP0I\", \"positive_value\": \"./images/B00B2OFP0I.png\", \"hn_image\": [\"./images/B008006CXG.png\"]}\n{\"q_img\": \"./images/B008BRLWA6.png\", \"q_text\": \"is a black and white dress, and is strapless\", \"positive_key\": \"B0069IWITS\", \"positive_value\": \"./images/B0069IWITS.png\", \"hn_image\": [\"./images/B008BRLWA6.png\"]}\n{\"q_img\": \"./images/B00AOWQJZ4.png\", \"q_text\": \"has thin straps and no slit, and has spaghetti straps and no slit\", \"positive_key\": \"B008AKNE3W\", \"positive_value\": \"./images/B008AKNE3W.png\", \"hn_image\": [\"./images/B00AOWQJZ4.png\"]}\n{\"q_img\": \"./images/B007PMBZ00.png\", \"q_text\": \"has a floral print and is less fitted, and is very light and more flowing\", \"positive_key\": \"B007TMBT5M\", \"positive_value\": \"./images/B007TMBT5M.png\", \"hn_image\": [\"./images/B007PMBZ00.png\"]}\n{\"q_img\": \"./images/B007NG6V6Q.png\", \"q_text\": \"has longer sleeves, and has a pattern and longer sleeves\", \"positive_key\": \"B004RJ8IHW\", \"positive_value\": \"./images/B004RJ8IHW.png\", \"hn_image\": [\"./images/B007NG6V6Q.png\"]}\n{\"q_img\": \"./images/B006UR05S4.png\", \"q_text\": \"is longer, and has red and white v stripes\", \"positive_key\": \"B007PPHY70\", \"positive_value\": \"./images/B007PPHY70.png\", \"hn_image\": [\"./images/B006UR05S4.png\"]}\n{\"q_img\": \"./images/B00F4N004Y.png\", \"q_text\": \"is a cream dress with an open bottom with no sleeves, and more revealing and more plain\", \"positive_key\": \"B007GSG3U0\", \"positive_value\": \"./images/B007GSG3U0.png\", \"hn_image\": [\"./images/B00F4N004Y.png\"]}\n{\"q_img\": \"./images/B007GSG3U0.png\", \"q_text\": \"has smaller straps, and is darker\", \"positive_key\": \"B0038KR31I\", \"positive_value\": \"./images/B0038KR31I.png\", \"hn_image\": [\"./images/B007GSG3U0.png\"]}\n{\"q_img\": \"./images/B004S0O8HO.png\", \"q_text\": \"is very stylish and long, and is black and longer\", \"positive_key\": \"B00B5MHQCW\", \"positive_value\": \"./images/B00B5MHQCW.png\", \"hn_image\": [\"./images/B004S0O8HO.png\"]}\n{\"q_img\": \"./images/B004DVT2SI.png\", \"q_text\": \"has stripes and more grren, and is shorter in length and multicolored\", \"positive_key\": \"B0064J5JPC\", \"positive_value\": \"./images/B0064J5JPC.png\", \"hn_image\": [\"./images/B004DVT2SI.png\"]}\n{\"q_img\": \"./images/B00C17P8W0.png\", \"q_text\": \"is short and beige, and is short and pink\", \"positive_key\": \"B004A15UO6\", \"positive_value\": \"./images/B004A15UO6.png\", \"hn_image\": [\"./images/B00C17P8W0.png\"]}\n{\"q_img\": \"./images/B00BWISVXW.png\", \"q_text\": \"has longer sleeves and shorter length, and is black\", \"positive_key\": \"B009AO2IP2\", \"positive_value\": \"./images/B009AO2IP2.png\", \"hn_image\": [\"./images/B00BWISVXW.png\"]}\n{\"q_img\": \"./images/B002NVCPQ6.png\", \"q_text\": \"has a neck strap and is darker, and is solid blue with silver highlights\", \"positive_key\": \"B007XTIENG\", \"positive_value\": \"./images/B007XTIENG.png\", \"hn_image\": [\"./images/B002NVCPQ6.png\"]}\n{\"q_img\": \"./images/B008VJ0Q2Y.png\", \"q_text\": \"is longer and solid black, and is solid black\", \"positive_key\": \"B00C3I0548\", \"positive_value\": \"./images/B00C3I0548.png\", \"hn_image\": [\"./images/B008VJ0Q2Y.png\"]}\n{\"q_img\": \"./images/B005IFCASW.png\", \"q_text\": \"is an off white with short sleeves, and shorter\", \"positive_key\": \"B00CIL95XW\", \"positive_value\": \"./images/B00CIL95XW.png\", \"hn_image\": [\"./images/B005IFCASW.png\"]}\n{\"q_img\": \"./images/B0048C7MMU.png\", \"q_text\": \"is a turquoise color dress. It's longer, and is blue and long\", \"positive_key\": \"B00CFY2ZWA\", \"positive_value\": \"./images/B00CFY2ZWA.png\", \"hn_image\": [\"./images/B0048C7MMU.png\"]}\n{\"q_img\": \"./images/B002Z7FONO.png\", \"q_text\": \"is more formal, and is longer\", \"positive_key\": \"B00BV3WNCI\", \"positive_value\": \"./images/B00BV3WNCI.png\", \"hn_image\": [\"./images/B002Z7FONO.png\"]}\n{\"q_img\": \"./images/B002DI8HQG.png\", \"q_text\": \"is a blue short dress, and is sleeveless\", \"positive_key\": \"B005T4G0F6\", \"positive_value\": \"./images/B005T4G0F6.png\", \"hn_image\": [\"./images/B002DI8HQG.png\"]}\n{\"q_img\": \"./images/B00ANR3LEM.png\", \"q_text\": \"has sleeves and is solid white, and It has sleeves and it is more white\", \"positive_key\": \"B00DLKEQGK\", \"positive_value\": \"./images/B00DLKEQGK.png\", \"hn_image\": [\"./images/B00ANR3LEM.png\"]}\n{\"q_img\": \"./images/B004L2L9X0.png\", \"q_text\": \"has short sleeves and a v shaped neck, and is dark and flowing\", \"positive_key\": \"B00C3M1IAO\", \"positive_value\": \"./images/B00C3M1IAO.png\", \"hn_image\": [\"./images/B004L2L9X0.png\"]}\n{\"q_img\": \"./images/B00AQUZKG8.png\", \"q_text\": \"is more classy, and has long sleeves and is a pink print\", \"positive_key\": \"B00BXBCUQW\", \"positive_value\": \"./images/B00BXBCUQW.png\", \"hn_image\": [\"./images/B00AQUZKG8.png\"]}\n{\"q_img\": \"./images/B00D178PQK.png\", \"q_text\": \"white, and It is shorted and with sleeves\", \"positive_key\": \"B0080E53WI\", \"positive_value\": \"./images/B0080E53WI.png\", \"hn_image\": [\"./images/B00D178PQK.png\"]}\n{\"q_img\": \"./images/B0097J2PVM.png\", \"q_text\": \"is more lacy and less shiney, and is less form fitting in the middle\", \"positive_key\": \"B00B7WGMXE\", \"positive_value\": \"./images/B00B7WGMXE.png\", \"hn_image\": [\"./images/B0097J2PVM.png\"]}\n{\"q_img\": \"./images/B0033UVILO.png\", \"q_text\": \"is black and red, and Is more colorful\", \"positive_key\": \"B009P1MYNQ\", \"positive_value\": \"./images/B009P1MYNQ.png\", \"hn_image\": [\"./images/B0033UVILO.png\"]}\n{\"q_img\": \"./images/B00DW61O90.png\", \"q_text\": \"has long sleeves and is shortrer, and is in a lighter brown and tan with slits in the midrift.\", \"positive_key\": \"B00AG3Y2JG\", \"positive_value\": \"./images/B00AG3Y2JG.png\", \"hn_image\": [\"./images/B00DW61O90.png\"]}\n{\"q_img\": \"./images/B005RUNC3K.png\", \"q_text\": \"is cream and a shiny top, and is tan with sleeves\", \"positive_key\": \"B00B7DONTI\", \"positive_value\": \"./images/B00B7DONTI.png\", \"hn_image\": [\"./images/B005RUNC3K.png\"]}\n{\"q_img\": \"./images/B003ILG26E.png\", \"q_text\": \"is sexy and navy blue and more revealing, and is more revealing and flowing\", \"positive_key\": \"B009X6E9E0\", \"positive_value\": \"./images/B009X6E9E0.png\", \"hn_image\": [\"./images/B003ILG26E.png\"]}\n{\"q_img\": \"./images/B00CTT41D2.png\", \"q_text\": \"has a looser fitting skirt and darker stripes, and Is less colorful and has short sleeves\", \"positive_key\": \"B00D9SL7JI\", \"positive_value\": \"./images/B00D9SL7JI.png\", \"hn_image\": [\"./images/B00CTT41D2.png\"]}\n{\"q_img\": \"./images/B00B71U376.png\", \"q_text\": \"is more revealing and royal, and is blue and shiny\", \"positive_key\": \"B00438RWVU\", \"positive_value\": \"./images/B00438RWVU.png\", \"hn_image\": [\"./images/B00B71U376.png\"]}\n{\"q_img\": \"./images/B008UH8MY6.png\", \"q_text\": \"is a black halter dress, and is black and is shorter and more fitted\", \"positive_key\": \"B009ES4PFA\", \"positive_value\": \"./images/B009ES4PFA.png\", \"hn_image\": [\"./images/B008UH8MY6.png\"]}\n{\"q_img\": \"./images/B007KHTV24.png\", \"q_text\": \"has longer sleeves and is shorter, and has long sleeves\", \"positive_key\": \"B00DI4A1AY\", \"positive_value\": \"./images/B00DI4A1AY.png\", \"hn_image\": [\"./images/B007KHTV24.png\"]}\n{\"q_img\": \"./images/B005YUC6B2.png\", \"q_text\": \"is more body fitting, and Is shorter and more revealing\", \"positive_key\": \"B009817FR8\", \"positive_value\": \"./images/B009817FR8.png\", \"hn_image\": [\"./images/B005YUC6B2.png\"]}\n{\"q_img\": \"./images/B00C5168K8.png\", \"q_text\": \"is a pink casual dress, and is more red\", \"positive_key\": \"B007678MDM\", \"positive_value\": \"./images/B007678MDM.png\", \"hn_image\": [\"./images/B00C5168K8.png\"]}\n{\"q_img\": \"./images/B00ARFVZ3Y.png\", \"q_text\": \"has pink and thin straps, and is pink and flowy and colorful\", \"positive_key\": \"B00ARFW8JE\", \"positive_value\": \"./images/B00ARFW8JE.png\", \"hn_image\": [\"./images/B00ARFVZ3Y.png\"]}\n{\"q_img\": \"./images/B007V0LBW8.png\", \"q_text\": \" and is less modest, and is red with no stripes\", \"positive_key\": \"B00CO2NVFS\", \"positive_value\": \"./images/B00CO2NVFS.png\", \"hn_image\": [\"./images/B007V0LBW8.png\"]}\n{\"q_img\": \"./images/B008R8BFFG.png\", \"q_text\": \" with a black matching belt, and is more blue\", \"positive_key\": \"B00C9OICDM\", \"positive_value\": \"./images/B00C9OICDM.png\", \"hn_image\": [\"./images/B008R8BFFG.png\"]}\n{\"q_img\": \"./images/B0009VT6MI.png\", \"q_text\": \"is shorter and wine colored, and Is more colorful\", \"positive_key\": \"B000Z4OH0U\", \"positive_value\": \"./images/B000Z4OH0U.png\", \"hn_image\": [\"./images/B0009VT6MI.png\"]}\n{\"q_img\": \"./images/B007IWOBBC.png\", \"q_text\": \"is black with v neck and sleeves, and is longer sleeves and darker\", \"positive_key\": \"B0052IGRDO\", \"positive_value\": \"./images/B0052IGRDO.png\", \"hn_image\": [\"./images/B007IWOBBC.png\"]}\n{\"q_img\": \"./images/B003TJA7YI.png\", \"q_text\": \"Is more belted and red, and is red and belted\", \"positive_key\": \"B005278U3U\", \"positive_value\": \"./images/B005278U3U.png\", \"hn_image\": [\"./images/B003TJA7YI.png\"]}\n{\"q_img\": \"./images/B0076QKN76.png\", \"q_text\": \"is brown with long sleeves and a u neck, and has more red\", \"positive_key\": \"B004XJI4I4\", \"positive_value\": \"./images/B004XJI4I4.png\", \"hn_image\": [\"./images/B0076QKN76.png\"]}\n{\"q_img\": \"./images/B0097BCW4U.png\", \"q_text\": \"is black and more revealing, and is black and short.\", \"positive_key\": \"B008X0ARIY\", \"positive_value\": \"./images/B008X0ARIY.png\", \"hn_image\": [\"./images/B0097BCW4U.png\"]}\n{\"q_img\": \"./images/B008KPIBWQ.png\", \"q_text\": \"has thin straps and is printed, and has straps and more colors\", \"positive_key\": \"B008J3LWG6\", \"positive_value\": \"./images/B008J3LWG6.png\", \"hn_image\": [\"./images/B008KPIBWQ.png\"]}\n{\"q_img\": \"./images/B005FUMMGU.png\", \"q_text\": \"is white with long sleeves, and is more revealing\", \"positive_key\": \"B00D2ON1H0\", \"positive_value\": \"./images/B00D2ON1H0.png\", \"hn_image\": [\"./images/B005FUMMGU.png\"]}\n{\"q_img\": \"./images/B007MLM38C.png\", \"q_text\": \"is lighter and longer, and is longer and colored red\", \"positive_key\": \"B008UH8MY6\", \"positive_value\": \"./images/B008UH8MY6.png\", \"hn_image\": [\"./images/B007MLM38C.png\"]}\n{\"q_img\": \"./images/B00A3EZC4M.png\", \"q_text\": \"is pink, and is pink\", \"positive_key\": \"B008XODTD0\", \"positive_value\": \"./images/B008XODTD0.png\", \"hn_image\": [\"./images/B00A3EZC4M.png\"]}\n{\"q_img\": \"./images/B004JXVQD4.png\", \"q_text\": \"is lighter and has shorter sleeves, and is white with no sleeves\", \"positive_key\": \"B004JU1ULU\", \"positive_value\": \"./images/B004JU1ULU.png\", \"hn_image\": [\"./images/B004JXVQD4.png\"]}\n{\"q_img\": \"./images/B009NBVQ5A.png\", \"q_text\": \"has more v neck with chevron print, and Is gray and has no sleeves\", \"positive_key\": \"B007SVFCAW\", \"positive_value\": \"./images/B007SVFCAW.png\", \"hn_image\": [\"./images/B009NBVQ5A.png\"]}\n{\"q_img\": \"./images/B005GPQLO8.png\", \"q_text\": \"has long and Vneck red color, and is white with red flowers and has a halterneck\", \"positive_key\": \"B004BUW6DO\", \"positive_value\": \"./images/B004BUW6DO.png\", \"hn_image\": [\"./images/B005GPQLO8.png\"]}\n{\"q_img\": \"./images/B007678MDM.png\", \"q_text\": \"short yellow and blue design dress, and is sleeveless with white on the top 3/4 and yellow and blue on the bottom\", \"positive_key\": \"B009YKCIIY\", \"positive_value\": \"./images/B009YKCIIY.png\", \"hn_image\": [\"./images/B007678MDM.png\"]}\n{\"q_img\": \"./images/B00DO9M2VE.png\", \"q_text\": \"is white baby doll dress, and is strapless with higher waste\", \"positive_key\": \"B008HSX5ZO\", \"positive_value\": \"./images/B008HSX5ZO.png\", \"hn_image\": [\"./images/B00DO9M2VE.png\"]}\n{\"q_img\": \"./images/B004A7XTUC.png\", \"q_text\": \" long sleeved and longer, and is white with spaghetti straps\", \"positive_key\": \"B005Z1XEBG\", \"positive_value\": \"./images/B005Z1XEBG.png\", \"hn_image\": [\"./images/B004A7XTUC.png\"]}\n{\"q_img\": \"./images/B00A3PEX7S.png\", \"q_text\": \"is longer and has a bare bak, and longer and slimmer\", \"positive_key\": \"B00BNXTOGY\", \"positive_value\": \"./images/B00BNXTOGY.png\", \"hn_image\": [\"./images/B00A3PEX7S.png\"]}\n{\"q_img\": \"./images/B008MN5HU0.png\", \"q_text\": \"has no sleeves and is solid burgandy, and is in reds with ruffled fabric.\", \"positive_key\": \"B008595HNS\", \"positive_value\": \"./images/B008595HNS.png\", \"hn_image\": [\"./images/B008MN5HU0.png\"]}\n{\"q_img\": \"./images/B00E0ORPVY.png\", \"q_text\": \"has a shorter hem length and smaller print, and is more red and shorter\", \"positive_key\": \"B00DOJ28UY\", \"positive_value\": \"./images/B00DOJ28UY.png\", \"hn_image\": [\"./images/B00E0ORPVY.png\"]}\n{\"q_img\": \"./images/B005N4L15G.png\", \"q_text\": \"Grey and looser, and is white with short sleeves\", \"positive_key\": \"B007HZJE08\", \"positive_value\": \"./images/B007HZJE08.png\", \"hn_image\": [\"./images/B005N4L15G.png\"]}\n{\"q_img\": \"./images/B005UX57P0.png\", \"q_text\": \"short blue dress, and and textured\", \"positive_key\": \"B0070L9U6M\", \"positive_value\": \"./images/B0070L9U6M.png\", \"hn_image\": [\"./images/B005UX57P0.png\"]}\n{\"q_img\": \"./images/B00EDQ7QZE.png\", \"q_text\": \"is black capped sleeved v-necked fitted dress for evening, and is more revealing\", \"positive_key\": \"B00B4GG33W\", \"positive_value\": \"./images/B00B4GG33W.png\", \"hn_image\": [\"./images/B00EDQ7QZE.png\"]}\n{\"q_img\": \"./images/B00AEW4384.png\", \"q_text\": \"is tight fitting and shiny, and white\", \"positive_key\": \"B00A6GCXA8\", \"positive_value\": \"./images/B00A6GCXA8.png\", \"hn_image\": [\"./images/B00AEW4384.png\"]}\n{\"q_img\": \"./images/B005VTKJWO.png\", \"q_text\": \"is a darker color, and is more see through\", \"positive_key\": \"B007XCS81G\", \"positive_value\": \"./images/B007XCS81G.png\", \"hn_image\": [\"./images/B005VTKJWO.png\"]}\n{\"q_img\": \"./images/B008LFKSF8.png\", \"q_text\": \"Pink and thicker shoulder straps, and has a lighter coloring.\", \"positive_key\": \"B00C3NG8RQ\", \"positive_value\": \"./images/B00C3NG8RQ.png\", \"hn_image\": [\"./images/B008LFKSF8.png\"]}\n{\"q_img\": \"./images/B00B1G5SPY.png\", \"q_text\": \"shows less shoulder and has a deeper neckline, and is black with capped sleeves and is more fitted\", \"positive_key\": \"B007TDELU6\", \"positive_value\": \"./images/B007TDELU6.png\", \"hn_image\": [\"./images/B00B1G5SPY.png\"]}\n{\"q_img\": \"./images/B008FPXR3Y.png\", \"q_text\": \"is more red and has sleeves, and is more colorful with sleeves\", \"positive_key\": \"B00CQ9U2UQ\", \"positive_value\": \"./images/B00CQ9U2UQ.png\", \"hn_image\": [\"./images/B008FPXR3Y.png\"]}\n{\"q_img\": \"./images/B00726H26K.png\", \"q_text\": \"The other product looks more like a flapper dress, and tan and salsa like\", \"positive_key\": \"B00DJHSWI8\", \"positive_value\": \"./images/B00DJHSWI8.png\", \"hn_image\": [\"./images/B00726H26K.png\"]}\n{\"q_img\": \"./images/B00G9WJ09A.png\", \"q_text\": \"has a darker color and a belt, and is darker with shorter sleeves\", \"positive_key\": \"B00DG7SOTS\", \"positive_value\": \"./images/B00DG7SOTS.png\", \"hn_image\": [\"./images/B00G9WJ09A.png\"]}\n{\"q_img\": \"./images/B00C12LOZU.png\", \"q_text\": \"is darker, and is orange\", \"positive_key\": \"B00CIEPROU\", \"positive_value\": \"./images/B00CIEPROU.png\", \"hn_image\": [\"./images/B00C12LOZU.png\"]}\n{\"q_img\": \"./images/B004RKM1YW.png\", \"q_text\": \"is solid red and has a high-low skirt, and is bright salmon with a belt\", \"positive_key\": \"B00AWP99H8\", \"positive_value\": \"./images/B00AWP99H8.png\", \"hn_image\": [\"./images/B004RKM1YW.png\"]}\n{\"q_img\": \"./images/B0099DC5Z2.png\", \"q_text\": \"Is darker and more simple, and has long sleeves and a lower belt\", \"positive_key\": \"B006VNFCNK\", \"positive_value\": \"./images/B006VNFCNK.png\", \"hn_image\": [\"./images/B0099DC5Z2.png\"]}\n{\"q_img\": \"./images/B003XFLQVG.png\", \"q_text\": \"is large purple with short sleeves and a u neck, and It is more plus sized and longer\", \"positive_key\": \"B000RZSA8M\", \"positive_value\": \"./images/B000RZSA8M.png\", \"hn_image\": [\"./images/B003XFLQVG.png\"]}\n{\"q_img\": \"./images/B00EIQK2XM.png\", \"q_text\": \"is blue in color, and An orange pattern longer dress\", \"positive_key\": \"B007CTLUP6\", \"positive_value\": \"./images/B007CTLUP6.png\", \"hn_image\": [\"./images/B00EIQK2XM.png\"]}\n{\"q_img\": \"./images/B00BM82UCK.png\", \"q_text\": \"has a see through sleeve, and is shorter and elegant\", \"positive_key\": \"B00GMIQG34\", \"positive_value\": \"./images/B00GMIQG34.png\", \"hn_image\": [\"./images/B00BM82UCK.png\"]}\n{\"q_img\": \"./images/B00BNRBLIE.png\", \"q_text\": \"is a black knee high dress, and is darker\", \"positive_key\": \"B0092QK1W0\", \"positive_value\": \"./images/B0092QK1W0.png\", \"hn_image\": [\"./images/B00BNRBLIE.png\"]}\n{\"q_img\": \"./images/B00FQANLX2.png\", \"q_text\": \"is printed and shorter with sleeves, and has short sleeves and is above the knee\", \"positive_key\": \"B005GA9D7K\", \"positive_value\": \"./images/B005GA9D7K.png\", \"hn_image\": [\"./images/B00FQANLX2.png\"]}\n{\"q_img\": \"./images/B0058T1JNA.png\", \"q_text\": \"has long sleeves, and is more solid colored\", \"positive_key\": \"B005FOPWOK\", \"positive_value\": \"./images/B005FOPWOK.png\", \"hn_image\": [\"./images/B0058T1JNA.png\"]}\n{\"q_img\": \"./images/B00ATPE8CW.png\", \"q_text\": \"has longer sleeves and solid black, and is a black mini dress with short sleeves\", \"positive_key\": \"B00BBW9YWQ\", \"positive_value\": \"./images/B00BBW9YWQ.png\", \"hn_image\": [\"./images/B00ATPE8CW.png\"]}\n{\"q_img\": \"./images/B00CIAE21S.png\", \"q_text\": \"black and grey stripped dress, and striped and short sleeved\", \"positive_key\": \"B00EIQK2XM\", \"positive_value\": \"./images/B00EIQK2XM.png\", \"hn_image\": [\"./images/B00CIAE21S.png\"]}\n{\"q_img\": \"./images/B0085U9J5E.png\", \"q_text\": \"has a longer hem with no lace, and is longer and more colourful\", \"positive_key\": \"B003Z2TRN6\", \"positive_value\": \"./images/B003Z2TRN6.png\", \"hn_image\": [\"./images/B0085U9J5E.png\"]}\n{\"q_img\": \"./images/B00C1BSDBO.png\", \"q_text\": \"black and white with vertical stripes and is much shorter, and is striped and not as flowing\", \"positive_key\": \"B00C9K0JQY\", \"positive_value\": \"./images/B00C9K0JQY.png\", \"hn_image\": [\"./images/B00C1BSDBO.png\"]}\n{\"q_img\": \"./images/B00AKCGNP4.png\", \"q_text\": \"a strapless yellow bubble dress, and is yellow with strappy sleeves\", \"positive_key\": \"B007FO27IW\", \"positive_value\": \"./images/B007FO27IW.png\", \"hn_image\": [\"./images/B00AKCGNP4.png\"]}\n{\"q_img\": \"./images/B007UZSPC8.png\", \"q_text\": \"has a v shaped neck with no sleeves, and The dress has more colors and more chest shown\", \"positive_key\": \"B006QSEC4K\", \"positive_value\": \"./images/B006QSEC4K.png\", \"hn_image\": [\"./images/B007UZSPC8.png\"]}\n{\"q_img\": \"./images/B00D8RZKMU.png\", \"q_text\": \"is more floral, and full dress with floral pattern\", \"positive_key\": \"B00AE4C2LC\", \"positive_value\": \"./images/B00AE4C2LC.png\", \"hn_image\": [\"./images/B00D8RZKMU.png\"]}\n{\"q_img\": \"./images/B00D68DXCA.png\", \"q_text\": \"Is more black and belted, and is black and short sleeves\", \"positive_key\": \"B00FIZWNSY\", \"positive_value\": \"./images/B00FIZWNSY.png\", \"hn_image\": [\"./images/B00D68DXCA.png\"]}\n{\"q_img\": \"./images/B00AYR2L4M.png\", \"q_text\": \"cowl neck brown dress with black belt, and is brown with a dark brown belt\", \"positive_key\": \"B005FOC9Z0\", \"positive_value\": \"./images/B005FOC9Z0.png\", \"hn_image\": [\"./images/B00AYR2L4M.png\"]}\n{\"q_img\": \"./images/B005FOLSE8.png\", \"q_text\": \"is blue with a large slit, and is blue and more revealing\", \"positive_key\": \"B009SI9OTS\", \"positive_value\": \"./images/B009SI9OTS.png\", \"hn_image\": [\"./images/B005FOLSE8.png\"]}\n{\"q_img\": \"./images/B009S3I7EG.png\", \"q_text\": \"is dark blue colored and two straps, and is black and not off the shoulder\", \"positive_key\": \"B007314UDM\", \"positive_value\": \"./images/B007314UDM.png\", \"hn_image\": [\"./images/B009S3I7EG.png\"]}\n{\"q_img\": \"./images/B007HZAFSI.png\", \"q_text\": \"has longer sleeves and plain white, and is white with longer sleeves\", \"positive_key\": \"B007E66P9I\", \"positive_value\": \"./images/B007E66P9I.png\", \"hn_image\": [\"./images/B007HZAFSI.png\"]}\n{\"q_img\": \"./images/B003XRKA9I.png\", \"q_text\": \"is white and plain pattern, and is all white\", \"positive_key\": \"B004PZHXZG\", \"positive_value\": \"./images/B004PZHXZG.png\", \"hn_image\": [\"./images/B003XRKA9I.png\"]}\n{\"q_img\": \"./images/B00913N1QC.png\", \"q_text\": \"is shorter and has longer sleeves, and is more simple looking\", \"positive_key\": \"B00EM95GJK\", \"positive_value\": \"./images/B00EM95GJK.png\", \"hn_image\": [\"./images/B00913N1QC.png\"]}\n{\"q_img\": \"./images/B00EUVTLPK.png\", \"q_text\": \"Is black and white printed with shorter red jacket, and has a red shrug and a white and black pattern\", \"positive_key\": \"B00715FZVG\", \"positive_value\": \"./images/B00715FZVG.png\", \"hn_image\": [\"./images/B00EUVTLPK.png\"]}\n{\"q_img\": \"./images/B006ZO6A0O.png\", \"q_text\": \" and pink skirt. It's tighter around the waist, and has no sleeves and design\", \"positive_key\": \"B007C3S564\", \"positive_value\": \"./images/B007C3S564.png\", \"hn_image\": [\"./images/B006ZO6A0O.png\"]}\n{\"q_img\": \"./images/B002Z7FWB8.png\", \"q_text\": \"is solid black with a v-neck and longer sleeves, and is darker and has long sleeces\", \"positive_key\": \"B009E2E3QM\", \"positive_value\": \"./images/B009E2E3QM.png\", \"hn_image\": [\"./images/B002Z7FWB8.png\"]}\n{\"q_img\": \"./images/B0087YZPJ2.png\", \"q_text\": \"is white and black with a flared skirt, and puffier skirt with a belt\", \"positive_key\": \"B00C6NLM5G\", \"positive_value\": \"./images/B00C6NLM5G.png\", \"hn_image\": [\"./images/B0087YZPJ2.png\"]}\n{\"q_img\": \"./images/B00CY2BD1M.png\", \"q_text\": \"is shorter and darker, and is darker in color\", \"positive_key\": \"B008583T7A\", \"positive_value\": \"./images/B008583T7A.png\", \"hn_image\": [\"./images/B00CY2BD1M.png\"]}\n{\"q_img\": \"./images/B0070IN282.png\", \"q_text\": \"is brighter in color, and with a strap around the foot.\", \"positive_key\": \"B004NAW3HQ\", \"positive_value\": \"./images/B004NAW3HQ.png\", \"hn_image\": [\"./images/B0070IN282.png\"]}\n{\"q_img\": \"./images/B00FN4A7QA.png\", \"q_text\": \"is darker in color and more loose, and is black and solid\", \"positive_key\": \"B0081JNTYG\", \"positive_value\": \"./images/B0081JNTYG.png\", \"hn_image\": [\"./images/B00FN4A7QA.png\"]}\n{\"q_img\": \"./images/B004W4YR4K.png\", \"q_text\": \"is a logo of a company, and is a brand name\", \"positive_key\": \"B00AO1VRF2\", \"positive_value\": \"./images/B00AO1VRF2.png\", \"hn_image\": [\"./images/B004W4YR4K.png\"]}\n{\"q_img\": \"./images/B0094DGGR0.png\", \"q_text\": \"is all black with long lace sleeves, and Is black and chic\", \"positive_key\": \"B0072C7PG6\", \"positive_value\": \"./images/B0072C7PG6.png\", \"hn_image\": [\"./images/B0094DGGR0.png\"]}\n{\"q_img\": \"./images/B008Z1TX2M.png\", \"q_text\": \"is more open at the cleavage area, and has thin straps and a pattern\", \"positive_key\": \"B00DTWDHWO\", \"positive_value\": \"./images/B00DTWDHWO.png\", \"hn_image\": [\"./images/B008Z1TX2M.png\"]}\n{\"q_img\": \"./images/B007B6WEA0.png\", \"q_text\": \"has half sleeves with a tie in the back belt, and is more sexual\", \"positive_key\": \"B007UDBI3S\", \"positive_value\": \"./images/B007UDBI3S.png\", \"hn_image\": [\"./images/B007B6WEA0.png\"]}\n{\"q_img\": \"./images/B00BDGE5DI.png\", \"q_text\": \"shorter sleeve and beige, and is lighter in color and is tighter\", \"positive_key\": \"B00BJEV35C\", \"positive_value\": \"./images/B00BJEV35C.png\", \"hn_image\": [\"./images/B00BDGE5DI.png\"]}\n{\"q_img\": \"./images/B0043RSVPM.png\", \"q_text\": \"This dress has a better mix of colorsthis dress, and the dress is purple and blue with no straps\", \"positive_key\": \"B004XBCBWM\", \"positive_value\": \"./images/B004XBCBWM.png\", \"hn_image\": [\"./images/B0043RSVPM.png\"]}\n{\"q_img\": \"./images/B004ZY06UQ.png\", \"q_text\": \"is more free and casual, and has sleeves and a floral pattern\", \"positive_key\": \"B004T20EFG\", \"positive_value\": \"./images/B004T20EFG.png\", \"hn_image\": [\"./images/B004ZY06UQ.png\"]}\n{\"q_img\": \"./images/B00AAD6KOM.png\", \"q_text\": \"has no sleeves and is gauzier, and is longer\", \"positive_key\": \"B007FWGBRW\", \"positive_value\": \"./images/B007FWGBRW.png\", \"hn_image\": [\"./images/B00AAD6KOM.png\"]}\n{\"q_img\": \"./images/B006330SY6.png\", \"q_text\": \"Is solid, and is darker and more revealing\", \"positive_key\": \"B002XUY8SA\", \"positive_value\": \"./images/B002XUY8SA.png\", \"hn_image\": [\"./images/B006330SY6.png\"]}\n{\"q_img\": \"./images/B004X0VYKI.png\", \"q_text\": \" is plain black and is shorter, and is black and has a halter top.\", \"positive_key\": \"B0098XU33E\", \"positive_value\": \"./images/B0098XU33E.png\", \"hn_image\": [\"./images/B004X0VYKI.png\"]}\n{\"q_img\": \"./images/B00EZK1EQK.png\", \"q_text\": \"is longer with ruffles, and Is navy and longer\", \"positive_key\": \"B008OY9QUE\", \"positive_value\": \"./images/B008OY9QUE.png\", \"hn_image\": [\"./images/B00EZK1EQK.png\"]}\n{\"q_img\": \"./images/B00CAAADZU.png\", \"q_text\": \"Pink, and is pink and less revealing\", \"positive_key\": \"B00CRNS11I\", \"positive_value\": \"./images/B00CRNS11I.png\", \"hn_image\": [\"./images/B00CAAADZU.png\"]}\n{\"q_img\": \"./images/B006ZO6A0O.png\", \"q_text\": \"is flirtier with shorter sleeves, and is shorter and fluffy with pink bottom\", \"positive_key\": \"B007IWO6JO\", \"positive_value\": \"./images/B007IWO6JO.png\", \"hn_image\": [\"./images/B006ZO6A0O.png\"]}\n{\"q_img\": \"./images/B00499DHK8.png\", \"q_text\": \"has a yellow belt, and is more fitting\", \"positive_key\": \"B00763VKX0\", \"positive_value\": \"./images/B00763VKX0.png\", \"hn_image\": [\"./images/B00499DHK8.png\"]}\n{\"q_img\": \"./images/B008MNM7Y4.png\", \"q_text\": \"is light pink with no sleeves, and has a collar and is lighter colored\", \"positive_key\": \"B002Z7ETL2\", \"positive_value\": \"./images/B002Z7ETL2.png\", \"hn_image\": [\"./images/B008MNM7Y4.png\"]}\n{\"q_img\": \"./images/B00EKY4UEY.png\", \"q_text\": \"is one color and long, and has a print and is shorter\", \"positive_key\": \"B0059BS4O4\", \"positive_value\": \"./images/B0059BS4O4.png\", \"hn_image\": [\"./images/B00EKY4UEY.png\"]}\n{\"q_img\": \"./images/B00CZC6JIS.png\", \"q_text\": \"Is longer with a paisley print., and has no shoulder straps and a longer skirt\", \"positive_key\": \"B00559SS32\", \"positive_value\": \"./images/B00559SS32.png\", \"hn_image\": [\"./images/B00CZC6JIS.png\"]}\n{\"q_img\": \"./images/B00AMDXN2C.png\", \"q_text\": \"is simple and white, and is white and has short sleeves\", \"positive_key\": \"B00C670RXA\", \"positive_value\": \"./images/B00C670RXA.png\", \"hn_image\": [\"./images/B00AMDXN2C.png\"]}\n{\"q_img\": \"./images/B006WM4LYG.png\", \"q_text\": \"has a round strap along the neck, and has a polka dot print\", \"positive_key\": \"B003BNYA04\", \"positive_value\": \"./images/B003BNYA04.png\", \"hn_image\": [\"./images/B006WM4LYG.png\"]}\n{\"q_img\": \"./images/B005278U3U.png\", \"q_text\": \"is longer and open at the chest, and is longer with grey markings\", \"positive_key\": \"B005GLSA0A\", \"positive_value\": \"./images/B005GLSA0A.png\", \"hn_image\": [\"./images/B005278U3U.png\"]}\n{\"q_img\": \"./images/B005VZ8MWC.png\", \"q_text\": \"its longer and not so revealing, and is longer and has a fuller top in all black.\", \"positive_key\": \"B0058T1JNA\", \"positive_value\": \"./images/B0058T1JNA.png\", \"hn_image\": [\"./images/B005VZ8MWC.png\"]}\n{\"q_img\": \"./images/B007JS1Y7Y.png\", \"q_text\": \"has more random colors and patterns, and comes in separate design in four quarters in blues\", \"positive_key\": \"B00B5KEVPY\", \"positive_value\": \"./images/B00B5KEVPY.png\", \"hn_image\": [\"./images/B007JS1Y7Y.png\"]}\n{\"q_img\": \"./images/B008RDI8MO.png\", \"q_text\": \"has thicker straps, and is grey and longer\", \"positive_key\": \"B008RCU9X6\", \"positive_value\": \"./images/B008RCU9X6.png\", \"hn_image\": [\"./images/B008RDI8MO.png\"]}\n{\"q_img\": \"./images/B0043D2HME.png\", \"q_text\": \"is longer and more fitting around the waist, and fitted\", \"positive_key\": \"B00AAL49CO\", \"positive_value\": \"./images/B00AAL49CO.png\", \"hn_image\": [\"./images/B0043D2HME.png\"]}\n{\"q_img\": \"./images/B009E739Q2.png\", \"q_text\": \"is yellow colored, and is long and dark yellow\", \"positive_key\": \"B007N0B248\", \"positive_value\": \"./images/B007N0B248.png\", \"hn_image\": [\"./images/B009E739Q2.png\"]}\n{\"q_img\": \"./images/B009IFOBF8.png\", \"q_text\": \"is shorter and darker, and is shorter and more flora\", \"positive_key\": \"B007IAHLOS\", \"positive_value\": \"./images/B007IAHLOS.png\", \"hn_image\": [\"./images/B009IFOBF8.png\"]}\n{\"q_img\": \"./images/B000BL6VJM.png\", \"q_text\": \"doesn't have embellishments, and has no sleeves and is dark colored\", \"positive_key\": \"B007HIL16U\", \"positive_value\": \"./images/B007HIL16U.png\", \"hn_image\": [\"./images/B000BL6VJM.png\"]}\n{\"q_img\": \"./images/B0091GQUE4.png\", \"q_text\": \"is a long print with tiny straps, and is longer and small straps\", \"positive_key\": \"B00CVTM6XC\", \"positive_value\": \"./images/B00CVTM6XC.png\", \"hn_image\": [\"./images/B0091GQUE4.png\"]}\n{\"q_img\": \"./images/B007EGKM5G.png\", \"q_text\": \"is green with a belt and longer, and is longer\", \"positive_key\": \"B007HSSKMI\", \"positive_value\": \"./images/B007HSSKMI.png\", \"hn_image\": [\"./images/B007EGKM5G.png\"]}\n{\"q_img\": \"./images/B00EY93064.png\", \"q_text\": \"Is more blue and floral, and has short sleeves and is multicolored\", \"positive_key\": \"B00CJTARFI\", \"positive_value\": \"./images/B00CJTARFI.png\", \"hn_image\": [\"./images/B00EY93064.png\"]}\n{\"q_img\": \"./images/B004ZWG4UE.png\", \"q_text\": \"Has a belted waist and is lighter, and Has longer sleeves and is grey in color\", \"positive_key\": \"B007NCIGQ8\", \"positive_value\": \"./images/B007NCIGQ8.png\", \"hn_image\": [\"./images/B004ZWG4UE.png\"]}\n{\"q_img\": \"./images/B00CLCYUCO.png\", \"q_text\": \"Is longer and not so tight., and is more multi colored\", \"positive_key\": \"B00BNRBD32\", \"positive_value\": \"./images/B00BNRBD32.png\", \"hn_image\": [\"./images/B00CLCYUCO.png\"]}\n{\"q_img\": \"./images/B002W77AY8.png\", \"q_text\": \"has longer sleeves and is shorter, and IS ABOVE THE KNEE AND FITTED\", \"positive_key\": \"B009LV9UGO\", \"positive_value\": \"./images/B009LV9UGO.png\", \"hn_image\": [\"./images/B002W77AY8.png\"]}\n{\"q_img\": \"./images/B0060RMLPO.png\", \"q_text\": \"is shorter and all white, and is white with a bow\", \"positive_key\": \"B007WA3RBK\", \"positive_value\": \"./images/B007WA3RBK.png\", \"hn_image\": [\"./images/B0060RMLPO.png\"]}\n{\"q_img\": \"./images/B007GSGKOO.png\", \"q_text\": \"is a blue and black dress and is less tighter, and is more frilly\", \"positive_key\": \"B0027CTRNK\", \"positive_value\": \"./images/B0027CTRNK.png\", \"hn_image\": [\"./images/B007GSGKOO.png\"]}\n{\"q_img\": \"./images/B008AIKMD4.png\", \"q_text\": \"is a cream blouse and purple skirt, and is a two-toned flowing purple\", \"positive_key\": \"B00BVX94NO\", \"positive_value\": \"./images/B00BVX94NO.png\", \"hn_image\": [\"./images/B008AIKMD4.png\"]}\n{\"q_img\": \"./images/B0072QWT2M.png\", \"q_text\": \"is shorter, and more fluffy and costumey\", \"positive_key\": \"B00AFR9G2Q\", \"positive_value\": \"./images/B00AFR9G2Q.png\", \"hn_image\": [\"./images/B0072QWT2M.png\"]}\n{\"q_img\": \"./images/B007G1015U.png\", \"q_text\": \"is strapless and pink colored, and doesn't have sleeves and is printed\", \"positive_key\": \"B0076R88V8\", \"positive_value\": \"./images/B0076R88V8.png\", \"hn_image\": [\"./images/B007G1015U.png\"]}\n{\"q_img\": \"./images/B0087YZVNC.png\", \"q_text\": \"is longer and darker, and is longer and more patterned\", \"positive_key\": \"B006ZVRIQM\", \"positive_value\": \"./images/B006ZVRIQM.png\", \"hn_image\": [\"./images/B0087YZVNC.png\"]}\n{\"q_img\": \"./images/B006362580.png\", \"q_text\": \"has buttons on the front, and is more of a casual dress\", \"positive_key\": \"B0095JQTLQ\", \"positive_value\": \"./images/B0095JQTLQ.png\", \"hn_image\": [\"./images/B006362580.png\"]}\n{\"q_img\": \"./images/B005PHZN7I.png\", \"q_text\": \"is less shiner, and is less silky looking\", \"positive_key\": \"B005CNVKCC\", \"positive_value\": \"./images/B005CNVKCC.png\", \"hn_image\": [\"./images/B005PHZN7I.png\"]}\n{\"q_img\": \"./images/B00B2JHXOE.png\", \"q_text\": \"is sleeveless and belted, and is sleeveless and fitted waistline\", \"positive_key\": \"B007ZDYK2E\", \"positive_value\": \"./images/B007ZDYK2E.png\", \"hn_image\": [\"./images/B00B2JHXOE.png\"]}\n{\"q_img\": \"./images/B009ND97G8.png\", \"q_text\": \"long sleeved black dress, and is black with longer sleeves\", \"positive_key\": \"B008D5A9W8\", \"positive_value\": \"./images/B008D5A9W8.png\", \"hn_image\": [\"./images/B009ND97G8.png\"]}\n{\"q_img\": \"./images/B008L7Z1B2.png\", \"q_text\": \"is darker and has longer sleeves, and is solid black.\", \"positive_key\": \"B00G64FYHI\", \"positive_value\": \"./images/B00G64FYHI.png\", \"hn_image\": [\"./images/B008L7Z1B2.png\"]}\n{\"q_img\": \"./images/B00CII41EI.png\", \"q_text\": \" white black belt and knee length., and It is more tight and not belted\", \"positive_key\": \"B008U4K4T0\", \"positive_value\": \"./images/B008U4K4T0.png\", \"hn_image\": [\"./images/B00CII41EI.png\"]}\n{\"q_img\": \"./images/B00D8RZKMU.png\", \"q_text\": \"is straight and more fitting, and is more fitted and is black and gold\", \"positive_key\": \"B00BEZTESO\", \"positive_value\": \"./images/B00BEZTESO.png\", \"hn_image\": [\"./images/B00D8RZKMU.png\"]}\n{\"q_img\": \"./images/B0084YMCBY.png\", \"q_text\": \"Has long sleeves and is navy blue, and is blue\", \"positive_key\": \"B0084YMBQK\", \"positive_value\": \"./images/B0084YMBQK.png\", \"hn_image\": [\"./images/B0084YMCBY.png\"]}\n{\"q_img\": \"./images/B001EA86KQ.png\", \"q_text\": \"is solid red and has longer sleeves, and Is more solid of a red color\", \"positive_key\": \"B005JWELJU\", \"positive_value\": \"./images/B005JWELJU.png\", \"hn_image\": [\"./images/B001EA86KQ.png\"]}\n{\"q_img\": \"./images/B005GMT86Y.png\", \"q_text\": \"is a short pink dress with short sleeve, and is pink shorter and cinched waist\", \"positive_key\": \"B00DN7KGRO\", \"positive_value\": \"./images/B00DN7KGRO.png\", \"hn_image\": [\"./images/B005GMT86Y.png\"]}\n{\"q_img\": \"./images/B002WV0S6G.png\", \"q_text\": \"is shorter and more casual, and is a lot shorter in tan coloring and the straps aren't similar.\", \"positive_key\": \"B0036ME5BE\", \"positive_value\": \"./images/B0036ME5BE.png\", \"hn_image\": [\"./images/B002WV0S6G.png\"]}\n{\"q_img\": \"./images/B008CO7MGQ.png\", \"q_text\": \"is teal colored and longer with sleeves., and Longer and with sleeves\", \"positive_key\": \"B00CO9MVT8\", \"positive_value\": \"./images/B00CO9MVT8.png\", \"hn_image\": [\"./images/B008CO7MGQ.png\"]}\n{\"q_img\": \"./images/B0047MEZNK.png\", \"q_text\": \"is less colorful and less blousey, and is black\", \"positive_key\": \"B00DEWU6AU\", \"positive_value\": \"./images/B00DEWU6AU.png\", \"hn_image\": [\"./images/B0047MEZNK.png\"]}\n{\"q_img\": \"./images/B006UCPPZ2.png\", \"q_text\": \"Is a colorful boho dress., and is much more flowing and long\", \"positive_key\": \"B00AA6F0GI\", \"positive_value\": \"./images/B00AA6F0GI.png\", \"hn_image\": [\"./images/B006UCPPZ2.png\"]}\n{\"q_img\": \"./images/B0055TBJL0.png\", \"q_text\": \"is purple and shiny, and is more purple and more loose\", \"positive_key\": \"B003VQRQ66\", \"positive_value\": \"./images/B003VQRQ66.png\", \"hn_image\": [\"./images/B0055TBJL0.png\"]}\n{\"q_img\": \"./images/B007WATI18.png\", \"q_text\": \"is shorter and light pink, and is pink and short\", \"positive_key\": \"B008PHQ8II\", \"positive_value\": \"./images/B008PHQ8II.png\", \"hn_image\": [\"./images/B007WATI18.png\"]}\n{\"q_img\": \"./images/B0055OGQ7W.png\", \"q_text\": \"has no straps with more ruffles, and Is belted and draped\", \"positive_key\": \"B0051N2MMA\", \"positive_value\": \"./images/B0051N2MMA.png\", \"hn_image\": [\"./images/B0055OGQ7W.png\"]}\n{\"q_img\": \"./images/B009S0IT7E.png\", \"q_text\": \"is longer and is colorful, and has a bright striped pattern\", \"positive_key\": \"B00CH3BZVG\", \"positive_value\": \"./images/B00CH3BZVG.png\", \"hn_image\": [\"./images/B009S0IT7E.png\"]}\n{\"q_img\": \"./images/B00DDX15TG.png\", \"q_text\": \"is purple colored, and and a darker green.\", \"positive_key\": \"B00DCC9ZJK\", \"positive_value\": \"./images/B00DCC9ZJK.png\", \"hn_image\": [\"./images/B00DDX15TG.png\"]}\n{\"q_img\": \"./images/B00559SS32.png\", \"q_text\": \"has more style, and is black with stripes\", \"positive_key\": \"B00EVLIQIM\", \"positive_value\": \"./images/B00EVLIQIM.png\", \"hn_image\": [\"./images/B00559SS32.png\"]}\n{\"q_img\": \"./images/B006WY8INO.png\", \"q_text\": \"is more colorful and has fewer sleeves, and is more of a summer dress with spaghetti strap sleeves\", \"positive_key\": \"B0075BEF4O\", \"positive_value\": \"./images/B0075BEF4O.png\", \"hn_image\": [\"./images/B006WY8INO.png\"]}\n{\"q_img\": \"./images/B0084Y2V6A.png\", \"q_text\": \"is gray and sleeveless, and is shorter and fitted\", \"positive_key\": \"B008AIKMD4\", \"positive_value\": \"./images/B008AIKMD4.png\", \"hn_image\": [\"./images/B0084Y2V6A.png\"]}\n{\"q_img\": \"./images/B004L0XCUK.png\", \"q_text\": \"has a floral print on the skirt, and pale pink and less patterned\", \"positive_key\": \"B004NAU396\", \"positive_value\": \"./images/B004NAU396.png\", \"hn_image\": [\"./images/B004L0XCUK.png\"]}\n{\"q_img\": \"./images/B004748I22.png\", \"q_text\": \"is shorter and has no sleeves, and sleeveless and no opening at chest\", \"positive_key\": \"B006MPQO2A\", \"positive_value\": \"./images/B006MPQO2A.png\", \"hn_image\": [\"./images/B004748I22.png\"]}\n{\"q_img\": \"./images/B0081TP5FM.png\", \"q_text\": \"has thinner straps with solid black skirt, and darker with no sleeves\", \"positive_key\": \"B007FKA7E2\", \"positive_value\": \"./images/B007FKA7E2.png\", \"hn_image\": [\"./images/B0081TP5FM.png\"]}\n{\"q_img\": \"./images/B001PKTT7E.png\", \"q_text\": \"no sleeves and a different pattern, and Is shorter and has a longer train\", \"positive_key\": \"B00CY8S5WG\", \"positive_value\": \"./images/B00CY8S5WG.png\", \"hn_image\": [\"./images/B001PKTT7E.png\"]}\n{\"q_img\": \"./images/B0090F42VY.png\", \"q_text\": \"less flared and more green, and is more comfortable and lighter\", \"positive_key\": \"B0038JE7LI\", \"positive_value\": \"./images/B0038JE7LI.png\", \"hn_image\": [\"./images/B0090F42VY.png\"]}\n{\"q_img\": \"./images/B008OXYAKG.png\", \"q_text\": \"is more body fitting and has long sleeves, and is a blue long sleeved top with jeans\", \"positive_key\": \"B00332FXRW\", \"positive_value\": \"./images/B00332FXRW.png\", \"hn_image\": [\"./images/B008OXYAKG.png\"]}\n{\"q_img\": \"./images/B004UE9940.png\", \"q_text\": \"has blue and white stripes and straps, and and is more purple.\", \"positive_key\": \"B007G3N9CU\", \"positive_value\": \"./images/B007G3N9CU.png\", \"hn_image\": [\"./images/B004UE9940.png\"]}\n{\"q_img\": \"./images/B007FHD0FI.png\", \"q_text\": \"is grey with a wider plaid pattern, and is darker colored\", \"positive_key\": \"B007FHE5IY\", \"positive_value\": \"./images/B007FHE5IY.png\", \"hn_image\": [\"./images/B007FHD0FI.png\"]}\n{\"q_img\": \"./images/B00BG4M4BM.png\", \"q_text\": \"is more of a dress, and is an actual dress\", \"positive_key\": \"B007H12PCG\", \"positive_value\": \"./images/B007H12PCG.png\", \"hn_image\": [\"./images/B00BG4M4BM.png\"]}\n{\"q_img\": \"./images/B0071TUI7S.png\", \"q_text\": \"is more orange, and is more coral looking\", \"positive_key\": \"B004DPAV0W\", \"positive_value\": \"./images/B004DPAV0W.png\", \"hn_image\": [\"./images/B0071TUI7S.png\"]}\n{\"q_img\": \"./images/B00FZ00I7U.png\", \"q_text\": \"has shorter sleeves and more ruffles, and is in solid white.\", \"positive_key\": \"B00AKGN67S\", \"positive_value\": \"./images/B00AKGN67S.png\", \"hn_image\": [\"./images/B00FZ00I7U.png\"]}\n{\"q_img\": \"./images/B00BJ1NFNI.png\", \"q_text\": \" a v neck and is much shorter, and no comparison to words\", \"positive_key\": \"B00936I6J4\", \"positive_value\": \"./images/B00936I6J4.png\", \"hn_image\": [\"./images/B00BJ1NFNI.png\"]}\n{\"q_img\": \"./images/B00BAHX7Z2.png\", \"q_text\": \"is dark blue floor lenght and sleeves, and has more dark blue\", \"positive_key\": \"B00EZSOR2U\", \"positive_value\": \"./images/B00EZSOR2U.png\", \"hn_image\": [\"./images/B00BAHX7Z2.png\"]}\n{\"q_img\": \"./images/B00CTRI7D4.png\", \"q_text\": \"has a print that covers the chest, and side rushed sweetheart\", \"positive_key\": \"B00E00IM92\", \"positive_value\": \"./images/B00E00IM92.png\", \"hn_image\": [\"./images/B00CTRI7D4.png\"]}\n{\"q_img\": \"./images/B00CTLJ1TY.png\", \"q_text\": \"is white, and is white and a little tighter\", \"positive_key\": \"B00BNRBJFY\", \"positive_value\": \"./images/B00BNRBJFY.png\", \"hn_image\": [\"./images/B00CTLJ1TY.png\"]}\n{\"q_img\": \"./images/B008SOOLGE.png\", \"q_text\": \"is more yellow in color and longer, and is beige and long\", \"positive_key\": \"B008596PSY\", \"positive_value\": \"./images/B008596PSY.png\", \"hn_image\": [\"./images/B008SOOLGE.png\"]}\n{\"q_img\": \"./images/B008OY9QUE.png\", \"q_text\": \"is black and white patterned, and is lighter colored\", \"positive_key\": \"B00BNCH1RO\", \"positive_value\": \"./images/B00BNCH1RO.png\", \"hn_image\": [\"./images/B008OY9QUE.png\"]}\n{\"q_img\": \"./images/B0091QSWH2.png\", \"q_text\": \"Is more colorful and flowing, and is less flowy and strapless\", \"positive_key\": \"B009N8Z2T4\", \"positive_value\": \"./images/B009N8Z2T4.png\", \"hn_image\": [\"./images/B0091QSWH2.png\"]}\n{\"q_img\": \"./images/B004NAU4G8.png\", \"q_text\": \"is black wit thin straps, and Has straps and is black\", \"positive_key\": \"B004ECLRKC\", \"positive_value\": \"./images/B004ECLRKC.png\", \"hn_image\": [\"./images/B004NAU4G8.png\"]}\n{\"q_img\": \"./images/B00CFXUI4S.png\", \"q_text\": \"Has more coverage, and Is losser fitting and has longer sleeves.\", \"positive_key\": \"B00B2JHXOE\", \"positive_value\": \"./images/B00B2JHXOE.png\", \"hn_image\": [\"./images/B00CFXUI4S.png\"]}\n{\"q_img\": \"./images/B009COQRZW.png\", \"q_text\": \"has a ruffled bottome, and has frills\", \"positive_key\": \"B00BY6HLDI\", \"positive_value\": \"./images/B00BY6HLDI.png\", \"hn_image\": [\"./images/B009COQRZW.png\"]}\n{\"q_img\": \"./images/B00BXB7LRA.png\", \"q_text\": \" with an elastic waste and thicker straps, and It is more plain and with not strap\", \"positive_key\": \"B00BXB755S\", \"positive_value\": \"./images/B00BXB755S.png\", \"hn_image\": [\"./images/B00BXB7LRA.png\"]}\n{\"q_img\": \"./images/B009NXZXDO.png\", \"q_text\": \"is solid yellow with sweetheart neckline, and is more bright\", \"positive_key\": \"B00ARMNY1I\", \"positive_value\": \"./images/B00ARMNY1I.png\", \"hn_image\": [\"./images/B009NXZXDO.png\"]}\n{\"q_img\": \"./images/B009A97XVG.png\", \"q_text\": \"is lighter, and has thinner straps and is shorter\", \"positive_key\": \"B005TDLRRS\", \"positive_value\": \"./images/B005TDLRRS.png\", \"hn_image\": [\"./images/B009A97XVG.png\"]}\n{\"q_img\": \"./images/B004U4R8IE.png\", \"q_text\": \"Is shorter and more casual, and is multi-colored patterned\", \"positive_key\": \"B0070TYNAM\", \"positive_value\": \"./images/B0070TYNAM.png\", \"hn_image\": [\"./images/B004U4R8IE.png\"]}\n{\"q_img\": \"./images/B007FHD0FI.png\", \"q_text\": \"is more a floral pattern with spaghetti straps, and is blue with patterns\", \"positive_key\": \"B00ALGUAOO\", \"positive_value\": \"./images/B00ALGUAOO.png\", \"hn_image\": [\"./images/B007FHD0FI.png\"]}\n{\"q_img\": \"./images/B007OX0MXG.png\", \"q_text\": \"is more sporty with a halter top, and Is more revealing and fitted\", \"positive_key\": \"B008VY5C0A\", \"positive_value\": \"./images/B008VY5C0A.png\", \"hn_image\": [\"./images/B007OX0MXG.png\"]}\n{\"q_img\": \"./images/B00BUVAI5K.png\", \"q_text\": \"is black with long sleeves and tight, and is darker in color and less revealing\", \"positive_key\": \"B00AQ8PVXW\", \"positive_value\": \"./images/B00AQ8PVXW.png\", \"hn_image\": [\"./images/B00BUVAI5K.png\"]}\n{\"q_img\": \"./images/B0083FTUI2.png\", \"q_text\": \"is shorter with a collar on it, and has more colors\", \"positive_key\": \"B002HEWD7A\", \"positive_value\": \"./images/B002HEWD7A.png\", \"hn_image\": [\"./images/B0083FTUI2.png\"]}\n{\"q_img\": \"./images/B002DI8HQG.png\", \"q_text\": \"is white and slightly longer, and It is more corporate and stripped\", \"positive_key\": \"B008RC9NQ0\", \"positive_value\": \"./images/B008RC9NQ0.png\", \"hn_image\": [\"./images/B002DI8HQG.png\"]}\n{\"q_img\": \"./images/B009LIFAV6.png\", \"q_text\": \"is a black belted dress, and is longer and has no sleeves\", \"positive_key\": \"B002Z7FWB8\", \"positive_value\": \"./images/B002Z7FWB8.png\", \"hn_image\": [\"./images/B009LIFAV6.png\"]}\n{\"q_img\": \"./images/B00B7DONTI.png\", \"q_text\": \"has slim straps and is black, and is sleeveless and darker\", \"positive_key\": \"B004DTTRA8\", \"positive_value\": \"./images/B004DTTRA8.png\", \"hn_image\": [\"./images/B00B7DONTI.png\"]}\n{\"q_img\": \"./images/B005O0PARY.png\", \"q_text\": \"is darker, and Is more ruched and black\", \"positive_key\": \"B004T6S8ZK\", \"positive_value\": \"./images/B004T6S8ZK.png\", \"hn_image\": [\"./images/B005O0PARY.png\"]}\n{\"q_img\": \"./images/B007HY9O5O.png\", \"q_text\": \"is darker and sexier, and is black and red\", \"positive_key\": \"B007WA3E8Q\", \"positive_value\": \"./images/B007WA3E8Q.png\", \"hn_image\": [\"./images/B007HY9O5O.png\"]}\n{\"q_img\": \"./images/B008LTJG3E.png\", \"q_text\": \"has a black belt with gray designs, and is more revealing and darker\", \"positive_key\": \"B00FGQRGCS\", \"positive_value\": \"./images/B00FGQRGCS.png\", \"hn_image\": [\"./images/B008LTJG3E.png\"]}\n{\"q_img\": \"./images/B003ILIR4E.png\", \"q_text\": \"has a darker color is more of a loose fit and is less revealing, and is black and has straps\", \"positive_key\": \"B0045EPT8A\", \"positive_value\": \"./images/B0045EPT8A.png\", \"hn_image\": [\"./images/B003ILIR4E.png\"]}\n{\"q_img\": \"./images/B006WM49XE.png\", \"q_text\": \"is solid orange, and Is orange and more plain\", \"positive_key\": \"B007CDX3AW\", \"positive_value\": \"./images/B007CDX3AW.png\", \"hn_image\": [\"./images/B006WM49XE.png\"]}\n{\"q_img\": \"./images/B002FOR462.png\", \"q_text\": \" not patterned with longer sleeves, and has a halter top and is more fitted\", \"positive_key\": \"B005644KAG\", \"positive_value\": \"./images/B005644KAG.png\", \"hn_image\": [\"./images/B002FOR462.png\"]}\n{\"q_img\": \"./images/B00F4N004Y.png\", \"q_text\": \"pink and black dress with Paris written on it, and is more multi colored\", \"positive_key\": \"B005ZLS51K\", \"positive_value\": \"./images/B005ZLS51K.png\", \"hn_image\": [\"./images/B00F4N004Y.png\"]}\n{\"q_img\": \"./images/B008OVCJ0Q.png\", \"q_text\": \"is a long maxi dress, and is floor lengthed\", \"positive_key\": \"B009AEQD3A\", \"positive_value\": \"./images/B009AEQD3A.png\", \"hn_image\": [\"./images/B008OVCJ0Q.png\"]}\n{\"q_img\": \"./images/B00AFOMEMI.png\", \"q_text\": \"is light grey with black sleeves, and has a gray bodice and zipper on the side\", \"positive_key\": \"B0097B86M2\", \"positive_value\": \"./images/B0097B86M2.png\", \"hn_image\": [\"./images/B00AFOMEMI.png\"]}\n{\"q_img\": \"./images/B00D68DXCA.png\", \"q_text\": \"has a different patter and wider straps, and comes in olive green with design on it.\", \"positive_key\": \"B00E1ZTATW\", \"positive_value\": \"./images/B00E1ZTATW.png\", \"hn_image\": [\"./images/B00D68DXCA.png\"]}\n{\"q_img\": \"./images/B000NB1FAU.png\", \"q_text\": \"is shorter and tighter, and is blue with long sleeves\", \"positive_key\": \"B009DMLERE\", \"positive_value\": \"./images/B009DMLERE.png\", \"hn_image\": [\"./images/B000NB1FAU.png\"]}\n{\"q_img\": \"./images/B00B1YKB26.png\", \"q_text\": \"has a different style at the neckline, and contains black and blue and isn't sleeveless\", \"positive_key\": \"B0071GD9D6\", \"positive_value\": \"./images/B0071GD9D6.png\", \"hn_image\": [\"./images/B00B1YKB26.png\"]}\n{\"q_img\": \"./images/B00E0ORPVY.png\", \"q_text\": \"is sleeveless, and has less sleeves\", \"positive_key\": \"B00E3XBZOA\", \"positive_value\": \"./images/B00E3XBZOA.png\", \"hn_image\": [\"./images/B00E0ORPVY.png\"]}\n{\"q_img\": \"./images/B007M8PCAQ.png\", \"q_text\": \"is shorter, and has black bordering lines around the sides.\", \"positive_key\": \"B008QXCMWW\", \"positive_value\": \"./images/B008QXCMWW.png\", \"hn_image\": [\"./images/B007M8PCAQ.png\"]}\n{\"q_img\": \"./images/B00DJHSWI8.png\", \"q_text\": \"has short sleeves and is white and lacy, and is all white with round neck\", \"positive_key\": \"B00BYMX1AY\", \"positive_value\": \"./images/B00BYMX1AY.png\", \"hn_image\": [\"./images/B00DJHSWI8.png\"]}\n{\"q_img\": \"./images/B007GP6PME.png\", \"q_text\": \"is black with spaghetti straps, and It has thin straps and more tight\", \"positive_key\": \"B0067LPEFW\", \"positive_value\": \"./images/B0067LPEFW.png\", \"hn_image\": [\"./images/B007GP6PME.png\"]}\n{\"q_img\": \"./images/B004VM5W1K.png\", \"q_text\": \"Is bright green with an empire waist., and Has a tight belt right in the center.\", \"positive_key\": \"B00CA38COQ\", \"positive_value\": \"./images/B00CA38COQ.png\", \"hn_image\": [\"./images/B004VM5W1K.png\"]}\n{\"q_img\": \"./images/B00ASIH0ZC.png\", \"q_text\": \"in blue and flowered, and has longer sleeves and is more blue\", \"positive_key\": \"B00B2JJ15S\", \"positive_value\": \"./images/B00B2JJ15S.png\", \"hn_image\": [\"./images/B00ASIH0ZC.png\"]}\n{\"q_img\": \"./images/B00DUL8IWS.png\", \"q_text\": \"is black with color pattern, and sleeveless\", \"positive_key\": \"B00C97IILK\", \"positive_value\": \"./images/B00C97IILK.png\", \"hn_image\": [\"./images/B00DUL8IWS.png\"]}\n{\"q_img\": \"./images/B0094KQ1NC.png\", \"q_text\": \"has no belt but buttons and is darker, and is dark and polkadots\", \"positive_key\": \"B009QMD6UY\", \"positive_value\": \"./images/B009QMD6UY.png\", \"hn_image\": [\"./images/B0094KQ1NC.png\"]}\n{\"q_img\": \"./images/B00EWOQ16M.png\", \"q_text\": \"is royal blue in color, and is blue with a V neck\", \"positive_key\": \"B00385IQY6\", \"positive_value\": \"./images/B00385IQY6.png\", \"hn_image\": [\"./images/B00EWOQ16M.png\"]}\n{\"q_img\": \"./images/B005TM5U7C.png\", \"q_text\": \"is white and black stripes, and is white with black stripe and shorter\", \"positive_key\": \"B009LJ8QR0\", \"positive_value\": \"./images/B009LJ8QR0.png\", \"hn_image\": [\"./images/B005TM5U7C.png\"]}\n{\"q_img\": \"./images/B00AWAZPS0.png\", \"q_text\": \"has visible thicker straps and is grayer, and has v neck shape\", \"positive_key\": \"B00CXO1ULE\", \"positive_value\": \"./images/B00CXO1ULE.png\", \"hn_image\": [\"./images/B00AWAZPS0.png\"]}\n{\"q_img\": \"./images/B008BHSSMG.png\", \"q_text\": \"has a brown belt, and is strapless and is belted\", \"positive_key\": \"B008AEEMYS\", \"positive_value\": \"./images/B008AEEMYS.png\", \"hn_image\": [\"./images/B008BHSSMG.png\"]}\n{\"q_img\": \"./images/B00BF8B8CA.png\", \"q_text\": \"is darker, and is see through\", \"positive_key\": \"B009PXRLIW\", \"positive_value\": \"./images/B009PXRLIW.png\", \"hn_image\": [\"./images/B00BF8B8CA.png\"]}\n{\"q_img\": \"./images/B004E0ZEBW.png\", \"q_text\": \"short grey dress, and the dress is grey and shorter\", \"positive_key\": \"B005LC87SO\", \"positive_value\": \"./images/B005LC87SO.png\", \"hn_image\": [\"./images/B004E0ZEBW.png\"]}\n{\"q_img\": \"./images/B008F48SN4.png\", \"q_text\": \"is solid gray and has short sleeves, and is more solid\", \"positive_key\": \"B009JY5GIY\", \"positive_value\": \"./images/B009JY5GIY.png\", \"hn_image\": [\"./images/B008F48SN4.png\"]}\n{\"q_img\": \"./images/B007FHE5IY.png\", \"q_text\": \"is black with longer sleeves, and is black with long sleeves\", \"positive_key\": \"B005VCC2MQ\", \"positive_value\": \"./images/B005VCC2MQ.png\", \"hn_image\": [\"./images/B007FHE5IY.png\"]}\n{\"q_img\": \"./images/B007FG0Y8U.png\", \"q_text\": \"is a multicolored blue strapless dress, and is in blues and purples.\", \"positive_key\": \"B00CLF9APS\", \"positive_value\": \"./images/B00CLF9APS.png\", \"hn_image\": [\"./images/B007FG0Y8U.png\"]}\n{\"q_img\": \"./images/B00BWKXOS2.png\", \"q_text\": \"has no sleeves and black and white, and shorter black skirt and geormetric pattern\", \"positive_key\": \"B007Y0T5T6\", \"positive_value\": \"./images/B007Y0T5T6.png\", \"hn_image\": [\"./images/B00BWKXOS2.png\"]}\n{\"q_img\": \"./images/B00DQ686X8.png\", \"q_text\": \"has less pattern at the waist, and Sleevless black dress\", \"positive_key\": \"B00DVFJ2UK\", \"positive_value\": \"./images/B00DVFJ2UK.png\", \"hn_image\": [\"./images/B00DQ686X8.png\"]}\n{\"q_img\": \"./images/B009A97XVG.png\", \"q_text\": \"is longer and lighter, and is a darker pattern\", \"positive_key\": \"B001MZ264K\", \"positive_value\": \"./images/B001MZ264K.png\", \"hn_image\": [\"./images/B009A97XVG.png\"]}\n{\"q_img\": \"./images/B009IFOBF8.png\", \"q_text\": \"shorter length, and is darker in color and shorter in length\", \"positive_key\": \"B003B00592\", \"positive_value\": \"./images/B003B00592.png\", \"hn_image\": [\"./images/B009IFOBF8.png\"]}\n{\"q_img\": \"./images/B002Y2X7NE.png\", \"q_text\": \"has white buttons, and has smaller straps\", \"positive_key\": \"B002Y2RC8A\", \"positive_value\": \"./images/B002Y2RC8A.png\", \"hn_image\": [\"./images/B002Y2X7NE.png\"]}\n{\"q_img\": \"./images/B00AKB3VPA.png\", \"q_text\": \"one that has lighter colors, and is grey with a belt\", \"positive_key\": \"B0087COXNS\", \"positive_value\": \"./images/B0087COXNS.png\", \"hn_image\": [\"./images/B00AKB3VPA.png\"]}\n{\"q_img\": \"./images/B007W9ZC3W.png\", \"q_text\": \"has a more revealing neckline, and more like a night-gown\", \"positive_key\": \"B006C4GIS6\", \"positive_value\": \"./images/B006C4GIS6.png\", \"hn_image\": [\"./images/B007W9ZC3W.png\"]}\n{\"q_img\": \"./images/B00GTNC0G4.png\", \"q_text\": \"has a tighter fit and blue patterns, and is white with a floaty skirt\", \"positive_key\": \"B00CHS82BC\", \"positive_value\": \"./images/B00CHS82BC.png\", \"hn_image\": [\"./images/B00GTNC0G4.png\"]}\n{\"q_img\": \"./images/B00CFUEHAW.png\", \"q_text\": \"has straps and is black, and is black with spaghetti straps\", \"positive_key\": \"B00BYF3GVA\", \"positive_value\": \"./images/B00BYF3GVA.png\", \"hn_image\": [\"./images/B00CFUEHAW.png\"]}\n{\"q_img\": \"./images/B00CTSFCE0.png\", \"q_text\": \"is black with image of skull, and is black and less revealing\", \"positive_key\": \"B0085SJZUK\", \"positive_value\": \"./images/B0085SJZUK.png\", \"hn_image\": [\"./images/B00CTSFCE0.png\"]}\n{\"q_img\": \"./images/B002Z7ETL2.png\", \"q_text\": \"Is shorter and has a design with sleeves., and is white and red with long sleeves\", \"positive_key\": \"B00CGTBWEG\", \"positive_value\": \"./images/B00CGTBWEG.png\", \"hn_image\": [\"./images/B002Z7ETL2.png\"]}\n{\"q_img\": \"./images/B00C2BIUXO.png\", \"q_text\": \"is darker in color with a shinier skirt and a plainer top, and is darker with long sleeves\", \"positive_key\": \"B009ND8UKC\", \"positive_value\": \"./images/B009ND8UKC.png\", \"hn_image\": [\"./images/B00C2BIUXO.png\"]}\n{\"q_img\": \"./images/B008LRQUW6.png\", \"q_text\": \"is a long armed dress, and is more pink and has longer sleeves\", \"positive_key\": \"B00AXVFMYA\", \"positive_value\": \"./images/B00AXVFMYA.png\", \"hn_image\": [\"./images/B008LRQUW6.png\"]}\n{\"q_img\": \"./images/B009YKCIIY.png\", \"q_text\": \"Has a darker color., and is solid colored and sleeveless\", \"positive_key\": \"B006057XVS\", \"positive_value\": \"./images/B006057XVS.png\", \"hn_image\": [\"./images/B009YKCIIY.png\"]}\n{\"q_img\": \"./images/B00FE0G1OU.png\", \"q_text\": \"is less sexy and shorter, and is sleeveless with polka dots\", \"positive_key\": \"B00COB4DF6\", \"positive_value\": \"./images/B00COB4DF6.png\", \"hn_image\": [\"./images/B00FE0G1OU.png\"]}\n{\"q_img\": \"./images/B00DTWDHWO.png\", \"q_text\": \"has longer sleeves and a different pattern, and is a slightly darker blue\", \"positive_key\": \"B00BXMPQZI\", \"positive_value\": \"./images/B00BXMPQZI.png\", \"hn_image\": [\"./images/B00DTWDHWO.png\"]}\n{\"q_img\": \"./images/B007WAT0S4.png\", \"q_text\": \"is black and shiny, and is black and shorter\", \"positive_key\": \"B007XD6GHS\", \"positive_value\": \"./images/B007XD6GHS.png\", \"hn_image\": [\"./images/B007WAT0S4.png\"]}\n{\"q_img\": \"./images/B0088D21Y4.png\", \"q_text\": \"half sleeved short blue dress, and is in all blue.\", \"positive_key\": \"B008QW7GOW\", \"positive_value\": \"./images/B008QW7GOW.png\", \"hn_image\": [\"./images/B0088D21Y4.png\"]}\n{\"q_img\": \"./images/B007RBIRK0.png\", \"q_text\": \"has sleeves and a more plunging neckline, and is all black with a deep V neck\", \"positive_key\": \"B008MA8UJI\", \"positive_value\": \"./images/B008MA8UJI.png\", \"hn_image\": [\"./images/B007RBIRK0.png\"]}\n{\"q_img\": \"./images/B007XD18IK.png\", \"q_text\": \"is high low and black and blue, and has a blue bottom and white and black top.\", \"positive_key\": \"B00CJTWWA6\", \"positive_value\": \"./images/B00CJTWWA6.png\", \"hn_image\": [\"./images/B007XD18IK.png\"]}\n{\"q_img\": \"./images/B00DJTS67S.png\", \"q_text\": \"Is a black cold shoulder short dress., and not as form fitting and has a sleeve\", \"positive_key\": \"B00BLZGJ1W\", \"positive_value\": \"./images/B00BLZGJ1W.png\", \"hn_image\": [\"./images/B00DJTS67S.png\"]}\n{\"q_img\": \"./images/B0055TBPNC.png\", \"q_text\": \"is purple with one strap, and is lilac and more loose fitting\", \"positive_key\": \"B008R9FAD8\", \"positive_value\": \"./images/B008R9FAD8.png\", \"hn_image\": [\"./images/B0055TBPNC.png\"]}\n{\"q_img\": \"./images/B00BYFESVM.png\", \"q_text\": \"is longer and less revealing, and has no sleeves and is a pink colored dress\", \"positive_key\": \"B00BY3R17W\", \"positive_value\": \"./images/B00BY3R17W.png\", \"hn_image\": [\"./images/B00BYFESVM.png\"]}\n{\"q_img\": \"./images/B00B90O740.png\", \"q_text\": \"black with a tighter fit around the hips, and is tighter and darker\", \"positive_key\": \"B00CBY97SE\", \"positive_value\": \"./images/B00CBY97SE.png\", \"hn_image\": [\"./images/B00B90O740.png\"]}\n{\"q_img\": \"./images/B00AXDA432.png\", \"q_text\": \"is beige with a high neck, and is a tan color with a belted waist\", \"positive_key\": \"B00E1ALO0K\", \"positive_value\": \"./images/B00E1ALO0K.png\", \"hn_image\": [\"./images/B00AXDA432.png\"]}\n{\"q_img\": \"./images/B008K3SX7G.png\", \"q_text\": \"is light purple with three-quarter length sleeves, and is lavender and has short sleeves\", \"positive_key\": \"B006PA7Q4M\", \"positive_value\": \"./images/B006PA7Q4M.png\", \"hn_image\": [\"./images/B008K3SX7G.png\"]}\n{\"q_img\": \"./images/B008ZB39FY.png\", \"q_text\": \"is dark red, and is red with elbow length sleeves\", \"positive_key\": \"B008HQYFB4\", \"positive_value\": \"./images/B008HQYFB4.png\", \"hn_image\": [\"./images/B008ZB39FY.png\"]}\n{\"q_img\": \"./images/B00CTT4N7G.png\", \"q_text\": \"is a short black tank dress, and is above thigh in length and is more sporty\", \"positive_key\": \"B004MAR55C\", \"positive_value\": \"./images/B004MAR55C.png\", \"hn_image\": [\"./images/B00CTT4N7G.png\"]}\n{\"q_img\": \"./images/B006QSEC4K.png\", \"q_text\": \"is darker and longer, and is longer and darker\", \"positive_key\": \"B0090U5OHK\", \"positive_value\": \"./images/B0090U5OHK.png\", \"hn_image\": [\"./images/B006QSEC4K.png\"]}\n{\"q_img\": \"./images/B007NJY9XA.png\", \"q_text\": \"is shorter with longer sleeves, and Is blue with longer sleeves\", \"positive_key\": \"B00B2JIJNI\", \"positive_value\": \"./images/B00B2JIJNI.png\", \"hn_image\": [\"./images/B007NJY9XA.png\"]}\n{\"q_img\": \"./images/B00E8AUR2O.png\", \"q_text\": \"is red colored and strapless, and A solid red long dress with no sleeves\", \"positive_key\": \"B007FG0Y8U\", \"positive_value\": \"./images/B007FG0Y8U.png\", \"hn_image\": [\"./images/B00E8AUR2O.png\"]}\n{\"q_img\": \"./images/B00A3PEX7S.png\", \"q_text\": \"Is darker with a bright floral pattern., and is darker with skinny straps\", \"positive_key\": \"B00C5TA8F6\", \"positive_value\": \"./images/B00C5TA8F6.png\", \"hn_image\": [\"./images/B00A3PEX7S.png\"]}\n{\"q_img\": \"./images/B009QW3VSQ.png\", \"q_text\": \"is more official and less blousey, and is two toned\", \"positive_key\": \"B00EJVX38C\", \"positive_value\": \"./images/B00EJVX38C.png\", \"hn_image\": [\"./images/B009QW3VSQ.png\"]}\n{\"q_img\": \"./images/B005QYY8F8.png\", \"q_text\": \"is short mini and shiny yellow, and white\", \"positive_key\": \"B007B86AF8\", \"positive_value\": \"./images/B007B86AF8.png\", \"hn_image\": [\"./images/B005QYY8F8.png\"]}\n{\"q_img\": \"./images/B0081CXJ4I.png\", \"q_text\": \"is shorter and darker, and longer and darker\", \"positive_key\": \"B00854663S\", \"positive_value\": \"./images/B00854663S.png\", \"hn_image\": [\"./images/B0081CXJ4I.png\"]}\n{\"q_img\": \"./images/B00598QDJK.png\", \"q_text\": \"has more lace, and shorter and has sleeves\", \"positive_key\": \"B00D9OXNW6\", \"positive_value\": \"./images/B00D9OXNW6.png\", \"hn_image\": [\"./images/B00598QDJK.png\"]}\n{\"q_img\": \"./images/B00CIEPROU.png\", \"q_text\": \"is longer sleeved and white, and is white and belted and shorter and trendy\", \"positive_key\": \"B005H7G65O\", \"positive_value\": \"./images/B005H7G65O.png\", \"hn_image\": [\"./images/B00CIEPROU.png\"]}\n{\"q_img\": \"./images/B0059BCNOG.png\", \"q_text\": \"has no sleeves and is solid black, and has more belt aspects\", \"positive_key\": \"B00BRBYOLC\", \"positive_value\": \"./images/B00BRBYOLC.png\", \"hn_image\": [\"./images/B0059BCNOG.png\"]}\n{\"q_img\": \"./images/B00508YRGU.png\", \"q_text\": \"plant print short black dress, and is shorter and has a different pattertn\", \"positive_key\": \"B003DIWQNA\", \"positive_value\": \"./images/B003DIWQNA.png\", \"hn_image\": [\"./images/B00508YRGU.png\"]}\n{\"q_img\": \"./images/B00C6PP5CA.png\", \"q_text\": \"is strapless with lighter colors, and is lighter and blue and sleeveless\", \"positive_key\": \"B0099DCINQ\", \"positive_value\": \"./images/B0099DCINQ.png\", \"hn_image\": [\"./images/B00C6PP5CA.png\"]}\n{\"q_img\": \"./images/B000YXD4R4.png\", \"q_text\": \"is longer and lighter in color, and has no sleeves and beige color\", \"positive_key\": \"B00BYTETAS\", \"positive_value\": \"./images/B00BYTETAS.png\", \"hn_image\": [\"./images/B000YXD4R4.png\"]}\n{\"q_img\": \"./images/B004AM5K4K.png\", \"q_text\": \"is off one shoulder, and is fancier looking\", \"positive_key\": \"B007G693H2\", \"positive_value\": \"./images/B007G693H2.png\", \"hn_image\": [\"./images/B004AM5K4K.png\"]}\n{\"q_img\": \"./images/B000X4TNTM.png\", \"q_text\": \"is black, and It is more black and more pleated\", \"positive_key\": \"B00A2G31AS\", \"positive_value\": \"./images/B00A2G31AS.png\", \"hn_image\": [\"./images/B000X4TNTM.png\"]}\n{\"q_img\": \"./images/B008AEEHA2.png\", \"q_text\": \"is light but less sexy, and Covers some of the arms and is beige\", \"positive_key\": \"B008596V88\", \"positive_value\": \"./images/B008596V88.png\", \"hn_image\": [\"./images/B008AEEHA2.png\"]}\n{\"q_img\": \"./images/B007FNGLZI.png\", \"q_text\": \"Is darker with longer length., and is darker and has straps\", \"positive_key\": \"B00AG41UFY\", \"positive_value\": \"./images/B00AG41UFY.png\", \"hn_image\": [\"./images/B007FNGLZI.png\"]}\n{\"q_img\": \"./images/B007520NYO.png\", \"q_text\": \"is solid black and more revealing, and is more black with thinner straps\", \"positive_key\": \"B00CFXUI4S\", \"positive_value\": \"./images/B00CFXUI4S.png\", \"hn_image\": [\"./images/B007520NYO.png\"]}\n{\"q_img\": \"./images/B002AQSPL8.png\", \"q_text\": \"has no sleeves and is sexier, and is much darker in color\", \"positive_key\": \"B003BT62A4\", \"positive_value\": \"./images/B003BT62A4.png\", \"hn_image\": [\"./images/B002AQSPL8.png\"]}\n{\"q_img\": \"./images/B00DYFK9N6.png\", \"q_text\": \"is darker and has longer sleeves, and and teal stripes\", \"positive_key\": \"B00DM0REWC\", \"positive_value\": \"./images/B00DM0REWC.png\", \"hn_image\": [\"./images/B00DYFK9N6.png\"]}\n{\"q_img\": \"./images/B009ND97G8.png\", \"q_text\": \"Is lighter with scoop neck., and is sleeveless and pink\", \"positive_key\": \"B00CIBN6H8\", \"positive_value\": \"./images/B00CIBN6H8.png\", \"hn_image\": [\"./images/B009ND97G8.png\"]}\n{\"q_img\": \"./images/B0076IK8OC.png\", \"q_text\": \"is more floral and lighter, and is more flowery and slim\", \"positive_key\": \"B00BP0WWBY\", \"positive_value\": \"./images/B00BP0WWBY.png\", \"hn_image\": [\"./images/B0076IK8OC.png\"]}\n{\"q_img\": \"./images/B00CQAJI7S.png\", \"q_text\": \"has long sleeves and is short, and is shorter with sleeves\", \"positive_key\": \"B00F9KUL8W\", \"positive_value\": \"./images/B00F9KUL8W.png\", \"hn_image\": [\"./images/B00CQAJI7S.png\"]}\n{\"q_img\": \"./images/B00B53A556.png\", \"q_text\": \"has longer sleeves, and is grey and has 3/4 length sleeves\", \"positive_key\": \"B008MC8438\", \"positive_value\": \"./images/B008MC8438.png\", \"hn_image\": [\"./images/B00B53A556.png\"]}\n{\"q_img\": \"./images/B006FRRQLS.png\", \"q_text\": \"is assymetrical with pattern, and Is a white/black print dress with one-sided sleeve\", \"positive_key\": \"B004UDYSDS\", \"positive_value\": \"./images/B004UDYSDS.png\", \"hn_image\": [\"./images/B006FRRQLS.png\"]}\n{\"q_img\": \"./images/B00A13GLG8.png\", \"q_text\": \"Is a solid color., and is solid black\", \"positive_key\": \"B00CD8K1W4\", \"positive_value\": \"./images/B00CD8K1W4.png\", \"hn_image\": [\"./images/B00A13GLG8.png\"]}\n{\"q_img\": \"./images/B0096C2UVA.png\", \"q_text\": \"is darker and has longer sleeves, and is black and more shiny\", \"positive_key\": \"B007W3ZADK\", \"positive_value\": \"./images/B007W3ZADK.png\", \"hn_image\": [\"./images/B0096C2UVA.png\"]}\n{\"q_img\": \"./images/B00AQUZKG8.png\", \"q_text\": \"short sleeves orange floral long blouse, and is more orange and is a blouse\", \"positive_key\": \"B00DHVW5N4\", \"positive_value\": \"./images/B00DHVW5N4.png\", \"hn_image\": [\"./images/B00AQUZKG8.png\"]}\n{\"q_img\": \"./images/B00DSSIGXO.png\", \"q_text\": \"has more design, and the dress is grey and white and more revealing\", \"positive_key\": \"B00EGJHGVW\", \"positive_value\": \"./images/B00EGJHGVW.png\", \"hn_image\": [\"./images/B00DSSIGXO.png\"]}\n{\"q_img\": \"./images/B005HP3HL2.png\", \"q_text\": \"light pink long dress with a bow, and is solid pink with a bow\", \"positive_key\": \"B00BWPEAP8\", \"positive_value\": \"./images/B00BWPEAP8.png\", \"hn_image\": [\"./images/B005HP3HL2.png\"]}\n{\"q_img\": \"./images/B0032FOQUU.png\", \"q_text\": \"has straps and royal blue colored, and is a form fitting blue sleeveless sheath\", \"positive_key\": \"B0082C616K\", \"positive_value\": \"./images/B0082C616K.png\", \"hn_image\": [\"./images/B0032FOQUU.png\"]}\n{\"q_img\": \"./images/B008MNM7Y4.png\", \"q_text\": \"is navy blue with subtle print, and is lighter and patterned\", \"positive_key\": \"B0076ODGBS\", \"positive_value\": \"./images/B0076ODGBS.png\", \"hn_image\": [\"./images/B008MNM7Y4.png\"]}\n{\"q_img\": \"./images/B00BPF5X4M.png\", \"q_text\": \"is greener with a different pattern, and is green and longer\", \"positive_key\": \"B008UC9MGS\", \"positive_value\": \"./images/B008UC9MGS.png\", \"hn_image\": [\"./images/B00BPF5X4M.png\"]}\n{\"q_img\": \"./images/B007Y0UKN6.png\", \"q_text\": \"has a rounder neckline, and has a higher neckline and is more black\", \"positive_key\": \"B0052WU704\", \"positive_value\": \"./images/B0052WU704.png\", \"hn_image\": [\"./images/B007Y0UKN6.png\"]}\n{\"q_img\": \"./images/B009C6OMMK.png\", \"q_text\": \"is brighter in color, and is lighter colored and more frilly\", \"positive_key\": \"B0071UXLDK\", \"positive_value\": \"./images/B0071UXLDK.png\", \"hn_image\": [\"./images/B009C6OMMK.png\"]}\n{\"q_img\": \"./images/B00ARFWBTG.png\", \"q_text\": \"Is more colorful and loud, and is light mint green\", \"positive_key\": \"B00A76P278\", \"positive_value\": \"./images/B00A76P278.png\", \"hn_image\": [\"./images/B00ARFWBTG.png\"]}\n{\"q_img\": \"./images/B007FG0Y8U.png\", \"q_text\": \"is sleeveless long loose dress and white, and is white and less fitted\", \"positive_key\": \"B009CPQZ12\", \"positive_value\": \"./images/B009CPQZ12.png\", \"hn_image\": [\"./images/B007FG0Y8U.png\"]}\n{\"q_img\": \"./images/B00BISLTIK.png\", \"q_text\": \"looks like jeans, and has a ruffled skirt\", \"positive_key\": \"B00B5KMPA2\", \"positive_value\": \"./images/B00B5KMPA2.png\", \"hn_image\": [\"./images/B00BISLTIK.png\"]}\n{\"q_img\": \"./images/B0074H8I6K.png\", \"q_text\": \"has no sleeves and brighter colors, and is white green\", \"positive_key\": \"B000JL47FE\", \"positive_value\": \"./images/B000JL47FE.png\", \"hn_image\": [\"./images/B0074H8I6K.png\"]}\n{\"q_img\": \"./images/B00EQ1PPEK.png\", \"q_text\": \"is longer, and Is longer and more elega\", \"positive_key\": \"B00C18EV0E\", \"positive_value\": \"./images/B00C18EV0E.png\", \"hn_image\": [\"./images/B00EQ1PPEK.png\"]}\n{\"q_img\": \"./images/B0094QXCKQ.png\", \"q_text\": \"short striped red and blue red, and has a tank top and a red\", \"positive_key\": \"B00870UVRC\", \"positive_value\": \"./images/B00870UVRC.png\", \"hn_image\": [\"./images/B0094QXCKQ.png\"]}\n{\"q_img\": \"./images/B00BBOI64Q.png\", \"q_text\": \"is black in color and slighly longer overall, and is sleeveless and red\", \"positive_key\": \"B009ZUHNE2\", \"positive_value\": \"./images/B009ZUHNE2.png\", \"hn_image\": [\"./images/B00BBOI64Q.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"is solid black, and is black and slender\", \"positive_key\": \"B008BHSFOW\", \"positive_value\": \"./images/B008BHSFOW.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B00F9AJ5Y8.png\", \"q_text\": \"is white, and has a blazer and is beige\", \"positive_key\": \"B005ENHHZO\", \"positive_value\": \"./images/B005ENHHZO.png\", \"hn_image\": [\"./images/B00F9AJ5Y8.png\"]}\n{\"q_img\": \"./images/B004Z56IPC.png\", \"q_text\": \" patterned, and grey\", \"positive_key\": \"B009401NI0\", \"positive_value\": \"./images/B009401NI0.png\", \"hn_image\": [\"./images/B004Z56IPC.png\"]}\n{\"q_img\": \"./images/B00D7ISRPC.png\", \"q_text\": \"Has darker colors., and is more revealing and less colorful\", \"positive_key\": \"B00EKY53XQ\", \"positive_value\": \"./images/B00EKY53XQ.png\", \"hn_image\": [\"./images/B00D7ISRPC.png\"]}\n{\"q_img\": \"./images/B005UX57P0.png\", \"q_text\": \"is black and sleeveless, and is more kinky\", \"positive_key\": \"B00DOS9HN6\", \"positive_value\": \"./images/B00DOS9HN6.png\", \"hn_image\": [\"./images/B005UX57P0.png\"]}\n{\"q_img\": \"./images/B005WY5LAS.png\", \"q_text\": \"has print of miss sixty, and is not a dress or clothing\", \"positive_key\": \"B00AYE3UIG\", \"positive_value\": \"./images/B00AYE3UIG.png\", \"hn_image\": [\"./images/B005WY5LAS.png\"]}\n{\"q_img\": \"./images/B003YKZ42W.png\", \"q_text\": \"is visually aesthetic, and has a flowered pattern\", \"positive_key\": \"B006QKKVZM\", \"positive_value\": \"./images/B006QKKVZM.png\", \"hn_image\": [\"./images/B003YKZ42W.png\"]}\n{\"q_img\": \"./images/B0059BCNOG.png\", \"q_text\": \"is shorter and has a tube top, and shows more cleavage and more blue\", \"positive_key\": \"B0058YU4AE\", \"positive_value\": \"./images/B0058YU4AE.png\", \"hn_image\": [\"./images/B0059BCNOG.png\"]}\n{\"q_img\": \"./images/B0033UVILO.png\", \"q_text\": \" tie dye, and and is longer\", \"positive_key\": \"B0084Y6UQC\", \"positive_value\": \"./images/B0084Y6UQC.png\", \"hn_image\": [\"./images/B0033UVILO.png\"]}\n{\"q_img\": \"./images/B00CII5YD0.png\", \"q_text\": \"has a lace detail, and has a lacey top and flouncy bottom\", \"positive_key\": \"B00D75WT4U\", \"positive_value\": \"./images/B00D75WT4U.png\", \"hn_image\": [\"./images/B00CII5YD0.png\"]}\n{\"q_img\": \"./images/B004ZRUT96.png\", \"q_text\": \"is a belted dress, and has a more solid color\", \"positive_key\": \"B007E9Y26S\", \"positive_value\": \"./images/B007E9Y26S.png\", \"hn_image\": [\"./images/B004ZRUT96.png\"]}\n{\"q_img\": \"./images/B00C9K0JQY.png\", \"q_text\": \"short black dress with 3 buttons on belt, and has a belt\", \"positive_key\": \"B00455GK1E\", \"positive_value\": \"./images/B00455GK1E.png\", \"hn_image\": [\"./images/B00C9K0JQY.png\"]}\n{\"q_img\": \"./images/B009VDSHKC.png\", \"q_text\": \"is short, and Is shorter and fitted\", \"positive_key\": \"B008ZYMM5Y\", \"positive_value\": \"./images/B008ZYMM5Y.png\", \"hn_image\": [\"./images/B009VDSHKC.png\"]}\n{\"q_img\": \"./images/B00CBYFJ3Q.png\", \"q_text\": \"has sleeves and is less shiny, and is less sexy\", \"positive_key\": \"B005NVQAUA\", \"positive_value\": \"./images/B005NVQAUA.png\", \"hn_image\": [\"./images/B00CBYFJ3Q.png\"]}\n{\"q_img\": \"./images/B002GU7D5M.png\", \"q_text\": \"is a line no sleeves and slight print, and is red and a line shaped\", \"positive_key\": \"B0083QFE0Y\", \"positive_value\": \"./images/B0083QFE0Y.png\", \"hn_image\": [\"./images/B002GU7D5M.png\"]}\n{\"q_img\": \"./images/B0039ITJ84.png\", \"q_text\": \"is more sexy, and is sexy and bold\", \"positive_key\": \"B00CD8JJ7M\", \"positive_value\": \"./images/B00CD8JJ7M.png\", \"hn_image\": [\"./images/B0039ITJ84.png\"]}\n{\"q_img\": \"./images/B00A7N4DC6.png\", \"q_text\": \"is short sleeved a solid color flair bottom, and is longer and formal\", \"positive_key\": \"B00E1C7SZI\", \"positive_value\": \"./images/B00E1C7SZI.png\", \"hn_image\": [\"./images/B00A7N4DC6.png\"]}\n{\"q_img\": \"./images/B009Z31KZM.png\", \"q_text\": \"has longer sleeves and is short, and is shorter and red with sleeves\", \"positive_key\": \"B009YJEIXS\", \"positive_value\": \"./images/B009YJEIXS.png\", \"hn_image\": [\"./images/B009Z31KZM.png\"]}\n{\"q_img\": \"./images/B00EJN90W8.png\", \"q_text\": \"is longer and more bridal, and the dress is cream and much longer\", \"positive_key\": \"B0097IX9NQ\", \"positive_value\": \"./images/B0097IX9NQ.png\", \"hn_image\": [\"./images/B00EJN90W8.png\"]}\n{\"q_img\": \"./images/B007ZOGQIO.png\", \"q_text\": \"is shorter and has shorter sleeves, and is printed and sleeveless\", \"positive_key\": \"B007KJ2M5K\", \"positive_value\": \"./images/B007KJ2M5K.png\", \"hn_image\": [\"./images/B007ZOGQIO.png\"]}\n{\"q_img\": \"./images/B00AEYZ35E.png\", \"q_text\": \"is all black and is more revealing, and is black and longer\", \"positive_key\": \"B006VVMYPQ\", \"positive_value\": \"./images/B006VVMYPQ.png\", \"hn_image\": [\"./images/B00AEYZ35E.png\"]}\n{\"q_img\": \"./images/B00BYMX1AY.png\", \"q_text\": \"is blue and one shouldered, and one shouldered\", \"positive_key\": \"B00CP3BTKU\", \"positive_value\": \"./images/B00CP3BTKU.png\", \"hn_image\": [\"./images/B00BYMX1AY.png\"]}\n{\"q_img\": \"./images/B00C4Q2RPO.png\", \"q_text\": \" with a blet belt around the waist, and is solid black\", \"positive_key\": \"B00C4Q356Y\", \"positive_value\": \"./images/B00C4Q356Y.png\", \"hn_image\": [\"./images/B00C4Q2RPO.png\"]}\n{\"q_img\": \"./images/B008LFKSF8.png\", \"q_text\": \" much longer and is orange, and is purple\", \"positive_key\": \"B00DQAKVT6\", \"positive_value\": \"./images/B00DQAKVT6.png\", \"hn_image\": [\"./images/B008LFKSF8.png\"]}\n{\"q_img\": \"./images/B00E62NUTG.png\", \"q_text\": \"Is darker with an empire waist., and is one color and classy\", \"positive_key\": \"B00D6IVL6K\", \"positive_value\": \"./images/B00D6IVL6K.png\", \"hn_image\": [\"./images/B00E62NUTG.png\"]}\n{\"q_img\": \"./images/B008NHSU3G.png\", \"q_text\": \"Is a white strapless gown., and is white and form fitted\", \"positive_key\": \"B002DI8HQG\", \"positive_value\": \"./images/B002DI8HQG.png\", \"hn_image\": [\"./images/B008NHSU3G.png\"]}\n{\"q_img\": \"./images/B005UASDM2.png\", \"q_text\": \"is a ling dress with no sleeves, and is longer\", \"positive_key\": \"B009E739Q2\", \"positive_value\": \"./images/B009E739Q2.png\", \"hn_image\": [\"./images/B005UASDM2.png\"]}\n{\"q_img\": \"./images/B002NVCPQ6.png\", \"q_text\": \"Is a ruffled black short grey dress, and purple\", \"positive_key\": \"B00D6D8EL0\", \"positive_value\": \"./images/B00D6D8EL0.png\", \"hn_image\": [\"./images/B002NVCPQ6.png\"]}\n{\"q_img\": \"./images/B009C6OMMK.png\", \"q_text\": \"has a more modest neck and a different pattern, and is cream colored with a higher neckline\", \"positive_key\": \"B007PYJLRC\", \"positive_value\": \"./images/B007PYJLRC.png\", \"hn_image\": [\"./images/B009C6OMMK.png\"]}\n{\"q_img\": \"./images/B006ZO54SI.png\", \"q_text\": \"has a bubble skirt and white, and white and more frivolous\", \"positive_key\": \"B007PKECMA\", \"positive_value\": \"./images/B007PKECMA.png\", \"hn_image\": [\"./images/B006ZO54SI.png\"]}\n{\"q_img\": \"./images/B007FDYS62.png\", \"q_text\": \"has straps with blue floral print, and has blue and yellow pattern\", \"positive_key\": \"B003L13FTI\", \"positive_value\": \"./images/B003L13FTI.png\", \"hn_image\": [\"./images/B007FDYS62.png\"]}\n{\"q_img\": \"./images/B00AO9RH30.png\", \"q_text\": \"is whiter, and white with big cross design\", \"positive_key\": \"B00DQAL0XW\", \"positive_value\": \"./images/B00DQAL0XW.png\", \"hn_image\": [\"./images/B00AO9RH30.png\"]}\n{\"q_img\": \"./images/B00AO1VVB2.png\", \"q_text\": \"n/a, and is longer and darker\", \"positive_key\": \"B004VQBVOI\", \"positive_value\": \"./images/B004VQBVOI.png\", \"hn_image\": [\"./images/B00AO1VVB2.png\"]}\n{\"q_img\": \"./images/B0070L9U6M.png\", \"q_text\": \"is black with longer sleeves, and black longer sleeves with cut outs\", \"positive_key\": \"B006MWMNRS\", \"positive_value\": \"./images/B006MWMNRS.png\", \"hn_image\": [\"./images/B0070L9U6M.png\"]}\n{\"q_img\": \"./images/B00BL9EE4C.png\", \"q_text\": \"is shorter and sexier, and is turquoise and more revealing\", \"positive_key\": \"B00CO6KDDM\", \"positive_value\": \"./images/B00CO6KDDM.png\", \"hn_image\": [\"./images/B00BL9EE4C.png\"]}\n{\"q_img\": \"./images/B00BSN0JOA.png\", \"q_text\": \"is black with gold accents, and is more sexy\", \"positive_key\": \"B008UWENTO\", \"positive_value\": \"./images/B008UWENTO.png\", \"hn_image\": [\"./images/B00BSN0JOA.png\"]}\n{\"q_img\": \"./images/B00BBOI64Q.png\", \"q_text\": \"Is solid red, and is white with a swing skirt\", \"positive_key\": \"B00EE0XOZA\", \"positive_value\": \"./images/B00EE0XOZA.png\", \"hn_image\": [\"./images/B00BBOI64Q.png\"]}\n{\"q_img\": \"./images/B00C9796UC.png\", \"q_text\": \"long white wedding gown with ruffles, and flared and floral\", \"positive_key\": \"B00AT9JFQC\", \"positive_value\": \"./images/B00AT9JFQC.png\", \"hn_image\": [\"./images/B00C9796UC.png\"]}\n{\"q_img\": \"./images/B005KMQQFQ.png\", \"q_text\": \"is gray with a gray belt, and It is less red and have both shoulder\", \"positive_key\": \"B005BU8MG8\", \"positive_value\": \"./images/B005BU8MG8.png\", \"hn_image\": [\"./images/B005KMQQFQ.png\"]}\n{\"q_img\": \"./images/B00F4MXLRI.png\", \"q_text\": \"Is a green sleeveless dress with a medallion., and is similar without sleeves\", \"positive_key\": \"B00DW8GVIW\", \"positive_value\": \"./images/B00DW8GVIW.png\", \"hn_image\": [\"./images/B00F4MXLRI.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"is a navy maxi dress, and solid blue\", \"positive_key\": \"B007P0Q6PQ\", \"positive_value\": \"./images/B007P0Q6PQ.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B005DRATG0.png\", \"q_text\": \"has no sleeves and is lighter, and has thinner straps\", \"positive_key\": \"B005JU13OS\", \"positive_value\": \"./images/B005JU13OS.png\", \"hn_image\": [\"./images/B005DRATG0.png\"]}\n{\"q_img\": \"./images/B004U88SW0.png\", \"q_text\": \"is blue with short sleeves, and is blue and has shorter sleeves\", \"positive_key\": \"B00DZ7AA8M\", \"positive_value\": \"./images/B00DZ7AA8M.png\", \"hn_image\": [\"./images/B004U88SW0.png\"]}\n{\"q_img\": \"./images/B0082BCNZY.png\", \"q_text\": \"has a plain design at the front, and is darker and has shorter sleeves\", \"positive_key\": \"B008CB707K\", \"positive_value\": \"./images/B008CB707K.png\", \"hn_image\": [\"./images/B0082BCNZY.png\"]}\n{\"q_img\": \"./images/B00CTSFCE0.png\", \"q_text\": \"is purple with patterns on it, and is shorter and has sleeves and is purple\", \"positive_key\": \"B00ED49C12\", \"positive_value\": \"./images/B00ED49C12.png\", \"hn_image\": [\"./images/B00CTSFCE0.png\"]}\n{\"q_img\": \"./images/B007W9ZC3W.png\", \"q_text\": \"is light pinked with even sides, and is colourfull\", \"positive_key\": \"B00BWJ9R2A\", \"positive_value\": \"./images/B00BWJ9R2A.png\", \"hn_image\": [\"./images/B007W9ZC3W.png\"]}\n{\"q_img\": \"./images/B00GP5TL6I.png\", \"q_text\": \"is light and darker red sleevless dress, and is salmon colored and sleeveless\", \"positive_key\": \"B007VU22WQ\", \"positive_value\": \"./images/B007VU22WQ.png\", \"hn_image\": [\"./images/B00GP5TL6I.png\"]}\n{\"q_img\": \"./images/B007JS1Y7Y.png\", \"q_text\": \"is shorter and bright zigzag stripes that are red and white, and more red and longer skirt\", \"positive_key\": \"B007G6JZV6\", \"positive_value\": \"./images/B007G6JZV6.png\", \"hn_image\": [\"./images/B007JS1Y7Y.png\"]}\n{\"q_img\": \"./images/B00DCC9ZJK.png\", \"q_text\": \"is a long green maxi dress, and has more green\", \"positive_key\": \"B00DDX15TG\", \"positive_value\": \"./images/B00DDX15TG.png\", \"hn_image\": [\"./images/B00DCC9ZJK.png\"]}\n{\"q_img\": \"./images/B00CE66AQG.png\", \"q_text\": \"is solid blue with a v-neck, and black and white\", \"positive_key\": \"B009Z31FXY\", \"positive_value\": \"./images/B009Z31FXY.png\", \"hn_image\": [\"./images/B00CE66AQG.png\"]}\n{\"q_img\": \"./images/B00AOBCAXA.png\", \"q_text\": \"is black witch short sleeves, and is solid black with long sleeves\", \"positive_key\": \"B00DH8X2OI\", \"positive_value\": \"./images/B00DH8X2OI.png\", \"hn_image\": [\"./images/B00AOBCAXA.png\"]}\n{\"q_img\": \"./images/B000AY2892.png\", \"q_text\": \"is shiny green with longer length, and is full length and shiny\", \"positive_key\": \"B000XTPPYO\", \"positive_value\": \"./images/B000XTPPYO.png\", \"hn_image\": [\"./images/B000AY2892.png\"]}\n{\"q_img\": \"./images/B007RM21BU.png\", \"q_text\": \"is black and shinier, and is black with sequins\", \"positive_key\": \"B00A4MST0C\", \"positive_value\": \"./images/B00A4MST0C.png\", \"hn_image\": [\"./images/B007RM21BU.png\"]}\n{\"q_img\": \"./images/B008596XXG.png\", \"q_text\": \"is shorter and green in color, and is shorter and green.\", \"positive_key\": \"B008P5453U\", \"positive_value\": \"./images/B008P5453U.png\", \"hn_image\": [\"./images/B008596XXG.png\"]}\n{\"q_img\": \"./images/B0069TD8DW.png\", \"q_text\": \"has thinner straps, and is darker\", \"positive_key\": \"B002UD64NW\", \"positive_value\": \"./images/B002UD64NW.png\", \"hn_image\": [\"./images/B0069TD8DW.png\"]}\n{\"q_img\": \"./images/B005WQCHPI.png\", \"q_text\": \"is shorter and solid black, and is black and thin straps\", \"positive_key\": \"B0080E6RN2\", \"positive_value\": \"./images/B0080E6RN2.png\", \"hn_image\": [\"./images/B005WQCHPI.png\"]}\n{\"q_img\": \"./images/B009C6OO8C.png\", \"q_text\": \"is a solid, and has a halter neckline and is a lighter color\", \"positive_key\": \"B009C6OMMK\", \"positive_value\": \"./images/B009C6OMMK.png\", \"hn_image\": [\"./images/B009C6OO8C.png\"]}\n{\"q_img\": \"./images/B009S3GIV0.png\", \"q_text\": \"is more red and is a solid color, and is more red\", \"positive_key\": \"B00595TNMM\", \"positive_value\": \"./images/B00595TNMM.png\", \"hn_image\": [\"./images/B009S3GIV0.png\"]}\n{\"q_img\": \"./images/B00BG3NQE2.png\", \"q_text\": \"strapless and blue print, and Is more floral and colorful\", \"positive_key\": \"B004LKRWWO\", \"positive_value\": \"./images/B004LKRWWO.png\", \"hn_image\": [\"./images/B00BG3NQE2.png\"]}\n{\"q_img\": \"./images/B00CW6HT7W.png\", \"q_text\": \"is a black laced dress with both shoulders covered, and is shorter and lighter\", \"positive_key\": \"B00A76OJHW\", \"positive_value\": \"./images/B00A76OJHW.png\", \"hn_image\": [\"./images/B00CW6HT7W.png\"]}\n{\"q_img\": \"./images/B0071MSFCU.png\", \"q_text\": \"is a blue dress, and is a longer length and a brighter color\", \"positive_key\": \"B00D6QTRRC\", \"positive_value\": \"./images/B00D6QTRRC.png\", \"hn_image\": [\"./images/B0071MSFCU.png\"]}\n{\"q_img\": \"./images/B007FO27IW.png\", \"q_text\": \"is shorter, and is blue and sexy\", \"positive_key\": \"B00342VIKW\", \"positive_value\": \"./images/B00342VIKW.png\", \"hn_image\": [\"./images/B007FO27IW.png\"]}\n{\"q_img\": \"./images/B007HZJE08.png\", \"q_text\": \"has longer sleeves and more blue, and has striped top\", \"positive_key\": \"B0069U00N2\", \"positive_value\": \"./images/B0069U00N2.png\", \"hn_image\": [\"./images/B007HZJE08.png\"]}\n{\"q_img\": \"./images/B0096C2UVA.png\", \"q_text\": \"is black and has half sleeves, and is black with longer sleeves\", \"positive_key\": \"B005FW6A4S\", \"positive_value\": \"./images/B005FW6A4S.png\", \"hn_image\": [\"./images/B0096C2UVA.png\"]}\n{\"q_img\": \"./images/B00BEZTESO.png\", \"q_text\": \"has a chiffon skirt and is less fitted, and so is not as revealing\", \"positive_key\": \"B00D8RZKMU\", \"positive_value\": \"./images/B00D8RZKMU.png\", \"hn_image\": [\"./images/B00BEZTESO.png\"]}\n{\"q_img\": \"./images/B007HI6KPM.png\", \"q_text\": \"is black and much shorter, and more modern and less sexy\", \"positive_key\": \"B005CT9ZJG\", \"positive_value\": \"./images/B005CT9ZJG.png\", \"hn_image\": [\"./images/B007HI6KPM.png\"]}\n{\"q_img\": \"./images/B0046Q4K6E.png\", \"q_text\": \"has a long slit, and Is lighter and more revealing.\", \"positive_key\": \"B002N8CDA2\", \"positive_value\": \"./images/B002N8CDA2.png\", \"hn_image\": [\"./images/B0046Q4K6E.png\"]}\n{\"q_img\": \"./images/B00B1OKIRO.png\", \"q_text\": \"is longer with one strap and pink, and is more formal\", \"positive_key\": \"B00CK2LIXY\", \"positive_value\": \"./images/B00CK2LIXY.png\", \"hn_image\": [\"./images/B00B1OKIRO.png\"]}\n{\"q_img\": \"./images/B008GRR6II.png\", \"q_text\": \"is a one sleeved mini black evening dress, and has one strap\", \"positive_key\": \"B004MKNOJ8\", \"positive_value\": \"./images/B004MKNOJ8.png\", \"hn_image\": [\"./images/B008GRR6II.png\"]}\n{\"q_img\": \"./images/B007SOUELG.png\", \"q_text\": \"is a darker shade, and is a darker and more casual\", \"positive_key\": \"B007SOUD26\", \"positive_value\": \"./images/B007SOUD26.png\", \"hn_image\": [\"./images/B007SOUELG.png\"]}\n{\"q_img\": \"./images/B00801W1H6.png\", \"q_text\": \"is red with black buttons, and has longer sleeves\", \"positive_key\": \"B00CFL3FDQ\", \"positive_value\": \"./images/B00CFL3FDQ.png\", \"hn_image\": [\"./images/B00801W1H6.png\"]}\n{\"q_img\": \"./images/B00ANC7JCW.png\", \"q_text\": \"is darker pink and shiny, and fitted\", \"positive_key\": \"B00ANR3NSQ\", \"positive_value\": \"./images/B00ANR3NSQ.png\", \"hn_image\": [\"./images/B00ANC7JCW.png\"]}\n{\"q_img\": \"./images/B0089DLVNA.png\", \"q_text\": \"is above knee and three quarter sleeve, and is blue with long sleeves\", \"positive_key\": \"B007ZT13QE\", \"positive_value\": \"./images/B007ZT13QE.png\", \"hn_image\": [\"./images/B0089DLVNA.png\"]}\n{\"q_img\": \"./images/B004IZRBDW.png\", \"q_text\": \"has longer sleeves and is a solid color, and has longer sleeves and more revealing\", \"positive_key\": \"B005LMZ2KU\", \"positive_value\": \"./images/B005LMZ2KU.png\", \"hn_image\": [\"./images/B004IZRBDW.png\"]}\n{\"q_img\": \"./images/B002VLK54C.png\", \"q_text\": \"is red, and comes in colors with white floral designs.\", \"positive_key\": \"B002VLDSRS\", \"positive_value\": \"./images/B002VLDSRS.png\", \"hn_image\": [\"./images/B002VLK54C.png\"]}\n{\"q_img\": \"./images/B00C62EUBA.png\", \"q_text\": \"is a t shirt with short sleeves, and is white with a collar\", \"positive_key\": \"B009QOHBUI\", \"positive_value\": \"./images/B009QOHBUI.png\", \"hn_image\": [\"./images/B00C62EUBA.png\"]}\n{\"q_img\": \"./images/B00A8VHMRK.png\", \"q_text\": \"is transparent and blue, and is flowing and blue\", \"positive_key\": \"B009VUUMBW\", \"positive_value\": \"./images/B009VUUMBW.png\", \"hn_image\": [\"./images/B00A8VHMRK.png\"]}\n{\"q_img\": \"./images/B00CY8T80Y.png\", \"q_text\": \"is less shiny with a longer skirt, and is dark a more fitted\", \"positive_key\": \"B00F4N004Y\", \"positive_value\": \"./images/B00F4N004Y.png\", \"hn_image\": [\"./images/B00CY8T80Y.png\"]}\n{\"q_img\": \"./images/B004EPXD9M.png\", \"q_text\": \"is more floral print with a shorter skirt, and is shorter with a brighter colored print\", \"positive_key\": \"B0091XR6GS\", \"positive_value\": \"./images/B0091XR6GS.png\", \"hn_image\": [\"./images/B004EPXD9M.png\"]}\n{\"q_img\": \"./images/B0059817JQ.png\", \"q_text\": \"has three colors, and has more colors\", \"positive_key\": \"B008UD2EAS\", \"positive_value\": \"./images/B008UD2EAS.png\", \"hn_image\": [\"./images/B0059817JQ.png\"]}\n{\"q_img\": \"./images/B00A16VH28.png\", \"q_text\": \"is lighter, and Is red and more plain\", \"positive_key\": \"B009FEMH76\", \"positive_value\": \"./images/B009FEMH76.png\", \"hn_image\": [\"./images/B00A16VH28.png\"]}\n{\"q_img\": \"./images/B0029YQQYE.png\", \"q_text\": \"more purple, and is longer and more maroon\", \"positive_key\": \"B004OLGO24\", \"positive_value\": \"./images/B004OLGO24.png\", \"hn_image\": [\"./images/B0029YQQYE.png\"]}\n{\"q_img\": \"./images/B004OHYU4M.png\", \"q_text\": \"has no straps. It's solid black with ruffles, and ruffled and darker\", \"positive_key\": \"B006O39V3Y\", \"positive_value\": \"./images/B006O39V3Y.png\", \"hn_image\": [\"./images/B004OHYU4M.png\"]}\n{\"q_img\": \"./images/B007TMBT5M.png\", \"q_text\": \"is bluer with shorter sleeves, and has a wider neck line and is blue.\", \"positive_key\": \"B006M49D42\", \"positive_value\": \"./images/B006M49D42.png\", \"hn_image\": [\"./images/B007TMBT5M.png\"]}\n{\"q_img\": \"./images/B005FUMMGU.png\", \"q_text\": \"a lace black dress with long sleeves, and has lace\", \"positive_key\": \"B0077G77OW\", \"positive_value\": \"./images/B0077G77OW.png\", \"hn_image\": [\"./images/B005FUMMGU.png\"]}\n{\"q_img\": \"./images/B005MGDYK0.png\", \"q_text\": \"has halter top and is bright white, and is holter top and more revealing\", \"positive_key\": \"B00EASZKQW\", \"positive_value\": \"./images/B00EASZKQW.png\", \"hn_image\": [\"./images/B005MGDYK0.png\"]}\n{\"q_img\": \"./images/B0045EPT8A.png\", \"q_text\": \"is off the shoulder and red, and is much more pink\", \"positive_key\": \"B008RXA5IO\", \"positive_value\": \"./images/B008RXA5IO.png\", \"hn_image\": [\"./images/B0045EPT8A.png\"]}\n{\"q_img\": \"./images/B0073WBRYQ.png\", \"q_text\": \"Is a green paisley print v neck dress, and has a floral design and is longer.\", \"positive_key\": \"B007SOUELG\", \"positive_value\": \"./images/B007SOUELG.png\", \"hn_image\": [\"./images/B0073WBRYQ.png\"]}\n{\"q_img\": \"./images/B00BXB7LRA.png\", \"q_text\": \"is black and shorter, and is darker ad has buttons\", \"positive_key\": \"B00CGYK5K8\", \"positive_value\": \"./images/B00CGYK5K8.png\", \"hn_image\": [\"./images/B00BXB7LRA.png\"]}\n{\"q_img\": \"./images/B005KMQQFQ.png\", \"q_text\": \"Is more charcoal and less satin, and is blacker\", \"positive_key\": \"B007KANUK0\", \"positive_value\": \"./images/B007KANUK0.png\", \"hn_image\": [\"./images/B005KMQQFQ.png\"]}\n{\"q_img\": \"./images/B00BSN0JOA.png\", \"q_text\": \"has a blue top and multi-colored bottom, and has a blue top and a multicolored skirt\", \"positive_key\": \"B00E1SQHY0\", \"positive_value\": \"./images/B00E1SQHY0.png\", \"hn_image\": [\"./images/B00BSN0JOA.png\"]}\n{\"q_img\": \"./images/B00FBRGJKM.png\", \"q_text\": \"is tighter and fancier, and tighter skirt and v neck\", \"positive_key\": \"B00FTA0HUE\", \"positive_value\": \"./images/B00FTA0HUE.png\", \"hn_image\": [\"./images/B00FBRGJKM.png\"]}\n{\"q_img\": \"./images/B00BPCXTVE.png\", \"q_text\": \"is floral with sleeves, and is a twin pack\", \"positive_key\": \"B003I62TM0\", \"positive_value\": \"./images/B003I62TM0.png\", \"hn_image\": [\"./images/B00BPCXTVE.png\"]}\n{\"q_img\": \"./images/B005X4PL1G.png\", \"q_text\": \"is more darker and minimal, and is black\", \"positive_key\": \"B0084DJQ6O\", \"positive_value\": \"./images/B0084DJQ6O.png\", \"hn_image\": [\"./images/B005X4PL1G.png\"]}\n{\"q_img\": \"./images/B009D3M86O.png\", \"q_text\": \"is pink with a low neck, and is more colorful and has two straps\", \"positive_key\": \"B00B4YXHF6\", \"positive_value\": \"./images/B00B4YXHF6.png\", \"hn_image\": [\"./images/B009D3M86O.png\"]}\n{\"q_img\": \"./images/B004MMF00W.png\", \"q_text\": \"is longer and a solid plum color, and is much longer\", \"positive_key\": \"B00C17P8W0\", \"positive_value\": \"./images/B00C17P8W0.png\", \"hn_image\": [\"./images/B004MMF00W.png\"]}\n{\"q_img\": \"./images/B00AAOD3LO.png\", \"q_text\": \"is cream colored with one side longer, and is white and more revealing\", \"positive_key\": \"B007W9ZC3W\", \"positive_value\": \"./images/B007W9ZC3W.png\", \"hn_image\": [\"./images/B00AAOD3LO.png\"]}\n{\"q_img\": \"./images/B005ZXS03G.png\", \"q_text\": \"has longer sleeves and is short, and is tan with sleeves and a belt\", \"positive_key\": \"B00DV6C0Y4\", \"positive_value\": \"./images/B00DV6C0Y4.png\", \"hn_image\": [\"./images/B005ZXS03G.png\"]}\n{\"q_img\": \"./images/B00DH8X2OI.png\", \"q_text\": \"is red and sleeveless, and is vibrant red with shorter sleeves\", \"positive_key\": \"B00AOAQ2JO\", \"positive_value\": \"./images/B00AOAQ2JO.png\", \"hn_image\": [\"./images/B00DH8X2OI.png\"]}\n{\"q_img\": \"./images/B009NW4TW6.png\", \"q_text\": \"is white and longer, and is longer and lighter\", \"positive_key\": \"B008DH1ZEM\", \"positive_value\": \"./images/B008DH1ZEM.png\", \"hn_image\": [\"./images/B009NW4TW6.png\"]}\n{\"q_img\": \"./images/B00B1YKB26.png\", \"q_text\": \" has a u shaped neck with red heels, and black and gray with no shoulder straps\", \"positive_key\": \"B00BUVAI5K\", \"positive_value\": \"./images/B00BUVAI5K.png\", \"hn_image\": [\"./images/B00B1YKB26.png\"]}\n{\"q_img\": \"./images/B00D3A3HRC.png\", \"q_text\": \"is lighter, and is in all white.\", \"positive_key\": \"B00E0XEJRI\", \"positive_value\": \"./images/B00E0XEJRI.png\", \"hn_image\": [\"./images/B00D3A3HRC.png\"]}\n{\"q_img\": \"./images/B00AX1SATY.png\", \"q_text\": \"is a red dress with a jacket, and the desiered probuct is more sexy\", \"positive_key\": \"B00CJRV3Z8\", \"positive_value\": \"./images/B00CJRV3Z8.png\", \"hn_image\": [\"./images/B00AX1SATY.png\"]}\n{\"q_img\": \"./images/B007HZAFSI.png\", \"q_text\": \"has summery straps and wild print, and patterned and much darker in colour\", \"positive_key\": \"B008MLXYYS\", \"positive_value\": \"./images/B008MLXYYS.png\", \"hn_image\": [\"./images/B007HZAFSI.png\"]}\n{\"q_img\": \"./images/B00B5859Z2.png\", \"q_text\": \"is denim with a brown belt, and is more bright\", \"positive_key\": \"B00CC9QFN8\", \"positive_value\": \"./images/B00CC9QFN8.png\", \"hn_image\": [\"./images/B00B5859Z2.png\"]}\n{\"q_img\": \"./images/B004Q9T840.png\", \"q_text\": \"is plain, and more plain and flowing\", \"positive_key\": \"B004T90LFM\", \"positive_value\": \"./images/B004T90LFM.png\", \"hn_image\": [\"./images/B004Q9T840.png\"]}\n{\"q_img\": \"./images/B00913N1QC.png\", \"q_text\": \"is more official, and is shorter and striped\", \"positive_key\": \"B00EVKYFWO\", \"positive_value\": \"./images/B00EVKYFWO.png\", \"hn_image\": [\"./images/B00913N1QC.png\"]}\n{\"q_img\": \"./images/B004U88SW0.png\", \"q_text\": \"has short sleeves, and lighter colored\", \"positive_key\": \"B00ALSDNIM\", \"positive_value\": \"./images/B00ALSDNIM.png\", \"hn_image\": [\"./images/B004U88SW0.png\"]}\n{\"q_img\": \"./images/B004VJS5QC.png\", \"q_text\": \"Is lighter with a loose fit top, and Is white and shorter\", \"positive_key\": \"B004F9PJEE\", \"positive_value\": \"./images/B004F9PJEE.png\", \"hn_image\": [\"./images/B004VJS5QC.png\"]}\n{\"q_img\": \"./images/B006ZWEMWE.png\", \"q_text\": \"is more colorful and longer, and is more colorful and printed\", \"positive_key\": \"B00CE4GUC2\", \"positive_value\": \"./images/B00CE4GUC2.png\", \"hn_image\": [\"./images/B006ZWEMWE.png\"]}\n{\"q_img\": \"./images/B0088AAXUG.png\", \"q_text\": \"is light white and less shiny with close neck design., and the dress is white and less revealing\", \"positive_key\": \"B009LDY5S0\", \"positive_value\": \"./images/B009LDY5S0.png\", \"hn_image\": [\"./images/B0088AAXUG.png\"]}\n{\"q_img\": \"./images/B00B4GJODI.png\", \"q_text\": \"is more tailored and and less ruffled, and is more professional work attire\", \"positive_key\": \"B004L2L9X0\", \"positive_value\": \"./images/B004L2L9X0.png\", \"hn_image\": [\"./images/B00B4GJODI.png\"]}\n{\"q_img\": \"./images/B008BHHSAY.png\", \"q_text\": \"is white colored and longer sleeves, and is more conservative\", \"positive_key\": \"B00BM1IJ18\", \"positive_value\": \"./images/B00BM1IJ18.png\", \"hn_image\": [\"./images/B008BHHSAY.png\"]}\n{\"q_img\": \"./images/B00B89H8NO.png\", \"q_text\": \"has two on the shoulder sleeves., and has two straps on the shoulders.\", \"positive_key\": \"B00DR8ZWAU\", \"positive_value\": \"./images/B00DR8ZWAU.png\", \"hn_image\": [\"./images/B00B89H8NO.png\"]}\n{\"q_img\": \"./images/B00CLV8QQ6.png\", \"q_text\": \"has a different patern and thin straps, and Is more colorful and tan\", \"positive_key\": \"B00CLF5ZFC\", \"positive_value\": \"./images/B00CLF5ZFC.png\", \"hn_image\": [\"./images/B00CLV8QQ6.png\"]}\n{\"q_img\": \"./images/B00EODXN1W.png\", \"q_text\": \"is shorter with long sleeves and a belt, and orange with ruffled hem\", \"positive_key\": \"B00655AJCS\", \"positive_value\": \"./images/B00655AJCS.png\", \"hn_image\": [\"./images/B00EODXN1W.png\"]}\n{\"q_img\": \"./images/B00F4N004Y.png\", \"q_text\": \"No sleeves and white, and is white and short\", \"positive_key\": \"B007GSFG76\", \"positive_value\": \"./images/B007GSFG76.png\", \"hn_image\": [\"./images/B00F4N004Y.png\"]}\n{\"q_img\": \"./images/B005HEJCDK.png\", \"q_text\": \"is satin pink with uneven ruffles, and Light & Dark color\", \"positive_key\": \"B00ASPCIF2\", \"positive_value\": \"./images/B00ASPCIF2.png\", \"hn_image\": [\"./images/B005HEJCDK.png\"]}\n{\"q_img\": \"./images/B005AQCKBQ.png\", \"q_text\": \"is more covering around neck and belted, and has a smaller collar and is not se through\", \"positive_key\": \"B008VTFOJ4\", \"positive_value\": \"./images/B008VTFOJ4.png\", \"hn_image\": [\"./images/B005AQCKBQ.png\"]}\n{\"q_img\": \"./images/B00CR02CF2.png\", \"q_text\": \"has stripes and is without sleeves, and is plain black\", \"positive_key\": \"B00C45XM6S\", \"positive_value\": \"./images/B00C45XM6S.png\", \"hn_image\": [\"./images/B00CR02CF2.png\"]}\n{\"q_img\": \"./images/B004YADUVS.png\", \"q_text\": \"is a shiny blue color, and is blue satin\", \"positive_key\": \"B0033UVILO\", \"positive_value\": \"./images/B0033UVILO.png\", \"hn_image\": [\"./images/B004YADUVS.png\"]}\n{\"q_img\": \"./images/B008VFX1IE.png\", \"q_text\": \"has a peekaboo cutout and a fitted waist, and is dark green and belted\", \"positive_key\": \"B008YSFCK8\", \"positive_value\": \"./images/B008YSFCK8.png\", \"hn_image\": [\"./images/B008VFX1IE.png\"]}\n{\"q_img\": \"./images/B00E8157H8.png\", \"q_text\": \"looks more formal `, and is darker with a deeper neckline\", \"positive_key\": \"B005DNQB9I\", \"positive_value\": \"./images/B005DNQB9I.png\", \"hn_image\": [\"./images/B00E8157H8.png\"]}\n{\"q_img\": \"./images/B008E6CKVY.png\", \"q_text\": \"is a v shaped neck dress, and has more of a red chinese design and up top has short sleeves in a v shape.\", \"positive_key\": \"B00A9T9N6O\", \"positive_value\": \"./images/B00A9T9N6O.png\", \"hn_image\": [\"./images/B008E6CKVY.png\"]}\n{\"q_img\": \"./images/B006UCPRSC.png\", \"q_text\": \"is black with a more v neck, and is black and is more fitted\", \"positive_key\": \"B007WPHD92\", \"positive_value\": \"./images/B007WPHD92.png\", \"hn_image\": [\"./images/B006UCPRSC.png\"]}\n{\"q_img\": \"./images/B00CL0VWTA.png\", \"q_text\": \"Is dark blue with a belted waist., and is solid blue without sleeves.\", \"positive_key\": \"B00BQX461E\", \"positive_value\": \"./images/B00BQX461E.png\", \"hn_image\": [\"./images/B00CL0VWTA.png\"]}\n{\"q_img\": \"./images/B00BXB7LRA.png\", \"q_text\": \"has a polka dot pattern, and is white\", \"positive_key\": \"B00CGYK1MU\", \"positive_value\": \"./images/B00CGYK1MU.png\", \"hn_image\": [\"./images/B00BXB7LRA.png\"]}\n{\"q_img\": \"./images/B007KANUK0.png\", \"q_text\": \"is a short brown skirt, and is short and shinier\", \"positive_key\": \"B0050JBVQS\", \"positive_value\": \"./images/B0050JBVQS.png\", \"hn_image\": [\"./images/B007KANUK0.png\"]}\n{\"q_img\": \"./images/B008IGRUCE.png\", \"q_text\": \"is lighter and has shorter sleeves, and has shorter sleeves\", \"positive_key\": \"B007XD6JWA\", \"positive_value\": \"./images/B007XD6JWA.png\", \"hn_image\": [\"./images/B008IGRUCE.png\"]}\n{\"q_img\": \"./images/B005N4L15G.png\", \"q_text\": \"is more feminine, and Dark Color\", \"positive_key\": \"B0088B30TG\", \"positive_value\": \"./images/B0088B30TG.png\", \"hn_image\": [\"./images/B005N4L15G.png\"]}\n{\"q_img\": \"./images/B00AG3Y2JG.png\", \"q_text\": \"its black with waist covered, and is black with seperated straps\", \"positive_key\": \"B004AY7K1Y\", \"positive_value\": \"./images/B004AY7K1Y.png\", \"hn_image\": [\"./images/B00AG3Y2JG.png\"]}\n{\"q_img\": \"./images/B00BNRBD32.png\", \"q_text\": \"this dress looks more formal, and has shorter sleeves\", \"positive_key\": \"B00BNRBLIE\", \"positive_value\": \"./images/B00BNRBLIE.png\", \"hn_image\": [\"./images/B00BNRBD32.png\"]}\n{\"q_img\": \"./images/B008H78T6K.png\", \"q_text\": \"is sea green blue with longer sleeves, and is blue-green and has long sleeves\", \"positive_key\": \"B009YKDK9U\", \"positive_value\": \"./images/B009YKDK9U.png\", \"hn_image\": [\"./images/B008H78T6K.png\"]}\n{\"q_img\": \"./images/B007WAT0S4.png\", \"q_text\": \"is purple with a rope belt, and has a pink and purple print and is looser\", \"positive_key\": \"B00CQBL6NG\", \"positive_value\": \"./images/B00CQBL6NG.png\", \"hn_image\": [\"./images/B007WAT0S4.png\"]}\n{\"q_img\": \"./images/B0009T5VWY.png\", \"q_text\": \"is black and shorter, and is shorter and has no sleeves\", \"positive_key\": \"B005QSQX52\", \"positive_value\": \"./images/B005QSQX52.png\", \"hn_image\": [\"./images/B0009T5VWY.png\"]}\n{\"q_img\": \"./images/B006YOU8HG.png\", \"q_text\": \"has no sleeves and is lighter grey, and has striped bottom\", \"positive_key\": \"B0072BCICS\", \"positive_value\": \"./images/B0072BCICS.png\", \"hn_image\": [\"./images/B006YOU8HG.png\"]}\n{\"q_img\": \"./images/B00B1VG358.png\", \"q_text\": \"Has lace colvering the shoulders, and has a black and white print instead of red.\", \"positive_key\": \"B008H62ENA\", \"positive_value\": \"./images/B008H62ENA.png\", \"hn_image\": [\"./images/B00B1VG358.png\"]}\n{\"q_img\": \"./images/B000Z4OH0U.png\", \"q_text\": \"is black with a white belt, and is darker with a collar\", \"positive_key\": \"B00CBYG6VA\", \"positive_value\": \"./images/B00CBYG6VA.png\", \"hn_image\": [\"./images/B000Z4OH0U.png\"]}\n{\"q_img\": \"./images/B002FLMMY4.png\", \"q_text\": \" with no straps, and is more see through\", \"positive_key\": \"B00DHN4WDS\", \"positive_value\": \"./images/B00DHN4WDS.png\", \"hn_image\": [\"./images/B002FLMMY4.png\"]}\n{\"q_img\": \"./images/B006GV3N56.png\", \"q_text\": \"is longer and lighter, and is white with straps\", \"positive_key\": \"B008NBYR2A\", \"positive_value\": \"./images/B008NBYR2A.png\", \"hn_image\": [\"./images/B006GV3N56.png\"]}\n{\"q_img\": \"./images/B00AKR44XM.png\", \"q_text\": \"is red with circular designs, and has shorter sleeves\", \"positive_key\": \"B00BWISRUE\", \"positive_value\": \"./images/B00BWISRUE.png\", \"hn_image\": [\"./images/B00AKR44XM.png\"]}\n{\"q_img\": \"./images/B00DR3TRJ2.png\", \"q_text\": \"is red with a low neck, and is red\", \"positive_key\": \"B00DU6GQ5O\", \"positive_value\": \"./images/B00DU6GQ5O.png\", \"hn_image\": [\"./images/B00DR3TRJ2.png\"]}\n{\"q_img\": \"./images/B008DS1E6K.png\", \"q_text\": \"is more white and has a pattern, and has a different color pattern\", \"positive_key\": \"B008AEELZS\", \"positive_value\": \"./images/B008AEELZS.png\", \"hn_image\": [\"./images/B008DS1E6K.png\"]}\n{\"q_img\": \"./images/B005ZG5MQG.png\", \"q_text\": \"has a ruffle on neck and light green, and is long and gray with a black lace up front\", \"positive_key\": \"B00D9SATFG\", \"positive_value\": \"./images/B00D9SATFG.png\", \"hn_image\": [\"./images/B005ZG5MQG.png\"]}\n{\"q_img\": \"./images/B008LQU49W.png\", \"q_text\": \"is neutral color trench coat top with belt, and coat with ruffled hem and buttons\", \"positive_key\": \"B007X5IFVQ\", \"positive_value\": \"./images/B007X5IFVQ.png\", \"hn_image\": [\"./images/B008LQU49W.png\"]}\n{\"q_img\": \"./images/B002SNAIWC.png\", \"q_text\": \"Is brighter colored and sleeveless., and is brighter\", \"positive_key\": \"B0036MEL4U\", \"positive_value\": \"./images/B0036MEL4U.png\", \"hn_image\": [\"./images/B002SNAIWC.png\"]}\n{\"q_img\": \"./images/B00COB4356.png\", \"q_text\": \"has longer sleeves with floral design, and is in floral print with peach and green.\", \"positive_key\": \"B00GCI8KRE\", \"positive_value\": \"./images/B00GCI8KRE.png\", \"hn_image\": [\"./images/B00COB4356.png\"]}\n{\"q_img\": \"./images/B0090NZVZC.png\", \"q_text\": \"is brighter blue, and is sleeveless\", \"positive_key\": \"B0065U2ZPW\", \"positive_value\": \"./images/B0065U2ZPW.png\", \"hn_image\": [\"./images/B0090NZVZC.png\"]}\n{\"q_img\": \"./images/B00BXX6PM0.png\", \"q_text\": \"Is a pink long sleeved shirt, and has flutter sleeves\", \"positive_key\": \"B008DS1E6K\", \"positive_value\": \"./images/B008DS1E6K.png\", \"hn_image\": [\"./images/B00BXX6PM0.png\"]}\n{\"q_img\": \"./images/B009DMCMNO.png\", \"q_text\": \"is solid white is short sleeves, and all white and has one sleeve\", \"positive_key\": \"B00AFX3FQ8\", \"positive_value\": \"./images/B00AFX3FQ8.png\", \"hn_image\": [\"./images/B009DMCMNO.png\"]}\n{\"q_img\": \"./images/B0095QLSJW.png\", \"q_text\": \"is a darker color, and is a solid darker color\", \"positive_key\": \"B0084ZS2YO\", \"positive_value\": \"./images/B0084ZS2YO.png\", \"hn_image\": [\"./images/B0095QLSJW.png\"]}\n{\"q_img\": \"./images/B0072QWT2M.png\", \"q_text\": \"is fancier with a defined bust, and is a long burgundy dress\", \"positive_key\": \"B0029YQQYE\", \"positive_value\": \"./images/B0029YQQYE.png\", \"hn_image\": [\"./images/B0072QWT2M.png\"]}\n{\"q_img\": \"./images/B0095FP9B6.png\", \"q_text\": \"is more country in a lighter color, and is coral and has a belt.\", \"positive_key\": \"B006ZWE2Y2\", \"positive_value\": \"./images/B006ZWE2Y2.png\", \"hn_image\": [\"./images/B0095FP9B6.png\"]}\n{\"q_img\": \"./images/B0048C7MMU.png\", \"q_text\": \"is shorter white color and has floral, and is orange and more casual\", \"positive_key\": \"B003XRKA9I\", \"positive_value\": \"./images/B003XRKA9I.png\", \"hn_image\": [\"./images/B0048C7MMU.png\"]}\n{\"q_img\": \"./images/B007HC3J1G.png\", \"q_text\": \"dark colored and more revealing, and A solid black short dress\", \"positive_key\": \"B007R1Q9M8\", \"positive_value\": \"./images/B007R1Q9M8.png\", \"hn_image\": [\"./images/B007HC3J1G.png\"]}\n{\"q_img\": \"./images/B00DOYX1VY.png\", \"q_text\": \"has no sleeves and is patterned, and no sleeves and lighter\", \"positive_key\": \"B00CGYJXJ2\", \"positive_value\": \"./images/B00CGYJXJ2.png\", \"hn_image\": [\"./images/B00DOYX1VY.png\"]}\n{\"q_img\": \"./images/B00DSSIGXO.png\", \"q_text\": \"has black stripes and white shoes, and orange print\", \"positive_key\": \"B00BF8B8CA\", \"positive_value\": \"./images/B00BF8B8CA.png\", \"hn_image\": [\"./images/B00DSSIGXO.png\"]}\n{\"q_img\": \"./images/B00FM7EAQQ.png\", \"q_text\": \"is pink mid-sleeved and a black jacket, and is pink with black overcoat\", \"positive_key\": \"B009NQUQBA\", \"positive_value\": \"./images/B009NQUQBA.png\", \"hn_image\": [\"./images/B00FM7EAQQ.png\"]}\n{\"q_img\": \"./images/B0036MEL0O.png\", \"q_text\": \"is more stylish, and is darker and looser\", \"positive_key\": \"B002SNAIWC\", \"positive_value\": \"./images/B002SNAIWC.png\", \"hn_image\": [\"./images/B0036MEL0O.png\"]}\n{\"q_img\": \"./images/B00GAMZJF8.png\", \"q_text\": \"is yellow in colour and has less straps, and is yellow with one strap\", \"positive_key\": \"B00BH9KMO2\", \"positive_value\": \"./images/B00BH9KMO2.png\", \"hn_image\": [\"./images/B00GAMZJF8.png\"]}\n{\"q_img\": \"./images/B00CIEPROU.png\", \"q_text\": \"is blue and white and shorter, and as white and blue\", \"positive_key\": \"B0098BV7YK\", \"positive_value\": \"./images/B0098BV7YK.png\", \"hn_image\": [\"./images/B00CIEPROU.png\"]}\n{\"q_img\": \"./images/B007WA2VB2.png\", \"q_text\": \"Is more striped and more short, and is shorter\", \"positive_key\": \"B008596SUO\", \"positive_value\": \"./images/B008596SUO.png\", \"hn_image\": [\"./images/B007WA2VB2.png\"]}\n{\"q_img\": \"./images/B00997L4QE.png\", \"q_text\": \" higher skirt, and is pinkand open\", \"positive_key\": \"B00DZUQHSQ\", \"positive_value\": \"./images/B00DZUQHSQ.png\", \"hn_image\": [\"./images/B00997L4QE.png\"]}\n{\"q_img\": \"./images/B006TALF54.png\", \"q_text\": \"is sleeveless, and yellow and flowy\", \"positive_key\": \"B001Q3KW9Y\", \"positive_value\": \"./images/B001Q3KW9Y.png\", \"hn_image\": [\"./images/B006TALF54.png\"]}\n{\"q_img\": \"./images/B008S4C8JG.png\", \"q_text\": \"has no sleeves with blue and white horizontal stripes, and tighter\", \"positive_key\": \"B00CGY6SV8\", \"positive_value\": \"./images/B00CGY6SV8.png\", \"hn_image\": [\"./images/B008S4C8JG.png\"]}\n{\"q_img\": \"./images/B0094QXCKQ.png\", \"q_text\": \"is more revealing with a design, and Is more whimsical\", \"positive_key\": \"B002DMJQTE\", \"positive_value\": \"./images/B002DMJQTE.png\", \"hn_image\": [\"./images/B0094QXCKQ.png\"]}\n{\"q_img\": \"./images/B00BTHR98E.png\", \"q_text\": \"is red in color, and is pink without patterns\", \"positive_key\": \"B00DYP24AM\", \"positive_value\": \"./images/B00DYP24AM.png\", \"hn_image\": [\"./images/B00BTHR98E.png\"]}\n{\"q_img\": \"./images/B00741MP7O.png\", \"q_text\": \"has short sleeves with prints, and has a more subtle print\", \"positive_key\": \"B005XW1C70\", \"positive_value\": \"./images/B005XW1C70.png\", \"hn_image\": [\"./images/B00741MP7O.png\"]}\n{\"q_img\": \"./images/B004VSDS2Y.png\", \"q_text\": \"is v-necked printed wrap dress with mid sleeves, and shorter sleeves\", \"positive_key\": \"B0059JPHP0\", \"positive_value\": \"./images/B0059JPHP0.png\", \"hn_image\": [\"./images/B004VSDS2Y.png\"]}\n{\"q_img\": \"./images/B008D6LE3A.png\", \"q_text\": \"has longer sleeves and an animal print pattern, and is darker and has thicker straps\", \"positive_key\": \"B00GDSPIYQ\", \"positive_value\": \"./images/B00GDSPIYQ.png\", \"hn_image\": [\"./images/B008D6LE3A.png\"]}\n{\"q_img\": \"./images/B007VZ35MM.png\", \"q_text\": \"short sleeved, and is 3/4 sleeves and solid pink\", \"positive_key\": \"B004QVUU2M\", \"positive_value\": \"./images/B004QVUU2M.png\", \"hn_image\": [\"./images/B007VZ35MM.png\"]}\n{\"q_img\": \"./images/B0096C2UVA.png\", \"q_text\": \"is darker and a different pattern, and wider straps and darker\", \"positive_key\": \"B006DYG6PA\", \"positive_value\": \"./images/B006DYG6PA.png\", \"hn_image\": [\"./images/B0096C2UVA.png\"]}\n{\"q_img\": \"./images/B003ZLIWEW.png\", \"q_text\": \"is black with long sleeves, and Is darker in color and less revealing\", \"positive_key\": \"B003ILEHQG\", \"positive_value\": \"./images/B003ILEHQG.png\", \"hn_image\": [\"./images/B003ZLIWEW.png\"]}\n{\"q_img\": \"./images/B0058XPHQG.png\", \"q_text\": \"is stripped, and has longer sleeves\", \"positive_key\": \"B008A5ZXZO\", \"positive_value\": \"./images/B008A5ZXZO.png\", \"hn_image\": [\"./images/B0058XPHQG.png\"]}\n{\"q_img\": \"./images/B00BXX6XBS.png\", \"q_text\": \"is shorter and solid red, and is red\", \"positive_key\": \"B00BTUXSYA\", \"positive_value\": \"./images/B00BTUXSYA.png\", \"hn_image\": [\"./images/B00BXX6XBS.png\"]}\n{\"q_img\": \"./images/B00AD11VKO.png\", \"q_text\": \"Is lighter with a ruffle hemline., and is beige and has sleeves\", \"positive_key\": \"B008D5IQFU\", \"positive_value\": \"./images/B008D5IQFU.png\", \"hn_image\": [\"./images/B00AD11VKO.png\"]}\n{\"q_img\": \"./images/B007MLVQ1C.png\", \"q_text\": \"has a pink tone with spagetti straps, and is pink and white\", \"positive_key\": \"B004NSUIUW\", \"positive_value\": \"./images/B004NSUIUW.png\", \"hn_image\": [\"./images/B007MLVQ1C.png\"]}\n{\"q_img\": \"./images/B008DSN0BW.png\", \"q_text\": \"has a fitted waist and belt and s blue with square necklne, and is solid and more fitted\", \"positive_key\": \"B0074H8CXE\", \"positive_value\": \"./images/B0074H8CXE.png\", \"hn_image\": [\"./images/B008DSN0BW.png\"]}\n{\"q_img\": \"./images/B006P044VU.png\", \"q_text\": \"is beige and has ruffles, and has a lower cut neckline with a ruffle\", \"positive_key\": \"B004XBD0JK\", \"positive_value\": \"./images/B004XBD0JK.png\", \"hn_image\": [\"./images/B006P044VU.png\"]}\n{\"q_img\": \"./images/B00DO9M2VE.png\", \"q_text\": \"is longer, and is long with a floral print\", \"positive_key\": \"B005TM7S68\", \"positive_value\": \"./images/B005TM7S68.png\", \"hn_image\": [\"./images/B00DO9M2VE.png\"]}\n{\"q_img\": \"./images/B00B72LAS6.png\", \"q_text\": \"is darker and has a shorted hemline, and is dark colored with longer sleeves\", \"positive_key\": \"B008RCSALO\", \"positive_value\": \"./images/B008RCSALO.png\", \"hn_image\": [\"./images/B00B72LAS6.png\"]}\n{\"q_img\": \"./images/B007XATMYA.png\", \"q_text\": \"is short with no sleeves, and is tighter with more black\", \"positive_key\": \"B004S0O8HO\", \"positive_value\": \"./images/B004S0O8HO.png\", \"hn_image\": [\"./images/B007XATMYA.png\"]}\n{\"q_img\": \"./images/B00B3P7MVG.png\", \"q_text\": \"is gold and black, and is pink and has floral\", \"positive_key\": \"B002PEXJD4\", \"positive_value\": \"./images/B002PEXJD4.png\", \"hn_image\": [\"./images/B00B3P7MVG.png\"]}\n{\"q_img\": \"./images/B009COQELY.png\", \"q_text\": \"v neck short grey dress, and is more slimming and curvier\", \"positive_key\": \"B00E1Q8HMM\", \"positive_value\": \"./images/B00E1Q8HMM.png\", \"hn_image\": [\"./images/B009COQELY.png\"]}\n{\"q_img\": \"./images/B00BXB755S.png\", \"q_text\": \"has spaghetti straps and short front long back, and is multicolored\", \"positive_key\": \"B0017RAI9S\", \"positive_value\": \"./images/B0017RAI9S.png\", \"hn_image\": [\"./images/B00BXB755S.png\"]}\n{\"q_img\": \"./images/B007SYWDWO.png\", \"q_text\": \"is longer with red white and blue strips, and is striped and longer\", \"positive_key\": \"B008VY0EIU\", \"positive_value\": \"./images/B008VY0EIU.png\", \"hn_image\": [\"./images/B007SYWDWO.png\"]}\n{\"q_img\": \"./images/B00AEMHDIG.png\", \"q_text\": \"is solid black with long sleeves, and is dark colored and long sleeves\", \"positive_key\": \"B006BQAHTG\", \"positive_value\": \"./images/B006BQAHTG.png\", \"hn_image\": [\"./images/B00AEMHDIG.png\"]}\n{\"q_img\": \"./images/B0085F4XEG.png\", \"q_text\": \"is pink and sleevless, and is longer in length and shorter sleeves\", \"positive_key\": \"B00FHOMIXG\", \"positive_value\": \"./images/B00FHOMIXG.png\", \"hn_image\": [\"./images/B0085F4XEG.png\"]}\n{\"q_img\": \"./images/B0080E53WI.png\", \"q_text\": \"is a longer fitted solid black, and is darker and longer\", \"positive_key\": \"B007WAELCO\", \"positive_value\": \"./images/B007WAELCO.png\", \"hn_image\": [\"./images/B0080E53WI.png\"]}\n{\"q_img\": \"./images/B008368ZQY.png\", \"q_text\": \"has short sleeves and is less fitted, and is solid black with white accents\", \"positive_key\": \"B007UN64Q4\", \"positive_value\": \"./images/B007UN64Q4.png\", \"hn_image\": [\"./images/B008368ZQY.png\"]}\n{\"q_img\": \"./images/B003Z59PYE.png\", \"q_text\": \"is just about the same with a slightly different design, and It is more pink and less tight\", \"positive_key\": \"B0027RAAUE\", \"positive_value\": \"./images/B0027RAAUE.png\", \"hn_image\": [\"./images/B003Z59PYE.png\"]}\n{\"q_img\": \"./images/B00B9D3JOG.png\", \"q_text\": \"v neck and lighter colored, and Has V-neck and pockets in light color.\", \"positive_key\": \"B0069TD8DW\", \"positive_value\": \"./images/B0069TD8DW.png\", \"hn_image\": [\"./images/B00B9D3JOG.png\"]}\n{\"q_img\": \"./images/B00BPU0D7E.png\", \"q_text\": \"is about the same design but cream in color, and is lighter in color and less flowy\", \"positive_key\": \"B00BPTPJ38\", \"positive_value\": \"./images/B00BPTPJ38.png\", \"hn_image\": [\"./images/B00BPU0D7E.png\"]}\n{\"q_img\": \"./images/B002HCNMTU.png\", \"q_text\": \"is fancier with shorter sleeves, and is teal and purple and has a long train and short in front and short sleeves\", \"positive_key\": \"B004HLHVKA\", \"positive_value\": \"./images/B004HLHVKA.png\", \"hn_image\": [\"./images/B002HCNMTU.png\"]}\n{\"q_img\": \"./images/B004WYVJNC.png\", \"q_text\": \"A long grey belted dress, and is more formal and darker colors\", \"positive_key\": \"B004XY62ZQ\", \"positive_value\": \"./images/B004XY62ZQ.png\", \"hn_image\": [\"./images/B004WYVJNC.png\"]}\n{\"q_img\": \"./images/B007HZAFSI.png\", \"q_text\": \"is lighter and shorter, and has longer sleeves and is brighter colored\", \"positive_key\": \"B00DN5ZX2O\", \"positive_value\": \"./images/B00DN5ZX2O.png\", \"hn_image\": [\"./images/B007HZAFSI.png\"]}\n{\"q_img\": \"./images/B009KQVCAM.png\", \"q_text\": \"has 3/4 sleeved and purple colored, and has longer sleeves\", \"positive_key\": \"B00CBBW5JA\", \"positive_value\": \"./images/B00CBBW5JA.png\", \"hn_image\": [\"./images/B009KQVCAM.png\"]}\n{\"q_img\": \"./images/B008DSN0BW.png\", \"q_text\": \"is shorter and darker, and is more black and fitted\", \"positive_key\": \"B0087YZMHW\", \"positive_value\": \"./images/B0087YZMHW.png\", \"hn_image\": [\"./images/B008DSN0BW.png\"]}\n{\"q_img\": \"./images/B00EZSOR2U.png\", \"q_text\": \"is short and a lighter blue, and is shorter in size with no arms.\", \"positive_key\": \"B003AM8B8S\", \"positive_value\": \"./images/B003AM8B8S.png\", \"hn_image\": [\"./images/B00EZSOR2U.png\"]}\n{\"q_img\": \"./images/B004EHON6W.png\", \"q_text\": \"is black and white striped, and has black and tan stripes\", \"positive_key\": \"B008GSMCEA\", \"positive_value\": \"./images/B008GSMCEA.png\", \"hn_image\": [\"./images/B004EHON6W.png\"]}\n{\"q_img\": \"./images/B007457CVE.png\", \"q_text\": \"is green with a u shaped neck, and is light green and has short sleeves\", \"positive_key\": \"B00EDS3BRO\", \"positive_value\": \"./images/B00EDS3BRO.png\", \"hn_image\": [\"./images/B007457CVE.png\"]}\n{\"q_img\": \"./images/B008NBYR2A.png\", \"q_text\": \"is darker with wider shoulder straps, and more revealing and thicker sleevs\", \"positive_key\": \"B005XC79VS\", \"positive_value\": \"./images/B005XC79VS.png\", \"hn_image\": [\"./images/B008NBYR2A.png\"]}\n{\"q_img\": \"./images/B00711V8Y8.png\", \"q_text\": \"is shorter with more print work, and is shorter and has a floral pattern\", \"positive_key\": \"B00858IL8C\", \"positive_value\": \"./images/B00858IL8C.png\", \"hn_image\": [\"./images/B00711V8Y8.png\"]}\n{\"q_img\": \"./images/B00DERHT5A.png\", \"q_text\": \"is patterned with no belt, and the dress is gray and pink and shorter\", \"positive_key\": \"B00B513RKS\", \"positive_value\": \"./images/B00B513RKS.png\", \"hn_image\": [\"./images/B00DERHT5A.png\"]}\n{\"q_img\": \"./images/B00G6TRZE8.png\", \"q_text\": \"dark colored and less fitted, and has shorter sleeves\", \"positive_key\": \"B00ECLMIKS\", \"positive_value\": \"./images/B00ECLMIKS.png\", \"hn_image\": [\"./images/B00G6TRZE8.png\"]}\n{\"q_img\": \"./images/B00D61ZCGW.png\", \"q_text\": \"is a solid black color. It's also shorter an tighter fitting, and is black and more skimpy\", \"positive_key\": \"B00CLGW2W0\", \"positive_value\": \"./images/B00CLGW2W0.png\", \"hn_image\": [\"./images/B00D61ZCGW.png\"]}\n{\"q_img\": \"./images/B0052B66GY.png\", \"q_text\": \"is short and blue in color, and and with no sleeves\", \"positive_key\": \"B008KYNQBI\", \"positive_value\": \"./images/B008KYNQBI.png\", \"hn_image\": [\"./images/B0052B66GY.png\"]}\n{\"q_img\": \"./images/B007P5H6HS.png\", \"q_text\": \"is black colored and shorter length, and not as long\", \"positive_key\": \"B007SOULGY\", \"positive_value\": \"./images/B007SOULGY.png\", \"hn_image\": [\"./images/B007P5H6HS.png\"]}\n{\"q_img\": \"./images/B006362580.png\", \"q_text\": \"is darker\\\\, and more formal\", \"positive_key\": \"B00462QU3Y\", \"positive_value\": \"./images/B00462QU3Y.png\", \"hn_image\": [\"./images/B006362580.png\"]}\n{\"q_img\": \"./images/B006UCPPZ2.png\", \"q_text\": \"Is a vintage print 3/4 sleeve dress., and is in floral blue and reds.\", \"positive_key\": \"B006W573YI\", \"positive_value\": \"./images/B006W573YI.png\", \"hn_image\": [\"./images/B006UCPPZ2.png\"]}\n{\"q_img\": \"./images/B004HD4V1K.png\", \"q_text\": \"is black, and is farker and more open\", \"positive_key\": \"B00405RUW2\", \"positive_value\": \"./images/B00405RUW2.png\", \"hn_image\": [\"./images/B004HD4V1K.png\"]}\n{\"q_img\": \"./images/B004HSQXPM.png\", \"q_text\": \"is purple and blue and shorter, and has a deeper purple print.\", \"positive_key\": \"B005GLSFJ6\", \"positive_value\": \"./images/B005GLSFJ6.png\", \"hn_image\": [\"./images/B004HSQXPM.png\"]}\n{\"q_img\": \"./images/B00EFE9MII.png\", \"q_text\": \"is pink colored, and is pink and elegant\", \"positive_key\": \"B00APFDIYK\", \"positive_value\": \"./images/B00APFDIYK.png\", \"hn_image\": [\"./images/B00EFE9MII.png\"]}\n{\"q_img\": \"./images/B0022BF5Q4.png\", \"q_text\": \"has straps and a u shaped neck, and has palm trees in the pattern and is sleeveless instead of straps\", \"positive_key\": \"B00B2OIJJM\", \"positive_value\": \"./images/B00B2OIJJM.png\", \"hn_image\": [\"./images/B0022BF5Q4.png\"]}\n{\"q_img\": \"./images/B005LC87SO.png\", \"q_text\": \"is white in color and more longer, and is more conservative\", \"positive_key\": \"B006HTLHY6\", \"positive_value\": \"./images/B006HTLHY6.png\", \"hn_image\": [\"./images/B005LC87SO.png\"]}\n{\"q_img\": \"./images/B008MN5HU0.png\", \"q_text\": \"has spaghetti straps and is solid color, and is shorter and has a sweetheart neckline\", \"positive_key\": \"B007XD5XH2\", \"positive_value\": \"./images/B007XD5XH2.png\", \"hn_image\": [\"./images/B008MN5HU0.png\"]}\n{\"q_img\": \"./images/B00BWKXOS2.png\", \"q_text\": \"is cinched in the middle and pink., and is red and black and more revealing\", \"positive_key\": \"B008E4O7OO\", \"positive_value\": \"./images/B008E4O7OO.png\", \"hn_image\": [\"./images/B00BWKXOS2.png\"]}\n{\"q_img\": \"./images/B00DM03I72.png\", \"q_text\": \"Is shorter and sexier., and is shorter and a shiny silver color\", \"positive_key\": \"B00BSMGUPI\", \"positive_value\": \"./images/B00BSMGUPI.png\", \"hn_image\": [\"./images/B00DM03I72.png\"]}\n{\"q_img\": \"./images/B00D3A3HRC.png\", \"q_text\": \"longer sleeved and longer skirt, and More plain and with sleeves\", \"positive_key\": \"B005UASDM2\", \"positive_value\": \"./images/B005UASDM2.png\", \"hn_image\": [\"./images/B00D3A3HRC.png\"]}\n{\"q_img\": \"./images/B0046Q2FCK.png\", \"q_text\": \"Is a pillow., and is a pillow not a gown\", \"positive_key\": \"B008L7Z1B2\", \"positive_value\": \"./images/B008L7Z1B2.png\", \"hn_image\": [\"./images/B0046Q2FCK.png\"]}\n{\"q_img\": \"./images/B00CJQI0I2.png\", \"q_text\": \"is more striped and shorter, and is light in color and fitted\", \"positive_key\": \"B00FN4A7QA\", \"positive_value\": \"./images/B00FN4A7QA.png\", \"hn_image\": [\"./images/B00CJQI0I2.png\"]}\n{\"q_img\": \"./images/B008E7IA0I.png\", \"q_text\": \"is blue with spaghetti straps, and It is more plain and less longer\", \"positive_key\": \"B00COADNNU\", \"positive_value\": \"./images/B00COADNNU.png\", \"hn_image\": [\"./images/B008E7IA0I.png\"]}\n{\"q_img\": \"./images/B007WATF2U.png\", \"q_text\": \"has shorter sleeves, and has short sleeves\", \"positive_key\": \"B007W9ZSEA\", \"positive_value\": \"./images/B007W9ZSEA.png\", \"hn_image\": [\"./images/B007WATF2U.png\"]}\n{\"q_img\": \"./images/B007GSGKOO.png\", \"q_text\": \"is longer and solid colored, and light color\", \"positive_key\": \"B00AOK25MC\", \"positive_value\": \"./images/B00AOK25MC.png\", \"hn_image\": [\"./images/B007GSGKOO.png\"]}\n{\"q_img\": \"./images/B005F0NOYO.png\", \"q_text\": \"is darker, and Has 2 shoulders and is darker\", \"positive_key\": \"B008368ZQY\", \"positive_value\": \"./images/B008368ZQY.png\", \"hn_image\": [\"./images/B005F0NOYO.png\"]}\n{\"q_img\": \"./images/B00CTSFCE0.png\", \"q_text\": \"Is shorter with an a line cut, and is red with frills\", \"positive_key\": \"B00FFL98LG\", \"positive_value\": \"./images/B00FFL98LG.png\", \"hn_image\": [\"./images/B00CTSFCE0.png\"]}\n{\"q_img\": \"./images/B004Q9T840.png\", \"q_text\": \"has a red belt and less revealing, and has more black and white\", \"positive_key\": \"B00BB92J9E\", \"positive_value\": \"./images/B00BB92J9E.png\", \"hn_image\": [\"./images/B004Q9T840.png\"]}\n{\"q_img\": \"./images/B00CY8T80Y.png\", \"q_text\": \"Is more stylish and preppy, and Is more chic and less flashy\", \"positive_key\": \"B00BAHV7K4\", \"positive_value\": \"./images/B00BAHV7K4.png\", \"hn_image\": [\"./images/B00CY8T80Y.png\"]}\n{\"q_img\": \"./images/B00AAD6KOM.png\", \"q_text\": \"Is more sexy and whimsical, and is longer and more casual.\", \"positive_key\": \"B00DQHXEO8\", \"positive_value\": \"./images/B00DQHXEO8.png\", \"hn_image\": [\"./images/B00AAD6KOM.png\"]}\n{\"q_img\": \"./images/B007FG0AAM.png\", \"q_text\": \"is green, and is lighter and has longer sleeves\", \"positive_key\": \"B004TOPB98\", \"positive_value\": \"./images/B004TOPB98.png\", \"hn_image\": [\"./images/B007FG0AAM.png\"]}\n{\"q_img\": \"./images/B00EP49AYY.png\", \"q_text\": \"Is shorter, and is shorter and more monotoned\", \"positive_key\": \"B007NKXANE\", \"positive_value\": \"./images/B007NKXANE.png\", \"hn_image\": [\"./images/B00EP49AYY.png\"]}\n{\"q_img\": \"./images/B009S3H54Y.png\", \"q_text\": \"less elegant warm, and is tighter and light blue\", \"positive_key\": \"B009S3I9A8\", \"positive_value\": \"./images/B009S3I9A8.png\", \"hn_image\": [\"./images/B009S3H54Y.png\"]}\n{\"q_img\": \"./images/B00B5KOBPY.png\", \"q_text\": \"its darker and less print, and Shiny black and white zebra print with zipper-end front\", \"positive_key\": \"B00A7HP3HQ\", \"positive_value\": \"./images/B00A7HP3HQ.png\", \"hn_image\": [\"./images/B00B5KOBPY.png\"]}\n{\"q_img\": \"./images/B006WARFS2.png\", \"q_text\": \"is darker, and is patterned and much darker\", \"positive_key\": \"B008PF2EHE\", \"positive_value\": \"./images/B008PF2EHE.png\", \"hn_image\": [\"./images/B006WARFS2.png\"]}\n{\"q_img\": \"./images/B008VIR88U.png\", \"q_text\": \"blue black and pink long v neck dress, and is patterned\", \"positive_key\": \"B002QQ9BEC\", \"positive_value\": \"./images/B002QQ9BEC.png\", \"hn_image\": [\"./images/B008VIR88U.png\"]}\n{\"q_img\": \"./images/B009MB17B4.png\", \"q_text\": \"is solid black with small design, and is black\", \"positive_key\": \"B005917FDA\", \"positive_value\": \"./images/B005917FDA.png\", \"hn_image\": [\"./images/B009MB17B4.png\"]}\n{\"q_img\": \"./images/B009CC9GUC.png\", \"q_text\": \"is long with black spots, and no sleeves and longer bottom\", \"positive_key\": \"B00DBDXEHY\", \"positive_value\": \"./images/B00DBDXEHY.png\", \"hn_image\": [\"./images/B009CC9GUC.png\"]}\n{\"q_img\": \"./images/B005WNQOQ4.png\", \"q_text\": \" off-shoulder and tighter fit, and is solid light brown\", \"positive_key\": \"B007W9YZ9O\", \"positive_value\": \"./images/B007W9YZ9O.png\", \"hn_image\": [\"./images/B005WNQOQ4.png\"]}\n{\"q_img\": \"./images/B008CUM27O.png\", \"q_text\": \"is shorter with sleeves, and is blue with sleeves\", \"positive_key\": \"B008ATJYUU\", \"positive_value\": \"./images/B008ATJYUU.png\", \"hn_image\": [\"./images/B008CUM27O.png\"]}\n{\"q_img\": \"./images/B007TYKJ42.png\", \"q_text\": \"has more contrasting colors, and is darker in color and less fitted\", \"positive_key\": \"B009XHO7NW\", \"positive_value\": \"./images/B009XHO7NW.png\", \"hn_image\": [\"./images/B007TYKJ42.png\"]}\n{\"q_img\": \"./images/B00CFMMG8K.png\", \"q_text\": \"is red and knee length, and is red and shorter\", \"positive_key\": \"B007XD18IK\", \"positive_value\": \"./images/B007XD18IK.png\", \"hn_image\": [\"./images/B00CFMMG8K.png\"]}\n{\"q_img\": \"./images/B005OOR6BI.png\", \"q_text\": \"Is a long sleeve short black dress., and is more form fitting\", \"positive_key\": \"B005PL4BTU\", \"positive_value\": \"./images/B005PL4BTU.png\", \"hn_image\": [\"./images/B005OOR6BI.png\"]}\n{\"q_img\": \"./images/B00332FXRW.png\", \"q_text\": \"has longer sleeves, and is black and more shiny\", \"positive_key\": \"B00BSKHRDO\", \"positive_value\": \"./images/B00BSKHRDO.png\", \"hn_image\": [\"./images/B00332FXRW.png\"]}\n{\"q_img\": \"./images/B008GS79VG.png\", \"q_text\": \"is black and a little loose, and has drop sleeves\", \"positive_key\": \"B00C5168K8\", \"positive_value\": \"./images/B00C5168K8.png\", \"hn_image\": [\"./images/B008GS79VG.png\"]}\n{\"q_img\": \"./images/B008N2O3NW.png\", \"q_text\": \"is a white strapless dress. Shorter than the other., and has no sleeves and green color\", \"positive_key\": \"B00A4FRDUQ\", \"positive_value\": \"./images/B00A4FRDUQ.png\", \"hn_image\": [\"./images/B008N2O3NW.png\"]}\n{\"q_img\": \"./images/B00FS3EAOQ.png\", \"q_text\": \"is shorter and thin straps and more revealing, and is yellow and more revealing\", \"positive_key\": \"B004L10OU0\", \"positive_value\": \"./images/B004L10OU0.png\", \"hn_image\": [\"./images/B00FS3EAOQ.png\"]}\n{\"q_img\": \"./images/B002XUY8SA.png\", \"q_text\": \" form fitted, and is blue and lacy\", \"positive_key\": \"B005PP424A\", \"positive_value\": \"./images/B005PP424A.png\", \"hn_image\": [\"./images/B002XUY8SA.png\"]}\n{\"q_img\": \"./images/B008OVRK0U.png\", \"q_text\": \"has more stripes and is lighter, and yellow and white stripes with angled skirt cut\", \"positive_key\": \"B00D83ENMC\", \"positive_value\": \"./images/B00D83ENMC.png\", \"hn_image\": [\"./images/B008OVRK0U.png\"]}\n{\"q_img\": \"./images/B00C2D4XA6.png\", \"q_text\": \"Is a white silk, and has denim\", \"positive_key\": \"B008IGSFKK\", \"positive_value\": \"./images/B008IGSFKK.png\", \"hn_image\": [\"./images/B00C2D4XA6.png\"]}\n{\"q_img\": \"./images/B00CD8JJ7M.png\", \"q_text\": \"is more free and skater, and has a flared bottom and is belted\", \"positive_key\": \"B00EPDRYQG\", \"positive_value\": \"./images/B00EPDRYQG.png\", \"hn_image\": [\"./images/B00CD8JJ7M.png\"]}\n{\"q_img\": \"./images/B0097IWC3Y.png\", \"q_text\": \"a strapless long white bride dress, and is white with no straps\", \"positive_key\": \"B005FOLK78\", \"positive_value\": \"./images/B005FOLK78.png\", \"hn_image\": [\"./images/B0097IWC3Y.png\"]}\n{\"q_img\": \"./images/B00BBKYD9C.png\", \"q_text\": \"Has a geometric pattern, and is fitted and bold black/white\", \"positive_key\": \"B0066CUDNU\", \"positive_value\": \"./images/B0066CUDNU.png\", \"hn_image\": [\"./images/B00BBKYD9C.png\"]}\n{\"q_img\": \"./images/B009UJ3NTW.png\", \"q_text\": \"longer and has a u shaped neck, and is blue with a longer skirt\", \"positive_key\": \"B0094M3I9A\", \"positive_value\": \"./images/B0094M3I9A.png\", \"hn_image\": [\"./images/B009UJ3NTW.png\"]}\n{\"q_img\": \"./images/B008E6CKVY.png\", \"q_text\": \"has shorter sleeves, and Is more sexy and elegant\", \"positive_key\": \"B005QYY5W4\", \"positive_value\": \"./images/B005QYY5W4.png\", \"hn_image\": [\"./images/B008E6CKVY.png\"]}\n{\"q_img\": \"./images/B008RXA5IO.png\", \"q_text\": \"is gray with straps, and is light grey with spaghetti straps\", \"positive_key\": \"B004TPRPNM\", \"positive_value\": \"./images/B004TPRPNM.png\", \"hn_image\": [\"./images/B008RXA5IO.png\"]}\n{\"q_img\": \"./images/B0017RAI9S.png\", \"q_text\": \"has longer sleeves and a different pattern, and with a white and pink stripe and strapless\", \"positive_key\": \"B004I3UEXS\", \"positive_value\": \"./images/B004I3UEXS.png\", \"hn_image\": [\"./images/B0017RAI9S.png\"]}\n{\"q_img\": \"./images/B009CC9GUC.png\", \"q_text\": \"is black with large colorful flowers, and has floral patterns and is darker\", \"positive_key\": \"B00DBDVI7W\", \"positive_value\": \"./images/B00DBDVI7W.png\", \"hn_image\": [\"./images/B009CC9GUC.png\"]}\n{\"q_img\": \"./images/B007FPDWDU.png\", \"q_text\": \"is shorter and shorter sleeved, and Shorter and more colorful\", \"positive_key\": \"B007TUPWAC\", \"positive_value\": \"./images/B007TUPWAC.png\", \"hn_image\": [\"./images/B007FPDWDU.png\"]}\n{\"q_img\": \"./images/B008YDEB6E.png\", \"q_text\": \" half sleeved sweater dress, and black and white\", \"positive_key\": \"B00DURATI8\", \"positive_value\": \"./images/B00DURATI8.png\", \"hn_image\": [\"./images/B008YDEB6E.png\"]}\n{\"q_img\": \"./images/B00DRIKVDI.png\", \"q_text\": \"is purple and white striped and longer, and is longer and striped\", \"positive_key\": \"B00D4BURP0\", \"positive_value\": \"./images/B00D4BURP0.png\", \"hn_image\": [\"./images/B00DRIKVDI.png\"]}\n{\"q_img\": \"./images/B008006FK6.png\", \"q_text\": \"is solid black, and is black with thinner straps\", \"positive_key\": \"B00BL9DCM2\", \"positive_value\": \"./images/B00BL9DCM2.png\", \"hn_image\": [\"./images/B008006FK6.png\"]}\n{\"q_img\": \"./images/B0091QSKHE.png\", \"q_text\": \"is more open at the chest, and is white with straps\", \"positive_key\": \"B00ASIUGVC\", \"positive_value\": \"./images/B00ASIUGVC.png\", \"hn_image\": [\"./images/B0091QSKHE.png\"]}\n{\"q_img\": \"./images/B00A3PEX7S.png\", \"q_text\": \"is less casual with thinner straps, and is a rose pattern with no sleeves\", \"positive_key\": \"B008W02KVC\", \"positive_value\": \"./images/B008W02KVC.png\", \"hn_image\": [\"./images/B00A3PEX7S.png\"]}\n{\"q_img\": \"./images/B0078KOUNI.png\", \"q_text\": \"has longer sleeves. It's also solid black, and has long sleeves and is solid colored\", \"positive_key\": \"B00FHLNKM2\", \"positive_value\": \"./images/B00FHLNKM2.png\", \"hn_image\": [\"./images/B0078KOUNI.png\"]}\n{\"q_img\": \"./images/B00CD8JJ7M.png\", \"q_text\": \"has more stripes, and has white and black stripes.\", \"positive_key\": \"B008HRLQ60\", \"positive_value\": \"./images/B008HRLQ60.png\", \"hn_image\": [\"./images/B00CD8JJ7M.png\"]}\n{\"q_img\": \"./images/B00BSONSN8.png\", \"q_text\": \"is pink with a checkered middle, and is pink and more flowy\", \"positive_key\": \"B009E73G1U\", \"positive_value\": \"./images/B009E73G1U.png\", \"hn_image\": [\"./images/B00BSONSN8.png\"]}\n{\"q_img\": \"./images/B008AEELZS.png\", \"q_text\": \"is gray with flowy sleeves, and is more solid colored\", \"positive_key\": \"B009YLJWUK\", \"positive_value\": \"./images/B009YLJWUK.png\", \"hn_image\": [\"./images/B008AEELZS.png\"]}\n{\"q_img\": \"./images/B00ENCGGZY.png\", \"q_text\": \"is a long blue maxi dress, and tie waist\", \"positive_key\": \"B00B89IP36\", \"positive_value\": \"./images/B00B89IP36.png\", \"hn_image\": [\"./images/B00ENCGGZY.png\"]}\n{\"q_img\": \"./images/B005UX57P0.png\", \"q_text\": \"is more official, and is white with black patterns\", \"positive_key\": \"B0076IK8OC\", \"positive_value\": \"./images/B0076IK8OC.png\", \"hn_image\": [\"./images/B005UX57P0.png\"]}\n{\"q_img\": \"./images/B006AN491W.png\", \"q_text\": \"Is shorter and strapless., and is more sexual\", \"positive_key\": \"B00B7DOJOC\", \"positive_value\": \"./images/B00B7DOJOC.png\", \"hn_image\": [\"./images/B006AN491W.png\"]}\n{\"q_img\": \"./images/B00APKHEMC.png\", \"q_text\": \"Is lighter pink and longer, and Is longer in length\", \"positive_key\": \"B00BUTRK62\", \"positive_value\": \"./images/B00BUTRK62.png\", \"hn_image\": [\"./images/B00APKHEMC.png\"]}\n{\"q_img\": \"./images/B00A4A8PRQ.png\", \"q_text\": \"is shiner, and Yellow\", \"positive_key\": \"B006J42TFU\", \"positive_value\": \"./images/B006J42TFU.png\", \"hn_image\": [\"./images/B00A4A8PRQ.png\"]}\n{\"q_img\": \"./images/B007OX0MXG.png\", \"q_text\": \"is tighter and is more grey, and has ruffled gray in a different pattern.\", \"positive_key\": \"B007W9ZK66\", \"positive_value\": \"./images/B007W9ZK66.png\", \"hn_image\": [\"./images/B007OX0MXG.png\"]}\n{\"q_img\": \"./images/B004U88SW0.png\", \"q_text\": \"is more patterned, and has more designs on it\", \"positive_key\": \"B008QZEOWG\", \"positive_value\": \"./images/B008QZEOWG.png\", \"hn_image\": [\"./images/B004U88SW0.png\"]}\n{\"q_img\": \"./images/B004PKZWNG.png\", \"q_text\": \"is blue in color, and Has long sleeves and a blue pattern\", \"positive_key\": \"B009LGLIIC\", \"positive_value\": \"./images/B009LGLIIC.png\", \"hn_image\": [\"./images/B004PKZWNG.png\"]}\n{\"q_img\": \"./images/B003A84EOM.png\", \"q_text\": \"has both straps, and is babydoll style and sassy\", \"positive_key\": \"B00749W6HA\", \"positive_value\": \"./images/B00749W6HA.png\", \"hn_image\": [\"./images/B003A84EOM.png\"]}\n{\"q_img\": \"./images/B008HQKK1S.png\", \"q_text\": \"has shorter sleeves and is lighter, and is green with shorter sleeves\", \"positive_key\": \"B00BLNL7G6\", \"positive_value\": \"./images/B00BLNL7G6.png\", \"hn_image\": [\"./images/B008HQKK1S.png\"]}\n{\"q_img\": \"./images/B004FPYVO2.png\", \"q_text\": \"is shorter and a darker color, and is shorter and in black.\", \"positive_key\": \"B007RUZ66E\", \"positive_value\": \"./images/B007RUZ66E.png\", \"hn_image\": [\"./images/B004FPYVO2.png\"]}\n{\"q_img\": \"./images/B00AEB8OQ2.png\", \"q_text\": \"blue and black and does not have sequins, and is less shiny\", \"positive_key\": \"B00GCR5OPG\", \"positive_value\": \"./images/B00GCR5OPG.png\", \"hn_image\": [\"./images/B00AEB8OQ2.png\"]}\n{\"q_img\": \"./images/B00CAMMV5S.png\", \"q_text\": \"is less flattering with short sleeves, and has sleeves and is a darker color\", \"positive_key\": \"B00E3HDPMQ\", \"positive_value\": \"./images/B00E3HDPMQ.png\", \"hn_image\": [\"./images/B00CAMMV5S.png\"]}\n{\"q_img\": \"./images/B00D9BZR4Q.png\", \"q_text\": \"is more colorful and lengthier, and is purple and long\", \"positive_key\": \"B00B5GRUR4\", \"positive_value\": \"./images/B00B5GRUR4.png\", \"hn_image\": [\"./images/B00D9BZR4Q.png\"]}\n{\"q_img\": \"./images/B007GSGKOO.png\", \"q_text\": \"high low red and brown dress, and has a shorter skirt and a lighter tan color.\", \"positive_key\": \"B00ARHO3ZY\", \"positive_value\": \"./images/B00ARHO3ZY.png\", \"hn_image\": [\"./images/B007GSGKOO.png\"]}\n{\"q_img\": \"./images/B008YIG3DI.png\", \"q_text\": \"Is a dark blue baby doll dress., and is solid bluish purple\", \"positive_key\": \"B002OEYH3Q\", \"positive_value\": \"./images/B002OEYH3Q.png\", \"hn_image\": [\"./images/B008YIG3DI.png\"]}\n{\"q_img\": \"./images/B004VM5W1K.png\", \"q_text\": \"Is more feminine with shorter sleeves, and is strapless and looser\", \"positive_key\": \"B007FDYS62\", \"positive_value\": \"./images/B007FDYS62.png\", \"hn_image\": [\"./images/B004VM5W1K.png\"]}\n{\"q_img\": \"./images/B0071KR3QG.png\", \"q_text\": \"is patterned with more blues, and It is less flowery and less pink\", \"positive_key\": \"B0027BDMB4\", \"positive_value\": \"./images/B0027BDMB4.png\", \"hn_image\": [\"./images/B0071KR3QG.png\"]}\n{\"q_img\": \"./images/B004JXVQD4.png\", \"q_text\": \"is a solid color and is sleeveless, and is more colorful\", \"positive_key\": \"B0070SSWKK\", \"positive_value\": \"./images/B0070SSWKK.png\", \"hn_image\": [\"./images/B004JXVQD4.png\"]}\n{\"q_img\": \"./images/B005KMQPS4.png\", \"q_text\": \"is longer and dark velvet, and is purple and is much longer\", \"positive_key\": \"B00FB7N4I2\", \"positive_value\": \"./images/B00FB7N4I2.png\", \"hn_image\": [\"./images/B005KMQPS4.png\"]}\n{\"q_img\": \"./images/B007CL8W1O.png\", \"q_text\": \"is a long dress with no straps, and is white and loose\", \"positive_key\": \"B00F150ZXQ\", \"positive_value\": \"./images/B00F150ZXQ.png\", \"hn_image\": [\"./images/B007CL8W1O.png\"]}\n{\"q_img\": \"./images/B00DHVW5N4.png\", \"q_text\": \"has shorter sleeves and different pattern, and a bit brighter and longer\", \"positive_key\": \"B00EY93064\", \"positive_value\": \"./images/B00EY93064.png\", \"hn_image\": [\"./images/B00DHVW5N4.png\"]}\n{\"q_img\": \"./images/B00BUVAI5K.png\", \"q_text\": \"has a v shaped neckline and is plain red, and is just orange with a V neck\", \"positive_key\": \"B00DWGMTHQ\", \"positive_value\": \"./images/B00DWGMTHQ.png\", \"hn_image\": [\"./images/B00BUVAI5K.png\"]}\n{\"q_img\": \"./images/B0063ASXFA.png\", \"q_text\": \"closed shoulder sleeves and dark in color, and is solid brown and covered neck line\", \"positive_key\": \"B00A17JXDC\", \"positive_value\": \"./images/B00A17JXDC.png\", \"hn_image\": [\"./images/B0063ASXFA.png\"]}\n{\"q_img\": \"./images/B00DSSIGXO.png\", \"q_text\": \"has longer sleeves and black and blue, and is blue and striped\", \"positive_key\": \"B0077M7ZUM\", \"positive_value\": \"./images/B0077M7ZUM.png\", \"hn_image\": [\"./images/B00DSSIGXO.png\"]}\n{\"q_img\": \"./images/B0071ALNLC.png\", \"q_text\": \"is a darker shade of blue, and is a darker color\", \"positive_key\": \"B00B6ETQIG\", \"positive_value\": \"./images/B00B6ETQIG.png\", \"hn_image\": [\"./images/B0071ALNLC.png\"]}\n{\"q_img\": \"./images/B00F3YJH8O.png\", \"q_text\": \"green with v neck and tight fit, and is in all blue with a v neck top.\", \"positive_key\": \"B004D85XZW\", \"positive_value\": \"./images/B004D85XZW.png\", \"hn_image\": [\"./images/B00F3YJH8O.png\"]}\n{\"q_img\": \"./images/B00A846WGO.png\", \"q_text\": \"is more green, and is red with latex\", \"positive_key\": \"B008N0MM8W\", \"positive_value\": \"./images/B008N0MM8W.png\", \"hn_image\": [\"./images/B00A846WGO.png\"]}\n{\"q_img\": \"./images/B00499DHK8.png\", \"q_text\": \"is red and white striped, and is red with white striped\", \"positive_key\": \"B00AIL3OAO\", \"positive_value\": \"./images/B00AIL3OAO.png\", \"hn_image\": [\"./images/B00499DHK8.png\"]}\n{\"q_img\": \"./images/B005FLWZCA.png\", \"q_text\": \"has a more symmetric neckline with a tan hue, and is a bronze color with capped sleeves\", \"positive_key\": \"B004YADUVS\", \"positive_value\": \"./images/B004YADUVS.png\", \"hn_image\": [\"./images/B005FLWZCA.png\"]}\n{\"q_img\": \"./images/B009S3GIV0.png\", \"q_text\": \"colorful one shouldered maxi dress, and is much brighter in color\", \"positive_key\": \"B007WPH9BY\", \"positive_value\": \"./images/B007WPH9BY.png\", \"hn_image\": [\"./images/B009S3GIV0.png\"]}\n{\"q_img\": \"./images/B0091EGPXW.png\", \"q_text\": \"has more of a flared skirt with bow tie belt, and lighter blue and more flowing\", \"positive_key\": \"B00BIJGMD6\", \"positive_value\": \"./images/B00BIJGMD6.png\", \"hn_image\": [\"./images/B0091EGPXW.png\"]}\n{\"q_img\": \"./images/B008MNM7Y4.png\", \"q_text\": \"is black and slightly shorter, and is longer and white with a different neckline\", \"positive_key\": \"B008VIQTZS\", \"positive_value\": \"./images/B008VIQTZS.png\", \"hn_image\": [\"./images/B008MNM7Y4.png\"]}\n{\"q_img\": \"./images/B005FSIX5G.png\", \"q_text\": \"the desired prdouct patten is diffrent and the colur is RED comapre to privided product, and has large soda graphic and plunging neckline\", \"positive_key\": \"B005JDB3UO\", \"positive_value\": \"./images/B005JDB3UO.png\", \"hn_image\": [\"./images/B005FSIX5G.png\"]}\n{\"q_img\": \"./images/B004Z56IPC.png\", \"q_text\": \"is tighter and has a leopard print., and leopard pattern and tighter\", \"positive_key\": \"B009401L6O\", \"positive_value\": \"./images/B009401L6O.png\", \"hn_image\": [\"./images/B004Z56IPC.png\"]}\n{\"q_img\": \"./images/B005QN9ET4.png\", \"q_text\": \"is shorter with stripes, and is a white and green v pattern\", \"positive_key\": \"B004VJS5QC\", \"positive_value\": \"./images/B004VJS5QC.png\", \"hn_image\": [\"./images/B005QN9ET4.png\"]}\n{\"q_img\": \"./images/B005VTJH4K.png\", \"q_text\": \"has long sleeves and is a darker color, and has longer sleeves and two stripes\", \"positive_key\": \"B0058XV3BY\", \"positive_value\": \"./images/B0058XV3BY.png\", \"hn_image\": [\"./images/B005VTJH4K.png\"]}\n{\"q_img\": \"./images/B009C6OO8C.png\", \"q_text\": \"is the same compared to provided product, and is more white\", \"positive_key\": \"B00B2FEFNU\", \"positive_value\": \"./images/B00B2FEFNU.png\", \"hn_image\": [\"./images/B009C6OO8C.png\"]}\n{\"q_img\": \"./images/B00C5168K8.png\", \"q_text\": \"is longer and more elegant, and is printed and is longer\", \"positive_key\": \"B0067M3VO2\", \"positive_value\": \"./images/B0067M3VO2.png\", \"hn_image\": [\"./images/B00C5168K8.png\"]}\n{\"q_img\": \"./images/B00CJRV3Z8.png\", \"q_text\": \"is black with white coat, and has all blacks and white colorings.\", \"positive_key\": \"B0087YYYEO\", \"positive_value\": \"./images/B0087YYYEO.png\", \"hn_image\": [\"./images/B00CJRV3Z8.png\"]}\n{\"q_img\": \"./images/B00BI5R31U.png\", \"q_text\": \"is red with belt and lenght above knee, and is maroon and shorter\", \"positive_key\": \"B00DURAWWQ\", \"positive_value\": \"./images/B00DURAWWQ.png\", \"hn_image\": [\"./images/B00BI5R31U.png\"]}\n{\"q_img\": \"./images/B006ZO5GC2.png\", \"q_text\": \"is mustard colour and longer., and is yellow and longer\", \"positive_key\": \"B0080N9HNU\", \"positive_value\": \"./images/B0080N9HNU.png\", \"hn_image\": [\"./images/B006ZO5GC2.png\"]}\n{\"q_img\": \"./images/B004748I22.png\", \"q_text\": \"Tighter and more exposed neck area, and has a shorter tighter skirt.\", \"positive_key\": \"B00466IEVG\", \"positive_value\": \"./images/B00466IEVG.png\", \"hn_image\": [\"./images/B004748I22.png\"]}\n{\"q_img\": \"./images/B005QVUUR6.png\", \"q_text\": \"has longer sleeves and shorter skirt, and is shorter in length\", \"positive_key\": \"B00DWFEPEC\", \"positive_value\": \"./images/B00DWFEPEC.png\", \"hn_image\": [\"./images/B005QVUUR6.png\"]}\n{\"q_img\": \"./images/B00C45XM6S.png\", \"q_text\": \"is more blue and has more sleeves, and is in solid blue with yellow lining.\", \"positive_key\": \"B00BNH31AU\", \"positive_value\": \"./images/B00BNH31AU.png\", \"hn_image\": [\"./images/B00C45XM6S.png\"]}\n{\"q_img\": \"./images/B005QN9ET4.png\", \"q_text\": \" shorter and has shorter sleeves, and is more black and white striped and shorter\", \"positive_key\": \"B00BRE2YD4\", \"positive_value\": \"./images/B00BRE2YD4.png\", \"hn_image\": [\"./images/B005QN9ET4.png\"]}\n{\"q_img\": \"./images/B0073M5RKG.png\", \"q_text\": \"is dark green with a lower neck, and has two straps\", \"positive_key\": \"B009AZRPRM\", \"positive_value\": \"./images/B009AZRPRM.png\", \"hn_image\": [\"./images/B0073M5RKG.png\"]}\n{\"q_img\": \"./images/B0061IXDTU.png\", \"q_text\": \"is a lighter color and has cold shoulder sleeves, and lighter and more forgiving\", \"positive_key\": \"B007WA4020\", \"positive_value\": \"./images/B007WA4020.png\", \"hn_image\": [\"./images/B0061IXDTU.png\"]}\n{\"q_img\": \"./images/B007XCS81G.png\", \"q_text\": \"Checked and brighter, and u shaped neckline\", \"positive_key\": \"B008PC5DGG\", \"positive_value\": \"./images/B008PC5DGG.png\", \"hn_image\": [\"./images/B007XCS81G.png\"]}\n{\"q_img\": \"./images/B0094PFE4E.png\", \"q_text\": \"is darker and has longer sleeves, and is black and white and less revealing at top\", \"positive_key\": \"B00336ESI8\", \"positive_value\": \"./images/B00336ESI8.png\", \"hn_image\": [\"./images/B0094PFE4E.png\"]}\n{\"q_img\": \"./images/B006U07GW4.png\", \"q_text\": \"is brighter and belted, and is light blue and shorter\", \"positive_key\": \"B00AEMHQU6\", \"positive_value\": \"./images/B00AEMHQU6.png\", \"hn_image\": [\"./images/B006U07GW4.png\"]}\n{\"q_img\": \"./images/B004L0XCUK.png\", \"q_text\": \"is purple, and is black with long sleeves\", \"positive_key\": \"B004NAU4G8\", \"positive_value\": \"./images/B004NAU4G8.png\", \"hn_image\": [\"./images/B004L0XCUK.png\"]}\n{\"q_img\": \"./images/B007TWJCBA.png\", \"q_text\": \"is tighter and shorter, and is shorter and lighter in color\", \"positive_key\": \"B005UX57P0\", \"positive_value\": \"./images/B005UX57P0.png\", \"hn_image\": [\"./images/B007TWJCBA.png\"]}\n{\"q_img\": \"./images/B007520NYO.png\", \"q_text\": \"has longer sleeves and is darker, and is black and long sleeves\", \"positive_key\": \"B00E3S6LH6\", \"positive_value\": \"./images/B00E3S6LH6.png\", \"hn_image\": [\"./images/B007520NYO.png\"]}\n{\"q_img\": \"./images/B00CNT5AMY.png\", \"q_text\": \"is longer and ligter, and Is longer and has a white satin effect\", \"positive_key\": \"B00C5XWX0K\", \"positive_value\": \"./images/B00C5XWX0K.png\", \"hn_image\": [\"./images/B00CNT5AMY.png\"]}\n{\"q_img\": \"./images/B005NXMDTU.png\", \"q_text\": \"is red with a high neck, and is a red patterned dress\", \"positive_key\": \"B0088N3HGU\", \"positive_value\": \"./images/B0088N3HGU.png\", \"hn_image\": [\"./images/B005NXMDTU.png\"]}\n{\"q_img\": \"./images/B005QN26TE.png\", \"q_text\": \"is shorter and yellow, and is yellow and shorter\", \"positive_key\": \"B0041553AW\", \"positive_value\": \"./images/B0041553AW.png\", \"hn_image\": [\"./images/B005QN26TE.png\"]}\n{\"q_img\": \"./images/B00AEW4384.png\", \"q_text\": \"is sleeveless with blue, and is white with straps\", \"positive_key\": \"B00F8LGGKE\", \"positive_value\": \"./images/B00F8LGGKE.png\", \"hn_image\": [\"./images/B00AEW4384.png\"]}\n{\"q_img\": \"./images/B00E6RBFFC.png\", \"q_text\": \"is long and is red with thicker straps, and is much longer\", \"positive_key\": \"B008VIV02K\", \"positive_value\": \"./images/B008VIV02K.png\", \"hn_image\": [\"./images/B00E6RBFFC.png\"]}\n{\"q_img\": \"./images/B00C1RQBBM.png\", \"q_text\": \"is more colorful, and is lighter in color and is looser fitted\", \"positive_key\": \"B00B2UEOEK\", \"positive_value\": \"./images/B00B2UEOEK.png\", \"hn_image\": [\"./images/B00C1RQBBM.png\"]}\n{\"q_img\": \"./images/B006TALRY8.png\", \"q_text\": \"is shorter and has no sleeves, and light pink and sleeves\", \"positive_key\": \"B005VTJH4K\", \"positive_value\": \"./images/B005VTJH4K.png\", \"hn_image\": [\"./images/B006TALRY8.png\"]}\n{\"q_img\": \"./images/B008YIG3DI.png\", \"q_text\": \"is pink, and It is more red and has a wider strap\", \"positive_key\": \"B009AMQ2WY\", \"positive_value\": \"./images/B009AMQ2WY.png\", \"hn_image\": [\"./images/B008YIG3DI.png\"]}\n{\"q_img\": \"./images/B00405RUW2.png\", \"q_text\": \"is more colorful and shiny, and has a collar and multicolored top\", \"positive_key\": \"B0043M5UR4\", \"positive_value\": \"./images/B0043M5UR4.png\", \"hn_image\": [\"./images/B00405RUW2.png\"]}\n{\"q_img\": \"./images/B00BTMWMYU.png\", \"q_text\": \"is darker and has longer sleeves, and resembles more closely a long jacket instead of a dress.\", \"positive_key\": \"B0045R3SUI\", \"positive_value\": \"./images/B0045R3SUI.png\", \"hn_image\": [\"./images/B00BTMWMYU.png\"]}\n{\"q_img\": \"./images/B0071MSFCU.png\", \"q_text\": \"is teal and has pockets, and is in a teal blue.\", \"positive_key\": \"B009JY687M\", \"positive_value\": \"./images/B009JY687M.png\", \"hn_image\": [\"./images/B0071MSFCU.png\"]}\n{\"q_img\": \"./images/B00BPBCKQK.png\", \"q_text\": \"Is shorter and light blue., and has sleeves and a v-neck\", \"positive_key\": \"B00F8KXFHW\", \"positive_value\": \"./images/B00F8KXFHW.png\", \"hn_image\": [\"./images/B00BPBCKQK.png\"]}\n{\"q_img\": \"./images/B00DJJILJQ.png\", \"q_text\": \"has collars with pockets and khaki colored, and The desired product is short-sleeved and tan.\", \"positive_key\": \"B004BDP0HU\", \"positive_value\": \"./images/B004BDP0HU.png\", \"hn_image\": [\"./images/B00DJJILJQ.png\"]}\n{\"q_img\": \"./images/B00DQHXEO8.png\", \"q_text\": \"is shorter and more casual, and Has a shorter skirt and longer sleeves\", \"positive_key\": \"B00CL0VWTA\", \"positive_value\": \"./images/B00CL0VWTA.png\", \"hn_image\": [\"./images/B00DQHXEO8.png\"]}\n{\"q_img\": \"./images/B002GU7D5M.png\", \"q_text\": \"is black and grey in color and thicker straps, and more black\", \"positive_key\": \"B0030IMZ1G\", \"positive_value\": \"./images/B0030IMZ1G.png\", \"hn_image\": [\"./images/B002GU7D5M.png\"]}\n{\"q_img\": \"./images/B00BSNJOS2.png\", \"q_text\": \"is darker with more sleeves, and is navy blue with tan flowers\", \"positive_key\": \"B008UBRJPU\", \"positive_value\": \"./images/B008UBRJPU.png\", \"hn_image\": [\"./images/B00BSNJOS2.png\"]}\n{\"q_img\": \"./images/B002YX0KZ6.png\", \"q_text\": \"has no sleeves and shorter, and has long sleeves\", \"positive_key\": \"B005I1NJPY\", \"positive_value\": \"./images/B005I1NJPY.png\", \"hn_image\": [\"./images/B002YX0KZ6.png\"]}\n{\"q_img\": \"./images/B00BVX94NO.png\", \"q_text\": \"is black with sleeves, and is solid black\", \"positive_key\": \"B008ZAD996\", \"positive_value\": \"./images/B008ZAD996.png\", \"hn_image\": [\"./images/B00BVX94NO.png\"]}\n{\"q_img\": \"./images/B0059817JQ.png\", \"q_text\": \"is lighter grey, and grey cotton\", \"positive_key\": \"B005SFZ6VU\", \"positive_value\": \"./images/B005SFZ6VU.png\", \"hn_image\": [\"./images/B0059817JQ.png\"]}\n{\"q_img\": \"./images/B00EM95GJK.png\", \"q_text\": \"is solid black with flared skirt, and is all black and has a looser bottom\", \"positive_key\": \"B00EIRMGKS\", \"positive_value\": \"./images/B00EIRMGKS.png\", \"hn_image\": [\"./images/B00EM95GJK.png\"]}\n{\"q_img\": \"./images/B00BDGMRGA.png\", \"q_text\": \"is long with beige stripes, and has a striped print in white and black with longer skirt.\", \"positive_key\": \"B0060VUQ0C\", \"positive_value\": \"./images/B0060VUQ0C.png\", \"hn_image\": [\"./images/B00BDGMRGA.png\"]}\n{\"q_img\": \"./images/B00BH9R3US.png\", \"q_text\": \"Is shorter and more white, and is shorter above the knees with a full top.\", \"positive_key\": \"B005JW6JRM\", \"positive_value\": \"./images/B005JW6JRM.png\", \"hn_image\": [\"./images/B00BH9R3US.png\"]}\n{\"q_img\": \"./images/B00BG4M4BM.png\", \"q_text\": \"is identical, and na\", \"positive_key\": \"B00B2JIFRS\", \"positive_value\": \"./images/B00B2JIFRS.png\", \"hn_image\": [\"./images/B00BG4M4BM.png\"]}\n{\"q_img\": \"./images/B009KQVCAM.png\", \"q_text\": \"is tan with a halter top, and is tan and haltered\", \"positive_key\": \"B007VP2C7Q\", \"positive_value\": \"./images/B007VP2C7Q.png\", \"hn_image\": [\"./images/B009KQVCAM.png\"]}\n{\"q_img\": \"./images/B009YKCYZ6.png\", \"q_text\": \"is dark blue but the same design, and It is a bit longer and still above knee\", \"positive_key\": \"B008GS79VG\", \"positive_value\": \"./images/B008GS79VG.png\", \"hn_image\": [\"./images/B009YKCYZ6.png\"]}\n{\"q_img\": \"./images/B008B2OOQK.png\", \"q_text\": \"is black and grey, and darker and strapless\", \"positive_key\": \"B0010VH27C\", \"positive_value\": \"./images/B0010VH27C.png\", \"hn_image\": [\"./images/B008B2OOQK.png\"]}\n{\"q_img\": \"./images/B005QYY8F8.png\", \"q_text\": \"has a blet belt, and is more revealing and is lighter colored\", \"positive_key\": \"B005FMSUQ4\", \"positive_value\": \"./images/B005FMSUQ4.png\", \"hn_image\": [\"./images/B005QYY8F8.png\"]}\n{\"q_img\": \"./images/B005DRAVT0.png\", \"q_text\": \"is brown and sleeveless, and is not as dark and has one sleeve\", \"positive_key\": \"B005DRATG0\", \"positive_value\": \"./images/B005DRATG0.png\", \"hn_image\": [\"./images/B005DRAVT0.png\"]}\n{\"q_img\": \"./images/B00B7Q9ZDO.png\", \"q_text\": \"is black red yellow and green, and is rasta colored and strapless\", \"positive_key\": \"B00C5TOAQO\", \"positive_value\": \"./images/B00C5TOAQO.png\", \"hn_image\": [\"./images/B00B7Q9ZDO.png\"]}\n{\"q_img\": \"./images/B00BI3CY4S.png\", \"q_text\": \"is peach with a longer hemline, and has more pink and is more frilly\", \"positive_key\": \"B00DB9S5B8\", \"positive_value\": \"./images/B00DB9S5B8.png\", \"hn_image\": [\"./images/B00BI3CY4S.png\"]}\n{\"q_img\": \"./images/B008KKNM4S.png\", \"q_text\": \"longer dress with long sleeves, and is longer and has longer sleeves\", \"positive_key\": \"B009PKDTEA\", \"positive_value\": \"./images/B009PKDTEA.png\", \"hn_image\": [\"./images/B008KKNM4S.png\"]}\n{\"q_img\": \"./images/B008D5IQFU.png\", \"q_text\": \"Black dress is more shiny, and has more polka dots and is less conservative\", \"positive_key\": \"B005T8EO6O\", \"positive_value\": \"./images/B005T8EO6O.png\", \"hn_image\": [\"./images/B008D5IQFU.png\"]}\n{\"q_img\": \"./images/B003N17E7U.png\", \"q_text\": \"is gold in color and is shinier, and Less Loose\", \"positive_key\": \"B006J3YCOC\", \"positive_value\": \"./images/B006J3YCOC.png\", \"hn_image\": [\"./images/B003N17E7U.png\"]}\n{\"q_img\": \"./images/B00B2JHXOE.png\", \"q_text\": \"is Jones New York, and cannot see the desired product.\", \"positive_key\": \"B00BI3DRHG\", \"positive_value\": \"./images/B00BI3DRHG.png\", \"hn_image\": [\"./images/B00B2JHXOE.png\"]}\n{\"q_img\": \"./images/B00BMAPIB8.png\", \"q_text\": \"less revealing with a turtle neck and long sleeves, and has longer sleeves and is more solid colored\", \"positive_key\": \"B005GA9DEI\", \"positive_value\": \"./images/B005GA9DEI.png\", \"hn_image\": [\"./images/B00BMAPIB8.png\"]}\n{\"q_img\": \"./images/B005UASDM2.png\", \"q_text\": \"is off the shoulders and fitted, and has sleeves and is a darker red\", \"positive_key\": \"B009GY7K4A\", \"positive_value\": \"./images/B009GY7K4A.png\", \"hn_image\": [\"./images/B005UASDM2.png\"]}\n{\"q_img\": \"./images/B00ANC7JCW.png\", \"q_text\": \"Is more fitted and has long-sleeves, and is black and blue with sleeves\", \"positive_key\": \"B00BNMJRDA\", \"positive_value\": \"./images/B00BNMJRDA.png\", \"hn_image\": [\"./images/B00ANC7JCW.png\"]}\n{\"q_img\": \"./images/B00FHXBQFS.png\", \"q_text\": \"is green in colour with shorter sleeves, and casual v neck with cut open sleeves\", \"positive_key\": \"B009KQVCAM\", \"positive_value\": \"./images/B009KQVCAM.png\", \"hn_image\": [\"./images/B00FHXBQFS.png\"]}\n{\"q_img\": \"./images/B0043D2HME.png\", \"q_text\": \"More revealing and more color, and is more white\", \"positive_key\": \"B00DV9636M\", \"positive_value\": \"./images/B00DV9636M.png\", \"hn_image\": [\"./images/B0043D2HME.png\"]}\n{\"q_img\": \"./images/B009ANVEHG.png\", \"q_text\": \"is plain black and longer, and is black and reveals more chest\", \"positive_key\": \"B00598QDJK\", \"positive_value\": \"./images/B00598QDJK.png\", \"hn_image\": [\"./images/B009ANVEHG.png\"]}\n{\"q_img\": \"./images/B00BBU6R46.png\", \"q_text\": \"is bright colored and asymmetrical hemmed, and Is all pink material\", \"positive_key\": \"B0098SKOQK\", \"positive_value\": \"./images/B0098SKOQK.png\", \"hn_image\": [\"./images/B00BBU6R46.png\"]}\n{\"q_img\": \"./images/B007WA38BY.png\", \"q_text\": \"is black in colour and has less straps, and is black and off the shoulder\", \"positive_key\": \"B00BW7Z49M\", \"positive_value\": \"./images/B00BW7Z49M.png\", \"hn_image\": [\"./images/B007WA38BY.png\"]}\n{\"q_img\": \"./images/B008PG0SOO.png\", \"q_text\": \"is brown with halter straps, and is more revealing\", \"positive_key\": \"B004DUM060\", \"positive_value\": \"./images/B004DUM060.png\", \"hn_image\": [\"./images/B008PG0SOO.png\"]}\n{\"q_img\": \"./images/B0092QK1W0.png\", \"q_text\": \"longer and strappier sleeves, and is loger and see through\", \"positive_key\": \"B007R0TNBI\", \"positive_value\": \"./images/B007R0TNBI.png\", \"hn_image\": [\"./images/B0092QK1W0.png\"]}\n{\"q_img\": \"./images/B00E9QQU46.png\", \"q_text\": \"has more ruffles with no straps, and has no sleeves and more texture\", \"positive_key\": \"B0091QSRCM\", \"positive_value\": \"./images/B0091QSRCM.png\", \"hn_image\": [\"./images/B00E9QQU46.png\"]}\n{\"q_img\": \"./images/B00AJ1P6XQ.png\", \"q_text\": \"is pink in color with ruffles, and is sleeveless and frilly\", \"positive_key\": \"B00B907L9S\", \"positive_value\": \"./images/B00B907L9S.png\", \"hn_image\": [\"./images/B00AJ1P6XQ.png\"]}\n{\"q_img\": \"./images/B00ASWY2GI.png\", \"q_text\": \"has a less poofy skirt, and is very similar without as much puff to the shirt.\", \"positive_key\": \"B0091QSWH2\", \"positive_value\": \"./images/B0091QSWH2.png\", \"hn_image\": [\"./images/B00ASWY2GI.png\"]}\n{\"q_img\": \"./images/B00FQANLX2.png\", \"q_text\": \"is patterned, and has darker colors and more sheer\", \"positive_key\": \"B00CBMO7JA\", \"positive_value\": \"./images/B00CBMO7JA.png\", \"hn_image\": [\"./images/B00FQANLX2.png\"]}\n{\"q_img\": \"./images/B004UE9940.png\", \"q_text\": \"Is pink with a sheer overlay., and deeper blue\", \"positive_key\": \"B00DEX03CA\", \"positive_value\": \"./images/B00DEX03CA.png\", \"hn_image\": [\"./images/B004UE9940.png\"]}\n{\"q_img\": \"./images/B00DAU8VIA.png\", \"q_text\": \"is blue with light trim, and is blue with tan trim\", \"positive_key\": \"B00BWRLQUS\", \"positive_value\": \"./images/B00BWRLQUS.png\", \"hn_image\": [\"./images/B00DAU8VIA.png\"]}\n{\"q_img\": \"./images/B0076R88V8.png\", \"q_text\": \"tight fit and dark in color, and has longer sleeves and is gray colored\", \"positive_key\": \"B007G1015U\", \"positive_value\": \"./images/B007G1015U.png\", \"hn_image\": [\"./images/B0076R88V8.png\"]}\n{\"q_img\": \"./images/B007JS1Y7Y.png\", \"q_text\": \"is dark colored and flowy and strapless, and is strapless with a print\", \"positive_key\": \"B004J3492O\", \"positive_value\": \"./images/B004J3492O.png\", \"hn_image\": [\"./images/B007JS1Y7Y.png\"]}\n{\"q_img\": \"./images/B00AZE6UV4.png\", \"q_text\": \"is longer and lighter, and has white and is longer\", \"positive_key\": \"B00B8QSAY8\", \"positive_value\": \"./images/B00B8QSAY8.png\", \"hn_image\": [\"./images/B00AZE6UV4.png\"]}\n{\"q_img\": \"./images/B004W88GB6.png\", \"q_text\": \"is longer and open at the chest, and is long floor length and lighter floral design\", \"positive_key\": \"B0077967DG\", \"positive_value\": \"./images/B0077967DG.png\", \"hn_image\": [\"./images/B004W88GB6.png\"]}\n{\"q_img\": \"./images/B009D3M86O.png\", \"q_text\": \"is pink with a right strap, and is pink and belted\", \"positive_key\": \"B008OKGFF2\", \"positive_value\": \"./images/B008OKGFF2.png\", \"hn_image\": [\"./images/B009D3M86O.png\"]}\n{\"q_img\": \"./images/B004HD4V1K.png\", \"q_text\": \" pink on bottom with smaller straps, and is darker\", \"positive_key\": \"B003TJA7YI\", \"positive_value\": \"./images/B003TJA7YI.png\", \"hn_image\": [\"./images/B004HD4V1K.png\"]}\n{\"q_img\": \"./images/B008E6CKVY.png\", \"q_text\": \"has a pattern and is a v-neck, and has no sleeves and dark colored\", \"positive_key\": \"B004HD55X8\", \"positive_value\": \"./images/B004HD55X8.png\", \"hn_image\": [\"./images/B008E6CKVY.png\"]}\n{\"q_img\": \"./images/B00EVODFJE.png\", \"q_text\": \"none, and is exactly the same\", \"positive_key\": \"B00EVODFOE\", \"positive_value\": \"./images/B00EVODFOE.png\", \"hn_image\": [\"./images/B00EVODFJE.png\"]}\n{\"q_img\": \"./images/B00ATPE8CW.png\", \"q_text\": \"Is a halter dress with polka dots., and has dots\", \"positive_key\": \"B00CELOWN4\", \"positive_value\": \"./images/B00CELOWN4.png\", \"hn_image\": [\"./images/B00ATPE8CW.png\"]}\n{\"q_img\": \"./images/B009G0RL4I.png\", \"q_text\": \"is a long purple maxi dress, and is longer\", \"positive_key\": \"B00B5J8HNW\", \"positive_value\": \"./images/B00B5J8HNW.png\", \"hn_image\": [\"./images/B009G0RL4I.png\"]}\n{\"q_img\": \"./images/B00CQ8JYLK.png\", \"q_text\": \"is more sleek and black, and is more shiny black material\", \"positive_key\": \"B00CIZZ6EK\", \"positive_value\": \"./images/B00CIZZ6EK.png\", \"hn_image\": [\"./images/B00CQ8JYLK.png\"]}\n{\"q_img\": \"./images/B004LQ0GZ8.png\", \"q_text\": \"is blue and less revealing, and is zebra striped and shorter\", \"positive_key\": \"B00AKB3PCY\", \"positive_value\": \"./images/B00AKB3PCY.png\", \"hn_image\": [\"./images/B004LQ0GZ8.png\"]}\n{\"q_img\": \"./images/B007PMBZ00.png\", \"q_text\": \"has no straps and is white, and More revealing and brighter\", \"positive_key\": \"B0076R8AVQ\", \"positive_value\": \"./images/B0076R8AVQ.png\", \"hn_image\": [\"./images/B007PMBZ00.png\"]}\n{\"q_img\": \"./images/B00H2FUGOQ.png\", \"q_text\": \"has shorter sleeves with white buttons and is red, and is a lighter red with shorter sleeves\", \"positive_key\": \"B00E5X7NRQ\", \"positive_value\": \"./images/B00E5X7NRQ.png\", \"hn_image\": [\"./images/B00H2FUGOQ.png\"]}\n{\"q_img\": \"./images/B005HEJCDK.png\", \"q_text\": \"is more adult like, and is pink and longer\", \"positive_key\": \"B0066U2DH6\", \"positive_value\": \"./images/B0066U2DH6.png\", \"hn_image\": [\"./images/B005HEJCDK.png\"]}\n{\"q_img\": \"./images/B003AKZMXM.png\", \"q_text\": \"is tighter with longer sleeves, and is more striaght\", \"positive_key\": \"B009CNGPHS\", \"positive_value\": \"./images/B009CNGPHS.png\", \"hn_image\": [\"./images/B003AKZMXM.png\"]}\n{\"q_img\": \"./images/B00DRZUAJ6.png\", \"q_text\": \"is less revealing, and has a pattern\", \"positive_key\": \"B008RV69JU\", \"positive_value\": \"./images/B008RV69JU.png\", \"hn_image\": [\"./images/B00DRZUAJ6.png\"]}\n{\"q_img\": \"./images/B00DYFK9N6.png\", \"q_text\": \"is more multi colored, and Longer and more colors\", \"positive_key\": \"B00DDHA7YQ\", \"positive_value\": \"./images/B00DDHA7YQ.png\", \"hn_image\": [\"./images/B00DYFK9N6.png\"]}\n{\"q_img\": \"./images/B00GP5TL6I.png\", \"q_text\": \"is flullier, and is sleeveless and two toned\", \"positive_key\": \"B00AEBAJU6\", \"positive_value\": \"./images/B00AEBAJU6.png\", \"hn_image\": [\"./images/B00GP5TL6I.png\"]}\n{\"q_img\": \"./images/B00AYXA3U0.png\", \"q_text\": \"Is a sheath with black and white flowers., and has a black and white pattren\", \"positive_key\": \"B007520NYO\", \"positive_value\": \"./images/B007520NYO.png\", \"hn_image\": [\"./images/B00AYXA3U0.png\"]}\n{\"q_img\": \"./images/B00AAOY6W4.png\", \"q_text\": \"is white and is short, and is white with a shorter hem\", \"positive_key\": \"B0066U2D7G\", \"positive_value\": \"./images/B0066U2D7G.png\", \"hn_image\": [\"./images/B00AAOY6W4.png\"]}\n{\"q_img\": \"./images/B00CLF4QKC.png\", \"q_text\": \"has balloon sleeves and is black, and has shorter sleeves\", \"positive_key\": \"B00EKY4UEY\", \"positive_value\": \"./images/B00EKY4UEY.png\", \"hn_image\": [\"./images/B00CLF4QKC.png\"]}\n{\"q_img\": \"./images/B00BY3GCD6.png\", \"q_text\": \"is more modest and lacier, and sheer top and wavy skirt bottom\", \"positive_key\": \"B00BY3GFWE\", \"positive_value\": \"./images/B00BY3GFWE.png\", \"hn_image\": [\"./images/B00BY3GCD6.png\"]}\n{\"q_img\": \"./images/B008R9FAD8.png\", \"q_text\": \"is tighter and multi-colored, and is striped and has sleeves and is fitted\", \"positive_key\": \"B006MHQUOU\", \"positive_value\": \"./images/B006MHQUOU.png\", \"hn_image\": [\"./images/B008R9FAD8.png\"]}\n{\"q_img\": \"./images/B008D5GUJY.png\", \"q_text\": \"all black and longer, and is black and long\", \"positive_key\": \"B00BJGVXNW\", \"positive_value\": \"./images/B00BJGVXNW.png\", \"hn_image\": [\"./images/B008D5GUJY.png\"]}\n{\"q_img\": \"./images/B0036DOZMC.png\", \"q_text\": \"is more casual and lighter color, and is lighter in color with shorter sleeves\", \"positive_key\": \"B00COB4356\", \"positive_value\": \"./images/B00COB4356.png\", \"hn_image\": [\"./images/B0036DOZMC.png\"]}\n{\"q_img\": \"./images/B0059BGIG0.png\", \"q_text\": \"Is white and more eveing, and is white\", \"positive_key\": \"B008XKC00W\", \"positive_value\": \"./images/B008XKC00W.png\", \"hn_image\": [\"./images/B0059BGIG0.png\"]}\n{\"q_img\": \"./images/B00B923WCQ.png\", \"q_text\": \"has two straps and is more green than blue, and is more conservative and darker in color\", \"positive_key\": \"B00BWA8H0C\", \"positive_value\": \"./images/B00BWA8H0C.png\", \"hn_image\": [\"./images/B00B923WCQ.png\"]}\n{\"q_img\": \"./images/B0076R5V0E.png\", \"q_text\": \"is longer with stripes, and is purple striped and is much longer\", \"positive_key\": \"B004VBELY0\", \"positive_value\": \"./images/B004VBELY0.png\", \"hn_image\": [\"./images/B0076R5V0E.png\"]}\n{\"q_img\": \"./images/B0091WZCE2.png\", \"q_text\": \"is red with no sleeves and flat shoes, and is red\", \"positive_key\": \"B00B72LAS6\", \"positive_value\": \"./images/B00B72LAS6.png\", \"hn_image\": [\"./images/B0091WZCE2.png\"]}\n{\"q_img\": \"./images/B008583T7A.png\", \"q_text\": \"is yellow and orange, and is in peach reds and a little longer\", \"positive_key\": \"B00CY2BD1M\", \"positive_value\": \"./images/B00CY2BD1M.png\", \"hn_image\": [\"./images/B008583T7A.png\"]}\n{\"q_img\": \"./images/B004RJ8IHW.png\", \"q_text\": \"has vest like sleeves, and tighter fitted and dark blue\", \"positive_key\": \"B005GT2DUK\", \"positive_value\": \"./images/B005GT2DUK.png\", \"hn_image\": [\"./images/B004RJ8IHW.png\"]}\n{\"q_img\": \"./images/B005QVUUR6.png\", \"q_text\": \"is white with red bow at waist and more patterned, and is printed and bow tie waist\", \"positive_key\": \"B004X9MA66\", \"positive_value\": \"./images/B004X9MA66.png\", \"hn_image\": [\"./images/B005QVUUR6.png\"]}\n{\"q_img\": \"./images/B006P044VU.png\", \"q_text\": \"is plain in design, and revealing the leg and is in purple only.\", \"positive_key\": \"B006YKWT18\", \"positive_value\": \"./images/B006YKWT18.png\", \"hn_image\": [\"./images/B006P044VU.png\"]}\n{\"q_img\": \"./images/B007TUPWAC.png\", \"q_text\": \"has a floral pattern, and is black and grey and has a belt.\", \"positive_key\": \"B005GT74NQ\", \"positive_value\": \"./images/B005GT74NQ.png\", \"hn_image\": [\"./images/B007TUPWAC.png\"]}\n{\"q_img\": \"./images/B00BAHT21U.png\", \"q_text\": \"has more graphics and is darker, and is more sparkly\", \"positive_key\": \"B00F4MZB7G\", \"positive_value\": \"./images/B00F4MZB7G.png\", \"hn_image\": [\"./images/B00BAHT21U.png\"]}\n{\"q_img\": \"./images/B007PENSBC.png\", \"q_text\": \"is longer and purple, and is longer with zebra stripes\", \"positive_key\": \"B0047MEZNK\", \"positive_value\": \"./images/B0047MEZNK.png\", \"hn_image\": [\"./images/B007PENSBC.png\"]}\n{\"q_img\": \"./images/B0030EHJCU.png\", \"q_text\": \"is longer and black, and is solid black\", \"positive_key\": \"B00A4AN1K2\", \"positive_value\": \"./images/B00A4AN1K2.png\", \"hn_image\": [\"./images/B0030EHJCU.png\"]}\n{\"q_img\": \"./images/B00BFGBJ3K.png\", \"q_text\": \"is a darker, and is solid black\", \"positive_key\": \"B006TD8IUQ\", \"positive_value\": \"./images/B006TD8IUQ.png\", \"hn_image\": [\"./images/B00BFGBJ3K.png\"]}\n{\"q_img\": \"./images/B00D993JHA.png\", \"q_text\": \"has one shoulder and is a different pattern, and Is more colorful\", \"positive_key\": \"B008HW2UT2\", \"positive_value\": \"./images/B008HW2UT2.png\", \"hn_image\": [\"./images/B00D993JHA.png\"]}\n{\"q_img\": \"./images/B00CY8S8P0.png\", \"q_text\": \"has boxed designs with strings, and is darker and more patterned\", \"positive_key\": \"B005F6MYA8\", \"positive_value\": \"./images/B005F6MYA8.png\", \"hn_image\": [\"./images/B00CY8S8P0.png\"]}\n{\"q_img\": \"./images/B00CGY6RJ6.png\", \"q_text\": \"Is lighter with a floral pattern., and is more see through\", \"positive_key\": \"B00833D9TU\", \"positive_value\": \"./images/B00833D9TU.png\", \"hn_image\": [\"./images/B00CGY6RJ6.png\"]}\n{\"q_img\": \"./images/B00FN82BN8.png\", \"q_text\": \"is plain, and has no sleeves and is a bit red\", \"positive_key\": \"B00EKVI27I\", \"positive_value\": \"./images/B00EKVI27I.png\", \"hn_image\": [\"./images/B00FN82BN8.png\"]}\n{\"q_img\": \"./images/B008FG57UE.png\", \"q_text\": \"is looser and off the shoulders, and longer\", \"positive_key\": \"B008BM1A0I\", \"positive_value\": \"./images/B008BM1A0I.png\", \"hn_image\": [\"./images/B008FG57UE.png\"]}\n{\"q_img\": \"./images/B009G8FXPY.png\", \"q_text\": \"is lighter and has shorter sleeves, and is white and less tight\", \"positive_key\": \"B005F0NOYO\", \"positive_value\": \"./images/B005F0NOYO.png\", \"hn_image\": [\"./images/B009G8FXPY.png\"]}\n{\"q_img\": \"./images/B00BJ9IUN0.png\", \"q_text\": \"is a lime green, and is more green and has bigger straps\", \"positive_key\": \"B00B5KKYFK\", \"positive_value\": \"./images/B00B5KKYFK.png\", \"hn_image\": [\"./images/B00BJ9IUN0.png\"]}\n{\"q_img\": \"./images/B007ORZ1PG.png\", \"q_text\": \"is patterned and sleeveless, and is dotted with straps\", \"positive_key\": \"B0092QK9AY\", \"positive_value\": \"./images/B0092QK9AY.png\", \"hn_image\": [\"./images/B007ORZ1PG.png\"]}\n{\"q_img\": \"./images/B00CE4GUC2.png\", \"q_text\": \"has sleeves and no pattern, and It is less plain with short sleeves\", \"positive_key\": \"B00ABSUHMC\", \"positive_value\": \"./images/B00ABSUHMC.png\", \"hn_image\": [\"./images/B00CE4GUC2.png\"]}\n{\"q_img\": \"./images/B00CBBWGQW.png\", \"q_text\": \"is more sexier and shorter, and has a pattern\", \"positive_key\": \"B0066NI17E\", \"positive_value\": \"./images/B0066NI17E.png\", \"hn_image\": [\"./images/B00CBBWGQW.png\"]}\n{\"q_img\": \"./images/B00A9TA2XM.png\", \"q_text\": \"is brown toned with abstract print, and is printed and rounded neckline\", \"positive_key\": \"B008BRLWA6\", \"positive_value\": \"./images/B008BRLWA6.png\", \"hn_image\": [\"./images/B00A9TA2XM.png\"]}\n{\"q_img\": \"./images/B004PYESUA.png\", \"q_text\": \"is darker, and is floral and dark\", \"positive_key\": \"B004P67ZYE\", \"positive_value\": \"./images/B004P67ZYE.png\", \"hn_image\": [\"./images/B004PYESUA.png\"]}\n{\"q_img\": \"./images/B00CBBZOW0.png\", \"q_text\": \"is meant for fatter people, and is more colorful\", \"positive_key\": \"B00CDJZR1S\", \"positive_value\": \"./images/B00CDJZR1S.png\", \"hn_image\": [\"./images/B00CBBZOW0.png\"]}\n{\"q_img\": \"./images/B004MMF00W.png\", \"q_text\": \"is more red and longer, and is darker in color and longer in length\", \"positive_key\": \"B009PKQ6FY\", \"positive_value\": \"./images/B009PKQ6FY.png\", \"hn_image\": [\"./images/B004MMF00W.png\"]}\n{\"q_img\": \"./images/B00BXB7LRA.png\", \"q_text\": \"has not pattern or no straps, and is slightly darker color\", \"positive_key\": \"B009311JG6\", \"positive_value\": \"./images/B009311JG6.png\", \"hn_image\": [\"./images/B00BXB7LRA.png\"]}\n{\"q_img\": \"./images/B00CI08M00.png\", \"q_text\": \"is longer and brighter, and not an A line\", \"positive_key\": \"B00CII5IT0\", \"positive_value\": \"./images/B00CII5IT0.png\", \"hn_image\": [\"./images/B00CI08M00.png\"]}\n{\"q_img\": \"./images/B00AFOMEMI.png\", \"q_text\": \"is lighter with a polka dot patter, and is lighter in color with spots\", \"positive_key\": \"B00AFODS96\", \"positive_value\": \"./images/B00AFODS96.png\", \"hn_image\": [\"./images/B00AFOMEMI.png\"]}\n{\"q_img\": \"./images/B005VZ8MWC.png\", \"q_text\": \"is more revealing and sexy, and has a pattern\", \"positive_key\": \"B0088OXE9Y\", \"positive_value\": \"./images/B0088OXE9Y.png\", \"hn_image\": [\"./images/B005VZ8MWC.png\"]}\n{\"q_img\": \"./images/B00ANK6ND0.png\", \"q_text\": \"is lighter and has longer sleeves, and off the shoulder and less fitted\", \"positive_key\": \"B00AO1VG9Y\", \"positive_value\": \"./images/B00AO1VG9Y.png\", \"hn_image\": [\"./images/B00ANK6ND0.png\"]}\n{\"q_img\": \"./images/B00C4Q2RPO.png\", \"q_text\": \"has longer sleeves, and halter necked\", \"positive_key\": \"B00D4K4IJC\", \"positive_value\": \"./images/B00D4K4IJC.png\", \"hn_image\": [\"./images/B00C4Q2RPO.png\"]}\n{\"q_img\": \"./images/B00DJ7YYD0.png\", \"q_text\": \"is blacker and more pink flowers, and is very similar\", \"positive_key\": \"B00BEXOQB6\", \"positive_value\": \"./images/B00BEXOQB6.png\", \"hn_image\": [\"./images/B00DJ7YYD0.png\"]}\n{\"q_img\": \"./images/B00B59UOBU.png\", \"q_text\": \"contains different shades of purple instead of one color, and is tie dyed and pinkish colors\", \"positive_key\": \"B008TYS0C4\", \"positive_value\": \"./images/B008TYS0C4.png\", \"hn_image\": [\"./images/B00B59UOBU.png\"]}\n{\"q_img\": \"./images/B00D9BZR4Q.png\", \"q_text\": \"Darker and more cleavage, and darker with shinky pieces\", \"positive_key\": \"B00D3A3HRC\", \"positive_value\": \"./images/B00D3A3HRC.png\", \"hn_image\": [\"./images/B00D9BZR4Q.png\"]}\n{\"q_img\": \"./images/B00ASIFVDU.png\", \"q_text\": \"is black and white with no straps, and is a dress\", \"positive_key\": \"B008PRJ394\", \"positive_value\": \"./images/B008PRJ394.png\", \"hn_image\": [\"./images/B00ASIFVDU.png\"]}\n{\"q_img\": \"./images/B0081TB10A.png\", \"q_text\": \" is strapless and is more flowing, and is lighter and shorter\", \"positive_key\": \"B00COL2E4S\", \"positive_value\": \"./images/B00COL2E4S.png\", \"hn_image\": [\"./images/B0081TB10A.png\"]}\n{\"q_img\": \"./images/B009HPIG00.png\", \"q_text\": \"has less ruffles, and is strapless and more fitted bottom\", \"positive_key\": \"B008YQM98I\", \"positive_value\": \"./images/B008YQM98I.png\", \"hn_image\": [\"./images/B009HPIG00.png\"]}\n{\"q_img\": \"./images/B00B5MK7N2.png\", \"q_text\": \"has a flared skirt and is white., and is much lighter in color\", \"positive_key\": \"B009FX8ODI\", \"positive_value\": \"./images/B009FX8ODI.png\", \"hn_image\": [\"./images/B00B5MK7N2.png\"]}\n{\"q_img\": \"./images/B00A846WGO.png\", \"q_text\": \"is less shiney, and is sleeveless\", \"positive_key\": \"B00AHHR22A\", \"positive_value\": \"./images/B00AHHR22A.png\", \"hn_image\": [\"./images/B00A846WGO.png\"]}\n{\"q_img\": \"./images/B00C6PP86S.png\", \"q_text\": \"is blue colored, and is blue and not so low-cut.\", \"positive_key\": \"B0062FN2EI\", \"positive_value\": \"./images/B0062FN2EI.png\", \"hn_image\": [\"./images/B00C6PP86S.png\"]}\n{\"q_img\": \"./images/B00DCC9ZJK.png\", \"q_text\": \"is gray colored, and is gray.\", \"positive_key\": \"B00EA3WE9S\", \"positive_value\": \"./images/B00EA3WE9S.png\", \"hn_image\": [\"./images/B00DCC9ZJK.png\"]}\n{\"q_img\": \"./images/B00EHYXCA0.png\", \"q_text\": \"is the same dress but is maroon, and is red\", \"positive_key\": \"B00968D8ZG\", \"positive_value\": \"./images/B00968D8ZG.png\", \"hn_image\": [\"./images/B00EHYXCA0.png\"]}\n{\"q_img\": \"./images/B00CRNS11I.png\", \"q_text\": \"is much shorter and sportier, and is white and sporty\", \"positive_key\": \"B005LURFWK\", \"positive_value\": \"./images/B005LURFWK.png\", \"hn_image\": [\"./images/B00CRNS11I.png\"]}\n{\"q_img\": \"./images/B00DQBBAAE.png\", \"q_text\": \" and is a darker blue, and fitted\", \"positive_key\": \"B000QSGNOI\", \"positive_value\": \"./images/B000QSGNOI.png\", \"hn_image\": [\"./images/B00DQBBAAE.png\"]}\n{\"q_img\": \"./images/B00CBWGQVM.png\", \"q_text\": \"sleeveless and small dark print, and has pattern and no sleeves\", \"positive_key\": \"B009B7MLZU\", \"positive_value\": \"./images/B009B7MLZU.png\", \"hn_image\": [\"./images/B00CBWGQVM.png\"]}\n{\"q_img\": \"./images/B0088Y0CLW.png\", \"q_text\": \"has shorter sleeves and is lighter, and is a darker deeper red with long sleeves.\", \"positive_key\": \"B006PBGK9I\", \"positive_value\": \"./images/B006PBGK9I.png\", \"hn_image\": [\"./images/B0088Y0CLW.png\"]}\n{\"q_img\": \"./images/B00BP84R3C.png\", \"q_text\": \"has purple designs with black stripes, and shiny and sleeveless\", \"positive_key\": \"B00EPFMQWQ\", \"positive_value\": \"./images/B00EPFMQWQ.png\", \"hn_image\": [\"./images/B00BP84R3C.png\"]}\n{\"q_img\": \"./images/B007OX0MXG.png\", \"q_text\": \"is slightly shorter and hugs the body more, and has a v neck and is tighter\", \"positive_key\": \"B00DNVUBK2\", \"positive_value\": \"./images/B00DNVUBK2.png\", \"hn_image\": [\"./images/B007OX0MXG.png\"]}\n{\"q_img\": \"./images/B007JS1Y7Y.png\", \"q_text\": \"is light blue with some patterns on top, and is lighter\", \"positive_key\": \"B00A76ZQQU\", \"positive_value\": \"./images/B00A76ZQQU.png\", \"hn_image\": [\"./images/B007JS1Y7Y.png\"]}\n{\"q_img\": \"./images/B00C93VT5G.png\", \"q_text\": \"is sexier and darker, and has stripes\", \"positive_key\": \"B00DBA0LLE\", \"positive_value\": \"./images/B00DBA0LLE.png\", \"hn_image\": [\"./images/B00C93VT5G.png\"]}\n{\"q_img\": \"./images/B008BWOCGC.png\", \"q_text\": \"black with lace on bottom, and is more plus size and less frivolous\", \"positive_key\": \"B00FRGH3S4\", \"positive_value\": \"./images/B00FRGH3S4.png\", \"hn_image\": [\"./images/B008BWOCGC.png\"]}\n{\"q_img\": \"./images/B003YKZ42W.png\", \"q_text\": \"is dark green colored and two straps, and more fitting and less frilly with round neck.\", \"positive_key\": \"B007959ZSW\", \"positive_value\": \"./images/B007959ZSW.png\", \"hn_image\": [\"./images/B003YKZ42W.png\"]}\n{\"q_img\": \"./images/B005HQ42E2.png\", \"q_text\": \"more revealing at the cleavage and is dark in color, and is brown with leopard print\", \"positive_key\": \"B0083M20HI\", \"positive_value\": \"./images/B0083M20HI.png\", \"hn_image\": [\"./images/B005HQ42E2.png\"]}\n{\"q_img\": \"./images/B0095JQTLQ.png\", \"q_text\": \"has no buttons, and is blue with no buttons\", \"positive_key\": \"B006362580\", \"positive_value\": \"./images/B006362580.png\", \"hn_image\": [\"./images/B0095JQTLQ.png\"]}\n{\"q_img\": \"./images/B002VLDSRS.png\", \"q_text\": \"does not have a floral pattern on it, and is orange and has large striped accent\", \"positive_key\": \"B004IZRBDW\", \"positive_value\": \"./images/B004IZRBDW.png\", \"hn_image\": [\"./images/B002VLDSRS.png\"]}\n{\"q_img\": \"./images/B007WA29BY.png\", \"q_text\": \"is beige and has thicker straps, and is a nude color\", \"positive_key\": \"B00AFOM43W\", \"positive_value\": \"./images/B00AFOM43W.png\", \"hn_image\": [\"./images/B007WA29BY.png\"]}\n{\"q_img\": \"./images/B005PHRKTW.png\", \"q_text\": \"is mini red satin dress with one sleeved and strap, and red one shoulder butterfly sleeve straight skirt design\", \"positive_key\": \"B005PHZN7I\", \"positive_value\": \"./images/B005PHZN7I.png\", \"hn_image\": [\"./images/B005PHRKTW.png\"]}\n{\"q_img\": \"./images/B00ARMNY1I.png\", \"q_text\": \"is more pink with straps, and is orange with straps\", \"positive_key\": \"B008XOXVBU\", \"positive_value\": \"./images/B008XOXVBU.png\", \"hn_image\": [\"./images/B00ARMNY1I.png\"]}\n{\"q_img\": \"./images/B004Q9T840.png\", \"q_text\": \"is darker and has longer sleeves, and but a different pattern\", \"positive_key\": \"B004V7KUQ2\", \"positive_value\": \"./images/B004V7KUQ2.png\", \"hn_image\": [\"./images/B004Q9T840.png\"]}\n{\"q_img\": \"./images/B00ENEC7K0.png\", \"q_text\": \"not revealing royal blue knee length dress, and Is more conservative\", \"positive_key\": \"B00DR3TRJ2\", \"positive_value\": \"./images/B00DR3TRJ2.png\", \"hn_image\": [\"./images/B00ENEC7K0.png\"]}\n{\"q_img\": \"./images/B00DEP9CUW.png\", \"q_text\": \"Bright and colorful, and is white with floral patterns\", \"positive_key\": \"B00C12LOZU\", \"positive_value\": \"./images/B00C12LOZU.png\", \"hn_image\": [\"./images/B00DEP9CUW.png\"]}\n{\"q_img\": \"./images/B006WARFS2.png\", \"q_text\": \" a-line, and has no sleeves but is longer\", \"positive_key\": \"B008CUM27O\", \"positive_value\": \"./images/B008CUM27O.png\", \"hn_image\": [\"./images/B006WARFS2.png\"]}\n{\"q_img\": \"./images/B00CGY6RKU.png\", \"q_text\": \"has long sleeves and v neck with belted waist, and the dress is black with longer sleeves\", \"positive_key\": \"B00CGY7236\", \"positive_value\": \"./images/B00CGY7236.png\", \"hn_image\": [\"./images/B00CGY6RKU.png\"]}\n{\"q_img\": \"./images/B008VFX1IE.png\", \"q_text\": \"is bright pink and ayered, and is red with seperated sleeves\", \"positive_key\": \"B00F4GWI0U\", \"positive_value\": \"./images/B00F4GWI0U.png\", \"hn_image\": [\"./images/B008VFX1IE.png\"]}\n{\"q_img\": \"./images/B009KQVCAM.png\", \"q_text\": \"Has longer sleeves and is a darker color, and has longer sleeves and an art pattern\", \"positive_key\": \"B00FHXBQFS\", \"positive_value\": \"./images/B00FHXBQFS.png\", \"hn_image\": [\"./images/B009KQVCAM.png\"]}\n{\"q_img\": \"./images/B004QL3YBG.png\", \"q_text\": \"is darker and shorter, and the dress is black and shorter\", \"positive_key\": \"B0037PVUW2\", \"positive_value\": \"./images/B0037PVUW2.png\", \"hn_image\": [\"./images/B004QL3YBG.png\"]}\n{\"q_img\": \"./images/B004BDP1BA.png\", \"q_text\": \"Is a short patterned dress., and is more patterned\", \"positive_key\": \"B007RLR0GC\", \"positive_value\": \"./images/B007RLR0GC.png\", \"hn_image\": [\"./images/B004BDP1BA.png\"]}\n{\"q_img\": \"./images/B007WAFUJW.png\", \"q_text\": \"has longer sleeves and is brown, and More girly and less dark\", \"positive_key\": \"B007WAE0SO\", \"positive_value\": \"./images/B007WAE0SO.png\", \"hn_image\": [\"./images/B007WAFUJW.png\"]}\n{\"q_img\": \"./images/B00CBBZOW0.png\", \"q_text\": \"Is longer and more vibrantly colored, and Is more colorful and longer\", \"positive_key\": \"B00CHICR3G\", \"positive_value\": \"./images/B00CHICR3G.png\", \"hn_image\": [\"./images/B00CBBZOW0.png\"]}\n{\"q_img\": \"./images/B00B2JJ15S.png\", \"q_text\": \"is a solid color and has shorter sleeves, and is blue with no sleeves\", \"positive_key\": \"B00BSNJOS2\", \"positive_value\": \"./images/B00BSNJOS2.png\", \"hn_image\": [\"./images/B00B2JJ15S.png\"]}\n{\"q_img\": \"./images/B009HPIG00.png\", \"q_text\": \"has no straps with black floral print, and Has a pattern\", \"positive_key\": \"B0097IXSGO\", \"positive_value\": \"./images/B0097IXSGO.png\", \"hn_image\": [\"./images/B009HPIG00.png\"]}\n{\"q_img\": \"./images/B00BJGVXNW.png\", \"q_text\": \"is shorter, and is sea through\", \"positive_key\": \"B00AD11LB8\", \"positive_value\": \"./images/B00AD11LB8.png\", \"hn_image\": [\"./images/B00BJGVXNW.png\"]}\n{\"q_img\": \"./images/B00A3PEX7S.png\", \"q_text\": \" strapless and longer in length, and Is less colorful\", \"positive_key\": \"B000FCY9XM\", \"positive_value\": \"./images/B000FCY9XM.png\", \"hn_image\": [\"./images/B00A3PEX7S.png\"]}\n{\"q_img\": \"./images/B007WPHD92.png\", \"q_text\": \"is a long sleeve long black dress, and is longer and more formal\", \"positive_key\": \"B007WPHQJ4\", \"positive_value\": \"./images/B007WPHQJ4.png\", \"hn_image\": [\"./images/B007WPHD92.png\"]}\n{\"q_img\": \"./images/B0091QI6KA.png\", \"q_text\": \"has shorter sleeves and is purple, and is less fitted at top\", \"positive_key\": \"B006QSDAJS\", \"positive_value\": \"./images/B006QSDAJS.png\", \"hn_image\": [\"./images/B0091QI6KA.png\"]}\n{\"q_img\": \"./images/B00BNH7KVQ.png\", \"q_text\": \"Is a short floral patterned blue dress, and has thin straps and a colorful blue pattern\", \"positive_key\": \"B007N6L9UE\", \"positive_value\": \"./images/B007N6L9UE.png\", \"hn_image\": [\"./images/B00BNH7KVQ.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \"v-neck and longed sleeved, and Longer sleeves and longer\", \"positive_key\": \"B00E3S6ERI\", \"positive_value\": \"./images/B00E3S6ERI.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B003ILEHQG.png\", \"q_text\": \"is purple and sleeveless, and is sleeveless and lighter in color\", \"positive_key\": \"B003ZLIWEW\", \"positive_value\": \"./images/B003ZLIWEW.png\", \"hn_image\": [\"./images/B003ILEHQG.png\"]}\n{\"q_img\": \"./images/B00GS6TTU2.png\", \"q_text\": \" black, and is shorter and has less sleeves\", \"positive_key\": \"B00E6X3Y1O\", \"positive_value\": \"./images/B00E6X3Y1O.png\", \"hn_image\": [\"./images/B00GS6TTU2.png\"]}\n{\"q_img\": \"./images/B007P048TC.png\", \"q_text\": \"has bolder stripes and is longer, and is a long dress\", \"positive_key\": \"B007SV9A5A\", \"positive_value\": \"./images/B007SV9A5A.png\", \"hn_image\": [\"./images/B007P048TC.png\"]}\n{\"q_img\": \"./images/B002Z7FONO.png\", \"q_text\": \"is maroon with sheer sleeves, and It is longer and has sleeves\", \"positive_key\": \"B002HCNMTU\", \"positive_value\": \"./images/B002HCNMTU.png\", \"hn_image\": [\"./images/B002Z7FONO.png\"]}\n{\"q_img\": \"./images/B00C17P8W0.png\", \"q_text\": \"is shorter and has a belt, and has floral pattern and more revealling\", \"positive_key\": \"B004MMF00W\", \"positive_value\": \"./images/B004MMF00W.png\", \"hn_image\": [\"./images/B00C17P8W0.png\"]}\n{\"q_img\": \"./images/B0027RAAUE.png\", \"q_text\": \"has a button around its cleavage, and is black and more sexy\", \"positive_key\": \"B0009VT6MI\", \"positive_value\": \"./images/B0009VT6MI.png\", \"hn_image\": [\"./images/B0027RAAUE.png\"]}\n{\"q_img\": \"./images/B00BXB7LRA.png\", \"q_text\": \"Red and more party orientated, and is red with quarter sleeves\", \"positive_key\": \"B00DOYX1VY\", \"positive_value\": \"./images/B00DOYX1VY.png\", \"hn_image\": [\"./images/B00BXB7LRA.png\"]}\n{\"q_img\": \"./images/B006U07GW4.png\", \"q_text\": \"is black with a high neck, and is sleeveless and black\", \"positive_key\": \"B00B5R9XQ4\", \"positive_value\": \"./images/B00B5R9XQ4.png\", \"hn_image\": [\"./images/B006U07GW4.png\"]}\n{\"q_img\": \"./images/B00DU6GMRG.png\", \"q_text\": \"has a more modest neckline and defined waist, and is a blueish green with a high neckline\", \"positive_key\": \"B00E7NYRRS\", \"positive_value\": \"./images/B00E7NYRRS.png\", \"hn_image\": [\"./images/B00DU6GMRG.png\"]}\n{\"q_img\": \"./images/B00ENEI9FW.png\", \"q_text\": \"is longer and floral, and more colorful\", \"positive_key\": \"B00CR02CF2\", \"positive_value\": \"./images/B00CR02CF2.png\", \"hn_image\": [\"./images/B00ENEI9FW.png\"]}\n{\"q_img\": \"./images/B004MMF00W.png\", \"q_text\": \"Is a short red sleeveless dress, and red and tighter skirt\", \"positive_key\": \"B0084DQY14\", \"positive_value\": \"./images/B0084DQY14.png\", \"hn_image\": [\"./images/B004MMF00W.png\"]}\n{\"q_img\": \"./images/B00FXAC5SM.png\", \"q_text\": \"Is darker and has longer sleeves, and has longer sleeves and is dark colored\", \"positive_key\": \"B00GP5TL6I\", \"positive_value\": \"./images/B00GP5TL6I.png\", \"hn_image\": [\"./images/B00FXAC5SM.png\"]}\n{\"q_img\": \"./images/B007KZHNY4.png\", \"q_text\": \"is more revealing, and is shiny and purple and is strapless\", \"positive_key\": \"B005DWYYFC\", \"positive_value\": \"./images/B005DWYYFC.png\", \"hn_image\": [\"./images/B007KZHNY4.png\"]}\n{\"q_img\": \"./images/B008BYUH9Q.png\", \"q_text\": \"is longer and has sleeves, and Is floral without the red.\", \"positive_key\": \"B005G8BDX4\", \"positive_value\": \"./images/B005G8BDX4.png\", \"hn_image\": [\"./images/B008BYUH9Q.png\"]}\n{\"q_img\": \"./images/B00BJFXUBG.png\", \"q_text\": \"A more colorful darker dress without straps, and more colorful with thin straps\", \"positive_key\": \"B007U3P8PC\", \"positive_value\": \"./images/B007U3P8PC.png\", \"hn_image\": [\"./images/B00BJFXUBG.png\"]}\n{\"q_img\": \"./images/B00ABSUHMC.png\", \"q_text\": \"is sleeveless and tighter, and is sleeveless in a darker shiny fabric\", \"positive_key\": \"B00BCMGH6G\", \"positive_value\": \"./images/B00BCMGH6G.png\", \"hn_image\": [\"./images/B00ABSUHMC.png\"]}\n{\"q_img\": \"./images/B007B6WEA0.png\", \"q_text\": \"is black and shorter, and is black with thin straps\", \"positive_key\": \"B007B98014\", \"positive_value\": \"./images/B007B98014.png\", \"hn_image\": [\"./images/B007B6WEA0.png\"]}\n{\"q_img\": \"./images/B005QVUUR6.png\", \"q_text\": \"Is more long-sleeve and fashionable, and has long draping sleeves and open white only design.\", \"positive_key\": \"B00CF3TCRW\", \"positive_value\": \"./images/B00CF3TCRW.png\", \"hn_image\": [\"./images/B005QVUUR6.png\"]}\n{\"q_img\": \"./images/B007EGKM5G.png\", \"q_text\": \"has a ribbon, and has a silver top and a white belt\", \"positive_key\": \"B007HSQM28\", \"positive_value\": \"./images/B007HSQM28.png\", \"hn_image\": [\"./images/B007EGKM5G.png\"]}\n{\"q_img\": \"./images/B00CFEVLBQ.png\", \"q_text\": \"has a higher neckline and shorter skirt, and shoulder straps and more orange\", \"positive_key\": \"B007HWN10K\", \"positive_value\": \"./images/B007HWN10K.png\", \"hn_image\": [\"./images/B00CFEVLBQ.png\"]}\n{\"q_img\": \"./images/B005FOPRRW.png\", \"q_text\": \"is more casual and looser, and is pink and longer and flowy\", \"positive_key\": \"B00DP6SWJW\", \"positive_value\": \"./images/B00DP6SWJW.png\", \"hn_image\": [\"./images/B005FOPRRW.png\"]}\n{\"q_img\": \"./images/B007XD6AMO.png\", \"q_text\": \"is a two piece, and White top with brown/black dots dress.\", \"positive_key\": \"B0080E5BJS\", \"positive_value\": \"./images/B0080E5BJS.png\", \"hn_image\": [\"./images/B007XD6AMO.png\"]}\n{\"q_img\": \"./images/B004U88SW0.png\", \"q_text\": \"has shorter sleeves, and is see through and shorter\", \"positive_key\": \"B0075N3XX6\", \"positive_value\": \"./images/B0075N3XX6.png\", \"hn_image\": [\"./images/B004U88SW0.png\"]}\n{\"q_img\": \"./images/B007FWGBRW.png\", \"q_text\": \"is a black with white dots dress below knee length, and is black and white with longer sleeves\", \"positive_key\": \"B00B3VB34W\", \"positive_value\": \"./images/B00B3VB34W.png\", \"hn_image\": [\"./images/B007FWGBRW.png\"]}\n{\"q_img\": \"./images/B00CJQI0I2.png\", \"q_text\": \"is two piece and beige at bottom., and is shorter and more revealing\", \"positive_key\": \"B00BLCXBA2\", \"positive_value\": \"./images/B00BLCXBA2.png\", \"hn_image\": [\"./images/B00CJQI0I2.png\"]}\n{\"q_img\": \"./images/B00CI1ELFO.png\", \"q_text\": \"is blue with no sleeves and a v neck, and is blue and more revealing\", \"positive_key\": \"B00EPSQ458\", \"positive_value\": \"./images/B00EPSQ458.png\", \"hn_image\": [\"./images/B00CI1ELFO.png\"]}\n{\"q_img\": \"./images/B00E00GL86.png\", \"q_text\": \"is shorter and darker, and is one length a bit above the knee and only has one strap\", \"positive_key\": \"B007FF4S10\", \"positive_value\": \"./images/B007FF4S10.png\", \"hn_image\": [\"./images/B00E00GL86.png\"]}\n{\"q_img\": \"./images/B007VP2C7Q.png\", \"q_text\": \"is longer with crossing straps, and tiered hemline\", \"positive_key\": \"B007VP2AIC\", \"positive_value\": \"./images/B007VP2AIC.png\", \"hn_image\": [\"./images/B007VP2C7Q.png\"]}\n{\"q_img\": \"./images/B00AOWQJZ4.png\", \"q_text\": \"the black dress looks more fashionable, and is white with a fuller bottom\", \"positive_key\": \"B007P7CNYM\", \"positive_value\": \"./images/B007P7CNYM.png\", \"hn_image\": [\"./images/B00AOWQJZ4.png\"]}\n{\"q_img\": \"./images/B007KZHNY4.png\", \"q_text\": \" has longer sleeves and more wintery, and is sleeveless\", \"positive_key\": \"B0055TBJL0\", \"positive_value\": \"./images/B0055TBJL0.png\", \"hn_image\": [\"./images/B007KZHNY4.png\"]}\n{\"q_img\": \"./images/B004GBESAC.png\", \"q_text\": \"is lighter, and Has a pattern and less frills\", \"positive_key\": \"B0080646GU\", \"positive_value\": \"./images/B0080646GU.png\", \"hn_image\": [\"./images/B004GBESAC.png\"]}\n{\"q_img\": \"./images/B008B2OOQK.png\", \"q_text\": \"is one shouldered, and is a muted pink off the shoulder with a ruffled skirt\", \"positive_key\": \"B0067PIC3E\", \"positive_value\": \"./images/B0067PIC3E.png\", \"hn_image\": [\"./images/B008B2OOQK.png\"]}\n{\"q_img\": \"./images/B002TYZGJ0.png\", \"q_text\": \"is of the same color with short sleeves and a u neck shape, and is longer and more flowing\", \"positive_key\": \"B0085F4XEG\", \"positive_value\": \"./images/B0085F4XEG.png\", \"hn_image\": [\"./images/B002TYZGJ0.png\"]}\n{\"q_img\": \"./images/B005HP3HL2.png\", \"q_text\": \"is white and strapless, and is white and gray colored\", \"positive_key\": \"B004XID1GA\", \"positive_value\": \"./images/B004XID1GA.png\", \"hn_image\": [\"./images/B005HP3HL2.png\"]}\n{\"q_img\": \"./images/B00GDSPIYQ.png\", \"q_text\": \"has no sleeves and a red belt, and is slimmer and one color\", \"positive_key\": \"B00DKII46G\", \"positive_value\": \"./images/B00DKII46G.png\", \"hn_image\": [\"./images/B00GDSPIYQ.png\"]}\n{\"q_img\": \"./images/B00ED49C12.png\", \"q_text\": \"is orange colored and sleeveless, and is solid orange and has a halter top\", \"positive_key\": \"B00CTSFCE0\", \"positive_value\": \"./images/B00CTSFCE0.png\", \"hn_image\": [\"./images/B00ED49C12.png\"]}\n{\"q_img\": \"./images/B00CF5GD3Q.png\", \"q_text\": \"has white and black stripes and is longer, and is longer and has blue and white stripes\", \"positive_key\": \"B00DBDCQ7I\", \"positive_value\": \"./images/B00DBDCQ7I.png\", \"hn_image\": [\"./images/B00CF5GD3Q.png\"]}\n{\"q_img\": \"./images/B00CTT41D2.png\", \"q_text\": \"Is shorter and more purple, and is strapless and purple\", \"positive_key\": \"B00CHQCC42\", \"positive_value\": \"./images/B00CHQCC42.png\", \"hn_image\": [\"./images/B00CTT41D2.png\"]}\n{\"q_img\": \"./images/B00BY6HLDI.png\", \"q_text\": \"is pink and ruffled, and is pink and more revealing\", \"positive_key\": \"B008ZYZ3EG\", \"positive_value\": \"./images/B008ZYZ3EG.png\", \"hn_image\": [\"./images/B00BY6HLDI.png\"]}\n{\"q_img\": \"./images/B00EYSAIPG.png\", \"q_text\": \"is all black with a rouched front, and is black and less revealing\", \"positive_key\": \"B00EKZYLS8\", \"positive_value\": \"./images/B00EKZYLS8.png\", \"hn_image\": [\"./images/B00EYSAIPG.png\"]}\n{\"q_img\": \"./images/B00FRS3RDM.png\", \"q_text\": \" with spaghetti straps, and is shorter and colorful\", \"positive_key\": \"B004M6S636\", \"positive_value\": \"./images/B004M6S636.png\", \"hn_image\": [\"./images/B00FRS3RDM.png\"]}\n{\"q_img\": \"./images/B00CSQ15KS.png\", \"q_text\": \"is plain black with short sleeves, and is black and more sexy\", \"positive_key\": \"B0056BKBN4\", \"positive_value\": \"./images/B0056BKBN4.png\", \"hn_image\": [\"./images/B00CSQ15KS.png\"]}\n{\"q_img\": \"./images/B009B8RMZI.png\", \"q_text\": \"no strap floral maxi dress, and long with color\", \"positive_key\": \"B004RNZA9M\", \"positive_value\": \"./images/B004RNZA9M.png\", \"hn_image\": [\"./images/B009B8RMZI.png\"]}\n{\"q_img\": \"./images/B00CQAJI7S.png\", \"q_text\": \"is shorter skirt with brighter colors, and is colorful with sleeves\", \"positive_key\": \"B00B79JENC\", \"positive_value\": \"./images/B00B79JENC.png\", \"hn_image\": [\"./images/B00CQAJI7S.png\"]}\n{\"q_img\": \"./images/B0084YMBQK.png\", \"q_text\": \"is longer and has more stripes, and is longer in length and less dressy\", \"positive_key\": \"B00622HIMS\", \"positive_value\": \"./images/B00622HIMS.png\", \"hn_image\": [\"./images/B0084YMBQK.png\"]}\n{\"q_img\": \"./images/B008S0HK3Y.png\", \"q_text\": \"is a darker red, and is a darker pink\", \"positive_key\": \"B007FG0AAM\", \"positive_value\": \"./images/B007FG0AAM.png\", \"hn_image\": [\"./images/B008S0HK3Y.png\"]}\n{\"q_img\": \"./images/B004RDQ97O.png\", \"q_text\": \"is a short white dress, and with one shoulder and is more fitted\", \"positive_key\": \"B0057WYVU6\", \"positive_value\": \"./images/B0057WYVU6.png\", \"hn_image\": [\"./images/B004RDQ97O.png\"]}\n{\"q_img\": \"./images/B008E6CKVY.png\", \"q_text\": \"has one sholder straps, and with long sleeves\", \"positive_key\": \"B003A84EOM\", \"positive_value\": \"./images/B003A84EOM.png\", \"hn_image\": [\"./images/B008E6CKVY.png\"]}\n{\"q_img\": \"./images/B00B1G5SPY.png\", \"q_text\": \"is shorter and is red, and shows more skin\", \"positive_key\": \"B009Z3QZ6Q\", \"positive_value\": \"./images/B009Z3QZ6Q.png\", \"hn_image\": [\"./images/B00B1G5SPY.png\"]}\n{\"q_img\": \"./images/B005DNSQVY.png\", \"q_text\": \"Is more whimsical and casual, and is more patterned and multi-colored\", \"positive_key\": \"B00COPGWRO\", \"positive_value\": \"./images/B00COPGWRO.png\", \"hn_image\": [\"./images/B005DNSQVY.png\"]}\n{\"q_img\": \"./images/B00E5YA82M.png\", \"q_text\": \"is black and shorter, and is shorter with a silver belt\", \"positive_key\": \"B00CPWOPJ8\", \"positive_value\": \"./images/B00CPWOPJ8.png\", \"hn_image\": [\"./images/B00E5YA82M.png\"]}\n{\"q_img\": \"./images/B00BB92J9E.png\", \"q_text\": \"is solid black and has a v-neck, and has a deep v neckline and is solid black\", \"positive_key\": \"B007GB2BZI\", \"positive_value\": \"./images/B007GB2BZI.png\", \"hn_image\": [\"./images/B00BB92J9E.png\"]}\n{\"q_img\": \"./images/B007M8PCAQ.png\", \"q_text\": \"is quaint and simple., and is grey and has shorter knee\", \"positive_key\": \"B000X4TNTM\", \"positive_value\": \"./images/B000X4TNTM.png\", \"hn_image\": [\"./images/B007M8PCAQ.png\"]}\n{\"q_img\": \"./images/B008BYUH9Q.png\", \"q_text\": \"is flowery with thicker straps, and is more leaves and hawaiian\", \"positive_key\": \"B005UA9538\", \"positive_value\": \"./images/B005UA9538.png\", \"hn_image\": [\"./images/B008BYUH9Q.png\"]}\n{\"q_img\": \"./images/B00DJ7YYD0.png\", \"q_text\": \"is solid black in color, and is shorter\", \"positive_key\": \"B00AW80GO0\", \"positive_value\": \"./images/B00AW80GO0.png\", \"hn_image\": [\"./images/B00DJ7YYD0.png\"]}\n{\"q_img\": \"./images/B007WA38BY.png\", \"q_text\": \"is more playful, and is more colorful\", \"positive_key\": \"B008SOEUGK\", \"positive_value\": \"./images/B008SOEUGK.png\", \"hn_image\": [\"./images/B007WA38BY.png\"]}\n{\"q_img\": \"./images/B00B0ANWFO.png\", \"q_text\": \"is white with longer sleeves and plain pattern, and is lighter and open\", \"positive_key\": \"B00B0AO1GI\", \"positive_value\": \"./images/B00B0AO1GI.png\", \"hn_image\": [\"./images/B00B0ANWFO.png\"]}\n{\"q_img\": \"./images/B007NKVTOQ.png\", \"q_text\": \"is white with orange and yellow pattern, and is white and sexy\", \"positive_key\": \"B0077H5AJU\", \"positive_value\": \"./images/B0077H5AJU.png\", \"hn_image\": [\"./images/B007NKVTOQ.png\"]}\n{\"q_img\": \"./images/B007GP6PME.png\", \"q_text\": \"The blue dress looks more mature party wear, and is more blue with sleeves\", \"positive_key\": \"B007GP6PMO\", \"positive_value\": \"./images/B007GP6PMO.png\", \"hn_image\": [\"./images/B007GP6PME.png\"]}\n{\"q_img\": \"./images/B00E808KYQ.png\", \"q_text\": \"has floral print with straps, and is sleeveless and has lighter colored\", \"positive_key\": \"B007S7AX6Y\", \"positive_value\": \"./images/B007S7AX6Y.png\", \"hn_image\": [\"./images/B00E808KYQ.png\"]}\n{\"q_img\": \"./images/B002W77AY8.png\", \"q_text\": \" longer and more colourful, and purple patterned and longer\", \"positive_key\": \"B00575HZZG\", \"positive_value\": \"./images/B00575HZZG.png\", \"hn_image\": [\"./images/B002W77AY8.png\"]}\n{\"q_img\": \"./images/B00E0ORPVY.png\", \"q_text\": \"is shorter and has pink and black strips, and has more black and is shorter\", \"positive_key\": \"B008OW6IYI\", \"positive_value\": \"./images/B008OW6IYI.png\", \"hn_image\": [\"./images/B00E0ORPVY.png\"]}\n{\"q_img\": \"./images/B004AY7K1Y.png\", \"q_text\": \"pin and striped., and has no sleeves and more stripes\", \"positive_key\": \"B004S35CWG\", \"positive_value\": \"./images/B004S35CWG.png\", \"hn_image\": [\"./images/B004AY7K1Y.png\"]}\n{\"q_img\": \"./images/B00CSQ15KS.png\", \"q_text\": \"has longer sleeves and even ruffles, and is grey with long sleeves\", \"positive_key\": \"B003L5BZSC\", \"positive_value\": \"./images/B003L5BZSC.png\", \"hn_image\": [\"./images/B00CSQ15KS.png\"]}\n{\"q_img\": \"./images/B00B59UOBU.png\", \"q_text\": \"A shorter length, and is more yellow\", \"positive_key\": \"B004TM36ME\", \"positive_value\": \"./images/B004TM36ME.png\", \"hn_image\": [\"./images/B00B59UOBU.png\"]}\n{\"q_img\": \"./images/B006WQZGXC.png\", \"q_text\": \"has no neck strap, and is a hot pink and form fitting\", \"positive_key\": \"B00ET5HWBM\", \"positive_value\": \"./images/B00ET5HWBM.png\", \"hn_image\": [\"./images/B006WQZGXC.png\"]}\n{\"q_img\": \"./images/B007RB7IJG.png\", \"q_text\": \" with fringe, and is longer and smaller stripes\", \"positive_key\": \"B004WI3TPY\", \"positive_value\": \"./images/B004WI3TPY.png\", \"hn_image\": [\"./images/B007RB7IJG.png\"]}\n{\"q_img\": \"./images/B00A0I6GSC.png\", \"q_text\": \"has no sleeves and tight black dress, and is black and striped\", \"positive_key\": \"B00CLF3JPA\", \"positive_value\": \"./images/B00CLF3JPA.png\", \"hn_image\": [\"./images/B00A0I6GSC.png\"]}\n{\"q_img\": \"./images/B00139HWLC.png\", \"q_text\": \"is white and puffy gown type, and is white and longer\", \"positive_key\": \"B001NMKQRG\", \"positive_value\": \"./images/B001NMKQRG.png\", \"hn_image\": [\"./images/B00139HWLC.png\"]}\n{\"q_img\": \"./images/B005DRAVT0.png\", \"q_text\": \"Is more bright and longer, and Has not sleeves and is a lighter color\", \"positive_key\": \"B004MDL5IC\", \"positive_value\": \"./images/B004MDL5IC.png\", \"hn_image\": [\"./images/B005DRAVT0.png\"]}\n{\"q_img\": \"./images/B00BNVAH6M.png\", \"q_text\": \"is grey and white striped, and has grey and white stripes and less revealing\", \"positive_key\": \"B0043W9Z1Q\", \"positive_value\": \"./images/B0043W9Z1Q.png\", \"hn_image\": [\"./images/B00BNVAH6M.png\"]}\n{\"q_img\": \"./images/B0076OCNJE.png\", \"q_text\": \"is blue and white, and has less revealing and is blue color\", \"positive_key\": \"B00C4Q2RPO\", \"positive_value\": \"./images/B00C4Q2RPO.png\", \"hn_image\": [\"./images/B0076OCNJE.png\"]}\n{\"q_img\": \"./images/B00E3HGLU4.png\", \"q_text\": \"is black, and is shorter and no sleeves\", \"positive_key\": \"B000MWSM6A\", \"positive_value\": \"./images/B000MWSM6A.png\", \"hn_image\": [\"./images/B00E3HGLU4.png\"]}\n{\"q_img\": \"./images/B00CRNS11I.png\", \"q_text\": \" green with short sleeves, and is shorter\", \"positive_key\": \"B00CD8JH9M\", \"positive_value\": \"./images/B00CD8JH9M.png\", \"hn_image\": [\"./images/B00CRNS11I.png\"]}\n{\"q_img\": \"./images/B00C1BSDBO.png\", \"q_text\": \"More vintage and girly, and Less black and longer sleeves\", \"positive_key\": \"B00ELLYWL2\", \"positive_value\": \"./images/B00ELLYWL2.png\", \"hn_image\": [\"./images/B00C1BSDBO.png\"]}\n{\"q_img\": \"./images/B00CC6WGQ6.png\", \"q_text\": \"Has lace sleeves and is shorter., and has a v shaped neck\", \"positive_key\": \"B00DGNSEBK\", \"positive_value\": \"./images/B00DGNSEBK.png\", \"hn_image\": [\"./images/B00CC6WGQ6.png\"]}\n{\"q_img\": \"./images/B00CF3TCRW.png\", \"q_text\": \"is more revealing and colorful, and has floral reds and blacks instead of white.\", \"positive_key\": \"B00DDYWTP4\", \"positive_value\": \"./images/B00DDYWTP4.png\", \"hn_image\": [\"./images/B00CF3TCRW.png\"]}\n{\"q_img\": \"./images/B00D3SCKPE.png\", \"q_text\": \"is shorter and darker, and is shorter and is a darker color.\", \"positive_key\": \"B00D6BYMK4\", \"positive_value\": \"./images/B00D6BYMK4.png\", \"hn_image\": [\"./images/B00D3SCKPE.png\"]}\n{\"q_img\": \"./images/B008BM17HY.png\", \"q_text\": \"is more black and revealing, and is more revealing and darker in color\", \"positive_key\": \"B007FFZT6I\", \"positive_value\": \"./images/B007FFZT6I.png\", \"hn_image\": [\"./images/B008BM17HY.png\"]}\n{\"q_img\": \"./images/B00EPC5D10.png\", \"q_text\": \"is more solid and less colorful, and is darker and longer\", \"positive_key\": \"B00BP84R3C\", \"positive_value\": \"./images/B00BP84R3C.png\", \"hn_image\": [\"./images/B00EPC5D10.png\"]}\n{\"q_img\": \"./images/B009B7MLZU.png\", \"q_text\": \"is solid colored and gold, and Is lighter in color with a crew neckline.\", \"positive_key\": \"B00E62JLB2\", \"positive_value\": \"./images/B00E62JLB2.png\", \"hn_image\": [\"./images/B009B7MLZU.png\"]}\n{\"q_img\": \"./images/B00EVODFJE.png\", \"q_text\": \"A low cut lacy dress., and zip front orange print strapless fitted sheath\", \"positive_key\": \"B008BG6R0W\", \"positive_value\": \"./images/B008BG6R0W.png\", \"hn_image\": [\"./images/B00EVODFJE.png\"]}\n{\"q_img\": \"./images/B005AQCKBQ.png\", \"q_text\": \"has a v cut neckline, and has a v neckline\", \"positive_key\": \"B008VQ6NNI\", \"positive_value\": \"./images/B008VQ6NNI.png\", \"hn_image\": [\"./images/B005AQCKBQ.png\"]}\n{\"q_img\": \"./images/B003JH7YJ6.png\", \"q_text\": \"is more sexier, and sweetheart neckline\", \"positive_key\": \"B005VZ8MWC\", \"positive_value\": \"./images/B005VZ8MWC.png\", \"hn_image\": [\"./images/B003JH7YJ6.png\"]}\n{\"q_img\": \"./images/B00CPCMP0Y.png\", \"q_text\": \"has an open neck and shoulder and fitted, and is lighter and more revealing\", \"positive_key\": \"B00AEW46F4\", \"positive_value\": \"./images/B00AEW46F4.png\", \"hn_image\": [\"./images/B00CPCMP0Y.png\"]}\n{\"q_img\": \"./images/B002X79Z72.png\", \"q_text\": \"is a lighter shade of purple with a belt tie belt, and and is tighter fitting\", \"positive_key\": \"B00CG5Q9XO\", \"positive_value\": \"./images/B00CG5Q9XO.png\", \"hn_image\": [\"./images/B002X79Z72.png\"]}\n{\"q_img\": \"./images/B00A784UKG.png\", \"q_text\": \"is gray with a floral design, and is see through\", \"positive_key\": \"B004RKVGDY\", \"positive_value\": \"./images/B004RKVGDY.png\", \"hn_image\": [\"./images/B00A784UKG.png\"]}\n{\"q_img\": \"./images/B00CY0CF0M.png\", \"q_text\": \"Is longer and more whimsical, and is a darker color\", \"positive_key\": \"B008MJWKAE\", \"positive_value\": \"./images/B008MJWKAE.png\", \"hn_image\": [\"./images/B00CY0CF0M.png\"]}\n{\"q_img\": \"./images/B009HK2P2A.png\", \"q_text\": \"has no sleeves and shiny red, and na\", \"positive_key\": \"B005X4RALA\", \"positive_value\": \"./images/B005X4RALA.png\", \"hn_image\": [\"./images/B009HK2P2A.png\"]}\n{\"q_img\": \"./images/B00DV2P9ZA.png\", \"q_text\": \"is black with large white flowers and short, and Is black with print and shorter in length\", \"positive_key\": \"B004FN1TTO\", \"positive_value\": \"./images/B004FN1TTO.png\", \"hn_image\": [\"./images/B00DV2P9ZA.png\"]}\n{\"q_img\": \"./images/B00AO9RH30.png\", \"q_text\": \"Has a patterned top and solid skirt., and red lace\", \"positive_key\": \"B00AEYZ35E\", \"positive_value\": \"./images/B00AEYZ35E.png\", \"hn_image\": [\"./images/B00AO9RH30.png\"]}\n{\"q_img\": \"./images/B00CTT41D2.png\", \"q_text\": \"is shinier and more golden, and shinier metallic gold\", \"positive_key\": \"B00CG70Y60\", \"positive_value\": \"./images/B00CG70Y60.png\", \"hn_image\": [\"./images/B00CTT41D2.png\"]}\n{\"q_img\": \"./images/B00B3YPJY4.png\", \"q_text\": \"has longer sleeves and is less bridal, and has long sleeves and is green\", \"positive_key\": \"B00D8BR0ZQ\", \"positive_value\": \"./images/B00D8BR0ZQ.png\", \"hn_image\": [\"./images/B00B3YPJY4.png\"]}\n{\"q_img\": \"./images/B00C45XM6S.png\", \"q_text\": \"is pink and black with thin straps, and Is floral with halter neck and empire waist.\", \"positive_key\": \"B0071KR3QG\", \"positive_value\": \"./images/B0071KR3QG.png\", \"hn_image\": [\"./images/B00C45XM6S.png\"]}\n{\"q_img\": \"./images/B00DR3TPIA.png\", \"q_text\": \"is striped all around, and Black and White stripe. Long sleeves\", \"positive_key\": \"B00DU5DJ8W\", \"positive_value\": \"./images/B00DU5DJ8W.png\", \"hn_image\": [\"./images/B00DR3TPIA.png\"]}\n{\"q_img\": \"./images/B0087PLHB6.png\", \"q_text\": \"is longer and more formal, and is much longer\", \"positive_key\": \"B00AAPBVBW\", \"positive_value\": \"./images/B00AAPBVBW.png\", \"hn_image\": [\"./images/B0087PLHB6.png\"]}\n{\"q_img\": \"./images/B00E95L22C.png\", \"q_text\": \"is blue with a black stripe, and Has more blue and is tighter\", \"positive_key\": \"B008H1C5IE\", \"positive_value\": \"./images/B008H1C5IE.png\", \"hn_image\": [\"./images/B00E95L22C.png\"]}\n{\"q_img\": \"./images/B00B2JJ15S.png\", \"q_text\": \"Is more evening and more pink, and is pink with straps\", \"positive_key\": \"B007ZDHJ3G\", \"positive_value\": \"./images/B007ZDHJ3G.png\", \"hn_image\": [\"./images/B00B2JJ15S.png\"]}\n{\"q_img\": \"./images/B00AGC26WM.png\", \"q_text\": \"is darker and has longer sleeves, and is multicolored with sleeves\", \"positive_key\": \"B008G0ERA0\", \"positive_value\": \"./images/B008G0ERA0.png\", \"hn_image\": [\"./images/B00AGC26WM.png\"]}\n{\"q_img\": \"./images/B007JS1Y7Y.png\", \"q_text\": \"is more black, and is solid colored and shorter sleeves\", \"positive_key\": \"B002Y2X7NE\", \"positive_value\": \"./images/B002Y2X7NE.png\", \"hn_image\": [\"./images/B007JS1Y7Y.png\"]}\n{\"q_img\": \"./images/B006603WEE.png\", \"q_text\": \"has brighter colors and a mod print, and does not have a belt and is slightly longer\", \"positive_key\": \"B00BYRIGD6\", \"positive_value\": \"./images/B00BYRIGD6.png\", \"hn_image\": [\"./images/B006603WEE.png\"]}\n{\"q_img\": \"./images/B009Q25IYG.png\", \"q_text\": \"is a halter top and two solid colors, and yellow and green striped\", \"positive_key\": \"B00DUFCJCY\", \"positive_value\": \"./images/B00DUFCJCY.png\", \"hn_image\": [\"./images/B009Q25IYG.png\"]}\n{\"q_img\": \"./images/B00DN2F1YM.png\", \"q_text\": \"halter neck and flower print, and Is short with halter neck.\", \"positive_key\": \"B0074W1CWC\", \"positive_value\": \"./images/B0074W1CWC.png\", \"hn_image\": [\"./images/B00DN2F1YM.png\"]}\n{\"q_img\": \"./images/B001JDGNS0.png\", \"q_text\": \"is tighter fitting and less revealing neckline, and is longer and more form fitting\", \"positive_key\": \"B003BKX12K\", \"positive_value\": \"./images/B003BKX12K.png\", \"hn_image\": [\"./images/B001JDGNS0.png\"]}\n{\"q_img\": \"./images/B00DW475WC.png\", \"q_text\": \"is sleeveless and tighter, and has a paisley print and is form fitted\", \"positive_key\": \"B00B7Q9ZDO\", \"positive_value\": \"./images/B00B7Q9ZDO.png\", \"hn_image\": [\"./images/B00DW475WC.png\"]}\n{\"q_img\": \"./images/B00BH9R3US.png\", \"q_text\": \"is more red, and has a waist strap and comes in solid red.\", \"positive_key\": \"B00E1CASRI\", \"positive_value\": \"./images/B00E1CASRI.png\", \"hn_image\": [\"./images/B00BH9R3US.png\"]}\n{\"q_img\": \"./images/B00E6RBFFC.png\", \"q_text\": \"is longer and white with thin straps, and is white and longer\", \"positive_key\": \"B007WA38BY\", \"positive_value\": \"./images/B007WA38BY.png\", \"hn_image\": [\"./images/B00E6RBFFC.png\"]}\n{\"q_img\": \"./images/B009LEFR4A.png\", \"q_text\": \"is longer and a halter toped, and has a black lining around the sides and top.\", \"positive_key\": \"B004988IIU\", \"positive_value\": \"./images/B004988IIU.png\", \"hn_image\": [\"./images/B009LEFR4A.png\"]}\n{\"q_img\": \"./images/B00C1BSDBO.png\", \"q_text\": \"is more standard blue thin strapped, and no belt and is a solid pink color\", \"positive_key\": \"B00C2LP2A8\", \"positive_value\": \"./images/B00C2LP2A8.png\", \"hn_image\": [\"./images/B00C1BSDBO.png\"]}\n{\"q_img\": \"./images/B004VM5W1K.png\", \"q_text\": \"has short sleeves and is darker, and is darker with short sleeves\", \"positive_key\": \"B00DJ7YYD0\", \"positive_value\": \"./images/B00DJ7YYD0.png\", \"hn_image\": [\"./images/B004VM5W1K.png\"]}\n{\"q_img\": \"./images/B0089XW7OC.png\", \"q_text\": \"is dark purple, and is more blue with short sleeves\", \"positive_key\": \"B007R1YUI8\", \"positive_value\": \"./images/B007R1YUI8.png\", \"hn_image\": [\"./images/B0089XW7OC.png\"]}\n{\"q_img\": \"./images/B007OVUV9S.png\", \"q_text\": \"A vintage black and white strapless dress, and is white and black with a bow and is strapless\", \"positive_key\": \"B00DI6AZAI\", \"positive_value\": \"./images/B00DI6AZAI.png\", \"hn_image\": [\"./images/B007OVUV9S.png\"]}\n{\"q_img\": \"./images/B00F4WI9H0.png\", \"q_text\": \"has sleeves on both shoulders and printer, and is blue with sleeves\", \"positive_key\": \"B00A76UFK2\", \"positive_value\": \"./images/B00A76UFK2.png\", \"hn_image\": [\"./images/B00F4WI9H0.png\"]}\n{\"q_img\": \"./images/B000PUGW8O.png\", \"q_text\": \"is solid white in color, and is white and more flared and has sleeves\", \"positive_key\": \"B0046Q2FCK\", \"positive_value\": \"./images/B0046Q2FCK.png\", \"hn_image\": [\"./images/B000PUGW8O.png\"]}\n{\"q_img\": \"./images/B00466ICP4.png\", \"q_text\": \"is more revealing, and has no sleeves and flower pattern\", \"positive_key\": \"B0051N2MOI\", \"positive_value\": \"./images/B0051N2MOI.png\", \"hn_image\": [\"./images/B00466ICP4.png\"]}\n{\"q_img\": \"./images/B008MB4OI8.png\", \"q_text\": \"is purple in color and has silk like appearance, and is pink and sleeveless\", \"positive_key\": \"B00A7DR1M0\", \"positive_value\": \"./images/B00A7DR1M0.png\", \"hn_image\": [\"./images/B008MB4OI8.png\"]}\n{\"q_img\": \"./images/B00CY8S5WG.png\", \"q_text\": \"is shorter, and is pale pink with spagetti straps and is shorter\", \"positive_key\": \"B0060JCWO2\", \"positive_value\": \"./images/B0060JCWO2.png\", \"hn_image\": [\"./images/B00CY8S5WG.png\"]}\n{\"q_img\": \"./images/B004H4WHVA.png\", \"q_text\": \"is black and pink patterned, and has a floral pattern and no sleeves.\", \"positive_key\": \"B0014IKPTS\", \"positive_value\": \"./images/B0014IKPTS.png\", \"hn_image\": [\"./images/B004H4WHVA.png\"]}\n{\"q_img\": \"./images/B004LGXB0K.png\", \"q_text\": \"Is more colorful and sexier, and is red and shorter\", \"positive_key\": \"B00297BG7I\", \"positive_value\": \"./images/B00297BG7I.png\", \"hn_image\": [\"./images/B004LGXB0K.png\"]}\n{\"q_img\": \"./images/B002G5GHCM.png\", \"q_text\": \"has sleeves and is darker, and is solid black in color\", \"positive_key\": \"B002QB70BI\", \"positive_value\": \"./images/B002QB70BI.png\", \"hn_image\": [\"./images/B002G5GHCM.png\"]}\n{\"q_img\": \"./images/B0052B66GY.png\", \"q_text\": \"is a blue dress that is short, and is printed and pastel\", \"positive_key\": \"B009LJJZS4\", \"positive_value\": \"./images/B009LJJZS4.png\", \"hn_image\": [\"./images/B0052B66GY.png\"]}\n{\"q_img\": \"./images/B003WT11KY.png\", \"q_text\": \"is looser with an animal print, and has shorter sleeves and a solid grey panel\", \"positive_key\": \"B007JTP2SA\", \"positive_value\": \"./images/B007JTP2SA.png\", \"hn_image\": [\"./images/B003WT11KY.png\"]}\n{\"q_img\": \"./images/B00CGY6RJ6.png\", \"q_text\": \"is a light blue color, and less billowy but bigger bow\", \"positive_key\": \"B009JY6AIE\", \"positive_value\": \"./images/B009JY6AIE.png\", \"hn_image\": [\"./images/B00CGY6RJ6.png\"]}\n{\"q_img\": \"./images/B0023D5Q4W.png\", \"q_text\": \"cream with straps and u neck shape, and is sleeveless and beige\", \"positive_key\": \"B004EWG5WW\", \"positive_value\": \"./images/B004EWG5WW.png\", \"hn_image\": [\"./images/B0023D5Q4W.png\"]}\n{\"q_img\": \"./images/B00BL9DCM2.png\", \"q_text\": \"has longer sleeves and is red, and has longer sleeves and red colored\", \"positive_key\": \"B005S9W2FE\", \"positive_value\": \"./images/B005S9W2FE.png\", \"hn_image\": [\"./images/B00BL9DCM2.png\"]}\n{\"q_img\": \"./images/B004FPYVO2.png\", \"q_text\": \"is more striped and multicolored, and is darker and striped\", \"positive_key\": \"B005X4RMW2\", \"positive_value\": \"./images/B005X4RMW2.png\", \"hn_image\": [\"./images/B004FPYVO2.png\"]}\n{\"q_img\": \"./images/B004DVT2SI.png\", \"q_text\": \"this back dress look too adult, and is darker and slimmer sleeves\", \"positive_key\": \"B000AY2892\", \"positive_value\": \"./images/B000AY2892.png\", \"hn_image\": [\"./images/B004DVT2SI.png\"]}\n{\"q_img\": \"./images/B00A4FQ4PG.png\", \"q_text\": \"is red and shows more bust, and is red and more shiny\", \"positive_key\": \"B00A7049XW\", \"positive_value\": \"./images/B00A7049XW.png\", \"hn_image\": [\"./images/B00A4FQ4PG.png\"]}\n{\"q_img\": \"./images/B008596V88.png\", \"q_text\": \"black and white short dress, and has a grey bottom and red top. A little longer.\", \"positive_key\": \"B00CLDVNSC\", \"positive_value\": \"./images/B00CLDVNSC.png\", \"hn_image\": [\"./images/B008596V88.png\"]}\n{\"q_img\": \"./images/B001TA2J9K.png\", \"q_text\": \"is red and shorter, and is less ethnic\", \"positive_key\": \"B0050JZTKW\", \"positive_value\": \"./images/B0050JZTKW.png\", \"hn_image\": [\"./images/B001TA2J9K.png\"]}\n{\"q_img\": \"./images/B00DUFRTQA.png\", \"q_text\": \"one shoulder and shorter dark colored, and is black with only one strap\", \"positive_key\": \"B0080ICEQC\", \"positive_value\": \"./images/B0080ICEQC.png\", \"hn_image\": [\"./images/B00DUFRTQA.png\"]}\n{\"q_img\": \"./images/B0073MVVPQ.png\", \"q_text\": \"is not as embellished and is lighter, and white and less decorative\", \"positive_key\": \"B00BG5TRZ2\", \"positive_value\": \"./images/B00BG5TRZ2.png\", \"hn_image\": [\"./images/B0073MVVPQ.png\"]}\n{\"q_img\": \"./images/B007EGKM5G.png\", \"q_text\": \"is shinier with a black belt, and is sleeveless\", \"positive_key\": \"B00D8JB44G\", \"positive_value\": \"./images/B00D8JB44G.png\", \"hn_image\": [\"./images/B007EGKM5G.png\"]}\n{\"q_img\": \"./images/B003AI6LNY.png\", \"q_text\": \"has less sleeves with floral print, and is spaghetti strap and short front and longer in the back\", \"positive_key\": \"B00BYRKJIQ\", \"positive_value\": \"./images/B00BYRKJIQ.png\", \"hn_image\": [\"./images/B003AI6LNY.png\"]}\n{\"q_img\": \"./images/B002M3TVJ4.png\", \"q_text\": \"has shorter sleeves and more color, and shorter skirt and pattern\", \"positive_key\": \"B00DNRAX5E\", \"positive_value\": \"./images/B00DNRAX5E.png\", \"hn_image\": [\"./images/B002M3TVJ4.png\"]}\n{\"q_img\": \"./images/B00DEP9CUW.png\", \"q_text\": \"is shorter knee length, and has no sleeves and more colors\", \"positive_key\": \"B006G3YVBE\", \"positive_value\": \"./images/B006G3YVBE.png\", \"hn_image\": [\"./images/B00DEP9CUW.png\"]}\n{\"q_img\": \"./images/B00C01N5K4.png\", \"q_text\": \"is all polka dots, and is polka dotted and fuller\", \"positive_key\": \"B0071NHF5W\", \"positive_value\": \"./images/B0071NHF5W.png\", \"hn_image\": [\"./images/B00C01N5K4.png\"]}\n{\"q_img\": \"./images/B00EUVTLPK.png\", \"q_text\": \"is longer and has long sleeves, and is much longer\", \"positive_key\": \"B008MWER8E\", \"positive_value\": \"./images/B008MWER8E.png\", \"hn_image\": [\"./images/B00EUVTLPK.png\"]}\n{\"q_img\": \"./images/B00AWKKHOC.png\", \"q_text\": \"is black and has puffy sleeves, and with short sleeves and more fitted\", \"positive_key\": \"B0087YZ0S8\", \"positive_value\": \"./images/B0087YZ0S8.png\", \"hn_image\": [\"./images/B00AWKKHOC.png\"]}\n{\"q_img\": \"./images/B0080ICEQC.png\", \"q_text\": \"is shorter and has longer sleeves, and is short sleeved and printed\", \"positive_key\": \"B0050SCUXC\", \"positive_value\": \"./images/B0050SCUXC.png\", \"hn_image\": [\"./images/B0080ICEQC.png\"]}\n{\"q_img\": \"./images/B005QYW7PG.png\", \"q_text\": \"More blue and stripped, and maxi dress in pink pattern with no bow\", \"positive_key\": \"B0076R807A\", \"positive_value\": \"./images/B0076R807A.png\", \"hn_image\": [\"./images/B005QYW7PG.png\"]}\n{\"q_img\": \"./images/B00DR3TRJ2.png\", \"q_text\": \"Is green and more revealing, and has a green color and no sleeves\", \"positive_key\": \"B00DU6GJJC\", \"positive_value\": \"./images/B00DU6GJJC.png\", \"hn_image\": [\"./images/B00DR3TRJ2.png\"]}\n{\"q_img\": \"./images/B004E9SNTS.png\", \"q_text\": \"has longer sleeves and a belt, and is shorter\", \"positive_key\": \"B00FHLNHK2\", \"positive_value\": \"./images/B00FHLNHK2.png\", \"hn_image\": [\"./images/B004E9SNTS.png\"]}\n{\"q_img\": \"./images/B005NXMDTU.png\", \"q_text\": \"shiny short black dress, and is shinier\", \"positive_key\": \"B007F7PAOC\", \"positive_value\": \"./images/B007F7PAOC.png\", \"hn_image\": [\"./images/B005NXMDTU.png\"]}\n{\"q_img\": \"./images/B002OEYH3Q.png\", \"q_text\": \"has shorter front longer back and longer sleeves, and more baby-doll styled\", \"positive_key\": \"B007FDOMTK\", \"positive_value\": \"./images/B007FDOMTK.png\", \"hn_image\": [\"./images/B002OEYH3Q.png\"]}\n{\"q_img\": \"./images/B005KMQPS4.png\", \"q_text\": \"is black with v neck below knee lenght, and is black with short sleeves\", \"positive_key\": \"B0060Q8032\", \"positive_value\": \"./images/B0060Q8032.png\", \"hn_image\": [\"./images/B005KMQPS4.png\"]}\n{\"q_img\": \"./images/B008HQKK1S.png\", \"q_text\": \"Has a white low cut top and blue skirt., and is more revealing with two colors and shorter in length\", \"positive_key\": \"B00AZE7306\", \"positive_value\": \"./images/B00AZE7306.png\", \"hn_image\": [\"./images/B008HQKK1S.png\"]}\n{\"q_img\": \"./images/B0046Q4K6E.png\", \"q_text\": \"less revealing, and is a solid cream color\", \"positive_key\": \"B00316G2DE\", \"positive_value\": \"./images/B00316G2DE.png\", \"hn_image\": [\"./images/B0046Q4K6E.png\"]}\n{\"q_img\": \"./images/B009P83GT0.png\", \"q_text\": \"lacy with burgandy at top with a high low hem, and is purple and blue\", \"positive_key\": \"B008PG0SOO\", \"positive_value\": \"./images/B008PG0SOO.png\", \"hn_image\": [\"./images/B009P83GT0.png\"]}\n{\"q_img\": \"./images/B00BPBCKQK.png\", \"q_text\": \"Is shorter and more simple., and is classier looking\", \"positive_key\": \"B003TAJUM2\", \"positive_value\": \"./images/B003TAJUM2.png\", \"hn_image\": [\"./images/B00BPBCKQK.png\"]}\n{\"q_img\": \"./images/B004BDP1BA.png\", \"q_text\": \"Is a tan dress with a ruched side, and is a tan coloring.\", \"positive_key\": \"B0065W6F5Q\", \"positive_value\": \"./images/B0065W6F5Q.png\", \"hn_image\": [\"./images/B004BDP1BA.png\"]}\n{\"q_img\": \"./images/B0016LHFE6.png\", \"q_text\": \"has more buttons, and is shorter with buttons down the front\", \"positive_key\": \"B00CF2VVJK\", \"positive_value\": \"./images/B00CF2VVJK.png\", \"hn_image\": [\"./images/B0016LHFE6.png\"]}\n{\"q_img\": \"./images/B00BTUULCC.png\", \"q_text\": \"is pink and purple striped floor lenght, and has strip0es and longer\", \"positive_key\": \"B00CMLUHF8\", \"positive_value\": \"./images/B00CMLUHF8.png\", \"hn_image\": [\"./images/B00BTUULCC.png\"]}\n{\"q_img\": \"./images/B00FHXBQFS.png\", \"q_text\": \"is more casual, and has cap sleeves and a silver skirt\", \"positive_key\": \"B00ABXKO02\", \"positive_value\": \"./images/B00ABXKO02.png\", \"hn_image\": [\"./images/B00FHXBQFS.png\"]}\n{\"q_img\": \"./images/B005VTKJWO.png\", \"q_text\": \"is black in color, and shows more neck\", \"positive_key\": \"B00AWMNYG8\", \"positive_value\": \"./images/B00AWMNYG8.png\", \"hn_image\": [\"./images/B005VTKJWO.png\"]}\n{\"q_img\": \"./images/B0060VUQ0C.png\", \"q_text\": \"has no straps with black and white strips and florals, and has more stripes and color\", \"positive_key\": \"B00AQUBNWI\", \"positive_value\": \"./images/B00AQUBNWI.png\", \"hn_image\": [\"./images/B0060VUQ0C.png\"]}\n{\"q_img\": \"./images/B005GYSR64.png\", \"q_text\": \"long pink maxi dress with no sleeves, and is longer and sleeveless\", \"positive_key\": \"B00BV46E9K\", \"positive_value\": \"./images/B00BV46E9K.png\", \"hn_image\": [\"./images/B005GYSR64.png\"]}\n{\"q_img\": \"./images/B0094QXCKQ.png\", \"q_text\": \"is lighter and has shorter sleeves, and is lighter colored and has a cutout in the back\", \"positive_key\": \"B00CLDNZ1K\", \"positive_value\": \"./images/B00CLDNZ1K.png\", \"hn_image\": [\"./images/B0094QXCKQ.png\"]}\n{\"q_img\": \"./images/B007NG77PU.png\", \"q_text\": \"has no ruffles and has sleeves, and mesh striped solid blue color\", \"positive_key\": \"B006M40UZ8\", \"positive_value\": \"./images/B006M40UZ8.png\", \"hn_image\": [\"./images/B007NG77PU.png\"]}\n{\"q_img\": \"./images/B007UDBI3S.png\", \"q_text\": \"has black lace and a red skirt, and has a black top and red bottom\", \"positive_key\": \"B007LTUAUS\", \"positive_value\": \"./images/B007LTUAUS.png\", \"hn_image\": [\"./images/B007UDBI3S.png\"]}\n{\"q_img\": \"./images/B008OVCJ0Q.png\", \"q_text\": \"has a low neck and has many rhinestones, and gray and longer sleeved\", \"positive_key\": \"B004TA9PVW\", \"positive_value\": \"./images/B004TA9PVW.png\", \"hn_image\": [\"./images/B008OVCJ0Q.png\"]}\n{\"q_img\": \"./images/B009NW4TW6.png\", \"q_text\": \"is more revaeling and darker, and has a floral pattern\", \"positive_key\": \"B0076RQLLW\", \"positive_value\": \"./images/B0076RQLLW.png\", \"hn_image\": [\"./images/B009NW4TW6.png\"]}\n{\"q_img\": \"./images/B004AM5K4K.png\", \"q_text\": \"is dark colored v-necked dress and sleeveless, and is black and blue\", \"positive_key\": \"B004AM5KA4\", \"positive_value\": \"./images/B004AM5KA4.png\", \"hn_image\": [\"./images/B004AM5K4K.png\"]}\n{\"q_img\": \"./images/B00A16KWZ6.png\", \"q_text\": \"is blue and has a belt, and is blue with shorter sleeves\", \"positive_key\": \"B00AYXA5D0\", \"positive_value\": \"./images/B00AYXA5D0.png\", \"hn_image\": [\"./images/B00A16KWZ6.png\"]}\n{\"q_img\": \"./images/B007XD6AMO.png\", \"q_text\": \"is solid black, and has thinner straps and is more black\", \"positive_key\": \"B0094DYEJC\", \"positive_value\": \"./images/B0094DYEJC.png\", \"hn_image\": [\"./images/B007XD6AMO.png\"]}\n{\"q_img\": \"./images/B00C2BIUXO.png\", \"q_text\": \"has more sleeve and is red with tie belt, and is red and has longer sleeves\", \"positive_key\": \"B00DEO5N8I\", \"positive_value\": \"./images/B00DEO5N8I.png\", \"hn_image\": [\"./images/B00C2BIUXO.png\"]}\n{\"q_img\": \"./images/B005278U3U.png\", \"q_text\": \"has longer sleeves and longer length, and has a similar colored belt\", \"positive_key\": \"B0019IIQYY\", \"positive_value\": \"./images/B0019IIQYY.png\", \"hn_image\": [\"./images/B005278U3U.png\"]}\n{\"q_img\": \"./images/B00EDS3BRO.png\", \"q_text\": \"is more sexy and shorter, and is darker and strappy\", \"positive_key\": \"B007457CVE\", \"positive_value\": \"./images/B007457CVE.png\", \"hn_image\": [\"./images/B00EDS3BRO.png\"]}\n{\"q_img\": \"./images/B003BKNUVC.png\", \"q_text\": \"is shorter and red, and is red and sexy\", \"positive_key\": \"B006X6XGT2\", \"positive_value\": \"./images/B006X6XGT2.png\", \"hn_image\": [\"./images/B003BKNUVC.png\"]}\n{\"q_img\": \"./images/B00AG3Y2JG.png\", \"q_text\": \"is black, and is longer and all black\", \"positive_key\": \"B00DEP9CUW\", \"positive_value\": \"./images/B00DEP9CUW.png\", \"hn_image\": [\"./images/B00AG3Y2JG.png\"]}\n{\"q_img\": \"./images/B00CRNS11I.png\", \"q_text\": \"is shorter and darker in color, and is shorter and is blue\", \"positive_key\": \"B008F48P7S\", \"positive_value\": \"./images/B008F48P7S.png\", \"hn_image\": [\"./images/B00CRNS11I.png\"]}\n{\"q_img\": \"./images/B00AYXA3G4.png\", \"q_text\": \"one shouldered printed dress, and is green and white\", \"positive_key\": \"B006JDHC8A\", \"positive_value\": \"./images/B006JDHC8A.png\", \"hn_image\": [\"./images/B00AYXA3G4.png\"]}\n{\"q_img\": \"./images/B00BUVAI5K.png\", \"q_text\": \"is light yellow with a bow on the belt, and flowing and white\", \"positive_key\": \"B00AODCC9U\", \"positive_value\": \"./images/B00AODCC9U.png\", \"hn_image\": [\"./images/B00BUVAI5K.png\"]}\n{\"q_img\": \"./images/B007IWO80Q.png\", \"q_text\": \"has a lower neckline, and is white and does not have stripes\", \"positive_key\": \"B008BWOCGC\", \"positive_value\": \"./images/B008BWOCGC.png\", \"hn_image\": [\"./images/B007IWO80Q.png\"]}\n{\"q_img\": \"./images/B00405RUW2.png\", \"q_text\": \"is more revealing and tight, and is more revealing\", \"positive_key\": \"B008500HGE\", \"positive_value\": \"./images/B008500HGE.png\", \"hn_image\": [\"./images/B00405RUW2.png\"]}\n{\"q_img\": \"./images/B00CLGVWLM.png\", \"q_text\": \"pink dress with half sleeves, and is pink with no stripes\", \"positive_key\": \"B00D61Z5Y6\", \"positive_value\": \"./images/B00D61Z5Y6.png\", \"hn_image\": [\"./images/B00CLGVWLM.png\"]}\n{\"q_img\": \"./images/B00C7SQQJW.png\", \"q_text\": \"Purple and more revealing, and maroon with deeper neck\", \"positive_key\": \"B001J2HRRW\", \"positive_value\": \"./images/B001J2HRRW.png\", \"hn_image\": [\"./images/B00C7SQQJW.png\"]}\n{\"q_img\": \"./images/B003BKNUVC.png\", \"q_text\": \"is solid black, and is solid black and v necked\", \"positive_key\": \"B001JDGNS0\", \"positive_value\": \"./images/B001JDGNS0.png\", \"hn_image\": [\"./images/B003BKNUVC.png\"]}\n{\"q_img\": \"./images/B00AT8CQIM.png\", \"q_text\": \"has more color contrast, and is black and white with longer sleeves\", \"positive_key\": \"B009XNN37W\", \"positive_value\": \"./images/B009XNN37W.png\", \"hn_image\": [\"./images/B00AT8CQIM.png\"]}\n{\"q_img\": \"./images/B0094QXCKQ.png\", \"q_text\": \"Is longer with colorful stripes., and has no sleeves and more color\", \"positive_key\": \"B008VJ0Q2Y\", \"positive_value\": \"./images/B008VJ0Q2Y.png\", \"hn_image\": [\"./images/B0094QXCKQ.png\"]}\n{\"q_img\": \"./images/B002YX0KZ6.png\", \"q_text\": \"has a skater design, and has more dress volume\", \"positive_key\": \"B005CMMWYI\", \"positive_value\": \"./images/B005CMMWYI.png\", \"hn_image\": [\"./images/B002YX0KZ6.png\"]}\n{\"q_img\": \"./images/B00EPFMQWQ.png\", \"q_text\": \"is golden with less sleeves, and More see through and double layered with Teal As the base color.\", \"positive_key\": \"B00FZVANC4\", \"positive_value\": \"./images/B00FZVANC4.png\", \"hn_image\": [\"./images/B00EPFMQWQ.png\"]}\n{\"q_img\": \"./images/B008FPXR3Y.png\", \"q_text\": \"has sleeves, and has different coloring and fuller shoulders.\", \"positive_key\": \"B00AESNPGY\", \"positive_value\": \"./images/B00AESNPGY.png\", \"hn_image\": [\"./images/B008FPXR3Y.png\"]}\n{\"q_img\": \"./images/B005KOYZWU.png\", \"q_text\": \"Is brighter with an asymmetrical hem, and is yellow and has straps\", \"positive_key\": \"B00BJFXX54\", \"positive_value\": \"./images/B00BJFXX54.png\", \"hn_image\": [\"./images/B005KOYZWU.png\"]}\n{\"q_img\": \"./images/B009DI5YSS.png\", \"q_text\": \"has no sleeves and is longer, and light color\", \"positive_key\": \"B004BH166A\", \"positive_value\": \"./images/B004BH166A.png\", \"hn_image\": [\"./images/B009DI5YSS.png\"]}\n{\"q_img\": \"./images/B008E7IA0I.png\", \"q_text\": \"has longer sleeves and a different pattern, and Is shorter\", \"positive_key\": \"B00FRS3RDM\", \"positive_value\": \"./images/B00FRS3RDM.png\", \"hn_image\": [\"./images/B008E7IA0I.png\"]}\n{\"q_img\": \"./images/B008TKM790.png\", \"q_text\": \" is brighter, and is shorter and one colored\", \"positive_key\": \"B00APER4ZK\", \"positive_value\": \"./images/B00APER4ZK.png\", \"hn_image\": [\"./images/B008TKM790.png\"]}\n{\"q_img\": \"./images/B004E9SNTS.png\", \"q_text\": \"has a different pattern and is shorter, and has no sleeves and is multicolored\", \"positive_key\": \"B0076R5V0E\", \"positive_value\": \"./images/B0076R5V0E.png\", \"hn_image\": [\"./images/B004E9SNTS.png\"]}\n{\"q_img\": \"./images/B00AKLK08G.png\", \"q_text\": \"is light brown, and is one-toned\", \"positive_key\": \"B00AFDH25I\", \"positive_value\": \"./images/B00AFDH25I.png\", \"hn_image\": [\"./images/B00AKLK08G.png\"]}\n{\"q_img\": \"./images/B0073M5RKG.png\", \"q_text\": \"Is lighter with a keyhole cut out, and is white\", \"positive_key\": \"B00BBU6R46\", \"positive_value\": \"./images/B00BBU6R46.png\", \"hn_image\": [\"./images/B0073M5RKG.png\"]}\n{\"q_img\": \"./images/B00CE4GUC2.png\", \"q_text\": \"is yellow, and is yellow and shorter\", \"positive_key\": \"B00CF1RK6Y\", \"positive_value\": \"./images/B00CF1RK6Y.png\", \"hn_image\": [\"./images/B00CE4GUC2.png\"]}\n{\"q_img\": \"./images/B00DFN4MSU.png\", \"q_text\": \"is shorter, and is shorter and darker\", \"positive_key\": \"B00806NEIQ\", \"positive_value\": \"./images/B00806NEIQ.png\", \"hn_image\": [\"./images/B00DFN4MSU.png\"]}\n{\"q_img\": \"./images/B007GSGKOO.png\", \"q_text\": \"is a solid body con dress, and is pink and thin\", \"positive_key\": \"B00CY914W8\", \"positive_value\": \"./images/B00CY914W8.png\", \"hn_image\": [\"./images/B007GSGKOO.png\"]}\n{\"q_img\": \"./images/B00AU1U2PC.png\", \"q_text\": \"is a pattern material with sleeves, and fitted\", \"positive_key\": \"B00BQZBG4W\", \"positive_value\": \"./images/B00BQZBG4W.png\", \"hn_image\": [\"./images/B00AU1U2PC.png\"]}\n{\"q_img\": \"./images/B00297BG7I.png\", \"q_text\": \"is purple and more revealing, and It has more purple and it is more plain\", \"positive_key\": \"B0063BBIEM\", \"positive_value\": \"./images/B0063BBIEM.png\", \"hn_image\": [\"./images/B00297BG7I.png\"]}\n{\"q_img\": \"./images/B005278U3U.png\", \"q_text\": \"is green with a v neck, and is green\", \"positive_key\": \"B00A9TA2XM\", \"positive_value\": \"./images/B00A9TA2XM.png\", \"hn_image\": [\"./images/B005278U3U.png\"]}\n{\"q_img\": \"./images/B003XCNN3I.png\", \"q_text\": \"Is darker with longer length., and is darker in color and longer\", \"positive_key\": \"B004HSN6W0\", \"positive_value\": \"./images/B004HSN6W0.png\", \"hn_image\": [\"./images/B003XCNN3I.png\"]}\n{\"q_img\": \"./images/B00CJHLQ3W.png\", \"q_text\": \"is two colors and less print, and is black with grey stripe\", \"positive_key\": \"B00ANT6K0M\", \"positive_value\": \"./images/B00ANT6K0M.png\", \"hn_image\": [\"./images/B00CJHLQ3W.png\"]}\n{\"q_img\": \"./images/B0032FOQUU.png\", \"q_text\": \"is blue and straight, and is longer\", \"positive_key\": \"B006YNDYF0\", \"positive_value\": \"./images/B006YNDYF0.png\", \"hn_image\": [\"./images/B0032FOQUU.png\"]}\n{\"q_img\": \"./images/B00D3V29MA.png\", \"q_text\": \"is more elgant, and is two-toned black and white\", \"positive_key\": \"B009S3GIV0\", \"positive_value\": \"./images/B009S3GIV0.png\", \"hn_image\": [\"./images/B00D3V29MA.png\"]}\n{\"q_img\": \"./images/B0091SFTVC.png\", \"q_text\": \"is a brown and white print dress, and longer ruffle hemmed\", \"positive_key\": \"B00E21AEUY\", \"positive_value\": \"./images/B00E21AEUY.png\", \"hn_image\": [\"./images/B0091SFTVC.png\"]}\n{\"q_img\": \"./images/B000BL6VJM.png\", \"q_text\": \"is shorter and sexier with no sleeves, and The dress is longer and less summery.\", \"positive_key\": \"B007OZSTY8\", \"positive_value\": \"./images/B007OZSTY8.png\", \"hn_image\": [\"./images/B000BL6VJM.png\"]}\n{\"q_img\": \"./images/B00AIBYV1K.png\", \"q_text\": \"has a white outline, and ruffle bottom\", \"positive_key\": \"B008UEW5RO\", \"positive_value\": \"./images/B008UEW5RO.png\", \"hn_image\": [\"./images/B00AIBYV1K.png\"]}\n{\"q_img\": \"./images/B00DY3XR1O.png\", \"q_text\": \"has a longer skirt and more pleats, and sleeveless and more two toned\", \"positive_key\": \"B008VIXCL2\", \"positive_value\": \"./images/B008VIXCL2.png\", \"hn_image\": [\"./images/B00DY3XR1O.png\"]}\n{\"q_img\": \"./images/B003ILG26E.png\", \"q_text\": \"Is less fitted and more ruffled, and is pink and wider shouldered\", \"positive_key\": \"B0083UY0LY\", \"positive_value\": \"./images/B0083UY0LY.png\", \"hn_image\": [\"./images/B003ILG26E.png\"]}\n{\"q_img\": \"./images/B00525WBU0.png\", \"q_text\": \"a coral pink strapless dress, and is pink with floral pattern\", \"positive_key\": \"B002ZLOQGQ\", \"positive_value\": \"./images/B002ZLOQGQ.png\", \"hn_image\": [\"./images/B00525WBU0.png\"]}\n{\"q_img\": \"./images/B005NXMDTU.png\", \"q_text\": \"shorter and pink with a bow on it, and Pink cold shoulder dress.\", \"positive_key\": \"B008GQ3BVK\", \"positive_value\": \"./images/B008GQ3BVK.png\", \"hn_image\": [\"./images/B005NXMDTU.png\"]}\n{\"q_img\": \"./images/B00CBWGQVM.png\", \"q_text\": \"is black with short sleeves, and is less revealing and darker.\", \"positive_key\": \"B0091SH4H4\", \"positive_value\": \"./images/B0091SH4H4.png\", \"hn_image\": [\"./images/B00CBWGQVM.png\"]}\n{\"q_img\": \"./images/B00D4E8ZW4.png\", \"q_text\": \"Is a dark blue cold shoulder dress., and is off one shoulder and blue\", \"positive_key\": \"B009XJMTSA\", \"positive_value\": \"./images/B009XJMTSA.png\", \"hn_image\": [\"./images/B00D4E8ZW4.png\"]}\n{\"q_img\": \"./images/B00ELLYWL2.png\", \"q_text\": \"is all blue with lacy bottom, and has shorter sleeves and a festive print\", \"positive_key\": \"B00CCPYPYI\", \"positive_value\": \"./images/B00CCPYPYI.png\", \"hn_image\": [\"./images/B00ELLYWL2.png\"]}\n{\"q_img\": \"./images/B007Z24BC4.png\", \"q_text\": \"is longer with black and white prints, and has a print with sections of black and white only without reds.\", \"positive_key\": \"B00COYGYPU\", \"positive_value\": \"./images/B00COYGYPU.png\", \"hn_image\": [\"./images/B007Z24BC4.png\"]}\n{\"q_img\": \"./images/B00F4MZ144.png\", \"q_text\": \"is solid gray and lighter color, and is lighter in color\", \"positive_key\": \"B00F4MX4SE\", \"positive_value\": \"./images/B00F4MX4SE.png\", \"hn_image\": [\"./images/B00F4MZ144.png\"]}\n{\"q_img\": \"./images/B003WT11KY.png\", \"q_text\": \"is white and much more plain, and is lighter in color\", \"positive_key\": \"B008GRR6II\", \"positive_value\": \"./images/B008GRR6II.png\", \"hn_image\": [\"./images/B003WT11KY.png\"]}\n{\"q_img\": \"./images/B00C97ISFG.png\", \"q_text\": \"is black, and is all black and two pieces\", \"positive_key\": \"B00A8VHMRK\", \"positive_value\": \"./images/B00A8VHMRK.png\", \"hn_image\": [\"./images/B00C97ISFG.png\"]}\n{\"q_img\": \"./images/B00CN46SMU.png\", \"q_text\": \"is off-white and one length, and is lacy and lighter in color\", \"positive_key\": \"B00BTUULCC\", \"positive_value\": \"./images/B00BTUULCC.png\", \"hn_image\": [\"./images/B00CN46SMU.png\"]}\n{\"q_img\": \"./images/B008MZS5Y8.png\", \"q_text\": \"is a bit longer and black, and is less revealing and black\", \"positive_key\": \"B008H78T6K\", \"positive_value\": \"./images/B008H78T6K.png\", \"hn_image\": [\"./images/B008MZS5Y8.png\"]}\n{\"q_img\": \"./images/B009MIRC6Q.png\", \"q_text\": \"is more transparent, and is more laced on the top\", \"positive_key\": \"B009G8FXPY\", \"positive_value\": \"./images/B009G8FXPY.png\", \"hn_image\": [\"./images/B009MIRC6Q.png\"]}\n{\"q_img\": \"./images/B00CP80N3O.png\", \"q_text\": \"is leopard print with a pink bow, and is multi-colored and less sexy\", \"positive_key\": \"B00EYNTEYW\", \"positive_value\": \"./images/B00EYNTEYW.png\", \"hn_image\": [\"./images/B00CP80N3O.png\"]}\n{\"q_img\": \"./images/B00466F2W0.png\", \"q_text\": \"is solid black with ruffles, and is darker colored with no sequins\", \"positive_key\": \"B0098BW31Q\", \"positive_value\": \"./images/B0098BW31Q.png\", \"hn_image\": [\"./images/B00466F2W0.png\"]}\n{\"q_img\": \"./images/B00530FEEY.png\", \"q_text\": \"has spaghetti straps, and is short.\", \"positive_key\": \"B0017LUFJM\", \"positive_value\": \"./images/B0017LUFJM.png\", \"hn_image\": [\"./images/B00530FEEY.png\"]}\n{\"q_img\": \"./images/B008DH1ZEM.png\", \"q_text\": \"is more modern and less frilly, and is red with black patterns\", \"positive_key\": \"B00CX0GGUI\", \"positive_value\": \"./images/B00CX0GGUI.png\", \"hn_image\": [\"./images/B008DH1ZEM.png\"]}\n{\"q_img\": \"./images/B00CQSPPLS.png\", \"q_text\": \"is white, and is white and more open\", \"positive_key\": \"B009815VKG\", \"positive_value\": \"./images/B009815VKG.png\", \"hn_image\": [\"./images/B00CQSPPLS.png\"]}\n{\"q_img\": \"./images/B00ANSYKPA.png\", \"q_text\": \"is shinier and has shorter sleeves, and is a tannish brass color and reveals a lot of breast around the straps.\", \"positive_key\": \"B00DHP4U0Q\", \"positive_value\": \"./images/B00DHP4U0Q.png\", \"hn_image\": [\"./images/B00ANSYKPA.png\"]}\n{\"q_img\": \"./images/B000BL6VJM.png\", \"q_text\": \"is short and blue in color with shorter sleeves, and is blue and knee length\", \"positive_key\": \"B00DC01APO\", \"positive_value\": \"./images/B00DC01APO.png\", \"hn_image\": [\"./images/B000BL6VJM.png\"]}\n{\"q_img\": \"./images/B00C7Z4VGU.png\", \"q_text\": \"High Low Dress Royal Blue Sheer Sleeves, and is brighter in color\", \"positive_key\": \"B00CJHLBFK\", \"positive_value\": \"./images/B00CJHLBFK.png\", \"hn_image\": [\"./images/B00C7Z4VGU.png\"]}\n{\"q_img\": \"./images/B00CTT51MC.png\", \"q_text\": \"is longer and black, and is much longer\", \"positive_key\": \"B00CTT4N7G\", \"positive_value\": \"./images/B00CTT4N7G.png\", \"hn_image\": [\"./images/B00CTT51MC.png\"]}\n{\"q_img\": \"./images/B0069IWITS.png\", \"q_text\": \"is pink and black with straps, and has no sleeves and more art\", \"positive_key\": \"B00428MWP2\", \"positive_value\": \"./images/B00428MWP2.png\", \"hn_image\": [\"./images/B0069IWITS.png\"]}\n{\"q_img\": \"./images/B003ILEHQG.png\", \"q_text\": \"dress body con dress, and has a cover over it and color is off\", \"positive_key\": \"B00GATDMUK\", \"positive_value\": \"./images/B00GATDMUK.png\", \"hn_image\": [\"./images/B003ILEHQG.png\"]}\n{\"q_img\": \"./images/B00DSME58U.png\", \"q_text\": \"a mint colored dress with straps, and is light blue\", \"positive_key\": \"B00BJZA08M\", \"positive_value\": \"./images/B00BJZA08M.png\", \"hn_image\": [\"./images/B00DSME58U.png\"]}\n{\"q_img\": \"./images/B00BJSECVA.png\", \"q_text\": \"has sleeves and is black, and has sleeves and is red and black floral print\", \"positive_key\": \"B008D6LE3A\", \"positive_value\": \"./images/B008D6LE3A.png\", \"hn_image\": [\"./images/B00BJSECVA.png\"]}\n{\"q_img\": \"./images/B00428MWP2.png\", \"q_text\": \"is longer and darker, and is longer and animal print\", \"positive_key\": \"B00DH7E7IE\", \"positive_value\": \"./images/B00DH7E7IE.png\", \"hn_image\": [\"./images/B00428MWP2.png\"]}\n{\"q_img\": \"./images/B007FG0AAM.png\", \"q_text\": \"is lighter, and is white with slightly higher neckline\", \"positive_key\": \"B00A7Z5MKQ\", \"positive_value\": \"./images/B00A7Z5MKQ.png\", \"hn_image\": [\"./images/B007FG0AAM.png\"]}\n{\"q_img\": \"./images/B00EFE9MII.png\", \"q_text\": \"Is darker and not sleeveless, and More black and less satin\", \"positive_key\": \"B00AXL6E3I\", \"positive_value\": \"./images/B00AXL6E3I.png\", \"hn_image\": [\"./images/B00EFE9MII.png\"]}\n{\"q_img\": \"./images/B005RYO18G.png\", \"q_text\": \"is lighter grey toned, and is lighter in color\", \"positive_key\": \"B006ZWEMWE\", \"positive_value\": \"./images/B006ZWEMWE.png\", \"hn_image\": [\"./images/B005RYO18G.png\"]}\n{\"q_img\": \"./images/B007FO27IW.png\", \"q_text\": \"Is a patterned short dress, and short sleeves with pattern\", \"positive_key\": \"B008LRQUW6\", \"positive_value\": \"./images/B008LRQUW6.png\", \"hn_image\": [\"./images/B007FO27IW.png\"]}\n{\"q_img\": \"./images/B006361ONM.png\", \"q_text\": \"is more form fitted with stripes, and is less formal and striped\", \"positive_key\": \"B00EDSJG2I\", \"positive_value\": \"./images/B00EDSJG2I.png\", \"hn_image\": [\"./images/B006361ONM.png\"]}\n{\"q_img\": \"./images/B00BTUXSYA.png\", \"q_text\": \"Is striped with asymmetrical hemline., and has stripes\", \"positive_key\": \"B00CN46SMU\", \"positive_value\": \"./images/B00CN46SMU.png\", \"hn_image\": [\"./images/B00BTUXSYA.png\"]}\n{\"q_img\": \"./images/B008TYS0C4.png\", \"q_text\": \"is shorter and ligther, and is more pink and is shorter\", \"positive_key\": \"B005O0PARY\", \"positive_value\": \"./images/B005O0PARY.png\", \"hn_image\": [\"./images/B008TYS0C4.png\"]}\n{\"q_img\": \"./images/B00A3PEX7S.png\", \"q_text\": \"is shorter and shiner with no straps, and is red and tight\", \"positive_key\": \"B0033PRQ2Y\", \"positive_value\": \"./images/B0033PRQ2Y.png\", \"hn_image\": [\"./images/B00A3PEX7S.png\"]}\n{\"q_img\": \"./images/B007FO27IW.png\", \"q_text\": \"is longer and more black, and is more elegant and longer in length\", \"positive_key\": \"B002ZCXTA4\", \"positive_value\": \"./images/B002ZCXTA4.png\", \"hn_image\": [\"./images/B007FO27IW.png\"]}\n{\"q_img\": \"./images/B005OOUCLE.png\", \"q_text\": \"long sleeves and collar, and is long sleeved and has collar\", \"positive_key\": \"B00CJJD4S0\", \"positive_value\": \"./images/B00CJJD4S0.png\", \"hn_image\": [\"./images/B005OOUCLE.png\"]}\n{\"q_img\": \"./images/B00D4B6OI4.png\", \"q_text\": \"has more sleeves and is tighter, and Is more fitted with longer sleeves and scoop neck\", \"positive_key\": \"B004XYPAL8\", \"positive_value\": \"./images/B004XYPAL8.png\", \"hn_image\": [\"./images/B00D4B6OI4.png\"]}\n{\"q_img\": \"./images/B00D993JHA.png\", \"q_text\": \"Is more masculine and white, and is underwear\", \"positive_key\": \"B000EC58G0\", \"positive_value\": \"./images/B000EC58G0.png\", \"hn_image\": [\"./images/B00D993JHA.png\"]}\n{\"q_img\": \"./images/B005OOUCLE.png\", \"q_text\": \"Is maroon with a ruffled top., and is a dark red cowlneck and long sleeves\", \"positive_key\": \"B000YXD4R4\", \"positive_value\": \"./images/B000YXD4R4.png\", \"hn_image\": [\"./images/B005OOUCLE.png\"]}\n{\"q_img\": \"./images/B007IWOBBC.png\", \"q_text\": \"is a darker color and has a halter neckline, and is black with spaghetti straps\", \"positive_key\": \"B0081V3WPU\", \"positive_value\": \"./images/B0081V3WPU.png\", \"hn_image\": [\"./images/B007IWOBBC.png\"]}\n{\"q_img\": \"./images/B000BL6VJM.png\", \"q_text\": \"is sleeveless and shorter, and is strapless and above the knee\", \"positive_key\": \"B00AIWGKY0\", \"positive_value\": \"./images/B00AIWGKY0.png\", \"hn_image\": [\"./images/B000BL6VJM.png\"]}\n{\"q_img\": \"./images/B007FDYS62.png\", \"q_text\": \"has thicker sleeves, and is more random\", \"positive_key\": \"B004VM5W1K\", \"positive_value\": \"./images/B004VM5W1K.png\", \"hn_image\": [\"./images/B007FDYS62.png\"]}\n{\"q_img\": \"./images/B00D9E1SSW.png\", \"q_text\": \"is longer with solid black and thicker straps, and is black with lace bottom\", \"positive_key\": \"B00E1SPJR6\", \"positive_value\": \"./images/B00E1SPJR6.png\", \"hn_image\": [\"./images/B00D9E1SSW.png\"]}\n{\"q_img\": \"./images/B00EJYPULM.png\", \"q_text\": \"has a more loose fit and long sleeves, and has long sleeves\", \"positive_key\": \"B006PAT5EQ\", \"positive_value\": \"./images/B006PAT5EQ.png\", \"hn_image\": [\"./images/B00EJYPULM.png\"]}\n{\"q_img\": \"./images/B00B5VXRTO.png\", \"q_text\": \"is white and black with cap sleeves, and is black and white\", \"positive_key\": \"B009DMGQM2\", \"positive_value\": \"./images/B009DMGQM2.png\", \"hn_image\": [\"./images/B00B5VXRTO.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"is a darker shade, and It is darker and more plain\", \"positive_key\": \"B00A787OEA\", \"positive_value\": \"./images/B00A787OEA.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B002Z7FWB8.png\", \"q_text\": \"is black with short sleeves, and is much shorter\", \"positive_key\": \"B009LIFAV6\", \"positive_value\": \"./images/B009LIFAV6.png\", \"hn_image\": [\"./images/B002Z7FWB8.png\"]}\n{\"q_img\": \"./images/B004TM36ME.png\", \"q_text\": \"yellow long boots, and is a pair of deep yellow suede knee boots.\", \"positive_key\": \"B009UJ5M7S\", \"positive_value\": \"./images/B009UJ5M7S.png\", \"hn_image\": [\"./images/B004TM36ME.png\"]}\n{\"q_img\": \"./images/B00CFYDMM2.png\", \"q_text\": \"is pink in color, and has shorter sleeves and is pink\", \"positive_key\": \"B00DONTLP0\", \"positive_value\": \"./images/B00DONTLP0.png\", \"hn_image\": [\"./images/B00CFYDMM2.png\"]}\n{\"q_img\": \"./images/B00CFXUI4S.png\", \"q_text\": \"longer sleeved and print, and has elbow length sleeves and a black print\", \"positive_key\": \"B008U8XP06\", \"positive_value\": \"./images/B008U8XP06.png\", \"hn_image\": [\"./images/B00CFXUI4S.png\"]}\n{\"q_img\": \"./images/B007FHE5IY.png\", \"q_text\": \"is lighter, and is turquoise and black\", \"positive_key\": \"B007FHD0FI\", \"positive_value\": \"./images/B007FHD0FI.png\", \"hn_image\": [\"./images/B007FHE5IY.png\"]}\n{\"q_img\": \"./images/B007KHTV24.png\", \"q_text\": \"is tighter and shorter, and one blue\", \"positive_key\": \"B00ASI7TQM\", \"positive_value\": \"./images/B00ASI7TQM.png\", \"hn_image\": [\"./images/B007KHTV24.png\"]}\n{\"q_img\": \"./images/B00BI5R31U.png\", \"q_text\": \"is dark blue with no patterns, and is form fitting and short and is blue\", \"positive_key\": \"B008R4XZ0I\", \"positive_value\": \"./images/B008R4XZ0I.png\", \"hn_image\": [\"./images/B00BI5R31U.png\"]}\n{\"q_img\": \"./images/B00BBOI64Q.png\", \"q_text\": \"is dark blue in color and is longer, and is full length\", \"positive_key\": \"B009P83GT0\", \"positive_value\": \"./images/B009P83GT0.png\", \"hn_image\": [\"./images/B00BBOI64Q.png\"]}\n{\"q_img\": \"./images/B007FWGBRW.png\", \"q_text\": \"is shorter and more sexy, and is more red and shorter.\", \"positive_key\": \"B00E1MEKLI\", \"positive_value\": \"./images/B00E1MEKLI.png\", \"hn_image\": [\"./images/B007FWGBRW.png\"]}\n{\"q_img\": \"./images/B00GDSPIYQ.png\", \"q_text\": \"has no straps, and ruffled and no sleeves\", \"positive_key\": \"B004ISKT38\", \"positive_value\": \"./images/B004ISKT38.png\", \"hn_image\": [\"./images/B00GDSPIYQ.png\"]}\n{\"q_img\": \"./images/B007UYY43I.png\", \"q_text\": \"long sleeved green printed dress, and is longer darker and less revealing\", \"positive_key\": \"B00BI5R31U\", \"positive_value\": \"./images/B00BI5R31U.png\", \"hn_image\": [\"./images/B007UYY43I.png\"]}\n{\"q_img\": \"./images/B00DEP9CUW.png\", \"q_text\": \"is shorter and more casual, and thinner material and shorter skirt\", \"positive_key\": \"B004PEB1KU\", \"positive_value\": \"./images/B004PEB1KU.png\", \"hn_image\": [\"./images/B00DEP9CUW.png\"]}\n{\"q_img\": \"./images/B00CL0VWTA.png\", \"q_text\": \"is black with white dots on it and sleeveless, and has polkadots\", \"positive_key\": \"B00C1BSDBO\", \"positive_value\": \"./images/B00C1BSDBO.png\", \"hn_image\": [\"./images/B00CL0VWTA.png\"]}\n{\"q_img\": \"./images/B00CW6HT7W.png\", \"q_text\": \"has two sleeves and a belt, and has two strapts\", \"positive_key\": \"B00ENCGGZY\", \"positive_value\": \"./images/B00ENCGGZY.png\", \"hn_image\": [\"./images/B00CW6HT7W.png\"]}\n{\"q_img\": \"./images/B005KMQQFQ.png\", \"q_text\": \"is teal and longer, and is mint green\", \"positive_key\": \"B00DBS04F4\", \"positive_value\": \"./images/B00DBS04F4.png\", \"hn_image\": [\"./images/B005KMQQFQ.png\"]}\n{\"q_img\": \"./images/B006UT2TPY.png\", \"q_text\": \"is less shiny and has sleeves, and is not sleek and rubberized. Instead it is made of fabric.\", \"positive_key\": \"B00BYD7DDO\", \"positive_value\": \"./images/B00BYD7DDO.png\", \"hn_image\": [\"./images/B006UT2TPY.png\"]}\n{\"q_img\": \"./images/B007OX0MXG.png\", \"q_text\": \"has one sleeve and is pink, and is much lighter in color\", \"positive_key\": \"B006YZLWSE\", \"positive_value\": \"./images/B006YZLWSE.png\", \"hn_image\": [\"./images/B007OX0MXG.png\"]}\n{\"q_img\": \"./images/B006DYG6PA.png\", \"q_text\": \"is less striped with longer sleeves, and is one-toned and longer\", \"positive_key\": \"B009DMEAL6\", \"positive_value\": \"./images/B009DMEAL6.png\", \"hn_image\": [\"./images/B006DYG6PA.png\"]}\n{\"q_img\": \"./images/B008GQ3H50.png\", \"q_text\": \" looser and has longer sleeves, and has much shorter sleeves\", \"positive_key\": \"B00CFD6ZBI\", \"positive_value\": \"./images/B00CFD6ZBI.png\", \"hn_image\": [\"./images/B008GQ3H50.png\"]}\n{\"q_img\": \"./images/B004LVNVDC.png\", \"q_text\": \"is solid black, and is not as tight\", \"positive_key\": \"B001HK23KM\", \"positive_value\": \"./images/B001HK23KM.png\", \"hn_image\": [\"./images/B004LVNVDC.png\"]}\n{\"q_img\": \"./images/B00EEERGP0.png\", \"q_text\": \"is red and has a laced bottom, and Less black and more plain\", \"positive_key\": \"B00DMEFORK\", \"positive_value\": \"./images/B00DMEFORK.png\", \"hn_image\": [\"./images/B00EEERGP0.png\"]}\n{\"q_img\": \"./images/B007WAETMG.png\", \"q_text\": \"is black and short length, and shorter and darker\", \"positive_key\": \"B00AFOMEMI\", \"positive_value\": \"./images/B00AFOMEMI.png\", \"hn_image\": [\"./images/B007WAETMG.png\"]}\n{\"q_img\": \"./images/B007959ZSW.png\", \"q_text\": \"is strapless in a lighter color, and is more revealing\", \"positive_key\": \"B007ECAZPM\", \"positive_value\": \"./images/B007ECAZPM.png\", \"hn_image\": [\"./images/B007959ZSW.png\"]}\n{\"q_img\": \"./images/B00E3HDPMQ.png\", \"q_text\": \"is shinier and shorter, and Is cream colored and fitted\", \"positive_key\": \"B00DRCZHJC\", \"positive_value\": \"./images/B00DRCZHJC.png\", \"hn_image\": [\"./images/B00E3HDPMQ.png\"]}\n{\"q_img\": \"./images/B006361YUA.png\", \"q_text\": \"is light pink shade with small sleeves, and is pink and grey in color\", \"positive_key\": \"B00CJF48D4\", \"positive_value\": \"./images/B00CJF48D4.png\", \"hn_image\": [\"./images/B006361YUA.png\"]}\n{\"q_img\": \"./images/B0058ZB4L6.png\", \"q_text\": \"Is more fitted and chic, and is more fitting\", \"positive_key\": \"B00BBOI64Q\", \"positive_value\": \"./images/B00BBOI64Q.png\", \"hn_image\": [\"./images/B0058ZB4L6.png\"]}\n{\"q_img\": \"./images/B00A7049XW.png\", \"q_text\": \"is more sexy and redder, and is more of a red color\", \"positive_key\": \"B005WY5LAS\", \"positive_value\": \"./images/B005WY5LAS.png\", \"hn_image\": [\"./images/B00A7049XW.png\"]}\n{\"q_img\": \"./images/B007HCAEKU.png\", \"q_text\": \"is shorter with sheer prints, and is more revealing and shorter\", \"positive_key\": \"B00726HLP2\", \"positive_value\": \"./images/B00726HLP2.png\", \"hn_image\": [\"./images/B007HCAEKU.png\"]}\n{\"q_img\": \"./images/B00ALGUAOO.png\", \"q_text\": \"is black and orange with no sleeves, and strapless\", \"positive_key\": \"B00ALGUEXG\", \"positive_value\": \"./images/B00ALGUEXG.png\", \"hn_image\": [\"./images/B00ALGUAOO.png\"]}\n{\"q_img\": \"./images/B00DWPDCGY.png\", \"q_text\": \"is short sleeved and black with pink outline, and is less revealing and more has shorter sleeves\", \"positive_key\": \"B00DR3TPIA\", \"positive_value\": \"./images/B00DR3TPIA.png\", \"hn_image\": [\"./images/B00DWPDCGY.png\"]}\n{\"q_img\": \"./images/B009PXRLIW.png\", \"q_text\": \"has narrower straps and is less dressy, and is lighter colored\", \"positive_key\": \"B00C7P935E\", \"positive_value\": \"./images/B00C7P935E.png\", \"hn_image\": [\"./images/B009PXRLIW.png\"]}\n{\"q_img\": \"./images/B00AOWQJZ4.png\", \"q_text\": \"is a long black lace dress, and It is more tight and has sleeves\", \"positive_key\": \"B00G73FUGI\", \"positive_value\": \"./images/B00G73FUGI.png\", \"hn_image\": [\"./images/B00AOWQJZ4.png\"]}\n{\"q_img\": \"./images/B00DR3TPIA.png\", \"q_text\": \" and v-necked, and is red and square neck line\", \"positive_key\": \"B00EQ1PPEK\", \"positive_value\": \"./images/B00EQ1PPEK.png\", \"hn_image\": [\"./images/B00DR3TPIA.png\"]}\n{\"q_img\": \"./images/B008DH1ZEM.png\", \"q_text\": \" longer, and is black with straps\", \"positive_key\": \"B006MWV676\", \"positive_value\": \"./images/B006MWV676.png\", \"hn_image\": [\"./images/B008DH1ZEM.png\"]}\n{\"q_img\": \"./images/B008H86H1I.png\", \"q_text\": \"is darker in colour with no sleeves, and is shorter and dark\", \"positive_key\": \"B0041AF33Y\", \"positive_value\": \"./images/B0041AF33Y.png\", \"hn_image\": [\"./images/B008H86H1I.png\"]}\n{\"q_img\": \"./images/B006361YUA.png\", \"q_text\": \"has no straps and striped, and is more black and white striped\", \"positive_key\": \"B00CMU5O7K\", \"positive_value\": \"./images/B00CMU5O7K.png\", \"hn_image\": [\"./images/B006361YUA.png\"]}\n{\"q_img\": \"./images/B003725DKU.png\", \"q_text\": \"is pastel green with mesh over spaghetti strap top, and is sleeveless and green and white in color\", \"positive_key\": \"B00B71U376\", \"positive_value\": \"./images/B00B71U376.png\", \"hn_image\": [\"./images/B003725DKU.png\"]}\n{\"q_img\": \"./images/B008W46FMS.png\", \"q_text\": \"is off the shoulder with long sleeves, and has sleeves and shoulderless\", \"positive_key\": \"B00FE0G1OU\", \"positive_value\": \"./images/B00FE0G1OU.png\", \"hn_image\": [\"./images/B008W46FMS.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \" shorter skirt, and na\", \"positive_key\": \"B00BG4M4BM\", \"positive_value\": \"./images/B00BG4M4BM.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B00BKUTYWO.png\", \"q_text\": \"is white without straps and is high low, and is white and strapless\", \"positive_key\": \"B00B1OKIRO\", \"positive_value\": \"./images/B00B1OKIRO.png\", \"hn_image\": [\"./images/B00BKUTYWO.png\"]}\n{\"q_img\": \"./images/B0099DCINQ.png\", \"q_text\": \"is shorter with thin straps and white with red, and is shorter\", \"positive_key\": \"B00AHOYKVO\", \"positive_value\": \"./images/B00AHOYKVO.png\", \"hn_image\": [\"./images/B0099DCINQ.png\"]}\n{\"q_img\": \"./images/B00766RTT6.png\", \"q_text\": \"is black and longer, and leopard print designs and black belt stripe\", \"positive_key\": \"B00F0W6US4\", \"positive_value\": \"./images/B00F0W6US4.png\", \"hn_image\": [\"./images/B00766RTT6.png\"]}\n{\"q_img\": \"./images/B00CY90TWE.png\", \"q_text\": \"is shorter and lighter colored, and is white and shorter in length\", \"positive_key\": \"B0091CXMOE\", \"positive_value\": \"./images/B0091CXMOE.png\", \"hn_image\": [\"./images/B00CY90TWE.png\"]}\n{\"q_img\": \"./images/B00AD11LB8.png\", \"q_text\": \"is a line and lighter with design, and is square in the neck and blue\", \"positive_key\": \"B00AD11VKO\", \"positive_value\": \"./images/B00AD11VKO.png\", \"hn_image\": [\"./images/B00AD11LB8.png\"]}\n{\"q_img\": \"./images/B0090NZVZC.png\", \"q_text\": \"is more revealing orange, and is more complex in red\", \"positive_key\": \"B0065U35CY\", \"positive_value\": \"./images/B0065U35CY.png\", \"hn_image\": [\"./images/B0090NZVZC.png\"]}\n{\"q_img\": \"./images/B004RDQ97O.png\", \"q_text\": \"is a half sleeved dress, and has a peach and black skirt\", \"positive_key\": \"B009F90MHS\", \"positive_value\": \"./images/B009F90MHS.png\", \"hn_image\": [\"./images/B004RDQ97O.png\"]}\n{\"q_img\": \"./images/B0093K54X6.png\", \"q_text\": \"is black and one shouldered, and is black with one strap\", \"positive_key\": \"B003WT19PQ\", \"positive_value\": \"./images/B003WT19PQ.png\", \"hn_image\": [\"./images/B0093K54X6.png\"]}\n{\"q_img\": \"./images/B009DMCMNO.png\", \"q_text\": \"one shoulder dark blue short dress, and blue with only one shoulder\", \"positive_key\": \"B0098BVPIS\", \"positive_value\": \"./images/B0098BVPIS.png\", \"hn_image\": [\"./images/B009DMCMNO.png\"]}\n{\"q_img\": \"./images/B00BBXX6LU.png\", \"q_text\": \"longer and more revealing, and is longer and has no sleeves\", \"positive_key\": \"B00756WDYI\", \"positive_value\": \"./images/B00756WDYI.png\", \"hn_image\": [\"./images/B00BBXX6LU.png\"]}\n{\"q_img\": \"./images/B008VJ0Q2Y.png\", \"q_text\": \"has a falling neck, and is shorter and different color\", \"positive_key\": \"B008BDRDPI\", \"positive_value\": \"./images/B008BDRDPI.png\", \"hn_image\": [\"./images/B008VJ0Q2Y.png\"]}\n{\"q_img\": \"./images/B00F2MA1FK.png\", \"q_text\": \"Is more colorful and more floral, and The dress rests lower with no straps and has floral and dark colors.\", \"positive_key\": \"B00A7N4DC6\", \"positive_value\": \"./images/B00A7N4DC6.png\", \"hn_image\": [\"./images/B00F2MA1FK.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"is longer and less revealing, and is full length\", \"positive_key\": \"B008GX3852\", \"positive_value\": \"./images/B008GX3852.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B00C01N5K4.png\", \"q_text\": \" with vneck and tight fit, and has no sleeves and is yellow white colored\", \"positive_key\": \"B00AN8LRX8\", \"positive_value\": \"./images/B00AN8LRX8.png\", \"hn_image\": [\"./images/B00C01N5K4.png\"]}\n{\"q_img\": \"./images/B00AXX83YE.png\", \"q_text\": \"has floral prints, and is printed and shiny\", \"positive_key\": \"B0047SD9WC\", \"positive_value\": \"./images/B0047SD9WC.png\", \"hn_image\": [\"./images/B00AXX83YE.png\"]}\n{\"q_img\": \"./images/B00D75WT4U.png\", \"q_text\": \"a lot longer with no straps, and is less black\", \"positive_key\": \"B00APLSSLC\", \"positive_value\": \"./images/B00APLSSLC.png\", \"hn_image\": [\"./images/B00D75WT4U.png\"]}\n{\"q_img\": \"./images/B008JI0MX0.png\", \"q_text\": \"is pinker and shorter, and Is strapless in a bright color.\", \"positive_key\": \"B0046ECPGI\", \"positive_value\": \"./images/B0046ECPGI.png\", \"hn_image\": [\"./images/B008JI0MX0.png\"]}\n{\"q_img\": \"./images/B009CCW1XG.png\", \"q_text\": \"is black and tighter, and is black\", \"positive_key\": \"B0094U99S6\", \"positive_value\": \"./images/B0094U99S6.png\", \"hn_image\": [\"./images/B009CCW1XG.png\"]}\n{\"q_img\": \"./images/B007ADFRHQ.png\", \"q_text\": \"is a long multi colored summer dress, and is multi colored and longer\", \"positive_key\": \"B00D68DXCA\", \"positive_value\": \"./images/B00D68DXCA.png\", \"hn_image\": [\"./images/B007ADFRHQ.png\"]}\n{\"q_img\": \"./images/B00DDX15TG.png\", \"q_text\": \"is mostly black, and is black instead of green.\", \"positive_key\": \"B00CHSE4V4\", \"positive_value\": \"./images/B00CHSE4V4.png\", \"hn_image\": [\"./images/B00DDX15TG.png\"]}\n{\"q_img\": \"./images/B009HIH1S0.png\", \"q_text\": \"Is black, and has longer sleeves\", \"positive_key\": \"B001UHFV7O\", \"positive_value\": \"./images/B001UHFV7O.png\", \"hn_image\": [\"./images/B009HIH1S0.png\"]}\n{\"q_img\": \"./images/B007RB7IJG.png\", \"q_text\": \"has a belt and chevron pattern, and is more revealing and has a belt\", \"positive_key\": \"B009PYP9WQ\", \"positive_value\": \"./images/B009PYP9WQ.png\", \"hn_image\": [\"./images/B007RB7IJG.png\"]}\n{\"q_img\": \"./images/B008006CXG.png\", \"q_text\": \"has a v neck and is red, and red and more revealing\", \"positive_key\": \"B00575VZM0\", \"positive_value\": \"./images/B00575VZM0.png\", \"hn_image\": [\"./images/B008006CXG.png\"]}\n{\"q_img\": \"./images/B0092PKPKE.png\", \"q_text\": \"has longer sleeves, and Has longer sleeves\", \"positive_key\": \"B0092PKYNM\", \"positive_value\": \"./images/B0092PKYNM.png\", \"hn_image\": [\"./images/B0092PKPKE.png\"]}\n{\"q_img\": \"./images/B00DSI8WYW.png\", \"q_text\": \"is sexier and blue., and is blue and black pattern with long black sleeves\", \"positive_key\": \"B00BYI7ZQO\", \"positive_value\": \"./images/B00BYI7ZQO.png\", \"hn_image\": [\"./images/B00DSI8WYW.png\"]}\n{\"q_img\": \"./images/B005YB2S5K.png\", \"q_text\": \"has red and beige strips, and It is one piece and has a shorter sleeve\", \"positive_key\": \"B007WSD2CQ\", \"positive_value\": \"./images/B007WSD2CQ.png\", \"hn_image\": [\"./images/B005YB2S5K.png\"]}\n{\"q_img\": \"./images/B008G0ERA0.png\", \"q_text\": \"is longer and lighter, and has long sleeves and brighter blue\", \"positive_key\": \"B005XKDZ5Y\", \"positive_value\": \"./images/B005XKDZ5Y.png\", \"hn_image\": [\"./images/B008G0ERA0.png\"]}\n{\"q_img\": \"./images/B0060MFHH8.png\", \"q_text\": \"is short sleeves, and Is a sleeveless sheath dress in red with black accents.\", \"positive_key\": \"B00F4MYMJO\", \"positive_value\": \"./images/B00F4MYMJO.png\", \"hn_image\": [\"./images/B0060MFHH8.png\"]}\n{\"q_img\": \"./images/B009TM8IG8.png\", \"q_text\": \"is sleeveless, and longer\", \"positive_key\": \"B008A4N1YK\", \"positive_value\": \"./images/B008A4N1YK.png\", \"hn_image\": [\"./images/B009TM8IG8.png\"]}\n{\"q_img\": \"./images/B00AA6F0GI.png\", \"q_text\": \" blue and less print, and is blue and strapless\", \"positive_key\": \"B006VA06KW\", \"positive_value\": \"./images/B006VA06KW.png\", \"hn_image\": [\"./images/B00AA6F0GI.png\"]}\n{\"q_img\": \"./images/B00BYHHYCA.png\", \"q_text\": \"is a darker shade of pink, and is darker\", \"positive_key\": \"B00CEJW206\", \"positive_value\": \"./images/B00CEJW206.png\", \"hn_image\": [\"./images/B00BYHHYCA.png\"]}\n{\"q_img\": \"./images/B006361YUA.png\", \"q_text\": \"has an orange tiger on it, and has a tiger design\", \"positive_key\": \"B00CQNXR7W\", \"positive_value\": \"./images/B00CQNXR7W.png\", \"hn_image\": [\"./images/B006361YUA.png\"]}\n{\"q_img\": \"./images/B005278U3U.png\", \"q_text\": \"has floral pattern and more casual, and is printer and has thin straps\", \"positive_key\": \"B004GGSFK6\", \"positive_value\": \"./images/B004GGSFK6.png\", \"hn_image\": [\"./images/B005278U3U.png\"]}\n{\"q_img\": \"./images/B00B4GJODI.png\", \"q_text\": \" lacy, and is coral with white color and shorter\", \"positive_key\": \"B009NCHDG0\", \"positive_value\": \"./images/B009NCHDG0.png\", \"hn_image\": [\"./images/B00B4GJODI.png\"]}\n{\"q_img\": \"./images/B004EPXWK2.png\", \"q_text\": \"is black with white stripes, and is black and red\", \"positive_key\": \"B009NJQCO2\", \"positive_value\": \"./images/B009NJQCO2.png\", \"hn_image\": [\"./images/B004EPXWK2.png\"]}\n{\"q_img\": \"./images/B0064J5JPC.png\", \"q_text\": \"is monochromatic, and has quarter length sleeves and skirt to the knee\", \"positive_key\": \"B00DGTZ7XM\", \"positive_value\": \"./images/B00DGTZ7XM.png\", \"hn_image\": [\"./images/B0064J5JPC.png\"]}\n{\"q_img\": \"./images/B004Q9T840.png\", \"q_text\": \"is red with ruffles on collar, and is red with a higher nexk\", \"positive_key\": \"B005P7BK84\", \"positive_value\": \"./images/B005P7BK84.png\", \"hn_image\": [\"./images/B004Q9T840.png\"]}\n{\"q_img\": \"./images/B0087PLHB6.png\", \"q_text\": \"more red, and is red and has 2 straps.\", \"positive_key\": \"B00CP5NEFG\", \"positive_value\": \"./images/B00CP5NEFG.png\", \"hn_image\": [\"./images/B0087PLHB6.png\"]}\n{\"q_img\": \"./images/B00C1RFQA4.png\", \"q_text\": \"white with a bird pattern and a belt, and is belted and printed\", \"positive_key\": \"B00CIAHQVG\", \"positive_value\": \"./images/B00CIAHQVG.png\", \"hn_image\": [\"./images/B00C1RFQA4.png\"]}\n{\"q_img\": \"./images/B006GF739E.png\", \"q_text\": \"is longer and has a bright blue pattern, and Is longer and a brighter geometric pattern\", \"positive_key\": \"B007RJP2SM\", \"positive_value\": \"./images/B007RJP2SM.png\", \"hn_image\": [\"./images/B006GF739E.png\"]}\n{\"q_img\": \"./images/B008F48SKC.png\", \"q_text\": \"is flowing and zigzagged., and has off shoulder and is pink chevron color\", \"positive_key\": \"B00CJHOUFI\", \"positive_value\": \"./images/B00CJHOUFI.png\", \"hn_image\": [\"./images/B008F48SKC.png\"]}\n{\"q_img\": \"./images/B006J7DBTK.png\", \"q_text\": \"is black and long and has multiple slits, and has shorter sleeves and a gold belt\", \"positive_key\": \"B00DSSHIX8\", \"positive_value\": \"./images/B00DSSHIX8.png\", \"hn_image\": [\"./images/B006J7DBTK.png\"]}\n{\"q_img\": \"./images/B009HMQOP2.png\", \"q_text\": \"Has a light purple color, and is in pink without a v neck.\", \"positive_key\": \"B004AVK514\", \"positive_value\": \"./images/B004AVK514.png\", \"hn_image\": [\"./images/B009HMQOP2.png\"]}\n{\"q_img\": \"./images/B005VTJH4K.png\", \"q_text\": \" with a floral pattern, and red with deeper v neck\", \"positive_key\": \"B006TALRY8\", \"positive_value\": \"./images/B006TALRY8.png\", \"hn_image\": [\"./images/B005VTJH4K.png\"]}\n{\"q_img\": \"./images/B004U8YYZU.png\", \"q_text\": \"is shorter and orange., and is a shorter dress and orange color\", \"positive_key\": \"B005QYW7PG\", \"positive_value\": \"./images/B005QYW7PG.png\", \"hn_image\": [\"./images/B004U8YYZU.png\"]}\n{\"q_img\": \"./images/B00B3Y6GNM.png\", \"q_text\": \"is longer and blue floral, and blue and white with longer skirt\", \"positive_key\": \"B004EPXD9M\", \"positive_value\": \"./images/B004EPXD9M.png\", \"hn_image\": [\"./images/B00B3Y6GNM.png\"]}\n{\"q_img\": \"./images/B00EPC5D10.png\", \"q_text\": \"has one shorter sleeve and a defined waist, and has a lighter print and spaghetti strap on one side\", \"positive_key\": \"B005278ZUI\", \"positive_value\": \"./images/B005278ZUI.png\", \"hn_image\": [\"./images/B00EPC5D10.png\"]}\n{\"q_img\": \"./images/B00BG4M4BM.png\", \"q_text\": \"is solid black with sleeves, and longer sleeves\", \"positive_key\": \"B0091QVR0G\", \"positive_value\": \"./images/B0091QVR0G.png\", \"hn_image\": [\"./images/B00BG4M4BM.png\"]}\n{\"q_img\": \"./images/B005644KAG.png\", \"q_text\": \"has lace at the top part, and has no racerback straps\", \"positive_key\": \"B00BUC1QUA\", \"positive_value\": \"./images/B00BUC1QUA.png\", \"hn_image\": [\"./images/B005644KAG.png\"]}\n{\"q_img\": \"./images/B0030EHJCU.png\", \"q_text\": \"short white dress, and is more white with no sleeves\", \"positive_key\": \"B00BY3GO0C\", \"positive_value\": \"./images/B00BY3GO0C.png\", \"hn_image\": [\"./images/B0030EHJCU.png\"]}\n{\"q_img\": \"./images/B0073HKUWG.png\", \"q_text\": \"Has blue and white tie dyed pattern., and is floor-length and patterned\", \"positive_key\": \"B007TC5ICW\", \"positive_value\": \"./images/B007TC5ICW.png\", \"hn_image\": [\"./images/B0073HKUWG.png\"]}\n{\"q_img\": \"./images/B0036ME5BE.png\", \"q_text\": \"is darker and longer, and is more red with a longer skirt\", \"positive_key\": \"B002WV0S6G\", \"positive_value\": \"./images/B002WV0S6G.png\", \"hn_image\": [\"./images/B0036ME5BE.png\"]}\n{\"q_img\": \"./images/B0059817JQ.png\", \"q_text\": \"Looser and colorful, and has more colors in it\", \"positive_key\": \"B00D8ICJNM\", \"positive_value\": \"./images/B00D8ICJNM.png\", \"hn_image\": [\"./images/B0059817JQ.png\"]}\n{\"q_img\": \"./images/B00C2D4XA6.png\", \"q_text\": \"has longer sleeves and is darker, and is red and has longer sleeve\", \"positive_key\": \"B00C2DDJXI\", \"positive_value\": \"./images/B00C2DDJXI.png\", \"hn_image\": [\"./images/B00C2D4XA6.png\"]}\n{\"q_img\": \"./images/B00DSSIGXO.png\", \"q_text\": \"shorter sleeve and shorter skirt, and pink\", \"positive_key\": \"B00BDGMRGA\", \"positive_value\": \"./images/B00BDGMRGA.png\", \"hn_image\": [\"./images/B00DSSIGXO.png\"]}\n{\"q_img\": \"./images/B00EXMSRW4.png\", \"q_text\": \"is solid black, and is black\", \"positive_key\": \"B0071MSFCU\", \"positive_value\": \"./images/B0071MSFCU.png\", \"hn_image\": [\"./images/B00EXMSRW4.png\"]}\n{\"q_img\": \"./images/B00CAMJMRS.png\", \"q_text\": \"is solid red with silver design on neck, and brighter red with jewled neckline\", \"positive_key\": \"B00DMZFD7K\", \"positive_value\": \"./images/B00DMZFD7K.png\", \"hn_image\": [\"./images/B00CAMJMRS.png\"]}\n{\"q_img\": \"./images/B00CJRV3Z8.png\", \"q_text\": \"is solid blue, and is black\", \"positive_key\": \"B00AX1SATY\", \"positive_value\": \"./images/B00AX1SATY.png\", \"hn_image\": [\"./images/B00CJRV3Z8.png\"]}\n{\"q_img\": \"./images/B005SGZXTY.png\", \"q_text\": \"Is darker and strapless., and is black and long\", \"positive_key\": \"B007R9JNPU\", \"positive_value\": \"./images/B007R9JNPU.png\", \"hn_image\": [\"./images/B005SGZXTY.png\"]}\n{\"q_img\": \"./images/B009E73G1U.png\", \"q_text\": \"is red white gray and black, and is black and striped\", \"positive_key\": \"B0084Y2V6A\", \"positive_value\": \"./images/B0084Y2V6A.png\", \"hn_image\": [\"./images/B009E73G1U.png\"]}\n{\"q_img\": \"./images/B00BEJ34Q8.png\", \"q_text\": \"is clack in color with white boxes at the tits, and the dress is black and white and shorter\", \"positive_key\": \"B00AWKKMWE\", \"positive_value\": \"./images/B00AWKKMWE.png\", \"hn_image\": [\"./images/B00BEJ34Q8.png\"]}\n{\"q_img\": \"./images/B007B2HS78.png\", \"q_text\": \"Is dark blue with a low necklne., and is brighter in color\", \"positive_key\": \"B009DI5YSS\", \"positive_value\": \"./images/B009DI5YSS.png\", \"hn_image\": [\"./images/B007B2HS78.png\"]}\n{\"q_img\": \"./images/B006YNDYF0.png\", \"q_text\": \"has long sleeves and is black, and is black with long sleeves\", \"positive_key\": \"B004748I22\", \"positive_value\": \"./images/B004748I22.png\", \"hn_image\": [\"./images/B006YNDYF0.png\"]}\n{\"q_img\": \"./images/B000NB1FAU.png\", \"q_text\": \"is shorter and has no sleeves, and is shorter and sleeveless\", \"positive_key\": \"B00CDJZYA2\", \"positive_value\": \"./images/B00CDJZYA2.png\", \"hn_image\": [\"./images/B000NB1FAU.png\"]}\n{\"q_img\": \"./images/B0063BBIEM.png\", \"q_text\": \"is more revealing and darker, and is darker\", \"positive_key\": \"B006D8SSOS\", \"positive_value\": \"./images/B006D8SSOS.png\", \"hn_image\": [\"./images/B0063BBIEM.png\"]}\n{\"q_img\": \"./images/B00FFDZT8K.png\", \"q_text\": \"has longer sleeves and less revealing, and is silver with sleeves\", \"positive_key\": \"B007WAFYU2\", \"positive_value\": \"./images/B007WAFYU2.png\", \"hn_image\": [\"./images/B00FFDZT8K.png\"]}\n{\"q_img\": \"./images/B00BJGW386.png\", \"q_text\": \"is more casual, and More western style with fit and flare\", \"positive_key\": \"B00DW475WC\", \"positive_value\": \"./images/B00DW475WC.png\", \"hn_image\": [\"./images/B00BJGW386.png\"]}\n{\"q_img\": \"./images/B00B8QSAY8.png\", \"q_text\": \"is more revealing, and is black with a shiny look\", \"positive_key\": \"B0088AAXUG\", \"positive_value\": \"./images/B0088AAXUG.png\", \"hn_image\": [\"./images/B00B8QSAY8.png\"]}\n{\"q_img\": \"./images/B008Z1TX2M.png\", \"q_text\": \"white and more rounded, and Is more flowing and white\", \"positive_key\": \"B00C11R13U\", \"positive_value\": \"./images/B00C11R13U.png\", \"hn_image\": [\"./images/B008Z1TX2M.png\"]}\n{\"q_img\": \"./images/B005WQCHPI.png\", \"q_text\": \"is solid black and shorter, and is black and more fitted.\", \"positive_key\": \"B00BM279C2\", \"positive_value\": \"./images/B00BM279C2.png\", \"hn_image\": [\"./images/B005WQCHPI.png\"]}\n{\"q_img\": \"./images/B006VRT354.png\", \"q_text\": \"is v necked and green, and is more frilly and has more green\", \"positive_key\": \"B006UCQXRG\", \"positive_value\": \"./images/B006UCQXRG.png\", \"hn_image\": [\"./images/B006VRT354.png\"]}\n{\"q_img\": \"./images/B009AUQVS6.png\", \"q_text\": \"Is brighter and more revealing, and is pink with puffy sleeves and more flowy\", \"positive_key\": \"B00ATPE8CW\", \"positive_value\": \"./images/B00ATPE8CW.png\", \"hn_image\": [\"./images/B009AUQVS6.png\"]}\n{\"q_img\": \"./images/B0083QFE0Y.png\", \"q_text\": \"Is a lighter color, and is in a lighter yellow color.\", \"positive_key\": \"B00AO3SNGQ\", \"positive_value\": \"./images/B00AO3SNGQ.png\", \"hn_image\": [\"./images/B0083QFE0Y.png\"]}\n{\"q_img\": \"./images/B00BUVAI5K.png\", \"q_text\": \"is peach with white collar, and pink and more flowing\", \"positive_key\": \"B006PDVQQI\", \"positive_value\": \"./images/B006PDVQQI.png\", \"hn_image\": [\"./images/B00BUVAI5K.png\"]}\n{\"q_img\": \"./images/B0017LUFJM.png\", \"q_text\": \" longer, and Has frilly sleeves and longer\", \"positive_key\": \"B00530FEEY\", \"positive_value\": \"./images/B00530FEEY.png\", \"hn_image\": [\"./images/B0017LUFJM.png\"]}\n{\"q_img\": \"./images/B0033UVILO.png\", \"q_text\": \"Has a bright floral pattern., and is less shiney\", \"positive_key\": \"B007C4CF7I\", \"positive_value\": \"./images/B007C4CF7I.png\", \"hn_image\": [\"./images/B0033UVILO.png\"]}\n{\"q_img\": \"./images/B00DP6RHIO.png\", \"q_text\": \"is black and white striped and is sleeveless, and is striped and has thin straps\", \"positive_key\": \"B008PG3FP8\", \"positive_value\": \"./images/B008PG3FP8.png\", \"hn_image\": [\"./images/B00DP6RHIO.png\"]}\n{\"q_img\": \"./images/B007678MDM.png\", \"q_text\": \"is solid black with no sleeves, and is sleeveless and black\", \"positive_key\": \"B009YKCYZ6\", \"positive_value\": \"./images/B009YKCYZ6.png\", \"hn_image\": [\"./images/B007678MDM.png\"]}\n{\"q_img\": \"./images/B007XD4IMS.png\", \"q_text\": \"Is light blue with halter straps., and is strapless and longer\", \"positive_key\": \"B007WA29BY\", \"positive_value\": \"./images/B007WA29BY.png\", \"hn_image\": [\"./images/B007XD4IMS.png\"]}\n{\"q_img\": \"./images/B006WBA2JK.png\", \"q_text\": \"has plant prints on dres, and is a printed dress and shorter in length\", \"positive_key\": \"B008BYUH9Q\", \"positive_value\": \"./images/B008BYUH9Q.png\", \"hn_image\": [\"./images/B006WBA2JK.png\"]}\n{\"q_img\": \"./images/B004DTTRA8.png\", \"q_text\": \"Is more intimate and cami, and has no pink in it\", \"positive_key\": \"B0048OYGF4\", \"positive_value\": \"./images/B0048OYGF4.png\", \"hn_image\": [\"./images/B004DTTRA8.png\"]}\n{\"q_img\": \"./images/B001NIYDSS.png\", \"q_text\": \"Is a black ruffle dress with lime green piping, and strapless with green line\", \"positive_key\": \"B00ANC7QIY\", \"positive_value\": \"./images/B00ANC7QIY.png\", \"hn_image\": [\"./images/B001NIYDSS.png\"]}\n{\"q_img\": \"./images/B008PG0SOO.png\", \"q_text\": \"more pink and strapless, and is pink with no straps\", \"positive_key\": \"B006WARFS2\", \"positive_value\": \"./images/B006WARFS2.png\", \"hn_image\": [\"./images/B008PG0SOO.png\"]}\n{\"q_img\": \"./images/B00AKB3VPA.png\", \"q_text\": \"has longer sleeves and light blue, and is purple with no sleeves\", \"positive_key\": \"B008E4O6TU\", \"positive_value\": \"./images/B008E4O6TU.png\", \"hn_image\": [\"./images/B00AKB3VPA.png\"]}\n{\"q_img\": \"./images/B007M8PCAQ.png\", \"q_text\": \"Shorter and more exotic, and is shorter and is a shinier material\", \"positive_key\": \"B0062GQFX2\", \"positive_value\": \"./images/B0062GQFX2.png\", \"hn_image\": [\"./images/B007M8PCAQ.png\"]}\n{\"q_img\": \"./images/B0088Y02XA.png\", \"q_text\": \"has a patterened bottom, and has white and green and a patterned bottom\", \"positive_key\": \"B00A2X398A\", \"positive_value\": \"./images/B00A2X398A.png\", \"hn_image\": [\"./images/B0088Y02XA.png\"]}\n{\"q_img\": \"./images/B005KOYZWU.png\", \"q_text\": \"is brighter and less revealing, and The desired product is a completely different style than the provided product.\", \"positive_key\": \"B00BY045C4\", \"positive_value\": \"./images/B00BY045C4.png\", \"hn_image\": [\"./images/B005KOYZWU.png\"]}\n{\"q_img\": \"./images/B0074W1CWC.png\", \"q_text\": \"Is a short purple and black dress with a bow, and Dark Color\", \"positive_key\": \"B002B55PES\", \"positive_value\": \"./images/B002B55PES.png\", \"hn_image\": [\"./images/B0074W1CWC.png\"]}\n{\"q_img\": \"./images/B0066CUZ6A.png\", \"q_text\": \"Is more western, and is more revealing and short\", \"positive_key\": \"B0078JAST4\", \"positive_value\": \"./images/B0078JAST4.png\", \"hn_image\": [\"./images/B0066CUZ6A.png\"]}\n{\"q_img\": \"./images/B007WAFYU2.png\", \"q_text\": \"is sleeveless, and is black and more revealing\", \"positive_key\": \"B009SBBLNM\", \"positive_value\": \"./images/B009SBBLNM.png\", \"hn_image\": [\"./images/B007WAFYU2.png\"]}\n{\"q_img\": \"./images/B008P3DTZC.png\", \"q_text\": \"is black and sexy, and Is darker and less patterned.\", \"positive_key\": \"B0061IQ32E\", \"positive_value\": \"./images/B0061IQ32E.png\", \"hn_image\": [\"./images/B008P3DTZC.png\"]}\n{\"q_img\": \"./images/B006ZO54SI.png\", \"q_text\": \"is white and frilly, and is white and has crochet\", \"positive_key\": \"B008LE9FLM\", \"positive_value\": \"./images/B008LE9FLM.png\", \"hn_image\": [\"./images/B006ZO54SI.png\"]}\n{\"q_img\": \"./images/B008PC5DGG.png\", \"q_text\": \"is red with a collar, and is pink with a belt and V neck\", \"positive_key\": \"B004I3UFKK\", \"positive_value\": \"./images/B004I3UFKK.png\", \"hn_image\": [\"./images/B008PC5DGG.png\"]}\n{\"q_img\": \"./images/B00CZC6JIS.png\", \"q_text\": \"is a greenish dress with neck strap, and is lighter colored\", \"positive_key\": \"B007J6W358\", \"positive_value\": \"./images/B007J6W358.png\", \"hn_image\": [\"./images/B00CZC6JIS.png\"]}\n{\"q_img\": \"./images/B004Q9T840.png\", \"q_text\": \"is a long maxi dress, and is more formal and less revealing\", \"positive_key\": \"B00A2X6J78\", \"positive_value\": \"./images/B00A2X6J78.png\", \"hn_image\": [\"./images/B004Q9T840.png\"]}\n{\"q_img\": \"./images/B005VZ8MWC.png\", \"q_text\": \"Black and sexier, and a darker colored dress with no straps.\", \"positive_key\": \"B005VZ8RFO\", \"positive_value\": \"./images/B005VZ8RFO.png\", \"hn_image\": [\"./images/B005VZ8MWC.png\"]}\n{\"q_img\": \"./images/B0084Y6UQC.png\", \"q_text\": \"has no straps, and has more green\", \"positive_key\": \"B0076OEBY4\", \"positive_value\": \"./images/B0076OEBY4.png\", \"hn_image\": [\"./images/B0084Y6UQC.png\"]}\n{\"q_img\": \"./images/B008PRJ394.png\", \"q_text\": \"more blue and white, and is v necked nd darker\", \"positive_key\": \"B006OR8A6E\", \"positive_value\": \"./images/B006OR8A6E.png\", \"hn_image\": [\"./images/B008PRJ394.png\"]}\n{\"q_img\": \"./images/B00D83ENMC.png\", \"q_text\": \" short, and is long sleeved and black and more white\", \"positive_key\": \"B00FM7EAQQ\", \"positive_value\": \"./images/B00FM7EAQQ.png\", \"hn_image\": [\"./images/B00D83ENMC.png\"]}\n{\"q_img\": \"./images/B007SYWE00.png\", \"q_text\": \"has straps on both shoulders, and is more flowing and darker\", \"positive_key\": \"B004AZLVJK\", \"positive_value\": \"./images/B004AZLVJK.png\", \"hn_image\": [\"./images/B007SYWE00.png\"]}\n{\"q_img\": \"./images/B00ED49C12.png\", \"q_text\": \"has spaghetti straps with halter top, and is longer and a haler\", \"positive_key\": \"B008B6QCH0\", \"positive_value\": \"./images/B008B6QCH0.png\", \"hn_image\": [\"./images/B00ED49C12.png\"]}\n{\"q_img\": \"./images/B009OCNXE0.png\", \"q_text\": \"is longer, and is long and has a blue and white pattern\", \"positive_key\": \"B006CR20VW\", \"positive_value\": \"./images/B006CR20VW.png\", \"hn_image\": [\"./images/B009OCNXE0.png\"]}\n{\"q_img\": \"./images/B003YYRKSE.png\", \"q_text\": \"is flowy and has thin straps, and is longer and lighter colored\", \"positive_key\": \"B00BJ8J4NQ\", \"positive_value\": \"./images/B00BJ8J4NQ.png\", \"hn_image\": [\"./images/B003YYRKSE.png\"]}\n{\"q_img\": \"./images/B0038KR31I.png\", \"q_text\": \"has more polka dots and is darker, and shinier and pleather\", \"positive_key\": \"B009T74R2C\", \"positive_value\": \"./images/B009T74R2C.png\", \"hn_image\": [\"./images/B0038KR31I.png\"]}\n{\"q_img\": \"./images/B00AFV75CU.png\", \"q_text\": \"is sexier and less revealing, and is darker colored black\", \"positive_key\": \"B00DSME58U\", \"positive_value\": \"./images/B00DSME58U.png\", \"hn_image\": [\"./images/B00AFV75CU.png\"]}\n{\"q_img\": \"./images/B009IE33SU.png\", \"q_text\": \"Is black and has gold spots., and Is more patterned\", \"positive_key\": \"B0091F39QC\", \"positive_value\": \"./images/B0091F39QC.png\", \"hn_image\": [\"./images/B009IE33SU.png\"]}\n{\"q_img\": \"./images/B008HSGOKC.png\", \"q_text\": \"is a more solid color and shorter, and is much shorter\", \"positive_key\": \"B0060C8RXE\", \"positive_value\": \"./images/B0060C8RXE.png\", \"hn_image\": [\"./images/B008HSGOKC.png\"]}\n{\"q_img\": \"./images/B00ARFW8JE.png\", \"q_text\": \"has horizontal stripes, and is white with black stripes\", \"positive_key\": \"B00ARFVZ3Y\", \"positive_value\": \"./images/B00ARFVZ3Y.png\", \"hn_image\": [\"./images/B00ARFW8JE.png\"]}\n{\"q_img\": \"./images/B0084DJO1Q.png\", \"q_text\": \"is black with floral pattern on top, and is muticolored and has no sleeves\", \"positive_key\": \"B007V0B9UC\", \"positive_value\": \"./images/B007V0B9UC.png\", \"hn_image\": [\"./images/B0084DJO1Q.png\"]}\n{\"q_img\": \"./images/B00E9QQU46.png\", \"q_text\": \"has thin straps, and is more elegant and more frilly\", \"positive_key\": \"B0097IWC3Y\", \"positive_value\": \"./images/B0097IWC3Y.png\", \"hn_image\": [\"./images/B00E9QQU46.png\"]}\n{\"q_img\": \"./images/B00BJ75S8C.png\", \"q_text\": \"more shirt-dress with longer sleeves, and is lighter purple and pink\", \"positive_key\": \"B00C12KACS\", \"positive_value\": \"./images/B00C12KACS.png\", \"hn_image\": [\"./images/B00BJ75S8C.png\"]}\n{\"q_img\": \"./images/B00AJ1P6XQ.png\", \"q_text\": \"is darker, and is gray and one short sleeve\", \"positive_key\": \"B00AEW4384\", \"positive_value\": \"./images/B00AEW4384.png\", \"hn_image\": [\"./images/B00AJ1P6XQ.png\"]}\n{\"q_img\": \"./images/B003VXVGRY.png\", \"q_text\": \"higher neck line more colored blocked, and has longer sleeves and darker colors\", \"positive_key\": \"B005KILV0A\", \"positive_value\": \"./images/B005KILV0A.png\", \"hn_image\": [\"./images/B003VXVGRY.png\"]}\n{\"q_img\": \"./images/B00BBXX6LU.png\", \"q_text\": \"is a sleeveless white and blue dress, and is strapless and blue\", \"positive_key\": \"B002VLJSQI\", \"positive_value\": \"./images/B002VLJSQI.png\", \"hn_image\": [\"./images/B00BBXX6LU.png\"]}\n{\"q_img\": \"./images/B008MNM7Y4.png\", \"q_text\": \"is longer and orange in color, and is longer and has a higher split\", \"positive_key\": \"B00ECTI3SG\", \"positive_value\": \"./images/B00ECTI3SG.png\", \"hn_image\": [\"./images/B008MNM7Y4.png\"]}\n{\"q_img\": \"./images/B009K7C2CS.png\", \"q_text\": \"Is more casual and has long-sleeves, and is pink and casual\", \"positive_key\": \"B0015SKXG2\", \"positive_value\": \"./images/B0015SKXG2.png\", \"hn_image\": [\"./images/B009K7C2CS.png\"]}\n{\"q_img\": \"./images/B00715FFX4.png\", \"q_text\": \"is solid red and shorter, and has shorter sleeves and more revealing\", \"positive_key\": \"B007UNTT6G\", \"positive_value\": \"./images/B007UNTT6G.png\", \"hn_image\": [\"./images/B00715FFX4.png\"]}\n{\"q_img\": \"./images/B005XW2EJK.png\", \"q_text\": \"is red with long sleeves, and is solid red\", \"positive_key\": \"B00E3S6BS0\", \"positive_value\": \"./images/B00E3S6BS0.png\", \"hn_image\": [\"./images/B005XW2EJK.png\"]}\n{\"q_img\": \"./images/B00DZUQHSQ.png\", \"q_text\": \"This dress is a lighter color and has a pattern, and is longer and lighter in color\", \"positive_key\": \"B00DZURB9K\", \"positive_value\": \"./images/B00DZURB9K.png\", \"hn_image\": [\"./images/B00DZUQHSQ.png\"]}\n{\"q_img\": \"./images/B0058BPMPO.png\", \"q_text\": \"blue and short, and is blue\", \"positive_key\": \"B009HMQOP2\", \"positive_value\": \"./images/B009HMQOP2.png\", \"hn_image\": [\"./images/B0058BPMPO.png\"]}\n{\"q_img\": \"./images/B00G1ZZDL4.png\", \"q_text\": \"Is more subdued, and is white instead of pink.\", \"positive_key\": \"B004LGXB0K\", \"positive_value\": \"./images/B004LGXB0K.png\", \"hn_image\": [\"./images/B00G1ZZDL4.png\"]}\n{\"q_img\": \"./images/B003BT62A4.png\", \"q_text\": \"is not strapless and is more colorful, and is more yellow and stripy\", \"positive_key\": \"B006WM4LYG\", \"positive_value\": \"./images/B006WM4LYG.png\", \"hn_image\": [\"./images/B003BT62A4.png\"]}\n{\"q_img\": \"./images/B00CGY6RJ6.png\", \"q_text\": \"is red and not flowy, and is more form fitting\", \"positive_key\": \"B004XGY21K\", \"positive_value\": \"./images/B004XGY21K.png\", \"hn_image\": [\"./images/B00CGY6RJ6.png\"]}\n{\"q_img\": \"./images/B00CI08M00.png\", \"q_text\": \"has an orange and purple floral print, and is orange\", \"positive_key\": \"B00BUVJTSC\", \"positive_value\": \"./images/B00BUVJTSC.png\", \"hn_image\": [\"./images/B00CI08M00.png\"]}\n{\"q_img\": \"./images/B00GP5TL6I.png\", \"q_text\": \"has a v shaped neck with no sleeves, and is sleeveless and less form fitting\", \"positive_key\": \"B00FBRGJKM\", \"positive_value\": \"./images/B00FBRGJKM.png\", \"hn_image\": [\"./images/B00GP5TL6I.png\"]}\n{\"q_img\": \"./images/B002TXI512.png\", \"q_text\": \"is darker and has longer sleeves, and is more flowing and blue\", \"positive_key\": \"B00DRF4NGW\", \"positive_value\": \"./images/B00DRF4NGW.png\", \"hn_image\": [\"./images/B002TXI512.png\"]}\n{\"q_img\": \"./images/B007ZOGQIO.png\", \"q_text\": \"pink and black 3/4 sleeved dress, and one shouldered and is much longer\", \"positive_key\": \"B008A54HPG\", \"positive_value\": \"./images/B008A54HPG.png\", \"hn_image\": [\"./images/B007ZOGQIO.png\"]}\n{\"q_img\": \"./images/B004HSN6W0.png\", \"q_text\": \"is v neck and black and white print, and It is more monochromatic and less flowery\", \"positive_key\": \"B008E7IBDO\", \"positive_value\": \"./images/B008E7IBDO.png\", \"hn_image\": [\"./images/B004HSN6W0.png\"]}\n{\"q_img\": \"./images/B008YF4VAS.png\", \"q_text\": \"Is shorter with a striped skirt., and the skirt has more white\", \"positive_key\": \"B008HH26XM\", \"positive_value\": \"./images/B008HH26XM.png\", \"hn_image\": [\"./images/B008YF4VAS.png\"]}\n{\"q_img\": \"./images/B00G6TRZE8.png\", \"q_text\": \"more revealing and shorter, and is more shiny\", \"positive_key\": \"B00CO7ZRNM\", \"positive_value\": \"./images/B00CO7ZRNM.png\", \"hn_image\": [\"./images/B00G6TRZE8.png\"]}\n{\"q_img\": \"./images/B009NW4TW6.png\", \"q_text\": \"Is darker, and is black with no patterns\", \"positive_key\": \"B00A3UT8GE\", \"positive_value\": \"./images/B00A3UT8GE.png\", \"hn_image\": [\"./images/B009NW4TW6.png\"]}\n{\"q_img\": \"./images/B0055TBPNC.png\", \"q_text\": \"Is more colorful and complex, and is 3/4 sleeves and bold printed\", \"positive_key\": \"B00BWKXOS2\", \"positive_value\": \"./images/B00BWKXOS2.png\", \"hn_image\": [\"./images/B0055TBPNC.png\"]}\n{\"q_img\": \"./images/B0029YQQYE.png\", \"q_text\": \"is darker with a tighter fitted skirt, and fitted\", \"positive_key\": \"B004KB5HQW\", \"positive_value\": \"./images/B004KB5HQW.png\", \"hn_image\": [\"./images/B0029YQQYE.png\"]}\n{\"q_img\": \"./images/B008QW7GOW.png\", \"q_text\": \"Is lighter and more fitted, and is a lighter color\", \"positive_key\": \"B005MGDYK0\", \"positive_value\": \"./images/B005MGDYK0.png\", \"hn_image\": [\"./images/B008QW7GOW.png\"]}\n{\"q_img\": \"./images/B00CHH5C2K.png\", \"q_text\": \"is red, and is maroon and has a belt\", \"positive_key\": \"B00B29NXUC\", \"positive_value\": \"./images/B00B29NXUC.png\", \"hn_image\": [\"./images/B00CHH5C2K.png\"]}\n{\"q_img\": \"./images/B005I63HRY.png\", \"q_text\": \"is shorter is chevron print, and has a striped print and shorter skirt\", \"positive_key\": \"B00E4SYADC\", \"positive_value\": \"./images/B00E4SYADC.png\", \"hn_image\": [\"./images/B005I63HRY.png\"]}\n{\"q_img\": \"./images/B00AD11FFU.png\", \"q_text\": \"is shorter and has a pattern, and has a completely floral print and is a mini\", \"positive_key\": \"B004W88GB6\", \"positive_value\": \"./images/B004W88GB6.png\", \"hn_image\": [\"./images/B00AD11FFU.png\"]}\n{\"q_img\": \"./images/B003TLO3R8.png\", \"q_text\": \"is an off shoulder floral dress, and has a blue coloring to the floral aspect and one strap.\", \"positive_key\": \"B008BOX1DA\", \"positive_value\": \"./images/B008BOX1DA.png\", \"hn_image\": [\"./images/B003TLO3R8.png\"]}\n{\"q_img\": \"./images/B003VTQA1K.png\", \"q_text\": \"is purple and silver and has a bow on the belt, and light color\", \"positive_key\": \"B00BOPQZ86\", \"positive_value\": \"./images/B00BOPQZ86.png\", \"hn_image\": [\"./images/B003VTQA1K.png\"]}\n{\"q_img\": \"./images/B006Q6E4FO.png\", \"q_text\": \"has a different pattern and a different neckline, and is a little longer and more filled.\", \"positive_key\": \"B00B4KJV4G\", \"positive_value\": \"./images/B00B4KJV4G.png\", \"hn_image\": [\"./images/B006Q6E4FO.png\"]}\n{\"q_img\": \"./images/B0063TUIB8.png\", \"q_text\": \"printed beige and black dress with white belt, and has black design in the print.\", \"positive_key\": \"B00ATEZA6Q\", \"positive_value\": \"./images/B00ATEZA6Q.png\", \"hn_image\": [\"./images/B0063TUIB8.png\"]}\n{\"q_img\": \"./images/B00CTT41D2.png\", \"q_text\": \"is a short skirt with brown, and is lighter and fluffier\", \"positive_key\": \"B00E8157H8\", \"positive_value\": \"./images/B00E8157H8.png\", \"hn_image\": [\"./images/B00CTT41D2.png\"]}\n{\"q_img\": \"./images/B00DHD5322.png\", \"q_text\": \"is longer and lighter, and is white and has longer sleeves\", \"positive_key\": \"B00B6C5H9K\", \"positive_value\": \"./images/B00B6C5H9K.png\", \"hn_image\": [\"./images/B00DHD5322.png\"]}\n{\"q_img\": \"./images/B00F20KR4M.png\", \"q_text\": \"has a plain black top and complete sleeves, and is a solid color top and floral bottom half\", \"positive_key\": \"B00FH7C5RC\", \"positive_value\": \"./images/B00FH7C5RC.png\", \"hn_image\": [\"./images/B00F20KR4M.png\"]}\n{\"q_img\": \"./images/B00FABRB4M.png\", \"q_text\": \"is strapless and black, and is black and short\", \"positive_key\": \"B006JVH5UC\", \"positive_value\": \"./images/B006JVH5UC.png\", \"hn_image\": [\"./images/B00FABRB4M.png\"]}\n{\"q_img\": \"./images/B00508YRGU.png\", \"q_text\": \"lighter color and print, and has buttons\", \"positive_key\": \"B004PKZWNG\", \"positive_value\": \"./images/B004PKZWNG.png\", \"hn_image\": [\"./images/B00508YRGU.png\"]}\n{\"q_img\": \"./images/B00E5YA82M.png\", \"q_text\": \"is looser and more elegant, and has slightly thinner straps\", \"positive_key\": \"B00740T5PK\", \"positive_value\": \"./images/B00740T5PK.png\", \"hn_image\": [\"./images/B00E5YA82M.png\"]}\n{\"q_img\": \"./images/B0087YZVNC.png\", \"q_text\": \"has a black-and-white striped dress, and has thinner straps and a lower neckline\", \"positive_key\": \"B00CIUH75Q\", \"positive_value\": \"./images/B00CIUH75Q.png\", \"hn_image\": [\"./images/B0087YZVNC.png\"]}\n{\"q_img\": \"./images/B006TALF54.png\", \"q_text\": \"is darker color and sleeveless, and Is more colorful and has shorter sleeves\", \"positive_key\": \"B006UAPPP4\", \"positive_value\": \"./images/B006UAPPP4.png\", \"hn_image\": [\"./images/B006TALF54.png\"]}\n{\"q_img\": \"./images/B00DG7SOTS.png\", \"q_text\": \"is a lighter color and is sleeveless, and is shorter\", \"positive_key\": \"B00DAUCYC4\", \"positive_value\": \"./images/B00DAUCYC4.png\", \"hn_image\": [\"./images/B00DG7SOTS.png\"]}\n{\"q_img\": \"./images/B00CFYDMM2.png\", \"q_text\": \"is pink in color, and has shorter sleeves and is pink\", \"positive_key\": \"B00CZ6XXF6\", \"positive_value\": \"./images/B00CZ6XXF6.png\", \"hn_image\": [\"./images/B00CFYDMM2.png\"]}\n{\"q_img\": \"./images/B004P67ZYE.png\", \"q_text\": \"is more white, and is white and dull\", \"positive_key\": \"B005IFCASW\", \"positive_value\": \"./images/B005IFCASW.png\", \"hn_image\": [\"./images/B004P67ZYE.png\"]}\n{\"q_img\": \"./images/B00ASI7TQM.png\", \"q_text\": \"has longer lace sleves and higher at neck, and is brighter\", \"positive_key\": \"B00BZ8FTB6\", \"positive_value\": \"./images/B00BZ8FTB6.png\", \"hn_image\": [\"./images/B00ASI7TQM.png\"]}\n{\"q_img\": \"./images/B008OW8EXQ.png\", \"q_text\": \"This dress has straps on the front., and has a sheer outer layer\", \"positive_key\": \"B0055OGJLU\", \"positive_value\": \"./images/B0055OGJLU.png\", \"hn_image\": [\"./images/B008OW8EXQ.png\"]}\n{\"q_img\": \"./images/B0083QFSRS.png\", \"q_text\": \"has a solid color, and is black with short sleeves\", \"positive_key\": \"B0083QEJL4\", \"positive_value\": \"./images/B0083QEJL4.png\", \"hn_image\": [\"./images/B0083QFSRS.png\"]}\n{\"q_img\": \"./images/B0067M3VO2.png\", \"q_text\": \"Is shorter with ruffles., and the dress is tan and has sleeves\", \"positive_key\": \"B00767PZZ0\", \"positive_value\": \"./images/B00767PZZ0.png\", \"hn_image\": [\"./images/B0067M3VO2.png\"]}\n{\"q_img\": \"./images/B00A13GLG8.png\", \"q_text\": \"is purple, and is purple and more form fitting\", \"positive_key\": \"B005E79D16\", \"positive_value\": \"./images/B005E79D16.png\", \"hn_image\": [\"./images/B00A13GLG8.png\"]}\n{\"q_img\": \"./images/B00D9OXNW6.png\", \"q_text\": \"is lighter, and is lighter colored and has buttons\", \"positive_key\": \"B00BG40HUW\", \"positive_value\": \"./images/B00BG40HUW.png\", \"hn_image\": [\"./images/B00D9OXNW6.png\"]}\n{\"q_img\": \"./images/B00BLLQCIQ.png\", \"q_text\": \"is flirter and has no sleeves, and is slightly shorter with smaller straps\", \"positive_key\": \"B00CW94CSS\", \"positive_value\": \"./images/B00CW94CSS.png\", \"hn_image\": [\"./images/B00BLLQCIQ.png\"]}\n{\"q_img\": \"./images/B005GLSA0A.png\", \"q_text\": \"is similar with short sleeves, and has sleeves\", \"positive_key\": \"B008E7IA12\", \"positive_value\": \"./images/B008E7IA12.png\", \"hn_image\": [\"./images/B005GLSA0A.png\"]}\n{\"q_img\": \"./images/B0097BCW4U.png\", \"q_text\": \"Is solid white, and Sleeveless white dress\", \"positive_key\": \"B008B8461W\", \"positive_value\": \"./images/B008B8461W.png\", \"hn_image\": [\"./images/B0097BCW4U.png\"]}\n{\"q_img\": \"./images/B008EPZKJE.png\", \"q_text\": \"Is a shorter length with thicker straps, and is pink\", \"positive_key\": \"B007UA3ZYQ\", \"positive_value\": \"./images/B007UA3ZYQ.png\", \"hn_image\": [\"./images/B008EPZKJE.png\"]}\n{\"q_img\": \"./images/B004MMF00W.png\", \"q_text\": \"Is darker and sleeveless., and is black and more revealing\", \"positive_key\": \"B002JZIO02\", \"positive_value\": \"./images/B002JZIO02.png\", \"hn_image\": [\"./images/B004MMF00W.png\"]}\n{\"q_img\": \"./images/B00297BG7I.png\", \"q_text\": \" darker, and has fuller skirt and strapless\", \"positive_key\": \"B00A0AAD20\", \"positive_value\": \"./images/B00A0AAD20.png\", \"hn_image\": [\"./images/B00297BG7I.png\"]}\n{\"q_img\": \"./images/B0082825YQ.png\", \"q_text\": \" that flows more at the bottom, and is darker and wider\", \"positive_key\": \"B007CU5A7E\", \"positive_value\": \"./images/B007CU5A7E.png\", \"hn_image\": [\"./images/B0082825YQ.png\"]}\n{\"q_img\": \"./images/B007Y0UKN6.png\", \"q_text\": \"is looser and brighter in color, and is lighter purple and is longer\", \"positive_key\": \"B008EQ6AM4\", \"positive_value\": \"./images/B008EQ6AM4.png\", \"hn_image\": [\"./images/B007Y0UKN6.png\"]}\n{\"q_img\": \"./images/B00CC6WGQ6.png\", \"q_text\": \"Is solid black, and has dolmen sleeves and is off shouldered\", \"positive_key\": \"B005ES247K\", \"positive_value\": \"./images/B005ES247K.png\", \"hn_image\": [\"./images/B00CC6WGQ6.png\"]}\n{\"q_img\": \"./images/B007H3JLBM.png\", \"q_text\": \"3/4 sleeved velvet blue green dress, and has sleeves and is a much shorter style\", \"positive_key\": \"B007W3ZR2E\", \"positive_value\": \"./images/B007W3ZR2E.png\", \"hn_image\": [\"./images/B007H3JLBM.png\"]}\n{\"q_img\": \"./images/B007ZOGQIO.png\", \"q_text\": \"is striped and has shorter sleeves, and Is lighter color and shorter with V-neck.\", \"positive_key\": \"B0071MJI0S\", \"positive_value\": \"./images/B0071MJI0S.png\", \"hn_image\": [\"./images/B007ZOGQIO.png\"]}\n{\"q_img\": \"./images/B008BHSSMG.png\", \"q_text\": \"is black and white in color, and is more revealing\", \"positive_key\": \"B009W328K6\", \"positive_value\": \"./images/B009W328K6.png\", \"hn_image\": [\"./images/B008BHSSMG.png\"]}\n{\"q_img\": \"./images/B0075AV660.png\", \"q_text\": \"Has a paisley print and long sleeves., and is longer and has longer sleeves\", \"positive_key\": \"B00ED0XSF2\", \"positive_value\": \"./images/B00ED0XSF2.png\", \"hn_image\": [\"./images/B0075AV660.png\"]}\n{\"q_img\": \"./images/B007FPDWDU.png\", \"q_text\": \"is a short bright colored floral dress, and is shorter and more vibrant colored\", \"positive_key\": \"B007RM21BU\", \"positive_value\": \"./images/B007RM21BU.png\", \"hn_image\": [\"./images/B007FPDWDU.png\"]}\n{\"q_img\": \"./images/B009HK2P2A.png\", \"q_text\": \"has colorful prints, and N/A other product isnt fashion produce\", \"positive_key\": \"B002TUT4UG\", \"positive_value\": \"./images/B002TUT4UG.png\", \"hn_image\": [\"./images/B009HK2P2A.png\"]}\n{\"q_img\": \"./images/B00DVPOZNY.png\", \"q_text\": \"has no sleeves and is whiter, and has no sleeves and is white\", \"positive_key\": \"B00BR20TRY\", \"positive_value\": \"./images/B00BR20TRY.png\", \"hn_image\": [\"./images/B00DVPOZNY.png\"]}\n{\"q_img\": \"./images/B002HCNMTU.png\", \"q_text\": \" and less revealing, and is much lighter\", \"positive_key\": \"B002G5TF3A\", \"positive_value\": \"./images/B002G5TF3A.png\", \"hn_image\": [\"./images/B002HCNMTU.png\"]}\n{\"q_img\": \"./images/B008FY9HPW.png\", \"q_text\": \"has polka dots, and is tan and longer\", \"positive_key\": \"B008OW8EXQ\", \"positive_value\": \"./images/B008OW8EXQ.png\", \"hn_image\": [\"./images/B008FY9HPW.png\"]}\n{\"q_img\": \"./images/B008ODSF7U.png\", \"q_text\": \"Has a flared skirt and low cut top., and is black with short sleeves\", \"positive_key\": \"B005LJDKHA\", \"positive_value\": \"./images/B005LJDKHA.png\", \"hn_image\": [\"./images/B008ODSF7U.png\"]}\n{\"q_img\": \"./images/B00763VJ6S.png\", \"q_text\": \"is more pink and concealing, and Has sleeves and is pink rather than blue.\", \"positive_key\": \"B0051BZ6HK\", \"positive_value\": \"./images/B0051BZ6HK.png\", \"hn_image\": [\"./images/B00763VJ6S.png\"]}\n{\"q_img\": \"./images/B006QSEC4K.png\", \"q_text\": \"Is a bright colored shirt dress., and is more orange and red\", \"positive_key\": \"B007P0Q796\", \"positive_value\": \"./images/B007P0Q796.png\", \"hn_image\": [\"./images/B006QSEC4K.png\"]}\n{\"q_img\": \"./images/B000PUGW8O.png\", \"q_text\": \"is more elegant, and is black and white and less shiny\", \"positive_key\": \"B00EKDGVL0\", \"positive_value\": \"./images/B00EKDGVL0.png\", \"hn_image\": [\"./images/B000PUGW8O.png\"]}\n{\"q_img\": \"./images/B009M56HNS.png\", \"q_text\": \"has a deeper neckline with more stripes, and has longer sleeves and more revealing\", \"positive_key\": \"B002VECPU6\", \"positive_value\": \"./images/B002VECPU6.png\", \"hn_image\": [\"./images/B009M56HNS.png\"]}\n{\"q_img\": \"./images/B005AQCKBQ.png\", \"q_text\": \"is lighter, and is solid red.\", \"positive_key\": \"B00DM2GBQK\", \"positive_value\": \"./images/B00DM2GBQK.png\", \"hn_image\": [\"./images/B005AQCKBQ.png\"]}\n{\"q_img\": \"./images/B0075BQQ82.png\", \"q_text\": \"has shorter sleeves, and is sleeveless and more pink\", \"positive_key\": \"B009MB17B4\", \"positive_value\": \"./images/B009MB17B4.png\", \"hn_image\": [\"./images/B0075BQQ82.png\"]}\n{\"q_img\": \"./images/B002RHPBO4.png\", \"q_text\": \"is dark pink, and is hot pink\", \"positive_key\": \"B0082825YQ\", \"positive_value\": \"./images/B0082825YQ.png\", \"hn_image\": [\"./images/B002RHPBO4.png\"]}\n{\"q_img\": \"./images/B00D61ZCGW.png\", \"q_text\": \"is a shorter solid black and buttons up, and Light & Dark color\", \"positive_key\": \"B008W46FMS\", \"positive_value\": \"./images/B008W46FMS.png\", \"hn_image\": [\"./images/B00D61ZCGW.png\"]}\n{\"q_img\": \"./images/B00BM8Q3NW.png\", \"q_text\": \"is more formal and is one color, and is darker in color\", \"positive_key\": \"B00A4A8PRQ\", \"positive_value\": \"./images/B00A4A8PRQ.png\", \"hn_image\": [\"./images/B00BM8Q3NW.png\"]}\n{\"q_img\": \"./images/B009X6E9E0.png\", \"q_text\": \"is longer and tighter, and red and longer\", \"positive_key\": \"B0051E7IYQ\", \"positive_value\": \"./images/B0051E7IYQ.png\", \"hn_image\": [\"./images/B009X6E9E0.png\"]}\n{\"q_img\": \"./images/B008VQWBRK.png\", \"q_text\": \"Has simple pattern and looks formal, and has a bow and shorter sleeves\", \"positive_key\": \"B00H2FUGOQ\", \"positive_value\": \"./images/B00H2FUGOQ.png\", \"hn_image\": [\"./images/B008VQWBRK.png\"]}\n{\"q_img\": \"./images/B00C6PP86S.png\", \"q_text\": \"is about the same design but with a u neck, and is light in color and less revealing\", \"positive_key\": \"B008VIRN62\", \"positive_value\": \"./images/B008VIRN62.png\", \"hn_image\": [\"./images/B00C6PP86S.png\"]}\n{\"q_img\": \"./images/B007314UDM.png\", \"q_text\": \"is shiny and metallic, and The dress is more revealing and short\", \"positive_key\": \"B007WPHLTE\", \"positive_value\": \"./images/B007WPHLTE.png\", \"hn_image\": [\"./images/B007314UDM.png\"]}\n{\"q_img\": \"./images/B005MN4RLS.png\", \"q_text\": \"is gray with a lace top and sleeves, and is silver with lace\", \"positive_key\": \"B00F4MY4OW\", \"positive_value\": \"./images/B00F4MY4OW.png\", \"hn_image\": [\"./images/B005MN4RLS.png\"]}\n{\"q_img\": \"./images/B00C7Z4VGU.png\", \"q_text\": \"is white and has fabric around the neck, and is lighter in color\", \"positive_key\": \"B00BISLTIK\", \"positive_value\": \"./images/B00BISLTIK.png\", \"hn_image\": [\"./images/B00C7Z4VGU.png\"]}\n{\"q_img\": \"./images/B00FFDZT8K.png\", \"q_text\": \"this black long dress looks more slim fit, and longer\", \"positive_key\": \"B0018PWU7C\", \"positive_value\": \"./images/B0018PWU7C.png\", \"hn_image\": [\"./images/B00FFDZT8K.png\"]}\n{\"q_img\": \"./images/B00CJGAPIU.png\", \"q_text\": \"has an asymetrical hem and is pink, and is more pink\", \"positive_key\": \"B00AHOYUPA\", \"positive_value\": \"./images/B00AHOYUPA.png\", \"hn_image\": [\"./images/B00CJGAPIU.png\"]}\n{\"q_img\": \"./images/B0093K54X6.png\", \"q_text\": \"is a orange halter, and has no sleeves and is flowing\", \"positive_key\": \"B007BV3XGE\", \"positive_value\": \"./images/B007BV3XGE.png\", \"hn_image\": [\"./images/B0093K54X6.png\"]}\n{\"q_img\": \"./images/B006WM4LYG.png\", \"q_text\": \"is longer and shinier, and is longer\", \"positive_key\": \"B006W8T1UO\", \"positive_value\": \"./images/B006W8T1UO.png\", \"hn_image\": [\"./images/B006WM4LYG.png\"]}\n{\"q_img\": \"./images/B00EEAOUCQ.png\", \"q_text\": \"More colorful and cute, and Less white and more pleated\", \"positive_key\": \"B00CRH9V0Y\", \"positive_value\": \"./images/B00CRH9V0Y.png\", \"hn_image\": [\"./images/B00EEAOUCQ.png\"]}\n{\"q_img\": \"./images/B00AXX83YE.png\", \"q_text\": \"has no pleats and has buttons., and black and see through\", \"positive_key\": \"B00CL0USIG\", \"positive_value\": \"./images/B00CL0USIG.png\", \"hn_image\": [\"./images/B00AXX83YE.png\"]}\n{\"q_img\": \"./images/B006WWY8YE.png\", \"q_text\": \"Is less colorful and more formal, and is black and white and more flowing\", \"positive_key\": \"B00D67853S\", \"positive_value\": \"./images/B00D67853S.png\", \"hn_image\": [\"./images/B006WWY8YE.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \" shorter skirt, and na\", \"positive_key\": \"B00BFPGDHI\", \"positive_value\": \"./images/B00BFPGDHI.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B00BL9DCM2.png\", \"q_text\": \"is dark blue colored and more tighter, and tighter skirt and thicker straps\", \"positive_key\": \"B006MPVW4U\", \"positive_value\": \"./images/B006MPVW4U.png\", \"hn_image\": [\"./images/B00BL9DCM2.png\"]}\n{\"q_img\": \"./images/B0080E6RN2.png\", \"q_text\": \"has a pink flowing bottom, and has a patterned\", \"positive_key\": \"B00EU8NZ8W\", \"positive_value\": \"./images/B00EU8NZ8W.png\", \"hn_image\": [\"./images/B0080E6RN2.png\"]}\n{\"q_img\": \"./images/B00CDJZYA2.png\", \"q_text\": \"is brighter in color, and has no sleeves but has art patterns\", \"positive_key\": \"B007TYKJ42\", \"positive_value\": \"./images/B007TYKJ42.png\", \"hn_image\": [\"./images/B00CDJZYA2.png\"]}\n{\"q_img\": \"./images/B005VU87VI.png\", \"q_text\": \"is solid black without strips, and is solid black and less fitted\", \"positive_key\": \"B008X711D2\", \"positive_value\": \"./images/B008X711D2.png\", \"hn_image\": [\"./images/B005VU87VI.png\"]}\n{\"q_img\": \"./images/B00EYDNSHG.png\", \"q_text\": \"is solid pink with black belt, and is pink and has short skeevs\", \"positive_key\": \"B008ZE09GI\", \"positive_value\": \"./images/B008ZE09GI.png\", \"hn_image\": [\"./images/B00EYDNSHG.png\"]}\n{\"q_img\": \"./images/B00BTMWMYU.png\", \"q_text\": \"has a longer hemline, and is an off shoulder cut and is longer\", \"positive_key\": \"B00DD7B30S\", \"positive_value\": \"./images/B00DD7B30S.png\", \"hn_image\": [\"./images/B00BTMWMYU.png\"]}\n{\"q_img\": \"./images/B00E3HDPMQ.png\", \"q_text\": \"The product has sleeves, and is purple with long lace sleeves and v-neck\", \"positive_key\": \"B00E3HGLU4\", \"positive_value\": \"./images/B00E3HGLU4.png\", \"hn_image\": [\"./images/B00E3HDPMQ.png\"]}\n{\"q_img\": \"./images/B004OP7MI0.png\", \"q_text\": \"has long sleeve and shiny pink color, and is pink and longer sleeves\", \"positive_key\": \"B00H1Z5KKC\", \"positive_value\": \"./images/B00H1Z5KKC.png\", \"hn_image\": [\"./images/B004OP7MI0.png\"]}\n{\"q_img\": \"./images/B006PA7Q4M.png\", \"q_text\": \"is printed and sleeveless, and is sleeveless\", \"positive_key\": \"B00BWKXQI0\", \"positive_value\": \"./images/B00BWKXQI0.png\", \"hn_image\": [\"./images/B006PA7Q4M.png\"]}\n{\"q_img\": \"./images/B007SOULGY.png\", \"q_text\": \"is a lighter color and has more sleeves, and is more brightly colored\", \"positive_key\": \"B0007WIZYE\", \"positive_value\": \"./images/B0007WIZYE.png\", \"hn_image\": [\"./images/B007SOULGY.png\"]}\n{\"q_img\": \"./images/B004NAU396.png\", \"q_text\": \"has no sleeves and has black and white designs, and has a lacy pattern and no sleeves\", \"positive_key\": \"B00B3P7MVG\", \"positive_value\": \"./images/B00B3P7MVG.png\", \"hn_image\": [\"./images/B004NAU396.png\"]}\n{\"q_img\": \"./images/B00BEM2JB6.png\", \"q_text\": \"more revealing with an open neck with no sleeves, and has no sleeves and two striped patterns\", \"positive_key\": \"B00B903O7G\", \"positive_value\": \"./images/B00B903O7G.png\", \"hn_image\": [\"./images/B00BEM2JB6.png\"]}\n{\"q_img\": \"./images/B008BM17HY.png\", \"q_text\": \"Is more formal and more revealing, and is strapless and pink\", \"positive_key\": \"B00BL9EE4C\", \"positive_value\": \"./images/B00BL9EE4C.png\", \"hn_image\": [\"./images/B008BM17HY.png\"]}\n{\"q_img\": \"./images/B002AQSPL8.png\", \"q_text\": \"is a black dress with short sleeves, and is shorter and tighter\", \"positive_key\": \"B007HI6KPM\", \"positive_value\": \"./images/B007HI6KPM.png\", \"hn_image\": [\"./images/B002AQSPL8.png\"]}\n{\"q_img\": \"./images/B00DDX15TG.png\", \"q_text\": \"is a flowery dress with spagetti straps and more colourful, and white and black\", \"positive_key\": \"B00EKQSAES\", \"positive_value\": \"./images/B00EKQSAES.png\", \"hn_image\": [\"./images/B00DDX15TG.png\"]}\n{\"q_img\": \"./images/B00FXAC5SM.png\", \"q_text\": \" with lace interwoven, and is less form fitting and one color\", \"positive_key\": \"B00AODC7BS\", \"positive_value\": \"./images/B00AODC7BS.png\", \"hn_image\": [\"./images/B00FXAC5SM.png\"]}\n{\"q_img\": \"./images/B00ANR3NSQ.png\", \"q_text\": \"Is darker and more patterned, and is more brown and less revealing\", \"positive_key\": \"B008BA7MPW\", \"positive_value\": \"./images/B008BA7MPW.png\", \"hn_image\": [\"./images/B00ANR3NSQ.png\"]}\n{\"q_img\": \"./images/B0058BPLPK.png\", \"q_text\": \"3/4 sleeve gold and black dress, and has shorter sleeves and is tighter.\", \"positive_key\": \"B0058BPMPO\", \"positive_value\": \"./images/B0058BPMPO.png\", \"hn_image\": [\"./images/B0058BPLPK.png\"]}\n{\"q_img\": \"./images/B0058BPMPO.png\", \"q_text\": \"red and shorter sleeves, and Has shorter sleeves with portrait neckline.\", \"positive_key\": \"B0091S4XXC\", \"positive_value\": \"./images/B0091S4XXC.png\", \"hn_image\": [\"./images/B0058BPMPO.png\"]}\n{\"q_img\": \"./images/B0051UT6AY.png\", \"q_text\": \"is less casual with wider bottom, and has lots of red and no flowers\", \"positive_key\": \"B001G8HWQA\", \"positive_value\": \"./images/B001G8HWQA.png\", \"hn_image\": [\"./images/B0051UT6AY.png\"]}\n{\"q_img\": \"./images/B0071GD9D6.png\", \"q_text\": \"is lighter and has a different pattern, and the dress is white and less revealing\", \"positive_key\": \"B00378LCPE\", \"positive_value\": \"./images/B00378LCPE.png\", \"hn_image\": [\"./images/B0071GD9D6.png\"]}\n{\"q_img\": \"./images/B007OZSTY8.png\", \"q_text\": \"is a black and white stripped one piece, and Has longer sleeves\", \"positive_key\": \"B005JD4SRO\", \"positive_value\": \"./images/B005JD4SRO.png\", \"hn_image\": [\"./images/B007OZSTY8.png\"]}\n{\"q_img\": \"./images/B00FTA0HUE.png\", \"q_text\": \"is more see through, and has flowers and thin straps\", \"positive_key\": \"B00CY0CF0M\", \"positive_value\": \"./images/B00CY0CF0M.png\", \"hn_image\": [\"./images/B00FTA0HUE.png\"]}\n{\"q_img\": \"./images/B008S0HK3Y.png\", \"q_text\": \"Is a green a line., and has more green\", \"positive_key\": \"B00BTJXG66\", \"positive_value\": \"./images/B00BTJXG66.png\", \"hn_image\": [\"./images/B008S0HK3Y.png\"]}\n{\"q_img\": \"./images/B004O6LVY0.png\", \"q_text\": \"The dress in black in color., and is strapless and black\", \"positive_key\": \"B002YX0KZ6\", \"positive_value\": \"./images/B002YX0KZ6.png\", \"hn_image\": [\"./images/B004O6LVY0.png\"]}\n{\"q_img\": \"./images/B00AZE6UV4.png\", \"q_text\": \"has no straps, and is more revealing and has a curvy design\", \"positive_key\": \"B0095T312Q\", \"positive_value\": \"./images/B0095T312Q.png\", \"hn_image\": [\"./images/B00AZE6UV4.png\"]}\n{\"q_img\": \"./images/B00DEP9CUW.png\", \"q_text\": \"is red, and is shorter and more red\", \"positive_key\": \"B00FABRB4M\", \"positive_value\": \"./images/B00FABRB4M.png\", \"hn_image\": [\"./images/B00DEP9CUW.png\"]}\n{\"q_img\": \"./images/B00BXX6PM0.png\", \"q_text\": \"little black dress, and is black and has sleeves\", \"positive_key\": \"B00C97JLZ2\", \"positive_value\": \"./images/B00C97JLZ2.png\", \"hn_image\": [\"./images/B00BXX6PM0.png\"]}\n{\"q_img\": \"./images/B003DIWQNA.png\", \"q_text\": \"is wide strapped black dress with white leaf pattern, and has less color\", \"positive_key\": \"B004VJ217Q\", \"positive_value\": \"./images/B004VJ217Q.png\", \"hn_image\": [\"./images/B003DIWQNA.png\"]}\n{\"q_img\": \"./images/B007WSD2CQ.png\", \"q_text\": \"is more tighter, and Is more fitted and black\", \"positive_key\": \"B00CM3595Y\", \"positive_value\": \"./images/B00CM3595Y.png\", \"hn_image\": [\"./images/B007WSD2CQ.png\"]}\n{\"q_img\": \"./images/B00CWSNY24.png\", \"q_text\": \"is long sleeves, and is black with long sleeves\", \"positive_key\": \"B00B9U19BY\", \"positive_value\": \"./images/B00B9U19BY.png\", \"hn_image\": [\"./images/B00CWSNY24.png\"]}\n{\"q_img\": \"./images/B0080646GU.png\", \"q_text\": \"is less geometric and more muted, and Is blue\", \"positive_key\": \"B00AYLAFOG\", \"positive_value\": \"./images/B00AYLAFOG.png\", \"hn_image\": [\"./images/B0080646GU.png\"]}\n{\"q_img\": \"./images/B00BUIF4YS.png\", \"q_text\": \"is lighter and has more contrast, and is more of a white color\", \"positive_key\": \"B00428MW7A\", \"positive_value\": \"./images/B00428MW7A.png\", \"hn_image\": [\"./images/B00BUIF4YS.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"is looser and a darker color, and is solid black and fitted waistline\", \"positive_key\": \"B0095JQQZK\", \"positive_value\": \"./images/B0095JQQZK.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B005EYTFS0.png\", \"q_text\": \"is a pastel color with tank top, and the dress has straps and is pink\", \"positive_key\": \"B00AFDIN42\", \"positive_value\": \"./images/B00AFDIN42.png\", \"hn_image\": [\"./images/B005EYTFS0.png\"]}\n{\"q_img\": \"./images/B009QW3VSQ.png\", \"q_text\": \"is a peplum shorter dress, and has no sleeves and more revealing\", \"positive_key\": \"B00CTIEB2O\", \"positive_value\": \"./images/B00CTIEB2O.png\", \"hn_image\": [\"./images/B009QW3VSQ.png\"]}\n{\"q_img\": \"./images/B0072QWT2M.png\", \"q_text\": \"is black colored and strapless, and and lighter colored\", \"positive_key\": \"B00AOWQJZ4\", \"positive_value\": \"./images/B00AOWQJZ4.png\", \"hn_image\": [\"./images/B0072QWT2M.png\"]}\n{\"q_img\": \"./images/B008KPIBWQ.png\", \"q_text\": \"is tight and has thin straps, and is much darker in color\", \"positive_key\": \"B0072NLS9U\", \"positive_value\": \"./images/B0072NLS9U.png\", \"hn_image\": [\"./images/B008KPIBWQ.png\"]}\n{\"q_img\": \"./images/B00BL9DCM2.png\", \"q_text\": \"is purple and has half sleeves, and has sleeves\", \"positive_key\": \"B008LRERE4\", \"positive_value\": \"./images/B008LRERE4.png\", \"hn_image\": [\"./images/B00BL9DCM2.png\"]}\n{\"q_img\": \"./images/B00ED49C12.png\", \"q_text\": \"Has a black top and a floral skirt., and short sleeves and pattern\", \"positive_key\": \"B009ZGNP5W\", \"positive_value\": \"./images/B009ZGNP5W.png\", \"hn_image\": [\"./images/B00ED49C12.png\"]}\n{\"q_img\": \"./images/B005VZ8RFO.png\", \"q_text\": \"is lighter, and is printed and longer\", \"positive_key\": \"B003JH7Y46\", \"positive_value\": \"./images/B003JH7Y46.png\", \"hn_image\": [\"./images/B005VZ8RFO.png\"]}\n{\"q_img\": \"./images/B005KMQQFQ.png\", \"q_text\": \"is white and black colored and shorter length, and sexy\", \"positive_key\": \"B003KQ9LWE\", \"positive_value\": \"./images/B003KQ9LWE.png\", \"hn_image\": [\"./images/B005KMQQFQ.png\"]}\n{\"q_img\": \"./images/B0061RGQEU.png\", \"q_text\": \"Is more white and flowing, and it white and looser\", \"positive_key\": \"B007NKVTOQ\", \"positive_value\": \"./images/B007NKVTOQ.png\", \"hn_image\": [\"./images/B0061RGQEU.png\"]}\n{\"q_img\": \"./images/B007JLP1DE.png\", \"q_text\": \"is black with capped sleeves, and is solid black\", \"positive_key\": \"B00BEU0S8E\", \"positive_value\": \"./images/B00BEU0S8E.png\", \"hn_image\": [\"./images/B007JLP1DE.png\"]}\n{\"q_img\": \"./images/B00ANOMIQ2.png\", \"q_text\": \"is a different color and more concealing, and is more orange with vertical stripes\", \"positive_key\": \"B009TM8ML4\", \"positive_value\": \"./images/B009TM8ML4.png\", \"hn_image\": [\"./images/B00ANOMIQ2.png\"]}\n{\"q_img\": \"./images/B00B5859Z2.png\", \"q_text\": \"Is a denim shirt dress., and is short sleeved and buttons up\", \"positive_key\": \"B00C2MGB6G\", \"positive_value\": \"./images/B00C2MGB6G.png\", \"hn_image\": [\"./images/B00B5859Z2.png\"]}\n{\"q_img\": \"./images/B007GP6PMO.png\", \"q_text\": \"knee length dress, and more contempary less sexy\", \"positive_key\": \"B00BQX4A2O\", \"positive_value\": \"./images/B00BQX4A2O.png\", \"hn_image\": [\"./images/B007GP6PMO.png\"]}\n{\"q_img\": \"./images/B005GQPE4K.png\", \"q_text\": \"longer sleeved and shorter, and is shorter and more evening.\", \"positive_key\": \"B004YHIH28\", \"positive_value\": \"./images/B004YHIH28.png\", \"hn_image\": [\"./images/B005GQPE4K.png\"]}\n{\"q_img\": \"./images/B006WM4LYG.png\", \"q_text\": \"has blue tones, and has more blue\", \"positive_key\": \"B00BG3NQE2\", \"positive_value\": \"./images/B00BG3NQE2.png\", \"hn_image\": [\"./images/B006WM4LYG.png\"]}\n{\"q_img\": \"./images/B0091W2E9S.png\", \"q_text\": \"is black, and It is more plain and longer\", \"positive_key\": \"B00G0PONNY\", \"positive_value\": \"./images/B00G0PONNY.png\", \"hn_image\": [\"./images/B0091W2E9S.png\"]}\n{\"q_img\": \"./images/B00C12LOZU.png\", \"q_text\": \"has see through lace, and The desired product is more fancy than the provided product\", \"positive_key\": \"B00EW6JZ4A\", \"positive_value\": \"./images/B00EW6JZ4A.png\", \"hn_image\": [\"./images/B00C12LOZU.png\"]}\n{\"q_img\": \"./images/B00A17JXDC.png\", \"q_text\": \"is off the shoulder, and hangs off the shoulders and is more gray\", \"positive_key\": \"B0063ASXFA\", \"positive_value\": \"./images/B0063ASXFA.png\", \"hn_image\": [\"./images/B00A17JXDC.png\"]}\n{\"q_img\": \"./images/B00BPYWKO4.png\", \"q_text\": \"Is darker in color., and is black and has buttons.\", \"positive_key\": \"B00C1RQBBM\", \"positive_value\": \"./images/B00C1RQBBM.png\", \"hn_image\": [\"./images/B00BPYWKO4.png\"]}\n{\"q_img\": \"./images/B00CXAP5HS.png\", \"q_text\": \"has straps, and is solid white and has straps\", \"positive_key\": \"B00CLZMWUI\", \"positive_value\": \"./images/B00CLZMWUI.png\", \"hn_image\": [\"./images/B00CXAP5HS.png\"]}\n{\"q_img\": \"./images/B00BG4M4BM.png\", \"q_text\": \"is pink belted dress, and n/a\", \"positive_key\": \"B00CE579WG\", \"positive_value\": \"./images/B00CE579WG.png\", \"hn_image\": [\"./images/B00BG4M4BM.png\"]}\n{\"q_img\": \"./images/B007HCAEKU.png\", \"q_text\": \"is lighter and shorter, and is shorter\", \"positive_key\": \"B007IWOH1Q\", \"positive_value\": \"./images/B007IWOH1Q.png\", \"hn_image\": [\"./images/B007HCAEKU.png\"]}\n{\"q_img\": \"./images/B0055TBPNC.png\", \"q_text\": \"is darker colored and has a looser fit, and has longer sleeves and is darker in colour\", \"positive_key\": \"B005DWYMPO\", \"positive_value\": \"./images/B005DWYMPO.png\", \"hn_image\": [\"./images/B0055TBPNC.png\"]}\n{\"q_img\": \"./images/B00DEPBDBI.png\", \"q_text\": \"is made of brown leather and is shoes, and is a pair of boots\", \"positive_key\": \"B006N02HTS\", \"positive_value\": \"./images/B006N02HTS.png\", \"hn_image\": [\"./images/B00DEPBDBI.png\"]}\n{\"q_img\": \"./images/B0071MSFCU.png\", \"q_text\": \"is brighter, and is pink and thinner strapped\", \"positive_key\": \"B00AFDIUWM\", \"positive_value\": \"./images/B00AFDIUWM.png\", \"hn_image\": [\"./images/B0071MSFCU.png\"]}\n{\"q_img\": \"./images/B00FXAC5SM.png\", \"q_text\": \"is black and white, and is not as tight\", \"positive_key\": \"B00AJ1P6XQ\", \"positive_value\": \"./images/B00AJ1P6XQ.png\", \"hn_image\": [\"./images/B00FXAC5SM.png\"]}\n{\"q_img\": \"./images/B00EPC65FS.png\", \"q_text\": \"short grey dress with floral prints, and the dress is more revealing and has a flower pattern\", \"positive_key\": \"B0051UT6AY\", \"positive_value\": \"./images/B0051UT6AY.png\", \"hn_image\": [\"./images/B00EPC65FS.png\"]}\n{\"q_img\": \"./images/B005VCC2MQ.png\", \"q_text\": \"has no straps and is a romper, and has no sleeves\", \"positive_key\": \"B00EPA3DFA\", \"positive_value\": \"./images/B00EPA3DFA.png\", \"hn_image\": [\"./images/B005VCC2MQ.png\"]}\n{\"q_img\": \"./images/B00D9SL7JI.png\", \"q_text\": \"is striped and has brighter colors, and is colorful with shorter sleeves\", \"positive_key\": \"B00CTT41D2\", \"positive_value\": \"./images/B00CTT41D2.png\", \"hn_image\": [\"./images/B00D9SL7JI.png\"]}\n{\"q_img\": \"./images/B0026IAOWI.png\", \"q_text\": \"is strapless, and is belted and strapless\", \"positive_key\": \"B006UAJT60\", \"positive_value\": \"./images/B006UAJT60.png\", \"hn_image\": [\"./images/B0026IAOWI.png\"]}\n{\"q_img\": \"./images/B00FFDZT8K.png\", \"q_text\": \"more sleeves with flared skirt and white belt, and is black with sleeves\", \"positive_key\": \"B0079F8A8I\", \"positive_value\": \"./images/B0079F8A8I.png\", \"hn_image\": [\"./images/B00FFDZT8K.png\"]}\n{\"q_img\": \"./images/B008BHSFOW.png\", \"q_text\": \"is brighter and one shoulder uncovered, and is blue and more vintage\", \"positive_key\": \"B005JR0XW4\", \"positive_value\": \"./images/B005JR0XW4.png\", \"hn_image\": [\"./images/B008BHSFOW.png\"]}\n{\"q_img\": \"./images/B0016CHUWW.png\", \"q_text\": \"is a black shirt with a pink and white logo, and The desired product is only black and have an animation design.\", \"positive_key\": \"B00762YDYO\", \"positive_value\": \"./images/B00762YDYO.png\", \"hn_image\": [\"./images/B0016CHUWW.png\"]}\n{\"q_img\": \"./images/B006J7DBTK.png\", \"q_text\": \"has a cutout in the mid section, and is longer and blue\", \"positive_key\": \"B00B79JOJG\", \"positive_value\": \"./images/B00B79JOJG.png\", \"hn_image\": [\"./images/B006J7DBTK.png\"]}\n{\"q_img\": \"./images/B007EGKM5G.png\", \"q_text\": \"a long sleeve blue miracle dress, and has a lot more blues.\", \"positive_key\": \"B00AEDCDZ8\", \"positive_value\": \"./images/B00AEDCDZ8.png\", \"hn_image\": [\"./images/B007EGKM5G.png\"]}\n{\"q_img\": \"./images/B004PZHXZG.png\", \"q_text\": \"patterned multi color dress, and has pink and blues decorating the dress.\", \"positive_key\": \"B007PENSBC\", \"positive_value\": \"./images/B007PENSBC.png\", \"hn_image\": [\"./images/B004PZHXZG.png\"]}\n{\"q_img\": \"./images/B00BISLTIK.png\", \"q_text\": \" sleeveless and a darker color, and has a geometric print with longer sleeves\", \"positive_key\": \"B00AKGFCY8\", \"positive_value\": \"./images/B00AKGFCY8.png\", \"hn_image\": [\"./images/B00BISLTIK.png\"]}\n{\"q_img\": \"./images/B005NXMDTU.png\", \"q_text\": \"is black with long sleeves, and is darker with long sleeves\", \"positive_key\": \"B008GQ3H50\", \"positive_value\": \"./images/B008GQ3H50.png\", \"hn_image\": [\"./images/B005NXMDTU.png\"]}\n{\"q_img\": \"./images/B005JDB3UO.png\", \"q_text\": \"is black and gold with designs, and is a lighter pattern with a similar neckline.\", \"positive_key\": \"B00CSNDFDG\", \"positive_value\": \"./images/B00CSNDFDG.png\", \"hn_image\": [\"./images/B005JDB3UO.png\"]}\n{\"q_img\": \"./images/B008H78T6K.png\", \"q_text\": \"is shorter and brighter, and has more red and patterns\", \"positive_key\": \"B0087AYQ7I\", \"positive_value\": \"./images/B0087AYQ7I.png\", \"hn_image\": [\"./images/B008H78T6K.png\"]}\n{\"q_img\": \"./images/B003ILRVZU.png\", \"q_text\": \"is blue and shiny, and is also a tight fitting dress\", \"positive_key\": \"B00ATFWZ04\", \"positive_value\": \"./images/B00ATFWZ04.png\", \"hn_image\": [\"./images/B003ILRVZU.png\"]}\n{\"q_img\": \"./images/B00ALGUAOO.png\", \"q_text\": \"burgundy floral above knee tank dress with thicker straps, and Has a scoop neck and darker colors\", \"positive_key\": \"B005MUYI2Y\", \"positive_value\": \"./images/B005MUYI2Y.png\", \"hn_image\": [\"./images/B00ALGUAOO.png\"]}\n{\"q_img\": \"./images/B004RKVGDY.png\", \"q_text\": \"is more colorful and has shorter sleeves, and floral pattern and crimped in middle\", \"positive_key\": \"B00BL9CN0E\", \"positive_value\": \"./images/B00BL9CN0E.png\", \"hn_image\": [\"./images/B004RKVGDY.png\"]}\n{\"q_img\": \"./images/B007TUECJ4.png\", \"q_text\": \"is a darker color, and is more darker\", \"positive_key\": \"B007XCGZNY\", \"positive_value\": \"./images/B007XCGZNY.png\", \"hn_image\": [\"./images/B007TUECJ4.png\"]}\n{\"q_img\": \"./images/B008PG0SOO.png\", \"q_text\": \" with medical symbols on the skirt and stockings, and is less solid color\", \"positive_key\": \"B007ZYW246\", \"positive_value\": \"./images/B007ZYW246.png\", \"hn_image\": [\"./images/B008PG0SOO.png\"]}\n{\"q_img\": \"./images/B00DDQX05A.png\", \"q_text\": \"is much more of a dress, and is a printed dress and not pants\", \"positive_key\": \"B00BTHR98E\", \"positive_value\": \"./images/B00BTHR98E.png\", \"hn_image\": [\"./images/B00DDQX05A.png\"]}\n{\"q_img\": \"./images/B007C3MKYC.png\", \"q_text\": \"Has a solid blue top and flowered skirt, and is longer\", \"positive_key\": \"B00DJVPZYS\", \"positive_value\": \"./images/B00DJVPZYS.png\", \"hn_image\": [\"./images/B007C3MKYC.png\"]}\n{\"q_img\": \"./images/B009KM5FUE.png\", \"q_text\": \"Is more blue and structured, and has a belt and is more blue in colour\", \"positive_key\": \"B007ZFHT1Q\", \"positive_value\": \"./images/B007ZFHT1Q.png\", \"hn_image\": [\"./images/B009KM5FUE.png\"]}\n{\"q_img\": \"./images/B0087YZVNC.png\", \"q_text\": \"is solid blue, and is blue and little longer\", \"positive_key\": \"B0091GR3HC\", \"positive_value\": \"./images/B0091GR3HC.png\", \"hn_image\": [\"./images/B0087YZVNC.png\"]}\n{\"q_img\": \"./images/B0098BW31Q.png\", \"q_text\": \"has sheer shoulder straps, and is black and has lace\", \"positive_key\": \"B007CL8W1O\", \"positive_value\": \"./images/B007CL8W1O.png\", \"hn_image\": [\"./images/B0098BW31Q.png\"]}\n{\"q_img\": \"./images/B008VJ0Q2Y.png\", \"q_text\": \"is brighter color with less sleeves, and grey and onger\", \"positive_key\": \"B00D6Y5VWY\", \"positive_value\": \"./images/B00D6Y5VWY.png\", \"hn_image\": [\"./images/B008VJ0Q2Y.png\"]}\n{\"q_img\": \"./images/B007KHTV24.png\", \"q_text\": \"is shorter and has squares, and Is shorter and more chic\", \"positive_key\": \"B00BOEMLWQ\", \"positive_value\": \"./images/B00BOEMLWQ.png\", \"hn_image\": [\"./images/B007KHTV24.png\"]}\n{\"q_img\": \"./images/B00A7Z5MKQ.png\", \"q_text\": \"has longer sleeves, and black and white blocks\", \"positive_key\": \"B00B4Z2I28\", \"positive_value\": \"./images/B00B4Z2I28.png\", \"hn_image\": [\"./images/B00A7Z5MKQ.png\"]}\n{\"q_img\": \"./images/B0071KR3QG.png\", \"q_text\": \"is a solid pink shade, and is solid pink and haltered\", \"positive_key\": \"B002DIBXH6\", \"positive_value\": \"./images/B002DIBXH6.png\", \"hn_image\": [\"./images/B0071KR3QG.png\"]}\n{\"q_img\": \"./images/B005KILV0A.png\", \"q_text\": \"longer in length and exposing more cleavage, and bohemian colorful strapless dress.\", \"positive_key\": \"B00CFMMG8K\", \"positive_value\": \"./images/B00CFMMG8K.png\", \"hn_image\": [\"./images/B005KILV0A.png\"]}\n{\"q_img\": \"./images/B009NXZXDO.png\", \"q_text\": \"is more modest and a solid color, and is made of denim\", \"positive_key\": \"B00CKV707I\", \"positive_value\": \"./images/B00CKV707I.png\", \"hn_image\": [\"./images/B009NXZXDO.png\"]}\n{\"q_img\": \"./images/B006J42TFU.png\", \"q_text\": \"Is a short sleeveless dress with a black skirt, and is black and white and shorter\", \"positive_key\": \"B005F32V6I\", \"positive_value\": \"./images/B005F32V6I.png\", \"hn_image\": [\"./images/B006J42TFU.png\"]}\n{\"q_img\": \"./images/B00BSMLF1M.png\", \"q_text\": \"has a u neck design, and Is black and not shiny.\", \"positive_key\": \"B004EHON6W\", \"positive_value\": \"./images/B004EHON6W.png\", \"hn_image\": [\"./images/B00BSMLF1M.png\"]}\n{\"q_img\": \"./images/B005WQCHPI.png\", \"q_text\": \"is sleeveless, and is shorter and black and white\", \"positive_key\": \"B005WNQOQ4\", \"positive_value\": \"./images/B005WNQOQ4.png\", \"hn_image\": [\"./images/B005WQCHPI.png\"]}\n{\"q_img\": \"./images/B009NQUQBA.png\", \"q_text\": \"black with a pattern throughout and is more casual, and It is less corporate and has more yellow\", \"positive_key\": \"B00FN7SME6\", \"positive_value\": \"./images/B00FN7SME6.png\", \"hn_image\": [\"./images/B009NQUQBA.png\"]}\n{\"q_img\": \"./images/B000BL6VJM.png\", \"q_text\": \"is shorter and more revealing, and the dress is purple and shorter\", \"positive_key\": \"B00D5UJAOY\", \"positive_value\": \"./images/B00D5UJAOY.png\", \"hn_image\": [\"./images/B000BL6VJM.png\"]}\n{\"q_img\": \"./images/B007PENSBC.png\", \"q_text\": \"is more revealing with a v shaped cleavage, and is warmer in color\", \"positive_key\": \"B004YKU3F4\", \"positive_value\": \"./images/B004YKU3F4.png\", \"hn_image\": [\"./images/B007PENSBC.png\"]}\n{\"q_img\": \"./images/B005DN88YY.png\", \"q_text\": \"is eggshell white, and is 3/4 sleeves and off white\", \"positive_key\": \"B007EY9NEE\", \"positive_value\": \"./images/B007EY9NEE.png\", \"hn_image\": [\"./images/B005DN88YY.png\"]}\n{\"q_img\": \"./images/B008YF4VAS.png\", \"q_text\": \"is more revealing, and It is more red and tight\", \"positive_key\": \"B008S1W49I\", \"positive_value\": \"./images/B008S1W49I.png\", \"hn_image\": [\"./images/B008YF4VAS.png\"]}\n{\"q_img\": \"./images/B007C3MKYC.png\", \"q_text\": \"is less shiny and light in color, and belted\", \"positive_key\": \"B00A52LQXI\", \"positive_value\": \"./images/B00A52LQXI.png\", \"hn_image\": [\"./images/B007C3MKYC.png\"]}\n{\"q_img\": \"./images/B002HCNMTU.png\", \"q_text\": \"is a short larger dress, and is knee length and silver with a jacket\", \"positive_key\": \"B0076IKR66\", \"positive_value\": \"./images/B0076IKR66.png\", \"hn_image\": [\"./images/B002HCNMTU.png\"]}\n{\"q_img\": \"./images/B009P5A9FW.png\", \"q_text\": \"is a blue print dress, and has a lighter color\", \"positive_key\": \"B00B0ANWFO\", \"positive_value\": \"./images/B00B0ANWFO.png\", \"hn_image\": [\"./images/B009P5A9FW.png\"]}\n{\"q_img\": \"./images/B005LC87SO.png\", \"q_text\": \"is flirtier with no straps, and has a lighter color\", \"positive_key\": \"B00BBKYD9C\", \"positive_value\": \"./images/B00BBKYD9C.png\", \"hn_image\": [\"./images/B005LC87SO.png\"]}\n{\"q_img\": \"./images/B0043W9Z1Q.png\", \"q_text\": \"has no sleeves and has chevrons, and has more black in it\", \"positive_key\": \"B00DR0LRRK\", \"positive_value\": \"./images/B00DR0LRRK.png\", \"hn_image\": [\"./images/B0043W9Z1Q.png\"]}\n{\"q_img\": \"./images/B00B2JJ15S.png\", \"q_text\": \"is longer with no sleeves, and is longer and has frills\", \"positive_key\": \"B008E65YXU\", \"positive_value\": \"./images/B008E65YXU.png\", \"hn_image\": [\"./images/B00B2JJ15S.png\"]}\n{\"q_img\": \"./images/B008XKC00W.png\", \"q_text\": \"Is more charcoal and fun, and Is darker in color\", \"positive_key\": \"B00ATFWVKI\", \"positive_value\": \"./images/B00ATFWVKI.png\", \"hn_image\": [\"./images/B008XKC00W.png\"]}\n{\"q_img\": \"./images/B0084YK2UW.png\", \"q_text\": \"a long black flowy dress, and is solid black and sleeveless\", \"positive_key\": \"B00BQVAVRY\", \"positive_value\": \"./images/B00BQVAVRY.png\", \"hn_image\": [\"./images/B0084YK2UW.png\"]}\n{\"q_img\": \"./images/B0076R807A.png\", \"q_text\": \"is a lighter blue and longer, and has brown striping and a longer hem\", \"positive_key\": \"B004U8YYZU\", \"positive_value\": \"./images/B004U8YYZU.png\", \"hn_image\": [\"./images/B0076R807A.png\"]}\n{\"q_img\": \"./images/B00A0I87AM.png\", \"q_text\": \"is black colored and longer in length, and fitted purple\", \"positive_key\": \"B00B8V4P9C\", \"positive_value\": \"./images/B00B8V4P9C.png\", \"hn_image\": [\"./images/B00A0I87AM.png\"]}\n{\"q_img\": \"./images/B00DM03I72.png\", \"q_text\": \"has no straps and is much shorter, and is shorter and more frilly\", \"positive_key\": \"B008YQVT6Q\", \"positive_value\": \"./images/B008YQVT6Q.png\", \"hn_image\": [\"./images/B00DM03I72.png\"]}\n{\"q_img\": \"./images/B005RYO18G.png\", \"q_text\": \"sexier and slim fit, and has horizontal striping\", \"positive_key\": \"B0052KMSYY\", \"positive_value\": \"./images/B0052KMSYY.png\", \"hn_image\": [\"./images/B005RYO18G.png\"]}\n{\"q_img\": \"./images/B008986NRY.png\", \"q_text\": \"is shorter and less sexy, and is shorter and striped\", \"positive_key\": \"B00BMTI2S0\", \"positive_value\": \"./images/B00BMTI2S0.png\", \"hn_image\": [\"./images/B008986NRY.png\"]}\n{\"q_img\": \"./images/B006W8T474.png\", \"q_text\": \"is lighter and shorter, and has a floral pattern\", \"positive_key\": \"B002AQSPL8\", \"positive_value\": \"./images/B002AQSPL8.png\", \"hn_image\": [\"./images/B006W8T474.png\"]}\n{\"q_img\": \"./images/B00BQVAVRY.png\", \"q_text\": \"is lighter and has longer sleeves, and is more sexy with lighter colored pattern\", \"positive_key\": \"B007U3Q6U8\", \"positive_value\": \"./images/B007U3Q6U8.png\", \"hn_image\": [\"./images/B00BQVAVRY.png\"]}\n{\"q_img\": \"./images/B00AYWXQ9Q.png\", \"q_text\": \"Is long with a lace pattern., and na\", \"positive_key\": \"B004TOUSQY\", \"positive_value\": \"./images/B004TOUSQY.png\", \"hn_image\": [\"./images/B00AYWXQ9Q.png\"]}\n{\"q_img\": \"./images/B007314UDM.png\", \"q_text\": \"Has a empire waist and lighter fabric, and is more colorful\", \"positive_key\": \"B0072QWT2M\", \"positive_value\": \"./images/B0072QWT2M.png\", \"hn_image\": [\"./images/B007314UDM.png\"]}\n{\"q_img\": \"./images/B00AOJVGYQ.png\", \"q_text\": \"is more revealing, and is black with straps\", \"positive_key\": \"B00967OFKE\", \"positive_value\": \"./images/B00967OFKE.png\", \"hn_image\": [\"./images/B00AOJVGYQ.png\"]}\n{\"q_img\": \"./images/B00APFQNP6.png\", \"q_text\": \"floor length and datk, and is black and longer\", \"positive_key\": \"B007ZWR7RK\", \"positive_value\": \"./images/B007ZWR7RK.png\", \"hn_image\": [\"./images/B00APFQNP6.png\"]}\n{\"q_img\": \"./images/B007HWN10K.png\", \"q_text\": \"is black and more patterned, and is more sexual\", \"positive_key\": \"B00CFIKMZ8\", \"positive_value\": \"./images/B00CFIKMZ8.png\", \"hn_image\": [\"./images/B007HWN10K.png\"]}\n{\"q_img\": \"./images/B00AHOY8H0.png\", \"q_text\": \"A coral and black asymmetrical sleeveless dress, and The dress is longer and is multi colored\", \"positive_key\": \"B006VRT354\", \"positive_value\": \"./images/B006VRT354.png\", \"hn_image\": [\"./images/B00AHOY8H0.png\"]}\n{\"q_img\": \"./images/B00BHPLQSC.png\", \"q_text\": \"is shorter and less formal, and is longer and less revealing\", \"positive_key\": \"B0073Y4HDW\", \"positive_value\": \"./images/B0073Y4HDW.png\", \"hn_image\": [\"./images/B00BHPLQSC.png\"]}\n{\"q_img\": \"./images/B0084YK2UW.png\", \"q_text\": \" simple, and is shorter\", \"positive_key\": \"B006WAQ3TE\", \"positive_value\": \"./images/B006WAQ3TE.png\", \"hn_image\": [\"./images/B0084YK2UW.png\"]}\n{\"q_img\": \"./images/B00EPFMQWQ.png\", \"q_text\": \"has no sleeves and is blue, and has shorter sleeves\", \"positive_key\": \"B00B8RAZE0\", \"positive_value\": \"./images/B00B8RAZE0.png\", \"hn_image\": [\"./images/B00EPFMQWQ.png\"]}\n{\"q_img\": \"./images/B005FW6A4S.png\", \"q_text\": \"has no sleeves and is lighter, and is pinker and more flowing\", \"positive_key\": \"B0025T0WUW\", \"positive_value\": \"./images/B0025T0WUW.png\", \"hn_image\": [\"./images/B005FW6A4S.png\"]}\n{\"q_img\": \"./images/B007904PDM.png\", \"q_text\": \"The product has spaghetti straps, and is less flowy\", \"positive_key\": \"B007RKKZPG\", \"positive_value\": \"./images/B007RKKZPG.png\", \"hn_image\": [\"./images/B007904PDM.png\"]}\n{\"q_img\": \"./images/B00A3PEX7S.png\", \"q_text\": \"is grey with thinner straps, and is darker green with pink flowers\", \"positive_key\": \"B002FOCY5I\", \"positive_value\": \"./images/B002FOCY5I.png\", \"hn_image\": [\"./images/B00A3PEX7S.png\"]}\n{\"q_img\": \"./images/B00DH8X2OI.png\", \"q_text\": \"has no sleeves and is tighter, and is more v-necked\", \"positive_key\": \"B00CWGF6JK\", \"positive_value\": \"./images/B00CWGF6JK.png\", \"hn_image\": [\"./images/B00DH8X2OI.png\"]}\n{\"q_img\": \"./images/B00ALSQQKY.png\", \"q_text\": \"has a half sleeve. It contains less lace, and is striped in black and a little white.\", \"positive_key\": \"B006GZN7TO\", \"positive_value\": \"./images/B006GZN7TO.png\", \"hn_image\": [\"./images/B00ALSQQKY.png\"]}\n{\"q_img\": \"./images/B008GSMCEA.png\", \"q_text\": \"Is black and more revealing with flowing bottom, and has no sleeves and is dark colored\", \"positive_key\": \"B00A6GXVJK\", \"positive_value\": \"./images/B00A6GXVJK.png\", \"hn_image\": [\"./images/B008GSMCEA.png\"]}\n{\"q_img\": \"./images/B009IFOBF8.png\", \"q_text\": \"is tighter, and is a darker color\", \"positive_key\": \"B008R8BFFG\", \"positive_value\": \"./images/B008R8BFFG.png\", \"hn_image\": [\"./images/B009IFOBF8.png\"]}\n{\"q_img\": \"./images/B001DXL1WY.png\", \"q_text\": \"is shorter has a closed chest, and is more brightly colored\", \"positive_key\": \"B00AMDI86S\", \"positive_value\": \"./images/B00AMDI86S.png\", \"hn_image\": [\"./images/B001DXL1WY.png\"]}\n{\"q_img\": \"./images/B0070IN282.png\", \"q_text\": \"is pink, and Is pink and longer\", \"positive_key\": \"B004OP7MI0\", \"positive_value\": \"./images/B004OP7MI0.png\", \"hn_image\": [\"./images/B0070IN282.png\"]}\n{\"q_img\": \"./images/B00DQHXEO8.png\", \"q_text\": \"Is more plain and has more coverage, and is darker and longer sleeves\", \"positive_key\": \"B0083Z1PAI\", \"positive_value\": \"./images/B0083Z1PAI.png\", \"hn_image\": [\"./images/B00DQHXEO8.png\"]}\n{\"q_img\": \"./images/B00AYR2L4M.png\", \"q_text\": \"is a red party dress, and is red with no sleeves or straps\", \"positive_key\": \"B006JXMU0U\", \"positive_value\": \"./images/B006JXMU0U.png\", \"hn_image\": [\"./images/B00AYR2L4M.png\"]}\n{\"q_img\": \"./images/B007NCIGQ8.png\", \"q_text\": \"Has a solid bottom and patterned top., and long sleeved\", \"positive_key\": \"B008RMIF5A\", \"positive_value\": \"./images/B008RMIF5A.png\", \"hn_image\": [\"./images/B007NCIGQ8.png\"]}\n{\"q_img\": \"./images/B004RDQ97O.png\", \"q_text\": \"has no sleeves and is looser, and floaty and transparent\", \"positive_key\": \"B004YVOPX4\", \"positive_value\": \"./images/B004YVOPX4.png\", \"hn_image\": [\"./images/B004RDQ97O.png\"]}\n{\"q_img\": \"./images/B007GMNK7U.png\", \"q_text\": \"is longer and darker, and no sleeves and\", \"positive_key\": \"B00749VA8Q\", \"positive_value\": \"./images/B00749VA8Q.png\", \"hn_image\": [\"./images/B007GMNK7U.png\"]}\n{\"q_img\": \"./images/B0054N0S6E.png\", \"q_text\": \"Is more comfortable and less bright, and has key hole sleeves and a lighter print\", \"positive_key\": \"B0077KMLT4\", \"positive_value\": \"./images/B0077KMLT4.png\", \"hn_image\": [\"./images/B0054N0S6E.png\"]}\n{\"q_img\": \"./images/B002SNAIWC.png\", \"q_text\": \"in orange and more sleeve, and has longer sleeves and is tighter\", \"positive_key\": \"B0036MEL0O\", \"positive_value\": \"./images/B0036MEL0O.png\", \"hn_image\": [\"./images/B002SNAIWC.png\"]}\n{\"q_img\": \"./images/B007L3WP5W.png\", \"q_text\": \"has a black belt and printed, and is pattered and has a belt\", \"positive_key\": \"B007L3W3FE\", \"positive_value\": \"./images/B007L3W3FE.png\", \"hn_image\": [\"./images/B007L3WP5W.png\"]}\n{\"q_img\": \"./images/B00BXBCUQW.png\", \"q_text\": \"blue and shorter, and is blue and shorter\", \"positive_key\": \"B005XDCT7G\", \"positive_value\": \"./images/B005XDCT7G.png\", \"hn_image\": [\"./images/B00BXBCUQW.png\"]}\n{\"q_img\": \"./images/B00A8CDCD2.png\", \"q_text\": \"white and less revealing between the legs, and the dress is white and black and more revealing\", \"positive_key\": \"B006VNCKJE\", \"positive_value\": \"./images/B006VNCKJE.png\", \"hn_image\": [\"./images/B00A8CDCD2.png\"]}\n{\"q_img\": \"./images/B000AY2892.png\", \"q_text\": \"is more casual and bubbly, and is looser fitted and more colorful\", \"positive_key\": \"B008BBCXB4\", \"positive_value\": \"./images/B008BBCXB4.png\", \"hn_image\": [\"./images/B000AY2892.png\"]}\n{\"q_img\": \"./images/B00DR8UEZ8.png\", \"q_text\": \"has longer sleeves with a pattern design, and Is black with a lower neck and slashed sleeves\", \"positive_key\": \"B00897AQ9G\", \"positive_value\": \"./images/B00897AQ9G.png\", \"hn_image\": [\"./images/B00DR8UEZ8.png\"]}\n{\"q_img\": \"./images/B008N05A40.png\", \"q_text\": \"is pink and less frilly., and is one solid color and is sleeveless\", \"positive_key\": \"B003AKZMXM\", \"positive_value\": \"./images/B003AKZMXM.png\", \"hn_image\": [\"./images/B008N05A40.png\"]}\n{\"q_img\": \"./images/B00ATEZA6Q.png\", \"q_text\": \" brighter color., and shorter and printed\", \"positive_key\": \"B0091EGPXW\", \"positive_value\": \"./images/B0091EGPXW.png\", \"hn_image\": [\"./images/B00ATEZA6Q.png\"]}\n{\"q_img\": \"./images/B00BPRK674.png\", \"q_text\": \"is a blue long dress, and is a blue striped maxi dress\", \"positive_key\": \"B0074578MW\", \"positive_value\": \"./images/B0074578MW.png\", \"hn_image\": [\"./images/B00BPRK674.png\"]}\n{\"q_img\": \"./images/B009NXZXDO.png\", \"q_text\": \"Has a white sleeveless top and black skirt., and More monochromatic and two piece\", \"positive_key\": \"B00B7QRPHC\", \"positive_value\": \"./images/B00B7QRPHC.png\", \"hn_image\": [\"./images/B009NXZXDO.png\"]}\n{\"q_img\": \"./images/B00DG7SOTS.png\", \"q_text\": \"has less sleeves and is longer, and longer and black\", \"positive_key\": \"B005ZWBSMM\", \"positive_value\": \"./images/B005ZWBSMM.png\", \"hn_image\": [\"./images/B00DG7SOTS.png\"]}\n{\"q_img\": \"./images/B00B5R9XQ4.png\", \"q_text\": \"is more grey in color and longer sleeves, and is tighter\", \"positive_key\": \"B00G3NAFFS\", \"positive_value\": \"./images/B00G3NAFFS.png\", \"hn_image\": [\"./images/B00B5R9XQ4.png\"]}\n{\"q_img\": \"./images/B00E00GL86.png\", \"q_text\": \"is longer and darker, and darker and longer\", \"positive_key\": \"B00CB73QNI\", \"positive_value\": \"./images/B00CB73QNI.png\", \"hn_image\": [\"./images/B00E00GL86.png\"]}\n{\"q_img\": \"./images/B00CN46SMU.png\", \"q_text\": \"is strapless, and has the colors red\", \"positive_key\": \"B00387EZGM\", \"positive_value\": \"./images/B00387EZGM.png\", \"hn_image\": [\"./images/B00CN46SMU.png\"]}\n{\"q_img\": \"./images/B0094QXCKQ.png\", \"q_text\": \"is longer and has no sleeves, and has shorter sleeves\", \"positive_key\": \"B003WE9TNA\", \"positive_value\": \"./images/B003WE9TNA.png\", \"hn_image\": [\"./images/B0094QXCKQ.png\"]}\n{\"q_img\": \"./images/B005QYY8F8.png\", \"q_text\": \"is a longer dress, and is longer and has a pattern\", \"positive_key\": \"B0043RSVPM\", \"positive_value\": \"./images/B0043RSVPM.png\", \"hn_image\": [\"./images/B005QYY8F8.png\"]}\n{\"q_img\": \"./images/B00B7Q9ZDO.png\", \"q_text\": \"is black with no straps, and is black and strapless\", \"positive_key\": \"B00DERHT5A\", \"positive_value\": \"./images/B00DERHT5A.png\", \"hn_image\": [\"./images/B00B7Q9ZDO.png\"]}\n{\"q_img\": \"./images/B00CRTE6OS.png\", \"q_text\": \"is striped and longer length, and is longer and stripped\", \"positive_key\": \"B00DH7DFXC\", \"positive_value\": \"./images/B00DH7DFXC.png\", \"hn_image\": [\"./images/B00CRTE6OS.png\"]}\n{\"q_img\": \"./images/B00CY0CF0M.png\", \"q_text\": \"is a strapless maroon dress, and is red and has a belt\", \"positive_key\": \"B00EAYWKNC\", \"positive_value\": \"./images/B00EAYWKNC.png\", \"hn_image\": [\"./images/B00CY0CF0M.png\"]}\n{\"q_img\": \"./images/B00997L4QE.png\", \"q_text\": \"is a white dress and is shorter, and is lighter in color\", \"positive_key\": \"B00EUGDI5O\", \"positive_value\": \"./images/B00EUGDI5O.png\", \"hn_image\": [\"./images/B00997L4QE.png\"]}\n{\"q_img\": \"./images/B008AM8NEA.png\", \"q_text\": \"is darker and more revealing, and is grey with red and black lines\", \"positive_key\": \"B00EQSG6M8\", \"positive_value\": \"./images/B00EQSG6M8.png\", \"hn_image\": [\"./images/B008AM8NEA.png\"]}\n{\"q_img\": \"./images/B008BM1A0I.png\", \"q_text\": \"is a long black strapless dress, and is black and longer\", \"positive_key\": \"B00DII4VBU\", \"positive_value\": \"./images/B00DII4VBU.png\", \"hn_image\": [\"./images/B008BM1A0I.png\"]}\n{\"q_img\": \"./images/B004TM36ME.png\", \"q_text\": \"zebra print dress with spaghetti strap, and is lighter in color\", \"positive_key\": \"B0058FOPLC\", \"positive_value\": \"./images/B0058FOPLC.png\", \"hn_image\": [\"./images/B004TM36ME.png\"]}\n{\"q_img\": \"./images/B00ARAY3OC.png\", \"q_text\": \"a dark pink, and is pink with white dots\", \"positive_key\": \"B00C3LJ93S\", \"positive_value\": \"./images/B00C3LJ93S.png\", \"hn_image\": [\"./images/B00ARAY3OC.png\"]}\n{\"q_img\": \"./images/B004HD4V1K.png\", \"q_text\": \"is more black, and black and sheer at top\", \"positive_key\": \"B00405RUWC\", \"positive_value\": \"./images/B00405RUWC.png\", \"hn_image\": [\"./images/B004HD4V1K.png\"]}\n{\"q_img\": \"./images/B00ELLYWL2.png\", \"q_text\": \"is a short dress, and shorter skirt with floral pattern\", \"positive_key\": \"B007VL6D9S\", \"positive_value\": \"./images/B007VL6D9S.png\", \"hn_image\": [\"./images/B00ELLYWL2.png\"]}\n{\"q_img\": \"./images/B00A7HGI8Y.png\", \"q_text\": \" and halter straps, and longer with orange\", \"positive_key\": \"B00D8YT7LS\", \"positive_value\": \"./images/B00D8YT7LS.png\", \"hn_image\": [\"./images/B00A7HGI8Y.png\"]}\n{\"q_img\": \"./images/B00746EJBY.png\", \"q_text\": \"two-toned and more relaxed fit, and is white with black bottom\", \"positive_key\": \"B006PDUYHU\", \"positive_value\": \"./images/B006PDUYHU.png\", \"hn_image\": [\"./images/B00746EJBY.png\"]}\n{\"q_img\": \"./images/B008G0E9UI.png\", \"q_text\": \"solid dark blue dress, and is a solid dark blue\", \"positive_key\": \"B009414WQO\", \"positive_value\": \"./images/B009414WQO.png\", \"hn_image\": [\"./images/B008G0E9UI.png\"]}\n{\"q_img\": \"./images/B008VPX9BS.png\", \"q_text\": \"is longer and sleeveless, and longer and cuter\", \"positive_key\": \"B007ZPXMXA\", \"positive_value\": \"./images/B007ZPXMXA.png\", \"hn_image\": [\"./images/B008VPX9BS.png\"]}\n{\"q_img\": \"./images/B007XD6SPI.png\", \"q_text\": \"is purple colored and thicker straps, and is hot pink with a bow around the waist\", \"positive_key\": \"B008VIR88U\", \"positive_value\": \"./images/B008VIR88U.png\", \"hn_image\": [\"./images/B007XD6SPI.png\"]}\n{\"q_img\": \"./images/B00CQLOKM0.png\", \"q_text\": \"has small straps, and More plain and more revealing\", \"positive_key\": \"B00DWGK5FO\", \"positive_value\": \"./images/B00DWGK5FO.png\", \"hn_image\": [\"./images/B00CQLOKM0.png\"]}\n{\"q_img\": \"./images/B008MA8UJI.png\", \"q_text\": \"is magenta and has a frill, and has longer sleeves and brighter colored\", \"positive_key\": \"B008GS77UO\", \"positive_value\": \"./images/B008GS77UO.png\", \"hn_image\": [\"./images/B008MA8UJI.png\"]}\n{\"q_img\": \"./images/B009HK2P2A.png\", \"q_text\": \"purple with no sleeves, and is a dress\", \"positive_key\": \"B00BXZ1PW8\", \"positive_value\": \"./images/B00BXZ1PW8.png\", \"hn_image\": [\"./images/B009HK2P2A.png\"]}\n{\"q_img\": \"./images/B00AEDCDZ8.png\", \"q_text\": \" black and more plain, and is more neutral coloring and short sleeves\", \"positive_key\": \"B00CBBZOW0\", \"positive_value\": \"./images/B00CBBZOW0.png\", \"hn_image\": [\"./images/B00AEDCDZ8.png\"]}\n{\"q_img\": \"./images/B008P5453U.png\", \"q_text\": \"is white and less modest, and is whiter\", \"positive_key\": \"B008AEEHA2\", \"positive_value\": \"./images/B008AEEHA2.png\", \"hn_image\": [\"./images/B008P5453U.png\"]}\n{\"q_img\": \"./images/B00ASPCIF2.png\", \"q_text\": \"the black color looks more attractive and more bold, and is less revealing\", \"positive_key\": \"B0040JGTJS\", \"positive_value\": \"./images/B0040JGTJS.png\", \"hn_image\": [\"./images/B00ASPCIF2.png\"]}\n{\"q_img\": \"./images/B001PLDUT6.png\", \"q_text\": \"is more patterned and has fewer sleeves, and is colorful and more revealing\", \"positive_key\": \"B0043M5PCY\", \"positive_value\": \"./images/B0043M5PCY.png\", \"hn_image\": [\"./images/B001PLDUT6.png\"]}\n{\"q_img\": \"./images/B006W5VWJU.png\", \"q_text\": \" tighter and less revealing, and white and plain\", \"positive_key\": \"B007G4ZTTK\", \"positive_value\": \"./images/B007G4ZTTK.png\", \"hn_image\": [\"./images/B006W5VWJU.png\"]}\n{\"q_img\": \"./images/B008R8BGT6.png\", \"q_text\": \"is a blue belted dress and longer, and is denim fabric with buttons\", \"positive_key\": \"B008SLY504\", \"positive_value\": \"./images/B008SLY504.png\", \"hn_image\": [\"./images/B008R8BGT6.png\"]}\n{\"q_img\": \"./images/B004QF0KN2.png\", \"q_text\": \"has more open neck and lacy, and is yellow and has a smoother neckline\", \"positive_key\": \"B004QVWTHQ\", \"positive_value\": \"./images/B004QVWTHQ.png\", \"hn_image\": [\"./images/B004QF0KN2.png\"]}\n{\"q_img\": \"./images/B008AEDVTU.png\", \"q_text\": \"is a solid black, and is more black and shorter\", \"positive_key\": \"B00CII3SOM\", \"positive_value\": \"./images/B00CII3SOM.png\", \"hn_image\": [\"./images/B008AEDVTU.png\"]}\n{\"q_img\": \"./images/B00CTT51MC.png\", \"q_text\": \" and halter straps, and is longer and has a darker print\", \"positive_key\": \"B00CYSO06G\", \"positive_value\": \"./images/B00CYSO06G.png\", \"hn_image\": [\"./images/B00CTT51MC.png\"]}\n{\"q_img\": \"./images/B00AEMHDIG.png\", \"q_text\": \"is darker and has a u shaped neck, and Is black and has no sleeves\", \"positive_key\": \"B008FD9MG2\", \"positive_value\": \"./images/B008FD9MG2.png\", \"hn_image\": [\"./images/B00AEMHDIG.png\"]}\n{\"q_img\": \"./images/B005KILV0A.png\", \"q_text\": \"is solid black with thin straps, and has spagetti straps and is more plain\", \"positive_key\": \"B0055KXHNM\", \"positive_value\": \"./images/B0055KXHNM.png\", \"hn_image\": [\"./images/B005KILV0A.png\"]}\n{\"q_img\": \"./images/B00F4M41J0.png\", \"q_text\": \"has longer sleeves and more lace, and had lace arms\", \"positive_key\": \"B00CJQI0I2\", \"positive_value\": \"./images/B00CJQI0I2.png\", \"hn_image\": [\"./images/B00F4M41J0.png\"]}\n{\"q_img\": \"./images/B004HX2DYW.png\", \"q_text\": \"is identical, and same product\", \"positive_key\": \"B004HX0CP4\", \"positive_value\": \"./images/B004HX0CP4.png\", \"hn_image\": [\"./images/B004HX2DYW.png\"]}\n{\"q_img\": \"./images/B009COQRZW.png\", \"q_text\": \"is a straight black dress, and is more fitted with peep shoulders\", \"positive_key\": \"B00E6RBFFC\", \"positive_value\": \"./images/B00E6RBFFC.png\", \"hn_image\": [\"./images/B009COQRZW.png\"]}\n{\"q_img\": \"./images/B008K3SX7G.png\", \"q_text\": \"is shinier and has more sleeves, and is much lighter in color\", \"positive_key\": \"B0055TBPNC\", \"positive_value\": \"./images/B0055TBPNC.png\", \"hn_image\": [\"./images/B008K3SX7G.png\"]}\n{\"q_img\": \"./images/B0097IXSGO.png\", \"q_text\": \"is solid white with a v-neck, and is plain white colored\", \"positive_key\": \"B00CEO4ETI\", \"positive_value\": \"./images/B00CEO4ETI.png\", \"hn_image\": [\"./images/B0097IXSGO.png\"]}\n{\"q_img\": \"./images/B00BBXX6LU.png\", \"q_text\": \"is darker, and has a collar and sleeves\", \"positive_key\": \"B009157FFI\", \"positive_value\": \"./images/B009157FFI.png\", \"hn_image\": [\"./images/B00BBXX6LU.png\"]}\n{\"q_img\": \"./images/B00CH50182.png\", \"q_text\": \"turtle neck 3/4 sleeve body con dress, and is tan with longer sleeves\", \"positive_key\": \"B00EAK3D8C\", \"positive_value\": \"./images/B00EAK3D8C.png\", \"hn_image\": [\"./images/B00CH50182.png\"]}\n{\"q_img\": \"./images/B00F150ZXQ.png\", \"q_text\": \"short grey short with ruffles, and is more skimpy and has straps\", \"positive_key\": \"B008CPNZ5W\", \"positive_value\": \"./images/B008CPNZ5W.png\", \"hn_image\": [\"./images/B00F150ZXQ.png\"]}\n{\"q_img\": \"./images/B00BQX461E.png\", \"q_text\": \"is lighter and has shorter sleeves, and is more grey and sleeveless\", \"positive_key\": \"B00E1XOZ3K\", \"positive_value\": \"./images/B00E1XOZ3K.png\", \"hn_image\": [\"./images/B00BQX461E.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \"is below the knee, and has a print and long sleeves\", \"positive_key\": \"B00EKTNDAQ\", \"positive_value\": \"./images/B00EKTNDAQ.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B005917FDA.png\", \"q_text\": \"is less sexy, and is less revealing and more floral\", \"positive_key\": \"B003K170GI\", \"positive_value\": \"./images/B003K170GI.png\", \"hn_image\": [\"./images/B005917FDA.png\"]}\n{\"q_img\": \"./images/B009C6OMMK.png\", \"q_text\": \"light pink dress looks more fashionable and party wear, and does not show the neck\", \"positive_key\": \"B009C6OLCQ\", \"positive_value\": \"./images/B009C6OLCQ.png\", \"hn_image\": [\"./images/B009C6OMMK.png\"]}\n{\"q_img\": \"./images/B005VTJH4K.png\", \"q_text\": \"Is more sexy, and is black with three quarter length sleeves\", \"positive_key\": \"B0058XPHQG\", \"positive_value\": \"./images/B0058XPHQG.png\", \"hn_image\": [\"./images/B005VTJH4K.png\"]}\n{\"q_img\": \"./images/B008YIG3DI.png\", \"q_text\": \"Is more western, and is a bit longer and more summery\", \"positive_key\": \"B0088A1FWG\", \"positive_value\": \"./images/B0088A1FWG.png\", \"hn_image\": [\"./images/B008YIG3DI.png\"]}\n{\"q_img\": \"./images/B00DBDCQ7I.png\", \"q_text\": \"has different sleeves and is black and white, and has wider stripes and no straps\", \"positive_key\": \"B00DSSIGXO\", \"positive_value\": \"./images/B00DSSIGXO.png\", \"hn_image\": [\"./images/B00DBDCQ7I.png\"]}\n{\"q_img\": \"./images/B002X79Z72.png\", \"q_text\": \"is shorter, and is black with a shorter skirt\", \"positive_key\": \"B00BG3HJKY\", \"positive_value\": \"./images/B00BG3HJKY.png\", \"hn_image\": [\"./images/B002X79Z72.png\"]}\n{\"q_img\": \"./images/B006WY8INO.png\", \"q_text\": \"its floral print no sleeves, and covers the chest and flowy\", \"positive_key\": \"B00AOBCAXA\", \"positive_value\": \"./images/B00AOBCAXA.png\", \"hn_image\": [\"./images/B006WY8INO.png\"]}\n{\"q_img\": \"./images/B008W46FMS.png\", \"q_text\": \"has longer sleeves and floral print, and is black with long sleeves\", \"positive_key\": \"B00AO9RH30\", \"positive_value\": \"./images/B00AO9RH30.png\", \"hn_image\": [\"./images/B008W46FMS.png\"]}\n{\"q_img\": \"./images/B0099DCINQ.png\", \"q_text\": \"this cream dress looks more bold, and Is more fitted and plain\", \"positive_key\": \"B007WATTEE\", \"positive_value\": \"./images/B007WATTEE.png\", \"hn_image\": [\"./images/B0099DCINQ.png\"]}\n{\"q_img\": \"./images/B007ZDYK2E.png\", \"q_text\": \"less mature and too simple, and has longer sleeves and a belted waist\", \"positive_key\": \"B007RBZ3GQ\", \"positive_value\": \"./images/B007RBZ3GQ.png\", \"hn_image\": [\"./images/B007ZDYK2E.png\"]}\n{\"q_img\": \"./images/B006ZR3WK2.png\", \"q_text\": \"is white with a low neck, and has a fuller skirt\", \"positive_key\": \"B006W20H0I\", \"positive_value\": \"./images/B006W20H0I.png\", \"hn_image\": [\"./images/B006ZR3WK2.png\"]}\n{\"q_img\": \"./images/B008HRLQ60.png\", \"q_text\": \"has more coverage and is more fun, and is red and has a graphic design\", \"positive_key\": \"B00AEJR8E8\", \"positive_value\": \"./images/B00AEJR8E8.png\", \"hn_image\": [\"./images/B008HRLQ60.png\"]}\n{\"q_img\": \"./images/B00GP5TL6I.png\", \"q_text\": \"has black and white stripes, and is striped and longer\", \"positive_key\": \"B00EKVHEWM\", \"positive_value\": \"./images/B00EKVHEWM.png\", \"hn_image\": [\"./images/B00GP5TL6I.png\"]}\n{\"q_img\": \"./images/B004S0O8HO.png\", \"q_text\": \"is blue with longer sleeves, and is blue and black and long sleeves\", \"positive_key\": \"B00EKH5ZFY\", \"positive_value\": \"./images/B00EKH5ZFY.png\", \"hn_image\": [\"./images/B004S0O8HO.png\"]}\n{\"q_img\": \"./images/B00CLZMWUI.png\", \"q_text\": \"Has a vintage pattern and an asymmetrical hemline., and is blue and long\", \"positive_key\": \"B00A7FRP10\", \"positive_value\": \"./images/B00A7FRP10.png\", \"hn_image\": [\"./images/B00CLZMWUI.png\"]}\n{\"q_img\": \"./images/B0046XRKNM.png\", \"q_text\": \"is silver and shiny, and Light & Dark color\", \"positive_key\": \"B006MQRRAW\", \"positive_value\": \"./images/B006MQRRAW.png\", \"hn_image\": [\"./images/B0046XRKNM.png\"]}\n{\"q_img\": \"./images/B0097IWC3Y.png\", \"q_text\": \"is whiter and has a smoother bodice, and is more fitted and bright.\", \"positive_key\": \"B00C9796UC\", \"positive_value\": \"./images/B00C9796UC.png\", \"hn_image\": [\"./images/B0097IWC3Y.png\"]}\n{\"q_img\": \"./images/B0070SSWKK.png\", \"q_text\": \"white and silver and asymmetrical, and is less colorful and has one strap\", \"positive_key\": \"B007REBDNK\", \"positive_value\": \"./images/B007REBDNK.png\", \"hn_image\": [\"./images/B0070SSWKK.png\"]}\n{\"q_img\": \"./images/B00CFUEHAW.png\", \"q_text\": \"Is a brighter color and has no sleeves., and has a red and black pattern\", \"positive_key\": \"B009W66L92\", \"positive_value\": \"./images/B009W66L92.png\", \"hn_image\": [\"./images/B00CFUEHAW.png\"]}\n{\"q_img\": \"./images/B00CJRV3Z8.png\", \"q_text\": \"is almost the same but slightly different color, and is darker and more summery\", \"positive_key\": \"B0074H854A\", \"positive_value\": \"./images/B0074H854A.png\", \"hn_image\": [\"./images/B00CJRV3Z8.png\"]}\n{\"q_img\": \"./images/B00CDJZYA2.png\", \"q_text\": \"has a u shaped neck with short sleeves, and has a different coloring in darker colors.\", \"positive_key\": \"B008L0GPLY\", \"positive_value\": \"./images/B008L0GPLY.png\", \"hn_image\": [\"./images/B00CDJZYA2.png\"]}\n{\"q_img\": \"./images/B0084Y2V6A.png\", \"q_text\": \"Is a short black a line dress., and has less color and is shorter\", \"positive_key\": \"B00BSMLF1M\", \"positive_value\": \"./images/B00BSMLF1M.png\", \"hn_image\": [\"./images/B0084Y2V6A.png\"]}\n{\"q_img\": \"./images/B008OZV6II.png\", \"q_text\": \"has a more modest bodice and more lace, and is black with thicker sleeves\", \"positive_key\": \"B006BFNE3I\", \"positive_value\": \"./images/B006BFNE3I.png\", \"hn_image\": [\"./images/B008OZV6II.png\"]}\n{\"q_img\": \"./images/B00DB20MFM.png\", \"q_text\": \"Sleeveless and black and white, and has straps and is more shiny\", \"positive_key\": \"B0073Y4KA2\", \"positive_value\": \"./images/B0073Y4KA2.png\", \"hn_image\": [\"./images/B00DB20MFM.png\"]}\n{\"q_img\": \"./images/B006C4GIS6.png\", \"q_text\": \"is strapless and teal colored, and is lighter and more intimate\", \"positive_key\": \"B0076ZZ0DE\", \"positive_value\": \"./images/B0076ZZ0DE.png\", \"hn_image\": [\"./images/B006C4GIS6.png\"]}\n{\"q_img\": \"./images/B009CC9GUC.png\", \"q_text\": \"is gray scale and a style, and is darker in color and a little more dressy\", \"positive_key\": \"B0081TB10A\", \"positive_value\": \"./images/B0081TB10A.png\", \"hn_image\": [\"./images/B009CC9GUC.png\"]}\n{\"q_img\": \"./images/B007WPHQJ4.png\", \"q_text\": \"is more colorful and less classy, and Shorter and more colorful\", \"positive_key\": \"B006UCQYMA\", \"positive_value\": \"./images/B006UCQYMA.png\", \"hn_image\": [\"./images/B007WPHQJ4.png\"]}\n{\"q_img\": \"./images/B008UH8MY6.png\", \"q_text\": \"is shorter with more ruffles, and has a shorter skirt and is more orange\", \"positive_key\": \"B004KKXNOQ\", \"positive_value\": \"./images/B004KKXNOQ.png\", \"hn_image\": [\"./images/B008UH8MY6.png\"]}\n{\"q_img\": \"./images/B008KPIBWQ.png\", \"q_text\": \"has long sleeves, and is black and long-sleeve\", \"positive_key\": \"B008Z1TX2M\", \"positive_value\": \"./images/B008Z1TX2M.png\", \"hn_image\": [\"./images/B008KPIBWQ.png\"]}\n{\"q_img\": \"./images/B007IGI8D0.png\", \"q_text\": \"has straps and is brown, and is in brown only.\", \"positive_key\": \"B004T7XD4U\", \"positive_value\": \"./images/B004T7XD4U.png\", \"hn_image\": [\"./images/B007IGI8D0.png\"]}\n{\"q_img\": \"./images/B005OOUCLE.png\", \"q_text\": \"high low printed dress with thin straps, and has red floral print\", \"positive_key\": \"B007U6KS0Y\", \"positive_value\": \"./images/B007U6KS0Y.png\", \"hn_image\": [\"./images/B005OOUCLE.png\"]}\n{\"q_img\": \"./images/B001TA2J9K.png\", \"q_text\": \"green and shorter, and Is more metallic and shorter\", \"positive_key\": \"B000YLE1XM\", \"positive_value\": \"./images/B000YLE1XM.png\", \"hn_image\": [\"./images/B001TA2J9K.png\"]}\n{\"q_img\": \"./images/B001DXL1WY.png\", \"q_text\": \"has a more modest neck and different pattern, and is shorterm lighter and has a higher neckline\", \"positive_key\": \"B008006CXG\", \"positive_value\": \"./images/B008006CXG.png\", \"hn_image\": [\"./images/B001DXL1WY.png\"]}\n{\"q_img\": \"./images/B007WA3E8Q.png\", \"q_text\": \"is white, and is white and more revealing\", \"positive_key\": \"B00925CYOY\", \"positive_value\": \"./images/B00925CYOY.png\", \"hn_image\": [\"./images/B007WA3E8Q.png\"]}\n{\"q_img\": \"./images/B008YF4VAS.png\", \"q_text\": \"is solid red, and is red and has off shoulder\", \"positive_key\": \"B00AFV75CU\", \"positive_value\": \"./images/B00AFV75CU.png\", \"hn_image\": [\"./images/B008YF4VAS.png\"]}\n{\"q_img\": \"./images/B0061RGQEU.png\", \"q_text\": \"is lighter solid red, and is brighter red\", \"positive_key\": \"B007OX0MXG\", \"positive_value\": \"./images/B007OX0MXG.png\", \"hn_image\": [\"./images/B0061RGQEU.png\"]}\n{\"q_img\": \"./images/B004CHGS2G.png\", \"q_text\": \"is a scarf, and a scarf and not a long dress\", \"positive_key\": \"B00CMVCTZ4\", \"positive_value\": \"./images/B00CMVCTZ4.png\", \"hn_image\": [\"./images/B004CHGS2G.png\"]}\n{\"q_img\": \"./images/B0090JSF7W.png\", \"q_text\": \"has polka dots and a belt, and has a polka dot print and is belted at the waist\", \"positive_key\": \"B00A0IE1X4\", \"positive_value\": \"./images/B00A0IE1X4.png\", \"hn_image\": [\"./images/B0090JSF7W.png\"]}\n{\"q_img\": \"./images/B007RM21BU.png\", \"q_text\": \"Is a short green tee shirt dress., and a light green mini dress with a belt\", \"positive_key\": \"B0099N7EA8\", \"positive_value\": \"./images/B0099N7EA8.png\", \"hn_image\": [\"./images/B007RM21BU.png\"]}\n{\"q_img\": \"./images/B0043D2HME.png\", \"q_text\": \"has longer hemline and exposed back, and backless\", \"positive_key\": \"B00CAAADZU\", \"positive_value\": \"./images/B00CAAADZU.png\", \"hn_image\": [\"./images/B0043D2HME.png\"]}\n{\"q_img\": \"./images/B007F7PAOC.png\", \"q_text\": \"is long and floral, and Is longer and more floral.\", \"positive_key\": \"B007F7OJ8K\", \"positive_value\": \"./images/B007F7OJ8K.png\", \"hn_image\": [\"./images/B007F7PAOC.png\"]}\n{\"q_img\": \"./images/B008D5A9W8.png\", \"q_text\": \"has shorter sleeves and is all black, and is black and has shorter sleeves\", \"positive_key\": \"B00B22FA14\", \"positive_value\": \"./images/B00B22FA14.png\", \"hn_image\": [\"./images/B008D5A9W8.png\"]}\n{\"q_img\": \"./images/B0017RAI9S.png\", \"q_text\": \"the pink dress looks more fashionable and mature, and is pink with no designs on it\", \"positive_key\": \"B00CZ8W7UQ\", \"positive_value\": \"./images/B00CZ8W7UQ.png\", \"hn_image\": [\"./images/B0017RAI9S.png\"]}\n{\"q_img\": \"./images/B005N4KUIA.png\", \"q_text\": \"is more revealing, and is black and less casual\", \"positive_key\": \"B00FMLS7PW\", \"positive_value\": \"./images/B00FMLS7PW.png\", \"hn_image\": [\"./images/B005N4KUIA.png\"]}\n{\"q_img\": \"./images/B007SX36PI.png\", \"q_text\": \"is black with longer sleeves, and is black and shorter with longer sleeves\", \"positive_key\": \"B002YX04ZW\", \"positive_value\": \"./images/B002YX04ZW.png\", \"hn_image\": [\"./images/B007SX36PI.png\"]}\n{\"q_img\": \"./images/B00AT9JFQC.png\", \"q_text\": \"has longer sleeves, and has less puffy bottom\", \"positive_key\": \"B00AWM5REK\", \"positive_value\": \"./images/B00AWM5REK.png\", \"hn_image\": [\"./images/B00AT9JFQC.png\"]}\n{\"q_img\": \"./images/B0068Z9QUQ.png\", \"q_text\": \"is more solid and sexier, and The Desired product is less loud than the provided product\", \"positive_key\": \"B009TYL08E\", \"positive_value\": \"./images/B009TYL08E.png\", \"hn_image\": [\"./images/B0068Z9QUQ.png\"]}\n{\"q_img\": \"./images/B005QYY8F8.png\", \"q_text\": \"is less bright red with black accents, and has black trim with a darker red\", \"positive_key\": \"B00DY3XR1O\", \"positive_value\": \"./images/B00DY3XR1O.png\", \"hn_image\": [\"./images/B005QYY8F8.png\"]}\n{\"q_img\": \"./images/B005TM5U7C.png\", \"q_text\": \"is darker and shorter, and is shorter and black\", \"positive_key\": \"B00BSONSN8\", \"positive_value\": \"./images/B00BSONSN8.png\", \"hn_image\": [\"./images/B005TM5U7C.png\"]}\n{\"q_img\": \"./images/B00DB20MFM.png\", \"q_text\": \"is cream in color, and is cream\", \"positive_key\": \"B00AWAZPS0\", \"positive_value\": \"./images/B00AWAZPS0.png\", \"hn_image\": [\"./images/B00DB20MFM.png\"]}\n{\"q_img\": \"./images/B0017RAI9S.png\", \"q_text\": \"is longer and darker in color, and is longer and darker colored\", \"positive_key\": \"B004NG9V2U\", \"positive_value\": \"./images/B004NG9V2U.png\", \"hn_image\": [\"./images/B0017RAI9S.png\"]}\n{\"q_img\": \"./images/B00CM7JH80.png\", \"q_text\": \"has a sweetheart neckline and defined bust, and is black and more revealing\", \"positive_key\": \"B001M072KS\", \"positive_value\": \"./images/B001M072KS.png\", \"hn_image\": [\"./images/B00CM7JH80.png\"]}\n{\"q_img\": \"./images/B006GHAY4S.png\", \"q_text\": \"is dark blue with one long sleeve, and is in solid blue with one long draping sleeve.\", \"positive_key\": \"B008S4C8JG\", \"positive_value\": \"./images/B008S4C8JG.png\", \"hn_image\": [\"./images/B006GHAY4S.png\"]}\n{\"q_img\": \"./images/B001TA2J9K.png\", \"q_text\": \"Red color looks more feminime, and is red orange\", \"positive_key\": \"B0009T8KS6\", \"positive_value\": \"./images/B0009T8KS6.png\", \"hn_image\": [\"./images/B001TA2J9K.png\"]}\n{\"q_img\": \"./images/B00EDSJG2I.png\", \"q_text\": \"has thinner straps and dark blue, and is dark blue with spaghetti shoulder straps\", \"positive_key\": \"B004O3CSF4\", \"positive_value\": \"./images/B004O3CSF4.png\", \"hn_image\": [\"./images/B00EDSJG2I.png\"]}\n{\"q_img\": \"./images/B00DUL8IWS.png\", \"q_text\": \"is longer with a collar, and Has a white collar and is slightly longer\", \"positive_key\": \"B00B8PYBII\", \"positive_value\": \"./images/B00B8PYBII.png\", \"hn_image\": [\"./images/B00DUL8IWS.png\"]}\n{\"q_img\": \"./images/B004BDP1BA.png\", \"q_text\": \"Maggy London Dress, and just words\", \"positive_key\": \"B00AO1VEBO\", \"positive_value\": \"./images/B00AO1VEBO.png\", \"hn_image\": [\"./images/B004BDP1BA.png\"]}\n{\"q_img\": \"./images/B009AO2IP2.png\", \"q_text\": \"has a pattern and is sleeveless, and has spaghetti straps and is ombre colored\", \"positive_key\": \"B00BKV2MH2\", \"positive_value\": \"./images/B00BKV2MH2.png\", \"hn_image\": [\"./images/B009AO2IP2.png\"]}\n{\"q_img\": \"./images/B008MGCDCM.png\", \"q_text\": \"has no sleeves and is solid, and doesn't have a black skirt and some draping fabric is included.\", \"positive_key\": \"B004PYESUA\", \"positive_value\": \"./images/B004PYESUA.png\", \"hn_image\": [\"./images/B008MGCDCM.png\"]}\n{\"q_img\": \"./images/B00C2G1HCA.png\", \"q_text\": \"is of the same design with black, and thicker lines with pink and yellow\", \"positive_key\": \"B00CH5GHJE\", \"positive_value\": \"./images/B00CH5GHJE.png\", \"hn_image\": [\"./images/B00C2G1HCA.png\"]}\n{\"q_img\": \"./images/B00A16VH28.png\", \"q_text\": \"is a solid pale pink color, and is a different color and sleeveless\", \"positive_key\": \"B007423G7Q\", \"positive_value\": \"./images/B007423G7Q.png\", \"hn_image\": [\"./images/B00A16VH28.png\"]}\n{\"q_img\": \"./images/B006ZO5GC2.png\", \"q_text\": \"is longer, and is long and grey with no patterns on it\", \"positive_key\": \"B00CIE7XWO\", \"positive_value\": \"./images/B00CIE7XWO.png\", \"hn_image\": [\"./images/B006ZO5GC2.png\"]}\n{\"q_img\": \"./images/B0058FOPLC.png\", \"q_text\": \"ruffled light blue and white dress, and has more blues and is more full and longer.\", \"positive_key\": \"B00766RTT6\", \"positive_value\": \"./images/B00766RTT6.png\", \"hn_image\": [\"./images/B0058FOPLC.png\"]}\n{\"q_img\": \"./images/B007IGI7UO.png\", \"q_text\": \"less sexy, and has short sleeves and even hemline\", \"positive_key\": \"B007IGI8D0\", \"positive_value\": \"./images/B007IGI8D0.png\", \"hn_image\": [\"./images/B007IGI7UO.png\"]}\n{\"q_img\": \"./images/B00CL0VWTA.png\", \"q_text\": \"Is more vintage and more patterned, and flowing\", \"positive_key\": \"B008SF8A56\", \"positive_value\": \"./images/B008SF8A56.png\", \"hn_image\": [\"./images/B00CL0VWTA.png\"]}\n{\"q_img\": \"./images/B00BM82UCK.png\", \"q_text\": \"is thigh high and blue, and Is blue and denim\", \"positive_key\": \"B0097B5ABC\", \"positive_value\": \"./images/B0097B5ABC.png\", \"hn_image\": [\"./images/B00BM82UCK.png\"]}\n{\"q_img\": \"./images/B00CZ6G09C.png\", \"q_text\": \"has longer sleeves and is brown, and is darker with longer sleeves\", \"positive_key\": \"B008ICQU24\", \"positive_value\": \"./images/B008ICQU24.png\", \"hn_image\": [\"./images/B00CZ6G09C.png\"]}\n{\"q_img\": \"./images/B005WQCHPI.png\", \"q_text\": \"is shorter and sexier, and is a short off shoulder multicolor dress\", \"positive_key\": \"B008IGT1TY\", \"positive_value\": \"./images/B008IGT1TY.png\", \"hn_image\": [\"./images/B005WQCHPI.png\"]}\n{\"q_img\": \"./images/B007L3W3FE.png\", \"q_text\": \"Is more revealing and sexier, and Purple and white leopard print short-sleeved neck\", \"positive_key\": \"B0082BI7WC\", \"positive_value\": \"./images/B0082BI7WC.png\", \"hn_image\": [\"./images/B007L3W3FE.png\"]}\n{\"q_img\": \"./images/B006TALF54.png\", \"q_text\": \"is black with no sleeves, and gold\", \"positive_key\": \"B004CR6DEY\", \"positive_value\": \"./images/B004CR6DEY.png\", \"hn_image\": [\"./images/B006TALF54.png\"]}\n{\"q_img\": \"./images/B0080ICEQC.png\", \"q_text\": \"has a lighter color and is longer, and is strapless and yellow riffles\", \"positive_key\": \"B0050SAMUK\", \"positive_value\": \"./images/B0050SAMUK.png\", \"hn_image\": [\"./images/B0080ICEQC.png\"]}\n{\"q_img\": \"./images/B00CIZZ6EK.png\", \"q_text\": \"is long and blue, and two-piece\", \"positive_key\": \"B007YBDKZK\", \"positive_value\": \"./images/B007YBDKZK.png\", \"hn_image\": [\"./images/B00CIZZ6EK.png\"]}\n{\"q_img\": \"./images/B008ZAD996.png\", \"q_text\": \"It has a v-neck and a tighter fit., and is less dark and more revealing\", \"positive_key\": \"B00D8GZNH8\", \"positive_value\": \"./images/B00D8GZNH8.png\", \"hn_image\": [\"./images/B008ZAD996.png\"]}\n{\"q_img\": \"./images/B0073Y4KA2.png\", \"q_text\": \"Is a short cream colored dress, and is less formal and white\", \"positive_key\": \"B00BHPLQSC\", \"positive_value\": \"./images/B00BHPLQSC.png\", \"hn_image\": [\"./images/B0073Y4KA2.png\"]}\n{\"q_img\": \"./images/B005F6NAWE.png\", \"q_text\": \"is shorter and has more cat, and Is pink with a cat motif\", \"positive_key\": \"B008OY314G\", \"positive_value\": \"./images/B008OY314G.png\", \"hn_image\": [\"./images/B005F6NAWE.png\"]}\n{\"q_img\": \"./images/B00FN4A7QA.png\", \"q_text\": \"Has a ruffled hem and straps., and printed\", \"positive_key\": \"B008CBA3RE\", \"positive_value\": \"./images/B008CBA3RE.png\", \"hn_image\": [\"./images/B00FN4A7QA.png\"]}\n{\"q_img\": \"./images/B006YOU8HG.png\", \"q_text\": \"Has pockets and a button up neck, and is longe sleeves and is gray colored\", \"positive_key\": \"B005C8DASY\", \"positive_value\": \"./images/B005C8DASY.png\", \"hn_image\": [\"./images/B006YOU8HG.png\"]}\n{\"q_img\": \"./images/B003N17E7U.png\", \"q_text\": \"has thin straps with ruffles and is black, and is more revealing\", \"positive_key\": \"B006GV3N56\", \"positive_value\": \"./images/B006GV3N56.png\", \"hn_image\": [\"./images/B003N17E7U.png\"]}\n{\"q_img\": \"./images/B0036SHD1W.png\", \"q_text\": \"is longer in length and has longer sleeves, and Is longer and patterned\", \"positive_key\": \"B00FGBX5HI\", \"positive_value\": \"./images/B00FGBX5HI.png\", \"hn_image\": [\"./images/B0036SHD1W.png\"]}\n{\"q_img\": \"./images/B006M49D42.png\", \"q_text\": \"is darker with flowers and short sleeves, and is black with red and yellow flowers\", \"positive_key\": \"B00A2IKF6O\", \"positive_value\": \"./images/B00A2IKF6O.png\", \"hn_image\": [\"./images/B006M49D42.png\"]}\n{\"q_img\": \"./images/B00AA6F0GI.png\", \"q_text\": \"is purple and sleeveless, and is purple with a big bow\", \"positive_key\": \"B006VA5H1K\", \"positive_value\": \"./images/B006VA5H1K.png\", \"hn_image\": [\"./images/B00AA6F0GI.png\"]}\n{\"q_img\": \"./images/B006LQCT6K.png\", \"q_text\": \"More hugging and more purple, and is solid purple\", \"positive_key\": \"B00AYWQV4I\", \"positive_value\": \"./images/B00AYWQV4I.png\", \"hn_image\": [\"./images/B006LQCT6K.png\"]}\n{\"q_img\": \"./images/B00DVG348U.png\", \"q_text\": \"looks more fashionable and party wear, and is cream and brown with animal print\", \"positive_key\": \"B009S3H54Y\", \"positive_value\": \"./images/B009S3H54Y.png\", \"hn_image\": [\"./images/B00DVG348U.png\"]}\n{\"q_img\": \"./images/B00A8CDCD2.png\", \"q_text\": \"is longer with solid maroon and silver belt, and is plain red and is more formal\", \"positive_key\": \"B008CV6AN0\", \"positive_value\": \"./images/B008CV6AN0.png\", \"hn_image\": [\"./images/B00A8CDCD2.png\"]}\n{\"q_img\": \"./images/B007VZ35MM.png\", \"q_text\": \"is long and one shoulder, and is longer and purple\", \"positive_key\": \"B00C2CTQPE\", \"positive_value\": \"./images/B00C2CTQPE.png\", \"hn_image\": [\"./images/B007VZ35MM.png\"]}\n{\"q_img\": \"./images/B002Z7FONO.png\", \"q_text\": \"is longer with longer sleeves and is shiny black, and Longer and more formal\", \"positive_key\": \"B00BPIUJCA\", \"positive_value\": \"./images/B00BPIUJCA.png\", \"hn_image\": [\"./images/B002Z7FONO.png\"]}\n{\"q_img\": \"./images/B007RB7IJG.png\", \"q_text\": \"Is a short black sleeveless dress, and is much plainer and more simple and all black.\", \"positive_key\": \"B0070QMT24\", \"positive_value\": \"./images/B0070QMT24.png\", \"hn_image\": [\"./images/B007RB7IJG.png\"]}\n{\"q_img\": \"./images/B00BXS8A3M.png\", \"q_text\": \"A blue and green chevron short dress, and is multi-colored and is sleeveless\", \"positive_key\": \"B0098JSNPS\", \"positive_value\": \"./images/B0098JSNPS.png\", \"hn_image\": [\"./images/B00BXS8A3M.png\"]}\n{\"q_img\": \"./images/B009DMFX6M.png\", \"q_text\": \"yellow and white short dress, and is more colorful\", \"positive_key\": \"B005TRZ9W8\", \"positive_value\": \"./images/B005TRZ9W8.png\", \"hn_image\": [\"./images/B009DMFX6M.png\"]}\n{\"q_img\": \"./images/B0016CFU3I.png\", \"q_text\": \"Is sleeveless with a short skirt and black color, and is black and v necked\", \"positive_key\": \"B0072CEQ5Y\", \"positive_value\": \"./images/B0072CEQ5Y.png\", \"hn_image\": [\"./images/B0016CFU3I.png\"]}\n{\"q_img\": \"./images/B004HSN6W0.png\", \"q_text\": \"is shorter and lighter in color, and is shorter and is more flowing\", \"positive_key\": \"B004HSQXPM\", \"positive_value\": \"./images/B004HSQXPM.png\", \"hn_image\": [\"./images/B004HSN6W0.png\"]}\n{\"q_img\": \"./images/B00BM279C2.png\", \"q_text\": \"is shorter and has longer sleeves, and has longer sleeves and blue color\", \"positive_key\": \"B00CFGYOE0\", \"positive_value\": \"./images/B00CFGYOE0.png\", \"hn_image\": [\"./images/B00BM279C2.png\"]}\n{\"q_img\": \"./images/B007W9ZSEA.png\", \"q_text\": \"is white in color and longer, and is white and floor length\", \"positive_key\": \"B00EEAOUCQ\", \"positive_value\": \"./images/B00EEAOUCQ.png\", \"hn_image\": [\"./images/B007W9ZSEA.png\"]}\n{\"q_img\": \"./images/B0051UT6AY.png\", \"q_text\": \"has sleeves and a white collar, and it lighter and has sleeves\", \"positive_key\": \"B007XD55T8\", \"positive_value\": \"./images/B007XD55T8.png\", \"hn_image\": [\"./images/B0051UT6AY.png\"]}\n{\"q_img\": \"./images/B008AYJC2K.png\", \"q_text\": \"has longer sleeve with belt, and is yellow with a belt\", \"positive_key\": \"B003AI6LNY\", \"positive_value\": \"./images/B003AI6LNY.png\", \"hn_image\": [\"./images/B008AYJC2K.png\"]}\n{\"q_img\": \"./images/B005SSNE0C.png\", \"q_text\": \"is blue high platoo shoes, and shoes are more blue\", \"positive_key\": \"B00EFYI5U4\", \"positive_value\": \"./images/B00EFYI5U4.png\", \"hn_image\": [\"./images/B005SSNE0C.png\"]}\n{\"q_img\": \"./images/B00ASIFVDU.png\", \"q_text\": \"is more floral and has less text, and and a solid color\", \"positive_key\": \"B0079G1GL0\", \"positive_value\": \"./images/B0079G1GL0.png\", \"hn_image\": [\"./images/B00ASIFVDU.png\"]}\n{\"q_img\": \"./images/B0046XRKNM.png\", \"q_text\": \"is blue, and has one sleeve and is more purple\", \"positive_key\": \"B004O6LVY0\", \"positive_value\": \"./images/B004O6LVY0.png\", \"hn_image\": [\"./images/B0046XRKNM.png\"]}\n{\"q_img\": \"./images/B003AQB1CM.png\", \"q_text\": \"Black and white and doesn't have a belt/strap, and has a black and white design and is slightly longer\", \"positive_key\": \"B0035WTC0O\", \"positive_value\": \"./images/B0035WTC0O.png\", \"hn_image\": [\"./images/B003AQB1CM.png\"]}\n{\"q_img\": \"./images/B00BQX3Z0M.png\", \"q_text\": \"perfect, and is identical\", \"positive_key\": \"B00CIQD2MW\", \"positive_value\": \"./images/B00CIQD2MW.png\", \"hn_image\": [\"./images/B00BQX3Z0M.png\"]}\n{\"q_img\": \"./images/B009D5TCY8.png\", \"q_text\": \"is draped and blue., and has floral and is blue color\", \"positive_key\": \"B00C2BIUXO\", \"positive_value\": \"./images/B00C2BIUXO.png\", \"hn_image\": [\"./images/B009D5TCY8.png\"]}\n{\"q_img\": \"./images/B00DZ02CJY.png\", \"q_text\": \"has straps and is black and white polka dots, and is black with white polka dots\", \"positive_key\": \"B00AMRNYLS\", \"positive_value\": \"./images/B00AMRNYLS.png\", \"hn_image\": [\"./images/B00DZ02CJY.png\"]}\n{\"q_img\": \"./images/B0060RMLPO.png\", \"q_text\": \"is black with white polka dots, and is polka dotten blue and white with white linings.\", \"positive_key\": \"B00E0EOUF8\", \"positive_value\": \"./images/B00E0EOUF8.png\", \"hn_image\": [\"./images/B0060RMLPO.png\"]}\n{\"q_img\": \"./images/B005GQPE4K.png\", \"q_text\": \"more revealing and v-neck, and is lighter in color\", \"positive_key\": \"B007E778LQ\", \"positive_value\": \"./images/B007E778LQ.png\", \"hn_image\": [\"./images/B005GQPE4K.png\"]}\n{\"q_img\": \"./images/B0091E3MCE.png\", \"q_text\": \"is shorter and less tighter, and is striped and shorter\", \"positive_key\": \"B00AWMO2PU\", \"positive_value\": \"./images/B00AWMO2PU.png\", \"hn_image\": [\"./images/B0091E3MCE.png\"]}\n{\"q_img\": \"./images/B00ANC7JCW.png\", \"q_text\": \"is light blue and strapless, and is blue\", \"positive_key\": \"B009HIH1S0\", \"positive_value\": \"./images/B009HIH1S0.png\", \"hn_image\": [\"./images/B00ANC7JCW.png\"]}\n{\"q_img\": \"./images/B00BG3HJKY.png\", \"q_text\": \"is lighter blue shades, and is blue with a lighter blue hem line\", \"positive_key\": \"B00CG5QJF2\", \"positive_value\": \"./images/B00CG5QJF2.png\", \"hn_image\": [\"./images/B00BG3HJKY.png\"]}\n{\"q_img\": \"./images/B004OHYU4M.png\", \"q_text\": \"is red with rhinestones, and is red and flouncy\", \"positive_key\": \"B00AIHGCMU\", \"positive_value\": \"./images/B00AIHGCMU.png\", \"hn_image\": [\"./images/B004OHYU4M.png\"]}\n{\"q_img\": \"./images/B005FMSW3U.png\", \"q_text\": \"Is a floor length black sleeveless dress, and the dress is black and long\", \"positive_key\": \"B00BF73EO6\", \"positive_value\": \"./images/B00BF73EO6.png\", \"hn_image\": [\"./images/B005FMSW3U.png\"]}\n{\"q_img\": \"./images/B00D1A2XZQ.png\", \"q_text\": \"shorter and black, and is black and a mini dress\", \"positive_key\": \"B00C7BSFX4\", \"positive_value\": \"./images/B00C7BSFX4.png\", \"hn_image\": [\"./images/B00D1A2XZQ.png\"]}\n{\"q_img\": \"./images/B00DZUQHSQ.png\", \"q_text\": \"is pink with spaghetti straps and is layered.uh, and is a more pink coloring\", \"positive_key\": \"B00BYFESVM\", \"positive_value\": \"./images/B00BYFESVM.png\", \"hn_image\": [\"./images/B00DZUQHSQ.png\"]}\n{\"q_img\": \"./images/B00DJJILJQ.png\", \"q_text\": \"has no sleeves, and is shorter sleeved\", \"positive_key\": \"B0067G1KQY\", \"positive_value\": \"./images/B0067G1KQY.png\", \"hn_image\": [\"./images/B00DJJILJQ.png\"]}\n{\"q_img\": \"./images/B00C12LOZU.png\", \"q_text\": \"is a tube dress, and shorter and strapless\", \"positive_key\": \"B007REFDFO\", \"positive_value\": \"./images/B007REFDFO.png\", \"hn_image\": [\"./images/B00C12LOZU.png\"]}\n{\"q_img\": \"./images/B00DEPBDBI.png\", \"q_text\": \"is a long solid black dress with half sleeves., and has grey with a center black top that is revealed.\", \"positive_key\": \"B00B92B2JG\", \"positive_value\": \"./images/B00B92B2JG.png\", \"hn_image\": [\"./images/B00DEPBDBI.png\"]}\n{\"q_img\": \"./images/B007XTIENG.png\", \"q_text\": \" one should mini dress., and is green and has one strap\", \"positive_key\": \"B009LM5RLA\", \"positive_value\": \"./images/B009LM5RLA.png\", \"hn_image\": [\"./images/B007XTIENG.png\"]}\n{\"q_img\": \"./images/B00715FFX4.png\", \"q_text\": \"is mostly white, and is lighter colored\", \"positive_key\": \"B00518957K\", \"positive_value\": \"./images/B00518957K.png\", \"hn_image\": [\"./images/B00715FFX4.png\"]}\n{\"q_img\": \"./images/B0075AW63M.png\", \"q_text\": \"has pattern and is red, and is red with floral patterns\", \"positive_key\": \"B00CTA22JG\", \"positive_value\": \"./images/B00CTA22JG.png\", \"hn_image\": [\"./images/B0075AW63M.png\"]}\n{\"q_img\": \"./images/B00F9KUL8W.png\", \"q_text\": \"Is solid black and pleated, and is maroon and has long sleeves\", \"positive_key\": \"B00AW7ZRM2\", \"positive_value\": \"./images/B00AW7ZRM2.png\", \"hn_image\": [\"./images/B00F9KUL8W.png\"]}\n{\"q_img\": \"./images/B00B2UEOEK.png\", \"q_text\": \"is long an black with small designs, and is longer with a lower cut neckline\", \"positive_key\": \"B00CEM8AHC\", \"positive_value\": \"./images/B00CEM8AHC.png\", \"hn_image\": [\"./images/B00B2UEOEK.png\"]}\n{\"q_img\": \"./images/B002M3TVJ4.png\", \"q_text\": \"has longer sleeves, and sweater dress above the knee\", \"positive_key\": \"B00A8KYZSK\", \"positive_value\": \"./images/B00A8KYZSK.png\", \"hn_image\": [\"./images/B002M3TVJ4.png\"]}\n{\"q_img\": \"./images/B003XCNN3I.png\", \"q_text\": \"has blue and oranges throughout and is longer, and has some orange\", \"positive_key\": \"B0081TBB1O\", \"positive_value\": \"./images/B0081TBB1O.png\", \"hn_image\": [\"./images/B003XCNN3I.png\"]}\n{\"q_img\": \"./images/B00CE2GUYM.png\", \"q_text\": \"is blue wide strapped dress with flaired bottom, and is a darker color with wide straps\", \"positive_key\": \"B004ZY06UQ\", \"positive_value\": \"./images/B004ZY06UQ.png\", \"hn_image\": [\"./images/B00CE2GUYM.png\"]}\n{\"q_img\": \"./images/B005PMRES4.png\", \"q_text\": \"is sleeveless and more dark coloured, and is green\", \"positive_key\": \"B00BNH7KVQ\", \"positive_value\": \"./images/B00BNH7KVQ.png\", \"hn_image\": [\"./images/B005PMRES4.png\"]}\n{\"q_img\": \"./images/B00A787OEA.png\", \"q_text\": \" fit and flair, and is red with long sleeves\", \"positive_key\": \"B005KMQPS4\", \"positive_value\": \"./images/B005KMQPS4.png\", \"hn_image\": [\"./images/B00A787OEA.png\"]}\n{\"q_img\": \"./images/B00CFEVLBQ.png\", \"q_text\": \"is black with a high neck line, and is black and dressy\", \"positive_key\": \"B0043D2HME\", \"positive_value\": \"./images/B0043D2HME.png\", \"hn_image\": [\"./images/B00CFEVLBQ.png\"]}\n{\"q_img\": \"./images/B00F4M41J0.png\", \"q_text\": \"Has a white top and a blue skirt, and has a white top and blue bottom under the breasts.\", \"positive_key\": \"B00DAN7QBK\", \"positive_value\": \"./images/B00DAN7QBK.png\", \"hn_image\": [\"./images/B00F4M41J0.png\"]}\n{\"q_img\": \"./images/B00ANT6K0M.png\", \"q_text\": \"is black and less tight, and has straps\", \"positive_key\": \"B00CPCMP0Y\", \"positive_value\": \"./images/B00CPCMP0Y.png\", \"hn_image\": [\"./images/B00ANT6K0M.png\"]}\n{\"q_img\": \"./images/B00E0OSMS4.png\", \"q_text\": \"longed sleeved and dark colored, and is black and has much longer sleeves\", \"positive_key\": \"B00CL14N3Q\", \"positive_value\": \"./images/B00CL14N3Q.png\", \"hn_image\": [\"./images/B00E0OSMS4.png\"]}\n{\"q_img\": \"./images/B0036Z2T3C.png\", \"q_text\": \"is pink in color, and is more brightly colored\", \"positive_key\": \"B00EZITMBQ\", \"positive_value\": \"./images/B00EZITMBQ.png\", \"hn_image\": [\"./images/B0036Z2T3C.png\"]}\n{\"q_img\": \"./images/B0094QX5SK.png\", \"q_text\": \"Is a gold lace up tunic., and is yelloe and has ties at waist\", \"positive_key\": \"B00G9WJ09A\", \"positive_value\": \"./images/B00G9WJ09A.png\", \"hn_image\": [\"./images/B0094QX5SK.png\"]}\n{\"q_img\": \"./images/B004MW4Y9A.png\", \"q_text\": \"is longer and darker, and is striped and without a belt\", \"positive_key\": \"B004S4ZD1K\", \"positive_value\": \"./images/B004S4ZD1K.png\", \"hn_image\": [\"./images/B004MW4Y9A.png\"]}\n{\"q_img\": \"./images/B00DO9M2VE.png\", \"q_text\": \"has longer sleeves and is black, and is longer sleeved and polka-dotted\", \"positive_key\": \"B00AAD6KOM\", \"positive_value\": \"./images/B00AAD6KOM.png\", \"hn_image\": [\"./images/B00DO9M2VE.png\"]}\n{\"q_img\": \"./images/B003ZJ6GCO.png\", \"q_text\": \"is more patterned with images, and is a print and shorter with a squared neckline\", \"positive_key\": \"B007O056R6\", \"positive_value\": \"./images/B007O056R6.png\", \"hn_image\": [\"./images/B003ZJ6GCO.png\"]}\n{\"q_img\": \"./images/B00CL0UK0C.png\", \"q_text\": \"has longer sleeves and black in color, and is darker and has longer sleeves\", \"positive_key\": \"B00F4MZ144\", \"positive_value\": \"./images/B00F4MZ144.png\", \"hn_image\": [\"./images/B00CL0UK0C.png\"]}\n{\"q_img\": \"./images/B005WQCHPI.png\", \"q_text\": \"is perfect, and looks the exact same\", \"positive_key\": \"B007JFW33Q\", \"positive_value\": \"./images/B007JFW33Q.png\", \"hn_image\": [\"./images/B005WQCHPI.png\"]}\n{\"q_img\": \"./images/B00BY3GO0C.png\", \"q_text\": \"Brighter and more playful looking, and is red with a floral print\", \"positive_key\": \"B0030EHJ66\", \"positive_value\": \"./images/B0030EHJ66.png\", \"hn_image\": [\"./images/B00BY3GO0C.png\"]}\n" }, { "path": "research/BGE_VL/eval/data/fashioniq_shirt_corpus.jsonl", "content": "{\"content\": \"B000KENMD8\", \"image\": \"./images/B000KENMD8.png\"}\n{\"content\": \"B005AD7WZI\", \"image\": \"./images/B005AD7WZI.png\"}\n{\"content\": \"B0013EO152\", \"image\": \"./images/B0013EO152.png\"}\n{\"content\": \"B00DI9YY8O\", \"image\": 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\"./images/B0083PWU70.png\"}\n{\"content\": \"B00BZJ5TRE\", \"image\": \"./images/B00BZJ5TRE.png\"}\n{\"content\": \"B004E4AEJ0\", \"image\": \"./images/B004E4AEJ0.png\"}\n{\"content\": \"B007ETIW0U\", \"image\": \"./images/B007ETIW0U.png\"}\n{\"content\": \"B00B4CTKHW\", \"image\": \"./images/B00B4CTKHW.png\"}\n{\"content\": \"B003ZHBS0Q\", \"image\": \"./images/B003ZHBS0Q.png\"}\n{\"content\": \"B009DEHLXI\", \"image\": \"./images/B009DEHLXI.png\"}\n{\"content\": \"B000VZK55E\", \"image\": \"./images/B000VZK55E.png\"}\n{\"content\": \"B00EB0AN6Q\", \"image\": \"./images/B00EB0AN6Q.png\"}\n{\"content\": \"B005IYZ57Q\", \"image\": \"./images/B005IYZ57Q.png\"}\n{\"content\": \"B001FO5LAY\", \"image\": \"./images/B001FO5LAY.png\"}\n{\"content\": \"B000TYT49U\", \"image\": \"./images/B000TYT49U.png\"}\n{\"content\": \"B009K8GOWQ\", \"image\": \"./images/B009K8GOWQ.png\"}\n{\"content\": \"B00BIXSATQ\", \"image\": \"./images/B00BIXSATQ.png\"}\n{\"content\": \"B003461J16\", \"image\": \"./images/B003461J16.png\"}\n{\"content\": \"B0001TOS62\", \"image\": \"./images/B0001TOS62.png\"}\n{\"content\": \"B00BL5C9FM\", \"image\": \"./images/B00BL5C9FM.png\"}\n{\"content\": \"B0009ALW8U\", \"image\": \"./images/B0009ALW8U.png\"}\n{\"content\": \"B006UJFF0K\", \"image\": \"./images/B006UJFF0K.png\"}\n{\"content\": \"B00C2D0JS6\", \"image\": \"./images/B00C2D0JS6.png\"}\n{\"content\": \"B002ZDTKFG\", \"image\": \"./images/B002ZDTKFG.png\"}\n{\"content\": \"B003UWAHE4\", \"image\": \"./images/B003UWAHE4.png\"}\n{\"content\": \"B005MGV7MW\", \"image\": \"./images/B005MGV7MW.png\"}\n{\"content\": \"B008T3X67O\", \"image\": \"./images/B008T3X67O.png\"}\n{\"content\": \"B004LETUB6\", \"image\": \"./images/B004LETUB6.png\"}\n{\"content\": \"B00BXAFQ3W\", \"image\": \"./images/B00BXAFQ3W.png\"}\n{\"content\": \"B0012O8DC0\", \"image\": \"./images/B0012O8DC0.png\"}\n{\"content\": \"B00DBDYW3Y\", \"image\": \"./images/B00DBDYW3Y.png\"}\n{\"content\": \"B004ORSH1E\", \"image\": \"./images/B004ORSH1E.png\"}\n{\"content\": \"B00E5SPW9W\", \"image\": \"./images/B00E5SPW9W.png\"}\n{\"content\": \"B009RRNPDQ\", \"image\": \"./images/B009RRNPDQ.png\"}\n{\"content\": \"B008TQRQCM\", \"image\": \"./images/B008TQRQCM.png\"}\n{\"content\": \"B00C51HF46\", \"image\": \"./images/B00C51HF46.png\"}\n{\"content\": \"B004Z21SHS\", \"image\": \"./images/B004Z21SHS.png\"}\n{\"content\": \"B006G7RN8I\", \"image\": \"./images/B006G7RN8I.png\"}\n{\"content\": \"B009DEGOSG\", \"image\": \"./images/B009DEGOSG.png\"}\n{\"content\": \"B003YPJTGY\", \"image\": \"./images/B003YPJTGY.png\"}\n{\"content\": \"B00ASK9HGA\", \"image\": \"./images/B00ASK9HGA.png\"}\n{\"content\": \"B00BP1Q1SS\", \"image\": \"./images/B00BP1Q1SS.png\"}\n{\"content\": \"B0083I6W08\", \"image\": \"./images/B0083I6W08.png\"}\n{\"content\": \"B0047WVDWG\", \"image\": \"./images/B0047WVDWG.png\"}\n{\"content\": \"B005G3VM80\", \"image\": \"./images/B005G3VM80.png\"}\n{\"content\": \"B0095BJB1Y\", \"image\": \"./images/B0095BJB1Y.png\"}\n{\"content\": \"B00AOANXI2\", \"image\": \"./images/B00AOANXI2.png\"}\n{\"content\": \"B00C1140PC\", \"image\": \"./images/B00C1140PC.png\"}\n{\"content\": \"B004QQJAF0\", \"image\": \"./images/B004QQJAF0.png\"}\n{\"content\": \"B0080HK1X6\", \"image\": \"./images/B0080HK1X6.png\"}\n{\"content\": \"B0096A1FVS\", \"image\": \"./images/B0096A1FVS.png\"}\n{\"content\": \"B000V2PEJ4\", \"image\": \"./images/B000V2PEJ4.png\"}\n{\"content\": \"B0027HFLY4\", \"image\": \"./images/B0027HFLY4.png\"}\n{\"content\": \"B001G0TKF4\", \"image\": \"./images/B001G0TKF4.png\"}\n{\"content\": \"B002MA2CMA\", \"image\": \"./images/B002MA2CMA.png\"}\n{\"content\": \"B006H17FEK\", \"image\": \"./images/B006H17FEK.png\"}\n{\"content\": \"B000FQ9R62\", \"image\": \"./images/B000FQ9R62.png\"}\n{\"content\": \"B004Y6XML0\", \"image\": \"./images/B004Y6XML0.png\"}\n{\"content\": \"B001XCT5OQ\", \"image\": \"./images/B001XCT5OQ.png\"}\n{\"content\": \"B007X4OJCQ\", \"image\": \"./images/B007X4OJCQ.png\"}\n{\"content\": \"B000F49R5A\", \"image\": \"./images/B000F49R5A.png\"}\n{\"content\": \"B00BV0YQR6\", \"image\": \"./images/B00BV0YQR6.png\"}\n{\"content\": \"B005U7E428\", \"image\": \"./images/B005U7E428.png\"}\n{\"content\": \"B004VPW5Y4\", \"image\": \"./images/B004VPW5Y4.png\"}\n{\"content\": \"B007U24CJ6\", \"image\": \"./images/B007U24CJ6.png\"}\n{\"content\": \"B0087DW93S\", \"image\": \"./images/B0087DW93S.png\"}\n{\"content\": \"B008R7FA3K\", \"image\": \"./images/B008R7FA3K.png\"}\n{\"content\": \"B00EDKMZ7Y\", \"image\": \"./images/B00EDKMZ7Y.png\"}\n{\"content\": \"B000CANHZ8\", \"image\": \"./images/B000CANHZ8.png\"}\n{\"content\": \"B003NUSAYC\", \"image\": \"./images/B003NUSAYC.png\"}\n{\"content\": \"B005KWPGNE\", \"image\": \"./images/B005KWPGNE.png\"}\n{\"content\": \"B00A353T14\", \"image\": \"./images/B00A353T14.png\"}\n{\"content\": \"B005F4O56G\", \"image\": \"./images/B005F4O56G.png\"}\n{\"content\": \"B00D78EKUS\", \"image\": \"./images/B00D78EKUS.png\"}\n" }, { "path": "research/BGE_VL/eval/data/fashioniq_shirt_query_val.jsonl", "content": "{\"q_img\": \"./images/B00CZ7QJUG.png\", \"q_text\": \"is solid white, and is a lighter color\", \"positive_key\": \"B005AD7WZI\", \"positive_value\": \"./images/B005AD7WZI.png\", \"hn_image\": [\"./images/B00CZ7QJUG.png\"]}\n{\"q_img\": \"./images/B0083I6W08.png\", \"q_text\": \"is green with a four leaf clover, and is green and has no text\", \"positive_key\": \"B00BPD4N5E\", \"positive_value\": \"./images/B00BPD4N5E.png\", \"hn_image\": [\"./images/B0083I6W08.png\"]}\n{\"q_img\": \"./images/B0083WOL16.png\", \"q_text\": \"Is a brown tee shirt with diseal logo, and is darker and has short sleeves\", \"positive_key\": \"B008VNAKSU\", \"positive_value\": \"./images/B008VNAKSU.png\", \"hn_image\": [\"./images/B0083WOL16.png\"]}\n{\"q_img\": \"./images/B005Y4KFPM.png\", \"q_text\": \"is dark blue, and is blue with a different character.\", \"positive_key\": \"B005Y4JJ0Y\", \"positive_value\": \"./images/B005Y4JJ0Y.png\", \"hn_image\": [\"./images/B005Y4KFPM.png\"]}\n{\"q_img\": \"./images/B006QHPYIY.png\", \"q_text\": \"is red and a tshirt, and more warmer colors\", \"positive_key\": \"B005NYBUF2\", \"positive_value\": \"./images/B005NYBUF2.png\", \"hn_image\": [\"./images/B006QHPYIY.png\"]}\n{\"q_img\": \"./images/B001OATAN8.png\", \"q_text\": \"is the provided product, and look exact same\", \"positive_key\": \"B001OAN6BK\", \"positive_value\": \"./images/B001OAN6BK.png\", \"hn_image\": [\"./images/B001OATAN8.png\"]}\n{\"q_img\": \"./images/B0075G2YFG.png\", \"q_text\": \"is white with mickey mouse on it, and has a mickey mouse graphic and is paler in colour\", \"positive_key\": \"B007A4H2IW\", \"positive_value\": \"./images/B007A4H2IW.png\", \"hn_image\": [\"./images/B0075G2YFG.png\"]}\n{\"q_img\": \"./images/B005XIGOL8.png\", \"q_text\": \"has short sleeves and hawaiian print, and is more tropical\", \"positive_key\": \"B003IB71I2\", \"positive_value\": \"./images/B003IB71I2.png\", \"hn_image\": [\"./images/B005XIGOL8.png\"]}\n{\"q_img\": \"./images/B009LM1ZJS.png\", \"q_text\": \"is purple in color with playing cards graphic, and is darker\", \"positive_key\": \"B00B4EF1AA\", \"positive_value\": \"./images/B00B4EF1AA.png\", \"hn_image\": [\"./images/B009LM1ZJS.png\"]}\n{\"q_img\": \"./images/B00EW84RMS.png\", \"q_text\": \"has white letters, and has more buttons\", \"positive_key\": \"B00BGVUTBC\", \"positive_value\": \"./images/B00BGVUTBC.png\", \"hn_image\": [\"./images/B00EW84RMS.png\"]}\n{\"q_img\": \"./images/B0073OGKDW.png\", \"q_text\": \"looks faded and cheaper, and is longer\", \"positive_key\": \"B004PLN0DO\", \"positive_value\": \"./images/B004PLN0DO.png\", \"hn_image\": [\"./images/B0073OGKDW.png\"]}\n{\"q_img\": \"./images/B00AZLY7QM.png\", \"q_text\": \"is blue with checkered and a dark gray tie, and is more formal\", \"positive_key\": \"B009MB8Q7C\", \"positive_value\": \"./images/B009MB8Q7C.png\", \"hn_image\": [\"./images/B00AZLY7QM.png\"]}\n{\"q_img\": \"./images/B008TP27PY.png\", \"q_text\": \"is a black T shirt with writing on it, and is black and has less graphics\", \"positive_key\": \"B004WSVYX8\", \"positive_value\": \"./images/B004WSVYX8.png\", \"hn_image\": [\"./images/B008TP27PY.png\"]}\n{\"q_img\": \"./images/B00CFZQIUE.png\", \"q_text\": \"Has no graphic, and Half and gray and white tang-top\", \"positive_key\": \"B0062UCEE2\", \"positive_value\": \"./images/B0062UCEE2.png\", \"hn_image\": [\"./images/B00CFZQIUE.png\"]}\n{\"q_img\": \"./images/B003WESSNM.png\", \"q_text\": \" red and grey stripes, and has longer sleeves and is a full button up\", \"positive_key\": \"B008LK6OUQ\", \"positive_value\": \"./images/B008LK6OUQ.png\", \"hn_image\": [\"./images/B003WESSNM.png\"]}\n{\"q_img\": \"./images/B002P7569Q.png\", \"q_text\": \"Is more patriotic rather than religious, and Is darker blue with a more colorful graphic.\", \"positive_key\": \"B009ZI10CK\", \"positive_value\": \"./images/B009ZI10CK.png\", \"hn_image\": [\"./images/B002P7569Q.png\"]}\n{\"q_img\": \"./images/B003X7AFR0.png\", \"q_text\": \"is gray and logo on top left, and Is gray smooth lines with small upper chest graphic\", \"positive_key\": \"B00EU03D1E\", \"positive_value\": \"./images/B00EU03D1E.png\", \"hn_image\": [\"./images/B003X7AFR0.png\"]}\n{\"q_img\": \"./images/B007POL3U0.png\", \"q_text\": \"is more revealing, and is tighter\", \"positive_key\": \"B008XDHOEQ\", \"positive_value\": \"./images/B008XDHOEQ.png\", \"hn_image\": [\"./images/B007POL3U0.png\"]}\n{\"q_img\": \"./images/B007X2DHT4.png\", \"q_text\": \"is lighter, and is whiter\", \"positive_key\": \"B0090YV46G\", \"positive_value\": \"./images/B0090YV46G.png\", \"hn_image\": [\"./images/B007X2DHT4.png\"]}\n{\"q_img\": \"./images/B00AYJ94AE.png\", \"q_text\": \"A black tee with a green frowning face, and has a sad face in green\", \"positive_key\": \"B004ZIJQVM\", \"positive_value\": \"./images/B004ZIJQVM.png\", \"hn_image\": [\"./images/B00AYJ94AE.png\"]}\n{\"q_img\": \"./images/B004J1JDH2.png\", \"q_text\": \"is darker, and Desired image contains stylized image of a motorcycle\", \"positive_key\": \"B0012C826O\", \"positive_value\": \"./images/B0012C826O.png\", \"hn_image\": [\"./images/B004J1JDH2.png\"]}\n{\"q_img\": \"./images/B0051D0X2Q.png\", \"q_text\": \"is blue plaid with white button shirt, and is lighter\", \"positive_key\": \"B003INE0Q6\", \"positive_value\": \"./images/B003INE0Q6.png\", \"hn_image\": [\"./images/B0051D0X2Q.png\"]}\n{\"q_img\": \"./images/B008VRA3AG.png\", \"q_text\": \" less graphics, and is more revealing and has shorter sleeves\", \"positive_key\": \"B008JBOODG\", \"positive_value\": \"./images/B008JBOODG.png\", \"hn_image\": [\"./images/B008VRA3AG.png\"]}\n{\"q_img\": \"./images/B00C51HF46.png\", \"q_text\": \"is black with blue designs, and is tighter\", \"positive_key\": \"B00BSGTYMA\", \"positive_value\": \"./images/B00BSGTYMA.png\", \"hn_image\": [\"./images/B00C51HF46.png\"]}\n{\"q_img\": \"./images/B006SDZ0JY.png\", \"q_text\": \"is bright blue, and is brighter in color\", \"positive_key\": \"B004M17G1Y\", \"positive_value\": \"./images/B004M17G1Y.png\", \"hn_image\": [\"./images/B006SDZ0JY.png\"]}\n{\"q_img\": \"./images/B00AYU2QZ8.png\", \"q_text\": \"is a long sleeved shirt, and has long sleeve and is grey color\", \"positive_key\": \"B00E3QGVFU\", \"positive_value\": \"./images/B00E3QGVFU.png\", \"hn_image\": [\"./images/B00AYU2QZ8.png\"]}\n{\"q_img\": \"./images/B005LCVMIG.png\", \"q_text\": \"is a red colored shirt with a center logo, and A red shirt with a black/white circle on front\", \"positive_key\": \"B00073GAX6\", \"positive_value\": \"./images/B00073GAX6.png\", \"hn_image\": [\"./images/B005LCVMIG.png\"]}\n{\"q_img\": \"./images/B0081ZLSWU.png\", \"q_text\": \"is black ad white striped with a collar and buttons, and has vertical stripes and chest pockets\", \"positive_key\": \"B0013EKE3K\", \"positive_value\": \"./images/B0013EKE3K.png\", \"hn_image\": [\"./images/B0081ZLSWU.png\"]}\n{\"q_img\": \"./images/B001SN7NRQ.png\", \"q_text\": \"is lighter, and Has a larger graphic\", \"positive_key\": \"B004OP2J56\", \"positive_value\": \"./images/B004OP2J56.png\", \"hn_image\": [\"./images/B001SN7NRQ.png\"]}\n{\"q_img\": \"./images/B00ALRS9OG.png\", \"q_text\": \"has more of a heavy metal look to it, and is more gothic\", \"positive_key\": \"B00EABHQPC\", \"positive_value\": \"./images/B00EABHQPC.png\", \"hn_image\": [\"./images/B00ALRS9OG.png\"]}\n{\"q_img\": \"./images/B0081V2U6C.png\", \"q_text\": \"is black with orange designs, and is black with orange lettering\", \"positive_key\": \"B00B8XSUSW\", \"positive_value\": \"./images/B00B8XSUSW.png\", \"hn_image\": [\"./images/B0081V2U6C.png\"]}\n{\"q_img\": \"./images/B00853SE8E.png\", \"q_text\": \"is darker with a different graphic, and A black shirt with pattern on front\", \"positive_key\": \"B004IP8H7Q\", \"positive_value\": \"./images/B004IP8H7Q.png\", \"hn_image\": [\"./images/B00853SE8E.png\"]}\n{\"q_img\": \"./images/B006TTQX6G.png\", \"q_text\": \"is short sleeve and burgundy colored, and is red with sleeves\", \"positive_key\": \"B009JOTMXY\", \"positive_value\": \"./images/B009JOTMXY.png\", \"hn_image\": [\"./images/B006TTQX6G.png\"]}\n{\"q_img\": \"./images/B003OUWT0W.png\", \"q_text\": \"Is lighter colored and depicts animals., and is alighter color with round neck .\", \"positive_key\": \"B0014UCUXU\", \"positive_value\": \"./images/B0014UCUXU.png\", \"hn_image\": [\"./images/B003OUWT0W.png\"]}\n{\"q_img\": \"./images/B008YCX6KC.png\", \"q_text\": \"is plaid blue an black, and is pale blue\", \"positive_key\": \"B008R7FM1A\", \"positive_value\": \"./images/B008R7FM1A.png\", \"hn_image\": [\"./images/B008YCX6KC.png\"]}\n{\"q_img\": \"./images/B0038PZMTI.png\", \"q_text\": \" and white colors, and is a rose shade\", \"positive_key\": \"B000BFH7FA\", \"positive_value\": \"./images/B000BFH7FA.png\", \"hn_image\": [\"./images/B0038PZMTI.png\"]}\n{\"q_img\": \"./images/B000QHCNV6.png\", \"q_text\": \"is shortsleeved and bright, and Is green and less wordy\", \"positive_key\": \"B00G6UFA94\", \"positive_value\": \"./images/B00G6UFA94.png\", \"hn_image\": [\"./images/B000QHCNV6.png\"]}\n{\"q_img\": \"./images/B001KBZ1QQ.png\", \"q_text\": \"is a blue long sleeve sports t-shirt, and Has longer sleeves\", \"positive_key\": \"B004XASV0O\", \"positive_value\": \"./images/B004XASV0O.png\", \"hn_image\": [\"./images/B001KBZ1QQ.png\"]}\n{\"q_img\": \"./images/B001XWTNGQ.png\", \"q_text\": \"is smaller in size and brighter in color, and is lighter\", \"positive_key\": \"B00BQ3R3CI\", \"positive_value\": \"./images/B00BQ3R3CI.png\", \"hn_image\": [\"./images/B001XWTNGQ.png\"]}\n{\"q_img\": \"./images/B0059A4A5C.png\", \"q_text\": \"is purple with white designs, and is a t-shirt with writing\", \"positive_key\": \"B004BGYQPO\", \"positive_value\": \"./images/B004BGYQPO.png\", \"hn_image\": [\"./images/B0059A4A5C.png\"]}\n{\"q_img\": \"./images/B009NUR66O.png\", \"q_text\": \"is blue with a yellow truck, and is blue\", \"positive_key\": \"B00DJ3UPPA\", \"positive_value\": \"./images/B00DJ3UPPA.png\", \"hn_image\": [\"./images/B009NUR66O.png\"]}\n{\"q_img\": \"./images/B00BK77UC8.png\", \"q_text\": \"has short sleeves, and has shorter sleeves\", \"positive_key\": \"B00D6G9NX0\", \"positive_value\": \"./images/B00D6G9NX0.png\", \"hn_image\": [\"./images/B00BK77UC8.png\"]}\n{\"q_img\": \"./images/B00EABHQPC.png\", \"q_text\": \"is blue and has two cartoon characters, and is black and white\", \"positive_key\": \"B00ALRS9OG\", \"positive_value\": \"./images/B00ALRS9OG.png\", \"hn_image\": [\"./images/B00EABHQPC.png\"]}\n{\"q_img\": \"./images/B007HZ9C6Y.png\", \"q_text\": \"is gray with black designs, and is lighter\", \"positive_key\": \"B004XJDKCO\", \"positive_value\": \"./images/B004XJDKCO.png\", \"hn_image\": [\"./images/B007HZ9C6Y.png\"]}\n{\"q_img\": \"./images/B001CWN1A6.png\", \"q_text\": \"is a red turtle neck, and is red and turtle necked\", \"positive_key\": \"B001VAF1G6\", \"positive_value\": \"./images/B001VAF1G6.png\", \"hn_image\": [\"./images/B001CWN1A6.png\"]}\n{\"q_img\": \"./images/B008HO2HZ2.png\", \"q_text\": \"has long sleeves and is dressier, and has long sleeves and buttons with pink stripes\", \"positive_key\": \"B0099S89HA\", \"positive_value\": \"./images/B0099S89HA.png\", \"hn_image\": [\"./images/B008HO2HZ2.png\"]}\n{\"q_img\": \"./images/B002NCIATQ.png\", \"q_text\": \"Is more blue and less wordy, and A light blue shirt with green design\", \"positive_key\": \"B008J4E76W\", \"positive_value\": \"./images/B008J4E76W.png\", \"hn_image\": [\"./images/B002NCIATQ.png\"]}\n{\"q_img\": \"./images/B007ZGZ80E.png\", \"q_text\": \"Has a larger graphic and is more blue, and blue tee shirt with a blue emergency sign\", \"positive_key\": \"B0060K7LLU\", \"positive_value\": \"./images/B0060K7LLU.png\", \"hn_image\": [\"./images/B007ZGZ80E.png\"]}\n{\"q_img\": \"./images/B009VZW24C.png\", \"q_text\": \"is more black, and is black\", \"positive_key\": \"B004ZITRGQ\", \"positive_value\": \"./images/B004ZITRGQ.png\", \"hn_image\": [\"./images/B009VZW24C.png\"]}\n{\"q_img\": \"./images/B0013FPI8K.png\", \"q_text\": \"is blue in color, and is a deeper blue\", \"positive_key\": \"B001GHIUDK\", \"positive_value\": \"./images/B001GHIUDK.png\", \"hn_image\": [\"./images/B0013FPI8K.png\"]}\n{\"q_img\": \"./images/B003TWOPKW.png\", \"q_text\": \"Longer sleeves with black red and white stripped, and is black with white and red stripes\", \"positive_key\": \"B001A92CC4\", \"positive_value\": \"./images/B001A92CC4.png\", \"hn_image\": [\"./images/B003TWOPKW.png\"]}\n{\"q_img\": \"./images/B003TSDPJI.png\", \"q_text\": \"Is green and black plaid, and is green and black checked\", \"positive_key\": \"B0062VX9YA\", \"positive_value\": \"./images/B0062VX9YA.png\", \"hn_image\": [\"./images/B003TSDPJI.png\"]}\n{\"q_img\": \"./images/B003XCKD3G.png\", \"q_text\": \"is black short sleeved t-shirt with Star Wars image print, and is darker\", \"positive_key\": \"B0061SL9LO\", \"positive_value\": \"./images/B0061SL9LO.png\", \"hn_image\": [\"./images/B003XCKD3G.png\"]}\n{\"q_img\": \"./images/B003Z6OPF2.png\", \"q_text\": \"has a neon color, and is yellow with a different graphic\", \"positive_key\": \"B00BARY35A\", \"positive_value\": \"./images/B00BARY35A.png\", \"hn_image\": [\"./images/B003Z6OPF2.png\"]}\n{\"q_img\": \"./images/B00AREPLXK.png\", \"q_text\": \"is black and white with checks, and is lighter\", \"positive_key\": \"B0081K48FY\", \"positive_value\": \"./images/B0081K48FY.png\", \"hn_image\": [\"./images/B00AREPLXK.png\"]}\n{\"q_img\": \"./images/B00449EW9S.png\", \"q_text\": \"has long sleeves, and is grey and green with longer sleeves\", \"positive_key\": \"B0035GKKEM\", \"positive_value\": \"./images/B0035GKKEM.png\", \"hn_image\": [\"./images/B00449EW9S.png\"]}\n{\"q_img\": \"./images/B004ZC2HIC.png\", \"q_text\": \"has longer sleeves and buttons going down, and is a white button down shirt\", \"positive_key\": \"B00BV2SB20\", \"positive_value\": \"./images/B00BV2SB20.png\", \"hn_image\": [\"./images/B004ZC2HIC.png\"]}\n{\"q_img\": \"./images/B004BO6CXA.png\", \"q_text\": \"is grey and white printed shirt, and is gray\", \"positive_key\": \"B003I7CZ8C\", \"positive_value\": \"./images/B003I7CZ8C.png\", \"hn_image\": [\"./images/B004BO6CXA.png\"]}\n{\"q_img\": \"./images/B009G0S8CM.png\", \"q_text\": \"has shorter sleeves and is more patterned, and Has flames and shorter sleeves\", \"positive_key\": \"B005OQ0QDG\", \"positive_value\": \"./images/B005OQ0QDG.png\", \"hn_image\": [\"./images/B009G0S8CM.png\"]}\n{\"q_img\": \"./images/B000BQ09GS.png\", \"q_text\": \"is less graphic, and A half blue on top and red striped on botton half shirt\", \"positive_key\": \"B0046R0BDE\", \"positive_value\": \"./images/B0046R0BDE.png\", \"hn_image\": [\"./images/B000BQ09GS.png\"]}\n{\"q_img\": \"./images/B00BT0JNWQ.png\", \"q_text\": \"Has Led Zeppelin in red letters., and is less colorful\", \"positive_key\": \"B00ADZ1H8G\", \"positive_value\": \"./images/B00ADZ1H8G.png\", \"hn_image\": [\"./images/B00BT0JNWQ.png\"]}\n{\"q_img\": \"./images/B005TGMERW.png\", \"q_text\": \"has a simpler chest logo, and is more faded\", \"positive_key\": \"B002SBOJHY\", \"positive_value\": \"./images/B002SBOJHY.png\", \"hn_image\": [\"./images/B005TGMERW.png\"]}\n{\"q_img\": \"./images/B003FGW8MO.png\", \"q_text\": \"round neck and white, and is an advertisement\", \"positive_key\": \"B00GHN6XLY\", \"positive_value\": \"./images/B00GHN6XLY.png\", \"hn_image\": [\"./images/B003FGW8MO.png\"]}\n{\"q_img\": \"./images/B0029529MG.png\", \"q_text\": \"The shirt is red with gray agray DG emblem., and is red colored and has no collar and buttons\", \"positive_key\": \"B0098ZHBY6\", \"positive_value\": \"./images/B0098ZHBY6.png\", \"hn_image\": [\"./images/B0029529MG.png\"]}\n{\"q_img\": \"./images/B0098GE9GI.png\", \"q_text\": \"is darker and the sleeves are longer, and A brown patterned jacket\", \"positive_key\": \"B00589BQSI\", \"positive_value\": \"./images/B00589BQSI.png\", \"hn_image\": [\"./images/B0098GE9GI.png\"]}\n{\"q_img\": \"./images/B000JM3A1A.png\", \"q_text\": \"is lighter colored with a larger graphic, and is white with only black markings\", \"positive_key\": \"B000QGPYP4\", \"positive_value\": \"./images/B000QGPYP4.png\", \"hn_image\": [\"./images/B000JM3A1A.png\"]}\n{\"q_img\": \"./images/B007V9SM0S.png\", \"q_text\": \" short sleeved and more casual, and is lavender in color\", \"positive_key\": \"B005AONB34\", \"positive_value\": \"./images/B005AONB34.png\", \"hn_image\": [\"./images/B007V9SM0S.png\"]}\n{\"q_img\": \"./images/B00B71D7E2.png\", \"q_text\": \"is darker and has a graphic, and has wu tang on it\", \"positive_key\": \"B00394FK5E\", \"positive_value\": \"./images/B00394FK5E.png\", \"hn_image\": [\"./images/B00B71D7E2.png\"]}\n{\"q_img\": \"./images/B0099AHZXW.png\", \"q_text\": \"is white in color with green and yellow graphic, and is lighter\", \"positive_key\": \"B002TEW3IM\", \"positive_value\": \"./images/B002TEW3IM.png\", \"hn_image\": [\"./images/B0099AHZXW.png\"]}\n{\"q_img\": \"./images/B004SBSY3W.png\", \"q_text\": \"The shirt is black and Gray with green designs., and is a button down black colored\", \"positive_key\": \"B008CS4MZ6\", \"positive_value\": \"./images/B008CS4MZ6.png\", \"hn_image\": [\"./images/B004SBSY3W.png\"]}\n{\"q_img\": \"./images/B00G6UFA94.png\", \"q_text\": \"is red with a star in the middle, and is red with a satanic graphic\", \"positive_key\": \"B0086F6HIK\", \"positive_value\": \"./images/B0086F6HIK.png\", \"hn_image\": [\"./images/B00G6UFA94.png\"]}\n{\"q_img\": \"./images/B005ILK060.png\", \"q_text\": \"is colorful and formal, and is blue and doesn't have a paisley print\", \"positive_key\": \"B007PYE17M\", \"positive_value\": \"./images/B007PYE17M.png\", \"hn_image\": [\"./images/B005ILK060.png\"]}\n{\"q_img\": \"./images/B0014J7Y8W.png\", \"q_text\": \"is a black cat, and is more casual and a head piece\", \"positive_key\": \"B000U0PPXC\", \"positive_value\": \"./images/B000U0PPXC.png\", \"hn_image\": [\"./images/B0014J7Y8W.png\"]}\n{\"q_img\": \"./images/B003T70M3G.png\", \"q_text\": \"is white, and is white and more pop culture\", \"positive_key\": \"B00AOCAELE\", \"positive_value\": \"./images/B00AOCAELE.png\", \"hn_image\": [\"./images/B003T70M3G.png\"]}\n{\"q_img\": \"./images/B007FGYDAK.png\", \"q_text\": \"is a navy blue t shirt, and with thinner stripes\", \"positive_key\": \"B0051VBW7S\", \"positive_value\": \"./images/B0051VBW7S.png\", \"hn_image\": [\"./images/B007FGYDAK.png\"]}\n{\"q_img\": \"./images/B00A9IW94I.png\", \"q_text\": \"is lighter in color, and is grey with black writing\", \"positive_key\": \"B00A9IVY5I\", \"positive_value\": \"./images/B00A9IVY5I.png\", \"hn_image\": [\"./images/B00A9IW94I.png\"]}\n{\"q_img\": \"./images/B0073PSC3M.png\", \"q_text\": \"is black colored with long sleeves, and is black colored\", \"positive_key\": \"B00DEIUVB8\", \"positive_value\": \"./images/B00DEIUVB8.png\", \"hn_image\": [\"./images/B0073PSC3M.png\"]}\n{\"q_img\": \"./images/B001U239PU.png\", \"q_text\": \"is more cartoonish and less strappy, and is a shirt and not a backpack\", \"positive_key\": \"B007JES6SI\", \"positive_value\": \"./images/B007JES6SI.png\", \"hn_image\": [\"./images/B001U239PU.png\"]}\n{\"q_img\": \"./images/B005W44GZO.png\", \"q_text\": \"has a longer sleeve, and is longer sleeved\", \"positive_key\": \"B007VN04BO\", \"positive_value\": \"./images/B007VN04BO.png\", \"hn_image\": [\"./images/B005W44GZO.png\"]}\n{\"q_img\": \"./images/B008588ZPQ.png\", \"q_text\": \"is more comic, and is darker in color\", \"positive_key\": \"B00FPBE0X2\", \"positive_value\": \"./images/B00FPBE0X2.png\", \"hn_image\": [\"./images/B008588ZPQ.png\"]}\n{\"q_img\": \"./images/B004APGLNG.png\", \"q_text\": \"is darker colored, and Red with all black lettering\", \"positive_key\": \"B00BLL79L0\", \"positive_value\": \"./images/B00BLL79L0.png\", \"hn_image\": [\"./images/B004APGLNG.png\"]}\n{\"q_img\": \"./images/B0051GDT4M.png\", \"q_text\": \"Is more white and has shorter sleeves, and is a white short sleeved polo\", \"positive_key\": \"B0027EBMU4\", \"positive_value\": \"./images/B0027EBMU4.png\", \"hn_image\": [\"./images/B0051GDT4M.png\"]}\n{\"q_img\": \"./images/B00926S118.png\", \"q_text\": \"is a shirt with collar, and is a light blue button up\", \"positive_key\": \"B008H6R3UY\", \"positive_value\": \"./images/B008H6R3UY.png\", \"hn_image\": [\"./images/B00926S118.png\"]}\n{\"q_img\": \"./images/B00B88ZKXA.png\", \"q_text\": \"is a lighter color with a cat graphic, and Is lighter and more graphic.\", \"positive_key\": \"B00EZUKCCM\", \"positive_value\": \"./images/B00EZUKCCM.png\", \"hn_image\": [\"./images/B00B88ZKXA.png\"]}\n{\"q_img\": \"./images/B00923GT8S.png\", \"q_text\": \"has different wording, and has a different logo\", \"positive_key\": \"B00844FELO\", \"positive_value\": \"./images/B00844FELO.png\", \"hn_image\": [\"./images/B00923GT8S.png\"]}\n{\"q_img\": \"./images/B0045KCG7Q.png\", \"q_text\": \"is a black T shirt with a blonde girl on it, and is a black tshirt with falling in reverse\", \"positive_key\": \"B006JPU6JA\", \"positive_value\": \"./images/B006JPU6JA.png\", \"hn_image\": [\"./images/B0045KCG7Q.png\"]}\n{\"q_img\": \"./images/B00461228C.png\", \"q_text\": \"is a white tank-top, and is white colored\", \"positive_key\": \"B00C5RAHMW\", \"positive_value\": \"./images/B00C5RAHMW.png\", \"hn_image\": [\"./images/B00461228C.png\"]}\n{\"q_img\": \"./images/B0093OQBCK.png\", \"q_text\": \"has a different graphic, and is camouflaged and wider\", \"positive_key\": \"B0071BYF0M\", \"positive_value\": \"./images/B0071BYF0M.png\", \"hn_image\": [\"./images/B0093OQBCK.png\"]}\n{\"q_img\": \"./images/B005344CDE.png\", \"q_text\": \"Has a more faded print, and has a less vibrant white color\", \"positive_key\": \"B000HRTB60\", \"positive_value\": \"./images/B000HRTB60.png\", \"hn_image\": [\"./images/B005344CDE.png\"]}\n{\"q_img\": \"./images/B00BRB2H8O.png\", \"q_text\": \"doesn't have a color, and has no collar and has a blue and white logo\", \"positive_key\": \"B00BRB29YQ\", \"positive_value\": \"./images/B00BRB29YQ.png\", \"hn_image\": [\"./images/B00BRB2H8O.png\"]}\n{\"q_img\": \"./images/B000XK9KKI.png\", \"q_text\": \"has more lettering and is less oval, and A black shirt with white words and face on front\", \"positive_key\": \"B005IR27EC\", \"positive_value\": \"./images/B005IR27EC.png\", \"hn_image\": [\"./images/B000XK9KKI.png\"]}\n{\"q_img\": \"./images/B004CO39EO.png\", \"q_text\": \"is white, and is a lighter and brighter color\", \"positive_key\": \"B006Z8HHGQ\", \"positive_value\": \"./images/B006Z8HHGQ.png\", \"hn_image\": [\"./images/B004CO39EO.png\"]}\n{\"q_img\": \"./images/B0067OAL7U.png\", \"q_text\": \"is black colored with a v-neck, and white lettering\", \"positive_key\": \"B00C68VOYA\", \"positive_value\": \"./images/B00C68VOYA.png\", \"hn_image\": [\"./images/B0067OAL7U.png\"]}\n{\"q_img\": \"./images/B000TYT49U.png\", \"q_text\": \"has buttons, and is sleeveless and buttons up\", \"positive_key\": \"B007IRLCPU\", \"positive_value\": \"./images/B007IRLCPU.png\", \"hn_image\": [\"./images/B000TYT49U.png\"]}\n{\"q_img\": \"./images/B001QL8NVA.png\", \"q_text\": \" white, and is black with a brighter graphic\", \"positive_key\": \"B008Z398FC\", \"positive_value\": \"./images/B008Z398FC.png\", \"hn_image\": [\"./images/B001QL8NVA.png\"]}\n{\"q_img\": \"./images/B007SH2NT4.png\", \"q_text\": \"has no creepy creature, and is darker and more scary\", \"positive_key\": \"B004KF9LM4\", \"positive_value\": \"./images/B004KF9LM4.png\", \"hn_image\": [\"./images/B007SH2NT4.png\"]}\n{\"q_img\": \"./images/B000QGPYP4.png\", \"q_text\": \"is brown with no image, and Is brown with tiny words\", \"positive_key\": \"B0083Z6QDO\", \"positive_value\": \"./images/B0083Z6QDO.png\", \"hn_image\": [\"./images/B000QGPYP4.png\"]}\n{\"q_img\": \"./images/B0072D8DD4.png\", \"q_text\": \"is blue with sea turtles, and Desired item is dark blue with sea creatures pictured\", \"positive_key\": \"B004LTR2XY\", \"positive_value\": \"./images/B004LTR2XY.png\", \"hn_image\": [\"./images/B0072D8DD4.png\"]}\n{\"q_img\": \"./images/B0012NBP4O.png\", \"q_text\": \"has longer sleeves., and is green and has long sleeves\", \"positive_key\": \"B0051IRDJC\", \"positive_value\": \"./images/B0051IRDJC.png\", \"hn_image\": [\"./images/B0012NBP4O.png\"]}\n{\"q_img\": \"./images/B0041IY8I2.png\", \"q_text\": \"is lighter, and is more colorful\", \"positive_key\": \"B007TW76EU\", \"positive_value\": \"./images/B007TW76EU.png\", \"hn_image\": [\"./images/B0041IY8I2.png\"]}\n{\"q_img\": \"./images/B005BLUUJY.png\", \"q_text\": \"is a gray tank top, and has less sleeves\", \"positive_key\": \"B00305G9I4\", \"positive_value\": \"./images/B00305G9I4.png\", \"hn_image\": [\"./images/B005BLUUJY.png\"]}\n{\"q_img\": \"./images/B008K644U8.png\", \"q_text\": \"is green plaid, and is black and grey checked with a navy tie\", \"positive_key\": \"B008K66VG8\", \"positive_value\": \"./images/B008K66VG8.png\", \"hn_image\": [\"./images/B008K644U8.png\"]}\n{\"q_img\": \"./images/B002PY6O0E.png\", \"q_text\": \"has plain pattern, and is black with small logo\", \"positive_key\": \"B000W34L2I\", \"positive_value\": \"./images/B000W34L2I.png\", \"hn_image\": [\"./images/B002PY6O0E.png\"]}\n{\"q_img\": \"./images/B000W7YJP8.png\", \"q_text\": \"have dark color short sleeves, and Has shorter sleeves and is more brown.\", \"positive_key\": \"B0096F6D6U\", \"positive_value\": \"./images/B0096F6D6U.png\", \"hn_image\": [\"./images/B000W7YJP8.png\"]}\n{\"q_img\": \"./images/B00CWVIPQ6.png\", \"q_text\": \"The shirt is black with white writing., and A black shirt with words JERSEY STRONG on front\", \"positive_key\": \"B00AFC4VDA\", \"positive_value\": \"./images/B00AFC4VDA.png\", \"hn_image\": [\"./images/B00CWVIPQ6.png\"]}\n{\"q_img\": \"./images/B003EKOBTY.png\", \"q_text\": \"has longer sleeves and is black, and is darker and has longer sleeves\", \"positive_key\": \"B001KUGM6U\", \"positive_value\": \"./images/B001KUGM6U.png\", \"hn_image\": [\"./images/B003EKOBTY.png\"]}\n{\"q_img\": \"./images/B004WU17MY.png\", \"q_text\": \"is a black T shirt with Anime on it, and has shorter sleeves\", \"positive_key\": \"B0046CZBPM\", \"positive_value\": \"./images/B0046CZBPM.png\", \"hn_image\": [\"./images/B004WU17MY.png\"]}\n{\"q_img\": \"./images/B006N1KIHK.png\", \"q_text\": \"is beige with a collar, and is lighter in color\", \"positive_key\": \"B006N1KCB2\", \"positive_value\": \"./images/B006N1KCB2.png\", \"hn_image\": [\"./images/B006N1KIHK.png\"]}\n{\"q_img\": \"./images/B007M68RR8.png\", \"q_text\": \"has a brighter color and art, and has logo and light yellow color\", \"positive_key\": \"B009ELWRYS\", \"positive_value\": \"./images/B009ELWRYS.png\", \"hn_image\": [\"./images/B007M68RR8.png\"]}\n{\"q_img\": \"./images/B000VSHLSK.png\", \"q_text\": \"Is white with long sleeves., and is grey with long sleeves\", \"positive_key\": \"B005588YAA\", \"positive_value\": \"./images/B005588YAA.png\", \"hn_image\": [\"./images/B000VSHLSK.png\"]}\n{\"q_img\": \"./images/B0033EFCIK.png\", \"q_text\": \"has stripes, and is brown and white striped\", \"positive_key\": \"B004FTPTR6\", \"positive_value\": \"./images/B004FTPTR6.png\", \"hn_image\": [\"./images/B0033EFCIK.png\"]}\n{\"q_img\": \"./images/B005XIGCYW.png\", \"q_text\": \"is more black with less graphics, and it is black and has a simple print\", \"positive_key\": \"B009Q7CK8I\", \"positive_value\": \"./images/B009Q7CK8I.png\", \"hn_image\": [\"./images/B005XIGCYW.png\"]}\n{\"q_img\": \"./images/B00844FELO.png\", \"q_text\": \"is light blue with white words, and is a lighter color and a different state logo\", \"positive_key\": \"B00CH1JZ92\", \"positive_value\": \"./images/B00CH1JZ92.png\", \"hn_image\": [\"./images/B00844FELO.png\"]}\n{\"q_img\": \"./images/B007TM9B70.png\", \"q_text\": \"longer sleeves and blue, and is long sleeved and solid blue\", \"positive_key\": \"B009G0S8CM\", \"positive_value\": \"./images/B009G0S8CM.png\", \"hn_image\": [\"./images/B007TM9B70.png\"]}\n{\"q_img\": \"./images/B000W8UAR8.png\", \"q_text\": \"is brown, and is lighter\", \"positive_key\": \"B00B4YHCJS\", \"positive_value\": \"./images/B00B4YHCJS.png\", \"hn_image\": [\"./images/B000W8UAR8.png\"]}\n{\"q_img\": \"./images/B009L80RS2.png\", \"q_text\": \"is bright with short sleeves, and has short sleeves\", \"positive_key\": \"B0089HEOHG\", \"positive_value\": \"./images/B0089HEOHG.png\", \"hn_image\": [\"./images/B009L80RS2.png\"]}\n{\"q_img\": \"./images/B005JQ5YBU.png\", \"q_text\": \"is darker with two buttons, and has a color and top buttons\", \"positive_key\": \"B0013HD6MS\", \"positive_value\": \"./images/B0013HD6MS.png\", \"hn_image\": [\"./images/B005JQ5YBU.png\"]}\n{\"q_img\": \"./images/B0032HJXKG.png\", \"q_text\": \"is lighter colored and has checkered pattern, and and has a different graphic\", \"positive_key\": \"B0043DWIR8\", \"positive_value\": \"./images/B0043DWIR8.png\", \"hn_image\": [\"./images/B0032HJXKG.png\"]}\n{\"q_img\": \"./images/B00CZ6OWA6.png\", \"q_text\": \"is more darker, and is solid gray and has a smaller plaid pattern\", \"positive_key\": \"B00CYJT2VS\", \"positive_value\": \"./images/B00CYJT2VS.png\", \"hn_image\": [\"./images/B00CZ6OWA6.png\"]}\n{\"q_img\": \"./images/B00A81ZUWO.png\", \"q_text\": \"is a white shirt with camouflage sleeves, and A white with army print on sleeves and pocket\", \"positive_key\": \"B00DD63R4E\", \"positive_value\": \"./images/B00DD63R4E.png\", \"hn_image\": [\"./images/B00A81ZUWO.png\"]}\n{\"q_img\": \"./images/B0094KCP96.png\", \"q_text\": \"is brown with HEY written on it, and has more maroon\", \"positive_key\": \"B009YQF6B4\", \"positive_value\": \"./images/B009YQF6B4.png\", \"hn_image\": [\"./images/B0094KCP96.png\"]}\n{\"q_img\": \"./images/B00BQ71QVI.png\", \"q_text\": \"is white with no stripes, and is more solid colored\", \"positive_key\": \"B00ANLQM44\", \"positive_value\": \"./images/B00ANLQM44.png\", \"hn_image\": [\"./images/B00BQ71QVI.png\"]}\n{\"q_img\": \"./images/B00B80RFOA.png\", \"q_text\": \"has a smaller, and does not have a picture but text\", \"positive_key\": \"B008DBTWIY\", \"positive_value\": \"./images/B008DBTWIY.png\", \"hn_image\": [\"./images/B00B80RFOA.png\"]}\n{\"q_img\": \"./images/B007ZNLENM.png\", \"q_text\": \"is smaller in size, and is longer and more colorful\", \"positive_key\": \"B00ARJ65AI\", \"positive_value\": \"./images/B00ARJ65AI.png\", \"hn_image\": [\"./images/B007ZNLENM.png\"]}\n{\"q_img\": \"./images/B0013FJWT6.png\", \"q_text\": \"has longer sleeves and is a different blue, and has long sleeves and is a brighter color\", \"positive_key\": \"B00A6JE1C8\", \"positive_value\": \"./images/B00A6JE1C8.png\", \"hn_image\": [\"./images/B0013FJWT6.png\"]}\n{\"q_img\": \"./images/B0048KUVYS.png\", \"q_text\": \"has square images in center, and is black with gray log\", \"positive_key\": \"B002PY6O0E\", \"positive_value\": \"./images/B002PY6O0E.png\", \"hn_image\": [\"./images/B0048KUVYS.png\"]}\n{\"q_img\": \"./images/B004AM0XUQ.png\", \"q_text\": \"is red and bolder, and is darker in color and has a larger design\", \"positive_key\": \"B00BY87U8W\", \"positive_value\": \"./images/B00BY87U8W.png\", \"hn_image\": [\"./images/B004AM0XUQ.png\"]}\n{\"q_img\": \"./images/B005ILK060.png\", \"q_text\": \"is pure white, and is white and has a pocket\", \"positive_key\": \"B007AEUC98\", \"positive_value\": \"./images/B007AEUC98.png\", \"hn_image\": [\"./images/B005ILK060.png\"]}\n{\"q_img\": \"./images/B002TUTMCG.png\", \"q_text\": \"is grey with a different picture, and is of a lighter color\", \"positive_key\": \"B008UXAEF0\", \"positive_value\": \"./images/B008UXAEF0.png\", \"hn_image\": [\"./images/B002TUTMCG.png\"]}\n{\"q_img\": \"./images/B001KQ3YDS.png\", \"q_text\": \"The shirt is gray in color with white writing., and is faded\", \"positive_key\": \"B00EIKWHN6\", \"positive_value\": \"./images/B00EIKWHN6.png\", \"hn_image\": [\"./images/B001KQ3YDS.png\"]}\n{\"q_img\": \"./images/B007Z8W47C.png\", \"q_text\": \"is a police t shirt, and is a lighter shade of blue\", \"positive_key\": \"B0077RZYKA\", \"positive_value\": \"./images/B0077RZYKA.png\", \"hn_image\": [\"./images/B007Z8W47C.png\"]}\n{\"q_img\": \"./images/B003H3B9ZM.png\", \"q_text\": \"has different graphics, and has brighter colors in it\", \"positive_key\": \"B00CTSBLU4\", \"positive_value\": \"./images/B00CTSBLU4.png\", \"hn_image\": [\"./images/B003H3B9ZM.png\"]}\n{\"q_img\": \"./images/B000MX1M98.png\", \"q_text\": \"is red in color and plain pattern, and is red\", \"positive_key\": \"B002DYJC3M\", \"positive_value\": \"./images/B002DYJC3M.png\", \"hn_image\": [\"./images/B000MX1M98.png\"]}\n{\"q_img\": \"./images/B005AJ1Q7M.png\", \"q_text\": \"is darker, and is darker in color\", \"positive_key\": \"B00AEMT5Z0\", \"positive_value\": \"./images/B00AEMT5Z0.png\", \"hn_image\": [\"./images/B005AJ1Q7M.png\"]}\n{\"q_img\": \"./images/B00BOHU9ZE.png\", \"q_text\": \"is green with a centered image, and has a smaller logo\", \"positive_key\": \"B00AFZ38RM\", \"positive_value\": \"./images/B00AFZ38RM.png\", \"hn_image\": [\"./images/B00BOHU9ZE.png\"]}\n{\"q_img\": \"./images/B00B6E7IHW.png\", \"q_text\": \"is more gray and plain, and is grey and is more loose fitted\", \"positive_key\": \"B0011RLPW8\", \"positive_value\": \"./images/B0011RLPW8.png\", \"hn_image\": [\"./images/B00B6E7IHW.png\"]}\n{\"q_img\": \"./images/B00166XERS.png\", \"q_text\": \"is blue with black writing, and is less formal\", \"positive_key\": \"B00GXF7CCK\", \"positive_value\": \"./images/B00GXF7CCK.png\", \"hn_image\": [\"./images/B00166XERS.png\"]}\n{\"q_img\": \"./images/B008C72B7S.png\", \"q_text\": \"Is less shiny and wider, and is brighter\", \"positive_key\": \"B00CWJ5K16\", \"positive_value\": \"./images/B00CWJ5K16.png\", \"hn_image\": [\"./images/B008C72B7S.png\"]}\n{\"q_img\": \"./images/B009NBSZOU.png\", \"q_text\": \"Is more striped and less pocketed, and has longer sleeves\", \"positive_key\": \"B00AOJHF8M\", \"positive_value\": \"./images/B00AOJHF8M.png\", \"hn_image\": [\"./images/B009NBSZOU.png\"]}\n{\"q_img\": \"./images/B007SJE6RE.png\", \"q_text\": \"a black tee with a movie logo, and has black and yellow colors\", \"positive_key\": \"B000XK9KKI\", \"positive_value\": \"./images/B000XK9KKI.png\", \"hn_image\": [\"./images/B007SJE6RE.png\"]}\n{\"q_img\": \"./images/B00DE1BDLC.png\", \"q_text\": \"Is blue with a red letter N on front, and A black shirt with a red N on front\", \"positive_key\": \"B0030BG78U\", \"positive_value\": \"./images/B0030BG78U.png\", \"hn_image\": [\"./images/B00DE1BDLC.png\"]}\n{\"q_img\": \"./images/B004TNLWVA.png\", \"q_text\": \"is dark in color with visual graphics on it., and is blue with a skeleton design\", \"positive_key\": \"B004LTL34S\", \"positive_value\": \"./images/B004LTL34S.png\", \"hn_image\": [\"./images/B004TNLWVA.png\"]}\n{\"q_img\": \"./images/B004K2NSGM.png\", \"q_text\": \"is gray and has a flag in the picture, and is darker\", \"positive_key\": \"B00B0R0JZ8\", \"positive_value\": \"./images/B00B0R0JZ8.png\", \"hn_image\": [\"./images/B004K2NSGM.png\"]}\n{\"q_img\": \"./images/B006H4KOTA.png\", \"q_text\": \"is white and has a different logo, and is black with crew neck\", \"positive_key\": \"B00EHYJ2EU\", \"positive_value\": \"./images/B00EHYJ2EU.png\", \"hn_image\": [\"./images/B006H4KOTA.png\"]}\n{\"q_img\": \"./images/B00AY2FULS.png\", \"q_text\": \"is black and has a meme graphic, and is black with a white face pattern\", \"positive_key\": \"B0075G2YFG\", \"positive_value\": \"./images/B0075G2YFG.png\", \"hn_image\": [\"./images/B00AY2FULS.png\"]}\n{\"q_img\": \"./images/B000EGBBTE.png\", \"q_text\": \"Has a darker print on front., and is much brighter in color\", \"positive_key\": \"B00C7QLO00\", \"positive_value\": \"./images/B00C7QLO00.png\", \"hn_image\": [\"./images/B000EGBBTE.png\"]}\n{\"q_img\": \"./images/B003INE0Q6.png\", \"q_text\": \"This is a solid color and short sleeves., and buttons\", \"positive_key\": \"B00BQ4IATW\", \"positive_value\": \"./images/B00BQ4IATW.png\", \"hn_image\": [\"./images/B003INE0Q6.png\"]}\n{\"q_img\": \"./images/B00563EGWE.png\", \"q_text\": \"Is more girly and bright, and is a pink hat and not a shirt\", \"positive_key\": \"B007KREYFI\", \"positive_value\": \"./images/B007KREYFI.png\", \"hn_image\": [\"./images/B00563EGWE.png\"]}\n{\"q_img\": \"./images/B008MATL6Y.png\", \"q_text\": \"is black with blue and pink designs, and has three colors to it\", \"positive_key\": \"B004VSEBNY\", \"positive_value\": \"./images/B004VSEBNY.png\", \"hn_image\": [\"./images/B008MATL6Y.png\"]}\n{\"q_img\": \"./images/B006ZZHEU8.png\", \"q_text\": \"has longer sleeves with stripes, and has longer sleeves\", \"positive_key\": \"B00AOI54L8\", \"positive_value\": \"./images/B00AOI54L8.png\", \"hn_image\": [\"./images/B006ZZHEU8.png\"]}\n{\"q_img\": \"./images/B00BSN9X6K.png\", \"q_text\": \"Is dark blue in color., and Is more simple.\", \"positive_key\": \"B00EBUFSAW\", \"positive_value\": \"./images/B00EBUFSAW.png\", \"hn_image\": [\"./images/B00BSN9X6K.png\"]}\n{\"q_img\": \"./images/B005HJPL0I.png\", \"q_text\": \"has longer sleeves, and is lighter and has longer sleeves\", \"positive_key\": \"B00CDRJ54A\", \"positive_value\": \"./images/B00CDRJ54A.png\", \"hn_image\": [\"./images/B005HJPL0I.png\"]}\n{\"q_img\": \"./images/B004CVDPAA.png\", \"q_text\": \"has a dog print on it, and Has a larger graphic\", \"positive_key\": \"B00AYXZ7S8\", \"positive_value\": \"./images/B00AYXZ7S8.png\", \"hn_image\": [\"./images/B004CVDPAA.png\"]}\n{\"q_img\": \"./images/B004GIZYTE.png\", \"q_text\": \"Shorter sleeves and blue, and has shorter sleeves\", \"positive_key\": \"B004JZWH0I\", \"positive_value\": \"./images/B004JZWH0I.png\", \"hn_image\": [\"./images/B004GIZYTE.png\"]}\n{\"q_img\": \"./images/B00BYQH3D6.png\", \"q_text\": \"is a loose fitting t shirt, and is blue and is more animated\", \"positive_key\": \"B0081QK3A2\", \"positive_value\": \"./images/B0081QK3A2.png\", \"hn_image\": [\"./images/B00BYQH3D6.png\"]}\n{\"q_img\": \"./images/B005ESA9SG.png\", \"q_text\": \"has a solid color., and is darker and has no stripes\", \"positive_key\": \"B0068ETQWA\", \"positive_value\": \"./images/B0068ETQWA.png\", \"hn_image\": [\"./images/B005ESA9SG.png\"]}\n{\"q_img\": \"./images/B001U9DJ0I.png\", \"q_text\": \"is darker and has a larger graphic, and has bolder graphics and less humorous\", \"positive_key\": \"B000WK6SDQ\", \"positive_value\": \"./images/B000WK6SDQ.png\", \"hn_image\": [\"./images/B001U9DJ0I.png\"]}\n{\"q_img\": \"./images/B00AOJE63E.png\", \"q_text\": \" and dark green, and is darker in color\", \"positive_key\": \"B00138MPS8\", \"positive_value\": \"./images/B00138MPS8.png\", \"hn_image\": [\"./images/B00AOJE63E.png\"]}\n{\"q_img\": \"./images/B005DIJSPM.png\", \"q_text\": \"is blue checked, and is bright blue plaid design\", \"positive_key\": \"B00A8D13CI\", \"positive_value\": \"./images/B00A8D13CI.png\", \"hn_image\": [\"./images/B005DIJSPM.png\"]}\n{\"q_img\": \"./images/B006SHCSEU.png\", \"q_text\": \"is blue t-shirt-style with arge logo graphic, and is less formal\", \"positive_key\": \"B007Y8L4VK\", \"positive_value\": \"./images/B007Y8L4VK.png\", \"hn_image\": [\"./images/B006SHCSEU.png\"]}\n{\"q_img\": \"./images/B005FYKZ8I.png\", \"q_text\": \"is large and blue, and is light blue with different logo\", \"positive_key\": \"B005OCJOEC\", \"positive_value\": \"./images/B005OCJOEC.png\", \"hn_image\": [\"./images/B005FYKZ8I.png\"]}\n{\"q_img\": \"./images/B004Y9NQ1S.png\", \"q_text\": \"Is more blue and pocketed, and is lighter\", \"positive_key\": \"B009EUI4G4\", \"positive_value\": \"./images/B009EUI4G4.png\", \"hn_image\": [\"./images/B004Y9NQ1S.png\"]}\n{\"q_img\": \"./images/B008MO9EY4.png\", \"q_text\": \"has longer sleeves and is grey, and is lighter and has longer sleeves\", \"positive_key\": \"B00EPDU2PG\", \"positive_value\": \"./images/B00EPDU2PG.png\", \"hn_image\": [\"./images/B008MO9EY4.png\"]}\n{\"q_img\": \"./images/B0024FAZ4A.png\", \"q_text\": \"has an astronaut on it, and has a different graphic\", \"positive_key\": \"B0044R8CE6\", \"positive_value\": \"./images/B0044R8CE6.png\", \"hn_image\": [\"./images/B0024FAZ4A.png\"]}\n{\"q_img\": \"./images/B002XGG0AS.png\", \"q_text\": \"has longer sleeves and white center with print, and has longer sleeves and a round logo\", \"positive_key\": \"B001HYRKS8\", \"positive_value\": \"./images/B001HYRKS8.png\", \"hn_image\": [\"./images/B002XGG0AS.png\"]}\n{\"q_img\": \"./images/B000Z3V2JA.png\", \"q_text\": \"green, and is green and less animal-like\", \"positive_key\": \"B003X46ZKO\", \"positive_value\": \"./images/B003X46ZKO.png\", \"hn_image\": [\"./images/B000Z3V2JA.png\"]}\n{\"q_img\": \"./images/B0048KPWFG.png\", \"q_text\": \"Is light grey with a black pattern, and is darker\", \"positive_key\": \"B00D6KCEI2\", \"positive_value\": \"./images/B00D6KCEI2.png\", \"hn_image\": [\"./images/B0048KPWFG.png\"]}\n{\"q_img\": \"./images/B0069IQPPQ.png\", \"q_text\": \"is red, and is red\", \"positive_key\": \"B00028I7R8\", \"positive_value\": \"./images/B00028I7R8.png\", \"hn_image\": [\"./images/B0069IQPPQ.png\"]}\n{\"q_img\": \"./images/B0020Q11LE.png\", \"q_text\": \"has writing on the t-shirt, and is gray with black lettering\", \"positive_key\": \"B000WB6WTU\", \"positive_value\": \"./images/B000WB6WTU.png\", \"hn_image\": [\"./images/B0020Q11LE.png\"]}\n{\"q_img\": \"./images/B002HNSH80.png\", \"q_text\": \"has a different graphic, and has a Volkswagen saying on front\", \"positive_key\": \"B0072Y3CWK\", \"positive_value\": \"./images/B0072Y3CWK.png\", \"hn_image\": [\"./images/B002HNSH80.png\"]}\n{\"q_img\": \"./images/B00AZWLNV8.png\", \"q_text\": \"has a v shaped neckline, and is darker\", \"positive_key\": \"B005KGF70M\", \"positive_value\": \"./images/B005KGF70M.png\", \"hn_image\": [\"./images/B00AZWLNV8.png\"]}\n{\"q_img\": \"./images/B0075BECBK.png\", \"q_text\": \"light gray t shirt with honey badger, and is darker\", \"positive_key\": \"B007CM1XC8\", \"positive_value\": \"./images/B007CM1XC8.png\", \"hn_image\": [\"./images/B0075BECBK.png\"]}\n{\"q_img\": \"./images/B005588YAA.png\", \"q_text\": \"Is dark blue., and is darker in color.\", \"positive_key\": \"B0002FHIMQ\", \"positive_value\": \"./images/B0002FHIMQ.png\", \"hn_image\": [\"./images/B005588YAA.png\"]}\n{\"q_img\": \"./images/B00C9T317O.png\", \"q_text\": \"is gray and black, and Black and gray with button neck and large front print\", \"positive_key\": \"B00BFA20GG\", \"positive_value\": \"./images/B00BFA20GG.png\", \"hn_image\": [\"./images/B00C9T317O.png\"]}\n{\"q_img\": \"./images/B004ZC5RL6.png\", \"q_text\": \"is a lighter color, and is brighter in color\", \"positive_key\": \"B004ZC5PXG\", \"positive_value\": \"./images/B004ZC5PXG.png\", \"hn_image\": [\"./images/B004ZC5RL6.png\"]}\n{\"q_img\": \"./images/B007VYS9I8.png\", \"q_text\": \"Is green, and is green\", \"positive_key\": \"B000N9FHLU\", \"positive_value\": \"./images/B000N9FHLU.png\", \"hn_image\": [\"./images/B007VYS9I8.png\"]}\n{\"q_img\": \"./images/B0089ODSU8.png\", \"q_text\": \"has green, and has more colorful graphics\", \"positive_key\": \"B0086YNVVM\", \"positive_value\": \"./images/B0086YNVVM.png\", \"hn_image\": [\"./images/B0089ODSU8.png\"]}\n{\"q_img\": \"./images/B006KNW59K.png\", \"q_text\": \"is black, and Has darker shades and devil graphic\", \"positive_key\": \"B003H3B9ZM\", \"positive_value\": \"./images/B003H3B9ZM.png\", \"hn_image\": [\"./images/B006KNW59K.png\"]}\n{\"q_img\": \"./images/B00BWTZIC8.png\", \"q_text\": \"is dark colored with an image, and is black and has letter logo\", \"positive_key\": \"B008QPAUAG\", \"positive_value\": \"./images/B008QPAUAG.png\", \"hn_image\": [\"./images/B00BWTZIC8.png\"]}\n{\"q_img\": \"./images/B00B9DYVZC.png\", \"q_text\": \"is black with white designs, and is black with grey and white logo\", \"positive_key\": \"B005LXFXIA\", \"positive_value\": \"./images/B005LXFXIA.png\", \"hn_image\": [\"./images/B00B9DYVZC.png\"]}\n{\"q_img\": \"./images/B005JQ4WEK.png\", \"q_text\": \"Is a darker color and short sleeves., and is a black button down\", \"positive_key\": \"B0013HB3KK\", \"positive_value\": \"./images/B0013HB3KK.png\", \"hn_image\": [\"./images/B005JQ4WEK.png\"]}\n{\"q_img\": \"./images/B0064SJ6EI.png\", \"q_text\": \"is brown with smaller graphic words, and is darker\", \"positive_key\": \"B00164ITHU\", \"positive_value\": \"./images/B00164ITHU.png\", \"hn_image\": [\"./images/B0064SJ6EI.png\"]}\n{\"q_img\": \"./images/B000GA5YPK.png\", \"q_text\": \"is sleeveless and black in color, and is sleeveless and is black\", \"positive_key\": \"B00075ZVH0\", \"positive_value\": \"./images/B00075ZVH0.png\", \"hn_image\": [\"./images/B000GA5YPK.png\"]}\n{\"q_img\": \"./images/B004FN2OYI.png\", \"q_text\": \"has lighter lettering, and It is more black\", \"positive_key\": \"B005XIFIRO\", \"positive_value\": \"./images/B005XIFIRO.png\", \"hn_image\": [\"./images/B004FN2OYI.png\"]}\n{\"q_img\": \"./images/B0098GE6OI.png\", \"q_text\": \"has a different logo, and has baggier sleeves\", \"positive_key\": \"B003XF2ULG\", \"positive_value\": \"./images/B003XF2ULG.png\", \"hn_image\": [\"./images/B0098GE6OI.png\"]}\n{\"q_img\": \"./images/B00BCXL3QE.png\", \"q_text\": \"is darker in color, and is black with slipknot\", \"positive_key\": \"B004YJVIQS\", \"positive_value\": \"./images/B004YJVIQS.png\", \"hn_image\": [\"./images/B00BCXL3QE.png\"]}\n{\"q_img\": \"./images/B00DX7P1IS.png\", \"q_text\": \"is dark blue colored and plain pattern, and is more simple and darker in colour\", \"positive_key\": \"B004GSOVXO\", \"positive_value\": \"./images/B004GSOVXO.png\", \"hn_image\": [\"./images/B00DX7P1IS.png\"]}\n{\"q_img\": \"./images/B00AOJG07E.png\", \"q_text\": \"is similar but with chest pockets on both sides, and has longer sleeves\", \"positive_key\": \"B009LIZU52\", \"positive_value\": \"./images/B009LIZU52.png\", \"hn_image\": [\"./images/B00AOJG07E.png\"]}\n{\"q_img\": \"./images/B005ISINDU.png\", \"q_text\": \"is red with black designs, and is longer\", \"positive_key\": \"B005LU36LY\", \"positive_value\": \"./images/B005LU36LY.png\", \"hn_image\": [\"./images/B005ISINDU.png\"]}\n{\"q_img\": \"./images/B006JATIK8.png\", \"q_text\": \"is gray with figures walking, and is gray with charlie brown\", \"positive_key\": \"B00BC41R46\", \"positive_value\": \"./images/B00BC41R46.png\", \"hn_image\": [\"./images/B006JATIK8.png\"]}\n{\"q_img\": \"./images/B006RI34ZC.png\", \"q_text\": \"is red with yellow designs, and is lighter\", \"positive_key\": \"B00DR4IZTE\", \"positive_value\": \"./images/B00DR4IZTE.png\", \"hn_image\": [\"./images/B006RI34ZC.png\"]}\n{\"q_img\": \"./images/B0084UUHB0.png\", \"q_text\": \"has a more intricate graphic, and is moe emo\", \"positive_key\": \"B009LJCL8A\", \"positive_value\": \"./images/B009LJCL8A.png\", \"hn_image\": [\"./images/B0084UUHB0.png\"]}\n{\"q_img\": \"./images/B004OKFF7K.png\", \"q_text\": \"is lighter colored and has a hairier dog, and has a lighter color\", \"positive_key\": \"B009ZYPCQO\", \"positive_value\": \"./images/B009ZYPCQO.png\", \"hn_image\": [\"./images/B004OKFF7K.png\"]}\n{\"q_img\": \"./images/B0012GILRK.png\", \"q_text\": \"has a gray and black design, and is a tye dye shirt\", \"positive_key\": \"B005YUZ678\", \"positive_value\": \"./images/B005YUZ678.png\", \"hn_image\": [\"./images/B0012GILRK.png\"]}\n{\"q_img\": \"./images/B005B6ZIE6.png\", \"q_text\": \"Is more fitted and has no buttons, and Black with one big gray stripes across belly\", \"positive_key\": \"B00ACDAOGU\", \"positive_value\": \"./images/B00ACDAOGU.png\", \"hn_image\": [\"./images/B005B6ZIE6.png\"]}\n{\"q_img\": \"./images/B007WMC41C.png\", \"q_text\": \"is black and plain, and is more black\", \"positive_key\": \"B00BXRE5UU\", \"positive_value\": \"./images/B00BXRE5UU.png\", \"hn_image\": [\"./images/B007WMC41C.png\"]}\n{\"q_img\": \"./images/B001OQYE24.png\", \"q_text\": \"The shirt is long sleeve and blue in color., and longer sleeves on it\", \"positive_key\": \"B0031TVNNK\", \"positive_value\": \"./images/B0031TVNNK.png\", \"hn_image\": [\"./images/B001OQYE24.png\"]}\n{\"q_img\": \"./images/B000783TCQ.png\", \"q_text\": \"is a long sleeved button up light plaid, and Black t-shirt with skeleton showing muscles and organs\", \"positive_key\": \"B007IRL1VA\", \"positive_value\": \"./images/B007IRL1VA.png\", \"hn_image\": [\"./images/B000783TCQ.png\"]}\n{\"q_img\": \"./images/B007CBS7OG.png\", \"q_text\": \"is blue in color, and is a solid green color\", \"positive_key\": \"B004SWYOZI\", \"positive_value\": \"./images/B004SWYOZI.png\", \"hn_image\": [\"./images/B007CBS7OG.png\"]}\n{\"q_img\": \"./images/B00APU4KE2.png\", \"q_text\": \"has short sleeves and a logo, and has shorter leeves\", \"positive_key\": \"B006AN59CA\", \"positive_value\": \"./images/B006AN59CA.png\", \"hn_image\": [\"./images/B00APU4KE2.png\"]}\n{\"q_img\": \"./images/B000X74R7C.png\", \"q_text\": \"Black and has a video game character, and is darker\", \"positive_key\": \"B003RW8XFW\", \"positive_value\": \"./images/B003RW8XFW.png\", \"hn_image\": [\"./images/B000X74R7C.png\"]}\n{\"q_img\": \"./images/B007T5JNJ8.png\", \"q_text\": \"is darker, and has longer sleeves\", \"positive_key\": \"B00GQWTJ6C\", \"positive_value\": \"./images/B00GQWTJ6C.png\", \"hn_image\": [\"./images/B007T5JNJ8.png\"]}\n{\"q_img\": \"./images/B00A758348.png\", \"q_text\": \"has no sleeves and is less sexy, and is yellow and a tank top\", \"positive_key\": \"B00FBBN4EW\", \"positive_value\": \"./images/B00FBBN4EW.png\", \"hn_image\": [\"./images/B00A758348.png\"]}\n{\"q_img\": \"./images/B0094D2LWE.png\", \"q_text\": \"Is grey with a purple logo, and is light grey and has KR3W graphic\", \"positive_key\": \"B007QAPB1A\", \"positive_value\": \"./images/B007QAPB1A.png\", \"hn_image\": [\"./images/B0094D2LWE.png\"]}\n{\"q_img\": \"./images/B00AR0ARLK.png\", \"q_text\": \"has red, and is light white and has a bit logo\", \"positive_key\": \"B00AYJNE3M\", \"positive_value\": \"./images/B00AYJNE3M.png\", \"hn_image\": [\"./images/B00AR0ARLK.png\"]}\n{\"q_img\": \"./images/B008LDGQK6.png\", \"q_text\": \"is blue, and is less brightly colored\", \"positive_key\": \"B00BLL7CB2\", \"positive_value\": \"./images/B00BLL7CB2.png\", \"hn_image\": [\"./images/B008LDGQK6.png\"]}\n{\"q_img\": \"./images/B008Z0FTB2.png\", \"q_text\": \"is a white polo shirt, and is white with shorter sleeves\", \"positive_key\": \"B007BLNDLO\", \"positive_value\": \"./images/B007BLNDLO.png\", \"hn_image\": [\"./images/B008Z0FTB2.png\"]}\n{\"q_img\": \"./images/B0020Q11LE.png\", \"q_text\": \"Is blue in color with a different logo, and wrestling\", \"positive_key\": \"B0030EX7Z8\", \"positive_value\": \"./images/B0030EX7Z8.png\", \"hn_image\": [\"./images/B0020Q11LE.png\"]}\n{\"q_img\": \"./images/B00112FYQG.png\", \"q_text\": \"is more colorful and more tropical, and is blue with floral pattern\", \"positive_key\": \"B005JQN9C6\", \"positive_value\": \"./images/B005JQN9C6.png\", \"hn_image\": [\"./images/B00112FYQG.png\"]}\n{\"q_img\": \"./images/B004QYHZ6I.png\", \"q_text\": \"Less stripped and more cool, and is light lilac\", \"positive_key\": \"B005UL02C0\", \"positive_value\": \"./images/B005UL02C0.png\", \"hn_image\": [\"./images/B004QYHZ6I.png\"]}\n{\"q_img\": \"./images/B008F39HRQ.png\", \"q_text\": \"is more formal and plain, and is a more formal shirt\", \"positive_key\": \"B000ZN8Z3G\", \"positive_value\": \"./images/B000ZN8Z3G.png\", \"hn_image\": [\"./images/B008F39HRQ.png\"]}\n{\"q_img\": \"./images/B00827XZUK.png\", \"q_text\": \"is brighter colored with green sleeves, and is white and green with longer sleeves\", \"positive_key\": \"B003XGPATY\", \"positive_value\": \"./images/B003XGPATY.png\", \"hn_image\": [\"./images/B00827XZUK.png\"]}\n{\"q_img\": \"./images/B0002X4OES.png\", \"q_text\": \"is a plain white t-shirt with short sleeves, and is all white and t-shirt style\", \"positive_key\": \"B0050OFMB8\", \"positive_value\": \"./images/B0050OFMB8.png\", \"hn_image\": [\"./images/B0002X4OES.png\"]}\n{\"q_img\": \"./images/B006N1KCB2.png\", \"q_text\": \"is the long light blue shirt, and has brighter colors and is more cartoonish\", \"positive_key\": \"B00F3YS42Y\", \"positive_value\": \"./images/B00F3YS42Y.png\", \"hn_image\": [\"./images/B006N1KCB2.png\"]}\n{\"q_img\": \"./images/B006WFFTTO.png\", \"q_text\": \"The shirt is white and has long sleeves., and is lighter and more western\", \"positive_key\": \"B00F3SJQ72\", \"positive_value\": \"./images/B00F3SJQ72.png\", \"hn_image\": [\"./images/B006WFFTTO.png\"]}\n{\"q_img\": \"./images/B005S6YGO2.png\", \"q_text\": \"is black and grey with strips, and is more striped and darker\", \"positive_key\": \"B005N56BXW\", \"positive_value\": \"./images/B005N56BXW.png\", \"hn_image\": [\"./images/B005S6YGO2.png\"]}\n{\"q_img\": \"./images/B005AJNKEE.png\", \"q_text\": \"is a dark grey shirt with front buttons, and is grey and has buttons\", \"positive_key\": \"B001M0MWSK\", \"positive_value\": \"./images/B001M0MWSK.png\", \"hn_image\": [\"./images/B005AJNKEE.png\"]}\n{\"q_img\": \"./images/B004TMGDME.png\", \"q_text\": \"is lighter, and it is blue\", \"positive_key\": \"B000WNEK7Y\", \"positive_value\": \"./images/B000WNEK7Y.png\", \"hn_image\": [\"./images/B004TMGDME.png\"]}\n{\"q_img\": \"./images/B003H7RKB0.png\", \"q_text\": \"is gray with black designs, and is darker\", \"positive_key\": \"B00AQFAWU2\", \"positive_value\": \"./images/B00AQFAWU2.png\", \"hn_image\": [\"./images/B003H7RKB0.png\"]}\n{\"q_img\": \"./images/B00BY24W7K.png\", \"q_text\": \"is solid and has long sleeves, and is orange and has longer sleeves\", \"positive_key\": \"B003AQQ370\", \"positive_value\": \"./images/B003AQQ370.png\", \"hn_image\": [\"./images/B00BY24W7K.png\"]}\n{\"q_img\": \"./images/B008T3V1YO.png\", \"q_text\": \"is grey with many minions, and is darker\", \"positive_key\": \"B00E9JZ6D4\", \"positive_value\": \"./images/B00E9JZ6D4.png\", \"hn_image\": [\"./images/B008T3V1YO.png\"]}\n{\"q_img\": \"./images/B00ARF686S.png\", \"q_text\": \"Is black wiith a graphic print, and is darker\", \"positive_key\": \"B00CL08O0A\", \"positive_value\": \"./images/B00CL08O0A.png\", \"hn_image\": [\"./images/B00ARF686S.png\"]}\n{\"q_img\": \"./images/B0060EYIU8.png\", \"q_text\": \"is smaller in size with different print design, and has more pattern\", \"positive_key\": \"B005XS00E0\", \"positive_value\": \"./images/B005XS00E0.png\", \"hn_image\": [\"./images/B0060EYIU8.png\"]}\n{\"q_img\": \"./images/B0057V76WM.png\", \"q_text\": \"is a cream color with an open color, and is a button down shirt\", \"positive_key\": \"B001ET7HSE\", \"positive_value\": \"./images/B001ET7HSE.png\", \"hn_image\": [\"./images/B0057V76WM.png\"]}\n{\"q_img\": \"./images/B006N1KIHK.png\", \"q_text\": \"is red colored, and A solid bright red shirt.\", \"positive_key\": \"B008YF7M2M\", \"positive_value\": \"./images/B008YF7M2M.png\", \"hn_image\": [\"./images/B006N1KIHK.png\"]}\n{\"q_img\": \"./images/B006ID7VIC.png\", \"q_text\": \"Green and has more text, and is green with white on red text\", \"positive_key\": \"B00FZ57788\", \"positive_value\": \"./images/B00FZ57788.png\", \"hn_image\": [\"./images/B006ID7VIC.png\"]}\n{\"q_img\": \"./images/B00AY9O4WC.png\", \"q_text\": \"has a yellow color with cartoon art, and is yellow colored\", \"positive_key\": \"B005ZCA5PI\", \"positive_value\": \"./images/B005ZCA5PI.png\", \"hn_image\": [\"./images/B00AY9O4WC.png\"]}\n{\"q_img\": \"./images/B00680JY4O.png\", \"q_text\": \"is more casual, and is more striped\", \"positive_key\": \"B0068YW1EA\", \"positive_value\": \"./images/B0068YW1EA.png\", \"hn_image\": [\"./images/B00680JY4O.png\"]}\n{\"q_img\": \"./images/B0092UH97G.png\", \"q_text\": \"is darker with long sleeves, and is darker and has longer sleeves\", \"positive_key\": \"B000N4U0LC\", \"positive_value\": \"./images/B000N4U0LC.png\", \"hn_image\": [\"./images/B0092UH97G.png\"]}\n{\"q_img\": \"./images/B001L0ZVIE.png\", \"q_text\": \"Is blue, and Is navy\", \"positive_key\": \"B0086WETGU\", \"positive_value\": \"./images/B0086WETGU.png\", \"hn_image\": [\"./images/B001L0ZVIE.png\"]}\n{\"q_img\": \"./images/B0009MIGVO.png\", \"q_text\": \"is a black v shaped neck t-shirt, and is black and more casual\", \"positive_key\": \"B00BOY9EWQ\", \"positive_value\": \"./images/B00BOY9EWQ.png\", \"hn_image\": [\"./images/B0009MIGVO.png\"]}\n{\"q_img\": \"./images/B003TQ05QQ.png\", \"q_text\": \"Is a red Rooney shirt., and is less political\", \"positive_key\": \"B006IZI2H4\", \"positive_value\": \"./images/B006IZI2H4.png\", \"hn_image\": [\"./images/B003TQ05QQ.png\"]}\n{\"q_img\": \"./images/B0064IPVGK.png\", \"q_text\": \"Has a standard logo on it, and is darker\", \"positive_key\": \"B0083SDC9M\", \"positive_value\": \"./images/B0083SDC9M.png\", \"hn_image\": [\"./images/B0064IPVGK.png\"]}\n{\"q_img\": \"./images/B00CC2O4PQ.png\", \"q_text\": \"is black and plain, and is black and has buttons\", \"positive_key\": \"B00B4BAP2M\", \"positive_value\": \"./images/B00B4BAP2M.png\", \"hn_image\": [\"./images/B00CC2O4PQ.png\"]}\n{\"q_img\": \"./images/B003DQKR1A.png\", \"q_text\": \"is floral and long sleeved, and has longer sleeves\", \"positive_key\": \"B0079085BA\", \"positive_value\": \"./images/B0079085BA.png\", \"hn_image\": [\"./images/B003DQKR1A.png\"]}\n{\"q_img\": \"./images/B003TW4V2Y.png\", \"q_text\": \"is darker and has different graphics, and is a darker color and has art printed on front\", \"positive_key\": \"B006OWP8GO\", \"positive_value\": \"./images/B006OWP8GO.png\", \"hn_image\": [\"./images/B003TW4V2Y.png\"]}\n{\"q_img\": \"./images/B0095XTJNW.png\", \"q_text\": \"is lighter and has different words, and is light grey with only text on front\", \"positive_key\": \"B00GM38ZBK\", \"positive_value\": \"./images/B00GM38ZBK.png\", \"hn_image\": [\"./images/B0095XTJNW.png\"]}\n{\"q_img\": \"./images/B007Y5A38I.png\", \"q_text\": \"is lighter colored and has no collar, and is white colored\", \"positive_key\": \"B0051EPWF8\", \"positive_value\": \"./images/B0051EPWF8.png\", \"hn_image\": [\"./images/B007Y5A38I.png\"]}\n{\"q_img\": \"./images/B000N4TP72.png\", \"q_text\": \"has more colors, and is a Doors t-shirt with rainbow lettering\", \"positive_key\": \"B004TMPIWK\", \"positive_value\": \"./images/B004TMPIWK.png\", \"hn_image\": [\"./images/B000N4TP72.png\"]}\n{\"q_img\": \"./images/B005ESA9SG.png\", \"q_text\": \"is pink with strips going down, and is pink colored striped long sleeved\", \"positive_key\": \"B009P9OF9O\", \"positive_value\": \"./images/B009P9OF9O.png\", \"hn_image\": [\"./images/B005ESA9SG.png\"]}\n{\"q_img\": \"./images/B00ALRS9OG.png\", \"q_text\": \"is a buttoned sleeved shirt with crazy visuals, and has longer sleeves\", \"positive_key\": \"B00DW88CA2\", \"positive_value\": \"./images/B00DW88CA2.png\", \"hn_image\": [\"./images/B00ALRS9OG.png\"]}\n{\"q_img\": \"./images/B00812S67S.png\", \"q_text\": \"its has a picture print on front from elda, and is lighter\", \"positive_key\": \"B005Y4L6KK\", \"positive_value\": \"./images/B005Y4L6KK.png\", \"hn_image\": [\"./images/B00812S67S.png\"]}\n{\"q_img\": \"./images/B004YO7ZNS.png\", \"q_text\": \"way less attractive, and is more coloorful\", \"positive_key\": \"B004JJVF48\", \"positive_value\": \"./images/B004JJVF48.png\", \"hn_image\": [\"./images/B004YO7ZNS.png\"]}\n{\"q_img\": \"./images/B00AZIB0ZG.png\", \"q_text\": \"is light blue with blue words, and is blue and no color or buttons\", \"positive_key\": \"B004LWZE54\", \"positive_value\": \"./images/B004LWZE54.png\", \"hn_image\": [\"./images/B00AZIB0ZG.png\"]}\n{\"q_img\": \"./images/B00E89R5KC.png\", \"q_text\": \"is a purple T shirt with Kittens on iy, and is more gay\", \"positive_key\": \"B000NPJ9H2\", \"positive_value\": \"./images/B000NPJ9H2.png\", \"hn_image\": [\"./images/B00E89R5KC.png\"]}\n{\"q_img\": \"./images/B00398WXHI.png\", \"q_text\": \"is multi-colored pattern and no sleeves, and has short sleeves\", \"positive_key\": \"B00D83U984\", \"positive_value\": \"./images/B00D83U984.png\", \"hn_image\": [\"./images/B00398WXHI.png\"]}\n{\"q_img\": \"./images/B00B31W2TC.png\", \"q_text\": \"has shorter sleeves and no pattern, and is blue with shorter sleeves\", \"positive_key\": \"B00C0CMGOY\", \"positive_value\": \"./images/B00C0CMGOY.png\", \"hn_image\": [\"./images/B00B31W2TC.png\"]}\n{\"q_img\": \"./images/B003Y5LY76.png\", \"q_text\": \"is green with a character, and Is brighter in color\", \"positive_key\": \"B005XIGH92\", \"positive_value\": \"./images/B005XIGH92.png\", \"hn_image\": [\"./images/B003Y5LY76.png\"]}\n{\"q_img\": \"./images/B00DGA6TWY.png\", \"q_text\": \"is more graphic and brighter, and is lighter in color\", \"positive_key\": \"B006YE9WLY\", \"positive_value\": \"./images/B006YE9WLY.png\", \"hn_image\": [\"./images/B00DGA6TWY.png\"]}\n{\"q_img\": \"./images/B004EDHI0Y.png\", \"q_text\": \"Is white and plain, and is lighter\", \"positive_key\": \"B00263KHX4\", \"positive_value\": \"./images/B00263KHX4.png\", \"hn_image\": [\"./images/B004EDHI0Y.png\"]}\n{\"q_img\": \"./images/B009YFB2Q8.png\", \"q_text\": \"is black with buttons and collar, and has more graphics\", \"positive_key\": \"B007LHVZJK\", \"positive_value\": \"./images/B007LHVZJK.png\", \"hn_image\": [\"./images/B009YFB2Q8.png\"]}\n{\"q_img\": \"./images/B009DLYLS4.png\", \"q_text\": \"has 'lifted' logo in center, and has pointed graphic and a word\", \"positive_key\": \"B00A1WCYXS\", \"positive_value\": \"./images/B00A1WCYXS.png\", \"hn_image\": [\"./images/B009DLYLS4.png\"]}\n{\"q_img\": \"./images/B00ECY8GWO.png\", \"q_text\": \"has a darker color, and is darker colored and white font\", \"positive_key\": \"B002PUSVT0\", \"positive_value\": \"./images/B002PUSVT0.png\", \"hn_image\": [\"./images/B00ECY8GWO.png\"]}\n{\"q_img\": \"./images/B002NCIATQ.png\", \"q_text\": \"is black colored short sleeved shirt with white colored art, and is darker\", \"positive_key\": \"B000JI5NY6\", \"positive_value\": \"./images/B000JI5NY6.png\", \"hn_image\": [\"./images/B002NCIATQ.png\"]}\n{\"q_img\": \"./images/B009REO9Q6.png\", \"q_text\": \"gray colored and v-neck, and is grey in color\", \"positive_key\": \"B00C6AVPXS\", \"positive_value\": \"./images/B00C6AVPXS.png\", \"hn_image\": [\"./images/B009REO9Q6.png\"]}\n{\"q_img\": \"./images/B003IT639C.png\", \"q_text\": \"is black with green writing, and is darker\", \"positive_key\": \"B00AMD5I7U\", \"positive_value\": \"./images/B00AMD5I7U.png\", \"hn_image\": [\"./images/B003IT639C.png\"]}\n{\"q_img\": \"./images/B00C26O5DS.png\", \"q_text\": \"has a brighter color and slogans on both sides, and is lighter\", \"positive_key\": \"B00B89J29W\", \"positive_value\": \"./images/B00B89J29W.png\", \"hn_image\": [\"./images/B00C26O5DS.png\"]}\n{\"q_img\": \"./images/B004KF7JM8.png\", \"q_text\": \"is black with a design of pacman, and Is black in color\", \"positive_key\": \"B00354MXJY\", \"positive_value\": \"./images/B00354MXJY.png\", \"hn_image\": [\"./images/B004KF7JM8.png\"]}\n{\"q_img\": \"./images/B00B88ZKXA.png\", \"q_text\": \" half sleeved shirt., and and green\", \"positive_key\": \"B00B88TB9O\", \"positive_value\": \"./images/B00B88TB9O.png\", \"hn_image\": [\"./images/B00B88ZKXA.png\"]}\n{\"q_img\": \"./images/B00A3OSBJ0.png\", \"q_text\": \"is darker colored and has no print, and Orange short-sleeved workout muscle shirt\", \"positive_key\": \"B0007UBWPK\", \"positive_value\": \"./images/B0007UBWPK.png\", \"hn_image\": [\"./images/B00A3OSBJ0.png\"]}\n{\"q_img\": \"./images/B00BAQ74CA.png\", \"q_text\": \"is a white tee shirt, and is tighter\", \"positive_key\": \"B004R89JYO\", \"positive_value\": \"./images/B004R89JYO.png\", \"hn_image\": [\"./images/B00BAQ74CA.png\"]}\n{\"q_img\": \"./images/B004OKFF7K.png\", \"q_text\": \"has BMS written on it, and is shorter\", \"positive_key\": \"B00BFTMTJA\", \"positive_value\": \"./images/B00BFTMTJA.png\", \"hn_image\": [\"./images/B004OKFF7K.png\"]}\n{\"q_img\": \"./images/B0048KPZO4.png\", \"q_text\": \" and with a collar., and A bright blue with red letters\", \"positive_key\": \"B004YO7H3G\", \"positive_value\": \"./images/B004YO7H3G.png\", \"hn_image\": [\"./images/B0048KPZO4.png\"]}\n{\"q_img\": \"./images/B00BT0JNWQ.png\", \"q_text\": \"is black with red and blue designs, and is more faded\", \"positive_key\": \"B007DJ2XQA\", \"positive_value\": \"./images/B007DJ2XQA.png\", \"hn_image\": [\"./images/B00BT0JNWQ.png\"]}\n{\"q_img\": \"./images/B00CU8PDPW.png\", \"q_text\": \"is a beige colored long sleeve with text, and is olive green with white logo\", \"positive_key\": \"B00D7N1178\", \"positive_value\": \"./images/B00D7N1178.png\", \"hn_image\": [\"./images/B00CU8PDPW.png\"]}\n{\"q_img\": \"./images/B008LOSNCE.png\", \"q_text\": \"less brightly pink, and is lighter\", \"positive_key\": \"B0019SBBPK\", \"positive_value\": \"./images/B0019SBBPK.png\", \"hn_image\": [\"./images/B008LOSNCE.png\"]}\n{\"q_img\": \"./images/B004Z21SHS.png\", \"q_text\": \"A lighter color ., and is black with blue words\", \"positive_key\": \"B008SAGOWW\", \"positive_value\": \"./images/B008SAGOWW.png\", \"hn_image\": [\"./images/B004Z21SHS.png\"]}\n{\"q_img\": \"./images/B009RUTFV4.png\", \"q_text\": \"has longer sleeves and is more fitted, and is purple with long sleeves\", \"positive_key\": \"B006Z2HG0Y\", \"positive_value\": \"./images/B006Z2HG0Y.png\", \"hn_image\": [\"./images/B009RUTFV4.png\"]}\n{\"q_img\": \"./images/B006VY6A12.png\", \"q_text\": \"is a green T shirt with balls on it, and is green and has more text\", \"positive_key\": \"B007JQKAFI\", \"positive_value\": \"./images/B007JQKAFI.png\", \"hn_image\": [\"./images/B006VY6A12.png\"]}\n{\"q_img\": \"./images/B00ANR1M8Y.png\", \"q_text\": \"has long sleeves, and is long sleeved and has a colored graphic\", \"positive_key\": \"B006MHJB1O\", \"positive_value\": \"./images/B006MHJB1O.png\", \"hn_image\": [\"./images/B00ANR1M8Y.png\"]}\n{\"q_img\": \"./images/B007X2DHT4.png\", \"q_text\": \"is white with a more humorous graphic, and It is more white and colorful\", \"positive_key\": \"B006TTQKU0\", \"positive_value\": \"./images/B006TTQKU0.png\", \"hn_image\": [\"./images/B007X2DHT4.png\"]}\n{\"q_img\": \"./images/B000XEBDA4.png\", \"q_text\": \"is short-sleeved and shinier material, and has shorter sleeves and is of a different fabric\", \"positive_key\": \"B00AKR3VPO\", \"positive_value\": \"./images/B00AKR3VPO.png\", \"hn_image\": [\"./images/B000XEBDA4.png\"]}\n{\"q_img\": \"./images/B0015GIECO.png\", \"q_text\": \"is a gray long sleeved shirt, and It has no pocket and has long sleeves\", \"positive_key\": \"B005XXYY2O\", \"positive_value\": \"./images/B005XXYY2O.png\", \"hn_image\": [\"./images/B0015GIECO.png\"]}\n{\"q_img\": \"./images/B0084NYYRK.png\", \"q_text\": \"has whiter cuffs, and is less formal\", \"positive_key\": \"B0084TQIUA\", \"positive_value\": \"./images/B0084TQIUA.png\", \"hn_image\": [\"./images/B0084NYYRK.png\"]}\n{\"q_img\": \"./images/B0046Q4F28.png\", \"q_text\": \"its cream colored and minimal print, and tan.\", \"positive_key\": \"B00CP5TV58\", \"positive_value\": \"./images/B00CP5TV58.png\", \"hn_image\": [\"./images/B0046Q4F28.png\"]}\n{\"q_img\": \"./images/B007P28IKK.png\", \"q_text\": \"is similar with army pants, and is white with black and different graphics\", \"positive_key\": \"B004OOXD6G\", \"positive_value\": \"./images/B004OOXD6G.png\", \"hn_image\": [\"./images/B007P28IKK.png\"]}\n{\"q_img\": \"./images/B004UR1MY2.png\", \"q_text\": \"lighter dark color, and is dark gray\", \"positive_key\": \"B003MCOPWW\", \"positive_value\": \"./images/B003MCOPWW.png\", \"hn_image\": [\"./images/B004UR1MY2.png\"]}\n{\"q_img\": \"./images/B0098AWTUW.png\", \"q_text\": \"is a short sleeve black shirt with a jolly roger, and has more red to it\", \"positive_key\": \"B000P47ZAE\", \"positive_value\": \"./images/B000P47ZAE.png\", \"hn_image\": [\"./images/B0098AWTUW.png\"]}\n{\"q_img\": \"./images/B006NCQPYO.png\", \"q_text\": \"is smaller of a lighter color and displays a phrase, and is a white tank top with writing on it\", \"positive_key\": \"B00C6BN2CO\", \"positive_value\": \"./images/B00C6BN2CO.png\", \"hn_image\": [\"./images/B006NCQPYO.png\"]}\n{\"q_img\": \"./images/B002GP7QO0.png\", \"q_text\": \"its exactly what I want, and is the same product\", \"positive_key\": \"B002GP7QNQ\", \"positive_value\": \"./images/B002GP7QNQ.png\", \"hn_image\": [\"./images/B002GP7QO0.png\"]}\n{\"q_img\": \"./images/B00DYR63RK.png\", \"q_text\": \"is blue with red lettering, and Is less wordy\", \"positive_key\": \"B00A8Q37YC\", \"positive_value\": \"./images/B00A8Q37YC.png\", \"hn_image\": [\"./images/B00DYR63RK.png\"]}\n{\"q_img\": \"./images/B000G1FDF0.png\", \"q_text\": \"is more formal, and It is less white\", \"positive_key\": \"B002WYUFIY\", \"positive_value\": \"./images/B002WYUFIY.png\", \"hn_image\": [\"./images/B000G1FDF0.png\"]}\n{\"q_img\": \"./images/B007MD9MN4.png\", \"q_text\": \"Is more complex, and has pink on the shoulder\", \"positive_key\": \"B001U0F5QI\", \"positive_value\": \"./images/B001U0F5QI.png\", \"hn_image\": [\"./images/B007MD9MN4.png\"]}\n{\"q_img\": \"./images/B00AZUNPNE.png\", \"q_text\": \"has no buttons or pockets, and is lighter\", \"positive_key\": \"B0058XVNC8\", \"positive_value\": \"./images/B0058XVNC8.png\", \"hn_image\": [\"./images/B00AZUNPNE.png\"]}\n{\"q_img\": \"./images/B004ZC7H0K.png\", \"q_text\": \"is a blue polo shirt, and is blue and has shorter sleeves\", \"positive_key\": \"B003C7NPA0\", \"positive_value\": \"./images/B003C7NPA0.png\", \"hn_image\": [\"./images/B004ZC7H0K.png\"]}\n{\"q_img\": \"./images/B0094D2LWE.png\", \"q_text\": \"is more formal, and is plaid and has buttons\", \"positive_key\": \"B00863JI88\", \"positive_value\": \"./images/B00863JI88.png\", \"hn_image\": [\"./images/B0094D2LWE.png\"]}\n{\"q_img\": \"./images/B00AESNT5Q.png\", \"q_text\": \"are darker colors, and is pastel colored\", \"positive_key\": \"B008447TR6\", \"positive_value\": \"./images/B008447TR6.png\", \"hn_image\": [\"./images/B00AESNT5Q.png\"]}\n{\"q_img\": \"./images/B00DSY5UOQ.png\", \"q_text\": \"The shirt is white in color., and is made of cotton\", \"positive_key\": \"B0090E0GLA\", \"positive_value\": \"./images/B0090E0GLA.png\", \"hn_image\": [\"./images/B00DSY5UOQ.png\"]}\n{\"q_img\": \"./images/B001PJ6ZX6.png\", \"q_text\": \"has image in yellow on it, and has yellow writting graphic in front\", \"positive_key\": \"B001PIWUZ4\", \"positive_value\": \"./images/B001PIWUZ4.png\", \"hn_image\": [\"./images/B001PJ6ZX6.png\"]}\n{\"q_img\": \"./images/B000CAZVI4.png\", \"q_text\": \"has long sleeves, and is darker and has longer sleeves\", \"positive_key\": \"B007JWZZIO\", \"positive_value\": \"./images/B007JWZZIO.png\", \"hn_image\": [\"./images/B000CAZVI4.png\"]}\n{\"q_img\": \"./images/B00A21JW60.png\", \"q_text\": \"is more formal and blue, and is blue with shorter sleeves\", \"positive_key\": \"B004L63QNW\", \"positive_value\": \"./images/B004L63QNW.png\", \"hn_image\": [\"./images/B00A21JW60.png\"]}\n{\"q_img\": \"./images/B005OCFG3K.png\", \"q_text\": \"is black color with text, and has a larger motif\", \"positive_key\": \"B00A3HIN6I\", \"positive_value\": \"./images/B00A3HIN6I.png\", \"hn_image\": [\"./images/B005OCFG3K.png\"]}\n{\"q_img\": \"./images/B00AZLH99O.png\", \"q_text\": \"has short sleeves and light in color, and is lighter and has longer sleeves\", \"positive_key\": \"B00AK47K0E\", \"positive_value\": \"./images/B00AK47K0E.png\", \"hn_image\": [\"./images/B00AZLH99O.png\"]}\n{\"q_img\": \"./images/B009G0SO62.png\", \"q_text\": \"is brown with a round neck, and has slightly shorter sleeves\", \"positive_key\": \"B002PZHYQ6\", \"positive_value\": \"./images/B002PZHYQ6.png\", \"hn_image\": [\"./images/B009G0SO62.png\"]}\n{\"q_img\": \"./images/B000VTMXZU.png\", \"q_text\": \"is black with lawyer written on it, and has less words\", \"positive_key\": \"B003HEYFBG\", \"positive_value\": \"./images/B003HEYFBG.png\", \"hn_image\": [\"./images/B000VTMXZU.png\"]}\n{\"q_img\": \"./images/B00140FIFC.png\", \"q_text\": \"Is purple and less casual, and is purple and has buttons\", \"positive_key\": \"B0045889NS\", \"positive_value\": \"./images/B0045889NS.png\", \"hn_image\": [\"./images/B00140FIFC.png\"]}\n{\"q_img\": \"./images/B00BGJ4OQA.png\", \"q_text\": \"is white with california republic writing, and is lighter\", \"positive_key\": \"B005F0JPDI\", \"positive_value\": \"./images/B005F0JPDI.png\", \"hn_image\": [\"./images/B00BGJ4OQA.png\"]}\n{\"q_img\": \"./images/B005MV78O8.png\", \"q_text\": \"Is more green and colorful, and Desired item contains white and green text images\", \"positive_key\": \"B00313Y4BY\", \"positive_value\": \"./images/B00313Y4BY.png\", \"hn_image\": [\"./images/B005MV78O8.png\"]}\n{\"q_img\": \"./images/B00AZ0180G.png\", \"q_text\": \"is navy blue with yellow lettering, and Is navy in color\", \"positive_key\": \"B005MGDZ04\", \"positive_value\": \"./images/B005MGDZ04.png\", \"hn_image\": [\"./images/B00AZ0180G.png\"]}\n{\"q_img\": \"./images/B004C43LO2.png\", \"q_text\": \"is black with white and red designs, and is a black tshirt with a white and red design\", \"positive_key\": \"B001KAN812\", \"positive_value\": \"./images/B001KAN812.png\", \"hn_image\": [\"./images/B004C43LO2.png\"]}\n{\"q_img\": \"./images/B00714Q6LA.png\", \"q_text\": \"is black with red words, and is blacker\", \"positive_key\": \"B007RJG5J2\", \"positive_value\": \"./images/B007RJG5J2.png\", \"hn_image\": [\"./images/B00714Q6LA.png\"]}\n{\"q_img\": \"./images/B00B4DXN4W.png\", \"q_text\": \"has longer sleeves with no art, and is black and has long sleeves\", \"positive_key\": \"B00BNBFJ7E\", \"positive_value\": \"./images/B00BNBFJ7E.png\", \"hn_image\": [\"./images/B00B4DXN4W.png\"]}\n{\"q_img\": \"./images/B007C3IEOC.png\", \"q_text\": \"More black and longer sleeve, and Has longer sleeves and is black\", \"positive_key\": \"B00804T1HQ\", \"positive_value\": \"./images/B00804T1HQ.png\", \"hn_image\": [\"./images/B007C3IEOC.png\"]}\n{\"q_img\": \"./images/B00AO1T42U.png\", \"q_text\": \"is black with blue writing, and is darker\", \"positive_key\": \"B008CP94U2\", \"positive_value\": \"./images/B008CP94U2.png\", \"hn_image\": [\"./images/B00AO1T42U.png\"]}\n{\"q_img\": \"./images/B005MV78O8.png\", \"q_text\": \"Is more white and animated, and is white with rockstar on it\", \"positive_key\": \"B005HMPACO\", \"positive_value\": \"./images/B005HMPACO.png\", \"hn_image\": [\"./images/B005MV78O8.png\"]}\n{\"q_img\": \"./images/B007JFCWQY.png\", \"q_text\": \"is luminous green, and is bright green\", \"positive_key\": \"B003IT639C\", \"positive_value\": \"./images/B003IT639C.png\", \"hn_image\": [\"./images/B007JFCWQY.png\"]}\n{\"q_img\": \"./images/B005G3VM80.png\", \"q_text\": \"is black and long sleeves with red and white designs, and is black with longer sleeves\", \"positive_key\": \"B008RMD5HS\", \"positive_value\": \"./images/B008RMD5HS.png\", \"hn_image\": [\"./images/B005G3VM80.png\"]}\n{\"q_img\": \"./images/B003FWLRD4.png\", \"q_text\": \"is a large dark t-shirt, and is less formal\", \"positive_key\": \"B001G8UIZW\", \"positive_value\": \"./images/B001G8UIZW.png\", \"hn_image\": [\"./images/B003FWLRD4.png\"]}\n{\"q_img\": \"./images/B000OM3WSQ.png\", \"q_text\": \"is black with other wording, and is darker\", \"positive_key\": \"B00ANR01C2\", \"positive_value\": \"./images/B00ANR01C2.png\", \"hn_image\": [\"./images/B000OM3WSQ.png\"]}\n{\"q_img\": \"./images/B003U4T928.png\", \"q_text\": \"Is black and longer sleeves, and has long sleeves\", \"positive_key\": \"B00EQ85MY6\", \"positive_value\": \"./images/B00EQ85MY6.png\", \"hn_image\": [\"./images/B003U4T928.png\"]}\n{\"q_img\": \"./images/B0053DV6AW.png\", \"q_text\": \"is a long sleeve button up with collar, and is light pink\", \"positive_key\": \"B00AA13RN6\", \"positive_value\": \"./images/B00AA13RN6.png\", \"hn_image\": [\"./images/B0053DV6AW.png\"]}\n{\"q_img\": \"./images/B002UFT5JA.png\", \"q_text\": \"is a darker color and has a larger graphic, and is darker\", \"positive_key\": \"B00CCR7O1C\", \"positive_value\": \"./images/B00CCR7O1C.png\", \"hn_image\": [\"./images/B002UFT5JA.png\"]}\n{\"q_img\": \"./images/B005IR27EC.png\", \"q_text\": \"is darker, and has more words on it\", \"positive_key\": \"B006OIVJCK\", \"positive_value\": \"./images/B006OIVJCK.png\", \"hn_image\": [\"./images/B005IR27EC.png\"]}\n{\"q_img\": \"./images/B004UIOL3K.png\", \"q_text\": \"is black and has a skull on it, and it is black\", \"positive_key\": \"B004UTATIU\", \"positive_value\": \"./images/B004UTATIU.png\", \"hn_image\": [\"./images/B004UIOL3K.png\"]}\n{\"q_img\": \"./images/B0081FLAGO.png\", \"q_text\": \"is more manly and faded, and has a white design on the chest\", \"positive_key\": \"B00AAJ67QW\", \"positive_value\": \"./images/B00AAJ67QW.png\", \"hn_image\": [\"./images/B0081FLAGO.png\"]}\n{\"q_img\": \"./images/B00AREHSMM.png\", \"q_text\": \"is gray short sleeved shirt with white designs, and is grey with a white pattern\", \"positive_key\": \"B006N7NITO\", \"positive_value\": \"./images/B006N7NITO.png\", \"hn_image\": [\"./images/B00AREHSMM.png\"]}\n{\"q_img\": \"./images/B00B7HVJZA.png\", \"q_text\": \"is darker, and Desired item is grey and purple with a pocket\", \"positive_key\": \"B008NAGF26\", \"positive_value\": \"./images/B008NAGF26.png\", \"hn_image\": [\"./images/B00B7HVJZA.png\"]}\n{\"q_img\": \"./images/B007TXNW9M.png\", \"q_text\": \"is black with white designs, and is black colored\", \"positive_key\": \"B006SDZ0JY\", \"positive_value\": \"./images/B006SDZ0JY.png\", \"hn_image\": [\"./images/B007TXNW9M.png\"]}\n{\"q_img\": \"./images/B0046IFV6K.png\", \"q_text\": \"has clear wording, and is lighter\", \"positive_key\": \"B006405WPI\", \"positive_value\": \"./images/B006405WPI.png\", \"hn_image\": [\"./images/B0046IFV6K.png\"]}\n{\"q_img\": \"./images/B001PIWUZ4.png\", \"q_text\": \"has longer sleeves with a logo, and is long sleeved with black font design\", \"positive_key\": \"B001TSZPM0\", \"positive_value\": \"./images/B001TSZPM0.png\", \"hn_image\": [\"./images/B001PIWUZ4.png\"]}\n{\"q_img\": \"./images/B008447TR6.png\", \"q_text\": \"is identical, and is only the blue one\", \"positive_key\": \"B004G4G00K\", \"positive_value\": \"./images/B004G4G00K.png\", \"hn_image\": [\"./images/B008447TR6.png\"]}\n{\"q_img\": \"./images/B009T5NMS4.png\", \"q_text\": \"is black in color, and is darker\", \"positive_key\": \"B004VFHTL8\", \"positive_value\": \"./images/B004VFHTL8.png\", \"hn_image\": [\"./images/B009T5NMS4.png\"]}\n{\"q_img\": \"./images/B0013F2JGE.png\", \"q_text\": \"Has dolphins on a light blue tee shirt, and is lighter\", \"positive_key\": \"B000H84DIA\", \"positive_value\": \"./images/B000H84DIA.png\", \"hn_image\": [\"./images/B0013F2JGE.png\"]}\n{\"q_img\": \"./images/B00CRZ1D66.png\", \"q_text\": \"Is grey with a less faded graphic, and is a lighter color and has different artwork\", \"positive_key\": \"B00BYEA52Y\", \"positive_value\": \"./images/B00BYEA52Y.png\", \"hn_image\": [\"./images/B00CRZ1D66.png\"]}\n{\"q_img\": \"./images/B004SWYOZI.png\", \"q_text\": \"is a long sleeved henley with a few buttons, and has longer sleeves\", \"positive_key\": \"B007G03S2Y\", \"positive_value\": \"./images/B007G03S2Y.png\", \"hn_image\": [\"./images/B004SWYOZI.png\"]}\n{\"q_img\": \"./images/B00AX2ZUCS.png\", \"q_text\": \"is grey with light blue bands but more fitted, and has an aquatic design\", \"positive_key\": \"B0081SF8AU\", \"positive_value\": \"./images/B0081SF8AU.png\", \"hn_image\": [\"./images/B00AX2ZUCS.png\"]}\n{\"q_img\": \"./images/B00DTIO7G8.png\", \"q_text\": \"is a zombie flip up t-shirt, and lighter and masculine\", \"positive_key\": \"B002SK8VGK\", \"positive_value\": \"./images/B002SK8VGK.png\", \"hn_image\": [\"./images/B00DTIO7G8.png\"]}\n{\"q_img\": \"./images/B00DTIO7G8.png\", \"q_text\": \"is gray with blue writing on it, and is more imature\", \"positive_key\": \"B00AYJA4G2\", \"positive_value\": \"./images/B00AYJA4G2.png\", \"hn_image\": [\"./images/B00DTIO7G8.png\"]}\n{\"q_img\": \"./images/B00EQV08XS.png\", \"q_text\": \"is a white button down, and it is white with collar and pocket\", \"positive_key\": \"B004UKNYZ4\", \"positive_value\": \"./images/B004UKNYZ4.png\", \"hn_image\": [\"./images/B00EQV08XS.png\"]}\n{\"q_img\": \"./images/B006G2Z1W8.png\", \"q_text\": \"has short sleeves with red image design, and is more scary and has a larger graphic\", \"positive_key\": \"B00FM6HAQY\", \"positive_value\": \"./images/B00FM6HAQY.png\", \"hn_image\": [\"./images/B006G2Z1W8.png\"]}\n{\"q_img\": \"./images/B00CD7L1WY.png\", \"q_text\": \"is green in color, and is greener\", \"positive_key\": \"B0009RIYES\", \"positive_value\": \"./images/B0009RIYES.png\", \"hn_image\": [\"./images/B00CD7L1WY.png\"]}\n{\"q_img\": \"./images/B00BAXTUN4.png\", \"q_text\": \"is darker, and is darker\", \"positive_key\": \"B009QR8QAE\", \"positive_value\": \"./images/B009QR8QAE.png\", \"hn_image\": [\"./images/B00BAXTUN4.png\"]}\n{\"q_img\": \"./images/B00BWDCPUW.png\", \"q_text\": \"has less design, and Is maroon and more simple.\", \"positive_key\": \"B00BJPLEYG\", \"positive_value\": \"./images/B00BJPLEYG.png\", \"hn_image\": [\"./images/B00BWDCPUW.png\"]}\n{\"q_img\": \"./images/B005FS7LEK.png\", \"q_text\": \"is lighter and less colorful, and is lighter in color\", \"positive_key\": \"B00BP47QWK\", \"positive_value\": \"./images/B00BP47QWK.png\", \"hn_image\": [\"./images/B005FS7LEK.png\"]}\n{\"q_img\": \"./images/B004RRB10U.png\", \"q_text\": \"is light colored and multicolored, and has lighter colors\", \"positive_key\": \"B007R1NWEG\", \"positive_value\": \"./images/B007R1NWEG.png\", \"hn_image\": [\"./images/B004RRB10U.png\"]}\n{\"q_img\": \"./images/B00CXMYX2E.png\", \"q_text\": \"Is blue and has a more complex logo, and is light blue in color\", \"positive_key\": \"B00DAMLQKS\", \"positive_value\": \"./images/B00DAMLQKS.png\", \"hn_image\": [\"./images/B00CXMYX2E.png\"]}\n{\"q_img\": \"./images/B0013FJWT6.png\", \"q_text\": \"is sleeveless and plain white, and is sleeveless and white\", \"positive_key\": \"B002C6FOII\", \"positive_value\": \"./images/B002C6FOII.png\", \"hn_image\": [\"./images/B0013FJWT6.png\"]}\n{\"q_img\": \"./images/B009QR8VNQ.png\", \"q_text\": \"is black in color, and is darker in color\", \"positive_key\": \"B00DB5ERM8\", \"positive_value\": \"./images/B00DB5ERM8.png\", \"hn_image\": [\"./images/B009QR8VNQ.png\"]}\n{\"q_img\": \"./images/B008447TR6.png\", \"q_text\": \"Is a white color, and is more formal\", \"positive_key\": \"B00AEF5VKA\", \"positive_value\": \"./images/B00AEF5VKA.png\", \"hn_image\": [\"./images/B008447TR6.png\"]}\n{\"q_img\": \"./images/B00B4WT108.png\", \"q_text\": \"Is blue and has stripes, and is blue colored with purple stripes\", \"positive_key\": \"B002KG6OOS\", \"positive_value\": \"./images/B002KG6OOS.png\", \"hn_image\": [\"./images/B00B4WT108.png\"]}\n{\"q_img\": \"./images/B003R4ZLE6.png\", \"q_text\": \"has strips and a pocket, and is white with grey horizontal stripes\", \"positive_key\": \"B007TFPS3S\", \"positive_value\": \"./images/B007TFPS3S.png\", \"hn_image\": [\"./images/B003R4ZLE6.png\"]}\n{\"q_img\": \"./images/B00DE1BDLC.png\", \"q_text\": \" long sleeved and more grey, and is light blue\", \"positive_key\": \"B004RDY5LQ\", \"positive_value\": \"./images/B004RDY5LQ.png\", \"hn_image\": [\"./images/B00DE1BDLC.png\"]}\n{\"q_img\": \"./images/B00CXMYX2E.png\", \"q_text\": \"is brighter in color, and is brighter\", \"positive_key\": \"B004LTGJYC\", \"positive_value\": \"./images/B004LTGJYC.png\", \"hn_image\": [\"./images/B00CXMYX2E.png\"]}\n{\"q_img\": \"./images/B0073PSC3M.png\", \"q_text\": \"has a darker color and a different print, and Is black with rock theme.\", \"positive_key\": \"B007MY9PUI\", \"positive_value\": \"./images/B007MY9PUI.png\", \"hn_image\": [\"./images/B0073PSC3M.png\"]}\n{\"q_img\": \"./images/B008VTKSMM.png\", \"q_text\": \"A white Tapout shirt with skull on it, and has red lettering\", \"positive_key\": \"B008P8A022\", \"positive_value\": \"./images/B008P8A022.png\", \"hn_image\": [\"./images/B008VTKSMM.png\"]}\n{\"q_img\": \"./images/B003JZCDFS.png\", \"q_text\": \"is a black t shirt with white lettering, and has no horizontal stripes\", \"positive_key\": \"B005H9ZP8Q\", \"positive_value\": \"./images/B005H9ZP8Q.png\", \"hn_image\": [\"./images/B003JZCDFS.png\"]}\n{\"q_img\": \"./images/B005LB3I6G.png\", \"q_text\": \"has only a logo and is blue, and it is more blue and has a tiny print\", \"positive_key\": \"B005BNN1U2\", \"positive_value\": \"./images/B005BNN1U2.png\", \"hn_image\": [\"./images/B005LB3I6G.png\"]}\n{\"q_img\": \"./images/B00BL5C9FM.png\", \"q_text\": \"is shinier, and has a different graphic\", \"positive_key\": \"B008ACDRIC\", \"positive_value\": \"./images/B008ACDRIC.png\", \"hn_image\": [\"./images/B00BL5C9FM.png\"]}\n{\"q_img\": \"./images/B001XWTNGQ.png\", \"q_text\": \"is a black shirt with a caption in the center, and Gray with white lettering 'got dinner' picture of man shooting\", \"positive_key\": \"B00563EO9O\", \"positive_value\": \"./images/B00563EO9O.png\", \"hn_image\": [\"./images/B001XWTNGQ.png\"]}\n{\"q_img\": \"./images/B0042RUEJY.png\", \"q_text\": \"has tan button and is denim colored, and is blue with buttons and a collar\", \"positive_key\": \"B00AZIN2P2\", \"positive_value\": \"./images/B00AZIN2P2.png\", \"hn_image\": [\"./images/B0042RUEJY.png\"]}\n{\"q_img\": \"./images/B000V999DY.png\", \"q_text\": \"is blue, and Desired item is blue with stick figure characters\", \"positive_key\": \"B004TEMWHC\", \"positive_value\": \"./images/B004TEMWHC.png\", \"hn_image\": [\"./images/B000V999DY.png\"]}\n{\"q_img\": \"./images/B00AY9O4WC.png\", \"q_text\": \"is white and has 'Zoo York' written, and is white with black lettering\", \"positive_key\": \"B004APGLNG\", \"positive_value\": \"./images/B004APGLNG.png\", \"hn_image\": [\"./images/B00AY9O4WC.png\"]}\n{\"q_img\": \"./images/B0085O4Q7Q.png\", \"q_text\": \"has short sleeves, and is lighter in color and has short sleeves\", \"positive_key\": \"B008AFYG9I\", \"positive_value\": \"./images/B008AFYG9I.png\", \"hn_image\": [\"./images/B0085O4Q7Q.png\"]}\n{\"q_img\": \"./images/B004JXAKD6.png\", \"q_text\": \"is a white t shirt with a Mexican on it, and is a T shirt with a man in a sombrero on it\", \"positive_key\": \"B005NYBNEU\", \"positive_value\": \"./images/B005NYBNEU.png\", \"hn_image\": [\"./images/B004JXAKD6.png\"]}\n{\"q_img\": \"./images/B004MYFR3K.png\", \"q_text\": \"dark colored with a Game of Thrones image, and Is darker black with a lighter colored graphic\", \"positive_key\": \"B005KXL6TQ\", \"positive_value\": \"./images/B005KXL6TQ.png\", \"hn_image\": [\"./images/B004MYFR3K.png\"]}\n{\"q_img\": \"./images/B0085UCF3M.png\", \"q_text\": \"Is grey, and is lighter colored\", \"positive_key\": \"B001V3ST94\", \"positive_value\": \"./images/B001V3ST94.png\", \"hn_image\": [\"./images/B0085UCF3M.png\"]}\n{\"q_img\": \"./images/B000KHDKJQ.png\", \"q_text\": \"is black with a positive self-image text, and is black with different writing\", \"positive_key\": \"B00642IYH4\", \"positive_value\": \"./images/B00642IYH4.png\", \"hn_image\": [\"./images/B000KHDKJQ.png\"]}\n{\"q_img\": \"./images/B00GA1Q00M.png\", \"q_text\": \"is grey with shorter sleeves, and is grey with short sleeves\", \"positive_key\": \"B00820LZAO\", \"positive_value\": \"./images/B00820LZAO.png\", \"hn_image\": [\"./images/B00GA1Q00M.png\"]}\n{\"q_img\": \"./images/B004J1HDHE.png\", \"q_text\": \"The black shirt has white writing., and is darker\", \"positive_key\": \"B004J1HFKE\", \"positive_value\": \"./images/B004J1HFKE.png\", \"hn_image\": [\"./images/B004J1HDHE.png\"]}\n{\"q_img\": \"./images/B0046ICFPU.png\", \"q_text\": \"is white with a different graphic, and is white with a colorful face on the front\", \"positive_key\": \"B002CJ7O2Y\", \"positive_value\": \"./images/B002CJ7O2Y.png\", \"hn_image\": [\"./images/B0046ICFPU.png\"]}\n{\"q_img\": \"./images/B0083Z6QDO.png\", \"q_text\": \"is black and has a more colourful graphic, and is a darker color\", \"positive_key\": \"B0051VMCEK\", \"positive_value\": \"./images/B0051VMCEK.png\", \"hn_image\": [\"./images/B0083Z6QDO.png\"]}\n{\"q_img\": \"./images/B003TYLO3G.png\", \"q_text\": \"is white with longer sleeves, and is white colored\", \"positive_key\": \"B005LJJ9AC\", \"positive_value\": \"./images/B005LJJ9AC.png\", \"hn_image\": [\"./images/B003TYLO3G.png\"]}\n{\"q_img\": \"./images/B00F3KMWX0.png\", \"q_text\": \"has not for sale written on it, and has a different graphic\", \"positive_key\": \"B005OODLGW\", \"positive_value\": \"./images/B005OODLGW.png\", \"hn_image\": [\"./images/B00F3KMWX0.png\"]}\n{\"q_img\": \"./images/B005344CDE.png\", \"q_text\": \"is a white short sleeved button down shirt with collar, and light colored short sleeve button up shirt with collar\", \"positive_key\": \"B00AUV3TQQ\", \"positive_value\": \"./images/B00AUV3TQQ.png\", \"hn_image\": [\"./images/B005344CDE.png\"]}\n{\"q_img\": \"./images/B008351BMA.png\", \"q_text\": \"Has longer sleeves and is green, and is green and long sleeved\", \"positive_key\": \"B007U6TYUY\", \"positive_value\": \"./images/B007U6TYUY.png\", \"hn_image\": [\"./images/B008351BMA.png\"]}\n{\"q_img\": \"./images/B009R8P6QO.png\", \"q_text\": \"is gray with different words, and has more grey\", \"positive_key\": \"B00GOGK2SO\", \"positive_value\": \"./images/B00GOGK2SO.png\", \"hn_image\": [\"./images/B009R8P6QO.png\"]}\n{\"q_img\": \"./images/B0001TP9FG.png\", \"q_text\": \"is black with yellow designs, and has brighter colors in it\", \"positive_key\": \"B0015N1DYI\", \"positive_value\": \"./images/B0015N1DYI.png\", \"hn_image\": [\"./images/B0001TP9FG.png\"]}\n{\"q_img\": \"./images/B007TB59A4.png\", \"q_text\": \"is blue with a collar, and Is navy in color\", \"positive_key\": \"B00FK45MG8\", \"positive_value\": \"./images/B00FK45MG8.png\", \"hn_image\": [\"./images/B007TB59A4.png\"]}\n{\"q_img\": \"./images/B003QCGEVS.png\", \"q_text\": \"is black, and is black\", \"positive_key\": \"B003QD1Q3I\", \"positive_value\": \"./images/B003QD1Q3I.png\", \"hn_image\": [\"./images/B003QCGEVS.png\"]}\n{\"q_img\": \"./images/B004VG7H4G.png\", \"q_text\": \"is blue with the same brand image, and has shield logo and is blue color\", \"positive_key\": \"B00B7SB6RA\", \"positive_value\": \"./images/B00B7SB6RA.png\", \"hn_image\": [\"./images/B004VG7H4G.png\"]}\n{\"q_img\": \"./images/B004D39JI4.png\", \"q_text\": \"has shorter sleeves and deeply black, and Is a red on the decal\", \"positive_key\": \"B007B8MGMY\", \"positive_value\": \"./images/B007B8MGMY.png\", \"hn_image\": [\"./images/B004D39JI4.png\"]}\n{\"q_img\": \"./images/B000LUIBH8.png\", \"q_text\": \"is more colorful with short sleeves, and has shorter sleeves and less athletic\", \"positive_key\": \"B00826ZXF6\", \"positive_value\": \"./images/B00826ZXF6.png\", \"hn_image\": [\"./images/B000LUIBH8.png\"]}\n{\"q_img\": \"./images/B0059DQYU8.png\", \"q_text\": \"is cream colored with a different logo, and is darker\", \"positive_key\": \"B005N1I9TK\", \"positive_value\": \"./images/B005N1I9TK.png\", \"hn_image\": [\"./images/B0059DQYU8.png\"]}\n{\"q_img\": \"./images/B007VHEYAM.png\", \"q_text\": \"is green with red and white designs, and is green with no buttons\", \"positive_key\": \"B00GUQOCLQ\", \"positive_value\": \"./images/B00GUQOCLQ.png\", \"hn_image\": [\"./images/B007VHEYAM.png\"]}\n{\"q_img\": \"./images/B0012FNHIO.png\", \"q_text\": \"This tshirt looks more loose, and is long sleeved and white buttoned\", \"positive_key\": \"B001MD8CZ4\", \"positive_value\": \"./images/B001MD8CZ4.png\", \"hn_image\": [\"./images/B0012FNHIO.png\"]}\n{\"q_img\": \"./images/B00AWQ7KG4.png\", \"q_text\": \"has no vneck and has a graphic, and is a crew neck with building logos\", \"positive_key\": \"B004PEMI7U\", \"positive_value\": \"./images/B004PEMI7U.png\", \"hn_image\": [\"./images/B00AWQ7KG4.png\"]}\n{\"q_img\": \"./images/B004XQF8FE.png\", \"q_text\": \"is a dark tee shirt with a dragon graphic, and is black and has short sleeves\", \"positive_key\": \"B004QY6QKY\", \"positive_value\": \"./images/B004QY6QKY.png\", \"hn_image\": [\"./images/B004XQF8FE.png\"]}\n{\"q_img\": \"./images/B007DJ3EYA.png\", \"q_text\": \"is more sexual, and is darker\", \"positive_key\": \"B0046JZMOK\", \"positive_value\": \"./images/B0046JZMOK.png\", \"hn_image\": [\"./images/B007DJ3EYA.png\"]}\n{\"q_img\": \"./images/B0016LURJ6.png\", \"q_text\": \"is yellow, and is yellow color\", \"positive_key\": \"B00184FGT2\", \"positive_value\": \"./images/B00184FGT2.png\", \"hn_image\": [\"./images/B0016LURJ6.png\"]}\n{\"q_img\": \"./images/B004FN2OYI.png\", \"q_text\": \"is dark and has more words, and Desired item is blue and references Pink Floyd\", \"positive_key\": \"B001UHMZHI\", \"positive_value\": \"./images/B001UHMZHI.png\", \"hn_image\": [\"./images/B004FN2OYI.png\"]}\n{\"q_img\": \"./images/B00AN9K6DE.png\", \"q_text\": \"Is more plain and fitted, and black tank top\", \"positive_key\": \"B00AE16OLO\", \"positive_value\": \"./images/B00AE16OLO.png\", \"hn_image\": [\"./images/B00AN9K6DE.png\"]}\n{\"q_img\": \"./images/B00AQR2JM4.png\", \"q_text\": \"is a blue v shaped neck short sleeve t-shirt, and has a V neck and is pale blue/green\", \"positive_key\": \"B004J8I4VQ\", \"positive_value\": \"./images/B004J8I4VQ.png\", \"hn_image\": [\"./images/B00AQR2JM4.png\"]}\n{\"q_img\": \"./images/B00G5N5VPK.png\", \"q_text\": \"has front pockets, and is light blue with pockets\", \"positive_key\": \"B002INKDGI\", \"positive_value\": \"./images/B002INKDGI.png\", \"hn_image\": [\"./images/B00G5N5VPK.png\"]}\n{\"q_img\": \"./images/B006C6XSFA.png\", \"q_text\": \"has less colors, and is dark blue with white lettering\", \"positive_key\": \"B00A7O1W0G\", \"positive_value\": \"./images/B00A7O1W0G.png\", \"hn_image\": [\"./images/B006C6XSFA.png\"]}\n{\"q_img\": \"./images/B005O76TKE.png\", \"q_text\": \"is lighter, and is lighter colored\", \"positive_key\": \"B00A8KZAIE\", \"positive_value\": \"./images/B00A8KZAIE.png\", \"hn_image\": [\"./images/B005O76TKE.png\"]}\n{\"q_img\": \"./images/B000L7G8OY.png\", \"q_text\": \"is darker with more earth tones, and has scull and cross bones and is more black\", \"positive_key\": \"B000MXQ7CA\", \"positive_value\": \"./images/B000MXQ7CA.png\", \"hn_image\": [\"./images/B000L7G8OY.png\"]}\n{\"q_img\": \"./images/B009NBSZOU.png\", \"q_text\": \"is green without buttons and collar, and is neon green with no buttons or collar\", \"positive_key\": \"B00BFG8QL8\", \"positive_value\": \"./images/B00BFG8QL8.png\", \"hn_image\": [\"./images/B009NBSZOU.png\"]}\n{\"q_img\": \"./images/B0091RIIRA.png\", \"q_text\": \"is grey colored, and is lighter colored and more adolescent\", \"positive_key\": \"B00DXZ2S5E\", \"positive_value\": \"./images/B00DXZ2S5E.png\", \"hn_image\": [\"./images/B0091RIIRA.png\"]}\n{\"q_img\": \"./images/B00501BIXW.png\", \"q_text\": \"has a graphical design, and is darker\", \"positive_key\": \"B001LZV83Q\", \"positive_value\": \"./images/B001LZV83Q.png\", \"hn_image\": [\"./images/B00501BIXW.png\"]}\n{\"q_img\": \"./images/B002EEP43I.png\", \"q_text\": \"is warmer and has a hood, and it has hood and it is long sleeved\", \"positive_key\": \"B001E2PNT6\", \"positive_value\": \"./images/B001E2PNT6.png\", \"hn_image\": [\"./images/B002EEP43I.png\"]}\n{\"q_img\": \"./images/B009WVA51M.png\", \"q_text\": \"is more formal and lighter colored, and is more formal and white with stripes\", \"positive_key\": \"B007ENCGTY\", \"positive_value\": \"./images/B007ENCGTY.png\", \"hn_image\": [\"./images/B009WVA51M.png\"]}\n{\"q_img\": \"./images/B00EKT8ASG.png\", \"q_text\": \"has longer sleeves and horizontal lines., and has longer sleeves and is stripe coloured\", \"positive_key\": \"B00822FD2I\", \"positive_value\": \"./images/B00822FD2I.png\", \"hn_image\": [\"./images/B00EKT8ASG.png\"]}\n{\"q_img\": \"./images/B00B31W2TC.png\", \"q_text\": \"is lighter with shorter sleeves, and is less striped\", \"positive_key\": \"B00B2JD2CQ\", \"positive_value\": \"./images/B00B2JD2CQ.png\", \"hn_image\": [\"./images/B00B31W2TC.png\"]}\n{\"q_img\": \"./images/B00AJSWYI4.png\", \"q_text\": \"lighter colored, and is a lighter color\", \"positive_key\": \"B007IF36VA\", \"positive_value\": \"./images/B007IF36VA.png\", \"hn_image\": [\"./images/B00AJSWYI4.png\"]}\n{\"q_img\": \"./images/B001ELCD7M.png\", \"q_text\": \"A blue tie dye Woodstock shirt, and has only blue colors\", \"positive_key\": \"B0024V5VKM\", \"positive_value\": \"./images/B0024V5VKM.png\", \"hn_image\": [\"./images/B001ELCD7M.png\"]}\n{\"q_img\": \"./images/B0013HB3KK.png\", \"q_text\": \"is darker with no collar or buttons, and is less formal\", \"positive_key\": \"B005L9UW26\", \"positive_value\": \"./images/B005L9UW26.png\", \"hn_image\": [\"./images/B0013HB3KK.png\"]}\n{\"q_img\": \"./images/B00BW5K82C.png\", \"q_text\": \"has short sleeves and some writings, and has no leggings\", \"positive_key\": \"B007VDLVSE\", \"positive_value\": \"./images/B007VDLVSE.png\", \"hn_image\": [\"./images/B00BW5K82C.png\"]}\n{\"q_img\": \"./images/B00AOJG2QS.png\", \"q_text\": \"more pattern and less bright, and has palm tree patterns\", \"positive_key\": \"B0014480CG\", \"positive_value\": \"./images/B0014480CG.png\", \"hn_image\": [\"./images/B00AOJG2QS.png\"]}\n{\"q_img\": \"./images/B0081SF8AU.png\", \"q_text\": \"The blue tshirt is light color & more reveling, and has no buttons and collar and has stripes\", \"positive_key\": \"B009E6STY0\", \"positive_value\": \"./images/B009E6STY0.png\", \"hn_image\": [\"./images/B0081SF8AU.png\"]}\n{\"q_img\": \"./images/B00BTYJGY2.png\", \"q_text\": \"is a v ck t shirt with stripes, and is grey with blue stripes\", \"positive_key\": \"B00AASO2OM\", \"positive_value\": \"./images/B00AASO2OM.png\", \"hn_image\": [\"./images/B00BTYJGY2.png\"]}\n{\"q_img\": \"./images/B009RVTT4G.png\", \"q_text\": \"has shorter sleeves and centered lettering., and is lighter and has shorter sleeves\", \"positive_key\": \"B003T70M3G\", \"positive_value\": \"./images/B003T70M3G.png\", \"hn_image\": [\"./images/B009RVTT4G.png\"]}\n{\"q_img\": \"./images/B000PNTVAC.png\", \"q_text\": \"is more blue with a different graphic, and is blue with a cool logo\", \"positive_key\": \"B000VT8W7S\", \"positive_value\": \"./images/B000VT8W7S.png\", \"hn_image\": [\"./images/B000PNTVAC.png\"]}\n{\"q_img\": \"./images/B00EJRN3HM.png\", \"q_text\": \"is much smaller and used for electronics, and is a phone cover case\", \"positive_key\": \"B006GCKVK0\", \"positive_value\": \"./images/B006GCKVK0.png\", \"hn_image\": [\"./images/B00EJRN3HM.png\"]}\n{\"q_img\": \"./images/B006XD31RC.png\", \"q_text\": \"is blue in color, and is blue and more athletic\", \"positive_key\": \"B00B0ZBOTA\", \"positive_value\": \"./images/B00B0ZBOTA.png\", \"hn_image\": [\"./images/B006XD31RC.png\"]}\n{\"q_img\": \"./images/B00ASK9HGA.png\", \"q_text\": \"is a white t shirt with eagles and an indian on it, and Has sleeves\", \"positive_key\": \"B000GHTGHK\", \"positive_value\": \"./images/B000GHTGHK.png\", \"hn_image\": [\"./images/B00ASK9HGA.png\"]}\n{\"q_img\": \"./images/B00AZIN2P2.png\", \"q_text\": \"has long sleeves and a lighter color, and is black\", \"positive_key\": \"B005A22HD6\", \"positive_value\": \"./images/B005A22HD6.png\", \"hn_image\": [\"./images/B00AZIN2P2.png\"]}\n{\"q_img\": \"./images/B004OYVAIY.png\", \"q_text\": \"Is yellow and long-sleeved, and is lighter in color and has more buttons\", \"positive_key\": \"B00CR90T2Q\", \"positive_value\": \"./images/B00CR90T2Q.png\", \"hn_image\": [\"./images/B004OYVAIY.png\"]}\n{\"q_img\": \"./images/B008BTI85Q.png\", \"q_text\": \"is red with a bird, and is red colored with a different graphic\", \"positive_key\": \"B005KN1S9Y\", \"positive_value\": \"./images/B005KN1S9Y.png\", \"hn_image\": [\"./images/B008BTI85Q.png\"]}\n{\"q_img\": \"./images/B00B0QP1XY.png\", \"q_text\": \"is white v-neck top with stripe pattern at the bottom, and is white and has a patterned bottom\", \"positive_key\": \"B009XP1HUA\", \"positive_value\": \"./images/B009XP1HUA.png\", \"hn_image\": [\"./images/B00B0QP1XY.png\"]}\n{\"q_img\": \"./images/B004PP120I.png\", \"q_text\": \"is black and has red lettering, and is darker and has a rainbow\", \"positive_key\": \"B00BFLIM6M\", \"positive_value\": \"./images/B00BFLIM6M.png\", \"hn_image\": [\"./images/B004PP120I.png\"]}\n{\"q_img\": \"./images/B007R1OJ7A.png\", \"q_text\": \"has a larger more colorful graphic, and is darker\", \"positive_key\": \"B00BAXTUN4\", \"positive_value\": \"./images/B00BAXTUN4.png\", \"hn_image\": [\"./images/B007R1OJ7A.png\"]}\n{\"q_img\": \"./images/B004GGUA9U.png\", \"q_text\": \"is lighter with different graphics, and A white shirt with black/blue letters on front\", \"positive_key\": \"B009LIZAES\", \"positive_value\": \"./images/B009LIZAES.png\", \"hn_image\": [\"./images/B004GGUA9U.png\"]}\n{\"q_img\": \"./images/B001H8TM80.png\", \"q_text\": \"Has Super Mario on it., and is more of a blue color\", \"positive_key\": \"B00BB50XXM\", \"positive_value\": \"./images/B00BB50XXM.png\", \"hn_image\": [\"./images/B001H8TM80.png\"]}\n{\"q_img\": \"./images/B00DEO56HQ.png\", \"q_text\": \"is ethnic, and is darker\", \"positive_key\": \"B0052U6EBC\", \"positive_value\": \"./images/B0052U6EBC.png\", \"hn_image\": [\"./images/B00DEO56HQ.png\"]}\n{\"q_img\": \"./images/B00EZLD1NI.png\", \"q_text\": \"is a short sleeved black T-Shirt, and is more solid colored\", \"positive_key\": \"B00D83J282\", \"positive_value\": \"./images/B00D83J282.png\", \"hn_image\": [\"./images/B00EZLD1NI.png\"]}\n{\"q_img\": \"./images/B0072ZCRLQ.png\", \"q_text\": \"Is more dark and animal-like, and is darker and has a turkey on it\", \"positive_key\": \"B00GJ1N5AG\", \"positive_value\": \"./images/B00GJ1N5AG.png\", \"hn_image\": [\"./images/B0072ZCRLQ.png\"]}\n{\"q_img\": \"./images/B003I86JVK.png\", \"q_text\": \"Is black and more whimsical, and Is black\", \"positive_key\": \"B0097TBXCE\", \"positive_value\": \"./images/B0097TBXCE.png\", \"hn_image\": [\"./images/B003I86JVK.png\"]}\n{\"q_img\": \"./images/B003ITZNYS.png\", \"q_text\": \"is blue in color, and is blue and has a different graphic\", \"positive_key\": \"B00BQVW6PY\", \"positive_value\": \"./images/B00BQVW6PY.png\", \"hn_image\": [\"./images/B003ITZNYS.png\"]}\n{\"q_img\": \"./images/B007IF36VA.png\", \"q_text\": \"is blue with no stripes, and is more colorful\", \"positive_key\": \"B008QT38YW\", \"positive_value\": \"./images/B008QT38YW.png\", \"hn_image\": [\"./images/B007IF36VA.png\"]}\n{\"q_img\": \"./images/B003XMOFP8.png\", \"q_text\": \"is more lighter, and is lighter\", \"positive_key\": \"B001GQZNQS\", \"positive_value\": \"./images/B001GQZNQS.png\", \"hn_image\": [\"./images/B003XMOFP8.png\"]}\n{\"q_img\": \"./images/B000BO1R2K.png\", \"q_text\": \"is black with a woman on it., and is darker and shorter\", \"positive_key\": \"B00512NGTY\", \"positive_value\": \"./images/B00512NGTY.png\", \"hn_image\": [\"./images/B000BO1R2K.png\"]}\n{\"q_img\": \"./images/B00CWJ5K16.png\", \"q_text\": \"has less colors, and less pockets\", \"positive_key\": \"B00CVJFQ2K\", \"positive_value\": \"./images/B00CVJFQ2K.png\", \"hn_image\": [\"./images/B00CWJ5K16.png\"]}\n{\"q_img\": \"./images/B0074AWK4S.png\", \"q_text\": \"is black with white lettering, and Dark blue Stupid people graphic shirt\", \"positive_key\": \"B009SDCLNY\", \"positive_value\": \"./images/B009SDCLNY.png\", \"hn_image\": [\"./images/B0074AWK4S.png\"]}\n{\"q_img\": \"./images/B00EZAK902.png\", \"q_text\": \"has shorter sleeves and is darker colored, and is darker\", \"positive_key\": \"B00AAG5H5C\", \"positive_value\": \"./images/B00AAG5H5C.png\", \"hn_image\": [\"./images/B00EZAK902.png\"]}\n{\"q_img\": \"./images/B0094KCP96.png\", \"q_text\": \"is dark green in color, and is shorter\", \"positive_key\": \"B009PQRI82\", \"positive_value\": \"./images/B009PQRI82.png\", \"hn_image\": [\"./images/B0094KCP96.png\"]}\n{\"q_img\": \"./images/B008CP95LA.png\", \"q_text\": \"is longer in length, and is lighter\", \"positive_key\": \"B003YLJOJU\", \"positive_value\": \"./images/B003YLJOJU.png\", \"hn_image\": [\"./images/B008CP95LA.png\"]}\n{\"q_img\": \"./images/B00CL0541M.png\", \"q_text\": \"is black with a white star on it, and A black shirt with white star on front\", \"positive_key\": \"B008MC2KTC\", \"positive_value\": \"./images/B008MC2KTC.png\", \"hn_image\": [\"./images/B00CL0541M.png\"]}\n{\"q_img\": \"./images/B007VHEYAM.png\", \"q_text\": \"is black with short sleeves, and is black with iamge\", \"positive_key\": \"B009P73OCK\", \"positive_value\": \"./images/B009P73OCK.png\", \"hn_image\": [\"./images/B007VHEYAM.png\"]}\n{\"q_img\": \"./images/B008TOOBWW.png\", \"q_text\": \"is gray with a different pattern, and has longer sleeves\", \"positive_key\": \"B00FRGP0CA\", \"positive_value\": \"./images/B00FRGP0CA.png\", \"hn_image\": [\"./images/B008TOOBWW.png\"]}\n{\"q_img\": \"./images/B00CFZQIUE.png\", \"q_text\": \"is an american flag colored vest, and has an american flag throughtout\", \"positive_key\": \"B007Q3HCSC\", \"positive_value\": \"./images/B007Q3HCSC.png\", \"hn_image\": [\"./images/B00CFZQIUE.png\"]}\n{\"q_img\": \"./images/B0006SCZRM.png\", \"q_text\": \"Has shorter sleeves and is more casual, and is less formal\", \"positive_key\": \"B00BWTZIC8\", \"positive_value\": \"./images/B00BWTZIC8.png\", \"hn_image\": [\"./images/B0006SCZRM.png\"]}\n{\"q_img\": \"./images/B005UQ9WKI.png\", \"q_text\": \"is a black sleeve shirt with Earth picture, and Black with earth picture and people flying\", \"positive_key\": \"B00AL8I7E2\", \"positive_value\": \"./images/B00AL8I7E2.png\", \"hn_image\": [\"./images/B005UQ9WKI.png\"]}\n{\"q_img\": \"./images/B006QSYMFO.png\", \"q_text\": \"is black with a collar, and is darker in color\", \"positive_key\": \"B006HT1XRC\", \"positive_value\": \"./images/B006HT1XRC.png\", \"hn_image\": [\"./images/B006QSYMFO.png\"]}\n{\"q_img\": \"./images/B0006SCZRM.png\", \"q_text\": \"The shirt is blue in color with short sleeves., and has smaller sleeves\", \"positive_key\": \"B00AYOY3BO\", \"positive_value\": \"./images/B00AYOY3BO.png\", \"hn_image\": [\"./images/B0006SCZRM.png\"]}\n{\"q_img\": \"./images/B00DV4AIL8.png\", \"q_text\": \"has stripes, and is more multi colored\", \"positive_key\": \"B007CGS5HK\", \"positive_value\": \"./images/B007CGS5HK.png\", \"hn_image\": [\"./images/B00DV4AIL8.png\"]}\n{\"q_img\": \"./images/B007POLATE.png\", \"q_text\": \"Is less casual and less patterned, and is pink with long sleeves\", \"positive_key\": \"B00C0CNKTE\", \"positive_value\": \"./images/B00C0CNKTE.png\", \"hn_image\": [\"./images/B007POLATE.png\"]}\n{\"q_img\": \"./images/B00BJO34NG.png\", \"q_text\": \"is more formal, and is lighter in shade and comes with a tie\", \"positive_key\": \"B00006NQ0N\", \"positive_value\": \"./images/B00006NQ0N.png\", \"hn_image\": [\"./images/B00BJO34NG.png\"]}\n{\"q_img\": \"./images/B0002X4R18.png\", \"q_text\": \"has no sleeves and is black, and has shorter sleeves\", \"positive_key\": \"B00075ZWR4\", \"positive_value\": \"./images/B00075ZWR4.png\", \"hn_image\": [\"./images/B0002X4R18.png\"]}\n{\"q_img\": \"./images/B00468PEQC.png\", \"q_text\": \"has more color, and Is red\", \"positive_key\": \"B009FTYOCM\", \"positive_value\": \"./images/B009FTYOCM.png\", \"hn_image\": [\"./images/B00468PEQC.png\"]}\n{\"q_img\": \"./images/B000NCVT3M.png\", \"q_text\": \"The shirt is red in color with big foot., and is lighter and more vintage\", \"positive_key\": \"B00383VNX4\", \"positive_value\": \"./images/B00383VNX4.png\", \"hn_image\": [\"./images/B000NCVT3M.png\"]}\n{\"q_img\": \"./images/B00AFR24ZW.png\", \"q_text\": \"Is blue and not patterned, and It is plain and has a longer sleeve\", \"positive_key\": \"B008EIXL38\", \"positive_value\": \"./images/B008EIXL38.png\", \"hn_image\": [\"./images/B00AFR24ZW.png\"]}\n{\"q_img\": \"./images/B00ARF686S.png\", \"q_text\": \"is a checked shirt, and White with one brown stripe going down each side\", \"positive_key\": \"B004MKMG1U\", \"positive_value\": \"./images/B004MKMG1U.png\", \"hn_image\": [\"./images/B00ARF686S.png\"]}\n{\"q_img\": \"./images/B005IZUOOE.png\", \"q_text\": \"Is more black and less colorful, and is darker in color\", \"positive_key\": \"B007YVZFYY\", \"positive_value\": \"./images/B007YVZFYY.png\", \"hn_image\": [\"./images/B005IZUOOE.png\"]}\n{\"q_img\": \"./images/B00BK77UC8.png\", \"q_text\": \"is checked with a deeper shade of color, and is bolder\", \"positive_key\": \"B00G46E9CY\", \"positive_value\": \"./images/B00G46E9CY.png\", \"hn_image\": [\"./images/B00BK77UC8.png\"]}\n{\"q_img\": \"./images/B008BSWH22.png\", \"q_text\": \"checked and more brighter, and is lighter\", \"positive_key\": \"B008BSWM4K\", \"positive_value\": \"./images/B008BSWM4K.png\", \"hn_image\": [\"./images/B008BSWH22.png\"]}\n{\"q_img\": \"./images/B00597L3W8.png\", \"q_text\": \"is darker and less sporty, and A black shirt with buttons on front\", \"positive_key\": \"B005HJPL0I\", \"positive_value\": \"./images/B005HJPL0I.png\", \"hn_image\": [\"./images/B00597L3W8.png\"]}\n{\"q_img\": \"./images/B0089ODSU8.png\", \"q_text\": \"has larger graphics, and has smaller lettering on it\", \"positive_key\": \"B005TX2N6M\", \"positive_value\": \"./images/B005TX2N6M.png\", \"hn_image\": [\"./images/B0089ODSU8.png\"]}\n{\"q_img\": \"./images/B0026GQET2.png\", \"q_text\": \"has longer sleeves and is greyer, and is grey with long sleeves\", \"positive_key\": \"B004ZBM5OY\", \"positive_value\": \"./images/B004ZBM5OY.png\", \"hn_image\": [\"./images/B0026GQET2.png\"]}\n{\"q_img\": \"./images/B0090OIMUW.png\", \"q_text\": \"is black and has shorter sleeves, and is black and shorter sleeves\", \"positive_key\": \"B00A9OC50U\", \"positive_value\": \"./images/B00A9OC50U.png\", \"hn_image\": [\"./images/B0090OIMUW.png\"]}\n{\"q_img\": \"./images/B00D0XHGGA.png\", \"q_text\": \"is white with a small fox logo, and is white with a smaller fox logo.\", \"positive_key\": \"B007V9SGZE\", \"positive_value\": \"./images/B007V9SGZE.png\", \"hn_image\": [\"./images/B00D0XHGGA.png\"]}\n{\"q_img\": \"./images/B00CMQ8ZXO.png\", \"q_text\": \"is white colored, and is lighter\", \"positive_key\": \"B009SREU1G\", \"positive_value\": \"./images/B009SREU1G.png\", \"hn_image\": [\"./images/B00CMQ8ZXO.png\"]}\n{\"q_img\": \"./images/B00C2CZTYG.png\", \"q_text\": \"The shirt is black in color with angry cat., and is darker with a I Had Fun Once Grumpy Cat\", \"positive_key\": \"B00BPDHMCU\", \"positive_value\": \"./images/B00BPDHMCU.png\", \"hn_image\": [\"./images/B00C2CZTYG.png\"]}\n{\"q_img\": \"./images/B00GGR9BSS.png\", \"q_text\": \"is white with shorter sleeves, and is short sleeved and white\", \"positive_key\": \"B004DCCKBI\", \"positive_value\": \"./images/B004DCCKBI.png\", \"hn_image\": [\"./images/B00GGR9BSS.png\"]}\n{\"q_img\": \"./images/B005BN2YN2.png\", \"q_text\": \"has long sleeves and brighter in color, and is white and long-sleeve.\", \"positive_key\": \"B0064DPOAS\", \"positive_value\": \"./images/B0064DPOAS.png\", \"hn_image\": [\"./images/B005BN2YN2.png\"]}\n{\"q_img\": \"./images/B005EJDQ0S.png\", \"q_text\": \"is a darker blue print, and is darker in color\", \"positive_key\": \"B006R1IXXW\", \"positive_value\": \"./images/B006R1IXXW.png\", \"hn_image\": [\"./images/B005EJDQ0S.png\"]}\n{\"q_img\": \"./images/B005LBXRWQ.png\", \"q_text\": \"Is less wordy, and is darker\", \"positive_key\": \"B005B4MD0K\", \"positive_value\": \"./images/B005B4MD0K.png\", \"hn_image\": [\"./images/B005LBXRWQ.png\"]}\n{\"q_img\": \"./images/B00DOYXAVA.png\", \"q_text\": \"is plain, and is solid color and has round logo\", \"positive_key\": \"B007R1OAP6\", \"positive_value\": \"./images/B007R1OAP6.png\", \"hn_image\": [\"./images/B00DOYXAVA.png\"]}\n{\"q_img\": \"./images/B008V2JXTS.png\", \"q_text\": \"is a tshirt, and is not socks\", \"positive_key\": \"B005Y0FUKQ\", \"positive_value\": \"./images/B005Y0FUKQ.png\", \"hn_image\": [\"./images/B008V2JXTS.png\"]}\n{\"q_img\": \"./images/B00AOCAUEK.png\", \"q_text\": \"is striped long sleeves and less black, and has longer sleeves\", \"positive_key\": \"B00B2ZMJOM\", \"positive_value\": \"./images/B00B2ZMJOM.png\", \"hn_image\": [\"./images/B00AOCAUEK.png\"]}\n{\"q_img\": \"./images/B001FO5LAY.png\", \"q_text\": \"is more pastel and more embroidered, and is darker\", \"positive_key\": \"B008X8YET4\", \"positive_value\": \"./images/B008X8YET4.png\", \"hn_image\": [\"./images/B001FO5LAY.png\"]}\n{\"q_img\": \"./images/B00B92J932.png\", \"q_text\": \"is more formal and lighter, and is a light colored button up\", \"positive_key\": \"B00DEKLOYO\", \"positive_value\": \"./images/B00DEKLOYO.png\", \"hn_image\": [\"./images/B00B92J932.png\"]}\n{\"q_img\": \"./images/B001H0F2J6.png\", \"q_text\": \"is a sporty sleeveless top, and has more blue and no sleeves\", \"positive_key\": \"B004LDLLG4\", \"positive_value\": \"./images/B004LDLLG4.png\", \"hn_image\": [\"./images/B001H0F2J6.png\"]}\n{\"q_img\": \"./images/B00F6FD5UG.png\", \"q_text\": \"is a black shirt, and is darker and has a pocket\", \"positive_key\": \"B000TUTVXS\", \"positive_value\": \"./images/B000TUTVXS.png\", \"hn_image\": [\"./images/B00F6FD5UG.png\"]}\n{\"q_img\": \"./images/B007QAPB1A.png\", \"q_text\": \"The shirt is black white and gray., and has darker sleeves\", \"positive_key\": \"B007EC5YKI\", \"positive_value\": \"./images/B007EC5YKI.png\", \"hn_image\": [\"./images/B007QAPB1A.png\"]}\n{\"q_img\": \"./images/B00AKOIQDE.png\", \"q_text\": \"is a striped shirt with long sleeves, and has buttons\", \"positive_key\": \"B00BBFV80E\", \"positive_value\": \"./images/B00BBFV80E.png\", \"hn_image\": [\"./images/B00AKOIQDE.png\"]}\n{\"q_img\": \"./images/B003LNPB70.png\", \"q_text\": \"is baby outfit and is green, and is a baby outfit\", \"positive_key\": \"B00FEQ60KY\", \"positive_value\": \"./images/B00FEQ60KY.png\", \"hn_image\": [\"./images/B003LNPB70.png\"]}\n{\"q_img\": \"./images/B00A4770HK.png\", \"q_text\": \"is black with white words, and has more words on it\", \"positive_key\": \"B003ZL943A\", \"positive_value\": \"./images/B003ZL943A.png\", \"hn_image\": [\"./images/B00A4770HK.png\"]}\n{\"q_img\": \"./images/B00DX7Q8O4.png\", \"q_text\": \"has a different graphic, and is a brighter green\", \"positive_key\": \"B009Z3YN4W\", \"positive_value\": \"./images/B009Z3YN4W.png\", \"hn_image\": [\"./images/B00DX7Q8O4.png\"]}\n{\"q_img\": \"./images/B004YO7CFY.png\", \"q_text\": \"has horizontal striped pattern, and has long sleeves and no buttons\", \"positive_key\": \"B0094IVR2E\", \"positive_value\": \"./images/B0094IVR2E.png\", \"hn_image\": [\"./images/B004YO7CFY.png\"]}\n{\"q_img\": \"./images/B003AU61WI.png\", \"q_text\": \"Is more blue and striped, and is more outdoorsy and less casual\", \"positive_key\": \"B004AHL5DU\", \"positive_value\": \"./images/B004AHL5DU.png\", \"hn_image\": [\"./images/B003AU61WI.png\"]}\n{\"q_img\": \"./images/B0046IB5PQ.png\", \"q_text\": \"has collar and buttons and no zipper, and has shorter sleeves and buttons\", \"positive_key\": \"B0046VB9PE\", \"positive_value\": \"./images/B0046VB9PE.png\", \"hn_image\": [\"./images/B0046IB5PQ.png\"]}\n{\"q_img\": \"./images/B0084TQIUA.png\", \"q_text\": \"is a navy blue shirt, and is pale blue with white lettering\", \"positive_key\": \"B0084NYEPW\", \"positive_value\": \"./images/B0084NYEPW.png\", \"hn_image\": [\"./images/B0084TQIUA.png\"]}\n{\"q_img\": \"./images/B009CCS6JO.png\", \"q_text\": \"is gray and has a different image, and is pale grey with a weather report\", \"positive_key\": \"B00G0IUSB2\", \"positive_value\": \"./images/B00G0IUSB2.png\", \"hn_image\": [\"./images/B009CCS6JO.png\"]}\n{\"q_img\": \"./images/B00AWK9TGE.png\", \"q_text\": \"Is more purple and bright, and is darker purple\", \"positive_key\": \"B00AX0SS24\", \"positive_value\": \"./images/B00AX0SS24.png\", \"hn_image\": [\"./images/B00AWK9TGE.png\"]}\n{\"q_img\": \"./images/B0096LL2W4.png\", \"q_text\": \"Is a jersey and is red with shorter sleeves, and has short sleeves and no hoodie\", \"positive_key\": \"B0099MIZH0\", \"positive_value\": \"./images/B0099MIZH0.png\", \"hn_image\": [\"./images/B0096LL2W4.png\"]}\n{\"q_img\": \"./images/B006VJXGAU.png\", \"q_text\": \"has writing on front and is more black, and is darker in color\", \"positive_key\": \"B0046IB5JW\", \"positive_value\": \"./images/B0046IB5JW.png\", \"hn_image\": [\"./images/B006VJXGAU.png\"]}\n{\"q_img\": \"./images/B005HWY12E.png\", \"q_text\": \"is a t-shirt with green writing on it, and is lighter in color and is more green\", \"positive_key\": \"B007ZTL5US\", \"positive_value\": \"./images/B007ZTL5US.png\", \"hn_image\": [\"./images/B005HWY12E.png\"]}\n{\"q_img\": \"./images/B00BHKFFAW.png\", \"q_text\": \"is a darker green and smaller logo, and is a darker green\", \"positive_key\": \"B0015D0PWY\", \"positive_value\": \"./images/B0015D0PWY.png\", \"hn_image\": [\"./images/B00BHKFFAW.png\"]}\n{\"q_img\": \"./images/B002NCIAU0.png\", \"q_text\": \"is darker and more manly, and black with a green graphic on the front\", \"positive_key\": \"B000PNTVAC\", \"positive_value\": \"./images/B000PNTVAC.png\", \"hn_image\": [\"./images/B002NCIAU0.png\"]}\n{\"q_img\": \"./images/B003L4LYB6.png\", \"q_text\": \"is more distressed-looking belt with square studding, and is a belt\", \"positive_key\": \"B000RQO9S6\", \"positive_value\": \"./images/B000RQO9S6.png\", \"hn_image\": [\"./images/B003L4LYB6.png\"]}\n{\"q_img\": \"./images/B00AO1FBBS.png\", \"q_text\": \"black in color, and is darker\", \"positive_key\": \"B004YWBYNW\", \"positive_value\": \"./images/B004YWBYNW.png\", \"hn_image\": [\"./images/B00AO1FBBS.png\"]}\n{\"q_img\": \"./images/B00EOYR308.png\", \"q_text\": \"says my girlfriend cant wrestle but you should see her box, and A black shirt with three rows of words\", \"positive_key\": \"B000VTMXZU\", \"positive_value\": \"./images/B000VTMXZU.png\", \"hn_image\": [\"./images/B00EOYR308.png\"]}\n{\"q_img\": \"./images/B009MMPFOS.png\", \"q_text\": \"is red with no stripes, and has no pockets\", \"positive_key\": \"B0090UWAVI\", \"positive_value\": \"./images/B0090UWAVI.png\", \"hn_image\": [\"./images/B009MMPFOS.png\"]}\n{\"q_img\": \"./images/B00C5RC12G.png\", \"q_text\": \"is a blue NASA t shirt, and is colored blue\", \"positive_key\": \"B00EI584U6\", \"positive_value\": \"./images/B00EI584U6.png\", \"hn_image\": [\"./images/B00C5RC12G.png\"]}\n{\"q_img\": \"./images/B00CR90T2Q.png\", \"q_text\": \"is darker in color and checked, and A black shirt.\", \"positive_key\": \"B009NI9M3Q\", \"positive_value\": \"./images/B009NI9M3Q.png\", \"hn_image\": [\"./images/B00CR90T2Q.png\"]}\n{\"q_img\": \"./images/B008GXHZ7O.png\", \"q_text\": \"has longer sleeves and is white, and is only white and longer sleeves.\", \"positive_key\": \"B005G6MYK2\", \"positive_value\": \"./images/B005G6MYK2.png\", \"hn_image\": [\"./images/B008GXHZ7O.png\"]}\n{\"q_img\": \"./images/B00BMR73SC.png\", \"q_text\": \"is darker and more manly, and is darker\", \"positive_key\": \"B00517FPKC\", \"positive_value\": \"./images/B00517FPKC.png\", \"hn_image\": [\"./images/B00BMR73SC.png\"]}\n{\"q_img\": \"./images/B001S9X34W.png\", \"q_text\": \"is blue with a pepsi logo, and Is more fitted & blue\", \"positive_key\": \"B007X5PGQS\", \"positive_value\": \"./images/B007X5PGQS.png\", \"hn_image\": [\"./images/B001S9X34W.png\"]}\n{\"q_img\": \"./images/B00611XBS0.png\", \"q_text\": \"is blue with yellow designs, and is a navy and grey two pack of t shirts\", \"positive_key\": \"B0001TOMJA\", \"positive_value\": \"./images/B0001TOMJA.png\", \"hn_image\": [\"./images/B00611XBS0.png\"]}\n{\"q_img\": \"./images/B005W44GZO.png\", \"q_text\": \"a t shirt with a bear logo, and is a grey t-shirt with a white polar bear.\", \"positive_key\": \"B0013L5KI2\", \"positive_value\": \"./images/B0013L5KI2.png\", \"hn_image\": [\"./images/B005W44GZO.png\"]}\n{\"q_img\": \"./images/B007VYRFX8.png\", \"q_text\": \"is red with cards on it, and is white with El Camino vehicles on it\", \"positive_key\": \"B000REM4EE\", \"positive_value\": \"./images/B000REM4EE.png\", \"hn_image\": [\"./images/B007VYRFX8.png\"]}\n{\"q_img\": \"./images/B003T70M3G.png\", \"q_text\": \"is lighter, and is white\", \"positive_key\": \"B007CCWITA\", \"positive_value\": \"./images/B007CCWITA.png\", \"hn_image\": [\"./images/B003T70M3G.png\"]}\n{\"q_img\": \"./images/B004QMA13O.png\", \"q_text\": \"is blue and short sleeved with a logo, and is blue\", \"positive_key\": \"B00AJJRKLY\", \"positive_value\": \"./images/B00AJJRKLY.png\", \"hn_image\": [\"./images/B004QMA13O.png\"]}\n{\"q_img\": \"./images/B004QZ9T9I.png\", \"q_text\": \"is a t-shirt with short sleeves and a dark color, and is black with shorter sleeves\", \"positive_key\": \"B007IOX6N4\", \"positive_value\": \"./images/B007IOX6N4.png\", \"hn_image\": [\"./images/B004QZ9T9I.png\"]}\n{\"q_img\": \"./images/B00ELPU4J2.png\", \"q_text\": \" buttons and checkered print, and is lighter blue\", \"positive_key\": \"B0094S068A\", \"positive_value\": \"./images/B0094S068A.png\", \"hn_image\": [\"./images/B00ELPU4J2.png\"]}\n{\"q_img\": \"./images/B003TP2GXM.png\", \"q_text\": \"is more blue and has shorter sleaves, and has shorter sleeves and blue\", \"positive_key\": \"B000PHI5L4\", \"positive_value\": \"./images/B000PHI5L4.png\", \"hn_image\": [\"./images/B003TP2GXM.png\"]}\n{\"q_img\": \"./images/B009B5O3NK.png\", \"q_text\": \"white and black t shirt looks more slim fir, and has wider stripes\", \"positive_key\": \"B005S2A9SI\", \"positive_value\": \"./images/B005S2A9SI.png\", \"hn_image\": [\"./images/B009B5O3NK.png\"]}\n{\"q_img\": \"./images/B001DDPIKA.png\", \"q_text\": \"a short sleeve solid color shirt with a few buttons, and has shorter slleves\", \"positive_key\": \"B009ZOJJ9K\", \"positive_value\": \"./images/B009ZOJJ9K.png\", \"hn_image\": [\"./images/B001DDPIKA.png\"]}\n{\"q_img\": \"./images/B00DE4GBUM.png\", \"q_text\": \"is black with yellow writing on it, and Is darker and more graphic\", \"positive_key\": \"B007FHZL7S\", \"positive_value\": \"./images/B007FHZL7S.png\", \"hn_image\": [\"./images/B00DE4GBUM.png\"]}\n{\"q_img\": \"./images/B005QCMXCU.png\", \"q_text\": \"is orange with black lettering, and is orange with black text\", \"positive_key\": \"B003MS2424\", \"positive_value\": \"./images/B003MS2424.png\", \"hn_image\": [\"./images/B005QCMXCU.png\"]}\n{\"q_img\": \"./images/B007OXEFOI.png\", \"q_text\": \"is darker colored, and is black with a different logo on it\", \"positive_key\": \"B0084DEKW4\", \"positive_value\": \"./images/B0084DEKW4.png\", \"hn_image\": [\"./images/B007OXEFOI.png\"]}\n{\"q_img\": \"./images/B007G2OMP4.png\", \"q_text\": \"is a solid color., and Solid white\", \"positive_key\": \"B00DDPTNS4\", \"positive_value\": \"./images/B00DDPTNS4.png\", \"hn_image\": [\"./images/B007G2OMP4.png\"]}\n{\"q_img\": \"./images/B0083FXVDC.png\", \"q_text\": \"is lighter, and has more strips\", \"positive_key\": \"B00CRVW3LY\", \"positive_value\": \"./images/B00CRVW3LY.png\", \"hn_image\": [\"./images/B0083FXVDC.png\"]}\n{\"q_img\": \"./images/B00CN6W5YI.png\", \"q_text\": \"Is more black and longer, and is darker and has a cityscape\", \"positive_key\": \"B0040B640K\", \"positive_value\": \"./images/B0040B640K.png\", \"hn_image\": [\"./images/B00CN6W5YI.png\"]}\n{\"q_img\": \"./images/B007PCKWDQ.png\", \"q_text\": \"is sleeveless and has contrast trim, and is lighter and is a tank top\", \"positive_key\": \"B00DFJODD8\", \"positive_value\": \"./images/B00DFJODD8.png\", \"hn_image\": [\"./images/B007PCKWDQ.png\"]}\n{\"q_img\": \"./images/B000LQVB9W.png\", \"q_text\": \"is green with a horse on it, and is more colourfull\", \"positive_key\": \"B00AWYU432\", \"positive_value\": \"./images/B00AWYU432.png\", \"hn_image\": [\"./images/B000LQVB9W.png\"]}\n{\"q_img\": \"./images/B0071LPNY4.png\", \"q_text\": \"has less detail and is darker, and has a Ramones emblem and other doesn't\", \"positive_key\": \"B000CQXJJ6\", \"positive_value\": \"./images/B000CQXJJ6.png\", \"hn_image\": [\"./images/B0071LPNY4.png\"]}\n{\"q_img\": \"./images/B000OTT8N2.png\", \"q_text\": \"is a Mohammed T shirt, and Is navy in color\", \"positive_key\": \"B002V4QF9S\", \"positive_value\": \"./images/B002V4QF9S.png\", \"hn_image\": [\"./images/B000OTT8N2.png\"]}\n{\"q_img\": \"./images/B00337XRJS.png\", \"q_text\": \"is black with white designs, and is more emo\", \"positive_key\": \"B005H98Z9W\", \"positive_value\": \"./images/B005H98Z9W.png\", \"hn_image\": [\"./images/B00337XRJS.png\"]}\n{\"q_img\": \"./images/B00D01V4OC.png\", \"q_text\": \"Is more black and bright, and is more loose fitting and less fitted\", \"positive_key\": \"B0085UCF3M\", \"positive_value\": \"./images/B0085UCF3M.png\", \"hn_image\": [\"./images/B00D01V4OC.png\"]}\n{\"q_img\": \"./images/B0099694TE.png\", \"q_text\": \"has shorter sleeves and lighter in color, and is white with red image\", \"positive_key\": \"B00580DVJO\", \"positive_value\": \"./images/B00580DVJO.png\", \"hn_image\": [\"./images/B0099694TE.png\"]}\n{\"q_img\": \"./images/B0084UUHB0.png\", \"q_text\": \"is smaller in size, and has a piggies graphic\", \"positive_key\": \"B004Q8J39Q\", \"positive_value\": \"./images/B004Q8J39Q.png\", \"hn_image\": [\"./images/B0084UUHB0.png\"]}\n{\"q_img\": \"./images/B00686R8MS.png\", \"q_text\": \"is a collared shirt, and is more formal\", \"positive_key\": \"B005EJFXKY\", \"positive_value\": \"./images/B005EJFXKY.png\", \"hn_image\": [\"./images/B00686R8MS.png\"]}\n{\"q_img\": \"./images/B001SN7QH8.png\", \"q_text\": \"is black with white characters, and has a darker background and smaller graphics\", \"positive_key\": \"B003WE9084\", \"positive_value\": \"./images/B003WE9084.png\", \"hn_image\": [\"./images/B001SN7QH8.png\"]}\n{\"q_img\": \"./images/B001TPFF0U.png\", \"q_text\": \"The shirt is black in color with Marilyn Monroe., and is faded\", \"positive_key\": \"B004JHP10E\", \"positive_value\": \"./images/B004JHP10E.png\", \"hn_image\": [\"./images/B001TPFF0U.png\"]}\n{\"q_img\": \"./images/B0081D7C7W.png\", \"q_text\": \"is lighter with different graphics, and It is less red and more designed\", \"positive_key\": \"B004LE9P8E\", \"positive_value\": \"./images/B004LE9P8E.png\", \"hn_image\": [\"./images/B0081D7C7W.png\"]}\n{\"q_img\": \"./images/B002U4LDZK.png\", \"q_text\": \"Is more pink and less wordy, and is pink\", \"positive_key\": \"B00CLCERMC\", \"positive_value\": \"./images/B00CLCERMC.png\", \"hn_image\": [\"./images/B002U4LDZK.png\"]}\n{\"q_img\": \"./images/B005K7SZGO.png\", \"q_text\": \" not words., and striped polo shirt of woven material with buttons\", \"positive_key\": \"B000NJGSWW\", \"positive_value\": \"./images/B000NJGSWW.png\", \"hn_image\": [\"./images/B005K7SZGO.png\"]}\n{\"q_img\": \"./images/B00DG9ISYM.png\", \"q_text\": \"Is specifically for hiking., and A grey and black vest\", \"positive_key\": \"B003R50LA4\", \"positive_value\": \"./images/B003R50LA4.png\", \"hn_image\": [\"./images/B00DG9ISYM.png\"]}\n{\"q_img\": \"./images/B00D6KCEI2.png\", \"q_text\": \"is more colorful, and is darker\", \"positive_key\": \"B00642S27Q\", \"positive_value\": \"./images/B00642S27Q.png\", \"hn_image\": [\"./images/B00D6KCEI2.png\"]}\n{\"q_img\": \"./images/B0013FI8JQ.png\", \"q_text\": \"has a rg3 logo, and it is purple and not plain\", \"positive_key\": \"B007XJBQCM\", \"positive_value\": \"./images/B007XJBQCM.png\", \"hn_image\": [\"./images/B0013FI8JQ.png\"]}\n{\"q_img\": \"./images/B007XX6790.png\", \"q_text\": \"is short sleeved polo, and shirt with short sleeves\", \"positive_key\": \"B0040I9L12\", \"positive_value\": \"./images/B0040I9L12.png\", \"hn_image\": [\"./images/B007XX6790.png\"]}\n{\"q_img\": \"./images/B006Y5SM2I.png\", \"q_text\": \"Is more whimsical and black, and is black with picture of a woman\", \"positive_key\": \"B006Y5SYO4\", \"positive_value\": \"./images/B006Y5SYO4.png\", \"hn_image\": [\"./images/B006Y5SM2I.png\"]}\n{\"q_img\": \"./images/B0012O8DC0.png\", \"q_text\": \"is solid black without a collar, and is matt black and has no buttons\", \"positive_key\": \"B0084UXV86\", \"positive_value\": \"./images/B0084UXV86.png\", \"hn_image\": [\"./images/B0012O8DC0.png\"]}\n{\"q_img\": \"./images/B00E9M1M5C.png\", \"q_text\": \"is a darker yellow, and is completely yellow in color\", \"positive_key\": \"B008T3V1YO\", \"positive_value\": \"./images/B008T3V1YO.png\", \"hn_image\": [\"./images/B00E9M1M5C.png\"]}\n{\"q_img\": \"./images/B000OM3WSQ.png\", \"q_text\": \"The shirt is dingy white and has a button up collar., and has longer sleeves\", \"positive_key\": \"B00AMBW9MO\", \"positive_value\": \"./images/B00AMBW9MO.png\", \"hn_image\": [\"./images/B000OM3WSQ.png\"]}\n{\"q_img\": \"./images/B00BS7FFQ8.png\", \"q_text\": \"Is a blue and navy polo, and has shorter sleeves and is darker coloured\", \"positive_key\": \"B00BEDXZAY\", \"positive_value\": \"./images/B00BEDXZAY.png\", \"hn_image\": [\"./images/B00BS7FFQ8.png\"]}\n{\"q_img\": \"./images/B004R9DY4E.png\", \"q_text\": \"is white with yellow words, and is more childish\", \"positive_key\": \"B002SAV10S\", \"positive_value\": \"./images/B002SAV10S.png\", \"hn_image\": [\"./images/B004R9DY4E.png\"]}\n{\"q_img\": \"./images/B009G0S8CM.png\", \"q_text\": \"Is more red and not long-sleeved, and is shorter sleeved and is plaid\", \"positive_key\": \"B007UNXLB0\", \"positive_value\": \"./images/B007UNXLB0.png\", \"hn_image\": [\"./images/B009G0S8CM.png\"]}\n{\"q_img\": \"./images/B002HNSH80.png\", \"q_text\": \"is blue with logo in the middle, and has a larger font on it\", \"positive_key\": \"B0072ZB1A4\", \"positive_value\": \"./images/B0072ZB1A4.png\", \"hn_image\": [\"./images/B002HNSH80.png\"]}\n{\"q_img\": \"./images/B000EMLDNC.png\", \"q_text\": \"is more festive, and is red and has longer sleeves\", \"positive_key\": \"B00GNCQQ34\", \"positive_value\": \"./images/B00GNCQQ34.png\", \"hn_image\": [\"./images/B000EMLDNC.png\"]}\n{\"q_img\": \"./images/B0087KODVM.png\", \"q_text\": \"has a smaller image with less colors, and has an orange colored graphic\", \"positive_key\": \"B007GB06WI\", \"positive_value\": \"./images/B007GB06WI.png\", \"hn_image\": [\"./images/B0087KODVM.png\"]}\n{\"q_img\": \"./images/B0060EYIU8.png\", \"q_text\": \"is black with gray designs, and has blacker colors\", \"positive_key\": \"B00827XZUK\", \"positive_value\": \"./images/B00827XZUK.png\", \"hn_image\": [\"./images/B0060EYIU8.png\"]}\n{\"q_img\": \"./images/B003BAEVJ2.png\", \"q_text\": \"is blue with stripes and buttons, and has more buttons\", \"positive_key\": \"B00CWRLCV0\", \"positive_value\": \"./images/B00CWRLCV0.png\", \"hn_image\": [\"./images/B003BAEVJ2.png\"]}\n{\"q_img\": \"./images/B0001TOMJA.png\", \"q_text\": \"Is black and less wordy, and has more red on it\", \"positive_key\": \"B007FBKECQ\", \"positive_value\": \"./images/B007FBKECQ.png\", \"hn_image\": [\"./images/B0001TOMJA.png\"]}\n{\"q_img\": \"./images/B00BLQFUX4.png\", \"q_text\": \"Has text on the front that says I'd rather be bodybuilding, and has I'd rather be bodyuilding text\", \"positive_key\": \"B008J8FDAM\", \"positive_value\": \"./images/B008J8FDAM.png\", \"hn_image\": [\"./images/B00BLQFUX4.png\"]}\n{\"q_img\": \"./images/B0093OQBCK.png\", \"q_text\": \"is a black vodka T shirt, and is wide fit with wording graphics\", \"positive_key\": \"B00294F366\", \"positive_value\": \"./images/B00294F366.png\", \"hn_image\": [\"./images/B0093OQBCK.png\"]}\n{\"q_img\": \"./images/B000FFB8D8.png\", \"q_text\": \"is lighter colored with a panda image, and is more animal-like and stylish\", \"positive_key\": \"B004M45KQ4\", \"positive_value\": \"./images/B004M45KQ4.png\", \"hn_image\": [\"./images/B000FFB8D8.png\"]}\n{\"q_img\": \"./images/B00DE1BDLC.png\", \"q_text\": \"Is more blue and white, and is more colorful and has more logos\", \"positive_key\": \"B006ID8WAI\", \"positive_value\": \"./images/B006ID8WAI.png\", \"hn_image\": [\"./images/B00DE1BDLC.png\"]}\n{\"q_img\": \"./images/B003MSNCM0.png\", \"q_text\": \"is more sexual and brighter, and is white with woman riding horse\", \"positive_key\": \"B00AR0ARLK\", \"positive_value\": \"./images/B00AR0ARLK.png\", \"hn_image\": [\"./images/B003MSNCM0.png\"]}\n{\"q_img\": \"./images/B00AVMPTQM.png\", \"q_text\": \"Is black and has a larger graphic, and is more colorful\", \"positive_key\": \"B007ENRNDI\", \"positive_value\": \"./images/B007ENRNDI.png\", \"hn_image\": [\"./images/B00AVMPTQM.png\"]}\n{\"q_img\": \"./images/B00BPXKESE.png\", \"q_text\": \"is lighter with a different logo, and is blue and wider\", \"positive_key\": \"B0084A8JCO\", \"positive_value\": \"./images/B0084A8JCO.png\", \"hn_image\": [\"./images/B00BPXKESE.png\"]}\n{\"q_img\": \"./images/B007VYS9I8.png\", \"q_text\": \"is a shirt with bright checkers, and is a red fleece with longer sleeves\", \"positive_key\": \"B005PJSVJS\", \"positive_value\": \"./images/B005PJSVJS.png\", \"hn_image\": [\"./images/B007VYS9I8.png\"]}\n{\"q_img\": \"./images/B003GWWK7K.png\", \"q_text\": \"is long sleeved and plaid, and is more formal\", \"positive_key\": \"B004S7ERQ4\", \"positive_value\": \"./images/B004S7ERQ4.png\", \"hn_image\": [\"./images/B003GWWK7K.png\"]}\n{\"q_img\": \"./images/B00CB3D1MS.png\", \"q_text\": \"Has a different graphic and is darker, and Is darker with smaller words\", \"positive_key\": \"B001DTIOFU\", \"positive_value\": \"./images/B001DTIOFU.png\", \"hn_image\": [\"./images/B00CB3D1MS.png\"]}\n{\"q_img\": \"./images/B005CZM4XE.png\", \"q_text\": \"is solid black with green lettering, and Has a larger decal\", \"positive_key\": \"B00931VPYM\", \"positive_value\": \"./images/B00931VPYM.png\", \"hn_image\": [\"./images/B005CZM4XE.png\"]}\n{\"q_img\": \"./images/B00E3OW73M.png\", \"q_text\": \"The shirt is white and black in .color, and has larger graphics and lighter color\", \"positive_key\": \"B00EQV0784\", \"positive_value\": \"./images/B00EQV0784.png\", \"hn_image\": [\"./images/B00E3OW73M.png\"]}\n{\"q_img\": \"./images/B004XMVOPQ.png\", \"q_text\": \"Is more red and simple, and with yellow stripes\", \"positive_key\": \"B005OCBIX2\", \"positive_value\": \"./images/B005OCBIX2.png\", \"hn_image\": [\"./images/B004XMVOPQ.png\"]}\n{\"q_img\": \"./images/B001DIQ02K.png\", \"q_text\": \"is yellow with a figure on it, and is a different color and a crew neck\", \"positive_key\": \"B000JM3A1A\", \"positive_value\": \"./images/B000JM3A1A.png\", \"hn_image\": [\"./images/B001DIQ02K.png\"]}\n{\"q_img\": \"./images/B003YPJTGY.png\", \"q_text\": \"has different graphics, and is grey with woodstock logo\", \"positive_key\": \"B0079K5NVK\", \"positive_value\": \"./images/B0079K5NVK.png\", \"hn_image\": [\"./images/B003YPJTGY.png\"]}\n{\"q_img\": \"./images/B006PGF5HQ.png\", \"q_text\": \"is a white t-shirt with small writing on top chest, and is a white t shirt and is much more plain\", \"positive_key\": \"B006JI53NQ\", \"positive_value\": \"./images/B006JI53NQ.png\", \"hn_image\": [\"./images/B006PGF5HQ.png\"]}\n{\"q_img\": \"./images/B0055KYMCW.png\", \"q_text\": \"is more photographic and less humorous, and is dark blue with colourful picture\", \"positive_key\": \"B000F5K9ZQ\", \"positive_value\": \"./images/B000F5K9ZQ.png\", \"hn_image\": [\"./images/B0055KYMCW.png\"]}\n{\"q_img\": \"./images/B0083SDCOC.png\", \"q_text\": \"White and longer sleeves, and is much lighter in color\", \"positive_key\": \"B00G37DN5S\", \"positive_value\": \"./images/B00G37DN5S.png\", \"hn_image\": [\"./images/B0083SDCOC.png\"]}\n{\"q_img\": \"./images/B00A353SXI.png\", \"q_text\": \"has longer sleeves and a demin look, and is a shirt and is more blue in colour\", \"positive_key\": \"B008OUP3RS\", \"positive_value\": \"./images/B008OUP3RS.png\", \"hn_image\": [\"./images/B00A353SXI.png\"]}\n{\"q_img\": \"./images/B00DSY5UOQ.png\", \"q_text\": \"The shirt is blue jean., and longer sleeves\", \"positive_key\": \"B007679AJ2\", \"positive_value\": \"./images/B007679AJ2.png\", \"hn_image\": [\"./images/B00DSY5UOQ.png\"]}\n{\"q_img\": \"./images/B00AJML1ZM.png\", \"q_text\": \"short sleeved and picture graphic, and has longer sleeves and a different graphic\", \"positive_key\": \"B00CFP6098\", \"positive_value\": \"./images/B00CFP6098.png\", \"hn_image\": [\"./images/B00AJML1ZM.png\"]}\n{\"q_img\": \"./images/B008B8NS0C.png\", \"q_text\": \"is darker gray, and has a flag logo and darker color\", \"positive_key\": \"B00B97REHU\", \"positive_value\": \"./images/B00B97REHU.png\", \"hn_image\": [\"./images/B008B8NS0C.png\"]}\n{\"q_img\": \"./images/B000YDCP06.png\", \"q_text\": \"is darker, and is a darker purple with a design on the front\", \"positive_key\": \"B000BNZCZ4\", \"positive_value\": \"./images/B000BNZCZ4.png\", \"hn_image\": [\"./images/B000YDCP06.png\"]}\n{\"q_img\": \"./images/B005MZ1V4C.png\", \"q_text\": \"is darker, and is gray\", \"positive_key\": \"B000Y1FWB2\", \"positive_value\": \"./images/B000Y1FWB2.png\", \"hn_image\": [\"./images/B005MZ1V4C.png\"]}\n{\"q_img\": \"./images/B005TL7L5W.png\", \"q_text\": \"is lighter with different graphics, and is lighter and has an avengers graphic\", \"positive_key\": \"B008K7ZNRU\", \"positive_value\": \"./images/B008K7ZNRU.png\", \"hn_image\": [\"./images/B005TL7L5W.png\"]}\n{\"q_img\": \"./images/B008WFQVVW.png\", \"q_text\": \"is a light blue color dress shirt, and light blue shirt with no stripes\", \"positive_key\": \"B008KGWOHS\", \"positive_value\": \"./images/B008KGWOHS.png\", \"hn_image\": [\"./images/B008WFQVVW.png\"]}\n{\"q_img\": \"./images/B003SZXF3S.png\", \"q_text\": \"has more color, and is red and has human figure\", \"positive_key\": \"B004TTHNIA\", \"positive_value\": \"./images/B004TTHNIA.png\", \"hn_image\": [\"./images/B003SZXF3S.png\"]}\n{\"q_img\": \"./images/B0047HKIYU.png\", \"q_text\": \"Is darker and has short sleeves., and has short sleeves and a logo\", \"positive_key\": \"B006428WQ2\", \"positive_value\": \"./images/B006428WQ2.png\", \"hn_image\": [\"./images/B0047HKIYU.png\"]}\n{\"q_img\": \"./images/B009E9J5HC.png\", \"q_text\": \"is darker colored, and is grey\", \"positive_key\": \"B0038OVKR2\", \"positive_value\": \"./images/B0038OVKR2.png\", \"hn_image\": [\"./images/B009E9J5HC.png\"]}\n{\"q_img\": \"./images/B0020Q11LE.png\", \"q_text\": \"is a black T shirt with a logo, and is black with red circular motif\", \"positive_key\": \"B00CIYDP94\", \"positive_value\": \"./images/B00CIYDP94.png\", \"hn_image\": [\"./images/B0020Q11LE.png\"]}\n{\"q_img\": \"./images/B007HZ9C6Y.png\", \"q_text\": \"is more brighter and green, and is solid green\", \"positive_key\": \"B005EG1LE4\", \"positive_value\": \"./images/B005EG1LE4.png\", \"hn_image\": [\"./images/B007HZ9C6Y.png\"]}\n{\"q_img\": \"./images/B0036XODB0.png\", \"q_text\": \"The short sleeve shirt is orange in color., and Desired item is light brownish-orange in color\", \"positive_key\": \"B0036XMY5M\", \"positive_value\": \"./images/B0036XMY5M.png\", \"hn_image\": [\"./images/B0036XODB0.png\"]}\n{\"q_img\": \"./images/B00D6URY78.png\", \"q_text\": \"is green with short sleeves and a slim fit, and is a lighter color and has shorter sleeves\", \"positive_key\": \"B0062B5JQQ\", \"positive_value\": \"./images/B0062B5JQQ.png\", \"hn_image\": [\"./images/B00D6URY78.png\"]}\n{\"q_img\": \"./images/B0012XKATA.png\", \"q_text\": \"is black with gray designs, and is darker in color\", \"positive_key\": \"B001GEHX1I\", \"positive_value\": \"./images/B001GEHX1I.png\", \"hn_image\": [\"./images/B0012XKATA.png\"]}\n{\"q_img\": \"./images/B006ID8WAI.png\", \"q_text\": \"is color blocked, and has no logos\", \"positive_key\": \"B00ARFOX60\", \"positive_value\": \"./images/B00ARFOX60.png\", \"hn_image\": [\"./images/B006ID8WAI.png\"]}\n{\"q_img\": \"./images/B005TON8VA.png\", \"q_text\": \"has different graphics, and has more colors\", \"positive_key\": \"B00C03UNC0\", \"positive_value\": \"./images/B00C03UNC0.png\", \"hn_image\": [\"./images/B005TON8VA.png\"]}\n{\"q_img\": \"./images/B007TXNXA0.png\", \"q_text\": \"is grey with a pocket, and is slate grey\", \"positive_key\": \"B009PXV0JS\", \"positive_value\": \"./images/B009PXV0JS.png\", \"hn_image\": [\"./images/B007TXNXA0.png\"]}\n{\"q_img\": \"./images/B00CPSDPCU.png\", \"q_text\": \"Is darker in color., and is gray and has a CSI graphic\", \"positive_key\": \"B001EAQHV6\", \"positive_value\": \"./images/B001EAQHV6.png\", \"hn_image\": [\"./images/B00CPSDPCU.png\"]}\n{\"q_img\": \"./images/B005HIQ9KA.png\", \"q_text\": \"is a darker color, and is darker and less graphic\", \"positive_key\": \"B005409ELW\", \"positive_value\": \"./images/B005409ELW.png\", \"hn_image\": [\"./images/B005HIQ9KA.png\"]}\n{\"q_img\": \"./images/B009P73OCK.png\", \"q_text\": \"is a fitted tee, and has more text on it\", \"positive_key\": \"B00CIBHIO0\", \"positive_value\": \"./images/B00CIBHIO0.png\", \"hn_image\": [\"./images/B009P73OCK.png\"]}\n{\"q_img\": \"./images/B005JQ5YBU.png\", \"q_text\": \"is a plain black, and is darker\", \"positive_key\": \"B005JQ4WEK\", \"positive_value\": \"./images/B005JQ4WEK.png\", \"hn_image\": [\"./images/B005JQ5YBU.png\"]}\n{\"q_img\": \"./images/B0046Q4F28.png\", \"q_text\": \"is dark grey with pi on it, and is darker colored and fewer fonts\", \"positive_key\": \"B00CAAJSYC\", \"positive_value\": \"./images/B00CAAJSYC.png\", \"hn_image\": [\"./images/B0046Q4F28.png\"]}\n{\"q_img\": \"./images/B003Z6Q7YY.png\", \"q_text\": \"is black in color, and is darker\", \"positive_key\": \"B0059A4A5C\", \"positive_value\": \"./images/B0059A4A5C.png\", \"hn_image\": [\"./images/B003Z6Q7YY.png\"]}\n{\"q_img\": \"./images/B008LJZV0G.png\", \"q_text\": \"has a short sleeve black color with a brand logo, and has no collar and is black\", \"positive_key\": \"B001RXMRKU\", \"positive_value\": \"./images/B001RXMRKU.png\", \"hn_image\": [\"./images/B008LJZV0G.png\"]}\n{\"q_img\": \"./images/B00AWA4LW6.png\", \"q_text\": \"Is more blue and has more buttons, and Is a light blue\", \"positive_key\": \"B006N1KIHK\", \"positive_value\": \"./images/B006N1KIHK.png\", \"hn_image\": [\"./images/B00AWA4LW6.png\"]}\n{\"q_img\": \"./images/B0091VAKBI.png\", \"q_text\": \"white colored and larger graphic, and is white I love NY T shirt\", \"positive_key\": \"B005MKSBAE\", \"positive_value\": \"./images/B005MKSBAE.png\", \"hn_image\": [\"./images/B0091VAKBI.png\"]}\n{\"q_img\": \"./images/B007VHEYAM.png\", \"q_text\": \"is less formal and has short sleeves, and is white with black lettering\", \"positive_key\": \"B008IJULM2\", \"positive_value\": \"./images/B008IJULM2.png\", \"hn_image\": [\"./images/B007VHEYAM.png\"]}\n{\"q_img\": \"./images/B009L4NRSS.png\", \"q_text\": \"has a different picture, and is black with a blue logo\", \"positive_key\": \"B007SH2NT4\", \"positive_value\": \"./images/B007SH2NT4.png\", \"hn_image\": [\"./images/B009L4NRSS.png\"]}\n{\"q_img\": \"./images/B001SN7NRQ.png\", \"q_text\": \"is darker and has a graphic, and different graphic shirt\", \"positive_key\": \"B001BZ6ILS\", \"positive_value\": \"./images/B001BZ6ILS.png\", \"hn_image\": [\"./images/B001SN7NRQ.png\"]}\n{\"q_img\": \"./images/B0061S0348.png\", \"q_text\": \"is black in color, and is less humorous and more goth\", \"positive_key\": \"B00AIAIO46\", \"positive_value\": \"./images/B00AIAIO46.png\", \"hn_image\": [\"./images/B0061S0348.png\"]}\n{\"q_img\": \"./images/B00AZIAZK2.png\", \"q_text\": \"Is more whimsical and black, and is a dark t-shirt with a graphic\", \"positive_key\": \"B001LY2VLK\", \"positive_value\": \"./images/B001LY2VLK.png\", \"hn_image\": [\"./images/B00AZIAZK2.png\"]}\n{\"q_img\": \"./images/B00AZLZ8UQ.png\", \"q_text\": \"is lighter, and is just a tie with no shirt\", \"positive_key\": \"B001QVJ6PM\", \"positive_value\": \"./images/B001QVJ6PM.png\", \"hn_image\": [\"./images/B00AZLZ8UQ.png\"]}\n{\"q_img\": \"./images/B003ZAH3AM.png\", \"q_text\": \"has shorter sleeves and is wordy, and is cream with short sleeves and no collar\", \"positive_key\": \"B004YOA0EE\", \"positive_value\": \"./images/B004YOA0EE.png\", \"hn_image\": [\"./images/B003ZAH3AM.png\"]}\n{\"q_img\": \"./images/B00BOHU9ZE.png\", \"q_text\": \"Is camo patterned and has a graphic, and has a vegetation pattern and is more colorful.\", \"positive_key\": \"B00AKYDNFA\", \"positive_value\": \"./images/B00AKYDNFA.png\", \"hn_image\": [\"./images/B00BOHU9ZE.png\"]}\n{\"q_img\": \"./images/B00BCXL3QE.png\", \"q_text\": \"is more scary, and has a darker color\", \"positive_key\": \"B007KDGECS\", \"positive_value\": \"./images/B007KDGECS.png\", \"hn_image\": [\"./images/B00BCXL3QE.png\"]}\n{\"q_img\": \"./images/B004U9F624.png\", \"q_text\": \"is more colorful and longer, and is yellow and brown plaid color\", \"positive_key\": \"B003NUSAVU\", \"positive_value\": \"./images/B003NUSAVU.png\", \"hn_image\": [\"./images/B004U9F624.png\"]}\n{\"q_img\": \"./images/B00CIYDP94.png\", \"q_text\": \"is gray and has a picture on the front, and is lighter colored\", \"positive_key\": \"B00BYINGXK\", \"positive_value\": \"./images/B00BYINGXK.png\", \"hn_image\": [\"./images/B00CIYDP94.png\"]}\n{\"q_img\": \"./images/B005MKSBAE.png\", \"q_text\": \"is black in color, and is black and has image logo\", \"positive_key\": \"B00DUEF8LY\", \"positive_value\": \"./images/B00DUEF8LY.png\", \"hn_image\": [\"./images/B005MKSBAE.png\"]}\n{\"q_img\": \"./images/B00AX0SS24.png\", \"q_text\": \"has a different pattern and is less colorful., and is grey with white spots\", \"positive_key\": \"B008MJI77Y\", \"positive_value\": \"./images/B008MJI77Y.png\", \"hn_image\": [\"./images/B00AX0SS24.png\"]}\n{\"q_img\": \"./images/B001DYFD6S.png\", \"q_text\": \"is white and has has no buttons, and completely different shirts. Desired is white and designed. The other is a dress shirt.\", \"positive_key\": \"B00BMV7WYI\", \"positive_value\": \"./images/B00BMV7WYI.png\", \"hn_image\": [\"./images/B001DYFD6S.png\"]}\n{\"q_img\": \"./images/B004NIZR44.png\", \"q_text\": \"is lighter and has longer sleeves, and is white and long sleeves\", \"positive_key\": \"B004RDZ39E\", \"positive_value\": \"./images/B004RDZ39E.png\", \"hn_image\": [\"./images/B004NIZR44.png\"]}\n{\"q_img\": \"./images/B00509BTHY.png\", \"q_text\": \"has yellow words on it, and has more graphics on it\", \"positive_key\": \"B00018A9XO\", \"positive_value\": \"./images/B00018A9XO.png\", \"hn_image\": [\"./images/B00509BTHY.png\"]}\n{\"q_img\": \"./images/B000J0M6BC.png\", \"q_text\": \"is black with white words, and black with different graphic\", \"positive_key\": \"B008TSM9CW\", \"positive_value\": \"./images/B008TSM9CW.png\", \"hn_image\": [\"./images/B000J0M6BC.png\"]}\n{\"q_img\": \"./images/B00AZ020X0.png\", \"q_text\": \"is white with black background logo, and is white and more graphic design\", \"positive_key\": \"B006ICWAWA\", \"positive_value\": \"./images/B006ICWAWA.png\", \"hn_image\": [\"./images/B00AZ020X0.png\"]}\n{\"q_img\": \"./images/B004W75SW2.png\", \"q_text\": \" pointing hand on the front, and darker plaid long sleeve shirt\", \"positive_key\": \"B0030DG7TC\", \"positive_value\": \"./images/B0030DG7TC.png\", \"hn_image\": [\"./images/B004W75SW2.png\"]}\n{\"q_img\": \"./images/B001E6GK8K.png\", \"q_text\": \"is a brown t-shirt with a pink haired anime girl on it., and is lighter\", \"positive_key\": \"B0059XUITG\", \"positive_value\": \"./images/B0059XUITG.png\", \"hn_image\": [\"./images/B001E6GK8K.png\"]}\n{\"q_img\": \"./images/B001GEHX1I.png\", \"q_text\": \"is a black T shirt with skeleton and ships wheel, and is tighter\", \"positive_key\": \"B001L4TRKS\", \"positive_value\": \"./images/B001L4TRKS.png\", \"hn_image\": [\"./images/B001GEHX1I.png\"]}\n{\"q_img\": \"./images/B00AQR2JM4.png\", \"q_text\": \"is more casual and darker, and is plain and has short sleeves\", \"positive_key\": \"B004OYVAIY\", \"positive_value\": \"./images/B004OYVAIY.png\", \"hn_image\": [\"./images/B00AQR2JM4.png\"]}\n{\"q_img\": \"./images/B004JJVF48.png\", \"q_text\": \"is a gray tshirt, and Different color and phrase. The other could be offensive.\", \"positive_key\": \"B005OCHMO6\", \"positive_value\": \"./images/B005OCHMO6.png\", \"hn_image\": [\"./images/B004JJVF48.png\"]}\n{\"q_img\": \"./images/B009T6010U.png\", \"q_text\": \"is lighter blue with white stripes, and is pale blue with white stripes\", \"positive_key\": \"B009T5NMS4\", \"positive_value\": \"./images/B009T5NMS4.png\", \"hn_image\": [\"./images/B009T6010U.png\"]}\n{\"q_img\": \"./images/B005MV00ES.png\", \"q_text\": \"has a face graphic of an actor, and has more neutral colored graphics\", \"positive_key\": \"B001L17S0C\", \"positive_value\": \"./images/B001L17S0C.png\", \"hn_image\": [\"./images/B005MV00ES.png\"]}\n{\"q_img\": \"./images/B007T4JJ5W.png\", \"q_text\": \"is olive green with short sleeves, and is brown with pockets\", \"positive_key\": \"B00A0UW5HG\", \"positive_value\": \"./images/B00A0UW5HG.png\", \"hn_image\": [\"./images/B007T4JJ5W.png\"]}\n{\"q_img\": \"./images/B0007R8KA8.png\", \"q_text\": \"The shirt is black in color with a blue shark., and is black\", \"positive_key\": \"B0007R8KCQ\", \"positive_value\": \"./images/B0007R8KCQ.png\", \"hn_image\": [\"./images/B0007R8KA8.png\"]}\n{\"q_img\": \"./images/B00A74AOSM.png\", \"q_text\": \"is whiter and has short sleeves, and is lighter and has shorter sleeves\", \"positive_key\": \"B004JF5VJW\", \"positive_value\": \"./images/B004JF5VJW.png\", \"hn_image\": [\"./images/B00A74AOSM.png\"]}\n{\"q_img\": \"./images/B0089SVHFW.png\", \"q_text\": \"is a pink shade, and Is pink with a larger graphic\", \"positive_key\": \"B003BSOLP8\", \"positive_value\": \"./images/B003BSOLP8.png\", \"hn_image\": [\"./images/B0089SVHFW.png\"]}\n{\"q_img\": \"./images/B00AYU2QZ8.png\", \"q_text\": \"has longer sleeves and is brown, and is long sleeved\", \"positive_key\": \"B00CBEYFHW\", \"positive_value\": \"./images/B00CBEYFHW.png\", \"hn_image\": [\"./images/B00AYU2QZ8.png\"]}\n{\"q_img\": \"./images/B00CMQ8ZXO.png\", \"q_text\": \"Is white, and is white with Colt 45 picture and other is black with different logo\", \"positive_key\": \"B00765MFB4\", \"positive_value\": \"./images/B00765MFB4.png\", \"hn_image\": [\"./images/B00CMQ8ZXO.png\"]}\n{\"q_img\": \"./images/B00D3DB5KU.png\", \"q_text\": \"is a darker blue with long sleeves, and Is blue and less casual.\", \"positive_key\": \"B005FOP470\", \"positive_value\": \"./images/B005FOP470.png\", \"hn_image\": [\"./images/B00D3DB5KU.png\"]}\n{\"q_img\": \"./images/B00542QOFE.png\", \"q_text\": \"has text written in green, and is more faded\", \"positive_key\": \"B0091HI4VK\", \"positive_value\": \"./images/B0091HI4VK.png\", \"hn_image\": [\"./images/B00542QOFE.png\"]}\n{\"q_img\": \"./images/B002EQ9C8O.png\", \"q_text\": \"is darker blue, and is darker\", \"positive_key\": \"B002KGFNS6\", \"positive_value\": \"./images/B002KGFNS6.png\", \"hn_image\": [\"./images/B002EQ9C8O.png\"]}\n{\"q_img\": \"./images/B002M11IU6.png\", \"q_text\": \"is different colors, and two tone color\", \"positive_key\": \"B003QCBBJI\", \"positive_value\": \"./images/B003QCBBJI.png\", \"hn_image\": [\"./images/B002M11IU6.png\"]}\n{\"q_img\": \"./images/B00BRB2H8O.png\", \"q_text\": \"is white with a woman on it without a collar, and is lighter\", \"positive_key\": \"B00BU7BST4\", \"positive_value\": \"./images/B00BU7BST4.png\", \"hn_image\": [\"./images/B00BRB2H8O.png\"]}\n{\"q_img\": \"./images/B00AH7QE2Y.png\", \"q_text\": \"is a green colored shirt with an image, and is army green and has less animated graphics\", \"positive_key\": \"B00B2NNXBM\", \"positive_value\": \"./images/B00B2NNXBM.png\", \"hn_image\": [\"./images/B00AH7QE2Y.png\"]}\n{\"q_img\": \"./images/B0057XC4P4.png\", \"q_text\": \"has longer sleeves and a collar, and less casual with longer sleever\", \"positive_key\": \"B00APU4PO2\", \"positive_value\": \"./images/B00APU4PO2.png\", \"hn_image\": [\"./images/B0057XC4P4.png\"]}\n{\"q_img\": \"./images/B00816NL4M.png\", \"q_text\": \"is a plain white T shirt, and is lighter\", \"positive_key\": \"B005C1QMSQ\", \"positive_value\": \"./images/B005C1QMSQ.png\", \"hn_image\": [\"./images/B00816NL4M.png\"]}\n{\"q_img\": \"./images/B004VG7H4G.png\", \"q_text\": \"is black with words, and is darker and less stylish\", \"positive_key\": \"B00CRMP2YS\", \"positive_value\": \"./images/B00CRMP2YS.png\", \"hn_image\": [\"./images/B004VG7H4G.png\"]}\n{\"q_img\": \"./images/B00164GQDY.png\", \"q_text\": \"The shirt is a orange hibiscus shirt., and button up orange tropical print\", \"positive_key\": \"B009D63E2I\", \"positive_value\": \"./images/B009D63E2I.png\", \"hn_image\": [\"./images/B00164GQDY.png\"]}\n{\"q_img\": \"./images/B0091GJHGW.png\", \"q_text\": \"Has no stripes and is black, and has finer vertical striping\", \"positive_key\": \"B009OX7692\", \"positive_value\": \"./images/B009OX7692.png\", \"hn_image\": [\"./images/B0091GJHGW.png\"]}\n{\"q_img\": \"./images/B006YYY8UO.png\", \"q_text\": \"has diferente words on it, and has words got missionaries?\", \"positive_key\": \"B008H7I2L2\", \"positive_value\": \"./images/B008H7I2L2.png\", \"hn_image\": [\"./images/B006YYY8UO.png\"]}\n{\"q_img\": \"./images/B003HM7PT2.png\", \"q_text\": \"A lighter color, and is white with a photo of a person\", \"positive_key\": \"B00640IQFQ\", \"positive_value\": \"./images/B00640IQFQ.png\", \"hn_image\": [\"./images/B003HM7PT2.png\"]}\n{\"q_img\": \"./images/B004VUFJK6.png\", \"q_text\": \"is light colored with a huge image, and is white and has full grey logo\", \"positive_key\": \"B004B3T6R0\", \"positive_value\": \"./images/B004B3T6R0.png\", \"hn_image\": [\"./images/B004VUFJK6.png\"]}\n{\"q_img\": \"./images/B000RQO9S6.png\", \"q_text\": \"is just a plain smooth brown color, and is a brown belt that is not distressed\", \"positive_key\": \"B00CTPTNKW\", \"positive_value\": \"./images/B00CTPTNKW.png\", \"hn_image\": [\"./images/B000RQO9S6.png\"]}\n{\"q_img\": \"./images/B006C25WGM.png\", \"q_text\": \"is more graphic and pocketless., and is black with image\", \"positive_key\": \"B00DBE0UI4\", \"positive_value\": \"./images/B00DBE0UI4.png\", \"hn_image\": [\"./images/B006C25WGM.png\"]}\n{\"q_img\": \"./images/B00B7SA29S.png\", \"q_text\": \"has a smaller logo, and is longer\", \"positive_key\": \"B00B9DUBMY\", \"positive_value\": \"./images/B00B9DUBMY.png\", \"hn_image\": [\"./images/B00B7SA29S.png\"]}\n{\"q_img\": \"./images/B00B1FCGFU.png\", \"q_text\": \"is a blue colored shirt with visual art, and a navy short sleeved tee with eagle imprint\", \"positive_key\": \"B000OMGLDY\", \"positive_value\": \"./images/B000OMGLDY.png\", \"hn_image\": [\"./images/B00B1FCGFU.png\"]}\n{\"q_img\": \"./images/B001F23NH4.png\", \"q_text\": \"has a different logo, and has larger graphics on it\", \"positive_key\": \"B00CACXSU0\", \"positive_value\": \"./images/B00CACXSU0.png\", \"hn_image\": [\"./images/B001F23NH4.png\"]}\n{\"q_img\": \"./images/B000297JN0.png\", \"q_text\": \"has a black color with a decent size image, and has a rectangle graphic design\", \"positive_key\": \"B001S2PP5E\", \"positive_value\": \"./images/B001S2PP5E.png\", \"hn_image\": [\"./images/B000297JN0.png\"]}\n{\"q_img\": \"./images/B009EUI4G4.png\", \"q_text\": \"Is more casual and has shorter sleeves, and is a dark blue T shirt\", \"positive_key\": \"B007CL304I\", \"positive_value\": \"./images/B007CL304I.png\", \"hn_image\": [\"./images/B009EUI4G4.png\"]}\n{\"q_img\": \"./images/B0080SUGWQ.png\", \"q_text\": \"It's the same thing, and the shirts are the same\", \"positive_key\": \"B0080SUGXU\", \"positive_value\": \"./images/B0080SUGXU.png\", \"hn_image\": [\"./images/B0080SUGWQ.png\"]}\n{\"q_img\": \"./images/B00BCWHW2E.png\", \"q_text\": \"is a blue collared shirt, and it is blue and has collar\", \"positive_key\": \"B007JGEFZY\", \"positive_value\": \"./images/B007JGEFZY.png\", \"hn_image\": [\"./images/B00BCWHW2E.png\"]}\n{\"q_img\": \"./images/B0059BG2SE.png\", \"q_text\": \"Brighter and checked, and has a lighter color\", \"positive_key\": \"B0056FTFB4\", \"positive_value\": \"./images/B0056FTFB4.png\", \"hn_image\": [\"./images/B0059BG2SE.png\"]}\n{\"q_img\": \"./images/B0041D811M.png\", \"q_text\": \"is maroon and more plain, and is black with bright blue detailing\", \"positive_key\": \"B00A2CR8R4\", \"positive_value\": \"./images/B00A2CR8R4.png\", \"hn_image\": [\"./images/B0041D811M.png\"]}\n{\"q_img\": \"./images/B00BY6SGZA.png\", \"q_text\": \"Is more whimsical and colorful, and different larger graphic\", \"positive_key\": \"B0064SJ6EI\", \"positive_value\": \"./images/B0064SJ6EI.png\", \"hn_image\": [\"./images/B00BY6SGZA.png\"]}\n{\"q_img\": \"./images/B005TVEOKW.png\", \"q_text\": \"is black in color, and is darker colored\", \"positive_key\": \"B005L50K6I\", \"positive_value\": \"./images/B005L50K6I.png\", \"hn_image\": [\"./images/B005TVEOKW.png\"]}\n{\"q_img\": \"./images/B008JVJI00.png\", \"q_text\": \"is green, and is green with a yellow motif\", \"positive_key\": \"B009G81G82\", \"positive_value\": \"./images/B009G81G82.png\", \"hn_image\": [\"./images/B008JVJI00.png\"]}\n{\"q_img\": \"./images/B00BT0JNWQ.png\", \"q_text\": \"has less colors, and has short sleeves with art\", \"positive_key\": \"B008YF3AE6\", \"positive_value\": \"./images/B008YF3AE6.png\", \"hn_image\": [\"./images/B00BT0JNWQ.png\"]}\n{\"q_img\": \"./images/B004CTW2HY.png\", \"q_text\": \"is orange with facial features, and is lighter\", \"positive_key\": \"B00BE3Z99O\", \"positive_value\": \"./images/B00BE3Z99O.png\", \"hn_image\": [\"./images/B004CTW2HY.png\"]}\n{\"q_img\": \"./images/B00AJML1ZM.png\", \"q_text\": \"is a green t shirt, and is green with sleeves\", \"positive_key\": \"B00BJKVKQS\", \"positive_value\": \"./images/B00BJKVKQS.png\", \"hn_image\": [\"./images/B00AJML1ZM.png\"]}\n{\"q_img\": \"./images/B00CRVW3LY.png\", \"q_text\": \"is dark blue with white stripes button shirt, and is more formal\", \"positive_key\": \"B005XI84F2\", \"positive_value\": \"./images/B005XI84F2.png\", \"hn_image\": [\"./images/B00CRVW3LY.png\"]}\n{\"q_img\": \"./images/B00AZLZ8UQ.png\", \"q_text\": \"Is black and more casual, and is less formal\", \"positive_key\": \"B006X23XXK\", \"positive_value\": \"./images/B006X23XXK.png\", \"hn_image\": [\"./images/B00AZLZ8UQ.png\"]}\n{\"q_img\": \"./images/B00BGVUTBC.png\", \"q_text\": \"has a different font, and is a red short sleeved collared tee\", \"positive_key\": \"B00A42QEE0\", \"positive_value\": \"./images/B00A42QEE0.png\", \"hn_image\": [\"./images/B00BGVUTBC.png\"]}\n{\"q_img\": \"./images/B005OK1YZQ.png\", \"q_text\": \"is a black shirt with Godzilla on it, and is a shirt only\", \"positive_key\": \"B001UQULU2\", \"positive_value\": \"./images/B001UQULU2.png\", \"hn_image\": [\"./images/B005OK1YZQ.png\"]}\n{\"q_img\": \"./images/B000GA5YPK.png\", \"q_text\": \"is a darker color and has longer sleeves, and has longer sleeves and is darker\", \"positive_key\": \"B008HTLHSK\", \"positive_value\": \"./images/B008HTLHSK.png\", \"hn_image\": [\"./images/B000GA5YPK.png\"]}\n{\"q_img\": \"./images/B00AZIB77W.png\", \"q_text\": \"is short sleeve and plaid, and is striped with short sleeves\", \"positive_key\": \"B003INE18S\", \"positive_value\": \"./images/B003INE18S.png\", \"hn_image\": [\"./images/B00AZIB77W.png\"]}\n{\"q_img\": \"./images/B00DCUQD7Y.png\", \"q_text\": \"is darker with more cats, and is black with multiple grumpy cats\", \"positive_key\": \"B00C2CZTYG\", \"positive_value\": \"./images/B00C2CZTYG.png\", \"hn_image\": [\"./images/B00DCUQD7Y.png\"]}\n{\"q_img\": \"./images/B00G37DN5S.png\", \"q_text\": \"has shorter sleeves and is darker, and is gray with shorter sleeves\", \"positive_key\": \"B00C4RULCE\", \"positive_value\": \"./images/B00C4RULCE.png\", \"hn_image\": [\"./images/B00G37DN5S.png\"]}\n{\"q_img\": \"./images/B0061SL9LO.png\", \"q_text\": \"is grey colored, and has a smaller graphic to it\", \"positive_key\": \"B00ACE0CD4\", \"positive_value\": \"./images/B00ACE0CD4.png\", \"hn_image\": [\"./images/B0061SL9LO.png\"]}\n{\"q_img\": \"./images/B00AZUNPNE.png\", \"q_text\": \"is white color with red text, and is shorter sleeved and has no buttons or collar\", \"positive_key\": \"B007E6M3UI\", \"positive_value\": \"./images/B007E6M3UI.png\", \"hn_image\": [\"./images/B00AZUNPNE.png\"]}\n{\"q_img\": \"./images/B006Y8Q79U.png\", \"q_text\": \"is black, and is darker\", \"positive_key\": \"B005GQC97U\", \"positive_value\": \"./images/B005GQC97U.png\", \"hn_image\": [\"./images/B006Y8Q79U.png\"]}\n{\"q_img\": \"./images/B004QL7B9C.png\", \"q_text\": \"is white with blue strips, and is white with blue and yellow stripes\", \"positive_key\": \"B0078J5Y2U\", \"positive_value\": \"./images/B0078J5Y2U.png\", \"hn_image\": [\"./images/B004QL7B9C.png\"]}\n{\"q_img\": \"./images/B0051EPWF8.png\", \"q_text\": \"is green in color with stripes and longer sleeves, and is more formal\", \"positive_key\": \"B008LJZV0G\", \"positive_value\": \"./images/B008LJZV0G.png\", \"hn_image\": [\"./images/B0051EPWF8.png\"]}\n{\"q_img\": \"./images/B000LQVB9W.png\", \"q_text\": \"Orange green and blue pattern with more buttons, and is more formal\", \"positive_key\": \"B006WRED6W\", \"positive_value\": \"./images/B006WRED6W.png\", \"hn_image\": [\"./images/B000LQVB9W.png\"]}\n{\"q_img\": \"./images/B000S3P946.png\", \"q_text\": \"is gray with figures on it, and is more vogue\", \"positive_key\": \"B000KOU8E4\", \"positive_value\": \"./images/B000KOU8E4.png\", \"hn_image\": [\"./images/B000S3P946.png\"]}\n{\"q_img\": \"./images/B0040B640K.png\", \"q_text\": \"is a long sleeve red shirt with zipper, and is orange and has a zipper\", \"positive_key\": \"B0059D0Q9I\", \"positive_value\": \"./images/B0059D0Q9I.png\", \"hn_image\": [\"./images/B0040B640K.png\"]}\n{\"q_img\": \"./images/B0045MS86W.png\", \"q_text\": \"is dark brown with a blue cartoon logo of earth, and is much darker with different image\", \"positive_key\": \"B005X0QH9A\", \"positive_value\": \"./images/B005X0QH9A.png\", \"hn_image\": [\"./images/B0045MS86W.png\"]}\n{\"q_img\": \"./images/B004VFHTL8.png\", \"q_text\": \"Is more monochromatic, and is more of a navy blue\", \"positive_key\": \"B00605YPSC\", \"positive_value\": \"./images/B00605YPSC.png\", \"hn_image\": [\"./images/B004VFHTL8.png\"]}\n{\"q_img\": \"./images/B008X6WIBC.png\", \"q_text\": \"is black with a different logo, and A black shirt with large print on front\", \"positive_key\": \"B0094D2LWE\", \"positive_value\": \"./images/B0094D2LWE.png\", \"hn_image\": [\"./images/B008X6WIBC.png\"]}\n{\"q_img\": \"./images/B005JQ4FEM.png\", \"q_text\": \"is a checked shirt, and has more of a plaid pattern\", \"positive_key\": \"B005LH4FEY\", \"positive_value\": \"./images/B005LH4FEY.png\", \"hn_image\": [\"./images/B005JQ4FEM.png\"]}\n{\"q_img\": \"./images/B0083Z6QDO.png\", \"q_text\": \"looks more fashionable, and is black\", \"positive_key\": \"B007R0I1UC\", \"positive_value\": \"./images/B007R0I1UC.png\", \"hn_image\": [\"./images/B0083Z6QDO.png\"]}\n{\"q_img\": \"./images/B001JTDJZY.png\", \"q_text\": \"Is more buttoned and less casual, and is a darker blue\", \"positive_key\": \"B0055AWQSO\", \"positive_value\": \"./images/B0055AWQSO.png\", \"hn_image\": [\"./images/B001JTDJZY.png\"]}\n{\"q_img\": \"./images/B00C2DBEY4.png\", \"q_text\": \"is a red colored shirt with a minimal logo, and is darker\", \"positive_key\": \"B00BNPCQ58\", \"positive_value\": \"./images/B00BNPCQ58.png\", \"hn_image\": [\"./images/B00C2DBEY4.png\"]}\n{\"q_img\": \"./images/B000JVKHLW.png\", \"q_text\": \"is a black back pack, and has shiny leather\", \"positive_key\": \"B001U239PU\", \"positive_value\": \"./images/B001U239PU.png\", \"hn_image\": [\"./images/B000JVKHLW.png\"]}\n{\"q_img\": \"./images/B004R1RB3M.png\", \"q_text\": \"is white and green plaid, and Desired product is lighter in color\", \"positive_key\": \"B004GIVMZO\", \"positive_value\": \"./images/B004GIVMZO.png\", \"hn_image\": [\"./images/B004R1RB3M.png\"]}\n{\"q_img\": \"./images/B006YDMYCY.png\", \"q_text\": \"has its an anime thing written on it, and has a graphic that says its an anime thing\", \"positive_key\": \"B00DH8E2F6\", \"positive_value\": \"./images/B00DH8E2F6.png\", \"hn_image\": [\"./images/B006YDMYCY.png\"]}\n{\"q_img\": \"./images/B000GA5YPK.png\", \"q_text\": \"is red and long sleeves, and is dark red with long sleeves\", \"positive_key\": \"B0002VN7CA\", \"positive_value\": \"./images/B0002VN7CA.png\", \"hn_image\": [\"./images/B000GA5YPK.png\"]}\n{\"q_img\": \"./images/B005HGTEPY.png\", \"q_text\": \"a simple light colored tee with a small logo, and has more yellow and less graphics\", \"positive_key\": \"B00AP5DK20\", \"positive_value\": \"./images/B00AP5DK20.png\", \"hn_image\": [\"./images/B005HGTEPY.png\"]}\n{\"q_img\": \"./images/B007POLATE.png\", \"q_text\": \"is white with no buttons or pattern, and Solid white with blue image print and no buttons\", \"positive_key\": \"B006ZQH11E\", \"positive_value\": \"./images/B006ZQH11E.png\", \"hn_image\": [\"./images/B007POLATE.png\"]}\n{\"q_img\": \"./images/B009DEGOSG.png\", \"q_text\": \"is black color with art, and is dark blue with green logo\", \"positive_key\": \"B005TF98AE\", \"positive_value\": \"./images/B005TF98AE.png\", \"hn_image\": [\"./images/B009DEGOSG.png\"]}\n{\"q_img\": \"./images/B0081V2U6C.png\", \"q_text\": \"is darker with a different logo, and has sentences on the front\", \"positive_key\": \"B00AIXUCG6\", \"positive_value\": \"./images/B00AIXUCG6.png\", \"hn_image\": [\"./images/B0081V2U6C.png\"]}\n{\"q_img\": \"./images/B00BS7FFQ8.png\", \"q_text\": \"is white and is in tshirt design, and is all white and no collar or buttons\", \"positive_key\": \"B00066ZKS0\", \"positive_value\": \"./images/B00066ZKS0.png\", \"hn_image\": [\"./images/B00BS7FFQ8.png\"]}\n{\"q_img\": \"./images/B00AFEOREQ.png\", \"q_text\": \"has a different graphic and a foreign language, and Has a black & white graphic\", \"positive_key\": \"B009UWZ8H4\", \"positive_value\": \"./images/B009UWZ8H4.png\", \"hn_image\": [\"./images/B00AFEOREQ.png\"]}\n{\"q_img\": \"./images/B004KF9LM4.png\", \"q_text\": \"Is more colorful and bright, and Is more wordy\", \"positive_key\": \"B0049J1RZA\", \"positive_value\": \"./images/B0049J1RZA.png\", \"hn_image\": [\"./images/B004KF9LM4.png\"]}\n{\"q_img\": \"./images/B002TUTM4Y.png\", \"q_text\": \"has a robotic image, and red shirt with two robots in love\", \"positive_key\": \"B00877MORU\", \"positive_value\": \"./images/B00877MORU.png\", \"hn_image\": [\"./images/B002TUTM4Y.png\"]}\n{\"q_img\": \"./images/B006QMKAUQ.png\", \"q_text\": \"Darker and not button up, and is a plain black tshirt with writing\", \"positive_key\": \"B0094JWU0G\", \"positive_value\": \"./images/B0094JWU0G.png\", \"hn_image\": [\"./images/B006QMKAUQ.png\"]}\n{\"q_img\": \"./images/B0077PMHIO.png\", \"q_text\": \"has a smaller logo, and has a smaller design and more white\", \"positive_key\": \"B002NCIAU0\", \"positive_value\": \"./images/B002NCIAU0.png\", \"hn_image\": [\"./images/B0077PMHIO.png\"]}\n{\"q_img\": \"./images/B008HO2HZ2.png\", \"q_text\": \"has longer sleeves, and is more formal\", \"positive_key\": \"B00B1DVMN4\", \"positive_value\": \"./images/B00B1DVMN4.png\", \"hn_image\": [\"./images/B008HO2HZ2.png\"]}\n{\"q_img\": \"./images/B0032CO7IO.png\", \"q_text\": \"Is a solid black t-shirt, and is a darker and a t shirt\", \"positive_key\": \"B0017QFRF4\", \"positive_value\": \"./images/B0017QFRF4.png\", \"hn_image\": [\"./images/B0032CO7IO.png\"]}\n{\"q_img\": \"./images/B00CIYDP94.png\", \"q_text\": \"is blue with yellow lettering, and is dark blue with yellow lettering\", \"positive_key\": \"B007Z8W47C\", \"positive_value\": \"./images/B007Z8W47C.png\", \"hn_image\": [\"./images/B00CIYDP94.png\"]}\n{\"q_img\": \"./images/B001JP5KAA.png\", \"q_text\": \"is a grey shirt with black stripes on the sides, and is lighter\", \"positive_key\": \"B0013WHNO0\", \"positive_value\": \"./images/B0013WHNO0.png\", \"hn_image\": [\"./images/B001JP5KAA.png\"]}\n{\"q_img\": \"./images/B000BQ09GS.png\", \"q_text\": \"is gray with blue designs, and is simpler\", \"positive_key\": \"B00ASK99IQ\", \"positive_value\": \"./images/B00ASK99IQ.png\", \"hn_image\": [\"./images/B000BQ09GS.png\"]}\n{\"q_img\": \"./images/B009E5ZBBU.png\", \"q_text\": \"is darker and has different graphics, and is black with a white advertising logo\", \"positive_key\": \"B005G7QM5E\", \"positive_value\": \"./images/B005G7QM5E.png\", \"hn_image\": [\"./images/B009E5ZBBU.png\"]}\n{\"q_img\": \"./images/B006Z8HHGQ.png\", \"q_text\": \"is short sleeves, and has shorter sleeves\", \"positive_key\": \"B0053WWJWC\", \"positive_value\": \"./images/B0053WWJWC.png\", \"hn_image\": [\"./images/B006Z8HHGQ.png\"]}\n{\"q_img\": \"./images/B007RGHP1W.png\", \"q_text\": \"has more print work., and is darker\", \"positive_key\": \"B009BBDBGY\", \"positive_value\": \"./images/B009BBDBGY.png\", \"hn_image\": [\"./images/B007RGHP1W.png\"]}\n{\"q_img\": \"./images/B001GU03DC.png\", \"q_text\": \"its black with print linkin park, and is black and blue and trendy\", \"positive_key\": \"B00106YNM4\", \"positive_value\": \"./images/B00106YNM4.png\", \"hn_image\": [\"./images/B001GU03DC.png\"]}\n{\"q_img\": \"./images/B00FFKQ3LK.png\", \"q_text\": \"has plain pattern, and has more colors to it\", \"positive_key\": \"B00006M7NJ\", \"positive_value\": \"./images/B00006M7NJ.png\", \"hn_image\": [\"./images/B00FFKQ3LK.png\"]}\n{\"q_img\": \"./images/B000VTIH5A.png\", \"q_text\": \"is black, and darker clue\", \"positive_key\": \"B008LOFIVS\", \"positive_value\": \"./images/B008LOFIVS.png\", \"hn_image\": [\"./images/B000VTIH5A.png\"]}\n{\"q_img\": \"./images/B00D3T6XG0.png\", \"q_text\": \"Is more whimsical and has shorter sleeves, and has more pattern to it\", \"positive_key\": \"B00E5G71Z2\", \"positive_value\": \"./images/B00E5G71Z2.png\", \"hn_image\": [\"./images/B00D3T6XG0.png\"]}\n{\"q_img\": \"./images/B00G3ZMAJU.png\", \"q_text\": \"Is more white and simple, and Is more white and has shorter sleeves\", \"positive_key\": \"B00D9ZW3I0\", \"positive_value\": \"./images/B00D9ZW3I0.png\", \"hn_image\": [\"./images/B00G3ZMAJU.png\"]}\n{\"q_img\": \"./images/B003J5CFXS.png\", \"q_text\": \"is boxier and more babyish, and has Pokemon graphic\", \"positive_key\": \"B005XQRSBU\", \"positive_value\": \"./images/B005XQRSBU.png\", \"hn_image\": [\"./images/B003J5CFXS.png\"]}\n{\"q_img\": \"./images/B004ZM3VVY.png\", \"q_text\": \"is a brown shirt, and is lighter and has longer sleeves\", \"positive_key\": \"B00EW46JWS\", \"positive_value\": \"./images/B00EW46JWS.png\", \"hn_image\": [\"./images/B004ZM3VVY.png\"]}\n{\"q_img\": \"./images/B004G71M28.png\", \"q_text\": \"is brighter colored, and is green colored with a different graphic\", \"positive_key\": \"B005XHQC8Y\", \"positive_value\": \"./images/B005XHQC8Y.png\", \"hn_image\": [\"./images/B004G71M28.png\"]}\n{\"q_img\": \"./images/B000NZW3JI.png\", \"q_text\": \"has a graphic with dolphins, and is black with blue logo\", \"positive_key\": \"B0036UG48S\", \"positive_value\": \"./images/B0036UG48S.png\", \"hn_image\": [\"./images/B000NZW3JI.png\"]}\n{\"q_img\": \"./images/B008075ZCS.png\", \"q_text\": \"is black with a skull on it, and is darker\", \"positive_key\": \"B004BHYA9U\", \"positive_value\": \"./images/B004BHYA9U.png\", \"hn_image\": [\"./images/B008075ZCS.png\"]}\n{\"q_img\": \"./images/B007UNXLB0.png\", \"q_text\": \"is button-up and plaid, and is t shirts with no buttons\", \"positive_key\": \"B003674EUQ\", \"positive_value\": \"./images/B003674EUQ.png\", \"hn_image\": [\"./images/B007UNXLB0.png\"]}\n{\"q_img\": \"./images/B00642IYH4.png\", \"q_text\": \"has no sleeves, and has a more colourful graphic with no sleeves\", \"positive_key\": \"B0050YAQ6O\", \"positive_value\": \"./images/B0050YAQ6O.png\", \"hn_image\": [\"./images/B00642IYH4.png\"]}\n{\"q_img\": \"./images/B008MWGSHM.png\", \"q_text\": \"is blue and red, and is red and blue\", \"positive_key\": \"B005MZ1V4C\", \"positive_value\": \"./images/B005MZ1V4C.png\", \"hn_image\": [\"./images/B008MWGSHM.png\"]}\n{\"q_img\": \"./images/B006ID8WAI.png\", \"q_text\": \"is blue in color, and is solid blue\", \"positive_key\": \"B00AFU8P2K\", \"positive_value\": \"./images/B00AFU8P2K.png\", \"hn_image\": [\"./images/B006ID8WAI.png\"]}\n{\"q_img\": \"./images/B006ZU9X16.png\", \"q_text\": \"is gray and white stripes, and is gray with stripes and shorter sleeves\", \"positive_key\": \"B009EB1WBW\", \"positive_value\": \"./images/B009EB1WBW.png\", \"hn_image\": [\"./images/B006ZU9X16.png\"]}\n{\"q_img\": \"./images/B00C28CCQS.png\", \"q_text\": \"is a purple T shirt with writing on it, and woven fabric\", \"positive_key\": \"B005X0RQ2C\", \"positive_value\": \"./images/B005X0RQ2C.png\", \"hn_image\": [\"./images/B00C28CCQS.png\"]}\n{\"q_img\": \"./images/B009ZYQ8RQ.png\", \"q_text\": \"The shirt is white with long sleeves., and is a white button down\", \"positive_key\": \"B0037OBZT6\", \"positive_value\": \"./images/B0037OBZT6.png\", \"hn_image\": [\"./images/B009ZYQ8RQ.png\"]}\n{\"q_img\": \"./images/B00C4RULCE.png\", \"q_text\": \"has longer sleeves and buttons and is denim material, and is a button down top\", \"positive_key\": \"B008YCX5DK\", \"positive_value\": \"./images/B008YCX5DK.png\", \"hn_image\": [\"./images/B00C4RULCE.png\"]}\n{\"q_img\": \"./images/B00B92J932.png\", \"q_text\": \"is short sleeved, and has a larger design on it\", \"positive_key\": \"B0041I3CNO\", \"positive_value\": \"./images/B0041I3CNO.png\", \"hn_image\": [\"./images/B00B92J932.png\"]}\n{\"q_img\": \"./images/B002TUTMCG.png\", \"q_text\": \"The shirt is black in color with long sleeves., and is long sleeves and leather\", \"positive_key\": \"B0015IP9XO\", \"positive_value\": \"./images/B0015IP9XO.png\", \"hn_image\": [\"./images/B002TUTMCG.png\"]}\n{\"q_img\": \"./images/B006Y5SM2I.png\", \"q_text\": \"is violet colored, and Is dark purple color\", \"positive_key\": \"B007X2DKWS\", \"positive_value\": \"./images/B007X2DKWS.png\", \"hn_image\": [\"./images/B006Y5SM2I.png\"]}\n{\"q_img\": \"./images/B0045MS86W.png\", \"q_text\": \"is darker, and A black shirt with white design on front\", \"positive_key\": \"B00563YWUA\", \"positive_value\": \"./images/B00563YWUA.png\", \"hn_image\": [\"./images/B0045MS86W.png\"]}\n{\"q_img\": \"./images/B00E80PTRC.png\", \"q_text\": \"is long sleeved and dark, and A solid grey shirt with stripes across the chest\", \"positive_key\": \"B009F7NHDQ\", \"positive_value\": \"./images/B009F7NHDQ.png\", \"hn_image\": [\"./images/B00E80PTRC.png\"]}\n{\"q_img\": \"./images/B00ELPU4J2.png\", \"q_text\": \"Is a solid light purple color, and is purple and is more plain\", \"positive_key\": \"B00AXOZ1XO\", \"positive_value\": \"./images/B00AXOZ1XO.png\", \"hn_image\": [\"./images/B00ELPU4J2.png\"]}\n{\"q_img\": \"./images/B00CXGH84A.png\", \"q_text\": \"is a sons of anarchy T shirt with red letters, and is darker\", \"positive_key\": \"B00CL5IDQK\", \"positive_value\": \"./images/B00CL5IDQK.png\", \"hn_image\": [\"./images/B00CXGH84A.png\"]}\n{\"q_img\": \"./images/B0053DV6AW.png\", \"q_text\": \"a light blue long sleeve tee shirt, and is lighter and has longer sleeves\", \"positive_key\": \"B008N67V8W\", \"positive_value\": \"./images/B008N67V8W.png\", \"hn_image\": [\"./images/B0053DV6AW.png\"]}\n{\"q_img\": \"./images/B009X6CURI.png\", \"q_text\": \"is plain white with short sleeves, and is white and not button down\", \"positive_key\": \"B00BWJ8T5G\", \"positive_value\": \"./images/B00BWJ8T5G.png\", \"hn_image\": [\"./images/B009X6CURI.png\"]}\n{\"q_img\": \"./images/B0014C09VI.png\", \"q_text\": \"has no sleeves and is navy blue, and has no sleeves\", \"positive_key\": \"B0039OO0R8\", \"positive_value\": \"./images/B0039OO0R8.png\", \"hn_image\": [\"./images/B0014C09VI.png\"]}\n{\"q_img\": \"./images/B0046RDF00.png\", \"q_text\": \"is plaid with pockets on the front, and has a flannel pattern\", \"positive_key\": \"B008UA22US\", \"positive_value\": \"./images/B008UA22US.png\", \"hn_image\": [\"./images/B0046RDF00.png\"]}\n{\"q_img\": \"./images/B002ZWYQ74.png\", \"q_text\": \"Is a lighter shirt with larger picture., and is brown with an indian\", \"positive_key\": \"B0072D8384\", \"positive_value\": \"./images/B0072D8384.png\", \"hn_image\": [\"./images/B002ZWYQ74.png\"]}\n{\"q_img\": \"./images/B00DOV9UJY.png\", \"q_text\": \"has shorter sleeves with brand text, and it is white and it is not plain\", \"positive_key\": \"B00DYY7QO2\", \"positive_value\": \"./images/B00DYY7QO2.png\", \"hn_image\": [\"./images/B00DOV9UJY.png\"]}\n{\"q_img\": \"./images/B003IBGLN8.png\", \"q_text\": \"is vacation themed, and has a different graphic.\", \"positive_key\": \"B003BGKDA2\", \"positive_value\": \"./images/B003BGKDA2.png\", \"hn_image\": [\"./images/B003IBGLN8.png\"]}\n{\"q_img\": \"./images/B00AAQT6ZY.png\", \"q_text\": \"has more buttons and a different pattern, and has a striped pattern and buttons down the front\", \"positive_key\": \"B00EM2ITEG\", \"positive_value\": \"./images/B00EM2ITEG.png\", \"hn_image\": [\"./images/B00AAQT6ZY.png\"]}\n{\"q_img\": \"./images/B004UIOL3K.png\", \"q_text\": \"is green with brown designs, and is less colorful\", \"positive_key\": \"B00389XFCA\", \"positive_value\": \"./images/B00389XFCA.png\", \"hn_image\": [\"./images/B004UIOL3K.png\"]}\n{\"q_img\": \"./images/B005LLE270.png\", \"q_text\": \"has sleeves, and is darker and has longer sleeves\", \"positive_key\": \"B007FBL1ZA\", \"positive_value\": \"./images/B007FBL1ZA.png\", \"hn_image\": [\"./images/B005LLE270.png\"]}\n{\"q_img\": \"./images/B007PIYT8O.png\", \"q_text\": \"has short sleeves and is silver, and Is grey and more casual\", \"positive_key\": \"B00BWJXU2S\", \"positive_value\": \"./images/B00BWJXU2S.png\", \"hn_image\": [\"./images/B007PIYT8O.png\"]}\n{\"q_img\": \"./images/B007WMC41C.png\", \"q_text\": \"The shirt is blue in color with yellow writing., and Is darker and more casual.\", \"positive_key\": \"B006LM75XG\", \"positive_value\": \"./images/B006LM75XG.png\", \"hn_image\": [\"./images/B007WMC41C.png\"]}\n{\"q_img\": \"./images/B005XO5K3U.png\", \"q_text\": \"is a black button down shirt with cars on it, and button up shirt with collar\", \"positive_key\": \"B00CIAX7WS\", \"positive_value\": \"./images/B00CIAX7WS.png\", \"hn_image\": [\"./images/B005XO5K3U.png\"]}\n{\"q_img\": \"./images/B005LA76RO.png\", \"q_text\": \"plaid long sleeve turquoise shirt, and has a plaid pattern and longer sleeves\", \"positive_key\": \"B00BU12D0I\", \"positive_value\": \"./images/B00BU12D0I.png\", \"hn_image\": [\"./images/B005LA76RO.png\"]}\n{\"q_img\": \"./images/B0080F3536.png\", \"q_text\": \"is a shirt with long sleeves and has a pocket., and has longer sleeves\", \"positive_key\": \"B006NR72OQ\", \"positive_value\": \"./images/B006NR72OQ.png\", \"hn_image\": [\"./images/B0080F3536.png\"]}\n{\"q_img\": \"./images/B005E7KJPK.png\", \"q_text\": \"is dark colored and longer, and has a bigger graphic and is more black\", \"positive_key\": \"B00BEIGTTI\", \"positive_value\": \"./images/B00BEIGTTI.png\", \"hn_image\": [\"./images/B005E7KJPK.png\"]}\n{\"q_img\": \"./images/B000E22CFA.png\", \"q_text\": \"is brighter, and is yellow with a black logo\", \"positive_key\": \"B00DEO35PQ\", \"positive_value\": \"./images/B00DEO35PQ.png\", \"hn_image\": [\"./images/B000E22CFA.png\"]}\n{\"q_img\": \"./images/B0047WT8AK.png\", \"q_text\": \"is blue in color, and is blue and has Batman symbol\", \"positive_key\": \"B00BPETZV0\", \"positive_value\": \"./images/B00BPETZV0.png\", \"hn_image\": [\"./images/B0047WT8AK.png\"]}\n{\"q_img\": \"./images/B000PHI5L4.png\", \"q_text\": \"has longer sleeves and is grey, and is a grey collared pullover\", \"positive_key\": \"B008B5HO2S\", \"positive_value\": \"./images/B008B5HO2S.png\", \"hn_image\": [\"./images/B000PHI5L4.png\"]}\n{\"q_img\": \"./images/B00B28FJ1E.png\", \"q_text\": \"is a lighter colored and has a larger graphic, and is closer to orange and a different design\", \"positive_key\": \"B00B7PLEUM\", \"positive_value\": \"./images/B00B7PLEUM.png\", \"hn_image\": [\"./images/B00B28FJ1E.png\"]}\n{\"q_img\": \"./images/B007DJ3436.png\", \"q_text\": \"has a graphic design, and is black with text\", \"positive_key\": \"B0035MGPW2\", \"positive_value\": \"./images/B0035MGPW2.png\", \"hn_image\": [\"./images/B007DJ3436.png\"]}\n{\"q_img\": \"./images/B0089681FI.png\", \"q_text\": \"is yellow, and is more formal\", \"positive_key\": \"B00C6A4LL6\", \"positive_value\": \"./images/B00C6A4LL6.png\", \"hn_image\": [\"./images/B0089681FI.png\"]}\n{\"q_img\": \"./images/B00305G3QC.png\", \"q_text\": \"is longer sleeved, and long sleeved blue\", \"positive_key\": \"B002MO7EAQ\", \"positive_value\": \"./images/B002MO7EAQ.png\", \"hn_image\": [\"./images/B00305G3QC.png\"]}\n{\"q_img\": \"./images/B0053DV6AW.png\", \"q_text\": \"is a gray fade color, and it has a long sleeve and has pocket\", \"positive_key\": \"B00AQOFCBW\", \"positive_value\": \"./images/B00AQOFCBW.png\", \"hn_image\": [\"./images/B0053DV6AW.png\"]}\n{\"q_img\": \"./images/B00CDL2BX8.png\", \"q_text\": \"The shirt is long sleeve and blue in color., and Desired item is solid navy blue\", \"positive_key\": \"B00CP1XSRO\", \"positive_value\": \"./images/B00CP1XSRO.png\", \"hn_image\": [\"./images/B00CDL2BX8.png\"]}\n{\"q_img\": \"./images/B0017ZBREA.png\", \"q_text\": \"is more masculine, and it has collar and pocket\", \"positive_key\": \"B004Y9NQ1S\", \"positive_value\": \"./images/B004Y9NQ1S.png\", \"hn_image\": [\"./images/B0017ZBREA.png\"]}\n{\"q_img\": \"./images/B003MU9OB6.png\", \"q_text\": \"Has shorter sleeves and is blue, and has short sleeves and plaid and other is long sleeves and black\", \"positive_key\": \"B009WN94IK\", \"positive_value\": \"./images/B009WN94IK.png\", \"hn_image\": [\"./images/B003MU9OB6.png\"]}\n{\"q_img\": \"./images/B001M0MYCE.png\", \"q_text\": \"is a black checkered shirt, and has plaid pattern\", \"positive_key\": \"B007HRO9B0\", \"positive_value\": \"./images/B007HRO9B0.png\", \"hn_image\": [\"./images/B001M0MYCE.png\"]}\n{\"q_img\": \"./images/B00BMPMJ6U.png\", \"q_text\": \"is less colorful, and has longer sleeves\", \"positive_key\": \"B009B0QAV8\", \"positive_value\": \"./images/B009B0QAV8.png\", \"hn_image\": [\"./images/B00BMPMJ6U.png\"]}\n{\"q_img\": \"./images/B00AWDQ1P8.png\", \"q_text\": \"is grey with a tiger on it, and is gray with colorful graphic and more casual\", \"positive_key\": \"B009WVK4OA\", \"positive_value\": \"./images/B009WVK4OA.png\", \"hn_image\": [\"./images/B00AWDQ1P8.png\"]}\n{\"q_img\": \"./images/B0083F5810.png\", \"q_text\": \"is a black t shirt, and is black with shorter sleeves\", \"positive_key\": \"B00BCWHW2E\", \"positive_value\": \"./images/B00BCWHW2E.png\", \"hn_image\": [\"./images/B0083F5810.png\"]}\n{\"q_img\": \"./images/B009A87P9W.png\", \"q_text\": \"has a shorter sleeve with four different colors, and is short sleeved black and gray color\", \"positive_key\": \"B003DQKR1A\", \"positive_value\": \"./images/B003DQKR1A.png\", \"hn_image\": [\"./images/B009A87P9W.png\"]}\n{\"q_img\": \"./images/B002PA0BWK.png\", \"q_text\": \"is a green and white long sleeve shirt, and is less formal with green sleeves and no buttons\", \"positive_key\": \"B006VK77MM\", \"positive_value\": \"./images/B006VK77MM.png\", \"hn_image\": [\"./images/B002PA0BWK.png\"]}\n{\"q_img\": \"./images/B00BD2XFGK.png\", \"q_text\": \"More long-sleeved and patterned, and is a button up with a collar\", \"positive_key\": \"B00FPT2XU6\", \"positive_value\": \"./images/B00FPT2XU6.png\", \"hn_image\": [\"./images/B00BD2XFGK.png\"]}\n{\"q_img\": \"./images/B007907Y6W.png\", \"q_text\": \"is not a pair of glasses, and Is a pair of glasses\", \"positive_key\": \"B00BN9YLZ2\", \"positive_value\": \"./images/B00BN9YLZ2.png\", \"hn_image\": [\"./images/B007907Y6W.png\"]}\n{\"q_img\": \"./images/B0016LURJ6.png\", \"q_text\": \"is white with green designs, and and has less buttons\", \"positive_key\": \"B006MORDT4\", \"positive_value\": \"./images/B006MORDT4.png\", \"hn_image\": [\"./images/B0016LURJ6.png\"]}\n{\"q_img\": \"./images/B0094IWPZM.png\", \"q_text\": \"The shirt is white with black writing., and is lighter\", \"positive_key\": \"B001G0QZ16\", \"positive_value\": \"./images/B001G0QZ16.png\", \"hn_image\": [\"./images/B0094IWPZM.png\"]}\n{\"q_img\": \"./images/B0080F3536.png\", \"q_text\": \"is a long sleeved army type sweater, and is grey with long sleeves\", \"positive_key\": \"B00D61JOKM\", \"positive_value\": \"./images/B00D61JOKM.png\", \"hn_image\": [\"./images/B0080F3536.png\"]}\n{\"q_img\": \"./images/B004KSSKXW.png\", \"q_text\": \"is grey and writing on it, and is lighter and less colorful\", \"positive_key\": \"B00B40E1K0\", \"positive_value\": \"./images/B00B40E1K0.png\", \"hn_image\": [\"./images/B004KSSKXW.png\"]}\n{\"q_img\": \"./images/B00D2VY082.png\", \"q_text\": \"has a red white and blue graphic, and a union jack graphic on front\", \"positive_key\": \"B00BDH4Q4A\", \"positive_value\": \"./images/B00BDH4Q4A.png\", \"hn_image\": [\"./images/B00D2VY082.png\"]}\n{\"q_img\": \"./images/B000JVKHLW.png\", \"q_text\": \"is gray with red designs, and is lighter in color\", \"positive_key\": \"B004D8FPRS\", \"positive_value\": \"./images/B004D8FPRS.png\", \"hn_image\": [\"./images/B000JVKHLW.png\"]}\n{\"q_img\": \"./images/B0085IG1NY.png\", \"q_text\": \"is blue and has shorter sleeves, and has shorter sleeves\", \"positive_key\": \"B004NNUWDK\", \"positive_value\": \"./images/B004NNUWDK.png\", \"hn_image\": [\"./images/B0085IG1NY.png\"]}\n{\"q_img\": \"./images/B006R1IWZQ.png\", \"q_text\": \"The shirt is green and white in color., and collared\", \"positive_key\": \"B0095STQ6C\", \"positive_value\": \"./images/B0095STQ6C.png\", \"hn_image\": [\"./images/B006R1IWZQ.png\"]}\n{\"q_img\": \"./images/B0098YLOJK.png\", \"q_text\": \" sleeveless and much more plain, and is a graphic t-shirt\", \"positive_key\": \"B00C9T2WBA\", \"positive_value\": \"./images/B00C9T2WBA.png\", \"hn_image\": [\"./images/B0098YLOJK.png\"]}\n{\"q_img\": \"./images/B004QQ5IMO.png\", \"q_text\": \"is a dark colored t-shirt with short sleeves, and darker in color\", \"positive_key\": \"B004GEC9V4\", \"positive_value\": \"./images/B004GEC9V4.png\", \"hn_image\": [\"./images/B004QQ5IMO.png\"]}\n{\"q_img\": \"./images/B00CDT7FHM.png\", \"q_text\": \"is gray with angry cat on it, and has more cat pictures to it\", \"positive_key\": \"B00DCUQD7Y\", \"positive_value\": \"./images/B00DCUQD7Y.png\", \"hn_image\": [\"./images/B00CDT7FHM.png\"]}\n{\"q_img\": \"./images/B009GFMPIU.png\", \"q_text\": \"Is lighter and less casual, and is slightly lighter\", \"positive_key\": \"B000TPFU2Y\", \"positive_value\": \"./images/B000TPFU2Y.png\", \"hn_image\": [\"./images/B009GFMPIU.png\"]}\n{\"q_img\": \"./images/B00DQDA9HC.png\", \"q_text\": \"is brighter colored, and Is brownish with elephant goddess on front\", \"positive_key\": \"B00DE0P8GO\", \"positive_value\": \"./images/B00DE0P8GO.png\", \"hn_image\": [\"./images/B00DQDA9HC.png\"]}\n{\"q_img\": \"./images/B00D2VC7VY.png\", \"q_text\": \"Black and not as exotic looking, and is black and shirt sleeved\", \"positive_key\": \"B006HT4EF0\", \"positive_value\": \"./images/B006HT4EF0.png\", \"hn_image\": [\"./images/B00D2VC7VY.png\"]}\n{\"q_img\": \"./images/B007JSCBD0.png\", \"q_text\": \"has short sleeves with a small image, and is heathered light gray design\", \"positive_key\": \"B000KD42BK\", \"positive_value\": \"./images/B000KD42BK.png\", \"hn_image\": [\"./images/B007JSCBD0.png\"]}\n{\"q_img\": \"./images/B00C2RX5QU.png\", \"q_text\": \"is black in color with text in red, and is t-shirt and has no graphics\", \"positive_key\": \"B00E4AE148\", \"positive_value\": \"./images/B00E4AE148.png\", \"hn_image\": [\"./images/B00C2RX5QU.png\"]}\n{\"q_img\": \"./images/B0036XOP54.png\", \"q_text\": \"Has more buttons and is blue, and is lighter\", \"positive_key\": \"B000NX7JM6\", \"positive_value\": \"./images/B000NX7JM6.png\", \"hn_image\": [\"./images/B0036XOP54.png\"]}\n{\"q_img\": \"./images/B003E6UMPK.png\", \"q_text\": \"is darker and more relaxed fit, and is black with white text\", \"positive_key\": \"B00AB6Z3TQ\", \"positive_value\": \"./images/B00AB6Z3TQ.png\", \"hn_image\": [\"./images/B003E6UMPK.png\"]}\n{\"q_img\": \"./images/B00CBU9L4I.png\", \"q_text\": \"has shorter sleeves and without buttons and collar, and short sleeves black face logo\", \"positive_key\": \"B004DTCFCA\", \"positive_value\": \"./images/B004DTCFCA.png\", \"hn_image\": [\"./images/B00CBU9L4I.png\"]}\n{\"q_img\": \"./images/B009RQPIQ4.png\", \"q_text\": \"is green with red designs, and is lighter and has shorter sleeves\", \"positive_key\": \"B005GHBKNI\", \"positive_value\": \"./images/B005GHBKNI.png\", \"hn_image\": [\"./images/B009RQPIQ4.png\"]}\n{\"q_img\": \"./images/B008CLE0RI.png\", \"q_text\": \"The shirt is short sleeve and is orange in color., and is orange and has no logo\", \"positive_key\": \"B003SE6GY4\", \"positive_value\": \"./images/B003SE6GY4.png\", \"hn_image\": [\"./images/B008CLE0RI.png\"]}\n{\"q_img\": \"./images/B000I2Y16Y.png\", \"q_text\": \"is a rectangular picture holder, and is not a t shirt\", \"positive_key\": \"B002Z2SFZ8\", \"positive_value\": \"./images/B002Z2SFZ8.png\", \"hn_image\": [\"./images/B000I2Y16Y.png\"]}\n{\"q_img\": \"./images/B0051GDT4M.png\", \"q_text\": \"is a cornflower blue, and is darker\", \"positive_key\": \"B002KG4U7G\", \"positive_value\": \"./images/B002KG4U7G.png\", \"hn_image\": [\"./images/B0051GDT4M.png\"]}\n{\"q_img\": \"./images/B00EFZ786K.png\", \"q_text\": \"has only two colors and buttons, and is white colored\", \"positive_key\": \"B008T479JO\", \"positive_value\": \"./images/B008T479JO.png\", \"hn_image\": [\"./images/B00EFZ786K.png\"]}\n{\"q_img\": \"./images/B004IUXQMM.png\", \"q_text\": \"The shirt is gray in color., and is less casual with grey patterns\", \"positive_key\": \"B00A9PLAEG\", \"positive_value\": \"./images/B00A9PLAEG.png\", \"hn_image\": [\"./images/B004IUXQMM.png\"]}\n{\"q_img\": \"./images/B0045VQCWA.png\", \"q_text\": \"has a flag of Chile, and is blue and is more stylish\", \"positive_key\": \"B003TQ05QQ\", \"positive_value\": \"./images/B003TQ05QQ.png\", \"hn_image\": [\"./images/B0045VQCWA.png\"]}\n{\"q_img\": \"./images/B007SYAVNC.png\", \"q_text\": \"is longer and lighter, and is gray with a logo\", \"positive_key\": \"B00BI886DG\", \"positive_value\": \"./images/B00BI886DG.png\", \"hn_image\": [\"./images/B007SYAVNC.png\"]}\n{\"q_img\": \"./images/B003HNA8WW.png\", \"q_text\": \"The shirt is long sleeve and white., and is grey\", \"positive_key\": \"B0000ANEC9\", \"positive_value\": \"./images/B0000ANEC9.png\", \"hn_image\": [\"./images/B003HNA8WW.png\"]}\n{\"q_img\": \"./images/B00018A9XO.png\", \"q_text\": \"has a logo behind lettering, and has a green motif\", \"positive_key\": \"B0019IT79C\", \"positive_value\": \"./images/B0019IT79C.png\", \"hn_image\": [\"./images/B00018A9XO.png\"]}\n{\"q_img\": \"./images/B00BF7T4C2.png\", \"q_text\": \"is less formal and has a graphic, and says lucky 13 on the back\", \"positive_key\": \"B00FEJEM6K\", \"positive_value\": \"./images/B00FEJEM6K.png\", \"hn_image\": [\"./images/B00BF7T4C2.png\"]}\n{\"q_img\": \"./images/B005FS7LEK.png\", \"q_text\": \"is more formal with buttons and a print, and Is plaid button-up\", \"positive_key\": \"B007GF53EK\", \"positive_value\": \"./images/B007GF53EK.png\", \"hn_image\": [\"./images/B005FS7LEK.png\"]}\n{\"q_img\": \"./images/B003IRMJYW.png\", \"q_text\": \"has long sleeves and a skeleton graphic, and is less adulty\", \"positive_key\": \"B009QR9DT2\", \"positive_value\": \"./images/B009QR9DT2.png\", \"hn_image\": [\"./images/B003IRMJYW.png\"]}\n{\"q_img\": \"./images/B007KDGECS.png\", \"q_text\": \"has a round neckline, and Has a white text logo\", \"positive_key\": \"B004BHVIDQ\", \"positive_value\": \"./images/B004BHVIDQ.png\", \"hn_image\": [\"./images/B007KDGECS.png\"]}\n{\"q_img\": \"./images/B005OQ0QDG.png\", \"q_text\": \"has flames that don't reach the top of the shirt, and is bolder\", \"positive_key\": \"B00A6VKWEW\", \"positive_value\": \"./images/B00A6VKWEW.png\", \"hn_image\": [\"./images/B005OQ0QDG.png\"]}\n{\"q_img\": \"./images/B00EYGE7X2.png\", \"q_text\": \"is red with words, and is a red t-shirt\", \"positive_key\": \"B005IGB4TC\", \"positive_value\": \"./images/B005IGB4TC.png\", \"hn_image\": [\"./images/B00EYGE7X2.png\"]}\n{\"q_img\": \"./images/B000JILAW0.png\", \"q_text\": \"Is a white button down dress shirt, and is lighter\", \"positive_key\": \"B00B07YQLG\", \"positive_value\": \"./images/B00B07YQLG.png\", \"hn_image\": [\"./images/B000JILAW0.png\"]}\n{\"q_img\": \"./images/B00C6AOL2K.png\", \"q_text\": \"is darker in color, and is blue and has logo\", \"positive_key\": \"B00B2QC36K\", \"positive_value\": \"./images/B00B2QC36K.png\", \"hn_image\": [\"./images/B00C6AOL2K.png\"]}\n{\"q_img\": \"./images/B0015GOC66.png\", \"q_text\": \"is more colorful, and is brighter in colour\", \"positive_key\": \"B0015IJ5MU\", \"positive_value\": \"./images/B0015IJ5MU.png\", \"hn_image\": [\"./images/B0015GOC66.png\"]}\n{\"q_img\": \"./images/B0087VDBP0.png\", \"q_text\": \"is more light colored with a huge image, and is lighter\", \"positive_key\": \"B0041A4L8C\", \"positive_value\": \"./images/B0041A4L8C.png\", \"hn_image\": [\"./images/B0087VDBP0.png\"]}\n{\"q_img\": \"./images/B00CMI6PSY.png\", \"q_text\": \"is a black shirt with a man on it, and is black with logo\", \"positive_key\": \"B005NYC4NE\", \"positive_value\": \"./images/B005NYC4NE.png\", \"hn_image\": [\"./images/B00CMI6PSY.png\"]}\n{\"q_img\": \"./images/B002Y7ABEC.png\", \"q_text\": \"has a darker color with a large logo, and is dark blue\", \"positive_key\": \"B004QS6S12\", \"positive_value\": \"./images/B004QS6S12.png\", \"hn_image\": [\"./images/B002Y7ABEC.png\"]}\n{\"q_img\": \"./images/B008ES82T6.png\", \"q_text\": \"is red with white floral designs, and red shirt with white Hawaiian flowers\", \"positive_key\": \"B007908AME\", \"positive_value\": \"./images/B007908AME.png\", \"hn_image\": [\"./images/B008ES82T6.png\"]}\n{\"q_img\": \"./images/B0041EIXIM.png\", \"q_text\": \"has shorter sleeves with buttons and a collar, and Desired item is black with short sleeves\", \"positive_key\": \"B0000C2VLK\", \"positive_value\": \"./images/B0000C2VLK.png\", \"hn_image\": [\"./images/B0041EIXIM.png\"]}\n{\"q_img\": \"./images/B0016BI1P8.png\", \"q_text\": \"is plainly blue, and is blue and not as colorful\", \"positive_key\": \"B004ZSHZ42\", \"positive_value\": \"./images/B004ZSHZ42.png\", \"hn_image\": [\"./images/B0016BI1P8.png\"]}\n{\"q_img\": \"./images/B001LQG1N2.png\", \"q_text\": \" with a symbol on the left side., and has a pattern and is pink\", \"positive_key\": \"B00CBMGV4O\", \"positive_value\": \"./images/B00CBMGV4O.png\", \"hn_image\": [\"./images/B001LQG1N2.png\"]}\n{\"q_img\": \"./images/B0089681FI.png\", \"q_text\": \"is blue in color with longer sleeves, and has long sleeve and is light blue color\", \"positive_key\": \"B00BIKAEUW\", \"positive_value\": \"./images/B00BIKAEUW.png\", \"hn_image\": [\"./images/B0089681FI.png\"]}\n{\"q_img\": \"./images/B003V1W8G4.png\", \"q_text\": \"has a graphic design and navy blue, and Has shorter sleeves\", \"positive_key\": \"B004I0F0LW\", \"positive_value\": \"./images/B004I0F0LW.png\", \"hn_image\": [\"./images/B003V1W8G4.png\"]}\n{\"q_img\": \"./images/B004M5I1R8.png\", \"q_text\": \"is short sleved, and is grey with a car motif\", \"positive_key\": \"B005FOSOPY\", \"positive_value\": \"./images/B005FOSOPY.png\", \"hn_image\": [\"./images/B004M5I1R8.png\"]}\n{\"q_img\": \"./images/B00CIA7QKW.png\", \"q_text\": \"is white colored, and is a white tshirt\", \"positive_key\": \"B009NB1DH6\", \"positive_value\": \"./images/B009NB1DH6.png\", \"hn_image\": [\"./images/B00CIA7QKW.png\"]}\n{\"q_img\": \"./images/B004Q3PSU4.png\", \"q_text\": \"is a black short sleeved shirt with minimal art, and Solid black shirt with white pattern on chest\", \"positive_key\": \"B004EHYK5G\", \"positive_value\": \"./images/B004EHYK5G.png\", \"hn_image\": [\"./images/B004Q3PSU4.png\"]}\n{\"q_img\": \"./images/B0083I6W08.png\", \"q_text\": \"is larger and darker, and is darker\", \"positive_key\": \"B001CK7TGA\", \"positive_value\": \"./images/B001CK7TGA.png\", \"hn_image\": [\"./images/B0083I6W08.png\"]}\n{\"q_img\": \"./images/B007T4JJCK.png\", \"q_text\": \"has shorter sleeves and larger print, and has shorter sleeves and is blue and white\", \"positive_key\": \"B004M8RY82\", \"positive_value\": \"./images/B004M8RY82.png\", \"hn_image\": [\"./images/B007T4JJCK.png\"]}\n{\"q_img\": \"./images/B003I86JVK.png\", \"q_text\": \"is a red shirt with printed words, and is red with white text\", \"positive_key\": \"B0095WFOW8\", \"positive_value\": \"./images/B0095WFOW8.png\", \"hn_image\": [\"./images/B003I86JVK.png\"]}\n{\"q_img\": \"./images/B000JI5NY6.png\", \"q_text\": \"is a sleeved sports wear, and is a long sleeved green tee\", \"positive_key\": \"B00A8YTGJE\", \"positive_value\": \"./images/B00A8YTGJE.png\", \"hn_image\": [\"./images/B000JI5NY6.png\"]}\n{\"q_img\": \"./images/B009UWZ8H4.png\", \"q_text\": \"Is a darker grey, and is darker\", \"positive_key\": \"B00AB2NZN6\", \"positive_value\": \"./images/B00AB2NZN6.png\", \"hn_image\": [\"./images/B009UWZ8H4.png\"]}\n{\"q_img\": \"./images/B009P8QMWS.png\", \"q_text\": \"Is darker and not a horror movie character, and is darker\", \"positive_key\": \"B0052YY01E\", \"positive_value\": \"./images/B0052YY01E.png\", \"hn_image\": [\"./images/B009P8QMWS.png\"]}\n{\"q_img\": \"./images/B006VJXGAU.png\", \"q_text\": \"has a logo on the pocket, and has a simpler graphic\", \"positive_key\": \"B00A64UD4I\", \"positive_value\": \"./images/B00A64UD4I.png\", \"hn_image\": [\"./images/B006VJXGAU.png\"]}\n{\"q_img\": \"./images/B004B2XMPI.png\", \"q_text\": \"has a collar and is more preppy, and is collared\", \"positive_key\": \"B005Q0B9ZY\", \"positive_value\": \"./images/B005Q0B9ZY.png\", \"hn_image\": [\"./images/B004B2XMPI.png\"]}\n{\"q_img\": \"./images/B0085X1DLE.png\", \"q_text\": \"is darker with a different logo, and is darker\", \"positive_key\": \"B009XE4MIA\", \"positive_value\": \"./images/B009XE4MIA.png\", \"hn_image\": [\"./images/B0085X1DLE.png\"]}\n{\"q_img\": \"./images/B007IDFV9M.png\", \"q_text\": \"The shirt is green in color with white writing., and has more green\", \"positive_key\": \"B00A0NDRUC\", \"positive_value\": \"./images/B00A0NDRUC.png\", \"hn_image\": [\"./images/B007IDFV9M.png\"]}\n{\"q_img\": \"./images/B004VFHTL8.png\", \"q_text\": \"is a different shade, and is blue\", \"positive_key\": \"B004D99N4S\", \"positive_value\": \"./images/B004D99N4S.png\", \"hn_image\": [\"./images/B004VFHTL8.png\"]}\n{\"q_img\": \"./images/B000HEC75U.png\", \"q_text\": \"is lighter with different graphics, and has a smaller design\", \"positive_key\": \"B00BWUD43W\", \"positive_value\": \"./images/B00BWUD43W.png\", \"hn_image\": [\"./images/B000HEC75U.png\"]}\n{\"q_img\": \"./images/B0052IW6MU.png\", \"q_text\": \"white ball cap old guys rule, and is a grey cap\", \"positive_key\": \"B00C2RX5QU\", \"positive_value\": \"./images/B00C2RX5QU.png\", \"hn_image\": [\"./images/B0052IW6MU.png\"]}\n{\"q_img\": \"./images/B004RL9ZEU.png\", \"q_text\": \"has a v shaped neckline, and has longer sleeves and slim fit\", \"positive_key\": \"B00593FR24\", \"positive_value\": \"./images/B00593FR24.png\", \"hn_image\": [\"./images/B004RL9ZEU.png\"]}\n{\"q_img\": \"./images/B0028TS5X0.png\", \"q_text\": \"is more lighter and patterned, and A green/white plaid with long sleeves\", \"positive_key\": \"B00EYYBPSO\", \"positive_value\": \"./images/B00EYYBPSO.png\", \"hn_image\": [\"./images/B0028TS5X0.png\"]}\n{\"q_img\": \"./images/B001M0MWSK.png\", \"q_text\": \"is green, and is less formal\", \"positive_key\": \"B005AJNKEE\", \"positive_value\": \"./images/B005AJNKEE.png\", \"hn_image\": [\"./images/B001M0MWSK.png\"]}\n{\"q_img\": \"./images/B00C4YRJKO.png\", \"q_text\": \"Is more charcoal and simple, and is black and white\", \"positive_key\": \"B00DL7PRD4\", \"positive_value\": \"./images/B00DL7PRD4.png\", \"hn_image\": [\"./images/B00C4YRJKO.png\"]}\n{\"q_img\": \"./images/B002RADJQS.png\", \"q_text\": \"blue colored with small print, and is more colorful\", \"positive_key\": \"B002RIPC92\", \"positive_value\": \"./images/B002RIPC92.png\", \"hn_image\": [\"./images/B002RADJQS.png\"]}\n{\"q_img\": \"./images/B00BXQTGGY.png\", \"q_text\": \"is more colorful, and is purple with frog graphic on front\", \"positive_key\": \"B004UIOL3K\", \"positive_value\": \"./images/B004UIOL3K.png\", \"hn_image\": [\"./images/B00BXQTGGY.png\"]}\n{\"q_img\": \"./images/B004M8R9O6.png\", \"q_text\": \"is identical, and is the same\", \"positive_key\": \"B004M8R9MI\", \"positive_value\": \"./images/B004M8R9MI.png\", \"hn_image\": [\"./images/B004M8R9O6.png\"]}\n{\"q_img\": \"./images/B004UJ14RU.png\", \"q_text\": \"is white with blue sleeves, and has buttons\", \"positive_key\": \"B00AU4OKW0\", \"positive_value\": \"./images/B00AU4OKW0.png\", \"hn_image\": [\"./images/B004UJ14RU.png\"]}\n{\"q_img\": \"./images/B005967P9Y.png\", \"q_text\": \"is darker, and has motorcycle graphic and has black background\", \"positive_key\": \"B007QPA6M4\", \"positive_value\": \"./images/B007QPA6M4.png\", \"hn_image\": [\"./images/B005967P9Y.png\"]}\n{\"q_img\": \"./images/B000YHF5K4.png\", \"q_text\": \"has black and blue colors, and it is black and has collar\", \"positive_key\": \"B003HLWPOI\", \"positive_value\": \"./images/B003HLWPOI.png\", \"hn_image\": [\"./images/B000YHF5K4.png\"]}\n{\"q_img\": \"./images/B001713ND2.png\", \"q_text\": \" with a striped tie, and has more busy looking graphics\", \"positive_key\": \"B004Z1828C\", \"positive_value\": \"./images/B004Z1828C.png\", \"hn_image\": [\"./images/B001713ND2.png\"]}\n{\"q_img\": \"./images/B001PJ6ZX6.png\", \"q_text\": \"is similar in color with fewer letters, and it has zane on it\", \"positive_key\": \"B001PIQSDY\", \"positive_value\": \"./images/B001PIQSDY.png\", \"hn_image\": [\"./images/B001PJ6ZX6.png\"]}\n{\"q_img\": \"./images/B003INE18S.png\", \"q_text\": \"has longer sleeves with a minimal line pattern, and is solid white\", \"positive_key\": \"B00AZIB77W\", \"positive_value\": \"./images/B00AZIB77W.png\", \"hn_image\": [\"./images/B003INE18S.png\"]}\n{\"q_img\": \"./images/B005I4MIO4.png\", \"q_text\": \"has writing on it, and is red with no collar or buttons\", \"positive_key\": \"B004FOCCA8\", \"positive_value\": \"./images/B004FOCCA8.png\", \"hn_image\": [\"./images/B005I4MIO4.png\"]}\n{\"q_img\": \"./images/B007Y2VXGW.png\", \"q_text\": \"is a blue no collared shirt with short sleeves, and it is darker blue and shorter sleeve\", \"positive_key\": \"B004A0BU3C\", \"positive_value\": \"./images/B004A0BU3C.png\", \"hn_image\": [\"./images/B007Y2VXGW.png\"]}\n{\"q_img\": \"./images/B00CQ3O38Y.png\", \"q_text\": \"is bright white colored with an image, and is lighter\", \"positive_key\": \"B005C91FL2\", \"positive_value\": \"./images/B005C91FL2.png\", \"hn_image\": [\"./images/B00CQ3O38Y.png\"]}\n{\"q_img\": \"./images/B008LWT8L6.png\", \"q_text\": \"is solid black with long sleeves, and is black with longer sleeves\", \"positive_key\": \"B003IWGZQA\", \"positive_value\": \"./images/B003IWGZQA.png\", \"hn_image\": [\"./images/B008LWT8L6.png\"]}\n{\"q_img\": \"./images/B005FSPIQS.png\", \"q_text\": \"is dark blue and plain, and has longer sleeves and a crew neck\", \"positive_key\": \"B0092KSEB6\", \"positive_value\": \"./images/B0092KSEB6.png\", \"hn_image\": [\"./images/B005FSPIQS.png\"]}\n{\"q_img\": \"./images/B007CCUJQE.png\", \"q_text\": \"is red colored, and is red and faded logo\", \"positive_key\": \"B003YGUW6E\", \"positive_value\": \"./images/B003YGUW6E.png\", \"hn_image\": [\"./images/B007CCUJQE.png\"]}\n{\"q_img\": \"./images/B005X71DGK.png\", \"q_text\": \"Is more white and has longer sleeves, and is white and a henley style shirt\", \"positive_key\": \"B003VTZ82C\", \"positive_value\": \"./images/B003VTZ82C.png\", \"hn_image\": [\"./images/B005X71DGK.png\"]}\n{\"q_img\": \"./images/B001J7LLOC.png\", \"q_text\": \"is a black tank top with skull on it, and v neck shorter sleeved\", \"positive_key\": \"B002IL3QHS\", \"positive_value\": \"./images/B002IL3QHS.png\", \"hn_image\": [\"./images/B001J7LLOC.png\"]}\n{\"q_img\": \"./images/B00CJLMHKO.png\", \"q_text\": \"a short sleeved AC/DC shirt, and is more colourfull\", \"positive_key\": \"B0032FPQ5E\", \"positive_value\": \"./images/B0032FPQ5E.png\", \"hn_image\": [\"./images/B00CJLMHKO.png\"]}\n{\"q_img\": \"./images/B00C2B0ESS.png\", \"q_text\": \"is green with words on it, and has larger words and is green\", \"positive_key\": \"B00EDT0SME\", \"positive_value\": \"./images/B00EDT0SME.png\", \"hn_image\": [\"./images/B00C2B0ESS.png\"]}\n{\"q_img\": \"./images/B0036XL2XW.png\", \"q_text\": \"is a blue colored short sleeve shirt, and is a lighter color\", \"positive_key\": \"B00GTTLEZQ\", \"positive_value\": \"./images/B00GTTLEZQ.png\", \"hn_image\": [\"./images/B0036XL2XW.png\"]}\n{\"q_img\": \"./images/B000783T2Q.png\", \"q_text\": \"is a dark orange sweater, and is a yellow sweatshirt\", \"positive_key\": \"B0002X4OES\", \"positive_value\": \"./images/B0002X4OES.png\", \"hn_image\": [\"./images/B000783T2Q.png\"]}\n{\"q_img\": \"./images/B003TMYVMO.png\", \"q_text\": \"is more striped and has longer sleeves, and Is striped in pattern\", \"positive_key\": \"B000KW4LIA\", \"positive_value\": \"./images/B000KW4LIA.png\", \"hn_image\": [\"./images/B003TMYVMO.png\"]}\n{\"q_img\": \"./images/B006ZJUB8G.png\", \"q_text\": \"is pink with black stars, and is pink with black logos\", \"positive_key\": \"B001KAFI6U\", \"positive_value\": \"./images/B001KAFI6U.png\", \"hn_image\": [\"./images/B006ZJUB8G.png\"]}\n{\"q_img\": \"./images/B00AREH9TO.png\", \"q_text\": \"is white, and with lighter\", \"positive_key\": \"B007GQ060K\", \"positive_value\": \"./images/B007GQ060K.png\", \"hn_image\": [\"./images/B00AREH9TO.png\"]}\n{\"q_img\": \"./images/B000QGPYP4.png\", \"q_text\": \"Black and no graphics, and is darker with smaller font\", \"positive_key\": \"B008GNPWNI\", \"positive_value\": \"./images/B008GNPWNI.png\", \"hn_image\": [\"./images/B000QGPYP4.png\"]}\n{\"q_img\": \"./images/B0042RUEJY.png\", \"q_text\": \"is red with a checker pattern, and has more red\", \"positive_key\": \"B00BXEQ132\", \"positive_value\": \"./images/B00BXEQ132.png\", \"hn_image\": [\"./images/B0042RUEJY.png\"]}\n{\"q_img\": \"./images/B005AJ1Q7M.png\", \"q_text\": \"is brighter, and colorful pattern\", \"positive_key\": \"B007E9T5EC\", \"positive_value\": \"./images/B007E9T5EC.png\", \"hn_image\": [\"./images/B005AJ1Q7M.png\"]}\n{\"q_img\": \"./images/B001CWN1A6.png\", \"q_text\": \"is gray with long sleeves, and is darker\", \"positive_key\": \"B001L0ZVIE\", \"positive_value\": \"./images/B001L0ZVIE.png\", \"hn_image\": [\"./images/B001CWN1A6.png\"]}\n{\"q_img\": \"./images/B00B02JOHM.png\", \"q_text\": \"is black colored, and is darker\", \"positive_key\": \"B00B1OE4T2\", \"positive_value\": \"./images/B00B1OE4T2.png\", \"hn_image\": [\"./images/B00B02JOHM.png\"]}\n{\"q_img\": \"./images/B008QU0HJ0.png\", \"q_text\": \"is red and black striped, and is darker\", \"positive_key\": \"B008CXZBG0\", \"positive_value\": \"./images/B008CXZBG0.png\", \"hn_image\": [\"./images/B008QU0HJ0.png\"]}\n{\"q_img\": \"./images/B008MATL6Y.png\", \"q_text\": \"The shirt is blue in color with long sleeves., and is darker and has long sleeves\", \"positive_key\": \"B001FBEFFY\", \"positive_value\": \"./images/B001FBEFFY.png\", \"hn_image\": [\"./images/B008MATL6Y.png\"]}\n{\"q_img\": \"./images/B00542QOFE.png\", \"q_text\": \"has bigger letters, and has more red\", \"positive_key\": \"B00347D72Y\", \"positive_value\": \"./images/B00347D72Y.png\", \"hn_image\": [\"./images/B00542QOFE.png\"]}\n{\"q_img\": \"./images/B006YOPUME.png\", \"q_text\": \"Is more blue and striped, and is navy and white striped\", \"positive_key\": \"B00DV0BIE8\", \"positive_value\": \"./images/B00DV0BIE8.png\", \"hn_image\": [\"./images/B006YOPUME.png\"]}\n{\"q_img\": \"./images/B008P4ZQMU.png\", \"q_text\": \"Ligher colored, and is more vibrant a color\", \"positive_key\": \"B00BYIUQXS\", \"positive_value\": \"./images/B00BYIUQXS.png\", \"hn_image\": [\"./images/B008P4ZQMU.png\"]}\n{\"q_img\": \"./images/B001B14H6K.png\", \"q_text\": \"has short sleeved and darker colored, and has no stripes and is shorter sleeved\", \"positive_key\": \"B00B4A2D6Y\", \"positive_value\": \"./images/B00B4A2D6Y.png\", \"hn_image\": [\"./images/B001B14H6K.png\"]}\n{\"q_img\": \"./images/B005XS00E0.png\", \"q_text\": \"has a gun, and has larger text and and more animated\", \"positive_key\": \"B007XT82VU\", \"positive_value\": \"./images/B007XT82VU.png\", \"hn_image\": [\"./images/B005XS00E0.png\"]}\n{\"q_img\": \"./images/B00DEO56HQ.png\", \"q_text\": \"Is white, and is of lighter color\", \"positive_key\": \"B005518UFQ\", \"positive_value\": \"./images/B005518UFQ.png\", \"hn_image\": [\"./images/B00DEO56HQ.png\"]}\n{\"q_img\": \"./images/B003CMJKGI.png\", \"q_text\": \"is grey, and is gray with red circle\", \"positive_key\": \"B00C518TIM\", \"positive_value\": \"./images/B00C518TIM.png\", \"hn_image\": [\"./images/B003CMJKGI.png\"]}\n{\"q_img\": \"./images/B008J8MLD4.png\", \"q_text\": \" not blue, and is fancier\", \"positive_key\": \"B0062QVQEK\", \"positive_value\": \"./images/B0062QVQEK.png\", \"hn_image\": [\"./images/B008J8MLD4.png\"]}\n{\"q_img\": \"./images/B00AZLZ8UQ.png\", \"q_text\": \"has a blue tie, and is white with a blue tie\", \"positive_key\": \"B00008JPSN\", \"positive_value\": \"./images/B00008JPSN.png\", \"hn_image\": [\"./images/B00AZLZ8UQ.png\"]}\n{\"q_img\": \"./images/B007I6HU02.png\", \"q_text\": \"has a darker color and is short sleeved, and is dark blue short sleeved\", \"positive_key\": \"B0089MUPWO\", \"positive_value\": \"./images/B0089MUPWO.png\", \"hn_image\": [\"./images/B007I6HU02.png\"]}\n{\"q_img\": \"./images/B004Q8J39Q.png\", \"q_text\": \"is bigger with a bright color, and is white with writing at the very top\", \"positive_key\": \"B0051VMI3K\", \"positive_value\": \"./images/B0051VMI3K.png\", \"hn_image\": [\"./images/B004Q8J39Q.png\"]}\n{\"q_img\": \"./images/B004UIOL3K.png\", \"q_text\": \"is grey in color with a snake logo, and has more snakes on it\", \"positive_key\": \"B007984YLM\", \"positive_value\": \"./images/B007984YLM.png\", \"hn_image\": [\"./images/B004UIOL3K.png\"]}\n{\"q_img\": \"./images/B001FBQ8L8.png\", \"q_text\": \"has short sleeves and buttons with a plaid pattern, and it is checked and has a shorter sleeve\", \"positive_key\": \"B004GIYGBQ\", \"positive_value\": \"./images/B004GIYGBQ.png\", \"hn_image\": [\"./images/B001FBQ8L8.png\"]}\n{\"q_img\": \"./images/B00AKAD912.png\", \"q_text\": \"longer sleeves black with blue stripes, and has long sleeves\", \"positive_key\": \"B00D6QT5II\", \"positive_value\": \"./images/B00D6QT5II.png\", \"hn_image\": [\"./images/B00AKAD912.png\"]}\n{\"q_img\": \"./images/B00C2WTGEU.png\", \"q_text\": \"has shorter sleeves and blue in color, and short sleeve darker in color\", \"positive_key\": \"B00A7ZSCNU\", \"positive_value\": \"./images/B00A7ZSCNU.png\", \"hn_image\": [\"./images/B00C2WTGEU.png\"]}\n{\"q_img\": \"./images/B001G2AEEI.png\", \"q_text\": \"is browner and simpler, and has more words on it\", \"positive_key\": \"B00ELWDOV0\", \"positive_value\": \"./images/B00ELWDOV0.png\", \"hn_image\": [\"./images/B001G2AEEI.png\"]}\n{\"q_img\": \"./images/B005F0NY26.png\", \"q_text\": \"is green in color and floral, and has a leaf pattern and is green\", \"positive_key\": \"B00DSNQSNY\", \"positive_value\": \"./images/B00DSNQSNY.png\", \"hn_image\": [\"./images/B005F0NY26.png\"]}\n{\"q_img\": \"./images/B0077UO9PS.png\", \"q_text\": \"The shirt is black with a cows face., and is black and white with a cow on it\", \"positive_key\": \"B0077SXLPO\", \"positive_value\": \"./images/B0077SXLPO.png\", \"hn_image\": [\"./images/B0077UO9PS.png\"]}\n{\"q_img\": \"./images/B0047ERHJM.png\", \"q_text\": \"has a white skull and flames, and is black with skull logo\", \"positive_key\": \"B0041EM9NM\", \"positive_value\": \"./images/B0041EM9NM.png\", \"hn_image\": [\"./images/B0047ERHJM.png\"]}\n{\"q_img\": \"./images/B005K7SZGO.png\", \"q_text\": \"is more political and colorful, and is white and more political\", \"positive_key\": \"B003SSVYXI\", \"positive_value\": \"./images/B003SSVYXI.png\", \"hn_image\": [\"./images/B005K7SZGO.png\"]}\n{\"q_img\": \"./images/B005KXL6TQ.png\", \"q_text\": \"has more words on it, and white and green striped\", \"positive_key\": \"B00FJW4OGK\", \"positive_value\": \"./images/B00FJW4OGK.png\", \"hn_image\": [\"./images/B005KXL6TQ.png\"]}\n{\"q_img\": \"./images/B007W4YUFI.png\", \"q_text\": \"Has a logo and is more casual, and is ess formal\", \"positive_key\": \"B00BJHF36O\", \"positive_value\": \"./images/B00BJHF36O.png\", \"hn_image\": [\"./images/B007W4YUFI.png\"]}\n{\"q_img\": \"./images/B009XDKZ3M.png\", \"q_text\": \"is a large white and black plaid, and is lighter with larger plaid pattern\", \"positive_key\": \"B00CJR3NUQ\", \"positive_value\": \"./images/B00CJR3NUQ.png\", \"hn_image\": [\"./images/B009XDKZ3M.png\"]}\n{\"q_img\": \"./images/B003YOPRJS.png\", \"q_text\": \"is white with a larger adidas logo, and has brand mark and is white color\", \"positive_key\": \"B003R4ZLE6\", \"positive_value\": \"./images/B003R4ZLE6.png\", \"hn_image\": [\"./images/B003YOPRJS.png\"]}\n{\"q_img\": \"./images/B009R7SGQW.png\", \"q_text\": \"has longer sleeves with a 'porsche' logo, and is more masculine and has longer sleeves\", \"positive_key\": \"B00CPOQX1Y\", \"positive_value\": \"./images/B00CPOQX1Y.png\", \"hn_image\": [\"./images/B009R7SGQW.png\"]}\n{\"q_img\": \"./images/B0051PMW8W.png\", \"q_text\": \"is fitted and black, and Is darker and more casual.\", \"positive_key\": \"B003LMVYRW\", \"positive_value\": \"./images/B003LMVYRW.png\", \"hn_image\": [\"./images/B0051PMW8W.png\"]}\n{\"q_img\": \"./images/B0038OVKR2.png\", \"q_text\": \"is a v neck with pocket, and is more faded\", \"positive_key\": \"B005TDPITQ\", \"positive_value\": \"./images/B005TDPITQ.png\", \"hn_image\": [\"./images/B0038OVKR2.png\"]}\n{\"q_img\": \"./images/B00AHEZ29I.png\", \"q_text\": \"is dark colored with contrasting colored art, and is more detailed and less colorful\", \"positive_key\": \"B004T5TDKU\", \"positive_value\": \"./images/B004T5TDKU.png\", \"hn_image\": [\"./images/B00AHEZ29I.png\"]}\n{\"q_img\": \"./images/B004F9QDFS.png\", \"q_text\": \"has an open front with side pockets, and is white with collar\", \"positive_key\": \"B008KAC0E6\", \"positive_value\": \"./images/B008KAC0E6.png\", \"hn_image\": [\"./images/B004F9QDFS.png\"]}\n{\"q_img\": \"./images/B0085IFWRA.png\", \"q_text\": \"has short sleeves, and has shorter sleeves and is black\", \"positive_key\": \"B003LNPB70\", \"positive_value\": \"./images/B003LNPB70.png\", \"hn_image\": [\"./images/B0085IFWRA.png\"]}\n{\"q_img\": \"./images/B005XXYY2O.png\", \"q_text\": \"is plaid, and A grey and white plaid with pockets on front\", \"positive_key\": \"B0015GIECO\", \"positive_value\": \"./images/B0015GIECO.png\", \"hn_image\": [\"./images/B005XXYY2O.png\"]}\n{\"q_img\": \"./images/B0038R5UDE.png\", \"q_text\": \"Has longer sleeves and is more plain, and Is white with longer sleeves.\", \"positive_key\": \"B00325MG16\", \"positive_value\": \"./images/B00325MG16.png\", \"hn_image\": [\"./images/B0038R5UDE.png\"]}\n{\"q_img\": \"./images/B005TG0JTW.png\", \"q_text\": \"is a v shaped neck and black, and has blue rectangular motif\", \"positive_key\": \"B007IDM3B6\", \"positive_value\": \"./images/B007IDM3B6.png\", \"hn_image\": [\"./images/B005TG0JTW.png\"]}\n{\"q_img\": \"./images/B000QHEOR2.png\", \"q_text\": \"is more striped and less casual, and has shorter sleeves and a v-neck\", \"positive_key\": \"B00AEJP21E\", \"positive_value\": \"./images/B00AEJP21E.png\", \"hn_image\": [\"./images/B000QHEOR2.png\"]}\n{\"q_img\": \"./images/B00AJML1ZM.png\", \"q_text\": \"has short sleeves, and Has longer sleeves and a larger graphic\", \"positive_key\": \"B0081MAU7M\", \"positive_value\": \"./images/B0081MAU7M.png\", \"hn_image\": [\"./images/B00AJML1ZM.png\"]}\n{\"q_img\": \"./images/B007SO7J46.png\", \"q_text\": \"Has no words on the graphic, and has poiinted gun and no wording\", \"positive_key\": \"B0089H17MG\", \"positive_value\": \"./images/B0089H17MG.png\", \"hn_image\": [\"./images/B007SO7J46.png\"]}\n{\"q_img\": \"./images/B00BYQDJH0.png\", \"q_text\": \"is a lighter color, and is lighter colored\", \"positive_key\": \"B006FKPRU2\", \"positive_value\": \"./images/B006FKPRU2.png\", \"hn_image\": [\"./images/B00BYQDJH0.png\"]}\n{\"q_img\": \"./images/B00ELPU4J2.png\", \"q_text\": \"Is more casual and nont patterned, and it is more casual and has a short sleeve\", \"positive_key\": \"B00CJLPB9I\", \"positive_value\": \"./images/B00CJLPB9I.png\", \"hn_image\": [\"./images/B00ELPU4J2.png\"]}\n{\"q_img\": \"./images/B003RW8XFW.png\", \"q_text\": \"is more graphic and less gamer, and is more gothic\", \"positive_key\": \"B001TPFF0U\", \"positive_value\": \"./images/B001TPFF0U.png\", \"hn_image\": [\"./images/B003RW8XFW.png\"]}\n{\"q_img\": \"./images/B007907Y6W.png\", \"q_text\": \"is floral long sleeved, and is lighter\", \"positive_key\": \"B005KQX65U\", \"positive_value\": \"./images/B005KQX65U.png\", \"hn_image\": [\"./images/B007907Y6W.png\"]}\n{\"q_img\": \"./images/B0085X1RZQ.png\", \"q_text\": \"Is yellow and more casual with no buttons, and is yellow with short sleeves and is more casual\", \"positive_key\": \"B008F39HRQ\", \"positive_value\": \"./images/B008F39HRQ.png\", \"hn_image\": [\"./images/B0085X1RZQ.png\"]}\n{\"q_img\": \"./images/B002WE9S7I.png\", \"q_text\": \"has words on it, and is black with white lettering\", \"positive_key\": \"B00D1G6Y2S\", \"positive_value\": \"./images/B00D1G6Y2S.png\", \"hn_image\": [\"./images/B002WE9S7I.png\"]}\n{\"q_img\": \"./images/B004D99N4S.png\", \"q_text\": \"is more colorful and a scarf, and has more fringe to it\", \"positive_key\": \"B00A0HWUY2\", \"positive_value\": \"./images/B00A0HWUY2.png\", \"hn_image\": [\"./images/B004D99N4S.png\"]}\n{\"q_img\": \"./images/B007IGKR52.png\", \"q_text\": \"is dark striped shirt, and has more colors in it\", \"positive_key\": \"B009EOYYNW\", \"positive_value\": \"./images/B009EOYYNW.png\", \"hn_image\": [\"./images/B007IGKR52.png\"]}\n{\"q_img\": \"./images/B005C91IGE.png\", \"q_text\": \"has a cartoon graphic, and has a red and white graphic\", \"positive_key\": \"B00AEJPB7O\", \"positive_value\": \"./images/B00AEJPB7O.png\", \"hn_image\": [\"./images/B005C91IGE.png\"]}\n{\"q_img\": \"./images/B00EKT8ASG.png\", \"q_text\": \"is a long sleeve blue button down shirt, and is much lighter in color\", \"positive_key\": \"B002CWNJXY\", \"positive_value\": \"./images/B002CWNJXY.png\", \"hn_image\": [\"./images/B00EKT8ASG.png\"]}\n{\"q_img\": \"./images/B009XE4MIA.png\", \"q_text\": \"has a different design, and is black and has large painting\", \"positive_key\": \"B0068OOI0A\", \"positive_value\": \"./images/B0068OOI0A.png\", \"hn_image\": [\"./images/B009XE4MIA.png\"]}\n{\"q_img\": \"./images/B00AKR3VPO.png\", \"q_text\": \"Is white and more graphic, and is white with black logo\", \"positive_key\": \"B001C1I46S\", \"positive_value\": \"./images/B001C1I46S.png\", \"hn_image\": [\"./images/B00AKR3VPO.png\"]}\n{\"q_img\": \"./images/B00E3MZ286.png\", \"q_text\": \"is blue with long sleeves, and Is less fitted and has longer sleeves\", \"positive_key\": \"B00F8D1UQM\", \"positive_value\": \"./images/B00F8D1UQM.png\", \"hn_image\": [\"./images/B00E3MZ286.png\"]}\n{\"q_img\": \"./images/B00B7P6FBK.png\", \"q_text\": \"is red colored without collar, and Is orange\", \"positive_key\": \"B007K3JLUU\", \"positive_value\": \"./images/B007K3JLUU.png\", \"hn_image\": [\"./images/B00B7P6FBK.png\"]}\n{\"q_img\": \"./images/B00EDOCC82.png\", \"q_text\": \"is lighter and more wordy, and is lighter and has a math-related pring\", \"positive_key\": \"B001C4AYBI\", \"positive_value\": \"./images/B001C4AYBI.png\", \"hn_image\": [\"./images/B00EDOCC82.png\"]}\n{\"q_img\": \"./images/B00BP4RT2C.png\", \"q_text\": \"is yellow colored with more stripes, and Has stripes\", \"positive_key\": \"B003FGW8MO\", \"positive_value\": \"./images/B003FGW8MO.png\", \"hn_image\": [\"./images/B00BP4RT2C.png\"]}\n{\"q_img\": \"./images/B008K1QJ5G.png\", \"q_text\": \" is long sleeved, and is lighter in color and more words\", \"positive_key\": \"B00B296GYC\", \"positive_value\": \"./images/B00B296GYC.png\", \"hn_image\": [\"./images/B008K1QJ5G.png\"]}\n{\"q_img\": \"./images/B007JQL71E.png\", \"q_text\": \"is gray with blue writing, and has a black graphic and is more grey in colour\", \"positive_key\": \"B00CHVAB5E\", \"positive_value\": \"./images/B00CHVAB5E.png\", \"hn_image\": [\"./images/B007JQL71E.png\"]}\n{\"q_img\": \"./images/B008K1QJ5G.png\", \"q_text\": \"is lighter with a different logo, and is greenish grey colored\", \"positive_key\": \"B00B8ACUXQ\", \"positive_value\": \"./images/B00B8ACUXQ.png\", \"hn_image\": [\"./images/B008K1QJ5G.png\"]}\n{\"q_img\": \"./images/B00BSY0YQC.png\", \"q_text\": \"has a lightning symbol, and has white logo\", \"positive_key\": \"B0085FSWVQ\", \"positive_value\": \"./images/B0085FSWVQ.png\", \"hn_image\": [\"./images/B00BSY0YQC.png\"]}\n{\"q_img\": \"./images/B00517ABLA.png\", \"q_text\": \"Is more blue and fun, and is more colourfull\", \"positive_key\": \"B00517AX5O\", \"positive_value\": \"./images/B00517AX5O.png\", \"hn_image\": [\"./images/B00517ABLA.png\"]}\n{\"q_img\": \"./images/B008AOZ2B0.png\", \"q_text\": \"has a design on half the shirt, and has only black patterning\", \"positive_key\": \"B000LUSGD2\", \"positive_value\": \"./images/B000LUSGD2.png\", \"hn_image\": [\"./images/B008AOZ2B0.png\"]}\n{\"q_img\": \"./images/B00ELPU4J2.png\", \"q_text\": \"is a shiny red dress shirt, and is more shiny\", \"positive_key\": \"B00F0DU0HK\", \"positive_value\": \"./images/B00F0DU0HK.png\", \"hn_image\": [\"./images/B00ELPU4J2.png\"]}\n{\"q_img\": \"./images/B0065ZE500.png\", \"q_text\": \"is a darker color, and is darker\", \"positive_key\": \"B00C4O1JSW\", \"positive_value\": \"./images/B00C4O1JSW.png\", \"hn_image\": [\"./images/B0065ZE500.png\"]}\n{\"q_img\": \"./images/B00BAXTUN4.png\", \"q_text\": \"is lighter and more trendy, and white with newspaper black lettering\", \"positive_key\": \"B001COZVCU\", \"positive_value\": \"./images/B001COZVCU.png\", \"hn_image\": [\"./images/B00BAXTUN4.png\"]}\n{\"q_img\": \"./images/B004YWBYNW.png\", \"q_text\": \"is white, and is a lighter color and has a looser fit\", \"positive_key\": \"B006Z8EV4W\", \"positive_value\": \"./images/B006Z8EV4W.png\", \"hn_image\": [\"./images/B004YWBYNW.png\"]}\n{\"q_img\": \"./images/B0012GILRK.png\", \"q_text\": \"is black colored, and is a darker black color\", \"positive_key\": \"B003ZHH21A\", \"positive_value\": \"./images/B003ZHH21A.png\", \"hn_image\": [\"./images/B0012GILRK.png\"]}\n{\"q_img\": \"./images/B00ABU0V4Y.png\", \"q_text\": \"is red and more bright, and is lighter\", \"positive_key\": \"B003RG34W0\", \"positive_value\": \"./images/B003RG34W0.png\", \"hn_image\": [\"./images/B00ABU0V4Y.png\"]}\n{\"q_img\": \"./images/B00BIFZU4C.png\", \"q_text\": \"Longer Sleeves and different style, and it is checked and has a long sleeve\", \"positive_key\": \"B004Q3PSU4\", \"positive_value\": \"./images/B004Q3PSU4.png\", \"hn_image\": [\"./images/B00BIFZU4C.png\"]}\n{\"q_img\": \"./images/B00A4LJAG0.png\", \"q_text\": \"is more plain, and has no graphic on it\", \"positive_key\": \"B009A3N3T8\", \"positive_value\": \"./images/B009A3N3T8.png\", \"hn_image\": [\"./images/B00A4LJAG0.png\"]}\n{\"q_img\": \"./images/B00FCM3K6C.png\", \"q_text\": \"is gray with a camel, and is light great with camel on the front\", \"positive_key\": \"B0067EWIK8\", \"positive_value\": \"./images/B0067EWIK8.png\", \"hn_image\": [\"./images/B00FCM3K6C.png\"]}\n{\"q_img\": \"./images/B008RMD8M0.png\", \"q_text\": \"is white, and is primarily white with less prominent logo\", \"positive_key\": \"B0060EYIU8\", \"positive_value\": \"./images/B0060EYIU8.png\", \"hn_image\": [\"./images/B008RMD8M0.png\"]}\n{\"q_img\": \"./images/B000V999DY.png\", \"q_text\": \"Is more of a shirt, and is a gray t-shirt\", \"positive_key\": \"B002Z8VH7K\", \"positive_value\": \"./images/B002Z8VH7K.png\", \"hn_image\": [\"./images/B000V999DY.png\"]}\n{\"q_img\": \"./images/B003OQTWS8.png\", \"q_text\": \"is darker, and is black\", \"positive_key\": \"B008N67FVK\", \"positive_value\": \"./images/B008N67FVK.png\", \"hn_image\": [\"./images/B003OQTWS8.png\"]}\n{\"q_img\": \"./images/B007P1GLKA.png\", \"q_text\": \"is black and has popeye image, and has longer sleeves\", \"positive_key\": \"B0035YJVGM\", \"positive_value\": \"./images/B0035YJVGM.png\", \"hn_image\": [\"./images/B007P1GLKA.png\"]}\n{\"q_img\": \"./images/B00FEET2J2.png\", \"q_text\": \"has green color with an amusing image, and is bright green colored with white graphic\", \"positive_key\": \"B009UTVW3Q\", \"positive_value\": \"./images/B009UTVW3Q.png\", \"hn_image\": [\"./images/B00FEET2J2.png\"]}\n{\"q_img\": \"./images/B009N29AO8.png\", \"q_text\": \"is for a female and fitted, and is lighter grey\", \"positive_key\": \"B002RCLYEK\", \"positive_value\": \"./images/B002RCLYEK.png\", \"hn_image\": [\"./images/B009N29AO8.png\"]}\n{\"q_img\": \"./images/B008VTKSMM.png\", \"q_text\": \"is black with white and yellow stripes, and it is black and has stripped print\", \"positive_key\": \"B0084ANB7C\", \"positive_value\": \"./images/B0084ANB7C.png\", \"hn_image\": [\"./images/B008VTKSMM.png\"]}\n{\"q_img\": \"./images/B0058YVMDM.png\", \"q_text\": \"is checked, and collar\", \"positive_key\": \"B008LWT8L6\", \"positive_value\": \"./images/B008LWT8L6.png\", \"hn_image\": [\"./images/B0058YVMDM.png\"]}\n{\"q_img\": \"./images/B0081365S4.png\", \"q_text\": \"is more colorful, and is blue with a design on the entire front\", \"positive_key\": \"B007RBBGOE\", \"positive_value\": \"./images/B007RBBGOE.png\", \"hn_image\": [\"./images/B0081365S4.png\"]}\n{\"q_img\": \"./images/B0050TB14K.png\", \"q_text\": \"is blue, and Is striped and more colorful\", \"positive_key\": \"B003NUSB6O\", \"positive_value\": \"./images/B003NUSB6O.png\", \"hn_image\": [\"./images/B0050TB14K.png\"]}\n{\"q_img\": \"./images/B0085428H6.png\", \"q_text\": \"Is more casual with shorter sleeves, and has shorter sleeves\", \"positive_key\": \"B009ZYOM60\", \"positive_value\": \"./images/B009ZYOM60.png\", \"hn_image\": [\"./images/B0085428H6.png\"]}\n{\"q_img\": \"./images/B004HEX7WI.png\", \"q_text\": \"is sleeveless and white, and is lighter\", \"positive_key\": \"B0033AG7DS\", \"positive_value\": \"./images/B0033AG7DS.png\", \"hn_image\": [\"./images/B004HEX7WI.png\"]}\n{\"q_img\": \"./images/B0086F6HIK.png\", \"q_text\": \"has a different logo, and is baggy and plain\", \"positive_key\": \"B007IL1XJG\", \"positive_value\": \"./images/B007IL1XJG.png\", \"hn_image\": [\"./images/B0086F6HIK.png\"]}\n{\"q_img\": \"./images/B007E9SJSU.png\", \"q_text\": \"is more formal and plain, and is more plain\", \"positive_key\": \"B007E9WZ12\", \"positive_value\": \"./images/B007E9WZ12.png\", \"hn_image\": [\"./images/B007E9SJSU.png\"]}\n{\"q_img\": \"./images/B003OBAWTG.png\", \"q_text\": \"The shirt is black in color with the Virgin Mary., and has a black background\", \"positive_key\": \"B001H9FPA8\", \"positive_value\": \"./images/B001H9FPA8.png\", \"hn_image\": [\"./images/B003OBAWTG.png\"]}\n{\"q_img\": \"./images/B005KQX65U.png\", \"q_text\": \"is more casual., and is less tropical\", \"positive_key\": \"B0039Q9USU\", \"positive_value\": \"./images/B0039Q9USU.png\", \"hn_image\": [\"./images/B005KQX65U.png\"]}\n{\"q_img\": \"./images/B009SNIEGW.png\", \"q_text\": \"Is blue and has a smaller graphiic, and has a smaller print\", \"positive_key\": \"B003IRY0XA\", \"positive_value\": \"./images/B003IRY0XA.png\", \"hn_image\": [\"./images/B009SNIEGW.png\"]}\n{\"q_img\": \"./images/B001O883VU.png\", \"q_text\": \"is black in color with a red strioe, and is black red and logos on it\", \"positive_key\": \"B00BQ31IIS\", \"positive_value\": \"./images/B00BQ31IIS.png\", \"hn_image\": [\"./images/B001O883VU.png\"]}\n{\"q_img\": \"./images/B005F0JPDI.png\", \"q_text\": \"is black with planets on it, and is black and has large painting\", \"positive_key\": \"B006BEONRA\", \"positive_value\": \"./images/B006BEONRA.png\", \"hn_image\": [\"./images/B005F0JPDI.png\"]}\n{\"q_img\": \"./images/B0098ZHBY6.png\", \"q_text\": \"is darker and more striped, and is black and has longer sleeves\", \"positive_key\": \"B0093JO492\", \"positive_value\": \"./images/B0093JO492.png\", \"hn_image\": [\"./images/B0098ZHBY6.png\"]}\n{\"q_img\": \"./images/B002HEWWVW.png\", \"q_text\": \"is a polo, and is black with a collar\", \"positive_key\": \"B008EYOK2S\", \"positive_value\": \"./images/B008EYOK2S.png\", \"hn_image\": [\"./images/B002HEWWVW.png\"]}\n{\"q_img\": \"./images/B008MWJ5PO.png\", \"q_text\": \"is very tight and sporty, and is short sleeved and white\", \"positive_key\": \"B004ISKVRW\", \"positive_value\": \"./images/B004ISKVRW.png\", \"hn_image\": [\"./images/B008MWJ5PO.png\"]}\n{\"q_img\": \"./images/B004K2NSGM.png\", \"q_text\": \"is blue, and is lighter\", \"positive_key\": \"B00DQELFQA\", \"positive_value\": \"./images/B00DQELFQA.png\", \"hn_image\": [\"./images/B004K2NSGM.png\"]}\n{\"q_img\": \"./images/B000LM7S2K.png\", \"q_text\": \"The shirt is blue with birds., and is navy with less patterned\", \"positive_key\": \"B0002JUEKU\", \"positive_value\": \"./images/B0002JUEKU.png\", \"hn_image\": [\"./images/B000LM7S2K.png\"]}\n{\"q_img\": \"./images/B005HJPL0I.png\", \"q_text\": \"Is more grey and casual, and is much lighter in color\", \"positive_key\": \"B004N86414\", \"positive_value\": \"./images/B004N86414.png\", \"hn_image\": [\"./images/B005HJPL0I.png\"]}\n{\"q_img\": \"./images/B0081GBULS.png\", \"q_text\": \"is a blue tshirt, and is more colorful\", \"positive_key\": \"B006VJXGAU\", \"positive_value\": \"./images/B006VJXGAU.png\", \"hn_image\": [\"./images/B0081GBULS.png\"]}\n{\"q_img\": \"./images/B005F0JPDI.png\", \"q_text\": \"Is more black and edgy, and it is black and has a white print\", \"positive_key\": \"B009CLF22E\", \"positive_value\": \"./images/B009CLF22E.png\", \"hn_image\": [\"./images/B005F0JPDI.png\"]}\n{\"q_img\": \"./images/B00BHKFFAW.png\", \"q_text\": \"is gray colored with dirty humor, and has a grey background\", \"positive_key\": \"B004X757OK\", \"positive_value\": \"./images/B004X757OK.png\", \"hn_image\": [\"./images/B00BHKFFAW.png\"]}\n{\"q_img\": \"./images/B00AQAZA68.png\", \"q_text\": \"Is darker and more blue, and has sleeves and is darker in colour\", \"positive_key\": \"B00BFA1TMC\", \"positive_value\": \"./images/B00BFA1TMC.png\", \"hn_image\": [\"./images/B00AQAZA68.png\"]}\n{\"q_img\": \"./images/B0095BJB1Y.png\", \"q_text\": \"is a striped button down, and have more vertical striping\", \"positive_key\": \"B005G4XMFK\", \"positive_value\": \"./images/B005G4XMFK.png\", \"hn_image\": [\"./images/B0095BJB1Y.png\"]}\n{\"q_img\": \"./images/B00C9T2MEW.png\", \"q_text\": \"has longer sleeves, and White short sleeved shirt with grey letter across the shirt\", \"positive_key\": \"B004DNXK0M\", \"positive_value\": \"./images/B004DNXK0M.png\", \"hn_image\": [\"./images/B00C9T2MEW.png\"]}\n{\"q_img\": \"./images/B001B2LL2W.png\", \"q_text\": \"is a darker color with short sleeves, and is green and shorter sleeves\", \"positive_key\": \"B001KLJJNM\", \"positive_value\": \"./images/B001KLJJNM.png\", \"hn_image\": [\"./images/B001B2LL2W.png\"]}\n{\"q_img\": \"./images/B00C6A28O8.png\", \"q_text\": \"is checked, and has checkered pattern\", \"positive_key\": \"B000F5IDAY\", \"positive_value\": \"./images/B000F5IDAY.png\", \"hn_image\": [\"./images/B00C6A28O8.png\"]}\n{\"q_img\": \"./images/B004OKFF7K.png\", \"q_text\": \"is solid grey with a red north face logo, and it is great and has a small print\", \"positive_key\": \"B004P9806S\", \"positive_value\": \"./images/B004P9806S.png\", \"hn_image\": [\"./images/B004OKFF7K.png\"]}\n{\"q_img\": \"./images/B00CBDH8EA.png\", \"q_text\": \"is gray with red, and has a larger graphic and is darker in colour\", \"positive_key\": \"B005IYVEQC\", \"positive_value\": \"./images/B005IYVEQC.png\", \"hn_image\": [\"./images/B00CBDH8EA.png\"]}\n{\"q_img\": \"./images/B00018AA74.png\", \"q_text\": \"is green and has a cartoon figure on it, and is more colorful\", \"positive_key\": \"B008T3X5CA\", \"positive_value\": \"./images/B008T3X5CA.png\", \"hn_image\": [\"./images/B00018AA74.png\"]}\n{\"q_img\": \"./images/B00BSJZ7HS.png\", \"q_text\": \"flag graphic print detail, and has a different logo printed on the front\", \"positive_key\": \"B0080WWFFS\", \"positive_value\": \"./images/B0080WWFFS.png\", \"hn_image\": [\"./images/B00BSJZ7HS.png\"]}\n{\"q_img\": \"./images/B0002I5T5G.png\", \"q_text\": \"Has longer sleeves and is for humans, and is made for humans and has buttons and long sleeves\", \"positive_key\": \"B003RZVXA6\", \"positive_value\": \"./images/B003RZVXA6.png\", \"hn_image\": [\"./images/B0002I5T5G.png\"]}\n{\"q_img\": \"./images/B00B0R0JZ8.png\", \"q_text\": \"Is bright blue and has a Tony the Tiger logo, and is bright blue with cartoon tiger on it\", \"positive_key\": \"B0088OX7ZA\", \"positive_value\": \"./images/B0088OX7ZA.png\", \"hn_image\": [\"./images/B00B0R0JZ8.png\"]}\n{\"q_img\": \"./images/B00DVMMVTW.png\", \"q_text\": \"different graphic, and Has a completely different name.\", \"positive_key\": \"B00BI6CNVY\", \"positive_value\": \"./images/B00BI6CNVY.png\", \"hn_image\": [\"./images/B00DVMMVTW.png\"]}\n{\"q_img\": \"./images/B0007QCPPK.png\", \"q_text\": \"is a green hat with white designs, and is a cap and is green.\", \"positive_key\": \"B001JDPUNO\", \"positive_value\": \"./images/B001JDPUNO.png\", \"hn_image\": [\"./images/B0007QCPPK.png\"]}\n{\"q_img\": \"./images/B00A78V6NU.png\", \"q_text\": \"is white in color, and is white and has a graphic\", \"positive_key\": \"B00DRKJ8TY\", \"positive_value\": \"./images/B00DRKJ8TY.png\", \"hn_image\": [\"./images/B00A78V6NU.png\"]}\n{\"q_img\": \"./images/B0032FPQ86.png\", \"q_text\": \"has more of an animal graphic on it, and is mainly black\", \"positive_key\": \"B002Y2BX4O\", \"positive_value\": \"./images/B002Y2BX4O.png\", \"hn_image\": [\"./images/B0032FPQ86.png\"]}\n{\"q_img\": \"./images/B009ZOJX3W.png\", \"q_text\": \"Is blue and not striped, and is blue colored and has no buttons\", \"positive_key\": \"B005HEV4YK\", \"positive_value\": \"./images/B005HEV4YK.png\", \"hn_image\": [\"./images/B009ZOJX3W.png\"]}\n{\"q_img\": \"./images/B005518UFQ.png\", \"q_text\": \" white graphic, and is darker\", \"positive_key\": \"B002WE9S7I\", \"positive_value\": \"./images/B002WE9S7I.png\", \"hn_image\": [\"./images/B005518UFQ.png\"]}\n{\"q_img\": \"./images/B007TRPF5W.png\", \"q_text\": \"is dark blue, and Desired item is blue and has white on the collar\", \"positive_key\": \"B0055NSFRM\", \"positive_value\": \"./images/B0055NSFRM.png\", \"hn_image\": [\"./images/B007TRPF5W.png\"]}\n{\"q_img\": \"./images/B0037KMEPO.png\", \"q_text\": \"is black with blue designs, and has longer sleeves\", \"positive_key\": \"B00345ZL8O\", \"positive_value\": \"./images/B00345ZL8O.png\", \"hn_image\": [\"./images/B0037KMEPO.png\"]}\n{\"q_img\": \"./images/B00DQDA9HC.png\", \"q_text\": \"has yellow on it, and is black with a man on it\", \"positive_key\": \"B0053DP72A\", \"positive_value\": \"./images/B0053DP72A.png\", \"hn_image\": [\"./images/B00DQDA9HC.png\"]}\n{\"q_img\": \"./images/B0085VJ4Y4.png\", \"q_text\": \"The shirt is blue with a smiley face., and is shorter\", \"positive_key\": \"B00A3XSIFS\", \"positive_value\": \"./images/B00A3XSIFS.png\", \"hn_image\": [\"./images/B0085VJ4Y4.png\"]}\n{\"q_img\": \"./images/B0052U6EBC.png\", \"q_text\": \"has a simpler graphic of a face with one color, and is a darker shade of black\", \"positive_key\": \"B0096EHVTO\", \"positive_value\": \"./images/B0096EHVTO.png\", \"hn_image\": [\"./images/B0052U6EBC.png\"]}\n{\"q_img\": \"./images/B0052YY01E.png\", \"q_text\": \"is darker, and Has silver print on the front\", \"positive_key\": \"B000F71EC6\", \"positive_value\": \"./images/B000F71EC6.png\", \"hn_image\": [\"./images/B0052YY01E.png\"]}\n{\"q_img\": \"./images/B005IYMNY4.png\", \"q_text\": \"has long sleeves, and is white colored and has longer sleeves\", \"positive_key\": \"B00B1SXGIS\", \"positive_value\": \"./images/B00B1SXGIS.png\", \"hn_image\": [\"./images/B005IYMNY4.png\"]}\n{\"q_img\": \"./images/B008YCX5DK.png\", \"q_text\": \"is lighter colored and has a larger print, and Shiner and lighter blue with white stripes\", \"positive_key\": \"B0068DJYAA\", \"positive_value\": \"./images/B0068DJYAA.png\", \"hn_image\": [\"./images/B008YCX5DK.png\"]}\n{\"q_img\": \"./images/B005GFHSI6.png\", \"q_text\": \"has a green and white logo, and has more color\", \"positive_key\": \"B00AJODAOA\", \"positive_value\": \"./images/B00AJODAOA.png\", \"hn_image\": [\"./images/B005GFHSI6.png\"]}\n{\"q_img\": \"./images/B004VSEBNY.png\", \"q_text\": \"its a white tight fitting t shirt, and white\", \"positive_key\": \"B00842GUDM\", \"positive_value\": \"./images/B00842GUDM.png\", \"hn_image\": [\"./images/B004VSEBNY.png\"]}\n{\"q_img\": \"./images/B0081ME1GI.png\", \"q_text\": \"is black in color, and is darker coloured\", \"positive_key\": \"B006TG7D2M\", \"positive_value\": \"./images/B006TG7D2M.png\", \"hn_image\": [\"./images/B0081ME1GI.png\"]}\n{\"q_img\": \"./images/B008YCX5DK.png\", \"q_text\": \"has a shorter sleeve with art, and is a crew neck with short sleeve and a print.\", \"positive_key\": \"B0062UBOOS\", \"positive_value\": \"./images/B0062UBOOS.png\", \"hn_image\": [\"./images/B008YCX5DK.png\"]}\n{\"q_img\": \"./images/B000BPUVQC.png\", \"q_text\": \"has umbrella written on it, and is lighter\", \"positive_key\": \"B0091RIIRA\", \"positive_value\": \"./images/B0091RIIRA.png\", \"hn_image\": [\"./images/B000BPUVQC.png\"]}\n{\"q_img\": \"./images/B003JY6WY2.png\", \"q_text\": \"has short sleeves and a circle logo, and is white with black logo\", \"positive_key\": \"B0076F7NF2\", \"positive_value\": \"./images/B0076F7NF2.png\", \"hn_image\": [\"./images/B003JY6WY2.png\"]}\n{\"q_img\": \"./images/B007ENCGTY.png\", \"q_text\": \"is warmer and stripped, and is more multi colored\", \"positive_key\": \"B008AL4EXK\", \"positive_value\": \"./images/B008AL4EXK.png\", \"hn_image\": [\"./images/B007ENCGTY.png\"]}\n{\"q_img\": \"./images/B006RI34ZC.png\", \"q_text\": \"is maroon with sports graphic, and is red with different logo\", \"positive_key\": \"B004LOHEEQ\", \"positive_value\": \"./images/B004LOHEEQ.png\", \"hn_image\": [\"./images/B006RI34ZC.png\"]}\n{\"q_img\": \"./images/B0067F9J1I.png\", \"q_text\": \"is more like graffiti and has more contrast, and is black in color\", \"positive_key\": \"B000LQU2XS\", \"positive_value\": \"./images/B000LQU2XS.png\", \"hn_image\": [\"./images/B0067F9J1I.png\"]}\n{\"q_img\": \"./images/B005CQAIYK.png\", \"q_text\": \"is a lighter color and has shorter sleeves, and Is white with shorter sleeves.\", \"positive_key\": \"B001B14HE2\", \"positive_value\": \"./images/B001B14HE2.png\", \"hn_image\": [\"./images/B005CQAIYK.png\"]}\n{\"q_img\": \"./images/B008MAIJBM.png\", \"q_text\": \"is t-shirt-style, and Is black\", \"positive_key\": \"B008Y7GGGI\", \"positive_value\": \"./images/B008Y7GGGI.png\", \"hn_image\": [\"./images/B008MAIJBM.png\"]}\n{\"q_img\": \"./images/B0045EY35K.png\", \"q_text\": \"has a lighter color and a different print, and It is more gray\", \"positive_key\": \"B00AES8W30\", \"positive_value\": \"./images/B00AES8W30.png\", \"hn_image\": [\"./images/B0045EY35K.png\"]}\n{\"q_img\": \"./images/B00A0KOAUG.png\", \"q_text\": \"is more darker with less art, and is darker\", \"positive_key\": \"B00318DGJA\", \"positive_value\": \"./images/B00318DGJA.png\", \"hn_image\": [\"./images/B00A0KOAUG.png\"]}\n{\"q_img\": \"./images/B0094IWPZM.png\", \"q_text\": \"has an image of a person and shorter sleeves, and has graphic on front and no buttons\", \"positive_key\": \"B00866FLXQ\", \"positive_value\": \"./images/B00866FLXQ.png\", \"hn_image\": [\"./images/B0094IWPZM.png\"]}\n{\"q_img\": \"./images/B002YE8R28.png\", \"q_text\": \"is grey in color without buttons and collar, and has no collar and has a graphic\", \"positive_key\": \"B00DE4GBUM\", \"positive_value\": \"./images/B00DE4GBUM.png\", \"hn_image\": [\"./images/B002YE8R28.png\"]}\n{\"q_img\": \"./images/B00AB9E3FI.png\", \"q_text\": \"is black colored, and is black with white text\", \"positive_key\": \"B00BXMUFWC\", \"positive_value\": \"./images/B00BXMUFWC.png\", \"hn_image\": [\"./images/B00AB9E3FI.png\"]}\n{\"q_img\": \"./images/B007YHY634.png\", \"q_text\": \"is darker and more feminine, and is darker and has less graphics\", \"positive_key\": \"B005IA9VO8\", \"positive_value\": \"./images/B005IA9VO8.png\", \"hn_image\": [\"./images/B007YHY634.png\"]}\n{\"q_img\": \"./images/B007TW76EU.png\", \"q_text\": \"has a smaller graphic and is less colorful, and is darker green with green image\", \"positive_key\": \"B000LZO27G\", \"positive_value\": \"./images/B000LZO27G.png\", \"hn_image\": [\"./images/B007TW76EU.png\"]}\n{\"q_img\": \"./images/B008BT8HRU.png\", \"q_text\": \"has no sleeves and black color, and has no sleeves\", \"positive_key\": \"B0087KO9TS\", \"positive_value\": \"./images/B0087KO9TS.png\", \"hn_image\": [\"./images/B008BT8HRU.png\"]}\n{\"q_img\": \"./images/B003YUBTBM.png\", \"q_text\": \"is black, and is solid color and has flap pockets\", \"positive_key\": \"B0032HJXKG\", \"positive_value\": \"./images/B0032HJXKG.png\", \"hn_image\": [\"./images/B003YUBTBM.png\"]}\n{\"q_img\": \"./images/B004W75SW2.png\", \"q_text\": \"is more bold, and is brown colored and has no buttons and pocket\", \"positive_key\": \"B004885WYY\", \"positive_value\": \"./images/B004885WYY.png\", \"hn_image\": [\"./images/B004W75SW2.png\"]}\n{\"q_img\": \"./images/B00CJLPB9I.png\", \"q_text\": \"has emblem 3 picture on it, and is white colored with image of three men\", \"positive_key\": \"B00DD1J4S2\", \"positive_value\": \"./images/B00DD1J4S2.png\", \"hn_image\": [\"./images/B00CJLPB9I.png\"]}\n{\"q_img\": \"./images/B00ER9PWY4.png\", \"q_text\": \"Is more black and has longer sleeves, and is long sleeved\", \"positive_key\": \"B004QMA13O\", \"positive_value\": \"./images/B004QMA13O.png\", \"hn_image\": [\"./images/B00ER9PWY4.png\"]}\n{\"q_img\": \"./images/B00BPETZV0.png\", \"q_text\": \"Black and has a non-fictional character, and is black\", \"positive_key\": \"B001KQ3YDS\", \"positive_value\": \"./images/B001KQ3YDS.png\", \"hn_image\": [\"./images/B00BPETZV0.png\"]}\n{\"q_img\": \"./images/B0038N83ZK.png\", \"q_text\": \"is black colored with a smaller image, and has a galaxy graphic\", \"positive_key\": \"B009QR94S2\", \"positive_value\": \"./images/B009QR94S2.png\", \"hn_image\": [\"./images/B0038N83ZK.png\"]}\n{\"q_img\": \"./images/B00BARYBBG.png\", \"q_text\": \"a sleeveless Adidas white shirt, and is white and sleeveless\", \"positive_key\": \"B004L627X2\", \"positive_value\": \"./images/B004L627X2.png\", \"hn_image\": [\"./images/B00BARYBBG.png\"]}\n{\"q_img\": \"./images/B005LLE270.png\", \"q_text\": \"is a black short sleeve tshirt, and Has slightly longer sleeves\", \"positive_key\": \"B005KN171I\", \"positive_value\": \"./images/B005KN171I.png\", \"hn_image\": [\"./images/B005LLE270.png\"]}\n{\"q_img\": \"./images/B007JQKWGU.png\", \"q_text\": \"White and has black text, and is lighter colored with text\", \"positive_key\": \"B00G96C2NC\", \"positive_value\": \"./images/B00G96C2NC.png\", \"hn_image\": [\"./images/B007JQKWGU.png\"]}\n{\"q_img\": \"./images/B00CXV9H26.png\", \"q_text\": \"is white and has a fish image on it, and is light gray colored\", \"positive_key\": \"B001CP1LLY\", \"positive_value\": \"./images/B001CP1LLY.png\", \"hn_image\": [\"./images/B00CXV9H26.png\"]}\n{\"q_img\": \"./images/B002UQJENQ.png\", \"q_text\": \"is a black t shirt, and has text in red\", \"positive_key\": \"B004KPD8FA\", \"positive_value\": \"./images/B004KPD8FA.png\", \"hn_image\": [\"./images/B002UQJENQ.png\"]}\n{\"q_img\": \"./images/B004SFV0ZC.png\", \"q_text\": \"is solid black in color, and is more darker\", \"positive_key\": \"B005GQEYVO\", \"positive_value\": \"./images/B005GQEYVO.png\", \"hn_image\": [\"./images/B004SFV0ZC.png\"]}\n{\"q_img\": \"./images/B000NZW3JI.png\", \"q_text\": \"is black with green designs, and is black with green logos\", \"positive_key\": \"B004UR1R2Y\", \"positive_value\": \"./images/B004UR1R2Y.png\", \"hn_image\": [\"./images/B000NZW3JI.png\"]}\n{\"q_img\": \"./images/B00AKYDNFA.png\", \"q_text\": \"Is black and has a more simple graphic, and is solid black with a Quiksilver design\", \"positive_key\": \"B007CCUJQE\", \"positive_value\": \"./images/B007CCUJQE.png\", \"hn_image\": [\"./images/B00AKYDNFA.png\"]}\n{\"q_img\": \"./images/B0093HZCAE.png\", \"q_text\": \"Has longer sleeves and is not wordy, and Is more sporty\", \"positive_key\": \"B00COXVGXQ\", \"positive_value\": \"./images/B00COXVGXQ.png\", \"hn_image\": [\"./images/B0093HZCAE.png\"]}\n{\"q_img\": \"./images/B00018A8ZI.png\", \"q_text\": \"The shirt is black with a skeleton., and is red\", \"positive_key\": \"B004LTJ0VG\", \"positive_value\": \"./images/B004LTJ0VG.png\", \"hn_image\": [\"./images/B00018A8ZI.png\"]}\n{\"q_img\": \"./images/B000WS6I56.png\", \"q_text\": \"Has no sleeves, and Has no sleeves\", \"positive_key\": \"B00COKA2CA\", \"positive_value\": \"./images/B00COKA2CA.png\", \"hn_image\": [\"./images/B000WS6I56.png\"]}\n{\"q_img\": \"./images/B00BQ9SMYU.png\", \"q_text\": \"Is white and has a slightly smaller graphic, and it is white\", \"positive_key\": \"B00F5XVN9Y\", \"positive_value\": \"./images/B00F5XVN9Y.png\", \"hn_image\": [\"./images/B00BQ9SMYU.png\"]}\n{\"q_img\": \"./images/B004VPW5Y4.png\", \"q_text\": \"Is a brighter color., and is bright blue\", \"positive_key\": \"B006QS2JEK\", \"positive_value\": \"./images/B006QS2JEK.png\", \"hn_image\": [\"./images/B004VPW5Y4.png\"]}\n{\"q_img\": \"./images/B001CFA16A.png\", \"q_text\": \"is sage with Hebrew text, and is lighter colored\", \"positive_key\": \"B00B1I1H5M\", \"positive_value\": \"./images/B00B1I1H5M.png\", \"hn_image\": [\"./images/B001CFA16A.png\"]}\n{\"q_img\": \"./images/B002AH1PSC.png\", \"q_text\": \"is a black T shirt with green and white logo, and has more green\", \"positive_key\": \"B002SQQ7L0\", \"positive_value\": \"./images/B002SQQ7L0.png\", \"hn_image\": [\"./images/B002AH1PSC.png\"]}\n{\"q_img\": \"./images/B00BWZI9M8.png\", \"q_text\": \"has collar and mini prints, and is more busy and has a collar\", \"positive_key\": \"B00A77H928\", \"positive_value\": \"./images/B00A77H928.png\", \"hn_image\": [\"./images/B00BWZI9M8.png\"]}\n{\"q_img\": \"./images/B004B7PMVK.png\", \"q_text\": \"This has brighter colors., and is orange\", \"positive_key\": \"B001BZX2VM\", \"positive_value\": \"./images/B001BZX2VM.png\", \"hn_image\": [\"./images/B004B7PMVK.png\"]}\n{\"q_img\": \"./images/B000CAZVI4.png\", \"q_text\": \"has a football team advertised on front and is more white in colour, and is white with shorter sleeves\", \"positive_key\": \"B0058KMS5W\", \"positive_value\": \"./images/B0058KMS5W.png\", \"hn_image\": [\"./images/B000CAZVI4.png\"]}\n{\"q_img\": \"./images/B003CTM0JA.png\", \"q_text\": \"The shirt is white with the London Olympics emblem., and is white with London Olympics graphic\", \"positive_key\": \"B008QUAX0S\", \"positive_value\": \"./images/B008QUAX0S.png\", \"hn_image\": [\"./images/B003CTM0JA.png\"]}\n{\"q_img\": \"./images/B008BSWH22.png\", \"q_text\": \"is dark colored and plain, and A solid black buttoned shirt\", \"positive_key\": \"B00A2NMCV0\", \"positive_value\": \"./images/B00A2NMCV0.png\", \"hn_image\": [\"./images/B008BSWH22.png\"]}\n{\"q_img\": \"./images/B005MKSBAE.png\", \"q_text\": \"is black with white designs, and is more emo\", \"positive_key\": \"B00ECGT5BI\", \"positive_value\": \"./images/B00ECGT5BI.png\", \"hn_image\": [\"./images/B005MKSBAE.png\"]}\n{\"q_img\": \"./images/B005VF6TK4.png\", \"q_text\": \"is a long sleeved shirt, and is long-sleeved with buttons and more color\", \"positive_key\": \"B008VRQ42C\", \"positive_value\": \"./images/B008VRQ42C.png\", \"hn_image\": [\"./images/B005VF6TK4.png\"]}\n{\"q_img\": \"./images/B009XPC5GA.png\", \"q_text\": \"is lighter colored and is more masculine, and is white with large design on the back\", \"positive_key\": \"B001MI1HMY\", \"positive_value\": \"./images/B001MI1HMY.png\", \"hn_image\": [\"./images/B009XPC5GA.png\"]}\n{\"q_img\": \"./images/B008BZANP8.png\", \"q_text\": \"is a black color with a logo slightly above the center, and is black colored\", \"positive_key\": \"B004XJ5L0I\", \"positive_value\": \"./images/B004XJ5L0I.png\", \"hn_image\": [\"./images/B008BZANP8.png\"]}\n{\"q_img\": \"./images/B003UPUYIA.png\", \"q_text\": \"is white, and is white with buttons and longer sleeves\", \"positive_key\": \"B00B4X5XLI\", \"positive_value\": \"./images/B00B4X5XLI.png\", \"hn_image\": [\"./images/B003UPUYIA.png\"]}\n{\"q_img\": \"./images/B003TWOPKW.png\", \"q_text\": \"is light colored and has horizontal stripes, and Is grey and pink stripes\", \"positive_key\": \"B00AZWLNV8\", \"positive_value\": \"./images/B00AZWLNV8.png\", \"hn_image\": [\"./images/B003TWOPKW.png\"]}\n{\"q_img\": \"./images/B004PEMI7U.png\", \"q_text\": \"is more formal with longer sleeves, and is a white shirt with pocket\", \"positive_key\": \"B005XI83X0\", \"positive_value\": \"./images/B005XI83X0.png\", \"hn_image\": [\"./images/B004PEMI7U.png\"]}\n{\"q_img\": \"./images/B000EGBBTE.png\", \"q_text\": \"The shirt is black with Superman., and A bright yellow shirt w/white letters\", \"positive_key\": \"B001JTDJZY\", \"positive_value\": \"./images/B001JTDJZY.png\", \"hn_image\": [\"./images/B000EGBBTE.png\"]}\n{\"q_img\": \"./images/B000CS0UGY.png\", \"q_text\": \"Red and is a novelty shirt, and is red\", \"positive_key\": \"B009KQ2S5U\", \"positive_value\": \"./images/B009KQ2S5U.png\", \"hn_image\": [\"./images/B000CS0UGY.png\"]}\n{\"q_img\": \"./images/B005HIQ9KA.png\", \"q_text\": \"Is red and has one angry bird., and is red with one bird\", \"positive_key\": \"B004O3ZBXU\", \"positive_value\": \"./images/B004O3ZBXU.png\", \"hn_image\": [\"./images/B005HIQ9KA.png\"]}\n{\"q_img\": \"./images/B008KGWOHS.png\", \"q_text\": \"is a darker blue and larger, and is darker blue\", \"positive_key\": \"B004UG6Q0S\", \"positive_value\": \"./images/B004UG6Q0S.png\", \"hn_image\": [\"./images/B008KGWOHS.png\"]}\n{\"q_img\": \"./images/B000EOZGWE.png\", \"q_text\": \"is red with yellow stars, and has stars and a red background\", \"positive_key\": \"B00BJ94VEC\", \"positive_value\": \"./images/B00BJ94VEC.png\", \"hn_image\": [\"./images/B000EOZGWE.png\"]}\n{\"q_img\": \"./images/B003M6FD4C.png\", \"q_text\": \"is rainbow tie dye shirt, and is more for hippies\", \"positive_key\": \"B002KDYT70\", \"positive_value\": \"./images/B002KDYT70.png\", \"hn_image\": [\"./images/B003M6FD4C.png\"]}\n{\"q_img\": \"./images/B00G3ZMAJU.png\", \"q_text\": \"Is more run and grey, and is gray with mickey mouse\", \"positive_key\": \"B00DT5P9N6\", \"positive_value\": \"./images/B00DT5P9N6.png\", \"hn_image\": [\"./images/B00G3ZMAJU.png\"]}\n{\"q_img\": \"./images/B00DS3DXJQ.png\", \"q_text\": \"is red with subtle white pattern, and is red\", \"positive_key\": \"B00AOJHO56\", \"positive_value\": \"./images/B00AOJHO56.png\", \"hn_image\": [\"./images/B00DS3DXJQ.png\"]}\n{\"q_img\": \"./images/B003IWBTOS.png\", \"q_text\": \"is plain and more formal, and The item has long sleeves and is grey\", \"positive_key\": \"B008EF1L5G\", \"positive_value\": \"./images/B008EF1L5G.png\", \"hn_image\": [\"./images/B003IWBTOS.png\"]}\n{\"q_img\": \"./images/B004TMGDME.png\", \"q_text\": \"is darker and has a different graphic, and is darker\", \"positive_key\": \"B004D8QCN4\", \"positive_value\": \"./images/B004D8QCN4.png\", \"hn_image\": [\"./images/B004TMGDME.png\"]}\n{\"q_img\": \"./images/B0032HJXKG.png\", \"q_text\": \"is darker in color, and is more of a dark gray color\", \"positive_key\": \"B008473Q20\", \"positive_value\": \"./images/B008473Q20.png\", \"hn_image\": [\"./images/B0032HJXKG.png\"]}\n{\"q_img\": \"./images/B005GN4ATS.png\", \"q_text\": \"is a darker color, and has no collar and is darker\", \"positive_key\": \"B0096HP0L2\", \"positive_value\": \"./images/B0096HP0L2.png\", \"hn_image\": [\"./images/B005GN4ATS.png\"]}\n{\"q_img\": \"./images/B009DLYLS4.png\", \"q_text\": \"is larger, and is orange and wider\", \"positive_key\": \"B007IDFV9M\", \"positive_value\": \"./images/B007IDFV9M.png\", \"hn_image\": [\"./images/B009DLYLS4.png\"]}\n{\"q_img\": \"./images/B007X22KSI.png\", \"q_text\": \"is light shade of red without print, and is more purple\", \"positive_key\": \"B003OQV4HA\", \"positive_value\": \"./images/B003OQV4HA.png\", \"hn_image\": [\"./images/B007X22KSI.png\"]}\n{\"q_img\": \"./images/B00B1FCGFU.png\", \"q_text\": \"is a T shirt with a dog soldier on it, and is black and has a larger graphic\", \"positive_key\": \"B0071LPNY4\", \"positive_value\": \"./images/B0071LPNY4.png\", \"hn_image\": [\"./images/B00B1FCGFU.png\"]}\n{\"q_img\": \"./images/B00B4X5XLI.png\", \"q_text\": \"is for children, and is one piece gray and no collar\", \"positive_key\": \"B00CC02P5O\", \"positive_value\": \"./images/B00CC02P5O.png\", \"hn_image\": [\"./images/B00B4X5XLI.png\"]}\n{\"q_img\": \"./images/B003NUSB6O.png\", \"q_text\": \" bright red with a larger, and is darker\", \"positive_key\": \"B002U4LDZK\", \"positive_value\": \"./images/B002U4LDZK.png\", \"hn_image\": [\"./images/B003NUSB6O.png\"]}\n{\"q_img\": \"./images/B00EASVUOI.png\", \"q_text\": \"The shirt is green in color with short sleeves., and Is shorter sleeved\", \"positive_key\": \"B0086EZ6WY\", \"positive_value\": \"./images/B0086EZ6WY.png\", \"hn_image\": [\"./images/B00EASVUOI.png\"]}\n{\"q_img\": \"./images/B006GJTLSG.png\", \"q_text\": \"is grey and oval neck, and gray with larger graphic on front\", \"positive_key\": \"B005MWDFGC\", \"positive_value\": \"./images/B005MWDFGC.png\", \"hn_image\": [\"./images/B006GJTLSG.png\"]}\n{\"q_img\": \"./images/B005ZAROU4.png\", \"q_text\": \"is a black tank top with diamond motif and lettering, and is a tank top\", \"positive_key\": \"B00CTP399S\", \"positive_value\": \"./images/B00CTP399S.png\", \"hn_image\": [\"./images/B005ZAROU4.png\"]}\n{\"q_img\": \"./images/B002HNSH80.png\", \"q_text\": \"has a different graphic, and is darker\", \"positive_key\": \"B006G2Z1W8\", \"positive_value\": \"./images/B006G2Z1W8.png\", \"hn_image\": [\"./images/B002HNSH80.png\"]}\n{\"q_img\": \"./images/B005JFAIQ2.png\", \"q_text\": \"is grey, and a patterned shirt\", \"positive_key\": \"B006HSCPUW\", \"positive_value\": \"./images/B006HSCPUW.png\", \"hn_image\": [\"./images/B005JFAIQ2.png\"]}\n{\"q_img\": \"./images/B003QC9E24.png\", \"q_text\": \"Is less patriotic, and is blue with white stripes\", \"positive_key\": \"B003QCESSE\", \"positive_value\": \"./images/B003QCESSE.png\", \"hn_image\": [\"./images/B003QC9E24.png\"]}\n{\"q_img\": \"./images/B0085O4Q7Q.png\", \"q_text\": \"Black and shorter, and is darker colored black\", \"positive_key\": \"B005XK8LOE\", \"positive_value\": \"./images/B005XK8LOE.png\", \"hn_image\": [\"./images/B0085O4Q7Q.png\"]}\n{\"q_img\": \"./images/B006IZI2H4.png\", \"q_text\": \"A darker color with a design on front., and is darker in color\", \"positive_key\": \"B0047WT8AK\", \"positive_value\": \"./images/B0047WT8AK.png\", \"hn_image\": [\"./images/B006IZI2H4.png\"]}\n{\"q_img\": \"./images/B0072Y3CWK.png\", \"q_text\": \"has the volkswagen logo on it, and is grey with a circular VW logo on front\", \"positive_key\": \"B00A2FC4ZW\", \"positive_value\": \"./images/B00A2FC4ZW.png\", \"hn_image\": [\"./images/B0072Y3CWK.png\"]}\n{\"q_img\": \"./images/B002HNSH80.png\", \"q_text\": \"has larger lettering and shorter message, and has the word rice on it\", \"positive_key\": \"B005GW1I2Q\", \"positive_value\": \"./images/B005GW1I2Q.png\", \"hn_image\": [\"./images/B002HNSH80.png\"]}\n{\"q_img\": \"./images/B007CBS7OG.png\", \"q_text\": \"is black with graphics, and Is darker with a white graphic\", \"positive_key\": \"B0064EIM7E\", \"positive_value\": \"./images/B0064EIM7E.png\", \"hn_image\": [\"./images/B007CBS7OG.png\"]}\n{\"q_img\": \"./images/B009G0S8CM.png\", \"q_text\": \"is dark colored, and is darker\", \"positive_key\": \"B00A2HPEZ2\", \"positive_value\": \"./images/B00A2HPEZ2.png\", \"hn_image\": [\"./images/B009G0S8CM.png\"]}\n{\"q_img\": \"./images/B00CCDB4WG.png\", \"q_text\": \"has long sleeves and a print, and is long-sleeved button down with small print\", \"positive_key\": \"B006HZH9MY\", \"positive_value\": \"./images/B006HZH9MY.png\", \"hn_image\": [\"./images/B00CCDB4WG.png\"]}\n{\"q_img\": \"./images/B002Y7ABEC.png\", \"q_text\": \"is black and has a different logo, and is darker\", \"positive_key\": \"B0079K1R2E\", \"positive_value\": \"./images/B0079K1R2E.png\", \"hn_image\": [\"./images/B002Y7ABEC.png\"]}\n{\"q_img\": \"./images/B006IJVUYW.png\", \"q_text\": \"is two-colored and has smaller graphic, and is half black and grey with a chest design.\", \"positive_key\": \"B00BRB2H8O\", \"positive_value\": \"./images/B00BRB2H8O.png\", \"hn_image\": [\"./images/B006IJVUYW.png\"]}\n{\"q_img\": \"./images/B00A4LJAG0.png\", \"q_text\": \"has a scarier picture on it, and is darker\", \"positive_key\": \"B001WSRW6Y\", \"positive_value\": \"./images/B001WSRW6Y.png\", \"hn_image\": [\"./images/B00A4LJAG0.png\"]}\n{\"q_img\": \"./images/B0071BYF0M.png\", \"q_text\": \"Is more wordy, and is darker and has text\", \"positive_key\": \"B009SOQ788\", \"positive_value\": \"./images/B009SOQ788.png\", \"hn_image\": [\"./images/B0071BYF0M.png\"]}\n{\"q_img\": \"./images/B00B71EDRC.png\", \"q_text\": \"is a t-shirt that is black, and has short sleeves and is all black\", \"positive_key\": \"B00BRGFCJK\", \"positive_value\": \"./images/B00BRGFCJK.png\", \"hn_image\": [\"./images/B00B71EDRC.png\"]}\n{\"q_img\": \"./images/B007XX6790.png\", \"q_text\": \"is a shirt, and is a shirt\", \"positive_key\": \"B00FZRK0YE\", \"positive_value\": \"./images/B00FZRK0YE.png\", \"hn_image\": [\"./images/B007XX6790.png\"]}\n{\"q_img\": \"./images/B005VM84X2.png\", \"q_text\": \"Is white and less colorful, and is lighter\", \"positive_key\": \"B004YO7ZNS\", \"positive_value\": \"./images/B004YO7ZNS.png\", \"hn_image\": [\"./images/B005VM84X2.png\"]}\n{\"q_img\": \"./images/B009QR9DT2.png\", \"q_text\": \"Is a light pink short sleeved shirt, and is much darker in color\", \"positive_key\": \"B003AOCZS8\", \"positive_value\": \"./images/B003AOCZS8.png\", \"hn_image\": [\"./images/B009QR9DT2.png\"]}\n{\"q_img\": \"./images/B00DQDA9HC.png\", \"q_text\": \"has a short black sleeve with yellow art, and It is black with yellow print\", \"positive_key\": \"B0044B0ZCE\", \"positive_value\": \"./images/B0044B0ZCE.png\", \"hn_image\": [\"./images/B00DQDA9HC.png\"]}\n{\"q_img\": \"./images/B008RJTPT8.png\", \"q_text\": \"has a red and blue pattern, and is checkered\", \"positive_key\": \"B007JR86O4\", \"positive_value\": \"./images/B007JR86O4.png\", \"hn_image\": [\"./images/B008RJTPT8.png\"]}\n{\"q_img\": \"./images/B008H7I2L2.png\", \"q_text\": \"is written 'got psychology?', and red\", \"positive_key\": \"B008H7ND9I\", \"positive_value\": \"./images/B008H7ND9I.png\", \"hn_image\": [\"./images/B008H7I2L2.png\"]}\n{\"q_img\": \"./images/B004L627X2.png\", \"q_text\": \"is a short sleeved checked shirt, and has short sleeve and is stripe shirt\", \"positive_key\": \"B00BF9Z6HC\", \"positive_value\": \"./images/B00BF9Z6HC.png\", \"hn_image\": [\"./images/B004L627X2.png\"]}\n{\"q_img\": \"./images/B008EJ0MMU.png\", \"q_text\": \"is white in color with red logo, and is lighter colored\", \"positive_key\": \"B001MQBBQS\", \"positive_value\": \"./images/B001MQBBQS.png\", \"hn_image\": [\"./images/B008EJ0MMU.png\"]}\n{\"q_img\": \"./images/B000JILAW0.png\", \"q_text\": \"is a white button down, and is solid white\", \"positive_key\": \"B000JIPI5U\", \"positive_value\": \"./images/B000JIPI5U.png\", \"hn_image\": [\"./images/B000JILAW0.png\"]}\n{\"q_img\": \"./images/B0087J58OE.png\", \"q_text\": \"is black with red designs, and is black with red motif\", \"positive_key\": \"B004UR1QJS\", \"positive_value\": \"./images/B004UR1QJS.png\", \"hn_image\": [\"./images/B0087J58OE.png\"]}\n{\"q_img\": \"./images/B00BU12D0I.png\", \"q_text\": \"Is solid in color and has short sleeves., and is grey and has short sleeves\", \"positive_key\": \"B005LA7TKI\", \"positive_value\": \"./images/B005LA7TKI.png\", \"hn_image\": [\"./images/B00BU12D0I.png\"]}\n{\"q_img\": \"./images/B001CFA16A.png\", \"q_text\": \"has slightly lighter and has less letters, and has a different graphic\", \"positive_key\": \"B001XWTNGQ\", \"positive_value\": \"./images/B001XWTNGQ.png\", \"hn_image\": [\"./images/B001CFA16A.png\"]}\n{\"q_img\": \"./images/B00AELH2JM.png\", \"q_text\": \"is white with shorter sleeves and buttons, and is lighter and has shorter sleeves\", \"positive_key\": \"B00AF3Y0HQ\", \"positive_value\": \"./images/B00AF3Y0HQ.png\", \"hn_image\": [\"./images/B00AELH2JM.png\"]}\n{\"q_img\": \"./images/B000LUIBH8.png\", \"q_text\": \"is blue colored and a logo, and has short sleeves\", \"positive_key\": \"B007RJG5BU\", \"positive_value\": \"./images/B007RJG5BU.png\", \"hn_image\": [\"./images/B000LUIBH8.png\"]}\n{\"q_img\": \"./images/B00DVX894M.png\", \"q_text\": \"Is more colorful and blue, and is lighter\", \"positive_key\": \"B009LM1ZJS\", \"positive_value\": \"./images/B009LM1ZJS.png\", \"hn_image\": [\"./images/B00DVX894M.png\"]}\n{\"q_img\": \"./images/B004GBM6N8.png\", \"q_text\": \"is a lighter color with no image just a slogan, and is lighter\", \"positive_key\": \"B00CMBWV0W\", \"positive_value\": \"./images/B00CMBWV0W.png\", \"hn_image\": [\"./images/B004GBM6N8.png\"]}\n{\"q_img\": \"./images/B0048KZ3FU.png\", \"q_text\": \"has larger sleeves, and is salmon colored\", \"positive_key\": \"B001DIKL4I\", \"positive_value\": \"./images/B001DIKL4I.png\", \"hn_image\": [\"./images/B0048KZ3FU.png\"]}\n{\"q_img\": \"./images/B0087J58OE.png\", \"q_text\": \"is black and represents a video game, and is black with text\", \"positive_key\": \"B00580L0YC\", \"positive_value\": \"./images/B00580L0YC.png\", \"hn_image\": [\"./images/B0087J58OE.png\"]}\n{\"q_img\": \"./images/B00AK46JK6.png\", \"q_text\": \"green tank top with white stripes, and is green and no sleeves\", \"positive_key\": \"B00C1EMHHW\", \"positive_value\": \"./images/B00C1EMHHW.png\", \"hn_image\": [\"./images/B00AK46JK6.png\"]}\n{\"q_img\": \"./images/B0064R5VW0.png\", \"q_text\": \"is darker, and has an image of a person\", \"positive_key\": \"B003UV4YRQ\", \"positive_value\": \"./images/B003UV4YRQ.png\", \"hn_image\": [\"./images/B0064R5VW0.png\"]}\n{\"q_img\": \"./images/B00C4RULCE.png\", \"q_text\": \"Darker black and blue print, and is darker and has blue text\", \"positive_key\": \"B0064IPSMC\", \"positive_value\": \"./images/B0064IPSMC.png\", \"hn_image\": [\"./images/B00C4RULCE.png\"]}\n{\"q_img\": \"./images/B0095VZ8JI.png\", \"q_text\": \"Is less animal-like and more wordy, and has a white chest logo\", \"positive_key\": \"B008MHFJIQ\", \"positive_value\": \"./images/B008MHFJIQ.png\", \"hn_image\": [\"./images/B0095VZ8JI.png\"]}\n{\"q_img\": \"./images/B000EJM6X6.png\", \"q_text\": \"is white colored, and is t shirt and white\", \"positive_key\": \"B009VZWHYW\", \"positive_value\": \"./images/B009VZWHYW.png\", \"hn_image\": [\"./images/B000EJM6X6.png\"]}\n{\"q_img\": \"./images/B001FO5LAY.png\", \"q_text\": \"is darker in color, and is more colorful\", \"positive_key\": \"B006GBQMT0\", \"positive_value\": \"./images/B006GBQMT0.png\", \"hn_image\": [\"./images/B001FO5LAY.png\"]}\n{\"q_img\": \"./images/B001U9DJ0I.png\", \"q_text\": \"Has a different graphic, and has more red\", \"positive_key\": \"B007OWVLFK\", \"positive_value\": \"./images/B007OWVLFK.png\", \"hn_image\": [\"./images/B001U9DJ0I.png\"]}\n{\"q_img\": \"./images/B003TW4UT8.png\", \"q_text\": \"has an american flag throughout and is longer sleeved, and it has a long sleeve and buttons\", \"positive_key\": \"B0020IQZ04\", \"positive_value\": \"./images/B0020IQZ04.png\", \"hn_image\": [\"./images/B003TW4UT8.png\"]}\n{\"q_img\": \"./images/B003FSSOA2.png\", \"q_text\": \"has a batman logo on it, and is grey with a batman motif\", \"positive_key\": \"B0040Z0XYO\", \"positive_value\": \"./images/B0040Z0XYO.png\", \"hn_image\": [\"./images/B003FSSOA2.png\"]}\n{\"q_img\": \"./images/B00AZWLNV8.png\", \"q_text\": \"is a plaid button down shirt with a collar, and is more flannel and pocketed\", \"positive_key\": \"B0098YLOJK\", \"positive_value\": \"./images/B0098YLOJK.png\", \"hn_image\": [\"./images/B00AZWLNV8.png\"]}\n{\"q_img\": \"./images/B001TB6OR2.png\", \"q_text\": \"has a smaller graphic and is lighter in color, and has more subtle looking graphics\", \"positive_key\": \"B000VX05IS\", \"positive_value\": \"./images/B000VX05IS.png\", \"hn_image\": [\"./images/B001TB6OR2.png\"]}\n{\"q_img\": \"./images/B0078KLOH8.png\", \"q_text\": \"Is less patterned and not long-sleeved, and has shorter sleeves\", \"positive_key\": \"B0063MJBZE\", \"positive_value\": \"./images/B0063MJBZE.png\", \"hn_image\": [\"./images/B0078KLOH8.png\"]}\n{\"q_img\": \"./images/B00E9KI4K0.png\", \"q_text\": \"Is black and more colorful, and is black colored and has autism written on it\", \"positive_key\": \"B00DOL44JK\", \"positive_value\": \"./images/B00DOL44JK.png\", \"hn_image\": [\"./images/B00E9KI4K0.png\"]}\n{\"q_img\": \"./images/B002BOUBHK.png\", \"q_text\": \"is a green tee-shirt, and A dark green shirt with black/white print\", \"positive_key\": \"B00AHTLYVI\", \"positive_value\": \"./images/B00AHTLYVI.png\", \"hn_image\": [\"./images/B002BOUBHK.png\"]}\n{\"q_img\": \"./images/B0079085BA.png\", \"q_text\": \"is a floral green shirt with short sleeves, and Desired item is short-sleeved and black with floral print\", \"positive_key\": \"B00CO5FLLW\", \"positive_value\": \"./images/B00CO5FLLW.png\", \"hn_image\": [\"./images/B0079085BA.png\"]}\n{\"q_img\": \"./images/B000783TCQ.png\", \"q_text\": \"is a sportswear, and A black shirt.\", \"positive_key\": \"B009M023QI\", \"positive_value\": \"./images/B009M023QI.png\", \"hn_image\": [\"./images/B000783TCQ.png\"]}\n{\"q_img\": \"./images/B00CC2O4PQ.png\", \"q_text\": \"is more dark with visual patterns, and is darker and floral printed\", \"positive_key\": \"B000V3MZNG\", \"positive_value\": \"./images/B000V3MZNG.png\", \"hn_image\": [\"./images/B00CC2O4PQ.png\"]}\n{\"q_img\": \"./images/B00BSXLX9U.png\", \"q_text\": \"Is more casual and green, and is more faded\", \"positive_key\": \"B00D2KGGOE\", \"positive_value\": \"./images/B00D2KGGOE.png\", \"hn_image\": [\"./images/B00BSXLX9U.png\"]}\n{\"q_img\": \"./images/B008CP95LA.png\", \"q_text\": \"has more simple graphics, and has a smaller graphic on front\", \"positive_key\": \"B002TUTMCG\", \"positive_value\": \"./images/B002TUTMCG.png\", \"hn_image\": [\"./images/B008CP95LA.png\"]}\n{\"q_img\": \"./images/B007IJZ2KO.png\", \"q_text\": \"darker color, and has a four-letter word and black background\", \"positive_key\": \"B0077DM1JQ\", \"positive_value\": \"./images/B0077DM1JQ.png\", \"hn_image\": [\"./images/B007IJZ2KO.png\"]}\n{\"q_img\": \"./images/B007JWZZIO.png\", \"q_text\": \"has more colors and is sportier, and is more green and has 3/4 sleeves\", \"positive_key\": \"B000CAZVI4\", \"positive_value\": \"./images/B000CAZVI4.png\", \"hn_image\": [\"./images/B007JWZZIO.png\"]}\n{\"q_img\": \"./images/B00AOJHO56.png\", \"q_text\": \"Has a Hawaii summery feel with more colors, and has more colors and more graphics\", \"positive_key\": \"245600258X\", \"positive_value\": \"./images/245600258X.png\", \"hn_image\": [\"./images/B00AOJHO56.png\"]}\n{\"q_img\": \"./images/B0024FAZ4A.png\", \"q_text\": \"The shirt is black in color with a picture of a man., and is black with square logo\", \"positive_key\": \"B00BCXLU30\", \"positive_value\": \"./images/B00BCXLU30.png\", \"hn_image\": [\"./images/B0024FAZ4A.png\"]}\n{\"q_img\": \"./images/B000PW38Y8.png\", \"q_text\": \"Is plaid and less bright green, and darker\", \"positive_key\": \"B0096A37SW\", \"positive_value\": \"./images/B0096A37SW.png\", \"hn_image\": [\"./images/B000PW38Y8.png\"]}\n{\"q_img\": \"./images/B00AREPLXK.png\", \"q_text\": \"is lighter colored with a print, and has a more white and gray stripped pattern\", \"positive_key\": \"B006VE8GR8\", \"positive_value\": \"./images/B006VE8GR8.png\", \"hn_image\": [\"./images/B00AREPLXK.png\"]}\n{\"q_img\": \"./images/B000LQVB9W.png\", \"q_text\": \"doesn't have a collar, and fewer words\", \"positive_key\": \"B004O95B0W\", \"positive_value\": \"./images/B004O95B0W.png\", \"hn_image\": [\"./images/B000LQVB9W.png\"]}\n{\"q_img\": \"./images/B0085428H6.png\", \"q_text\": \"is light colored plaid shirt with collar and short sleeves, and has a collar\", \"positive_key\": \"B00BCXNBA0\", \"positive_value\": \"./images/B00BCXNBA0.png\", \"hn_image\": [\"./images/B0085428H6.png\"]}\n{\"q_img\": \"./images/B009MB8Q7C.png\", \"q_text\": \"is darker and not patterned, and is solid color and has gold tie\", \"positive_key\": \"B000VIMKXG\", \"positive_value\": \"./images/B000VIMKXG.png\", \"hn_image\": [\"./images/B009MB8Q7C.png\"]}\n{\"q_img\": \"./images/B0042AOT8S.png\", \"q_text\": \"Is a shirt, and is white with a small logo\", \"positive_key\": \"B002612YSW\", \"positive_value\": \"./images/B002612YSW.png\", \"hn_image\": [\"./images/B0042AOT8S.png\"]}\n{\"q_img\": \"./images/B00AWA4RF2.png\", \"q_text\": \"has shorter sleeves and is less formal, and is white and red with shorter sleeves\", \"positive_key\": \"B00C61GXEI\", \"positive_value\": \"./images/B00C61GXEI.png\", \"hn_image\": [\"./images/B00AWA4RF2.png\"]}\n{\"q_img\": \"./images/B004M488W2.png\", \"q_text\": \"is black and slim, and is black and has a different graphic\", \"positive_key\": \"B001AYANYI\", \"positive_value\": \"./images/B001AYANYI.png\", \"hn_image\": [\"./images/B004M488W2.png\"]}\n{\"q_img\": \"./images/B0015N1DYI.png\", \"q_text\": \"is slim and red, and is red with a yellow log\", \"positive_key\": \"B005Y5C7PC\", \"positive_value\": \"./images/B005Y5C7PC.png\", \"hn_image\": [\"./images/B0015N1DYI.png\"]}\n{\"q_img\": \"./images/B000UZCE2M.png\", \"q_text\": \"is multicolored, and is lighter with dark horizontal stripes\", \"positive_key\": \"B00BYQH3D6\", \"positive_value\": \"./images/B00BYQH3D6.png\", \"hn_image\": [\"./images/B000UZCE2M.png\"]}\n{\"q_img\": \"./images/B0046JZMOK.png\", \"q_text\": \"has shorter sleeves, and sleeveless\", \"positive_key\": \"B00EBH4ALI\", \"positive_value\": \"./images/B00EBH4ALI.png\", \"hn_image\": [\"./images/B0046JZMOK.png\"]}\n{\"q_img\": \"./images/B007X6BEF4.png\", \"q_text\": \"is darker, and is more formal\", \"positive_key\": \"B004AQ5O0Q\", \"positive_value\": \"./images/B004AQ5O0Q.png\", \"hn_image\": [\"./images/B007X6BEF4.png\"]}\n{\"q_img\": \"./images/B005JQ2IFK.png\", \"q_text\": \"is green and long sleeves, and is grey only\", \"positive_key\": \"B0002FHIWQ\", \"positive_value\": \"./images/B0002FHIWQ.png\", \"hn_image\": [\"./images/B005JQ2IFK.png\"]}\n{\"q_img\": \"./images/B00383INJQ.png\", \"q_text\": \"is pale yellow, and Is red in color and buttoned\", \"positive_key\": \"B0037OJRTQ\", \"positive_value\": \"./images/B0037OJRTQ.png\", \"hn_image\": [\"./images/B00383INJQ.png\"]}\n{\"q_img\": \"./images/B00BGLZK2K.png\", \"q_text\": \"has long sleeves and is patterend, and Has longer sleeves and is button up plaid\", \"positive_key\": \"B00639ED32\", \"positive_value\": \"./images/B00639ED32.png\", \"hn_image\": [\"./images/B00BGLZK2K.png\"]}\n{\"q_img\": \"./images/B004YO8QCC.png\", \"q_text\": \"is a long sleeve black tee, and has longer sleeves\", \"positive_key\": \"B00426DRYO\", \"positive_value\": \"./images/B00426DRYO.png\", \"hn_image\": [\"./images/B004YO8QCC.png\"]}\n{\"q_img\": \"./images/B000JIPI5U.png\", \"q_text\": \"is purple, and is purple and less formal.\", \"positive_key\": \"B002N8P7MI\", \"positive_value\": \"./images/B002N8P7MI.png\", \"hn_image\": [\"./images/B000JIPI5U.png\"]}\n{\"q_img\": \"./images/B00CGSITX4.png\", \"q_text\": \"is less formal with less buttons, and is gray with no collar\", \"positive_key\": \"B008J83O62\", \"positive_value\": \"./images/B008J83O62.png\", \"hn_image\": [\"./images/B00CGSITX4.png\"]}\n{\"q_img\": \"./images/B0036SQCKK.png\", \"q_text\": \"is the same as, and and black colored\", \"positive_key\": \"B003ZHH2MY\", \"positive_value\": \"./images/B003ZHH2MY.png\", \"hn_image\": [\"./images/B0036SQCKK.png\"]}\n{\"q_img\": \"./images/B00GOGK2SO.png\", \"q_text\": \" button-down in checkered print, and is green\", \"positive_key\": \"B0051VNAJG\", \"positive_value\": \"./images/B0051VNAJG.png\", \"hn_image\": [\"./images/B00GOGK2SO.png\"]}\n{\"q_img\": \"./images/B007T4JJ5W.png\", \"q_text\": \"has vertical green stripes, and is sold blue\", \"positive_key\": \"B008GS195S\", \"positive_value\": \"./images/B008GS195S.png\", \"hn_image\": [\"./images/B007T4JJ5W.png\"]}\n{\"q_img\": \"./images/B006QNB1M6.png\", \"q_text\": \"is grey with sith academy on it, and is darker colored and has a different graphic\", \"positive_key\": \"B0088LMPCY\", \"positive_value\": \"./images/B0088LMPCY.png\", \"hn_image\": [\"./images/B006QNB1M6.png\"]}\n{\"q_img\": \"./images/B007R1NWUU.png\", \"q_text\": \"is red with white designs, and is a budweiser t shirt and is more red\", \"positive_key\": \"B007R1OKNS\", \"positive_value\": \"./images/B007R1OKNS.png\", \"hn_image\": [\"./images/B007R1NWUU.png\"]}\n{\"q_img\": \"./images/B000KOU8E4.png\", \"q_text\": \"is sleeveless and has text on it, and has no sleeves\", \"positive_key\": \"B00FDLENDQ\", \"positive_value\": \"./images/B00FDLENDQ.png\", \"hn_image\": [\"./images/B000KOU8E4.png\"]}\n{\"q_img\": \"./images/B00DEKLOYO.png\", \"q_text\": \"is pink with white cuffs and neck collar., and Is more pink.\", \"positive_key\": \"B00CBQ9V0G\", \"positive_value\": \"./images/B00CBQ9V0G.png\", \"hn_image\": [\"./images/B00DEKLOYO.png\"]}\n{\"q_img\": \"./images/B009M9PACI.png\", \"q_text\": \"is lighter with smaller pinstripes, and is white colored and plain pattern\", \"positive_key\": \"B00AZIAZK2\", \"positive_value\": \"./images/B00AZIAZK2.png\", \"hn_image\": [\"./images/B009M9PACI.png\"]}\n{\"q_img\": \"./images/B009P9TQ5M.png\", \"q_text\": \"is more formal and pink, and Is striped in pattern\", \"positive_key\": \"B008RAOF42\", \"positive_value\": \"./images/B008RAOF42.png\", \"hn_image\": [\"./images/B009P9TQ5M.png\"]}\n{\"q_img\": \"./images/B007CBS7OG.png\", \"q_text\": \"has NCIS cast on it, and has a chest logo\", \"positive_key\": \"B003IROW6K\", \"positive_value\": \"./images/B003IROW6K.png\", \"hn_image\": [\"./images/B007CBS7OG.png\"]}\n{\"q_img\": \"./images/B00C865HBQ.png\", \"q_text\": \"is white with a different pic, and is white with a different image\", \"positive_key\": \"B0091G8YOI\", \"positive_value\": \"./images/B0091G8YOI.png\", \"hn_image\": [\"./images/B00C865HBQ.png\"]}\n{\"q_img\": \"./images/B00644P0N8.png\", \"q_text\": \"is a white button down shirt, and is more formal\", \"positive_key\": \"B004R0XJM0\", \"positive_value\": \"./images/B004R0XJM0.png\", \"hn_image\": [\"./images/B00644P0N8.png\"]}\n{\"q_img\": \"./images/B00018A8ZI.png\", \"q_text\": \"has floral pattern, and floral design\", \"positive_key\": \"B00DGXRR8Q\", \"positive_value\": \"./images/B00DGXRR8Q.png\", \"hn_image\": [\"./images/B00018A8ZI.png\"]}\n{\"q_img\": \"./images/B005FR87IA.png\", \"q_text\": \"is darker with a different 'tap out' logo, and is black with an American flag image\", \"positive_key\": \"B005L385PI\", \"positive_value\": \"./images/B005L385PI.png\", \"hn_image\": [\"./images/B005FR87IA.png\"]}\n{\"q_img\": \"./images/B00GUQOCLQ.png\", \"q_text\": \"is a blue t-shirt and has writing on it, and is blue with colorful lettering\", \"positive_key\": \"B001AGEBZI\", \"positive_value\": \"./images/B001AGEBZI.png\", \"hn_image\": [\"./images/B00GUQOCLQ.png\"]}\n{\"q_img\": \"./images/B00B9DYVZC.png\", \"q_text\": \"is blue with a more colourful picture, and is blue and more graphic\", \"positive_key\": \"B00CPRKNNA\", \"positive_value\": \"./images/B00CPRKNNA.png\", \"hn_image\": [\"./images/B00B9DYVZC.png\"]}\n{\"q_img\": \"./images/B009OMU5V4.png\", \"q_text\": \"is similar, and is lighter\", \"positive_key\": \"B00AWJT5LY\", \"positive_value\": \"./images/B00AWJT5LY.png\", \"hn_image\": [\"./images/B009OMU5V4.png\"]}\n{\"q_img\": \"./images/B0081GBQFS.png\", \"q_text\": \"is a darker shade of black, and is darker in color\", \"positive_key\": \"B00B91705M\", \"positive_value\": \"./images/B00B91705M.png\", \"hn_image\": [\"./images/B0081GBQFS.png\"]}\n{\"q_img\": \"./images/B001YIOAOE.png\", \"q_text\": \"has a black background color with two goldfish, and is more faded\", \"positive_key\": \"B008AGL136\", \"positive_value\": \"./images/B008AGL136.png\", \"hn_image\": [\"./images/B001YIOAOE.png\"]}\n{\"q_img\": \"./images/B006W79ZGA.png\", \"q_text\": \"Is black and has a man's face on the front, and Black with male comedian pictured\", \"positive_key\": \"B00452WXMC\", \"positive_value\": \"./images/B00452WXMC.png\", \"hn_image\": [\"./images/B006W79ZGA.png\"]}\n{\"q_img\": \"./images/B002RL87FK.png\", \"q_text\": \" more colorful print, and black and turquoise stripes\", \"positive_key\": \"B00CJSTVJC\", \"positive_value\": \"./images/B00CJSTVJC.png\", \"hn_image\": [\"./images/B002RL87FK.png\"]}\n{\"q_img\": \"./images/B000OMGLDY.png\", \"q_text\": \"pink eagle men tee, and is pink with a smaller logo\", \"positive_key\": \"B0050OVVCM\", \"positive_value\": \"./images/B0050OVVCM.png\", \"hn_image\": [\"./images/B000OMGLDY.png\"]}\n{\"q_img\": \"./images/B00CIYDP94.png\", \"q_text\": \"is more simple, and has no red in it\", \"positive_key\": \"B000FVAR52\", \"positive_value\": \"./images/B000FVAR52.png\", \"hn_image\": [\"./images/B00CIYDP94.png\"]}\n{\"q_img\": \"./images/B00GOGK2SO.png\", \"q_text\": \"has a large movie quote graphic and profanity, and is lighter\", \"positive_key\": \"B0047QYJOQ\", \"positive_value\": \"./images/B0047QYJOQ.png\", \"hn_image\": [\"./images/B00GOGK2SO.png\"]}\n{\"q_img\": \"./images/B004LYG40Q.png\", \"q_text\": \"has a huge graphic and is darker colored, and it is blue with a colorful print\", \"positive_key\": \"B0083J832W\", \"positive_value\": \"./images/B0083J832W.png\", \"hn_image\": [\"./images/B004LYG40Q.png\"]}\n{\"q_img\": \"./images/B0067PC8OS.png\", \"q_text\": \"is a lighter color and has a round neck, and is lighter\", \"positive_key\": \"B000BO1R2K\", \"positive_value\": \"./images/B000BO1R2K.png\", \"hn_image\": [\"./images/B0067PC8OS.png\"]}\n{\"q_img\": \"./images/B00BCX4MWG.png\", \"q_text\": \"is a black vest, and has no sleeves\", \"positive_key\": \"B00CL5HKPK\", \"positive_value\": \"./images/B00CL5HKPK.png\", \"hn_image\": [\"./images/B00BCX4MWG.png\"]}\n{\"q_img\": \"./images/B00DAMLQKS.png\", \"q_text\": \"is darker, and is red\", \"positive_key\": \"B006EKJJ1G\", \"positive_value\": \"./images/B006EKJJ1G.png\", \"hn_image\": [\"./images/B00DAMLQKS.png\"]}\n{\"q_img\": \"./images/B004ZC5RWK.png\", \"q_text\": \"is a lighter color, and is brighter in color\", \"positive_key\": \"B004ZC5QLC\", \"positive_value\": \"./images/B004ZC5QLC.png\", \"hn_image\": [\"./images/B004ZC5RWK.png\"]}\n{\"q_img\": \"./images/B002CJLUOW.png\", \"q_text\": \"Has smaller picture and letters. Is darker., and is black with red lettering\", \"positive_key\": \"B005X0A2UU\", \"positive_value\": \"./images/B005X0A2UU.png\", \"hn_image\": [\"./images/B002CJLUOW.png\"]}\n{\"q_img\": \"./images/B003OPUWH4.png\", \"q_text\": \"The tank top is black with a white wolf., and is darke\", \"positive_key\": \"B005BW2RE4\", \"positive_value\": \"./images/B005BW2RE4.png\", \"hn_image\": [\"./images/B003OPUWH4.png\"]}\n{\"q_img\": \"./images/B004K2NSGM.png\", \"q_text\": \"has a different graphic, and is darker\", \"positive_key\": \"B009ZGNJ6W\", \"positive_value\": \"./images/B009ZGNJ6W.png\", \"hn_image\": [\"./images/B004K2NSGM.png\"]}\n{\"q_img\": \"./images/B006VE8IQC.png\", \"q_text\": \"is long sleeved button down dark plaid shirt with collar, and is maroon and white and more checked\", \"positive_key\": \"B00AREPMT8\", \"positive_value\": \"./images/B00AREPMT8.png\", \"hn_image\": [\"./images/B006VE8IQC.png\"]}\n{\"q_img\": \"./images/B00DI5GTNG.png\", \"q_text\": \"Has a photo on it not a character and is darker colored., and Has a larger graphic\", \"positive_key\": \"B00E1Z10FE\", \"positive_value\": \"./images/B00E1Z10FE.png\", \"hn_image\": [\"./images/B00DI5GTNG.png\"]}\n{\"q_img\": \"./images/B006MHJB1O.png\", \"q_text\": \"is more minimal and visually stylistic, and but does have a large print.\", \"positive_key\": \"B00ANR1M8Y\", \"positive_value\": \"./images/B00ANR1M8Y.png\", \"hn_image\": [\"./images/B006MHJB1O.png\"]}\n{\"q_img\": \"./images/B003O0NXZ2.png\", \"q_text\": \"is a red and blue flannel shirt, and lighter plaid with reds and blues\", \"positive_key\": \"B000GOSLJW\", \"positive_value\": \"./images/B000GOSLJW.png\", \"hn_image\": [\"./images/B003O0NXZ2.png\"]}\n{\"q_img\": \"./images/B004UHPEU0.png\", \"q_text\": \"has a bright color, and is lighter\", \"positive_key\": \"B00D8HJ3C8\", \"positive_value\": \"./images/B00D8HJ3C8.png\", \"hn_image\": [\"./images/B004UHPEU0.png\"]}\n{\"q_img\": \"./images/B008H1MLOW.png\", \"q_text\": \"Has shorter sleeves and is more casual, and is a t shirt\", \"positive_key\": \"B002RS6IWW\", \"positive_value\": \"./images/B002RS6IWW.png\", \"hn_image\": [\"./images/B008H1MLOW.png\"]}\n{\"q_img\": \"./images/B00F6S5GUK.png\", \"q_text\": \"has an image of a person, and has a different graphic\", \"positive_key\": \"B00BW9IQBS\", \"positive_value\": \"./images/B00BW9IQBS.png\", \"hn_image\": [\"./images/B00F6S5GUK.png\"]}\n{\"q_img\": \"./images/B00CMAHVE4.png\", \"q_text\": \"is black with words, and A black shirt with white letters on front\", \"positive_key\": \"B00AO9UKDO\", \"positive_value\": \"./images/B00AO9UKDO.png\", \"hn_image\": [\"./images/B00CMAHVE4.png\"]}\n{\"q_img\": \"./images/B008JVJI00.png\", \"q_text\": \"is a darker solid gray in color, and gray with words\", \"positive_key\": \"B003IRS13K\", \"positive_value\": \"./images/B003IRS13K.png\", \"hn_image\": [\"./images/B008JVJI00.png\"]}\n{\"q_img\": \"./images/B002VK1EZM.png\", \"q_text\": \"is patriotic, and Is all white with patriotic smiley face on front\", \"positive_key\": \"B000R4LN32\", \"positive_value\": \"./images/B000R4LN32.png\", \"hn_image\": [\"./images/B002VK1EZM.png\"]}\n{\"q_img\": \"./images/B00C4RULCE.png\", \"q_text\": \"is a blue plaid button up shirt with long sleeves, and Has longer sleeves and is plaid\", \"positive_key\": \"B003Y8Z3SY\", \"positive_value\": \"./images/B003Y8Z3SY.png\", \"hn_image\": [\"./images/B00C4RULCE.png\"]}\n{\"q_img\": \"./images/B007VHEYAM.png\", \"q_text\": \"Is darker and has print on it., and is a black t-shirt with a logo\", \"positive_key\": \"B004EGSXHI\", \"positive_value\": \"./images/B004EGSXHI.png\", \"hn_image\": [\"./images/B007VHEYAM.png\"]}\n{\"q_img\": \"./images/B0058P5XHM.png\", \"q_text\": \"is black with white words, and is darker and has text on the front\", \"positive_key\": \"B00D2XJ5SA\", \"positive_value\": \"./images/B00D2XJ5SA.png\", \"hn_image\": [\"./images/B0058P5XHM.png\"]}\n{\"q_img\": \"./images/B00383INJQ.png\", \"q_text\": \"The shirt is tan and long sleeve., and has buttons\", \"positive_key\": \"B002PA0BWK\", \"positive_value\": \"./images/B002PA0BWK.png\", \"hn_image\": [\"./images/B00383INJQ.png\"]}\n{\"q_img\": \"./images/B00BPIA3QW.png\", \"q_text\": \"is more cartoonish and darker-colored, and is dark blue\", \"positive_key\": \"B001SR5OYQ\", \"positive_value\": \"./images/B001SR5OYQ.png\", \"hn_image\": [\"./images/B00BPIA3QW.png\"]}\n{\"q_img\": \"./images/B003TYLO3G.png\", \"q_text\": \"is red in color and plain pattern, and is red and less busy\", \"positive_key\": \"B000AT8GEI\", \"positive_value\": \"./images/B000AT8GEI.png\", \"hn_image\": [\"./images/B003TYLO3G.png\"]}\n{\"q_img\": \"./images/B0002H6ZUU.png\", \"q_text\": \"darker colored and blue graphic print, and it is black with blue print\", \"positive_key\": \"B00D8GIBYU\", \"positive_value\": \"./images/B00D8GIBYU.png\", \"hn_image\": [\"./images/B0002H6ZUU.png\"]}\n{\"q_img\": \"./images/B0044NVJEU.png\", \"q_text\": \"a solid color short sleeve top with a collar and a few buttons in red, and is brighter blue and doesn't have stripes\", \"positive_key\": \"B00853KHV6\", \"positive_value\": \"./images/B00853KHV6.png\", \"hn_image\": [\"./images/B0044NVJEU.png\"]}\n{\"q_img\": \"./images/B001S2PP5E.png\", \"q_text\": \"is dark with red images in the middle, and with no sleeves\", \"positive_key\": \"B009K1O7XG\", \"positive_value\": \"./images/B009K1O7XG.png\", \"hn_image\": [\"./images/B001S2PP5E.png\"]}\n{\"q_img\": \"./images/B0050CFTU4.png\", \"q_text\": \"is black with a jordan logo, and is darker\", \"positive_key\": \"B007GDNXWG\", \"positive_value\": \"./images/B007GDNXWG.png\", \"hn_image\": [\"./images/B0050CFTU4.png\"]}\n{\"q_img\": \"./images/B002QEBQ3I.png\", \"q_text\": \"is white and black checkered with a collar, and is lighter and plaid\", \"positive_key\": \"B007T4JJCK\", \"positive_value\": \"./images/B007T4JJCK.png\", \"hn_image\": [\"./images/B002QEBQ3I.png\"]}\n{\"q_img\": \"./images/B006MV5MV8.png\", \"q_text\": \"Is more grey, and has more trucks on it\", \"positive_key\": \"B004UHP3IS\", \"positive_value\": \"./images/B004UHP3IS.png\", \"hn_image\": [\"./images/B006MV5MV8.png\"]}\n{\"q_img\": \"./images/B000X74R7C.png\", \"q_text\": \"is a black collared shirt, and is black with wide sleeves\", \"positive_key\": \"B001UZ7IHM\", \"positive_value\": \"./images/B001UZ7IHM.png\", \"hn_image\": [\"./images/B000X74R7C.png\"]}\n{\"q_img\": \"./images/B00BOHU9ZE.png\", \"q_text\": \"is gray not black and has birds on it, and is light gray with back lettering\", \"positive_key\": \"B009SRQAF0\", \"positive_value\": \"./images/B009SRQAF0.png\", \"hn_image\": [\"./images/B00BOHU9ZE.png\"]}\n{\"q_img\": \"./images/B0091V9DR0.png\", \"q_text\": \"is a light grey with green logo, and is gray with camping graphic\", \"positive_key\": \"B00CP77EEG\", \"positive_value\": \"./images/B00CP77EEG.png\", \"hn_image\": [\"./images/B0091V9DR0.png\"]}\n{\"q_img\": \"./images/B00FZRK0YE.png\", \"q_text\": \"has longer sleeves with more buttons, and has longer sleeves and is completely button up\", \"positive_key\": \"B006TXXE46\", \"positive_value\": \"./images/B006TXXE46.png\", \"hn_image\": [\"./images/B00FZRK0YE.png\"]}\n{\"q_img\": \"./images/B007DJ2UK4.png\", \"q_text\": \"is tan, and is beige color\", \"positive_key\": \"B008EYEBMM\", \"positive_value\": \"./images/B008EYEBMM.png\", \"hn_image\": [\"./images/B007DJ2UK4.png\"]}\n{\"q_img\": \"./images/B0041EIXIM.png\", \"q_text\": \"has shorter sleeves, and is short sleeved\", \"positive_key\": \"B00AHNV18U\", \"positive_value\": \"./images/B00AHNV18U.png\", \"hn_image\": [\"./images/B0041EIXIM.png\"]}\n{\"q_img\": \"./images/B00DT5P9N6.png\", \"q_text\": \"is black with red and yellow design, and is darker and more masculine\", \"positive_key\": \"B004B70D8M\", \"positive_value\": \"./images/B004B70D8M.png\", \"hn_image\": [\"./images/B00DT5P9N6.png\"]}\n{\"q_img\": \"./images/B00BW9IQBS.png\", \"q_text\": \"has image of star wars, and is gray with black and red logos\", \"positive_key\": \"B005XIGCYW\", \"positive_value\": \"./images/B005XIGCYW.png\", \"hn_image\": [\"./images/B00BW9IQBS.png\"]}\n{\"q_img\": \"./images/B00B5FTHIK.png\", \"q_text\": \"has longer sleeves, and Tightly blue squared long-sleeved shirt\", \"positive_key\": \"B009XDKZ3M\", \"positive_value\": \"./images/B009XDKZ3M.png\", \"hn_image\": [\"./images/B00B5FTHIK.png\"]}\n{\"q_img\": \"./images/B00B1FCGFU.png\", \"q_text\": \"is blue with image of dolphins, and is closer to blue\", \"positive_key\": \"B000F5LTXW\", \"positive_value\": \"./images/B000F5LTXW.png\", \"hn_image\": [\"./images/B00B1FCGFU.png\"]}\n{\"q_img\": \"./images/B002DESA4E.png\", \"q_text\": \"Is wordy, and has a more centered design on it\", \"positive_key\": \"B0054419WA\", \"positive_value\": \"./images/B0054419WA.png\", \"hn_image\": [\"./images/B002DESA4E.png\"]}\n{\"q_img\": \"./images/B00DR4IZTE.png\", \"q_text\": \"is black in color, and has gaspari graphic and is more black in colour\", \"positive_key\": \"B00380DSDU\", \"positive_value\": \"./images/B00380DSDU.png\", \"hn_image\": [\"./images/B00DR4IZTE.png\"]}\n{\"q_img\": \"./images/B005Y8684K.png\", \"q_text\": \"has more contrast and is darker, and is black with a red stripe and a button up\", \"positive_key\": \"B004CS0IZS\", \"positive_value\": \"./images/B004CS0IZS.png\", \"hn_image\": [\"./images/B005Y8684K.png\"]}\n{\"q_img\": \"./images/B0045Y0YKS.png\", \"q_text\": \"This is a black t-shirt with short sleeves., and is elss formal\", \"positive_key\": \"B00D3OTP5Q\", \"positive_value\": \"./images/B00D3OTP5Q.png\", \"hn_image\": [\"./images/B0045Y0YKS.png\"]}\n{\"q_img\": \"./images/B00DFJODD8.png\", \"q_text\": \"is black with white designs, and is black with a white design\", \"positive_key\": \"B00CC0TY1W\", \"positive_value\": \"./images/B00CC0TY1W.png\", \"hn_image\": [\"./images/B00DFJODD8.png\"]}\n{\"q_img\": \"./images/B0041FZUL4.png\", \"q_text\": \"is more colorful and more solid, and solid black shirt with green\", \"positive_key\": \"B00C45IEES\", \"positive_value\": \"./images/B00C45IEES.png\", \"hn_image\": [\"./images/B0041FZUL4.png\"]}\n{\"q_img\": \"./images/B0006MUZLG.png\", \"q_text\": \"has longer sleeves and is no collar, and has longer sleeves and no collar.\", \"positive_key\": \"B00F91Q5T0\", \"positive_value\": \"./images/B00F91Q5T0.png\", \"hn_image\": [\"./images/B0006MUZLG.png\"]}\n{\"q_img\": \"./images/B00028I7R8.png\", \"q_text\": \"is bright white colored and plain, and is a lighter color\", \"positive_key\": \"B000ULN7H2\", \"positive_value\": \"./images/B000ULN7H2.png\", \"hn_image\": [\"./images/B00028I7R8.png\"]}\n{\"q_img\": \"./images/B009DLYLS4.png\", \"q_text\": \"has a different graphic, and it has a lighter red and a white print\", \"positive_key\": \"B004E77QOS\", \"positive_value\": \"./images/B004E77QOS.png\", \"hn_image\": [\"./images/B009DLYLS4.png\"]}\n{\"q_img\": \"./images/B0058YVMDM.png\", \"q_text\": \"is gray, and Is grey and not buttoned\", \"positive_key\": \"B006RDOWRQ\", \"positive_value\": \"./images/B006RDOWRQ.png\", \"hn_image\": [\"./images/B0058YVMDM.png\"]}\n{\"q_img\": \"./images/B005GN4ATS.png\", \"q_text\": \" more black with a picture on front, and has shorter sleeves and black stripes\", \"positive_key\": \"B00BEEF04W\", \"positive_value\": \"./images/B00BEEF04W.png\", \"hn_image\": [\"./images/B005GN4ATS.png\"]}\n{\"q_img\": \"./images/B005G4XMFK.png\", \"q_text\": \"is blue striped, and is blue in color with a different pattern\", \"positive_key\": \"B002DYJG74\", \"positive_value\": \"./images/B002DYJG74.png\", \"hn_image\": [\"./images/B005G4XMFK.png\"]}\n{\"q_img\": \"./images/B004VZIML4.png\", \"q_text\": \"is a checked brighter shirt, and is green and white checked\", \"positive_key\": \"B00B9ALKF4\", \"positive_value\": \"./images/B00B9ALKF4.png\", \"hn_image\": [\"./images/B004VZIML4.png\"]}\n{\"q_img\": \"./images/B009ADCOP2.png\", \"q_text\": \"has more active figure and earthier color, and is a different color and has a different image\", \"positive_key\": \"B006ZP77FK\", \"positive_value\": \"./images/B006ZP77FK.png\", \"hn_image\": [\"./images/B009ADCOP2.png\"]}\n{\"q_img\": \"./images/B00CR90B0Q.png\", \"q_text\": \"is darker, and is solid black with a tiny logo\", \"positive_key\": \"B004VDCRW6\", \"positive_value\": \"./images/B004VDCRW6.png\", \"hn_image\": [\"./images/B00CR90B0Q.png\"]}\n{\"q_img\": \"./images/B00DDXR4QO.png\", \"q_text\": \"is more fitted and has more buttons, and is more solid grey\", \"positive_key\": \"B005B6ZIE6\", \"positive_value\": \"./images/B005B6ZIE6.png\", \"hn_image\": [\"./images/B00DDXR4QO.png\"]}\n{\"q_img\": \"./images/B004LTR2XY.png\", \"q_text\": \"Is more patriotic and grey, and it is grey\", \"positive_key\": \"B004LYSV9I\", \"positive_value\": \"./images/B004LYSV9I.png\", \"hn_image\": [\"./images/B004LTR2XY.png\"]}\n{\"q_img\": \"./images/B0027EBMU4.png\", \"q_text\": \"is black, and is darker and more bold\", \"positive_key\": \"B006DI2W36\", \"positive_value\": \"./images/B006DI2W36.png\", \"hn_image\": [\"./images/B0027EBMU4.png\"]}\n{\"q_img\": \"./images/B003XEFOQK.png\", \"q_text\": \"is gray with buttons down, and is grey and has buttons\", \"positive_key\": \"B003YLOVQ6\", \"positive_value\": \"./images/B003YLOVQ6.png\", \"hn_image\": [\"./images/B003XEFOQK.png\"]}\n{\"q_img\": \"./images/B006ZPQ0EY.png\", \"q_text\": \"is yellow with small characters, and is yellow in color\", \"positive_key\": \"B006ZP4OZQ\", \"positive_value\": \"./images/B006ZP4OZQ.png\", \"hn_image\": [\"./images/B006ZPQ0EY.png\"]}\n{\"q_img\": \"./images/B001LY2U70.png\", \"q_text\": \"a khaki and long sleeved with buttons., and is long sleeve beige colored\", \"positive_key\": \"B002GCGLOE\", \"positive_value\": \"./images/B002GCGLOE.png\", \"hn_image\": [\"./images/B001LY2U70.png\"]}\n{\"q_img\": \"./images/B003YOPRJS.png\", \"q_text\": \"is a tight fitness black long sleeved shirt, and is a blue tshirt\", \"positive_key\": \"B003R4ZS8U\", \"positive_value\": \"./images/B003R4ZS8U.png\", \"hn_image\": [\"./images/B003YOPRJS.png\"]}\n{\"q_img\": \"./images/B004UIOL3K.png\", \"q_text\": \"is brown with a different picture, and is brown\", \"positive_key\": \"B003HEYHV4\", \"positive_value\": \"./images/B003HEYHV4.png\", \"hn_image\": [\"./images/B004UIOL3K.png\"]}\n{\"q_img\": \"./images/B0051SN0LC.png\", \"q_text\": \"has a red and white image with text, and has a image in red and white on it with text\", \"positive_key\": \"B004REC7ZQ\", \"positive_value\": \"./images/B004REC7ZQ.png\", \"hn_image\": [\"./images/B0051SN0LC.png\"]}\n{\"q_img\": \"./images/B0068RJFC8.png\", \"q_text\": \"has longer sleeves and a different graphic, and is longer sleeved and red\", \"positive_key\": \"B002TR4X6O\", \"positive_value\": \"./images/B002TR4X6O.png\", \"hn_image\": [\"./images/B0068RJFC8.png\"]}\n{\"q_img\": \"./images/B000NZW3JI.png\", \"q_text\": \"has white words on it, and has text present\", \"positive_key\": \"B003XS78ZG\", \"positive_value\": \"./images/B003XS78ZG.png\", \"hn_image\": [\"./images/B000NZW3JI.png\"]}\n{\"q_img\": \"./images/B00611XBS0.png\", \"q_text\": \"is lighter with different graphics, and is purple with a red logo\", \"positive_key\": \"B005EQRGZW\", \"positive_value\": \"./images/B005EQRGZW.png\", \"hn_image\": [\"./images/B00611XBS0.png\"]}\n{\"q_img\": \"./images/B0081V2U6C.png\", \"q_text\": \"is black with yellow designs, and is darker\", \"positive_key\": \"B008BUX24C\", \"positive_value\": \"./images/B008BUX24C.png\", \"hn_image\": [\"./images/B0081V2U6C.png\"]}\n{\"q_img\": \"./images/B007XCSP9G.png\", \"q_text\": \" button down with collar, and has a larger graphic\", \"positive_key\": \"B00962JHKC\", \"positive_value\": \"./images/B00962JHKC.png\", \"hn_image\": [\"./images/B007XCSP9G.png\"]}\n{\"q_img\": \"./images/B004G09IXK.png\", \"q_text\": \"has no sleeves with a blue and white color, and is a light blue tank\", \"positive_key\": \"B00BQ9SS8U\", \"positive_value\": \"./images/B00BQ9SS8U.png\", \"hn_image\": [\"./images/B004G09IXK.png\"]}\n{\"q_img\": \"./images/B00729P2UA.png\", \"q_text\": \"is more casual, and is darker\", \"positive_key\": \"B0036XL7ZA\", \"positive_value\": \"./images/B0036XL7ZA.png\", \"hn_image\": [\"./images/B00729P2UA.png\"]}\n{\"q_img\": \"./images/B009ZZ7PYU.png\", \"q_text\": \"is gray with different wording, and is grey\", \"positive_key\": \"B00BQK0P7Q\", \"positive_value\": \"./images/B00BQK0P7Q.png\", \"hn_image\": [\"./images/B009ZZ7PYU.png\"]}\n{\"q_img\": \"./images/B0050OFMB8.png\", \"q_text\": \"is grey without sleeves, and is darker and has shorter sleeves\", \"positive_key\": \"B00B4DXN4W\", \"positive_value\": \"./images/B00B4DXN4W.png\", \"hn_image\": [\"./images/B0050OFMB8.png\"]}\n{\"q_img\": \"./images/B005KGF70M.png\", \"q_text\": \"a full rounded neckline, and Has a blue pattern across the front\", \"positive_key\": \"B005LCVMIG\", \"positive_value\": \"./images/B005LCVMIG.png\", \"hn_image\": [\"./images/B005KGF70M.png\"]}\n{\"q_img\": \"./images/B003OPUWH4.png\", \"q_text\": \"is black in color, and black with picture graphic on front\", \"positive_key\": \"B00EQI4GYS\", \"positive_value\": \"./images/B00EQI4GYS.png\", \"hn_image\": [\"./images/B003OPUWH4.png\"]}\n{\"q_img\": \"./images/B001O84MJC.png\", \"q_text\": \"the stripes are purple not blue, and darker color\", \"positive_key\": \"B001O869LG\", \"positive_value\": \"./images/B001O869LG.png\", \"hn_image\": [\"./images/B001O84MJC.png\"]}\n{\"q_img\": \"./images/B00DV4AIL8.png\", \"q_text\": \"has a lighter color with an image on the pocket, and is white colored and has a graphic\", \"positive_key\": \"B00BY2VXU4\", \"positive_value\": \"./images/B00BY2VXU4.png\", \"hn_image\": [\"./images/B00DV4AIL8.png\"]}\n{\"q_img\": \"./images/B005FS7LEK.png\", \"q_text\": \"Is more whimsical and vintage, and has longer sleeves and more pattern\", \"positive_key\": \"B004V2UC9M\", \"positive_value\": \"./images/B004V2UC9M.png\", \"hn_image\": [\"./images/B005FS7LEK.png\"]}\n{\"q_img\": \"./images/B003HLWPOI.png\", \"q_text\": \"is lighter colored, and is more blue\", \"positive_key\": \"B00BAWW596\", \"positive_value\": \"./images/B00BAWW596.png\", \"hn_image\": [\"./images/B003HLWPOI.png\"]}\n{\"q_img\": \"./images/B00CLZRTWE.png\", \"q_text\": \"a dark colored long sleeve sporty ribbed shirt, and is brown and has longer sleeves\", \"positive_key\": \"B001TSAF2U\", \"positive_value\": \"./images/B001TSAF2U.png\", \"hn_image\": [\"./images/B00CLZRTWE.png\"]}\n{\"q_img\": \"./images/B009B5OVU0.png\", \"q_text\": \"is a v-neck t-shirt with wide stripes, and has more stripes\", \"positive_key\": \"B00AAQTONI\", \"positive_value\": \"./images/B00AAQTONI.png\", \"hn_image\": [\"./images/B009B5OVU0.png\"]}\n{\"q_img\": \"./images/B001R1TYSA.png\", \"q_text\": \"The shirt is blue and long sleeve., and has shorter sleeves\", \"positive_key\": \"B004GC5XZU\", \"positive_value\": \"./images/B004GC5XZU.png\", \"hn_image\": [\"./images/B001R1TYSA.png\"]}\n{\"q_img\": \"./images/B004XYGILO.png\", \"q_text\": \"has a large logo, and darker and does not have a collar\", \"positive_key\": \"B006VDYBG4\", \"positive_value\": \"./images/B006VDYBG4.png\", \"hn_image\": [\"./images/B004XYGILO.png\"]}\n{\"q_img\": \"./images/B009DLYLS4.png\", \"q_text\": \"Has longer sleeves and has a pattern, and is more formal\", \"positive_key\": \"B00974VG2G\", \"positive_value\": \"./images/B00974VG2G.png\", \"hn_image\": [\"./images/B009DLYLS4.png\"]}\n{\"q_img\": \"./images/B007G03S2Y.png\", \"q_text\": \"is green, and Is brighter with shorter sleeves.\", \"positive_key\": \"B005KLXWRM\", \"positive_value\": \"./images/B005KLXWRM.png\", \"hn_image\": [\"./images/B007G03S2Y.png\"]}\n{\"q_img\": \"./images/B00BCX3BHI.png\", \"q_text\": \"blue colored and white graphic print, and is blue and has an image of a genie's lamp on it\", \"positive_key\": \"B00AD5BU6U\", \"positive_value\": \"./images/B00AD5BU6U.png\", \"hn_image\": [\"./images/B00BCX3BHI.png\"]}\n{\"q_img\": \"./images/B004JQOLKG.png\", \"q_text\": \"is a lighter color, and is lighter in color\", \"positive_key\": \"B00CUXLRDE\", \"positive_value\": \"./images/B00CUXLRDE.png\", \"hn_image\": [\"./images/B004JQOLKG.png\"]}\n{\"q_img\": \"./images/B001AYANYI.png\", \"q_text\": \"is more lighter and plain, and has long sleeves and is not a t-shirt\", \"positive_key\": \"B002X115DK\", \"positive_value\": \"./images/B002X115DK.png\", \"hn_image\": [\"./images/B001AYANYI.png\"]}\n{\"q_img\": \"./images/B004HTURAS.png\", \"q_text\": \"Is more sexy and longer, and is black in color\", \"positive_key\": \"B006WO450Y\", \"positive_value\": \"./images/B006WO450Y.png\", \"hn_image\": [\"./images/B004HTURAS.png\"]}\n{\"q_img\": \"./images/B0085X1RZQ.png\", \"q_text\": \"is white colored with stripes, and is blue and has longer sleeves\", \"positive_key\": \"B008AKNGYY\", \"positive_value\": \"./images/B008AKNGYY.png\", \"hn_image\": [\"./images/B0085X1RZQ.png\"]}\n{\"q_img\": \"./images/B00ABDT1II.png\", \"q_text\": \"is grey in color, and A grey color with black print on front\", \"positive_key\": \"B0056B0LFC\", \"positive_value\": \"./images/B0056B0LFC.png\", \"hn_image\": [\"./images/B00ABDT1II.png\"]}\n{\"q_img\": \"./images/B001S2PQ4O.png\", \"q_text\": \"is white with black designs, and is white and has faces on it\", \"positive_key\": \"B001S2PPF4\", \"positive_value\": \"./images/B001S2PPF4.png\", \"hn_image\": [\"./images/B001S2PQ4O.png\"]}\n{\"q_img\": \"./images/B002CWNJXY.png\", \"q_text\": \"is more casual and has shorter sleeves, and is less formal\", \"positive_key\": \"B00A3BNT36\", \"positive_value\": \"./images/B00A3BNT36.png\", \"hn_image\": [\"./images/B002CWNJXY.png\"]}\n{\"q_img\": \"./images/B0039228GK.png\", \"q_text\": \"has a different pattern and is darker, and is less formal\", \"positive_key\": \"B0040JZKSE\", \"positive_value\": \"./images/B0040JZKSE.png\", \"hn_image\": [\"./images/B0039228GK.png\"]}\n{\"q_img\": \"./images/B000LJASMK.png\", \"q_text\": \"is blue with short sleeves, and is light blue and a short sleeved polo\", \"positive_key\": \"B0029L3104\", \"positive_value\": \"./images/B0029L3104.png\", \"hn_image\": [\"./images/B000LJASMK.png\"]}\n{\"q_img\": \"./images/B00493VVOS.png\", \"q_text\": \"The shirt is black and tan., and is darker and has shorter sleeves\", \"positive_key\": \"B00F6FD5UG\", \"positive_value\": \"./images/B00F6FD5UG.png\", \"hn_image\": [\"./images/B00493VVOS.png\"]}\n{\"q_img\": \"./images/B005IZUOOE.png\", \"q_text\": \"Is more black and less colorful, and is darker in color\", \"positive_key\": \"B007YVZFRG\", \"positive_value\": \"./images/B007YVZFRG.png\", \"hn_image\": [\"./images/B005IZUOOE.png\"]}\n{\"q_img\": \"./images/B00BFCV6A0.png\", \"q_text\": \"is navy blue with a Might Mouse logo, and is darker and masculine\", \"positive_key\": \"B001HKGHVS\", \"positive_value\": \"./images/B001HKGHVS.png\", \"hn_image\": [\"./images/B00BFCV6A0.png\"]}\n{\"q_img\": \"./images/B006VE8IQC.png\", \"q_text\": \"Is black, and has white stripes on inner sleeve\", \"positive_key\": \"B009RQPIQ4\", \"positive_value\": \"./images/B009RQPIQ4.png\", \"hn_image\": [\"./images/B006VE8IQC.png\"]}\n{\"q_img\": \"./images/B00CL08O0A.png\", \"q_text\": \"Is blue and brighter, and is light blue with a yellow print\", \"positive_key\": \"B00157I6DK\", \"positive_value\": \"./images/B00157I6DK.png\", \"hn_image\": [\"./images/B00CL08O0A.png\"]}\n{\"q_img\": \"./images/B001A5UHAW.png\", \"q_text\": \"has long sleeves and an English motif, and is a Manchester United long sleeved sweatshirt\", \"positive_key\": \"B0076AQKM4\", \"positive_value\": \"./images/B0076AQKM4.png\", \"hn_image\": [\"./images/B001A5UHAW.png\"]}\n{\"q_img\": \"./images/B00DHN6WU4.png\", \"q_text\": \"Is less whimsical, and Is brown with Stones 1972 World Tour on it\", \"positive_key\": \"B003WE90A2\", \"positive_value\": \"./images/B003WE90A2.png\", \"hn_image\": [\"./images/B00DHN6WU4.png\"]}\n{\"q_img\": \"./images/B001B16GCS.png\", \"q_text\": \"has no sleeves and is ombre blue, and is blue with no sleeves\", \"positive_key\": \"B00BP71JV6\", \"positive_value\": \"./images/B00BP71JV6.png\", \"hn_image\": [\"./images/B001B16GCS.png\"]}\n{\"q_img\": \"./images/B008B5HO2S.png\", \"q_text\": \"is a white shirt, and is more formal\", \"positive_key\": \"B003TP2GXM\", \"positive_value\": \"./images/B003TP2GXM.png\", \"hn_image\": [\"./images/B008B5HO2S.png\"]}\n{\"q_img\": \"./images/B005DIJKXW.png\", \"q_text\": \"is a gray sweater cardigan, and is much lighter in color\", \"positive_key\": \"B00EDF7U2Y\", \"positive_value\": \"./images/B00EDF7U2Y.png\", \"hn_image\": [\"./images/B005DIJKXW.png\"]}\n{\"q_img\": \"./images/B006YDJJDG.png\", \"q_text\": \"is completely red with no text, and is less sporty and more humorous\", \"positive_key\": \"B00B7Q8HK6\", \"positive_value\": \"./images/B00B7Q8HK6.png\", \"hn_image\": [\"./images/B006YDJJDG.png\"]}\n{\"q_img\": \"./images/B00A00772O.png\", \"q_text\": \"has clear wording, and is blue and white check\", \"positive_key\": \"B006MHI8XQ\", \"positive_value\": \"./images/B006MHI8XQ.png\", \"hn_image\": [\"./images/B00A00772O.png\"]}\n{\"q_img\": \"./images/B004UHP3IS.png\", \"q_text\": \"is similar but blue with differnt picture on front, and is lighter\", \"positive_key\": \"B003SQ2YS4\", \"positive_value\": \"./images/B003SQ2YS4.png\", \"hn_image\": [\"./images/B004UHP3IS.png\"]}\n{\"q_img\": \"./images/B000EXU5VW.png\", \"q_text\": \"is official and with short sleeves, and is more formal\", \"positive_key\": \"B00DGCXJ0W\", \"positive_value\": \"./images/B00DGCXJ0W.png\", \"hn_image\": [\"./images/B000EXU5VW.png\"]}\n{\"q_img\": \"./images/B00APU4KE2.png\", \"q_text\": \"is a blue tee shirt, and has short sleeves\", \"positive_key\": \"B00EINT7N6\", \"positive_value\": \"./images/B00EINT7N6.png\", \"hn_image\": [\"./images/B00APU4KE2.png\"]}\n{\"q_img\": \"./images/B000NRXXBS.png\", \"q_text\": \"is solid black with shorter sleeves, and Is black with shorter sleeves\", \"positive_key\": \"B001T3UBVA\", \"positive_value\": \"./images/B001T3UBVA.png\", \"hn_image\": [\"./images/B000NRXXBS.png\"]}\n{\"q_img\": \"./images/B006NCQPYO.png\", \"q_text\": \"Has a logo and is black, and is black with white lettering\", \"positive_key\": \"B005LBXRWQ\", \"positive_value\": \"./images/B005LBXRWQ.png\", \"hn_image\": [\"./images/B006NCQPYO.png\"]}\n{\"q_img\": \"./images/B001B2LL2W.png\", \"q_text\": \"is less tight and has short sleeves, and shorter sleeves\", \"positive_key\": \"B004VE4GF6\", \"positive_value\": \"./images/B004VE4GF6.png\", \"hn_image\": [\"./images/B001B2LL2W.png\"]}\n{\"q_img\": \"./images/B005K7SZGO.png\", \"q_text\": \"is a long sleeve purple shirt with a colar, and is collared\", \"positive_key\": \"B0083J2RAG\", \"positive_value\": \"./images/B0083J2RAG.png\", \"hn_image\": [\"./images/B005K7SZGO.png\"]}\n{\"q_img\": \"./images/B00AY7XJSO.png\", \"q_text\": \"is red and has no stripes, and is red without stripes\", \"positive_key\": \"B004SFV0ZC\", \"positive_value\": \"./images/B004SFV0ZC.png\", \"hn_image\": [\"./images/B00AY7XJSO.png\"]}\n{\"q_img\": \"./images/B00CJRW0LO.png\", \"q_text\": \"Is more patriotic, and is black with white and red and blue logo\", \"positive_key\": \"B00DWC9QL2\", \"positive_value\": \"./images/B00DWC9QL2.png\", \"hn_image\": [\"./images/B00CJRW0LO.png\"]}\n{\"q_img\": \"./images/B00A7EQAWG.png\", \"q_text\": \"is striped black and white, and is striped\", \"positive_key\": \"B00BEYS666\", \"positive_value\": \"./images/B00BEYS666.png\", \"hn_image\": [\"./images/B00A7EQAWG.png\"]}\n{\"q_img\": \"./images/B00523OC9A.png\", \"q_text\": \"is gray with a black stripe, and is two toned\", \"positive_key\": \"B004S92O7Q\", \"positive_value\": \"./images/B004S92O7Q.png\", \"hn_image\": [\"./images/B00523OC9A.png\"]}\n{\"q_img\": \"./images/B007T4JJCK.png\", \"q_text\": \"is solid blue, and has long sleeve and is blue color\", \"positive_key\": \"B002QEBQ3I\", \"positive_value\": \"./images/B002QEBQ3I.png\", \"hn_image\": [\"./images/B007T4JJCK.png\"]}\n{\"q_img\": \"./images/B007VN04BO.png\", \"q_text\": \"is a long sleeve and brown coloered, and Is less fitted in a darker color\", \"positive_key\": \"B008BSWMFE\", \"positive_value\": \"./images/B008BSWMFE.png\", \"hn_image\": [\"./images/B007VN04BO.png\"]}\n{\"q_img\": \"./images/B0016LURJ6.png\", \"q_text\": \"has a dark collar, and is more athletic and has a ringed collar\", \"positive_key\": \"B0009GV35G\", \"positive_value\": \"./images/B0009GV35G.png\", \"hn_image\": [\"./images/B0016LURJ6.png\"]}\n{\"q_img\": \"./images/B0076ET5AY.png\", \"q_text\": \"is light blue with art, and is a light blue t shirt\", \"positive_key\": \"B00A9J9VRA\", \"positive_value\": \"./images/B00A9J9VRA.png\", \"hn_image\": [\"./images/B0076ET5AY.png\"]}\n{\"q_img\": \"./images/B00D0XHGGA.png\", \"q_text\": \"Grend and doesn't have logo, and is more of a green color\", \"positive_key\": \"B003URVV8A\", \"positive_value\": \"./images/B003URVV8A.png\", \"hn_image\": [\"./images/B00D0XHGGA.png\"]}\n{\"q_img\": \"./images/B008UT67K0.png\", \"q_text\": \"Is less casual and more striped, and is red\", \"positive_key\": \"B007WPJNBS\", \"positive_value\": \"./images/B007WPJNBS.png\", \"hn_image\": [\"./images/B008UT67K0.png\"]}\n{\"q_img\": \"./images/B00BSXLX9U.png\", \"q_text\": \"is a dark hoodie, and is a navy blue lightweight hoodie.\", \"positive_key\": \"B008FVT5FC\", \"positive_value\": \"./images/B008FVT5FC.png\", \"hn_image\": [\"./images/B00BSXLX9U.png\"]}\n{\"q_img\": \"./images/B005V4B9NM.png\", \"q_text\": \"is loose blue t-shirt with fishing print, and is a graphic tee\", \"positive_key\": \"B003FL7SME\", \"positive_value\": \"./images/B003FL7SME.png\", \"hn_image\": [\"./images/B005V4B9NM.png\"]}\n{\"q_img\": \"./images/B004K2NSGM.png\", \"q_text\": \" has buttons and a collar, and is darker with different image\", \"positive_key\": \"B0036F7QEY\", \"positive_value\": \"./images/B0036F7QEY.png\", \"hn_image\": [\"./images/B004K2NSGM.png\"]}\n{\"q_img\": \"./images/B008E7RDYW.png\", \"q_text\": \"is black with green designs, and has shorter sleeves\", \"positive_key\": \"B005C6B9UW\", \"positive_value\": \"./images/B005C6B9UW.png\", \"hn_image\": [\"./images/B008E7RDYW.png\"]}\n{\"q_img\": \"./images/B006PKHQTM.png\", \"q_text\": \"is black with a person, and has more black and has a photo\", \"positive_key\": \"B009ZPT41W\", \"positive_value\": \"./images/B009ZPT41W.png\", \"hn_image\": [\"./images/B006PKHQTM.png\"]}\n{\"q_img\": \"./images/B002S788KM.png\", \"q_text\": \"Is more wordy, and is more wordy\", \"positive_key\": \"B000GNYYDA\", \"positive_value\": \"./images/B000GNYYDA.png\", \"hn_image\": [\"./images/B002S788KM.png\"]}\n{\"q_img\": \"./images/B00CDL2BX8.png\", \"q_text\": \"is a mutlicolored abstract pattern button down shirt with collar, and is much more patterned and colourful\", \"positive_key\": \"B00DIY0UJQ\", \"positive_value\": \"./images/B00DIY0UJQ.png\", \"hn_image\": [\"./images/B00CDL2BX8.png\"]}\n{\"q_img\": \"./images/B007HB6JI2.png\", \"q_text\": \"is a blue t-shirt, and is blue with a graphic and is more casual\", \"positive_key\": \"B00BQX5XPM\", \"positive_value\": \"./images/B00BQX5XPM.png\", \"hn_image\": [\"./images/B007HB6JI2.png\"]}\n{\"q_img\": \"./images/B00BI4R9V0.png\", \"q_text\": \"is green with yellow words, and is green and more bold\", \"positive_key\": \"B000FON8K0\", \"positive_value\": \"./images/B000FON8K0.png\", \"hn_image\": [\"./images/B00BI4R9V0.png\"]}\n{\"q_img\": \"./images/B00932CW0W.png\", \"q_text\": \"more western and pocketed, and has a elaborate design\", \"positive_key\": \"B00FFKQ3LK\", \"positive_value\": \"./images/B00FFKQ3LK.png\", \"hn_image\": [\"./images/B00932CW0W.png\"]}\n{\"q_img\": \"./images/B006Z2HG0Y.png\", \"q_text\": \"is white handbag, and is a bag\", \"positive_key\": \"B008FPJHVK\", \"positive_value\": \"./images/B008FPJHVK.png\", \"hn_image\": [\"./images/B006Z2HG0Y.png\"]}\n{\"q_img\": \"./images/B009GFMPIU.png\", \"q_text\": \"is a denim vest, and Is sleeveless and darker\", \"positive_key\": \"B008HTCGMG\", \"positive_value\": \"./images/B008HTCGMG.png\", \"hn_image\": [\"./images/B009GFMPIU.png\"]}\n{\"q_img\": \"./images/B003IWGZQA.png\", \"q_text\": \"is brighter in color, and is red\", \"positive_key\": \"B00B6EB530\", \"positive_value\": \"./images/B00B6EB530.png\", \"hn_image\": [\"./images/B003IWGZQA.png\"]}\n{\"q_img\": \"./images/B00AAXIHXO.png\", \"q_text\": \" blue, and says AHHD\", \"positive_key\": \"B000S3P946\", \"positive_value\": \"./images/B000S3P946.png\", \"hn_image\": [\"./images/B00AAXIHXO.png\"]}\n{\"q_img\": \"./images/B0071BYF0M.png\", \"q_text\": \"is black with a logo of american flag, and has a lighter logo\", \"positive_key\": \"B001F8IK0S\", \"positive_value\": \"./images/B001F8IK0S.png\", \"hn_image\": [\"./images/B0071BYF0M.png\"]}\n{\"q_img\": \"./images/B0001TOU74.png\", \"q_text\": \"is lighter with different graphics, and is white with a green alien logo\", \"positive_key\": \"B006OW1I6I\", \"positive_value\": \"./images/B006OW1I6I.png\", \"hn_image\": [\"./images/B0001TOU74.png\"]}\n{\"q_img\": \"./images/B0045VQCWA.png\", \"q_text\": \"is gray in color, and is a black Argentina themed shirt\", \"positive_key\": \"B003R4PEN4\", \"positive_value\": \"./images/B003R4PEN4.png\", \"hn_image\": [\"./images/B0045VQCWA.png\"]}\n{\"q_img\": \"./images/B00EOYR308.png\", \"q_text\": \"is darker colored and depicts tv characters, and Dark blue and cast group of people in color\", \"positive_key\": \"B00C7YCQ04\", \"positive_value\": \"./images/B00C7YCQ04.png\", \"hn_image\": [\"./images/B00EOYR308.png\"]}\n{\"q_img\": \"./images/B000EMUCTS.png\", \"q_text\": \"is lighter, and A grey color with white and orange design\", \"positive_key\": \"B000BTFW9E\", \"positive_value\": \"./images/B000BTFW9E.png\", \"hn_image\": [\"./images/B000EMUCTS.png\"]}\n{\"q_img\": \"./images/B00680K0D8.png\", \"q_text\": \"has longer sleeves and is more colorful, and is more blue and red\", \"positive_key\": \"B00CH59Q38\", \"positive_value\": \"./images/B00CH59Q38.png\", \"hn_image\": [\"./images/B00680K0D8.png\"]}\n{\"q_img\": \"./images/B00932CW0W.png\", \"q_text\": \"has a checkered red pattern, and has a plaid pattern and is red and beige\", \"positive_key\": \"B0085709D8\", \"positive_value\": \"./images/B0085709D8.png\", \"hn_image\": [\"./images/B00932CW0W.png\"]}\n{\"q_img\": \"./images/B00AHEZ29I.png\", \"q_text\": \"is darker, and has a different graphic on the front\", \"positive_key\": \"B0080SY1T0\", \"positive_value\": \"./images/B0080SY1T0.png\", \"hn_image\": [\"./images/B00AHEZ29I.png\"]}\n{\"q_img\": \"./images/B007F85FQO.png\", \"q_text\": \"is black with white words, and has a v neck\", \"positive_key\": \"B004YO78VW\", \"positive_value\": \"./images/B004YO78VW.png\", \"hn_image\": [\"./images/B007F85FQO.png\"]}\n{\"q_img\": \"./images/B009XE4O7E.png\", \"q_text\": \"is black in color, and is darker colored and branded\", \"positive_key\": \"B00509BT38\", \"positive_value\": \"./images/B00509BT38.png\", \"hn_image\": [\"./images/B009XE4O7E.png\"]}\n{\"q_img\": \"./images/B00DEKLOYO.png\", \"q_text\": \"A dark green relaxed fit tee shirt, and is not collared\", \"positive_key\": \"B007VQG410\", \"positive_value\": \"./images/B007VQG410.png\", \"hn_image\": [\"./images/B00DEKLOYO.png\"]}\n{\"q_img\": \"./images/B004L6PSCO.png\", \"q_text\": \"is more formal and light, and is light blue\", \"positive_key\": \"B008WFQVVW\", \"positive_value\": \"./images/B008WFQVVW.png\", \"hn_image\": [\"./images/B004L6PSCO.png\"]}\n{\"q_img\": \"./images/B007LIWE6C.png\", \"q_text\": \"is more plain, and is more solid colored\", \"positive_key\": \"B007G24YP2\", \"positive_value\": \"./images/B007G24YP2.png\", \"hn_image\": [\"./images/B007LIWE6C.png\"]}\n{\"q_img\": \"./images/B004U940JY.png\", \"q_text\": \"is a t-shirt has a darker plain color with a samell print, and Is gray with out a collar.\", \"positive_key\": \"B002A9JVZE\", \"positive_value\": \"./images/B002A9JVZE.png\", \"hn_image\": [\"./images/B004U940JY.png\"]}\n{\"q_img\": \"./images/B002R7U7Q6.png\", \"q_text\": \"is faded and dark, and has a different graphic\", \"positive_key\": \"B00906L8L0\", \"positive_value\": \"./images/B00906L8L0.png\", \"hn_image\": [\"./images/B002R7U7Q6.png\"]}\n{\"q_img\": \"./images/B00E40LJI4.png\", \"q_text\": \"The shirt is light blue in color., and is plain blue with a tie and is more formal\", \"positive_key\": \"B005ZNWKKU\", \"positive_value\": \"./images/B005ZNWKKU.png\", \"hn_image\": [\"./images/B00E40LJI4.png\"]}\n{\"q_img\": \"./images/B008AY9X6K.png\", \"q_text\": \"has longer sleeves and is blue, and Is darker with longer sleeves\", \"positive_key\": \"B0038MLMHC\", \"positive_value\": \"./images/B0038MLMHC.png\", \"hn_image\": [\"./images/B008AY9X6K.png\"]}\n{\"q_img\": \"./images/B008EJ0MMU.png\", \"q_text\": \"is larger and darker, and is darker in color\", \"positive_key\": \"B001MX8V8C\", \"positive_value\": \"./images/B001MX8V8C.png\", \"hn_image\": [\"./images/B008EJ0MMU.png\"]}\n{\"q_img\": \"./images/B004ZC2HIC.png\", \"q_text\": \"is a checked shirt and is more formal, and has longer sleeves\", \"positive_key\": \"B00EZLD1NI\", \"positive_value\": \"./images/B00EZLD1NI.png\", \"hn_image\": [\"./images/B004ZC2HIC.png\"]}\n{\"q_img\": \"./images/B004FN2OYI.png\", \"q_text\": \"Is a white David Bowie T shirt, and is white with David Bowie picture\", \"positive_key\": \"B00C865IWY\", \"positive_value\": \"./images/B00C865IWY.png\", \"hn_image\": [\"./images/B004FN2OYI.png\"]}\n{\"q_img\": \"./images/B003WF2ECW.png\", \"q_text\": \"has a darker blue color, and is just a polo\", \"positive_key\": \"B003YHF2BS\", \"positive_value\": \"./images/B003YHF2BS.png\", \"hn_image\": [\"./images/B003WF2ECW.png\"]}\n{\"q_img\": \"./images/B000PHI5L4.png\", \"q_text\": \"is lighter and has longer sleeves, and is long sleeved and cream colored\", \"positive_key\": \"B004B2PUWQ\", \"positive_value\": \"./images/B004B2PUWQ.png\", \"hn_image\": [\"./images/B000PHI5L4.png\"]}\n{\"q_img\": \"./images/B003U6HHS4.png\", \"q_text\": \"has grey sleeves and no buttons, and is blue and grey with shorter sleeves\", \"positive_key\": \"B007ISC8UM\", \"positive_value\": \"./images/B007ISC8UM.png\", \"hn_image\": [\"./images/B003U6HHS4.png\"]}\n{\"q_img\": \"./images/B00BR2B6W6.png\", \"q_text\": \" and gray print lower half, and is purple and has long sleeve\", \"positive_key\": \"B002FYW6HE\", \"positive_value\": \"./images/B002FYW6HE.png\", \"hn_image\": [\"./images/B00BR2B6W6.png\"]}\n{\"q_img\": \"./images/B00DTIO7G8.png\", \"q_text\": \"is a black color short sleeve with clown art, and is darker\", \"positive_key\": \"B007FX9LAA\", \"positive_value\": \"./images/B007FX9LAA.png\", \"hn_image\": [\"./images/B00DTIO7G8.png\"]}\n{\"q_img\": \"./images/B0025U4X5G.png\", \"q_text\": \"is grey in color, and has no shiny and is grey color\", \"positive_key\": \"B002Q0QYZW\", \"positive_value\": \"./images/B002Q0QYZW.png\", \"hn_image\": [\"./images/B0025U4X5G.png\"]}\n{\"q_img\": \"./images/B0032HJXKG.png\", \"q_text\": \"is brown and folded, and A solid brown shirt long sleeved with pocket\", \"positive_key\": \"B00BR2B6W6\", \"positive_value\": \"./images/B00BR2B6W6.png\", \"hn_image\": [\"./images/B0032HJXKG.png\"]}\n{\"q_img\": \"./images/B00F5XVN9Y.png\", \"q_text\": \"short blue tee shirt with slight v neck, and Is medium blue in color\", \"positive_key\": \"B00EQV0DC4\", \"positive_value\": \"./images/B00EQV0DC4.png\", \"hn_image\": [\"./images/B00F5XVN9Y.png\"]}\n{\"q_img\": \"./images/B003URVV8A.png\", \"q_text\": \"is darker, and is black with a fox hound logo\", \"positive_key\": \"B004GB3XJ4\", \"positive_value\": \"./images/B004GB3XJ4.png\", \"hn_image\": [\"./images/B003URVV8A.png\"]}\n{\"q_img\": \"./images/B003OQV4HA.png\", \"q_text\": \"has longer sleeves with collars, and has longer sleeves and more pocketed\", \"positive_key\": \"B003AU61WI\", \"positive_value\": \"./images/B003AU61WI.png\", \"hn_image\": [\"./images/B003OQV4HA.png\"]}\n{\"q_img\": \"./images/B006JATIK8.png\", \"q_text\": \"has a different graphic, and has lighter colored image\", \"positive_key\": \"B006JARC9C\", \"positive_value\": \"./images/B006JARC9C.png\", \"hn_image\": [\"./images/B006JATIK8.png\"]}\n{\"q_img\": \"./images/B004DZAWJM.png\", \"q_text\": \"is blue and doesn't have a collar, and A solid black short sleeved shirt\", \"positive_key\": \"B00E3I72W4\", \"positive_value\": \"./images/B00E3I72W4.png\", \"hn_image\": [\"./images/B004DZAWJM.png\"]}\n{\"q_img\": \"./images/B001TSZPM0.png\", \"q_text\": \"has short sleeves, and is darker and has shorter sleeves\", \"positive_key\": \"B001PJ6ZX6\", \"positive_value\": \"./images/B001PJ6ZX6.png\", \"hn_image\": [\"./images/B001TSZPM0.png\"]}\n{\"q_img\": \"./images/B00CX6BDFU.png\", \"q_text\": \"is solid black in color, and is less sporty and more casual\", \"positive_key\": \"B005DIJJBK\", \"positive_value\": \"./images/B005DIJJBK.png\", \"hn_image\": [\"./images/B00CX6BDFU.png\"]}\n{\"q_img\": \"./images/B002KQ6MY0.png\", \"q_text\": \"is grey with the beattles on it, and is lighter\", \"positive_key\": \"B008P07SDE\", \"positive_value\": \"./images/B008P07SDE.png\", \"hn_image\": [\"./images/B002KQ6MY0.png\"]}\n{\"q_img\": \"./images/B00DLBJ18M.png\", \"q_text\": \"The shirt is white in color with Albert Einstein., and Is white with Einstein's Theory of Football on it\", \"positive_key\": \"B005HT4N0M\", \"positive_value\": \"./images/B005HT4N0M.png\", \"hn_image\": [\"./images/B00DLBJ18M.png\"]}\n{\"q_img\": \"./images/B00B67EIJK.png\", \"q_text\": \"Is striped and colorful, and is multi-colored with no buttons\", \"positive_key\": \"B00A8YYHWK\", \"positive_value\": \"./images/B00A8YYHWK.png\", \"hn_image\": [\"./images/B00B67EIJK.png\"]}\n{\"q_img\": \"./images/B003EEN7MM.png\", \"q_text\": \"is a blue fitness short sleeve shirt, and is blue with yellow\", \"positive_key\": \"B0049EODT2\", \"positive_value\": \"./images/B0049EODT2.png\", \"hn_image\": [\"./images/B003EEN7MM.png\"]}\n{\"q_img\": \"./images/B00BYQDJH0.png\", \"q_text\": \"is less formal and dark blue, and is less formal\", \"positive_key\": \"B004CR4ZC6\", \"positive_value\": \"./images/B004CR4ZC6.png\", \"hn_image\": [\"./images/B00BYQDJH0.png\"]}\n{\"q_img\": \"./images/B002CNTSVA.png\", \"q_text\": \"is white, and is lighter\", \"positive_key\": \"B0006SL92Y\", \"positive_value\": \"./images/B0006SL92Y.png\", \"hn_image\": [\"./images/B002CNTSVA.png\"]}\n{\"q_img\": \"./images/B00AECN92K.png\", \"q_text\": \"is black with long sleeves, and with a hood and longer sleeves\", \"positive_key\": \"B009PTECLU\", \"positive_value\": \"./images/B009PTECLU.png\", \"hn_image\": [\"./images/B00AECN92K.png\"]}\n{\"q_img\": \"./images/B00AFRC2EU.png\", \"q_text\": \"is black and says sons of anarchy, and is blac and has a different logo\", \"positive_key\": \"B006JVZCS4\", \"positive_value\": \"./images/B006JVZCS4.png\", \"hn_image\": [\"./images/B00AFRC2EU.png\"]}\n{\"q_img\": \"./images/B004IAMEI4.png\", \"q_text\": \"is more masculine, and is black with a logo on front\", \"positive_key\": \"B0096T97J6\", \"positive_value\": \"./images/B0096T97J6.png\", \"hn_image\": [\"./images/B004IAMEI4.png\"]}\n{\"q_img\": \"./images/B008YEKTO6.png\", \"q_text\": \"is maroon colored short sleeved shirt with button collar, and is dark red and has no graphics\", \"positive_key\": \"B008KKDN92\", \"positive_value\": \"./images/B008KKDN92.png\", \"hn_image\": [\"./images/B008YEKTO6.png\"]}\n{\"q_img\": \"./images/B00CH59Q38.png\", \"q_text\": \"has a brighter color and no pockets, and A grey/white plaid long sleeved shirt\", \"positive_key\": \"B009OFPY8A\", \"positive_value\": \"./images/B009OFPY8A.png\", \"hn_image\": [\"./images/B00CH59Q38.png\"]}\n{\"q_img\": \"./images/B00BGVUTBC.png\", \"q_text\": \"has a different graphic on it., and Blond zombie female with gray hat and black clevage shirt\", \"positive_key\": \"B00AYHQDXC\", \"positive_value\": \"./images/B00AYHQDXC.png\", \"hn_image\": [\"./images/B00BGVUTBC.png\"]}\n{\"q_img\": \"./images/B002G9UC1A.png\", \"q_text\": \"Is tan with no graphic print, and is lighter in color and has more buttons\", \"positive_key\": \"B003HNA8WW\", \"positive_value\": \"./images/B003HNA8WW.png\", \"hn_image\": [\"./images/B002G9UC1A.png\"]}\n{\"q_img\": \"./images/B009ZZ7PYU.png\", \"q_text\": \"is bright in color, and is less colorful and has a military graphic\", \"positive_key\": \"B00AL1LOEE\", \"positive_value\": \"./images/B00AL1LOEE.png\", \"hn_image\": [\"./images/B009ZZ7PYU.png\"]}\n{\"q_img\": \"./images/B006NCQPYO.png\", \"q_text\": \"The tank top is light gray with black trim., and is a lighter color\", \"positive_key\": \"B006C25WGM\", \"positive_value\": \"./images/B006C25WGM.png\", \"hn_image\": [\"./images/B006NCQPYO.png\"]}\n{\"q_img\": \"./images/B0071N0J8M.png\", \"q_text\": \"has a graphic representation of a dog, and is darker colored\", \"positive_key\": \"B004N8SX1I\", \"positive_value\": \"./images/B004N8SX1I.png\", \"hn_image\": [\"./images/B0071N0J8M.png\"]}\n{\"q_img\": \"./images/B004QS6S12.png\", \"q_text\": \"is a lighter blue, and is lighter in color\", \"positive_key\": \"B000EGBBTE\", \"positive_value\": \"./images/B000EGBBTE.png\", \"hn_image\": [\"./images/B004QS6S12.png\"]}\n{\"q_img\": \"./images/B00AENLJ4E.png\", \"q_text\": \"is white in color with black and red text, and is a whtie tshirt\", \"positive_key\": \"B00AHTM05C\", \"positive_value\": \"./images/B00AHTM05C.png\", \"hn_image\": [\"./images/B00AENLJ4E.png\"]}\n{\"q_img\": \"./images/B00AR19X1O.png\", \"q_text\": \"is beige in color, and is lighter and more yellow\", \"positive_key\": \"B00AREHPXE\", \"positive_value\": \"./images/B00AREHPXE.png\", \"hn_image\": [\"./images/B00AR19X1O.png\"]}\n{\"q_img\": \"./images/B00ATQ60M2.png\", \"q_text\": \"the black color jacket is too losse, and is darker in color and has more of a zipper\", \"positive_key\": \"B009CYLW52\", \"positive_value\": \"./images/B009CYLW52.png\", \"hn_image\": [\"./images/B00ATQ60M2.png\"]}\n{\"q_img\": \"./images/B0085428H6.png\", \"q_text\": \"has collars and shorter sleeves, and is short sleeved with a collar\", \"positive_key\": \"B0033EFCIK\", \"positive_value\": \"./images/B0033EFCIK.png\", \"hn_image\": [\"./images/B0085428H6.png\"]}\n{\"q_img\": \"./images/B008LJZV0G.png\", \"q_text\": \"is a blue checkered shirt, and is more blue\", \"positive_key\": \"B007EDJM6Y\", \"positive_value\": \"./images/B007EDJM6Y.png\", \"hn_image\": [\"./images/B008LJZV0G.png\"]}\n{\"q_img\": \"./images/B006SHCSEU.png\", \"q_text\": \"is checked with a deeper shade of color, and darker blue\", \"positive_key\": \"B004M5I1IW\", \"positive_value\": \"./images/B004M5I1IW.png\", \"hn_image\": [\"./images/B006SHCSEU.png\"]}\n{\"q_img\": \"./images/B00931JHSS.png\", \"q_text\": \"The short sleeve shirt is white in color., and is more plain\", \"positive_key\": \"B006X0AKYM\", \"positive_value\": \"./images/B006X0AKYM.png\", \"hn_image\": [\"./images/B00931JHSS.png\"]}\n{\"q_img\": \"./images/B00FD8I2HC.png\", \"q_text\": \"is white colored, and is light gray\", \"positive_key\": \"B0019RM524\", \"positive_value\": \"./images/B0019RM524.png\", \"hn_image\": [\"./images/B00FD8I2HC.png\"]}\n{\"q_img\": \"./images/B005DIJKXW.png\", \"q_text\": \"is shortsleeved and bright, and is white and has shorter sleeves\", \"positive_key\": \"B001CGKLJQ\", \"positive_value\": \"./images/B001CGKLJQ.png\", \"hn_image\": [\"./images/B005DIJKXW.png\"]}\n{\"q_img\": \"./images/B005IZUOOE.png\", \"q_text\": \"is a black T shirt that says That's what, and is black in color\", \"positive_key\": \"B00821C5SY\", \"positive_value\": \"./images/B00821C5SY.png\", \"hn_image\": [\"./images/B005IZUOOE.png\"]}\n{\"q_img\": \"./images/B001O883U6.png\", \"q_text\": \"has blue stripes and not purple, and is darker\", \"positive_key\": \"B001O84MJC\", \"positive_value\": \"./images/B001O84MJC.png\", \"hn_image\": [\"./images/B001O883U6.png\"]}\n{\"q_img\": \"./images/B00CDT7FHM.png\", \"q_text\": \"is slim fit, and has more words\", \"positive_key\": \"B003LR3ES4\", \"positive_value\": \"./images/B003LR3ES4.png\", \"hn_image\": [\"./images/B00CDT7FHM.png\"]}\n{\"q_img\": \"./images/B0043RJLWY.png\", \"q_text\": \"is a lighter graphic shirt, and has more white\", \"positive_key\": \"B001SN7PB0\", \"positive_value\": \"./images/B001SN7PB0.png\", \"hn_image\": [\"./images/B0043RJLWY.png\"]}\n{\"q_img\": \"./images/B002I00AE6.png\", \"q_text\": \"is a black tshirt, and is a black t shirt with a Rush graphic\", \"positive_key\": \"B0055KYMGS\", \"positive_value\": \"./images/B0055KYMGS.png\", \"hn_image\": [\"./images/B002I00AE6.png\"]}\n{\"q_img\": \"./images/B0016LG70E.png\", \"q_text\": \"is more casual with shorter sleeves, and Is more casual with a colorful graphic.\", \"positive_key\": \"B00BOWT1E4\", \"positive_value\": \"./images/B00BOWT1E4.png\", \"hn_image\": [\"./images/B0016LG70E.png\"]}\n{\"q_img\": \"./images/B0049CLHTS.png\", \"q_text\": \"has smaller letters, and does not have an image\", \"positive_key\": \"B001XXTJIC\", \"positive_value\": \"./images/B001XXTJIC.png\", \"hn_image\": [\"./images/B0049CLHTS.png\"]}\n{\"q_img\": \"./images/B00B40E1K0.png\", \"q_text\": \"is black colored, and is black with a his and hers design\", \"positive_key\": \"B00FFUSAD4\", \"positive_value\": \"./images/B00FFUSAD4.png\", \"hn_image\": [\"./images/B00B40E1K0.png\"]}\n{\"q_img\": \"./images/B008OCFYYI.png\", \"q_text\": \"is more of a t-shirt with v-neck and darker, and has one pocket\", \"positive_key\": \"B009MMPFOS\", \"positive_value\": \"./images/B009MMPFOS.png\", \"hn_image\": [\"./images/B008OCFYYI.png\"]}\n{\"q_img\": \"./images/B001O84MJC.png\", \"q_text\": \"has greay, and is black and white\", \"positive_key\": \"B001O883TW\", \"positive_value\": \"./images/B001O883TW.png\", \"hn_image\": [\"./images/B001O84MJC.png\"]}\n{\"q_img\": \"./images/B00AZIN2P2.png\", \"q_text\": \"is made of cotton and is formal, and is striped and only top buttons\", \"positive_key\": \"B00776LY8W\", \"positive_value\": \"./images/B00776LY8W.png\", \"hn_image\": [\"./images/B00AZIN2P2.png\"]}\n{\"q_img\": \"./images/B002DZR9JK.png\", \"q_text\": \"Is more blue, and Purple superman backwards sign in purple and yellow\", \"positive_key\": \"B002Y7ABEC\", \"positive_value\": \"./images/B002Y7ABEC.png\", \"hn_image\": [\"./images/B002DZR9JK.png\"]}\n{\"q_img\": \"./images/B000CS0UGY.png\", \"q_text\": \"is white with blue designs, and is lighter colored with a graphic print that isn't similar.\", \"positive_key\": \"B003FON3BG\", \"positive_value\": \"./images/B003FON3BG.png\", \"hn_image\": [\"./images/B000CS0UGY.png\"]}\n{\"q_img\": \"./images/B005I63XM8.png\", \"q_text\": \"has longer sleeves and different graphic, and is more graphic and has longer sleeves\", \"positive_key\": \"B006C6XSFA\", \"positive_value\": \"./images/B006C6XSFA.png\", \"hn_image\": [\"./images/B005I63XM8.png\"]}\n{\"q_img\": \"./images/B00F3KMWX0.png\", \"q_text\": \"is white in color, and is grey and has a bloody zombie graphic\", \"positive_key\": \"B00641RTYE\", \"positive_value\": \"./images/B00641RTYE.png\", \"hn_image\": [\"./images/B00F3KMWX0.png\"]}\n{\"q_img\": \"./images/B004Q3PSU4.png\", \"q_text\": \"has short sleeves and is solid black, and A solid black shirt with short sleeves\", \"positive_key\": \"B0057V76WM\", \"positive_value\": \"./images/B0057V76WM.png\", \"hn_image\": [\"./images/B004Q3PSU4.png\"]}\n{\"q_img\": \"./images/B0072701PS.png\", \"q_text\": \"a light colored tee shirt with a large colorful slogan on front and back, and is darker\", \"positive_key\": \"B000A35AMK\", \"positive_value\": \"./images/B000A35AMK.png\", \"hn_image\": [\"./images/B0072701PS.png\"]}\n{\"q_img\": \"./images/B005BSLCWQ.png\", \"q_text\": \"has a black top, and has lower white and is black upper chest\", \"positive_key\": \"B00BCCMELS\", \"positive_value\": \"./images/B00BCCMELS.png\", \"hn_image\": [\"./images/B005BSLCWQ.png\"]}\n{\"q_img\": \"./images/B00AMD5I7U.png\", \"q_text\": \"is blue, and has a lighter logo\", \"positive_key\": \"B005LIXPPI\", \"positive_value\": \"./images/B005LIXPPI.png\", \"hn_image\": [\"./images/B00AMD5I7U.png\"]}\n{\"q_img\": \"./images/B005R961PM.png\", \"q_text\": \"is a no sleeved shirt with a logo, and has no sleeves\", \"positive_key\": \"B003Z9SNLG\", \"positive_value\": \"./images/B003Z9SNLG.png\", \"hn_image\": [\"./images/B005R961PM.png\"]}\n{\"q_img\": \"./images/B003QR06EO.png\", \"q_text\": \"is a lighter color with a team symbol and name, and is a white tee\", \"positive_key\": \"B0058KLRPE\", \"positive_value\": \"./images/B0058KLRPE.png\", \"hn_image\": [\"./images/B003QR06EO.png\"]}\n{\"q_img\": \"./images/B00CWRLCV0.png\", \"q_text\": \"A light grey tee with a panther, and is grey with a cat motif\", \"positive_key\": \"B003BAEVJ2\", \"positive_value\": \"./images/B003BAEVJ2.png\", \"hn_image\": [\"./images/B00CWRLCV0.png\"]}\n{\"q_img\": \"./images/B00BRB29YQ.png\", \"q_text\": \"is a marvel comic t shirt, and has a smaller design\", \"positive_key\": \"B00A6Y9C40\", \"positive_value\": \"./images/B00A6Y9C40.png\", \"hn_image\": [\"./images/B00BRB29YQ.png\"]}\n{\"q_img\": \"./images/B00BQZ7OCK.png\", \"q_text\": \"is similar, and has a larger car on it\", \"positive_key\": \"B008K9C9Q6\", \"positive_value\": \"./images/B008K9C9Q6.png\", \"hn_image\": [\"./images/B00BQZ7OCK.png\"]}\n{\"q_img\": \"./images/B00112FYQG.png\", \"q_text\": \"is black, and is darker\", \"positive_key\": \"B004T4M9E8\", \"positive_value\": \"./images/B004T4M9E8.png\", \"hn_image\": [\"./images/B00112FYQG.png\"]}\n{\"q_img\": \"./images/B002G9UC1A.png\", \"q_text\": \"is button-up with a checkered print, and plaid\", \"positive_key\": \"B0081RM7XW\", \"positive_value\": \"./images/B0081RM7XW.png\", \"hn_image\": [\"./images/B002G9UC1A.png\"]}\n{\"q_img\": \"./images/B009OKKA5W.png\", \"q_text\": \"is grey with a different logo, and is lighter in color\", \"positive_key\": \"B0076Z9GS4\", \"positive_value\": \"./images/B0076Z9GS4.png\", \"hn_image\": [\"./images/B009OKKA5W.png\"]}\n{\"q_img\": \"./images/B001FQIYO2.png\", \"q_text\": \"is a lighter pink, and is more light pink\", \"positive_key\": \"B000JHQGVQ\", \"positive_value\": \"./images/B000JHQGVQ.png\", \"hn_image\": [\"./images/B001FQIYO2.png\"]}\n{\"q_img\": \"./images/B005KP3UPW.png\", \"q_text\": \"has a plaid design, and is lighter\", \"positive_key\": \"B008B18A8Y\", \"positive_value\": \"./images/B008B18A8Y.png\", \"hn_image\": [\"./images/B005KP3UPW.png\"]}\n{\"q_img\": \"./images/B00EPDU2PG.png\", \"q_text\": \"is more anti-religious and darker colored, and is anti religious\", \"positive_key\": \"B008EJ0MMU\", \"positive_value\": \"./images/B008EJ0MMU.png\", \"hn_image\": [\"./images/B00EPDU2PG.png\"]}\n{\"q_img\": \"./images/B00BV2QEV0.png\", \"q_text\": \"is bright pink, and is short sleeved and pink\", \"positive_key\": \"B00BV2QNUM\", \"positive_value\": \"./images/B00BV2QNUM.png\", \"hn_image\": [\"./images/B00BV2QEV0.png\"]}\n{\"q_img\": \"./images/B003BWYEQ0.png\", \"q_text\": \"has longer sleeves with a red circle, and is black and less shiny\", \"positive_key\": \"B0040M6G16\", \"positive_value\": \"./images/B0040M6G16.png\", \"hn_image\": [\"./images/B003BWYEQ0.png\"]}\n{\"q_img\": \"./images/B000WEB0D0.png\", \"q_text\": \"Is more wordy, and is black with white lettering\", \"positive_key\": \"B006BGJACQ\", \"positive_value\": \"./images/B006BGJACQ.png\", \"hn_image\": [\"./images/B000WEB0D0.png\"]}\n{\"q_img\": \"./images/B007X6BEF4.png\", \"q_text\": \"is a plaid shirt and has long sleeves, and White with black\", \"positive_key\": \"B005CVKLWE\", \"positive_value\": \"./images/B005CVKLWE.png\", \"hn_image\": [\"./images/B007X6BEF4.png\"]}\n{\"q_img\": \"./images/B006IJVUYW.png\", \"q_text\": \"has more realistic picture, and has a 3d printed logo\", \"positive_key\": \"B00CLIV9ZE\", \"positive_value\": \"./images/B00CLIV9ZE.png\", \"hn_image\": [\"./images/B006IJVUYW.png\"]}\n{\"q_img\": \"./images/B008H7I2L2.png\", \"q_text\": \"light black color is less revealing, and Has a different saying.\", \"positive_key\": \"B006YYY8UO\", \"positive_value\": \"./images/B006YYY8UO.png\", \"hn_image\": [\"./images/B008H7I2L2.png\"]}\n{\"q_img\": \"./images/B00509NSFU.png\", \"q_text\": \"Is colored darker, and is darker\", \"positive_key\": \"B009Z41GL4\", \"positive_value\": \"./images/B009Z41GL4.png\", \"hn_image\": [\"./images/B00509NSFU.png\"]}\n{\"q_img\": \"./images/B009L4NRSS.png\", \"q_text\": \"is dark green, and is grey and less masculine\", \"positive_key\": \"B007GYHINK\", \"positive_value\": \"./images/B007GYHINK.png\", \"hn_image\": [\"./images/B009L4NRSS.png\"]}\n{\"q_img\": \"./images/B00FDV30N0.png\", \"q_text\": \"This is brighter in color. A bright pink., and is pink and has a collar and buttons\", \"positive_key\": \"B00A8D1Q8Y\", \"positive_value\": \"./images/B00A8D1Q8Y.png\", \"hn_image\": [\"./images/B00FDV30N0.png\"]}\n{\"q_img\": \"./images/B002SVEJYC.png\", \"q_text\": \"is white button down with collar, and is the same\", \"positive_key\": \"B002SVEKNW\", \"positive_value\": \"./images/B002SVEKNW.png\", \"hn_image\": [\"./images/B002SVEJYC.png\"]}\n{\"q_img\": \"./images/B00CJLPB9I.png\", \"q_text\": \"has different graphics, and is darker grey with a text logo\", \"positive_key\": \"B008VHXGLY\", \"positive_value\": \"./images/B008VHXGLY.png\", \"hn_image\": [\"./images/B00CJLPB9I.png\"]}\n{\"q_img\": \"./images/B005TG0O6K.png\", \"q_text\": \"Is more elegant with the graphic, and has a picture of a portrait\", \"positive_key\": \"B007E4WL4S\", \"positive_value\": \"./images/B007E4WL4S.png\", \"hn_image\": [\"./images/B005TG0O6K.png\"]}\n{\"q_img\": \"./images/B001KOUGCW.png\", \"q_text\": \"has a v-neck with stripes, and has stripes\", \"positive_key\": \"B00AO1T42U\", \"positive_value\": \"./images/B00AO1T42U.png\", \"hn_image\": [\"./images/B001KOUGCW.png\"]}\n{\"q_img\": \"./images/B007HRO9B0.png\", \"q_text\": \"is more darker with striped lines, and has longer sleeves and is a dark grey\", \"positive_key\": \"B006Y8Q79U\", \"positive_value\": \"./images/B006Y8Q79U.png\", \"hn_image\": [\"./images/B007HRO9B0.png\"]}\n{\"q_img\": \"./images/B00EBUFSAW.png\", \"q_text\": \"has less color and more repeated lettering, and is more humorous and darker colored\", \"positive_key\": \"B00B1W6FWI\", \"positive_value\": \"./images/B00B1W6FWI.png\", \"hn_image\": [\"./images/B00EBUFSAW.png\"]}\n{\"q_img\": \"./images/B0039BE27Y.png\", \"q_text\": \"has long sleeves, and is more bright orange and long sleeves\", \"positive_key\": \"B001KFURAW\", \"positive_value\": \"./images/B001KFURAW.png\", \"hn_image\": [\"./images/B0039BE27Y.png\"]}\n{\"q_img\": \"./images/B00EHN44IA.png\", \"q_text\": \"is larger in size and has larger letters, and and has no graphic\", \"positive_key\": \"B00726ORDQ\", \"positive_value\": \"./images/B00726ORDQ.png\", \"hn_image\": [\"./images/B00EHN44IA.png\"]}\n{\"q_img\": \"./images/B00066ZKS0.png\", \"q_text\": \"is long sleeved and dark, and is long-sleeved and dark blue in color\", \"positive_key\": \"B008ZBQYLU\", \"positive_value\": \"./images/B008ZBQYLU.png\", \"hn_image\": [\"./images/B00066ZKS0.png\"]}\n{\"q_img\": \"./images/B000YHF5K4.png\", \"q_text\": \"The shirt is brown in color with the word Texas., and less graphics on it\", \"positive_key\": \"B008RYZTWK\", \"positive_value\": \"./images/B008RYZTWK.png\", \"hn_image\": [\"./images/B000YHF5K4.png\"]}\n{\"q_img\": \"./images/B009ADCOP2.png\", \"q_text\": \"has longer sleeves, and has longer sleeves and more masculine\", \"positive_key\": \"B006ZP4SU2\", \"positive_value\": \"./images/B006ZP4SU2.png\", \"hn_image\": [\"./images/B009ADCOP2.png\"]}\n{\"q_img\": \"./images/B004YO9IVU.png\", \"q_text\": \"is bright yellow, and is a shirt tshirt with a skeleton on it.\", \"positive_key\": \"B0048KRJ98\", \"positive_value\": \"./images/B0048KRJ98.png\", \"hn_image\": [\"./images/B004YO9IVU.png\"]}\n{\"q_img\": \"./images/B00916YLW2.png\", \"q_text\": \"is a polo shirt with horizontal bands, and has more stripes\", \"positive_key\": \"B007ZOLQGG\", \"positive_value\": \"./images/B007ZOLQGG.png\", \"hn_image\": [\"./images/B00916YLW2.png\"]}\n{\"q_img\": \"./images/B006NCQPYO.png\", \"q_text\": \"has an american flag on it, and Is more colorful\", \"positive_key\": \"B00DJBDZ6I\", \"positive_value\": \"./images/B00DJBDZ6I.png\", \"hn_image\": [\"./images/B006NCQPYO.png\"]}\n{\"q_img\": \"./images/B000JM3A1A.png\", \"q_text\": \"is dark and more text, and is black and more humorous\", \"positive_key\": \"B007WGBT1O\", \"positive_value\": \"./images/B007WGBT1O.png\", \"hn_image\": [\"./images/B000JM3A1A.png\"]}\n{\"q_img\": \"./images/B0097YZI2K.png\", \"q_text\": \"is blue colored, and is blue and more simple\", \"positive_key\": \"B003OQTWS8\", \"positive_value\": \"./images/B003OQTWS8.png\", \"hn_image\": [\"./images/B0097YZI2K.png\"]}\n{\"q_img\": \"./images/B000A38K4A.png\", \"q_text\": \"is a smaller size, and is lighter\", \"positive_key\": \"B000G7BCRW\", \"positive_value\": \"./images/B000G7BCRW.png\", \"hn_image\": [\"./images/B000A38K4A.png\"]}\n{\"q_img\": \"./images/B00BAQ3TWY.png\", \"q_text\": \"is white with black designs, and is more patterned and less solid colored\", \"positive_key\": \"B00E9K6MUO\", \"positive_value\": \"./images/B00E9K6MUO.png\", \"hn_image\": [\"./images/B00BAQ3TWY.png\"]}\n{\"q_img\": \"./images/B00BT0JNWQ.png\", \"q_text\": \"is plaid, and has longer sleeves\", \"positive_key\": \"B00AG3YJTY\", \"positive_value\": \"./images/B00AG3YJTY.png\", \"hn_image\": [\"./images/B00BT0JNWQ.png\"]}\n{\"q_img\": \"./images/B001PIXJTA.png\", \"q_text\": \"is a Jesus loves Iceland T shirt, and is white and has more graphics\", \"positive_key\": \"B005E0TR32\", \"positive_value\": \"./images/B005E0TR32.png\", \"hn_image\": [\"./images/B001PIXJTA.png\"]}\n{\"q_img\": \"./images/B00G6UFA94.png\", \"q_text\": \"is grey with a image of two person and blackboard, and has long sleeve and is grey color\", \"positive_key\": \"B000QHHX2K\", \"positive_value\": \"./images/B000QHHX2K.png\", \"hn_image\": [\"./images/B00G6UFA94.png\"]}\n{\"q_img\": \"./images/B00C865HBQ.png\", \"q_text\": \"has star wars characters, and is darker with more colorful image\", \"positive_key\": \"B004FN2OYI\", \"positive_value\": \"./images/B004FN2OYI.png\", \"hn_image\": [\"./images/B00C865HBQ.png\"]}\n{\"q_img\": \"./images/B004I0C5XI.png\", \"q_text\": \"has longer sleeves with plaid, and is more formal\", \"positive_key\": \"B00A21JW60\", \"positive_value\": \"./images/B00A21JW60.png\", \"hn_image\": [\"./images/B004I0C5XI.png\"]}\n{\"q_img\": \"./images/B007QPAWNM.png\", \"q_text\": \"has no skull art at the front, and A solid black shirt with batman on the front\", \"positive_key\": \"B0017MXLL0\", \"positive_value\": \"./images/B0017MXLL0.png\", \"hn_image\": [\"./images/B007QPAWNM.png\"]}\n{\"q_img\": \"./images/B00GKENI2W.png\", \"q_text\": \"is black and has a different logo, and is black and has a larger graphic\", \"positive_key\": \"B009L4ZBNM\", \"positive_value\": \"./images/B009L4ZBNM.png\", \"hn_image\": [\"./images/B00GKENI2W.png\"]}\n{\"q_img\": \"./images/B001WAL2PE.png\", \"q_text\": \"is more colorful and has less graphics on it, and is a black tee\", \"positive_key\": \"B004IWHEAU\", \"positive_value\": \"./images/B004IWHEAU.png\", \"hn_image\": [\"./images/B001WAL2PE.png\"]}\n{\"q_img\": \"./images/B0070DEQP0.png\", \"q_text\": \"has a different logo similar to a flag, and Has Not Forgotten on front\", \"positive_key\": \"B00DXRW6DQ\", \"positive_value\": \"./images/B00DXRW6DQ.png\", \"hn_image\": [\"./images/B0070DEQP0.png\"]}\n{\"q_img\": \"./images/B00CIYDP94.png\", \"q_text\": \"is more abstract art, and is is blue\", \"positive_key\": \"B00CJSTN5O\", \"positive_value\": \"./images/B00CJSTN5O.png\", \"hn_image\": [\"./images/B00CIYDP94.png\"]}\n{\"q_img\": \"./images/B0098GE6OI.png\", \"q_text\": \"is gray, and Is gray not white\", \"positive_key\": \"B002O1UMAG\", \"positive_value\": \"./images/B002O1UMAG.png\", \"hn_image\": [\"./images/B0098GE6OI.png\"]}\n{\"q_img\": \"./images/B008AYYVVM.png\", \"q_text\": \"has a graphic on front and longer sleeves, and has longer sleeves and text on the front\", \"positive_key\": \"B005QS5P92\", \"positive_value\": \"./images/B005QS5P92.png\", \"hn_image\": [\"./images/B008AYYVVM.png\"]}\n{\"q_img\": \"./images/B005X0A2UU.png\", \"q_text\": \"is black in color without any text, and is black colored with red and blue graphic\", \"positive_key\": \"B007RKULSW\", \"positive_value\": \"./images/B007RKULSW.png\", \"hn_image\": [\"./images/B005X0A2UU.png\"]}\n{\"q_img\": \"./images/B009BHF8XW.png\", \"q_text\": \"Is a blue hockey tee shirt, and is darker\", \"positive_key\": \"B0081GC3AK\", \"positive_value\": \"./images/B0081GC3AK.png\", \"hn_image\": [\"./images/B009BHF8XW.png\"]}\n{\"q_img\": \"./images/B007DJ7XOC.png\", \"q_text\": \"Is blue, and is blue\", \"positive_key\": \"B004LK9GZ0\", \"positive_value\": \"./images/B004LK9GZ0.png\", \"hn_image\": [\"./images/B007DJ7XOC.png\"]}\n{\"q_img\": \"./images/B00BIFZU4C.png\", \"q_text\": \"has longer sleeves and a collared neckline, and it is long sleeved and plaid\", \"positive_key\": \"B004Q3PQYM\", \"positive_value\": \"./images/B004Q3PQYM.png\", \"hn_image\": [\"./images/B00BIFZU4C.png\"]}\n{\"q_img\": \"./images/B002TLTIA6.png\", \"q_text\": \"has more if a visual aesthetic, and has more red and has a plane graphic\", \"positive_key\": \"B005BU5OIM\", \"positive_value\": \"./images/B005BU5OIM.png\", \"hn_image\": [\"./images/B002TLTIA6.png\"]}\n{\"q_img\": \"./images/B001LQG1N2.png\", \"q_text\": \"buttoned collar, and has dark stripes and is white.\", \"positive_key\": \"B000VZOJG0\", \"positive_value\": \"./images/B000VZOJG0.png\", \"hn_image\": [\"./images/B001LQG1N2.png\"]}\n{\"q_img\": \"./images/B003IWGZQA.png\", \"q_text\": \"is burgundy and short sleeved, and A solid red shirt\", \"positive_key\": \"B004VMHQ8M\", \"positive_value\": \"./images/B004VMHQ8M.png\", \"hn_image\": [\"./images/B003IWGZQA.png\"]}\n{\"q_img\": \"./images/B000MX1M98.png\", \"q_text\": \"The shirt is gray, and is colorful with longer sleeves\", \"positive_key\": \"B00A78V6NU\", \"positive_value\": \"./images/B00A78V6NU.png\", \"hn_image\": [\"./images/B000MX1M98.png\"]}\n{\"q_img\": \"./images/B008EYOK2S.png\", \"q_text\": \"is a brown shirt, and has longer sleeves and buttons down the front\", \"positive_key\": \"B00GINTLRQ\", \"positive_value\": \"./images/B00GINTLRQ.png\", \"hn_image\": [\"./images/B008EYOK2S.png\"]}\n{\"q_img\": \"./images/B0077RS0VU.png\", \"q_text\": \"is green colored with a centered logo, and Desired item is green with a large white clover\", \"positive_key\": \"B008K856DK\", \"positive_value\": \"./images/B008K856DK.png\", \"hn_image\": [\"./images/B0077RS0VU.png\"]}\n{\"q_img\": \"./images/B001WAL2UE.png\", \"q_text\": \"is grey with a logo, and is brown with red and yellow logo\", \"positive_key\": \"B003I86JVK\", \"positive_value\": \"./images/B003I86JVK.png\", \"hn_image\": [\"./images/B001WAL2UE.png\"]}\n{\"q_img\": \"./images/B009W2ETIQ.png\", \"q_text\": \"has shorter sleeves and is blue, and is brighter in color and has short sleeves\", \"positive_key\": \"B005NH73N2\", \"positive_value\": \"./images/B005NH73N2.png\", \"hn_image\": [\"./images/B009W2ETIQ.png\"]}\n{\"q_img\": \"./images/B001GHIUDK.png\", \"q_text\": \"is light blue with buttons, and is a shirt and is lighter blue\", \"positive_key\": \"B0011MPO0M\", \"positive_value\": \"./images/B0011MPO0M.png\", \"hn_image\": [\"./images/B001GHIUDK.png\"]}\n{\"q_img\": \"./images/B00BJO34NG.png\", \"q_text\": \"is less colorful and has more buttons, and it is white with no pocket\", \"positive_key\": \"B002QEBQCO\", \"positive_value\": \"./images/B002QEBQCO.png\", \"hn_image\": [\"./images/B00BJO34NG.png\"]}\n{\"q_img\": \"./images/B005BSLCWQ.png\", \"q_text\": \"is a tank top with a simpler graphic in a darker color, and is darker with no sleeves\", \"positive_key\": \"B0084YZBAI\", \"positive_value\": \"./images/B0084YZBAI.png\", \"hn_image\": [\"./images/B005BSLCWQ.png\"]}\n{\"q_img\": \"./images/B0084OPRIO.png\", \"q_text\": \"is pants with fading, and are pants\", \"positive_key\": \"B003E420MU\", \"positive_value\": \"./images/B003E420MU.png\", \"hn_image\": [\"./images/B0084OPRIO.png\"]}\n{\"q_img\": \"./images/B00599EYZY.png\", \"q_text\": \"is blue, and is more blue\", \"positive_key\": \"B0059E5E80\", \"positive_value\": \"./images/B0059E5E80.png\", \"hn_image\": [\"./images/B00599EYZY.png\"]}\n{\"q_img\": \"./images/B00DYY7QO2.png\", \"q_text\": \"is black with a v neck, and is dark gray with a v-neck\", \"positive_key\": \"B0048KPYFY\", \"positive_value\": \"./images/B0048KPYFY.png\", \"hn_image\": [\"./images/B00DYY7QO2.png\"]}\n{\"q_img\": \"./images/B009ADCOP2.png\", \"q_text\": \"Is grey with a different logo, and has a lighter color\", \"positive_key\": \"B00483AQX6\", \"positive_value\": \"./images/B00483AQX6.png\", \"hn_image\": [\"./images/B009ADCOP2.png\"]}\n{\"q_img\": \"./images/B005HGTEPY.png\", \"q_text\": \"has a different logo, and is more simple\", \"positive_key\": \"B007CCWNBI\", \"positive_value\": \"./images/B007CCWNBI.png\", \"hn_image\": [\"./images/B005HGTEPY.png\"]}\n{\"q_img\": \"./images/B0054PCK4U.png\", \"q_text\": \"has a gray color with big art, and is light gray\", \"positive_key\": \"B0049W8IGI\", \"positive_value\": \"./images/B0049W8IGI.png\", \"hn_image\": [\"./images/B0054PCK4U.png\"]}\n{\"q_img\": \"./images/B004QQJAF0.png\", \"q_text\": \"is lighter with different graphics, and is white with boxing logo and fitted\", \"positive_key\": \"B00BJXOE36\", \"positive_value\": \"./images/B00BJXOE36.png\", \"hn_image\": [\"./images/B004QQJAF0.png\"]}\n{\"q_img\": \"./images/B00637KYVY.png\", \"q_text\": \"The shirt is gray with long sleeves., and is darker and has longer sleeves\", \"positive_key\": \"B003YQJFWG\", \"positive_value\": \"./images/B003YQJFWG.png\", \"hn_image\": [\"./images/B00637KYVY.png\"]}\n{\"q_img\": \"./images/B0083Z6QDO.png\", \"q_text\": \"is more colorful with no sleeves, and has no sleeves\", \"positive_key\": \"B00CA33WFU\", \"positive_value\": \"./images/B00CA33WFU.png\", \"hn_image\": [\"./images/B0083Z6QDO.png\"]}\n{\"q_img\": \"./images/B00CE1SWLM.png\", \"q_text\": \"is navy blue with a chicken in the middle, and is more faded\", \"positive_key\": \"B00CMQWPS0\", \"positive_value\": \"./images/B00CMQWPS0.png\", \"hn_image\": [\"./images/B00CE1SWLM.png\"]}\n{\"q_img\": \"./images/B004XWCRKC.png\", \"q_text\": \"has short sleeves and an orange logo, and Has shorter sleeves and orange graphics\", \"positive_key\": \"B007JPU7G6\", \"positive_value\": \"./images/B007JPU7G6.png\", \"hn_image\": [\"./images/B004XWCRKC.png\"]}\n{\"q_img\": \"./images/B0013KOIL8.png\", \"q_text\": \"is bright and has no bow-tie, and Is less formal\", \"positive_key\": \"B002QLE37C\", \"positive_value\": \"./images/B002QLE37C.png\", \"hn_image\": [\"./images/B0013KOIL8.png\"]}\n{\"q_img\": \"./images/B008V2JXTS.png\", \"q_text\": \"has striped socks without character, and is orange and blue striped\", \"positive_key\": \"B00CI4EWXW\", \"positive_value\": \"./images/B00CI4EWXW.png\", \"hn_image\": [\"./images/B008V2JXTS.png\"]}\n{\"q_img\": \"./images/B005JFAIQ2.png\", \"q_text\": \"is darker, and is colored purple\", \"positive_key\": \"B000V5DSCG\", \"positive_value\": \"./images/B000V5DSCG.png\", \"hn_image\": [\"./images/B005JFAIQ2.png\"]}\n{\"q_img\": \"./images/B00DI9YY8O.png\", \"q_text\": \"Is white, and Is white\", \"positive_key\": \"B00CFPGOH6\", \"positive_value\": \"./images/B00CFPGOH6.png\", \"hn_image\": [\"./images/B00DI9YY8O.png\"]}\n{\"q_img\": \"./images/B003URVV8A.png\", \"q_text\": \"is a red T shirt with a dog on it, and is red with dog on front\", \"positive_key\": \"B00F6BZOWM\", \"positive_value\": \"./images/B00F6BZOWM.png\", \"hn_image\": [\"./images/B003URVV8A.png\"]}\n{\"q_img\": \"./images/B0088B8CM6.png\", \"q_text\": \"The desired product is blue, and is blue with a photo and a t-shirt.\", \"positive_key\": \"B007YHY634\", \"positive_value\": \"./images/B007YHY634.png\", \"hn_image\": [\"./images/B0088B8CM6.png\"]}\n{\"q_img\": \"./images/B00BW9IQBS.png\", \"q_text\": \"is lighter colored, and is lighter and less graphic\", \"positive_key\": \"B005X0895K\", \"positive_value\": \"./images/B005X0895K.png\", \"hn_image\": [\"./images/B00BW9IQBS.png\"]}\n{\"q_img\": \"./images/B000RZ3L68.png\", \"q_text\": \"has longer sleeves and less buttons, and is blue with long sleeves\", \"positive_key\": \"B001UFZIJ2\", \"positive_value\": \"./images/B001UFZIJ2.png\", \"hn_image\": [\"./images/B000RZ3L68.png\"]}\n{\"q_img\": \"./images/B007N2RG00.png\", \"q_text\": \"has a v-neck and is brown, and is darker\", \"positive_key\": \"B003WER4FK\", \"positive_value\": \"./images/B003WER4FK.png\", \"hn_image\": [\"./images/B007N2RG00.png\"]}\n{\"q_img\": \"./images/B000L9S5NY.png\", \"q_text\": \"is less embroidered and more cartoonish, and has a Donald Duck image on the front\", \"positive_key\": \"B007TYWH74\", \"positive_value\": \"./images/B007TYWH74.png\", \"hn_image\": [\"./images/B000L9S5NY.png\"]}\n{\"q_img\": \"./images/B007S41PRS.png\", \"q_text\": \"is a darker color and has no buttons, and Is darker and more casual.\", \"positive_key\": \"B00DGA6TWY\", \"positive_value\": \"./images/B00DGA6TWY.png\", \"hn_image\": [\"./images/B007S41PRS.png\"]}\n{\"q_img\": \"./images/B00ATREDFC.png\", \"q_text\": \"is brown and white plaid, and is full of buttons and stripes\", \"positive_key\": \"B007TMU7Z0\", \"positive_value\": \"./images/B007TMU7Z0.png\", \"hn_image\": [\"./images/B00ATREDFC.png\"]}\n{\"q_img\": \"./images/B00CJSA0L0.png\", \"q_text\": \"is grey with buttons, and Is grey and buttoned\", \"positive_key\": \"B00CJRUN9U\", \"positive_value\": \"./images/B00CJRUN9U.png\", \"hn_image\": [\"./images/B00CJSA0L0.png\"]}\n{\"q_img\": \"./images/B001F23NH4.png\", \"q_text\": \"a blue tee with a phrase on it, and Is a dark grey in color\", \"positive_key\": \"B00BGKZEKY\", \"positive_value\": \"./images/B00BGKZEKY.png\", \"hn_image\": [\"./images/B001F23NH4.png\"]}\n{\"q_img\": \"./images/B00A1W3NB0.png\", \"q_text\": \"is a purple colored shirt with textured art, and is more feminine\", \"positive_key\": \"B0072D9SJ2\", \"positive_value\": \"./images/B0072D9SJ2.png\", \"hn_image\": [\"./images/B00A1W3NB0.png\"]}\n{\"q_img\": \"./images/B007R78YK2.png\", \"q_text\": \"is black with short sleeves, and is a dark T with a graphic.\", \"positive_key\": \"B008AOKHCE\", \"positive_value\": \"./images/B008AOKHCE.png\", \"hn_image\": [\"./images/B007R78YK2.png\"]}\n{\"q_img\": \"./images/B008QT38YW.png\", \"q_text\": \"Its has no sleeves and is darker color., and is grey with no collar\", \"positive_key\": \"B000KK0O8S\", \"positive_value\": \"./images/B000KK0O8S.png\", \"hn_image\": [\"./images/B008QT38YW.png\"]}\n{\"q_img\": \"./images/B00BARY35A.png\", \"q_text\": \"is a bright blue colored shirt with text, and is bright blue with an orange and yellow design\", \"positive_key\": \"B006PGW58I\", \"positive_value\": \"./images/B006PGW58I.png\", \"hn_image\": [\"./images/B00BARY35A.png\"]}\n{\"q_img\": \"./images/B008K1QJ5G.png\", \"q_text\": \"is dressier and less graphic, and Has a different person on the front.\", \"positive_key\": \"B005A3Q2W2\", \"positive_value\": \"./images/B005A3Q2W2.png\", \"hn_image\": [\"./images/B008K1QJ5G.png\"]}\n{\"q_img\": \"./images/B007P28IKK.png\", \"q_text\": \"this white t shirt has darker color, and is white with image of men\", \"positive_key\": \"B001SN7QH8\", \"positive_value\": \"./images/B001SN7QH8.png\", \"hn_image\": [\"./images/B007P28IKK.png\"]}\n{\"q_img\": \"./images/B0076Z9GS4.png\", \"q_text\": \"Is less casual and longer sleeves, and is lighter in color and has more buttons\", \"positive_key\": \"B006VWU4TI\", \"positive_value\": \"./images/B006VWU4TI.png\", \"hn_image\": [\"./images/B0076Z9GS4.png\"]}\n{\"q_img\": \"./images/B00BJHF36O.png\", \"q_text\": \"is maroon with a v neck, and is solid red and has no text\", \"positive_key\": \"B0091600H2\", \"positive_value\": \"./images/B0091600H2.png\", \"hn_image\": [\"./images/B00BJHF36O.png\"]}\n{\"q_img\": \"./images/B004LAO7PE.png\", \"q_text\": \"has white design around shoulders, and is more shiny\", \"positive_key\": \"B006HT40OK\", \"positive_value\": \"./images/B006HT40OK.png\", \"hn_image\": [\"./images/B004LAO7PE.png\"]}\n{\"q_img\": \"./images/B007TZ3DNA.png\", \"q_text\": \"has no logo, and Is more wordy\", \"positive_key\": \"B00CMQ8ZXO\", \"positive_value\": \"./images/B00CMQ8ZXO.png\", \"hn_image\": [\"./images/B007TZ3DNA.png\"]}\n{\"q_img\": \"./images/B00CAHM74S.png\", \"q_text\": \"is red with prints on it, and is busier pattern and fewer stripes\", \"positive_key\": \"B008STCL4I\", \"positive_value\": \"./images/B008STCL4I.png\", \"hn_image\": [\"./images/B00CAHM74S.png\"]}\n{\"q_img\": \"./images/B00DG9ISYM.png\", \"q_text\": \"is light blue and also larger, and is blue and has a white text logo\", \"positive_key\": \"B008Y24H1E\", \"positive_value\": \"./images/B008Y24H1E.png\", \"hn_image\": [\"./images/B00DG9ISYM.png\"]}\n{\"q_img\": \"./images/B000YHJDOI.png\", \"q_text\": \"is lighter and more casual, and is a white tee shirt\", \"positive_key\": \"B005H2CBC6\", \"positive_value\": \"./images/B005H2CBC6.png\", \"hn_image\": [\"./images/B000YHJDOI.png\"]}\n{\"q_img\": \"./images/B00BIQ1XCE.png\", \"q_text\": \"Darker and has longer sleeves, and is less detailed and darker in color\", \"positive_key\": \"B007E82H7A\", \"positive_value\": \"./images/B007E82H7A.png\", \"hn_image\": [\"./images/B00BIQ1XCE.png\"]}\n{\"q_img\": \"./images/B004IK83WU.png\", \"q_text\": \"Is multi colored and button down., and is more western\", \"positive_key\": \"B00590Q14U\", \"positive_value\": \"./images/B00590Q14U.png\", \"hn_image\": [\"./images/B004IK83WU.png\"]}\n{\"q_img\": \"./images/B0002NZ11I.png\", \"q_text\": \"is similar but grey., and is shorter\", \"positive_key\": \"B001086JAQ\", \"positive_value\": \"./images/B001086JAQ.png\", \"hn_image\": [\"./images/B0002NZ11I.png\"]}\n{\"q_img\": \"./images/B0091V9DR0.png\", \"q_text\": \"is lighter in color, and is lighter\", \"positive_key\": \"B00653QDEI\", \"positive_value\": \"./images/B00653QDEI.png\", \"hn_image\": [\"./images/B0091V9DR0.png\"]}\n{\"q_img\": \"./images/B009RVTT4G.png\", \"q_text\": \"Is a denim button down shirt., and is lighter in color and dressier\", \"positive_key\": \"B004HW68YY\", \"positive_value\": \"./images/B004HW68YY.png\", \"hn_image\": [\"./images/B009RVTT4G.png\"]}\n{\"q_img\": \"./images/B0085IGAU8.png\", \"q_text\": \"has buttons and is short sleeved., and it is checked and has a short sleeve\", \"positive_key\": \"B00AOLMMLK\", \"positive_value\": \"./images/B00AOLMMLK.png\", \"hn_image\": [\"./images/B0085IGAU8.png\"]}\n{\"q_img\": \"./images/B007PVC4R4.png\", \"q_text\": \"Is more plain and monochromatic, and Is long-sleeved with Jack Daniels bottle logo on front\", \"positive_key\": \"B0038MJA8U\", \"positive_value\": \"./images/B0038MJA8U.png\", \"hn_image\": [\"./images/B007PVC4R4.png\"]}\n{\"q_img\": \"./images/B0072701PS.png\", \"q_text\": \"has characters on it, and it is blue\", \"positive_key\": \"B00GMRXOYY\", \"positive_value\": \"./images/B00GMRXOYY.png\", \"hn_image\": [\"./images/B0072701PS.png\"]}\n{\"q_img\": \"./images/B008G0SU26.png\", \"q_text\": \"is black and short sleeved, and has shorter sleeves\", \"positive_key\": \"B0077RS0VU\", \"positive_value\": \"./images/B0077RS0VU.png\", \"hn_image\": [\"./images/B008G0SU26.png\"]}\n{\"q_img\": \"./images/B0087DW93S.png\", \"q_text\": \"is grey with a deer on it, and is darker\", \"positive_key\": \"B009YJ8O60\", \"positive_value\": \"./images/B009YJ8O60.png\", \"hn_image\": [\"./images/B0087DW93S.png\"]}\n{\"q_img\": \"./images/B00542QOFE.png\", \"q_text\": \"has a prominent slogan and is lighter colored, and is blue with more sentences\", \"positive_key\": \"B0050WOT9Q\", \"positive_value\": \"./images/B0050WOT9Q.png\", \"hn_image\": [\"./images/B00542QOFE.png\"]}\n{\"q_img\": \"./images/B00F6S5GUK.png\", \"q_text\": \"Is not as wordy and has more color, and is shorter\", \"positive_key\": \"B0058RUUH8\", \"positive_value\": \"./images/B0058RUUH8.png\", \"hn_image\": [\"./images/B00F6S5GUK.png\"]}\n{\"q_img\": \"./images/B00CSRZ7VA.png\", \"q_text\": \"has short sleeves and more yellow, and Yellow shirt with black/white stripes\", \"positive_key\": \"B00C6AOL2K\", \"positive_value\": \"./images/B00C6AOL2K.png\", \"hn_image\": [\"./images/B00CSRZ7VA.png\"]}\n{\"q_img\": \"./images/B008N67FVK.png\", \"q_text\": \"is grey colored, and is gray with longer sleeves\", \"positive_key\": \"B002KQ6H4U\", \"positive_value\": \"./images/B002KQ6H4U.png\", \"hn_image\": [\"./images/B008N67FVK.png\"]}\n{\"q_img\": \"./images/B00CJLPB9I.png\", \"q_text\": \"has more graphics with a cartoon robot, and has cartoon graphic and is more colorful\", \"positive_key\": \"B007MI4PIG\", \"positive_value\": \"./images/B007MI4PIG.png\", \"hn_image\": [\"./images/B00CJLPB9I.png\"]}\n{\"q_img\": \"./images/B00261C2V6.png\", \"q_text\": \"is more colorful and casual, and is a gray tshirt\", \"positive_key\": \"B00CKOR1GA\", \"positive_value\": \"./images/B00CKOR1GA.png\", \"hn_image\": [\"./images/B00261C2V6.png\"]}\n{\"q_img\": \"./images/B009VE8P1C.png\", \"q_text\": \"is lighter with a different logo, and is gray\", \"positive_key\": \"B007E7R9ZQ\", \"positive_value\": \"./images/B007E7R9ZQ.png\", \"hn_image\": [\"./images/B009VE8P1C.png\"]}\n{\"q_img\": \"./images/B004MDLAF0.png\", \"q_text\": \"buttons up and looks more presentable, and is a grey button down\", \"positive_key\": \"B007P3PMHQ\", \"positive_value\": \"./images/B007P3PMHQ.png\", \"hn_image\": [\"./images/B004MDLAF0.png\"]}\n{\"q_img\": \"./images/B00CJSA0L0.png\", \"q_text\": \"has shorter sleeves and different graphic, and is white and has shorter sleeves\", \"positive_key\": \"B006H4LI7C\", \"positive_value\": \"./images/B006H4LI7C.png\", \"hn_image\": [\"./images/B00CJSA0L0.png\"]}\n{\"q_img\": \"./images/B0085OZ8HS.png\", \"q_text\": \"has long sleeves, and is dark purple and has long sleeve\", \"positive_key\": \"B0085O4Q7Q\", \"positive_value\": \"./images/B0085O4Q7Q.png\", \"hn_image\": [\"./images/B0085OZ8HS.png\"]}\n{\"q_img\": \"./images/B003QCBBJI.png\", \"q_text\": \"is white with strips on collar, and is more solid and has striped collar\", \"positive_key\": \"B003QCGEVS\", \"positive_value\": \"./images/B003QCGEVS.png\", \"hn_image\": [\"./images/B003QCBBJI.png\"]}\n{\"q_img\": \"./images/B0067K5MNC.png\", \"q_text\": \"The shirt is black in color with white and black symbols., and has short sleeves and no hood\", \"positive_key\": \"B00AAJ687U\", \"positive_value\": \"./images/B00AAJ687U.png\", \"hn_image\": [\"./images/B0067K5MNC.png\"]}\n{\"q_img\": \"./images/B003IB71I2.png\", \"q_text\": \"is lighter, and is a lighter color\", \"positive_key\": \"B0016JD8PI\", \"positive_value\": \"./images/B0016JD8PI.png\", \"hn_image\": [\"./images/B003IB71I2.png\"]}\n{\"q_img\": \"./images/B005TL7L5W.png\", \"q_text\": \"its black and bigger writing, and has more black and white\", \"positive_key\": \"B00A6ZEW5S\", \"positive_value\": \"./images/B00A6ZEW5S.png\", \"hn_image\": [\"./images/B005TL7L5W.png\"]}\n{\"q_img\": \"./images/B005H9ZP8Q.png\", \"q_text\": \"has a different logo, and has a cow graphic\", \"positive_key\": \"B000BTMG0C\", \"positive_value\": \"./images/B000BTMG0C.png\", \"hn_image\": [\"./images/B005H9ZP8Q.png\"]}\n{\"q_img\": \"./images/B004TIH9N0.png\", \"q_text\": \"has a black bow-tie, and is a tuxedo shirt with black bow tie\", \"positive_key\": \"B00DII1AGO\", \"positive_value\": \"./images/B00DII1AGO.png\", \"hn_image\": [\"./images/B004TIH9N0.png\"]}\n{\"q_img\": \"./images/B004B7PMVK.png\", \"q_text\": \"Is darker and flower design stands out more, and is darker\", \"positive_key\": \"B004B0BAUY\", \"positive_value\": \"./images/B004B0BAUY.png\", \"hn_image\": [\"./images/B004B7PMVK.png\"]}\n{\"q_img\": \"./images/B006BZ8690.png\", \"q_text\": \"is white with red designs, and is white with a Leroy Jenkins design\", \"positive_key\": \"B0068RJFC8\", \"positive_value\": \"./images/B0068RJFC8.png\", \"hn_image\": [\"./images/B006BZ8690.png\"]}\n{\"q_img\": \"./images/B008E0UE6I.png\", \"q_text\": \"The shirts are blue red and dark blue., and has a collar and small logo\", \"positive_key\": \"B00897ZREK\", \"positive_value\": \"./images/B00897ZREK.png\", \"hn_image\": [\"./images/B008E0UE6I.png\"]}\n{\"q_img\": \"./images/B000WNEK7Y.png\", \"q_text\": \"is green with words on it, and Is more wordy\", \"positive_key\": \"B005HWTURA\", \"positive_value\": \"./images/B005HWTURA.png\", \"hn_image\": [\"./images/B000WNEK7Y.png\"]}\n{\"q_img\": \"./images/B0081SF8AU.png\", \"q_text\": \"is stripped blue, and Has longer sleeves and is more business like.\", \"positive_key\": \"B006WUUQR4\", \"positive_value\": \"./images/B006WUUQR4.png\", \"hn_image\": [\"./images/B0081SF8AU.png\"]}\n{\"q_img\": \"./images/B00C2CZTYG.png\", \"q_text\": \"has shorter sleeves with a different graphic, and Has no sleeves and diffferent graphics\", \"positive_key\": \"B00CJH05O8\", \"positive_value\": \"./images/B00CJH05O8.png\", \"hn_image\": [\"./images/B00C2CZTYG.png\"]}\n{\"q_img\": \"./images/B0089681FI.png\", \"q_text\": \"is a dress shirt and long sleeves, and is lighter\", \"positive_key\": \"B00C69YVRG\", \"positive_value\": \"./images/B00C69YVRG.png\", \"hn_image\": [\"./images/B0089681FI.png\"]}\n{\"q_img\": \"./images/B0009MDF9W.png\", \"q_text\": \"is black, and is tighter\", \"positive_key\": \"B00A7BR9XS\", \"positive_value\": \"./images/B00A7BR9XS.png\", \"hn_image\": [\"./images/B0009MDF9W.png\"]}\n{\"q_img\": \"./images/B00C6AOL2K.png\", \"q_text\": \"is a white shirt with a small brand logo, and Desired item is white with an insignia on the left\", \"positive_key\": \"B00CEJLS7E\", \"positive_value\": \"./images/B00CEJLS7E.png\", \"hn_image\": [\"./images/B00C6AOL2K.png\"]}\n{\"q_img\": \"./images/B005GT20QM.png\", \"q_text\": \"is a short sleeve collared button up, and is a beige button down\", \"positive_key\": \"B007BYRUYW\", \"positive_value\": \"./images/B007BYRUYW.png\", \"hn_image\": [\"./images/B005GT20QM.png\"]}\n{\"q_img\": \"./images/B001QL8NVA.png\", \"q_text\": \"Is more blue and long-sleeve, and is blue and has long sleeves\", \"positive_key\": \"B00608DPQC\", \"positive_value\": \"./images/B00608DPQC.png\", \"hn_image\": [\"./images/B001QL8NVA.png\"]}\n{\"q_img\": \"./images/B009H420K8.png\", \"q_text\": \"Is more black and less comical, and is a darker color\", \"positive_key\": \"B002QH4BLO\", \"positive_value\": \"./images/B002QH4BLO.png\", \"hn_image\": [\"./images/B009H420K8.png\"]}\n{\"q_img\": \"./images/B007APJQJY.png\", \"q_text\": \"Button up with a collar, and has a collar and more purple\", \"positive_key\": \"B0011MNWSI\", \"positive_value\": \"./images/B0011MNWSI.png\", \"hn_image\": [\"./images/B007APJQJY.png\"]}\n{\"q_img\": \"./images/B005J4C8HA.png\", \"q_text\": \"a dark colored shirt with a red logo in the middle, and is black with red motif\", \"positive_key\": \"B00A5IQHAY\", \"positive_value\": \"./images/B00A5IQHAY.png\", \"hn_image\": [\"./images/B005J4C8HA.png\"]}\n{\"q_img\": \"./images/B0063TRZD2.png\", \"q_text\": \"is all white, and has shorter sleeves\", \"positive_key\": \"B009VCN7IA\", \"positive_value\": \"./images/B009VCN7IA.png\", \"hn_image\": [\"./images/B0063TRZD2.png\"]}\n{\"q_img\": \"./images/B003OPUWH4.png\", \"q_text\": \"is a sleeveless black t-shirt, and is black and has a different graphic\", \"positive_key\": \"B00F6886DY\", \"positive_value\": \"./images/B00F6886DY.png\", \"hn_image\": [\"./images/B003OPUWH4.png\"]}\n{\"q_img\": \"./images/B0081ST50O.png\", \"q_text\": \"is a blue short sleeved t-shirt and phrase saying motif, and is dark blue and has text\", \"positive_key\": \"B007SWH4HK\", \"positive_value\": \"./images/B007SWH4HK.png\", \"hn_image\": [\"./images/B0081ST50O.png\"]}\n{\"q_img\": \"./images/B002PZHYQ6.png\", \"q_text\": \"Is more blue and wordy, and is a blue T.\", \"positive_key\": \"B00DHX3Y5U\", \"positive_value\": \"./images/B00DHX3Y5U.png\", \"hn_image\": [\"./images/B002PZHYQ6.png\"]}\n{\"q_img\": \"./images/B006Q8U9D8.png\", \"q_text\": \"is gray, and is darker\", \"positive_key\": \"B009PIK8BY\", \"positive_value\": \"./images/B009PIK8BY.png\", \"hn_image\": [\"./images/B006Q8U9D8.png\"]}\n{\"q_img\": \"./images/B009MB8Q7C.png\", \"q_text\": \"is a white colored dress shirt with beige colored tie, and is more plain\", \"positive_key\": \"B00AZLZ8UQ\", \"positive_value\": \"./images/B00AZLZ8UQ.png\", \"hn_image\": [\"./images/B009MB8Q7C.png\"]}\n{\"q_img\": \"./images/B001LZKV6Q.png\", \"q_text\": \"is black with Grateful Dead written on it, and is darker\", \"positive_key\": \"B00451AT6U\", \"positive_value\": \"./images/B00451AT6U.png\", \"hn_image\": [\"./images/B001LZKV6Q.png\"]}\n{\"q_img\": \"./images/B00CH4QR7M.png\", \"q_text\": \"Is lighter with different TV graphic., and has a photo of buck rogers\", \"positive_key\": \"B00H9FBM5Q\", \"positive_value\": \"./images/B00H9FBM5Q.png\", \"hn_image\": [\"./images/B00CH4QR7M.png\"]}\n{\"q_img\": \"./images/B007IJZ2KO.png\", \"q_text\": \"Has a vintage logo on a green shirt, and is wrinkled and have a different picture.\", \"positive_key\": \"B00BHKFFAW\", \"positive_value\": \"./images/B00BHKFFAW.png\", \"hn_image\": [\"./images/B007IJZ2KO.png\"]}\n{\"q_img\": \"./images/B003WE9084.png\", \"q_text\": \"has green lettering, and it has a print at the back\", \"positive_key\": \"B003ZZM6G8\", \"positive_value\": \"./images/B003ZZM6G8.png\", \"hn_image\": [\"./images/B003WE9084.png\"]}\n{\"q_img\": \"./images/B003X46ZKO.png\", \"q_text\": \"Is red, and has more design\", \"positive_key\": \"B00BLS4998\", \"positive_value\": \"./images/B00BLS4998.png\", \"hn_image\": [\"./images/B003X46ZKO.png\"]}\n{\"q_img\": \"./images/B004YOAT9A.png\", \"q_text\": \"has less color, and is black with white logo\", \"positive_key\": \"B0093OQBCK\", \"positive_value\": \"./images/B0093OQBCK.png\", \"hn_image\": [\"./images/B004YOAT9A.png\"]}\n{\"q_img\": \"./images/B005B6ZIE6.png\", \"q_text\": \"is green with striped brightly colored, and is grey with blue stripes\", \"positive_key\": \"B00CMS4SB0\", \"positive_value\": \"./images/B00CMS4SB0.png\", \"hn_image\": [\"./images/B005B6ZIE6.png\"]}\n{\"q_img\": \"./images/B004AM0XUQ.png\", \"q_text\": \"is black with pictures of guns, and has lot of guns\", \"positive_key\": \"B0036C4NFW\", \"positive_value\": \"./images/B0036C4NFW.png\", \"hn_image\": [\"./images/B004AM0XUQ.png\"]}\n{\"q_img\": \"./images/B004QDCRZS.png\", \"q_text\": \"is purple with image of pacman, and is purple with yellow image\", \"positive_key\": \"B00C7CCMEG\", \"positive_value\": \"./images/B00C7CCMEG.png\", \"hn_image\": [\"./images/B004QDCRZS.png\"]}\n{\"q_img\": \"./images/B00AKYDNFA.png\", \"q_text\": \"is black with a yellow smiley face, and has a picture\", \"positive_key\": \"B00943FZTA\", \"positive_value\": \"./images/B00943FZTA.png\", \"hn_image\": [\"./images/B00AKYDNFA.png\"]}\n{\"q_img\": \"./images/B009RUTFV4.png\", \"q_text\": \"is yellow lettering, and is more yellow and has more graphics\", \"positive_key\": \"B008L40C7S\", \"positive_value\": \"./images/B008L40C7S.png\", \"hn_image\": [\"./images/B009RUTFV4.png\"]}\n{\"q_img\": \"./images/B00DYA9R5M.png\", \"q_text\": \"is darker and has u shaped neck, and is black with white logo\", \"positive_key\": \"B0094KCP96\", \"positive_value\": \"./images/B0094KCP96.png\", \"hn_image\": [\"./images/B00DYA9R5M.png\"]}\n{\"q_img\": \"./images/B0083WQ3MQ.png\", \"q_text\": \"is outerwear with longer sleeves, and is blue with longer sleeves\", \"positive_key\": \"B0009G3YQW\", \"positive_value\": \"./images/B0009G3YQW.png\", \"hn_image\": [\"./images/B0083WQ3MQ.png\"]}\n{\"q_img\": \"./images/B008BSWM4K.png\", \"q_text\": \"is dark brown and not plaid, and is not as brightly colored\", \"positive_key\": \"B008BSWH22\", \"positive_value\": \"./images/B008BSWH22.png\", \"hn_image\": [\"./images/B008BSWM4K.png\"]}\n{\"q_img\": \"./images/B007IE40W0.png\", \"q_text\": \"is stripped black and white vertically., and is white and has stripes\", \"positive_key\": \"B0033BGD0E\", \"positive_value\": \"./images/B0033BGD0E.png\", \"hn_image\": [\"./images/B007IE40W0.png\"]}\n{\"q_img\": \"./images/B007SA6SZQ.png\", \"q_text\": \"is denim material, and Is denim and more plain\", \"positive_key\": \"B00AA2BC0U\", \"positive_value\": \"./images/B00AA2BC0U.png\", \"hn_image\": [\"./images/B007SA6SZQ.png\"]}\n{\"q_img\": \"./images/B004CVDPAA.png\", \"q_text\": \"Is a black lucky's speed shop short sleeve tee, and is darker\", \"positive_key\": \"B00030LHBS\", \"positive_value\": \"./images/B00030LHBS.png\", \"hn_image\": [\"./images/B004CVDPAA.png\"]}\n{\"q_img\": \"./images/B00BJHF36O.png\", \"q_text\": \"has a darker color and shorter sleeves, and has shorter sleeves and is darker colored\", \"positive_key\": \"B004UJ14FM\", \"positive_value\": \"./images/B004UJ14FM.png\", \"hn_image\": [\"./images/B00BJHF36O.png\"]}\n{\"q_img\": \"./images/B00AAQT6ZY.png\", \"q_text\": \"is less casual with long sleeves., and it has a long sleeve\", \"positive_key\": \"B0085HCEL8\", \"positive_value\": \"./images/B0085HCEL8.png\", \"hn_image\": [\"./images/B00AAQT6ZY.png\"]}\n{\"q_img\": \"./images/B00E3I72W4.png\", \"q_text\": \"is grey with logo, and is faded\", \"positive_key\": \"B005ZMUZH6\", \"positive_value\": \"./images/B005ZMUZH6.png\", \"hn_image\": [\"./images/B00E3I72W4.png\"]}\n{\"q_img\": \"./images/B004YU7HNU.png\", \"q_text\": \"Has a graphic., and has a blue flaming skull\", \"positive_key\": \"B007QPAWNM\", \"positive_value\": \"./images/B007QPAWNM.png\", \"hn_image\": [\"./images/B004YU7HNU.png\"]}\n{\"q_img\": \"./images/B0074D0W68.png\", \"q_text\": \"Is more white and less striped, and is white color with a spider graphic design\", \"positive_key\": \"B001B16GCS\", \"positive_value\": \"./images/B001B16GCS.png\", \"hn_image\": [\"./images/B0074D0W68.png\"]}\n{\"q_img\": \"./images/B007VYRFX8.png\", \"q_text\": \"is yellow in color with red text in center, and is yellow and has letter logo\", \"positive_key\": \"B005KSBJL6\", \"positive_value\": \"./images/B005KSBJL6.png\", \"hn_image\": [\"./images/B007VYRFX8.png\"]}\n{\"q_img\": \"./images/B009T5NMS4.png\", \"q_text\": \"is green, and is more multi colored\", \"positive_key\": \"B007TRPF5W\", \"positive_value\": \"./images/B007TRPF5W.png\", \"hn_image\": [\"./images/B009T5NMS4.png\"]}\n{\"q_img\": \"./images/B005KB7QSS.png\", \"q_text\": \"has colors, and has blue\", \"positive_key\": \"B00CLAZZOS\", \"positive_value\": \"./images/B00CLAZZOS.png\", \"hn_image\": [\"./images/B005KB7QSS.png\"]}\n{\"q_img\": \"./images/B00CEJEA8I.png\", \"q_text\": \"is white with red stripes, and is lighter\", \"positive_key\": \"B006043E2Q\", \"positive_value\": \"./images/B006043E2Q.png\", \"hn_image\": [\"./images/B00CEJEA8I.png\"]}\n{\"q_img\": \"./images/B00BYQDJH0.png\", \"q_text\": \"is more minimalist, and is mainly plan brown with small amount white trim\", \"positive_key\": \"B008LTDW1G\", \"positive_value\": \"./images/B008LTDW1G.png\", \"hn_image\": [\"./images/B00BYQDJH0.png\"]}\n{\"q_img\": \"./images/B000MYC2YG.png\", \"q_text\": \"is long sleeved and yellow, and is lighter and has longer sleeves\", \"positive_key\": \"B0057XC6S4\", \"positive_value\": \"./images/B0057XC6S4.png\", \"hn_image\": [\"./images/B000MYC2YG.png\"]}\n{\"q_img\": \"./images/B00C51HF46.png\", \"q_text\": \"has a graphic and a feminist slogan, and has larger graphics on it\", \"positive_key\": \"B00DPQ8FX0\", \"positive_value\": \"./images/B00DPQ8FX0.png\", \"hn_image\": [\"./images/B00C51HF46.png\"]}\n{\"q_img\": \"./images/B009L97W5M.png\", \"q_text\": \"has a dinosaur with open mouth, and is gray with a large dinosaur head\", \"positive_key\": \"B009QR8VNQ\", \"positive_value\": \"./images/B009QR8VNQ.png\", \"hn_image\": [\"./images/B009L97W5M.png\"]}\n{\"q_img\": \"./images/B00BFTS1DI.png\", \"q_text\": \"is more segmented and is longer, and has more colors in it\", \"positive_key\": \"B004S92OEE\", \"positive_value\": \"./images/B004S92OEE.png\", \"hn_image\": [\"./images/B00BFTS1DI.png\"]}\n{\"q_img\": \"./images/B001GW13QG.png\", \"q_text\": \"is darker and more evil, and Black t-shirt with green skull\", \"positive_key\": \"B005JQ22DI\", \"positive_value\": \"./images/B005JQ22DI.png\", \"hn_image\": [\"./images/B001GW13QG.png\"]}\n{\"q_img\": \"./images/B005UVPW5W.png\", \"q_text\": \"is a jersey and more colorful, and is shorter\", \"positive_key\": \"B005E7KJPK\", \"positive_value\": \"./images/B005E7KJPK.png\", \"hn_image\": [\"./images/B005UVPW5W.png\"]}\n{\"q_img\": \"./images/B0086F6HIK.png\", \"q_text\": \"is black with hwhite designs, and Black with white lettering and drawing figure\", \"positive_key\": \"B00B2B9JJY\", \"positive_value\": \"./images/B00B2B9JJY.png\", \"hn_image\": [\"./images/B0086F6HIK.png\"]}\n{\"q_img\": \"./images/B00398WXHI.png\", \"q_text\": \"is more of a solid color and lighter, and is lighter\", \"positive_key\": \"B0042FOL8Q\", \"positive_value\": \"./images/B0042FOL8Q.png\", \"hn_image\": [\"./images/B00398WXHI.png\"]}\n{\"q_img\": \"./images/B0039Q9USU.png\", \"q_text\": \"is lighter with different graphics, and is blue and more bold\", \"positive_key\": \"B008WJ68NY\", \"positive_value\": \"./images/B008WJ68NY.png\", \"hn_image\": [\"./images/B0039Q9USU.png\"]}\n{\"q_img\": \"./images/B002N8P7MI.png\", \"q_text\": \"is yellow, and is lighter\", \"positive_key\": \"B00B4WT108\", \"positive_value\": \"./images/B00B4WT108.png\", \"hn_image\": [\"./images/B002N8P7MI.png\"]}\n{\"q_img\": \"./images/B004R1NSL6.png\", \"q_text\": \"has short sleeves, and Is lavender with shorter sleeves\", \"positive_key\": \"B00B4WVJKI\", \"positive_value\": \"./images/B00B4WVJKI.png\", \"hn_image\": [\"./images/B004R1NSL6.png\"]}\n{\"q_img\": \"./images/B00DU9IRGC.png\", \"q_text\": \"is long sleeved with a plaid colorful print, and is more multi colored\", \"positive_key\": \"B009ZRV3GY\", \"positive_value\": \"./images/B009ZRV3GY.png\", \"hn_image\": [\"./images/B00DU9IRGC.png\"]}\n{\"q_img\": \"./images/B00AFQ1JD6.png\", \"q_text\": \"has shorter sleeves with less text, and Has a Dakine logo\", \"positive_key\": \"B0025V4H4M\", \"positive_value\": \"./images/B0025V4H4M.png\", \"hn_image\": [\"./images/B00AFQ1JD6.png\"]}\n{\"q_img\": \"./images/B00C40R3FY.png\", \"q_text\": \"Is less casual and has more buttons, and is more formal\", \"positive_key\": \"B004YZINB0\", \"positive_value\": \"./images/B004YZINB0.png\", \"hn_image\": [\"./images/B00C40R3FY.png\"]}\n{\"q_img\": \"./images/B00CB3EQDG.png\", \"q_text\": \"is light brown with a zipper, and is more bright coloureed\", \"positive_key\": \"B00700WVBO\", \"positive_value\": \"./images/B00700WVBO.png\", \"hn_image\": [\"./images/B00CB3EQDG.png\"]}\n{\"q_img\": \"./images/B003AU61WI.png\", \"q_text\": \"Is more casual and black and white, and is a black and white tshirt\", \"positive_key\": \"B00D8UCM00\", \"positive_value\": \"./images/B00D8UCM00.png\", \"hn_image\": [\"./images/B003AU61WI.png\"]}\n{\"q_img\": \"./images/B004BOV7DK.png\", \"q_text\": \"The shirt is blue with white writing., and is lighter\", \"positive_key\": \"B00FH65X5E\", \"positive_value\": \"./images/B00FH65X5E.png\", \"hn_image\": [\"./images/B004BOV7DK.png\"]}\n{\"q_img\": \"./images/B006Y5SM2I.png\", \"q_text\": \"The shirt is gray with black and white lettering., and is gray colored with a large centered graphic\", \"positive_key\": \"B00C9UU1D0\", \"positive_value\": \"./images/B00C9UU1D0.png\", \"hn_image\": [\"./images/B006Y5SM2I.png\"]}\n{\"q_img\": \"./images/B005LBXRWQ.png\", \"q_text\": \"has longer sleeves and is lighter in color, and is much lighter in color\", \"positive_key\": \"B005LBWAV0\", \"positive_value\": \"./images/B005LBWAV0.png\", \"hn_image\": [\"./images/B005LBXRWQ.png\"]}\n{\"q_img\": \"./images/B0084ERQFG.png\", \"q_text\": \"is darker with buttons, and is black and has buttons down the front\", \"positive_key\": \"B0056EJG8M\", \"positive_value\": \"./images/B0056EJG8M.png\", \"hn_image\": [\"./images/B0084ERQFG.png\"]}\n{\"q_img\": \"./images/B004ZC7YKI.png\", \"q_text\": \"identical, and has pocket and tailored button tab\", \"positive_key\": \"B00CIZI8Y0\", \"positive_value\": \"./images/B00CIZI8Y0.png\", \"hn_image\": [\"./images/B004ZC7YKI.png\"]}\n{\"q_img\": \"./images/B006ZZV0L2.png\", \"q_text\": \"is political, and Is black and different phrase.\", \"positive_key\": \"B003S0W9EO\", \"positive_value\": \"./images/B003S0W9EO.png\", \"hn_image\": [\"./images/B006ZZV0L2.png\"]}\n{\"q_img\": \"./images/B004R1CKX8.png\", \"q_text\": \"is a short sleeved t-shirt with entire dog's face, and Dark colors with dog\", \"positive_key\": \"B00AFJK8E4\", \"positive_value\": \"./images/B00AFJK8E4.png\", \"hn_image\": [\"./images/B004R1CKX8.png\"]}\n{\"q_img\": \"./images/B0042AOT8S.png\", \"q_text\": \"is gray with black words, and is a lighter grey color\", \"positive_key\": \"B0091BZBHQ\", \"positive_value\": \"./images/B0091BZBHQ.png\", \"hn_image\": [\"./images/B0042AOT8S.png\"]}\n{\"q_img\": \"./images/B0042VK7GA.png\", \"q_text\": \"is blue with a collar or buttons, and is less formal\", \"positive_key\": \"B00CBVC8GA\", \"positive_value\": \"./images/B00CBVC8GA.png\", \"hn_image\": [\"./images/B0042VK7GA.png\"]}\n{\"q_img\": \"./images/B002HNSH80.png\", \"q_text\": \"is light blue with yellow logo, and is light blue\", \"positive_key\": \"B00FV0FKXQ\", \"positive_value\": \"./images/B00FV0FKXQ.png\", \"hn_image\": [\"./images/B002HNSH80.png\"]}\n{\"q_img\": \"./images/B00BBSZEQ0.png\", \"q_text\": \"a light colored button down dress shirt, and is more formal\", \"positive_key\": \"B00AOI5DM8\", \"positive_value\": \"./images/B00AOI5DM8.png\", \"hn_image\": [\"./images/B00BBSZEQ0.png\"]}\n{\"q_img\": \"./images/B006ZP78WC.png\", \"q_text\": \"a light colored long sleeve shirt with a small grill graphic, and has a lighter color to it\", \"positive_key\": \"B006ZP4RPI\", \"positive_value\": \"./images/B006ZP4RPI.png\", \"hn_image\": [\"./images/B006ZP78WC.png\"]}\n{\"q_img\": \"./images/B007OXCG1C.png\", \"q_text\": \"is brighter and less formal, and is a lighter blue without a tie\", \"positive_key\": \"B009X6CURI\", \"positive_value\": \"./images/B009X6CURI.png\", \"hn_image\": [\"./images/B007OXCG1C.png\"]}\n{\"q_img\": \"./images/B00AK46JK6.png\", \"q_text\": \"has no sleeves, and it has no sleeve and it is more colorful\", \"positive_key\": \"B00C1ENRT4\", \"positive_value\": \"./images/B00C1ENRT4.png\", \"hn_image\": [\"./images/B00AK46JK6.png\"]}\n{\"q_img\": \"./images/B00F3KMWX0.png\", \"q_text\": \"is gray with black designs, and is blue gray colored\", \"positive_key\": \"B00BXVISC2\", \"positive_value\": \"./images/B00BXVISC2.png\", \"hn_image\": [\"./images/B00F3KMWX0.png\"]}\n{\"q_img\": \"./images/B00DQDA9HC.png\", \"q_text\": \"is a larger bright t-shirt, and has a white and black pattern\", \"positive_key\": \"B0015SWA4A\", \"positive_value\": \"./images/B0015SWA4A.png\", \"hn_image\": [\"./images/B00DQDA9HC.png\"]}\n{\"q_img\": \"./images/B006C5IY82.png\", \"q_text\": \"is beige and has more text, and is more brown in colour\", \"positive_key\": \"B00686QH8E\", \"positive_value\": \"./images/B00686QH8E.png\", \"hn_image\": [\"./images/B006C5IY82.png\"]}\n{\"q_img\": \"./images/B0083Z6QDO.png\", \"q_text\": \"is green, and A green shirt with white letters\", \"positive_key\": \"B007BMSSW2\", \"positive_value\": \"./images/B007BMSSW2.png\", \"hn_image\": [\"./images/B0083Z6QDO.png\"]}\n{\"q_img\": \"./images/B0028TS5X0.png\", \"q_text\": \"has a different logo, and is more faded\", \"positive_key\": \"B00BEH3S48\", \"positive_value\": \"./images/B00BEH3S48.png\", \"hn_image\": [\"./images/B0028TS5X0.png\"]}\n{\"q_img\": \"./images/B009P8QMWS.png\", \"q_text\": \"is gray and loose with red lettering, and A grey shirt with red letters on front\", \"positive_key\": \"B005HFBLQA\", \"positive_value\": \"./images/B005HFBLQA.png\", \"hn_image\": [\"./images/B009P8QMWS.png\"]}\n{\"q_img\": \"./images/B002BOHPNS.png\", \"q_text\": \"has a smaller photo, and is more fitted an has less graphics\", \"positive_key\": \"B005J4CCRG\", \"positive_value\": \"./images/B005J4CCRG.png\", \"hn_image\": [\"./images/B002BOHPNS.png\"]}\n{\"q_img\": \"./images/B003TWOPKW.png\", \"q_text\": \"is black in color with a v neck, and A black buttoned shirt with white letters on one side\", \"positive_key\": \"B008E5CDEO\", \"positive_value\": \"./images/B008E5CDEO.png\", \"hn_image\": [\"./images/B003TWOPKW.png\"]}\n{\"q_img\": \"./images/B00AFU8P2K.png\", \"q_text\": \"has graphic and is lighter, and is a beige colored tee with a blue graphic\", \"positive_key\": \"B004KIOM4S\", \"positive_value\": \"./images/B004KIOM4S.png\", \"hn_image\": [\"./images/B00AFU8P2K.png\"]}\n{\"q_img\": \"./images/B0088YPBA4.png\", \"q_text\": \"is lighter, and is much lighter in color\", \"positive_key\": \"B004I5NVL8\", \"positive_value\": \"./images/B004I5NVL8.png\", \"hn_image\": [\"./images/B0088YPBA4.png\"]}\n{\"q_img\": \"./images/B0096UD3FO.png\", \"q_text\": \"lighter color, and A white shirt with black pattern on bottom\", \"positive_key\": \"B001PTD0KM\", \"positive_value\": \"./images/B001PTD0KM.png\", \"hn_image\": [\"./images/B0096UD3FO.png\"]}\n{\"q_img\": \"./images/B00BLL7CB2.png\", \"q_text\": \"is shorter, and is darker in color\", \"positive_key\": \"B004EDHI0Y\", \"positive_value\": \"./images/B004EDHI0Y.png\", \"hn_image\": [\"./images/B00BLL7CB2.png\"]}\n{\"q_img\": \"./images/B0015GMX80.png\", \"q_text\": \"brighter plaid print, and Is lighter colored and shorter\", \"positive_key\": \"B00BEEF9OI\", \"positive_value\": \"./images/B00BEEF9OI.png\", \"hn_image\": [\"./images/B0015GMX80.png\"]}\n{\"q_img\": \"./images/B001H0F2J6.png\", \"q_text\": \"is white in color, and is white and stylish\", \"positive_key\": \"B008RMEXKG\", \"positive_value\": \"./images/B008RMEXKG.png\", \"hn_image\": [\"./images/B001H0F2J6.png\"]}\n{\"q_img\": \"./images/B009DLYLS4.png\", \"q_text\": \"is red with a cat logo on front, and is more pop culture and adolescent\", \"positive_key\": \"B001T4F46Q\", \"positive_value\": \"./images/B001T4F46Q.png\", \"hn_image\": [\"./images/B009DLYLS4.png\"]}\n{\"q_img\": \"./images/B00DQDA9HC.png\", \"q_text\": \"Is more charcoal, and is more black\", \"positive_key\": \"B00DPOJCL6\", \"positive_value\": \"./images/B00DPOJCL6.png\", \"hn_image\": [\"./images/B00DQDA9HC.png\"]}\n{\"q_img\": \"./images/B008VPIL52.png\", \"q_text\": \"has a print and is darker colored, and it is checked and has pocket\", \"positive_key\": \"B000KLV1CA\", \"positive_value\": \"./images/B000KLV1CA.png\", \"hn_image\": [\"./images/B008VPIL52.png\"]}\n{\"q_img\": \"./images/B00EAP7JAK.png\", \"q_text\": \"is a gray long sleeve, and is gray and long sleeved\", \"positive_key\": \"B0085428H6\", \"positive_value\": \"./images/B0085428H6.png\", \"hn_image\": [\"./images/B00EAP7JAK.png\"]}\n{\"q_img\": \"./images/B00641VADU.png\", \"q_text\": \"has zombie face print on inside., and is more emo\", \"positive_key\": \"B000TMDSOY\", \"positive_value\": \"./images/B000TMDSOY.png\", \"hn_image\": [\"./images/B00641VADU.png\"]}\n{\"q_img\": \"./images/B002UQJENQ.png\", \"q_text\": \"is dark colored, and is darker\", \"positive_key\": \"B00CPSD8A4\", \"positive_value\": \"./images/B00CPSD8A4.png\", \"hn_image\": [\"./images/B002UQJENQ.png\"]}\n{\"q_img\": \"./images/B0085OX8LQ.png\", \"q_text\": \"is similar but brighter, and is white with longer sleeves\", \"positive_key\": \"B007RAZN6M\", \"positive_value\": \"./images/B007RAZN6M.png\", \"hn_image\": [\"./images/B0085OX8LQ.png\"]}\n{\"q_img\": \"./images/B0089681FI.png\", \"q_text\": \"has different pattern, and has more brown in it\", \"positive_key\": \"B004DLNRSE\", \"positive_value\": \"./images/B004DLNRSE.png\", \"hn_image\": [\"./images/B0089681FI.png\"]}\n{\"q_img\": \"./images/B004REC7ZQ.png\", \"q_text\": \"is white with orange designs, and is lighter\", \"positive_key\": \"B007JQKD7S\", \"positive_value\": \"./images/B007JQKD7S.png\", \"hn_image\": [\"./images/B004REC7ZQ.png\"]}\n{\"q_img\": \"./images/B005Q0YSU2.png\", \"q_text\": \"has text 'saab', and has different words on it\", \"positive_key\": \"B0067PC8OS\", \"positive_value\": \"./images/B0067PC8OS.png\", \"hn_image\": [\"./images/B005Q0YSU2.png\"]}\n{\"q_img\": \"./images/B004U7I0SI.png\", \"q_text\": \"is black with a dog on it, and is darker in color\", \"positive_key\": \"B004OKFF7K\", \"positive_value\": \"./images/B004OKFF7K.png\", \"hn_image\": [\"./images/B004U7I0SI.png\"]}\n{\"q_img\": \"./images/B003H8KK0M.png\", \"q_text\": \"a short sleeve darker colored tee shirt, and is more colourfull\", \"positive_key\": \"B0013FPI8K\", \"positive_value\": \"./images/B0013FPI8K.png\", \"hn_image\": [\"./images/B003H8KK0M.png\"]}\n{\"q_img\": \"./images/B003OPUWH4.png\", \"q_text\": \"is lighter, and is more plain\", \"positive_key\": \"B003CHNXQQ\", \"positive_value\": \"./images/B003CHNXQQ.png\", \"hn_image\": [\"./images/B003OPUWH4.png\"]}\n{\"q_img\": \"./images/B004QDCRZS.png\", \"q_text\": \"has a different graphic on the front, and has a patriotic graphic\", \"positive_key\": \"B004T3K0FE\", \"positive_value\": \"./images/B004T3K0FE.png\", \"hn_image\": [\"./images/B004QDCRZS.png\"]}\n{\"q_img\": \"./images/B008D6Q7DC.png\", \"q_text\": \"has a different logo, and is bold and less graphic\", \"positive_key\": \"B007MEZRC8\", \"positive_value\": \"./images/B007MEZRC8.png\", \"hn_image\": [\"./images/B008D6Q7DC.png\"]}\n{\"q_img\": \"./images/B008K2HF6C.png\", \"q_text\": \"is a grey tee with army tank icons and lettering, and is grey in color\", \"positive_key\": \"B009JD7XUO\", \"positive_value\": \"./images/B009JD7XUO.png\", \"hn_image\": [\"./images/B008K2HF6C.png\"]}\n{\"q_img\": \"./images/B00CLCHVSY.png\", \"q_text\": \"The shirt is gray with white writing., and Is darker with white words\", \"positive_key\": \"B00E9LPGG4\", \"positive_value\": \"./images/B00E9LPGG4.png\", \"hn_image\": [\"./images/B00CLCHVSY.png\"]}\n{\"q_img\": \"./images/B004QDCRZS.png\", \"q_text\": \"is red colored with minimal art, and is orange\", \"positive_key\": \"B005J4C8HA\", \"positive_value\": \"./images/B005J4C8HA.png\", \"hn_image\": [\"./images/B004QDCRZS.png\"]}\n{\"q_img\": \"./images/B000EOZGWE.png\", \"q_text\": \"The shirt is gray with the word Croatia and the Croatian Flag., and is darker\", \"positive_key\": \"B002R7U7Q6\", \"positive_value\": \"./images/B002R7U7Q6.png\", \"hn_image\": [\"./images/B000EOZGWE.png\"]}\n{\"q_img\": \"./images/B0069UTN1W.png\", \"q_text\": \"has no collar and is dark grey with royal purple written on it, and is grey with no collar\", \"positive_key\": \"B00B9ZFVJ0\", \"positive_value\": \"./images/B00B9ZFVJ0.png\", \"hn_image\": [\"./images/B0069UTN1W.png\"]}\n{\"q_img\": \"./images/B004ASDKKA.png\", \"q_text\": \"Is more aloha with the print and is red, and is lighter\", \"positive_key\": \"B0007XMDL4\", \"positive_value\": \"./images/B0007XMDL4.png\", \"hn_image\": [\"./images/B004ASDKKA.png\"]}\n{\"q_img\": \"./images/B00451AT6U.png\", \"q_text\": \"Is more colorful, and has a Spider-man graphic\", \"positive_key\": \"B0041IY8I2\", \"positive_value\": \"./images/B0041IY8I2.png\", \"hn_image\": [\"./images/B00451AT6U.png\"]}\n{\"q_img\": \"./images/B003B2KVCQ.png\", \"q_text\": \"Is more blue and less casual, and Is a dress shirt.\", \"positive_key\": \"B00CYMFFVQ\", \"positive_value\": \"./images/B00CYMFFVQ.png\", \"hn_image\": [\"./images/B003B2KVCQ.png\"]}\n{\"q_img\": \"./images/B0032HJXKG.png\", \"q_text\": \"is less formal and has designs, and has a different fabric that is finer.\", \"positive_key\": \"B000WLVLDW\", \"positive_value\": \"./images/B000WLVLDW.png\", \"hn_image\": [\"./images/B0032HJXKG.png\"]}\n{\"q_img\": \"./images/B0038OVKR2.png\", \"q_text\": \"is black, and it is black\", \"positive_key\": \"B001086JBK\", \"positive_value\": \"./images/B001086JBK.png\", \"hn_image\": [\"./images/B0038OVKR2.png\"]}\n{\"q_img\": \"./images/B004R1CKX8.png\", \"q_text\": \"is similar but with shorter sleeves, and Is more white with shorter sleeves.\", \"positive_key\": \"B0015IMRNE\", \"positive_value\": \"./images/B0015IMRNE.png\", \"hn_image\": [\"./images/B004R1CKX8.png\"]}\n{\"q_img\": \"./images/B007LVJSW2.png\", \"q_text\": \"is gray with checkered button down shirt, and is more formal\", \"positive_key\": \"B002SG72FU\", \"positive_value\": \"./images/B002SG72FU.png\", \"hn_image\": [\"./images/B007LVJSW2.png\"]}\n{\"q_img\": \"./images/B003ICY3OQ.png\", \"q_text\": \"is darker and has longer sleeves, and is darker in color and has longer sleeves\", \"positive_key\": \"B007BA6MNQ\", \"positive_value\": \"./images/B007BA6MNQ.png\", \"hn_image\": [\"./images/B003ICY3OQ.png\"]}\n{\"q_img\": \"./images/B004KV0604.png\", \"q_text\": \"is the same, and Is darker in color\", \"positive_key\": \"B00C7BNA66\", \"positive_value\": \"./images/B00C7BNA66.png\", \"hn_image\": [\"./images/B004KV0604.png\"]}\n{\"q_img\": \"./images/B00008JPSN.png\", \"q_text\": \"has a darker color with a print tie, and is more colorful\", \"positive_key\": \"B00AZLY7QM\", \"positive_value\": \"./images/B00AZLY7QM.png\", \"hn_image\": [\"./images/B00008JPSN.png\"]}\n{\"q_img\": \"./images/B009R8P6QO.png\", \"q_text\": \"Is a darker color with more of a political statement, and is navy blue\", \"positive_key\": \"B0097OTU7Y\", \"positive_value\": \"./images/B0097OTU7Y.png\", \"hn_image\": [\"./images/B009R8P6QO.png\"]}\n{\"q_img\": \"./images/B0071SPPAO.png\", \"q_text\": \"is plain, and is one solid color\", \"positive_key\": \"B00ADD03FG\", \"positive_value\": \"./images/B00ADD03FG.png\", \"hn_image\": [\"./images/B0071SPPAO.png\"]}\n{\"q_img\": \"./images/B00DNI5GI2.png\", \"q_text\": \"has longer sleeves with buttons and a collar, and is more formal\", \"positive_key\": \"B000EDM01A\", \"positive_value\": \"./images/B000EDM01A.png\", \"hn_image\": [\"./images/B00DNI5GI2.png\"]}\n{\"q_img\": \"./images/B00DSOHOBI.png\", \"q_text\": \"light gray polo, and is grey with red outlining\", \"positive_key\": \"B008G3KOC2\", \"positive_value\": \"./images/B008G3KOC2.png\", \"hn_image\": [\"./images/B00DSOHOBI.png\"]}\n{\"q_img\": \"./images/B004TNLWVA.png\", \"q_text\": \"is beige color with abstract art, and is brown with black patterns\", \"positive_key\": \"B0074JA762\", \"positive_value\": \"./images/B0074JA762.png\", \"hn_image\": [\"./images/B004TNLWVA.png\"]}\n{\"q_img\": \"./images/B00AZLZ8UQ.png\", \"q_text\": \"this is a darker colored striped shirt with no tie, and less professional and more striped\", \"positive_key\": \"B00DRG0TS2\", \"positive_value\": \"./images/B00DRG0TS2.png\", \"hn_image\": [\"./images/B00AZLZ8UQ.png\"]}\n{\"q_img\": \"./images/B008LK7CAW.png\", \"q_text\": \"Is white and button down., and face tan\", \"positive_key\": \"B000RZ3L68\", \"positive_value\": \"./images/B000RZ3L68.png\", \"hn_image\": [\"./images/B008LK7CAW.png\"]}\n{\"q_img\": \"./images/B00CPS2NE6.png\", \"q_text\": \"has sleeves and is plainer, and has more grey\", \"positive_key\": \"B001FBQ8L8\", \"positive_value\": \"./images/B001FBQ8L8.png\", \"hn_image\": [\"./images/B00CPS2NE6.png\"]}\n{\"q_img\": \"./images/B0054419WA.png\", \"q_text\": \"Has shorter sleeves, and is darker\", \"positive_key\": \"B0093NHAQW\", \"positive_value\": \"./images/B0093NHAQW.png\", \"hn_image\": [\"./images/B0054419WA.png\"]}\n{\"q_img\": \"./images/B009N29AO8.png\", \"q_text\": \"is green with a belt printed on it, and Is green Zelda shirt\", \"positive_key\": \"B00EYGE7X2\", \"positive_value\": \"./images/B00EYGE7X2.png\", \"hn_image\": [\"./images/B009N29AO8.png\"]}\n{\"q_img\": \"./images/B008435IOI.png\", \"q_text\": \"is lighter and has a different graphic, and is light colored with a different graphic\", \"positive_key\": \"B004B6NXIK\", \"positive_value\": \"./images/B004B6NXIK.png\", \"hn_image\": [\"./images/B008435IOI.png\"]}\n{\"q_img\": \"./images/B000SJFEAE.png\", \"q_text\": \"is black with a skull on it, and is elss formal\", \"positive_key\": \"B000RP49KK\", \"positive_value\": \"./images/B000RP49KK.png\", \"hn_image\": [\"./images/B000SJFEAE.png\"]}\n{\"q_img\": \"./images/B007VPMP8C.png\", \"q_text\": \"has a smaller image and text, and A black shirt with white letters on front\", \"positive_key\": \"B000S97HBS\", \"positive_value\": \"./images/B000S97HBS.png\", \"hn_image\": [\"./images/B007VPMP8C.png\"]}\n{\"q_img\": \"./images/B00502EZX6.png\", \"q_text\": \"The shirt is green with no sleeves., and is green with no sleeves\", \"positive_key\": \"B004U28BBY\", \"positive_value\": \"./images/B004U28BBY.png\", \"hn_image\": [\"./images/B00502EZX6.png\"]}\n{\"q_img\": \"./images/B00A4LC2II.png\", \"q_text\": \"is black with yellow letters, and has no color\", \"positive_key\": \"B006H6TYSK\", \"positive_value\": \"./images/B006H6TYSK.png\", \"hn_image\": [\"./images/B00A4LC2II.png\"]}\n{\"q_img\": \"./images/B00DR4IZTE.png\", \"q_text\": \"is black and has a woman on it, and is darker\", \"positive_key\": \"B0078F2F80\", \"positive_value\": \"./images/B0078F2F80.png\", \"hn_image\": [\"./images/B00DR4IZTE.png\"]}\n{\"q_img\": \"./images/B00BYIN01I.png\", \"q_text\": \"Is dark blue with a Doctor Who logo on front, and is blue with DW and multiple hash marks on front\", \"positive_key\": \"B006W969LW\", \"positive_value\": \"./images/B006W969LW.png\", \"hn_image\": [\"./images/B00BYIN01I.png\"]}\n{\"q_img\": \"./images/B00861KWY4.png\", \"q_text\": \"Is black with a larger logo, and Is black in color and has a larger graphic\", \"positive_key\": \"B009P8QMWS\", \"positive_value\": \"./images/B009P8QMWS.png\", \"hn_image\": [\"./images/B00861KWY4.png\"]}\n{\"q_img\": \"./images/B007UP0B4S.png\", \"q_text\": \"is lighter, and is sleeveless with a camo print\", \"positive_key\": \"B0088B8HBC\", \"positive_value\": \"./images/B0088B8HBC.png\", \"hn_image\": [\"./images/B007UP0B4S.png\"]}\n{\"q_img\": \"./images/B00009ETTU.png\", \"q_text\": \"is pink and more plain, and is a dog shirt\", \"positive_key\": \"B0002I5T5G\", \"positive_value\": \"./images/B0002I5T5G.png\", \"hn_image\": [\"./images/B00009ETTU.png\"]}\n{\"q_img\": \"./images/B00G6UFA94.png\", \"q_text\": \"is darker with white and yellow lettering, and has more writing and is more black in colour\", \"positive_key\": \"B00CBP45Q2\", \"positive_value\": \"./images/B00CBP45Q2.png\", \"hn_image\": [\"./images/B00G6UFA94.png\"]}\n{\"q_img\": \"./images/B001FBQ8L8.png\", \"q_text\": \"is white, and is lighter colored and has shorter sleeves\", \"positive_key\": \"B0089CCDUQ\", \"positive_value\": \"./images/B0089CCDUQ.png\", \"hn_image\": [\"./images/B001FBQ8L8.png\"]}\n{\"q_img\": \"./images/B00AECN92K.png\", \"q_text\": \"is red in color, and is red with different facial drawings\", \"positive_key\": \"B00AECKNR4\", \"positive_value\": \"./images/B00AECKNR4.png\", \"hn_image\": [\"./images/B00AECN92K.png\"]}\n{\"q_img\": \"./images/B009G0SO62.png\", \"q_text\": \"Is blue and has a graphic, and has no collar and is dark blue\", \"positive_key\": \"B0090WXH9K\", \"positive_value\": \"./images/B0090WXH9K.png\", \"hn_image\": [\"./images/B009G0SO62.png\"]}\n{\"q_img\": \"./images/B002RCLYEK.png\", \"q_text\": \"Tighter and black, and is black\", \"positive_key\": \"B006MHI516\", \"positive_value\": \"./images/B006MHI516.png\", \"hn_image\": [\"./images/B002RCLYEK.png\"]}\n{\"q_img\": \"./images/B006C25WGM.png\", \"q_text\": \"Is more striped and colorful, and has multiple colours including red\", \"positive_key\": \"B006ZYZFNM\", \"positive_value\": \"./images/B006ZYZFNM.png\", \"hn_image\": [\"./images/B006C25WGM.png\"]}\n{\"q_img\": \"./images/B00CLZVGAA.png\", \"q_text\": \"is red and has a different logo, and is grey\", \"positive_key\": \"B003TPUV0M\", \"positive_value\": \"./images/B003TPUV0M.png\", \"hn_image\": [\"./images/B00CLZVGAA.png\"]}\n{\"q_img\": \"./images/B00B9JJY76.png\", \"q_text\": \"is more tropical and less muted, and A red shirt with grey/white pattern on shirt\", \"positive_key\": \"B0093NPKO6\", \"positive_value\": \"./images/B0093NPKO6.png\", \"hn_image\": [\"./images/B00B9JJY76.png\"]}\n{\"q_img\": \"./images/B002EIMNY2.png\", \"q_text\": \"has a symblol and a tie dye heart, and has a heart in the center\", \"positive_key\": \"B0014C09VI\", \"positive_value\": \"./images/B0014C09VI.png\", \"hn_image\": [\"./images/B002EIMNY2.png\"]}\n{\"q_img\": \"./images/B004OKFF7K.png\", \"q_text\": \"is dark colored with a dog art, and has a black and white dog on it\", \"positive_key\": \"B00774HWNA\", \"positive_value\": \"./images/B00774HWNA.png\", \"hn_image\": [\"./images/B004OKFF7K.png\"]}\n{\"q_img\": \"./images/B007T4JJ5W.png\", \"q_text\": \"is a blue button down, and is more blue\", \"positive_key\": \"B00BTJJVPQ\", \"positive_value\": \"./images/B00BTJJVPQ.png\", \"hn_image\": [\"./images/B007T4JJ5W.png\"]}\n{\"q_img\": \"./images/B00GOGK2SO.png\", \"q_text\": \"is red and has long sleeves, and has longer sleeves\", \"positive_key\": \"B009LM4LYO\", \"positive_value\": \"./images/B009LM4LYO.png\", \"hn_image\": [\"./images/B00GOGK2SO.png\"]}\n{\"q_img\": \"./images/B00B7SA29S.png\", \"q_text\": \"bright pink shirt, and is has a lighter color\", \"positive_key\": \"B00CUU350U\", \"positive_value\": \"./images/B00CUU350U.png\", \"hn_image\": [\"./images/B00B7SA29S.png\"]}\n{\"q_img\": \"./images/B008T3V1YO.png\", \"q_text\": \"is darker colored with text, and is darker\", \"positive_key\": \"B00EUEB41G\", \"positive_value\": \"./images/B00EUEB41G.png\", \"hn_image\": [\"./images/B008T3V1YO.png\"]}\n{\"q_img\": \"./images/B002O17HOU.png\", \"q_text\": \"is darker, and is grey colored\", \"positive_key\": \"B004QQ5IMO\", \"positive_value\": \"./images/B004QQ5IMO.png\", \"hn_image\": [\"./images/B002O17HOU.png\"]}\n{\"q_img\": \"./images/B003BWWH8M.png\", \"q_text\": \"has a longer sleeve with text and art, and is black long sleeve and less shiny\", \"positive_key\": \"B004BTX0GW\", \"positive_value\": \"./images/B004BTX0GW.png\", \"hn_image\": [\"./images/B003BWWH8M.png\"]}\n{\"q_img\": \"./images/B0002DQSS8.png\", \"q_text\": \"does not a collar, and is blue and not collared\", \"positive_key\": \"B002WOSLVM\", \"positive_value\": \"./images/B002WOSLVM.png\", \"hn_image\": [\"./images/B0002DQSS8.png\"]}\n{\"q_img\": \"./images/B00CUA9J5A.png\", \"q_text\": \"is a button down with a plaid pattern, and has a checkered pattern\", \"positive_key\": \"B0065EBKS6\", \"positive_value\": \"./images/B0065EBKS6.png\", \"hn_image\": [\"./images/B00CUA9J5A.png\"]}\n{\"q_img\": \"./images/B003BWRJ1C.png\", \"q_text\": \"is light blue but plain, and is longer and one color\", \"positive_key\": \"B00CXSBQ8M\", \"positive_value\": \"./images/B00CXSBQ8M.png\", \"hn_image\": [\"./images/B003BWRJ1C.png\"]}\n{\"q_img\": \"./images/B007F85LVI.png\", \"q_text\": \"navy with a stig graphic and longer sleeved, and has longer sleeves\", \"positive_key\": \"B00D8GO1E4\", \"positive_value\": \"./images/B00D8GO1E4.png\", \"hn_image\": [\"./images/B007F85LVI.png\"]}\n{\"q_img\": \"./images/B009LJCL8A.png\", \"q_text\": \"has injustice written on it, and is a little longer\", \"positive_key\": \"B00EW0UFUY\", \"positive_value\": \"./images/B00EW0UFUY.png\", \"hn_image\": [\"./images/B009LJCL8A.png\"]}\n{\"q_img\": \"./images/B006WFFTTO.png\", \"q_text\": \"has shorter sleeves, and has less black color\", \"positive_key\": \"B008JBP5L6\", \"positive_value\": \"./images/B008JBP5L6.png\", \"hn_image\": [\"./images/B006WFFTTO.png\"]}\n{\"q_img\": \"./images/B008QUAX0S.png\", \"q_text\": \"has the american flag on it, and is more bright\", \"positive_key\": \"B008PG61WM\", \"positive_value\": \"./images/B008PG61WM.png\", \"hn_image\": [\"./images/B008QUAX0S.png\"]}\n{\"q_img\": \"./images/B000BO1R2K.png\", \"q_text\": \"is a white shirt with a message, and has black\", \"positive_key\": \"B0061S0348\", \"positive_value\": \"./images/B0061S0348.png\", \"hn_image\": [\"./images/B000BO1R2K.png\"]}\n{\"q_img\": \"./images/B00CNE6EEW.png\", \"q_text\": \"is black with image of inverted triangle, and is darker\", \"positive_key\": \"B00BHA7IBG\", \"positive_value\": \"./images/B00BHA7IBG.png\", \"hn_image\": [\"./images/B00CNE6EEW.png\"]}\n{\"q_img\": \"./images/B002CMLPNK.png\", \"q_text\": \"is a darker t shirt with no buttons, and is a printed t shirt\", \"positive_key\": \"B00BJV7YIA\", \"positive_value\": \"./images/B00BJV7YIA.png\", \"hn_image\": [\"./images/B002CMLPNK.png\"]}\n{\"q_img\": \"./images/B00EFZ786K.png\", \"q_text\": \"is more blue, and is blue with white palm trees\", \"positive_key\": \"B0002KV5OS\", \"positive_value\": \"./images/B0002KV5OS.png\", \"hn_image\": [\"./images/B00EFZ786K.png\"]}\n{\"q_img\": \"./images/B0013FPI8K.png\", \"q_text\": \"is white less color., and is white\", \"positive_key\": \"B005X2NZ9S\", \"positive_value\": \"./images/B005X2NZ9S.png\", \"hn_image\": [\"./images/B0013FPI8K.png\"]}\n{\"q_img\": \"./images/B00EA93IKQ.png\", \"q_text\": \"is darker and has shorter sleeves, and Has a tank top fit & is darker\", \"positive_key\": \"B009I1PKKC\", \"positive_value\": \"./images/B009I1PKKC.png\", \"hn_image\": [\"./images/B00EA93IKQ.png\"]}\n{\"q_img\": \"./images/B0038MHG18.png\", \"q_text\": \"is light brown with short sleeves, and is more brown colored\", \"positive_key\": \"B003POFQZM\", \"positive_value\": \"./images/B003POFQZM.png\", \"hn_image\": [\"./images/B0038MHG18.png\"]}\n{\"q_img\": \"./images/B005GL63X6.png\", \"q_text\": \"has bottons and is black, and is lighter\", \"positive_key\": \"B006HAHZ7I\", \"positive_value\": \"./images/B006HAHZ7I.png\", \"hn_image\": [\"./images/B005GL63X6.png\"]}\n{\"q_img\": \"./images/B006AJXW0A.png\", \"q_text\": \"is black with red and yellow pattern, and has a red logo\", \"positive_key\": \"B008F061QO\", \"positive_value\": \"./images/B008F061QO.png\", \"hn_image\": [\"./images/B006AJXW0A.png\"]}\n{\"q_img\": \"./images/B000G1FDF0.png\", \"q_text\": \"is more formal and has more pleats, and has a black bow tie included\", \"positive_key\": \"B0013KOIL8\", \"positive_value\": \"./images/B0013KOIL8.png\", \"hn_image\": [\"./images/B000G1FDF0.png\"]}\n{\"q_img\": \"./images/B0016BK4T4.png\", \"q_text\": \"is colorful tie dyed, and has more vivid colors\", \"positive_key\": \"B001L3GYSW\", \"positive_value\": \"./images/B001L3GYSW.png\", \"hn_image\": [\"./images/B0016BK4T4.png\"]}\n{\"q_img\": \"./images/B00C4AZVA8.png\", \"q_text\": \"is more light colored with the same brand, and is light blue and has a bit logo\", \"positive_key\": \"B004WH0J58\", \"positive_value\": \"./images/B004WH0J58.png\", \"hn_image\": [\"./images/B00C4AZVA8.png\"]}\n{\"q_img\": \"./images/B00CIYDP94.png\", \"q_text\": \"has a different graphic, and is nerdier\", \"positive_key\": \"B0087J4VGK\", \"positive_value\": \"./images/B0087J4VGK.png\", \"hn_image\": [\"./images/B00CIYDP94.png\"]}\n{\"q_img\": \"./images/B0012FNHIO.png\", \"q_text\": \"is a usb charger, and is a glittery strap\", \"positive_key\": \"B00C4XARMM\", \"positive_value\": \"./images/B00C4XARMM.png\", \"hn_image\": [\"./images/B0012FNHIO.png\"]}\n{\"q_img\": \"./images/B0054I421G.png\", \"q_text\": \"is lighter, and is grey with short sleeves\", \"positive_key\": \"B005BN2YN2\", \"positive_value\": \"./images/B005BN2YN2.png\", \"hn_image\": [\"./images/B0054I421G.png\"]}\n{\"q_img\": \"./images/B006WRED6W.png\", \"q_text\": \"is light blue colored with patterns, and is less colorful and less sporty\", \"positive_key\": \"B001TKTN1M\", \"positive_value\": \"./images/B001TKTN1M.png\", \"hn_image\": [\"./images/B006WRED6W.png\"]}\n{\"q_img\": \"./images/B004CR4ZC6.png\", \"q_text\": \"is shinier, and is a blue button down\", \"positive_key\": \"B004SKM6SC\", \"positive_value\": \"./images/B004SKM6SC.png\", \"hn_image\": [\"./images/B004CR4ZC6.png\"]}\n{\"q_img\": \"./images/B004QQ5IMO.png\", \"q_text\": \"Green and not a logo, and is green and is more flowy\", \"positive_key\": \"B002O17HOU\", \"positive_value\": \"./images/B002O17HOU.png\", \"hn_image\": [\"./images/B004QQ5IMO.png\"]}\n{\"q_img\": \"./images/B00AJJJPNA.png\", \"q_text\": \"is a white vest, and has no sleeves\", \"positive_key\": \"B00C5RC12G\", \"positive_value\": \"./images/B00C5RC12G.png\", \"hn_image\": [\"./images/B00AJJJPNA.png\"]}\n{\"q_img\": \"./images/B007JR83CE.png\", \"q_text\": \"are denim pants, and is denim and are jeans\", \"positive_key\": \"B00192IJZG\", \"positive_value\": \"./images/B00192IJZG.png\", \"hn_image\": [\"./images/B007JR83CE.png\"]}\n{\"q_img\": \"./images/B006TG84O8.png\", \"q_text\": \"is black with white logo, and islighter\", \"positive_key\": \"B006TG7W3M\", \"positive_value\": \"./images/B006TG7W3M.png\", \"hn_image\": [\"./images/B006TG84O8.png\"]}\n{\"q_img\": \"./images/B008BTI85Q.png\", \"q_text\": \"is brown with a figure on it, and is olive green/tan with blue motif\", \"positive_key\": \"B004N5H9D4\", \"positive_value\": \"./images/B004N5H9D4.png\", \"hn_image\": [\"./images/B008BTI85Q.png\"]}\n{\"q_img\": \"./images/B009ZC3SAS.png\", \"q_text\": \"is a darker color and similar print, and is red and has white lines\", \"positive_key\": \"B003QMM0SO\", \"positive_value\": \"./images/B003QMM0SO.png\", \"hn_image\": [\"./images/B009ZC3SAS.png\"]}\n{\"q_img\": \"./images/B0045VQCWA.png\", \"q_text\": \"is a blue Finland soccer T shirt, and is dark blue colored with a different graphic\", \"positive_key\": \"B0045EY35K\", \"positive_value\": \"./images/B0045EY35K.png\", \"hn_image\": [\"./images/B0045VQCWA.png\"]}\n{\"q_img\": \"./images/B005NK3GCG.png\", \"q_text\": \"is a striped shirt with short sleeves, and it has a short sleeve and has no collar\", \"positive_key\": \"B003IT740Y\", \"positive_value\": \"./images/B003IT740Y.png\", \"hn_image\": [\"./images/B005NK3GCG.png\"]}\n{\"q_img\": \"./images/B0048KZ3FU.png\", \"q_text\": \"has a orange snake, and has a different graphic\", \"positive_key\": \"B005REIERK\", \"positive_value\": \"./images/B005REIERK.png\", \"hn_image\": [\"./images/B0048KZ3FU.png\"]}\n{\"q_img\": \"./images/B004XMVOPQ.png\", \"q_text\": \"is large and black, and is darker in color with more realistic graphics\", \"positive_key\": \"B001F6TDJW\", \"positive_value\": \"./images/B001F6TDJW.png\", \"hn_image\": [\"./images/B004XMVOPQ.png\"]}\n{\"q_img\": \"./images/B006XD31RC.png\", \"q_text\": \"has long sleeves, and is darker and has longer sleeves\", \"positive_key\": \"B00A6YFYXS\", \"positive_value\": \"./images/B00A6YFYXS.png\", \"hn_image\": [\"./images/B006XD31RC.png\"]}\n{\"q_img\": \"./images/B008VI65PW.png\", \"q_text\": \"has a funny image, and has a different graphic\", \"positive_key\": \"B00092SAR4\", \"positive_value\": \"./images/B00092SAR4.png\", \"hn_image\": [\"./images/B008VI65PW.png\"]}\n{\"q_img\": \"./images/B00A64TGV4.png\", \"q_text\": \"is white coloreds with baby blue stripes, and is lighter\", \"positive_key\": \"B00C96U8RI\", \"positive_value\": \"./images/B00C96U8RI.png\", \"hn_image\": [\"./images/B00A64TGV4.png\"]}\n{\"q_img\": \"./images/B005CXDOA8.png\", \"q_text\": \"It is a buttoned shirt., and black with buttons and a collar\", \"positive_key\": \"B006HT3WBC\", \"positive_value\": \"./images/B006HT3WBC.png\", \"hn_image\": [\"./images/B005CXDOA8.png\"]}\n{\"q_img\": \"./images/B00BOHU9ZE.png\", \"q_text\": \"has a logo with multiple personages, and Is more colorful with a larger logo.\", \"positive_key\": \"B00EHQR0FQ\", \"positive_value\": \"./images/B00EHQR0FQ.png\", \"hn_image\": [\"./images/B00BOHU9ZE.png\"]}\n{\"q_img\": \"./images/B001E2038M.png\", \"q_text\": \"has a more simple graphic on the front, and is grey and one large circle as logo\", \"positive_key\": \"B00DDXIRV0\", \"positive_value\": \"./images/B00DDXIRV0.png\", \"hn_image\": [\"./images/B001E2038M.png\"]}\n{\"q_img\": \"./images/B0093OQREM.png\", \"q_text\": \"has a white crossed pattern, and is darker blue with white logo\", \"positive_key\": \"B00FDL73BK\", \"positive_value\": \"./images/B00FDL73BK.png\", \"hn_image\": [\"./images/B0093OQREM.png\"]}\n{\"q_img\": \"./images/B00B7SA29S.png\", \"q_text\": \"is a green t shirt with a light material, and is more colorful\", \"positive_key\": \"B008OZKW56\", \"positive_value\": \"./images/B008OZKW56.png\", \"hn_image\": [\"./images/B00B7SA29S.png\"]}\n{\"q_img\": \"./images/B001PFBE2C.png\", \"q_text\": \"is a white ping pong tee, and is a white\", \"positive_key\": \"B004O92RP4\", \"positive_value\": \"./images/B004O92RP4.png\", \"hn_image\": [\"./images/B001PFBE2C.png\"]}\n{\"q_img\": \"./images/B003XMOFP8.png\", \"q_text\": \"darker blue with checks, and is darker\", \"positive_key\": \"B000W6WB6I\", \"positive_value\": \"./images/B000W6WB6I.png\", \"hn_image\": [\"./images/B003XMOFP8.png\"]}\n{\"q_img\": \"./images/B003MSNCM0.png\", \"q_text\": \"is blue, and has a bit logo and is blue color\", \"positive_key\": \"B00DIAJ7Z8\", \"positive_value\": \"./images/B00DIAJ7Z8.png\", \"hn_image\": [\"./images/B003MSNCM0.png\"]}\n{\"q_img\": \"./images/B004UJ14FM.png\", \"q_text\": \"is brown in color, and is darker and more masculine\", \"positive_key\": \"B000L9S5NY\", \"positive_value\": \"./images/B000L9S5NY.png\", \"hn_image\": [\"./images/B004UJ14FM.png\"]}\n{\"q_img\": \"./images/B001CFA16A.png\", \"q_text\": \"is light black colored with a different text, and has a different saying\", \"positive_key\": \"B002HNSH80\", \"positive_value\": \"./images/B002HNSH80.png\", \"hn_image\": [\"./images/B001CFA16A.png\"]}\n{\"q_img\": \"./images/B004CS0IZS.png\", \"q_text\": \"is less formal and bright, and is a T in blue with white logo on the front.\", \"positive_key\": \"B005Y8684K\", \"positive_value\": \"./images/B005Y8684K.png\", \"hn_image\": [\"./images/B004CS0IZS.png\"]}\n{\"q_img\": \"./images/B00CQLJ1Y2.png\", \"q_text\": \"has more buttons., and Black with white bottons\", \"positive_key\": \"B00DN6JHNE\", \"positive_value\": \"./images/B00DN6JHNE.png\", \"hn_image\": [\"./images/B00CQLJ1Y2.png\"]}\n{\"q_img\": \"./images/B003BAEVJ2.png\", \"q_text\": \"is yellow and black, and is more colorful\", \"positive_key\": \"B006NPNCBA\", \"positive_value\": \"./images/B006NPNCBA.png\", \"hn_image\": [\"./images/B003BAEVJ2.png\"]}\n{\"q_img\": \"./images/B00AWA4RF2.png\", \"q_text\": \"is a casual v-neck shirt with no buttons, and Has a v-neck and is more casual.\", \"positive_key\": \"B0098CQTP6\", \"positive_value\": \"./images/B0098CQTP6.png\", \"hn_image\": [\"./images/B00AWA4RF2.png\"]}\n{\"q_img\": \"./images/B00B7679FU.png\", \"q_text\": \"is red colored with a clock in center, and is more colorful\", \"positive_key\": \"B009BHF8XW\", \"positive_value\": \"./images/B009BHF8XW.png\", \"hn_image\": [\"./images/B00B7679FU.png\"]}\n{\"q_img\": \"./images/B009PTECLU.png\", \"q_text\": \"has shorter sleeves with no graphic, and has shorter sleeves and only printed words\", \"positive_key\": \"B00FYXXTY2\", \"positive_value\": \"./images/B00FYXXTY2.png\", \"hn_image\": [\"./images/B009PTECLU.png\"]}\n{\"q_img\": \"./images/B0087KODVM.png\", \"q_text\": \"is smaller, and is black with orange lettering\", \"positive_key\": \"B008BT8HRU\", \"positive_value\": \"./images/B008BT8HRU.png\", \"hn_image\": [\"./images/B0087KODVM.png\"]}\n{\"q_img\": \"./images/B0071FEZAI.png\", \"q_text\": \"has a smaller sons of anarchy logo on it, and is much darker\", \"positive_key\": \"B004XWCRKC\", \"positive_value\": \"./images/B004XWCRKC.png\", \"hn_image\": [\"./images/B0071FEZAI.png\"]}\n{\"q_img\": \"./images/B008VI0ZP8.png\", \"q_text\": \"is puffier and has longer sleeves, and is a brown and a button up jacket\", \"positive_key\": \"B005958LUC\", \"positive_value\": \"./images/B005958LUC.png\", \"hn_image\": [\"./images/B008VI0ZP8.png\"]}\n{\"q_img\": \"./images/B009QR94S2.png\", \"q_text\": \"is lighter colored and has a bigger graphic, and is white colored and has a different graphic\", \"positive_key\": \"B005YJAP3O\", \"positive_value\": \"./images/B005YJAP3O.png\", \"hn_image\": [\"./images/B009QR94S2.png\"]}\n{\"q_img\": \"./images/B00BHA7IBG.png\", \"q_text\": \"has a darker color and has no image only words on it, and has orange lettering\", \"positive_key\": \"B003C1R750\", \"positive_value\": \"./images/B003C1R750.png\", \"hn_image\": [\"./images/B00BHA7IBG.png\"]}\n{\"q_img\": \"./images/B0036C4NFW.png\", \"q_text\": \"has a black color with math humor, and has long sleeves\", \"positive_key\": \"B002OT93GM\", \"positive_value\": \"./images/B002OT93GM.png\", \"hn_image\": [\"./images/B0036C4NFW.png\"]}\n{\"q_img\": \"./images/B007G03S2Y.png\", \"q_text\": \"Is bright red with collar, and is more colorufl with less buttons\", \"positive_key\": \"B00B60JFFO\", \"positive_value\": \"./images/B00B60JFFO.png\", \"hn_image\": [\"./images/B007G03S2Y.png\"]}\n{\"q_img\": \"./images/B00CDT7FHM.png\", \"q_text\": \"Has letters instead of pictures on it., and It is more blue with simple design\", \"positive_key\": \"B00A5GM3RC\", \"positive_value\": \"./images/B00A5GM3RC.png\", \"hn_image\": [\"./images/B00CDT7FHM.png\"]}\n{\"q_img\": \"./images/B00F6886DY.png\", \"q_text\": \"It has sleeves., and has longer sleeves\", \"positive_key\": \"B006YDMYCY\", \"positive_value\": \"./images/B006YDMYCY.png\", \"hn_image\": [\"./images/B00F6886DY.png\"]}\n{\"q_img\": \"./images/B007E9SJSU.png\", \"q_text\": \"is darker and has no buttons, and is a blue-t-shirt with three lighter blue stripes\", \"positive_key\": \"B00AF3YKSA\", \"positive_value\": \"./images/B00AF3YKSA.png\", \"hn_image\": [\"./images/B007E9SJSU.png\"]}\n{\"q_img\": \"./images/B005EJDQ0S.png\", \"q_text\": \"has a different print, and Desired item pictures motor vehicles\", \"positive_key\": \"B00AYVSZ4I\", \"positive_value\": \"./images/B00AYVSZ4I.png\", \"hn_image\": [\"./images/B005EJDQ0S.png\"]}\n{\"q_img\": \"./images/B003BP07R2.png\", \"q_text\": \"has a star wars print on it, and and has a different graphic\", \"positive_key\": \"B00AEYXOX2\", \"positive_value\": \"./images/B00AEYXOX2.png\", \"hn_image\": [\"./images/B003BP07R2.png\"]}\n{\"q_img\": \"./images/B004KV0604.png\", \"q_text\": \"is grey with a mustang, and is gray\", \"positive_key\": \"B00DJ6QOUC\", \"positive_value\": \"./images/B00DJ6QOUC.png\", \"hn_image\": [\"./images/B004KV0604.png\"]}\n{\"q_img\": \"./images/B00D7SXFS6.png\", \"q_text\": \"is light blue and striped, and is more red\", \"positive_key\": \"B00CPHNAQ2\", \"positive_value\": \"./images/B00CPHNAQ2.png\", \"hn_image\": [\"./images/B00D7SXFS6.png\"]}\n{\"q_img\": \"./images/B003RW8XFW.png\", \"q_text\": \"is slim fit, and Has letters on the decal\", \"positive_key\": \"B007ULMQO0\", \"positive_value\": \"./images/B007ULMQO0.png\", \"hn_image\": [\"./images/B003RW8XFW.png\"]}\n{\"q_img\": \"./images/B00166XERS.png\", \"q_text\": \"is plaid, and A white/red checkered long sleeved shirt\", \"positive_key\": \"B00579E6JK\", \"positive_value\": \"./images/B00579E6JK.png\", \"hn_image\": [\"./images/B00166XERS.png\"]}\n{\"q_img\": \"./images/B0013WHNO0.png\", \"q_text\": \"has shorter sleeves and a tighter collar, and has a yellow stripe and a crew neck\", \"positive_key\": \"B001JP5KAA\", \"positive_value\": \"./images/B001JP5KAA.png\", \"hn_image\": [\"./images/B0013WHNO0.png\"]}\n{\"q_img\": \"./images/B00BJ90X7Q.png\", \"q_text\": \"Is more colorful with sleeves., and is more brightly colored\", \"positive_key\": \"B001TYLKNM\", \"positive_value\": \"./images/B001TYLKNM.png\", \"hn_image\": [\"./images/B00BJ90X7Q.png\"]}\n{\"q_img\": \"./images/B009JJG3JA.png\", \"q_text\": \"is less formal, and A black long sleeved with green design on front\", \"positive_key\": \"B00D2CXAOG\", \"positive_value\": \"./images/B00D2CXAOG.png\", \"hn_image\": [\"./images/B009JJG3JA.png\"]}\n{\"q_img\": \"./images/B00018A8ZI.png\", \"q_text\": \"is more formal, and is more formal\", \"positive_key\": \"B002CMLPNK\", \"positive_value\": \"./images/B002CMLPNK.png\", \"hn_image\": [\"./images/B00018A8ZI.png\"]}\n{\"q_img\": \"./images/B005HF6TGW.png\", \"q_text\": \"Has a more intense graphic, and is darker\", \"positive_key\": \"B0053H927W\", \"positive_value\": \"./images/B0053H927W.png\", \"hn_image\": [\"./images/B005HF6TGW.png\"]}\n{\"q_img\": \"./images/B00D8YF87U.png\", \"q_text\": \"is a long sleeved line patterned shirt, and has longer sleeves\", \"positive_key\": \"B00DVHNG2I\", \"positive_value\": \"./images/B00DVHNG2I.png\", \"hn_image\": [\"./images/B00D8YF87U.png\"]}\n{\"q_img\": \"./images/B006NR72OQ.png\", \"q_text\": \"is a t-shirt with a red picture of a girl, and is a t shirt and printed logo\", \"positive_key\": \"B004NES8GW\", \"positive_value\": \"./images/B004NES8GW.png\", \"hn_image\": [\"./images/B006NR72OQ.png\"]}\n{\"q_img\": \"./images/B009ZGNJ6W.png\", \"q_text\": \"is a lighter color, and is lighter in color\", \"positive_key\": \"B00A7BYWM4\", \"positive_value\": \"./images/B00A7BYWM4.png\", \"hn_image\": [\"./images/B009ZGNJ6W.png\"]}\n{\"q_img\": \"./images/B0001TOU74.png\", \"q_text\": \"is more text and darker, and has lettering in only white\", \"positive_key\": \"B0042AOT8S\", \"positive_value\": \"./images/B0042AOT8S.png\", \"hn_image\": [\"./images/B0001TOU74.png\"]}\n{\"q_img\": \"./images/B0081ST50O.png\", \"q_text\": \"has more words on it, and has a bigger logo\", \"positive_key\": \"B00865CPJ0\", \"positive_value\": \"./images/B00865CPJ0.png\", \"hn_image\": [\"./images/B0081ST50O.png\"]}\n{\"q_img\": \"./images/B004TNLWVA.png\", \"q_text\": \"is a white button down long sleeved shirt with collar, and is white and collared\", \"positive_key\": \"B00AAHPHQA\", \"positive_value\": \"./images/B00AAHPHQA.png\", \"hn_image\": [\"./images/B004TNLWVA.png\"]}\n{\"q_img\": \"./images/B00AJODAOA.png\", \"q_text\": \"A dark blue life is simple shirt, and has a quote on the shirt and is blud\", \"positive_key\": \"B006D40LJW\", \"positive_value\": \"./images/B006D40LJW.png\", \"hn_image\": [\"./images/B00AJODAOA.png\"]}\n{\"q_img\": \"./images/B000CP9TY6.png\", \"q_text\": \"Shorter sleeves with pirate graphics, and has a different graphic.\", \"positive_key\": \"B001QJ2EXA\", \"positive_value\": \"./images/B001QJ2EXA.png\", \"hn_image\": [\"./images/B000CP9TY6.png\"]}\n{\"q_img\": \"./images/B0085X31DC.png\", \"q_text\": \"The shirt is blue in color with dark blue writing., and is light blue\", \"positive_key\": \"B009XE4O7E\", \"positive_value\": \"./images/B009XE4O7E.png\", \"hn_image\": [\"./images/B0085X31DC.png\"]}\n{\"q_img\": \"./images/B001GW13QG.png\", \"q_text\": \"is black colored with buttons and collar, and is a black button down\", \"positive_key\": \"B000YHJDOI\", \"positive_value\": \"./images/B000YHJDOI.png\", \"hn_image\": [\"./images/B001GW13QG.png\"]}\n{\"q_img\": \"./images/B00509NSFU.png\", \"q_text\": \"is black with a different print, and is darker in color\", \"positive_key\": \"B00BOO7OLO\", \"positive_value\": \"./images/B00BOO7OLO.png\", \"hn_image\": [\"./images/B00509NSFU.png\"]}\n{\"q_img\": \"./images/B002YL3YQ0.png\", \"q_text\": \"is red with physical ed written on it, and no sleeves\", \"positive_key\": \"B005TVEOKW\", \"positive_value\": \"./images/B005TVEOKW.png\", \"hn_image\": [\"./images/B002YL3YQ0.png\"]}\n{\"q_img\": \"./images/B002R7U7Q6.png\", \"q_text\": \"is more simpler graphic, and has a family crest on it\", \"positive_key\": \"B00ABBX8FW\", \"positive_value\": \"./images/B00ABBX8FW.png\", \"hn_image\": [\"./images/B002R7U7Q6.png\"]}\n{\"q_img\": \"./images/B006ZJUB8G.png\", \"q_text\": \"is a gray long sleeved striped shirt, and has longer sleeves and dark stripes\", \"positive_key\": \"B003WF2ECW\", \"positive_value\": \"./images/B003WF2ECW.png\", \"hn_image\": [\"./images/B006ZJUB8G.png\"]}\n{\"q_img\": \"./images/B0064DPOAS.png\", \"q_text\": \"The shirt is white in color., and is white with shorter sleeves\", \"positive_key\": \"B00AR1XB9O\", \"positive_value\": \"./images/B00AR1XB9O.png\", \"hn_image\": [\"./images/B0064DPOAS.png\"]}\n{\"q_img\": \"./images/B007QQVE7E.png\", \"q_text\": \"has a animated woman on it, and has large tan and red graphic\", \"positive_key\": \"B00CIA7QKW\", \"positive_value\": \"./images/B00CIA7QKW.png\", \"hn_image\": [\"./images/B007QQVE7E.png\"]}\n{\"q_img\": \"./images/B00B2HDZ98.png\", \"q_text\": \"has stripes and is darker, and is black with white stripes\", \"positive_key\": \"B00B2FIHBQ\", \"positive_value\": \"./images/B00B2FIHBQ.png\", \"hn_image\": [\"./images/B00B2HDZ98.png\"]}\n{\"q_img\": \"./images/B0085X31DC.png\", \"q_text\": \"is yellow with black words, and has a bit logo and is yellow color\", \"positive_key\": \"B00BP4RT2C\", \"positive_value\": \"./images/B00BP4RT2C.png\", \"hn_image\": [\"./images/B0085X31DC.png\"]}\n{\"q_img\": \"./images/B00E3QSK2W.png\", \"q_text\": \"is and orange, and red plaid shirt\", \"positive_key\": \"B006G7OC46\", \"positive_value\": \"./images/B006G7OC46.png\", \"hn_image\": [\"./images/B00E3QSK2W.png\"]}\n{\"q_img\": \"./images/B0085OX8LQ.png\", \"q_text\": \"is darker blue with no patterns, and It is more plain and more pocketed\", \"positive_key\": \"B004G09EE8\", \"positive_value\": \"./images/B004G09EE8.png\", \"hn_image\": [\"./images/B0085OX8LQ.png\"]}\n{\"q_img\": \"./images/B008RJTPT8.png\", \"q_text\": \"is grey in colour with more writing at front, and is blue with shorter sleeves\", \"positive_key\": \"B007JR83CE\", \"positive_value\": \"./images/B007JR83CE.png\", \"hn_image\": [\"./images/B008RJTPT8.png\"]}\n{\"q_img\": \"./images/B009ADCOP2.png\", \"q_text\": \"is darker, and is more beige\", \"positive_key\": \"B006ZP60QC\", \"positive_value\": \"./images/B006ZP60QC.png\", \"hn_image\": [\"./images/B009ADCOP2.png\"]}\n{\"q_img\": \"./images/B003BSOA0Y.png\", \"q_text\": \"is a tote bag with a saying on the front, and and buttons\", \"positive_key\": \"B009IQIZA4\", \"positive_value\": \"./images/B009IQIZA4.png\", \"hn_image\": [\"./images/B003BSOA0Y.png\"]}\n{\"q_img\": \"./images/B007HRO9B0.png\", \"q_text\": \"is a solid black button up with long sleeves, and has buttons and longer sleeves.\", \"positive_key\": \"B005DIJKXW\", \"positive_value\": \"./images/B005DIJKXW.png\", \"hn_image\": [\"./images/B007HRO9B0.png\"]}\n{\"q_img\": \"./images/B00C51HF46.png\", \"q_text\": \"is black, and A brown long sleeved shirt\", \"positive_key\": \"B007P885ZC\", \"positive_value\": \"./images/B007P885ZC.png\", \"hn_image\": [\"./images/B00C51HF46.png\"]}\n{\"q_img\": \"./images/B003R4ZLE6.png\", \"q_text\": \"is a black and white sneaker, and Is a tennis shoe\", \"positive_key\": \"B0007QCPPK\", \"positive_value\": \"./images/B0007QCPPK.png\", \"hn_image\": [\"./images/B003R4ZLE6.png\"]}\n{\"q_img\": \"./images/B0071BJQVU.png\", \"q_text\": \"Is darker in color., and is black\", \"positive_key\": \"B000N4TP72\", \"positive_value\": \"./images/B000N4TP72.png\", \"hn_image\": [\"./images/B0071BJQVU.png\"]}\n{\"q_img\": \"./images/B008FVT2Y6.png\", \"q_text\": \"Is a darker shirt with long sleeves., and is less formal\", \"positive_key\": \"B00E8IO0VA\", \"positive_value\": \"./images/B00E8IO0VA.png\", \"hn_image\": [\"./images/B008FVT2Y6.png\"]}\n{\"q_img\": \"./images/B00BF9FBJ0.png\", \"q_text\": \"is black color on sleeves, and has longer sleeves and smaller font\", \"positive_key\": \"B00A8OL6PG\", \"positive_value\": \"./images/B00A8OL6PG.png\", \"hn_image\": [\"./images/B00BF9FBJ0.png\"]}\n{\"q_img\": \"./images/B0051EPWF8.png\", \"q_text\": \"is more formal and more colorful, and is green with a collar\", \"positive_key\": \"B00C6A3ODM\", \"positive_value\": \"./images/B00C6A3ODM.png\", \"hn_image\": [\"./images/B0051EPWF8.png\"]}\n{\"q_img\": \"./images/B008ILTI24.png\", \"q_text\": \"has 'game of thrones' logo in center, and is large logo and less black color\", \"positive_key\": \"B00A2BD56I\", \"positive_value\": \"./images/B00A2BD56I.png\", \"hn_image\": [\"./images/B008ILTI24.png\"]}\n{\"q_img\": \"./images/B000PHI5L4.png\", \"q_text\": \"has more of a design, and is darker\", \"positive_key\": \"B0049U3VGW\", \"positive_value\": \"./images/B0049U3VGW.png\", \"hn_image\": [\"./images/B000PHI5L4.png\"]}\n{\"q_img\": \"./images/B000NBOIZE.png\", \"q_text\": \"is darker, and has a simple design\", \"positive_key\": \"B002P7569Q\", \"positive_value\": \"./images/B002P7569Q.png\", \"hn_image\": [\"./images/B000NBOIZE.png\"]}\n{\"q_img\": \"./images/B003JY6WY2.png\", \"q_text\": \"is gray colored with green and yellow design, and Is grey in color\", \"positive_key\": \"B004JHRX5A\", \"positive_value\": \"./images/B004JHRX5A.png\", \"hn_image\": [\"./images/B003JY6WY2.png\"]}\n{\"q_img\": \"./images/B001CFA16A.png\", \"q_text\": \"has different words on it, and has smaller font and less humorous.\", \"positive_key\": \"B0016KDSRU\", \"positive_value\": \"./images/B0016KDSRU.png\", \"hn_image\": [\"./images/B001CFA16A.png\"]}\n{\"q_img\": \"./images/B005XI84F2.png\", \"q_text\": \"is solid white, and has fewer stripes\", \"positive_key\": \"B00C7P5QGY\", \"positive_value\": \"./images/B00C7P5QGY.png\", \"hn_image\": [\"./images/B005XI84F2.png\"]}\n{\"q_img\": \"./images/B000BQ09GS.png\", \"q_text\": \"is professional and high quality, and It is more satin and less casual\", \"positive_key\": \"B0068UBR1W\", \"positive_value\": \"./images/B0068UBR1W.png\", \"hn_image\": [\"./images/B000BQ09GS.png\"]}\n{\"q_img\": \"./images/B0064R5VW0.png\", \"q_text\": \"has a more colorful logo, and has pink lettering\", \"positive_key\": \"B00C9RVPME\", \"positive_value\": \"./images/B00C9RVPME.png\", \"hn_image\": [\"./images/B0064R5VW0.png\"]}\n{\"q_img\": \"./images/B009F7NHDQ.png\", \"q_text\": \"is a casual short sleeved tee with a saying and graphic, and Is more wordy\", \"positive_key\": \"B00EMT9N4E\", \"positive_value\": \"./images/B00EMT9N4E.png\", \"hn_image\": [\"./images/B009F7NHDQ.png\"]}\n{\"q_img\": \"./images/B006X1BA90.png\", \"q_text\": \"The shirt is white with a gray image., and is white with gray vertical lines\", \"positive_key\": \"B002S0NVEW\", \"positive_value\": \"./images/B002S0NVEW.png\", \"hn_image\": [\"./images/B006X1BA90.png\"]}\n{\"q_img\": \"./images/B00644P0N8.png\", \"q_text\": \"has cartoons, and is darker and more graphic\", \"positive_key\": \"B00E7YEAEM\", \"positive_value\": \"./images/B00E7YEAEM.png\", \"hn_image\": [\"./images/B00644P0N8.png\"]}\n{\"q_img\": \"./images/B0036XODB0.png\", \"q_text\": \"is a pink polo style shirt, and is pink and has two buttons\", \"positive_key\": \"B00743YLR4\", \"positive_value\": \"./images/B00743YLR4.png\", \"hn_image\": [\"./images/B0036XODB0.png\"]}\n{\"q_img\": \"./images/B004JZY5H6.png\", \"q_text\": \"is blue in color, and is blue and has a different graphic\", \"positive_key\": \"B0030ZR9TC\", \"positive_value\": \"./images/B0030ZR9TC.png\", \"hn_image\": [\"./images/B004JZY5H6.png\"]}\n{\"q_img\": \"./images/B004FN2OYI.png\", \"q_text\": \"a light colored tee shirt with a car graphic, and is a bright color and different image.\", \"positive_key\": \"B00C865OS2\", \"positive_value\": \"./images/B00C865OS2.png\", \"hn_image\": [\"./images/B004FN2OYI.png\"]}\n{\"q_img\": \"./images/B00A9WRH00.png\", \"q_text\": \"Has longer sleeves, and is black colored and has longer sleeves\", \"positive_key\": \"B009K8BJJY\", \"positive_value\": \"./images/B009K8BJJY.png\", \"hn_image\": [\"./images/B00A9WRH00.png\"]}\n{\"q_img\": \"./images/B00AX46SOA.png\", \"q_text\": \" collar or pocket, and is a black womens shirt\", \"positive_key\": \"B00D83LFBO\", \"positive_value\": \"./images/B00D83LFBO.png\", \"hn_image\": [\"./images/B00AX46SOA.png\"]}\n{\"q_img\": \"./images/B008KAC0E6.png\", \"q_text\": \"is a tight gray sleeved shirt, and is grey with long sleeves\", \"positive_key\": \"B007POR2M8\", \"positive_value\": \"./images/B007POR2M8.png\", \"hn_image\": [\"./images/B008KAC0E6.png\"]}\n{\"q_img\": \"./images/B001MX8V8C.png\", \"q_text\": \"is a tighter shirt, and has fewer words\", \"positive_key\": \"B00B5TV78E\", \"positive_value\": \"./images/B00B5TV78E.png\", \"hn_image\": [\"./images/B001MX8V8C.png\"]}\n{\"q_img\": \"./images/B00CC0TY1W.png\", \"q_text\": \"is dark and no image design, and is a gray tank top\", \"positive_key\": \"B00AZLH99O\", \"positive_value\": \"./images/B00AZLH99O.png\", \"hn_image\": [\"./images/B00CC0TY1W.png\"]}\n{\"q_img\": \"./images/B0096LL2W4.png\", \"q_text\": \"has no hood and has no kangaroo pocket, and is a black tshirt with a batman symbol.\", \"positive_key\": \"B001M1JBYW\", \"positive_value\": \"./images/B001M1JBYW.png\", \"hn_image\": [\"./images/B0096LL2W4.png\"]}\n{\"q_img\": \"./images/B007KDY2GI.png\", \"q_text\": \"is darker with no pocket, and is a darker color with a color and no pocket\", \"positive_key\": \"B004U9INTW\", \"positive_value\": \"./images/B004U9INTW.png\", \"hn_image\": [\"./images/B007KDY2GI.png\"]}\n{\"q_img\": \"./images/B004GB3XJ4.png\", \"q_text\": \"is a black shirt with a smaller design, and has the graphic on the front\", \"positive_key\": \"B004QU6XU6\", \"positive_value\": \"./images/B004QU6XU6.png\", \"hn_image\": [\"./images/B004GB3XJ4.png\"]}\n{\"q_img\": \"./images/B0085X0R6Q.png\", \"q_text\": \"has pockets and is soild, and is white and short sleeves\", \"positive_key\": \"B006H4JR84\", \"positive_value\": \"./images/B006H4JR84.png\", \"hn_image\": [\"./images/B0085X0R6Q.png\"]}\n{\"q_img\": \"./images/B009OKKA5W.png\", \"q_text\": \"is white with black buttons, and is bright and has collar\", \"positive_key\": \"B0058JIFRI\", \"positive_value\": \"./images/B0058JIFRI.png\", \"hn_image\": [\"./images/B009OKKA5W.png\"]}\n{\"q_img\": \"./images/B003AQQ370.png\", \"q_text\": \"is lighter, and Desired item is neutral in color(s) and multiples\", \"positive_key\": \"B0012ECKFG\", \"positive_value\": \"./images/B0012ECKFG.png\", \"hn_image\": [\"./images/B003AQQ370.png\"]}\n{\"q_img\": \"./images/B005GL63X6.png\", \"q_text\": \"Has a more fun graphic, and has more arrows on it\", \"positive_key\": \"B00667268A\", \"positive_value\": \"./images/B00667268A.png\", \"hn_image\": [\"./images/B005GL63X6.png\"]}\n{\"q_img\": \"./images/B001YIOAOE.png\", \"q_text\": \"The shirt is black in color with Star Wars characters., and has busier looking graphics\", \"positive_key\": \"B008C72B7S\", \"positive_value\": \"./images/B008C72B7S.png\", \"hn_image\": [\"./images/B001YIOAOE.png\"]}\n{\"q_img\": \"./images/B0068549E4.png\", \"q_text\": \"has green lettering, and Has a green graphic\", \"positive_key\": \"B00DVX894M\", \"positive_value\": \"./images/B00DVX894M.png\", \"hn_image\": [\"./images/B0068549E4.png\"]}\n{\"q_img\": \"./images/B005X5L6SC.png\", \"q_text\": \"has shorter sleeves, and has horizontal stripes and shorter sleeves\", \"positive_key\": \"B00BF6L5HA\", \"positive_value\": \"./images/B00BF6L5HA.png\", \"hn_image\": [\"./images/B005X5L6SC.png\"]}\n{\"q_img\": \"./images/B002X115DK.png\", \"q_text\": \"is blue colored and plain, and and no collar\", \"positive_key\": \"B000SXEG5Y\", \"positive_value\": \"./images/B000SXEG5Y.png\", \"hn_image\": [\"./images/B002X115DK.png\"]}\n{\"q_img\": \"./images/B00AFJTU0M.png\", \"q_text\": \"has whit lettering, and has an autism graphic\", \"positive_key\": \"B00AFJQH0I\", \"positive_value\": \"./images/B00AFJQH0I.png\", \"hn_image\": [\"./images/B00AFJTU0M.png\"]}\n{\"q_img\": \"./images/B0074TVTM8.png\", \"q_text\": \"is a light yellow tee with words, and is yellow with black writing\", \"positive_key\": \"B002UKTZM2\", \"positive_value\": \"./images/B002UKTZM2.png\", \"hn_image\": [\"./images/B0074TVTM8.png\"]}\n{\"q_img\": \"./images/B0080WWFFS.png\", \"q_text\": \"is white with blue words and red designs, and Has an anti LSU image\", \"positive_key\": \"B00BSJZ7HS\", \"positive_value\": \"./images/B00BSJZ7HS.png\", \"hn_image\": [\"./images/B0080WWFFS.png\"]}\n{\"q_img\": \"./images/B00916YLW2.png\", \"q_text\": \"is black with people, and is blacker and has graphics with humans\", \"positive_key\": \"B007ZOLQC0\", \"positive_value\": \"./images/B007ZOLQC0.png\", \"hn_image\": [\"./images/B00916YLW2.png\"]}\n{\"q_img\": \"./images/B000GA5YPK.png\", \"q_text\": \"is solid black in color, and is sleeveless and black with crewneck\", \"positive_key\": \"B000MN87D2\", \"positive_value\": \"./images/B000MN87D2.png\", \"hn_image\": [\"./images/B000GA5YPK.png\"]}\n{\"q_img\": \"./images/B000TFWVQ2.png\", \"q_text\": \"has shorter sleeves and is red, and got honda?\", \"positive_key\": \"B004EPYKO4\", \"positive_value\": \"./images/B004EPYKO4.png\", \"hn_image\": [\"./images/B000TFWVQ2.png\"]}\n{\"q_img\": \"./images/B00BIFZU4C.png\", \"q_text\": \"has red and white plaid, and is collared\", \"positive_key\": \"B007CGRWWY\", \"positive_value\": \"./images/B007CGRWWY.png\", \"hn_image\": [\"./images/B00BIFZU4C.png\"]}\n{\"q_img\": \"./images/B0079K0YBO.png\", \"q_text\": \"Is grey and has a less whimsical graphic, and is lighter\", \"positive_key\": \"B00E3WVAD2\", \"positive_value\": \"./images/B00E3WVAD2.png\", \"hn_image\": [\"./images/B0079K0YBO.png\"]}\n{\"q_img\": \"./images/B004VUFJK6.png\", \"q_text\": \"is light gray with an image, and has a crewneck and large graphics\", \"positive_key\": \"B0041IVTJ8\", \"positive_value\": \"./images/B0041IVTJ8.png\", \"hn_image\": [\"./images/B004VUFJK6.png\"]}\n{\"q_img\": \"./images/B003IT639C.png\", \"q_text\": \"is more white, and is white and has clear logo\", \"positive_key\": \"B0098GE6OI\", \"positive_value\": \"./images/B0098GE6OI.png\", \"hn_image\": [\"./images/B003IT639C.png\"]}\n{\"q_img\": \"./images/B004YZINB0.png\", \"q_text\": \"is bolder and white, and Is white in color\", \"positive_key\": \"B00AZIAY1W\", \"positive_value\": \"./images/B00AZIAY1W.png\", \"hn_image\": [\"./images/B004YZINB0.png\"]}\n{\"q_img\": \"./images/B006FSM7V6.png\", \"q_text\": \"is white, and is white\", \"positive_key\": \"B002CIZP3K\", \"positive_value\": \"./images/B002CIZP3K.png\", \"hn_image\": [\"./images/B006FSM7V6.png\"]}\n{\"q_img\": \"./images/B004OYVAIY.png\", \"q_text\": \"is white, and is white with collar\", \"positive_key\": \"B007RAZYIO\", \"positive_value\": \"./images/B007RAZYIO.png\", \"hn_image\": [\"./images/B004OYVAIY.png\"]}\n{\"q_img\": \"./images/B004L6PSCO.png\", \"q_text\": \"is navy blue in color with long sleeves, and has more buttons\", \"positive_key\": \"B007PET9GA\", \"positive_value\": \"./images/B007PET9GA.png\", \"hn_image\": [\"./images/B004L6PSCO.png\"]}\n{\"q_img\": \"./images/B004N86414.png\", \"q_text\": \"is black with a yellow and white graphic, and is darker colored and has a centered logo.\", \"positive_key\": \"B005U1WRFK\", \"positive_value\": \"./images/B005U1WRFK.png\", \"hn_image\": [\"./images/B004N86414.png\"]}\n{\"q_img\": \"./images/B007KREYFI.png\", \"q_text\": \"is a military hat, and is all black with tassel on top\", \"positive_key\": \"B008FRAUFU\", \"positive_value\": \"./images/B008FRAUFU.png\", \"hn_image\": [\"./images/B007KREYFI.png\"]}\n{\"q_img\": \"./images/B002RL9T2U.png\", \"q_text\": \"has no collar and a logo, and is darker and not collared\", \"positive_key\": \"B00801Q8P2\", \"positive_value\": \"./images/B00801Q8P2.png\", \"hn_image\": [\"./images/B002RL9T2U.png\"]}\n{\"q_img\": \"./images/B006ZZV0L2.png\", \"q_text\": \"is more of a costume and is redder, and is a red costume\", \"positive_key\": \"B00BW5K82C\", \"positive_value\": \"./images/B00BW5K82C.png\", \"hn_image\": [\"./images/B006ZZV0L2.png\"]}\n{\"q_img\": \"./images/B0053DV6AW.png\", \"q_text\": \"Has more stripes and lighter color., and it is white and stripped\", \"positive_key\": \"B007Q8O26C\", \"positive_value\": \"./images/B007Q8O26C.png\", \"hn_image\": [\"./images/B0053DV6AW.png\"]}\n{\"q_img\": \"./images/B00BGVS61W.png\", \"q_text\": \"has more graphics, and is lighter\", \"positive_key\": \"B008RMD8M0\", \"positive_value\": \"./images/B008RMD8M0.png\", \"hn_image\": [\"./images/B00BGVS61W.png\"]}\n{\"q_img\": \"./images/B00BIFZU4C.png\", \"q_text\": \"is red, and is more red\", \"positive_key\": \"B00CAL68WQ\", \"positive_value\": \"./images/B00CAL68WQ.png\", \"hn_image\": [\"./images/B00BIFZU4C.png\"]}\n{\"q_img\": \"./images/B009LJPSV2.png\", \"q_text\": \"darker colored and white graphic logo, and has a bit logo and is black color\", \"positive_key\": \"B00DNQCFP6\", \"positive_value\": \"./images/B00DNQCFP6.png\", \"hn_image\": [\"./images/B009LJPSV2.png\"]}\n{\"q_img\": \"./images/B00517ABLA.png\", \"q_text\": \"is white color and blue graphic detail, and is white colored with a different graphic\", \"positive_key\": \"B00CUL3A2M\", \"positive_value\": \"./images/B00CUL3A2M.png\", \"hn_image\": [\"./images/B00517ABLA.png\"]}\n{\"q_img\": \"./images/B00DSOHOBI.png\", \"q_text\": \"is a gray long sleeved shirt, and has longer sleeves and is more formal\", \"positive_key\": \"B00AWA4RF2\", \"positive_value\": \"./images/B00AWA4RF2.png\", \"hn_image\": [\"./images/B00DSOHOBI.png\"]}\n{\"q_img\": \"./images/B001COZVCU.png\", \"q_text\": \"has a glass with wording on it, and has less words and more white\", \"positive_key\": \"B000QHEOR2\", \"positive_value\": \"./images/B000QHEOR2.png\", \"hn_image\": [\"./images/B001COZVCU.png\"]}\n{\"q_img\": \"./images/B000MYC2YG.png\", \"q_text\": \"has a brighter color., and is neon green with no picture or logo\", \"positive_key\": \"B0057XC4P4\", \"positive_value\": \"./images/B0057XC4P4.png\", \"hn_image\": [\"./images/B000MYC2YG.png\"]}\n{\"q_img\": \"./images/B001G0D1TA.png\", \"q_text\": \"is darker and has longer sleeves, and has holiday graphic and black background\", \"positive_key\": \"B00GA1Q00M\", \"positive_value\": \"./images/B00GA1Q00M.png\", \"hn_image\": [\"./images/B001G0D1TA.png\"]}\n{\"q_img\": \"./images/B00AZIB1DM.png\", \"q_text\": \"has a plaid design, and is a lighter color and has a checker pattern\", \"positive_key\": \"B008ZB50KG\", \"positive_value\": \"./images/B008ZB50KG.png\", \"hn_image\": [\"./images/B00AZIB1DM.png\"]}\n{\"q_img\": \"./images/B004MYFRVW.png\", \"q_text\": \"has larger letters, and has a larger graphic\", \"positive_key\": \"B005407FGI\", \"positive_value\": \"./images/B005407FGI.png\", \"hn_image\": [\"./images/B004MYFRVW.png\"]}\n{\"q_img\": \"./images/B005XIGH92.png\", \"q_text\": \"is black with smurf logo, and Is black with Papa Smurf on front\", \"positive_key\": \"B005XIGOL8\", \"positive_value\": \"./images/B005XIGOL8.png\", \"hn_image\": [\"./images/B005XIGH92.png\"]}\n{\"q_img\": \"./images/B001BISBCY.png\", \"q_text\": \"is grey with batman logo on it, and is gray colored\", \"positive_key\": \"B00F96M5LC\", \"positive_value\": \"./images/B00F96M5LC.png\", \"hn_image\": [\"./images/B001BISBCY.png\"]}\n{\"q_img\": \"./images/B00A9YBC0Y.png\", \"q_text\": \"is less formal with different colored stripes, and does not have a collar\", \"positive_key\": \"B007Y4P08W\", \"positive_value\": \"./images/B007Y4P08W.png\", \"hn_image\": [\"./images/B00A9YBC0Y.png\"]}\n{\"q_img\": \"./images/B009XE4MIA.png\", \"q_text\": \"is dark brown colored, and Is light brown in color\", \"positive_key\": \"B005FSOBZW\", \"positive_value\": \"./images/B005FSOBZW.png\", \"hn_image\": [\"./images/B009XE4MIA.png\"]}\n{\"q_img\": \"./images/B001G0TKF4.png\", \"q_text\": \"is written GOOMBA, and is more colorful\", \"positive_key\": \"B004PKPM2C\", \"positive_value\": \"./images/B004PKPM2C.png\", \"hn_image\": [\"./images/B001G0TKF4.png\"]}\n{\"q_img\": \"./images/B00AY2G1MU.png\", \"q_text\": \"is a short sleeve black shirt with text, and has less buttons and more text on it\", \"positive_key\": \"B003ZKB0O2\", \"positive_value\": \"./images/B003ZKB0O2.png\", \"hn_image\": [\"./images/B00AY2G1MU.png\"]}\n{\"q_img\": \"./images/B00BGVUTBC.png\", \"q_text\": \"is lighter colored, and is white with red lettering\", \"positive_key\": \"B00AYGEYL6\", \"positive_value\": \"./images/B00AYGEYL6.png\", \"hn_image\": [\"./images/B00BGVUTBC.png\"]}\n{\"q_img\": \"./images/B001S2PP5E.png\", \"q_text\": \"is a ozzy osbourne logo, and Gold Ozzy Osborne lettering with black and white picture of his face\", \"positive_key\": \"B001S2PQ0S\", \"positive_value\": \"./images/B001S2PQ0S.png\", \"hn_image\": [\"./images/B001S2PP5E.png\"]}\n{\"q_img\": \"./images/B007XX6790.png\", \"q_text\": \"is a blue t-shirt, and is a shirt\", \"positive_key\": \"B00A7DSIHM\", \"positive_value\": \"./images/B00A7DSIHM.png\", \"hn_image\": [\"./images/B007XX6790.png\"]}\n{\"q_img\": \"./images/B009B5OVU0.png\", \"q_text\": \"is short sleeves with black writing, and has more words\", \"positive_key\": \"B006CWV270\", \"positive_value\": \"./images/B006CWV270.png\", \"hn_image\": [\"./images/B009B5OVU0.png\"]}\n{\"q_img\": \"./images/B00A2URGOG.png\", \"q_text\": \"is blue colored with less text, and is long sleeve and blue\", \"positive_key\": \"B008EZTK2M\", \"positive_value\": \"./images/B008EZTK2M.png\", \"hn_image\": [\"./images/B00A2URGOG.png\"]}\n{\"q_img\": \"./images/B0033BGD0E.png\", \"q_text\": \"Is green and not striped, and is light green with a graphic\", \"positive_key\": \"B00B4YIHJ2\", \"positive_value\": \"./images/B00B4YIHJ2.png\", \"hn_image\": [\"./images/B0033BGD0E.png\"]}\n{\"q_img\": \"./images/B008E0UE6I.png\", \"q_text\": \"is plain colored and more formal, and polo shirt with collar solid colors\", \"positive_key\": \"B0076S3F88\", \"positive_value\": \"./images/B0076S3F88.png\", \"hn_image\": [\"./images/B008E0UE6I.png\"]}\n{\"q_img\": \"./images/B009279FB2.png\", \"q_text\": \"has a 'damon' graphic, and is less wordy\", \"positive_key\": \"B006RI31XW\", \"positive_value\": \"./images/B006RI31XW.png\", \"hn_image\": [\"./images/B009279FB2.png\"]}\n{\"q_img\": \"./images/B0038OVKR2.png\", \"q_text\": \"has no sleeves, and is a tank top\", \"positive_key\": \"B001MDCLH4\", \"positive_value\": \"./images/B001MDCLH4.png\", \"hn_image\": [\"./images/B0038OVKR2.png\"]}\n{\"q_img\": \"./images/B008F061QO.png\", \"q_text\": \"is more casual and has lettering, and is more bold and not collared\", \"positive_key\": \"B004RL9ZEU\", \"positive_value\": \"./images/B004RL9ZEU.png\", \"hn_image\": [\"./images/B008F061QO.png\"]}\n{\"q_img\": \"./images/B003WEQMDU.png\", \"q_text\": \"is a shirt with stripes on the sleeve, and is more dark\", \"positive_key\": \"B003WESSNM\", \"positive_value\": \"./images/B003WESSNM.png\", \"hn_image\": [\"./images/B003WEQMDU.png\"]}\n{\"q_img\": \"./images/B0017MXLL0.png\", \"q_text\": \"has more fitted sleeves and no flames on logo, and is only black with a white graphic\", \"positive_key\": \"B00687SPR4\", \"positive_value\": \"./images/B00687SPR4.png\", \"hn_image\": [\"./images/B0017MXLL0.png\"]}\n{\"q_img\": \"./images/B00BOO7OLO.png\", \"q_text\": \"is black with walking dead, and is brighter coloured\", \"positive_key\": \"B0071MR88M\", \"positive_value\": \"./images/B0071MR88M.png\", \"hn_image\": [\"./images/B00BOO7OLO.png\"]}\n{\"q_img\": \"./images/B00B3PKZUG.png\", \"q_text\": \"has long sleeves, and Is less colorful and has longer sleeves.\", \"positive_key\": \"B00B3PNS5K\", \"positive_value\": \"./images/B00B3PNS5K.png\", \"hn_image\": [\"./images/B00B3PKZUG.png\"]}\n{\"q_img\": \"./images/B00CFZQIUE.png\", \"q_text\": \"is a blue and white tank top depicting a surfer, and is cream with blue picture\", \"positive_key\": \"B00F1Z73G8\", \"positive_value\": \"./images/B00F1Z73G8.png\", \"hn_image\": [\"./images/B00CFZQIUE.png\"]}\n{\"q_img\": \"./images/B007OXCG1C.png\", \"q_text\": \"has more stripes and is less dressy, and has stripes and more purple\", \"positive_key\": \"B003ZU55K2\", \"positive_value\": \"./images/B003ZU55K2.png\", \"hn_image\": [\"./images/B007OXCG1C.png\"]}\n{\"q_img\": \"./images/B00BMR73SC.png\", \"q_text\": \"has multiple blue tones, and is three multicolored shirts\", \"positive_key\": \"B00B7E2L8W\", \"positive_value\": \"./images/B00B7E2L8W.png\", \"hn_image\": [\"./images/B00BMR73SC.png\"]}\n{\"q_img\": \"./images/B0064RL2BY.png\", \"q_text\": \"has a collar and buttons and is more green, and is lighter\", \"positive_key\": \"B00776GNBU\", \"positive_value\": \"./images/B00776GNBU.png\", \"hn_image\": [\"./images/B0064RL2BY.png\"]}\n{\"q_img\": \"./images/B006VY6A12.png\", \"q_text\": \"Is a red tee shirt with Asian characters, and is lighter\", \"positive_key\": \"B00527M7SO\", \"positive_value\": \"./images/B00527M7SO.png\", \"hn_image\": [\"./images/B006VY6A12.png\"]}\n{\"q_img\": \"./images/B008YCX6KC.png\", \"q_text\": \"is white without a collar or buttons with shorter sleeves, and is white and a t-shirt\", \"positive_key\": \"B00BJJJ6YM\", \"positive_value\": \"./images/B00BJJJ6YM.png\", \"hn_image\": [\"./images/B008YCX6KC.png\"]}\n{\"q_img\": \"./images/B00B3PNS5K.png\", \"q_text\": \"has no collar or buttons, and is a white t-shirt with tropical graphic on front\", \"positive_key\": \"B007JSCBD0\", \"positive_value\": \"./images/B007JSCBD0.png\", \"hn_image\": [\"./images/B00B3PNS5K.png\"]}\n{\"q_img\": \"./images/B00BK77UC8.png\", \"q_text\": \"is a navy blue t-shirt and has writing on it, and is darker and has shorter sleeves\", \"positive_key\": \"B00AO7K6TE\", \"positive_value\": \"./images/B00AO7K6TE.png\", \"hn_image\": [\"./images/B00BK77UC8.png\"]}\n{\"q_img\": \"./images/B00BGVS61W.png\", \"q_text\": \"is less formal with more graphics, and Black t-shirt with kitchen bloody picture in color\", \"positive_key\": \"B001M5E0BC\", \"positive_value\": \"./images/B001M5E0BC.png\", \"hn_image\": [\"./images/B00BGVS61W.png\"]}\n{\"q_img\": \"./images/B00BK77UC8.png\", \"q_text\": \"is less formal and more brighter, and is orange with black stripes\", \"positive_key\": \"B00A0M1UHA\", \"positive_value\": \"./images/B00A0M1UHA.png\", \"hn_image\": [\"./images/B00BK77UC8.png\"]}\n{\"q_img\": \"./images/B002UQJENQ.png\", \"q_text\": \"is more math oriented and less sex oriented, and is green with a triangle\", \"positive_key\": \"B0064OQR26\", \"positive_value\": \"./images/B0064OQR26.png\", \"hn_image\": [\"./images/B002UQJENQ.png\"]}\n{\"q_img\": \"./images/B00593M4M0.png\", \"q_text\": \"Has a different graphic and is darker, and is a black tee\", \"positive_key\": \"B004N1BFR4\", \"positive_value\": \"./images/B004N1BFR4.png\", \"hn_image\": [\"./images/B00593M4M0.png\"]}\n{\"q_img\": \"./images/B006IE7JLA.png\", \"q_text\": \"is more colorful and darker, and is black and red checked and is more casual\", \"positive_key\": \"B003OYJO5Q\", \"positive_value\": \"./images/B003OYJO5Q.png\", \"hn_image\": [\"./images/B006IE7JLA.png\"]}\n{\"q_img\": \"./images/B00018A8ZI.png\", \"q_text\": \"is more attractive, and A grey t-shirt with outdoors scene on front\", \"positive_key\": \"B003LLZBK4\", \"positive_value\": \"./images/B003LLZBK4.png\", \"hn_image\": [\"./images/B00018A8ZI.png\"]}\n{\"q_img\": \"./images/B00B71EDRC.png\", \"q_text\": \"is a blue long sleeved collar shirt, and is a shirt and is gray\", \"positive_key\": \"B00DQ5PBSM\", \"positive_value\": \"./images/B00DQ5PBSM.png\", \"hn_image\": [\"./images/B00B71EDRC.png\"]}\n{\"q_img\": \"./images/B008KAC0E6.png\", \"q_text\": \"is a brown t-shirt, and is a fitted\", \"positive_key\": \"B00ABU0V4Y\", \"positive_value\": \"./images/B00ABU0V4Y.png\", \"hn_image\": [\"./images/B008KAC0E6.png\"]}\n{\"q_img\": \"./images/B009VIGPC4.png\", \"q_text\": \"Is more black and blue, and is darker and shorter sleeves\", \"positive_key\": \"B00DR5TW2M\", \"positive_value\": \"./images/B00DR5TW2M.png\", \"hn_image\": [\"./images/B009VIGPC4.png\"]}\n{\"q_img\": \"./images/B0050TEWVO.png\", \"q_text\": \"has shorter sleeves and blue stripes, and is short sleeves with vertical stripes\", \"positive_key\": \"B0060H4ZWQ\", \"positive_value\": \"./images/B0060H4ZWQ.png\", \"hn_image\": [\"./images/B0050TEWVO.png\"]}\n{\"q_img\": \"./images/B009PTECLU.png\", \"q_text\": \"is white, and is a white sweater\", \"positive_key\": \"B0090OIMUW\", \"positive_value\": \"./images/B0090OIMUW.png\", \"hn_image\": [\"./images/B009PTECLU.png\"]}\n{\"q_img\": \"./images/B0052D1RXE.png\", \"q_text\": \"is black with white figures, and is black with picture\", \"positive_key\": \"B009QR8LS6\", \"positive_value\": \"./images/B009QR8LS6.png\", \"hn_image\": [\"./images/B0052D1RXE.png\"]}\n{\"q_img\": \"./images/B00C3TWRM0.png\", \"q_text\": \"is a lighter color, and is lighter\", \"positive_key\": \"B0084NYJ5C\", \"positive_value\": \"./images/B0084NYJ5C.png\", \"hn_image\": [\"./images/B00C3TWRM0.png\"]}\n{\"q_img\": \"./images/B003BWWH8M.png\", \"q_text\": \" yellow, and is a long sleeved hoodie with bigger logo\", \"positive_key\": \"B0060K7MEG\", \"positive_value\": \"./images/B0060K7MEG.png\", \"hn_image\": [\"./images/B003BWWH8M.png\"]}\n{\"q_img\": \"./images/B003YUBTBM.png\", \"q_text\": \"is a short sleeved colored button down shirt with collar, and has shorter sleeves\", \"positive_key\": \"B008VI0ZP8\", \"positive_value\": \"./images/B008VI0ZP8.png\", \"hn_image\": [\"./images/B003YUBTBM.png\"]}\n{\"q_img\": \"./images/B00AREPLXK.png\", \"q_text\": \"has strips on it and a light shade of blue, and has stripes\", \"positive_key\": \"B00AWK9TGE\", \"positive_value\": \"./images/B00AWK9TGE.png\", \"hn_image\": [\"./images/B00AREPLXK.png\"]}\n{\"q_img\": \"./images/B003EEN7MM.png\", \"q_text\": \"has shorter sleeves and text, and has shorter sleeves and more words\", \"positive_key\": \"B007ZU2UVU\", \"positive_value\": \"./images/B007ZU2UVU.png\", \"hn_image\": [\"./images/B003EEN7MM.png\"]}\n{\"q_img\": \"./images/B002BOHPNS.png\", \"q_text\": \"Longer selves and lighter colored., and is brown with longer sleeves\", \"positive_key\": \"B008AXCUF2\", \"positive_value\": \"./images/B008AXCUF2.png\", \"hn_image\": [\"./images/B002BOHPNS.png\"]}\n{\"q_img\": \"./images/B0049EODT2.png\", \"q_text\": \"is green with yellow words, and is less athletic and more humorous\", \"positive_key\": \"B00BBSZEQ0\", \"positive_value\": \"./images/B00BBSZEQ0.png\", \"hn_image\": [\"./images/B0049EODT2.png\"]}\n{\"q_img\": \"./images/B000CP9TY6.png\", \"q_text\": \"is light colored with more images, and shorter sleeved\", \"positive_key\": \"B0007R8KA8\", \"positive_value\": \"./images/B0007R8KA8.png\", \"hn_image\": [\"./images/B000CP9TY6.png\"]}\n{\"q_img\": \"./images/B00B71EDRC.png\", \"q_text\": \"Longer sleeves blue and grey check pattern with buttons, and is a button up flannel.\", \"positive_key\": \"B009DCZT8Y\", \"positive_value\": \"./images/B009DCZT8Y.png\", \"hn_image\": [\"./images/B00B71EDRC.png\"]}\n{\"q_img\": \"./images/B006QSYMFO.png\", \"q_text\": \"is a long sleeve tan brown shrit with buttons, and is solid brown\", \"positive_key\": \"B009E9J5HC\", \"positive_value\": \"./images/B009E9J5HC.png\", \"hn_image\": [\"./images/B006QSYMFO.png\"]}\n{\"q_img\": \"./images/B004XASV0O.png\", \"q_text\": \"is more bright and white colored, and has buttons\", \"positive_key\": \"B004Z5EMUK\", \"positive_value\": \"./images/B004Z5EMUK.png\", \"hn_image\": [\"./images/B004XASV0O.png\"]}\n{\"q_img\": \"./images/B00DV4AIL8.png\", \"q_text\": \"is not faded, and is darker\", \"positive_key\": \"B00BIFZU4C\", \"positive_value\": \"./images/B00BIFZU4C.png\", \"hn_image\": [\"./images/B00DV4AIL8.png\"]}\n{\"q_img\": \"./images/B004M45KQ4.png\", \"q_text\": \"is white with cartoon print, and is white with cartoon graphic\", \"positive_key\": \"B00A8RGFO0\", \"positive_value\": \"./images/B00A8RGFO0.png\", \"hn_image\": [\"./images/B004M45KQ4.png\"]}\n{\"q_img\": \"./images/B0058XVNC8.png\", \"q_text\": \"Is more grey and buttoned, and Gray solid long-sleeved buttoned down\", \"positive_key\": \"B0058Y7AYM\", \"positive_value\": \"./images/B0058Y7AYM.png\", \"hn_image\": [\"./images/B0058XVNC8.png\"]}\n{\"q_img\": \"./images/B009G0S8CM.png\", \"q_text\": \"has shorter sleeve and a plaid pattern, and is more colorful\", \"positive_key\": \"B008135X1Y\", \"positive_value\": \"./images/B008135X1Y.png\", \"hn_image\": [\"./images/B009G0S8CM.png\"]}\n{\"q_img\": \"./images/B000Q53KRE.png\", \"q_text\": \"has longer sleeves, and is a long sleeved button down shirt with pockets\", \"positive_key\": \"B004GIZYTE\", \"positive_value\": \"./images/B004GIZYTE.png\", \"hn_image\": [\"./images/B000Q53KRE.png\"]}\n{\"q_img\": \"./images/B005W44GZO.png\", \"q_text\": \"is grey, and is darker\", \"positive_key\": \"B007PZ3WIA\", \"positive_value\": \"./images/B007PZ3WIA.png\", \"hn_image\": [\"./images/B005W44GZO.png\"]}\n{\"q_img\": \"./images/B00F96M5LC.png\", \"q_text\": \"The shirt is gray in color with a classic car picture., and Is less wordy\", \"positive_key\": \"B0081TJWXI\", \"positive_value\": \"./images/B0081TJWXI.png\", \"hn_image\": [\"./images/B00F96M5LC.png\"]}\n{\"q_img\": \"./images/B0077RZYKA.png\", \"q_text\": \"Is more striped and buttoned, and has a collar and stripes\", \"positive_key\": \"B001QSG162\", \"positive_value\": \"./images/B001QSG162.png\", \"hn_image\": [\"./images/B0077RZYKA.png\"]}\n{\"q_img\": \"./images/B00D9ZW3I0.png\", \"q_text\": \"more dark and longer, and is darker\", \"positive_key\": \"B00FXJ5T22\", \"positive_value\": \"./images/B00FXJ5T22.png\", \"hn_image\": [\"./images/B00D9ZW3I0.png\"]}\n{\"q_img\": \"./images/B0085HCN82.png\", \"q_text\": \"is light blue short sleeved button down and has collar, and is darker\", \"positive_key\": \"B0085HBOPA\", \"positive_value\": \"./images/B0085HBOPA.png\", \"hn_image\": [\"./images/B0085HCN82.png\"]}\n{\"q_img\": \"./images/B00166XERS.png\", \"q_text\": \" stripes, and is less formal\", \"positive_key\": \"B008B10SOS\", \"positive_value\": \"./images/B008B10SOS.png\", \"hn_image\": [\"./images/B00166XERS.png\"]}\n{\"q_img\": \"./images/B00AHAH7B8.png\", \"q_text\": \"is darker, and is darker and more slim\", \"positive_key\": \"B0085UC7EY\", \"positive_value\": \"./images/B0085UC7EY.png\", \"hn_image\": [\"./images/B00AHAH7B8.png\"]}\n{\"q_img\": \"./images/B00066ZKS0.png\", \"q_text\": \"is yellow colored, and yellow solder stripes\", \"positive_key\": \"B003T0G1LU\", \"positive_value\": \"./images/B003T0G1LU.png\", \"hn_image\": [\"./images/B00066ZKS0.png\"]}\n{\"q_img\": \"./images/B0089ODSU8.png\", \"q_text\": \"is white and a huge image design, and is white and has graphics of people\", \"positive_key\": \"B002RV2BWA\", \"positive_value\": \"./images/B002RV2BWA.png\", \"hn_image\": [\"./images/B0089ODSU8.png\"]}\n{\"q_img\": \"./images/B003INE0Q6.png\", \"q_text\": \"is short sleeves with deers on it without collar and buttons, and is darker and has shorter sleeves\", \"positive_key\": \"B000MX1M98\", \"positive_value\": \"./images/B000MX1M98.png\", \"hn_image\": [\"./images/B003INE0Q6.png\"]}\n{\"q_img\": \"./images/B004VSE9CM.png\", \"q_text\": \"has brighter colors, and is very colorful and has a white kneeling man on it\", \"positive_key\": \"B009PPO9KI\", \"positive_value\": \"./images/B009PPO9KI.png\", \"hn_image\": [\"./images/B004VSE9CM.png\"]}\n{\"q_img\": \"./images/B000U6KLMG.png\", \"q_text\": \"Is less faded, and is darker red\", \"positive_key\": \"B005TF42KA\", \"positive_value\": \"./images/B005TF42KA.png\", \"hn_image\": [\"./images/B000U6KLMG.png\"]}\n{\"q_img\": \"./images/B007P28IKK.png\", \"q_text\": \"Is darker with a silver print., and is black\", \"positive_key\": \"B004GGUA9U\", \"positive_value\": \"./images/B004GGUA9U.png\", \"hn_image\": [\"./images/B007P28IKK.png\"]}\n{\"q_img\": \"./images/B00G6UFA94.png\", \"q_text\": \"Black and colorful graphic on front, and is darker\", \"positive_key\": \"B0096SKKJS\", \"positive_value\": \"./images/B0096SKKJS.png\", \"hn_image\": [\"./images/B00G6UFA94.png\"]}\n{\"q_img\": \"./images/B005TG0JTW.png\", \"q_text\": \"is similar, and has a more simple logo\", \"positive_key\": \"B00AHEZ29I\", \"positive_value\": \"./images/B00AHEZ29I.png\", \"hn_image\": [\"./images/B005TG0JTW.png\"]}\n{\"q_img\": \"./images/B001MIMB1A.png\", \"q_text\": \"Is a lighter color with letters on front., and is green with white text\", \"positive_key\": \"B000JOVTW0\", \"positive_value\": \"./images/B000JOVTW0.png\", \"hn_image\": [\"./images/B001MIMB1A.png\"]}\n{\"q_img\": \"./images/B00BJO34NG.png\", \"q_text\": \"Is red but otherwise identical, and is bright red and has no pockets\", \"positive_key\": \"B002GCIN54\", \"positive_value\": \"./images/B002GCIN54.png\", \"hn_image\": [\"./images/B00BJO34NG.png\"]}\n{\"q_img\": \"./images/B00318CIIA.png\", \"q_text\": \"has separate images of the band members and has a smaller image, and is darker\", \"positive_key\": \"B003LYYQF2\", \"positive_value\": \"./images/B003LYYQF2.png\", \"hn_image\": [\"./images/B00318CIIA.png\"]}\n{\"q_img\": \"./images/B002UQJENQ.png\", \"q_text\": \"is cleaner, and has a human figure and red lettering\", \"positive_key\": \"B0085VJ4Y4\", \"positive_value\": \"./images/B0085VJ4Y4.png\", \"hn_image\": [\"./images/B002UQJENQ.png\"]}\n{\"q_img\": \"./images/B000TYT49U.png\", \"q_text\": \"has shorter sleeves, and Doesn't have long sleeves\", \"positive_key\": \"B00CADKWXK\", \"positive_value\": \"./images/B00CADKWXK.png\", \"hn_image\": [\"./images/B000TYT49U.png\"]}\n{\"q_img\": \"./images/B0011MNWSI.png\", \"q_text\": \"has cartoons, and has no collar\", \"positive_key\": \"B005OCDS94\", \"positive_value\": \"./images/B005OCDS94.png\", \"hn_image\": [\"./images/B0011MNWSI.png\"]}\n{\"q_img\": \"./images/B00CH1JZ92.png\", \"q_text\": \"is black and chevy car logo, and White t-shirt with two chevy's\", \"positive_key\": \"B008HK470O\", \"positive_value\": \"./images/B008HK470O.png\", \"hn_image\": [\"./images/B00CH1JZ92.png\"]}\n{\"q_img\": \"./images/B004KV0604.png\", \"q_text\": \"is darker colored and less nostalgic, and is black\", \"positive_key\": \"B0040NB3PE\", \"positive_value\": \"./images/B0040NB3PE.png\", \"hn_image\": [\"./images/B004KV0604.png\"]}\n{\"q_img\": \"./images/B0085HBOPA.png\", \"q_text\": \"is black in color polo shirt, and is black with white stripes\", \"positive_key\": \"B00AAQTMT4\", \"positive_value\": \"./images/B00AAQTMT4.png\", \"hn_image\": [\"./images/B0085HBOPA.png\"]}\n{\"q_img\": \"./images/B004HTURAS.png\", \"q_text\": \"has longer sleeves with collar and prints, and has more red\", \"positive_key\": \"B00DS5HO0S\", \"positive_value\": \"./images/B00DS5HO0S.png\", \"hn_image\": [\"./images/B004HTURAS.png\"]}\n{\"q_img\": \"./images/B000F5LTXW.png\", \"q_text\": \"is a T shirt with a dancing tribe on it, and is lighter\", \"positive_key\": \"B000ID2PZ2\", \"positive_value\": \"./images/B000ID2PZ2.png\", \"hn_image\": [\"./images/B000F5LTXW.png\"]}\n{\"q_img\": \"./images/B00C40RA8Y.png\", \"q_text\": \"is darker, and has more stripes\", \"positive_key\": \"B00842GMLW\", \"positive_value\": \"./images/B00842GMLW.png\", \"hn_image\": [\"./images/B00C40RA8Y.png\"]}\n{\"q_img\": \"./images/B00EUEB41G.png\", \"q_text\": \"is black with a different logo, and has a different graphic\", \"positive_key\": \"B003XCKD3G\", \"positive_value\": \"./images/B003XCKD3G.png\", \"hn_image\": [\"./images/B00EUEB41G.png\"]}\n{\"q_img\": \"./images/B00AOJHF8M.png\", \"q_text\": \"The shirt is light blue in color., and is blue with shorter sleeves\", \"positive_key\": \"B00AOJG2QS\", \"positive_value\": \"./images/B00AOJG2QS.png\", \"hn_image\": [\"./images/B00AOJHF8M.png\"]}\n{\"q_img\": \"./images/B004OKFF7K.png\", \"q_text\": \"has more color, and is light colored\", \"positive_key\": \"B00774HYH4\", \"positive_value\": \"./images/B00774HYH4.png\", \"hn_image\": [\"./images/B004OKFF7K.png\"]}\n{\"q_img\": \"./images/B00C6A4LL6.png\", \"q_text\": \"has longer sleeves with stripes, and isstripped and button down\", \"positive_key\": \"B004XWMCZC\", \"positive_value\": \"./images/B004XWMCZC.png\", \"hn_image\": [\"./images/B00C6A4LL6.png\"]}\n{\"q_img\": \"./images/B00BCLMPQS.png\", \"q_text\": \"is black with gray patterns, and has short sleeve and is black color\", \"positive_key\": \"B004E5OGAM\", \"positive_value\": \"./images/B004E5OGAM.png\", \"hn_image\": [\"./images/B00BCLMPQS.png\"]}\n{\"q_img\": \"./images/B009279FB2.png\", \"q_text\": \"black tshirt is more fashionable, and is black colored and person graphic design\", \"positive_key\": \"B006RI34ZC\", \"positive_value\": \"./images/B006RI34ZC.png\", \"hn_image\": [\"./images/B009279FB2.png\"]}\n{\"q_img\": \"./images/B008135X1Y.png\", \"q_text\": \"is more ethnic, and is a patch with a red\", \"positive_key\": \"B0024YV8R4\", \"positive_value\": \"./images/B0024YV8R4.png\", \"hn_image\": [\"./images/B008135X1Y.png\"]}\n{\"q_img\": \"./images/B007JQKD7S.png\", \"q_text\": \"Black with large text, and is black with white text\", \"positive_key\": \"B00BFWJK0S\", \"positive_value\": \"./images/B00BFWJK0S.png\", \"hn_image\": [\"./images/B007JQKD7S.png\"]}\n{\"q_img\": \"./images/B009IUQ4R6.png\", \"q_text\": \"is a white polo shirt, and is light in color with a collar\", \"positive_key\": \"B0038298J6\", \"positive_value\": \"./images/B0038298J6.png\", \"hn_image\": [\"./images/B009IUQ4R6.png\"]}\n{\"q_img\": \"./images/B0013PPA8S.png\", \"q_text\": \"a shiny light colored dress shirt and tie, and n/a\", \"positive_key\": \"B0068UB0XC\", \"positive_value\": \"./images/B0068UB0XC.png\", \"hn_image\": [\"./images/B0013PPA8S.png\"]}\n{\"q_img\": \"./images/B001G0D1TA.png\", \"q_text\": \"is black with white writing, and is black with white lettering\", \"positive_key\": \"B008DXKT9S\", \"positive_value\": \"./images/B008DXKT9S.png\", \"hn_image\": [\"./images/B001G0D1TA.png\"]}\n{\"q_img\": \"./images/B008MC2GY6.png\", \"q_text\": \"is a collared shirt, and is totally plain with no picture\", \"positive_key\": \"B007E4SN6S\", \"positive_value\": \"./images/B007E4SN6S.png\", \"hn_image\": [\"./images/B008MC2GY6.png\"]}\n{\"q_img\": \"./images/B005C91FL2.png\", \"q_text\": \"is black, and is darker\", \"positive_key\": \"B005W8LMAC\", \"positive_value\": \"./images/B005W8LMAC.png\", \"hn_image\": [\"./images/B005C91FL2.png\"]}\n{\"q_img\": \"./images/B001WNSTAW.png\", \"q_text\": \"is black with solid white design, and has a white logo\", \"positive_key\": \"B0041YRX6K\", \"positive_value\": \"./images/B0041YRX6K.png\", \"hn_image\": [\"./images/B001WNSTAW.png\"]}\n{\"q_img\": \"./images/B008L40DC2.png\", \"q_text\": \"has a white bat on i t, and has less red color\", \"positive_key\": \"B006DUMY1O\", \"positive_value\": \"./images/B006DUMY1O.png\", \"hn_image\": [\"./images/B008L40DC2.png\"]}\n{\"q_img\": \"./images/B008H1MLOW.png\", \"q_text\": \"is dark blue in color with shorter sleeves, and tee shirt with v neck\", \"positive_key\": \"B008P4ZQMU\", \"positive_value\": \"./images/B008P4ZQMU.png\", \"hn_image\": [\"./images/B008H1MLOW.png\"]}\n{\"q_img\": \"./images/B00CACXSU0.png\", \"q_text\": \"is lighter, and is gray\", \"positive_key\": \"B005OJ9D32\", \"positive_value\": \"./images/B005OJ9D32.png\", \"hn_image\": [\"./images/B00CACXSU0.png\"]}\n{\"q_img\": \"./images/B004G09IXK.png\", \"q_text\": \"is sleeveless and a darker color, and has no sleeves\", \"positive_key\": \"B00842GS3O\", \"positive_value\": \"./images/B00842GS3O.png\", \"hn_image\": [\"./images/B004G09IXK.png\"]}\n{\"q_img\": \"./images/B001UFZIJ2.png\", \"q_text\": \"is darker and shorter sleeves, and has short sleeve and is dark blue\", \"positive_key\": \"B00CC3WCLI\", \"positive_value\": \"./images/B00CC3WCLI.png\", \"hn_image\": [\"./images/B001UFZIJ2.png\"]}\n{\"q_img\": \"./images/B004LYSV9I.png\", \"q_text\": \"is black with a graphic, and is black colored with a different graphic\", \"positive_key\": \"B005HF6TGW\", \"positive_value\": \"./images/B005HF6TGW.png\", \"hn_image\": [\"./images/B004LYSV9I.png\"]}\n{\"q_img\": \"./images/B007T5JNJ8.png\", \"q_text\": \"is a black and white long sleeved shirt, and has longer sleeves\", \"positive_key\": \"B0089823LY\", \"positive_value\": \"./images/B0089823LY.png\", \"hn_image\": [\"./images/B007T5JNJ8.png\"]}\n{\"q_img\": \"./images/B007X4OJCQ.png\", \"q_text\": \"Is light blue and has a Pony Up Mustang image on the front, and gray shirt with atoms in red\", \"positive_key\": \"B006AD5HJA\", \"positive_value\": \"./images/B006AD5HJA.png\", \"hn_image\": [\"./images/B007X4OJCQ.png\"]}\n{\"q_img\": \"./images/B008F9SIZ2.png\", \"q_text\": \"is darker, and is more oclorful\", \"positive_key\": \"B003RWG8KO\", \"positive_value\": \"./images/B003RWG8KO.png\", \"hn_image\": [\"./images/B008F9SIZ2.png\"]}\n{\"q_img\": \"./images/B003JY6WY2.png\", \"q_text\": \"is white and red short sleeved sport shirt with collar, and with buttons\", \"positive_key\": \"B005CT0T8C\", \"positive_value\": \"./images/B005CT0T8C.png\", \"hn_image\": [\"./images/B003JY6WY2.png\"]}\n{\"q_img\": \"./images/B0090UWAVI.png\", \"q_text\": \"is blue in color with text, and is more simple\", \"positive_key\": \"B0099RQYTQ\", \"positive_value\": \"./images/B0099RQYTQ.png\", \"hn_image\": [\"./images/B0090UWAVI.png\"]}\n{\"q_img\": \"./images/B00FV0FKXQ.png\", \"q_text\": \"Is more black and narrow, and is black with white words\", \"positive_key\": \"B00B2856XA\", \"positive_value\": \"./images/B00B2856XA.png\", \"hn_image\": [\"./images/B00FV0FKXQ.png\"]}\n{\"q_img\": \"./images/B00DAMLQKS.png\", \"q_text\": \"is black in color, and is darker\", \"positive_key\": \"B006QNBXC4\", \"positive_value\": \"./images/B006QNBXC4.png\", \"hn_image\": [\"./images/B00DAMLQKS.png\"]}\n{\"q_img\": \"./images/B008MON3NC.png\", \"q_text\": \"is a darker blue with white designs, and has no stripes and has black letters\", \"positive_key\": \"B004G7LM8C\", \"positive_value\": \"./images/B004G7LM8C.png\", \"hn_image\": [\"./images/B008MON3NC.png\"]}\n{\"q_img\": \"./images/B00256FLCO.png\", \"q_text\": \"is gray with blue stripes, and has blue stripes and longer sleeves\", \"positive_key\": \"B003C9Z1L4\", \"positive_value\": \"./images/B003C9Z1L4.png\", \"hn_image\": [\"./images/B00256FLCO.png\"]}\n{\"q_img\": \"./images/B001RXMRKU.png\", \"q_text\": \"is lighter and has a different collar, and has horizontal stripes and white background\", \"positive_key\": \"B005ESA9SG\", \"positive_value\": \"./images/B005ESA9SG.png\", \"hn_image\": [\"./images/B001RXMRKU.png\"]}\n{\"q_img\": \"./images/B0094J9WPM.png\", \"q_text\": \"has a graphic, and has an indian graphic on front\", \"positive_key\": \"B00CL4JTB4\", \"positive_value\": \"./images/B00CL4JTB4.png\", \"hn_image\": [\"./images/B0094J9WPM.png\"]}\n{\"q_img\": \"./images/B00EDKJAUE.png\", \"q_text\": \"is a grey t shirt with red lettering, and is gray and has larger text\", \"positive_key\": \"B0087RWANS\", \"positive_value\": \"./images/B0087RWANS.png\", \"hn_image\": [\"./images/B00EDKJAUE.png\"]}\n{\"q_img\": \"./images/B00BFCV6A0.png\", \"q_text\": \"is black with white words, and is black with a white logo.\", \"positive_key\": \"B003CMQITA\", \"positive_value\": \"./images/B003CMQITA.png\", \"hn_image\": [\"./images/B00BFCV6A0.png\"]}\n{\"q_img\": \"./images/B003U02384.png\", \"q_text\": \"is darker, and is gray with a soccar ball\", \"positive_key\": \"B003UHLGZI\", \"positive_value\": \"./images/B003UHLGZI.png\", \"hn_image\": [\"./images/B003U02384.png\"]}\n{\"q_img\": \"./images/B009R7SGQW.png\", \"q_text\": \"Is red and less wordy, and is red with yellow and white lettering\", \"positive_key\": \"B009H4RRGA\", \"positive_value\": \"./images/B009H4RRGA.png\", \"hn_image\": [\"./images/B009R7SGQW.png\"]}\n{\"q_img\": \"./images/B007E9RDH8.png\", \"q_text\": \"white, and is very similar\", \"positive_key\": \"B005I4MIO4\", \"positive_value\": \"./images/B005I4MIO4.png\", \"hn_image\": [\"./images/B007E9RDH8.png\"]}\n{\"q_img\": \"./images/B0032HJXKG.png\", \"q_text\": \"is light blue with shorter sleeves, and is a light blue short sleeved athletic polo\", \"positive_key\": \"B0089MW1E4\", \"positive_value\": \"./images/B0089MW1E4.png\", \"hn_image\": [\"./images/B0032HJXKG.png\"]}\n{\"q_img\": \"./images/B00CXMYX2E.png\", \"q_text\": \"is solid black with a skull in bricks, and is darker in color\", \"positive_key\": \"B00DLBISG8\", \"positive_value\": \"./images/B00DLBISG8.png\", \"hn_image\": [\"./images/B00CXMYX2E.png\"]}\n{\"q_img\": \"./images/B009IH5M86.png\", \"q_text\": \" sleeveless and more casual, and has a different graphic\", \"positive_key\": \"B003DEBW70\", \"positive_value\": \"./images/B003DEBW70.png\", \"hn_image\": [\"./images/B009IH5M86.png\"]}\n{\"q_img\": \"./images/B00CDT7FHM.png\", \"q_text\": \"is more dark and minimal, and a collar\", \"positive_key\": \"B00CKZ5XIW\", \"positive_value\": \"./images/B00CKZ5XIW.png\", \"hn_image\": [\"./images/B00CDT7FHM.png\"]}\n{\"q_img\": \"./images/B00B3PNS5K.png\", \"q_text\": \"Is more striped and has more blue, and has a zipper and stripes\", \"positive_key\": \"B0086EK3XG\", \"positive_value\": \"./images/B0086EK3XG.png\", \"hn_image\": [\"./images/B00B3PNS5K.png\"]}\n{\"q_img\": \"./images/B002U0K0XA.png\", \"q_text\": \"is lighter, and has rock-band graphic and is tee shirt\", \"positive_key\": \"B0032FPQ86\", \"positive_value\": \"./images/B0032FPQ86.png\", \"hn_image\": [\"./images/B002U0K0XA.png\"]}\n{\"q_img\": \"./images/B0084ERQFG.png\", \"q_text\": \"A lighter print, and has a light colored print\", \"positive_key\": \"B008392XJ6\", \"positive_value\": \"./images/B008392XJ6.png\", \"hn_image\": [\"./images/B0084ERQFG.png\"]}\n{\"q_img\": \"./images/B005PQ02G6.png\", \"q_text\": \", and is grey with a design on the back\", \"positive_key\": \"B008D6Q7DC\", \"positive_value\": \"./images/B008D6Q7DC.png\", \"hn_image\": [\"./images/B005PQ02G6.png\"]}\n{\"q_img\": \"./images/B005JQ2GP2.png\", \"q_text\": \"is red with a fish on it, and Desired product is pink with an orange fish\", \"positive_key\": \"B000YHF5K4\", \"positive_value\": \"./images/B000YHF5K4.png\", \"hn_image\": [\"./images/B005JQ2GP2.png\"]}\n{\"q_img\": \"./images/B003C1R750.png\", \"q_text\": \"is black with white words, and is less colorful\", \"positive_key\": \"B00BCX3WRC\", \"positive_value\": \"./images/B00BCX3WRC.png\", \"hn_image\": [\"./images/B003C1R750.png\"]}\n{\"q_img\": \"./images/B00AF3YKSA.png\", \"q_text\": \"Is brigher blue and has a full frontal logo., and is darker\", \"positive_key\": \"B007E5DGBO\", \"positive_value\": \"./images/B007E5DGBO.png\", \"hn_image\": [\"./images/B00AF3YKSA.png\"]}\n{\"q_img\": \"./images/B0087DW93S.png\", \"q_text\": \"is more graphic and less political, and is more emo\", \"positive_key\": \"B00851M3YW\", \"positive_value\": \"./images/B00851M3YW.png\", \"hn_image\": [\"./images/B0087DW93S.png\"]}\n{\"q_img\": \"./images/B0042ZB3PA.png\", \"q_text\": \"The shirt is black with Mexico and the Mexican Flag., and has a black collar on it\", \"positive_key\": \"B00479681K\", \"positive_value\": \"./images/B00479681K.png\", \"hn_image\": [\"./images/B0042ZB3PA.png\"]}\n{\"q_img\": \"./images/B005LXFXIA.png\", \"q_text\": \"is black short sleeve and more black, and has shorter sleeves\", \"positive_key\": \"B00A0XKK98\", \"positive_value\": \"./images/B00A0XKK98.png\", \"hn_image\": [\"./images/B005LXFXIA.png\"]}\n{\"q_img\": \"./images/B008VD5WE2.png\", \"q_text\": \"is more solid and less busy, and is light blue in color\", \"positive_key\": \"B004XN5L1S\", \"positive_value\": \"./images/B004XN5L1S.png\", \"hn_image\": [\"./images/B008VD5WE2.png\"]}\n{\"q_img\": \"./images/B004QZB40K.png\", \"q_text\": \"Different color and lettering, and is white and has a v-neck\", \"positive_key\": \"B0048KPZO4\", \"positive_value\": \"./images/B0048KPZO4.png\", \"hn_image\": [\"./images/B004QZB40K.png\"]}\n{\"q_img\": \"./images/B008PHCDA0.png\", \"q_text\": \"is black with writing on it, and is black with a different graphic\", \"positive_key\": \"B00DNTZ00U\", \"positive_value\": \"./images/B00DNTZ00U.png\", \"hn_image\": [\"./images/B008PHCDA0.png\"]}\n{\"q_img\": \"./images/B00C3TELTM.png\", \"q_text\": \"is black with white floral patterns, and is black and grey\", \"positive_key\": \"B00FOR1WUQ\", \"positive_value\": \"./images/B00FOR1WUQ.png\", \"hn_image\": [\"./images/B00C3TELTM.png\"]}\n{\"q_img\": \"./images/B00BGVUTBC.png\", \"q_text\": \"is darker, and is more faded\", \"positive_key\": \"B00A163QXG\", \"positive_value\": \"./images/B00A163QXG.png\", \"hn_image\": [\"./images/B00BGVUTBC.png\"]}\n{\"q_img\": \"./images/B0019IT79C.png\", \"q_text\": \"Is less wordy and has a larger graphic, and is darker in color\", \"positive_key\": \"B003OUW9KW\", \"positive_value\": \"./images/B003OUW9KW.png\", \"hn_image\": [\"./images/B0019IT79C.png\"]}\n{\"q_img\": \"./images/B004R6IQG8.png\", \"q_text\": \"is similar with different colors, and is black with blue bottom\", \"positive_key\": \"B007PVC4R4\", \"positive_value\": \"./images/B007PVC4R4.png\", \"hn_image\": [\"./images/B004R6IQG8.png\"]}\n{\"q_img\": \"./images/B004IWS9C2.png\", \"q_text\": \"is a darker color and has long sleeves, and is a grey hoodie\", \"positive_key\": \"B00CTSW4B4\", \"positive_value\": \"./images/B00CTSW4B4.png\", \"hn_image\": [\"./images/B004IWS9C2.png\"]}\n{\"q_img\": \"./images/B000VX05IS.png\", \"q_text\": \"dark colored and letter graphic, and it is black\", \"positive_key\": \"B000S6TI72\", \"positive_value\": \"./images/B000S6TI72.png\", \"hn_image\": [\"./images/B000VX05IS.png\"]}\n{\"q_img\": \"./images/B003WF2ECW.png\", \"q_text\": \"Is more blue and formal, and is lighter in color\", \"positive_key\": \"B00BXXX3PM\", \"positive_value\": \"./images/B00BXXX3PM.png\", \"hn_image\": [\"./images/B003WF2ECW.png\"]}\n{\"q_img\": \"./images/B002DQYGFE.png\", \"q_text\": \"has longer sleeves with black and green boxes, and has more buttons and longer sleeves\", \"positive_key\": \"B0088B8CM6\", \"positive_value\": \"./images/B0088B8CM6.png\", \"hn_image\": [\"./images/B002DQYGFE.png\"]}\n{\"q_img\": \"./images/B000ULWMIC.png\", \"q_text\": \"are darker colored, and Is more drab in color\", \"positive_key\": \"B00DTZAZUS\", \"positive_value\": \"./images/B00DTZAZUS.png\", \"hn_image\": [\"./images/B000ULWMIC.png\"]}\n{\"q_img\": \"./images/B0037A2O7W.png\", \"q_text\": \"has short sleeves with a big logo, and Desired is blue picturing large medical symbols\", \"positive_key\": \"B0067N9P32\", \"positive_value\": \"./images/B0067N9P32.png\", \"hn_image\": [\"./images/B0037A2O7W.png\"]}\n{\"q_img\": \"./images/B0089MUPWO.png\", \"q_text\": \"has a short sleeve gray shirt with cartoon art, and has a large graphic on it\", \"positive_key\": \"B00CS97P2C\", \"positive_value\": \"./images/B00CS97P2C.png\", \"hn_image\": [\"./images/B0089MUPWO.png\"]}\n{\"q_img\": \"./images/B001SN7QH8.png\", \"q_text\": \"is longer and has more colors, and A white shirt with yellow\", \"positive_key\": \"B000XTO4KK\", \"positive_value\": \"./images/B000XTO4KK.png\", \"hn_image\": [\"./images/B001SN7QH8.png\"]}\n{\"q_img\": \"./images/B00E5IEY3C.png\", \"q_text\": \"Is more grey and logo, and has less text and a darker grey\", \"positive_key\": \"B004HZQTXG\", \"positive_value\": \"./images/B004HZQTXG.png\", \"hn_image\": [\"./images/B00E5IEY3C.png\"]}\n{\"q_img\": \"./images/B00AEJPFL6.png\", \"q_text\": \"is brighter and less centered, and is brighter\", \"positive_key\": \"B00AEJOVYS\", \"positive_value\": \"./images/B00AEJOVYS.png\", \"hn_image\": [\"./images/B00AEJPFL6.png\"]}\n{\"q_img\": \"./images/B00A476WU6.png\", \"q_text\": \"is green, and is much darker in color\", \"positive_key\": \"B00C9VY1XU\", \"positive_value\": \"./images/B00C9VY1XU.png\", \"hn_image\": [\"./images/B00A476WU6.png\"]}\n{\"q_img\": \"./images/B000XK9KKI.png\", \"q_text\": \"has multiple pictures of a green head, and is black with green skulls\", \"positive_key\": \"B009RV4US6\", \"positive_value\": \"./images/B009RV4US6.png\", \"hn_image\": [\"./images/B000XK9KKI.png\"]}\n{\"q_img\": \"./images/B00C5KHZTM.png\", \"q_text\": \"A blue short sleeved Famous tee shirt., and Is blue rather than black.\", \"positive_key\": \"B006Y5SM2I\", \"positive_value\": \"./images/B006Y5SM2I.png\", \"hn_image\": [\"./images/B00C5KHZTM.png\"]}\n{\"q_img\": \"./images/B008QT38YW.png\", \"q_text\": \"Pink and buttons don't stand out, and A yellow long sleeved buttoned shirt\", \"positive_key\": \"B0009PRMSE\", \"positive_value\": \"./images/B0009PRMSE.png\", \"hn_image\": [\"./images/B008QT38YW.png\"]}\n{\"q_img\": \"./images/B002TSURL8.png\", \"q_text\": \"is light blue with black designs, and has longer blue sleeves and a different graphic\", \"positive_key\": \"B000GG5PDK\", \"positive_value\": \"./images/B000GG5PDK.png\", \"hn_image\": [\"./images/B002TSURL8.png\"]}\n{\"q_img\": \"./images/B00GKENI2W.png\", \"q_text\": \"is darker, and is a black Sleeping with Sirens t-shirt\", \"positive_key\": \"B00CJLMHKO\", \"positive_value\": \"./images/B00CJLMHKO.png\", \"hn_image\": [\"./images/B00GKENI2W.png\"]}\n{\"q_img\": \"./images/B005F0JPDI.png\", \"q_text\": \"is blue with logo on side, and Is darker with a smaller logo\", \"positive_key\": \"B008D8R4NC\", \"positive_value\": \"./images/B008D8R4NC.png\", \"hn_image\": [\"./images/B005F0JPDI.png\"]}\n{\"q_img\": \"./images/B005O76QW0.png\", \"q_text\": \"is a short sleeved shirt. It's half buttoned down, and has less buttons no pattern and shorter sleeves\", \"positive_key\": \"B006O69E8S\", \"positive_value\": \"./images/B006O69E8S.png\", \"hn_image\": [\"./images/B005O76QW0.png\"]}\n{\"q_img\": \"./images/B005SYP7WO.png\", \"q_text\": \"Has a picture of Captain America on it, and is black and more colorful graphics\", \"positive_key\": \"B005LW3XZ6\", \"positive_value\": \"./images/B005LW3XZ6.png\", \"hn_image\": [\"./images/B005SYP7WO.png\"]}\n{\"q_img\": \"./images/B0094D2LWE.png\", \"q_text\": \"is a long v-necked grey shirt with long sleeves, and no graphic\", \"positive_key\": \"B0044BCR2U\", \"positive_value\": \"./images/B0044BCR2U.png\", \"hn_image\": [\"./images/B0094D2LWE.png\"]}\n{\"q_img\": \"./images/B00A0NDRUC.png\", \"q_text\": \"is blue with a logo, and is darker\", \"positive_key\": \"B0048KRAWE\", \"positive_value\": \"./images/B0048KRAWE.png\", \"hn_image\": [\"./images/B00A0NDRUC.png\"]}\n{\"q_img\": \"./images/B006042IYG.png\", \"q_text\": \"is a white v neck with top pocket, and has a pocket\", \"positive_key\": \"B006OK7ZM6\", \"positive_value\": \"./images/B006OK7ZM6.png\", \"hn_image\": [\"./images/B006042IYG.png\"]}\n{\"q_img\": \"./images/B00BGKZEKY.png\", \"q_text\": \"Is more fitted and white, and is more tight fitting\", \"positive_key\": \"B00DU8PH9I\", \"positive_value\": \"./images/B00DU8PH9I.png\", \"hn_image\": [\"./images/B00BGKZEKY.png\"]}\n{\"q_img\": \"./images/B0096UD3FO.png\", \"q_text\": \"has collars and is darker, and is more formal\", \"positive_key\": \"B00C3AD6OC\", \"positive_value\": \"./images/B00C3AD6OC.png\", \"hn_image\": [\"./images/B0096UD3FO.png\"]}\n{\"q_img\": \"./images/B005G22KO6.png\", \"q_text\": \"is darker with a different print, and is brown with long sleeves\", \"positive_key\": \"B00D8V7G64\", \"positive_value\": \"./images/B00D8V7G64.png\", \"hn_image\": [\"./images/B005G22KO6.png\"]}\n{\"q_img\": \"./images/B0084YZBAI.png\", \"q_text\": \"has short sleeves, and is light grey and sleeved\", \"positive_key\": \"B005BSLCWQ\", \"positive_value\": \"./images/B005BSLCWQ.png\", \"hn_image\": [\"./images/B0084YZBAI.png\"]}\n{\"q_img\": \"./images/B00D2VC7VY.png\", \"q_text\": \"Pink and longer sleeves, and is more formal\", \"positive_key\": \"B001710FXS\", \"positive_value\": \"./images/B001710FXS.png\", \"hn_image\": [\"./images/B00D2VC7VY.png\"]}\n{\"q_img\": \"./images/B005MKSBAE.png\", \"q_text\": \"darker color, and black tee shirt with orange and blue graphics\", \"positive_key\": \"B008MT8ASA\", \"positive_value\": \"./images/B008MT8ASA.png\", \"hn_image\": [\"./images/B005MKSBAE.png\"]}\n{\"q_img\": \"./images/B00CDT7FHM.png\", \"q_text\": \"is tighter fitting, and is more of a dark gray color\", \"positive_key\": \"B004ZI7LFU\", \"positive_value\": \"./images/B004ZI7LFU.png\", \"hn_image\": [\"./images/B00CDT7FHM.png\"]}\n{\"q_img\": \"./images/B004K2NSGM.png\", \"q_text\": \"is mainly grey with big tiger face on front, and is grey with white\", \"positive_key\": \"B004U7I0SI\", \"positive_value\": \"./images/B004U7I0SI.png\", \"hn_image\": [\"./images/B004K2NSGM.png\"]}\n{\"q_img\": \"./images/B00AK46JK6.png\", \"q_text\": \"is black and white plaid with long sleeves, and has more of a plaid print\", \"positive_key\": \"B009EOL9ZI\", \"positive_value\": \"./images/B009EOL9ZI.png\", \"hn_image\": [\"./images/B00AK46JK6.png\"]}\n{\"q_img\": \"./images/B00166XERS.png\", \"q_text\": \"is dark blue, and is blue and tighter\", \"positive_key\": \"B00CN9LEQK\", \"positive_value\": \"./images/B00CN9LEQK.png\", \"hn_image\": [\"./images/B00166XERS.png\"]}\n{\"q_img\": \"./images/B007T5JNJ8.png\", \"q_text\": \"Has buttons and stripes, and Is more striped\", \"positive_key\": \"B004C43LO2\", \"positive_value\": \"./images/B004C43LO2.png\", \"hn_image\": [\"./images/B007T5JNJ8.png\"]}\n{\"q_img\": \"./images/B00854N5U0.png\", \"q_text\": \"is black with a pixel character, and is darker\", \"positive_key\": \"B009N29AO8\", \"positive_value\": \"./images/B009N29AO8.png\", \"hn_image\": [\"./images/B00854N5U0.png\"]}\n{\"q_img\": \"./images/B004OKFF7K.png\", \"q_text\": \"is beige colored, and is tan and a ferrett\", \"positive_key\": \"B0071BJQVU\", \"positive_value\": \"./images/B0071BJQVU.png\", \"hn_image\": [\"./images/B004OKFF7K.png\"]}\n{\"q_img\": \"./images/B0068VM5T4.png\", \"q_text\": \"is long sleeved, and has longer sleeves\", \"positive_key\": \"B003Y74AOI\", \"positive_value\": \"./images/B003Y74AOI.png\", \"hn_image\": [\"./images/B0068VM5T4.png\"]}\n{\"q_img\": \"./images/B00DEO35PQ.png\", \"q_text\": \"is black colored, and is black with white text\", \"positive_key\": \"B009M1QCLE\", \"positive_value\": \"./images/B009M1QCLE.png\", \"hn_image\": [\"./images/B00DEO35PQ.png\"]}\n{\"q_img\": \"./images/B009G0S8CM.png\", \"q_text\": \"is purple, and is purple and has a different collar\", \"positive_key\": \"B00BJO34NG\", \"positive_value\": \"./images/B00BJO34NG.png\", \"hn_image\": [\"./images/B009G0S8CM.png\"]}\n{\"q_img\": \"./images/B00BV2QNUM.png\", \"q_text\": \"is white with a black sleeve, and has black pocket and black sleeve\", \"positive_key\": \"B006WFFTTO\", \"positive_value\": \"./images/B006WFFTTO.png\", \"hn_image\": [\"./images/B00BV2QNUM.png\"]}\n{\"q_img\": \"./images/B000FQ9R62.png\", \"q_text\": \"has a darker plain color, and Is blue and no stripes.\", \"positive_key\": \"B000B8K9MK\", \"positive_value\": \"./images/B000B8K9MK.png\", \"hn_image\": [\"./images/B000FQ9R62.png\"]}\n{\"q_img\": \"./images/B0053OI72G.png\", \"q_text\": \"fitting sporty long sleeve shirt with a half-zipper, and is pullover and more fitted\", \"positive_key\": \"B007MD9MN4\", \"positive_value\": \"./images/B007MD9MN4.png\", \"hn_image\": [\"./images/B0053OI72G.png\"]}\n{\"q_img\": \"./images/B00BYIN01I.png\", \"q_text\": \"Is more wordy, and has less color on it\", \"positive_key\": \"B004L6ZSVU\", \"positive_value\": \"./images/B004L6ZSVU.png\", \"hn_image\": [\"./images/B00BYIN01I.png\"]}\n{\"q_img\": \"./images/B006PGW58I.png\", \"q_text\": \"is blue with white patterns, and is black with white inverted cross\", \"positive_key\": \"B007G1ZE0W\", \"positive_value\": \"./images/B007G1ZE0W.png\", \"hn_image\": [\"./images/B006PGW58I.png\"]}\n{\"q_img\": \"./images/B000OFIYP4.png\", \"q_text\": \"Is more blue and yellow, and is a blue color\", \"positive_key\": \"B000EMUCTS\", \"positive_value\": \"./images/B000EMUCTS.png\", \"hn_image\": [\"./images/B000OFIYP4.png\"]}\n{\"q_img\": \"./images/B0081GC10M.png\", \"q_text\": \"does not have words but people, and has less words on it\", \"positive_key\": \"B009BHEGLM\", \"positive_value\": \"./images/B009BHEGLM.png\", \"hn_image\": [\"./images/B0081GC10M.png\"]}\n{\"q_img\": \"./images/B00974VG2G.png\", \"q_text\": \"is blue and has short sleeves, and is plain colored\", \"positive_key\": \"B009DEHF2K\", \"positive_value\": \"./images/B009DEHF2K.png\", \"hn_image\": [\"./images/B00974VG2G.png\"]}\n{\"q_img\": \"./images/B005Y4L6KK.png\", \"q_text\": \"is darker, and is blue\", \"positive_key\": \"B00812S67S\", \"positive_value\": \"./images/B00812S67S.png\", \"hn_image\": [\"./images/B005Y4L6KK.png\"]}\n{\"q_img\": \"./images/B00CH59Q38.png\", \"q_text\": \"is black and white, and is more black and white checked\", \"positive_key\": \"B00AGEIWZK\", \"positive_value\": \"./images/B00AGEIWZK.png\", \"hn_image\": [\"./images/B00CH59Q38.png\"]}\n{\"q_img\": \"./images/B0030DG7TC.png\", \"q_text\": \"has short sleeves without collar, and It has short sleeve and has no pocket\", \"positive_key\": \"B00C2O3K72\", \"positive_value\": \"./images/B00C2O3K72.png\", \"hn_image\": [\"./images/B0030DG7TC.png\"]}\n{\"q_img\": \"./images/B00C2AXB66.png\", \"q_text\": \"is brown with white words, and is more brown with a different graphic\", \"positive_key\": \"B00A1QJ1TE\", \"positive_value\": \"./images/B00A1QJ1TE.png\", \"hn_image\": [\"./images/B00C2AXB66.png\"]}\n{\"q_img\": \"./images/B00680K0D8.png\", \"q_text\": \"is a gray long sleeved shirt, and is solid gray with rolled sleeves\", \"positive_key\": \"B0050TB14K\", \"positive_value\": \"./images/B0050TB14K.png\", \"hn_image\": [\"./images/B00680K0D8.png\"]}\n{\"q_img\": \"./images/B005AJ1Q7M.png\", \"q_text\": \"is darker and more sporty, and is black and tight\", \"positive_key\": \"B006PGE428\", \"positive_value\": \"./images/B006PGE428.png\", \"hn_image\": [\"./images/B005AJ1Q7M.png\"]}\n{\"q_img\": \"./images/B00CH1JZ92.png\", \"q_text\": \"its black with red print, and is black with red logo\", \"positive_key\": \"B006U6ENVK\", \"positive_value\": \"./images/B006U6ENVK.png\", \"hn_image\": [\"./images/B00CH1JZ92.png\"]}\n{\"q_img\": \"./images/B005173VTO.png\", \"q_text\": \"Is striped and more colorful, and is pliad and not gray\", \"positive_key\": \"B00BK77UC8\", \"positive_value\": \"./images/B00BK77UC8.png\", \"hn_image\": [\"./images/B005173VTO.png\"]}\n{\"q_img\": \"./images/B004F9QDFS.png\", \"q_text\": \"is brown in color, and is dark yellow with a different graphic\", \"positive_key\": \"B008588ZPQ\", \"positive_value\": \"./images/B008588ZPQ.png\", \"hn_image\": [\"./images/B004F9QDFS.png\"]}\n{\"q_img\": \"./images/B00FN9J26Q.png\", \"q_text\": \"Black and less neck opening, and is darker\", \"positive_key\": \"B004Y4G6XI\", \"positive_value\": \"./images/B004Y4G6XI.png\", \"hn_image\": [\"./images/B00FN9J26Q.png\"]}\n{\"q_img\": \"./images/B006YDMYCY.png\", \"q_text\": \"is a checked shirt with front pockets, and is more formal\", \"positive_key\": \"B0099RSPBG\", \"positive_value\": \"./images/B0099RSPBG.png\", \"hn_image\": [\"./images/B006YDMYCY.png\"]}\n{\"q_img\": \"./images/B001GW76D0.png\", \"q_text\": \"is more colorful and stripped, and has stripes\", \"positive_key\": \"B00DBAV1K4\", \"positive_value\": \"./images/B00DBAV1K4.png\", \"hn_image\": [\"./images/B001GW76D0.png\"]}\n{\"q_img\": \"./images/B00CH59Q38.png\", \"q_text\": \" is a shirt and has a dotted pattern, and has more white and wider checks\", \"positive_key\": \"B004AHLE0Y\", \"positive_value\": \"./images/B004AHLE0Y.png\", \"hn_image\": [\"./images/B00CH59Q38.png\"]}\n{\"q_img\": \"./images/B00750Y3DI.png\", \"q_text\": \"is a white long sleeved shirt, and is solid white in color\", \"positive_key\": \"B0083WOL16\", \"positive_value\": \"./images/B0083WOL16.png\", \"hn_image\": [\"./images/B00750Y3DI.png\"]}\n{\"q_img\": \"./images/B001PLAIAU.png\", \"q_text\": \"is green, and is more of a light green color\", \"positive_key\": \"B0084OPP72\", \"positive_value\": \"./images/B0084OPP72.png\", \"hn_image\": [\"./images/B001PLAIAU.png\"]}\n{\"q_img\": \"./images/B000RQO9S6.png\", \"q_text\": \"is made of fabric and has no studs, and is a t shirt that resembles a vest\", \"positive_key\": \"B007P88LU6\", \"positive_value\": \"./images/B007P88LU6.png\", \"hn_image\": [\"./images/B000RQO9S6.png\"]}\n{\"q_img\": \"./images/B004R9DY4E.png\", \"q_text\": \"Is brighter and yellow in color, and Yellow with words in different colors\", \"positive_key\": \"B005UZEMT0\", \"positive_value\": \"./images/B005UZEMT0.png\", \"hn_image\": [\"./images/B004R9DY4E.png\"]}\n{\"q_img\": \"./images/B003IWGZQA.png\", \"q_text\": \"has shorter sleeves, and shorter sleeved\", \"positive_key\": \"B006SHCSEU\", \"positive_value\": \"./images/B006SHCSEU.png\", \"hn_image\": [\"./images/B003IWGZQA.png\"]}\n{\"q_img\": \"./images/B00B4XUVKG.png\", \"q_text\": \"has longer sleeves and only one color, and is more formal\", \"positive_key\": \"B008L0BEAQ\", \"positive_value\": \"./images/B008L0BEAQ.png\", \"hn_image\": [\"./images/B00B4XUVKG.png\"]}\n{\"q_img\": \"./images/B001LNGY1E.png\", \"q_text\": \"has a collar and is light blue, and has buttons and a lighter color\", \"positive_key\": \"B00A81MMOS\", \"positive_value\": \"./images/B00A81MMOS.png\", \"hn_image\": [\"./images/B001LNGY1E.png\"]}\n{\"q_img\": \"./images/B008B18A8Y.png\", \"q_text\": \"is white without collar, and is white and not colored\", \"positive_key\": \"B0043EVFC6\", \"positive_value\": \"./images/B0043EVFC6.png\", \"hn_image\": [\"./images/B008B18A8Y.png\"]}\n{\"q_img\": \"./images/B0064POSPI.png\", \"q_text\": \"has a different logo and words, and has a solider's silhoutte\", \"positive_key\": \"B003UKCZBE\", \"positive_value\": \"./images/B003UKCZBE.png\", \"hn_image\": [\"./images/B0064POSPI.png\"]}\n{\"q_img\": \"./images/B004YOAT9A.png\", \"q_text\": \"is white in color, and is white with different colored lettering\", \"positive_key\": \"B0048KPWFG\", \"positive_value\": \"./images/B0048KPWFG.png\", \"hn_image\": [\"./images/B004YOAT9A.png\"]}\n{\"q_img\": \"./images/B0081D7EQQ.png\", \"q_text\": \"is black with colorful words, and has rainbow text instead of white\", \"positive_key\": \"B007F85FQO\", \"positive_value\": \"./images/B007F85FQO.png\", \"hn_image\": [\"./images/B0081D7EQQ.png\"]}\n{\"q_img\": \"./images/B005TON8VA.png\", \"q_text\": \"is lighter, and has an airplane on it\", \"positive_key\": \"B004XVTPJO\", \"positive_value\": \"./images/B004XVTPJO.png\", \"hn_image\": [\"./images/B005TON8VA.png\"]}\n{\"q_img\": \"./images/B0085709D8.png\", \"q_text\": \"Is not as colorful and is blue, and is a lighter shirt\", \"positive_key\": \"B0094S1AG2\", \"positive_value\": \"./images/B0094S1AG2.png\", \"hn_image\": [\"./images/B0085709D8.png\"]}\n{\"q_img\": \"./images/B00C3TJJ9E.png\", \"q_text\": \"is navy blue with an orange pocket, and has no collar\", \"positive_key\": \"B007ETIW0U\", \"positive_value\": \"./images/B007ETIW0U.png\", \"hn_image\": [\"./images/B00C3TJJ9E.png\"]}\n{\"q_img\": \"./images/B0036SQCKK.png\", \"q_text\": \"is brighter and more colorful, and is more red\", \"positive_key\": \"B003ZHBS0Q\", \"positive_value\": \"./images/B003ZHBS0Q.png\", \"hn_image\": [\"./images/B0036SQCKK.png\"]}\n{\"q_img\": \"./images/B009QCMOO8.png\", \"q_text\": \"has a larger logo, and is less faded\", \"positive_key\": \"B005IYZ57Q\", \"positive_value\": \"./images/B005IYZ57Q.png\", \"hn_image\": [\"./images/B009QCMOO8.png\"]}\n{\"q_img\": \"./images/B009BDY8AA.png\", \"q_text\": \"Is a brighter color and button down., and It is less casual and less design\", \"positive_key\": \"B00BIXSATQ\", \"positive_value\": \"./images/B00BIXSATQ.png\", \"hn_image\": [\"./images/B009BDY8AA.png\"]}\n{\"q_img\": \"./images/B007VHEYAM.png\", \"q_text\": \"is a white tshirt with emoji es on it, and is not formal\", \"positive_key\": \"B0009ALW8U\", \"positive_value\": \"./images/B0009ALW8U.png\", \"hn_image\": [\"./images/B007VHEYAM.png\"]}\n{\"q_img\": \"./images/B00A87A77Q.png\", \"q_text\": \"is tropical with buttons and a collar, and is button up and more floral\", \"positive_key\": \"B004LETUB6\", \"positive_value\": \"./images/B004LETUB6.png\", \"hn_image\": [\"./images/B00A87A77Q.png\"]}\n{\"q_img\": \"./images/B005R961PM.png\", \"q_text\": \"Is darker in color., and has a bigger graphic and is much darker in colour\", \"positive_key\": \"B00BXAFQ3W\", \"positive_value\": \"./images/B00BXAFQ3W.png\", \"hn_image\": [\"./images/B005R961PM.png\"]}\n{\"q_img\": \"./images/B008JY0X4W.png\", \"q_text\": \"The shirt is beige in color., and is tan and more t-shirt style\", \"positive_key\": \"B004ORSH1E\", \"positive_value\": \"./images/B004ORSH1E.png\", \"hn_image\": [\"./images/B008JY0X4W.png\"]}\n{\"q_img\": \"./images/B004YWBYNW.png\", \"q_text\": \"is white and looks looser, and is lighter\", \"positive_key\": \"B008TQRQCM\", \"positive_value\": \"./images/B008TQRQCM.png\", \"hn_image\": [\"./images/B004YWBYNW.png\"]}\n{\"q_img\": \"./images/B009SNIEGW.png\", \"q_text\": \"is more of a muscle shirt with skull, and has no sleeves\", \"positive_key\": \"B00ASK9HGA\", \"positive_value\": \"./images/B00ASK9HGA.png\", \"hn_image\": [\"./images/B009SNIEGW.png\"]}\n{\"q_img\": \"./images/B00AKYDNFA.png\", \"q_text\": \"is a blue T shirt that says super dad, and says super dad\", \"positive_key\": \"B0083I6W08\", \"positive_value\": \"./images/B0083I6W08.png\", \"hn_image\": [\"./images/B00AKYDNFA.png\"]}\n{\"q_img\": \"./images/B005G4XMFK.png\", \"q_text\": \"Is more monochromatic and grey, and solid colored no shoulder straps\", \"positive_key\": \"B0095BJB1Y\", \"positive_value\": \"./images/B0095BJB1Y.png\", \"hn_image\": [\"./images/B005G4XMFK.png\"]}\n{\"q_img\": \"./images/B00BJXOE36.png\", \"q_text\": \"is darker and has a more prominent design, and is black with an eagle on it\", \"positive_key\": \"B004QQJAF0\", \"positive_value\": \"./images/B004QQJAF0.png\", \"hn_image\": [\"./images/B00BJXOE36.png\"]}\n{\"q_img\": \"./images/B008MATC54.png\", \"q_text\": \"is a black short sleeve tshirt full white logo, and is a black tshirt with a white design covering it\", \"positive_key\": \"B0096A1FVS\", \"positive_value\": \"./images/B0096A1FVS.png\", \"hn_image\": [\"./images/B008MATC54.png\"]}\n{\"q_img\": \"./images/B0042RUEJY.png\", \"q_text\": \"is black with white and green words, and is black with shorter sleeves\", \"positive_key\": \"B002MA2CMA\", \"positive_value\": \"./images/B002MA2CMA.png\", \"hn_image\": [\"./images/B0042RUEJY.png\"]}\n{\"q_img\": \"./images/B009WVHAXI.png\", \"q_text\": \"Has stripes and button down with pocket on front., and is a button down with longer sleeves\", \"positive_key\": \"B000FQ9R62\", \"positive_value\": \"./images/B000FQ9R62.png\", \"hn_image\": [\"./images/B009WVHAXI.png\"]}\n{\"q_img\": \"./images/B003QAKA2Y.png\", \"q_text\": \"is black with horses on it, and is black colored and has a different graphic\", \"positive_key\": \"B000F49R5A\", \"positive_value\": \"./images/B000F49R5A.png\", \"hn_image\": [\"./images/B003QAKA2Y.png\"]}\n{\"q_img\": \"./images/B004VPW6HU.png\", \"q_text\": \"is lighter, and has a lighter graphic design\", \"positive_key\": \"B004VPW5Y4\", \"positive_value\": \"./images/B004VPW5Y4.png\", \"hn_image\": [\"./images/B004VPW6HU.png\"]}\n{\"q_img\": \"./images/B0026HE9CU.png\", \"q_text\": \"is blue and has sleeves, and is a gray graphic tee\", \"positive_key\": \"B007U24CJ6\", \"positive_value\": \"./images/B007U24CJ6.png\", \"hn_image\": [\"./images/B0026HE9CU.png\"]}\n{\"q_img\": \"./images/B00CB7F3E8.png\", \"q_text\": \"has more patterns, and is darker in color and has more stripes\", \"positive_key\": \"B008R7FA3K\", \"positive_value\": \"./images/B008R7FA3K.png\", \"hn_image\": [\"./images/B00CB7F3E8.png\"]}\n{\"q_img\": \"./images/B004U9F624.png\", \"q_text\": \"has longer sleeves and blue in the plaid, and Is blue in color\", \"positive_key\": \"B003NUSAYC\", \"positive_value\": \"./images/B003NUSAYC.png\", \"hn_image\": [\"./images/B004U9F624.png\"]}\n{\"q_img\": \"./images/B00AGEIWZK.png\", \"q_text\": \"is a blue T shirt with a Mega cartoon on it, and A blue shirt with yellow/red print on front\", \"positive_key\": \"B005KWPGNE\", \"positive_value\": \"./images/B005KWPGNE.png\", \"hn_image\": [\"./images/B00AGEIWZK.png\"]}\n{\"q_img\": \"./images/B0083KGFLC.png\", \"q_text\": \" short sleeved with graphics, and is a bracelet and not a shirt.\", \"positive_key\": \"B005F4O56G\", \"positive_value\": \"./images/B005F4O56G.png\", \"hn_image\": [\"./images/B0083KGFLC.png\"]}\n{\"q_img\": \"./images/B0042P6BVG.png\", \"q_text\": \"is solid black, and is more formal\", \"positive_key\": \"B00D78EKUS\", \"positive_value\": \"./images/B00D78EKUS.png\", \"hn_image\": [\"./images/B0042P6BVG.png\"]}\n" }, { "path": "research/BGE_VL/eval/data/fashioniq_toptee_corpus.jsonl", "content": "{\"content\": \"B008CG1JJ0\", \"image\": \"./images/B008CG1JJ0.png\"}\n{\"content\": \"B001AS562I\", \"image\": \"./images/B001AS562I.png\"}\n{\"content\": \"B00D2EVETW\", \"image\": \"./images/B00D2EVETW.png\"}\n{\"content\": \"B009P4JF1W\", \"image\": \"./images/B009P4JF1W.png\"}\n{\"content\": 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\"B007ZTO9HE\", \"image\": \"./images/B007ZTO9HE.png\"}\n{\"content\": \"B003AM8C1E\", \"image\": \"./images/B003AM8C1E.png\"}\n{\"content\": \"B001R9LU2U\", \"image\": \"./images/B001R9LU2U.png\"}\n{\"content\": \"B00CBU3EN2\", \"image\": \"./images/B00CBU3EN2.png\"}\n{\"content\": \"B004O54OXQ\", \"image\": \"./images/B004O54OXQ.png\"}\n{\"content\": \"B000831PT4\", \"image\": \"./images/B000831PT4.png\"}\n{\"content\": \"B00CDWRPE2\", \"image\": \"./images/B00CDWRPE2.png\"}\n{\"content\": \"B000IEK1B6\", \"image\": \"./images/B000IEK1B6.png\"}\n{\"content\": \"B008SBXUNW\", \"image\": \"./images/B008SBXUNW.png\"}\n{\"content\": \"B004KUYKGG\", \"image\": \"./images/B004KUYKGG.png\"}\n{\"content\": \"B005ETA59I\", \"image\": \"./images/B005ETA59I.png\"}\n{\"content\": \"B00846UD0Y\", \"image\": \"./images/B00846UD0Y.png\"}\n{\"content\": \"B00BIQADUW\", \"image\": \"./images/B00BIQADUW.png\"}\n{\"content\": \"B006LWH1DU\", \"image\": \"./images/B006LWH1DU.png\"}\n{\"content\": \"B00CNH9ZQS\", \"image\": \"./images/B00CNH9ZQS.png\"}\n{\"content\": \"B002IN0LK6\", \"image\": \"./images/B002IN0LK6.png\"}\n{\"content\": \"B00CQBNFZS\", \"image\": \"./images/B00CQBNFZS.png\"}\n{\"content\": \"B006W7SUXO\", \"image\": \"./images/B006W7SUXO.png\"}\n{\"content\": \"B00ESMW1DA\", \"image\": \"./images/B00ESMW1DA.png\"}\n{\"content\": \"B00BJ88CTI\", \"image\": \"./images/B00BJ88CTI.png\"}\n{\"content\": \"B00CVE03BE\", \"image\": \"./images/B00CVE03BE.png\"}\n{\"content\": \"B007E3720C\", \"image\": \"./images/B007E3720C.png\"}\n{\"content\": \"B008LZLH3K\", \"image\": \"./images/B008LZLH3K.png\"}\n{\"content\": \"B005IMGTFU\", \"image\": \"./images/B005IMGTFU.png\"}\n{\"content\": \"B00CFTC7CS\", \"image\": \"./images/B00CFTC7CS.png\"}\n{\"content\": \"B00AKHJYBY\", \"image\": \"./images/B00AKHJYBY.png\"}\n{\"content\": \"B005SSMI04\", \"image\": \"./images/B005SSMI04.png\"}\n{\"content\": \"B007X4BP50\", \"image\": \"./images/B007X4BP50.png\"}\n{\"content\": \"B00BTKWH56\", \"image\": \"./images/B00BTKWH56.png\"}\n{\"content\": \"B00CQBDKEO\", \"image\": \"./images/B00CQBDKEO.png\"}\n{\"content\": \"B006ZIOH6E\", \"image\": \"./images/B006ZIOH6E.png\"}\n{\"content\": \"B00AF2O4VO\", \"image\": \"./images/B00AF2O4VO.png\"}\n{\"content\": \"B001T92JIM\", \"image\": \"./images/B001T92JIM.png\"}\n{\"content\": \"B004070YLE\", \"image\": \"./images/B004070YLE.png\"}\n{\"content\": \"B003BHB50I\", \"image\": \"./images/B003BHB50I.png\"}\n" }, { "path": "research/BGE_VL/eval/data/fashioniq_toptee_query_val.jsonl", "content": "{\"q_img\": \"./images/B008CFZW76.png\", \"q_text\": \"is the same, and appears to be exactly the same\", \"positive_key\": \"B008CG1JJ0\", \"positive_value\": \"./images/B008CG1JJ0.png\", \"hn_image\": [\"./images/B008CFZW76.png\"]}\n{\"q_img\": \"./images/B0088WRQVS.png\", \"q_text\": \"i taank top, and has spaghetti straps\", \"positive_key\": \"B001AS562I\", \"positive_value\": \"./images/B001AS562I.png\", \"hn_image\": [\"./images/B0088WRQVS.png\"]}\n{\"q_img\": \"./images/B00BM27KWQ.png\", \"q_text\": \"is yellow with fringe, and is yellow with shorter sleeves\", \"positive_key\": \"B007TGJOMS\", \"positive_value\": \"./images/B007TGJOMS.png\", \"hn_image\": [\"./images/B00BM27KWQ.png\"]}\n{\"q_img\": \"./images/B00H03TS7Q.png\", \"q_text\": \"The shirt is black with the cat in hat., and has the Cat in the Hat on it\", \"positive_key\": \"B0085YC1G4\", \"positive_value\": \"./images/B0085YC1G4.png\", \"hn_image\": [\"./images/B00H03TS7Q.png\"]}\n{\"q_img\": \"./images/B00B02GQ1E.png\", \"q_text\": \"has longer lace sleeves in white, and Is longer sleeved\", \"positive_key\": \"B00BB1UAMU\", \"positive_value\": \"./images/B00BB1UAMU.png\", \"hn_image\": [\"./images/B00B02GQ1E.png\"]}\n{\"q_img\": \"./images/B007GO8A00.png\", \"q_text\": \"is red in color, and is red and less revealing\", \"positive_key\": \"B00CXN5WUA\", \"positive_value\": \"./images/B00CXN5WUA.png\", \"hn_image\": [\"./images/B007GO8A00.png\"]}\n{\"q_img\": \"./images/B0081XJQZI.png\", \"q_text\": \"The shirt is white with gray designs., and is long sleeved and button up\", \"positive_key\": \"B00CTA13AK\", \"positive_value\": \"./images/B00CTA13AK.png\", \"hn_image\": [\"./images/B0081XJQZI.png\"]}\n{\"q_img\": \"./images/B00DEJR2EQ.png\", \"q_text\": \"The shirt is purple with ruffles., and is purple and long sleeved\", \"positive_key\": \"B00CD23F8W\", \"positive_value\": \"./images/B00CD23F8W.png\", \"hn_image\": [\"./images/B00DEJR2EQ.png\"]}\n{\"q_img\": \"./images/B00ADM33S6.png\", \"q_text\": \"has a black color and no v-neck, and is darker and has a higher neckline\", \"positive_key\": \"B004045DEA\", \"positive_value\": \"./images/B004045DEA.png\", \"hn_image\": [\"./images/B00ADM33S6.png\"]}\n{\"q_img\": \"./images/B00EE0DYZ0.png\", \"q_text\": \"is orange with shorter sleeves, and is of orange camo pattern\", \"positive_key\": \"B001UEUTF6\", \"positive_value\": \"./images/B001UEUTF6.png\", \"hn_image\": [\"./images/B00EE0DYZ0.png\"]}\n{\"q_img\": \"./images/B00D5703AW.png\", \"q_text\": \"has longer sleeves with collar and buttons, and has long sleeves and teal\", \"positive_key\": \"B00ENUC9X4\", \"positive_value\": \"./images/B00ENUC9X4.png\", \"hn_image\": [\"./images/B00D5703AW.png\"]}\n{\"q_img\": \"./images/B007OTSRDM.png\", \"q_text\": \"is grey colored with orange sleeves, and is black with orange sleeves and different graphic\", \"positive_key\": \"B00CDVICKY\", \"positive_value\": \"./images/B00CDVICKY.png\", \"hn_image\": [\"./images/B007OTSRDM.png\"]}\n{\"q_img\": \"./images/B00BE75USU.png\", \"q_text\": \"has long sleeves, and is more yellow and red color\", \"positive_key\": \"B00D55169Q\", \"positive_value\": \"./images/B00D55169Q.png\", \"hn_image\": [\"./images/B00BE75USU.png\"]}\n{\"q_img\": \"./images/B008ZAD9A0.png\", \"q_text\": \"Has cropped sleeves and black, and is black with v-neck and shorter sleeves\", \"positive_key\": \"B00APEF5Q0\", \"positive_value\": \"./images/B00APEF5Q0.png\", \"hn_image\": [\"./images/B008ZAD9A0.png\"]}\n{\"q_img\": \"./images/B007G11JF6.png\", \"q_text\": \"The tank top has a flower pattern., and Has brighter flowers & a cinched waist\", \"positive_key\": \"B00F6EB91O\", \"positive_value\": \"./images/B00F6EB91O.png\", \"hn_image\": [\"./images/B007G11JF6.png\"]}\n{\"q_img\": \"./images/B006MHUH0I.png\", \"q_text\": \"is shorter and more bold, and is orange\", \"positive_key\": \"B00C2G46RS\", \"positive_value\": \"./images/B00C2G46RS.png\", \"hn_image\": [\"./images/B006MHUH0I.png\"]}\n{\"q_img\": \"./images/B006TCJTUK.png\", \"q_text\": \"is light green and sleeveless, and is green and has no sleeve\", \"positive_key\": \"B008SCHF7I\", \"positive_value\": \"./images/B008SCHF7I.png\", \"hn_image\": [\"./images/B006TCJTUK.png\"]}\n{\"q_img\": \"./images/B009I8DIC2.png\", \"q_text\": \"is green and has legs on it, and has more green\", \"positive_key\": \"B0068POKEI\", \"positive_value\": \"./images/B0068POKEI.png\", \"hn_image\": [\"./images/B009I8DIC2.png\"]}\n{\"q_img\": \"./images/B004D8QCN4.png\", \"q_text\": \"is navy blue with a mustang on it, and is a lighter color\", \"positive_key\": \"B004KUUK3S\", \"positive_value\": \"./images/B004KUUK3S.png\", \"hn_image\": [\"./images/B004D8QCN4.png\"]}\n{\"q_img\": \"./images/B00CN3WUP0.png\", \"q_text\": \"Is longer and more loose fitting, and is much looser and lighter colored\", \"positive_key\": \"B00BM0OYPO\", \"positive_value\": \"./images/B00BM0OYPO.png\", \"hn_image\": [\"./images/B00CN3WUP0.png\"]}\n{\"q_img\": \"./images/B006C4Y45A.png\", \"q_text\": \"is sleeveless and more grunge, and has shorter sleeves\", \"positive_key\": \"B00AJQVNJM\", \"positive_value\": \"./images/B00AJQVNJM.png\", \"hn_image\": [\"./images/B006C4Y45A.png\"]}\n{\"q_img\": \"./images/B007S3TENQ.png\", \"q_text\": \"is a short sleeve v neck, and is short sleeved and pink\", \"positive_key\": \"B0081U1TNI\", \"positive_value\": \"./images/B0081U1TNI.png\", \"hn_image\": [\"./images/B007S3TENQ.png\"]}\n{\"q_img\": \"./images/B00DLYL2ZO.png\", \"q_text\": \"has floral and shiny color, and is more multi colored\", \"positive_key\": \"B00DDXH9E6\", \"positive_value\": \"./images/B00DDXH9E6.png\", \"hn_image\": [\"./images/B00DLYL2ZO.png\"]}\n{\"q_img\": \"./images/B008A4OUC2.png\", \"q_text\": \"orange pleating in front, and is orange\", \"positive_key\": \"B004I6OAQW\", \"positive_value\": \"./images/B004I6OAQW.png\", \"hn_image\": [\"./images/B008A4OUC2.png\"]}\n{\"q_img\": \"./images/B005V9YOS4.png\", \"q_text\": \"has a long sleeve and black color, and is darker colored and has a v-neck\", \"positive_key\": \"B005EVZKJG\", \"positive_value\": \"./images/B005EVZKJG.png\", \"hn_image\": [\"./images/B005V9YOS4.png\"]}\n{\"q_img\": \"./images/B00AIZCMKI.png\", \"q_text\": \"The shirt is white and sheer., and is white and short sleeved\", \"positive_key\": \"B00BB1UC5K\", \"positive_value\": \"./images/B00BB1UC5K.png\", \"hn_image\": [\"./images/B00AIZCMKI.png\"]}\n{\"q_img\": \"./images/B004L83ZEU.png\", \"q_text\": \"The short sleeve shirt is a Ed Hardy shirt., and is darker\", \"positive_key\": \"B001NZVD4I\", \"positive_value\": \"./images/B001NZVD4I.png\", \"hn_image\": [\"./images/B004L83ZEU.png\"]}\n{\"q_img\": \"./images/B00DLAFC0O.png\", \"q_text\": \"is striped down and has wide neck, and is a light fabric in gray colors and a non button up.\", \"positive_key\": \"B007XD6ZME\", \"positive_value\": \"./images/B007XD6ZME.png\", \"hn_image\": [\"./images/B00DLAFC0O.png\"]}\n{\"q_img\": \"./images/B008WZC38C.png\", \"q_text\": \"The vest has blue and white stripes., and is much shorter\", \"positive_key\": \"B001F4A45G\", \"positive_value\": \"./images/B001F4A45G.png\", \"hn_image\": [\"./images/B008WZC38C.png\"]}\n{\"q_img\": \"./images/B00CTA3OT8.png\", \"q_text\": \"a short sleeve striped loose shirt, and has no buttons\", \"positive_key\": \"B00CTA4DSO\", \"positive_value\": \"./images/B00CTA4DSO.png\", \"hn_image\": [\"./images/B00CTA3OT8.png\"]}\n{\"q_img\": \"./images/B004KF5DSU.png\", \"q_text\": \"is blue with a different logo, and Desired product is blue and is religious\", \"positive_key\": \"B0051CMKH8\", \"positive_value\": \"./images/B0051CMKH8.png\", \"hn_image\": [\"./images/B004KF5DSU.png\"]}\n{\"q_img\": \"./images/B006E3ZMA0.png\", \"q_text\": \"The peasant shirt is blue in color., and is royal blue in color\", \"positive_key\": \"B00A13DD8M\", \"positive_value\": \"./images/B00A13DD8M.png\", \"hn_image\": [\"./images/B006E3ZMA0.png\"]}\n{\"q_img\": \"./images/B00BB9UBNU.png\", \"q_text\": \"is black and has longer sleeves, and Has longer sleeves and a tighter neckline\", \"positive_key\": \"B005LCCOI8\", \"positive_value\": \"./images/B005LCCOI8.png\", \"hn_image\": [\"./images/B00BB9UBNU.png\"]}\n{\"q_img\": \"./images/B002Q2O178.png\", \"q_text\": \"has short sleeves with colorful art, and has a solid background\", \"positive_key\": \"B008H3W3GQ\", \"positive_value\": \"./images/B008H3W3GQ.png\", \"hn_image\": [\"./images/B002Q2O178.png\"]}\n{\"q_img\": \"./images/B00BP9DXPO.png\", \"q_text\": \"is darker with no print, and white t-shirt\", \"positive_key\": \"B001PKTQSQ\", \"positive_value\": \"./images/B001PKTQSQ.png\", \"hn_image\": [\"./images/B00BP9DXPO.png\"]}\n{\"q_img\": \"./images/B00DV6C8LE.png\", \"q_text\": \"More colorful and patterned, and has more of a pattern to it\", \"positive_key\": \"B00C7CT7TO\", \"positive_value\": \"./images/B00C7CT7TO.png\", \"hn_image\": [\"./images/B00DV6C8LE.png\"]}\n{\"q_img\": \"./images/B000PZQ0MW.png\", \"q_text\": \"It is darker in color with not sleeves., and black spaghetti strap tank top\", \"positive_key\": \"B003WLL8TQ\", \"positive_value\": \"./images/B003WLL8TQ.png\", \"hn_image\": [\"./images/B000PZQ0MW.png\"]}\n{\"q_img\": \"./images/B000YYBM54.png\", \"q_text\": \"is darker and has longer sleeves, and is grey and more flowing\", \"positive_key\": \"B006PE13Y2\", \"positive_value\": \"./images/B006PE13Y2.png\", \"hn_image\": [\"./images/B000YYBM54.png\"]}\n{\"q_img\": \"./images/B000F4U6QE.png\", \"q_text\": \"is darker, and is an elegant black and gray top with embroidered neckline\", \"positive_key\": \"B00F6518JG\", \"positive_value\": \"./images/B00F6518JG.png\", \"hn_image\": [\"./images/B000F4U6QE.png\"]}\n{\"q_img\": \"./images/B001HBKGB4.png\", \"q_text\": \"has a less bright color and a different print, and is gray with bigfoot figure\", \"positive_key\": \"B0075PQCFK\", \"positive_value\": \"./images/B0075PQCFK.png\", \"hn_image\": [\"./images/B001HBKGB4.png\"]}\n{\"q_img\": \"./images/B0077497O2.png\", \"q_text\": \"The shirt is long sleeved white in color with black cuffs and a black stripe., and is a lighter color and has a gaphic\", \"positive_key\": \"B008MJAZF6\", \"positive_value\": \"./images/B008MJAZF6.png\", \"hn_image\": [\"./images/B0077497O2.png\"]}\n{\"q_img\": \"./images/B00DZP4XO6.png\", \"q_text\": \"has small neck and darker black, and has Farmall on the front\", \"positive_key\": \"B008A6VSNY\", \"positive_value\": \"./images/B008A6VSNY.png\", \"hn_image\": [\"./images/B00DZP4XO6.png\"]}\n{\"q_img\": \"./images/B00BADDV6Q.png\", \"q_text\": \"is white sleeveless with floral print, and is white with floral pattern\", \"positive_key\": \"B00EOJLM7S\", \"positive_value\": \"./images/B00EOJLM7S.png\", \"hn_image\": [\"./images/B00BADDV6Q.png\"]}\n{\"q_img\": \"./images/B005Z94IMM.png\", \"q_text\": \"is sleeveless solid black and ruffled, and is darker and has shorter sleeves\", \"positive_key\": \"B001SHNCL8\", \"positive_value\": \"./images/B001SHNCL8.png\", \"hn_image\": [\"./images/B005Z94IMM.png\"]}\n{\"q_img\": \"./images/B00BF6VD2M.png\", \"q_text\": \"is white with longer sleeves, and with v neckline\", \"positive_key\": \"B00COYDU9I\", \"positive_value\": \"./images/B00COYDU9I.png\", \"hn_image\": [\"./images/B00BF6VD2M.png\"]}\n{\"q_img\": \"./images/B007O3TPJI.png\", \"q_text\": \"has no sleeves and only black color, and Is more black and fitted.\", \"positive_key\": \"B009ZMX47A\", \"positive_value\": \"./images/B009ZMX47A.png\", \"hn_image\": [\"./images/B007O3TPJI.png\"]}\n{\"q_img\": \"./images/B0090YFTEE.png\", \"q_text\": \"has v neck and color is less blue, and is blue in color\", \"positive_key\": \"B0081OYIIM\", \"positive_value\": \"./images/B0081OYIIM.png\", \"hn_image\": [\"./images/B0090YFTEE.png\"]}\n{\"q_img\": \"./images/B007E67LT6.png\", \"q_text\": \"is black colored and less revealing, and Has a higher neckline and is less sporty\", \"positive_key\": \"B008JC4MEG\", \"positive_value\": \"./images/B008JC4MEG.png\", \"hn_image\": [\"./images/B007E67LT6.png\"]}\n{\"q_img\": \"./images/B00AK1OD66.png\", \"q_text\": \"is a short sleeve with gray and white stripes, and is pale grey and white stripes\", \"positive_key\": \"B00FHRZI58\", \"positive_value\": \"./images/B00FHRZI58.png\", \"hn_image\": [\"./images/B00AK1OD66.png\"]}\n{\"q_img\": \"./images/B007WAE516.png\", \"q_text\": \"is more colorful and tighter, and is blue\", \"positive_key\": \"B007WAU7WM\", \"positive_value\": \"./images/B007WAU7WM.png\", \"hn_image\": [\"./images/B007WAE516.png\"]}\n{\"q_img\": \"./images/B008TBS8UG.png\", \"q_text\": \"The shirt is black, and is in a floral design\", \"positive_key\": \"B0084YK36U\", \"positive_value\": \"./images/B0084YK36U.png\", \"hn_image\": [\"./images/B008TBS8UG.png\"]}\n{\"q_img\": \"./images/B00BDGQK90.png\", \"q_text\": \"is a wrapped up tube, and exposes the midriff\", \"positive_key\": \"B005U5K6XQ\", \"positive_value\": \"./images/B005U5K6XQ.png\", \"hn_image\": [\"./images/B00BDGQK90.png\"]}\n{\"q_img\": \"./images/B009NOC3TA.png\", \"q_text\": \"is pink striped color and has longer sleeves, and is pink and longer sleeve\", \"positive_key\": \"B009E8RMSC\", \"positive_value\": \"./images/B009E8RMSC.png\", \"hn_image\": [\"./images/B009NOC3TA.png\"]}\n{\"q_img\": \"./images/B00BDGQK90.png\", \"q_text\": \"is less revealing and official, and is black colored with sleeves and less revealing\", \"positive_key\": \"B008MP0KYQ\", \"positive_value\": \"./images/B008MP0KYQ.png\", \"hn_image\": [\"./images/B00BDGQK90.png\"]}\n{\"q_img\": \"./images/B00BU9F40G.png\", \"q_text\": \"is solid colored, and more femanen\", \"positive_key\": \"B00BWUL5LA\", \"positive_value\": \"./images/B00BWUL5LA.png\", \"hn_image\": [\"./images/B00BU9F40G.png\"]}\n{\"q_img\": \"./images/B005JR1KA8.png\", \"q_text\": \"is gold glitter with black top, and has longer sleeves and is tighter\", \"positive_key\": \"B009W1HUM4\", \"positive_value\": \"./images/B009W1HUM4.png\", \"hn_image\": [\"./images/B005JR1KA8.png\"]}\n{\"q_img\": \"./images/B00CQNY0EG.png\", \"q_text\": \"It is a brighter color in pink and blue., and has light sleeve blue and is pink color\", \"positive_key\": \"B007KBMC58\", \"positive_value\": \"./images/B007KBMC58.png\", \"hn_image\": [\"./images/B00CQNY0EG.png\"]}\n{\"q_img\": \"./images/B00FJF5ZEC.png\", \"q_text\": \"is sleeveless, and is black and white and sleeveless\", \"positive_key\": \"B00A8FD9G4\", \"positive_value\": \"./images/B00A8FD9G4.png\", \"hn_image\": [\"./images/B00FJF5ZEC.png\"]}\n{\"q_img\": \"./images/B00BXIPAQ2.png\", \"q_text\": \"is a solid color, and is red and has a dot pattern\", \"positive_key\": \"B000FTH9QO\", \"positive_value\": \"./images/B000FTH9QO.png\", \"hn_image\": [\"./images/B00BXIPAQ2.png\"]}\n{\"q_img\": \"./images/B009K2N8GW.png\", \"q_text\": \"is a fitted blue t shirt, and is lighter colored\", \"positive_key\": \"B0079K3RI6\", \"positive_value\": \"./images/B0079K3RI6.png\", \"hn_image\": [\"./images/B009K2N8GW.png\"]}\n{\"q_img\": \"./images/B004BZE6S2.png\", \"q_text\": \"has shorter sleeves with v-neck, and is sleeveless with a v-neck\", \"positive_key\": \"B004BZ9ECA\", \"positive_value\": \"./images/B004BZ9ECA.png\", \"hn_image\": [\"./images/B004BZE6S2.png\"]}\n{\"q_img\": \"./images/B00A0I88VU.png\", \"q_text\": \"The long sleeve shirt is red and gray in color., and it is red and has a longer sleeve\", \"positive_key\": \"B00AMYTG5E\", \"positive_value\": \"./images/B00AMYTG5E.png\", \"hn_image\": [\"./images/B00A0I88VU.png\"]}\n{\"q_img\": \"./images/B009LHR3P8.png\", \"q_text\": \"has more images and shorter sleeves, and is blue with short sleeves\", \"positive_key\": \"B007RWJT4C\", \"positive_value\": \"./images/B007RWJT4C.png\", \"hn_image\": [\"./images/B009LHR3P8.png\"]}\n{\"q_img\": \"./images/B003IM4JU4.png\", \"q_text\": \"Has longer sleeves and is shorter, and has long sleeves and is darker\", \"positive_key\": \"B00AQ46LIU\", \"positive_value\": \"./images/B00AQ46LIU.png\", \"hn_image\": [\"./images/B003IM4JU4.png\"]}\n{\"q_img\": \"./images/B003QORJKQ.png\", \"q_text\": \"The shirt is short sleeved and light green in color., and is soft green colored\", \"positive_key\": \"B004S2QS9I\", \"positive_value\": \"./images/B004S2QS9I.png\", \"hn_image\": [\"./images/B003QORJKQ.png\"]}\n{\"q_img\": \"./images/B008MAU19U.png\", \"q_text\": \"is black and has image of wolf, and Is black with starting print Those who act like sheep\", \"positive_key\": \"B00BXMUFWC\", \"positive_value\": \"./images/B00BXMUFWC.png\", \"hn_image\": [\"./images/B008MAU19U.png\"]}\n{\"q_img\": \"./images/B004NE6UZ8.png\", \"q_text\": \"is red with yellow lettering, and red different logo\", \"positive_key\": \"B009VSPPZ2\", \"positive_value\": \"./images/B009VSPPZ2.png\", \"hn_image\": [\"./images/B004NE6UZ8.png\"]}\n{\"q_img\": \"./images/B00A3MV3CO.png\", \"q_text\": \"Is more colorful and has a pattern, and is more plaid and longer sleeves\", \"positive_key\": \"B00A3MVCUC\", \"positive_value\": \"./images/B00A3MVCUC.png\", \"hn_image\": [\"./images/B00A3MV3CO.png\"]}\n{\"q_img\": \"./images/B005JWECI0.png\", \"q_text\": \"is black with short sleeves, and is darker in color\", \"positive_key\": \"B0061QIMZM\", \"positive_value\": \"./images/B0061QIMZM.png\", \"hn_image\": [\"./images/B005JWECI0.png\"]}\n{\"q_img\": \"./images/B007C3LIX6.png\", \"q_text\": \"is lighter, and is dark grey and has round neck\", \"positive_key\": \"B007RCOVUO\", \"positive_value\": \"./images/B007RCOVUO.png\", \"hn_image\": [\"./images/B007C3LIX6.png\"]}\n{\"q_img\": \"./images/B007R1QAJA.png\", \"q_text\": \"has short sleeves and is a larger fit, and is black with white logo\", \"positive_key\": \"B00BGJ4OQA\", \"positive_value\": \"./images/B00BGJ4OQA.png\", \"hn_image\": [\"./images/B007R1QAJA.png\"]}\n{\"q_img\": \"./images/B00CHYIX56.png\", \"q_text\": \"is darker and looser, and has a bigger logo and more blue in it\", \"positive_key\": \"B009S8OYTS\", \"positive_value\": \"./images/B009S8OYTS.png\", \"hn_image\": [\"./images/B00CHYIX56.png\"]}\n{\"q_img\": \"./images/B0035EPUBW.png\", \"q_text\": \"short sleeves, and is white with shorter sleeves\", \"positive_key\": \"B006YN4J2C\", \"positive_value\": \"./images/B006YN4J2C.png\", \"hn_image\": [\"./images/B0035EPUBW.png\"]}\n{\"q_img\": \"./images/B008JCFX3A.png\", \"q_text\": \"Is more satin and more chic, and is sleeveless and a solid color\", \"positive_key\": \"B006J4H356\", \"positive_value\": \"./images/B006J4H356.png\", \"hn_image\": [\"./images/B008JCFX3A.png\"]}\n{\"q_img\": \"./images/B00ALWZLS8.png\", \"q_text\": \"has shorter sleeves and a design, and has short sleeves\", \"positive_key\": \"B007RGHP1W\", \"positive_value\": \"./images/B007RGHP1W.png\", \"hn_image\": [\"./images/B00ALWZLS8.png\"]}\n{\"q_img\": \"./images/B00C8FZDCK.png\", \"q_text\": \"sleeveless, and Is more shiny and sexy\", \"positive_key\": \"B00GAMZZUM\", \"positive_value\": \"./images/B00GAMZZUM.png\", \"hn_image\": [\"./images/B00C8FZDCK.png\"]}\n{\"q_img\": \"./images/B0092TZZ1Y.png\", \"q_text\": \"is more revealing, and Is more sexy and revealing\", \"positive_key\": \"B00BC02S6Q\", \"positive_value\": \"./images/B00BC02S6Q.png\", \"hn_image\": [\"./images/B0092TZZ1Y.png\"]}\n{\"q_img\": \"./images/B0076OB20A.png\", \"q_text\": \"is solid black with a v neck, and is black and has short sleeves\", \"positive_key\": \"B002HOQ5J2\", \"positive_value\": \"./images/B002HOQ5J2.png\", \"hn_image\": [\"./images/B0076OB20A.png\"]}\n{\"q_img\": \"./images/B003FK79ZQ.png\", \"q_text\": \"is lighter in color with less stripes, and is gray without sleeves\", \"positive_key\": \"B003D8K7SQ\", \"positive_value\": \"./images/B003D8K7SQ.png\", \"hn_image\": [\"./images/B003FK79ZQ.png\"]}\n{\"q_img\": \"./images/B0092SC112.png\", \"q_text\": \"The shirt is backless and yellow and black in color., and has an open back\", \"positive_key\": \"B00857OFFG\", \"positive_value\": \"./images/B00857OFFG.png\", \"hn_image\": [\"./images/B0092SC112.png\"]}\n{\"q_img\": \"./images/B007TWL7L8.png\", \"q_text\": \"is navy blue with white words, and is darker colored\", \"positive_key\": \"B000RO0X0Q\", \"positive_value\": \"./images/B000RO0X0Q.png\", \"hn_image\": [\"./images/B007TWL7L8.png\"]}\n{\"q_img\": \"./images/B00BGV7G9A.png\", \"q_text\": \"has a shorter sleeve with different art patterns, and has 3/4 sleeves\", \"positive_key\": \"B009YJEDT2\", \"positive_value\": \"./images/B009YJEDT2.png\", \"hn_image\": [\"./images/B00BGV7G9A.png\"]}\n{\"q_img\": \"./images/B00CJGBH1Y.png\", \"q_text\": \"is yellow and has short sleeves, and is beige and shorter sleeves\", \"positive_key\": \"B009YKO0AI\", \"positive_value\": \"./images/B009YKO0AI.png\", \"hn_image\": [\"./images/B00CJGBH1Y.png\"]}\n{\"q_img\": \"./images/B008P6YR6E.png\", \"q_text\": \"has short sleeves and white shiny, and is white\", \"positive_key\": \"B00EQ2AYUO\", \"positive_value\": \"./images/B00EQ2AYUO.png\", \"hn_image\": [\"./images/B008P6YR6E.png\"]}\n{\"q_img\": \"./images/B009LLBI3C.png\", \"q_text\": \"is green colored and has shorter sleeves, and is less trendy and more junior\", \"positive_key\": \"B002EB464E\", \"positive_value\": \"./images/B002EB464E.png\", \"hn_image\": [\"./images/B009LLBI3C.png\"]}\n{\"q_img\": \"./images/B004F3NKGO.png\", \"q_text\": \"is gray with colorful prints, and Is more colorful and less formal.\", \"positive_key\": \"B00EUH9AB4\", \"positive_value\": \"./images/B00EUH9AB4.png\", \"hn_image\": [\"./images/B004F3NKGO.png\"]}\n{\"q_img\": \"./images/B009B60324.png\", \"q_text\": \"is loading, and has a bit print and is less black color\", \"positive_key\": \"B007R8V5J8\", \"positive_value\": \"./images/B007R8V5J8.png\", \"hn_image\": [\"./images/B009B60324.png\"]}\n{\"q_img\": \"./images/B00522PG28.png\", \"q_text\": \"is more flowy and darker, and is solid black in color\", \"positive_key\": \"B008WZC5EE\", \"positive_value\": \"./images/B008WZC5EE.png\", \"hn_image\": [\"./images/B00522PG28.png\"]}\n{\"q_img\": \"./images/B00CIB6S96.png\", \"q_text\": \"is a short sleeve black gothic shirt, and is darker and more scary looking\", \"positive_key\": \"B007Y2Z3FE\", \"positive_value\": \"./images/B007Y2Z3FE.png\", \"hn_image\": [\"./images/B00CIB6S96.png\"]}\n{\"q_img\": \"./images/B007HCADFQ.png\", \"q_text\": \"is a long sleeve button up animal print, and has longer sleeves and length with a different pattern\", \"positive_key\": \"B00726H3LE\", \"positive_value\": \"./images/B00726H3LE.png\", \"hn_image\": [\"./images/B007HCADFQ.png\"]}\n{\"q_img\": \"./images/B008J7WSS8.png\", \"q_text\": \"has large red and blue flower, and has flower motif in red and blue\", \"positive_key\": \"B00BQSWLC0\", \"positive_value\": \"./images/B00BQSWLC0.png\", \"hn_image\": [\"./images/B008J7WSS8.png\"]}\n{\"q_img\": \"./images/B008E093WY.png\", \"q_text\": \"The tank top is brown in color., and it is less tight and not a plain color\", \"positive_key\": \"B000E791Y0\", \"positive_value\": \"./images/B000E791Y0.png\", \"hn_image\": [\"./images/B008E093WY.png\"]}\n{\"q_img\": \"./images/B0086JXCEI.png\", \"q_text\": \"is red and orange with prints, and is red and has more embroidery\", \"positive_key\": \"B009HIQTFQ\", \"positive_value\": \"./images/B009HIQTFQ.png\", \"hn_image\": [\"./images/B0086JXCEI.png\"]}\n{\"q_img\": \"./images/B005Y8GDYK.png\", \"q_text\": \"has shorter sleeves with small hearts, and is pink with bird pattern\", \"positive_key\": \"B003DWBO7U\", \"positive_value\": \"./images/B003DWBO7U.png\", \"hn_image\": [\"./images/B005Y8GDYK.png\"]}\n{\"q_img\": \"./images/B006C2CO40.png\", \"q_text\": \"is darker and less nerdy, and is dark brown and has more print\", \"positive_key\": \"B0014E41QU\", \"positive_value\": \"./images/B0014E41QU.png\", \"hn_image\": [\"./images/B006C2CO40.png\"]}\n{\"q_img\": \"./images/B0083WYHCO.png\", \"q_text\": \"Is red with more sleeves, and has short sleeves and is entirely in red.\", \"positive_key\": \"B009R8P3I0\", \"positive_value\": \"./images/B009R8P3I0.png\", \"hn_image\": [\"./images/B0083WYHCO.png\"]}\n{\"q_img\": \"./images/B00ATF1P8M.png\", \"q_text\": \"Is purple and more plain, and is darker\", \"positive_key\": \"B005X5RMCG\", \"positive_value\": \"./images/B005X5RMCG.png\", \"hn_image\": [\"./images/B00ATF1P8M.png\"]}\n{\"q_img\": \"./images/B00DEZTDCE.png\", \"q_text\": \"is more brighter in color and has lace, and pink\", \"positive_key\": \"B00BB8VGL2\", \"positive_value\": \"./images/B00BB8VGL2.png\", \"hn_image\": [\"./images/B00DEZTDCE.png\"]}\n{\"q_img\": \"./images/B0043Y8L5K.png\", \"q_text\": \"is lighter with a different logo, and has a lighter blue\", \"positive_key\": \"B002CJ5LAQ\", \"positive_value\": \"./images/B002CJ5LAQ.png\", \"hn_image\": [\"./images/B0043Y8L5K.png\"]}\n{\"q_img\": \"./images/B008LZL9EW.png\", \"q_text\": \"has a bolder pattern and is less sophisticated, and has large round neck and is light blue color\", \"positive_key\": \"B00BQJVXEQ\", \"positive_value\": \"./images/B00BQJVXEQ.png\", \"hn_image\": [\"./images/B008LZL9EW.png\"]}\n{\"q_img\": \"./images/B008WYCFGI.png\", \"q_text\": \"is black and has short sleeves, and is grey with image\", \"positive_key\": \"B000VCJIM8\", \"positive_value\": \"./images/B000VCJIM8.png\", \"hn_image\": [\"./images/B008WYCFGI.png\"]}\n{\"q_img\": \"./images/B003TY105S.png\", \"q_text\": \"is wider, and is more wordy and less form fitting\", \"positive_key\": \"B000PAOV5A\", \"positive_value\": \"./images/B000PAOV5A.png\", \"hn_image\": [\"./images/B003TY105S.png\"]}\n{\"q_img\": \"./images/B004H3XMYM.png\", \"q_text\": \"The long sleeve shirt is pink in color., and is pink with regular sleeves\", \"positive_key\": \"B00B5SKOMU\", \"positive_value\": \"./images/B00B5SKOMU.png\", \"hn_image\": [\"./images/B004H3XMYM.png\"]}\n{\"q_img\": \"./images/B00C1180OE.png\", \"q_text\": \"is a white lacy long sleeve to the hips, and a sexy white long sleeve shirt with see through on the back.\", \"positive_key\": \"B00BCQIC1A\", \"positive_value\": \"./images/B00BCQIC1A.png\", \"hn_image\": [\"./images/B00C1180OE.png\"]}\n{\"q_img\": \"./images/B0039819TG.png\", \"q_text\": \"is monochrome, and is black and white polka dot bow tie neckline\", \"positive_key\": \"B00AIIQJ5E\", \"positive_value\": \"./images/B00AIIQJ5E.png\", \"hn_image\": [\"./images/B0039819TG.png\"]}\n{\"q_img\": \"./images/B00C0G8TI2.png\", \"q_text\": \"is white t shirt and masculine, and is white\", \"positive_key\": \"B006VQMAPU\", \"positive_value\": \"./images/B006VQMAPU.png\", \"hn_image\": [\"./images/B00C0G8TI2.png\"]}\n{\"q_img\": \"./images/B005NY5YK4.png\", \"q_text\": \"is tunic Tacy and less yellow, and is frizzy and brown and sexy\", \"positive_key\": \"B006ZIOJP8\", \"positive_value\": \"./images/B006ZIOJP8.png\", \"hn_image\": [\"./images/B005NY5YK4.png\"]}\n{\"q_img\": \"./images/B003EACZ9M.png\", \"q_text\": \"is yellow colored, and is yellow\", \"positive_key\": \"B000YVMK30\", \"positive_value\": \"./images/B000YVMK30.png\", \"hn_image\": [\"./images/B003EACZ9M.png\"]}\n{\"q_img\": \"./images/B004TSAE9Q.png\", \"q_text\": \"is purple, and has smaller round neck and is pink color\", \"positive_key\": \"B0091DQZRY\", \"positive_value\": \"./images/B0091DQZRY.png\", \"hn_image\": [\"./images/B004TSAE9Q.png\"]}\n{\"q_img\": \"./images/B007HEWEWE.png\", \"q_text\": \"Is more whimsical and colorful, and it is colorful and has a short sleeve\", \"positive_key\": \"B00DN2F6NI\", \"positive_value\": \"./images/B00DN2F6NI.png\", \"hn_image\": [\"./images/B007HEWEWE.png\"]}\n{\"q_img\": \"./images/B00C67YB50.png\", \"q_text\": \"is yellow and has shorter sleeves, and is brighter in color\", \"positive_key\": \"B0079G1DI6\", \"positive_value\": \"./images/B0079G1DI6.png\", \"hn_image\": [\"./images/B00C67YB50.png\"]}\n{\"q_img\": \"./images/B007GO8A00.png\", \"q_text\": \"has long sleeves, and has long sleeves and a blue fabric decorating the midriff.\", \"positive_key\": \"B0051SQTVK\", \"positive_value\": \"./images/B0051SQTVK.png\", \"hn_image\": [\"./images/B007GO8A00.png\"]}\n{\"q_img\": \"./images/B00BUD3KMQ.png\", \"q_text\": \"has short sleeves with white lettering, and is less revealing\", \"positive_key\": \"B00A2WJ3EA\", \"positive_value\": \"./images/B00A2WJ3EA.png\", \"hn_image\": [\"./images/B00BUD3KMQ.png\"]}\n{\"q_img\": \"./images/B006MQ5XBW.png\", \"q_text\": \"is orange and has long sleeves, and white graphic\", \"positive_key\": \"B00BNFNJOA\", \"positive_value\": \"./images/B00BNFNJOA.png\", \"hn_image\": [\"./images/B006MQ5XBW.png\"]}\n{\"q_img\": \"./images/B00C64M1BY.png\", \"q_text\": \"is sleeveless and pink, and Is shorter and more beachy\", \"positive_key\": \"B007ZQ59SK\", \"positive_value\": \"./images/B007ZQ59SK.png\", \"hn_image\": [\"./images/B00C64M1BY.png\"]}\n{\"q_img\": \"./images/B00AEYP3WC.png\", \"q_text\": \"is back striped and black shoulder up, and is lighter and has longer sleeves\", \"positive_key\": \"B00AX353WY\", \"positive_value\": \"./images/B00AX353WY.png\", \"hn_image\": [\"./images/B00AEYP3WC.png\"]}\n{\"q_img\": \"./images/B001BISBCY.png\", \"q_text\": \"The shirt is white with black writing., and is a white crew neck with black lettering.\", \"positive_key\": \"B00AZ01658\", \"positive_value\": \"./images/B00AZ01658.png\", \"hn_image\": [\"./images/B001BISBCY.png\"]}\n{\"q_img\": \"./images/B00E9OXH4Y.png\", \"q_text\": \"The shirt has short sleeves is tight and blue in color., and Dark blue solid\", \"positive_key\": \"B00AK1OD66\", \"positive_value\": \"./images/B00AK1OD66.png\", \"hn_image\": [\"./images/B00E9OXH4Y.png\"]}\n{\"q_img\": \"./images/B006M6B9ME.png\", \"q_text\": \"is more of a tank top with less floral pattern, and is dark pink and has smaller v-neck\", \"positive_key\": \"B00CWZT0NY\", \"positive_value\": \"./images/B00CWZT0NY.png\", \"hn_image\": [\"./images/B006M6B9ME.png\"]}\n{\"q_img\": \"./images/B009F1HC8I.png\", \"q_text\": \"The shirt has cut outs in the back and is black in color., and has a zipper back and with no sleeves.\", \"positive_key\": \"B00BFA8AK6\", \"positive_value\": \"./images/B00BFA8AK6.png\", \"hn_image\": [\"./images/B009F1HC8I.png\"]}\n{\"q_img\": \"./images/B000W9XX8U.png\", \"q_text\": \"The shirt is black with a count down clock., and is darker coloured\", \"positive_key\": \"B009B0K0KU\", \"positive_value\": \"./images/B009B0K0KU.png\", \"hn_image\": [\"./images/B000W9XX8U.png\"]}\n{\"q_img\": \"./images/B00AX2S3TU.png\", \"q_text\": \"is darker, and Has narrower straps and is more grungy\", \"positive_key\": \"B00AN545PI\", \"positive_value\": \"./images/B00AN545PI.png\", \"hn_image\": [\"./images/B00AX2S3TU.png\"]}\n{\"q_img\": \"./images/B005X6RYYQ.png\", \"q_text\": \"is brighter, and is yellow in color\", \"positive_key\": \"B007TT9MSQ\", \"positive_value\": \"./images/B007TT9MSQ.png\", \"hn_image\": [\"./images/B005X6RYYQ.png\"]}\n{\"q_img\": \"./images/B007Z4WRC8.png\", \"q_text\": \"is brighter in color, and is lighter and masculine\", \"positive_key\": \"B00ER9PWY4\", \"positive_value\": \"./images/B00ER9PWY4.png\", \"hn_image\": [\"./images/B007Z4WRC8.png\"]}\n{\"q_img\": \"./images/B00CTA3OT8.png\", \"q_text\": \"has shorter sleeves and is solid white, and cream with shorter sleeves\", \"positive_key\": \"B00767NH1Y\", \"positive_value\": \"./images/B00767NH1Y.png\", \"hn_image\": [\"./images/B00CTA3OT8.png\"]}\n{\"q_img\": \"./images/B007W9ZJK8.png\", \"q_text\": \"The shirt is loose fitting and brown in color., and is a brown color\", \"positive_key\": \"B00BGHR3XI\", \"positive_value\": \"./images/B00BGHR3XI.png\", \"hn_image\": [\"./images/B007W9ZJK8.png\"]}\n{\"q_img\": \"./images/B005MGB0S8.png\", \"q_text\": \"is sleevelass and a solid color., and is white with straps\", \"positive_key\": \"B003F67C4I\", \"positive_value\": \"./images/B003F67C4I.png\", \"hn_image\": [\"./images/B005MGB0S8.png\"]}\n{\"q_img\": \"./images/B00FIYMN9Y.png\", \"q_text\": \"is a black with white loose fitting dress, and Is less colorful and less floral.\", \"positive_key\": \"B008P3A0I6\", \"positive_value\": \"./images/B008P3A0I6.png\", \"hn_image\": [\"./images/B00FIYMN9Y.png\"]}\n{\"q_img\": \"./images/B007E94S12.png\", \"q_text\": \"is short sleeved, and is red with longer sleeves\", \"positive_key\": \"B007NG4BN6\", \"positive_value\": \"./images/B007NG4BN6.png\", \"hn_image\": [\"./images/B007E94S12.png\"]}\n{\"q_img\": \"./images/B007UJVK8A.png\", \"q_text\": \"is lighter and has longer sleeves, and is pure white colored\", \"positive_key\": \"B00D0SZHOI\", \"positive_value\": \"./images/B00D0SZHOI.png\", \"hn_image\": [\"./images/B007UJVK8A.png\"]}\n{\"q_img\": \"./images/B00EC7J7U6.png\", \"q_text\": \"The shirt is lavender with back cut outs., and is a shirt with a design at the back\", \"positive_key\": \"B00EDIGBUS\", \"positive_value\": \"./images/B00EDIGBUS.png\", \"hn_image\": [\"./images/B00EC7J7U6.png\"]}\n{\"q_img\": \"./images/B009SSTEPW.png\", \"q_text\": \"The long sleeved shirt is olive green., and is a button down grey long sleeve shirt\", \"positive_key\": \"B007X43KX0\", \"positive_value\": \"./images/B007X43KX0.png\", \"hn_image\": [\"./images/B009SSTEPW.png\"]}\n{\"q_img\": \"./images/B005ZSMYVU.png\", \"q_text\": \"is darker, and is brown in color\", \"positive_key\": \"B004WSJVRY\", \"positive_value\": \"./images/B004WSJVRY.png\", \"hn_image\": [\"./images/B005ZSMYVU.png\"]}\n{\"q_img\": \"./images/B00DEZTDCE.png\", \"q_text\": \"has long sleeves and classy, and it has a longer sleeve and it is white\", \"positive_key\": \"B008B68XO0\", \"positive_value\": \"./images/B008B68XO0.png\", \"hn_image\": [\"./images/B00DEZTDCE.png\"]}\n{\"q_img\": \"./images/B00CIH60TI.png\", \"q_text\": \"is beige colored with longer sleeves, and has long sleeves and a swoop neckline\", \"positive_key\": \"B00F3WDAIE\", \"positive_value\": \"./images/B00F3WDAIE.png\", \"hn_image\": [\"./images/B00CIH60TI.png\"]}\n{\"q_img\": \"./images/B00DHLT0T6.png\", \"q_text\": \"is white colored, and is less religious and more humorous\", \"positive_key\": \"B00B8A4BQA\", \"positive_value\": \"./images/B00B8A4BQA.png\", \"hn_image\": [\"./images/B00DHLT0T6.png\"]}\n{\"q_img\": \"./images/B002RL9VFA.png\", \"q_text\": \"is black and has thiner straps, and is a black tank top instead of a gray one.\", \"positive_key\": \"B0085K0LQU\", \"positive_value\": \"./images/B0085K0LQU.png\", \"hn_image\": [\"./images/B002RL9VFA.png\"]}\n{\"q_img\": \"./images/B00BM27KWQ.png\", \"q_text\": \"has more colored patterns, and is more colorful\", \"positive_key\": \"B00E1JQ56Y\", \"positive_value\": \"./images/B00E1JQ56Y.png\", \"hn_image\": [\"./images/B00BM27KWQ.png\"]}\n{\"q_img\": \"./images/B0058GP0K6.png\", \"q_text\": \"is more revealing, and is blue with a skirted bottom\", \"positive_key\": \"B00A04D1RU\", \"positive_value\": \"./images/B00A04D1RU.png\", \"hn_image\": [\"./images/B0058GP0K6.png\"]}\n{\"q_img\": \"./images/B009LHR3P8.png\", \"q_text\": \"is a black and white neck stripe and a slash of flowers, and Is more striped\", \"positive_key\": \"B0098I66QM\", \"positive_value\": \"./images/B0098I66QM.png\", \"hn_image\": [\"./images/B009LHR3P8.png\"]}\n{\"q_img\": \"./images/B0042VIV8G.png\", \"q_text\": \" gray and white color, and Is more frilly and less sporty\", \"positive_key\": \"B00BL8J34O\", \"positive_value\": \"./images/B00BL8J34O.png\", \"hn_image\": [\"./images/B0042VIV8G.png\"]}\n{\"q_img\": \"./images/B000GHI53Q.png\", \"q_text\": \"has deeper neckline and longer sleeves, and Has longer sleeves and a lower neckline\", \"positive_key\": \"B0040ZKZVA\", \"positive_value\": \"./images/B0040ZKZVA.png\", \"hn_image\": [\"./images/B000GHI53Q.png\"]}\n{\"q_img\": \"./images/B007M47VUY.png\", \"q_text\": \"The shirt is white in color., and is a white tshirt\", \"positive_key\": \"B003PGQ4Q0\", \"positive_value\": \"./images/B003PGQ4Q0.png\", \"hn_image\": [\"./images/B007M47VUY.png\"]}\n{\"q_img\": \"./images/B0093Y4HTE.png\", \"q_text\": \"Is more plain and ruched at the bottom, and is grey and longer sleeved\", \"positive_key\": \"B005EOA21E\", \"positive_value\": \"./images/B005EOA21E.png\", \"hn_image\": [\"./images/B0093Y4HTE.png\"]}\n{\"q_img\": \"./images/B007312U2A.png\", \"q_text\": \"purple and has Colbert, and has a larger graphic and is more purple\", \"positive_key\": \"B006ZZV0L2\", \"positive_value\": \"./images/B006ZZV0L2.png\", \"hn_image\": [\"./images/B007312U2A.png\"]}\n{\"q_img\": \"./images/B0074HOAEO.png\", \"q_text\": \"is red and less revealing, and is solid red\", \"positive_key\": \"B0096Q2R8M\", \"positive_value\": \"./images/B0096Q2R8M.png\", \"hn_image\": [\"./images/B0074HOAEO.png\"]}\n{\"q_img\": \"./images/B00C8FZDCK.png\", \"q_text\": \"has a shorter sleeve with multiple stripes, and Is multi colored scoop neck with 1/4 sleeves\", \"positive_key\": \"B00BWUX66M\", \"positive_value\": \"./images/B00BWUX66M.png\", \"hn_image\": [\"./images/B00C8FZDCK.png\"]}\n{\"q_img\": \"./images/B00CMVLDQU.png\", \"q_text\": \"The shirt is green with a Lion., and hot pink\", \"positive_key\": \"B0094I4IOI\", \"positive_value\": \"./images/B0094I4IOI.png\", \"hn_image\": [\"./images/B00CMVLDQU.png\"]}\n{\"q_img\": \"./images/B000ACJV9Y.png\", \"q_text\": \"is more sheer and one color, and strap sleeves pink under shirt with long sleeve lace over shirt\", \"positive_key\": \"B00B46I3PI\", \"positive_value\": \"./images/B00B46I3PI.png\", \"hn_image\": [\"./images/B000ACJV9Y.png\"]}\n{\"q_img\": \"./images/B0018DWZJC.png\", \"q_text\": \"is a three quarter sleeve v neck footbal shirt, and is black colored and gray stripe sleeve detail\", \"positive_key\": \"B004568EM6\", \"positive_value\": \"./images/B004568EM6.png\", \"hn_image\": [\"./images/B0018DWZJC.png\"]}\n{\"q_img\": \"./images/B00AMOM4FI.png\", \"q_text\": \"is lighter and longer with no buttons, and is white with brown stripes\", \"positive_key\": \"B00BMVOCM8\", \"positive_value\": \"./images/B00BMVOCM8.png\", \"hn_image\": [\"./images/B00AMOM4FI.png\"]}\n{\"q_img\": \"./images/B001BJXXD0.png\", \"q_text\": \"The tank top has the American Flag., and has shorter sleeves and is brighter colored\", \"positive_key\": \"B00D2UHPUI\", \"positive_value\": \"./images/B00D2UHPUI.png\", \"hn_image\": [\"./images/B001BJXXD0.png\"]}\n{\"q_img\": \"./images/B004JWPKPA.png\", \"q_text\": \"is gray with red words, and has rock band theme and four figures\", \"positive_key\": \"B007U7ACCC\", \"positive_value\": \"./images/B007U7ACCC.png\", \"hn_image\": [\"./images/B004JWPKPA.png\"]}\n{\"q_img\": \"./images/B00BQLAARU.png\", \"q_text\": \"Is more casual and fitted, and is a green form-fitting T-shirt\", \"positive_key\": \"B002GJ9UHM\", \"positive_value\": \"./images/B002GJ9UHM.png\", \"hn_image\": [\"./images/B00BQLAARU.png\"]}\n{\"q_img\": \"./images/B007WA3ZE4.png\", \"q_text\": \"is two toned and casual, and has red color to it\", \"positive_key\": \"B007XD5SPE\", \"positive_value\": \"./images/B007XD5SPE.png\", \"hn_image\": [\"./images/B007WA3ZE4.png\"]}\n{\"q_img\": \"./images/B00BDJ1DQM.png\", \"q_text\": \"The shirt is loss fitting and black and white in color., and Is darker in color and has sleeves\", \"positive_key\": \"B00B2T8KVY\", \"positive_value\": \"./images/B00B2T8KVY.png\", \"hn_image\": [\"./images/B00BDJ1DQM.png\"]}\n{\"q_img\": \"./images/B00BADDV6Q.png\", \"q_text\": \"The shirt is blue and light bllue., and is a solid blue color\", \"positive_key\": \"B00EPAB75S\", \"positive_value\": \"./images/B00EPAB75S.png\", \"hn_image\": [\"./images/B00BADDV6Q.png\"]}\n{\"q_img\": \"./images/B00AO4NI3I.png\", \"q_text\": \"is darker and has shorter sleeves, and purple tunic teeshirt suicide squad logo\", \"positive_key\": \"B009UIKO5O\", \"positive_value\": \"./images/B009UIKO5O.png\", \"hn_image\": [\"./images/B00AO4NI3I.png\"]}\n{\"q_img\": \"./images/B007MMYOQU.png\", \"q_text\": \"is blue with white lettering, and is blue\", \"positive_key\": \"B00282J5LI\", \"positive_value\": \"./images/B00282J5LI.png\", \"hn_image\": [\"./images/B007MMYOQU.png\"]}\n{\"q_img\": \"./images/B0036MPEUU.png\", \"q_text\": \"is longer with a vibrant print, and Is longer with zigzag\", \"positive_key\": \"B00A8YXLPE\", \"positive_value\": \"./images/B00A8YXLPE.png\", \"hn_image\": [\"./images/B0036MPEUU.png\"]}\n{\"q_img\": \"./images/B009M8MWK2.png\", \"q_text\": \"The shirt is black in color with a yellow and white design, and goes on the torso and is black colored\", \"positive_key\": \"B002SKELD2\", \"positive_value\": \"./images/B002SKELD2.png\", \"hn_image\": [\"./images/B009M8MWK2.png\"]}\n{\"q_img\": \"./images/B00CY51HPG.png\", \"q_text\": \"has no sleeves with a tile pattern, and is lighter and has shorter sleeves\", \"positive_key\": \"B00BAY489E\", \"positive_value\": \"./images/B00BAY489E.png\", \"hn_image\": [\"./images/B00CY51HPG.png\"]}\n{\"q_img\": \"./images/B009YK91RA.png\", \"q_text\": \"is black with cheetah designs, and has sleeves and is black\", \"positive_key\": \"B009KPMON2\", \"positive_value\": \"./images/B009KPMON2.png\", \"hn_image\": [\"./images/B009YK91RA.png\"]}\n{\"q_img\": \"./images/B00987K8EO.png\", \"q_text\": \"has no sleeves and has a blue color, and strap sleeves blue\", \"positive_key\": \"B00CY7NCB6\", \"positive_value\": \"./images/B00CY7NCB6.png\", \"hn_image\": [\"./images/B00987K8EO.png\"]}\n{\"q_img\": \"./images/B00A6OUUQY.png\", \"q_text\": \"is dark blue, and is navy blue tee shirt with different writing\", \"positive_key\": \"B00CHJ1DHQ\", \"positive_value\": \"./images/B00CHJ1DHQ.png\", \"hn_image\": [\"./images/B00A6OUUQY.png\"]}\n{\"q_img\": \"./images/B00AIQRCMU.png\", \"q_text\": \"Is a plain color and no design., and has black printing on white background\", \"positive_key\": \"B00A94M23K\", \"positive_value\": \"./images/B00A94M23K.png\", \"hn_image\": [\"./images/B00AIQRCMU.png\"]}\n{\"q_img\": \"./images/B0086URG8A.png\", \"q_text\": \"The off the shoulder shirt is pink., and is more pink in color\", \"positive_key\": \"B00C4YWU8K\", \"positive_value\": \"./images/B00C4YWU8K.png\", \"hn_image\": [\"./images/B0086URG8A.png\"]}\n{\"q_img\": \"./images/B00BOFWF2G.png\", \"q_text\": \"Is lacy and more fitted, and has no sleeves and is black\", \"positive_key\": \"B0091BALYY\", \"positive_value\": \"./images/B0091BALYY.png\", \"hn_image\": [\"./images/B00BOFWF2G.png\"]}\n{\"q_img\": \"./images/B008B68XO0.png\", \"q_text\": \"is more boldy colored and more casual, and is light pink and has short sleeve\", \"positive_key\": \"B007OUZVD0\", \"positive_value\": \"./images/B007OUZVD0.png\", \"hn_image\": [\"./images/B008B68XO0.png\"]}\n{\"q_img\": \"./images/B006TAEHZO.png\", \"q_text\": \"Is more aloha and colorful, and is a tropical\", \"positive_key\": \"B0088B3TAG\", \"positive_value\": \"./images/B0088B3TAG.png\", \"hn_image\": [\"./images/B006TAEHZO.png\"]}\n{\"q_img\": \"./images/B00APM86BS.png\", \"q_text\": \"has no buttons and is darker, and is black in color\", \"positive_key\": \"B006HT1QFQ\", \"positive_value\": \"./images/B006HT1QFQ.png\", \"hn_image\": [\"./images/B00APM86BS.png\"]}\n{\"q_img\": \"./images/B0096HONUG.png\", \"q_text\": \"is a solid teal, and is lighter\", \"positive_key\": \"B006ZIOC7I\", \"positive_value\": \"./images/B006ZIOC7I.png\", \"hn_image\": [\"./images/B0096HONUG.png\"]}\n{\"q_img\": \"./images/B00522PG28.png\", \"q_text\": \"is blue, and is a bright blue\", \"positive_key\": \"B006M44H7A\", \"positive_value\": \"./images/B006M44H7A.png\", \"hn_image\": [\"./images/B00522PG28.png\"]}\n{\"q_img\": \"./images/B0045ZH0AO.png\", \"q_text\": \"less fitted and green, and is lighter in colour with shorter sleeves\", \"positive_key\": \"B008HRFSQE\", \"positive_value\": \"./images/B008HRFSQE.png\", \"hn_image\": [\"./images/B0045ZH0AO.png\"]}\n{\"q_img\": \"./images/B00BI1E7AE.png\", \"q_text\": \"The shirt is loose and black in color., and has long sleeves and a v neck\", \"positive_key\": \"B00265T6ES\", \"positive_value\": \"./images/B00265T6ES.png\", \"hn_image\": [\"./images/B00BI1E7AE.png\"]}\n{\"q_img\": \"./images/B004HO6MI4.png\", \"q_text\": \"Is more casual and wordy, and has no buttons and is more casual\", \"positive_key\": \"B0046H85X2\", \"positive_value\": \"./images/B0046H85X2.png\", \"hn_image\": [\"./images/B004HO6MI4.png\"]}\n{\"q_img\": \"./images/B00APM86BS.png\", \"q_text\": \"has deep blue and less solid, and has a blue color\", \"positive_key\": \"B002KG5WD2\", \"positive_value\": \"./images/B002KG5WD2.png\", \"hn_image\": [\"./images/B00APM86BS.png\"]}\n{\"q_img\": \"./images/B008L13H1O.png\", \"q_text\": \"is pink and sheer lace, and has a more covered chest area\", \"positive_key\": \"B005NIVH0Q\", \"positive_value\": \"./images/B005NIVH0Q.png\", \"hn_image\": [\"./images/B008L13H1O.png\"]}\n{\"q_img\": \"./images/B0018ZUW40.png\", \"q_text\": \"has a skull pattern, and is shinier and more feminine\", \"positive_key\": \"B00BBG0ADE\", \"positive_value\": \"./images/B00BBG0ADE.png\", \"hn_image\": [\"./images/B0018ZUW40.png\"]}\n{\"q_img\": \"./images/B0069HMDM6.png\", \"q_text\": \"The shirt is white, and Is pinker with a black undershirt\", \"positive_key\": \"B00BWH086G\", \"positive_value\": \"./images/B00BWH086G.png\", \"hn_image\": [\"./images/B0069HMDM6.png\"]}\n{\"q_img\": \"./images/B004G0A72G.png\", \"q_text\": \"is dark brown colored and has shorter sleeves, and Desired item is brown with ruffles\", \"positive_key\": \"B0043U6O9O\", \"positive_value\": \"./images/B0043U6O9O.png\", \"hn_image\": [\"./images/B004G0A72G.png\"]}\n{\"q_img\": \"./images/B006Q8AMUI.png\", \"q_text\": \"is floral print and less animal print, and is a shirt with a floral design\", \"positive_key\": \"B00DAFYR88\", \"positive_value\": \"./images/B00DAFYR88.png\", \"hn_image\": [\"./images/B006Q8AMUI.png\"]}\n{\"q_img\": \"./images/B006BOKB30.png\", \"q_text\": \"is black with white spots, and is black and white and less revealing\", \"positive_key\": \"B00CAC6AOQ\", \"positive_value\": \"./images/B00CAC6AOQ.png\", \"hn_image\": [\"./images/B006BOKB30.png\"]}\n{\"q_img\": \"./images/B008JYNN30.png\", \"q_text\": \"its a striped sweater with a higher nackline, and is sleeveless and has a brighter color pattern\", \"positive_key\": \"B008DVXGO0\", \"positive_value\": \"./images/B008DVXGO0.png\", \"hn_image\": [\"./images/B008JYNN30.png\"]}\n{\"q_img\": \"./images/B00A0I86S0.png\", \"q_text\": \"has a lot of striped patterns, and Is plaid with a hood.\", \"positive_key\": \"B005WYCZ6G\", \"positive_value\": \"./images/B005WYCZ6G.png\", \"hn_image\": [\"./images/B00A0I86S0.png\"]}\n{\"q_img\": \"./images/B003YJG5S0.png\", \"q_text\": \"has longer sleeves, and it has long sleeve and less plain\", \"positive_key\": \"B00CLBK7JU\", \"positive_value\": \"./images/B00CLBK7JU.png\", \"hn_image\": [\"./images/B003YJG5S0.png\"]}\n{\"q_img\": \"./images/B00F4635ZW.png\", \"q_text\": \"has no sleeves with a different logo, and has no sleeves\", \"positive_key\": \"B00CIQ15SK\", \"positive_value\": \"./images/B00CIQ15SK.png\", \"hn_image\": [\"./images/B00F4635ZW.png\"]}\n{\"q_img\": \"./images/B00AU4N846.png\", \"q_text\": \"has a v neck, and has a flower pattern\", \"positive_key\": \"B000ACJV9Y\", \"positive_value\": \"./images/B000ACJV9Y.png\", \"hn_image\": [\"./images/B00AU4N846.png\"]}\n{\"q_img\": \"./images/B002SW2QGE.png\", \"q_text\": \"The shirt is long sleeved and multi-colored., and has more colors\", \"positive_key\": \"B008AJL2NC\", \"positive_value\": \"./images/B008AJL2NC.png\", \"hn_image\": [\"./images/B002SW2QGE.png\"]}\n{\"q_img\": \"./images/B004GHO03G.png\", \"q_text\": \"white tunic ruffled bodice short sleeves, and has longer sleeves\", \"positive_key\": \"B001D6Y466\", \"positive_value\": \"./images/B001D6Y466.png\", \"hn_image\": [\"./images/B004GHO03G.png\"]}\n{\"q_img\": \"./images/B008X04NMK.png\", \"q_text\": \"is lighter in color and more lacy, and is white with lace\", \"positive_key\": \"B00C9UDEAM\", \"positive_value\": \"./images/B00C9UDEAM.png\", \"hn_image\": [\"./images/B008X04NMK.png\"]}\n{\"q_img\": \"./images/B0051H8U86.png\", \"q_text\": \"is black with flowers, and black flowery poncho\", \"positive_key\": \"B00CF1Q2NQ\", \"positive_value\": \"./images/B00CF1Q2NQ.png\", \"hn_image\": [\"./images/B0051H8U86.png\"]}\n{\"q_img\": \"./images/B00C9NQNSY.png\", \"q_text\": \"The silicone coverUps are pink in color., and They\\u2019re coverup cutlets & not clothes\", \"positive_key\": \"B0051H8U86\", \"positive_value\": \"./images/B0051H8U86.png\", \"hn_image\": [\"./images/B00C9NQNSY.png\"]}\n{\"q_img\": \"./images/B00AG35TN4.png\", \"q_text\": \"The shirt is low cut and purple in color., and is pink with longer sleeves\", \"positive_key\": \"B0083UVR06\", \"positive_value\": \"./images/B0083UVR06.png\", \"hn_image\": [\"./images/B00AG35TN4.png\"]}\n{\"q_img\": \"./images/B0036TFZV6.png\", \"q_text\": \"is white with longer sleeves, and Is lighter & more colorful\", \"positive_key\": \"B002UD5UU0\", \"positive_value\": \"./images/B002UD5UU0.png\", \"hn_image\": [\"./images/B0036TFZV6.png\"]}\n{\"q_img\": \"./images/B00BXMUFWC.png\", \"q_text\": \"is purple, and Is more colorful\", \"positive_key\": \"B000FAE1SW\", \"positive_value\": \"./images/B000FAE1SW.png\", \"hn_image\": [\"./images/B00BXMUFWC.png\"]}\n{\"q_img\": \"./images/B008OSJKFG.png\", \"q_text\": \"The shirt is white with gray flowers., and is darker and has a design\", \"positive_key\": \"B005GYSG4C\", \"positive_value\": \"./images/B005GYSG4C.png\", \"hn_image\": [\"./images/B008OSJKFG.png\"]}\n{\"q_img\": \"./images/B000JWVPYO.png\", \"q_text\": \"is black colored and has longer sleeves, and no zipper\", \"positive_key\": \"B00DOJZJ6Y\", \"positive_value\": \"./images/B00DOJZJ6Y.png\", \"hn_image\": [\"./images/B000JWVPYO.png\"]}\n{\"q_img\": \"./images/B00GIOKE8U.png\", \"q_text\": \"The long sleeve shirt is white., and is lighter color and has no graphic.\", \"positive_key\": \"B00686OHK4\", \"positive_value\": \"./images/B00686OHK4.png\", \"hn_image\": [\"./images/B00GIOKE8U.png\"]}\n{\"q_img\": \"./images/B000P4ZQYG.png\", \"q_text\": \"is a short sleeve white shirt with Star Wars art, and is white and has Darth Vader on it\", \"positive_key\": \"B00AQQLQY2\", \"positive_value\": \"./images/B00AQQLQY2.png\", \"hn_image\": [\"./images/B000P4ZQYG.png\"]}\n{\"q_img\": \"./images/B007U4PP18.png\", \"q_text\": \"is light colored and has belt, and is a lighter blue and has slightly shorter sleeves\", \"positive_key\": \"B00D991S6E\", \"positive_value\": \"./images/B00D991S6E.png\", \"hn_image\": [\"./images/B007U4PP18.png\"]}\n{\"q_img\": \"./images/B000PJDKPS.png\", \"q_text\": \"The shirt is white with a colorful design., and more colors and no words\", \"positive_key\": \"B0074HOAEO\", \"positive_value\": \"./images/B0074HOAEO.png\", \"hn_image\": [\"./images/B000PJDKPS.png\"]}\n{\"q_img\": \"./images/B00A159E2O.png\", \"q_text\": \"Is more casual and darker in color, and tee shirt black with girl power logo\", \"positive_key\": \"B003MYBP0K\", \"positive_value\": \"./images/B003MYBP0K.png\", \"hn_image\": [\"./images/B00A159E2O.png\"]}\n{\"q_img\": \"./images/B00BPPALLM.png\", \"q_text\": \"is less yellow and shorter sleeves, and has short sleeves and yellow\", \"positive_key\": \"B00BVX9470\", \"positive_value\": \"./images/B00BVX9470.png\", \"hn_image\": [\"./images/B00BPPALLM.png\"]}\n{\"q_img\": \"./images/B00E0L2FAI.png\", \"q_text\": \"is looser, and is less graphic and buttons up\", \"positive_key\": \"B00COE18RO\", \"positive_value\": \"./images/B00COE18RO.png\", \"hn_image\": [\"./images/B00E0L2FAI.png\"]}\n{\"q_img\": \"./images/B0071NJ5IW.png\", \"q_text\": \"is patterned but with less colours, and is white with longer sleeves\", \"positive_key\": \"B009J61BRW\", \"positive_value\": \"./images/B009J61BRW.png\", \"hn_image\": [\"./images/B0071NJ5IW.png\"]}\n{\"q_img\": \"./images/B009YJEDT2.png\", \"q_text\": \"white long sleeve tunic, and long sleeves white drees shirt\", \"positive_key\": \"B0045EP3N6\", \"positive_value\": \"./images/B0045EP3N6.png\", \"hn_image\": [\"./images/B009YJEDT2.png\"]}\n{\"q_img\": \"./images/B007850FV4.png\", \"q_text\": \"is brown with shorter sleeves, and is a sheer camisole\", \"positive_key\": \"B00D426QSQ\", \"positive_value\": \"./images/B00D426QSQ.png\", \"hn_image\": [\"./images/B007850FV4.png\"]}\n{\"q_img\": \"./images/B0079K2OAI.png\", \"q_text\": \"has long sleeves and patterns, and is red with long sleeves\", \"positive_key\": \"B00FLNLKU0\", \"positive_value\": \"./images/B00FLNLKU0.png\", \"hn_image\": [\"./images/B0079K2OAI.png\"]}\n{\"q_img\": \"./images/B005E9GLD2.png\", \"q_text\": \"is less revealing, and Has longer sleeves with a graphic\", \"positive_key\": \"B00B3XTZQI\", \"positive_value\": \"./images/B00B3XTZQI.png\", \"hn_image\": [\"./images/B005E9GLD2.png\"]}\n{\"q_img\": \"./images/B00945C5QE.png\", \"q_text\": \"has different shades of pink, and has a less busy look to it\", \"positive_key\": \"B00BHIFMDO\", \"positive_value\": \"./images/B00BHIFMDO.png\", \"hn_image\": [\"./images/B00945C5QE.png\"]}\n{\"q_img\": \"./images/B00CCHVW88.png\", \"q_text\": \"is longer sleeved with a different pattern, and is floral and has sleeves.\", \"positive_key\": \"B00BTGQXYQ\", \"positive_value\": \"./images/B00BTGQXYQ.png\", \"hn_image\": [\"./images/B00CCHVW88.png\"]}\n{\"q_img\": \"./images/B00ECRIKTA.png\", \"q_text\": \"is plain black and more fitted, and is solid black\", \"positive_key\": \"B00D7112VA\", \"positive_value\": \"./images/B00D7112VA.png\", \"hn_image\": [\"./images/B00ECRIKTA.png\"]}\n{\"q_img\": \"./images/B00980K5X0.png\", \"q_text\": \"is more teal, and Has longer sleeves and is less lacey\", \"positive_key\": \"B0097YCPSU\", \"positive_value\": \"./images/B0097YCPSU.png\", \"hn_image\": [\"./images/B00980K5X0.png\"]}\n{\"q_img\": \"./images/B008AEE5VS.png\", \"q_text\": \"Is more flowy and more plain, and is lighter\", \"positive_key\": \"B0080E50NU\", \"positive_value\": \"./images/B0080E50NU.png\", \"hn_image\": [\"./images/B008AEE5VS.png\"]}\n{\"q_img\": \"./images/B009GL0KKO.png\", \"q_text\": \"is lighter and has shorter sleeves, and is white\", \"positive_key\": \"B00BA6KWNS\", \"positive_value\": \"./images/B00BA6KWNS.png\", \"hn_image\": [\"./images/B009GL0KKO.png\"]}\n{\"q_img\": \"./images/B00E4W5P2S.png\", \"q_text\": \"has shorter sleeves with black and white pattern, and is black and white patterned\", \"positive_key\": \"B00BMF3D6U\", \"positive_value\": \"./images/B00BMF3D6U.png\", \"hn_image\": [\"./images/B00E4W5P2S.png\"]}\n{\"q_img\": \"./images/B005TVOFNS.png\", \"q_text\": \"The long sleeve shirt is white in color., and is white with shorter sleeves\", \"positive_key\": \"B008DAQ1F2\", \"positive_value\": \"./images/B008DAQ1F2.png\", \"hn_image\": [\"./images/B005TVOFNS.png\"]}\n{\"q_img\": \"./images/B007PU81HM.png\", \"q_text\": \"has shorter sleeves and less grey, and white crew neck tee\", \"positive_key\": \"B0089OJC08\", \"positive_value\": \"./images/B0089OJC08.png\", \"hn_image\": [\"./images/B007PU81HM.png\"]}\n{\"q_img\": \"./images/B0084ENHNQ.png\", \"q_text\": \"is a floral pattern see thru blouse, and has one sleeve and is more revealing\", \"positive_key\": \"B005H15OVW\", \"positive_value\": \"./images/B005H15OVW.png\", \"hn_image\": [\"./images/B0084ENHNQ.png\"]}\n{\"q_img\": \"./images/B0087AYOE8.png\", \"q_text\": \"has side way striped, and is striped\", \"positive_key\": \"B007GO6Z1G\", \"positive_value\": \"./images/B007GO6Z1G.png\", \"hn_image\": [\"./images/B0087AYOE8.png\"]}\n{\"q_img\": \"./images/B007YSB65E.png\", \"q_text\": \"The tank top is loose fitting and is black in color., and is a tank in solid black.\", \"positive_key\": \"B0084SSYYY\", \"positive_value\": \"./images/B0084SSYYY.png\", \"hn_image\": [\"./images/B007YSB65E.png\"]}\n{\"q_img\": \"./images/B00794W7JC.png\", \"q_text\": \"is more casual, and is a yellow graphic tee\", \"positive_key\": \"B00CZD7IYQ\", \"positive_value\": \"./images/B00CZD7IYQ.png\", \"hn_image\": [\"./images/B00794W7JC.png\"]}\n{\"q_img\": \"./images/B00BSUEASE.png\", \"q_text\": \"is darker, and it has collar and pockets\", \"positive_key\": \"B00BU9F40G\", \"positive_value\": \"./images/B00BU9F40G.png\", \"hn_image\": [\"./images/B00BSUEASE.png\"]}\n{\"q_img\": \"./images/B00D01FO34.png\", \"q_text\": \"is monochromatic and less beachy, and is a 3/4 blue shirt\", \"positive_key\": \"B00D4FN1Y0\", \"positive_value\": \"./images/B00D4FN1Y0.png\", \"hn_image\": [\"./images/B00D01FO34.png\"]}\n{\"q_img\": \"./images/B001NOFE4Y.png\", \"q_text\": \"animal print tighter shirt, and Is less casual and more patterned.\", \"positive_key\": \"B00BXXO4WI\", \"positive_value\": \"./images/B00BXXO4WI.png\", \"hn_image\": [\"./images/B001NOFE4Y.png\"]}\n{\"q_img\": \"./images/B009IQD6E4.png\", \"q_text\": \"is more revaeling, and Is a blue crop top\", \"positive_key\": \"B00C30MK80\", \"positive_value\": \"./images/B00C30MK80.png\", \"hn_image\": [\"./images/B009IQD6E4.png\"]}\n{\"q_img\": \"./images/B005ZSMYVU.png\", \"q_text\": \"The tank top is white with brown buttons., and is white loose tee with button on front\", \"positive_key\": \"B00DEKPMQU\", \"positive_value\": \"./images/B00DEKPMQU.png\", \"hn_image\": [\"./images/B005ZSMYVU.png\"]}\n{\"q_img\": \"./images/B00BRCKMGC.png\", \"q_text\": \"is pink with longer sleeves, and is dressier looking\", \"positive_key\": \"B0039819XM\", \"positive_value\": \"./images/B0039819XM.png\", \"hn_image\": [\"./images/B00BRCKMGC.png\"]}\n{\"q_img\": \"./images/B008NA8BTG.png\", \"q_text\": \"The shirt is green in color., and is blue with white trim\", \"positive_key\": \"B008NA89FC\", \"positive_value\": \"./images/B008NA89FC.png\", \"hn_image\": [\"./images/B008NA8BTG.png\"]}\n{\"q_img\": \"./images/B00CDVICKY.png\", \"q_text\": \"is darker colored, and lower neckline\", \"positive_key\": \"B00CIVS0KG\", \"positive_value\": \"./images/B00CIVS0KG.png\", \"hn_image\": [\"./images/B00CDVICKY.png\"]}\n{\"q_img\": \"./images/B00BKUAH6Q.png\", \"q_text\": \"The tank top is red with white polka dots., and is sleeveless and red pattern.\", \"positive_key\": \"B007H12LT8\", \"positive_value\": \"./images/B007H12LT8.png\", \"hn_image\": [\"./images/B00BKUAH6Q.png\"]}\n{\"q_img\": \"./images/B00BNFFJ4I.png\", \"q_text\": \"has a long sleeve gray color, and Is gray with skeleton ribs and red heart\", \"positive_key\": \"B00C6D1QZM\", \"positive_value\": \"./images/B00C6D1QZM.png\", \"hn_image\": [\"./images/B00BNFFJ4I.png\"]}\n{\"q_img\": \"./images/B003HB8MIQ.png\", \"q_text\": \"The shirt is black with yellow an white writing., and is black with long sleeves\", \"positive_key\": \"B001CTIWQW\", \"positive_value\": \"./images/B001CTIWQW.png\", \"hn_image\": [\"./images/B003HB8MIQ.png\"]}\n{\"q_img\": \"./images/B001PIUPZ6.png\", \"q_text\": \"says university of beekeepers, and has a different logo\", \"positive_key\": \"B001PJB6YO\", \"positive_value\": \"./images/B001PJB6YO.png\", \"hn_image\": [\"./images/B001PIUPZ6.png\"]}\n{\"q_img\": \"./images/B006DUA614.png\", \"q_text\": \"is black with black writing, and Is black and has a red emblem with Team Jacob\", \"positive_key\": \"B003MEC004\", \"positive_value\": \"./images/B003MEC004.png\", \"hn_image\": [\"./images/B006DUA614.png\"]}\n{\"q_img\": \"./images/B00DCZSCUA.png\", \"q_text\": \"is lighter, and is white and black\", \"positive_key\": \"B008NDWZJ0\", \"positive_value\": \"./images/B008NDWZJ0.png\", \"hn_image\": [\"./images/B00DCZSCUA.png\"]}\n{\"q_img\": \"./images/B009ZRKSEM.png\", \"q_text\": \"The tank top is black with fringe., and is solid black and has fringes\", \"positive_key\": \"B00938DQNI\", \"positive_value\": \"./images/B00938DQNI.png\", \"hn_image\": [\"./images/B009ZRKSEM.png\"]}\n{\"q_img\": \"./images/B00G2GMBD0.png\", \"q_text\": \"is red colored, and is wine colored and has fitted sleeves\", \"positive_key\": \"B00GOCLHNM\", \"positive_value\": \"./images/B00GOCLHNM.png\", \"hn_image\": [\"./images/B00G2GMBD0.png\"]}\n{\"q_img\": \"./images/B007ZT9R94.png\", \"q_text\": \"Is more lacy and revealing, and is darker and has longer sleeves\", \"positive_key\": \"B00AB36UCS\", \"positive_value\": \"./images/B00AB36UCS.png\", \"hn_image\": [\"./images/B007ZT9R94.png\"]}\n{\"q_img\": \"./images/B006OMWE2U.png\", \"q_text\": \"is cotton and darker in color, and light brown\", \"positive_key\": \"B00E6MGG9M\", \"positive_value\": \"./images/B00E6MGG9M.png\", \"hn_image\": [\"./images/B006OMWE2U.png\"]}\n{\"q_img\": \"./images/B0035EPRWY.png\", \"q_text\": \"The shirt is black with long sleeves., and long sleeved and to the hip\", \"positive_key\": \"B0035EPUBW\", \"positive_value\": \"./images/B0035EPUBW.png\", \"hn_image\": [\"./images/B0035EPRWY.png\"]}\n{\"q_img\": \"./images/B004KSP3ZK.png\", \"q_text\": \"The shirt is blue with a white emblem., and Is dark blue with white emblem\", \"positive_key\": \"B00EJOHA2O\", \"positive_value\": \"./images/B00EJOHA2O.png\", \"hn_image\": [\"./images/B004KSP3ZK.png\"]}\n{\"q_img\": \"./images/B006E3ZMA0.png\", \"q_text\": \"has no sleeves and sexy open wide neck, and is sleeveless\", \"positive_key\": \"B006MXLXHI\", \"positive_value\": \"./images/B006MXLXHI.png\", \"hn_image\": [\"./images/B006E3ZMA0.png\"]}\n{\"q_img\": \"./images/B00BB1UE9E.png\", \"q_text\": \"The backless shirt is black in color., and high-low hem\", \"positive_key\": \"B001F4PUV4\", \"positive_value\": \"./images/B001F4PUV4.png\", \"hn_image\": [\"./images/B00BB1UE9E.png\"]}\n{\"q_img\": \"./images/B004070YLE.png\", \"q_text\": \"is solid black, and is more fitting\", \"positive_key\": \"B005748QPU\", \"positive_value\": \"./images/B005748QPU.png\", \"hn_image\": [\"./images/B004070YLE.png\"]}\n{\"q_img\": \"./images/B006BZ8690.png\", \"q_text\": \"numbered colts shirt blue white, and it is a lighter blue\", \"positive_key\": \"B00826O9SS\", \"positive_value\": \"./images/B00826O9SS.png\", \"hn_image\": [\"./images/B006BZ8690.png\"]}\n{\"q_img\": \"./images/B00F092ZKO.png\", \"q_text\": \"is more revealing, and has shorter sleeves and brighter colors\", \"positive_key\": \"B00CQ0AGJ2\", \"positive_value\": \"./images/B00CQ0AGJ2.png\", \"hn_image\": [\"./images/B00F092ZKO.png\"]}\n{\"q_img\": \"./images/B00ATTNOVE.png\", \"q_text\": \"has a big image in the middle and short sleeves, and Has shorter sleeves\", \"positive_key\": \"B005DHWXF0\", \"positive_value\": \"./images/B005DHWXF0.png\", \"hn_image\": [\"./images/B00ATTNOVE.png\"]}\n{\"q_img\": \"./images/B0026JOJQ4.png\", \"q_text\": \"is more darker, and Is shorter & darker\", \"positive_key\": \"B009T3Q18E\", \"positive_value\": \"./images/B009T3Q18E.png\", \"hn_image\": [\"./images/B0026JOJQ4.png\"]}\n{\"q_img\": \"./images/B007QSN628.png\", \"q_text\": \"has no sleeves and is v neck, and is black with straps\", \"positive_key\": \"B008BIUNOQ\", \"positive_value\": \"./images/B008BIUNOQ.png\", \"hn_image\": [\"./images/B007QSN628.png\"]}\n{\"q_img\": \"./images/B00ELK79BI.png\", \"q_text\": \"is blue colored, and is more blue in color\", \"positive_key\": \"B008OBD1PI\", \"positive_value\": \"./images/B008OBD1PI.png\", \"hn_image\": [\"./images/B00ELK79BI.png\"]}\n{\"q_img\": \"./images/B003IKOOTW.png\", \"q_text\": \"is black colored with sleeves, and is black with white print and full sleeves on the crew neck.\", \"positive_key\": \"B005ISP19Y\", \"positive_value\": \"./images/B005ISP19Y.png\", \"hn_image\": [\"./images/B003IKOOTW.png\"]}\n{\"q_img\": \"./images/B003U2T0DI.png\", \"q_text\": \"The tank top has ruffles and is black in color., and is a sleeveless frilled non-button up shirt.\", \"positive_key\": \"B00AEMHKKC\", \"positive_value\": \"./images/B00AEMHKKC.png\", \"hn_image\": [\"./images/B003U2T0DI.png\"]}\n{\"q_img\": \"./images/B0014JXLMK.png\", \"q_text\": \"has a lower neckine and is less premium, and is a short sleeved tee shirt\", \"positive_key\": \"B0014JZ5ZQ\", \"positive_value\": \"./images/B0014JZ5ZQ.png\", \"hn_image\": [\"./images/B0014JXLMK.png\"]}\n{\"q_img\": \"./images/B00C7USRYW.png\", \"q_text\": \"has floral and open wide neck, and has more yellow and red colors\", \"positive_key\": \"B007HLK1T0\", \"positive_value\": \"./images/B007HLK1T0.png\", \"hn_image\": [\"./images/B00C7USRYW.png\"]}\n{\"q_img\": \"./images/B004ZUFIZS.png\", \"q_text\": \"is red colored and has sleeves, and has red and pink flowers on it\", \"positive_key\": \"B0053Y6WG4\", \"positive_value\": \"./images/B0053Y6WG4.png\", \"hn_image\": [\"./images/B004ZUFIZS.png\"]}\n{\"q_img\": \"./images/B0094I4IOI.png\", \"q_text\": \"Is more wordy, and is a grey crew neck with print.\", \"positive_key\": \"B00DI5EP8M\", \"positive_value\": \"./images/B00DI5EP8M.png\", \"hn_image\": [\"./images/B0094I4IOI.png\"]}\n{\"q_img\": \"./images/B00D8AWLL0.png\", \"q_text\": \"The polo shirt is pink in color., and has long sleeves\", \"positive_key\": \"B00F092ZKO\", \"positive_value\": \"./images/B00F092ZKO.png\", \"hn_image\": [\"./images/B00D8AWLL0.png\"]}\n{\"q_img\": \"./images/B009T57HNK.png\", \"q_text\": \"is low cut and has bright stripes, and is more junior and fun\", \"positive_key\": \"B007HZJ516\", \"positive_value\": \"./images/B007HZJ516.png\", \"hn_image\": [\"./images/B009T57HNK.png\"]}\n{\"q_img\": \"./images/B001NQ01H2.png\", \"q_text\": \"is shiney and tight, and is white with black star pattern\", \"positive_key\": \"B00EV6AO8M\", \"positive_value\": \"./images/B00EV6AO8M.png\", \"hn_image\": [\"./images/B001NQ01H2.png\"]}\n{\"q_img\": \"./images/B007Z4WSA4.png\", \"q_text\": \"is darker and more vintage, and has a bit print and is black color\", \"positive_key\": \"B001HT27C2\", \"positive_value\": \"./images/B001HT27C2.png\", \"hn_image\": [\"./images/B007Z4WSA4.png\"]}\n{\"q_img\": \"./images/B00951IWFK.png\", \"q_text\": \"is orange, and has short sleeves\", \"positive_key\": \"B00DYTDAYW\", \"positive_value\": \"./images/B00DYTDAYW.png\", \"hn_image\": [\"./images/B00951IWFK.png\"]}\n{\"q_img\": \"./images/B00DY3WYSQ.png\", \"q_text\": \"has a very inappropriate message, and is a wordy tee shirt for men\", \"positive_key\": \"B00CBAJCQU\", \"positive_value\": \"./images/B00CBAJCQU.png\", \"hn_image\": [\"./images/B00DY3WYSQ.png\"]}\n{\"q_img\": \"./images/B009LEG3XO.png\", \"q_text\": \" button up and solid pink, and is black and has no sleeve\", \"positive_key\": \"B00872X8QQ\", \"positive_value\": \"./images/B00872X8QQ.png\", \"hn_image\": [\"./images/B009LEG3XO.png\"]}\n{\"q_img\": \"./images/B008PFGQP0.png\", \"q_text\": \"I need short sleeves and black in color with some kind of design on the front., and Has shorter sleeves & is black\", \"positive_key\": \"B00EE0Y0GC\", \"positive_value\": \"./images/B00EE0Y0GC.png\", \"hn_image\": [\"./images/B008PFGQP0.png\"]}\n{\"q_img\": \"./images/B005C5RPYW.png\", \"q_text\": \"is lighter, and is blue with a pocket\", \"positive_key\": \"B0030IMH9Q\", \"positive_value\": \"./images/B0030IMH9Q.png\", \"hn_image\": [\"./images/B005C5RPYW.png\"]}\n{\"q_img\": \"./images/B00CTYA5U0.png\", \"q_text\": \"The shirt is pink with multi-colored flowers., and is pink and orange with long sleeves\", \"positive_key\": \"B00CQ9ICKI\", \"positive_value\": \"./images/B00CQ9ICKI.png\", \"hn_image\": [\"./images/B00CTYA5U0.png\"]}\n{\"q_img\": \"./images/B00BZF3B3M.png\", \"q_text\": \"is black and white plaid, and Is longer with a flannel pattern.\", \"positive_key\": \"B00AEZZR2W\", \"positive_value\": \"./images/B00AEZZR2W.png\", \"hn_image\": [\"./images/B00BZF3B3M.png\"]}\n{\"q_img\": \"./images/B006SF8D3M.png\", \"q_text\": \"has a lower scoop neck and print, and is lighter and has a print with light sleeves\", \"positive_key\": \"B007FAENV0\", \"positive_value\": \"./images/B007FAENV0.png\", \"hn_image\": [\"./images/B006SF8D3M.png\"]}\n{\"q_img\": \"./images/B00CF53NUW.png\", \"q_text\": \"is solid grey with short sleeves, and it is grey and plain\", \"positive_key\": \"B009LLT866\", \"positive_value\": \"./images/B009LLT866.png\", \"hn_image\": [\"./images/B00CF53NUW.png\"]}\n{\"q_img\": \"./images/B007WAE516.png\", \"q_text\": \"is blue and more complex, and has an off the shoulder\", \"positive_key\": \"B008OUM4X4\", \"positive_value\": \"./images/B008OUM4X4.png\", \"hn_image\": [\"./images/B007WAE516.png\"]}\n{\"q_img\": \"./images/B008IZLWTM.png\", \"q_text\": \"has a less revealing neckline and is edgier, and is a darker color and has a higher neckline\", \"positive_key\": \"B008BDB1LK\", \"positive_value\": \"./images/B008BDB1LK.png\", \"hn_image\": [\"./images/B008IZLWTM.png\"]}\n{\"q_img\": \"./images/B007Q2KFQO.png\", \"q_text\": \"is more graphic and less preppy, and has graphic images in front\", \"positive_key\": \"B00440GIHG\", \"positive_value\": \"./images/B00440GIHG.png\", \"hn_image\": [\"./images/B007Q2KFQO.png\"]}\n{\"q_img\": \"./images/B006YDMYCY.png\", \"q_text\": \"The shirt is white with the word California in red and white., and is light colored and fitted\", \"positive_key\": \"B00B2WMY5E\", \"positive_value\": \"./images/B00B2WMY5E.png\", \"hn_image\": [\"./images/B006YDMYCY.png\"]}\n{\"q_img\": \"./images/B008ZBL9YM.png\", \"q_text\": \"short sleeved adornment on neckline, and is solid blue with decorative neckline\", \"positive_key\": \"B00B3Z8D96\", \"positive_value\": \"./images/B00B3Z8D96.png\", \"hn_image\": [\"./images/B008ZBL9YM.png\"]}\n{\"q_img\": \"./images/B00B9OQ1YA.png\", \"q_text\": \"is blue with thin straps, and is more multi colored\", \"positive_key\": \"B006VTEEJW\", \"positive_value\": \"./images/B006VTEEJW.png\", \"hn_image\": [\"./images/B00B9OQ1YA.png\"]}\n{\"q_img\": \"./images/B0040T2820.png\", \"q_text\": \"has a darker colored shirt with Domo, and is brown with a face\", \"positive_key\": \"B001HSBUGC\", \"positive_value\": \"./images/B001HSBUGC.png\", \"hn_image\": [\"./images/B0040T2820.png\"]}\n{\"q_img\": \"./images/B006ZJ1SMY.png\", \"q_text\": \"is beige colored, and has more tan colors\", \"positive_key\": \"B007RMN0CE\", \"positive_value\": \"./images/B007RMN0CE.png\", \"hn_image\": [\"./images/B006ZJ1SMY.png\"]}\n{\"q_img\": \"./images/B009LHR3P8.png\", \"q_text\": \"is solid blue, and Desired item is grey with short sleeves and decorative neckline\", \"positive_key\": \"B007M47VUY\", \"positive_value\": \"./images/B007M47VUY.png\", \"hn_image\": [\"./images/B009LHR3P8.png\"]}\n{\"q_img\": \"./images/B00DQTE3W8.png\", \"q_text\": \"is a brown ladies bag, and A brown leather purse with pockets on front\", \"positive_key\": \"B002NIL2AE\", \"positive_value\": \"./images/B002NIL2AE.png\", \"hn_image\": [\"./images/B00DQTE3W8.png\"]}\n{\"q_img\": \"./images/B005NY5YK4.png\", \"q_text\": \"is brighter colored, and is white with pink trim\", \"positive_key\": \"B004XFX3H0\", \"positive_value\": \"./images/B004XFX3H0.png\", \"hn_image\": [\"./images/B005NY5YK4.png\"]}\n{\"q_img\": \"./images/B00BIQADUW.png\", \"q_text\": \"is a darker color., and is in blue with pink lettering.\", \"positive_key\": \"B003KQJHR8\", \"positive_value\": \"./images/B003KQJHR8.png\", \"hn_image\": [\"./images/B00BIQADUW.png\"]}\n{\"q_img\": \"./images/B0030LH1QC.png\", \"q_text\": \"is black and shorter sleeves, and is darker\", \"positive_key\": \"B0027APC5Y\", \"positive_value\": \"./images/B0027APC5Y.png\", \"hn_image\": [\"./images/B0030LH1QC.png\"]}\n{\"q_img\": \"./images/B008J7WSS8.png\", \"q_text\": \"is darker and more sleek, and is black and has a skirt\", \"positive_key\": \"B008QW7G9M\", \"positive_value\": \"./images/B008QW7G9M.png\", \"hn_image\": [\"./images/B008J7WSS8.png\"]}\n{\"q_img\": \"./images/B007RBI9P8.png\", \"q_text\": \"is white colored and sleeveless, and is white with no sleeves\", \"positive_key\": \"B009MMJFFS\", \"positive_value\": \"./images/B009MMJFFS.png\", \"hn_image\": [\"./images/B007RBI9P8.png\"]}\n{\"q_img\": \"./images/B00AR014KI.png\", \"q_text\": \"The shirt is blue with blue and red strips., and has longer sleeves and is blue and red striped\", \"positive_key\": \"B00BSUEASE\", \"positive_value\": \"./images/B00BSUEASE.png\", \"hn_image\": [\"./images/B00AR014KI.png\"]}\n{\"q_img\": \"./images/B003RW1XTK.png\", \"q_text\": \" belted, and is less political and more punk rocki\", \"positive_key\": \"B000NMF34I\", \"positive_value\": \"./images/B000NMF34I.png\", \"hn_image\": [\"./images/B003RW1XTK.png\"]}\n{\"q_img\": \"./images/B0023RJDIS.png\", \"q_text\": \"sleeveless and yellow color, and has almost no sleeves on an orange tee.\", \"positive_key\": \"B00BQMFTD4\", \"positive_value\": \"./images/B00BQMFTD4.png\", \"hn_image\": [\"./images/B0023RJDIS.png\"]}\n{\"q_img\": \"./images/B008CMAZW6.png\", \"q_text\": \"is more solid and more fitted at waist, and is coral sleeveless design\", \"positive_key\": \"B00BXYXTSM\", \"positive_value\": \"./images/B00BXYXTSM.png\", \"hn_image\": [\"./images/B008CMAZW6.png\"]}\n{\"q_img\": \"./images/B00CP7TJ4E.png\", \"q_text\": \"It has a v-neck., and is more solid tan\", \"positive_key\": \"B009DO128S\", \"positive_value\": \"./images/B009DO128S.png\", \"hn_image\": [\"./images/B00CP7TJ4E.png\"]}\n{\"q_img\": \"./images/B007C4EYY0.png\", \"q_text\": \"Is white with a graphic print, and is a tank top\", \"positive_key\": \"B004H0MME6\", \"positive_value\": \"./images/B004H0MME6.png\", \"hn_image\": [\"./images/B007C4EYY0.png\"]}\n{\"q_img\": \"./images/B004S7QHVW.png\", \"q_text\": \"is yellow, and is yellow\", \"positive_key\": \"B005OCKU18\", \"positive_value\": \"./images/B005OCKU18.png\", \"hn_image\": [\"./images/B004S7QHVW.png\"]}\n{\"q_img\": \"./images/B005HPV9VC.png\", \"q_text\": \"The shirt is white with animal print., and longer sleeves\", \"positive_key\": \"B00CS24LOE\", \"positive_value\": \"./images/B00CS24LOE.png\", \"hn_image\": [\"./images/B005HPV9VC.png\"]}\n{\"q_img\": \"./images/B008DEUD7A.png\", \"q_text\": \"is more fitting and plain black, and is a solid black color\", \"positive_key\": \"B007WAFEWK\", \"positive_value\": \"./images/B007WAFEWK.png\", \"hn_image\": [\"./images/B008DEUD7A.png\"]}\n{\"q_img\": \"./images/B005BYTQ3W.png\", \"q_text\": \"has short sleeves with an image, and grey with different shoe logo\", \"positive_key\": \"B00DNGZ706\", \"positive_value\": \"./images/B00DNGZ706.png\", \"hn_image\": [\"./images/B005BYTQ3W.png\"]}\n{\"q_img\": \"./images/B00B1GLPWE.png\", \"q_text\": \"is more fitted and feminine, and has shorter sleeves\", \"positive_key\": \"B008QQH4S6\", \"positive_value\": \"./images/B008QQH4S6.png\", \"hn_image\": [\"./images/B00B1GLPWE.png\"]}\n{\"q_img\": \"./images/B009GGSKAG.png\", \"q_text\": \"has a lighter color, and is more white and has a bigger graphic\", \"positive_key\": \"B00843CFNK\", \"positive_value\": \"./images/B00843CFNK.png\", \"hn_image\": [\"./images/B009GGSKAG.png\"]}\n{\"q_img\": \"./images/B00ES89826.png\", \"q_text\": \"is blue with long sleeves, and lighter\", \"positive_key\": \"B003EEMH82\", \"positive_value\": \"./images/B003EEMH82.png\", \"hn_image\": [\"./images/B00ES89826.png\"]}\n{\"q_img\": \"./images/B007WAFEWK.png\", \"q_text\": \"is more flowing, and is looser fitted and is lighter in color\", \"positive_key\": \"B00E6RAWS8\", \"positive_value\": \"./images/B00E6RAWS8.png\", \"hn_image\": [\"./images/B007WAFEWK.png\"]}\n{\"q_img\": \"./images/B00DMP10Y0.png\", \"q_text\": \"more gathered at the waist, and is tan with short sleeves\", \"positive_key\": \"B00CF42390\", \"positive_value\": \"./images/B00CF42390.png\", \"hn_image\": [\"./images/B00DMP10Y0.png\"]}\n{\"q_img\": \"./images/B006OC3YR4.png\", \"q_text\": \"Is more flowing and darker, and is more sheer and loose\", \"positive_key\": \"B008IE0VYK\", \"positive_value\": \"./images/B008IE0VYK.png\", \"hn_image\": [\"./images/B006OC3YR4.png\"]}\n{\"q_img\": \"./images/B007T8HCPW.png\", \"q_text\": \"has sleeves and less revealing, and has sleeves\", \"positive_key\": \"B008L13H1O\", \"positive_value\": \"./images/B008L13H1O.png\", \"hn_image\": [\"./images/B007T8HCPW.png\"]}\n{\"q_img\": \"./images/B00BWYJ9XM.png\", \"q_text\": \"The shirt is loose fitting and gray, and Is not as sheer\", \"positive_key\": \"B00AU4N846\", \"positive_value\": \"./images/B00AU4N846.png\", \"hn_image\": [\"./images/B00BWYJ9XM.png\"]}\n{\"q_img\": \"./images/B005GI9BQ0.png\", \"q_text\": \"Is more ruffled and fitted, and is grey with longer sleeves.\", \"positive_key\": \"B00B2TF87S\", \"positive_value\": \"./images/B00B2TF87S.png\", \"hn_image\": [\"./images/B005GI9BQ0.png\"]}\n{\"q_img\": \"./images/B008DLCSL2.png\", \"q_text\": \"Brown with graphic on front, and is a tan tee shirt with animal face\", \"positive_key\": \"B0071BOHGO\", \"positive_value\": \"./images/B0071BOHGO.png\", \"hn_image\": [\"./images/B008DLCSL2.png\"]}\n{\"q_img\": \"./images/B00BRCKMGC.png\", \"q_text\": \"is button up with sleeves, and Is more sheer\", \"positive_key\": \"B00A159E2O\", \"positive_value\": \"./images/B00A159E2O.png\", \"hn_image\": [\"./images/B00BRCKMGC.png\"]}\n{\"q_img\": \"./images/B007SOBBBI.png\", \"q_text\": \"has longer sleeves and is solid white, and has longer sleeves and is white\", \"positive_key\": \"B003BQ07UI\", \"positive_value\": \"./images/B003BQ07UI.png\", \"hn_image\": [\"./images/B007SOBBBI.png\"]}\n{\"q_img\": \"./images/B001NZVD4I.png\", \"q_text\": \"is lighter in color and has american flag, and is more patriotic and less designer\", \"positive_key\": \"B004L83ZEU\", \"positive_value\": \"./images/B004L83ZEU.png\", \"hn_image\": [\"./images/B001NZVD4I.png\"]}\n{\"q_img\": \"./images/B009325DWG.png\", \"q_text\": \"has no sleeves and is purple, and is purple and has crisscross drape\", \"positive_key\": \"B00D7ZJQM8\", \"positive_value\": \"./images/B00D7ZJQM8.png\", \"hn_image\": [\"./images/B009325DWG.png\"]}\n{\"q_img\": \"./images/B006VJXGAU.png\", \"q_text\": \"is lighter in color with a different print, and is a lighter color\", \"positive_key\": \"B0051C9FHQ\", \"positive_value\": \"./images/B0051C9FHQ.png\", \"hn_image\": [\"./images/B006VJXGAU.png\"]}\n{\"q_img\": \"./images/B005FMVQ1U.png\", \"q_text\": \"is green and fuller, and is green and red\", \"positive_key\": \"B00FH695XA\", \"positive_value\": \"./images/B00FH695XA.png\", \"hn_image\": [\"./images/B005FMVQ1U.png\"]}\n{\"q_img\": \"./images/B003QOR38E.png\", \"q_text\": \"has shorter sleeves with red color, and is red in color and short-sleeved\", \"positive_key\": \"B003QP16LI\", \"positive_value\": \"./images/B003QP16LI.png\", \"hn_image\": [\"./images/B003QOR38E.png\"]}\n{\"q_img\": \"./images/B00AK1OD66.png\", \"q_text\": \"is white with buttons down and lace, and is sleeveless and buttons up\", \"positive_key\": \"B00BAD1DI4\", \"positive_value\": \"./images/B00BAD1DI4.png\", \"hn_image\": [\"./images/B00AK1OD66.png\"]}\n{\"q_img\": \"./images/B008DVXGO0.png\", \"q_text\": \"black buttoned pink blouse, and is pink and has a v neck and collar\", \"positive_key\": \"B008DVXI34\", \"positive_value\": \"./images/B008DVXI34.png\", \"hn_image\": [\"./images/B008DVXGO0.png\"]}\n{\"q_img\": \"./images/B00CD8H0N2.png\", \"q_text\": \"is white with a different logo, and it is white\", \"positive_key\": \"B00CS6IV1E\", \"positive_value\": \"./images/B00CS6IV1E.png\", \"hn_image\": [\"./images/B00CD8H0N2.png\"]}\n{\"q_img\": \"./images/B0071XH3WM.png\", \"q_text\": \"has a darker color and a text print, and is black and doesn't have a collar\", \"positive_key\": \"B008IHCNVQ\", \"positive_value\": \"./images/B008IHCNVQ.png\", \"hn_image\": [\"./images/B0071XH3WM.png\"]}\n{\"q_img\": \"./images/B00ADX6GYS.png\", \"q_text\": \"is more adult, and is more navy blue colored\", \"positive_key\": \"B00AKHUAL2\", \"positive_value\": \"./images/B00AKHUAL2.png\", \"hn_image\": [\"./images/B00ADX6GYS.png\"]}\n{\"q_img\": \"./images/B004CPWN1I.png\", \"q_text\": \"is all black and has white buttons, and short sleeves black design buttons down front\", \"positive_key\": \"B0010XXAA8\", \"positive_value\": \"./images/B0010XXAA8.png\", \"hn_image\": [\"./images/B004CPWN1I.png\"]}\n{\"q_img\": \"./images/B000Q6K9CM.png\", \"q_text\": \"is darker, and has stripes and is loose on the shoulders.\", \"positive_key\": \"B006MD5LYE\", \"positive_value\": \"./images/B006MD5LYE.png\", \"hn_image\": [\"./images/B000Q6K9CM.png\"]}\n{\"q_img\": \"./images/B003ICLHAO.png\", \"q_text\": \"The shirt is loose fitting and brown in color., and has shorter sleeves and is darker in colour\", \"positive_key\": \"B00A13D3WI\", \"positive_value\": \"./images/B00A13D3WI.png\", \"hn_image\": [\"./images/B003ICLHAO.png\"]}\n{\"q_img\": \"./images/B0069J3HWY.png\", \"q_text\": \"The shirt is long sleeved and black in color., and is tighter fitting\", \"positive_key\": \"B0036MPF0E\", \"positive_value\": \"./images/B0036MPF0E.png\", \"hn_image\": [\"./images/B0069J3HWY.png\"]}\n{\"q_img\": \"./images/B002KG5WD2.png\", \"q_text\": \"The shirt is white with cowboy boots., and is light grey with picture and text\", \"positive_key\": \"B009EWFGV8\", \"positive_value\": \"./images/B009EWFGV8.png\", \"hn_image\": [\"./images/B002KG5WD2.png\"]}\n{\"q_img\": \"./images/B009YK8DFQ.png\", \"q_text\": \"is long sleeve solid black, and is plain color and has long sleeves.\", \"positive_key\": \"B003M2XW1M\", \"positive_value\": \"./images/B003M2XW1M.png\", \"hn_image\": [\"./images/B009YK8DFQ.png\"]}\n{\"q_img\": \"./images/B0036MPEUU.png\", \"q_text\": \"is light blue with buttons down, and is a bright button up shirt with pocket\", \"positive_key\": \"B0036Z9ZJI\", \"positive_value\": \"./images/B0036Z9ZJI.png\", \"hn_image\": [\"./images/B0036MPEUU.png\"]}\n{\"q_img\": \"./images/B00858UOEQ.png\", \"q_text\": \"is violet and sleeveless, and Is lavender and sleeveless\", \"positive_key\": \"B004HFRFH0\", \"positive_value\": \"./images/B004HFRFH0.png\", \"hn_image\": [\"./images/B00858UOEQ.png\"]}\n{\"q_img\": \"./images/B0064XXI42.png\", \"q_text\": \"The shirt has short sleeves and is melon in color., and has short sleeves on it\", \"positive_key\": \"B0075CJZ0W\", \"positive_value\": \"./images/B0075CJZ0W.png\", \"hn_image\": [\"./images/B0064XXI42.png\"]}\n{\"q_img\": \"./images/B00BG0FKN0.png\", \"q_text\": \"is purple and solid colored, and is solid purple color\", \"positive_key\": \"B00D02G23O\", \"positive_value\": \"./images/B00D02G23O.png\", \"hn_image\": [\"./images/B00BG0FKN0.png\"]}\n{\"q_img\": \"./images/B00BULGQGK.png\", \"q_text\": \"is more revealing and solid blue, and is not a ruffled button up look\", \"positive_key\": \"B007JRRY06\", \"positive_value\": \"./images/B007JRRY06.png\", \"hn_image\": [\"./images/B00BULGQGK.png\"]}\n{\"q_img\": \"./images/B0091DWZ9G.png\", \"q_text\": \"is mint green with long sleeves, and has drawstring at waist\", \"positive_key\": \"B00AYCLHYM\", \"positive_value\": \"./images/B00AYCLHYM.png\", \"hn_image\": [\"./images/B0091DWZ9G.png\"]}\n{\"q_img\": \"./images/B0071G9W8M.png\", \"q_text\": \"is whiter, and Desired item is white with tank top sleeves\", \"positive_key\": \"B00A7QHUSW\", \"positive_value\": \"./images/B00A7QHUSW.png\", \"hn_image\": [\"./images/B0071G9W8M.png\"]}\n{\"q_img\": \"./images/B00E8CMIK6.png\", \"q_text\": \"is red, and Is brighter colored\", \"positive_key\": \"B00DQND01E\", \"positive_value\": \"./images/B00DQND01E.png\", \"hn_image\": [\"./images/B00E8CMIK6.png\"]}\n{\"q_img\": \"./images/B002FU6SQS.png\", \"q_text\": \"It is a brown t-shirt with letters., and Is darker and more casual\", \"positive_key\": \"B007OTSRDM\", \"positive_value\": \"./images/B007OTSRDM.png\", \"hn_image\": [\"./images/B002FU6SQS.png\"]}\n{\"q_img\": \"./images/B0045ZH0AO.png\", \"q_text\": \"is lighter and has shorter sleeves, and has longer sleeve and is white color\", \"positive_key\": \"B0043EVLRU\", \"positive_value\": \"./images/B0043EVLRU.png\", \"hn_image\": [\"./images/B0045ZH0AO.png\"]}\n{\"q_img\": \"./images/B0016C0VIW.png\", \"q_text\": \"The shirt is a black button up shirt., and is black in color\", \"positive_key\": \"B00265PFKC\", \"positive_value\": \"./images/B00265PFKC.png\", \"hn_image\": [\"./images/B0016C0VIW.png\"]}\n{\"q_img\": \"./images/B0089JCK3O.png\", \"q_text\": \"is darker, and Desired item is a graphic tee with white lettering\", \"positive_key\": \"B005TUKIGW\", \"positive_value\": \"./images/B005TUKIGW.png\", \"hn_image\": [\"./images/B0089JCK3O.png\"]}\n{\"q_img\": \"./images/B00CNH9ZQS.png\", \"q_text\": \"has a yellow and black color design, and long sleeve black and yellow artsy dress\", \"positive_key\": \"B00ESFH2D6\", \"positive_value\": \"./images/B00ESFH2D6.png\", \"hn_image\": [\"./images/B00CNH9ZQS.png\"]}\n{\"q_img\": \"./images/B002V0N9M8.png\", \"q_text\": \"is crisper looking and less muted, and is pink with white logo\", \"positive_key\": \"B002YF05M2\", \"positive_value\": \"./images/B002YF05M2.png\", \"hn_image\": [\"./images/B002V0N9M8.png\"]}\n{\"q_img\": \"./images/B003RW1XTK.png\", \"q_text\": \"is grey colored, and is lighter in color\", \"positive_key\": \"B0070S5STS\", \"positive_value\": \"./images/B0070S5STS.png\", \"hn_image\": [\"./images/B003RW1XTK.png\"]}\n{\"q_img\": \"./images/B00ADSHGFG.png\", \"q_text\": \"white color and v-neck, and is white color with font graphic design\", \"positive_key\": \"B00365F5CY\", \"positive_value\": \"./images/B00365F5CY.png\", \"hn_image\": [\"./images/B00ADSHGFG.png\"]}\n{\"q_img\": \"./images/B00644M0ZE.png\", \"q_text\": \"The hat is rainbow colored., and is a hat\", \"positive_key\": \"B003C27BE6\", \"positive_value\": \"./images/B003C27BE6.png\", \"hn_image\": [\"./images/B00644M0ZE.png\"]}\n{\"q_img\": \"./images/B00E8DA258.png\", \"q_text\": \"is blue with deep neck, and blue\", \"positive_key\": \"B009Q2ETA0\", \"positive_value\": \"./images/B009Q2ETA0.png\", \"hn_image\": [\"./images/B00E8DA258.png\"]}\n{\"q_img\": \"./images/B00BG0FKN0.png\", \"q_text\": \"is a summery, and Has brighter colors & is fitted\", \"positive_key\": \"B00ATQ740Y\", \"positive_value\": \"./images/B00ATQ740Y.png\", \"hn_image\": [\"./images/B00BG0FKN0.png\"]}\n{\"q_img\": \"./images/B00APD8ZDQ.png\", \"q_text\": \"The shirt is bright purple in color and loose fitting., and shorter sleeves\", \"positive_key\": \"B004WHUH7I\", \"positive_value\": \"./images/B004WHUH7I.png\", \"hn_image\": [\"./images/B00APD8ZDQ.png\"]}\n{\"q_img\": \"./images/B00CP55VFM.png\", \"q_text\": \"The shirt is black with white lettering., and is more casual with a higher neckline\", \"positive_key\": \"B008C24CU2\", \"positive_value\": \"./images/B008C24CU2.png\", \"hn_image\": [\"./images/B00CP55VFM.png\"]}\n{\"q_img\": \"./images/B004T6ZAO2.png\", \"q_text\": \"is lighter and has longer sleeves, and is a solid button down\", \"positive_key\": \"B007H5GJTM\", \"positive_value\": \"./images/B007H5GJTM.png\", \"hn_image\": [\"./images/B004T6ZAO2.png\"]}\n{\"q_img\": \"./images/B00B02GQ1E.png\", \"q_text\": \"is green in color with a v neck, and Is lower cut neckline and kakhi green\", \"positive_key\": \"B0073YHI9W\", \"positive_value\": \"./images/B0073YHI9W.png\", \"hn_image\": [\"./images/B00B02GQ1E.png\"]}\n{\"q_img\": \"./images/B00APD9FKS.png\", \"q_text\": \"is darker, and has striped end of sleevedesign\", \"positive_key\": \"B00APD9CPQ\", \"positive_value\": \"./images/B00APD9CPQ.png\", \"hn_image\": [\"./images/B00APD9FKS.png\"]}\n{\"q_img\": \"./images/B0036WSZVA.png\", \"q_text\": \"The shirt is white in color with black and red lettering., and pink round neck with large graphic\", \"positive_key\": \"B005WKSX5M\", \"positive_value\": \"./images/B005WKSX5M.png\", \"hn_image\": [\"./images/B0036WSZVA.png\"]}\n{\"q_img\": \"./images/B00092SAR4.png\", \"q_text\": \"is purple, and Is blue Batman t-shirt\", \"positive_key\": \"B005X4DNHA\", \"positive_value\": \"./images/B005X4DNHA.png\", \"hn_image\": [\"./images/B00092SAR4.png\"]}\n{\"q_img\": \"./images/B005A1U8OC.png\", \"q_text\": \"has no sleeves and is white, and is lighter in color and has no sleeves\", \"positive_key\": \"B0053AJX9G\", \"positive_value\": \"./images/B0053AJX9G.png\", \"hn_image\": [\"./images/B005A1U8OC.png\"]}\n{\"q_img\": \"./images/B005TFBF50.png\", \"q_text\": \"is a black short sleeved t-shirt with american eagle emblem, and more colorful logo\", \"positive_key\": \"B007E3LL4U\", \"positive_value\": \"./images/B007E3LL4U.png\", \"hn_image\": [\"./images/B005TFBF50.png\"]}\n{\"q_img\": \"./images/B009FF1MDA.png\", \"q_text\": \"a dark colored loong sleeve button down shirt with no pockets, and is darker colored\", \"positive_key\": \"B003U2T0DI\", \"positive_value\": \"./images/B003U2T0DI.png\", \"hn_image\": [\"./images/B009FF1MDA.png\"]}\n{\"q_img\": \"./images/B008P57GXG.png\", \"q_text\": \"has lower neckline and has iconic graphic, and is white with an open neck\", \"positive_key\": \"B00C664F5M\", \"positive_value\": \"./images/B00C664F5M.png\", \"hn_image\": [\"./images/B008P57GXG.png\"]}\n{\"q_img\": \"./images/B00CUU2RUO.png\", \"q_text\": \"is more photographic and less witty, and has a man on it.\", \"positive_key\": \"B008EMP6UK\", \"positive_value\": \"./images/B008EMP6UK.png\", \"hn_image\": [\"./images/B00CUU2RUO.png\"]}\n{\"q_img\": \"./images/B00CDZC2VA.png\", \"q_text\": \" and doesn't have buttons, and it has long sleeve and it is red\", \"positive_key\": \"B00F4AMFE0\", \"positive_value\": \"./images/B00F4AMFE0.png\", \"hn_image\": [\"./images/B00CDZC2VA.png\"]}\n{\"q_img\": \"./images/B008DVXGO0.png\", \"q_text\": \"Frilly and button up, and is a button up with loose fitting.\", \"positive_key\": \"B006FIZGVE\", \"positive_value\": \"./images/B006FIZGVE.png\", \"hn_image\": [\"./images/B008DVXGO0.png\"]}\n{\"q_img\": \"./images/B00980K5X0.png\", \"q_text\": \"has no sleeves, and lighter colored\", \"positive_key\": \"B0097ZNH7M\", \"positive_value\": \"./images/B0097ZNH7M.png\", \"hn_image\": [\"./images/B00980K5X0.png\"]}\n{\"q_img\": \"./images/B000J41PCO.png\", \"q_text\": \"The shirt is red and white plaid., and is a button down shirt\", \"positive_key\": \"B0043OA0YA\", \"positive_value\": \"./images/B0043OA0YA.png\", \"hn_image\": [\"./images/B000J41PCO.png\"]}\n{\"q_img\": \"./images/B009325DWG.png\", \"q_text\": \"has a darker purple color, and orange block\", \"positive_key\": \"B008A32ZVG\", \"positive_value\": \"./images/B008A32ZVG.png\", \"hn_image\": [\"./images/B009325DWG.png\"]}\n{\"q_img\": \"./images/B001BJXXD0.png\", \"q_text\": \"The shirt is black with the American Flag., and is darker\", \"positive_key\": \"B005CJYYM4\", \"positive_value\": \"./images/B005CJYYM4.png\", \"hn_image\": [\"./images/B001BJXXD0.png\"]}\n{\"q_img\": \"./images/B003XII7B0.png\", \"q_text\": \"is black and white striped, and has shorter sleeves and puffier shoulders\", \"positive_key\": \"B004HKI7M2\", \"positive_value\": \"./images/B004HKI7M2.png\", \"hn_image\": [\"./images/B003XII7B0.png\"]}\n{\"q_img\": \"./images/B00GR9QGL0.png\", \"q_text\": \"is more sexy and shorter, and orange print\", \"positive_key\": \"B00EV6D0G0\", \"positive_value\": \"./images/B00EV6D0G0.png\", \"hn_image\": [\"./images/B00GR9QGL0.png\"]}\n{\"q_img\": \"./images/B007IFMWA6.png\", \"q_text\": \"red, and is red button-up with long sleeves\", \"positive_key\": \"B0083F8T20\", \"positive_value\": \"./images/B0083F8T20.png\", \"hn_image\": [\"./images/B007IFMWA6.png\"]}\n{\"q_img\": \"./images/B007GO8A00.png\", \"q_text\": \"is more ethnic and more patterned, and is aqua colored and patterned design\", \"positive_key\": \"B00EB6PRV6\", \"positive_value\": \"./images/B00EB6PRV6.png\", \"hn_image\": [\"./images/B007GO8A00.png\"]}\n{\"q_img\": \"./images/B00BNMTGEA.png\", \"q_text\": \"strapless pink tube top, and is more revealing and shorter\", \"positive_key\": \"B00CO951MW\", \"positive_value\": \"./images/B00CO951MW.png\", \"hn_image\": [\"./images/B00BNMTGEA.png\"]}\n{\"q_img\": \"./images/B006PATFFU.png\", \"q_text\": \"is short sleeved with a large graphic on it, and Desired item is a graphic tee\", \"positive_key\": \"B002SW2QGE\", \"positive_value\": \"./images/B002SW2QGE.png\", \"hn_image\": [\"./images/B006PATFFU.png\"]}\n{\"q_img\": \"./images/B008L464E8.png\", \"q_text\": \"has shorter sleeves with an image in the center, and Is darker in color and more casual\", \"positive_key\": \"B005OD69BC\", \"positive_value\": \"./images/B005OD69BC.png\", \"hn_image\": [\"./images/B008L464E8.png\"]}\n{\"q_img\": \"./images/B008UFVTP2.png\", \"q_text\": \"The tank top is purple and pink in color., and has graphic and is darker purple with stribes\", \"positive_key\": \"B00DH8ZIOA\", \"positive_value\": \"./images/B00DH8ZIOA.png\", \"hn_image\": [\"./images/B008UFVTP2.png\"]}\n{\"q_img\": \"./images/B00C0QCRHG.png\", \"q_text\": \" purple and white and is loose fitting., and is black with long sleeves\", \"positive_key\": \"B0097ATVDQ\", \"positive_value\": \"./images/B0097ATVDQ.png\", \"hn_image\": [\"./images/B00C0QCRHG.png\"]}\n{\"q_img\": \"./images/B00A3HL3Y2.png\", \"q_text\": \"is slippers, and is pink and more fuzzy\", \"positive_key\": \"B00DUZL2OU\", \"positive_value\": \"./images/B00DUZL2OU.png\", \"hn_image\": [\"./images/B00A3HL3Y2.png\"]}\n{\"q_img\": \"./images/B007OZIXJO.png\", \"q_text\": \"has longer sleeves and color is black, and Black solid with white button-down mid-sleeve\", \"positive_key\": \"B003R6P2P2\", \"positive_value\": \"./images/B003R6P2P2.png\", \"hn_image\": [\"./images/B007OZIXJO.png\"]}\n{\"q_img\": \"./images/B0051DQXH0.png\", \"q_text\": \"is blue with no sleeves, and is blue and a tank top\", \"positive_key\": \"B003ICJSXC\", \"positive_value\": \"./images/B003ICJSXC.png\", \"hn_image\": [\"./images/B0051DQXH0.png\"]}\n{\"q_img\": \"./images/B00CE5Z5OA.png\", \"q_text\": \" and red with Alice in Wonderland., and is in solid red\", \"positive_key\": \"B0086RPNL0\", \"positive_value\": \"./images/B0086RPNL0.png\", \"hn_image\": [\"./images/B00CE5Z5OA.png\"]}\n{\"q_img\": \"./images/B0083SO3AO.png\", \"q_text\": \"is brown, and has the Constitution written graphic on it\", \"positive_key\": \"B002EIS4BS\", \"positive_value\": \"./images/B002EIS4BS.png\", \"hn_image\": [\"./images/B0083SO3AO.png\"]}\n{\"q_img\": \"./images/B003EUVTHG.png\", \"q_text\": \"The shirt is red with the Flash Symbol., and is lighter\", \"positive_key\": \"B001R5QU5Q\", \"positive_value\": \"./images/B001R5QU5Q.png\", \"hn_image\": [\"./images/B003EUVTHG.png\"]}\n{\"q_img\": \"./images/B00D6ANVMU.png\", \"q_text\": \"The shirt is gray with black lettering., and is mostly gray\", \"positive_key\": \"B00B3599SA\", \"positive_value\": \"./images/B00B3599SA.png\", \"hn_image\": [\"./images/B00D6ANVMU.png\"]}\n{\"q_img\": \"./images/B00CMD9IKQ.png\", \"q_text\": \"is darker, and is dark gray colored\", \"positive_key\": \"B005QQGOTO\", \"positive_value\": \"./images/B005QQGOTO.png\", \"hn_image\": [\"./images/B00CMD9IKQ.png\"]}\n{\"q_img\": \"./images/B0036MPEUU.png\", \"q_text\": \"The tight fitting shirt has long sleeves and is black in color., and is very similar\", \"positive_key\": \"B00BBQB4E8\", \"positive_value\": \"./images/B00BBQB4E8.png\", \"hn_image\": [\"./images/B0036MPEUU.png\"]}\n{\"q_img\": \"./images/B0026JKY9A.png\", \"q_text\": \"is darker with longer sleeves, and purple with grey trim and slim fit\", \"positive_key\": \"B0047WSZM2\", \"positive_value\": \"./images/B0047WSZM2.png\", \"hn_image\": [\"./images/B0026JKY9A.png\"]}\n{\"q_img\": \"./images/B0092S985O.png\", \"q_text\": \"is a short sleeve light blue color with musical notes art, and light blue with music notes\", \"positive_key\": \"B00E9KDNLK\", \"positive_value\": \"./images/B00E9KDNLK.png\", \"hn_image\": [\"./images/B0092S985O.png\"]}\n{\"q_img\": \"./images/B00BQSWLC0.png\", \"q_text\": \"has longer sleeves and beige colored, and Is more sheer and whimsical\", \"positive_key\": \"B008646FOC\", \"positive_value\": \"./images/B008646FOC.png\", \"hn_image\": [\"./images/B00BQSWLC0.png\"]}\n{\"q_img\": \"./images/B00AKHPDDW.png\", \"q_text\": \"has no sleeve with white and black colors, and Black/yellow patterned shirt with white lace on top\", \"positive_key\": \"B007XD4TEA\", \"positive_value\": \"./images/B007XD4TEA.png\", \"hn_image\": [\"./images/B00AKHPDDW.png\"]}\n{\"q_img\": \"./images/B00ECRIKTA.png\", \"q_text\": \"its black with some glitter an no button down, and it has one color and it is pleated\", \"positive_key\": \"B00EKIQ8GS\", \"positive_value\": \"./images/B00EKIQ8GS.png\", \"hn_image\": [\"./images/B00ECRIKTA.png\"]}\n{\"q_img\": \"./images/B0085OCK88.png\", \"q_text\": \"is light pink with small writing, and is pink with an I Love JJ image on it\", \"positive_key\": \"B00CH21TQ8\", \"positive_value\": \"./images/B00CH21TQ8.png\", \"hn_image\": [\"./images/B0085OCK88.png\"]}\n{\"q_img\": \"./images/B00A3MVCUC.png\", \"q_text\": \"has shorter sleeves and lighter color, and is lighter and has shorter sleeves\", \"positive_key\": \"B00A3MV3CO\", \"positive_value\": \"./images/B00A3MV3CO.png\", \"hn_image\": [\"./images/B00A3MVCUC.png\"]}\n{\"q_img\": \"./images/B005E0TR32.png\", \"q_text\": \"is feminine fitting and pink, and is pink with shorter sleeves\", \"positive_key\": \"B003HB8MIQ\", \"positive_value\": \"./images/B003HB8MIQ.png\", \"hn_image\": [\"./images/B005E0TR32.png\"]}\n{\"q_img\": \"./images/B00DEMQAH8.png\", \"q_text\": \"is pink colored and checkered pattern, and Is longer\", \"positive_key\": \"B004YXESRA\", \"positive_value\": \"./images/B004YXESRA.png\", \"hn_image\": [\"./images/B00DEMQAH8.png\"]}\n{\"q_img\": \"./images/B00DP8U3GU.png\", \"q_text\": \"is less see-thru and less feminine, and draped\", \"positive_key\": \"B0085CNLL0\", \"positive_value\": \"./images/B0085CNLL0.png\", \"hn_image\": [\"./images/B00DP8U3GU.png\"]}\n{\"q_img\": \"./images/B0070TYTWO.png\", \"q_text\": \"is lighter and has longer sleeves, and is white with long sleeves\", \"positive_key\": \"B00AFRSNKM\", \"positive_value\": \"./images/B00AFRSNKM.png\", \"hn_image\": [\"./images/B0070TYTWO.png\"]}\n{\"q_img\": \"./images/B00BUSQ4H4.png\", \"q_text\": \"is light blue with white stripes, and is blue with white stripes and shorter sleeves\", \"positive_key\": \"B00AZ5BIAG\", \"positive_value\": \"./images/B00AZ5BIAG.png\", \"hn_image\": [\"./images/B00BUSQ4H4.png\"]}\n{\"q_img\": \"./images/B007ZTA748.png\", \"q_text\": \"has longer sleeves and has a print, and has a blue and white print with longer sleeves\", \"positive_key\": \"B005M2DCNS\", \"positive_value\": \"./images/B005M2DCNS.png\", \"hn_image\": [\"./images/B007ZTA748.png\"]}\n{\"q_img\": \"./images/B00AG42HGU.png\", \"q_text\": \"is black with long sleeves, and is black and less revealing\", \"positive_key\": \"B00AKN42A6\", \"positive_value\": \"./images/B00AKN42A6.png\", \"hn_image\": [\"./images/B00AG42HGU.png\"]}\n{\"q_img\": \"./images/B00H03TS7Q.png\", \"q_text\": \"is blue colored with image of rainbow and car, and has more blue\", \"positive_key\": \"B009CEZ6KY\", \"positive_value\": \"./images/B009CEZ6KY.png\", \"hn_image\": [\"./images/B00H03TS7Q.png\"]}\n{\"q_img\": \"./images/B0089FEXNI.png\", \"q_text\": \"has a short sleeve blue color, and more blue more polo style\", \"positive_key\": \"B008DVKTPY\", \"positive_value\": \"./images/B008DVKTPY.png\", \"hn_image\": [\"./images/B0089FEXNI.png\"]}\n{\"q_img\": \"./images/B00CHYIX56.png\", \"q_text\": \"The shirt is gray with an image of Popeye., and is a lighter color\", \"positive_key\": \"B004JHNKR0\", \"positive_value\": \"./images/B004JHNKR0.png\", \"hn_image\": [\"./images/B00CHYIX56.png\"]}\n{\"q_img\": \"./images/B0059AA3AS.png\", \"q_text\": \"is a red sports bra, and orange\", \"positive_key\": \"B004S9C0VQ\", \"positive_value\": \"./images/B004S9C0VQ.png\", \"hn_image\": [\"./images/B0059AA3AS.png\"]}\n{\"q_img\": \"./images/B002RL9VFA.png\", \"q_text\": \"is a fitted black tank top, and is black with thin straps\", \"positive_key\": \"B003R1E3LQ\", \"positive_value\": \"./images/B003R1E3LQ.png\", \"hn_image\": [\"./images/B002RL9VFA.png\"]}\n{\"q_img\": \"./images/B00CS5PAZU.png\", \"q_text\": \"is black and has no words, and has a bigger graphic\", \"positive_key\": \"B004VNVMO0\", \"positive_value\": \"./images/B004VNVMO0.png\", \"hn_image\": [\"./images/B00CS5PAZU.png\"]}\n{\"q_img\": \"./images/B000Q6K9CM.png\", \"q_text\": \"short sleeved orange top, and is more orange\", \"positive_key\": \"B003XZOM9O\", \"positive_value\": \"./images/B003XZOM9O.png\", \"hn_image\": [\"./images/B000Q6K9CM.png\"]}\n{\"q_img\": \"./images/B007YSB65E.png\", \"q_text\": \"is beige with a design, and is grey and has more print\", \"positive_key\": \"B002OTJUUG\", \"positive_value\": \"./images/B002OTJUUG.png\", \"hn_image\": [\"./images/B007YSB65E.png\"]}\n{\"q_img\": \"./images/B008OGHR94.png\", \"q_text\": \"The shirt is long sleeved and is blue and gray her shorts are pink., and Is a grey top with long blue sleeves\", \"positive_key\": \"B00ALWZLS8\", \"positive_value\": \"./images/B00ALWZLS8.png\", \"hn_image\": [\"./images/B008OGHR94.png\"]}\n{\"q_img\": \"./images/B00E0L1X3I.png\", \"q_text\": \"The shirt is light blue in color., and is lower cut and less sexy\", \"positive_key\": \"B001UJP6TU\", \"positive_value\": \"./images/B001UJP6TU.png\", \"hn_image\": [\"./images/B00E0L1X3I.png\"]}\n{\"q_img\": \"./images/B008NA89DE.png\", \"q_text\": \"The shirt has white and red stripes., and red and white stripes\", \"positive_key\": \"B008M4UGVO\", \"positive_value\": \"./images/B008M4UGVO.png\", \"hn_image\": [\"./images/B008NA89DE.png\"]}\n{\"q_img\": \"./images/B00BJYIR9C.png\", \"q_text\": \"short sleeved top with different colors, and is sleeveless with a pattern\", \"positive_key\": \"B00BWI7CT6\", \"positive_value\": \"./images/B00BWI7CT6.png\", \"hn_image\": [\"./images/B00BJYIR9C.png\"]}\n{\"q_img\": \"./images/B002UNLYPK.png\", \"q_text\": \"has sleeves, and is ligher pink\", \"positive_key\": \"B006ZQ9284\", \"positive_value\": \"./images/B006ZQ9284.png\", \"hn_image\": [\"./images/B002UNLYPK.png\"]}\n{\"q_img\": \"./images/B003EACZ9M.png\", \"q_text\": \"is yellow colored, and is yellow\", \"positive_key\": \"B000YVF80C\", \"positive_value\": \"./images/B000YVF80C.png\", \"hn_image\": [\"./images/B003EACZ9M.png\"]}\n{\"q_img\": \"./images/B0057UCUEC.png\", \"q_text\": \"has long sleeves and a collar, and is a blue button up with collar.\", \"positive_key\": \"B0006UZHZM\", \"positive_value\": \"./images/B0006UZHZM.png\", \"hn_image\": [\"./images/B0057UCUEC.png\"]}\n{\"q_img\": \"./images/B003RW60ES.png\", \"q_text\": \"The shirt is black with white a white picture., and has a different graphic\", \"positive_key\": \"B008LP6XWU\", \"positive_value\": \"./images/B008LP6XWU.png\", \"hn_image\": [\"./images/B003RW60ES.png\"]}\n{\"q_img\": \"./images/B00B2T8KVY.png\", \"q_text\": \"is blue and has short sleeves, and Solid black shirt with blue scarf on front\", \"positive_key\": \"B00816I10Q\", \"positive_value\": \"./images/B00816I10Q.png\", \"hn_image\": [\"./images/B00B2T8KVY.png\"]}\n{\"q_img\": \"./images/B00CWMDY20.png\", \"q_text\": \"has multiple v shaped stripes, and is red blouse and has large round neck\", \"positive_key\": \"B00BNY0BGU\", \"positive_value\": \"./images/B00BNY0BGU.png\", \"hn_image\": [\"./images/B00CWMDY20.png\"]}\n{\"q_img\": \"./images/B002Q2O178.png\", \"q_text\": \"is more blousey and more dressy, and Has long sleeves and is black\", \"positive_key\": \"B00AAXLPNI\", \"positive_value\": \"./images/B00AAXLPNI.png\", \"hn_image\": [\"./images/B002Q2O178.png\"]}\n{\"q_img\": \"./images/B00A0I8G08.png\", \"q_text\": \"is a long sleeve black dress, and is darker with longer sleeves\", \"positive_key\": \"B00A0I81TO\", \"positive_value\": \"./images/B00A0I81TO.png\", \"hn_image\": [\"./images/B00A0I8G08.png\"]}\n{\"q_img\": \"./images/B00B8QSVT2.png\", \"q_text\": \"is lighter, and is pink and has more metallic silver\", \"positive_key\": \"B00B19GWBK\", \"positive_value\": \"./images/B00B19GWBK.png\", \"hn_image\": [\"./images/B00B8QSVT2.png\"]}\n{\"q_img\": \"./images/B00CJGBH1Y.png\", \"q_text\": \"is a turquoise scoop neck shirt with white decorative motif, and Has a decal design.\", \"positive_key\": \"B00980LBAQ\", \"positive_value\": \"./images/B00980LBAQ.png\", \"hn_image\": [\"./images/B00CJGBH1Y.png\"]}\n{\"q_img\": \"./images/B008P36HLK.png\", \"q_text\": \"Is longer and more chic, and is solid black with white borders.\", \"positive_key\": \"B007HYFREG\", \"positive_value\": \"./images/B007HYFREG.png\", \"hn_image\": [\"./images/B008P36HLK.png\"]}\n{\"q_img\": \"./images/B00FLX8STQ.png\", \"q_text\": \"is more feminine, and is more sexy and revealing\", \"positive_key\": \"B005ONXMI0\", \"positive_value\": \"./images/B005ONXMI0.png\", \"hn_image\": [\"./images/B00FLX8STQ.png\"]}\n{\"q_img\": \"./images/B00FN8311Y.png\", \"q_text\": \"is short sleeves and is gray, and Desired item has leather short sleeves and lower neckline\", \"positive_key\": \"B00A4N2S3K\", \"positive_value\": \"./images/B00A4N2S3K.png\", \"hn_image\": [\"./images/B00FN8311Y.png\"]}\n{\"q_img\": \"./images/B0097YF730.png\", \"q_text\": \"The tank top is green with the lucky charms character., and is more green colored\", \"positive_key\": \"B0018MRUVG\", \"positive_value\": \"./images/B0018MRUVG.png\", \"hn_image\": [\"./images/B0097YF730.png\"]}\n{\"q_img\": \"./images/B009LV2QW4.png\", \"q_text\": \"The tank top is peach in color., and is orange with thin straps and tighter fit\", \"positive_key\": \"B002DMJVQM\", \"positive_value\": \"./images/B002DMJVQM.png\", \"hn_image\": [\"./images/B009LV2QW4.png\"]}\n{\"q_img\": \"./images/B009YJEDT2.png\", \"q_text\": \"more natural colored, and longer sleeves\", \"positive_key\": \"B009YJB6PG\", \"positive_value\": \"./images/B009YJB6PG.png\", \"hn_image\": [\"./images/B009YJEDT2.png\"]}\n{\"q_img\": \"./images/B008QW03K6.png\", \"q_text\": \"The short sleeve shirt is white in color., and has shorter sleeves in blue and white color\", \"positive_key\": \"B00CN7XUWI\", \"positive_value\": \"./images/B00CN7XUWI.png\", \"hn_image\": [\"./images/B008QW03K6.png\"]}\n{\"q_img\": \"./images/B004045DEA.png\", \"q_text\": \"is white, and is lighter and has a graphic\", \"positive_key\": \"B0027IIDA2\", \"positive_value\": \"./images/B0027IIDA2.png\", \"hn_image\": [\"./images/B004045DEA.png\"]}\n{\"q_img\": \"./images/B00GGQZOHG.png\", \"q_text\": \"is yellow colored and shorter sleeves, and is bright yellow\", \"positive_key\": \"B0051DQXH0\", \"positive_value\": \"./images/B0051DQXH0.png\", \"hn_image\": [\"./images/B00GGQZOHG.png\"]}\n{\"q_img\": \"./images/B00AWMNV8Y.png\", \"q_text\": \"Has shorter sleeves and more buttons, and has shorter sleeves\", \"positive_key\": \"B00C2VHLS4\", \"positive_value\": \"./images/B00C2VHLS4.png\", \"hn_image\": [\"./images/B00AWMNV8Y.png\"]}\n{\"q_img\": \"./images/B007X4CRXE.png\", \"q_text\": \"is lighter, and is blue and more casual\", \"positive_key\": \"B000BTBPWW\", \"positive_value\": \"./images/B000BTBPWW.png\", \"hn_image\": [\"./images/B007X4CRXE.png\"]}\n{\"q_img\": \"./images/B00EWDMU7M.png\", \"q_text\": \"Is layered and brighter in color, and orange tank with high neckline\", \"positive_key\": \"B00A3QDXKU\", \"positive_value\": \"./images/B00A3QDXKU.png\", \"hn_image\": [\"./images/B00EWDMU7M.png\"]}\n{\"q_img\": \"./images/B008BT60TM.png\", \"q_text\": \"has less print, and has long sleeves\", \"positive_key\": \"B006UD94AS\", \"positive_value\": \"./images/B006UD94AS.png\", \"hn_image\": [\"./images/B008BT60TM.png\"]}\n{\"q_img\": \"./images/B0090KE5XE.png\", \"q_text\": \"is more revealing and more pastel colored, and is white with no sleeves\", \"positive_key\": \"B0070TYTWO\", \"positive_value\": \"./images/B0070TYTWO.png\", \"hn_image\": [\"./images/B0090KE5XE.png\"]}\n{\"q_img\": \"./images/B00BNQVHEI.png\", \"q_text\": \"The shirt is blue and red with white stripes., and is striped with blue trim\", \"positive_key\": \"B00AE6IWMS\", \"positive_value\": \"./images/B00AE6IWMS.png\", \"hn_image\": [\"./images/B00BNQVHEI.png\"]}\n{\"q_img\": \"./images/B009ZYOP1C.png\", \"q_text\": \"is lighter colored and more photographic, and has dog print and is dark grey color\", \"positive_key\": \"B009ZYYBVG\", \"positive_value\": \"./images/B009ZYYBVG.png\", \"hn_image\": [\"./images/B009ZYOP1C.png\"]}\n{\"q_img\": \"./images/B0093PJJLO.png\", \"q_text\": \"white 3/4 sleeve longer shirt, and is white with double pockets\", \"positive_key\": \"B00980LE0S\", \"positive_value\": \"./images/B00980LE0S.png\", \"hn_image\": [\"./images/B0093PJJLO.png\"]}\n{\"q_img\": \"./images/B00C2G46RS.png\", \"q_text\": \"has buttons and has pockets, and has buttons and pockets\", \"positive_key\": \"B00BSWFTJG\", \"positive_value\": \"./images/B00BSWFTJG.png\", \"hn_image\": [\"./images/B00C2G46RS.png\"]}\n{\"q_img\": \"./images/B00CY7NCB6.png\", \"q_text\": \"is lighter and has longer sleeves, and has more orange\", \"positive_key\": \"B009YJR60K\", \"positive_value\": \"./images/B009YJR60K.png\", \"hn_image\": [\"./images/B00CY7NCB6.png\"]}\n{\"q_img\": \"./images/B0089M6R2G.png\", \"q_text\": \"is multicolored, and solid color t-shirts\", \"positive_key\": \"B005DA4Q2K\", \"positive_value\": \"./images/B005DA4Q2K.png\", \"hn_image\": [\"./images/B0089M6R2G.png\"]}\n{\"q_img\": \"./images/B008XJWRCY.png\", \"q_text\": \"is darker with white lettering`, and has more text on it\", \"positive_key\": \"B0001TOS2Q\", \"positive_value\": \"./images/B0001TOS2Q.png\", \"hn_image\": [\"./images/B008XJWRCY.png\"]}\n{\"q_img\": \"./images/B0072C7GV0.png\", \"q_text\": \"The shirt is short sleeves and light in color., and short sleeves lighter color waterfall effect\", \"positive_key\": \"B00B9ZF3H0\", \"positive_value\": \"./images/B00B9ZF3H0.png\", \"hn_image\": [\"./images/B0072C7GV0.png\"]}\n{\"q_img\": \"./images/B00BN7XJNO.png\", \"q_text\": \"is off-shoulder and Crochet white, and has no sleeves\", \"positive_key\": \"B00B78M2XC\", \"positive_value\": \"./images/B00B78M2XC.png\", \"hn_image\": [\"./images/B00BN7XJNO.png\"]}\n{\"q_img\": \"./images/B00G3J4GB6.png\", \"q_text\": \"is more casual., and is darker and has shorter sleeves\", \"positive_key\": \"B004HE4K7E\", \"positive_value\": \"./images/B004HE4K7E.png\", \"hn_image\": [\"./images/B00G3J4GB6.png\"]}\n{\"q_img\": \"./images/B00408M6QO.png\", \"q_text\": \"is blue with a different logo, and is blue with bigger graphic\", \"positive_key\": \"B0099UJ9JK\", \"positive_value\": \"./images/B0099UJ9JK.png\", \"hn_image\": [\"./images/B00408M6QO.png\"]}\n{\"q_img\": \"./images/B0045DTP72.png\", \"q_text\": \"is light blue and shorter sleeves, and womens fit rounded neck light blue with graphic\", \"positive_key\": \"B008FJZ7LK\", \"positive_value\": \"./images/B008FJZ7LK.png\", \"hn_image\": [\"./images/B0045DTP72.png\"]}\n{\"q_img\": \"./images/B0058GP0K6.png\", \"q_text\": \"has longer sleeves, and is black and red with long sleeves\", \"positive_key\": \"B00EALYO5M\", \"positive_value\": \"./images/B00EALYO5M.png\", \"hn_image\": [\"./images/B0058GP0K6.png\"]}\n{\"q_img\": \"./images/B0067EUKX0.png\", \"q_text\": \"more colorful, and has text on it\", \"positive_key\": \"B001HBKGB4\", \"positive_value\": \"./images/B001HBKGB4.png\", \"hn_image\": [\"./images/B0067EUKX0.png\"]}\n{\"q_img\": \"./images/B007UR329M.png\", \"q_text\": \"is a solid color, and Is green and more plain\", \"positive_key\": \"B00B1Y4WNU\", \"positive_value\": \"./images/B00B1Y4WNU.png\", \"hn_image\": [\"./images/B007UR329M.png\"]}\n{\"q_img\": \"./images/B00FQUAV9E.png\", \"q_text\": \"is blue jersey and has short sleeves, and is blue in color\", \"positive_key\": \"B002UUXZS2\", \"positive_value\": \"./images/B002UUXZS2.png\", \"hn_image\": [\"./images/B00FQUAV9E.png\"]}\n{\"q_img\": \"./images/B005M02XIA.png\", \"q_text\": \"is blue and has sleeves, and Is blue with short sleeves.\", \"positive_key\": \"B0057UCUEC\", \"positive_value\": \"./images/B0057UCUEC.png\", \"hn_image\": [\"./images/B005M02XIA.png\"]}\n{\"q_img\": \"./images/B0045ZH0AO.png\", \"q_text\": \"The shirt is tight fitting with short sleeves and white in color., and is lighter in color\", \"positive_key\": \"B005WUPBZW\", \"positive_value\": \"./images/B005WUPBZW.png\", \"hn_image\": [\"./images/B0045ZH0AO.png\"]}\n{\"q_img\": \"./images/B008FMRCQK.png\", \"q_text\": \"is darker, and Is darker and less musical\", \"positive_key\": \"B000KD64ZC\", \"positive_value\": \"./images/B000KD64ZC.png\", \"hn_image\": [\"./images/B008FMRCQK.png\"]}\n{\"q_img\": \"./images/B00A061DH8.png\", \"q_text\": \"is more attractive, and is lighter and features a different animal\", \"positive_key\": \"B000K7GA66\", \"positive_value\": \"./images/B000K7GA66.png\", \"hn_image\": [\"./images/B00A061DH8.png\"]}\n{\"q_img\": \"./images/B00C9WFPGQ.png\", \"q_text\": \"is red button down with long sleeves, and is darker coloured\", \"positive_key\": \"B001AQOT0A\", \"positive_value\": \"./images/B001AQOT0A.png\", \"hn_image\": [\"./images/B00C9WFPGQ.png\"]}\n{\"q_img\": \"./images/B002BA5ZWK.png\", \"q_text\": \"is red and a button-up shirt, and is red with buttons\", \"positive_key\": \"B007E94EY8\", \"positive_value\": \"./images/B007E94EY8.png\", \"hn_image\": [\"./images/B002BA5ZWK.png\"]}\n{\"q_img\": \"./images/B002TNOCNW.png\", \"q_text\": \"is blue with short sleeves, and is blue and has shorter sleeve\", \"positive_key\": \"B00AF2OJHI\", \"positive_value\": \"./images/B00AF2OJHI.png\", \"hn_image\": [\"./images/B002TNOCNW.png\"]}\n{\"q_img\": \"./images/B0061QIMZM.png\", \"q_text\": \"is identical, and is same\", \"positive_key\": \"B0061QILTE\", \"positive_value\": \"./images/B0061QILTE.png\", \"hn_image\": [\"./images/B0061QIMZM.png\"]}\n{\"q_img\": \"./images/B00BNQW9CM.png\", \"q_text\": \"is more sweater and longer sleeves, and is lighter and has longer sleeves\", \"positive_key\": \"B008KYI2SK\", \"positive_value\": \"./images/B008KYI2SK.png\", \"hn_image\": [\"./images/B00BNQW9CM.png\"]}\n{\"q_img\": \"./images/B008KRDRF0.png\", \"q_text\": \"multi colored wih waist tie, and is blue and white with longer sleeves.\", \"positive_key\": \"B006CA80F4\", \"positive_value\": \"./images/B006CA80F4.png\", \"hn_image\": [\"./images/B008KRDRF0.png\"]}\n{\"q_img\": \"./images/B00C7SRYCA.png\", \"q_text\": \"is lighter, and is red with buttons and longer sleeves\", \"positive_key\": \"B008BPVQGS\", \"positive_value\": \"./images/B008BPVQGS.png\", \"hn_image\": [\"./images/B00C7SRYCA.png\"]}\n{\"q_img\": \"./images/B00C64M1BY.png\", \"q_text\": \"pocket tee with stripes, and pink and strips\", \"positive_key\": \"B00C64LT08\", \"positive_value\": \"./images/B00C64LT08.png\", \"hn_image\": [\"./images/B00C64M1BY.png\"]}\n{\"q_img\": \"./images/B000FVAR52.png\", \"q_text\": \"is darker, and is a lighter black with a British flag on it.\", \"positive_key\": \"B009BHFGKC\", \"positive_value\": \"./images/B009BHFGKC.png\", \"hn_image\": [\"./images/B000FVAR52.png\"]}\n{\"q_img\": \"./images/B0073PZW2Q.png\", \"q_text\": \"is red without stripes, and more fitted\", \"positive_key\": \"B009DIEYZM\", \"positive_value\": \"./images/B009DIEYZM.png\", \"hn_image\": [\"./images/B0073PZW2Q.png\"]}\n{\"q_img\": \"./images/B00AL1QEPS.png\", \"q_text\": \"The shirt is tight and dark blue in coloring., and is identical\", \"positive_key\": \"B00C7APZP6\", \"positive_value\": \"./images/B00C7APZP6.png\", \"hn_image\": [\"./images/B00AL1QEPS.png\"]}\n{\"q_img\": \"./images/B005NAEZXU.png\", \"q_text\": \"The shirt is white with black wording with Jesus., and is white men's tee with Jesus Loves Iceland on front\", \"positive_key\": \"B005E0TR32\", \"positive_value\": \"./images/B005E0TR32.png\", \"hn_image\": [\"./images/B005NAEZXU.png\"]}\n{\"q_img\": \"./images/B00B02GQ1E.png\", \"q_text\": \"is darker with thicker straps, and is black and less revealing\", \"positive_key\": \"B00AM3ICQO\", \"positive_value\": \"./images/B00AM3ICQO.png\", \"hn_image\": [\"./images/B00B02GQ1E.png\"]}\n{\"q_img\": \"./images/B004KJE0DU.png\", \"q_text\": \"The short sleeve shirt has a zig zag pattern of red, and is a colorful designed top\", \"positive_key\": \"B0081P9UPC\", \"positive_value\": \"./images/B0081P9UPC.png\", \"hn_image\": [\"./images/B004KJE0DU.png\"]}\n{\"q_img\": \"./images/B009ZYYBVG.png\", \"q_text\": \"is white colored, and Is white with a more colorful pattern\", \"positive_key\": \"B005VSPMEK\", \"positive_value\": \"./images/B005VSPMEK.png\", \"hn_image\": [\"./images/B009ZYYBVG.png\"]}\n{\"q_img\": \"./images/B007PAD4YC.png\", \"q_text\": \"the top is less revealing and the bottom is a trouser, and Is more chic\", \"positive_key\": \"B005QWOSX2\", \"positive_value\": \"./images/B005QWOSX2.png\", \"hn_image\": [\"./images/B007PAD4YC.png\"]}\n{\"q_img\": \"./images/B005J29QHW.png\", \"q_text\": \"The shirt is melon in color., and is orange in color with short sleeves\", \"positive_key\": \"B00AHGBX80\", \"positive_value\": \"./images/B00AHGBX80.png\", \"hn_image\": [\"./images/B005J29QHW.png\"]}\n{\"q_img\": \"./images/B008HZFIUM.png\", \"q_text\": \"Is less plain and more whimsical, and is yellow with a colourful pattern\", \"positive_key\": \"B0097B8O0Q\", \"positive_value\": \"./images/B0097B8O0Q.png\", \"hn_image\": [\"./images/B008HZFIUM.png\"]}\n{\"q_img\": \"./images/B0085K0LQU.png\", \"q_text\": \"is no sleeves and more lighter, and is lighter colored with less shoulder straps\", \"positive_key\": \"B002RL9VFA\", \"positive_value\": \"./images/B002RL9VFA.png\", \"hn_image\": [\"./images/B0085K0LQU.png\"]}\n{\"q_img\": \"./images/B0087KODUS.png\", \"q_text\": \"is a short sleeve Harley-Davidson shirt, and has small print and is less black color\", \"positive_key\": \"B009Q55MKI\", \"positive_value\": \"./images/B009Q55MKI.png\", \"hn_image\": [\"./images/B0087KODUS.png\"]}\n{\"q_img\": \"./images/B003RW1XTK.png\", \"q_text\": \"is less feminine and is brown, and sleeveless\", \"positive_key\": \"B001J5SK66\", \"positive_value\": \"./images/B001J5SK66.png\", \"hn_image\": [\"./images/B003RW1XTK.png\"]}\n{\"q_img\": \"./images/B00BWUM7YE.png\", \"q_text\": \"is darker and has longer sleeves, and Is solid black with sleeves.\", \"positive_key\": \"B008SAYUQY\", \"positive_value\": \"./images/B008SAYUQY.png\", \"hn_image\": [\"./images/B00BWUM7YE.png\"]}\n{\"q_img\": \"./images/B007RMNIE4.png\", \"q_text\": \"Is more flowing and white, and is shiny\", \"positive_key\": \"B007RMNP8I\", \"positive_value\": \"./images/B007RMNP8I.png\", \"hn_image\": [\"./images/B007RMNIE4.png\"]}\n{\"q_img\": \"./images/B00BEQD79U.png\", \"q_text\": \"has blue floral print around neck, and has blue embroidery and longer hem\", \"positive_key\": \"B00BQJWS6I\", \"positive_value\": \"./images/B00BQJWS6I.png\", \"hn_image\": [\"./images/B00BEQD79U.png\"]}\n{\"q_img\": \"./images/B00C7AFSIA.png\", \"q_text\": \"The shirt is tight and dark blue in coloring., and is identical\", \"positive_key\": \"B00D48X886\", \"positive_value\": \"./images/B00D48X886.png\", \"hn_image\": [\"./images/B00C7AFSIA.png\"]}\n{\"q_img\": \"./images/B00CFELT5O.png\", \"q_text\": \"has animal print and is shinier, and has shorter sleeves and pink\", \"positive_key\": \"B005G0TR94\", \"positive_value\": \"./images/B005G0TR94.png\", \"hn_image\": [\"./images/B00CFELT5O.png\"]}\n{\"q_img\": \"./images/B00CNCURMO.png\", \"q_text\": \"is darker, and Is darker with a lighter emblem\", \"positive_key\": \"B00AA1RC0A\", \"positive_value\": \"./images/B00AA1RC0A.png\", \"hn_image\": [\"./images/B00CNCURMO.png\"]}\n{\"q_img\": \"./images/B008VOY01M.png\", \"q_text\": \"The shirt is white with flowers., and is lighter\", \"positive_key\": \"B00980K6RA\", \"positive_value\": \"./images/B00980K6RA.png\", \"hn_image\": [\"./images/B008VOY01M.png\"]}\n{\"q_img\": \"./images/B00BPPALLM.png\", \"q_text\": \"is more frilly and more revealing, and is pink and has off shoulder\", \"positive_key\": \"B004GB27NM\", \"positive_value\": \"./images/B004GB27NM.png\", \"hn_image\": [\"./images/B00BPPALLM.png\"]}\n{\"q_img\": \"./images/B006GHSVKC.png\", \"q_text\": \"has a very distinct art for the whole shirt, and is blue with dolphins on front\", \"positive_key\": \"B001AYHVZ2\", \"positive_value\": \"./images/B001AYHVZ2.png\", \"hn_image\": [\"./images/B006GHSVKC.png\"]}\n{\"q_img\": \"./images/B00DS0TDAW.png\", \"q_text\": \"is white colored, and is white lace\", \"positive_key\": \"B00BTV1FZI\", \"positive_value\": \"./images/B00BTV1FZI.png\", \"hn_image\": [\"./images/B00DS0TDAW.png\"]}\n{\"q_img\": \"./images/B007D1K6U8.png\", \"q_text\": \"The loose fitting tank top is pink in color., and is light tan in color and sheer\", \"positive_key\": \"B009IV3DQU\", \"positive_value\": \"./images/B009IV3DQU.png\", \"hn_image\": [\"./images/B007D1K6U8.png\"]}\n{\"q_img\": \"./images/B008DVXGO0.png\", \"q_text\": \"has a shorter sleeve and is brighter colored, and is pink in color\", \"positive_key\": \"B00COMJHRO\", \"positive_value\": \"./images/B00COMJHRO.png\", \"hn_image\": [\"./images/B008DVXGO0.png\"]}\n{\"q_img\": \"./images/B000EOZGWE.png\", \"q_text\": \"has greek letters on it, and Is less graphic and more collegate\", \"positive_key\": \"B00EUSTQQW\", \"positive_value\": \"./images/B00EUSTQQW.png\", \"hn_image\": [\"./images/B000EOZGWE.png\"]}\n{\"q_img\": \"./images/B00D3ZHQ5G.png\", \"q_text\": \"is darker, and is blue.\", \"positive_key\": \"B00AECN92K\", \"positive_value\": \"./images/B00AECN92K.png\", \"hn_image\": [\"./images/B00D3ZHQ5G.png\"]}\n{\"q_img\": \"./images/B008X04NWA.png\", \"q_text\": \"Grey and has shorter sleeves, and white lace tank top\", \"positive_key\": \"B00BM27KWQ\", \"positive_value\": \"./images/B00BM27KWQ.png\", \"hn_image\": [\"./images/B008X04NWA.png\"]}\n{\"q_img\": \"./images/B00ADM33S6.png\", \"q_text\": \"It is a darker blue and long sleeves., and has longer sleeves and is darker\", \"positive_key\": \"B0083VXWJY\", \"positive_value\": \"./images/B0083VXWJY.png\", \"hn_image\": [\"./images/B00ADM33S6.png\"]}\n{\"q_img\": \"./images/B008MC6KIE.png\", \"q_text\": \"The tank top is white in color., and has a different pattern\", \"positive_key\": \"B008MC6RW8\", \"positive_value\": \"./images/B008MC6RW8.png\", \"hn_image\": [\"./images/B008MC6KIE.png\"]}\n{\"q_img\": \"./images/B007POA5NG.png\", \"q_text\": \"is darker and has longer sleeves, and is more casual and less breezy\", \"positive_key\": \"B008CEJM82\", \"positive_value\": \"./images/B008CEJM82.png\", \"hn_image\": [\"./images/B007POA5NG.png\"]}\n{\"q_img\": \"./images/B00ESB1S80.png\", \"q_text\": \"is less black and has short sleeves, and has smaller graphics on it\", \"positive_key\": \"B0087FOSXK\", \"positive_value\": \"./images/B0087FOSXK.png\", \"hn_image\": [\"./images/B00ESB1S80.png\"]}\n{\"q_img\": \"./images/B00AO4O7NI.png\", \"q_text\": \"has a smaller graphic and looks less feminine, and is a solid pink tee\", \"positive_key\": \"B009E6CMVG\", \"positive_value\": \"./images/B009E6CMVG.png\", \"hn_image\": [\"./images/B00AO4O7NI.png\"]}\n{\"q_img\": \"./images/B00CS24LOE.png\", \"q_text\": \"is a fitted tank and colorful, and has shorter sleeves and brighter colors\", \"positive_key\": \"B0095KBFXM\", \"positive_value\": \"./images/B0095KBFXM.png\", \"hn_image\": [\"./images/B00CS24LOE.png\"]}\n{\"q_img\": \"./images/B003N1BT76.png\", \"q_text\": \"is black, and has shorter hem and rock-band theme\", \"positive_key\": \"B00CA5TTCI\", \"positive_value\": \"./images/B00CA5TTCI.png\", \"hn_image\": [\"./images/B003N1BT76.png\"]}\n{\"q_img\": \"./images/B00EW1H8XK.png\", \"q_text\": \"has shorter sleeves and darker colored, and is tighter\", \"positive_key\": \"B00DOJ2BSS\", \"positive_value\": \"./images/B00DOJ2BSS.png\", \"hn_image\": [\"./images/B00EW1H8XK.png\"]}\n{\"q_img\": \"./images/B00579WNTA.png\", \"q_text\": \"is black with red words, and black with a print.\", \"positive_key\": \"B00AWRUOE8\", \"positive_value\": \"./images/B00AWRUOE8.png\", \"hn_image\": [\"./images/B00579WNTA.png\"]}\n{\"q_img\": \"./images/B004P5P1I2.png\", \"q_text\": \"The shirt is loose fitting with blue and white checkers., and has longer sleeves\", \"positive_key\": \"B0049MOEBQ\", \"positive_value\": \"./images/B0049MOEBQ.png\", \"hn_image\": [\"./images/B004P5P1I2.png\"]}\n{\"q_img\": \"./images/B0067F9A0S.png\", \"q_text\": \"The short sleeve shirt is white with a white and black ball., and is white with a different graphic\", \"positive_key\": \"B003KS9NCA\", \"positive_value\": \"./images/B003KS9NCA.png\", \"hn_image\": [\"./images/B0067F9A0S.png\"]}\n{\"q_img\": \"./images/B004U32SXK.png\", \"q_text\": \"no sleeves different logo, and is darker colored\", \"positive_key\": \"B00D9JXPFG\", \"positive_value\": \"./images/B00D9JXPFG.png\", \"hn_image\": [\"./images/B004U32SXK.png\"]}\n{\"q_img\": \"./images/B005TGIFKC.png\", \"q_text\": \"Is green and not as fitted, and has a darker color and larger graphics\", \"positive_key\": \"B007Y5JWO4\", \"positive_value\": \"./images/B007Y5JWO4.png\", \"hn_image\": [\"./images/B005TGIFKC.png\"]}\n{\"q_img\": \"./images/B006Z8F286.png\", \"q_text\": \"Blue and has sleeves, and is sporty and more revealing\", \"positive_key\": \"B006Z8GQAE\", \"positive_value\": \"./images/B006Z8GQAE.png\", \"hn_image\": [\"./images/B006Z8F286.png\"]}\n{\"q_img\": \"./images/B008K7SPQQ.png\", \"q_text\": \"is blue in color, and is a blue tee printed shirt\", \"positive_key\": \"B007PUOJAU\", \"positive_value\": \"./images/B007PUOJAU.png\", \"hn_image\": [\"./images/B008K7SPQQ.png\"]}\n{\"q_img\": \"./images/B0043VDDUQ.png\", \"q_text\": \"is longer and less revealing, and Is longer\", \"positive_key\": \"B0067MGY9G\", \"positive_value\": \"./images/B0067MGY9G.png\", \"hn_image\": [\"./images/B0043VDDUQ.png\"]}\n{\"q_img\": \"./images/B008G3M3IK.png\", \"q_text\": \"is white with colorful figures, and is white color with colorful people graphic design\", \"positive_key\": \"B00A7UPC6U\", \"positive_value\": \"./images/B00A7UPC6U.png\", \"hn_image\": [\"./images/B008G3M3IK.png\"]}\n{\"q_img\": \"./images/B008H48MI8.png\", \"q_text\": \"has no print and is darker in color, and is darker in color\", \"positive_key\": \"B005G5XGH8\", \"positive_value\": \"./images/B005G5XGH8.png\", \"hn_image\": [\"./images/B008H48MI8.png\"]}\n{\"q_img\": \"./images/B001PO54QU.png\", \"q_text\": \"has floral and color is black``, and has more graphics and is darker\", \"positive_key\": \"B0099SH8YU\", \"positive_value\": \"./images/B0099SH8YU.png\", \"hn_image\": [\"./images/B001PO54QU.png\"]}\n{\"q_img\": \"./images/B00BG0FKN0.png\", \"q_text\": \"is lighter, and is purple with a gathered bottom hem\", \"positive_key\": \"B00BJ9RAFY\", \"positive_value\": \"./images/B00BJ9RAFY.png\", \"hn_image\": [\"./images/B00BG0FKN0.png\"]}\n{\"q_img\": \"./images/B005LVNTOW.png\", \"q_text\": \"Is blue and has longer sleeves, and is short sleeved and blue\", \"positive_key\": \"B005IB1TYM\", \"positive_value\": \"./images/B005IB1TYM.png\", \"hn_image\": [\"./images/B005LVNTOW.png\"]}\n{\"q_img\": \"./images/B007RWJ3UC.png\", \"q_text\": \"is more open and flowing, and is black with a gray geometric design\", \"positive_key\": \"B009LHQR7I\", \"positive_value\": \"./images/B009LHQR7I.png\", \"hn_image\": [\"./images/B007RWJ3UC.png\"]}\n{\"q_img\": \"./images/B00C7CF1R6.png\", \"q_text\": \"is darker colored with wider stripes, and has a v-neck\", \"positive_key\": \"B0062SQ1GQ\", \"positive_value\": \"./images/B0062SQ1GQ.png\", \"hn_image\": [\"./images/B00C7CF1R6.png\"]}\n{\"q_img\": \"./images/B00B99TW1E.png\", \"q_text\": \"has no sleeves and different color patterns, and is colorful with more sleeves\", \"positive_key\": \"B006QHC6BM\", \"positive_value\": \"./images/B006QHC6BM.png\", \"hn_image\": [\"./images/B00B99TW1E.png\"]}\n{\"q_img\": \"./images/B00B99NYWC.png\", \"q_text\": \"is blue with no print, and is turquoise colored with no pattern\", \"positive_key\": \"B00BKU6QXE\", \"positive_value\": \"./images/B00BKU6QXE.png\", \"hn_image\": [\"./images/B00B99NYWC.png\"]}\n{\"q_img\": \"./images/B004KVE9Y8.png\", \"q_text\": \"It has a v-neck.., and has panda or mickey mouse looking garphic thing\", \"positive_key\": \"B00D8IEETE\", \"positive_value\": \"./images/B00D8IEETE.png\", \"hn_image\": [\"./images/B004KVE9Y8.png\"]}\n{\"q_img\": \"./images/B00FM8D476.png\", \"q_text\": \"The shirt is light purple., and lacy white gloves\", \"positive_key\": \"B00F4PTN5Y\", \"positive_value\": \"./images/B00F4PTN5Y.png\", \"hn_image\": [\"./images/B00FM8D476.png\"]}\n{\"q_img\": \"./images/B007MO6BNM.png\", \"q_text\": \"is brighter colored with short sleeves, and is lighter in color\", \"positive_key\": \"B004HFOQIG\", \"positive_value\": \"./images/B004HFOQIG.png\", \"hn_image\": [\"./images/B007MO6BNM.png\"]}\n{\"q_img\": \"./images/B00CNP18BA.png\", \"q_text\": \"is black and has shorter sleeves, and Is black and more fitted\", \"positive_key\": \"B005LJX4L2\", \"positive_value\": \"./images/B005LJX4L2.png\", \"hn_image\": [\"./images/B00CNP18BA.png\"]}\n{\"q_img\": \"./images/B007LLWQMQ.png\", \"q_text\": \"has more lettering and is less like costume, and Is a short sleeved tee shirt with letters\", \"positive_key\": \"B003H3O7MO\", \"positive_value\": \"./images/B003H3O7MO.png\", \"hn_image\": [\"./images/B007LLWQMQ.png\"]}\n{\"q_img\": \"./images/B00G3DOW3O.png\", \"q_text\": \"has a warmer color and longer sleeves, and has longer red sleeves\", \"positive_key\": \"B00D4K3BFO\", \"positive_value\": \"./images/B00D4K3BFO.png\", \"hn_image\": [\"./images/B00G3DOW3O.png\"]}\n{\"q_img\": \"./images/B00CD0KQ4A.png\", \"q_text\": \"is black with pink designs, and is solid black\", \"positive_key\": \"B009B5PDIO\", \"positive_value\": \"./images/B009B5PDIO.png\", \"hn_image\": [\"./images/B00CD0KQ4A.png\"]}\n{\"q_img\": \"./images/B005GA8YB6.png\", \"q_text\": \"has shorter sleeves, and shorter\", \"positive_key\": \"B00CF21LHM\", \"positive_value\": \"./images/B00CF21LHM.png\", \"hn_image\": [\"./images/B005GA8YB6.png\"]}\n{\"q_img\": \"./images/B000EOXMAM.png\", \"q_text\": \"is darker with a different logo, and is black in color\", \"positive_key\": \"B009DTUFG8\", \"positive_value\": \"./images/B009DTUFG8.png\", \"hn_image\": [\"./images/B000EOXMAM.png\"]}\n{\"q_img\": \"./images/B00B99NYWC.png\", \"q_text\": \"is a black waste fitted top with scoop necked collar, and is darker and has longer sleeves\", \"positive_key\": \"B00BB9UBNU\", \"positive_value\": \"./images/B00BB9UBNU.png\", \"hn_image\": [\"./images/B00B99NYWC.png\"]}\n{\"q_img\": \"./images/B00BF3FG0U.png\", \"q_text\": \"is black in color, and is black with white image\", \"positive_key\": \"B00H03TS7Q\", \"positive_value\": \"./images/B00H03TS7Q.png\", \"hn_image\": [\"./images/B00BF3FG0U.png\"]}\n{\"q_img\": \"./images/B00ADM3HIC.png\", \"q_text\": \"is darker, and is similar but black\", \"positive_key\": \"B005WS1TUA\", \"positive_value\": \"./images/B005WS1TUA.png\", \"hn_image\": [\"./images/B00ADM3HIC.png\"]}\n{\"q_img\": \"./images/B00AE6IWMS.png\", \"q_text\": \"is lighter, and is a light gray casual top with red-lined collar\", \"positive_key\": \"B00C85K4V0\", \"positive_value\": \"./images/B00C85K4V0.png\", \"hn_image\": [\"./images/B00AE6IWMS.png\"]}\n{\"q_img\": \"./images/B0078PHUX0.png\", \"q_text\": \"The tank top has a flag of Haiti., and no sleeved\", \"positive_key\": \"B00AFGVMDI\", \"positive_value\": \"./images/B00AFGVMDI.png\", \"hn_image\": [\"./images/B0078PHUX0.png\"]}\n{\"q_img\": \"./images/B008PFGQP0.png\", \"q_text\": \"3/4 sleeved blouse with shapes on it., and is black\", \"positive_key\": \"B00926VDVS\", \"positive_value\": \"./images/B00926VDVS.png\", \"hn_image\": [\"./images/B008PFGQP0.png\"]}\n{\"q_img\": \"./images/B00AG35TN4.png\", \"q_text\": \" has buttons in middle, and Is darker blue with longer sleeves\", \"positive_key\": \"B00861X3PE\", \"positive_value\": \"./images/B00861X3PE.png\", \"hn_image\": [\"./images/B00AG35TN4.png\"]}\n{\"q_img\": \"./images/B0013WQ03U.png\", \"q_text\": \"is long sleeves with purple color, and is purple and less revealing\", \"positive_key\": \"B007U4B7BA\", \"positive_value\": \"./images/B007U4B7BA.png\", \"hn_image\": [\"./images/B0013WQ03U.png\"]}\n{\"q_img\": \"./images/B008UAL5YM.png\", \"q_text\": \"is white with long sleeves, and it has longer sleeve and embroidered\", \"positive_key\": \"B009XG2TYW\", \"positive_value\": \"./images/B009XG2TYW.png\", \"hn_image\": [\"./images/B008UAL5YM.png\"]}\n{\"q_img\": \"./images/B005ZSMYVU.png\", \"q_text\": \"Is more whimsical and colorful, and is a different fabric with blue and black stars.\", \"positive_key\": \"B006FIU3HQ\", \"positive_value\": \"./images/B006FIU3HQ.png\", \"hn_image\": [\"./images/B005ZSMYVU.png\"]}\n{\"q_img\": \"./images/B008E093WY.png\", \"q_text\": \"The shirt has capped sleeves and is white., and is white tee\", \"positive_key\": \"B00BEPIF0W\", \"positive_value\": \"./images/B00BEPIF0W.png\", \"hn_image\": [\"./images/B008E093WY.png\"]}\n{\"q_img\": \"./images/B00608J3R2.png\", \"q_text\": \"has no sleeves and is black, and is sleeveless and has an animal print\", \"positive_key\": \"B004T6ZAO2\", \"positive_value\": \"./images/B004T6ZAO2.png\", \"hn_image\": [\"./images/B00608J3R2.png\"]}\n{\"q_img\": \"./images/B005E1LG9Y.png\", \"q_text\": \"is darker, and light blue color and v-neck collar.\", \"positive_key\": \"B0079K2OAI\", \"positive_value\": \"./images/B0079K2OAI.png\", \"hn_image\": [\"./images/B005E1LG9Y.png\"]}\n{\"q_img\": \"./images/B00BI3DVGS.png\", \"q_text\": \"has short sleeves, and is black and has longer sleeves\", \"positive_key\": \"B008H1D1AK\", \"positive_value\": \"./images/B008H1D1AK.png\", \"hn_image\": [\"./images/B00BI3DVGS.png\"]}\n{\"q_img\": \"./images/B00BB1UC5K.png\", \"q_text\": \"is looser short sleeved v necked, and has longer sleeves and is darker\", \"positive_key\": \"B00FABG19I\", \"positive_value\": \"./images/B00FABG19I.png\", \"hn_image\": [\"./images/B00BB1UC5K.png\"]}\n{\"q_img\": \"./images/B009P2LMWE.png\", \"q_text\": \"has shorter sleeves and two more characters, and is lighter in color\", \"positive_key\": \"B00E0YE9KO\", \"positive_value\": \"./images/B00E0YE9KO.png\", \"hn_image\": [\"./images/B009P2LMWE.png\"]}\n{\"q_img\": \"./images/B0067F94EK.png\", \"q_text\": \"`has large painting and deeply purple, and has native-folklore graphic and purple background\", \"positive_key\": \"B003UWZXAC\", \"positive_value\": \"./images/B003UWZXAC.png\", \"hn_image\": [\"./images/B0067F94EK.png\"]}\n{\"q_img\": \"./images/B00BF727US.png\", \"q_text\": \"has no sleeves, and has thin straps and is all white\", \"positive_key\": \"B0014K4O0W\", \"positive_value\": \"./images/B0014K4O0W.png\", \"hn_image\": [\"./images/B00BF727US.png\"]}\n{\"q_img\": \"./images/B0063GHGVG.png\", \"q_text\": \"different color different design, and is red\", \"positive_key\": \"B0055B67XI\", \"positive_value\": \"./images/B0055B67XI.png\", \"hn_image\": [\"./images/B0063GHGVG.png\"]}\n{\"q_img\": \"./images/B004VRQFDE.png\", \"q_text\": \"sleeveless and more pink, and is pink with no straps\", \"positive_key\": \"B00BKZ2UUM\", \"positive_value\": \"./images/B00BKZ2UUM.png\", \"hn_image\": [\"./images/B004VRQFDE.png\"]}\n{\"q_img\": \"./images/B002UKPUD0.png\", \"q_text\": \"has shorter sleeves, and quarter sleeves more colorfull\", \"positive_key\": \"B00EHZCYE4\", \"positive_value\": \"./images/B00EHZCYE4.png\", \"hn_image\": [\"./images/B002UKPUD0.png\"]}\n{\"q_img\": \"./images/B008D18TG0.png\", \"q_text\": \"The shirt is red with black writing., and Desired item is pink and references the South Pole\", \"positive_key\": \"B002LSI9FC\", \"positive_value\": \"./images/B002LSI9FC.png\", \"hn_image\": [\"./images/B008D18TG0.png\"]}\n{\"q_img\": \"./images/B0036JQOZM.png\", \"q_text\": \"is white and has short sleeves, and Has longer sleeves and is less graphic\", \"positive_key\": \"B000VOM5JY\", \"positive_value\": \"./images/B000VOM5JY.png\", \"hn_image\": [\"./images/B0036JQOZM.png\"]}\n{\"q_img\": \"./images/B00BXI78D0.png\", \"q_text\": \"has no sleeve with two different stripe patterns, and is black and less revealing\", \"positive_key\": \"B0058XRN72\", \"positive_value\": \"./images/B0058XRN72.png\", \"hn_image\": [\"./images/B00BXI78D0.png\"]}\n{\"q_img\": \"./images/B008R786SQ.png\", \"q_text\": \"has no sleeves and is plain white, and is solid white\", \"positive_key\": \"B00DNJNBHE\", \"positive_value\": \"./images/B00DNJNBHE.png\", \"hn_image\": [\"./images/B008R786SQ.png\"]}\n{\"q_img\": \"./images/B00A3NAH6G.png\", \"q_text\": \"is gold colored, and has more brown and tan pattern\", \"positive_key\": \"B001UB7POC\", \"positive_value\": \"./images/B001UB7POC.png\", \"hn_image\": [\"./images/B00A3NAH6G.png\"]}\n{\"q_img\": \"./images/B00BXIPAQ2.png\", \"q_text\": \"is a green tie die v neck shirt, and is olive green and short sleeved\", \"positive_key\": \"B004ISLMWK\", \"positive_value\": \"./images/B004ISLMWK.png\", \"hn_image\": [\"./images/B00BXIPAQ2.png\"]}\n{\"q_img\": \"./images/B008VA57I6.png\", \"q_text\": \"is red colored, and Is red with collar.\", \"positive_key\": \"B000BU0BF8\", \"positive_value\": \"./images/B000BU0BF8.png\", \"hn_image\": [\"./images/B008VA57I6.png\"]}\n{\"q_img\": \"./images/B000GNYYDA.png\", \"q_text\": \"is white, and is white woman's tee\", \"positive_key\": \"B008OGHR94\", \"positive_value\": \"./images/B008OGHR94.png\", \"hn_image\": [\"./images/B000GNYYDA.png\"]}\n{\"q_img\": \"./images/B007QPJ3Q4.png\", \"q_text\": \"I want something thats one solid color and form fitting, and is black with no other colors\", \"positive_key\": \"B004TUD7D4\", \"positive_value\": \"./images/B004TUD7D4.png\", \"hn_image\": [\"./images/B007QPJ3Q4.png\"]}\n{\"q_img\": \"./images/B00GGQZOHG.png\", \"q_text\": \"is solid white, and is short sleeved and mint colored\", \"positive_key\": \"B0038OVRMK\", \"positive_value\": \"./images/B0038OVRMK.png\", \"hn_image\": [\"./images/B00GGQZOHG.png\"]}\n{\"q_img\": \"./images/B005VA01DK.png\", \"q_text\": \"is more revealing, and has a deeper neck and shorter sleeves\", \"positive_key\": \"B005V9YGKK\", \"positive_value\": \"./images/B005V9YGKK.png\", \"hn_image\": [\"./images/B005VA01DK.png\"]}\n{\"q_img\": \"./images/B0068VGVK8.png\", \"q_text\": \"fits tighter and is light pink, and is lighter colored and more tshirt\", \"positive_key\": \"B005H63RJ8\", \"positive_value\": \"./images/B005H63RJ8.png\", \"hn_image\": [\"./images/B0068VGVK8.png\"]}\n{\"q_img\": \"./images/B00C5NMEPE.png\", \"q_text\": \"is brighter colored, and short sleeve tunic brown and white\", \"positive_key\": \"B008X0HEZS\", \"positive_value\": \"./images/B008X0HEZS.png\", \"hn_image\": [\"./images/B00C5NMEPE.png\"]}\n{\"q_img\": \"./images/B00BZF3B3M.png\", \"q_text\": \"is printed and short sleeved, and is lighter with a decorative top and forms to a skirt near the waist.\", \"positive_key\": \"B008XMLIVC\", \"positive_value\": \"./images/B008XMLIVC.png\", \"hn_image\": [\"./images/B00BZF3B3M.png\"]}\n{\"q_img\": \"./images/B001OOLPBY.png\", \"q_text\": \"The shirt is light green in color., and is very light olive\", \"positive_key\": \"B004RBMYMA\", \"positive_value\": \"./images/B004RBMYMA.png\", \"hn_image\": [\"./images/B001OOLPBY.png\"]}\n{\"q_img\": \"./images/B004W4CV52.png\", \"q_text\": \"has longer sleeves with a skull graphic, and is black with logo\", \"positive_key\": \"B00AR1TNN2\", \"positive_value\": \"./images/B00AR1TNN2.png\", \"hn_image\": [\"./images/B004W4CV52.png\"]}\n{\"q_img\": \"./images/B00C6IRB42.png\", \"q_text\": \"is brighter and less gothic, and is yellow and shorter\", \"positive_key\": \"B00E5RNKN8\", \"positive_value\": \"./images/B00E5RNKN8.png\", \"hn_image\": [\"./images/B00C6IRB42.png\"]}\n{\"q_img\": \"./images/B005MVACSM.png\", \"q_text\": \"has a graphic, and is more sporty and darker colored\", \"positive_key\": \"B003BQR0AI\", \"positive_value\": \"./images/B003BQR0AI.png\", \"hn_image\": [\"./images/B005MVACSM.png\"]}\n{\"q_img\": \"./images/B00FPT42EG.png\", \"q_text\": \"Has shorter sleeves and is patterned, and has more designs and a darker color\", \"positive_key\": \"B00BT95KVK\", \"positive_value\": \"./images/B00BT95KVK.png\", \"hn_image\": [\"./images/B00FPT42EG.png\"]}\n{\"q_img\": \"./images/B00BNQVHEI.png\", \"q_text\": \"has red and white colors, and is more junior and less casual\", \"positive_key\": \"B009DZON1A\", \"positive_value\": \"./images/B009DZON1A.png\", \"hn_image\": [\"./images/B00BNQVHEI.png\"]}\n{\"q_img\": \"./images/B00E5RNKN8.png\", \"q_text\": \"Is green with ruffles and sheer on the bottom, and more loose and darker in fabric.\", \"positive_key\": \"B00C11V9JM\", \"positive_value\": \"./images/B00C11V9JM.png\", \"hn_image\": [\"./images/B00E5RNKN8.png\"]}\n{\"q_img\": \"./images/B00AEXWZ7O.png\", \"q_text\": \"is black with brown designs, and tight fitting\", \"positive_key\": \"B009EXKQEY\", \"positive_value\": \"./images/B009EXKQEY.png\", \"hn_image\": [\"./images/B00AEXWZ7O.png\"]}\n{\"q_img\": \"./images/B007Y9F3MA.png\", \"q_text\": \"is sleeveless white smock, and is sleeveless and white colored\", \"positive_key\": \"B005J29QHW\", \"positive_value\": \"./images/B005J29QHW.png\", \"hn_image\": [\"./images/B007Y9F3MA.png\"]}\n{\"q_img\": \"./images/B007G0I5EK.png\", \"q_text\": \"is a sleeveless white with small black polka dots, and Is sleeveless with a dot pattern.\", \"positive_key\": \"B00AGFB1YI\", \"positive_value\": \"./images/B00AGFB1YI.png\", \"hn_image\": [\"./images/B007G0I5EK.png\"]}\n{\"q_img\": \"./images/B008I8YP52.png\", \"q_text\": \"The shirt is ruffled with pink, and is multi-colored and longer\", \"positive_key\": \"B007WASLUM\", \"positive_value\": \"./images/B007WASLUM.png\", \"hn_image\": [\"./images/B008I8YP52.png\"]}\n{\"q_img\": \"./images/B003IEWVRK.png\", \"q_text\": \"is a yellow night;s watch shirt, and is yellow with a black design on front\", \"positive_key\": \"B00A4NGTUI\", \"positive_value\": \"./images/B00A4NGTUI.png\", \"hn_image\": [\"./images/B003IEWVRK.png\"]}\n{\"q_img\": \"./images/B00CD8H0N2.png\", \"q_text\": \"The shirt is white with colorful bikes., and is white in color\", \"positive_key\": \"B004W9MRYW\", \"positive_value\": \"./images/B004W9MRYW.png\", \"hn_image\": [\"./images/B00CD8H0N2.png\"]}\n{\"q_img\": \"./images/B007ZTGZSA.png\", \"q_text\": \"is purple with black accents, and It is less plain with a longer sleeve\", \"positive_key\": \"B0080A333S\", \"positive_value\": \"./images/B0080A333S.png\", \"hn_image\": [\"./images/B007ZTGZSA.png\"]}\n{\"q_img\": \"./images/B001R63KMQ.png\", \"q_text\": \"Is black and more masculine, and is black\", \"positive_key\": \"B001OQZC3O\", \"positive_value\": \"./images/B001OQZC3O.png\", \"hn_image\": [\"./images/B001R63KMQ.png\"]}\n{\"q_img\": \"./images/B0054S3X0C.png\", \"q_text\": \"is lighter in color and longer sleeved, and Has longer sleeves and is lighter\", \"positive_key\": \"B00DET0O00\", \"positive_value\": \"./images/B00DET0O00.png\", \"hn_image\": [\"./images/B0054S3X0C.png\"]}\n{\"q_img\": \"./images/B0037TJOEO.png\", \"q_text\": \"The shirt is blue with white lettering that states geek., and has the word geek\", \"positive_key\": \"B00018A7Y0\", \"positive_value\": \"./images/B00018A7Y0.png\", \"hn_image\": [\"./images/B0037TJOEO.png\"]}\n{\"q_img\": \"./images/B003KW7PXK.png\", \"q_text\": \"has more graphics, and is black colored with a different graphic\", \"positive_key\": \"B003O1HGTA\", \"positive_value\": \"./images/B003O1HGTA.png\", \"hn_image\": [\"./images/B003KW7PXK.png\"]}\n{\"q_img\": \"./images/B004S7QHVW.png\", \"q_text\": \"is blue with black designs, and is lighter\", \"positive_key\": \"B0084APYQ8\", \"positive_value\": \"./images/B0084APYQ8.png\", \"hn_image\": [\"./images/B004S7QHVW.png\"]}\n{\"q_img\": \"./images/B0095YHVUY.png\", \"q_text\": \"The shirt is aqua in color., and is short sleeved and light blue\", \"positive_key\": \"B005N5GWCC\", \"positive_value\": \"./images/B005N5GWCC.png\", \"hn_image\": [\"./images/B0095YHVUY.png\"]}\n{\"q_img\": \"./images/B004HHNMRK.png\", \"q_text\": \"is worn above the waist, and is black and short sleeve t-shirt\", \"positive_key\": \"B008LUUKHO\", \"positive_value\": \"./images/B008LUUKHO.png\", \"hn_image\": [\"./images/B004HHNMRK.png\"]}\n{\"q_img\": \"./images/B000VCWJIS.png\", \"q_text\": \"is a short sleeved tshirt, and is a dark top\", \"positive_key\": \"B003BWL0EE\", \"positive_value\": \"./images/B003BWL0EE.png\", \"hn_image\": [\"./images/B000VCWJIS.png\"]}\n{\"q_img\": \"./images/B005HFYGOO.png\", \"q_text\": \"is more patterend and more feminine, and shorter sleeves red / white dress\", \"positive_key\": \"B0096HQC9Q\", \"positive_value\": \"./images/B0096HQC9Q.png\", \"hn_image\": [\"./images/B005HFYGOO.png\"]}\n{\"q_img\": \"./images/B00BF71UBU.png\", \"q_text\": \"is yellow colored, and is yellow in color.\", \"positive_key\": \"B00B7YFTOK\", \"positive_value\": \"./images/B00B7YFTOK.png\", \"hn_image\": [\"./images/B00BF71UBU.png\"]}\n{\"q_img\": \"./images/B00A4FC3HE.png\", \"q_text\": \"is black and white and is more dressy, and is black and white in color\", \"positive_key\": \"B00A0I88VU\", \"positive_value\": \"./images/B00A0I88VU.png\", \"hn_image\": [\"./images/B00A4FC3HE.png\"]}\n{\"q_img\": \"./images/B005ZO99DA.png\", \"q_text\": \"is a mustard color, and is yellow colored with a different graphic\", \"positive_key\": \"B003H3PTL2\", \"positive_value\": \"./images/B003H3PTL2.png\", \"hn_image\": [\"./images/B005ZO99DA.png\"]}\n{\"q_img\": \"./images/B003Q4VE8O.png\", \"q_text\": \"is white and sleeveless., and is white and sleeveless\", \"positive_key\": \"B003MSWJ1U\", \"positive_value\": \"./images/B003MSWJ1U.png\", \"hn_image\": [\"./images/B003Q4VE8O.png\"]}\n{\"q_img\": \"./images/B003DWBO7U.png\", \"q_text\": \"is yellow with a pink heart, and is yellow with pink graphic\", \"positive_key\": \"B007C7XJYS\", \"positive_value\": \"./images/B007C7XJYS.png\", \"hn_image\": [\"./images/B003DWBO7U.png\"]}\n{\"q_img\": \"./images/B0094U96UW.png\", \"q_text\": \"is lighter and has shorter sleeves, and it has a short sleeve and it has no collar\", \"positive_key\": \"B00BK2Z1TM\", \"positive_value\": \"./images/B00BK2Z1TM.png\", \"hn_image\": [\"./images/B0094U96UW.png\"]}\n{\"q_img\": \"./images/B002RL9VFA.png\", \"q_text\": \"is neon green with a cami, and Is more neon green and strappy\", \"positive_key\": \"B009NGX6U8\", \"positive_value\": \"./images/B009NGX6U8.png\", \"hn_image\": [\"./images/B002RL9VFA.png\"]}\n{\"q_img\": \"./images/B00CAAKD3M.png\", \"q_text\": \"The shirt is blue with white writing., and is a blue top\", \"positive_key\": \"B00AMSFVK4\", \"positive_value\": \"./images/B00AMSFVK4.png\", \"hn_image\": [\"./images/B00CAAKD3M.png\"]}\n{\"q_img\": \"./images/B00DDUSOWU.png\", \"q_text\": \"is black colored and longer sleeves, and is more flowing and less casual\", \"positive_key\": \"B00AFSPI0O\", \"positive_value\": \"./images/B00AFSPI0O.png\", \"hn_image\": [\"./images/B00DDUSOWU.png\"]}\n{\"q_img\": \"./images/B0067EHRQI.png\", \"q_text\": \"is darker, and is pink\", \"positive_key\": \"B00A4TMAPK\", \"positive_value\": \"./images/B00A4TMAPK.png\", \"hn_image\": [\"./images/B0067EHRQI.png\"]}\n{\"q_img\": \"./images/B0052CHM9S.png\", \"q_text\": \"is black and white patterned pants, and is feminine and sexy\", \"positive_key\": \"B0070TPNGA\", \"positive_value\": \"./images/B0070TPNGA.png\", \"hn_image\": [\"./images/B0052CHM9S.png\"]}\n{\"q_img\": \"./images/B00E0L1X3I.png\", \"q_text\": \"Is lighter in color and more wordy, and Is less colorful and more casual\", \"positive_key\": \"B008SAYDK2\", \"positive_value\": \"./images/B008SAYDK2.png\", \"hn_image\": [\"./images/B00E0L1X3I.png\"]}\n{\"q_img\": \"./images/B00AHGAKZM.png\", \"q_text\": \"Is white, and is white and more professional\", \"positive_key\": \"B0007MVXES\", \"positive_value\": \"./images/B0007MVXES.png\", \"hn_image\": [\"./images/B00AHGAKZM.png\"]}\n{\"q_img\": \"./images/B008OGUW32.png\", \"q_text\": \"is lighter in color, and but rather similar.\", \"positive_key\": \"B00EUAKBEG\", \"positive_value\": \"./images/B00EUAKBEG.png\", \"hn_image\": [\"./images/B008OGUW32.png\"]}\n{\"q_img\": \"./images/B00186WJ36.png\", \"q_text\": \"is sleeveless with more yellow, and is a yellow shirt and not a purse\", \"positive_key\": \"B00BRBHTJ6\", \"positive_value\": \"./images/B00BRBHTJ6.png\", \"hn_image\": [\"./images/B00186WJ36.png\"]}\n{\"q_img\": \"./images/B00APE63FM.png\", \"q_text\": \"is less flared and more pink, and is salmon and straight cut\", \"positive_key\": \"B00EK6N7XM\", \"positive_value\": \"./images/B00EK6N7XM.png\", \"hn_image\": [\"./images/B00APE63FM.png\"]}\n{\"q_img\": \"./images/B005I4KNX2.png\", \"q_text\": \"Is not colorful, and is more revealing\", \"positive_key\": \"B005CP04UE\", \"positive_value\": \"./images/B005CP04UE.png\", \"hn_image\": [\"./images/B005I4KNX2.png\"]}\n{\"q_img\": \"./images/B0094I4IOI.png\", \"q_text\": \"is blue and red colored, and american tie dye\", \"positive_key\": \"B003FOG0EI\", \"positive_value\": \"./images/B003FOG0EI.png\", \"hn_image\": [\"./images/B0094I4IOI.png\"]}\n{\"q_img\": \"./images/B001AS562I.png\", \"q_text\": \"is blue, and is a pair of pants\", \"positive_key\": \"B00AO4NAN6\", \"positive_value\": \"./images/B00AO4NAN6.png\", \"hn_image\": [\"./images/B001AS562I.png\"]}\n{\"q_img\": \"./images/B001D6Y466.png\", \"q_text\": \"has longer sleeves with red color, and is red with longer sleeves.\", \"positive_key\": \"B0085J2S6C\", \"positive_value\": \"./images/B0085J2S6C.png\", \"hn_image\": [\"./images/B001D6Y466.png\"]}\n{\"q_img\": \"./images/B00902GR0Q.png\", \"q_text\": \"is sheer and darker with longer sleeves, and is blue and more revealing\", \"positive_key\": \"B007UM4TDA\", \"positive_value\": \"./images/B007UM4TDA.png\", \"hn_image\": [\"./images/B00902GR0Q.png\"]}\n{\"q_img\": \"./images/B009FRGUN0.png\", \"q_text\": \"raglan graphic shirt, and Is whiter with no Superman emblem\", \"positive_key\": \"B000GHXMTI\", \"positive_value\": \"./images/B000GHXMTI.png\", \"hn_image\": [\"./images/B009FRGUN0.png\"]}\n{\"q_img\": \"./images/B007DQ8SHG.png\", \"q_text\": \"Has short sleeves and is longer., and is mint blue with logo\", \"positive_key\": \"B00BX4K30Y\", \"positive_value\": \"./images/B00BX4K30Y.png\", \"hn_image\": [\"./images/B007DQ8SHG.png\"]}\n{\"q_img\": \"./images/B00BR5R2JO.png\", \"q_text\": \"has no sleeves with big art, and has a cross printed and no sleeves\", \"positive_key\": \"B00D8MWDL6\", \"positive_value\": \"./images/B00D8MWDL6.png\", \"hn_image\": [\"./images/B00BR5R2JO.png\"]}\n{\"q_img\": \"./images/B0056L9QU8.png\", \"q_text\": \"Is darker in color and has shorter sleeves, and Has shorter sleeves and is darker\", \"positive_key\": \"B0049EP2BA\", \"positive_value\": \"./images/B0049EP2BA.png\", \"hn_image\": [\"./images/B0056L9QU8.png\"]}\n{\"q_img\": \"./images/B00DBDSZY6.png\", \"q_text\": \"has more ruffle in animal print, and black and white swirl lines slightly longer\", \"positive_key\": \"B008UAECJM\", \"positive_value\": \"./images/B008UAECJM.png\", \"hn_image\": [\"./images/B00DBDSZY6.png\"]}\n{\"q_img\": \"./images/B00066ZPNA.png\", \"q_text\": \"is a black t shirt with sleeves, and Has longer sleeves and is black\", \"positive_key\": \"B0058YTCIE\", \"positive_value\": \"./images/B0058YTCIE.png\", \"hn_image\": [\"./images/B00066ZPNA.png\"]}\n{\"q_img\": \"./images/B00CP11HK4.png\", \"q_text\": \" sleevless, and has no sleeves and is black\", \"positive_key\": \"B00CKFVNRM\", \"positive_value\": \"./images/B00CKFVNRM.png\", \"hn_image\": [\"./images/B00CP11HK4.png\"]}\n{\"q_img\": \"./images/B00DGXJUF4.png\", \"q_text\": \"is navy blue with pink flowers, and is black with a pink bow\", \"positive_key\": \"B00C9AJAL4\", \"positive_value\": \"./images/B00C9AJAL4.png\", \"hn_image\": [\"./images/B00DGXJUF4.png\"]}\n{\"q_img\": \"./images/B009NGX6U8.png\", \"q_text\": \"has a longer sleeve with different stripes, and has longer sleeves\", \"positive_key\": \"B00BNQWFKI\", \"positive_value\": \"./images/B00BNQWFKI.png\", \"hn_image\": [\"./images/B009NGX6U8.png\"]}\n{\"q_img\": \"./images/B009NGX6U8.png\", \"q_text\": \"The shirt is dark gray in color., and it has sleeves and it is longer\", \"positive_key\": \"B0041E10A0\", \"positive_value\": \"./images/B0041E10A0.png\", \"hn_image\": [\"./images/B009NGX6U8.png\"]}\n{\"q_img\": \"./images/B00DSS1KAK.png\", \"q_text\": \"is grey colored and has text on it, and is black with yellow lettering\", \"positive_key\": \"B00CXXIGTY\", \"positive_value\": \"./images/B00CXXIGTY.png\", \"hn_image\": [\"./images/B00DSS1KAK.png\"]}\n{\"q_img\": \"./images/B007WASYOA.png\", \"q_text\": \"The shirt is long and black with white and black stripes., and is black and white striped\", \"positive_key\": \"B0085977Y0\", \"positive_value\": \"./images/B0085977Y0.png\", \"hn_image\": [\"./images/B007WASYOA.png\"]}\n{\"q_img\": \"./images/B00BQMFTD4.png\", \"q_text\": \"is green with brown designs, and is multi-colored\", \"positive_key\": \"B00AKOWGUS\", \"positive_value\": \"./images/B00AKOWGUS.png\", \"hn_image\": [\"./images/B00BQMFTD4.png\"]}\n{\"q_img\": \"./images/B00A13DXIW.png\", \"q_text\": \"is green in color, and it is with shorter sleeve and v-neck\", \"positive_key\": \"B00BPNZ3ZI\", \"positive_value\": \"./images/B00BPNZ3ZI.png\", \"hn_image\": [\"./images/B00A13DXIW.png\"]}\n{\"q_img\": \"./images/B00751ZL9M.png\", \"q_text\": \"is black and white with buttons down and no sleeves, and is sleeveless and has pockets\", \"positive_key\": \"B006ZUDD1C\", \"positive_value\": \"./images/B006ZUDD1C.png\", \"hn_image\": [\"./images/B00751ZL9M.png\"]}\n{\"q_img\": \"./images/B008LCWM82.png\", \"q_text\": \"has a longer sleeve with buttons and pockets, and is darker and has longer sleeves\", \"positive_key\": \"B0075GVVXC\", \"positive_value\": \"./images/B0075GVVXC.png\", \"hn_image\": [\"./images/B008LCWM82.png\"]}\n{\"q_img\": \"./images/B00F0TPBS2.png\", \"q_text\": \"has a shorter sleeve with Hello Kitty, and is darker\", \"positive_key\": \"B008Z9Z3R8\", \"positive_value\": \"./images/B008Z9Z3R8.png\", \"hn_image\": [\"./images/B00F0TPBS2.png\"]}\n{\"q_img\": \"./images/B0087CC0GK.png\", \"q_text\": \"is blue with buttons down, and is more of a blue color\", \"positive_key\": \"B00C7Z6JA6\", \"positive_value\": \"./images/B00C7Z6JA6.png\", \"hn_image\": [\"./images/B0087CC0GK.png\"]}\n{\"q_img\": \"./images/B005HTENIO.png\", \"q_text\": \"The shirt is black with white and yellow., and is black\", \"positive_key\": \"B00FDKCY56\", \"positive_value\": \"./images/B00FDKCY56.png\", \"hn_image\": [\"./images/B005HTENIO.png\"]}\n{\"q_img\": \"./images/B006MXLXHI.png\", \"q_text\": \"is lighter in color and more casual, and is lighter and has longer sleeves\", \"positive_key\": \"B001GVIOWI\", \"positive_value\": \"./images/B001GVIOWI.png\", \"hn_image\": [\"./images/B006MXLXHI.png\"]}\n{\"q_img\": \"./images/B00B9Z7DNM.png\", \"q_text\": \"is darker and has longer sleeves, and is black and less sheer\", \"positive_key\": \"B00GI6IASO\", \"positive_value\": \"./images/B00GI6IASO.png\", \"hn_image\": [\"./images/B00B9Z7DNM.png\"]}\n{\"q_img\": \"./images/B00B0NQ7CG.png\", \"q_text\": \"is black colored, and is black and has button\", \"positive_key\": \"B006IV779W\", \"positive_value\": \"./images/B006IV779W.png\", \"hn_image\": [\"./images/B00B0NQ7CG.png\"]}\n{\"q_img\": \"./images/B008ADPLAI.png\", \"q_text\": \"The loose fitting shirt is a mixture of dark and light blues., and is much lighter\", \"positive_key\": \"B00BF61DR2\", \"positive_value\": \"./images/B00BF61DR2.png\", \"hn_image\": [\"./images/B008ADPLAI.png\"]}\n{\"q_img\": \"./images/B008BPVQGS.png\", \"q_text\": \"is plaid with longer sleeves, and is plaid patterned with no collar\", \"positive_key\": \"B00D0RC8NC\", \"positive_value\": \"./images/B00D0RC8NC.png\", \"hn_image\": [\"./images/B008BPVQGS.png\"]}\n{\"q_img\": \"./images/B00BYCEBKS.png\", \"q_text\": \"is lighter in color and more simple, and is grey with long sleeves\", \"positive_key\": \"B00F56HN22\", \"positive_value\": \"./images/B00F56HN22.png\", \"hn_image\": [\"./images/B00BYCEBKS.png\"]}\n{\"q_img\": \"./images/B008TV79LK.png\", \"q_text\": \"is lighter, and grey with a design.\", \"positive_key\": \"B009LHGE5S\", \"positive_value\": \"./images/B009LHGE5S.png\", \"hn_image\": [\"./images/B008TV79LK.png\"]}\n{\"q_img\": \"./images/B00C10NLIA.png\", \"q_text\": \"is more revealing with spagetti straps and elasticated waist, and has very thin straps and a fitted elastic waist\", \"positive_key\": \"B00B7CODWQ\", \"positive_value\": \"./images/B00B7CODWQ.png\", \"hn_image\": [\"./images/B00C10NLIA.png\"]}\n{\"q_img\": \"./images/B005L2NJKU.png\", \"q_text\": \"has a long sleeve black color shirt, and is solid black with a full button front\", \"positive_key\": \"B005BY3KN4\", \"positive_value\": \"./images/B005BY3KN4.png\", \"hn_image\": [\"./images/B005L2NJKU.png\"]}\n{\"q_img\": \"./images/B00819Q63W.png\", \"q_text\": \"is darker, and has long sleeves and stripes\", \"positive_key\": \"B00ANKBR0O\", \"positive_value\": \"./images/B00ANKBR0O.png\", \"hn_image\": [\"./images/B00819Q63W.png\"]}\n{\"q_img\": \"./images/B0085YC1G4.png\", \"q_text\": \"is gray with brown designs, and is gray with a different graphic\", \"positive_key\": \"B0058ZNIU6\", \"positive_value\": \"./images/B0058ZNIU6.png\", \"hn_image\": [\"./images/B0085YC1G4.png\"]}\n{\"q_img\": \"./images/B008293HO2.png\", \"q_text\": \"The long sleeve shirt is green in color., and is green with no pattern\", \"positive_key\": \"B0082993AO\", \"positive_value\": \"./images/B0082993AO.png\", \"hn_image\": [\"./images/B008293HO2.png\"]}\n{\"q_img\": \"./images/B009Q55MKI.png\", \"q_text\": \"Has longer sleeves and is more feminine, and has longer sleeves and buttons\", \"positive_key\": \"B008FPW6IG\", \"positive_value\": \"./images/B008FPW6IG.png\", \"hn_image\": [\"./images/B009Q55MKI.png\"]}\n{\"q_img\": \"./images/B0054P9Q1K.png\", \"q_text\": \"has short sleeves and is pink in color, and in pink with shorter sleeves\", \"positive_key\": \"B00B1MFXY4\", \"positive_value\": \"./images/B00B1MFXY4.png\", \"hn_image\": [\"./images/B0054P9Q1K.png\"]}\n{\"q_img\": \"./images/B00F44KDMW.png\", \"q_text\": \"is gray colored with no sleeves, and is gray and a tank top style\", \"positive_key\": \"B00BFG9Q8A\", \"positive_value\": \"./images/B00BFG9Q8A.png\", \"hn_image\": [\"./images/B00F44KDMW.png\"]}\n{\"q_img\": \"./images/B00E0L2FAI.png\", \"q_text\": \"is lighter and has shorter sleeves, and is colored only white\", \"positive_key\": \"B008BHS9H0\", \"positive_value\": \"./images/B008BHS9H0.png\", \"hn_image\": [\"./images/B00E0L2FAI.png\"]}\n{\"q_img\": \"./images/B001AQ42SO.png\", \"q_text\": \"is navy with shorter sleeves, and is darker\", \"positive_key\": \"B00143TIBY\", \"positive_value\": \"./images/B00143TIBY.png\", \"hn_image\": [\"./images/B001AQ42SO.png\"]}\n{\"q_img\": \"./images/B006DUA614.png\", \"q_text\": \"is red colored and more revealing, and has red color and buttons to it\", \"positive_key\": \"B004QDXXGU\", \"positive_value\": \"./images/B004QDXXGU.png\", \"hn_image\": [\"./images/B006DUA614.png\"]}\n{\"q_img\": \"./images/B001P9TNF8.png\", \"q_text\": \"is darker, and is black and has a bit logo print\", \"positive_key\": \"B003TNMKTO\", \"positive_value\": \"./images/B003TNMKTO.png\", \"hn_image\": [\"./images/B001P9TNF8.png\"]}\n{\"q_img\": \"./images/B00CHYIX56.png\", \"q_text\": \"is less feminine and is brown, and has a more yellow graphic\", \"positive_key\": \"B005VGBXPY\", \"positive_value\": \"./images/B005VGBXPY.png\", \"hn_image\": [\"./images/B00CHYIX56.png\"]}\n{\"q_img\": \"./images/B008CFZW76.png\", \"q_text\": \"is red colored and floral pattern, and has flowery print\", \"positive_key\": \"B00AZL7U7U\", \"positive_value\": \"./images/B00AZL7U7U.png\", \"hn_image\": [\"./images/B008CFZW76.png\"]}\n{\"q_img\": \"./images/B00891HSNE.png\", \"q_text\": \"Is a shirt, and green with lots of characters\", \"positive_key\": \"B00DC4HCSO\", \"positive_value\": \"./images/B00DC4HCSO.png\", \"hn_image\": [\"./images/B00891HSNE.png\"]}\n{\"q_img\": \"./images/B008A4OUC2.png\", \"q_text\": \"has shorter sleeves, and has no sleeve and is brown color\", \"positive_key\": \"B0052RN92W\", \"positive_value\": \"./images/B0052RN92W.png\", \"hn_image\": [\"./images/B008A4OUC2.png\"]}\n{\"q_img\": \"./images/B002KG5WD2.png\", \"q_text\": \"is gray, and is white with black logo\", \"positive_key\": \"B00CUU2RUO\", \"positive_value\": \"./images/B00CUU2RUO.png\", \"hn_image\": [\"./images/B002KG5WD2.png\"]}\n{\"q_img\": \"./images/B004IOL0UI.png\", \"q_text\": \"Is pink and less elegant, and is sleeveless and is a brighter color\", \"positive_key\": \"B00BGVR06Y\", \"positive_value\": \"./images/B00BGVR06Y.png\", \"hn_image\": [\"./images/B004IOL0UI.png\"]}\n{\"q_img\": \"./images/B008BHSRFO.png\", \"q_text\": \"has a cheetah pattern, and Is lighter colored & more flowy\", \"positive_key\": \"B007XD6D8A\", \"positive_value\": \"./images/B007XD6D8A.png\", \"hn_image\": [\"./images/B008BHSRFO.png\"]}\n{\"q_img\": \"./images/B00591CB0W.png\", \"q_text\": \"The shirt has no sleeves and is red in color., and red v-neck short sleeve\", \"positive_key\": \"B0072B2BHU\", \"positive_value\": \"./images/B0072B2BHU.png\", \"hn_image\": [\"./images/B00591CB0W.png\"]}\n{\"q_img\": \"./images/B00B7CODWQ.png\", \"q_text\": \"is darker and has longer sleeves, and is grey and has pink stribe with longer sleeves\", \"positive_key\": \"B0094VARXQ\", \"positive_value\": \"./images/B0094VARXQ.png\", \"hn_image\": [\"./images/B00B7CODWQ.png\"]}\n{\"q_img\": \"./images/B00CF574EI.png\", \"q_text\": \"is green see through with short sleeves, and longer sleeves more frilly colar blue\", \"positive_key\": \"B008UAD2T8\", \"positive_value\": \"./images/B008UAD2T8.png\", \"hn_image\": [\"./images/B00CF574EI.png\"]}\n{\"q_img\": \"./images/B00A3QE0QQ.png\", \"q_text\": \"is lighter, and has shorter sleeves and more blue\", \"positive_key\": \"B00BZQZLF2\", \"positive_value\": \"./images/B00BZQZLF2.png\", \"hn_image\": [\"./images/B00A3QE0QQ.png\"]}\n{\"q_img\": \"./images/B001Q5CH2C.png\", \"q_text\": \"The shirt is black with a light bulb., and is darker in color and less religious\", \"positive_key\": \"B004KF5DSU\", \"positive_value\": \"./images/B004KF5DSU.png\", \"hn_image\": [\"./images/B001Q5CH2C.png\"]}\n{\"q_img\": \"./images/B00AN9Y388.png\", \"q_text\": \"The shirt is black with white writing., and is black with sleeves\", \"positive_key\": \"B001797RB8\", \"positive_value\": \"./images/B001797RB8.png\", \"hn_image\": [\"./images/B00AN9Y388.png\"]}\n{\"q_img\": \"./images/B00C85FTH4.png\", \"q_text\": \"a button up and much longer, and is buttoned and has collar\", \"positive_key\": \"B007S9U9SE\", \"positive_value\": \"./images/B007S9U9SE.png\", \"hn_image\": [\"./images/B00C85FTH4.png\"]}\n{\"q_img\": \"./images/B007P5GAJS.png\", \"q_text\": \"is an orange dress with no sleeves, and is lighter and has shorter sleeves\", \"positive_key\": \"B008AW6A5O\", \"positive_value\": \"./images/B008AW6A5O.png\", \"hn_image\": [\"./images/B007P5GAJS.png\"]}\n{\"q_img\": \"./images/B005Y8GDYK.png\", \"q_text\": \"Is more plan and not patterned, and Is sleevless and more plain\", \"positive_key\": \"B002UNLYPK\", \"positive_value\": \"./images/B002UNLYPK.png\", \"hn_image\": [\"./images/B005Y8GDYK.png\"]}\n{\"q_img\": \"./images/B005GG68BI.png\", \"q_text\": \"has shorter sleeves with text and light pink color, and Solid pink shirt with grey letters\", \"positive_key\": \"B00E5V11AS\", \"positive_value\": \"./images/B00E5V11AS.png\", \"hn_image\": [\"./images/B005GG68BI.png\"]}\n{\"q_img\": \"./images/B00DEZTDCE.png\", \"q_text\": \"has no sleeves and gray colored, and is sleeveless and gray\", \"positive_key\": \"B000E78ZHY\", \"positive_value\": \"./images/B000E78ZHY.png\", \"hn_image\": [\"./images/B00DEZTDCE.png\"]}\n{\"q_img\": \"./images/B00B7X6HWE.png\", \"q_text\": \"Is brighter in color and has horizontal stripes, and is red striped instead of blue.\", \"positive_key\": \"B00BM0V36C\", \"positive_value\": \"./images/B00BM0V36C.png\", \"hn_image\": [\"./images/B00B7X6HWE.png\"]}\n{\"q_img\": \"./images/B008NDWPP4.png\", \"q_text\": \"is more patterned and less animalistic, and is green peacock design\", \"positive_key\": \"B008NDTKR0\", \"positive_value\": \"./images/B008NDTKR0.png\", \"hn_image\": [\"./images/B008NDWPP4.png\"]}\n{\"q_img\": \"./images/B003ECZMGI.png\", \"q_text\": \"is black colored with image of a pickup truck, and has car-theme graphic and is black\", \"positive_key\": \"B002XA89DA\", \"positive_value\": \"./images/B002XA89DA.png\", \"hn_image\": [\"./images/B003ECZMGI.png\"]}\n{\"q_img\": \"./images/B00CMVSRDC.png\", \"q_text\": \"is brown, and is a brown top\", \"positive_key\": \"B007N8OAI0\", \"positive_value\": \"./images/B007N8OAI0.png\", \"hn_image\": [\"./images/B00CMVSRDC.png\"]}\n{\"q_img\": \"./images/B004GHO03G.png\", \"q_text\": \"is white and has large neck, and is a sleeveless white top\", \"positive_key\": \"B002ODJ0D4\", \"positive_value\": \"./images/B002ODJ0D4.png\", \"hn_image\": [\"./images/B004GHO03G.png\"]}\n{\"q_img\": \"./images/B002EIS4BS.png\", \"q_text\": \"is lighter, and has a graphic print on it with no words\", \"positive_key\": \"B0047AOUJG\", \"positive_value\": \"./images/B0047AOUJG.png\", \"hn_image\": [\"./images/B002EIS4BS.png\"]}\n{\"q_img\": \"./images/B003BWL0EE.png\", \"q_text\": \"is more colorful, and is red and more everyday\", \"positive_key\": \"B000V9W4CM\", \"positive_value\": \"./images/B000V9W4CM.png\", \"hn_image\": [\"./images/B003BWL0EE.png\"]}\n{\"q_img\": \"./images/B008OGUW32.png\", \"q_text\": \"has a tighter neckline and is more animalistic, and longer sleeved\", \"positive_key\": \"B006Q8AMUI\", \"positive_value\": \"./images/B006Q8AMUI.png\", \"hn_image\": [\"./images/B008OGUW32.png\"]}\n{\"q_img\": \"./images/B003CLCIRC.png\", \"q_text\": \"is polka dotted pattern with sleeves, and Desired item is black with white dots\", \"positive_key\": \"B00AIITLPO\", \"positive_value\": \"./images/B00AIITLPO.png\", \"hn_image\": [\"./images/B003CLCIRC.png\"]}\n{\"q_img\": \"./images/B003Q6C28I.png\", \"q_text\": \"The shirt is white in color., and is gray with shorter sleeves\", \"positive_key\": \"B007URUC2M\", \"positive_value\": \"./images/B007URUC2M.png\", \"hn_image\": [\"./images/B003Q6C28I.png\"]}\n{\"q_img\": \"./images/B00FGDQ2VM.png\", \"q_text\": \"has a white back ground with small black dots, and purple colored\", \"positive_key\": \"B007X5IFF2\", \"positive_value\": \"./images/B007X5IFF2.png\", \"hn_image\": [\"./images/B00FGDQ2VM.png\"]}\n{\"q_img\": \"./images/B00018A7Y0.png\", \"q_text\": \"is grey, and is grey and blue logo\", \"positive_key\": \"B0001TOLG4\", \"positive_value\": \"./images/B0001TOLG4.png\", \"hn_image\": [\"./images/B00018A7Y0.png\"]}\n{\"q_img\": \"./images/B004I72CMU.png\", \"q_text\": \"has no sleeves, and Is sleeveless and sheer white with blue shoulders\", \"positive_key\": \"B00AZP2T7M\", \"positive_value\": \"./images/B00AZP2T7M.png\", \"hn_image\": [\"./images/B004I72CMU.png\"]}\n{\"q_img\": \"./images/B00D5703AW.png\", \"q_text\": \"is fur with black lace, and has full length sleeves\", \"positive_key\": \"B00EKB856G\", \"positive_value\": \"./images/B00EKB856G.png\", \"hn_image\": [\"./images/B00D5703AW.png\"]}\n{\"q_img\": \"./images/B002I48IIC.png\", \"q_text\": \"graphic t-shirt, and White shirt with kitten/rose on front\", \"positive_key\": \"B000Y4GM1I\", \"positive_value\": \"./images/B000Y4GM1I.png\", \"hn_image\": [\"./images/B002I48IIC.png\"]}\n{\"q_img\": \"./images/B00BL8ISHW.png\", \"q_text\": \"is more slouche and lighter, and is longer with a lighter color\", \"positive_key\": \"B00AWVA2IW\", \"positive_value\": \"./images/B00AWVA2IW.png\", \"hn_image\": [\"./images/B00BL8ISHW.png\"]}\n{\"q_img\": \"./images/B005GA8YB6.png\", \"q_text\": \"The shirt is red and lace., and is dressy and has lace see through sleeves\", \"positive_key\": \"B0084L7IRU\", \"positive_value\": \"./images/B0084L7IRU.png\", \"hn_image\": [\"./images/B005GA8YB6.png\"]}\n{\"q_img\": \"./images/B004O0TNHS.png\", \"q_text\": \"is a white color shirt, and is white and long flared sleeved\", \"positive_key\": \"B0030K9KPS\", \"positive_value\": \"./images/B0030K9KPS.png\", \"hn_image\": [\"./images/B004O0TNHS.png\"]}\n{\"q_img\": \"./images/B004ZFGRXA.png\", \"q_text\": \"is lighter with a different graphic, and Has a v-neck and is gray.\", \"positive_key\": \"B004BJM950\", \"positive_value\": \"./images/B004BJM950.png\", \"hn_image\": [\"./images/B004ZFGRXA.png\"]}\n{\"q_img\": \"./images/B004I6PMUK.png\", \"q_text\": \"is more revealing, and is solid black\", \"positive_key\": \"B008BMY45Q\", \"positive_value\": \"./images/B008BMY45Q.png\", \"hn_image\": [\"./images/B004I6PMUK.png\"]}\n{\"q_img\": \"./images/B000V9W4CM.png\", \"q_text\": \"is lighter and has shorter sleeves, and it has no sleeves and it is white\", \"positive_key\": \"B004R1I7L2\", \"positive_value\": \"./images/B004R1I7L2.png\", \"hn_image\": [\"./images/B000V9W4CM.png\"]}\n{\"q_img\": \"./images/B0060Q7E0W.png\", \"q_text\": \"Is much less revealing and white, and is a short sleeved tunis\", \"positive_key\": \"B007RMNSYE\", \"positive_value\": \"./images/B007RMNSYE.png\", \"hn_image\": [\"./images/B0060Q7E0W.png\"]}\n{\"q_img\": \"./images/B00A13DXIW.png\", \"q_text\": \", and is an off the shoulder top\", \"positive_key\": \"B005XKO35U\", \"positive_value\": \"./images/B005XKO35U.png\", \"hn_image\": [\"./images/B00A13DXIW.png\"]}\n{\"q_img\": \"./images/B00BHM7F9Y.png\", \"q_text\": \"The button up shirt has long sleeves and is black in color., and is less humorous and more junior\", \"positive_key\": \"B004PNFNGE\", \"positive_value\": \"./images/B004PNFNGE.png\", \"hn_image\": [\"./images/B00BHM7F9Y.png\"]}\n{\"q_img\": \"./images/B00A8YYL1C.png\", \"q_text\": \"is lighter, and has more grey and longer sleeves\", \"positive_key\": \"B007HY3GWG\", \"positive_value\": \"./images/B007HY3GWG.png\", \"hn_image\": [\"./images/B00A8YYL1C.png\"]}\n{\"q_img\": \"./images/B00CYP6OX6.png\", \"q_text\": \"has a longer sleeve with polka dots, and is orange and has buttons and longer sleeves\", \"positive_key\": \"B009727I8Y\", \"positive_value\": \"./images/B009727I8Y.png\", \"hn_image\": [\"./images/B00CYP6OX6.png\"]}\n{\"q_img\": \"./images/B003A6NYSG.png\", \"q_text\": \"is less solid and more gritty, and is short sleeve and miney mouse logo\", \"positive_key\": \"B0076G0M0Y\", \"positive_value\": \"./images/B0076G0M0Y.png\", \"hn_image\": [\"./images/B003A6NYSG.png\"]}\n{\"q_img\": \"./images/B001OOLPBY.png\", \"q_text\": \"has a darker color and a print, and is darker in color\", \"positive_key\": \"B00CBS8D4Y\", \"positive_value\": \"./images/B00CBS8D4Y.png\", \"hn_image\": [\"./images/B001OOLPBY.png\"]}\n{\"q_img\": \"./images/B0093AO0F4.png\", \"q_text\": \"has horizontal stripes and black and white in color, and has a round neckline and is stripped\", \"positive_key\": \"B0091IXT5K\", \"positive_value\": \"./images/B0091IXT5K.png\", \"hn_image\": [\"./images/B0093AO0F4.png\"]}\n{\"q_img\": \"./images/B009ZMX47A.png\", \"q_text\": \"is a less revealing top, and has sleeves\", \"positive_key\": \"B004UO4R1A\", \"positive_value\": \"./images/B004UO4R1A.png\", \"hn_image\": [\"./images/B009ZMX47A.png\"]}\n{\"q_img\": \"./images/B002076ABE.png\", \"q_text\": \"is a white tank top, and has no sleeves\", \"positive_key\": \"B00AT92F82\", \"positive_value\": \"./images/B00AT92F82.png\", \"hn_image\": [\"./images/B002076ABE.png\"]}\n{\"q_img\": \"./images/B0092SC112.png\", \"q_text\": \"has one shoulder and is darker in color, and has a one-shoulder strap and is smoother\", \"positive_key\": \"B00A7HBMEO\", \"positive_value\": \"./images/B00A7HBMEO.png\", \"hn_image\": [\"./images/B0092SC112.png\"]}\n{\"q_img\": \"./images/B00AYCOA0K.png\", \"q_text\": \"is lighter shinier and has shorter sleeves, and has no sleeves\", \"positive_key\": \"B004SGM7KS\", \"positive_value\": \"./images/B004SGM7KS.png\", \"hn_image\": [\"./images/B00AYCOA0K.png\"]}\n{\"q_img\": \"./images/B006IMURPC.png\", \"q_text\": \"has shorter sleeves with black and white polka dots, and has white polka dots\", \"positive_key\": \"B00A3HL3Y2\", \"positive_value\": \"./images/B00A3HL3Y2.png\", \"hn_image\": [\"./images/B006IMURPC.png\"]}\n{\"q_img\": \"./images/B006PAX3HG.png\", \"q_text\": \"is golden and more shiny, and is golden and more shiny\", \"positive_key\": \"B00FQ75N3Q\", \"positive_value\": \"./images/B00FQ75N3Q.png\", \"hn_image\": [\"./images/B006PAX3HG.png\"]}\n{\"q_img\": \"./images/B00BWCOOHA.png\", \"q_text\": \"is green colored and more loose, and is green and rounded neck\", \"positive_key\": \"B005LJX534\", \"positive_value\": \"./images/B005LJX534.png\", \"hn_image\": [\"./images/B00BWCOOHA.png\"]}\n{\"q_img\": \"./images/B0077Z6LQ8.png\", \"q_text\": \"is grey and says I ramen datterbay, and is lighter gray\", \"positive_key\": \"B00F62JQEI\", \"positive_value\": \"./images/B00F62JQEI.png\", \"hn_image\": [\"./images/B0077Z6LQ8.png\"]}\n{\"q_img\": \"./images/B00CU89THK.png\", \"q_text\": \"is purple with white designs, and is purple and has sleeves\", \"positive_key\": \"B0091O2IGU\", \"positive_value\": \"./images/B0091O2IGU.png\", \"hn_image\": [\"./images/B00CU89THK.png\"]}\n{\"q_img\": \"./images/B00B7EU09O.png\", \"q_text\": \"Is less eccentric and more subdued, and Is all black & longer\", \"positive_key\": \"B00AIXH7BO\", \"positive_value\": \"./images/B00AIXH7BO.png\", \"hn_image\": [\"./images/B00B7EU09O.png\"]}\n{\"q_img\": \"./images/B00FA670M0.png\", \"q_text\": \"is less revealing and more pink, and has no buttons on it\", \"positive_key\": \"B001HNM49E\", \"positive_value\": \"./images/B001HNM49E.png\", \"hn_image\": [\"./images/B00FA670M0.png\"]}\n{\"q_img\": \"./images/B003KQJHR8.png\", \"q_text\": \"has a short sleeve with orange art, and is white and long\", \"positive_key\": \"B001E6QWUQ\", \"positive_value\": \"./images/B001E6QWUQ.png\", \"hn_image\": [\"./images/B003KQJHR8.png\"]}\n{\"q_img\": \"./images/B009XAYQM6.png\", \"q_text\": \"The shirt is loose with white sleeves and yellow in color., and yellow\", \"positive_key\": \"B00AYCOA0K\", \"positive_value\": \"./images/B00AYCOA0K.png\", \"hn_image\": [\"./images/B009XAYQM6.png\"]}\n{\"q_img\": \"./images/B005GVB93K.png\", \"q_text\": \"has mp sleeved and is darker colored, and is darker colored\", \"positive_key\": \"B003CF1B6M\", \"positive_value\": \"./images/B003CF1B6M.png\", \"hn_image\": [\"./images/B005GVB93K.png\"]}\n{\"q_img\": \"./images/B00902GR0Q.png\", \"q_text\": \"is off the shoulders., and is strapless and has a ruffle\", \"positive_key\": \"B00DEPW47K\", \"positive_value\": \"./images/B00DEPW47K.png\", \"hn_image\": [\"./images/B00902GR0Q.png\"]}\n{\"q_img\": \"./images/B005GIF2JA.png\", \"q_text\": \"has long sleeves, and has longer sleeves\", \"positive_key\": \"B008ZBLSC0\", \"positive_value\": \"./images/B008ZBLSC0.png\", \"hn_image\": [\"./images/B005GIF2JA.png\"]}\n{\"q_img\": \"./images/B00751ZL9M.png\", \"q_text\": \"is white colored and has longer sleeves, and is frilled as well in solid white.\", \"positive_key\": \"B005IURP9G\", \"positive_value\": \"./images/B005IURP9G.png\", \"hn_image\": [\"./images/B00751ZL9M.png\"]}\n{\"q_img\": \"./images/B00BGVT030.png\", \"q_text\": \"dressier without print, and is color tan and has an opening on front.\", \"positive_key\": \"B00C1RCVP2\", \"positive_value\": \"./images/B00C1RCVP2.png\", \"hn_image\": [\"./images/B00BGVT030.png\"]}\n{\"q_img\": \"./images/B003XII7B0.png\", \"q_text\": \"is more floral and has rounder neck, and is lighter grey in color with graphic of flowers\", \"positive_key\": \"B008OJR7Q4\", \"positive_value\": \"./images/B008OJR7Q4.png\", \"hn_image\": [\"./images/B003XII7B0.png\"]}\n{\"q_img\": \"./images/B0009GG2RA.png\", \"q_text\": \"more revealing with no sleeves. black, and has no sleeves and is darker\", \"positive_key\": \"B003KYTQUI\", \"positive_value\": \"./images/B003KYTQUI.png\", \"hn_image\": [\"./images/B0009GG2RA.png\"]}\n{\"q_img\": \"./images/B00CXXIGTY.png\", \"q_text\": \"has no sleeves and is black and white, and is white with black stribe and is sleeveless\", \"positive_key\": \"B00E83BKNG\", \"positive_value\": \"./images/B00E83BKNG.png\", \"hn_image\": [\"./images/B00CXXIGTY.png\"]}\n{\"q_img\": \"./images/B00BXF5DKI.png\", \"q_text\": \"is white and has pink stripes on sleeves, and Is whiter with pink words\", \"positive_key\": \"B00CLE86I6\", \"positive_value\": \"./images/B00CLE86I6.png\", \"hn_image\": [\"./images/B00BXF5DKI.png\"]}\n{\"q_img\": \"./images/B008I8YP52.png\", \"q_text\": \"is tight fitting with a bow, and no sleeves blouse\", \"positive_key\": \"B008P6Z0XI\", \"positive_value\": \"./images/B008P6Z0XI.png\", \"hn_image\": [\"./images/B008I8YP52.png\"]}\n{\"q_img\": \"./images/B00DEZTDCE.png\", \"q_text\": \"Is brighter and less chic, and is a red tank with buttons on the neck to button down.\", \"positive_key\": \"B00C3XXETQ\", \"positive_value\": \"./images/B00C3XXETQ.png\", \"hn_image\": [\"./images/B00DEZTDCE.png\"]}\n{\"q_img\": \"./images/B006DQSSLI.png\", \"q_text\": \"The shirt is black with colorful writing., and hit length with barth without art is just - eh logo\", \"positive_key\": \"B008VWCZ5W\", \"positive_value\": \"./images/B008VWCZ5W.png\", \"hn_image\": [\"./images/B006DQSSLI.png\"]}\n{\"q_img\": \"./images/B00D63XY6U.png\", \"q_text\": \"is more feminine, and is lavender short sleeved\", \"positive_key\": \"B004P5P1I2\", \"positive_value\": \"./images/B004P5P1I2.png\", \"hn_image\": [\"./images/B00D63XY6U.png\"]}\n{\"q_img\": \"./images/B00DI5EP8M.png\", \"q_text\": \"has longer sleeves and is sportier, and white pennstate long sleeve\", \"positive_key\": \"B004BX08L8\", \"positive_value\": \"./images/B004BX08L8.png\", \"hn_image\": [\"./images/B00DI5EP8M.png\"]}\n{\"q_img\": \"./images/B004KUYKGG.png\", \"q_text\": \"black tee with car logo, and black with larger colorful graphic\", \"positive_key\": \"B00BQZ7OCK\", \"positive_value\": \"./images/B00BQZ7OCK.png\", \"hn_image\": [\"./images/B004KUYKGG.png\"]}\n{\"q_img\": \"./images/B006YLXHFY.png\", \"q_text\": \"The shirt is loose fitting and red in color., and Has no buttons and is less ethnic\", \"positive_key\": \"B007BKCNRU\", \"positive_value\": \"./images/B007BKCNRU.png\", \"hn_image\": [\"./images/B006YLXHFY.png\"]}\n{\"q_img\": \"./images/B000A35AMK.png\", \"q_text\": \"has no sleeves, and is darker in color\", \"positive_key\": \"B00CO4GP72\", \"positive_value\": \"./images/B00CO4GP72.png\", \"hn_image\": [\"./images/B000A35AMK.png\"]}\n{\"q_img\": \"./images/B00BA6KWNS.png\", \"q_text\": \"The shirt is black in color with a picture of Old Dirty Bastard., and is black with a white panel\", \"positive_key\": \"B005D1WV8U\", \"positive_value\": \"./images/B005D1WV8U.png\", \"hn_image\": [\"./images/B00BA6KWNS.png\"]}\n{\"q_img\": \"./images/B007MB2OAE.png\", \"q_text\": \"is blue and has tiny hips, and is royal blue with white logo\", \"positive_key\": \"B009GPNPP2\", \"positive_value\": \"./images/B009GPNPP2.png\", \"hn_image\": [\"./images/B007MB2OAE.png\"]}\n{\"q_img\": \"./images/B007W9Z8WM.png\", \"q_text\": \"The shirt has no sleeves and has cut out panels., and has shorter sleeves and is blue\", \"positive_key\": \"B0094DG0N0\", \"positive_value\": \"./images/B0094DG0N0.png\", \"hn_image\": [\"./images/B007W9Z8WM.png\"]}\n{\"q_img\": \"./images/B001D6Y466.png\", \"q_text\": \"is grey and shinier with one strap, and Is a satin and a draped one shoulder style\", \"positive_key\": \"B0043XYYLQ\", \"positive_value\": \"./images/B0043XYYLQ.png\", \"hn_image\": [\"./images/B001D6Y466.png\"]}\n{\"q_img\": \"./images/B00AL1QEXU.png\", \"q_text\": \"The shirt is tight and dark blue in coloring., and is identical\", \"positive_key\": \"B00C7AFSIA\", \"positive_value\": \"./images/B00C7AFSIA.png\", \"hn_image\": [\"./images/B00AL1QEXU.png\"]}\n{\"q_img\": \"./images/B00H879A5Y.png\", \"q_text\": \"has more images in the middle, and is lighter in color\", \"positive_key\": \"B004YDELJA\", \"positive_value\": \"./images/B004YDELJA.png\", \"hn_image\": [\"./images/B00H879A5Y.png\"]}\n{\"q_img\": \"./images/B00D8MWDL6.png\", \"q_text\": \"is black and more premium, and is black and has large round neck\", \"positive_key\": \"B00CMPGWOY\", \"positive_value\": \"./images/B00CMPGWOY.png\", \"hn_image\": [\"./images/B00D8MWDL6.png\"]}\n{\"q_img\": \"./images/B000VCWJIS.png\", \"q_text\": \"id tan and longer, and is brown and a top\", \"positive_key\": \"B0058K7QCM\", \"positive_value\": \"./images/B0058K7QCM.png\", \"hn_image\": [\"./images/B000VCWJIS.png\"]}\n{\"q_img\": \"./images/B008MN5ONU.png\", \"q_text\": \"Is more lacy and flowing, and is less revealing and more feminine\", \"positive_key\": \"B008OY5EM8\", \"positive_value\": \"./images/B008OY5EM8.png\", \"hn_image\": [\"./images/B008MN5ONU.png\"]}\n{\"q_img\": \"./images/B003BYA9H6.png\", \"q_text\": \"is lighter and has shorter sleeves, and is white and has a more simple neckline\", \"positive_key\": \"B0052HY6XI\", \"positive_value\": \"./images/B0052HY6XI.png\", \"hn_image\": [\"./images/B003BYA9H6.png\"]}\n{\"q_img\": \"./images/B002Q2O178.png\", \"q_text\": \"short sleeve checker design black and white, and is plaid and longer sleeves\", \"positive_key\": \"B006TCJE1E\", \"positive_value\": \"./images/B006TCJE1E.png\", \"hn_image\": [\"./images/B002Q2O178.png\"]}\n{\"q_img\": \"./images/B007YSB65E.png\", \"q_text\": \"is less feminine, and Is black with a face design with words with disobey\", \"positive_key\": \"B00DEJR2EQ\", \"positive_value\": \"./images/B00DEJR2EQ.png\", \"hn_image\": [\"./images/B007YSB65E.png\"]}\n{\"q_img\": \"./images/B007GRPJ5Q.png\", \"q_text\": \"The shirt is a turtle neck and green in color., and is teal-colored\", \"positive_key\": \"B004AYYOOK\", \"positive_value\": \"./images/B004AYYOOK.png\", \"hn_image\": [\"./images/B007GRPJ5Q.png\"]}\n{\"q_img\": \"./images/B008NF7N5E.png\", \"q_text\": \"is blue colored blouse, and is a bra-top in white and blue\", \"positive_key\": \"B007ATKF7W\", \"positive_value\": \"./images/B007ATKF7W.png\", \"hn_image\": [\"./images/B008NF7N5E.png\"]}\n{\"q_img\": \"./images/B00133IN7K.png\", \"q_text\": \"is less scarier, and is a shirt\", \"positive_key\": \"B00DEBOY2M\", \"positive_value\": \"./images/B00DEBOY2M.png\", \"hn_image\": [\"./images/B00133IN7K.png\"]}\n{\"q_img\": \"./images/B00BX4K30Y.png\", \"q_text\": \"Is white with a v neck, and is white with chest pattern\", \"positive_key\": \"B00BBZVWNC\", \"positive_value\": \"./images/B00BBZVWNC.png\", \"hn_image\": [\"./images/B00BX4K30Y.png\"]}\n{\"q_img\": \"./images/B008WZC5EE.png\", \"q_text\": \"is colorful, and Is white and has long sleeves\", \"positive_key\": \"B007WAD0TE\", \"positive_value\": \"./images/B007WAD0TE.png\", \"hn_image\": [\"./images/B008WZC5EE.png\"]}\n{\"q_img\": \"./images/B004SWH4I2.png\", \"q_text\": \"The boat neck shirt is lavender in color., and is shorter and tan with a round neck\", \"positive_key\": \"B004GJWB5I\", \"positive_value\": \"./images/B004GJWB5I.png\", \"hn_image\": [\"./images/B004SWH4I2.png\"]}\n{\"q_img\": \"./images/B006M6B9ME.png\", \"q_text\": \"is tighter and is red and navy, and is black with red dots\", \"positive_key\": \"B00ASR45EC\", \"positive_value\": \"./images/B00ASR45EC.png\", \"hn_image\": [\"./images/B006M6B9ME.png\"]}\n{\"q_img\": \"./images/B007PUOJAU.png\", \"q_text\": \"is darker, and longer sleeve\", \"positive_key\": \"B007RUN7LA\", \"positive_value\": \"./images/B007RUN7LA.png\", \"hn_image\": [\"./images/B007PUOJAU.png\"]}\n{\"q_img\": \"./images/B008H865JM.png\", \"q_text\": \"is a black t-shirt with a logo, and it is black\", \"positive_key\": \"B001QLCLD6\", \"positive_value\": \"./images/B001QLCLD6.png\", \"hn_image\": [\"./images/B008H865JM.png\"]}\n{\"q_img\": \"./images/B002YAEG7C.png\", \"q_text\": \"has shorter sleeves with a pink color, and is pink with graphics\", \"positive_key\": \"B0057M0EUW\", \"positive_value\": \"./images/B0057M0EUW.png\", \"hn_image\": [\"./images/B002YAEG7C.png\"]}\n{\"q_img\": \"./images/B0079EIEQM.png\", \"q_text\": \"is white, and is cream colored\", \"positive_key\": \"B0096I9KZS\", \"positive_value\": \"./images/B0096I9KZS.png\", \"hn_image\": [\"./images/B0079EIEQM.png\"]}\n{\"q_img\": \"./images/B005GI9FCK.png\", \"q_text\": \"has longer sleeves with dot patterns, and no animal graphics\", \"positive_key\": \"B0094U96UW\", \"positive_value\": \"./images/B0094U96UW.png\", \"hn_image\": [\"./images/B005GI9FCK.png\"]}\n{\"q_img\": \"./images/B007WASLUM.png\", \"q_text\": \"is grey with collar and short sleeves, and is grey with red trim\", \"positive_key\": \"B007RVNR8W\", \"positive_value\": \"./images/B007RVNR8W.png\", \"hn_image\": [\"./images/B007WASLUM.png\"]}\n{\"q_img\": \"./images/B00BJYIR9C.png\", \"q_text\": \"has a darker color, and Is black with a more open neck\", \"positive_key\": \"B0015UTLJU\", \"positive_value\": \"./images/B0015UTLJU.png\", \"hn_image\": [\"./images/B00BJYIR9C.png\"]}\n{\"q_img\": \"./images/B00DLWPKZO.png\", \"q_text\": \"is a lime green t-shirt with printing, and is green with short sleeves\", \"positive_key\": \"B0062PZM8M\", \"positive_value\": \"./images/B0062PZM8M.png\", \"hn_image\": [\"./images/B00DLWPKZO.png\"]}\n{\"q_img\": \"./images/B009PIM60A.png\", \"q_text\": \"has short sleeves and is a-line shaped, and has pink skull with sheerer fabric\", \"positive_key\": \"B009EPLLVY\", \"positive_value\": \"./images/B009EPLLVY.png\", \"hn_image\": [\"./images/B009PIM60A.png\"]}\n{\"q_img\": \"./images/B00BHRICZU.png\", \"q_text\": \"has long sleeves and is less feminine, and is brown and has long sleeve\", \"positive_key\": \"B009C7QVXC\", \"positive_value\": \"./images/B009C7QVXC.png\", \"hn_image\": [\"./images/B00BHRICZU.png\"]}\n{\"q_img\": \"./images/B00296O9IM.png\", \"q_text\": \"is grey, and is a gray tank\", \"positive_key\": \"B008A1FJMK\", \"positive_value\": \"./images/B008A1FJMK.png\", \"hn_image\": [\"./images/B00296O9IM.png\"]}\n{\"q_img\": \"./images/B008O55QZW.png\", \"q_text\": \"Is brighter and has longer sleeves, and has mid sleeves\", \"positive_key\": \"B005KNCFL4\", \"positive_value\": \"./images/B005KNCFL4.png\", \"hn_image\": [\"./images/B008O55QZW.png\"]}\n{\"q_img\": \"./images/B00BKTH75G.png\", \"q_text\": \"is yellow, and has sleeves\", \"positive_key\": \"B00BCFZUAM\", \"positive_value\": \"./images/B00BCFZUAM.png\", \"hn_image\": [\"./images/B00BKTH75G.png\"]}\n{\"q_img\": \"./images/B00DO49KKK.png\", \"q_text\": \"is a long blue blouse, and is blue and more casual\", \"positive_key\": \"B005DA4P3A\", \"positive_value\": \"./images/B005DA4P3A.png\", \"hn_image\": [\"./images/B00DO49KKK.png\"]}\n{\"q_img\": \"./images/B00FH695XA.png\", \"q_text\": \"The capped sleeved shirt is brown and white in color., and is more brown colored\", \"positive_key\": \"B002QEAWJW\", \"positive_value\": \"./images/B002QEAWJW.png\", \"hn_image\": [\"./images/B00FH695XA.png\"]}\n{\"q_img\": \"./images/B0081ZRYN2.png\", \"q_text\": \"is black and has floral, and has a separate print with purple text.\", \"positive_key\": \"B009F3JJ2S\", \"positive_value\": \"./images/B009F3JJ2S.png\", \"hn_image\": [\"./images/B0081ZRYN2.png\"]}\n{\"q_img\": \"./images/B00AG4409C.png\", \"q_text\": \"is longer and grey, and Is more flowing and plain\", \"positive_key\": \"B0053GPWQS\", \"positive_value\": \"./images/B0053GPWQS.png\", \"hn_image\": [\"./images/B00AG4409C.png\"]}\n{\"q_img\": \"./images/B0089KF6T8.png\", \"q_text\": \"has longer sleeves with faded blue print, and Has shorter sleeves and is more casual\", \"positive_key\": \"B009YIOMJE\", \"positive_value\": \"./images/B009YIOMJE.png\", \"hn_image\": [\"./images/B0089KF6T8.png\"]}\n{\"q_img\": \"./images/B008MC1V1U.png\", \"q_text\": \"is black with a dong knock on it, and is solid black with white logo\", \"positive_key\": \"B0056K4L40\", \"positive_value\": \"./images/B0056K4L40.png\", \"hn_image\": [\"./images/B008MC1V1U.png\"]}\n{\"q_img\": \"./images/B00DULOFW0.png\", \"q_text\": \" red and yellow pictures., and has a full halter top to the waist.\", \"positive_key\": \"B00522PGW8\", \"positive_value\": \"./images/B00522PGW8.png\", \"hn_image\": [\"./images/B00DULOFW0.png\"]}\n{\"q_img\": \"./images/B00BXI5XOG.png\", \"q_text\": \"has a long sleeve with brighter color, and is grey and has longer sleeves.\", \"positive_key\": \"B003IKOO38\", \"positive_value\": \"./images/B003IKOO38.png\", \"hn_image\": [\"./images/B00BXI5XOG.png\"]}\n{\"q_img\": \"./images/B00754F60S.png\", \"q_text\": \"is black with white words, and has white text on it\", \"positive_key\": \"B0045DYIVK\", \"positive_value\": \"./images/B0045DYIVK.png\", \"hn_image\": [\"./images/B00754F60S.png\"]}\n{\"q_img\": \"./images/B00CU89THK.png\", \"q_text\": \"is back with green designs, and has longer sleeves\", \"positive_key\": \"B0079AI3I0\", \"positive_value\": \"./images/B0079AI3I0.png\", \"hn_image\": [\"./images/B00CU89THK.png\"]}\n{\"q_img\": \"./images/B001AYHVZ2.png\", \"q_text\": \"Is more flowing and girly, and is lace tank shirt with pink designs\", \"positive_key\": \"B005LCCRJY\", \"positive_value\": \"./images/B005LCCRJY.png\", \"hn_image\": [\"./images/B001AYHVZ2.png\"]}\n{\"q_img\": \"./images/B00342VD8O.png\", \"q_text\": \"is more flowy and longer, and is more frilly and mature\", \"positive_key\": \"B0011TQJUO\", \"positive_value\": \"./images/B0011TQJUO.png\", \"hn_image\": [\"./images/B00342VD8O.png\"]}\n{\"q_img\": \"./images/B00D6QDJS0.png\", \"q_text\": \"The shirt is pink with a pink dog., and is full pink with a pig print.\", \"positive_key\": \"B00961EPP0\", \"positive_value\": \"./images/B00961EPP0.png\", \"hn_image\": [\"./images/B00D6QDJS0.png\"]}\n{\"q_img\": \"./images/B0095Z37OW.png\", \"q_text\": \"has stripes and is more flowing, and has stripes\", \"positive_key\": \"B00D2N8CRK\", \"positive_value\": \"./images/B00D2N8CRK.png\", \"hn_image\": [\"./images/B0095Z37OW.png\"]}\n{\"q_img\": \"./images/B003PD0G30.png\", \"q_text\": \"is dark blue colored, and is blue and lighter in color\", \"positive_key\": \"B006BZ8690\", \"positive_value\": \"./images/B006BZ8690.png\", \"hn_image\": [\"./images/B003PD0G30.png\"]}\n{\"q_img\": \"./images/B0019IKZ16.png\", \"q_text\": \"has 3/4 length sleeve and orange color, and is yellow and orange colored\", \"positive_key\": \"B0085IN6M8\", \"positive_value\": \"./images/B0085IN6M8.png\", \"hn_image\": [\"./images/B0019IKZ16.png\"]}\n{\"q_img\": \"./images/B005LIXPPI.png\", \"q_text\": \"The shirt is black with red writing and a woman in red., and Has redder colors on the graphic\", \"positive_key\": \"B003IE8TXU\", \"positive_value\": \"./images/B003IE8TXU.png\", \"hn_image\": [\"./images/B005LIXPPI.png\"]}\n{\"q_img\": \"./images/B0001W1KSI.png\", \"q_text\": \"a tie dye tank top with light and dark colors, and has no sleeves\", \"positive_key\": \"B009LHGDLS\", \"positive_value\": \"./images/B009LHGDLS.png\", \"hn_image\": [\"./images/B0001W1KSI.png\"]}\n{\"q_img\": \"./images/B0090SGW4Q.png\", \"q_text\": \"The shirt is pink in color with white writing., and Has longer sleeves\", \"positive_key\": \"B00B7O1P3E\", \"positive_value\": \"./images/B00B7O1P3E.png\", \"hn_image\": [\"./images/B0090SGW4Q.png\"]}\n{\"q_img\": \"./images/B0081XJQZI.png\", \"q_text\": \"is more casual, and has straps and is grey\", \"positive_key\": \"B00AWNHCCO\", \"positive_value\": \"./images/B00AWNHCCO.png\", \"hn_image\": [\"./images/B0081XJQZI.png\"]}\n{\"q_img\": \"./images/B003WT218K.png\", \"q_text\": \"is green colored and has shorter sleeves, and is green and striped\", \"positive_key\": \"B00AA7DJF6\", \"positive_value\": \"./images/B00AA7DJF6.png\", \"hn_image\": [\"./images/B003WT218K.png\"]}\n{\"q_img\": \"./images/B00AU8BW5Y.png\", \"q_text\": \"is blue and has short sleeves, and is blue and has larger round neck\", \"positive_key\": \"B005EE3676\", \"positive_value\": \"./images/B005EE3676.png\", \"hn_image\": [\"./images/B00AU8BW5Y.png\"]}\n{\"q_img\": \"./images/B00CU89THK.png\", \"q_text\": \"is a black and white striped socks, and has long shock and is black stripe color\", \"positive_key\": \"B00A8CNPLQ\", \"positive_value\": \"./images/B00A8CNPLQ.png\", \"hn_image\": [\"./images/B00CU89THK.png\"]}\n{\"q_img\": \"./images/B007H9KGT2.png\", \"q_text\": \"is sleeveless, and is a strapped tank with blue added to the coloring.\", \"positive_key\": \"B00BQLT6FC\", \"positive_value\": \"./images/B00BQLT6FC.png\", \"hn_image\": [\"./images/B007H9KGT2.png\"]}\n{\"q_img\": \"./images/B008P3A0I6.png\", \"q_text\": \"Is a scarf, and is pink in color\", \"positive_key\": \"B006OO56AK\", \"positive_value\": \"./images/B006OO56AK.png\", \"hn_image\": [\"./images/B008P3A0I6.png\"]}\n{\"q_img\": \"./images/B0086JXCEI.png\", \"q_text\": \"Has a less colorful graphic, and has a different pattern\", \"positive_key\": \"B0086JXBYE\", \"positive_value\": \"./images/B0086JXBYE.png\", \"hn_image\": [\"./images/B0086JXCEI.png\"]}\n{\"q_img\": \"./images/B00G3J4GB6.png\", \"q_text\": \"is darker, and is in plain black\", \"positive_key\": \"B00FN8311Y\", \"positive_value\": \"./images/B00FN8311Y.png\", \"hn_image\": [\"./images/B00G3J4GB6.png\"]}\n{\"q_img\": \"./images/B006LN0WHG.png\", \"q_text\": \"is less busty and is navy, and has longer sleeves and is dark blue\", \"positive_key\": \"B007C3HHZ4\", \"positive_value\": \"./images/B007C3HHZ4.png\", \"hn_image\": [\"./images/B006LN0WHG.png\"]}\n{\"q_img\": \"./images/B008RDDD8I.png\", \"q_text\": \"is long sleeved and more fitted, and is brown with longer sleeves\", \"positive_key\": \"B009OKGWMM\", \"positive_value\": \"./images/B009OKGWMM.png\", \"hn_image\": [\"./images/B008RDDD8I.png\"]}\n{\"q_img\": \"./images/B00836H3JE.png\", \"q_text\": \"is more shinier and is brown, and is dark pink shiny and less revealing neck\", \"positive_key\": \"B0061M50AG\", \"positive_value\": \"./images/B0061M50AG.png\", \"hn_image\": [\"./images/B00836H3JE.png\"]}\n{\"q_img\": \"./images/B00ESMW1DA.png\", \"q_text\": \"is red colored and shorter sleeves, and is red with draped sleeves\", \"positive_key\": \"B00CSANZVQ\", \"positive_value\": \"./images/B00CSANZVQ.png\", \"hn_image\": [\"./images/B00ESMW1DA.png\"]}\n{\"q_img\": \"./images/B00DEJR2EQ.png\", \"q_text\": \"The short sleeved shirt is white, and has no sleeves\", \"positive_key\": \"B003NYEWPE\", \"positive_value\": \"./images/B003NYEWPE.png\", \"hn_image\": [\"./images/B00DEJR2EQ.png\"]}\n{\"q_img\": \"./images/B00BWULMFY.png\", \"q_text\": \"is pinched more below the bust, and is brown in color\", \"positive_key\": \"B007T4BN88\", \"positive_value\": \"./images/B007T4BN88.png\", \"hn_image\": [\"./images/B00BWULMFY.png\"]}\n{\"q_img\": \"./images/B008QW7RWS.png\", \"q_text\": \"is scoop neck with shorter sleeves, and is shorter sleeved\", \"positive_key\": \"B00E8DA258\", \"positive_value\": \"./images/B00E8DA258.png\", \"hn_image\": [\"./images/B008QW7RWS.png\"]}\n{\"q_img\": \"./images/B008DI1PAU.png\", \"q_text\": \"is solid white with 3/4 sleeves, and has white long sleeves\", \"positive_key\": \"B00AWMNV8Y\", \"positive_value\": \"./images/B00AWMNV8Y.png\", \"hn_image\": [\"./images/B008DI1PAU.png\"]}\n{\"q_img\": \"./images/B007PY6GJ8.png\", \"q_text\": \"is similar but has a different picture on front, and the shirt i want has man and woman\", \"positive_key\": \"B005UVOSB6\", \"positive_value\": \"./images/B005UVOSB6.png\", \"hn_image\": [\"./images/B007PY6GJ8.png\"]}\n{\"q_img\": \"./images/B005PXI0A4.png\", \"q_text\": \"has long sleeves, and is a blouse with long sleeves and a ruffled bottom\", \"positive_key\": \"B00CPA475W\", \"positive_value\": \"./images/B00CPA475W.png\", \"hn_image\": [\"./images/B005PXI0A4.png\"]}\n{\"q_img\": \"./images/B00BR8K0TA.png\", \"q_text\": \"doesn't have buttons, and is orange colored with a looser neckline\", \"positive_key\": \"B00AVXYJRQ\", \"positive_value\": \"./images/B00AVXYJRQ.png\", \"hn_image\": [\"./images/B00BR8K0TA.png\"]}\n{\"q_img\": \"./images/B0087AYJRA.png\", \"q_text\": \"The shirt is red and white in color., and is reddish pink and has shorter sleeves\", \"positive_key\": \"B00BQOYCF8\", \"positive_value\": \"./images/B00BQOYCF8.png\", \"hn_image\": [\"./images/B0087AYJRA.png\"]}\n{\"q_img\": \"./images/B00E4OQWQY.png\", \"q_text\": \"has no sleeves but an image, and has off shoulder and is black color\", \"positive_key\": \"B00FDY5AKS\", \"positive_value\": \"./images/B00FDY5AKS.png\", \"hn_image\": [\"./images/B00E4OQWQY.png\"]}\n{\"q_img\": \"./images/B009YJB6PG.png\", \"q_text\": \"is coral, and is pink and has off shoulder\", \"positive_key\": \"B007563G3K\", \"positive_value\": \"./images/B007563G3K.png\", \"hn_image\": [\"./images/B009YJB6PG.png\"]}\n{\"q_img\": \"./images/B00BXIPAQ2.png\", \"q_text\": \"is dark grey colored and has shorter sleeves, and is darker coloured\", \"positive_key\": \"B009YJRYDE\", \"positive_value\": \"./images/B009YJRYDE.png\", \"hn_image\": [\"./images/B00BXIPAQ2.png\"]}\n{\"q_img\": \"./images/B007S3RM9Y.png\", \"q_text\": \"has a square chest and long sleeves, and has long sleeves\", \"positive_key\": \"B00E6JTXLS\", \"positive_value\": \"./images/B00E6JTXLS.png\", \"hn_image\": [\"./images/B007S3RM9Y.png\"]}\n{\"q_img\": \"./images/B00775KKV0.png\", \"q_text\": \"is grey with sleeves, and is with short sleeves and has a wider neckline\", \"positive_key\": \"B007QSN628\", \"positive_value\": \"./images/B007QSN628.png\", \"hn_image\": [\"./images/B00775KKV0.png\"]}\n{\"q_img\": \"./images/B003V6J31C.png\", \"q_text\": \"is curvier and less solid, and has a green round color and is dark grey\", \"positive_key\": \"B003K1EIR2\", \"positive_value\": \"./images/B003K1EIR2.png\", \"hn_image\": [\"./images/B003V6J31C.png\"]}\n{\"q_img\": \"./images/B009R8P3I0.png\", \"q_text\": \"is darker with a purple logo, and Is black in color\", \"positive_key\": \"B00EOL6WJ4\", \"positive_value\": \"./images/B00EOL6WJ4.png\", \"hn_image\": [\"./images/B009R8P3I0.png\"]}\n{\"q_img\": \"./images/B00AZ0180G.png\", \"q_text\": \"The shirt is short sleeved gray in color with red writing., and is grey with red print.\", \"positive_key\": \"B00655SJ20\", \"positive_value\": \"./images/B00655SJ20.png\", \"hn_image\": [\"./images/B00AZ0180G.png\"]}\n{\"q_img\": \"./images/B009WOYY3E.png\", \"q_text\": \"is long sleeved v neck, and is solid cream color\", \"positive_key\": \"B001J8IK8Q\", \"positive_value\": \"./images/B001J8IK8Q.png\", \"hn_image\": [\"./images/B009WOYY3E.png\"]}\n{\"q_img\": \"./images/B008NDWZJ0.png\", \"q_text\": \"is black with red and yellow designs, and is less animal-like and more graphic inspirsed\", \"positive_key\": \"B0053GNYA4\", \"positive_value\": \"./images/B0053GNYA4.png\", \"hn_image\": [\"./images/B008NDWZJ0.png\"]}\n{\"q_img\": \"./images/B0052XIUXO.png\", \"q_text\": \"is brighter and much more revealing, and is sleeveless colored green\", \"positive_key\": \"B007SB3R40\", \"positive_value\": \"./images/B007SB3R40.png\", \"hn_image\": [\"./images/B0052XIUXO.png\"]}\n{\"q_img\": \"./images/B00BLYSR1I.png\", \"q_text\": \"is a t shirt with a design, and and darker and has shorter sleeves\", \"positive_key\": \"B008REUJ20\", \"positive_value\": \"./images/B008REUJ20.png\", \"hn_image\": [\"./images/B00BLYSR1I.png\"]}\n{\"q_img\": \"./images/B00CWAF7SG.png\", \"q_text\": \"has long sleeves, and has longer sleeves\", \"positive_key\": \"B00DP4XAQE\", \"positive_value\": \"./images/B00DP4XAQE.png\", \"hn_image\": [\"./images/B00CWAF7SG.png\"]}\n{\"q_img\": \"./images/B00DNZNKKQ.png\", \"q_text\": \"has a pink color with text in the middle, and is light pink colored\", \"positive_key\": \"B00B2E0MQU\", \"positive_value\": \"./images/B00B2E0MQU.png\", \"hn_image\": [\"./images/B00DNZNKKQ.png\"]}\n{\"q_img\": \"./images/B007R0TX6S.png\", \"q_text\": \"has no sleeves and two different colors, and strap sleeves pink no logo blue trip on sleeves and neck\", \"positive_key\": \"B006HT244S\", \"positive_value\": \"./images/B006HT244S.png\", \"hn_image\": [\"./images/B007R0TX6S.png\"]}\n{\"q_img\": \"./images/B00E65KUB4.png\", \"q_text\": \"The tank top is multi-colored., and brown\", \"positive_key\": \"B008NFKLRQ\", \"positive_value\": \"./images/B008NFKLRQ.png\", \"hn_image\": [\"./images/B00E65KUB4.png\"]}\n{\"q_img\": \"./images/B008OPRRQI.png\", \"q_text\": \"The shirt is gray in color with Stefan from Vampire Diaries, and is light gray with short sleeves\", \"positive_key\": \"B006M3I7I6\", \"positive_value\": \"./images/B006M3I7I6.png\", \"hn_image\": [\"./images/B008OPRRQI.png\"]}\n{\"q_img\": \"./images/B00BGEPG76.png\", \"q_text\": \"has blue jean color, and longer sleeves\", \"positive_key\": \"B005TJUA9I\", \"positive_value\": \"./images/B005TJUA9I.png\", \"hn_image\": [\"./images/B00BGEPG76.png\"]}\n{\"q_img\": \"./images/B0089HEOHG.png\", \"q_text\": \"The shirt is pink with a black camel and wording of Hump Daaay!, and is pink with black image\", \"positive_key\": \"B00GVHV6YA\", \"positive_value\": \"./images/B00GVHV6YA.png\", \"hn_image\": [\"./images/B0089HEOHG.png\"]}\n{\"q_img\": \"./images/B0051SM8SS.png\", \"q_text\": \"has longer sleeves, and is black cowl neck long sleeved\", \"positive_key\": \"B007TV5VOI\", \"positive_value\": \"./images/B007TV5VOI.png\", \"hn_image\": [\"./images/B0051SM8SS.png\"]}\n{\"q_img\": \"./images/B0009MGZQ2.png\", \"q_text\": \"has buttons and is brighter colored, and is green and white buttons\", \"positive_key\": \"B002BJL5XO\", \"positive_value\": \"./images/B002BJL5XO.png\", \"hn_image\": [\"./images/B0009MGZQ2.png\"]}\n{\"q_img\": \"./images/B00961EPP0.png\", \"q_text\": \"is light blue with a flying pig, and Solid light blue shirt with pig on front\", \"positive_key\": \"B0067EFMAQ\", \"positive_value\": \"./images/B0067EFMAQ.png\", \"hn_image\": [\"./images/B00961EPP0.png\"]}\n{\"q_img\": \"./images/B006MHUHM6.png\", \"q_text\": \"has the same design, and is black and orange and more revealing\", \"positive_key\": \"B007QJ3C08\", \"positive_value\": \"./images/B007QJ3C08.png\", \"hn_image\": [\"./images/B006MHUHM6.png\"]}\n{\"q_img\": \"./images/B004KUUKCO.png\", \"q_text\": \"is grey colored, and is cool gray with white lettering\", \"positive_key\": \"B003ECZMGI\", \"positive_value\": \"./images/B003ECZMGI.png\", \"hn_image\": [\"./images/B004KUUKCO.png\"]}\n{\"q_img\": \"./images/B006LD9FJ2.png\", \"q_text\": \"The shirt has fringe sleeves white in color with a copper neckline., and is short-sleeved and more flowing\", \"positive_key\": \"B006LD9DU8\", \"positive_value\": \"./images/B006LD9DU8.png\", \"hn_image\": [\"./images/B006LD9FJ2.png\"]}\n{\"q_img\": \"./images/B004KE48Y6.png\", \"q_text\": \"has more color, and has less black and is less sexy\", \"positive_key\": \"B00AQLLDZY\", \"positive_value\": \"./images/B00AQLLDZY.png\", \"hn_image\": [\"./images/B004KE48Y6.png\"]}\n{\"q_img\": \"./images/B005VSPMEK.png\", \"q_text\": \"is black with writing on it, and is black with white lettering\", \"positive_key\": \"B009B60324\", \"positive_value\": \"./images/B009B60324.png\", \"hn_image\": [\"./images/B005VSPMEK.png\"]}\n{\"q_img\": \"./images/B006IV779W.png\", \"q_text\": \"is similar with black buttons, and has a red logo\", \"positive_key\": \"B002DYJGGA\", \"positive_value\": \"./images/B002DYJGGA.png\", \"hn_image\": [\"./images/B006IV779W.png\"]}\n{\"q_img\": \"./images/B00522PG28.png\", \"q_text\": \"The shirt is black in color., and Is black\", \"positive_key\": \"B004S08TT2\", \"positive_value\": \"./images/B004S08TT2.png\", \"hn_image\": [\"./images/B00522PG28.png\"]}\n{\"q_img\": \"./images/B0075623M0.png\", \"q_text\": \"a floral shirt, and is a flowered top\", \"positive_key\": \"B003AICZLQ\", \"positive_value\": \"./images/B003AICZLQ.png\", \"hn_image\": [\"./images/B0075623M0.png\"]}\n{\"q_img\": \"./images/B00C64M1BY.png\", \"q_text\": \"is more formal with longer sleeves and buttons, and is solid white with collar and buttons\", \"positive_key\": \"B009QWJ3WE\", \"positive_value\": \"./images/B009QWJ3WE.png\", \"hn_image\": [\"./images/B00C64M1BY.png\"]}\n{\"q_img\": \"./images/B004F3NKGO.png\", \"q_text\": \"is lighter, and is grey and purple with longer sleeves\", \"positive_key\": \"B00CY3714O\", \"positive_value\": \"./images/B00CY3714O.png\", \"hn_image\": [\"./images/B004F3NKGO.png\"]}\n{\"q_img\": \"./images/B005M3UGH2.png\", \"q_text\": \"is darker, and is black with a different graphic\", \"positive_key\": \"B0054S3X0C\", \"positive_value\": \"./images/B0054S3X0C.png\", \"hn_image\": [\"./images/B005M3UGH2.png\"]}\n{\"q_img\": \"./images/B00FBI8K08.png\", \"q_text\": \"has shorter sleeves and is black, and Is darker with shorter sleeves.\", \"positive_key\": \"B0051SM8SS\", \"positive_value\": \"./images/B0051SM8SS.png\", \"hn_image\": [\"./images/B00FBI8K08.png\"]}\n{\"q_img\": \"./images/B00E6793SS.png\", \"q_text\": \"is sleeveless, and Has shorter sleeves\", \"positive_key\": \"B0086URG8K\", \"positive_value\": \"./images/B0086URG8K.png\", \"hn_image\": [\"./images/B00E6793SS.png\"]}\n{\"q_img\": \"./images/B004X80K3M.png\", \"q_text\": \"has no sleeves but more stripes, and is more colourfull\", \"positive_key\": \"B007NE48KE\", \"positive_value\": \"./images/B007NE48KE.png\", \"hn_image\": [\"./images/B004X80K3M.png\"]}\n{\"q_img\": \"./images/B00BXYXTSM.png\", \"q_text\": \"The shirt has no sleeves and has a floral print., and it's longer and patterned\", \"positive_key\": \"B00DOOFU90\", \"positive_value\": \"./images/B00DOOFU90.png\", \"hn_image\": [\"./images/B00BXYXTSM.png\"]}\n{\"q_img\": \"./images/B004F3NKGO.png\", \"q_text\": \"has more colors, and has shorter sleeves and is more colourful\", \"positive_key\": \"B00CV9ZD64\", \"positive_value\": \"./images/B00CV9ZD64.png\", \"hn_image\": [\"./images/B004F3NKGO.png\"]}\n{\"q_img\": \"./images/B007SVC5P2.png\", \"q_text\": \"this white dress looks so fashionable, and has a solid white color to it\", \"positive_key\": \"B006J3EPJO\", \"positive_value\": \"./images/B006J3EPJO.png\", \"hn_image\": [\"./images/B007SVC5P2.png\"]}\n{\"q_img\": \"./images/B003F2H36E.png\", \"q_text\": \"is a short sleeve pink color, and Solid dark pink v neck short sleeved shirt\", \"positive_key\": \"B002C7T5LO\", \"positive_value\": \"./images/B002C7T5LO.png\", \"hn_image\": [\"./images/B003F2H36E.png\"]}\n{\"q_img\": \"./images/B003Y5LY76.png\", \"q_text\": \"blue tee with logo, and is navy with a different graphic\", \"positive_key\": \"B007JI3V8Y\", \"positive_value\": \"./images/B007JI3V8Y.png\", \"hn_image\": [\"./images/B003Y5LY76.png\"]}\n{\"q_img\": \"./images/B002EIS4BS.png\", \"q_text\": \"is orange colored, and is tie-dyed and orange\", \"positive_key\": \"B00475Q2HO\", \"positive_value\": \"./images/B00475Q2HO.png\", \"hn_image\": [\"./images/B002EIS4BS.png\"]}\n{\"q_img\": \"./images/B007NG76LK.png\", \"q_text\": \"no sleeves more print, and Has shorter sleeves and is animal print\", \"positive_key\": \"B00BOFWMAQ\", \"positive_value\": \"./images/B00BOFWMAQ.png\", \"hn_image\": [\"./images/B007NG76LK.png\"]}\n{\"q_img\": \"./images/B003ECTX36.png\", \"q_text\": \"is black colored with more text, and is dark blue\", \"positive_key\": \"B001T7CRDG\", \"positive_value\": \"./images/B001T7CRDG.png\", \"hn_image\": [\"./images/B003ECTX36.png\"]}\n{\"q_img\": \"./images/B009PIM60A.png\", \"q_text\": \"Is more eccentric and longer, and is multi-colored\", \"positive_key\": \"B008WZC38C\", \"positive_value\": \"./images/B008WZC38C.png\", \"hn_image\": [\"./images/B009PIM60A.png\"]}\n{\"q_img\": \"./images/B003MU958S.png\", \"q_text\": \"has a tie in the front and has keyholes at the shoulders., and ties in the front and has peek a boo sleeves\", \"positive_key\": \"B00A666VHE\", \"positive_value\": \"./images/B00A666VHE.png\", \"hn_image\": [\"./images/B003MU958S.png\"]}\n{\"q_img\": \"./images/B006HT244S.png\", \"q_text\": \"is green with more sleeves, and is green colored and short sleeved\", \"positive_key\": \"B00B5TX0VG\", \"positive_value\": \"./images/B00B5TX0VG.png\", \"hn_image\": [\"./images/B006HT244S.png\"]}\n{\"q_img\": \"./images/B008DEXUSY.png\", \"q_text\": \"is black with gray sides, and has a tighter fit and is two toned\", \"positive_key\": \"B008AEE2F2\", \"positive_value\": \"./images/B008AEE2F2.png\", \"hn_image\": [\"./images/B008DEXUSY.png\"]}\n{\"q_img\": \"./images/B005ABVTOU.png\", \"q_text\": \"is blue with white and red designs, and has longer sleeves and is more blue\", \"positive_key\": \"B00D3ZHQ5G\", \"positive_value\": \"./images/B00D3ZHQ5G.png\", \"hn_image\": [\"./images/B005ABVTOU.png\"]}\n{\"q_img\": \"./images/B004KSP3ZK.png\", \"q_text\": \"is lighter, and is white with black letters\", \"positive_key\": \"B00F46ZN66\", \"positive_value\": \"./images/B00F46ZN66.png\", \"hn_image\": [\"./images/B004KSP3ZK.png\"]}\n{\"q_img\": \"./images/B00AG42HGU.png\", \"q_text\": \"is more revealing, and Solid black long sleeved high neck shirt.\", \"positive_key\": \"B0094KPUK2\", \"positive_value\": \"./images/B0094KPUK2.png\", \"hn_image\": [\"./images/B00AG42HGU.png\"]}\n{\"q_img\": \"./images/B0077BSFCA.png\", \"q_text\": \"is a brown t-shirt, and has shorter sleeves and a darker color\", \"positive_key\": \"B0041SI2AW\", \"positive_value\": \"./images/B0041SI2AW.png\", \"hn_image\": [\"./images/B0077BSFCA.png\"]}\n{\"q_img\": \"./images/B007Z4WSA4.png\", \"q_text\": \"The shirt is black with a colorful picture., and is darker coloured\", \"positive_key\": \"B009Z3XFQO\", \"positive_value\": \"./images/B009Z3XFQO.png\", \"hn_image\": [\"./images/B007Z4WSA4.png\"]}\n{\"q_img\": \"./images/B00AIAIO46.png\", \"q_text\": \"Is more wordy and has no sleeves, and has no sleeves\", \"positive_key\": \"B00FBBPMOW\", \"positive_value\": \"./images/B00FBBPMOW.png\", \"hn_image\": [\"./images/B00AIAIO46.png\"]}\n{\"q_img\": \"./images/B003ENGNX8.png\", \"q_text\": \"is lighter, and is tie dyed\", \"positive_key\": \"B001LV9ZQM\", \"positive_value\": \"./images/B001LV9ZQM.png\", \"hn_image\": [\"./images/B003ENGNX8.png\"]}\n{\"q_img\": \"./images/B0084S6I4C.png\", \"q_text\": \"is black with purple, and is darker colored and more pop culture\", \"positive_key\": \"B00DDX2LBM\", \"positive_value\": \"./images/B00DDX2LBM.png\", \"hn_image\": [\"./images/B0084S6I4C.png\"]}\n{\"q_img\": \"./images/B000YVMK30.png\", \"q_text\": \"is similar but more orange in colour, and is darker\", \"positive_key\": \"B006Z8F286\", \"positive_value\": \"./images/B006Z8F286.png\", \"hn_image\": [\"./images/B000YVMK30.png\"]}\n{\"q_img\": \"./images/B008V64YZC.png\", \"q_text\": \"The t-shirt is blue with cartoon characters., and is dark blue and has smaller print\", \"positive_key\": \"B007Q57FHI\", \"positive_value\": \"./images/B007Q57FHI.png\", \"hn_image\": [\"./images/B008V64YZC.png\"]}\n{\"q_img\": \"./images/B000EVG98W.png\", \"q_text\": \"is patterned, and it is black\", \"positive_key\": \"B001GPR9DO\", \"positive_value\": \"./images/B001GPR9DO.png\", \"hn_image\": [\"./images/B000EVG98W.png\"]}\n{\"q_img\": \"./images/B008ZBL9YM.png\", \"q_text\": \"is bright pink with short sleeves, and is darker in color and has shorter sleeves\", \"positive_key\": \"B004P4S9MS\", \"positive_value\": \"./images/B004P4S9MS.png\", \"hn_image\": [\"./images/B008ZBL9YM.png\"]}\n{\"q_img\": \"./images/B0083E6IS8.png\", \"q_text\": \"long sleeved and below waist long, and with a frilly bottom\", \"positive_key\": \"B006S3EQ4O\", \"positive_value\": \"./images/B006S3EQ4O.png\", \"hn_image\": [\"./images/B0083E6IS8.png\"]}\n{\"q_img\": \"./images/B00D4FQJQW.png\", \"q_text\": \"is blue with pocket, and sleeveless\", \"positive_key\": \"B00H7K6YAG\", \"positive_value\": \"./images/B00H7K6YAG.png\", \"hn_image\": [\"./images/B00D4FQJQW.png\"]}\n{\"q_img\": \"./images/B00GGYKWFW.png\", \"q_text\": \"has more dense text, and is black with a World War Champs design\", \"positive_key\": \"B0088LSAQO\", \"positive_value\": \"./images/B0088LSAQO.png\", \"hn_image\": [\"./images/B00GGYKWFW.png\"]}\n{\"q_img\": \"./images/B00C89WXFG.png\", \"q_text\": \"has black color and off-shoulder, and is solid colored\", \"positive_key\": \"B006MHUH0I\", \"positive_value\": \"./images/B006MHUH0I.png\", \"hn_image\": [\"./images/B00C89WXFG.png\"]}\n{\"q_img\": \"./images/B00BQOYCF8.png\", \"q_text\": \"Is more skimpy and is black, and black tank top with butterfly graphic\", \"positive_key\": \"B004JO6X2C\", \"positive_value\": \"./images/B004JO6X2C.png\", \"hn_image\": [\"./images/B00BQOYCF8.png\"]}\n{\"q_img\": \"./images/B00CV2DS40.png\", \"q_text\": \"The shirt is blue characters., and is blue\", \"positive_key\": \"B002RNT270\", \"positive_value\": \"./images/B002RNT270.png\", \"hn_image\": [\"./images/B00CV2DS40.png\"]}\n{\"q_img\": \"./images/B006K1ISPM.png\", \"q_text\": \"is more of a pull over with graphics, and is purple and wording logo\", \"positive_key\": \"B0059JIWA2\", \"positive_value\": \"./images/B0059JIWA2.png\", \"hn_image\": [\"./images/B006K1ISPM.png\"]}\n{\"q_img\": \"./images/B0092S985O.png\", \"q_text\": \"is blue, and Is more fitted and bright blue\", \"positive_key\": \"B0085OCQVO\", \"positive_value\": \"./images/B0085OCQVO.png\", \"hn_image\": [\"./images/B0092S985O.png\"]}\n{\"q_img\": \"./images/B005C5RPYW.png\", \"q_text\": \"The shirt is black with white writing., and has a more detailed graphic\", \"positive_key\": \"B00GBDV7JI\", \"positive_value\": \"./images/B00GBDV7JI.png\", \"hn_image\": [\"./images/B005C5RPYW.png\"]}\n{\"q_img\": \"./images/B007WA3P44.png\", \"q_text\": \"is lighter, and is less fashionable and more shirty\", \"positive_key\": \"B009XNQPXG\", \"positive_value\": \"./images/B009XNQPXG.png\", \"hn_image\": [\"./images/B007WA3P44.png\"]}\n{\"q_img\": \"./images/B000AS2OVA.png\", \"q_text\": \"is white colored shirt with shorter sleeves, and has button on it\", \"positive_key\": \"B0038MJ4HM\", \"positive_value\": \"./images/B0038MJ4HM.png\", \"hn_image\": [\"./images/B000AS2OVA.png\"]}\n{\"q_img\": \"./images/B0053Y6WG4.png\", \"q_text\": \"has skinny straps and one color, and is smaller straps and fitted waist\", \"positive_key\": \"B0044EHUIS\", \"positive_value\": \"./images/B0044EHUIS.png\", \"hn_image\": [\"./images/B0053Y6WG4.png\"]}\n{\"q_img\": \"./images/B00FDZ8PRM.png\", \"q_text\": \"is a short sleeve gray shirt with an image and text, and is grey with image\", \"positive_key\": \"B0014UJR6S\", \"positive_value\": \"./images/B0014UJR6S.png\", \"hn_image\": [\"./images/B00FDZ8PRM.png\"]}\n{\"q_img\": \"./images/B008SHUIP4.png\", \"q_text\": \"is grey in color with red and green logo, and is dark colored\", \"positive_key\": \"B00BFW3720\", \"positive_value\": \"./images/B00BFW3720.png\", \"hn_image\": [\"./images/B008SHUIP4.png\"]}\n{\"q_img\": \"./images/B008YX6N5Q.png\", \"q_text\": \"is white with pink floral, and has short sleeves\", \"positive_key\": \"B00AG4409C\", \"positive_value\": \"./images/B00AG4409C.png\", \"hn_image\": [\"./images/B008YX6N5Q.png\"]}\n{\"q_img\": \"./images/B00CP7TJ4E.png\", \"q_text\": \"is shorter, and has animal print and more revealing\", \"positive_key\": \"B00BPEWGTS\", \"positive_value\": \"./images/B00BPEWGTS.png\", \"hn_image\": [\"./images/B00CP7TJ4E.png\"]}\n{\"q_img\": \"./images/B004XJJRIU.png\", \"q_text\": \"is white and has longer sleeves, and has short sleeves and is white\", \"positive_key\": \"B00DDN6Z6O\", \"positive_value\": \"./images/B00DDN6Z6O.png\", \"hn_image\": [\"./images/B004XJJRIU.png\"]}\n{\"q_img\": \"./images/B00C5AQHXW.png\", \"q_text\": \"is mainly black and pink trimmings, and has sheerer sleeves and more flared skirt\", \"positive_key\": \"B0096HQW00\", \"positive_value\": \"./images/B0096HQW00.png\", \"hn_image\": [\"./images/B00C5AQHXW.png\"]}\n{\"q_img\": \"./images/B009M64VEO.png\", \"q_text\": \"The purse is white and black in color., and is a bag\", \"positive_key\": \"B00B1EDU7O\", \"positive_value\": \"./images/B00B1EDU7O.png\", \"hn_image\": [\"./images/B009M64VEO.png\"]}\n{\"q_img\": \"./images/B008293HO2.png\", \"q_text\": \"Is white and grey, and is lighter colored and more busy\", \"positive_key\": \"B008UB4D7M\", \"positive_value\": \"./images/B008UB4D7M.png\", \"hn_image\": [\"./images/B008293HO2.png\"]}\n{\"q_img\": \"./images/B0001TOS2Q.png\", \"q_text\": \"contains no sleeves and a lower cut chest, and is a rank with no sleeves and a green print.\", \"positive_key\": \"B00D70HRIS\", \"positive_value\": \"./images/B00D70HRIS.png\", \"hn_image\": [\"./images/B0001TOS2Q.png\"]}\n{\"q_img\": \"./images/B008JC4MEG.png\", \"q_text\": \"is long sleeve with purple color, and is purple\", \"positive_key\": \"B00DURCPRG\", \"positive_value\": \"./images/B00DURCPRG.png\", \"hn_image\": [\"./images/B008JC4MEG.png\"]}\n{\"q_img\": \"./images/B00FR621O0.png\", \"q_text\": \"is red with letters printed on it, and Is red with larger graphics\", \"positive_key\": \"B009WK2V0G\", \"positive_value\": \"./images/B009WK2V0G.png\", \"hn_image\": [\"./images/B00FR621O0.png\"]}\n{\"q_img\": \"./images/B00AJ3NGWC.png\", \"q_text\": \"is see through with stripes, and is red with stripes\", \"positive_key\": \"B00AMOM4FI\", \"positive_value\": \"./images/B00AMOM4FI.png\", \"hn_image\": [\"./images/B00AJ3NGWC.png\"]}\n{\"q_img\": \"./images/B00EY69KF2.png\", \"q_text\": \"has an inappropriate message, and is a black tee\", \"positive_key\": \"B00BFWJK0S\", \"positive_value\": \"./images/B00BFWJK0S.png\", \"hn_image\": [\"./images/B00EY69KF2.png\"]}\n{\"q_img\": \"./images/B008UALEK2.png\", \"q_text\": \"is more revealing with one color, and is black with white stripes\", \"positive_key\": \"B008HTBNHU\", \"positive_value\": \"./images/B008HTBNHU.png\", \"hn_image\": [\"./images/B008UALEK2.png\"]}\n{\"q_img\": \"./images/B006LGV2JK.png\", \"q_text\": \"is dark colored and transparent with sleeves, and it is more lacy and long sleeved\", \"positive_key\": \"B0035VAACY\", \"positive_value\": \"./images/B0035VAACY.png\", \"hn_image\": [\"./images/B006LGV2JK.png\"]}\n{\"q_img\": \"./images/B00E197JJ6.png\", \"q_text\": \"has short sleeves but a fox image, and is gray and has an animal logo\", \"positive_key\": \"B00F6BYI0Q\", \"positive_value\": \"./images/B00F6BYI0Q.png\", \"hn_image\": [\"./images/B00E197JJ6.png\"]}\n{\"q_img\": \"./images/B007H9KGT2.png\", \"q_text\": \"is red and has bit longer sleeves, and is not as tight and has a solid color\", \"positive_key\": \"B009NA63V8\", \"positive_value\": \"./images/B009NA63V8.png\", \"hn_image\": [\"./images/B007H9KGT2.png\"]}\n{\"q_img\": \"./images/B00E7NZL9G.png\", \"q_text\": \"is three quarter sleeves and shorter in length, and it has a longer sleeve and less transparent\", \"positive_key\": \"B00361FLPO\", \"positive_value\": \"./images/B00361FLPO.png\", \"hn_image\": [\"./images/B00E7NZL9G.png\"]}\n{\"q_img\": \"./images/B003NTI0OS.png\", \"q_text\": \"is longer and brighter colored with collars, and has a collar and is longer\", \"positive_key\": \"B0037W5MAQ\", \"positive_value\": \"./images/B0037W5MAQ.png\", \"hn_image\": [\"./images/B003NTI0OS.png\"]}\n{\"q_img\": \"./images/B00DEOOUGO.png\", \"q_text\": \"is brighter colored and has long sleeves, and is blue with sleeves\", \"positive_key\": \"B00DHO1NAC\", \"positive_value\": \"./images/B00DHO1NAC.png\", \"hn_image\": [\"./images/B00DEOOUGO.png\"]}\n{\"q_img\": \"./images/B003EACZ9M.png\", \"q_text\": \"The tank top is white in color., and is the same\", \"positive_key\": \"B00B036R4Y\", \"positive_value\": \"./images/B00B036R4Y.png\", \"hn_image\": [\"./images/B003EACZ9M.png\"]}\n{\"q_img\": \"./images/B00E48RKWA.png\", \"q_text\": \"is black colored, and is a black loose tee with rounded neckline\", \"positive_key\": \"B00BMYFLX4\", \"positive_value\": \"./images/B00BMYFLX4.png\", \"hn_image\": [\"./images/B00E48RKWA.png\"]}\n{\"q_img\": \"./images/B0037TJOEO.png\", \"q_text\": \"is longer and has shorter sleeves, and has off shoulder and is black color\", \"positive_key\": \"B00C5JLNH8\", \"positive_value\": \"./images/B00C5JLNH8.png\", \"hn_image\": [\"./images/B0037TJOEO.png\"]}\n{\"q_img\": \"./images/B003QONL9Y.png\", \"q_text\": \"Has shorter sleeves, and is red with shorter sleeves.\", \"positive_key\": \"B007OZIXJO\", \"positive_value\": \"./images/B007OZIXJO.png\", \"hn_image\": [\"./images/B003QONL9Y.png\"]}\n{\"q_img\": \"./images/B00CQ9IVL8.png\", \"q_text\": \"is colorful print tighter fit, and has busier pattern and shorter sleeves\", \"positive_key\": \"B005JI9EY6\", \"positive_value\": \"./images/B005JI9EY6.png\", \"hn_image\": [\"./images/B00CQ9IVL8.png\"]}\n{\"q_img\": \"./images/B008FV3CXS.png\", \"q_text\": \"is a lighter color and less feminine, and is of a lighter tan\", \"positive_key\": \"B008EQWXQQ\", \"positive_value\": \"./images/B008EQWXQQ.png\", \"hn_image\": [\"./images/B008FV3CXS.png\"]}\n{\"q_img\": \"./images/B000YVF80C.png\", \"q_text\": \"is similar but more orange in colour, and is darker\", \"positive_key\": \"B006Z8F2E0\", \"positive_value\": \"./images/B006Z8F2E0.png\", \"hn_image\": [\"./images/B000YVF80C.png\"]}\n{\"q_img\": \"./images/B008CISTR8.png\", \"q_text\": \"is blue with long sleeves, and is blue and long sleeved\", \"positive_key\": \"B008GT8N7E\", \"positive_value\": \"./images/B008GT8N7E.png\", \"hn_image\": [\"./images/B008CISTR8.png\"]}\n{\"q_img\": \"./images/B007BKC578.png\", \"q_text\": \"has a shorter sleeve with blue colors, and has shorter sleeves and is darker blue\", \"positive_key\": \"B00C7F9DQI\", \"positive_value\": \"./images/B00C7F9DQI.png\", \"hn_image\": [\"./images/B007BKC578.png\"]}\n{\"q_img\": \"./images/B0045EPNF4.png\", \"q_text\": \"is blue with long sleeves, and is long sleeved with a collar\", \"positive_key\": \"B004R8PZNS\", \"positive_value\": \"./images/B004R8PZNS.png\", \"hn_image\": [\"./images/B0045EPNF4.png\"]}\n{\"q_img\": \"./images/B005GVJD3I.png\", \"q_text\": \"is a more traditional t-shirt, and has a graphic on it\", \"positive_key\": \"B002Y3UZJW\", \"positive_value\": \"./images/B002Y3UZJW.png\", \"hn_image\": [\"./images/B005GVJD3I.png\"]}\n{\"q_img\": \"./images/B009LHGDLS.png\", \"q_text\": \"has fancy rips and three quarter sleeves, and is purple with a round neck and text logo\", \"positive_key\": \"B009LHGEZI\", \"positive_value\": \"./images/B009LHGEZI.png\", \"hn_image\": [\"./images/B009LHGDLS.png\"]}\n{\"q_img\": \"./images/B008WYCFGI.png\", \"q_text\": \"has a black and white color striped shirt, and short sleeves black and white stripes white frilly bow\", \"positive_key\": \"B008JBOZ62\", \"positive_value\": \"./images/B008JBOZ62.png\", \"hn_image\": [\"./images/B008WYCFGI.png\"]}\n{\"q_img\": \"./images/B0081OYV1Q.png\", \"q_text\": \"has no sleeves but a transparent white color, and is sleeveless and is white\", \"positive_key\": \"B00B5U0810\", \"positive_value\": \"./images/B00B5U0810.png\", \"hn_image\": [\"./images/B0081OYV1Q.png\"]}\n{\"q_img\": \"./images/B009727I8Y.png\", \"q_text\": \"The tank top is loose fitting and is white in color., and is white and strappy\", \"positive_key\": \"B00EE0D5NG\", \"positive_value\": \"./images/B00EE0D5NG.png\", \"hn_image\": [\"./images/B009727I8Y.png\"]}\n{\"q_img\": \"./images/B005C5RPYW.png\", \"q_text\": \"The shirt has short sleeves and is purple in color with black writing., and is purple and has human-figure graphic\", \"positive_key\": \"B009IT35FQ\", \"positive_value\": \"./images/B009IT35FQ.png\", \"hn_image\": [\"./images/B005C5RPYW.png\"]}\n{\"q_img\": \"./images/B004JPLHGS.png\", \"q_text\": \"The shirt is red in color with the wording England., and is a red tee\", \"positive_key\": \"B0076PA5TS\", \"positive_value\": \"./images/B0076PA5TS.png\", \"hn_image\": [\"./images/B004JPLHGS.png\"]}\n{\"q_img\": \"./images/B00A3MV3CO.png\", \"q_text\": \"is more revealing and has stripes, and Is less modest with straps.\", \"positive_key\": \"B00BT9UHDQ\", \"positive_value\": \"./images/B00BT9UHDQ.png\", \"hn_image\": [\"./images/B00A3MV3CO.png\"]}\n{\"q_img\": \"./images/B008LV15H2.png\", \"q_text\": \"is tighter and has a print on it, and is lighter black with a white logo.\", \"positive_key\": \"B00ESQWCWG\", \"positive_value\": \"./images/B00ESQWCWG.png\", \"hn_image\": [\"./images/B008LV15H2.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"has no sleeves with a halter top, and has no sleeves and is black and white\", \"positive_key\": \"B004ULP43S\", \"positive_value\": \"./images/B004ULP43S.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B008J68VGW.png\", \"q_text\": \"is black with white words, and white\", \"positive_key\": \"B00CFRD472\", \"positive_value\": \"./images/B00CFRD472.png\", \"hn_image\": [\"./images/B008J68VGW.png\"]}\n{\"q_img\": \"./images/B00C67YB50.png\", \"q_text\": \"is a v neck t shirt with green flower logo, and is lighter\", \"positive_key\": \"B008JCFX3A\", \"positive_value\": \"./images/B008JCFX3A.png\", \"hn_image\": [\"./images/B00C67YB50.png\"]}\n{\"q_img\": \"./images/B004HPH7B4.png\", \"q_text\": \"The shirt is light purple and dark purple., and is looser and deeper toned\", \"positive_key\": \"B0054QOMY0\", \"positive_value\": \"./images/B0054QOMY0.png\", \"hn_image\": [\"./images/B004HPH7B4.png\"]}\n{\"q_img\": \"./images/B007WAE516.png\", \"q_text\": \"Is more flowing and whimsical, and Is more flowing and elegant\", \"positive_key\": \"B007D5ERVS\", \"positive_value\": \"./images/B007D5ERVS.png\", \"hn_image\": [\"./images/B007WAE516.png\"]}\n{\"q_img\": \"./images/B001R5QU5Q.png\", \"q_text\": \"The shirt is black with the flash symbol., and is black with white and red logo\", \"positive_key\": \"B003KSJ2KI\", \"positive_value\": \"./images/B003KSJ2KI.png\", \"hn_image\": [\"./images/B001R5QU5Q.png\"]}\n{\"q_img\": \"./images/B007Z2PRTA.png\", \"q_text\": \"Is more flowy and less fitted, and is red with long sleeves\", \"positive_key\": \"B006N03Q9S\", \"positive_value\": \"./images/B006N03Q9S.png\", \"hn_image\": [\"./images/B007Z2PRTA.png\"]}\n{\"q_img\": \"./images/B008MZJHX6.png\", \"q_text\": \"is long sleeves with pastel printing, and it is more colorful and less lacy\", \"positive_key\": \"B006RB0TMA\", \"positive_value\": \"./images/B006RB0TMA.png\", \"hn_image\": [\"./images/B008MZJHX6.png\"]}\n{\"q_img\": \"./images/B00AW1L97K.png\", \"q_text\": \"The shirt is black and sheer., and is more loose fitting and is a lighter color\", \"positive_key\": \"B00AIZCMKI\", \"positive_value\": \"./images/B00AIZCMKI.png\", \"hn_image\": [\"./images/B00AW1L97K.png\"]}\n{\"q_img\": \"./images/B000YVF80C.png\", \"q_text\": \"is similar but much whiter in colour, and it is white\", \"positive_key\": \"B003EACZ9M\", \"positive_value\": \"./images/B003EACZ9M.png\", \"hn_image\": [\"./images/B000YVF80C.png\"]}\n{\"q_img\": \"./images/B00A3MVA7M.png\", \"q_text\": \"Is more whimsical and colorful, and Is more whimsical\", \"positive_key\": \"B008SRKLFG\", \"positive_value\": \"./images/B008SRKLFG.png\", \"hn_image\": [\"./images/B00A3MVA7M.png\"]}\n{\"q_img\": \"./images/B00980K6RA.png\", \"q_text\": \"has no pattern on it, and a collar\", \"positive_key\": \"B004U8BTTY\", \"positive_value\": \"./images/B004U8BTTY.png\", \"hn_image\": [\"./images/B00980K6RA.png\"]}\n{\"q_img\": \"./images/B00CLZWXSE.png\", \"q_text\": \"has sleeves with a blue batman, and has longer sleeves and a higher neck\", \"positive_key\": \"B0079K0YBO\", \"positive_value\": \"./images/B0079K0YBO.png\", \"hn_image\": [\"./images/B00CLZWXSE.png\"]}\n{\"q_img\": \"./images/B005JWECI0.png\", \"q_text\": \"Is more flowing and sheer, and is white and blue with shorter sleeves\", \"positive_key\": \"B00BPPALLM\", \"positive_value\": \"./images/B00BPPALLM.png\", \"hn_image\": [\"./images/B005JWECI0.png\"]}\n{\"q_img\": \"./images/B00GIOKE8U.png\", \"q_text\": \"is light colored and has lacey sleeves, and is more open around the neck.\", \"positive_key\": \"B00DDJGA6S\", \"positive_value\": \"./images/B00DDJGA6S.png\", \"hn_image\": [\"./images/B00GIOKE8U.png\"]}\n{\"q_img\": \"./images/B000RZ45TK.png\", \"q_text\": \"is black color with a logo, and is black with red blue and white logo\", \"positive_key\": \"B0077DN4P6\", \"positive_value\": \"./images/B0077DN4P6.png\", \"hn_image\": [\"./images/B000RZ45TK.png\"]}\n{\"q_img\": \"./images/B003IEWVRK.png\", \"q_text\": \"is lighter, and lighter and more detailed\", \"positive_key\": \"B003ILC93Y\", \"positive_value\": \"./images/B003ILC93Y.png\", \"hn_image\": [\"./images/B003IEWVRK.png\"]}\n{\"q_img\": \"./images/B00265PFKC.png\", \"q_text\": \"is yellow colored, and is peach-colored with buttons\", \"positive_key\": \"B001E0BSXI\", \"positive_value\": \"./images/B001E0BSXI.png\", \"hn_image\": [\"./images/B00265PFKC.png\"]}\n{\"q_img\": \"./images/B007R1QAJA.png\", \"q_text\": \"Is brown and looser with longer sleeves, and it has sleeves\", \"positive_key\": \"B004BJ1AIW\", \"positive_value\": \"./images/B004BJ1AIW.png\", \"hn_image\": [\"./images/B007R1QAJA.png\"]}\n{\"q_img\": \"./images/B008NDWPP4.png\", \"q_text\": \"is more hawaiian, and Is a brighter color and has a floral pattern\", \"positive_key\": \"B008H46S48\", \"positive_value\": \"./images/B008H46S48.png\", \"hn_image\": [\"./images/B008NDWPP4.png\"]}\n{\"q_img\": \"./images/B00B77AD0W.png\", \"q_text\": \"is lighter and has shorter sleeves, and wider neck\", \"positive_key\": \"B007O3FNY4\", \"positive_value\": \"./images/B007O3FNY4.png\", \"hn_image\": [\"./images/B00B77AD0W.png\"]}\n{\"q_img\": \"./images/B003ZFHZXM.png\", \"q_text\": \"is lighter, and has a light blue graphic\", \"positive_key\": \"B000VTB0OK\", \"positive_value\": \"./images/B000VTB0OK.png\", \"hn_image\": [\"./images/B003ZFHZXM.png\"]}\n{\"q_img\": \"./images/B005MVACSM.png\", \"q_text\": \"is darker, and is less athletic and more chic\", \"positive_key\": \"B00CNWHQPU\", \"positive_value\": \"./images/B00CNWHQPU.png\", \"hn_image\": [\"./images/B005MVACSM.png\"]}\n{\"q_img\": \"./images/B0018DWZJC.png\", \"q_text\": \"more colorful with bolder graphic, and is a men's shirt\", \"positive_key\": \"B00D4AM3S0\", \"positive_value\": \"./images/B00D4AM3S0.png\", \"hn_image\": [\"./images/B0018DWZJC.png\"]}\n{\"q_img\": \"./images/B0081EN1F8.png\", \"q_text\": \"The tank top has white and brown stripes and she is wearing red pants., and has black and white stripes and is looser\", \"positive_key\": \"B00BTSUSZY\", \"positive_value\": \"./images/B00BTSUSZY.png\", \"hn_image\": [\"./images/B0081EN1F8.png\"]}\n{\"q_img\": \"./images/B007JS1Y88.png\", \"q_text\": \"is white and no sleeves, and is more bright\", \"positive_key\": \"B0091EITD6\", \"positive_value\": \"./images/B0091EITD6.png\", \"hn_image\": [\"./images/B007JS1Y88.png\"]}\n{\"q_img\": \"./images/B0070VYHN8.png\", \"q_text\": \"has a shorter sleeve with two different color stripes, and is a striped pocketed shirt\", \"positive_key\": \"B0070VYI0U\", \"positive_value\": \"./images/B0070VYI0U.png\", \"hn_image\": [\"./images/B0070VYHN8.png\"]}\n{\"q_img\": \"./images/B009NGX6U8.png\", \"q_text\": \"is blue tank top, and a light blue tank top.\", \"positive_key\": \"B00DFN08YC\", \"positive_value\": \"./images/B00DFN08YC.png\", \"hn_image\": [\"./images/B009NGX6U8.png\"]}\n{\"q_img\": \"./images/B0058ZGNDK.png\", \"q_text\": \"has long sleeves and large neck, and Has longer sleeves with a graphic\", \"positive_key\": \"B00A4M3F2Y\", \"positive_value\": \"./images/B00A4M3F2Y.png\", \"hn_image\": [\"./images/B0058ZGNDK.png\"]}\n{\"q_img\": \"./images/B00AFYJTQM.png\", \"q_text\": \"is burgundy with shorter sleeves, and is red and shorter sleeves\", \"positive_key\": \"B007ZTGZSA\", \"positive_value\": \"./images/B007ZTGZSA.png\", \"hn_image\": [\"./images/B00AFYJTQM.png\"]}\n{\"q_img\": \"./images/B008E093WY.png\", \"q_text\": \"is pinker and more sheer, and is bright pink and sheer long sleeve\", \"positive_key\": \"B00B9Z7DNM\", \"positive_value\": \"./images/B00B9Z7DNM.png\", \"hn_image\": [\"./images/B008E093WY.png\"]}\n{\"q_img\": \"./images/B005ETA59I.png\", \"q_text\": \"It is white with a larger collar., and is a lighter color\", \"positive_key\": \"B0089K33O8\", \"positive_value\": \"./images/B0089K33O8.png\", \"hn_image\": [\"./images/B005ETA59I.png\"]}\n{\"q_img\": \"./images/B003IEWVRK.png\", \"q_text\": \"is white in color with no sleeves, and Is more of a white shirt and shorter sleeves\", \"positive_key\": \"B006HE4PW2\", \"positive_value\": \"./images/B006HE4PW2.png\", \"hn_image\": [\"./images/B003IEWVRK.png\"]}\n{\"q_img\": \"./images/B005J6ACWG.png\", \"q_text\": \"The shirt is loose fitting pink in color with a ruffle collar., and is lighter and has longer sleeves\", \"positive_key\": \"B0079MB7ZE\", \"positive_value\": \"./images/B0079MB7ZE.png\", \"hn_image\": [\"./images/B005J6ACWG.png\"]}\n{\"q_img\": \"./images/B005SW1YE6.png\", \"q_text\": \"is pink with no plaid or buttons, and has no buttons to it\", \"positive_key\": \"B00ES89826\", \"positive_value\": \"./images/B00ES89826.png\", \"hn_image\": [\"./images/B005SW1YE6.png\"]}\n{\"q_img\": \"./images/B0090A7P18.png\", \"q_text\": \"A darker t-shirt with short sleeves., and is a blue tee shirt\", \"positive_key\": \"B003E20OFC\", \"positive_value\": \"./images/B003E20OFC.png\", \"hn_image\": [\"./images/B0090A7P18.png\"]}\n{\"q_img\": \"./images/B00CF1XBYY.png\", \"q_text\": \"has longer sleeves and is much looser, and is less black and has long sleeve\", \"positive_key\": \"B00EE0DYZ0\", \"positive_value\": \"./images/B00EE0DYZ0.png\", \"hn_image\": [\"./images/B00CF1XBYY.png\"]}\n{\"q_img\": \"./images/B007GRPJ5Q.png\", \"q_text\": \"has cheetah prints with thin straps, and has an animal print\", \"positive_key\": \"B00CWJ9JAY\", \"positive_value\": \"./images/B00CWJ9JAY.png\", \"hn_image\": [\"./images/B007GRPJ5Q.png\"]}\n{\"q_img\": \"./images/B00FVZQOKO.png\", \"q_text\": \"The shawl is green in color with long sleeves., and is longer and green\", \"positive_key\": \"B00941PAT2\", \"positive_value\": \"./images/B00941PAT2.png\", \"hn_image\": [\"./images/B00FVZQOKO.png\"]}\n{\"q_img\": \"./images/B00AZ9I2PG.png\", \"q_text\": \"has spaghetti straps and lace, and it is more red with embroideries.\", \"positive_key\": \"B00B1XHI1E\", \"positive_value\": \"./images/B00B1XHI1E.png\", \"hn_image\": [\"./images/B00AZ9I2PG.png\"]}\n{\"q_img\": \"./images/B00CMD9IKQ.png\", \"q_text\": \"is a t-shirt, and is a grey graphic tee\", \"positive_key\": \"B004JKT87S\", \"positive_value\": \"./images/B004JKT87S.png\", \"hn_image\": [\"./images/B00CMD9IKQ.png\"]}\n{\"q_img\": \"./images/B00EV6D0G0.png\", \"q_text\": \"is a black satchel, and is a bag\", \"positive_key\": \"B004O4JT9G\", \"positive_value\": \"./images/B004O4JT9G.png\", \"hn_image\": [\"./images/B00EV6D0G0.png\"]}\n{\"q_img\": \"./images/B007Z2PRTA.png\", \"q_text\": \"has less color, and has no sleeves and more blue\", \"positive_key\": \"B00A6GX42O\", \"positive_value\": \"./images/B00A6GX42O.png\", \"hn_image\": [\"./images/B007Z2PRTA.png\"]}\n{\"q_img\": \"./images/B00C5NMEPE.png\", \"q_text\": \"is pink with shorter sleeves and white trim, and is shorter sleeved with a white collar\", \"positive_key\": \"B008X0WGAG\", \"positive_value\": \"./images/B008X0WGAG.png\", \"hn_image\": [\"./images/B00C5NMEPE.png\"]}\n{\"q_img\": \"./images/B008D0E320.png\", \"q_text\": \"has a red plaid color, and is lighter\", \"positive_key\": \"B00BM9J4TG\", \"positive_value\": \"./images/B00BM9J4TG.png\", \"hn_image\": [\"./images/B008D0E320.png\"]}\n{\"q_img\": \"./images/B0052AJVHG.png\", \"q_text\": \" is blue and has collars, and has looser sleeves\", \"positive_key\": \"B00785OEH0\", \"positive_value\": \"./images/B00785OEH0.png\", \"hn_image\": [\"./images/B0052AJVHG.png\"]}\n{\"q_img\": \"./images/B00767NH1Y.png\", \"q_text\": \"is white with black sleeves and more fitted, and Has a graphic\", \"positive_key\": \"B00ALYLB6M\", \"positive_value\": \"./images/B00ALYLB6M.png\", \"hn_image\": [\"./images/B00767NH1Y.png\"]}\n{\"q_img\": \"./images/B0052RN92W.png\", \"q_text\": \"is black with pink flowers, and has a higher collar with belt\", \"positive_key\": \"B00BHZVKR4\", \"positive_value\": \"./images/B00BHZVKR4.png\", \"hn_image\": [\"./images/B0052RN92W.png\"]}\n{\"q_img\": \"./images/B0081EN1F8.png\", \"q_text\": \" tan and red printed, and has thin white and red stripes\", \"positive_key\": \"B007W31QU6\", \"positive_value\": \"./images/B007W31QU6.png\", \"hn_image\": [\"./images/B0081EN1F8.png\"]}\n{\"q_img\": \"./images/B007WAE516.png\", \"q_text\": \"is more transparent, and is a black sheer top\", \"positive_key\": \"B0036UN2VA\", \"positive_value\": \"./images/B0036UN2VA.png\", \"hn_image\": [\"./images/B007WAE516.png\"]}\n{\"q_img\": \"./images/B007N1GDNC.png\", \"q_text\": \"is a red long sleeve with small stripes, and has buttons on it\", \"positive_key\": \"B0041Q2ZN4\", \"positive_value\": \"./images/B0041Q2ZN4.png\", \"hn_image\": [\"./images/B007N1GDNC.png\"]}\n{\"q_img\": \"./images/B007Y9F3MA.png\", \"q_text\": \"Is more whimsical and colorful, and has more color and shorter sleeves\", \"positive_key\": \"B008N6K1M0\", \"positive_value\": \"./images/B008N6K1M0.png\", \"hn_image\": [\"./images/B007Y9F3MA.png\"]}\n{\"q_img\": \"./images/B005LCMEDI.png\", \"q_text\": \"It is green with short sleeves., and is less masculine and more cool\", \"positive_key\": \"B00342V9SS\", \"positive_value\": \"./images/B00342V9SS.png\", \"hn_image\": [\"./images/B005LCMEDI.png\"]}\n{\"q_img\": \"./images/B00CMPGWOY.png\", \"q_text\": \"Has longer sleeves and is more flowing, and long sleeve v neck solid black\", \"positive_key\": \"B00BGBO7ZG\", \"positive_value\": \"./images/B00BGBO7ZG.png\", \"hn_image\": [\"./images/B00CMPGWOY.png\"]}\n{\"q_img\": \"./images/B0016SJTH0.png\", \"q_text\": \"Is white with shorter sleeves, and is white with collar\", \"positive_key\": \"B000Z902M2\", \"positive_value\": \"./images/B000Z902M2.png\", \"hn_image\": [\"./images/B0016SJTH0.png\"]}\n{\"q_img\": \"./images/B00AMH2L2G.png\", \"q_text\": \"black slim t shirt with a what the hell pink writing, and is black with pink text\", \"positive_key\": \"B004MF6A7Q\", \"positive_value\": \"./images/B004MF6A7Q.png\", \"hn_image\": [\"./images/B00AMH2L2G.png\"]}\n{\"q_img\": \"./images/B000BU0BF8.png\", \"q_text\": \"is white and doesn't have a collar, and is white with a v-neck\", \"positive_key\": \"B004U5T7IC\", \"positive_value\": \"./images/B004U5T7IC.png\", \"hn_image\": [\"./images/B000BU0BF8.png\"]}\n{\"q_img\": \"./images/B00B47WZHE.png\", \"q_text\": \"is more conservative, and is orange colored with longer bottom\", \"positive_key\": \"B00CO944O8\", \"positive_value\": \"./images/B00CO944O8.png\", \"hn_image\": [\"./images/B00B47WZHE.png\"]}\n{\"q_img\": \"./images/B006Y5YM0O.png\", \"q_text\": \"is gray with an apple logo, and Is darker with a more standard neckline.\", \"positive_key\": \"B008P57GXG\", \"positive_value\": \"./images/B008P57GXG.png\", \"hn_image\": [\"./images/B006Y5YM0O.png\"]}\n{\"q_img\": \"./images/B008BUX1A2.png\", \"q_text\": \"is white with a different logo, and white i love veggies\", \"positive_key\": \"B000FA9MNQ\", \"positive_value\": \"./images/B000FA9MNQ.png\", \"hn_image\": [\"./images/B008BUX1A2.png\"]}\n{\"q_img\": \"./images/B00BIPLT6K.png\", \"q_text\": \"has lettering and is less religious, and is a black t-shirt with graphic\", \"positive_key\": \"B006CWHHNI\", \"positive_value\": \"./images/B006CWHHNI.png\", \"hn_image\": [\"./images/B00BIPLT6K.png\"]}\n{\"q_img\": \"./images/B00775KKV0.png\", \"q_text\": \"It is more fitted and v-neck., and is red in color\", \"positive_key\": \"B004ZWEFPU\", \"positive_value\": \"./images/B004ZWEFPU.png\", \"hn_image\": [\"./images/B00775KKV0.png\"]}\n{\"q_img\": \"./images/B0064XXI42.png\", \"q_text\": \"has more sleeves with an image, and Is less revealing and more casual.\", \"positive_key\": \"B008UAL5YM\", \"positive_value\": \"./images/B008UAL5YM.png\", \"hn_image\": [\"./images/B0064XXI42.png\"]}\n{\"q_img\": \"./images/B00GOCLHNM.png\", \"q_text\": \"is darker and has shorter sleeves, and Is more festive with short sleeves.\", \"positive_key\": \"B005TF4TS0\", \"positive_value\": \"./images/B005TF4TS0.png\", \"hn_image\": [\"./images/B00GOCLHNM.png\"]}\n{\"q_img\": \"./images/B007WA3P44.png\", \"q_text\": \"The shirt is long sleeved and is melon in color., and is red with longer sleeves\", \"positive_key\": \"B00AFVH8P4\", \"positive_value\": \"./images/B00AFVH8P4.png\", \"hn_image\": [\"./images/B007WA3P44.png\"]}\n{\"q_img\": \"./images/B00AOA61KE.png\", \"q_text\": \"is blue with a collar and button down, and is light blue with buttons down the front\", \"positive_key\": \"B0089M6R2G\", \"positive_value\": \"./images/B0089M6R2G.png\", \"hn_image\": [\"./images/B00AOA61KE.png\"]}\n{\"q_img\": \"./images/B00DSEFH7G.png\", \"q_text\": \"The shirt is blue with an outer space design., and has longer sleeves and a collar.\", \"positive_key\": \"B00FFR8QK4\", \"positive_value\": \"./images/B00FFR8QK4.png\", \"hn_image\": [\"./images/B00DSEFH7G.png\"]}\n{\"q_img\": \"./images/B007ZQ58ZY.png\", \"q_text\": \"has stripes, and is dark with white stripes\", \"positive_key\": \"B004SGEZWQ\", \"positive_value\": \"./images/B004SGEZWQ.png\", \"hn_image\": [\"./images/B007ZQ58ZY.png\"]}\n{\"q_img\": \"./images/B00AIZCMKI.png\", \"q_text\": \"is grey colored and floral print, and is lighter\", \"positive_key\": \"B00AZPKQEA\", \"positive_value\": \"./images/B00AZPKQEA.png\", \"hn_image\": [\"./images/B00AIZCMKI.png\"]}\n{\"q_img\": \"./images/B008MZJHX6.png\", \"q_text\": \"is a darker color, and darker collared\", \"positive_key\": \"B006K32WWA\", \"positive_value\": \"./images/B006K32WWA.png\", \"hn_image\": [\"./images/B008MZJHX6.png\"]}\n{\"q_img\": \"./images/B008C3HJDS.png\", \"q_text\": \"is deeply grey and less revealing, and is less feminine and more masculine\", \"positive_key\": \"B0080XODS4\", \"positive_value\": \"./images/B0080XODS4.png\", \"hn_image\": [\"./images/B008C3HJDS.png\"]}\n{\"q_img\": \"./images/B003CUKHNK.png\", \"q_text\": \"has a gray color with smaller art, and is a gray tee shirt with a twilight cast\", \"positive_key\": \"B005UVNZXI\", \"positive_value\": \"./images/B005UVNZXI.png\", \"hn_image\": [\"./images/B003CUKHNK.png\"]}\n{\"q_img\": \"./images/B008P3A0I6.png\", \"q_text\": \"The white shirt has not sleeves with a melon colored scarf., and Is sleeveless and more solid\", \"positive_key\": \"B008DRX2TS\", \"positive_value\": \"./images/B008DRX2TS.png\", \"hn_image\": [\"./images/B008P3A0I6.png\"]}\n{\"q_img\": \"./images/B004O0TNMI.png\", \"q_text\": \"3/4 sleeves and short sleeves, and darker colored and shows more sleeves\", \"positive_key\": \"B004O0TNHS\", \"positive_value\": \"./images/B004O0TNHS.png\", \"hn_image\": [\"./images/B004O0TNMI.png\"]}\n{\"q_img\": \"./images/B00C5SIA98.png\", \"q_text\": \"has longer sleeves with shiny fabric, and has long sleeves\", \"positive_key\": \"B003UV9XUY\", \"positive_value\": \"./images/B003UV9XUY.png\", \"hn_image\": [\"./images/B00C5SIA98.png\"]}\n{\"q_img\": \"./images/B008UFVTP2.png\", \"q_text\": \"has longer sleeves and two pockets, and is peach colored 3/4 sleeved\", \"positive_key\": \"B008G0HBGM\", \"positive_value\": \"./images/B008G0HBGM.png\", \"hn_image\": [\"./images/B008UFVTP2.png\"]}\n{\"q_img\": \"./images/B007GO8A00.png\", \"q_text\": \"has long sleeves and is pink, and is pink\", \"positive_key\": \"B00980K37I\", \"positive_value\": \"./images/B00980K37I.png\", \"hn_image\": [\"./images/B007GO8A00.png\"]}\n{\"q_img\": \"./images/B007BKC578.png\", \"q_text\": \"is pink with shorter sleeves, and has shorter sleeves\", \"positive_key\": \"B008S5DJQQ\", \"positive_value\": \"./images/B008S5DJQQ.png\", \"hn_image\": [\"./images/B007BKC578.png\"]}\n{\"q_img\": \"./images/B008QW7G9M.png\", \"q_text\": \"is dark red colored and sleeveless, and is a burgundy and black slim fit tank top\", \"positive_key\": \"B007N11B6Q\", \"positive_value\": \"./images/B007N11B6Q.png\", \"hn_image\": [\"./images/B008QW7G9M.png\"]}\n{\"q_img\": \"./images/B007KS1DEW.png\", \"q_text\": \"is pink colored and less revealing, and is pink\", \"positive_key\": \"B005QA9NJS\", \"positive_value\": \"./images/B005QA9NJS.png\", \"hn_image\": [\"./images/B007KS1DEW.png\"]}\n{\"q_img\": \"./images/B007850FV4.png\", \"q_text\": \"is solid red and more revealing, and is red and way more revealing\", \"positive_key\": \"B006MJNKAK\", \"positive_value\": \"./images/B006MJNKAK.png\", \"hn_image\": [\"./images/B007850FV4.png\"]}\n{\"q_img\": \"./images/B00BLKRYK2.png\", \"q_text\": \"The tank top is black in color., and is a turtleneck without sleeves\", \"positive_key\": \"B00FL48H96\", \"positive_value\": \"./images/B00FL48H96.png\", \"hn_image\": [\"./images/B00BLKRYK2.png\"]}\n{\"q_img\": \"./images/B007ZQHYPG.png\", \"q_text\": \"is lighter and has shorter sleeves, and is white and has floral\", \"positive_key\": \"B00DI7EWY2\", \"positive_value\": \"./images/B00DI7EWY2.png\", \"hn_image\": [\"./images/B007ZQHYPG.png\"]}\n{\"q_img\": \"./images/B00A3QE0QQ.png\", \"q_text\": \"The loose fitting shirt is gray and white in color., and is grey in color with longer sleeves\", \"positive_key\": \"B00BZQZSOQ\", \"positive_value\": \"./images/B00BZQZSOQ.png\", \"hn_image\": [\"./images/B00A3QE0QQ.png\"]}\n{\"q_img\": \"./images/B004SWL0JQ.png\", \"q_text\": \"is one red shorts, and is solid red\", \"positive_key\": \"B004HHNMRK\", \"positive_value\": \"./images/B004HHNMRK.png\", \"hn_image\": [\"./images/B004SWL0JQ.png\"]}\n{\"q_img\": \"./images/B008VQ6OJQ.png\", \"q_text\": \"is darker and has shorter sleeves, and is royal blue colored\", \"positive_key\": \"B006LAMBCI\", \"positive_value\": \"./images/B006LAMBCI.png\", \"hn_image\": [\"./images/B008VQ6OJQ.png\"]}\n{\"q_img\": \"./images/B007RBI9P8.png\", \"q_text\": \"is black colored and more revealing, and is a black tank\", \"positive_key\": \"B007RBU2DU\", \"positive_value\": \"./images/B007RBU2DU.png\", \"hn_image\": [\"./images/B007RBI9P8.png\"]}\n{\"q_img\": \"./images/B004ID09K6.png\", \"q_text\": \"The shirt is is long and gray., and darker colored\", \"positive_key\": \"B007A3DDTA\", \"positive_value\": \"./images/B007A3DDTA.png\", \"hn_image\": [\"./images/B004ID09K6.png\"]}\n{\"q_img\": \"./images/B004U8FZE4.png\", \"q_text\": \"is dark brown and has sleeves, and Has longer sleeves and a darker pattern\", \"positive_key\": \"B00DV1FBTK\", \"positive_value\": \"./images/B00DV1FBTK.png\", \"hn_image\": [\"./images/B004U8FZE4.png\"]}\n{\"q_img\": \"./images/B007ZTA748.png\", \"q_text\": \"is darker pink with low neck, and Is sleeveless & darker pink\", \"positive_key\": \"B007ZT9R94\", \"positive_value\": \"./images/B007ZT9R94.png\", \"hn_image\": [\"./images/B007ZTA748.png\"]}\n{\"q_img\": \"./images/B007PU81HM.png\", \"q_text\": \"is two toned, and Is black without a collar.\", \"positive_key\": \"B007Q3A0J0\", \"positive_value\": \"./images/B007Q3A0J0.png\", \"hn_image\": [\"./images/B007PU81HM.png\"]}\n{\"q_img\": \"./images/B007312Q06.png\", \"q_text\": \"is the same, and has more words on it\", \"positive_key\": \"B007OOQHFC\", \"positive_value\": \"./images/B007OOQHFC.png\", \"hn_image\": [\"./images/B007312Q06.png\"]}\n{\"q_img\": \"./images/B009EYSJF6.png\", \"q_text\": \"is darker, and is black with blue text\", \"positive_key\": \"B003PD0G30\", \"positive_value\": \"./images/B003PD0G30.png\", \"hn_image\": [\"./images/B009EYSJF6.png\"]}\n{\"q_img\": \"./images/B007WAFEWU.png\", \"q_text\": \"is a tank top and has no lace, and it has wider strap and it is plain\", \"positive_key\": \"B000FIB8B2\", \"positive_value\": \"./images/B000FIB8B2.png\", \"hn_image\": [\"./images/B007WAFEWU.png\"]}\n{\"q_img\": \"./images/B007HCADFQ.png\", \"q_text\": \"Is more fitted and plain, and black tank top\", \"positive_key\": \"B005D2T18M\", \"positive_value\": \"./images/B005D2T18M.png\", \"hn_image\": [\"./images/B007HCADFQ.png\"]}\n{\"q_img\": \"./images/B00BHM7F9Y.png\", \"q_text\": \"is lighter, and is grey and a men's shirt\", \"positive_key\": \"B00E3U13N6\", \"positive_value\": \"./images/B00E3U13N6.png\", \"hn_image\": [\"./images/B00BHM7F9Y.png\"]}\n{\"q_img\": \"./images/B00ATF1P8M.png\", \"q_text\": \"is more wild, and has sleeves and a more bold pattern\", \"positive_key\": \"B00AT8E2WK\", \"positive_value\": \"./images/B00AT8E2WK.png\", \"hn_image\": [\"./images/B00ATF1P8M.png\"]}\n{\"q_img\": \"./images/B005GW1R5E.png\", \"q_text\": \"is more colorful, and more fitted tee\", \"positive_key\": \"B009PPCOFU\", \"positive_value\": \"./images/B009PPCOFU.png\", \"hn_image\": [\"./images/B005GW1R5E.png\"]}\n{\"q_img\": \"./images/B004WKWMOQ.png\", \"q_text\": \"has a lighter color, and is a gray tee\", \"positive_key\": \"B0058IED7U\", \"positive_value\": \"./images/B0058IED7U.png\", \"hn_image\": [\"./images/B004WKWMOQ.png\"]}\n{\"q_img\": \"./images/B00DEY4E2E.png\", \"q_text\": \"is pink with gold designs, and has sleeves\", \"positive_key\": \"B006UJI9V2\", \"positive_value\": \"./images/B006UJI9V2.png\", \"hn_image\": [\"./images/B00DEY4E2E.png\"]}\n{\"q_img\": \"./images/B0073N9LHK.png\", \"q_text\": \"is not off the shoulder., and has long sleeves\", \"positive_key\": \"B009X6TCGU\", \"positive_value\": \"./images/B009X6TCGU.png\", \"hn_image\": [\"./images/B0073N9LHK.png\"]}\n{\"q_img\": \"./images/B007NE48KE.png\", \"q_text\": \"is lighter, and is a light solid top with ribbon\", \"positive_key\": \"B0058YDMQM\", \"positive_value\": \"./images/B0058YDMQM.png\", \"hn_image\": [\"./images/B007NE48KE.png\"]}\n{\"q_img\": \"./images/B00DOZ60T8.png\", \"q_text\": \"is darker, and is darker in color\", \"positive_key\": \"B00092SAR4\", \"positive_value\": \"./images/B00092SAR4.png\", \"hn_image\": [\"./images/B00DOZ60T8.png\"]}\n{\"q_img\": \"./images/B004S36WB6.png\", \"q_text\": \"is lighter, and is sleeveless wiith a black bow\", \"positive_key\": \"B0079EI9GW\", \"positive_value\": \"./images/B0079EI9GW.png\", \"hn_image\": [\"./images/B004S36WB6.png\"]}\n{\"q_img\": \"./images/B008SDELXI.png\", \"q_text\": \"has a long sleeve with black color, and is black with long sleeves\", \"positive_key\": \"B00CW79MMQ\", \"positive_value\": \"./images/B00CW79MMQ.png\", \"hn_image\": [\"./images/B008SDELXI.png\"]}\n{\"q_img\": \"./images/B00EP49XUK.png\", \"q_text\": \"is lighter and has shorter sleeves, and is black and more revealing\", \"positive_key\": \"B00A41K9GA\", \"positive_value\": \"./images/B00A41K9GA.png\", \"hn_image\": [\"./images/B00EP49XUK.png\"]}\n{\"q_img\": \"./images/B00C8S6TNE.png\", \"q_text\": \"has a darker background, and is black with yellow lettering\", \"positive_key\": \"B00BCX6F1C\", \"positive_value\": \"./images/B00BCX6F1C.png\", \"hn_image\": [\"./images/B00C8S6TNE.png\"]}\n{\"q_img\": \"./images/B00BB1UE9E.png\", \"q_text\": \"is revealing, and is strapless and solid colored\", \"positive_key\": \"B00BL8ISHW\", \"positive_value\": \"./images/B00BL8ISHW.png\", \"hn_image\": [\"./images/B00BB1UE9E.png\"]}\n{\"q_img\": \"./images/B004W9MRYW.png\", \"q_text\": \"is black and white button up with a collar, and is darker coloured with floral print\", \"positive_key\": \"B0084XZE3I\", \"positive_value\": \"./images/B0084XZE3I.png\", \"hn_image\": [\"./images/B004W9MRYW.png\"]}\n{\"q_img\": \"./images/B00C3CVDH2.png\", \"q_text\": \" has longer sleeves, and is pink with white lettering\", \"positive_key\": \"B0089OG5TO\", \"positive_value\": \"./images/B0089OG5TO.png\", \"hn_image\": [\"./images/B00C3CVDH2.png\"]}\n{\"q_img\": \"./images/B00AIXH7BO.png\", \"q_text\": \"The shirt has short sleeves and is red in color., and is red with a collar and buttons\", \"positive_key\": \"B00C5UPBDO\", \"positive_value\": \"./images/B00C5UPBDO.png\", \"hn_image\": [\"./images/B00AIXH7BO.png\"]}\n{\"q_img\": \"./images/B00CP1G416.png\", \"q_text\": \"is blue colored and has sleeves, and has short sleeves\", \"positive_key\": \"B001HDTRJ4\", \"positive_value\": \"./images/B001HDTRJ4.png\", \"hn_image\": [\"./images/B00CP1G416.png\"]}\n{\"q_img\": \"./images/B00B5MEQMA.png\", \"q_text\": \"has collar and is less flowing, and is yellow and a button up.\", \"positive_key\": \"B004I77TLO\", \"positive_value\": \"./images/B004I77TLO.png\", \"hn_image\": [\"./images/B00B5MEQMA.png\"]}\n{\"q_img\": \"./images/B003IRNVLC.png\", \"q_text\": \"long sleeved and purple color, and is magenta v-neck long sleeve\", \"positive_key\": \"B00FN5ASIG\", \"positive_value\": \"./images/B00FN5ASIG.png\", \"hn_image\": [\"./images/B003IRNVLC.png\"]}\n{\"q_img\": \"./images/B00EYYEYAA.png\", \"q_text\": \"is purple, and is purple with shorter sleeves\", \"positive_key\": \"B0085U64UM\", \"positive_value\": \"./images/B0085U64UM.png\", \"hn_image\": [\"./images/B00EYYEYAA.png\"]}\n{\"q_img\": \"./images/B008QW7G9M.png\", \"q_text\": \"The shirt is black with a diamond pattern., and is looser and see-through\", \"positive_key\": \"B006JXJHVA\", \"positive_value\": \"./images/B006JXJHVA.png\", \"hn_image\": [\"./images/B008QW7G9M.png\"]}\n{\"q_img\": \"./images/B00591CB0W.png\", \"q_text\": \"is less colorful, and is lighter colored\", \"positive_key\": \"B008LYUYT4\", \"positive_value\": \"./images/B008LYUYT4.png\", \"hn_image\": [\"./images/B00591CB0W.png\"]}\n{\"q_img\": \"./images/B00CQ73OXK.png\", \"q_text\": \"is black and white, and is white with short sleeves\", \"positive_key\": \"B00B7BKI62\", \"positive_value\": \"./images/B00B7BKI62.png\", \"hn_image\": [\"./images/B00CQ73OXK.png\"]}\n{\"q_img\": \"./images/B005X4EAGS.png\", \"q_text\": \"white only one large scull on front, and has a scarier graphic\", \"positive_key\": \"B008MP3UOI\", \"positive_value\": \"./images/B008MP3UOI.png\", \"hn_image\": [\"./images/B005X4EAGS.png\"]}\n{\"q_img\": \"./images/B00BYOUPD8.png\", \"q_text\": \"long sleeve purple multi collored design, and has longer purple sleeves\", \"positive_key\": \"B0058Z614Q\", \"positive_value\": \"./images/B0058Z614Q.png\", \"hn_image\": [\"./images/B00BYOUPD8.png\"]}\n{\"q_img\": \"./images/B009C97W88.png\", \"q_text\": \"does not have black sleeves on the side, and has a white neckline and white sleeves\", \"positive_key\": \"B0056YO6R8\", \"positive_value\": \"./images/B0056YO6R8.png\", \"hn_image\": [\"./images/B009C97W88.png\"]}\n{\"q_img\": \"./images/B00F62JQEI.png\", \"q_text\": \"is yellow with a 'my little pony logo, and is yellow colored with a different graphic\", \"positive_key\": \"B00DQTE3W8\", \"positive_value\": \"./images/B00DQTE3W8.png\", \"hn_image\": [\"./images/B00F62JQEI.png\"]}\n{\"q_img\": \"./images/B00DCZSCUA.png\", \"q_text\": \"The shirt is gray with a wolf., and is gray and graphic design\", \"positive_key\": \"B008BJT3A0\", \"positive_value\": \"./images/B008BJT3A0.png\", \"hn_image\": [\"./images/B00DCZSCUA.png\"]}\n{\"q_img\": \"./images/B000ES2D9E.png\", \"q_text\": \"is black with pink/yellow, and is darker\", \"positive_key\": \"B00BR5MQJU\", \"positive_value\": \"./images/B00BR5MQJU.png\", \"hn_image\": [\"./images/B000ES2D9E.png\"]}\n{\"q_img\": \"./images/B00A26IWFC.png\", \"q_text\": \"has a short sleeve white and black stripes, and has a finer horizontal stripe pattern to it\", \"positive_key\": \"B00C62DKIE\", \"positive_value\": \"./images/B00C62DKIE.png\", \"hn_image\": [\"./images/B00A26IWFC.png\"]}\n{\"q_img\": \"./images/B00AWS1IG0.png\", \"q_text\": \"is white with less detailing, and is white with short sleeves\", \"positive_key\": \"B003YD528A\", \"positive_value\": \"./images/B003YD528A.png\", \"hn_image\": [\"./images/B00AWS1IG0.png\"]}\n{\"q_img\": \"./images/B008WYDP1C.png\", \"q_text\": \"is short sleeve and fuller, and is black with skull pattern\", \"positive_key\": \"B008FCAUIW\", \"positive_value\": \"./images/B008FCAUIW.png\", \"hn_image\": [\"./images/B008WYDP1C.png\"]}\n{\"q_img\": \"./images/B00AYGYY70.png\", \"q_text\": \"is a tank top, and Has strappy top woth no words\", \"positive_key\": \"B00B8QSUCA\", \"positive_value\": \"./images/B00B8QSUCA.png\", \"hn_image\": [\"./images/B00AYGYY70.png\"]}\n{\"q_img\": \"./images/B007HLK1T0.png\", \"q_text\": \"is purple with a different print, and is dark purple\", \"positive_key\": \"B00B7ETMLG\", \"positive_value\": \"./images/B00B7ETMLG.png\", \"hn_image\": [\"./images/B007HLK1T0.png\"]}\n{\"q_img\": \"./images/B007XXJOR2.png\", \"q_text\": \"is teal colored and more revealing, and is more revealing and has no stripes\", \"positive_key\": \"B00A7HBLW2\", \"positive_value\": \"./images/B00A7HBLW2.png\", \"hn_image\": [\"./images/B007XXJOR2.png\"]}\n{\"q_img\": \"./images/B007WACPOU.png\", \"q_text\": \"Is black with shorter sleeves and more pleats, and shorter sleeves black no collar lacy\", \"positive_key\": \"B00EP49SP0\", \"positive_value\": \"./images/B00EP49SP0.png\", \"hn_image\": [\"./images/B007WACPOU.png\"]}\n{\"q_img\": \"./images/B007GO8A00.png\", \"q_text\": \"is v neck and has longer sleeves, and has longer sleeves with tied collar\", \"positive_key\": \"B00CJGBH1Y\", \"positive_value\": \"./images/B00CJGBH1Y.png\", \"hn_image\": [\"./images/B007GO8A00.png\"]}\n{\"q_img\": \"./images/B001HBKGB4.png\", \"q_text\": \"The shirt has short sleeves is black in color with a white outline of a bird., and is a black shirt with white design\", \"positive_key\": \"B0067EYPX6\", \"positive_value\": \"./images/B0067EYPX6.png\", \"hn_image\": [\"./images/B001HBKGB4.png\"]}\n{\"q_img\": \"./images/B005GLHN8K.png\", \"q_text\": \"is darker, and has long sleeves\", \"positive_key\": \"B003VQS99E\", \"positive_value\": \"./images/B003VQS99E.png\", \"hn_image\": [\"./images/B005GLHN8K.png\"]}\n{\"q_img\": \"./images/B0081P9UPC.png\", \"q_text\": \"is black with a blue flower, and is darker with a v-necl\", \"positive_key\": \"B004IWRQRG\", \"positive_value\": \"./images/B004IWRQRG.png\", \"hn_image\": [\"./images/B0081P9UPC.png\"]}\n{\"q_img\": \"./images/B0055R3Q7C.png\", \"q_text\": \"a short sleeve v-neck tee shirt with a singer graphic in many colors, and higher neck\", \"positive_key\": \"B00CIHR6BE\", \"positive_value\": \"./images/B00CIHR6BE.png\", \"hn_image\": [\"./images/B0055R3Q7C.png\"]}\n{\"q_img\": \"./images/B003V1ZV6S.png\", \"q_text\": \"is pink with black letters, and has blue letters only on a printed pink shirt.\", \"positive_key\": \"B003JK09S6\", \"positive_value\": \"./images/B003JK09S6.png\", \"hn_image\": [\"./images/B003V1ZV6S.png\"]}\n{\"q_img\": \"./images/B00D7ZJQM8.png\", \"q_text\": \"The tank top in blue in color., and has straps over the shoulder\", \"positive_key\": \"B009NB8BU8\", \"positive_value\": \"./images/B009NB8BU8.png\", \"hn_image\": [\"./images/B00D7ZJQM8.png\"]}\n{\"q_img\": \"./images/B002DLLZVC.png\", \"q_text\": \"is black with a skull on it, and has no sleeve and is black color\", \"positive_key\": \"B003VSDEGA\", \"positive_value\": \"./images/B003VSDEGA.png\", \"hn_image\": [\"./images/B002DLLZVC.png\"]}\n{\"q_img\": \"./images/B007O3TPJI.png\", \"q_text\": \"is blue with a more flowery appearance, and is a ruffled blue tube top instead of polka dots it has just plain fabric.\", \"positive_key\": \"B003OPXHBC\", \"positive_value\": \"./images/B003OPXHBC.png\", \"hn_image\": [\"./images/B007O3TPJI.png\"]}\n{\"q_img\": \"./images/B008ADPLAI.png\", \"q_text\": \"is lighter, and has wine-bottle graphic and red highlights\", \"positive_key\": \"B00AUXJFSK\", \"positive_value\": \"./images/B00AUXJFSK.png\", \"hn_image\": [\"./images/B008ADPLAI.png\"]}\n{\"q_img\": \"./images/B00CP7TJ4E.png\", \"q_text\": \"is more revealing, and is more revealing and is grey and black\", \"positive_key\": \"B00CPR36UW\", \"positive_value\": \"./images/B00CPR36UW.png\", \"hn_image\": [\"./images/B00CP7TJ4E.png\"]}\n{\"q_img\": \"./images/B005FVREDU.png\", \"q_text\": \"Is more whimsical and has shorter sleeves, and Has shorter sleeves\", \"positive_key\": \"B002ASAI6G\", \"positive_value\": \"./images/B002ASAI6G.png\", \"hn_image\": [\"./images/B005FVREDU.png\"]}\n{\"q_img\": \"./images/B007A4XOES.png\", \"q_text\": \"Is blue and more feminine, and is printed with a new design on a blue tee.\", \"positive_key\": \"B0037TJOEO\", \"positive_value\": \"./images/B0037TJOEO.png\", \"hn_image\": [\"./images/B007A4XOES.png\"]}\n{\"q_img\": \"./images/B00AYCOA0K.png\", \"q_text\": \"The halter shirt is shiny and purple., and is sleeveless and is a darker color\", \"positive_key\": \"B0042VIGTK\", \"positive_value\": \"./images/B0042VIGTK.png\", \"hn_image\": [\"./images/B00AYCOA0K.png\"]}\n{\"q_img\": \"./images/B007WTG6CI.png\", \"q_text\": \"is green with pink words, and is green with a dog graphics on it\", \"positive_key\": \"B007PUVJXU\", \"positive_value\": \"./images/B007PUVJXU.png\", \"hn_image\": [\"./images/B007WTG6CI.png\"]}\n{\"q_img\": \"./images/B004I90MCU.png\", \"q_text\": \"has a black background, and it is black\", \"positive_key\": \"B008BUX7XI\", \"positive_value\": \"./images/B008BUX7XI.png\", \"hn_image\": [\"./images/B004I90MCU.png\"]}\n{\"q_img\": \"./images/B007XD6ZME.png\", \"q_text\": \"has more stripes, and has longer sleeves and is green and white\", \"positive_key\": \"B008MN5F3E\", \"positive_value\": \"./images/B008MN5F3E.png\", \"hn_image\": [\"./images/B007XD6ZME.png\"]}\n{\"q_img\": \"./images/B0045DTP72.png\", \"q_text\": \"has no sleeves and is brigther, and shorter sleeved\", \"positive_key\": \"B00AFJ9L24\", \"positive_value\": \"./images/B00AFJ9L24.png\", \"hn_image\": [\"./images/B0045DTP72.png\"]}\n{\"q_img\": \"./images/B008ZBL9YM.png\", \"q_text\": \"is darker, and is black and has large round neck\", \"positive_key\": \"B005GI9BQ0\", \"positive_value\": \"./images/B005GI9BQ0.png\", \"hn_image\": [\"./images/B008ZBL9YM.png\"]}\n{\"q_img\": \"./images/B00839LUD6.png\", \"q_text\": \"is sleeveless and yellow, and is sleeveless tank top in yellow\", \"positive_key\": \"B007YC03C2\", \"positive_value\": \"./images/B007YC03C2.png\", \"hn_image\": [\"./images/B00839LUD6.png\"]}\n{\"q_img\": \"./images/B008622MGO.png\", \"q_text\": \"is more revealing and black stripes on it, and it is more revealing\", \"positive_key\": \"B00F3W04GU\", \"positive_value\": \"./images/B00F3W04GU.png\", \"hn_image\": [\"./images/B008622MGO.png\"]}\n{\"q_img\": \"./images/B00BIQADUW.png\", \"q_text\": \"is more graphic and more red, and is red with a skulls design\", \"positive_key\": \"B0058P62B8\", \"positive_value\": \"./images/B0058P62B8.png\", \"hn_image\": [\"./images/B00BIQADUW.png\"]}\n{\"q_img\": \"./images/B00EE0Y0GC.png\", \"q_text\": \"has no graphic and is less childish, and has no graphic on it\", \"positive_key\": \"B005ERXFK6\", \"positive_value\": \"./images/B005ERXFK6.png\", \"hn_image\": [\"./images/B00EE0Y0GC.png\"]}\n{\"q_img\": \"./images/B00AZPKQEA.png\", \"q_text\": \"The tank top is tight fitting and pink in color., and is lighter and has shorter sleeves\", \"positive_key\": \"B00AZ57EYK\", \"positive_value\": \"./images/B00AZ57EYK.png\", \"hn_image\": [\"./images/B00AZPKQEA.png\"]}\n{\"q_img\": \"./images/B007WAEQ6U.png\", \"q_text\": \"The shirt is red in color with black writing., and is a red graphic tee\", \"positive_key\": \"B009IQD6E4\", \"positive_value\": \"./images/B009IQD6E4.png\", \"hn_image\": [\"./images/B007WAEQ6U.png\"]}\n{\"q_img\": \"./images/B008622MGO.png\", \"q_text\": \"has no stripes and has no sleeves, and has straps and is blue\", \"positive_key\": \"B0050SFJJY\", \"positive_value\": \"./images/B0050SFJJY.png\", \"hn_image\": [\"./images/B008622MGO.png\"]}\n{\"q_img\": \"./images/B0074YUMHQ.png\", \"q_text\": \"is lighter and has shorter sleeves, and neutral colors and shorter sleeves\", \"positive_key\": \"B005IPLQYQ\", \"positive_value\": \"./images/B005IPLQYQ.png\", \"hn_image\": [\"./images/B0074YUMHQ.png\"]}\n{\"q_img\": \"./images/B00CFELT5O.png\", \"q_text\": \"a white button down with a much more lose fit, and is a button up in solid grey.\", \"positive_key\": \"B009SSTEPW\", \"positive_value\": \"./images/B009SSTEPW.png\", \"hn_image\": [\"./images/B00CFELT5O.png\"]}\n{\"q_img\": \"./images/B00699EPO8.png\", \"q_text\": \"has longer sleeves with a monkey in it, and has long sleeves\", \"positive_key\": \"B003W3RBFY\", \"positive_value\": \"./images/B003W3RBFY.png\", \"hn_image\": [\"./images/B00699EPO8.png\"]}\n{\"q_img\": \"./images/B005ZO99DA.png\", \"q_text\": \"Is grey and more girly, and gray with colorful graphic\", \"positive_key\": \"B005KYW62Q\", \"positive_value\": \"./images/B005KYW62Q.png\", \"hn_image\": [\"./images/B005ZO99DA.png\"]}\n{\"q_img\": \"./images/B00AU4EKCA.png\", \"q_text\": \"has a cami with ruffles in middle with thin straps, and is a white tee with a different print.\", \"positive_key\": \"B00AZ81CD6\", \"positive_value\": \"./images/B00AZ81CD6.png\", \"hn_image\": [\"./images/B00AU4EKCA.png\"]}\n{\"q_img\": \"./images/B00BPVO5A4.png\", \"q_text\": \"is long sleeved and white, and has long sleeves and is lighter gray\", \"positive_key\": \"B00F9NS154\", \"positive_value\": \"./images/B00F9NS154.png\", \"hn_image\": [\"./images/B00BPVO5A4.png\"]}\n{\"q_img\": \"./images/B00GIOKE8U.png\", \"q_text\": \"is longer and darker, and Is a darker color and sexier with long bell sleeves\", \"positive_key\": \"B008DUZLX0\", \"positive_value\": \"./images/B008DUZLX0.png\", \"hn_image\": [\"./images/B00GIOKE8U.png\"]}\n{\"q_img\": \"./images/B00BJYIR9C.png\", \"q_text\": \"Is more edgy and has no sleeves, and is a black tank top\", \"positive_key\": \"B00DS0TDAW\", \"positive_value\": \"./images/B00DS0TDAW.png\", \"hn_image\": [\"./images/B00BJYIR9C.png\"]}\n{\"q_img\": \"./images/B004GHO03G.png\", \"q_text\": \"is plain grey with longer sleeves, and Has long sleeves and lower neckline.\", \"positive_key\": \"B003VQRFNU\", \"positive_value\": \"./images/B003VQRFNU.png\", \"hn_image\": [\"./images/B004GHO03G.png\"]}\n{\"q_img\": \"./images/B0060OSQXS.png\", \"q_text\": \"has a red pattern and thinner straps, and has spaghetti straps and is white with red and pink\", \"positive_key\": \"B007M7B28I\", \"positive_value\": \"./images/B007M7B28I.png\", \"hn_image\": [\"./images/B0060OSQXS.png\"]}\n{\"q_img\": \"./images/B000EMDA1A.png\", \"q_text\": \"is brown colored with image of giraffe, and has more of a brownish color\", \"positive_key\": \"B00BR8ES16\", \"positive_value\": \"./images/B00BR8ES16.png\", \"hn_image\": [\"./images/B000EMDA1A.png\"]}\n{\"q_img\": \"./images/B008ADPLAI.png\", \"q_text\": \"is blue colored with floral print on it, and is light blue\", \"positive_key\": \"B00AVYW7JM\", \"positive_value\": \"./images/B00AVYW7JM.png\", \"hn_image\": [\"./images/B008ADPLAI.png\"]}\n{\"q_img\": \"./images/B00AOOWR80.png\", \"q_text\": \"black sweatshirt with cats on front, and is black with long sleeves\", \"positive_key\": \"B00CEVB3JU\", \"positive_value\": \"./images/B00CEVB3JU.png\", \"hn_image\": [\"./images/B00AOOWR80.png\"]}\n{\"q_img\": \"./images/B00CF53NUW.png\", \"q_text\": \"has no sleeves and only pink color, and is more pink and sleeveless\", \"positive_key\": \"B0000DZQD6\", \"positive_value\": \"./images/B0000DZQD6.png\", \"hn_image\": [\"./images/B00CF53NUW.png\"]}\n{\"q_img\": \"./images/B004O9WZ92.png\", \"q_text\": \"The shirt is white with red lettering and a picture of Jaws., and is less revealing and is blue\", \"positive_key\": \"B004U32SXK\", \"positive_value\": \"./images/B004U32SXK.png\", \"hn_image\": [\"./images/B004O9WZ92.png\"]}\n{\"q_img\": \"./images/B00C93NG0M.png\", \"q_text\": \" white and pink plaid., and is yellow with pink large circles\", \"positive_key\": \"B00C93N4CW\", \"positive_value\": \"./images/B00C93N4CW.png\", \"hn_image\": [\"./images/B00C93NG0M.png\"]}\n{\"q_img\": \"./images/B00DJ7XEY0.png\", \"q_text\": \"is sleeveless and button down coral, and Is sleeveless and more patterned.\", \"positive_key\": \"B008IGAAX0\", \"positive_value\": \"./images/B008IGAAX0.png\", \"hn_image\": [\"./images/B00DJ7XEY0.png\"]}\n{\"q_img\": \"./images/B004KUUMUY.png\", \"q_text\": \"is more lighter, and is lighter blue\", \"positive_key\": \"B006V4ITGQ\", \"positive_value\": \"./images/B006V4ITGQ.png\", \"hn_image\": [\"./images/B004KUUMUY.png\"]}\n{\"q_img\": \"./images/B00CLD3UH4.png\", \"q_text\": \"has no sleeves., and has no sleeves and black text\", \"positive_key\": \"B007VXNU0Q\", \"positive_value\": \"./images/B007VXNU0Q.png\", \"hn_image\": [\"./images/B00CLD3UH4.png\"]}\n{\"q_img\": \"./images/B008IHCNVQ.png\", \"q_text\": \"is less graphic, and has a v-neck collar and a solid front\", \"positive_key\": \"B0071XG152\", \"positive_value\": \"./images/B0071XG152.png\", \"hn_image\": [\"./images/B008IHCNVQ.png\"]}\n{\"q_img\": \"./images/B00A3S02YI.png\", \"q_text\": \"has long sleeves and white, and has less buttons on it\", \"positive_key\": \"B00700FVDY\", \"positive_value\": \"./images/B00700FVDY.png\", \"hn_image\": [\"./images/B00A3S02YI.png\"]}\n{\"q_img\": \"./images/B00B5TX0VG.png\", \"q_text\": \"is white and marley, and is more bright\", \"positive_key\": \"B002VMHF58\", \"positive_value\": \"./images/B002VMHF58.png\", \"hn_image\": [\"./images/B00B5TX0VG.png\"]}\n{\"q_img\": \"./images/B00AEXWZ7O.png\", \"q_text\": \"has darker color with a logo, and gray color\", \"positive_key\": \"B001UA3038\", \"positive_value\": \"./images/B001UA3038.png\", \"hn_image\": [\"./images/B00AEXWZ7O.png\"]}\n{\"q_img\": \"./images/B006XL920E.png\", \"q_text\": \"Is blue, and is blue with white lettering\", \"positive_key\": \"B007IWEU8Q\", \"positive_value\": \"./images/B007IWEU8Q.png\", \"hn_image\": [\"./images/B006XL920E.png\"]}\n{\"q_img\": \"./images/B00BM9J4TG.png\", \"q_text\": \", and fades from red to orange\", \"positive_key\": \"B009NEGGSO\", \"positive_value\": \"./images/B009NEGGSO.png\", \"hn_image\": [\"./images/B00BM9J4TG.png\"]}\n{\"q_img\": \"./images/B00C5DXJMQ.png\", \"q_text\": \"has long sleeves and a collar, and is more everyday and breathable\", \"positive_key\": \"B007ZQI72K\", \"positive_value\": \"./images/B007ZQI72K.png\", \"hn_image\": [\"./images/B00C5DXJMQ.png\"]}\n{\"q_img\": \"./images/B007XCWQM8.png\", \"q_text\": \"has no sleeves but a fading gray color, and is short sleeved and has ruffles\", \"positive_key\": \"B00336FRHY\", \"positive_value\": \"./images/B00336FRHY.png\", \"hn_image\": [\"./images/B007XCWQM8.png\"]}\n{\"q_img\": \"./images/B00DQF873S.png\", \"q_text\": \"is a red hat, and is red and floral printed\", \"positive_key\": \"B009YLK6MI\", \"positive_value\": \"./images/B009YLK6MI.png\", \"hn_image\": [\"./images/B00DQF873S.png\"]}\n{\"q_img\": \"./images/B005ZSMYVU.png\", \"q_text\": \"has shorter sleeves, and has shorter sleeves and a lower neck line\", \"positive_key\": \"B005PXI0A4\", \"positive_value\": \"./images/B005PXI0A4.png\", \"hn_image\": [\"./images/B005ZSMYVU.png\"]}\n{\"q_img\": \"./images/B0049U3UHW.png\", \"q_text\": \"has three quarter sleeves and animal print, and has shorter sleeves and floral coloured\", \"positive_key\": \"B004DMX8W8\", \"positive_value\": \"./images/B004DMX8W8.png\", \"hn_image\": [\"./images/B0049U3UHW.png\"]}\n{\"q_img\": \"./images/B00495YV3O.png\", \"q_text\": \"The tank top is loose fitting and brown in color., and has no sleeves\", \"positive_key\": \"B005M02XIA\", \"positive_value\": \"./images/B005M02XIA.png\", \"hn_image\": [\"./images/B00495YV3O.png\"]}\n{\"q_img\": \"./images/B00DLYL2ZO.png\", \"q_text\": \"has dogs with long sleeves, and Black\", \"positive_key\": \"B004CMPHN2\", \"positive_value\": \"./images/B004CMPHN2.png\", \"hn_image\": [\"./images/B00DLYL2ZO.png\"]}\n{\"q_img\": \"./images/B0052HY6XI.png\", \"q_text\": \"It is a yellow shirt with no sleeves., and is darker and has shorter sleeves\", \"positive_key\": \"B0089QXF5O\", \"positive_value\": \"./images/B0089QXF5O.png\", \"hn_image\": [\"./images/B0052HY6XI.png\"]}\n{\"q_img\": \"./images/B00CD8H0N2.png\", \"q_text\": \"The shirt is black with white wording., and is black with gray lettering\", \"positive_key\": \"B001KWHF28\", \"positive_value\": \"./images/B001KWHF28.png\", \"hn_image\": [\"./images/B00CD8H0N2.png\"]}\n{\"q_img\": \"./images/B00ADYWYXY.png\", \"q_text\": \"is long sleeves and open in front neck line, and has longer sleeves and is less fashionable\", \"positive_key\": \"B005JJGDPI\", \"positive_value\": \"./images/B005JJGDPI.png\", \"hn_image\": [\"./images/B00ADYWYXY.png\"]}\n{\"q_img\": \"./images/B00CF574EI.png\", \"q_text\": \"is green with sleeves with black collar, and has a collar and is a brighter color\", \"positive_key\": \"B00EYIFL1W\", \"positive_value\": \"./images/B00EYIFL1W.png\", \"hn_image\": [\"./images/B00CF574EI.png\"]}\n{\"q_img\": \"./images/B00B78M2XC.png\", \"q_text\": \"The tank top is purple in color., and has long sleeves\", \"positive_key\": \"B00A435VDY\", \"positive_value\": \"./images/B00A435VDY.png\", \"hn_image\": [\"./images/B00B78M2XC.png\"]}\n{\"q_img\": \"./images/B008PFGQP0.png\", \"q_text\": \"is white wit brown designs, and is a black\", \"positive_key\": \"B008PFK90I\", \"positive_value\": \"./images/B008PFK90I.png\", \"hn_image\": [\"./images/B008PFGQP0.png\"]}\n{\"q_img\": \"./images/B007ZQ58ZY.png\", \"q_text\": \"is lighter and has longer sleeves, and is white with longer sleeves.\", \"positive_key\": \"B00FMK87TE\", \"positive_value\": \"./images/B00FMK87TE.png\", \"hn_image\": [\"./images/B007ZQ58ZY.png\"]}\n{\"q_img\": \"./images/B0093AO0F4.png\", \"q_text\": \"is pink and has a lace overlay, and is pink and flowy\", \"positive_key\": \"B00D4DCHXS\", \"positive_value\": \"./images/B00D4DCHXS.png\", \"hn_image\": [\"./images/B0093AO0F4.png\"]}\n{\"q_img\": \"./images/B00DEZTDCE.png\", \"q_text\": \"Has a v neck with more ruched sleeves, and long sleeve with v-neck\", \"positive_key\": \"B006BNQZWC\", \"positive_value\": \"./images/B006BNQZWC.png\", \"hn_image\": [\"./images/B00DEZTDCE.png\"]}\n{\"q_img\": \"./images/B006GHSVKC.png\", \"q_text\": \"is a tank top., and Is a pink tank top\", \"positive_key\": \"B004IOILQY\", \"positive_value\": \"./images/B004IOILQY.png\", \"hn_image\": [\"./images/B006GHSVKC.png\"]}\n{\"q_img\": \"./images/B00B1H5NSU.png\", \"q_text\": \"is white and sleeveless and more revealing, and is lighter in colour with vertical stripes\", \"positive_key\": \"B005FAOHIQ\", \"positive_value\": \"./images/B005FAOHIQ.png\", \"hn_image\": [\"./images/B00B1H5NSU.png\"]}\n{\"q_img\": \"./images/B00AYDWP4M.png\", \"q_text\": \"It is a solid white color shirt., and Is white and more flowy\", \"positive_key\": \"B00BIFKQSM\", \"positive_value\": \"./images/B00BIFKQSM.png\", \"hn_image\": [\"./images/B00AYDWP4M.png\"]}\n{\"q_img\": \"./images/B005IDUZKY.png\", \"q_text\": \"is darker and edgier, and is darker in color\", \"positive_key\": \"B009QP114O\", \"positive_value\": \"./images/B009QP114O.png\", \"hn_image\": [\"./images/B005IDUZKY.png\"]}\n{\"q_img\": \"./images/B00D5XII9O.png\", \"q_text\": \"The black shirt has white pictures and white lettering., and is a black top with white logo.\", \"positive_key\": \"B00BQWOBG0\", \"positive_value\": \"./images/B00BQWOBG0.png\", \"hn_image\": [\"./images/B00D5XII9O.png\"]}\n{\"q_img\": \"./images/B0027IIDA2.png\", \"q_text\": \"has engineer written on it, and is gray with ENGINEER on front\", \"positive_key\": \"B0046IDVOE\", \"positive_value\": \"./images/B0046IDVOE.png\", \"hn_image\": [\"./images/B0027IIDA2.png\"]}\n{\"q_img\": \"./images/B00AZIH38O.png\", \"q_text\": \"has tiny straps, and has no sleeves and reveals more\", \"positive_key\": \"B002ZCXVFW\", \"positive_value\": \"./images/B002ZCXVFW.png\", \"hn_image\": [\"./images/B00AZIH38O.png\"]}\n{\"q_img\": \"./images/B0038KABPI.png\", \"q_text\": \"is white colored tshirt, and doesn't have buttons and is gray\", \"positive_key\": \"B007J7J65C\", \"positive_value\": \"./images/B007J7J65C.png\", \"hn_image\": [\"./images/B0038KABPI.png\"]}\n{\"q_img\": \"./images/B0036JQOZM.png\", \"q_text\": \"is more graphic and artistic, and is darker coloured\", \"positive_key\": \"B006JX99GS\", \"positive_value\": \"./images/B006JX99GS.png\", \"hn_image\": [\"./images/B0036JQOZM.png\"]}\n{\"q_img\": \"./images/B009VI0H7S.png\", \"q_text\": \"is brighter and has a motivational quote., and is brighter in color\", \"positive_key\": \"B005OCD7O0\", \"positive_value\": \"./images/B005OCD7O0.png\", \"hn_image\": [\"./images/B009VI0H7S.png\"]}\n{\"q_img\": \"./images/B00CF2YB6A.png\", \"q_text\": \"has more colors, and has a green graphic on it\", \"positive_key\": \"B006ZCQB28\", \"positive_value\": \"./images/B006ZCQB28.png\", \"hn_image\": [\"./images/B00CF2YB6A.png\"]}\n{\"q_img\": \"./images/B00ATQ740Y.png\", \"q_text\": \"The shirt has many abstract colors., and is black with lack on the sleeves\", \"positive_key\": \"B00BG0FKN0\", \"positive_value\": \"./images/B00BG0FKN0.png\", \"hn_image\": [\"./images/B00ATQ740Y.png\"]}\n{\"q_img\": \"./images/B009ZGQ0F4.png\", \"q_text\": \"is lighter, and is lighter colored with a different graphic\", \"positive_key\": \"B00774HYH4\", \"positive_value\": \"./images/B00774HYH4.png\", \"hn_image\": [\"./images/B009ZGQ0F4.png\"]}\n{\"q_img\": \"./images/B003IEWVRK.png\", \"q_text\": \"is darker and much shorter, and is of shorter length\", \"positive_key\": \"B003ILHFUG\", \"positive_value\": \"./images/B003ILHFUG.png\", \"hn_image\": [\"./images/B003IEWVRK.png\"]}\n{\"q_img\": \"./images/B00AG35TN4.png\", \"q_text\": \"is a no sleeve black shirt, and has wider straps and is black\", \"positive_key\": \"B00AG3ZI4E\", \"positive_value\": \"./images/B00AG3ZI4E.png\", \"hn_image\": [\"./images/B00AG35TN4.png\"]}\n{\"q_img\": \"./images/B001HBKGB4.png\", \"q_text\": \"is darker, and is less font and more graphic\", \"positive_key\": \"B000O1J04C\", \"positive_value\": \"./images/B000O1J04C.png\", \"hn_image\": [\"./images/B001HBKGB4.png\"]}\n{\"q_img\": \"./images/B003RW60ES.png\", \"q_text\": \"is white with a black logo, and is white with only 2 colors\", \"positive_key\": \"B008LP73WE\", \"positive_value\": \"./images/B008LP73WE.png\", \"hn_image\": [\"./images/B003RW60ES.png\"]}\n{\"q_img\": \"./images/B0094KPUK2.png\", \"q_text\": \"is longer and has longer sleeves, and longer\", \"positive_key\": \"B00EIWYT08\", \"positive_value\": \"./images/B00EIWYT08.png\", \"hn_image\": [\"./images/B0094KPUK2.png\"]}\n{\"q_img\": \"./images/B00836H3JE.png\", \"q_text\": \"is solid black and three quarter sleeves, and Has long sleeves and is black.\", \"positive_key\": \"B002UKPUD0\", \"positive_value\": \"./images/B002UKPUD0.png\", \"hn_image\": [\"./images/B00836H3JE.png\"]}\n{\"q_img\": \"./images/B0081ZRYN2.png\", \"q_text\": \"is white lace with long sleeves, and is more sheer and is white\", \"positive_key\": \"B008AUHYZ6\", \"positive_value\": \"./images/B008AUHYZ6.png\", \"hn_image\": [\"./images/B0081ZRYN2.png\"]}\n{\"q_img\": \"./images/B007C3MI3A.png\", \"q_text\": \"has more of a print and straps, and has a graphic pattern and sheer sleeves\", \"positive_key\": \"B007HCB0SU\", \"positive_value\": \"./images/B007HCB0SU.png\", \"hn_image\": [\"./images/B007C3MI3A.png\"]}\n{\"q_img\": \"./images/B00FPT42EG.png\", \"q_text\": \"It is more solid in color., and banded waist\", \"positive_key\": \"B00FZ9SD2S\", \"positive_value\": \"./images/B00FZ9SD2S.png\", \"hn_image\": [\"./images/B00FPT42EG.png\"]}\n{\"q_img\": \"./images/B009B0K0KU.png\", \"q_text\": \"The shirt is black in color with a picture of Robocop., and is darker\", \"positive_key\": \"B00945QZJM\", \"positive_value\": \"./images/B00945QZJM.png\", \"hn_image\": [\"./images/B009B0K0KU.png\"]}\n{\"q_img\": \"./images/B00EYIOCIA.png\", \"q_text\": \"is yellow colored, and is yellow orangeish in color with short sleeves\", \"positive_key\": \"B007TZ00UE\", \"positive_value\": \"./images/B007TZ00UE.png\", \"hn_image\": [\"./images/B00EYIOCIA.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"has shorter sleeves, and is black with shorter sleeves\", \"positive_key\": \"B00BU5HXG8\", \"positive_value\": \"./images/B00BU5HXG8.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B00AOOWRZI.png\", \"q_text\": \"The shirt is green and black in color., and Has a solid color with shorter sleeves.\", \"positive_key\": \"B00BLA9DH4\", \"positive_value\": \"./images/B00BLA9DH4.png\", \"hn_image\": [\"./images/B00AOOWRZI.png\"]}\n{\"q_img\": \"./images/B00BE6KR4S.png\", \"q_text\": \"is darker in color, and Is grey in color\", \"positive_key\": \"B000YYBM54\", \"positive_value\": \"./images/B000YYBM54.png\", \"hn_image\": [\"./images/B00BE6KR4S.png\"]}\n{\"q_img\": \"./images/B007LWRFP8.png\", \"q_text\": \"The shirt is black with purple writing and a purple character., and is black with sleeves and a different graphic\", \"positive_key\": \"B00432IFU8\", \"positive_value\": \"./images/B00432IFU8.png\", \"hn_image\": [\"./images/B007LWRFP8.png\"]}\n{\"q_img\": \"./images/B00B5NVG3Q.png\", \"q_text\": \"is black colored and has sleeves, and is darker and has no designs\", \"positive_key\": \"B00940K34K\", \"positive_value\": \"./images/B00940K34K.png\", \"hn_image\": [\"./images/B00B5NVG3Q.png\"]}\n{\"q_img\": \"./images/B008NDUUO2.png\", \"q_text\": \"a bright colored shirt with a large dog graphic, and has a dog.\", \"positive_key\": \"B008VENGX0\", \"positive_value\": \"./images/B008VENGX0.png\", \"hn_image\": [\"./images/B008NDUUO2.png\"]}\n{\"q_img\": \"./images/B00GIOKE8U.png\", \"q_text\": \"is a long sleeve brown shirt, and is tan with shorter sleeves\", \"positive_key\": \"B002AA8JS8\", \"positive_value\": \"./images/B002AA8JS8.png\", \"hn_image\": [\"./images/B00GIOKE8U.png\"]}\n{\"q_img\": \"./images/B004L83ZEU.png\", \"q_text\": \" sleek, and has spaghetti straps and a floral print\", \"positive_key\": \"B004ULIAMK\", \"positive_value\": \"./images/B004ULIAMK.png\", \"hn_image\": [\"./images/B004L83ZEU.png\"]}\n{\"q_img\": \"./images/B00DSS1KAK.png\", \"q_text\": \"is yellow, and is yellow and covers the mid section fully\", \"positive_key\": \"B00DZ0LL2S\", \"positive_value\": \"./images/B00DZ0LL2S.png\", \"hn_image\": [\"./images/B00DSS1KAK.png\"]}\n{\"q_img\": \"./images/B008VWCZ5W.png\", \"q_text\": \"is white with black words, and is a lighter color\", \"positive_key\": \"B009QR9MIO\", \"positive_value\": \"./images/B009QR9MIO.png\", \"hn_image\": [\"./images/B008VWCZ5W.png\"]}\n{\"q_img\": \"./images/B004ID09K6.png\", \"q_text\": \"The shirt is red and long sleeve., and is red with long sleeves\", \"positive_key\": \"B005L2NJKU\", \"positive_value\": \"./images/B005L2NJKU.png\", \"hn_image\": [\"./images/B004ID09K6.png\"]}\n{\"q_img\": \"./images/B008P57GXG.png\", \"q_text\": \"is a long sleeve two colored shirt with art, and long sleeves two tone black sleeves white body\", \"positive_key\": \"B009C97W88\", \"positive_value\": \"./images/B009C97W88.png\", \"hn_image\": [\"./images/B008P57GXG.png\"]}\n{\"q_img\": \"./images/B00C64M1BY.png\", \"q_text\": \"has no sleeves with colorful strips, and has no sleeves and more stripes\", \"positive_key\": \"B004S59HMA\", \"positive_value\": \"./images/B004S59HMA.png\", \"hn_image\": [\"./images/B00C64M1BY.png\"]}\n{\"q_img\": \"./images/B00BNF2PVI.png\", \"q_text\": \"has image with text in purple, and has word and graphic images cover most of the front\", \"positive_key\": \"B0080ETRWA\", \"positive_value\": \"./images/B0080ETRWA.png\", \"hn_image\": [\"./images/B00BNF2PVI.png\"]}\n{\"q_img\": \"./images/B008CLNW1I.png\", \"q_text\": \"longer sleeves and white, and has a printed graphic that isn't stripes.\", \"positive_key\": \"B0097O1ERS\", \"positive_value\": \"./images/B0097O1ERS.png\", \"hn_image\": [\"./images/B008CLNW1I.png\"]}\n{\"q_img\": \"./images/B00FR621O0.png\", \"q_text\": \"is white with black lettering, and is white with numbers 1-40 on front\", \"positive_key\": \"B00A7T0LBC\", \"positive_value\": \"./images/B00A7T0LBC.png\", \"hn_image\": [\"./images/B00FR621O0.png\"]}\n{\"q_img\": \"./images/B00BQLAARU.png\", \"q_text\": \" green and purple., and has no sleeves\", \"positive_key\": \"B009PIM60A\", \"positive_value\": \"./images/B009PIM60A.png\", \"hn_image\": [\"./images/B00BQLAARU.png\"]}\n{\"q_img\": \"./images/B00CMUVC1C.png\", \"q_text\": \"has short sleeves and less blue, and is blue and has longer sleeves\", \"positive_key\": \"B001LXXQ7O\", \"positive_value\": \"./images/B001LXXQ7O.png\", \"hn_image\": [\"./images/B00CMUVC1C.png\"]}\n{\"q_img\": \"./images/B00CX0FNWU.png\", \"q_text\": \"is black with a character on one side, and black v-neck large wite graphic\", \"positive_key\": \"B009G6LU8A\", \"positive_value\": \"./images/B009G6LU8A.png\", \"hn_image\": [\"./images/B00CX0FNWU.png\"]}\n{\"q_img\": \"./images/B00C93N0NA.png\", \"q_text\": \"is blue with a collar and some buttons, and is blue and shorter sleeved\", \"positive_key\": \"B00C93NG0M\", \"positive_value\": \"./images/B00C93NG0M.png\", \"hn_image\": [\"./images/B00C93N0NA.png\"]}\n{\"q_img\": \"./images/B00BXYXTSM.png\", \"q_text\": \"has longer sleeves and is lighter colored, and is gray with long sleeves\", \"positive_key\": \"B004LQ17D8\", \"positive_value\": \"./images/B004LQ17D8.png\", \"hn_image\": [\"./images/B00BXYXTSM.png\"]}\n{\"q_img\": \"./images/B00E3J81RS.png\", \"q_text\": \"is lighter and has shorter sleeves, and has shorter sleeves\", \"positive_key\": \"B005QDCSHE\", \"positive_value\": \"./images/B005QDCSHE.png\", \"hn_image\": [\"./images/B00E3J81RS.png\"]}\n{\"q_img\": \"./images/B003M8HWVM.png\", \"q_text\": \"is black with white stripes, and it has stripes and it is black\", \"positive_key\": \"B006MQ5WTK\", \"positive_value\": \"./images/B006MQ5WTK.png\", \"hn_image\": [\"./images/B003M8HWVM.png\"]}\n{\"q_img\": \"./images/B003QOR38E.png\", \"q_text\": \"is cranberry in color, and is maroon with long sleeves\", \"positive_key\": \"B003QONL9Y\", \"positive_value\": \"./images/B003QONL9Y.png\", \"hn_image\": [\"./images/B003QOR38E.png\"]}\n{\"q_img\": \"./images/B00EKWB7CY.png\", \"q_text\": \"has no sleeves but a strip gray pattern, and is sheer with white and black\", \"positive_key\": \"B00CYP43U2\", \"positive_value\": \"./images/B00CYP43U2.png\", \"hn_image\": [\"./images/B00EKWB7CY.png\"]}\n{\"q_img\": \"./images/B007K4Y0KU.png\", \"q_text\": \"is stripped with a short sleeves, and has sleeves and has black and white stripes\", \"positive_key\": \"B00C2VK24Y\", \"positive_value\": \"./images/B00C2VK24Y.png\", \"hn_image\": [\"./images/B007K4Y0KU.png\"]}\n{\"q_img\": \"./images/B00CY5KNE2.png\", \"q_text\": \"is a t-shirt with text, and is solid black with white logo\", \"positive_key\": \"B009F1HC8I\", \"positive_value\": \"./images/B009F1HC8I.png\", \"hn_image\": [\"./images/B00CY5KNE2.png\"]}\n{\"q_img\": \"./images/B003VWLPLM.png\", \"q_text\": \"has more sleeve and green color, and is solid green\", \"positive_key\": \"B003U0TTIQ\", \"positive_value\": \"./images/B003U0TTIQ.png\", \"hn_image\": [\"./images/B003VWLPLM.png\"]}\n{\"q_img\": \"./images/B00B7ALSVM.png\", \"q_text\": \"is lighter and has shorter sleeves, and is pink and short sleeved\", \"positive_key\": \"B004F8FH92\", \"positive_value\": \"./images/B004F8FH92.png\", \"hn_image\": [\"./images/B00B7ALSVM.png\"]}\n{\"q_img\": \"./images/B00DDXIC1U.png\", \"q_text\": \"is lighter and has shorter sleeves, and and that's reason enough not to take it home\", \"positive_key\": \"B007BDJXUM\", \"positive_value\": \"./images/B007BDJXUM.png\", \"hn_image\": [\"./images/B00DDXIC1U.png\"]}\n{\"q_img\": \"./images/B00APD8ZDQ.png\", \"q_text\": \"Is purple and has less buttons, and is blue with longer sleeves\", \"positive_key\": \"B008566P82\", \"positive_value\": \"./images/B008566P82.png\", \"hn_image\": [\"./images/B00APD8ZDQ.png\"]}\n{\"q_img\": \"./images/B007225EKU.png\", \"q_text\": \"is longer and less revealing, and longer sleeves\", \"positive_key\": \"B005X6RYYQ\", \"positive_value\": \"./images/B005X6RYYQ.png\", \"hn_image\": [\"./images/B007225EKU.png\"]}\n{\"q_img\": \"./images/B00AHH92Z0.png\", \"q_text\": \"Is more flowing, and is longer sleeved and lighter\", \"positive_key\": \"B00591CB0W\", \"positive_value\": \"./images/B00591CB0W.png\", \"hn_image\": [\"./images/B00AHH92Z0.png\"]}\n{\"q_img\": \"./images/B004SH0MI6.png\", \"q_text\": \"is long sleeved and blue, and has longer sleeves and is blue\", \"positive_key\": \"B00EPC7JMQ\", \"positive_value\": \"./images/B00EPC7JMQ.png\", \"hn_image\": [\"./images/B004SH0MI6.png\"]}\n{\"q_img\": \"./images/B0098TSBOQ.png\", \"q_text\": \"is green and sleeveless, and has no sleeves and is bright green\", \"positive_key\": \"B008BIMNVC\", \"positive_value\": \"./images/B008BIMNVC.png\", \"hn_image\": [\"./images/B0098TSBOQ.png\"]}\n{\"q_img\": \"./images/B005UAHTCC.png\", \"q_text\": \"has no sleeves with a v-neck, and has more straps\", \"positive_key\": \"B00FIYMWYU\", \"positive_value\": \"./images/B00FIYMWYU.png\", \"hn_image\": [\"./images/B005UAHTCC.png\"]}\n{\"q_img\": \"./images/B000Q8SC9M.png\", \"q_text\": \"Has a less faded graphic, and has skull graphic on the dront\", \"positive_key\": \"B000VTEN12\", \"positive_value\": \"./images/B000VTEN12.png\", \"hn_image\": [\"./images/B000Q8SC9M.png\"]}\n{\"q_img\": \"./images/B00FL2U8NG.png\", \"q_text\": \"The shirt is a belted shirt that is black, and Has bright floral patterns\", \"positive_key\": \"B00FBP4AIM\", \"positive_value\": \"./images/B00FBP4AIM.png\", \"hn_image\": [\"./images/B00FL2U8NG.png\"]}\n{\"q_img\": \"./images/B00CIY810C.png\", \"q_text\": \"is more short sleeved, and Is black\", \"positive_key\": \"B0063L45YC\", \"positive_value\": \"./images/B0063L45YC.png\", \"hn_image\": [\"./images/B00CIY810C.png\"]}\n{\"q_img\": \"./images/B0081NZ7CY.png\", \"q_text\": \"is longer and more peasant like, and Is lighter colored and more sloppy\", \"positive_key\": \"B00EAIXVWC\", \"positive_value\": \"./images/B00EAIXVWC.png\", \"hn_image\": [\"./images/B0081NZ7CY.png\"]}\n{\"q_img\": \"./images/B008BHSRFO.png\", \"q_text\": \"The shirt has capped sleeves and is yellow and white the pants are aqua., and is grey and black with longer sleeves\", \"positive_key\": \"B008WYDI7I\", \"positive_value\": \"./images/B008WYDI7I.png\", \"hn_image\": [\"./images/B008BHSRFO.png\"]}\n{\"q_img\": \"./images/B005GV81EA.png\", \"q_text\": \"is white and sleeveless, and Is white in color and sleeveless\", \"positive_key\": \"B005GPKWLQ\", \"positive_value\": \"./images/B005GPKWLQ.png\", \"hn_image\": [\"./images/B005GV81EA.png\"]}\n{\"q_img\": \"./images/B008TWBPR8.png\", \"q_text\": \"Is longer and black, and is darker in color\", \"positive_key\": \"B006N04X2M\", \"positive_value\": \"./images/B006N04X2M.png\", \"hn_image\": [\"./images/B008TWBPR8.png\"]}\n{\"q_img\": \"./images/B003R6P3EM.png\", \"q_text\": \"The long sleeved shirt is light in color., and is grey colored with a pattern and no collar or buttons\", \"positive_key\": \"B0092XEYJY\", \"positive_value\": \"./images/B0092XEYJY.png\", \"hn_image\": [\"./images/B003R6P3EM.png\"]}\n{\"q_img\": \"./images/B00EP49DS2.png\", \"q_text\": \"has a shorter sleeve with spiral colors, and is more colorful tie-dye and t-shirt style top\", \"positive_key\": \"B0046QRK6G\", \"positive_value\": \"./images/B0046QRK6G.png\", \"hn_image\": [\"./images/B00EP49DS2.png\"]}\n{\"q_img\": \"./images/B004NEB94U.png\", \"q_text\": \" purple with black strips, and deep pink\", \"positive_key\": \"B0051VMERA\", \"positive_value\": \"./images/B0051VMERA.png\", \"hn_image\": [\"./images/B004NEB94U.png\"]}\n{\"q_img\": \"./images/B008X04NWA.png\", \"q_text\": \"is t shirt with pocket, and has shorter sleeves\", \"positive_key\": \"B00CQNY2WQ\", \"positive_value\": \"./images/B00CQNY2WQ.png\", \"hn_image\": [\"./images/B008X04NWA.png\"]}\n{\"q_img\": \"./images/B00F62JQEI.png\", \"q_text\": \"is purple colored with image of dinosaur, and is purple with dinosaur motif\", \"positive_key\": \"B004I90MCU\", \"positive_value\": \"./images/B004I90MCU.png\", \"hn_image\": [\"./images/B00F62JQEI.png\"]}\n{\"q_img\": \"./images/B0094I4IOI.png\", \"q_text\": \"has no sleeve and multiple green colors, and has less graphics on it\", \"positive_key\": \"B00CMVLDQU\", \"positive_value\": \"./images/B00CMVLDQU.png\", \"hn_image\": [\"./images/B0094I4IOI.png\"]}\n{\"q_img\": \"./images/B0073PZW2Q.png\", \"q_text\": \"is tighter and colorful, and is a t shirt and orange with trim\", \"positive_key\": \"B009DIG3D8\", \"positive_value\": \"./images/B009DIG3D8.png\", \"hn_image\": [\"./images/B0073PZW2Q.png\"]}\n{\"q_img\": \"./images/B00C11UWZE.png\", \"q_text\": \"Is more colorful and has thinner straps, and Has thinnder straps and is more patterened\", \"positive_key\": \"B0096I96XE\", \"positive_value\": \"./images/B0096I96XE.png\", \"hn_image\": [\"./images/B00C11UWZE.png\"]}\n{\"q_img\": \"./images/B008QIV35O.png\", \"q_text\": \"is brown with beige and white patterns, and has shorter sleeves and is lighter in color\", \"positive_key\": \"B004R96VLW\", \"positive_value\": \"./images/B004R96VLW.png\", \"hn_image\": [\"./images/B008QIV35O.png\"]}\n{\"q_img\": \"./images/B00BXXO4WI.png\", \"q_text\": \"is red, and is pink and has collar\", \"positive_key\": \"B00BZSE4Y4\", \"positive_value\": \"./images/B00BZSE4Y4.png\", \"hn_image\": [\"./images/B00BXXO4WI.png\"]}\n{\"q_img\": \"./images/B00BA3AHKO.png\", \"q_text\": \"is black with stripes, and darker colors with stripes\", \"positive_key\": \"B005LCMEDI\", \"positive_value\": \"./images/B005LCMEDI.png\", \"hn_image\": [\"./images/B00BA3AHKO.png\"]}\n{\"q_img\": \"./images/B0090YFTEE.png\", \"q_text\": \" blue, and has long sleeves\", \"positive_key\": \"B00AYDWP4M\", \"positive_value\": \"./images/B00AYDWP4M.png\", \"hn_image\": [\"./images/B0090YFTEE.png\"]}\n{\"q_img\": \"./images/B001QVT1TI.png\", \"q_text\": \"is black with a green tie, and has a V-neck and white\", \"positive_key\": \"B00BHM7F9Y\", \"positive_value\": \"./images/B00BHM7F9Y.png\", \"hn_image\": [\"./images/B001QVT1TI.png\"]}\n{\"q_img\": \"./images/B007E94S12.png\", \"q_text\": \"Is more casual and striped, and has sleeves and is red and black striped\", \"positive_key\": \"B0052HYADO\", \"positive_value\": \"./images/B0052HYADO.png\", \"hn_image\": [\"./images/B007E94S12.png\"]}\n{\"q_img\": \"./images/B00AIIQJ5E.png\", \"q_text\": \"has a longer sleeve, and is more tonal and long sleeves\", \"positive_key\": \"B007XD5BMY\", \"positive_value\": \"./images/B007XD5BMY.png\", \"hn_image\": [\"./images/B00AIIQJ5E.png\"]}\n{\"q_img\": \"./images/B0018DWZJC.png\", \"q_text\": \"The shirt is blue and red with white writing., and has a different print added to it.\", \"positive_key\": \"B00CORRYVK\", \"positive_value\": \"./images/B00CORRYVK.png\", \"hn_image\": [\"./images/B0018DWZJC.png\"]}\n{\"q_img\": \"./images/B00AYQMTPO.png\", \"q_text\": \"The sleeves shirt is sheer and white in color., and Is sleeveless and more chic\", \"positive_key\": \"B00COQ5O9K\", \"positive_value\": \"./images/B00COQ5O9K.png\", \"hn_image\": [\"./images/B00AYQMTPO.png\"]}\n{\"q_img\": \"./images/B008BOJ2TM.png\", \"q_text\": \"The shirt is off the shoulders and gray in color., and is grey with a low fur neckline\", \"positive_key\": \"B0076FANN6\", \"positive_value\": \"./images/B0076FANN6.png\", \"hn_image\": [\"./images/B008BOJ2TM.png\"]}\n{\"q_img\": \"./images/B00BYV5G0I.png\", \"q_text\": \"Is more revealing and sexy, and is sleeveless and dray laced\", \"positive_key\": \"B007G11JF6\", \"positive_value\": \"./images/B007G11JF6.png\", \"hn_image\": [\"./images/B00BYV5G0I.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"is lighter, and is dark grey\", \"positive_key\": \"B008UU2HC6\", \"positive_value\": \"./images/B008UU2HC6.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B00AEXWZ7O.png\", \"q_text\": \"Is more wordy and is black, and is black with white font.\", \"positive_key\": \"B001BISBCY\", \"positive_value\": \"./images/B001BISBCY.png\", \"hn_image\": [\"./images/B00AEXWZ7O.png\"]}\n{\"q_img\": \"./images/B00CXV9H26.png\", \"q_text\": \"is dark blue in color, and is more of a navy color\", \"positive_key\": \"B0067EVHQO\", \"positive_value\": \"./images/B0067EVHQO.png\", \"hn_image\": [\"./images/B00CXV9H26.png\"]}\n{\"q_img\": \"./images/B004IP8G3Q.png\", \"q_text\": \"is tighter and nerdy, and has a graphic of a man on it\", \"positive_key\": \"B008SHUIP4\", \"positive_value\": \"./images/B008SHUIP4.png\", \"hn_image\": [\"./images/B004IP8G3Q.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"has no sleeves and is blue, and has no sleeves but a similar floral weave.\", \"positive_key\": \"B008VT1Q5U\", \"positive_value\": \"./images/B008VT1Q5U.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B005P5ARGM.png\", \"q_text\": \"The shirt is black with lace., and is black and lace detail\", \"positive_key\": \"B004K1EDLC\", \"positive_value\": \"./images/B004K1EDLC.png\", \"hn_image\": [\"./images/B005P5ARGM.png\"]}\n{\"q_img\": \"./images/B0026C4H2C.png\", \"q_text\": \"is cotton shirt and less blue, and graphics\", \"positive_key\": \"B0042P87ZY\", \"positive_value\": \"./images/B0042P87ZY.png\", \"hn_image\": [\"./images/B0026C4H2C.png\"]}\n{\"q_img\": \"./images/B008NF7N5E.png\", \"q_text\": \"The shirt is purple and black., and has slightly longer sleeves and is purple and black\", \"positive_key\": \"B008UA8M3Y\", \"positive_value\": \"./images/B008UA8M3Y.png\", \"hn_image\": [\"./images/B008NF7N5E.png\"]}\n{\"q_img\": \"./images/B006C4Y45A.png\", \"q_text\": \"is a darker color, and is a hat\", \"positive_key\": \"B00EOH0D48\", \"positive_value\": \"./images/B00EOH0D48.png\", \"hn_image\": [\"./images/B006C4Y45A.png\"]}\n{\"q_img\": \"./images/B00EDKMINA.png\", \"q_text\": \"is navy blue with red words, and is darker with a different logo\", \"positive_key\": \"B00C5T0MK2\", \"positive_value\": \"./images/B00C5T0MK2.png\", \"hn_image\": [\"./images/B00EDKMINA.png\"]}\n{\"q_img\": \"./images/B0077DN4P6.png\", \"q_text\": \"is blue, and comes in blue\", \"positive_key\": \"B00B7SB6RA\", \"positive_value\": \"./images/B00B7SB6RA.png\", \"hn_image\": [\"./images/B0077DN4P6.png\"]}\n{\"q_img\": \"./images/B00BHLIS8C.png\", \"q_text\": \"is a solid green tee, and is more of a green color\", \"positive_key\": \"B001QVT1TI\", \"positive_value\": \"./images/B001QVT1TI.png\", \"hn_image\": [\"./images/B00BHLIS8C.png\"]}\n{\"q_img\": \"./images/B00EYYEYAA.png\", \"q_text\": \"is royal blue with high neck, and has a more complex neckline and is brighter\", \"positive_key\": \"B00415UFAA\", \"positive_value\": \"./images/B00415UFAA.png\", \"hn_image\": [\"./images/B00EYYEYAA.png\"]}\n{\"q_img\": \"./images/B00579WNTA.png\", \"q_text\": \"The shirt is short sleeves and is pink., and is pink and says fight like a girl\", \"positive_key\": \"B00FI9WO9I\", \"positive_value\": \"./images/B00FI9WO9I.png\", \"hn_image\": [\"./images/B00579WNTA.png\"]}\n{\"q_img\": \"./images/B0085IN9GQ.png\", \"q_text\": \"has no sleeves but beige color, and is tan with black buckle\", \"positive_key\": \"B0078LJIU2\", \"positive_value\": \"./images/B0078LJIU2.png\", \"hn_image\": [\"./images/B0085IN9GQ.png\"]}\n{\"q_img\": \"./images/B00836H3JE.png\", \"q_text\": \"has a darker color and an animal print, and is more dressy but still looks like a tank top\", \"positive_key\": \"B0094R0LDG\", \"positive_value\": \"./images/B0094R0LDG.png\", \"hn_image\": [\"./images/B00836H3JE.png\"]}\n{\"q_img\": \"./images/B00E0COGUE.png\", \"q_text\": \"is pink and has sleeves, and is pink with long sleeves\", \"positive_key\": \"B00DGXJUF4\", \"positive_value\": \"./images/B00DGXJUF4.png\", \"hn_image\": [\"./images/B00E0COGUE.png\"]}\n{\"q_img\": \"./images/B002XQ37FE.png\", \"q_text\": \"is blue colored and has image of a skull, and has a round graphic on it\", \"positive_key\": \"B00ADSHGFG\", \"positive_value\": \"./images/B00ADSHGFG.png\", \"hn_image\": [\"./images/B002XQ37FE.png\"]}\n{\"q_img\": \"./images/B001T92JIM.png\", \"q_text\": \"is blue and has long sleeves, and Has pockets and sleeves\", \"positive_key\": \"B002RASCE2\", \"positive_value\": \"./images/B002RASCE2.png\", \"hn_image\": [\"./images/B001T92JIM.png\"]}\n{\"q_img\": \"./images/B0092TZZ1Y.png\", \"q_text\": \"is yellow long sleeves and shiny, and is lighter and shinier\", \"positive_key\": \"B00B9BFGQ2\", \"positive_value\": \"./images/B00B9BFGQ2.png\", \"hn_image\": [\"./images/B0092TZZ1Y.png\"]}\n{\"q_img\": \"./images/B00A7HBMEO.png\", \"q_text\": \" pink and red., and but isn't a full dress.\", \"positive_key\": \"B0092SC112\", \"positive_value\": \"./images/B0092SC112.png\", \"hn_image\": [\"./images/B00A7HBMEO.png\"]}\n{\"q_img\": \"./images/B00A13FSRQ.png\", \"q_text\": \"is red short sleeved with white words, and Is looser & has an emblem\", \"positive_key\": \"B005GA8YB6\", \"positive_value\": \"./images/B005GA8YB6.png\", \"hn_image\": [\"./images/B00A13FSRQ.png\"]}\n{\"q_img\": \"./images/B0036WSZVA.png\", \"q_text\": \"is black with blue designs, and has a crew neck and different graphic\", \"positive_key\": \"B007R5VURS\", \"positive_value\": \"./images/B007R5VURS.png\", \"hn_image\": [\"./images/B0036WSZVA.png\"]}\n{\"q_img\": \"./images/B0078UMBN4.png\", \"q_text\": \"has a longer sleeve with more USA flags, and it is blue\", \"positive_key\": \"B00A6YTKWE\", \"positive_value\": \"./images/B00A6YTKWE.png\", \"hn_image\": [\"./images/B0078UMBN4.png\"]}\n{\"q_img\": \"./images/B005J29QHW.png\", \"q_text\": \"has abstract graphic and is less feminine, and has more of a busy looking pattern\", \"positive_key\": \"B005D66TH4\", \"positive_value\": \"./images/B005D66TH4.png\", \"hn_image\": [\"./images/B005J29QHW.png\"]}\n{\"q_img\": \"./images/B00870UYYW.png\", \"q_text\": \"has smooth ruffles and a tie around the waist., and is yellow with bow\", \"positive_key\": \"B00809LQ60\", \"positive_value\": \"./images/B00809LQ60.png\", \"hn_image\": [\"./images/B00870UYYW.png\"]}\n{\"q_img\": \"./images/B0098D9IBC.png\", \"q_text\": \"is black with with writing on it, and is navy blue\", \"positive_key\": \"B005C5RPYW\", \"positive_value\": \"./images/B005C5RPYW.png\", \"hn_image\": [\"./images/B0098D9IBC.png\"]}\n{\"q_img\": \"./images/B00346FBN8.png\", \"q_text\": \"The shit is a polo shirt that is gray in color., and has flowing short sleeves and vee neck\", \"positive_key\": \"B003QORIMA\", \"positive_value\": \"./images/B003QORIMA.png\", \"hn_image\": [\"./images/B00346FBN8.png\"]}\n{\"q_img\": \"./images/B0096HR11O.png\", \"q_text\": \"has long sleeves, and is blue and white patterned\", \"positive_key\": \"B0096HO4NM\", \"positive_value\": \"./images/B0096HO4NM.png\", \"hn_image\": [\"./images/B0096HR11O.png\"]}\n{\"q_img\": \"./images/B006ZIP0R4.png\", \"q_text\": \"has short sleeves with checkered patterns, and is white and black patterned\", \"positive_key\": \"B006ZJ1NA6\", \"positive_value\": \"./images/B006ZJ1NA6.png\", \"hn_image\": [\"./images/B006ZIP0R4.png\"]}\n{\"q_img\": \"./images/B003SPQCLU.png\", \"q_text\": \"The shirt is black, and short sleeves with a vneck and full picture on front\", \"positive_key\": \"B00BGJ8DAS\", \"positive_value\": \"./images/B00BGJ8DAS.png\", \"hn_image\": [\"./images/B003SPQCLU.png\"]}\n{\"q_img\": \"./images/B00A13DXIW.png\", \"q_text\": \"has more wording written on it, and no draping striped\", \"positive_key\": \"B0062X3CDG\", \"positive_value\": \"./images/B0062X3CDG.png\", \"hn_image\": [\"./images/B00A13DXIW.png\"]}\n{\"q_img\": \"./images/B00AFYJTQM.png\", \"q_text\": \"has a print and is flowing, and comes in black abstract design\", \"positive_key\": \"B00ASI7KXO\", \"positive_value\": \"./images/B00ASI7KXO.png\", \"hn_image\": [\"./images/B00AFYJTQM.png\"]}\n{\"q_img\": \"./images/B00BGJ4OQA.png\", \"q_text\": \"is sleeveless, and is sleeveless and has more white\", \"positive_key\": \"B00EE0DGTE\", \"positive_value\": \"./images/B00EE0DGTE.png\", \"hn_image\": [\"./images/B00BGJ4OQA.png\"]}\n{\"q_img\": \"./images/B007RTJ3K0.png\", \"q_text\": \"is green with black designs, and Is brighter and green\", \"positive_key\": \"B009OLI0N0\", \"positive_value\": \"./images/B009OLI0N0.png\", \"hn_image\": [\"./images/B007RTJ3K0.png\"]}\n{\"q_img\": \"./images/B00BNQVHEI.png\", \"q_text\": \"has longer sleeves and color is red, and Has longer sleeves and is less sporty\", \"positive_key\": \"B00C85JTSY\", \"positive_value\": \"./images/B00C85JTSY.png\", \"hn_image\": [\"./images/B00BNQVHEI.png\"]}\n{\"q_img\": \"./images/B004J1KGHS.png\", \"q_text\": \"The shirt is black with a female character., and is black.\", \"positive_key\": \"B0086DBCC8\", \"positive_value\": \"./images/B0086DBCC8.png\", \"hn_image\": [\"./images/B004J1KGHS.png\"]}\n{\"q_img\": \"./images/B00BKU6QXE.png\", \"q_text\": \"The shirt is short sleeve and yellow in color., and Desired item is yellow\", \"positive_key\": \"B00B99NYWC\", \"positive_value\": \"./images/B00B99NYWC.png\", \"hn_image\": [\"./images/B00BKU6QXE.png\"]}\n{\"q_img\": \"./images/B00CF574EI.png\", \"q_text\": \"The shirt is a pink tank top., and is more maternity and sexy\", \"positive_key\": \"B004TS1TIG\", \"positive_value\": \"./images/B004TS1TIG.png\", \"hn_image\": [\"./images/B00CF574EI.png\"]}\n{\"q_img\": \"./images/B000F0C6L6.png\", \"q_text\": \"The shirt is black with a person's face., and draped front\", \"positive_key\": \"B001EPIXHW\", \"positive_value\": \"./images/B001EPIXHW.png\", \"hn_image\": [\"./images/B000F0C6L6.png\"]}\n{\"q_img\": \"./images/B00FE2CEXU.png\", \"q_text\": \"is monochrome and loose, and blue.\", \"positive_key\": \"B00E3J81RS\", \"positive_value\": \"./images/B00E3J81RS.png\", \"hn_image\": [\"./images/B00FE2CEXU.png\"]}\n{\"q_img\": \"./images/B00ANKBR0O.png\", \"q_text\": \"Has shorter sleeves and is not striped, and is checkered and tighter\", \"positive_key\": \"B00BCE0IA0\", \"positive_value\": \"./images/B00BCE0IA0.png\", \"hn_image\": [\"./images/B00ANKBR0O.png\"]}\n{\"q_img\": \"./images/B005N3XKZQ.png\", \"q_text\": \"is red striped and has short sleeves, and has a scooped neck\", \"positive_key\": \"B005ERKX88\", \"positive_value\": \"./images/B005ERKX88.png\", \"hn_image\": [\"./images/B005N3XKZQ.png\"]}\n{\"q_img\": \"./images/B00A76XQGC.png\", \"q_text\": \"longer multi color print, and has no sleeves\", \"positive_key\": \"B00A7NAP8W\", \"positive_value\": \"./images/B00A7NAP8W.png\", \"hn_image\": [\"./images/B00A76XQGC.png\"]}\n{\"q_img\": \"./images/B00980K6RA.png\", \"q_text\": \"is lighter, and is white and only has graphics on the sleeves\", \"positive_key\": \"B006GHSVKC\", \"positive_value\": \"./images/B006GHSVKC.png\", \"hn_image\": [\"./images/B00980K6RA.png\"]}\n{\"q_img\": \"./images/B007BGV2J4.png\", \"q_text\": \"is black with a couple words, and Is darker and less graphic.\", \"positive_key\": \"B0013FUI12\", \"positive_value\": \"./images/B0013FUI12.png\", \"hn_image\": [\"./images/B007BGV2J4.png\"]}\n{\"q_img\": \"./images/B005M02XIA.png\", \"q_text\": \"is white and has short sleeves, and is white with sleeves\", \"positive_key\": \"B0050G1IUU\", \"positive_value\": \"./images/B0050G1IUU.png\", \"hn_image\": [\"./images/B005M02XIA.png\"]}\n{\"q_img\": \"./images/B004K1EDLC.png\", \"q_text\": \"is more colorful, and is blue and more solid colored\", \"positive_key\": \"B008FU0GB0\", \"positive_value\": \"./images/B008FU0GB0.png\", \"hn_image\": [\"./images/B004K1EDLC.png\"]}\n{\"q_img\": \"./images/B005AZ0WR6.png\", \"q_text\": \"The shirt is red with Hello Kitty., and is a cap sleeve pink tshirt.\", \"positive_key\": \"B007UI3OGM\", \"positive_value\": \"./images/B007UI3OGM.png\", \"hn_image\": [\"./images/B005AZ0WR6.png\"]}\n{\"q_img\": \"./images/B004H7NSPQ.png\", \"q_text\": \"is darker and has longer sleeves, and is black and slimmer\", \"positive_key\": \"B00E6XQSB2\", \"positive_value\": \"./images/B00E6XQSB2.png\", \"hn_image\": [\"./images/B004H7NSPQ.png\"]}\n{\"q_img\": \"./images/B007LWRFP8.png\", \"q_text\": \"has sleeves with higher neckline, and has more sleeve and redder graphic\", \"positive_key\": \"B000WK5WYW\", \"positive_value\": \"./images/B000WK5WYW.png\", \"hn_image\": [\"./images/B007LWRFP8.png\"]}\n{\"q_img\": \"./images/B00EYYEYAA.png\", \"q_text\": \"is a blue jersey and more masculine, and A blue jersey with number 17 and LINSANITY\", \"positive_key\": \"B007A9W52U\", \"positive_value\": \"./images/B007A9W52U.png\", \"hn_image\": [\"./images/B00EYYEYAA.png\"]}\n{\"q_img\": \"./images/B0096I96XE.png\", \"q_text\": \"The shirt has no sleeves and is lavender in color., and is darker in color and has a bow\", \"positive_key\": \"B0079EIDDG\", \"positive_value\": \"./images/B0079EIDDG.png\", \"hn_image\": [\"./images/B0096I96XE.png\"]}\n{\"q_img\": \"./images/B008MC6RW8.png\", \"q_text\": \"Is more flowing and more coverage in the shoulders, and is a gray peplum top\", \"positive_key\": \"B00B5MEQMA\", \"positive_value\": \"./images/B00B5MEQMA.png\", \"hn_image\": [\"./images/B008MC6RW8.png\"]}\n{\"q_img\": \"./images/B00GH0TW42.png\", \"q_text\": \"The shirt has white and light gray stripes., and has horizontal stripes\", \"positive_key\": \"B00FZ9NF08\", \"positive_value\": \"./images/B00FZ9NF08.png\", \"hn_image\": [\"./images/B00GH0TW42.png\"]}\n{\"q_img\": \"./images/B000G260UQ.png\", \"q_text\": \"is less feminine and white, and is lighter and has longer sleeves\", \"positive_key\": \"B003A71PUO\", \"positive_value\": \"./images/B003A71PUO.png\", \"hn_image\": [\"./images/B000G260UQ.png\"]}\n{\"q_img\": \"./images/B008L13H1O.png\", \"q_text\": \"has no sleeves with flower patterns, and is less casual and more dlirty\", \"positive_key\": \"B00CBHQ4NC\", \"positive_value\": \"./images/B00CBHQ4NC.png\", \"hn_image\": [\"./images/B008L13H1O.png\"]}\n{\"q_img\": \"./images/B0038J5YEC.png\", \"q_text\": \"The sweater is gray with green Greek letters., and is gray with longer sleeves and warmer\", \"positive_key\": \"B00GI1BW34\", \"positive_value\": \"./images/B00GI1BW34.png\", \"hn_image\": [\"./images/B0038J5YEC.png\"]}\n{\"q_img\": \"./images/B009DZON1A.png\", \"q_text\": \"is monochromatic and more sporty, and is yellow and solid colored\", \"positive_key\": \"B00BNQVHEI\", \"positive_value\": \"./images/B00BNQVHEI.png\", \"hn_image\": [\"./images/B009DZON1A.png\"]}\n{\"q_img\": \"./images/B004XBR0XM.png\", \"q_text\": \"has long sleeves and turtleneck, and is longer sleeved\", \"positive_key\": \"B008PUGRYK\", \"positive_value\": \"./images/B008PUGRYK.png\", \"hn_image\": [\"./images/B004XBR0XM.png\"]}\n{\"q_img\": \"./images/B008CG1JJ0.png\", \"q_text\": \"Is more flowing and has an animal print, and but is a soft polyester with frills.\", \"positive_key\": \"B00EDMNRUG\", \"positive_value\": \"./images/B00EDMNRUG.png\", \"hn_image\": [\"./images/B008CG1JJ0.png\"]}\n{\"q_img\": \"./images/B004I6CTXS.png\", \"q_text\": \"Is satin and offers more coverage, and shorter in length\", \"positive_key\": \"B005JWECI0\", \"positive_value\": \"./images/B005JWECI0.png\", \"hn_image\": [\"./images/B004I6CTXS.png\"]}\n{\"q_img\": \"./images/B008SHUIP4.png\", \"q_text\": \"is a black t-shirt and the picture on shirt is colored, and is darker colored\", \"positive_key\": \"B00D9JXNM6\", \"positive_value\": \"./images/B00D9JXNM6.png\", \"hn_image\": [\"./images/B008SHUIP4.png\"]}\n{\"q_img\": \"./images/B006JX99GS.png\", \"q_text\": \"is lighting green and small neck, and is solid green\", \"positive_key\": \"B00GOIAM4Q\", \"positive_value\": \"./images/B00GOIAM4Q.png\", \"hn_image\": [\"./images/B006JX99GS.png\"]}\n{\"q_img\": \"./images/B009W65CGU.png\", \"q_text\": \"is pink and purple and short sleeve, and is lighter in color and has more stripes\", \"positive_key\": \"B0012YO0UE\", \"positive_value\": \"./images/B0012YO0UE.png\", \"hn_image\": [\"./images/B009W65CGU.png\"]}\n{\"q_img\": \"./images/B00BHRICZU.png\", \"q_text\": \"The halter top is melon in color., and is strapless and red colored\", \"positive_key\": \"B00BUKCLA6\", \"positive_value\": \"./images/B00BUKCLA6.png\", \"hn_image\": [\"./images/B00BHRICZU.png\"]}\n{\"q_img\": \"./images/B009JR23TG.png\", \"q_text\": \"has a monkey, and is more junior and shorter length\", \"positive_key\": \"B00A5MMIJE\", \"positive_value\": \"./images/B00A5MMIJE.png\", \"hn_image\": [\"./images/B009JR23TG.png\"]}\n{\"q_img\": \"./images/B00ECWR21C.png\", \"q_text\": \"has a longer sleeve shirt, and is black and much longer\", \"positive_key\": \"B008X04NWA\", \"positive_value\": \"./images/B008X04NWA.png\", \"hn_image\": [\"./images/B00ECWR21C.png\"]}\n{\"q_img\": \"./images/B00AEXWZ7O.png\", \"q_text\": \"is a black colored shirt with text, and is a wordy tee shirt\", \"positive_key\": \"B009EWRY6S\", \"positive_value\": \"./images/B009EWRY6S.png\", \"hn_image\": [\"./images/B00AEXWZ7O.png\"]}\n{\"q_img\": \"./images/B0058UWR08.png\", \"q_text\": \"is black colored and beige pattern on it, and it black base colored with white designs\", \"positive_key\": \"B0058UWE3I\", \"positive_value\": \"./images/B0058UWE3I.png\", \"hn_image\": [\"./images/B0058UWR08.png\"]}\n{\"q_img\": \"./images/B0095YHVUY.png\", \"q_text\": \"has short sleeves and is less form fitting, and is orange and has shorter sleeves\", \"positive_key\": \"B00AOAPJ7U\", \"positive_value\": \"./images/B00AOAPJ7U.png\", \"hn_image\": [\"./images/B0095YHVUY.png\"]}\n{\"q_img\": \"./images/B007ZQ58ZY.png\", \"q_text\": \"is white colored and has sleeves, and is white in color and has long sleeves\", \"positive_key\": \"B00DIZVGZW\", \"positive_value\": \"./images/B00DIZVGZW.png\", \"hn_image\": [\"./images/B007ZQ58ZY.png\"]}\n{\"q_img\": \"./images/B00DBDSZY6.png\", \"q_text\": \"is green with a tie, and has a button front\", \"positive_key\": \"B00CF5FRZ6\", \"positive_value\": \"./images/B00CF5FRZ6.png\", \"hn_image\": [\"./images/B00DBDSZY6.png\"]}\n{\"q_img\": \"./images/B003JK09S6.png\", \"q_text\": \"The shirt is gray with batgirl., and is a gray tee\", \"positive_key\": \"B009JOHNBW\", \"positive_value\": \"./images/B009JOHNBW.png\", \"hn_image\": [\"./images/B003JK09S6.png\"]}\n{\"q_img\": \"./images/B006KNGG90.png\", \"q_text\": \"is white and has a more open neckline, and is white with a face\", \"positive_key\": \"B0056YO5NS\", \"positive_value\": \"./images/B0056YO5NS.png\", \"hn_image\": [\"./images/B006KNGG90.png\"]}\n{\"q_img\": \"./images/B00D3ZHQ5G.png\", \"q_text\": \"has no sleeves and is grey, and has no sleeves\", \"positive_key\": \"B0069XQJUW\", \"positive_value\": \"./images/B0069XQJUW.png\", \"hn_image\": [\"./images/B00D3ZHQ5G.png\"]}\n{\"q_img\": \"./images/B00BPPALLM.png\", \"q_text\": \"The shirt is black, and is bold colors and striped\", \"positive_key\": \"B00A7QGZ8I\", \"positive_value\": \"./images/B00A7QGZ8I.png\", \"hn_image\": [\"./images/B00BPPALLM.png\"]}\n{\"q_img\": \"./images/B0077M73N6.png\", \"q_text\": \"is lacier, and has longer sleeves and is lacy\", \"positive_key\": \"B004F9QC72\", \"positive_value\": \"./images/B004F9QC72.png\", \"hn_image\": [\"./images/B0077M73N6.png\"]}\n{\"q_img\": \"./images/B00749QXWO.png\", \"q_text\": \"Has no sleeves and has a pattern, and is less athletic and more everyday\", \"positive_key\": \"B0060OSQXS\", \"positive_value\": \"./images/B0060OSQXS.png\", \"hn_image\": [\"./images/B00749QXWO.png\"]}\n{\"q_img\": \"./images/B000XZ6740.png\", \"q_text\": \"is black colored, and Is more wordy and less outdoorsy\", \"positive_key\": \"B00G70SUTA\", \"positive_value\": \"./images/B00G70SUTA.png\", \"hn_image\": [\"./images/B000XZ6740.png\"]}\n{\"q_img\": \"./images/B00CD8H0N2.png\", \"q_text\": \"is red colored, and is red and has square logo\", \"positive_key\": \"B005A0LPW2\", \"positive_value\": \"./images/B005A0LPW2.png\", \"hn_image\": [\"./images/B00CD8H0N2.png\"]}\n{\"q_img\": \"./images/B007JRRY06.png\", \"q_text\": \"is dark colored and has longer sleeves, and has longer sleeves and is darker\", \"positive_key\": \"B008M4SKWQ\", \"positive_value\": \"./images/B008M4SKWQ.png\", \"hn_image\": [\"./images/B007JRRY06.png\"]}\n{\"q_img\": \"./images/B0069HMDM6.png\", \"q_text\": \"is shiner and more premium, and has leopard print and a chain across the front\", \"positive_key\": \"B002TNOCNW\", \"positive_value\": \"./images/B002TNOCNW.png\", \"hn_image\": [\"./images/B0069HMDM6.png\"]}\n{\"q_img\": \"./images/B000YYBM54.png\", \"q_text\": \"is a long sleeve purple shirt, and has sleeves and is maroon\", \"positive_key\": \"B003ZYDAMS\", \"positive_value\": \"./images/B003ZYDAMS.png\", \"hn_image\": [\"./images/B000YYBM54.png\"]}\n{\"q_img\": \"./images/B0064SLGDW.png\", \"q_text\": \"is more elegant and darker, and Solid black short sleeved long blouse\", \"positive_key\": \"B004VS50FM\", \"positive_value\": \"./images/B004VS50FM.png\", \"hn_image\": [\"./images/B0064SLGDW.png\"]}\n{\"q_img\": \"./images/B00A8ZDV1M.png\", \"q_text\": \"is sleeveless and pink, and it has no sleeve\", \"positive_key\": \"B008UFVTP2\", \"positive_value\": \"./images/B008UFVTP2.png\", \"hn_image\": [\"./images/B00A8ZDV1M.png\"]}\n{\"q_img\": \"./images/B008MC6KIE.png\", \"q_text\": \"is more revealing and darker, and Solid black v neck half sleeved blouse\", \"positive_key\": \"B00B538OCC\", \"positive_value\": \"./images/B00B538OCC.png\", \"hn_image\": [\"./images/B008MC6KIE.png\"]}\n{\"q_img\": \"./images/B008Z5TCU6.png\", \"q_text\": \"is denim and a vest, and is blue button vest\", \"positive_key\": \"B008NFA97I\", \"positive_value\": \"./images/B008NFA97I.png\", \"hn_image\": [\"./images/B008Z5TCU6.png\"]}\n{\"q_img\": \"./images/B00BN1IO30.png\", \"q_text\": \"Has longer sleeves and is less casual, and is darker and has longer sleeves\", \"positive_key\": \"B0055X72XK\", \"positive_value\": \"./images/B0055X72XK.png\", \"hn_image\": [\"./images/B00BN1IO30.png\"]}\n{\"q_img\": \"./images/B000LMLFO2.png\", \"q_text\": \"is plain black with no sleeves, and is white with straight long sleeves\", \"positive_key\": \"B006JXL1YG\", \"positive_value\": \"./images/B006JXL1YG.png\", \"hn_image\": [\"./images/B000LMLFO2.png\"]}\n{\"q_img\": \"./images/B007HEWEWE.png\", \"q_text\": \"is white with short sleeves, and is solid white and short sleeved\", \"positive_key\": \"B00A2ACUSI\", \"positive_value\": \"./images/B00A2ACUSI.png\", \"hn_image\": [\"./images/B007HEWEWE.png\"]}\n{\"q_img\": \"./images/B006HT23VM.png\", \"q_text\": \"The short sleeve shirt is a bright blue., and has shorter sleeves and less buttons\", \"positive_key\": \"B0038J5YHE\", \"positive_value\": \"./images/B0038J5YHE.png\", \"hn_image\": [\"./images/B006HT23VM.png\"]}\n{\"q_img\": \"./images/B006HE4PW2.png\", \"q_text\": \"is darker, and is black with a graphic\", \"positive_key\": \"B001QLCC62\", \"positive_value\": \"./images/B001QLCC62.png\", \"hn_image\": [\"./images/B006HE4PW2.png\"]}\n{\"q_img\": \"./images/B003WLL8TQ.png\", \"q_text\": \"white short sleeves longer, and is lighter\", \"positive_key\": \"B000PZQ0MW\", \"positive_value\": \"./images/B000PZQ0MW.png\", \"hn_image\": [\"./images/B003WLL8TQ.png\"]}\n{\"q_img\": \"./images/B000YJ0R6Y.png\", \"q_text\": \"sheer white 3/4 sleeve dressy, and has a thin cloth around the shoulders and a white top.\", \"positive_key\": \"B007CC4H30\", \"positive_value\": \"./images/B007CC4H30.png\", \"hn_image\": [\"./images/B000YJ0R6Y.png\"]}\n{\"q_img\": \"./images/B00BRBU4M0.png\", \"q_text\": \"is tighter, and has a scoop neck with three people on it\", \"positive_key\": \"B00EWJWQHU\", \"positive_value\": \"./images/B00EWJWQHU.png\", \"hn_image\": [\"./images/B00BRBU4M0.png\"]}\n{\"q_img\": \"./images/B00F62JQEI.png\", \"q_text\": \"The shirt is long sleeved sheer is gray with black stripes., and comes in black\", \"positive_key\": \"B00EQ21PI4\", \"positive_value\": \"./images/B00EQ21PI4.png\", \"hn_image\": [\"./images/B00F62JQEI.png\"]}\n{\"q_img\": \"./images/B008OJR7Q4.png\", \"q_text\": \"is darker with shorter sleeves, and is black\", \"positive_key\": \"B006MQ6JWE\", \"positive_value\": \"./images/B006MQ6JWE.png\", \"hn_image\": [\"./images/B008OJR7Q4.png\"]}\n{\"q_img\": \"./images/B00588V1UW.png\", \"q_text\": \"Has shorter sleeves and is orange, and it is more colorful and has a shorter sleeve\", \"positive_key\": \"B009KS9OJQ\", \"positive_value\": \"./images/B009KS9OJQ.png\", \"hn_image\": [\"./images/B00588V1UW.png\"]}\n{\"q_img\": \"./images/B004MUGNLO.png\", \"q_text\": \"its white and black with belt around waist, and is more frilly and blousy\", \"positive_key\": \"B007CL8QDI\", \"positive_value\": \"./images/B007CL8QDI.png\", \"hn_image\": [\"./images/B004MUGNLO.png\"]}\n{\"q_img\": \"./images/B004KUUKCO.png\", \"q_text\": \"is a v neck and is navy blue, and is blue with bigger image\", \"positive_key\": \"B00BCOZK3U\", \"positive_value\": \"./images/B00BCOZK3U.png\", \"hn_image\": [\"./images/B004KUUKCO.png\"]}\n{\"q_img\": \"./images/B0084DLUN6.png\", \"q_text\": \"has V-neck and embroidery, and has large v-neck and is white color\", \"positive_key\": \"B005UUF5I2\", \"positive_value\": \"./images/B005UUF5I2.png\", \"hn_image\": [\"./images/B0084DLUN6.png\"]}\n{\"q_img\": \"./images/B00F62JQEI.png\", \"q_text\": \"is more revealing, and is darker in color\", \"positive_key\": \"B0060B9554\", \"positive_value\": \"./images/B0060B9554.png\", \"hn_image\": [\"./images/B00F62JQEI.png\"]}\n{\"q_img\": \"./images/B009VI0H7S.png\", \"q_text\": \" lace and more form fitting, and has stripes that go horizontal\", \"positive_key\": \"B009IFP2ZQ\", \"positive_value\": \"./images/B009IFP2ZQ.png\", \"hn_image\": [\"./images/B009VI0H7S.png\"]}\n{\"q_img\": \"./images/B0073PZW2Q.png\", \"q_text\": \"is sheer and has longer sleeves, and Is pink toned and shorter\", \"positive_key\": \"B008QXAVJI\", \"positive_value\": \"./images/B008QXAVJI.png\", \"hn_image\": [\"./images/B0073PZW2Q.png\"]}\n{\"q_img\": \"./images/B003NYEWPE.png\", \"q_text\": \"has no sleeves and has a white shiny color, and spaggeti strap sleeves white\", \"positive_key\": \"B0053GGD1Q\", \"positive_value\": \"./images/B0053GGD1Q.png\", \"hn_image\": [\"./images/B003NYEWPE.png\"]}\n{\"q_img\": \"./images/B00DOJ2BSS.png\", \"q_text\": \"has vertical lines., and has colorful stripes and is more revealing\", \"positive_key\": \"B00CTSGLYU\", \"positive_value\": \"./images/B00CTSGLYU.png\", \"hn_image\": [\"./images/B00DOJ2BSS.png\"]}\n{\"q_img\": \"./images/B00ATUGPDM.png\", \"q_text\": \"is beige colored and sleeveless, and is blue t-shirt with owls on front\", \"positive_key\": \"B00B11YTXG\", \"positive_value\": \"./images/B00B11YTXG.png\", \"hn_image\": [\"./images/B00ATUGPDM.png\"]}\n{\"q_img\": \"./images/B006JFW0L2.png\", \"q_text\": \"The shirt is blue with a picture of a mustache and glasses., and has longer sleeves and is darker coloured\", \"positive_key\": \"B00FN67ZHC\", \"positive_value\": \"./images/B00FN67ZHC.png\", \"hn_image\": [\"./images/B006JFW0L2.png\"]}\n{\"q_img\": \"./images/B006852UH2.png\", \"q_text\": \"is light blue color with an image, and is pale blue\", \"positive_key\": \"B0015VAYHC\", \"positive_value\": \"./images/B0015VAYHC.png\", \"hn_image\": [\"./images/B006852UH2.png\"]}\n{\"q_img\": \"./images/B00C8FZDCK.png\", \"q_text\": \"has shorter sleeves with abstract print, and is multicolored and patterned\", \"positive_key\": \"B00EOYZJKO\", \"positive_value\": \"./images/B00EOYZJKO.png\", \"hn_image\": [\"./images/B00C8FZDCK.png\"]}\n{\"q_img\": \"./images/B004ZWEFPU.png\", \"q_text\": \"has a graphic and is more casual, and has shorter sleeves\", \"positive_key\": \"B002VHDH96\", \"positive_value\": \"./images/B002VHDH96.png\", \"hn_image\": [\"./images/B004ZWEFPU.png\"]}\n{\"q_img\": \"./images/B00CTVFMME.png\", \"q_text\": \"lighter and has fringe, and is a full top in white without the bust.\", \"positive_key\": \"B00CXUSE8U\", \"positive_value\": \"./images/B00CXUSE8U.png\", \"hn_image\": [\"./images/B00CTVFMME.png\"]}\n{\"q_img\": \"./images/B00BR5R2JO.png\", \"q_text\": \"is sleeveless and lighter in color, and is a tank top with an asymmetrical hem line\", \"positive_key\": \"B00B87DVXC\", \"positive_value\": \"./images/B00B87DVXC.png\", \"hn_image\": [\"./images/B00BR5R2JO.png\"]}\n{\"q_img\": \"./images/B006C4Y45A.png\", \"q_text\": \"is a purse, and has a handle\", \"positive_key\": \"B00186WJ36\", \"positive_value\": \"./images/B00186WJ36.png\", \"hn_image\": [\"./images/B006C4Y45A.png\"]}\n{\"q_img\": \"./images/B00A7HBMEO.png\", \"q_text\": \"The shirt is loose black and sheer., and Is see-through & looser\", \"positive_key\": \"B008K6BYIS\", \"positive_value\": \"./images/B008K6BYIS.png\", \"hn_image\": [\"./images/B00A7HBMEO.png\"]}\n{\"q_img\": \"./images/B0047P5KEA.png\", \"q_text\": \"has a brand name, and is white with red white and blue logo\", \"positive_key\": \"B004W2HAVO\", \"positive_value\": \"./images/B004W2HAVO.png\", \"hn_image\": [\"./images/B0047P5KEA.png\"]}\n{\"q_img\": \"./images/B00B2WMY5E.png\", \"q_text\": \"is more colorful, and is a red tee with yellow dsign print.\", \"positive_key\": \"B0083ITPWA\", \"positive_value\": \"./images/B0083ITPWA.png\", \"hn_image\": [\"./images/B00B2WMY5E.png\"]}\n{\"q_img\": \"./images/B00F3W04GU.png\", \"q_text\": \"Is blue with a logo and more casual, and is a blue t-shirt\", \"positive_key\": \"B0043Y8L5K\", \"positive_value\": \"./images/B0043Y8L5K.png\", \"hn_image\": [\"./images/B00F3W04GU.png\"]}\n{\"q_img\": \"./images/B008GT8N7E.png\", \"q_text\": \"is lighter, and is lighter and patterned\", \"positive_key\": \"B00AO4NBFI\", \"positive_value\": \"./images/B00AO4NBFI.png\", \"hn_image\": [\"./images/B008GT8N7E.png\"]}\n{\"q_img\": \"./images/B007DQ8SHG.png\", \"q_text\": \"is more feminine and whiter, and is white with green accents\", \"positive_key\": \"B0078AFDSO\", \"positive_value\": \"./images/B0078AFDSO.png\", \"hn_image\": [\"./images/B007DQ8SHG.png\"]}\n{\"q_img\": \"./images/B006SF8D3M.png\", \"q_text\": \"is lighter, and is brown with wider necklink\", \"positive_key\": \"B00C75HTBY\", \"positive_value\": \"./images/B00C75HTBY.png\", \"hn_image\": [\"./images/B006SF8D3M.png\"]}\n{\"q_img\": \"./images/B001HBKGB4.png\", \"q_text\": \"is darker and has a more colorful graphic, and is black in color\", \"positive_key\": \"B00DEKN4VA\", \"positive_value\": \"./images/B00DEKN4VA.png\", \"hn_image\": [\"./images/B001HBKGB4.png\"]}\n{\"q_img\": \"./images/B00133IN7K.png\", \"q_text\": \"is more drab and less scary, and has a motorcycle graphic on it\", \"positive_key\": \"B004UMQG5M\", \"positive_value\": \"./images/B004UMQG5M.png\", \"hn_image\": [\"./images/B00133IN7K.png\"]}\n{\"q_img\": \"./images/B005VSPMEK.png\", \"q_text\": \"has a different graphic, and has an image of a small dog on front\", \"positive_key\": \"B000GQW06K\", \"positive_value\": \"./images/B000GQW06K.png\", \"hn_image\": [\"./images/B005VSPMEK.png\"]}\n{\"q_img\": \"./images/B009C7QVXC.png\", \"q_text\": \"is blue shorter sleeves and strips, and is blue with white stripes\", \"positive_key\": \"B00307SY3U\", \"positive_value\": \"./images/B00307SY3U.png\", \"hn_image\": [\"./images/B009C7QVXC.png\"]}\n{\"q_img\": \"./images/B00AW1EBM0.png\", \"q_text\": \"It is a brighter color and a shorter sleeve., and it is purple and less transparent\", \"positive_key\": \"B003JL55VQ\", \"positive_value\": \"./images/B003JL55VQ.png\", \"hn_image\": [\"./images/B00AW1EBM0.png\"]}\n{\"q_img\": \"./images/B00CIHR6BE.png\", \"q_text\": \"is darker and less casual, and is grey and more flowing\", \"positive_key\": \"B0055R3KXM\", \"positive_value\": \"./images/B0055R3KXM.png\", \"hn_image\": [\"./images/B00CIHR6BE.png\"]}\n{\"q_img\": \"./images/B002ZNKR3K.png\", \"q_text\": \"needs to be more feminine looking as less sporty, and it has long sleeve and has no collar\", \"positive_key\": \"B003Q6C28I\", \"positive_value\": \"./images/B003Q6C28I.png\", \"hn_image\": [\"./images/B002ZNKR3K.png\"]}\n{\"q_img\": \"./images/B00BG5LNL8.png\", \"q_text\": \"is grey and a racer back top, and Is athletic fit & sleeveless\", \"positive_key\": \"B00GABCAKG\", \"positive_value\": \"./images/B00GABCAKG.png\", \"hn_image\": [\"./images/B00BG5LNL8.png\"]}\n{\"q_img\": \"./images/B008DVXI34.png\", \"q_text\": \"The loose fitting shirt is melon in color., and is a salmon color\", \"positive_key\": \"B008JYNN30\", \"positive_value\": \"./images/B008JYNN30.png\", \"hn_image\": [\"./images/B008DVXI34.png\"]}\n{\"q_img\": \"./images/B007FO5H1Q.png\", \"q_text\": \"Is less casual and more structured., and Is long sleeved white shirt with a color\", \"positive_key\": \"B009T6MLL2\", \"positive_value\": \"./images/B009T6MLL2.png\", \"hn_image\": [\"./images/B007FO5H1Q.png\"]}\n{\"q_img\": \"./images/B007Z224PA.png\", \"q_text\": \"is gray with beige designs, and dress-length with shorter sleeves\", \"positive_key\": \"B003IRMQI6\", \"positive_value\": \"./images/B003IRMQI6.png\", \"hn_image\": [\"./images/B007Z224PA.png\"]}\n{\"q_img\": \"./images/B004VNVMO0.png\", \"q_text\": \"has a black color with text, and has text on it\", \"positive_key\": \"B00CS5PAZU\", \"positive_value\": \"./images/B00CS5PAZU.png\", \"hn_image\": [\"./images/B004VNVMO0.png\"]}\n{\"q_img\": \"./images/B007W9Z994.png\", \"q_text\": \"is cheetah print, and has no buttons and is more patterened\", \"positive_key\": \"B00D82U8FO\", \"positive_value\": \"./images/B00D82U8FO.png\", \"hn_image\": [\"./images/B007W9Z994.png\"]}\n{\"q_img\": \"./images/B00ABG1BXS.png\", \"q_text\": \"printed brown 3/4 sleeved top, and is darker and has buttons\", \"positive_key\": \"B008KCNSFO\", \"positive_value\": \"./images/B008KCNSFO.png\", \"hn_image\": [\"./images/B00ABG1BXS.png\"]}\n{\"q_img\": \"./images/B004MKN18C.png\", \"q_text\": \"is darker with longer sleeves, and is longer sleeved and black\", \"positive_key\": \"B007CLSJ1C\", \"positive_value\": \"./images/B007CLSJ1C.png\", \"hn_image\": [\"./images/B004MKN18C.png\"]}\n{\"q_img\": \"./images/B008UABPG0.png\", \"q_text\": \"is more zigzag and less striped, and is multi color and longer sleeves.\", \"positive_key\": \"B008UACJR4\", \"positive_value\": \"./images/B008UACJR4.png\", \"hn_image\": [\"./images/B008UABPG0.png\"]}\n{\"q_img\": \"./images/B008D8ZZDI.png\", \"q_text\": \"is lighter, and Is more floral and less animalistic\", \"positive_key\": \"B0058UWR08\", \"positive_value\": \"./images/B0058UWR08.png\", \"hn_image\": [\"./images/B008D8ZZDI.png\"]}\n{\"q_img\": \"./images/B000O1J04C.png\", \"q_text\": \"has no sleeves but more concentrated color, and Is less graphic and more feminine\", \"positive_key\": \"B007POA5NG\", \"positive_value\": \"./images/B007POA5NG.png\", \"hn_image\": [\"./images/B000O1J04C.png\"]}\n{\"q_img\": \"./images/B00BJO2R4I.png\", \"q_text\": \"is lighter, and green.\", \"positive_key\": \"B0080E76I2\", \"positive_value\": \"./images/B0080E76I2.png\", \"hn_image\": [\"./images/B00BJO2R4I.png\"]}\n{\"q_img\": \"./images/B008Y2QBQS.png\", \"q_text\": \"is a purple top with long sleeves, and is blue\", \"positive_key\": \"B00A3Q3QAM\", \"positive_value\": \"./images/B00A3Q3QAM.png\", \"hn_image\": [\"./images/B008Y2QBQS.png\"]}\n{\"q_img\": \"./images/B0085OCK88.png\", \"q_text\": \"is black with red lettering, and is purple and has a higher neck\", \"positive_key\": \"B0092S985O\", \"positive_value\": \"./images/B0092S985O.png\", \"hn_image\": [\"./images/B0085OCK88.png\"]}\n{\"q_img\": \"./images/B002076ABE.png\", \"q_text\": \"The shirt is yellow with red writing., and is more colourfull\", \"positive_key\": \"B00AH4PUL8\", \"positive_value\": \"./images/B00AH4PUL8.png\", \"hn_image\": [\"./images/B002076ABE.png\"]}\n{\"q_img\": \"./images/B008CLI9C0.png\", \"q_text\": \"The tank top is peach in color., and is more junior and lighter colored\", \"positive_key\": \"B00BLYSDWG\", \"positive_value\": \"./images/B00BLYSDWG.png\", \"hn_image\": [\"./images/B008CLI9C0.png\"]}\n{\"q_img\": \"./images/B003DTEPVU.png\", \"q_text\": \"has longer sleeves, and is mint green in color\", \"positive_key\": \"B009388EFS\", \"positive_value\": \"./images/B009388EFS.png\", \"hn_image\": [\"./images/B003DTEPVU.png\"]}\n{\"q_img\": \"./images/B00AG35TN4.png\", \"q_text\": \"has longer sleeves and is black colored, and black with longer sleeves\", \"positive_key\": \"B0014JXLMK\", \"positive_value\": \"./images/B0014JXLMK.png\", \"hn_image\": [\"./images/B00AG35TN4.png\"]}\n{\"q_img\": \"./images/B00ATF1P8M.png\", \"q_text\": \"3/4 sleeve animal print, and has longer sleeves\", \"positive_key\": \"B008YDK852\", \"positive_value\": \"./images/B008YDK852.png\", \"hn_image\": [\"./images/B00ATF1P8M.png\"]}\n{\"q_img\": \"./images/B008O7XEGI.png\", \"q_text\": \"is longer sleeves and solid dark pink, and has better sleeves in a single color.\", \"positive_key\": \"B00B13SRVE\", \"positive_value\": \"./images/B00B13SRVE.png\", \"hn_image\": [\"./images/B008O7XEGI.png\"]}\n{\"q_img\": \"./images/B004K1EDLC.png\", \"q_text\": \"The shirt is loose fitting and green in color., and has shorter sleeves and is green\", \"positive_key\": \"B00APITT5Y\", \"positive_value\": \"./images/B00APITT5Y.png\", \"hn_image\": [\"./images/B004K1EDLC.png\"]}\n{\"q_img\": \"./images/B00415UFAA.png\", \"q_text\": \"is transparent, and has a lower neckline and is moer sheer\", \"positive_key\": \"B0038LJDWO\", \"positive_value\": \"./images/B0038LJDWO.png\", \"hn_image\": [\"./images/B00415UFAA.png\"]}\n{\"q_img\": \"./images/B008JDV59U.png\", \"q_text\": \"is identical, and is a perfect match\", \"positive_key\": \"B008JDV50Y\", \"positive_value\": \"./images/B008JDV50Y.png\", \"hn_image\": [\"./images/B008JDV59U.png\"]}\n{\"q_img\": \"./images/B00DS0TDAW.png\", \"q_text\": \"is jean with fray, and is a pair of jeans\", \"positive_key\": \"B00DYO4Y6K\", \"positive_value\": \"./images/B00DYO4Y6K.png\", \"hn_image\": [\"./images/B00DS0TDAW.png\"]}\n{\"q_img\": \"./images/B008NF7N5E.png\", \"q_text\": \"has no sleeves and a distinct pattern, and Black/white patterned tank top\", \"positive_key\": \"B003XJDT3K\", \"positive_value\": \"./images/B003XJDT3K.png\", \"hn_image\": [\"./images/B008NF7N5E.png\"]}\n{\"q_img\": \"./images/B00CQ9ICKI.png\", \"q_text\": \"its black and has one shoulder free, and is darker colored and more chic\", \"positive_key\": \"B009FV9I08\", \"positive_value\": \"./images/B009FV9I08.png\", \"hn_image\": [\"./images/B00CQ9ICKI.png\"]}\n{\"q_img\": \"./images/B00EFG5N48.png\", \"q_text\": \"gray turtlneck, and is in grey color.\", \"positive_key\": \"B00FWFX9NS\", \"positive_value\": \"./images/B00FWFX9NS.png\", \"hn_image\": [\"./images/B00EFG5N48.png\"]}\n{\"q_img\": \"./images/B005XKO35U.png\", \"q_text\": \"has short sleeves and branch-like graphic, and tight neckline\", \"positive_key\": \"B009N07W9U\", \"positive_value\": \"./images/B009N07W9U.png\", \"hn_image\": [\"./images/B005XKO35U.png\"]}\n{\"q_img\": \"./images/B004CTFLCM.png\", \"q_text\": \"is white with shorter sleeves and a flower, and is shorter sleeved\", \"positive_key\": \"B004V7F2AG\", \"positive_value\": \"./images/B004V7F2AG.png\", \"hn_image\": [\"./images/B004CTFLCM.png\"]}\n{\"q_img\": \"./images/B003JO9Z8W.png\", \"q_text\": \"is black with a large colorful logo, and is black in color\", \"positive_key\": \"B003ENGNX8\", \"positive_value\": \"./images/B003ENGNX8.png\", \"hn_image\": [\"./images/B003JO9Z8W.png\"]}\n{\"q_img\": \"./images/B005DA3P86.png\", \"q_text\": \"The shirt is long and black in color., and has no buttons\", \"positive_key\": \"B00A6AUVJ4\", \"positive_value\": \"./images/B00A6AUVJ4.png\", \"hn_image\": [\"./images/B005DA3P86.png\"]}\n{\"q_img\": \"./images/B009ZIFU4O.png\", \"q_text\": \"is teal colored with text in yellow, and is less animal-like and has more font\", \"positive_key\": \"B00AZ02OCM\", \"positive_value\": \"./images/B00AZ02OCM.png\", \"hn_image\": [\"./images/B009ZIFU4O.png\"]}\n{\"q_img\": \"./images/B00AN545PI.png\", \"q_text\": \"is white with a different logo, and is brighter coloured\", \"positive_key\": \"B00AX2S3TU\", \"positive_value\": \"./images/B00AX2S3TU.png\", \"hn_image\": [\"./images/B00AN545PI.png\"]}\n{\"q_img\": \"./images/B00B7SB6RA.png\", \"q_text\": \"is brown with a v neck, and is a shorter sleeved blouse\", \"positive_key\": \"B004RDYOTY\", \"positive_value\": \"./images/B004RDYOTY.png\", \"hn_image\": [\"./images/B00B7SB6RA.png\"]}\n{\"q_img\": \"./images/B0036WSZVA.png\", \"q_text\": \"is grey with an image of a person, and Is gray with political theme.\", \"positive_key\": \"B0099VTSI6\", \"positive_value\": \"./images/B0099VTSI6.png\", \"hn_image\": [\"./images/B0036WSZVA.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"is dark blue colored with stripes on it, and has more blue and more stripes\", \"positive_key\": \"B005EY7GC2\", \"positive_value\": \"./images/B005EY7GC2.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B00EDOVZ30.png\", \"q_text\": \"The shirt is pink in color with black writing., and is a white tee with pink stripes on short sleeves\", \"positive_key\": \"B00ENGQM2W\", \"positive_value\": \"./images/B00ENGQM2W.png\", \"hn_image\": [\"./images/B00EDOVZ30.png\"]}\n{\"q_img\": \"./images/B007M850QC.png\", \"q_text\": \"is lighter, and is multi-colored and has long sleeves\", \"positive_key\": \"B008DSJUYI\", \"positive_value\": \"./images/B008DSJUYI.png\", \"hn_image\": [\"./images/B007M850QC.png\"]}\n{\"q_img\": \"./images/B00BHRICZU.png\", \"q_text\": \"has more green color and floral pattern, and has turquoise background\", \"positive_key\": \"B007P0P5ZI\", \"positive_value\": \"./images/B007P0P5ZI.png\", \"hn_image\": [\"./images/B00BHRICZU.png\"]}\n{\"q_img\": \"./images/B00BXRKLBW.png\", \"q_text\": \"is dark blue colored and sleeveless, and has no sleeves and buttons\", \"positive_key\": \"B00CC3ZQX4\", \"positive_value\": \"./images/B00CC3ZQX4.png\", \"hn_image\": [\"./images/B00BXRKLBW.png\"]}\n{\"q_img\": \"./images/B00ASI7KXO.png\", \"q_text\": \"has a different pattern, and is more colourfull\", \"positive_key\": \"B00AFY0JE8\", \"positive_value\": \"./images/B00AFY0JE8.png\", \"hn_image\": [\"./images/B00ASI7KXO.png\"]}\n{\"q_img\": \"./images/B008QW7RWS.png\", \"q_text\": \"Pink with no sleeves, and Is lighter & sleeveless\", \"positive_key\": \"B00AC5W0II\", \"positive_value\": \"./images/B00AC5W0II.png\", \"hn_image\": [\"./images/B008QW7RWS.png\"]}\n{\"q_img\": \"./images/B00AZ9IM4W.png\", \"q_text\": \"has more of a print and is less sheer, and is not as loose and more floral.\", \"positive_key\": \"B00AZOSAQM\", \"positive_value\": \"./images/B00AZOSAQM.png\", \"hn_image\": [\"./images/B00AZ9IM4W.png\"]}\n{\"q_img\": \"./images/B004I72CMU.png\", \"q_text\": \"The shirt is blue in color., and is a button down with a collar\", \"positive_key\": \"B00ECWR21C\", \"positive_value\": \"./images/B00ECWR21C.png\", \"hn_image\": [\"./images/B004I72CMU.png\"]}\n{\"q_img\": \"./images/B0061N1AO0.png\", \"q_text\": \"is red and has off-soulder, and strap sleeves solid red\", \"positive_key\": \"B008CISTR8\", \"positive_value\": \"./images/B008CISTR8.png\", \"hn_image\": [\"./images/B0061N1AO0.png\"]}\n{\"q_img\": \"./images/B005GA8YB6.png\", \"q_text\": \"is a red t-shirt with the letter 'i' at the center, and shows a graphic with yellow in it\", \"positive_key\": \"B009HU9HN0\", \"positive_value\": \"./images/B009HU9HN0.png\", \"hn_image\": [\"./images/B005GA8YB6.png\"]}\n{\"q_img\": \"./images/B00FFR8QK4.png\", \"q_text\": \"is tank top with marilynn monroe, and has a different logo and a different pattern on it\", \"positive_key\": \"B00D2NFS9U\", \"positive_value\": \"./images/B00D2NFS9U.png\", \"hn_image\": [\"./images/B00FFR8QK4.png\"]}\n{\"q_img\": \"./images/B005H7CO6Y.png\", \"q_text\": \"Is black with fitted waist and long sleeves, and is less open\", \"positive_key\": \"B006559GSQ\", \"positive_value\": \"./images/B006559GSQ.png\", \"hn_image\": [\"./images/B005H7CO6Y.png\"]}\n{\"q_img\": \"./images/B006VTEEJW.png\", \"q_text\": \"The tank top is clack with palm trees., and is a tank top\", \"positive_key\": \"B00D93DB2E\", \"positive_value\": \"./images/B00D93DB2E.png\", \"hn_image\": [\"./images/B006VTEEJW.png\"]}\n{\"q_img\": \"./images/B008STDYFI.png\", \"q_text\": \"is very similar but deeper red, and is red\", \"positive_key\": \"B0047LA6HA\", \"positive_value\": \"./images/B0047LA6HA.png\", \"hn_image\": [\"./images/B008STDYFI.png\"]}\n{\"q_img\": \"./images/B00CV2DS40.png\", \"q_text\": \" long sleeved and more fitted, and is black tee shirt\", \"positive_key\": \"B00C99Q4FA\", \"positive_value\": \"./images/B00C99Q4FA.png\", \"hn_image\": [\"./images/B00CV2DS40.png\"]}\n{\"q_img\": \"./images/B002GSXMYK.png\", \"q_text\": \"has net sheer sleeves, and it is transparent\", \"positive_key\": \"B006E3ZMA0\", \"positive_value\": \"./images/B006E3ZMA0.png\", \"hn_image\": [\"./images/B002GSXMYK.png\"]}\n{\"q_img\": \"./images/B00D0SZHOI.png\", \"q_text\": \"is a loose vest, and is more multi colored\", \"positive_key\": \"B007UJVK8A\", \"positive_value\": \"./images/B007UJVK8A.png\", \"hn_image\": [\"./images/B00D0SZHOI.png\"]}\n{\"q_img\": \"./images/B00EE0DVPI.png\", \"q_text\": \"The shirt is black in color., and is looser and black\", \"positive_key\": \"B00AYQMTPO\", \"positive_value\": \"./images/B00AYQMTPO.png\", \"hn_image\": [\"./images/B00EE0DVPI.png\"]}\n{\"q_img\": \"./images/B003VWLPLM.png\", \"q_text\": \"is darker and more edgy, and is darker\", \"positive_key\": \"B003O01IF4\", \"positive_value\": \"./images/B003O01IF4.png\", \"hn_image\": [\"./images/B003VWLPLM.png\"]}\n{\"q_img\": \"./images/B00D9JWW1O.png\", \"q_text\": \"The shirt is black with a gray figure., and is a black tee shirt\", \"positive_key\": \"B009ZYOP1C\", \"positive_value\": \"./images/B009ZYOP1C.png\", \"hn_image\": [\"./images/B00D9JWW1O.png\"]}\n{\"q_img\": \"./images/B00E65KUB4.png\", \"q_text\": \"is grey and has a skull, and is a grey printed tee with short sleeves and open back.\", \"positive_key\": \"B0079K6C1A\", \"positive_value\": \"./images/B0079K6C1A.png\", \"hn_image\": [\"./images/B00E65KUB4.png\"]}\n{\"q_img\": \"./images/B000YVF80C.png\", \"q_text\": \"is similar but more orange in colour, and is darker\", \"positive_key\": \"B006Z8F1ZU\", \"positive_value\": \"./images/B006Z8F1ZU.png\", \"hn_image\": [\"./images/B000YVF80C.png\"]}\n{\"q_img\": \"./images/B003YJG5S0.png\", \"q_text\": \"The shirt is long sleeve and gray., and has longer sleeves and buttons up\", \"positive_key\": \"B008RX9J8Q\", \"positive_value\": \"./images/B008RX9J8Q.png\", \"hn_image\": [\"./images/B003YJG5S0.png\"]}\n{\"q_img\": \"./images/B008RDDD8I.png\", \"q_text\": \"is fishnet and black, and and a pattern\", \"positive_key\": \"B007TXYYCG\", \"positive_value\": \"./images/B007TXYYCG.png\", \"hn_image\": [\"./images/B008RDDD8I.png\"]}\n{\"q_img\": \"./images/B0085FSDMY.png\", \"q_text\": \"is blue in color, and is more blue\", \"positive_key\": \"B0021K4BPC\", \"positive_value\": \"./images/B0021K4BPC.png\", \"hn_image\": [\"./images/B0085FSDMY.png\"]}\n{\"q_img\": \"./images/B006JW32N0.png\", \"q_text\": \"has short sleeves and is blue, and is lighter and has shorter sleeves\", \"positive_key\": \"B00H2680W0\", \"positive_value\": \"./images/B00H2680W0.png\", \"hn_image\": [\"./images/B006JW32N0.png\"]}\n{\"q_img\": \"./images/B00C5AQHXW.png\", \"q_text\": \" not revealing with a zipper, and is sleeveless and less traditional\", \"positive_key\": \"B0096HR11O\", \"positive_value\": \"./images/B0096HR11O.png\", \"hn_image\": [\"./images/B00C5AQHXW.png\"]}\n{\"q_img\": \"./images/B005TF7H9S.png\", \"q_text\": \"is lighter and has shorter sleeves, and is white with no graphic\", \"positive_key\": \"B00DF4AT3G\", \"positive_value\": \"./images/B00DF4AT3G.png\", \"hn_image\": [\"./images/B005TF7H9S.png\"]}\n{\"q_img\": \"./images/B007RBI9P8.png\", \"q_text\": \"is lighter and has front buttons, and is cream with buttons\", \"positive_key\": \"B00590KE3O\", \"positive_value\": \"./images/B00590KE3O.png\", \"hn_image\": [\"./images/B007RBI9P8.png\"]}\n{\"q_img\": \"./images/B00GABCAKG.png\", \"q_text\": \"has a longer sleeve and dress shirt attire, and is blue with long sleeves\", \"positive_key\": \"B004G0A72G\", \"positive_value\": \"./images/B004G0A72G.png\", \"hn_image\": [\"./images/B00GABCAKG.png\"]}\n{\"q_img\": \"./images/B00G31733C.png\", \"q_text\": \"is shorter and lighter and has longer sleeves, and is cropped with 3/4 length sleeves\", \"positive_key\": \"B00FR84H9A\", \"positive_value\": \"./images/B00FR84H9A.png\", \"hn_image\": [\"./images/B00G31733C.png\"]}\n{\"q_img\": \"./images/B00C1187T2.png\", \"q_text\": \"has shorter sleeves and dark blue, and is dark blue\", \"positive_key\": \"B00AOCQ9N6\", \"positive_value\": \"./images/B00AOCQ9N6.png\", \"hn_image\": [\"./images/B00C1187T2.png\"]}\n{\"q_img\": \"./images/B0045EPNF4.png\", \"q_text\": \"is blue and orange plaid, and is less casual and more fun\", \"positive_key\": \"B005SW1YE6\", \"positive_value\": \"./images/B005SW1YE6.png\", \"hn_image\": [\"./images/B0045EPNF4.png\"]}\n{\"q_img\": \"./images/B00522PG0A.png\", \"q_text\": \"has more lace and is more revealing, and is sexier and more revealing\", \"positive_key\": \"B00DULOFW0\", \"positive_value\": \"./images/B00DULOFW0.png\", \"hn_image\": [\"./images/B00522PG0A.png\"]}\n{\"q_img\": \"./images/B004HO6MI4.png\", \"q_text\": \"has longer sleees and is less premium, and has longer sleeves\", \"positive_key\": \"B00CDAZWV2\", \"positive_value\": \"./images/B00CDAZWV2.png\", \"hn_image\": [\"./images/B004HO6MI4.png\"]}\n{\"q_img\": \"./images/B004F3NKGO.png\", \"q_text\": \"The long shirt is green and multi-colored., and is green with a different pattern\", \"positive_key\": \"B00A09D0VW\", \"positive_value\": \"./images/B00A09D0VW.png\", \"hn_image\": [\"./images/B004F3NKGO.png\"]}\n{\"q_img\": \"./images/B007RKNZNA.png\", \"q_text\": \"has larger dots and has short sleeves, and has short sleeves\", \"positive_key\": \"B00CA7FKM4\", \"positive_value\": \"./images/B00CA7FKM4.png\", \"hn_image\": [\"./images/B007RKNZNA.png\"]}\n{\"q_img\": \"./images/B00CP11HK4.png\", \"q_text\": \"is darker and monochrome, and is a darker color\", \"positive_key\": \"B00DGYGFBK\", \"positive_value\": \"./images/B00DGYGFBK.png\", \"hn_image\": [\"./images/B00CP11HK4.png\"]}\n{\"q_img\": \"./images/B00CIHR6BE.png\", \"q_text\": \"has long sleeves and is plain, and Has longer sleeves and no print\", \"positive_key\": \"B004GB1ZEY\", \"positive_value\": \"./images/B004GB1ZEY.png\", \"hn_image\": [\"./images/B00CIHR6BE.png\"]}\n{\"q_img\": \"./images/B00CKFVNRM.png\", \"q_text\": \"a dark colored short sleeve tee with 2 large cats on it, and has two tigers and is tee shirt\", \"positive_key\": \"B00CIQQ922\", \"positive_value\": \"./images/B00CIQQ922.png\", \"hn_image\": [\"./images/B00CKFVNRM.png\"]}\n{\"q_img\": \"./images/B007XPH5TO.png\", \"q_text\": \"is darker with no buttons, and is a tank top\", \"positive_key\": \"B001TEKJH0\", \"positive_value\": \"./images/B001TEKJH0.png\", \"hn_image\": [\"./images/B007XPH5TO.png\"]}\n{\"q_img\": \"./images/B00EKD4LLW.png\", \"q_text\": \"is lighting grey and pure wrap, and is slightly lighter\", \"positive_key\": \"B00EKD4LK8\", \"positive_value\": \"./images/B00EKD4LK8.png\", \"hn_image\": [\"./images/B00EKD4LLW.png\"]}\n{\"q_img\": \"./images/B002TNOCNW.png\", \"q_text\": \"The shirt is a tank top and is light gray in color., and is more layered and casual\", \"positive_key\": \"B0069HMDM6\", \"positive_value\": \"./images/B0069HMDM6.png\", \"hn_image\": [\"./images/B002TNOCNW.png\"]}\n{\"q_img\": \"./images/B0046VNAO2.png\", \"q_text\": \"has long sleeves, and has longer sleeves\", \"positive_key\": \"B003V224D0\", \"positive_value\": \"./images/B003V224D0.png\", \"hn_image\": [\"./images/B0046VNAO2.png\"]}\n{\"q_img\": \"./images/B009F3JJ2S.png\", \"q_text\": \"is lighter, and has longer sleeves and is pink\", \"positive_key\": \"B008AUHTVU\", \"positive_value\": \"./images/B008AUHTVU.png\", \"hn_image\": [\"./images/B009F3JJ2S.png\"]}\n{\"q_img\": \"./images/B00A3QDXKU.png\", \"q_text\": \"is yellow with longer sleeves, and is more yellow with longer sleeves\", \"positive_key\": \"B00A3QM374\", \"positive_value\": \"./images/B00A3QM374.png\", \"hn_image\": [\"./images/B00A3QDXKU.png\"]}\n{\"q_img\": \"./images/B009BOJQP6.png\", \"q_text\": \"The shirt is red with the England emblem., and Is reddish and looser\", \"positive_key\": \"B0052UX13G\", \"positive_value\": \"./images/B0052UX13G.png\", \"hn_image\": [\"./images/B009BOJQP6.png\"]}\n{\"q_img\": \"./images/B008P6Z0XI.png\", \"q_text\": \"is solid black, and is darker coloured\", \"positive_key\": \"B008I8YP52\", \"positive_value\": \"./images/B008I8YP52.png\", \"hn_image\": [\"./images/B008P6Z0XI.png\"]}\n{\"q_img\": \"./images/B007EEAA9Q.png\", \"q_text\": \"is cheetah brown shirt, and is more leopard print and darker\", \"positive_key\": \"B00C89WXFG\", \"positive_value\": \"./images/B00C89WXFG.png\", \"hn_image\": [\"./images/B007EEAA9Q.png\"]}\n{\"q_img\": \"./images/B00387TSOG.png\", \"q_text\": \"is deeply black and has v neck, and is burgundy color\", \"positive_key\": \"B00B0NQ7CG\", \"positive_value\": \"./images/B00B0NQ7CG.png\", \"hn_image\": [\"./images/B00387TSOG.png\"]}\n{\"q_img\": \"./images/B00BT8UASO.png\", \"q_text\": \"is dark blue colored, and is darker in color\", \"positive_key\": \"B000VX6TAG\", \"positive_value\": \"./images/B000VX6TAG.png\", \"hn_image\": [\"./images/B00BT8UASO.png\"]}\n{\"q_img\": \"./images/B005VSPMEK.png\", \"q_text\": \"has a different print but is of the same design, and has a dog and more words\", \"positive_key\": \"B000EP329W\", \"positive_value\": \"./images/B000EP329W.png\", \"hn_image\": [\"./images/B005VSPMEK.png\"]}\n{\"q_img\": \"./images/B007XCWQM8.png\", \"q_text\": \"is purple and has longer sleeves, and it is transparent and has longer sleeves\", \"positive_key\": \"B008QIV35O\", \"positive_value\": \"./images/B008QIV35O.png\", \"hn_image\": [\"./images/B007XCWQM8.png\"]}\n{\"q_img\": \"./images/B00CD8H0N2.png\", \"q_text\": \"is white in color with no sleeves, and is lighter and has shorter sleeves\", \"positive_key\": \"B003QJWIRU\", \"positive_value\": \"./images/B003QJWIRU.png\", \"hn_image\": [\"./images/B00CD8H0N2.png\"]}\n{\"q_img\": \"./images/B009HU9HN0.png\", \"q_text\": \"is more of a tank top, and with no polka dots\", \"positive_key\": \"B008T44C8U\", \"positive_value\": \"./images/B008T44C8U.png\", \"hn_image\": [\"./images/B009HU9HN0.png\"]}\n{\"q_img\": \"./images/B00CRHEXMU.png\", \"q_text\": \"Is blue and more flowing, and Has a denser pattern and is more feminine\", \"positive_key\": \"B00CFRU1SM\", \"positive_value\": \"./images/B00CFRU1SM.png\", \"hn_image\": [\"./images/B00CRHEXMU.png\"]}\n{\"q_img\": \"./images/B00CF3CGRU.png\", \"q_text\": \"The off the shoulders shirt is blue., and is a off the shoulder long sleeved dark blue\", \"positive_key\": \"B00DGXXWAI\", \"positive_value\": \"./images/B00DGXXWAI.png\", \"hn_image\": [\"./images/B00CF3CGRU.png\"]}\n{\"q_img\": \"./images/B001D6Y466.png\", \"q_text\": \"The shirt is gray in color., and is darker and has a simpler neck\", \"positive_key\": \"B007Y0MC1Y\", \"positive_value\": \"./images/B007Y0MC1Y.png\", \"hn_image\": [\"./images/B001D6Y466.png\"]}\n{\"q_img\": \"./images/B009YKBNJO.png\", \"q_text\": \"has longer sleeves and is shorter, and Has longer sleeves and a lighter pattern\", \"positive_key\": \"B009880Y1K\", \"positive_value\": \"./images/B009880Y1K.png\", \"hn_image\": [\"./images/B009YKBNJO.png\"]}\n{\"q_img\": \"./images/B0046QRK6G.png\", \"q_text\": \"is black and has a different logo, and is darker\", \"positive_key\": \"B008NBK3PA\", \"positive_value\": \"./images/B008NBK3PA.png\", \"hn_image\": [\"./images/B0046QRK6G.png\"]}\n{\"q_img\": \"./images/B008QQH4S6.png\", \"q_text\": \"The shirt is black with white writing., and has more white text\", \"positive_key\": \"B00B1GLPWE\", \"positive_value\": \"./images/B00B1GLPWE.png\", \"hn_image\": [\"./images/B008QQH4S6.png\"]}\n{\"q_img\": \"./images/B006OMWE2U.png\", \"q_text\": \"has longer sleeves with a large red flower, and has a white background with a red flower\", \"positive_key\": \"B00713UM4S\", \"positive_value\": \"./images/B00713UM4S.png\", \"hn_image\": [\"./images/B006OMWE2U.png\"]}\n{\"q_img\": \"./images/B008D58I3U.png\", \"q_text\": \" orange and green strips., and is white near neckline\", \"positive_key\": \"B00764CZ92\", \"positive_value\": \"./images/B00764CZ92.png\", \"hn_image\": [\"./images/B008D58I3U.png\"]}\n{\"q_img\": \"./images/B004GECGL2.png\", \"q_text\": \"is darker and has longer sleeves, and black lacy neck\", \"positive_key\": \"B00A7GQLMI\", \"positive_value\": \"./images/B00A7GQLMI.png\", \"hn_image\": [\"./images/B004GECGL2.png\"]}\n{\"q_img\": \"./images/B00DHJP5HY.png\", \"q_text\": \"is liss frilly and more sexy, and has larger neck and is black color\", \"positive_key\": \"B005523LHM\", \"positive_value\": \"./images/B005523LHM.png\", \"hn_image\": [\"./images/B00DHJP5HY.png\"]}\n{\"q_img\": \"./images/B00EJYNIDY.png\", \"q_text\": \"is long sleeve with a purple stripe design, and is purple and white with longer sleeves\", \"positive_key\": \"B00778BRN2\", \"positive_value\": \"./images/B00778BRN2.png\", \"hn_image\": [\"./images/B00EJYNIDY.png\"]}\n{\"q_img\": \"./images/B00CEITXVI.png\", \"q_text\": \"is orange with ruffles, and is orange with short sleeves\", \"positive_key\": \"B00CXV3Z38\", \"positive_value\": \"./images/B00CXV3Z38.png\", \"hn_image\": [\"./images/B00CEITXVI.png\"]}\n{\"q_img\": \"./images/B005FZVEBE.png\", \"q_text\": \"is nerdier and girly, and white\", \"positive_key\": \"B009UYFST0\", \"positive_value\": \"./images/B009UYFST0.png\", \"hn_image\": [\"./images/B005FZVEBE.png\"]}\n{\"q_img\": \"./images/B007LWRFP8.png\", \"q_text\": \"is white with sleeves and animated girl, and is white colored\", \"positive_key\": \"B004J1KGHS\", \"positive_value\": \"./images/B004J1KGHS.png\", \"hn_image\": [\"./images/B007LWRFP8.png\"]}\n{\"q_img\": \"./images/B008COX0M6.png\", \"q_text\": \"Is light pink and has a heart on it, and is pink\", \"positive_key\": \"B006ZQV5AC\", \"positive_value\": \"./images/B006ZQV5AC.png\", \"hn_image\": [\"./images/B008COX0M6.png\"]}\n{\"q_img\": \"./images/B005LCNXNS.png\", \"q_text\": \"has floral and color is black, and is more patterened and less sleek\", \"positive_key\": \"B0058Z62OA\", \"positive_value\": \"./images/B0058Z62OA.png\", \"hn_image\": [\"./images/B005LCNXNS.png\"]}\n{\"q_img\": \"./images/B00FBQRX8U.png\", \"q_text\": \"has a longer sleeve with one color, and v neck with face graphics\", \"positive_key\": \"B00FKG3UJ2\", \"positive_value\": \"./images/B00FKG3UJ2.png\", \"hn_image\": [\"./images/B00FBQRX8U.png\"]}\n{\"q_img\": \"./images/B00CJI37MO.png\", \"q_text\": \"is less transparent, and has a thinner strap and is lighter colored\", \"positive_key\": \"B00C4XDT36\", \"positive_value\": \"./images/B00C4XDT36.png\", \"hn_image\": [\"./images/B00CJI37MO.png\"]}\n{\"q_img\": \"./images/B00BB1ZCAA.png\", \"q_text\": \"is shorter sleeved with 'kiss' graphic, and has shorter sleeves\", \"positive_key\": \"B002V0N9M8\", \"positive_value\": \"./images/B002V0N9M8.png\", \"hn_image\": [\"./images/B00BB1ZCAA.png\"]}\n{\"q_img\": \"./images/B007H9KGT2.png\", \"q_text\": \"is looser fitting and has angular hem, and is dark brown and has more strips\", \"positive_key\": \"B007225EKU\", \"positive_value\": \"./images/B007225EKU.png\", \"hn_image\": [\"./images/B007H9KGT2.png\"]}\n{\"q_img\": \"./images/B007MB2OAE.png\", \"q_text\": \"is light blue, and is a light blue with different graphic\", \"positive_key\": \"B000OSQ6DI\", \"positive_value\": \"./images/B000OSQ6DI.png\", \"hn_image\": [\"./images/B007MB2OAE.png\"]}\n{\"q_img\": \"./images/B00987K8EO.png\", \"q_text\": \"is strapless and has a more subtle pattern, and plaid\", \"positive_key\": \"B005WYDAYC\", \"positive_value\": \"./images/B005WYDAYC.png\", \"hn_image\": [\"./images/B00987K8EO.png\"]}\n{\"q_img\": \"./images/B00836H3JE.png\", \"q_text\": \"has a longer sleeve with spots, and is brown and has no sleeve\", \"positive_key\": \"B008HZ8II6\", \"positive_value\": \"./images/B008HZ8II6.png\", \"hn_image\": [\"./images/B00836H3JE.png\"]}\n{\"q_img\": \"./images/B00DSS1KAK.png\", \"q_text\": \"is deeply grey and has logo, and is grey with a pattern\", \"positive_key\": \"B00AYTKSG8\", \"positive_value\": \"./images/B00AYTKSG8.png\", \"hn_image\": [\"./images/B00DSS1KAK.png\"]}\n{\"q_img\": \"./images/B0056L9QU8.png\", \"q_text\": \"Is darker with shorter sleeves, and is soft black short sleeved\", \"positive_key\": \"B00CD6N4PW\", \"positive_value\": \"./images/B00CD6N4PW.png\", \"hn_image\": [\"./images/B0056L9QU8.png\"]}\n{\"q_img\": \"./images/B00H03TS7Q.png\", \"q_text\": \"has a black color with meme art, and has cute kittens on burgers\", \"positive_key\": \"B00G3DOW3O\", \"positive_value\": \"./images/B00G3DOW3O.png\", \"hn_image\": [\"./images/B00H03TS7Q.png\"]}\n{\"q_img\": \"./images/B008O5619C.png\", \"q_text\": \"has a different graphic and is more blue in colour, and has different graphic with more green print\", \"positive_key\": \"B0089EELWM\", \"positive_value\": \"./images/B0089EELWM.png\", \"hn_image\": [\"./images/B008O5619C.png\"]}\n{\"q_img\": \"./images/B0079K0YBO.png\", \"q_text\": \"Is grey and has a less whimsical graphic, and is lighter\", \"positive_key\": \"B00E3WVAD2\", \"positive_value\": \"./images/B00E3WVAD2.png\", \"hn_image\": [\"./images/B0079K0YBO.png\"]}\n{\"q_img\": \"./images/B00FIYMN9Y.png\", \"q_text\": \"is solid black, and is black with no other colour\", \"positive_key\": \"B003IRNVLC\", \"positive_value\": \"./images/B003IRNVLC.png\", \"hn_image\": [\"./images/B00FIYMN9Y.png\"]}\n{\"q_img\": \"./images/B008CISTR8.png\", \"q_text\": \"has a cooler color and long sleeves, and has longer sleeves\", \"positive_key\": \"B0097YF3AC\", \"positive_value\": \"./images/B0097YF3AC.png\", \"hn_image\": [\"./images/B008CISTR8.png\"]}\n{\"q_img\": \"./images/B00B7ALSVM.png\", \"q_text\": \"is violet colored and has shorter sleeves, and has shorter sleeves and more purple\", \"positive_key\": \"B008COX0M6\", \"positive_value\": \"./images/B008COX0M6.png\", \"hn_image\": [\"./images/B00B7ALSVM.png\"]}\n{\"q_img\": \"./images/B000VTB0OK.png\", \"q_text\": \"The shirt is green in color with white lettering and picture., and Is brighter and more sarcastic\", \"positive_key\": \"B0084S6I4C\", \"positive_value\": \"./images/B0084S6I4C.png\", \"hn_image\": [\"./images/B000VTB0OK.png\"]}\n{\"q_img\": \"./images/B004KPD9JU.png\", \"q_text\": \"is a darker color., and is much darker\", \"positive_key\": \"B003KJ86YK\", \"positive_value\": \"./images/B003KJ86YK.png\", \"hn_image\": [\"./images/B004KPD9JU.png\"]}\n{\"q_img\": \"./images/B00C8FZDCK.png\", \"q_text\": \"Is longer and a tunic, and has shorter sleeves\", \"positive_key\": \"B004VS52HS\", \"positive_value\": \"./images/B004VS52HS.png\", \"hn_image\": [\"./images/B00C8FZDCK.png\"]}\n{\"q_img\": \"./images/B0018DWZJC.png\", \"q_text\": \"Is less fitted and darker, and is black with image\", \"positive_key\": \"B00E67JALI\", \"positive_value\": \"./images/B00E67JALI.png\", \"hn_image\": [\"./images/B0018DWZJC.png\"]}\n{\"q_img\": \"./images/B0085U64UM.png\", \"q_text\": \"The mid capped sleeved shirt is blue and white., and has straps and is a darker color\", \"positive_key\": \"B00C1806H6\", \"positive_value\": \"./images/B00C1806H6.png\", \"hn_image\": [\"./images/B0085U64UM.png\"]}\n{\"q_img\": \"./images/B0001TOLG4.png\", \"q_text\": \"is darker, and is darker\", \"positive_key\": \"B000W15XMM\", \"positive_value\": \"./images/B000W15XMM.png\", \"hn_image\": [\"./images/B0001TOLG4.png\"]}\n{\"q_img\": \"./images/B005GI9BQ0.png\", \"q_text\": \"Is more casual and less edugy, and Is less patterned and more business like\", \"positive_key\": \"B00B72HEX6\", \"positive_value\": \"./images/B00B72HEX6.png\", \"hn_image\": [\"./images/B005GI9BQ0.png\"]}\n{\"q_img\": \"./images/B0038P24VC.png\", \"q_text\": \"is a v necked floral print, and is lighter colored\", \"positive_key\": \"B007JOFXCK\", \"positive_value\": \"./images/B007JOFXCK.png\", \"hn_image\": [\"./images/B0038P24VC.png\"]}\n{\"q_img\": \"./images/B005ZO99DA.png\", \"q_text\": \"is no sleeve with purple and art, and is a purple tank\", \"positive_key\": \"B00CBCWJXG\", \"positive_value\": \"./images/B00CBCWJXG.png\", \"hn_image\": [\"./images/B005ZO99DA.png\"]}\n{\"q_img\": \"./images/B009PLIRGO.png\", \"q_text\": \"The halter shirt is long and pink in color., and has no sleeves\", \"positive_key\": \"B00CEHCUFK\", \"positive_value\": \"./images/B00CEHCUFK.png\", \"hn_image\": [\"./images/B009PLIRGO.png\"]}\n{\"q_img\": \"./images/B0029PIERK.png\", \"q_text\": \"has longer sleeves with multiple stripes, and has longer sleeves and is more stylish\", \"positive_key\": \"B006FTCDO6\", \"positive_value\": \"./images/B006FTCDO6.png\", \"hn_image\": [\"./images/B0029PIERK.png\"]}\n{\"q_img\": \"./images/B00AZ9G7X0.png\", \"q_text\": \"has no sleeves but an upside down arch pattern, and has two colors to it\", \"positive_key\": \"B0093B1WCM\", \"positive_value\": \"./images/B0093B1WCM.png\", \"hn_image\": [\"./images/B00AZ9G7X0.png\"]}\n{\"q_img\": \"./images/B0096HPTPE.png\", \"q_text\": \"is more patterned and more colorful, and has more of a pattern to it\", \"positive_key\": \"B0096HOBZ8\", \"positive_value\": \"./images/B0096HOBZ8.png\", \"hn_image\": [\"./images/B0096HPTPE.png\"]}\n{\"q_img\": \"./images/B00AYVKA0K.png\", \"q_text\": \"is red and has long sleeves, and is red with long sleeves\", \"positive_key\": \"B008QW7RWS\", \"positive_value\": \"./images/B008QW7RWS.png\", \"hn_image\": [\"./images/B00AYVKA0K.png\"]}\n{\"q_img\": \"./images/B008O566BK.png\", \"q_text\": \"has a different picture, and has shorter sleeves and a different graphic\", \"positive_key\": \"B00DGZCDV0\", \"positive_value\": \"./images/B00DGZCDV0.png\", \"hn_image\": [\"./images/B008O566BK.png\"]}\n{\"q_img\": \"./images/B008DVXI34.png\", \"q_text\": \"The shirt has an animal print., and has a colorful zebra print\", \"positive_key\": \"B00A7NKKGO\", \"positive_value\": \"./images/B00A7NKKGO.png\", \"hn_image\": [\"./images/B008DVXI34.png\"]}\n{\"q_img\": \"./images/B004ZT0ZCA.png\", \"q_text\": \"is brown with ruffles, and purple with long sleeves\", \"positive_key\": \"B00967OFL8\", \"positive_value\": \"./images/B00967OFL8.png\", \"hn_image\": [\"./images/B004ZT0ZCA.png\"]}\n{\"q_img\": \"./images/B00ABG1BXS.png\", \"q_text\": \"has 3/4 sleeves and is grey, and is 3/4 sleeves and embroidered\", \"positive_key\": \"B00BLYSR1I\", \"positive_value\": \"./images/B00BLYSR1I.png\", \"hn_image\": [\"./images/B00ABG1BXS.png\"]}\n{\"q_img\": \"./images/B00E80IR5I.png\", \"q_text\": \"is pink and has shorter sleeves, and pink round neck fitted waist 3/4 sleeve\", \"positive_key\": \"B0051896LU\", \"positive_value\": \"./images/B0051896LU.png\", \"hn_image\": [\"./images/B00E80IR5I.png\"]}\n{\"q_img\": \"./images/B00A7Y35WY.png\", \"q_text\": \"is purple colored and has pockets, and is purple colored and longer length\", \"positive_key\": \"B008WYCFGI\", \"positive_value\": \"./images/B008WYCFGI.png\", \"hn_image\": [\"./images/B00A7Y35WY.png\"]}\n{\"q_img\": \"./images/B004MNM91E.png\", \"q_text\": \"is black with orange designs, and More solid black with small logo\", \"positive_key\": \"B004TMGDME\", \"positive_value\": \"./images/B004TMGDME.png\", \"hn_image\": [\"./images/B004MNM91E.png\"]}\n{\"q_img\": \"./images/B007RWJSUC.png\", \"q_text\": \"is plain blue and more patterned at the neckline, and Is lighter blue\", \"positive_key\": \"B004NSUGCC\", \"positive_value\": \"./images/B004NSUGCC.png\", \"hn_image\": [\"./images/B007RWJSUC.png\"]}\n{\"q_img\": \"./images/B006BOKB30.png\", \"q_text\": \"is white and lacy, and is a solid white tank\", \"positive_key\": \"B008597ENE\", \"positive_value\": \"./images/B008597ENE.png\", \"hn_image\": [\"./images/B006BOKB30.png\"]}\n{\"q_img\": \"./images/B0052RN92W.png\", \"q_text\": \"is white and has longer sleeves, and is white colored and ahs longer sleeves\", \"positive_key\": \"B00BHZVSSA\", \"positive_value\": \"./images/B00BHZVSSA.png\", \"hn_image\": [\"./images/B0052RN92W.png\"]}\n{\"q_img\": \"./images/B0041E10A0.png\", \"q_text\": \"The shirt has capped sleeves and is orange in color., and Desired item has multi-color stripes and lace back\", \"positive_key\": \"B00ALR3QBM\", \"positive_value\": \"./images/B00ALR3QBM.png\", \"hn_image\": [\"./images/B0041E10A0.png\"]}\n{\"q_img\": \"./images/B008CLI9C0.png\", \"q_text\": \"is more patterned and fitted, and is black and yellow and more revealing\", \"positive_key\": \"B008396CNO\", \"positive_value\": \"./images/B008396CNO.png\", \"hn_image\": [\"./images/B008CLI9C0.png\"]}\n{\"q_img\": \"./images/B007E67LT6.png\", \"q_text\": \"is black colored, and is darker\", \"positive_key\": \"B005W4MAX4\", \"positive_value\": \"./images/B005W4MAX4.png\", \"hn_image\": [\"./images/B007E67LT6.png\"]}\n{\"q_img\": \"./images/B008OGUW32.png\", \"q_text\": \"is lighter and cooler, and white with light pink stripes buttoned down v-neck\", \"positive_key\": \"B00342VD8O\", \"positive_value\": \"./images/B00342VD8O.png\", \"hn_image\": [\"./images/B008OGUW32.png\"]}\n{\"q_img\": \"./images/B007Z3OINA.png\", \"q_text\": \"white tee with guitar logo, and white with large graphic\", \"positive_key\": \"B000KRJK2M\", \"positive_value\": \"./images/B000KRJK2M.png\", \"hn_image\": [\"./images/B007Z3OINA.png\"]}\n{\"q_img\": \"./images/B00C93N0NA.png\", \"q_text\": \"is yellow with floral prints, and is yellow with floral print\", \"positive_key\": \"B0091NSIXI\", \"positive_value\": \"./images/B0091NSIXI.png\", \"hn_image\": [\"./images/B00C93N0NA.png\"]}\n{\"q_img\": \"./images/B00BXXO4WI.png\", \"q_text\": \"The shirt is black and white in color., and is more blousy and bigger pattern\", \"positive_key\": \"B008TQN9P0\", \"positive_value\": \"./images/B008TQN9P0.png\", \"hn_image\": [\"./images/B00BXXO4WI.png\"]}\n{\"q_img\": \"./images/B00BUKJ2JY.png\", \"q_text\": \"is darker, and is a black shirt with flouncy bottom hem\", \"positive_key\": \"B00CN3WUP0\", \"positive_value\": \"./images/B00CN3WUP0.png\", \"hn_image\": [\"./images/B00BUKJ2JY.png\"]}\n{\"q_img\": \"./images/B008OGUW32.png\", \"q_text\": \"is more mature and has pink, and Is lighter more casual color.\", \"positive_key\": \"B007RWJ3UC\", \"positive_value\": \"./images/B007RWJ3UC.png\", \"hn_image\": [\"./images/B008OGUW32.png\"]}\n{\"q_img\": \"./images/B00BSKNZSA.png\", \"q_text\": \"Is more flowing and less plain, and Is darker & looser\", \"positive_key\": \"B00C65T8B4\", \"positive_value\": \"./images/B00C65T8B4.png\", \"hn_image\": [\"./images/B00BSKNZSA.png\"]}\n{\"q_img\": \"./images/B00B30HF3Q.png\", \"q_text\": \"is more flirty and has ruffles, and is black and has a low neck\", \"positive_key\": \"B007WA3ZE4\", \"positive_value\": \"./images/B007WA3ZE4.png\", \"hn_image\": [\"./images/B00B30HF3Q.png\"]}\n{\"q_img\": \"./images/B0058K6VF0.png\", \"q_text\": \"is lighter, and is light blue\", \"positive_key\": \"B00557QE8U\", \"positive_value\": \"./images/B00557QE8U.png\", \"hn_image\": [\"./images/B0058K6VF0.png\"]}\n{\"q_img\": \"./images/B003YPXJMY.png\", \"q_text\": \"has no sleeves and one color, and Is black with spaghetti straps\", \"positive_key\": \"B005OT2USW\", \"positive_value\": \"./images/B005OT2USW.png\", \"hn_image\": [\"./images/B003YPXJMY.png\"]}\n{\"q_img\": \"./images/B00GIOKE8U.png\", \"q_text\": \"is two different color stripes, and has black and white stripes\", \"positive_key\": \"B00C0QCRHG\", \"positive_value\": \"./images/B00C0QCRHG.png\", \"hn_image\": [\"./images/B00GIOKE8U.png\"]}\n{\"q_img\": \"./images/B008N03XMQ.png\", \"q_text\": \"is darker, and is black fabric and has black buttons\", \"positive_key\": \"B003BZFKQ0\", \"positive_value\": \"./images/B003BZFKQ0.png\", \"hn_image\": [\"./images/B008N03XMQ.png\"]}\n{\"q_img\": \"./images/B0058K771C.png\", \"q_text\": \"is cream colored, and has a lighter color\", \"positive_key\": \"B0058K7LBS\", \"positive_value\": \"./images/B0058K7LBS.png\", \"hn_image\": [\"./images/B0058K771C.png\"]}\n{\"q_img\": \"./images/B00B96CBQA.png\", \"q_text\": \"is white with black designs, and has a complex pattern design\", \"positive_key\": \"B007ISRV9U\", \"positive_value\": \"./images/B007ISRV9U.png\", \"hn_image\": [\"./images/B00B96CBQA.png\"]}\n{\"q_img\": \"./images/B00DF4AT3G.png\", \"q_text\": \"is blue long sleeved with black flower, and has longer sleeves and is darker colored\", \"positive_key\": \"B004CTFLCM\", \"positive_value\": \"./images/B004CTFLCM.png\", \"hn_image\": [\"./images/B00DF4AT3G.png\"]}\n{\"q_img\": \"./images/B007LWNRRI.png\", \"q_text\": \"is more casual has more shoulder coverage, and is black with red logo and longer sleeves\", \"positive_key\": \"B003Y7PFM4\", \"positive_value\": \"./images/B003Y7PFM4.png\", \"hn_image\": [\"./images/B007LWNRRI.png\"]}\n{\"q_img\": \"./images/B009HI8RII.png\", \"q_text\": \"has a short sleeve black color, and is black with white text\", \"positive_key\": \"B00BPWBYNE\", \"positive_value\": \"./images/B00BPWBYNE.png\", \"hn_image\": [\"./images/B009HI8RII.png\"]}\n{\"q_img\": \"./images/B00F56HN22.png\", \"q_text\": \"Is longer and has shorter sleeves, and is pink and longer\", \"positive_key\": \"B00B61WSC0\", \"positive_value\": \"./images/B00B61WSC0.png\", \"hn_image\": [\"./images/B00F56HN22.png\"]}\n{\"q_img\": \"./images/B007N11B6Q.png\", \"q_text\": \"Has more coverage and plaid, and is plaid and has waist tie\", \"positive_key\": \"B002GKC97Y\", \"positive_value\": \"./images/B002GKC97Y.png\", \"hn_image\": [\"./images/B007N11B6Q.png\"]}\n{\"q_img\": \"./images/B00522PGV4.png\", \"q_text\": \"is white with lace detail, and is white and less revealing\", \"positive_key\": \"B0082BHLLA\", \"positive_value\": \"./images/B0082BHLLA.png\", \"hn_image\": [\"./images/B00522PGV4.png\"]}\n{\"q_img\": \"./images/B009YJUNGO.png\", \"q_text\": \"is lighter, and is black\", \"positive_key\": \"B007P0QK02\", \"positive_value\": \"./images/B007P0QK02.png\", \"hn_image\": [\"./images/B009YJUNGO.png\"]}\n{\"q_img\": \"./images/B00FBPRWPA.png\", \"q_text\": \"is less fitting and is pink, and Is less revealing and more trendy\", \"positive_key\": \"B002N6NGK0\", \"positive_value\": \"./images/B002N6NGK0.png\", \"hn_image\": [\"./images/B00FBPRWPA.png\"]}\n{\"q_img\": \"./images/B00C4YP0YQ.png\", \"q_text\": \"has brown stripes, and is longer sleeved and green\", \"positive_key\": \"B00AG35TN4\", \"positive_value\": \"./images/B00AG35TN4.png\", \"hn_image\": [\"./images/B00C4YP0YQ.png\"]}\n{\"q_img\": \"./images/B0095YHVUY.png\", \"q_text\": \"has a short sleeve with art and text, and is black with white letters and shorter sleeves\", \"positive_key\": \"B00DHX4L2K\", \"positive_value\": \"./images/B00DHX4L2K.png\", \"hn_image\": [\"./images/B0095YHVUY.png\"]}\n{\"q_img\": \"./images/B0046IDVOE.png\", \"q_text\": \"is darker and less wordy, and is black with bill murray on it\", \"positive_key\": \"B00CC21O5E\", \"positive_value\": \"./images/B00CC21O5E.png\", \"hn_image\": [\"./images/B0046IDVOE.png\"]}\n{\"q_img\": \"./images/B00D7FABAO.png\", \"q_text\": \"black shirt with writing, and olive tee shirt with graphics and short sleeves\", \"positive_key\": \"B000X756HC\", \"positive_value\": \"./images/B000X756HC.png\", \"hn_image\": [\"./images/B00D7FABAO.png\"]}\n{\"q_img\": \"./images/B0054QOMY0.png\", \"q_text\": \"is light pink colored and has shorter sleeves, and has shorter sleeves and is flesh coloured\", \"positive_key\": \"B00B4WMM22\", \"positive_value\": \"./images/B00B4WMM22.png\", \"hn_image\": [\"./images/B0054QOMY0.png\"]}\n{\"q_img\": \"./images/B00DCZSCUA.png\", \"q_text\": \"is tighter gitting and dark, and is darker in color\", \"positive_key\": \"B00DY636DA\", \"positive_value\": \"./images/B00DY636DA.png\", \"hn_image\": [\"./images/B00DCZSCUA.png\"]}\n{\"q_img\": \"./images/B006IJ9MXI.png\", \"q_text\": \"is sleeveless and more revealing, and is a tank top\", \"positive_key\": \"B008OVL5WE\", \"positive_value\": \"./images/B008OVL5WE.png\", \"hn_image\": [\"./images/B006IJ9MXI.png\"]}\n{\"q_img\": \"./images/B0061M50AG.png\", \"q_text\": \"The tank top is tight fitting and black and red in color., and is less revealing\", \"positive_key\": \"B003I7POCG\", \"positive_value\": \"./images/B003I7POCG.png\", \"hn_image\": [\"./images/B0061M50AG.png\"]}\n{\"q_img\": \"./images/B00AKN42A6.png\", \"q_text\": \"Black sleeves are lace, and has a more open neck and lacy arms\", \"positive_key\": \"B00BMEKW74\", \"positive_value\": \"./images/B00BMEKW74.png\", \"hn_image\": [\"./images/B00AKN42A6.png\"]}\n{\"q_img\": \"./images/B00BFL3XS4.png\", \"q_text\": \"has no sleeves and multiple colors, and is colorful with shorter sleeves\", \"positive_key\": \"B00D40AXDW\", \"positive_value\": \"./images/B00D40AXDW.png\", \"hn_image\": [\"./images/B00BFL3XS4.png\"]}\n{\"q_img\": \"./images/B00B1EDU7O.png\", \"q_text\": \"is pink with long sleeves, and is purple with longer sleeves\", \"positive_key\": \"B00CEZJLUE\", \"positive_value\": \"./images/B00CEZJLUE.png\", \"hn_image\": [\"./images/B00B1EDU7O.png\"]}\n{\"q_img\": \"./images/B000VTMHFG.png\", \"q_text\": \"is skinnier, and is a woman's top\", \"positive_key\": \"B009FRGUN0\", \"positive_value\": \"./images/B009FRGUN0.png\", \"hn_image\": [\"./images/B000VTMHFG.png\"]}\n{\"q_img\": \"./images/B00BZSE6HY.png\", \"q_text\": \"The shirt is long sleeved and white., and it is white ad plain\", \"positive_key\": \"B00CQKVVXC\", \"positive_value\": \"./images/B00CQKVVXC.png\", \"hn_image\": [\"./images/B00BZSE6HY.png\"]}\n{\"q_img\": \"./images/B0043Y8L5K.png\", \"q_text\": \"Is red with a smaller graphic, and is read with a small logo on it\", \"positive_key\": \"B002TYKGBS\", \"positive_value\": \"./images/B002TYKGBS.png\", \"hn_image\": [\"./images/B0043Y8L5K.png\"]}\n{\"q_img\": \"./images/B00AOOWRZI.png\", \"q_text\": \"is darker colored, and is longer sleeved with more blue\", \"positive_key\": \"B00CWMDY20\", \"positive_value\": \"./images/B00CWMDY20.png\", \"hn_image\": [\"./images/B00AOOWRZI.png\"]}\n{\"q_img\": \"./images/B0093PJJLO.png\", \"q_text\": \"is more green, and is green with longer sleeves and lacy hem\", \"positive_key\": \"B0097YD8F4\", \"positive_value\": \"./images/B0097YD8F4.png\", \"hn_image\": [\"./images/B0093PJJLO.png\"]}\n{\"q_img\": \"./images/B00ADYWYXY.png\", \"q_text\": \"is brighter and more casual, and studded purse\", \"positive_key\": \"B006PBBTCG\", \"positive_value\": \"./images/B006PBBTCG.png\", \"hn_image\": [\"./images/B00ADYWYXY.png\"]}\n{\"q_img\": \"./images/B004I90MCU.png\", \"q_text\": \"The shirt is black with rainbow letter stating Free Hugs., and is darker with colorful logo\", \"positive_key\": \"B008BUX1A2\", \"positive_value\": \"./images/B008BUX1A2.png\", \"hn_image\": [\"./images/B004I90MCU.png\"]}\n{\"q_img\": \"./images/B0030IMH9Q.png\", \"q_text\": \"is long sleeve and darker, and has longer sleeves and is darker\", \"positive_key\": \"B002F7B7Y4\", \"positive_value\": \"./images/B002F7B7Y4.png\", \"hn_image\": [\"./images/B0030IMH9Q.png\"]}\n{\"q_img\": \"./images/B008VDQHZU.png\", \"q_text\": \"different color and cut no buttons, and is white and yellow with no straps.\", \"positive_key\": \"B007Y5U5A4\", \"positive_value\": \"./images/B007Y5U5A4.png\", \"hn_image\": [\"./images/B008VDQHZU.png\"]}\n{\"q_img\": \"./images/B007OZIXJO.png\", \"q_text\": \"is green and long sleeved, and Has longer sleeves and more buttons\", \"positive_key\": \"B007X4DCLU\", \"positive_value\": \"./images/B007X4DCLU.png\", \"hn_image\": [\"./images/B007OZIXJO.png\"]}\n{\"q_img\": \"./images/B00BLYSDWG.png\", \"q_text\": \"is more casual and less frilly, and blue wresting logo tee shirt\", \"positive_key\": \"B00A7V294W\", \"positive_value\": \"./images/B00A7V294W.png\", \"hn_image\": [\"./images/B00BLYSDWG.png\"]}\n{\"q_img\": \"./images/B007738INY.png\", \"q_text\": \"has short sleeves and is black, and is darker and sleeveless\", \"positive_key\": \"B003PNTWGW\", \"positive_value\": \"./images/B003PNTWGW.png\", \"hn_image\": [\"./images/B007738INY.png\"]}\n{\"q_img\": \"./images/B008Y7BHM6.png\", \"q_text\": \"is dark grey with longer sleeves, and Gray mid-sleeve\", \"positive_key\": \"B00B065FH6\", \"positive_value\": \"./images/B00B065FH6.png\", \"hn_image\": [\"./images/B008Y7BHM6.png\"]}\n{\"q_img\": \"./images/B00A3Q3QAM.png\", \"q_text\": \"is black with yellow designs, and has a tighter neckline\", \"positive_key\": \"B006C4Y45A\", \"positive_value\": \"./images/B006C4Y45A.png\", \"hn_image\": [\"./images/B00A3Q3QAM.png\"]}\n{\"q_img\": \"./images/B008H3W8S4.png\", \"q_text\": \"is cream and less abstract, and is cream with red picture and text\", \"positive_key\": \"B008H3VS12\", \"positive_value\": \"./images/B008H3VS12.png\", \"hn_image\": [\"./images/B008H3W8S4.png\"]}\n{\"q_img\": \"./images/B003EACZ9M.png\", \"q_text\": \"is black with blue designs, and has a sequined graphic and is a darker color\", \"positive_key\": \"B003LIERE8\", \"positive_value\": \"./images/B003LIERE8.png\", \"hn_image\": [\"./images/B003EACZ9M.png\"]}\n{\"q_img\": \"./images/B009IOEIXY.png\", \"q_text\": \"is black with a JEDI sign, and Is black and has different wording\", \"positive_key\": \"B002827628\", \"positive_value\": \"./images/B002827628.png\", \"hn_image\": [\"./images/B009IOEIXY.png\"]}\n{\"q_img\": \"./images/B00ADYWYXY.png\", \"q_text\": \"Has longer sleeves and is more casual, and is a striped red and white shirt with longer sleeves.\", \"positive_key\": \"B0063P09AM\", \"positive_value\": \"./images/B0063P09AM.png\", \"hn_image\": [\"./images/B00ADYWYXY.png\"]}\n{\"q_img\": \"./images/B007O3US12.png\", \"q_text\": \"is white and more flowy, and is white and flowy\", \"positive_key\": \"B00BRA0QI8\", \"positive_value\": \"./images/B00BRA0QI8.png\", \"hn_image\": [\"./images/B007O3US12.png\"]}\n{\"q_img\": \"./images/B00BG0FKN0.png\", \"q_text\": \"is yellow with black collar, and is yellow and longer sleeves\", \"positive_key\": \"B00C7US69I\", \"positive_value\": \"./images/B00C7US69I.png\", \"hn_image\": [\"./images/B00BG0FKN0.png\"]}\n{\"q_img\": \"./images/B003QOTHLA.png\", \"q_text\": \"is darker, and is black\", \"positive_key\": \"B003QORJKQ\", \"positive_value\": \"./images/B003QORJKQ.png\", \"hn_image\": [\"./images/B003QOTHLA.png\"]}\n{\"q_img\": \"./images/B002518SN8.png\", \"q_text\": \"has short sleeves, and has short sleeves\", \"positive_key\": \"B00251GQ78\", \"positive_value\": \"./images/B00251GQ78.png\", \"hn_image\": [\"./images/B002518SN8.png\"]}\n{\"q_img\": \"./images/B0067EYPX6.png\", \"q_text\": \"is black with pandas on it, and is darker and bold\", \"positive_key\": \"B000OT9CEM\", \"positive_value\": \"./images/B000OT9CEM.png\", \"hn_image\": [\"./images/B0067EYPX6.png\"]}\n{\"q_img\": \"./images/B00B8C4KK0.png\", \"q_text\": \"Is more edgy and masculine, and it has sleeves and it is tight\", \"positive_key\": \"B005OCIITO\", \"positive_value\": \"./images/B005OCIITO.png\", \"hn_image\": [\"./images/B00B8C4KK0.png\"]}\n{\"q_img\": \"./images/B007J7J3P0.png\", \"q_text\": \"has more of a bold line print, and is white and pink chevron stripe\", \"positive_key\": \"B00AKIB7ZE\", \"positive_value\": \"./images/B00AKIB7ZE.png\", \"hn_image\": [\"./images/B007J7J3P0.png\"]}\n{\"q_img\": \"./images/B009VMVAU2.png\", \"q_text\": \"has larger graphic and is less solid, and is black colored\", \"positive_key\": \"B008BTA8U4\", \"positive_value\": \"./images/B008BTA8U4.png\", \"hn_image\": [\"./images/B009VMVAU2.png\"]}\n{\"q_img\": \"./images/B005LIXP08.png\", \"q_text\": \"The shirt is black with Betty Page., and is black with white writing and short sleeves\", \"positive_key\": \"B001QLATCQ\", \"positive_value\": \"./images/B001QLATCQ.png\", \"hn_image\": [\"./images/B005LIXP08.png\"]}\n{\"q_img\": \"./images/B00CTA4DSO.png\", \"q_text\": \"is lighter and has longer sleeves, and is plaid with longer sleeves\", \"positive_key\": \"B004XV2IIE\", \"positive_value\": \"./images/B004XV2IIE.png\", \"hn_image\": [\"./images/B00CTA4DSO.png\"]}\n{\"q_img\": \"./images/B00EV6BXVO.png\", \"q_text\": \"Is more fitted and lacy, and Is less blousey and more revealing\", \"positive_key\": \"B00BI44Q9S\", \"positive_value\": \"./images/B00BI44Q9S.png\", \"hn_image\": [\"./images/B00EV6BXVO.png\"]}\n{\"q_img\": \"./images/B007M7B28I.png\", \"q_text\": \"It is a black shirt with no sleeves., and has no sleeves and is black\", \"positive_key\": \"B0080DIYY8\", \"positive_value\": \"./images/B0080DIYY8.png\", \"hn_image\": [\"./images/B007M7B28I.png\"]}\n{\"q_img\": \"./images/B007PUOJAU.png\", \"q_text\": \"is more wordy and darker, and is black with a smaller similar graphic.\", \"positive_key\": \"B009ZICD80\", \"positive_value\": \"./images/B009ZICD80.png\", \"hn_image\": [\"./images/B007PUOJAU.png\"]}\n{\"q_img\": \"./images/B004SWI1G6.png\", \"q_text\": \"is a t-shirt, and has shorter sleeves\", \"positive_key\": \"B0062CJEAW\", \"positive_value\": \"./images/B0062CJEAW.png\", \"hn_image\": [\"./images/B004SWI1G6.png\"]}\n{\"q_img\": \"./images/B005P0P4VU.png\", \"q_text\": \"The shirt is purple with white buttons., and is darker\", \"positive_key\": \"B009LO455C\", \"positive_value\": \"./images/B009LO455C.png\", \"hn_image\": [\"./images/B005P0P4VU.png\"]}\n{\"q_img\": \"./images/B008H3W8S4.png\", \"q_text\": \"is light green with a large white circle, and is green with white image\", \"positive_key\": \"B008H3VXTO\", \"positive_value\": \"./images/B008H3VXTO.png\", \"hn_image\": [\"./images/B008H3W8S4.png\"]}\n{\"q_img\": \"./images/B008BT599E.png\", \"q_text\": \"The shirt is black with the word DADD., and Is a regular t-shirt with different graphic.\", \"positive_key\": \"B008DO6OFK\", \"positive_value\": \"./images/B008DO6OFK.png\", \"hn_image\": [\"./images/B008BT599E.png\"]}\n{\"q_img\": \"./images/B002DMJRO8.png\", \"q_text\": \"has brown collar with meaningful text, and Is brown and more casual\", \"positive_key\": \"B006VHY2NW\", \"positive_value\": \"./images/B006VHY2NW.png\", \"hn_image\": [\"./images/B002DMJRO8.png\"]}\n{\"q_img\": \"./images/B003V224D0.png\", \"q_text\": \"is coral with no sleeves, and is sleeveless and dark peach color\", \"positive_key\": \"B007W4SK04\", \"positive_value\": \"./images/B007W4SK04.png\", \"hn_image\": [\"./images/B003V224D0.png\"]}\n{\"q_img\": \"./images/B00AZ01658.png\", \"q_text\": \"has a black background., and is black with a different logo\", \"positive_key\": \"B00D30CNK4\", \"positive_value\": \"./images/B00D30CNK4.png\", \"hn_image\": [\"./images/B00AZ01658.png\"]}\n{\"q_img\": \"./images/B0084S6I4C.png\", \"q_text\": \"is purple and fitted, and is purple\", \"positive_key\": \"B00DRYHJSC\", \"positive_value\": \"./images/B00DRYHJSC.png\", \"hn_image\": [\"./images/B0084S6I4C.png\"]}\n{\"q_img\": \"./images/B00B0YM9V8.png\", \"q_text\": \"is a white, and Is white and more colorful\", \"positive_key\": \"B00B9OQ1YA\", \"positive_value\": \"./images/B00B9OQ1YA.png\", \"hn_image\": [\"./images/B00B0YM9V8.png\"]}\n{\"q_img\": \"./images/B00480IRIU.png\", \"q_text\": \"has long sleeves, and longer sleeves with more open neck\", \"positive_key\": \"B00A13GW4Y\", \"positive_value\": \"./images/B00A13GW4Y.png\", \"hn_image\": [\"./images/B00480IRIU.png\"]}\n{\"q_img\": \"./images/B0046VWUJS.png\", \"q_text\": \"grey pink letters with logo, and has more text and more grey\", \"positive_key\": \"B009W7DKKE\", \"positive_value\": \"./images/B009W7DKKE.png\", \"hn_image\": [\"./images/B0046VWUJS.png\"]}\n{\"q_img\": \"./images/B006ZUDD1C.png\", \"q_text\": \"The capped sleeve shirt is white., and is white colored with a looser bottom\", \"positive_key\": \"B007EQ03VY\", \"positive_value\": \"./images/B007EQ03VY.png\", \"hn_image\": [\"./images/B006ZUDD1C.png\"]}\n{\"q_img\": \"./images/B003HIX6TE.png\", \"q_text\": \"is sleeveless and has a print, and has no sleeves and is bright green with pattern\", \"positive_key\": \"B00318CS0S\", \"positive_value\": \"./images/B00318CS0S.png\", \"hn_image\": [\"./images/B003HIX6TE.png\"]}\n{\"q_img\": \"./images/B0074QH1GO.png\", \"q_text\": \"is more colorful and larger logo, and looser neck line\", \"positive_key\": \"B00AEUKZ60\", \"positive_value\": \"./images/B00AEUKZ60.png\", \"hn_image\": [\"./images/B0074QH1GO.png\"]}\n{\"q_img\": \"./images/B005EY7GC2.png\", \"q_text\": \"is darker, and is blue and more revleaing\", \"positive_key\": \"B00686E880\", \"positive_value\": \"./images/B00686E880.png\", \"hn_image\": [\"./images/B005EY7GC2.png\"]}\n{\"q_img\": \"./images/B0085M32JQ.png\", \"q_text\": \"The tank top is leopard print., and is a leopard print patern\", \"positive_key\": \"B0089XPCZI\", \"positive_value\": \"./images/B0089XPCZI.png\", \"hn_image\": [\"./images/B0085M32JQ.png\"]}\n{\"q_img\": \"./images/B005ORSDV2.png\", \"q_text\": \"The halter to is tight and blue in color., and is more revealing\", \"positive_key\": \"B005LM5D0E\", \"positive_value\": \"./images/B005LM5D0E.png\", \"hn_image\": [\"./images/B005ORSDV2.png\"]}\n{\"q_img\": \"./images/B00CQKVVXC.png\", \"q_text\": \"Is more edgy and sexy, and is black with no buttons\", \"positive_key\": \"B00CII5TJO\", \"positive_value\": \"./images/B00CII5TJO.png\", \"hn_image\": [\"./images/B00CQKVVXC.png\"]}\n{\"q_img\": \"./images/B006VJXGAU.png\", \"q_text\": \"has a graphic design, and is darker and has bolder font\", \"positive_key\": \"B0097DMFKO\", \"positive_value\": \"./images/B0097DMFKO.png\", \"hn_image\": [\"./images/B006VJXGAU.png\"]}\n{\"q_img\": \"./images/B005GC3RXY.png\", \"q_text\": \"is striped with long sleeves, and has long sleeves and striped colorful design.\", \"positive_key\": \"B00BULLBJC\", \"positive_value\": \"./images/B00BULLBJC.png\", \"hn_image\": [\"./images/B005GC3RXY.png\"]}\n{\"q_img\": \"./images/B006J4H356.png\", \"q_text\": \"is more sheer and see through, and is light grey and has revealing\", \"positive_key\": \"B005QA9WNK\", \"positive_value\": \"./images/B005QA9WNK.png\", \"hn_image\": [\"./images/B006J4H356.png\"]}\n{\"q_img\": \"./images/B00DCZSCUA.png\", \"q_text\": \"is pink, and is red and purple of color\", \"positive_key\": \"B008NDVF2S\", \"positive_value\": \"./images/B008NDVF2S.png\", \"hn_image\": [\"./images/B00DCZSCUA.png\"]}\n{\"q_img\": \"./images/B002EO85OS.png\", \"q_text\": \"is green, and Is darker & has a fitted look\", \"positive_key\": \"B002LOASP0\", \"positive_value\": \"./images/B002LOASP0.png\", \"hn_image\": [\"./images/B002EO85OS.png\"]}\n{\"q_img\": \"./images/B007WAD2QU.png\", \"q_text\": \"The shirt is orange with white trim., and is orange colored with shorter sleeves and white collar\", \"positive_key\": \"B00CWAF7SG\", \"positive_value\": \"./images/B00CWAF7SG.png\", \"hn_image\": [\"./images/B007WAD2QU.png\"]}\n{\"q_img\": \"./images/B001RV8APS.png\", \"q_text\": \"is a floral print tank top loose fit, and is light gray with a black flower design\", \"positive_key\": \"B00B71A1S2\", \"positive_value\": \"./images/B00B71A1S2.png\", \"hn_image\": [\"./images/B001RV8APS.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"is more colorful and shorter sleeves, and Is more floral and shorter sleeves\", \"positive_key\": \"B0091HZ3WS\", \"positive_value\": \"./images/B0091HZ3WS.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B007XD4IXM.png\", \"q_text\": \"is beige with a black flower, and is brown and short sleeved\", \"positive_key\": \"B004S36WB6\", \"positive_value\": \"./images/B004S36WB6.png\", \"hn_image\": [\"./images/B007XD4IXM.png\"]}\n{\"q_img\": \"./images/B001ZR75XW.png\", \"q_text\": \"has more distinct color art, and Flowers of colorful garden\", \"positive_key\": \"B00BSLBVHG\", \"positive_value\": \"./images/B00BSLBVHG.png\", \"hn_image\": [\"./images/B001ZR75XW.png\"]}\n{\"q_img\": \"./images/B001R63KMQ.png\", \"q_text\": \"is black with writing, and Is darker and less feminine.\", \"positive_key\": \"B006G132LQ\", \"positive_value\": \"./images/B006G132LQ.png\", \"hn_image\": [\"./images/B001R63KMQ.png\"]}\n{\"q_img\": \"./images/B0086JXCEI.png\", \"q_text\": \"has written 'casino' on it, and has more imagery and color\", \"positive_key\": \"B001O0OPI8\", \"positive_value\": \"./images/B001O0OPI8.png\", \"hn_image\": [\"./images/B0086JXCEI.png\"]}\n{\"q_img\": \"./images/B005I4KNX2.png\", \"q_text\": \"is darker and has longer sleeves, and has blue floral print and has sleeves.\", \"positive_key\": \"B005X6RHYI\", \"positive_value\": \"./images/B005X6RHYI.png\", \"hn_image\": [\"./images/B005I4KNX2.png\"]}\n{\"q_img\": \"./images/B001PJEI8U.png\", \"q_text\": \"has a humorous message in the center, and has a different graphic\", \"positive_key\": \"B001PIY68I\", \"positive_value\": \"./images/B001PIY68I.png\", \"hn_image\": [\"./images/B001PJEI8U.png\"]}\n{\"q_img\": \"./images/B00ATUGPDM.png\", \"q_text\": \"The shirt is tan in color, and is brown with grey trim\", \"positive_key\": \"B00749QXWO\", \"positive_value\": \"./images/B00749QXWO.png\", \"hn_image\": [\"./images/B00ATUGPDM.png\"]}\n{\"q_img\": \"./images/B004I8WBQG.png\", \"q_text\": \"is darker, and has buttons on it\", \"positive_key\": \"B004VMHX8A\", \"positive_value\": \"./images/B004VMHX8A.png\", \"hn_image\": [\"./images/B004I8WBQG.png\"]}\n{\"q_img\": \"./images/B002DGRTNU.png\", \"q_text\": \"has longer sleeves with a floral pattern, and is less shirty and more flowing\", \"positive_key\": \"B006O5RSR8\", \"positive_value\": \"./images/B006O5RSR8.png\", \"hn_image\": [\"./images/B002DGRTNU.png\"]}\n{\"q_img\": \"./images/B00AU3Q8DK.png\", \"q_text\": \"is black with blue front stripes, and has stripe graphic design\", \"positive_key\": \"B006VTHA98\", \"positive_value\": \"./images/B006VTHA98.png\", \"hn_image\": [\"./images/B00AU3Q8DK.png\"]}\n{\"q_img\": \"./images/B00980K5X0.png\", \"q_text\": \"is black with polka dots, and is darker\", \"positive_key\": \"B009YJBFGQ\", \"positive_value\": \"./images/B009YJBFGQ.png\", \"hn_image\": [\"./images/B00980K5X0.png\"]}\n{\"q_img\": \"./images/B002IN0LK6.png\", \"q_text\": \"has bit longer sleeves and color is yellow, and is yellow in color\", \"positive_key\": \"B00AZ0180G\", \"positive_value\": \"./images/B00AZ0180G.png\", \"hn_image\": [\"./images/B002IN0LK6.png\"]}\n{\"q_img\": \"./images/B007T8HCPW.png\", \"q_text\": \"is pinker and longer, and Is more wordy and more casual\", \"positive_key\": \"B005CIRXXW\", \"positive_value\": \"./images/B005CIRXXW.png\", \"hn_image\": [\"./images/B007T8HCPW.png\"]}\n{\"q_img\": \"./images/B00AEXWZ7O.png\", \"q_text\": \"is black with an image of Elvis, and has a different logo\", \"positive_key\": \"B002HTOIC8\", \"positive_value\": \"./images/B002HTOIC8.png\", \"hn_image\": [\"./images/B00AEXWZ7O.png\"]}\n{\"q_img\": \"./images/B00BB9UBNU.png\", \"q_text\": \"has shorter sleeves and is a louder color, and is lighter and has shorter sleeves\", \"positive_key\": \"B00C3CVDH2\", \"positive_value\": \"./images/B00C3CVDH2.png\", \"hn_image\": [\"./images/B00BB9UBNU.png\"]}\n{\"q_img\": \"./images/B00CD5NHMI.png\", \"q_text\": \"Has longer sleeves and is more geometric, and a solid darker color and has shorter sleeves\", \"positive_key\": \"B004CPWN1I\", \"positive_value\": \"./images/B004CPWN1I.png\", \"hn_image\": [\"./images/B00CD5NHMI.png\"]}\n{\"q_img\": \"./images/B00BL8ISHW.png\", \"q_text\": \"has longer sleeves and is less revealing, and is a black turtleneck with long sleeves\", \"positive_key\": \"B007VG0E4I\", \"positive_value\": \"./images/B007VG0E4I.png\", \"hn_image\": [\"./images/B00BL8ISHW.png\"]}\n{\"q_img\": \"./images/B007E6A50S.png\", \"q_text\": \"is white, and is white and rouched\", \"positive_key\": \"B008WZC5KS\", \"positive_value\": \"./images/B008WZC5KS.png\", \"hn_image\": [\"./images/B007E6A50S.png\"]}\n{\"q_img\": \"./images/B00DDXIC1U.png\", \"q_text\": \"The shirt is blue with the Union Jack Flag, and comes in blue\", \"positive_key\": \"B00BUE7DHS\", \"positive_value\": \"./images/B00BUE7DHS.png\", \"hn_image\": [\"./images/B00DDXIC1U.png\"]}\n{\"q_img\": \"./images/B00ATQ740Y.png\", \"q_text\": \"long sleeve sheer blue blouse, and is see through\", \"positive_key\": \"B00CJHMG0E\", \"positive_value\": \"./images/B00CJHMG0E.png\", \"hn_image\": [\"./images/B00ATQ740Y.png\"]}\n{\"q_img\": \"./images/B00DP5ZIX6.png\", \"q_text\": \"has longer sleeves with a different graphic, and has more colors to it\", \"positive_key\": \"B00FR621O0\", \"positive_value\": \"./images/B00FR621O0.png\", \"hn_image\": [\"./images/B00DP5ZIX6.png\"]}\n{\"q_img\": \"./images/B004WSJVRY.png\", \"q_text\": \"The shirt has no sleeves and is lace and black in color., and is sleeveless\", \"positive_key\": \"B0090NGZ0W\", \"positive_value\": \"./images/B0090NGZ0W.png\", \"hn_image\": [\"./images/B004WSJVRY.png\"]}\n{\"q_img\": \"./images/B00AZPKQEA.png\", \"q_text\": \"is tighter and less layers, and is a leopard print top\", \"positive_key\": \"B00DQBXWNM\", \"positive_value\": \"./images/B00DQBXWNM.png\", \"hn_image\": [\"./images/B00AZPKQEA.png\"]}\n{\"q_img\": \"./images/B0030K9KPS.png\", \"q_text\": \"Is less flowing and is darker in color, and is black and shorter sleeved\", \"positive_key\": \"B00065XUBU\", \"positive_value\": \"./images/B00065XUBU.png\", \"hn_image\": [\"./images/B0030K9KPS.png\"]}\n{\"q_img\": \"./images/B00BGT0PEA.png\", \"q_text\": \"The shirt is a dark lace color., and is darker in color and has longer sleeves\", \"positive_key\": \"B00AZWRBJG\", \"positive_value\": \"./images/B00AZWRBJG.png\", \"hn_image\": [\"./images/B00BGT0PEA.png\"]}\n{\"q_img\": \"./images/B00CEZJLUE.png\", \"q_text\": \"is lighter, and is lighter in color\", \"positive_key\": \"B00A3QKAEW\", \"positive_value\": \"./images/B00A3QKAEW.png\", \"hn_image\": [\"./images/B00CEZJLUE.png\"]}\n{\"q_img\": \"./images/B0081ZRYN2.png\", \"q_text\": \"is black with England flag on it, and it is black and not stripped\", \"positive_key\": \"B004JPLHGS\", \"positive_value\": \"./images/B004JPLHGS.png\", \"hn_image\": [\"./images/B0081ZRYN2.png\"]}\n{\"q_img\": \"./images/B007T4AUO6.png\", \"q_text\": \" sleeveless and more fitted, and is more fitted and frilly collared\", \"positive_key\": \"B008FXRDW2\", \"positive_value\": \"./images/B008FXRDW2.png\", \"hn_image\": [\"./images/B007T4AUO6.png\"]}\n{\"q_img\": \"./images/B0064SLGDW.png\", \"q_text\": \"is a lighter green and is more form fitting, and has shorter sleeves\", \"positive_key\": \"B00BCMGKSQ\", \"positive_value\": \"./images/B00BCMGKSQ.png\", \"hn_image\": [\"./images/B0064SLGDW.png\"]}\n{\"q_img\": \"./images/B008QW7RWS.png\", \"q_text\": \"its a greenish long dress, and is a long blue dress\", \"positive_key\": \"B008TBS8UG\", \"positive_value\": \"./images/B008TBS8UG.png\", \"hn_image\": [\"./images/B008QW7RWS.png\"]}\n{\"q_img\": \"./images/B009MMJFFS.png\", \"q_text\": \"is grey colored and has sleeves, and has longer sleeves and is grey and white\", \"positive_key\": \"B007FO5H1Q\", \"positive_value\": \"./images/B007FO5H1Q.png\", \"hn_image\": [\"./images/B009MMJFFS.png\"]}\n{\"q_img\": \"./images/B0040JHGO0.png\", \"q_text\": \"is two toned and dark, and has two colors to it\", \"positive_key\": \"B008D58I3U\", \"positive_value\": \"./images/B008D58I3U.png\", \"hn_image\": [\"./images/B0040JHGO0.png\"]}\n{\"q_img\": \"./images/B00B7ETMLG.png\", \"q_text\": \"The shirt is sleeveless and low cut and black in color., and is more revealing and shorter.\", \"positive_key\": \"B009XTUSXI\", \"positive_value\": \"./images/B009XTUSXI.png\", \"hn_image\": [\"./images/B00B7ETMLG.png\"]}\n{\"q_img\": \"./images/B00361FLMC.png\", \"q_text\": \"It has a floral print and long sleeves., and has longer sleeves and is leopard print\", \"positive_key\": \"B000BI4DIG\", \"positive_value\": \"./images/B000BI4DIG.png\", \"hn_image\": [\"./images/B00361FLMC.png\"]}\n{\"q_img\": \"./images/B00AYO9A3A.png\", \"q_text\": \"has no strips on it, and is black with yellow text\", \"positive_key\": \"B004L6631O\", \"positive_value\": \"./images/B004L6631O.png\", \"hn_image\": [\"./images/B00AYO9A3A.png\"]}\n{\"q_img\": \"./images/B00COQ5O9K.png\", \"q_text\": \"Is green with longer sleeves, and is a long sleeved olive shirt\", \"positive_key\": \"B00EPQ7X00\", \"positive_value\": \"./images/B00EPQ7X00.png\", \"hn_image\": [\"./images/B00COQ5O9K.png\"]}\n{\"q_img\": \"./images/B003BVK410.png\", \"q_text\": \"is black with a flower, and is black sleeveless\", \"positive_key\": \"B0058W3G4W\", \"positive_value\": \"./images/B0058W3G4W.png\", \"hn_image\": [\"./images/B003BVK410.png\"]}\n{\"q_img\": \"./images/B00BUL8VJA.png\", \"q_text\": \"Is more colorful and has no sleeves, and more bold with no sleeves\", \"positive_key\": \"B00B55ME9E\", \"positive_value\": \"./images/B00B55ME9E.png\", \"hn_image\": [\"./images/B00BUL8VJA.png\"]}\n{\"q_img\": \"./images/B004SWI1G6.png\", \"q_text\": \"is black colored with red sleeves, and has longer sleeves and is red and black\", \"positive_key\": \"B0009GAOYC\", \"positive_value\": \"./images/B0009GAOYC.png\", \"hn_image\": [\"./images/B004SWI1G6.png\"]}\n{\"q_img\": \"./images/B008OPRRQI.png\", \"q_text\": \"is more revealing, and is sleeveless and gray\", \"positive_key\": \"B005113T7Y\", \"positive_value\": \"./images/B005113T7Y.png\", \"hn_image\": [\"./images/B008OPRRQI.png\"]}\n{\"q_img\": \"./images/B008YDERGI.png\", \"q_text\": \"is orange with long sleeves, and has a v neck and long sleeves\", \"positive_key\": \"B008VQ6OJQ\", \"positive_value\": \"./images/B008VQ6OJQ.png\", \"hn_image\": [\"./images/B008YDERGI.png\"]}\n{\"q_img\": \"./images/B00BM0OYPO.png\", \"q_text\": \"is more strappy and has lace, and is darker and has shorter sleeves\", \"positive_key\": \"B007WAFEWU\", \"positive_value\": \"./images/B007WAFEWU.png\", \"hn_image\": [\"./images/B00BM0OYPO.png\"]}\n{\"q_img\": \"./images/B003HIWK7S.png\", \"q_text\": \"has a v neck, and is darker\", \"positive_key\": \"B005J29SL6\", \"positive_value\": \"./images/B005J29SL6.png\", \"hn_image\": [\"./images/B003HIWK7S.png\"]}\n{\"q_img\": \"./images/B009ZGQ0F4.png\", \"q_text\": \"is lighter, and white with cat face with sunglasses logo\", \"positive_key\": \"B00CV2DS40\", \"positive_value\": \"./images/B00CV2DS40.png\", \"hn_image\": [\"./images/B009ZGQ0F4.png\"]}\n{\"q_img\": \"./images/B004WG7UZQ.png\", \"q_text\": \"is a navy blue shirt with USA logo, and is a darker color with different graphic\", \"positive_key\": \"B003R46JOW\", \"positive_value\": \"./images/B003R46JOW.png\", \"hn_image\": [\"./images/B004WG7UZQ.png\"]}\n{\"q_img\": \"./images/B00DDXH9E6.png\", \"q_text\": \"is navy blue, and is mostly black\", \"positive_key\": \"B00DDXI6N4\", \"positive_value\": \"./images/B00DDXI6N4.png\", \"hn_image\": [\"./images/B00DDXH9E6.png\"]}\n{\"q_img\": \"./images/B00AG2Z2ZU.png\", \"q_text\": \"The short is long sleeve and white., and The desired item is white with long sleeves\", \"positive_key\": \"B00CF5E1G2\", \"positive_value\": \"./images/B00CF5E1G2.png\", \"hn_image\": [\"./images/B00AG2Z2ZU.png\"]}\n{\"q_img\": \"./images/B00E955EOO.png\", \"q_text\": \"is lighter with longer sleeves and whit trim, and is long sleeve and full gray and white\", \"positive_key\": \"B00EKD4LHG\", \"positive_value\": \"./images/B00EKD4LHG.png\", \"hn_image\": [\"./images/B00E955EOO.png\"]}\n{\"q_img\": \"./images/B004TMGDME.png\", \"q_text\": \"is darker and has a different graphic, and is darker\", \"positive_key\": \"B004D8QCN4\", \"positive_value\": \"./images/B004D8QCN4.png\", \"hn_image\": [\"./images/B004TMGDME.png\"]}\n{\"q_img\": \"./images/B009BHFGKC.png\", \"q_text\": \"The shirt is white with the Union Jack Flag., and is lighter\", \"positive_key\": \"B00884SGEM\", \"positive_value\": \"./images/B00884SGEM.png\", \"hn_image\": [\"./images/B009BHFGKC.png\"]}\n{\"q_img\": \"./images/B009ZYYBVG.png\", \"q_text\": \"is blue colored with a meme picture, and is blue and has undersea graphic\", \"positive_key\": \"B004X7HL9O\", \"positive_value\": \"./images/B004X7HL9O.png\", \"hn_image\": [\"./images/B009ZYYBVG.png\"]}\n{\"q_img\": \"./images/B0051SM8SS.png\", \"q_text\": \"The shirt is tight fitting and blue in color., and has longer sleeves and blue colored\", \"positive_key\": \"B00FBI8K08\", \"positive_value\": \"./images/B00FBI8K08.png\", \"hn_image\": [\"./images/B0051SM8SS.png\"]}\n{\"q_img\": \"./images/B00CAAKD3M.png\", \"q_text\": \"is dark blue colored, and it is blue\", \"positive_key\": \"B009IOEIXY\", \"positive_value\": \"./images/B009IOEIXY.png\", \"hn_image\": [\"./images/B00CAAKD3M.png\"]}\n{\"q_img\": \"./images/B005TGOCYK.png\", \"q_text\": \"has one sleeve and is more feminine, and exposes one shoulder\", \"positive_key\": \"B0053AM230\", \"positive_value\": \"./images/B0053AM230.png\", \"hn_image\": [\"./images/B005TGOCYK.png\"]}\n{\"q_img\": \"./images/B00FR621O0.png\", \"q_text\": \"Is purple, and is purple\", \"positive_key\": \"B00DZTID5C\", \"positive_value\": \"./images/B00DZTID5C.png\", \"hn_image\": [\"./images/B00FR621O0.png\"]}\n{\"q_img\": \"./images/B0058K7LBS.png\", \"q_text\": \"The shirt is short sleeved and blue in color., and is darker and has longer sleeves\", \"positive_key\": \"B007W6K6CM\", \"positive_value\": \"./images/B007W6K6CM.png\", \"hn_image\": [\"./images/B0058K7LBS.png\"]}\n{\"q_img\": \"./images/B0063AHHHA.png\", \"q_text\": \"is lighter in color with shorter sleeves, and is a white short sleeve scoop neck\", \"positive_key\": \"B006GZEC1Q\", \"positive_value\": \"./images/B006GZEC1Q.png\", \"hn_image\": [\"./images/B0063AHHHA.png\"]}\n{\"q_img\": \"./images/B006E3ZMA0.png\", \"q_text\": \"Is more patterned and less sexy, and has more opaque sleeves\", \"positive_key\": \"B005MGB0S8\", \"positive_value\": \"./images/B005MGB0S8.png\", \"hn_image\": [\"./images/B006E3ZMA0.png\"]}\n{\"q_img\": \"./images/B005VSPMEK.png\", \"q_text\": \"is more glowing and less ethnic, and is black and has radar logo\", \"positive_key\": \"B000HRTB60\", \"positive_value\": \"./images/B000HRTB60.png\", \"hn_image\": [\"./images/B005VSPMEK.png\"]}\n{\"q_img\": \"./images/B00C7AFSIA.png\", \"q_text\": \"The shirt is tight and dark blue in coloring., and is identical\", \"positive_key\": \"B00AAMOH3Y\", \"positive_value\": \"./images/B00AAMOH3Y.png\", \"hn_image\": [\"./images/B00C7AFSIA.png\"]}\n{\"q_img\": \"./images/B004I75LNM.png\", \"q_text\": \"has a shorter sleeve but brighter color, and is lighter in color\", \"positive_key\": \"B008K2OPMO\", \"positive_value\": \"./images/B008K2OPMO.png\", \"hn_image\": [\"./images/B004I75LNM.png\"]}\n{\"q_img\": \"./images/B009ZYY1OS.png\", \"q_text\": \"is lighter and has longer sleeves, and Is fitted and has longer sleeves\", \"positive_key\": \"B00FC7KLZU\", \"positive_value\": \"./images/B00FC7KLZU.png\", \"hn_image\": [\"./images/B009ZYY1OS.png\"]}\n{\"q_img\": \"./images/B00BLKRYK2.png\", \"q_text\": \"sleeveless printed with belt, and has a colored pattern to it\", \"positive_key\": \"B00BFH34UU\", \"positive_value\": \"./images/B00BFH34UU.png\", \"hn_image\": [\"./images/B00BLKRYK2.png\"]}\n{\"q_img\": \"./images/B007NE48KE.png\", \"q_text\": \"The loose fitting jersey shirt is gray in color., and is shiny grey and has long sleeve\", \"positive_key\": \"B006UZR1MY\", \"positive_value\": \"./images/B006UZR1MY.png\", \"hn_image\": [\"./images/B007NE48KE.png\"]}\n{\"q_img\": \"./images/B00945QZJM.png\", \"q_text\": \"is darker, and is black colored with font graphic design\", \"positive_key\": \"B00BXL4BRI\", \"positive_value\": \"./images/B00BXL4BRI.png\", \"hn_image\": [\"./images/B00945QZJM.png\"]}\n{\"q_img\": \"./images/B006WAP98U.png\", \"q_text\": \"a badge, and white with scoop neck and tulip sleeves\", \"positive_key\": \"B0024XNP2Q\", \"positive_value\": \"./images/B0024XNP2Q.png\", \"hn_image\": [\"./images/B006WAP98U.png\"]}\n{\"q_img\": \"./images/B002CJ5LAQ.png\", \"q_text\": \"is darker, and navy with smaller graphics\", \"positive_key\": \"B0059631QK\", \"positive_value\": \"./images/B0059631QK.png\", \"hn_image\": [\"./images/B002CJ5LAQ.png\"]}\n{\"q_img\": \"./images/B0074QH1GO.png\", \"q_text\": \"has a longer sleeve with stripes, and has longer sleeves and is red\", \"positive_key\": \"B008BT5CPU\", \"positive_value\": \"./images/B008BT5CPU.png\", \"hn_image\": [\"./images/B0074QH1GO.png\"]}\n{\"q_img\": \"./images/B006GHSP6C.png\", \"q_text\": \"has more colors and long sleeve, and has a flower paatern\", \"positive_key\": \"B009L66M1A\", \"positive_value\": \"./images/B009L66M1A.png\", \"hn_image\": [\"./images/B006GHSP6C.png\"]}\n{\"q_img\": \"./images/B002DGHCHS.png\", \"q_text\": \"is light pink with low neck, and has shorter sleeves and a more revealing neck line\", \"positive_key\": \"B005H7CO6Y\", \"positive_value\": \"./images/B005H7CO6Y.png\", \"hn_image\": [\"./images/B002DGHCHS.png\"]}\n{\"q_img\": \"./images/B00C99PYQK.png\", \"q_text\": \"is girl cut with different words, and it is black\", \"positive_key\": \"B001KWO4YU\", \"positive_value\": \"./images/B001KWO4YU.png\", \"hn_image\": [\"./images/B00C99PYQK.png\"]}\n{\"q_img\": \"./images/B00E8DA258.png\", \"q_text\": \"is lighter, and has tie at the hem and is looser\", \"positive_key\": \"B00B1N6PYK\", \"positive_value\": \"./images/B00B1N6PYK.png\", \"hn_image\": [\"./images/B00E8DA258.png\"]}\n{\"q_img\": \"./images/B00DQX1YLM.png\", \"q_text\": \"has no sleeves and is burgundy, and floral and dark color.\", \"positive_key\": \"B007AIK2DU\", \"positive_value\": \"./images/B007AIK2DU.png\", \"hn_image\": [\"./images/B00DQX1YLM.png\"]}\n{\"q_img\": \"./images/B00B55ME9E.png\", \"q_text\": \"is long sleeved with black color, and has holes\", \"positive_key\": \"B00A8FCYEW\", \"positive_value\": \"./images/B00A8FCYEW.png\", \"hn_image\": [\"./images/B00B55ME9E.png\"]}\n{\"q_img\": \"./images/B005MVACSM.png\", \"q_text\": \"is white and less sporty, and is white with lower collar\", \"positive_key\": \"B004AHKZG8\", \"positive_value\": \"./images/B004AHKZG8.png\", \"hn_image\": [\"./images/B005MVACSM.png\"]}\n{\"q_img\": \"./images/B0018ZUW40.png\", \"q_text\": \"is black with long sleeves, and has longer sleeves and single skull picture\", \"positive_key\": \"B008ICHIMK\", \"positive_value\": \"./images/B008ICHIMK.png\", \"hn_image\": [\"./images/B0018ZUW40.png\"]}\n{\"q_img\": \"./images/B007TV73QC.png\", \"q_text\": \"has longer sleeves and a different pattern, and has longer sleeves\", \"positive_key\": \"B008BT60TM\", \"positive_value\": \"./images/B008BT60TM.png\", \"hn_image\": [\"./images/B007TV73QC.png\"]}\n{\"q_img\": \"./images/B004XFX3H0.png\", \"q_text\": \"The shirt is long and white., and is all white\", \"positive_key\": \"B004XFWZ4M\", \"positive_value\": \"./images/B004XFWZ4M.png\", \"hn_image\": [\"./images/B004XFX3H0.png\"]}\n{\"q_img\": \"./images/B002UNLYPK.png\", \"q_text\": \"is darker, and is black colored\", \"positive_key\": \"B002UNLRA2\", \"positive_value\": \"./images/B002UNLRA2.png\", \"hn_image\": [\"./images/B002UNLYPK.png\"]}\n{\"q_img\": \"./images/B007WAU92U.png\", \"q_text\": \"is short sleeve zebra print with belt, and is shorter sleeved and more white\", \"positive_key\": \"B008VDBWVE\", \"positive_value\": \"./images/B008VDBWVE.png\", \"hn_image\": [\"./images/B007WAU92U.png\"]}\n{\"q_img\": \"./images/B00EYIOCIA.png\", \"q_text\": \"Is white and more frilly, and is white with ruffles\", \"positive_key\": \"B00D1G46F0\", \"positive_value\": \"./images/B00D1G46F0.png\", \"hn_image\": [\"./images/B00EYIOCIA.png\"]}\n{\"q_img\": \"./images/B00BFL3XS4.png\", \"q_text\": \"The shirt has an animal print., and is tiger print.\", \"positive_key\": \"B00ARBVWE0\", \"positive_value\": \"./images/B00ARBVWE0.png\", \"hn_image\": [\"./images/B00BFL3XS4.png\"]}\n{\"q_img\": \"./images/B003QOTMNI.png\", \"q_text\": \"The long sleeve shirt is blue., and has longer sleeves and is a darker color\", \"positive_key\": \"B003Q4VE8O\", \"positive_value\": \"./images/B003Q4VE8O.png\", \"hn_image\": [\"./images/B003QOTMNI.png\"]}\n{\"q_img\": \"./images/B007M7B28I.png\", \"q_text\": \"is solid black with long sleeves, and is darker in color\", \"positive_key\": \"B008G0ECRI\", \"positive_value\": \"./images/B008G0ECRI.png\", \"hn_image\": [\"./images/B007M7B28I.png\"]}\n{\"q_img\": \"./images/B008NF7N5E.png\", \"q_text\": \"is gray and has a round neck, and is gray with shorter sleeves\", \"positive_key\": \"B00DJ85VU4\", \"positive_value\": \"./images/B00DJ85VU4.png\", \"hn_image\": [\"./images/B008NF7N5E.png\"]}\n{\"q_img\": \"./images/B005LM5D0E.png\", \"q_text\": \"is a lighter blue with sleeves, and striped & green\", \"positive_key\": \"B00BTN2RIU\", \"positive_value\": \"./images/B00BTN2RIU.png\", \"hn_image\": [\"./images/B005LM5D0E.png\"]}\n{\"q_img\": \"./images/B00CBSGXTQ.png\", \"q_text\": \"has a colorful print, and it is darker and it had more print on it\", \"positive_key\": \"B007VP69O8\", \"positive_value\": \"./images/B007VP69O8.png\", \"hn_image\": [\"./images/B00CBSGXTQ.png\"]}\n{\"q_img\": \"./images/B00BWUX66M.png\", \"q_text\": \"is a long sleeve turquoise sweater, and is more solid blue colored\", \"positive_key\": \"B00ET8J06E\", \"positive_value\": \"./images/B00ET8J06E.png\", \"hn_image\": [\"./images/B00BWUX66M.png\"]}\n{\"q_img\": \"./images/B00836H3JE.png\", \"q_text\": \" sleeveless and has a collar, and has short sleeves and less draping\", \"positive_key\": \"B004I6IQO4\", \"positive_value\": \"./images/B004I6IQO4.png\", \"hn_image\": [\"./images/B00836H3JE.png\"]}\n{\"q_img\": \"./images/B0001TP406.png\", \"q_text\": \"only has an image, and has a V neck and is more pink\", \"positive_key\": \"B003RW1XTK\", \"positive_value\": \"./images/B003RW1XTK.png\", \"hn_image\": [\"./images/B0001TP406.png\"]}\n{\"q_img\": \"./images/B00DLYL2ZO.png\", \"q_text\": \"has a design graphic, and is covered in a graphic pattern\", \"positive_key\": \"B00DNIF8ZS\", \"positive_value\": \"./images/B00DNIF8ZS.png\", \"hn_image\": [\"./images/B00DLYL2ZO.png\"]}\n{\"q_img\": \"./images/B0015UTLJU.png\", \"q_text\": \"is more sexy and is white, and has no sleeves but red spaghetti straps\", \"positive_key\": \"B007LWNRRI\", \"positive_value\": \"./images/B007LWNRRI.png\", \"hn_image\": [\"./images/B0015UTLJU.png\"]}\n{\"q_img\": \"./images/B00B5MEQMA.png\", \"q_text\": \"The tank top is loose fitting and purple in color., and is sleeveless and is a brighter shade of purple\", \"positive_key\": \"B00EIVRN8E\", \"positive_value\": \"./images/B00EIVRN8E.png\", \"hn_image\": [\"./images/B00B5MEQMA.png\"]}\n{\"q_img\": \"./images/B008I7A07U.png\", \"q_text\": \"has longer sleeves with a picture, and is lighter shade of grey with graphic image in front\", \"positive_key\": \"B005M3UGH2\", \"positive_value\": \"./images/B005M3UGH2.png\", \"hn_image\": [\"./images/B008I7A07U.png\"]}\n{\"q_img\": \"./images/B00CBSGXTQ.png\", \"q_text\": \"is more masculine, and is lighter\", \"positive_key\": \"B002214OT8\", \"positive_value\": \"./images/B002214OT8.png\", \"hn_image\": [\"./images/B00CBSGXTQ.png\"]}\n{\"q_img\": \"./images/B001HBKGB4.png\", \"q_text\": \"is red with no lettering, and vertical stripe alternating print\", \"positive_key\": \"B00ATTNOVE\", \"positive_value\": \"./images/B00ATTNOVE.png\", \"hn_image\": [\"./images/B001HBKGB4.png\"]}\n{\"q_img\": \"./images/B004G0A72G.png\", \"q_text\": \"has shorter sleeves and is tighter, and is lighter and has shorter sleeves\", \"positive_key\": \"B00A00H7N8\", \"positive_value\": \"./images/B00A00H7N8.png\", \"hn_image\": [\"./images/B004G0A72G.png\"]}\n{\"q_img\": \"./images/B00CYP43U2.png\", \"q_text\": \"is more revealing and more colors, and is teal\", \"positive_key\": \"B00CA7FEUW\", \"positive_value\": \"./images/B00CA7FEUW.png\", \"hn_image\": [\"./images/B00CYP43U2.png\"]}\n{\"q_img\": \"./images/B009YKBNJO.png\", \"q_text\": \"is lighter, and has more colorful pattern\", \"positive_key\": \"B009YK8DFQ\", \"positive_value\": \"./images/B009YK8DFQ.png\", \"hn_image\": [\"./images/B009YKBNJO.png\"]}\n{\"q_img\": \"./images/B00AFEKXXA.png\", \"q_text\": \"The shirt is white in color and tight fitting., and is white and less revealing\", \"positive_key\": \"B004RS75HC\", \"positive_value\": \"./images/B004RS75HC.png\", \"hn_image\": [\"./images/B00AFEKXXA.png\"]}\n{\"q_img\": \"./images/B0085J2S6C.png\", \"q_text\": \"is pink and white stripes, and Red/white striped half sleeved blouse\", \"positive_key\": \"B005Y8GDYK\", \"positive_value\": \"./images/B005Y8GDYK.png\", \"hn_image\": [\"./images/B0085J2S6C.png\"]}\n{\"q_img\": \"./images/B008TWBPR8.png\", \"q_text\": \"is longer and fuller iwith dark print, and is darker\", \"positive_key\": \"B0092TLPGS\", \"positive_value\": \"./images/B0092TLPGS.png\", \"hn_image\": [\"./images/B008TWBPR8.png\"]}\n{\"q_img\": \"./images/B00BQWOBG0.png\", \"q_text\": \"has different graphics, and has a different logo\", \"positive_key\": \"B00D5XII9O\", \"positive_value\": \"./images/B00D5XII9O.png\", \"hn_image\": [\"./images/B00BQWOBG0.png\"]}\n{\"q_img\": \"./images/B008CG10YO.png\", \"q_text\": \"has long sleeves and is white, and is white with long sleeves\", \"positive_key\": \"B007EX22S4\", \"positive_value\": \"./images/B007EX22S4.png\", \"hn_image\": [\"./images/B008CG10YO.png\"]}\n{\"q_img\": \"./images/B003WE9T80.png\", \"q_text\": \"its more pink with shoulder free sleeves, and is more structured\", \"positive_key\": \"B006N4RO8S\", \"positive_value\": \"./images/B006N4RO8S.png\", \"hn_image\": [\"./images/B003WE9T80.png\"]}\n{\"q_img\": \"./images/B002WQE9N4.png\", \"q_text\": \"has image of cross on it, and has christian broncos on front\", \"positive_key\": \"B005GLHN8K\", \"positive_value\": \"./images/B005GLHN8K.png\", \"hn_image\": [\"./images/B002WQE9N4.png\"]}\n{\"q_img\": \"./images/B00C3CU92C.png\", \"q_text\": \"The shirt is loose fitting with red and white stripes., and it is red and stripped\", \"positive_key\": \"B009XAYQM6\", \"positive_value\": \"./images/B009XAYQM6.png\", \"hn_image\": [\"./images/B00C3CU92C.png\"]}\n{\"q_img\": \"./images/B00BTHS64A.png\", \"q_text\": \"is purple, and is purple and has buttons on shoulder\", \"positive_key\": \"B00BP83ZIU\", \"positive_value\": \"./images/B00BP83ZIU.png\", \"hn_image\": [\"./images/B00BTHS64A.png\"]}\n{\"q_img\": \"./images/B008D0E320.png\", \"q_text\": \"has a tie waste and brighter in color, and A green/blue/white/red checked shirt\", \"positive_key\": \"B0097C8DP6\", \"positive_value\": \"./images/B0097C8DP6.png\", \"hn_image\": [\"./images/B008D0E320.png\"]}\n{\"q_img\": \"./images/B008UALEK2.png\", \"q_text\": \"The short sleeved shirt is green in color with white writing., and is more of a green color and looser\", \"positive_key\": \"B008RXAE5S\", \"positive_value\": \"./images/B008RXAE5S.png\", \"hn_image\": [\"./images/B008UALEK2.png\"]}\n{\"q_img\": \"./images/B0089HEOHG.png\", \"q_text\": \"has longer sleeves and is grey and purple, and has long sleeve and is dark grey color\", \"positive_key\": \"B0081ZRZ0O\", \"positive_value\": \"./images/B0081ZRZ0O.png\", \"hn_image\": [\"./images/B0089HEOHG.png\"]}\n{\"q_img\": \"./images/B005E0TR32.png\", \"q_text\": \"has a different graphic and is more blue in colour, and Has a feminine cut and blue.\", \"positive_key\": \"B005NAEZXU\", \"positive_value\": \"./images/B005NAEZXU.png\", \"hn_image\": [\"./images/B005E0TR32.png\"]}\n{\"q_img\": \"./images/B0064XXI42.png\", \"q_text\": \"is darker and less revealing, and has buttons\", \"positive_key\": \"B00CTA3OT8\", \"positive_value\": \"./images/B00CTA3OT8.png\", \"hn_image\": [\"./images/B0064XXI42.png\"]}\n{\"q_img\": \"./images/B009ZYQ61Y.png\", \"q_text\": \"The zip up jacket is black in color., and is black with longer sleeves and a zipper.\", \"positive_key\": \"B004ODK7H0\", \"positive_value\": \"./images/B004ODK7H0.png\", \"hn_image\": [\"./images/B009ZYQ61Y.png\"]}\n{\"q_img\": \"./images/B0052CHM9S.png\", \"q_text\": \"The shirt is white with a skull., and is white with gray letters\", \"positive_key\": \"B00D2O8EAE\", \"positive_value\": \"./images/B00D2O8EAE.png\", \"hn_image\": [\"./images/B0052CHM9S.png\"]}\n{\"q_img\": \"./images/B005EOA21E.png\", \"q_text\": \"is more fitted with shorter sleeves, and has a tighter fit nd lower scoop neck\", \"positive_key\": \"B00746ECKM\", \"positive_value\": \"./images/B00746ECKM.png\", \"hn_image\": [\"./images/B005EOA21E.png\"]}\n{\"q_img\": \"./images/B00AZXM5QY.png\", \"q_text\": \"is less revealing and has a text print on it, and has more text and has sleeves\", \"positive_key\": \"B0046IDPWC\", \"positive_value\": \"./images/B0046IDPWC.png\", \"hn_image\": [\"./images/B00AZXM5QY.png\"]}\n{\"q_img\": \"./images/B00F56HN22.png\", \"q_text\": \"is white, and is long sleeve white color\", \"positive_key\": \"B005V9YOS4\", \"positive_value\": \"./images/B005V9YOS4.png\", \"hn_image\": [\"./images/B00F56HN22.png\"]}\n{\"q_img\": \"./images/B004WKWMOQ.png\", \"q_text\": \"is lighter, and is gray colored\", \"positive_key\": \"B0052FQP6Q\", \"positive_value\": \"./images/B0052FQP6Q.png\", \"hn_image\": [\"./images/B004WKWMOQ.png\"]}\n{\"q_img\": \"./images/B008622J80.png\", \"q_text\": \"is smaller and lacier, and is white and more sheer\", \"positive_key\": \"B004EEVRT6\", \"positive_value\": \"./images/B004EEVRT6.png\", \"hn_image\": [\"./images/B008622J80.png\"]}\n{\"q_img\": \"./images/B009Z1YTV6.png\", \"q_text\": \"is black colored and has sleeves, and black teeshirt game of thrones logo\", \"positive_key\": \"B006H6WNQ0\", \"positive_value\": \"./images/B006H6WNQ0.png\", \"hn_image\": [\"./images/B009Z1YTV6.png\"]}\n{\"q_img\": \"./images/B0096HHMC2.png\", \"q_text\": \"The shirt is the torso of a hula dancer., and has sleeves and has a design of a body.\", \"positive_key\": \"B00CLZ14S4\", \"positive_value\": \"./images/B00CLZ14S4.png\", \"hn_image\": [\"./images/B0096HHMC2.png\"]}\n{\"q_img\": \"./images/B00AU8BR5O.png\", \"q_text\": \"is darker colored with sleeves, and is darker and shinier\", \"positive_key\": \"B00AG2Z2ZU\", \"positive_value\": \"./images/B00AG2Z2ZU.png\", \"hn_image\": [\"./images/B00AU8BR5O.png\"]}\n{\"q_img\": \"./images/B003RKKF2I.png\", \"q_text\": \"is black with yellow lettering, and Is black with a band name.\", \"positive_key\": \"B00BT0SHEQ\", \"positive_value\": \"./images/B00BT0SHEQ.png\", \"hn_image\": [\"./images/B003RKKF2I.png\"]}\n{\"q_img\": \"./images/B0073WBH6O.png\", \"q_text\": \"has a long sleeve purple color long v-neck, and long sleeve purple deep v neck\", \"positive_key\": \"B00EU7OYYW\", \"positive_value\": \"./images/B00EU7OYYW.png\", \"hn_image\": [\"./images/B0073WBH6O.png\"]}\n{\"q_img\": \"./images/B001HBKGB4.png\", \"q_text\": \"Is black and more colorful for the graphic, and black with different graphic\", \"positive_key\": \"B000LRU7NM\", \"positive_value\": \"./images/B000LRU7NM.png\", \"hn_image\": [\"./images/B001HBKGB4.png\"]}\n{\"q_img\": \"./images/B00BBI9NIU.png\", \"q_text\": \"has small straps and is more revealingg, and has spaghetti straps and is black and white\", \"positive_key\": \"B00B02GQ1E\", \"positive_value\": \"./images/B00B02GQ1E.png\", \"hn_image\": [\"./images/B00BBI9NIU.png\"]}\n{\"q_img\": \"./images/B0030K9KPS.png\", \"q_text\": \"is darker, and is brown with different collar\", \"positive_key\": \"B006EJEMPU\", \"positive_value\": \"./images/B006EJEMPU.png\", \"hn_image\": [\"./images/B0030K9KPS.png\"]}\n{\"q_img\": \"./images/B001COZV98.png\", \"q_text\": \"The shirt has short sleeves red in color and is white in color., and is red in color\", \"positive_key\": \"B00GPA3MHC\", \"positive_value\": \"./images/B00GPA3MHC.png\", \"hn_image\": [\"./images/B001COZV98.png\"]}\n{\"q_img\": \"./images/B0001TOK8S.png\", \"q_text\": \"is grey colored and sleeveless, and Is gray and sleeveless\", \"positive_key\": \"B002BZWYNS\", \"positive_value\": \"./images/B002BZWYNS.png\", \"hn_image\": [\"./images/B0001TOK8S.png\"]}\n{\"q_img\": \"./images/B0077Z6LQ8.png\", \"q_text\": \"teal muscle shirt with hero logo, and it is blue\", \"positive_key\": \"B00CP1G416\", \"positive_value\": \"./images/B00CP1G416.png\", \"hn_image\": [\"./images/B0077Z6LQ8.png\"]}\n{\"q_img\": \"./images/B008J9Z6YY.png\", \"q_text\": \"is red and black striped, and is bolder and striped\", \"positive_key\": \"B008622N1S\", \"positive_value\": \"./images/B008622N1S.png\", \"hn_image\": [\"./images/B008J9Z6YY.png\"]}\n{\"q_img\": \"./images/B00BQOYCF8.png\", \"q_text\": \"is lighter, and has longer sleeves and a lighter color\", \"positive_key\": \"B009XG4102\", \"positive_value\": \"./images/B009XG4102.png\", \"hn_image\": [\"./images/B00BQOYCF8.png\"]}\n{\"q_img\": \"./images/B00G4Y6EWO.png\", \"q_text\": \"is black and has shorter sleeves, and is shiny black and is short with short sleeves\", \"positive_key\": \"B00C69C5HO\", \"positive_value\": \"./images/B00C69C5HO.png\", \"hn_image\": [\"./images/B00G4Y6EWO.png\"]}\n{\"q_img\": \"./images/B008NF7N5E.png\", \"q_text\": \"has short sleeves and is darker colored, and has longer sleeves and black and red design\", \"positive_key\": \"B00DYVVKHO\", \"positive_value\": \"./images/B00DYVVKHO.png\", \"hn_image\": [\"./images/B008NF7N5E.png\"]}\n{\"q_img\": \"./images/B0075622PS.png\", \"q_text\": \"is darker and has longer sleeves, and is blue and has long sleeves\", \"positive_key\": \"B00AYDN2A8\", \"positive_value\": \"./images/B00AYDN2A8.png\", \"hn_image\": [\"./images/B0075622PS.png\"]}\n{\"q_img\": \"./images/B004I5BDKY.png\", \"q_text\": \"is gray with no stripes, and has more of a v-neck collar\", \"positive_key\": \"B002KHNEXG\", \"positive_value\": \"./images/B002KHNEXG.png\", \"hn_image\": [\"./images/B004I5BDKY.png\"]}\n{\"q_img\": \"./images/B009W4OV0U.png\", \"q_text\": \"Is more flowing and pink, and is solid pink\", \"positive_key\": \"B005U345LC\", \"positive_value\": \"./images/B005U345LC.png\", \"hn_image\": [\"./images/B009W4OV0U.png\"]}\n{\"q_img\": \"./images/B0053GG7JY.png\", \"q_text\": \"is darker colored, and is darker in color\", \"positive_key\": \"B00DHJP5HY\", \"positive_value\": \"./images/B00DHJP5HY.png\", \"hn_image\": [\"./images/B0053GG7JY.png\"]}\n{\"q_img\": \"./images/B006SF8D3M.png\", \"q_text\": \"is darker, and Is a lighter color and is more sporty\", \"positive_key\": \"B005EE3SRE\", \"positive_value\": \"./images/B005EE3SRE.png\", \"hn_image\": [\"./images/B006SF8D3M.png\"]}\n{\"q_img\": \"./images/B00AA1RC0A.png\", \"q_text\": \"Is grey and has a different graphic, and a black womens cut tshirt\", \"positive_key\": \"B00CNCURMO\", \"positive_value\": \"./images/B00CNCURMO.png\", \"hn_image\": [\"./images/B00AA1RC0A.png\"]}\n{\"q_img\": \"./images/B0096I9KZS.png\", \"q_text\": \"has black trim and longer sleeves, and is wider\", \"positive_key\": \"B007FNY26S\", \"positive_value\": \"./images/B007FNY26S.png\", \"hn_image\": [\"./images/B0096I9KZS.png\"]}\n{\"q_img\": \"./images/B006ZIPC86.png\", \"q_text\": \"has shorter sleeved and a darker print, and is lighter\", \"positive_key\": \"B006ZIP0R4\", \"positive_value\": \"./images/B006ZIP0R4.png\", \"hn_image\": [\"./images/B006ZIPC86.png\"]}\n{\"q_img\": \"./images/B008PUGRYK.png\", \"q_text\": \"Has shorter sleeves and is more casual, and is a tee shirt with star-and-circle graphic\", \"positive_key\": \"B008MC2KTC\", \"positive_value\": \"./images/B008MC2KTC.png\", \"hn_image\": [\"./images/B008PUGRYK.png\"]}\n{\"q_img\": \"./images/B006ZU804C.png\", \"q_text\": \"is white with no sleeves, and is sleeveless and white\", \"positive_key\": \"B00DPUP016\", \"positive_value\": \"./images/B00DPUP016.png\", \"hn_image\": [\"./images/B006ZU804C.png\"]}\n{\"q_img\": \"./images/B008IGIO7Y.png\", \"q_text\": \"is black and has text on it, and is black with words\", \"positive_key\": \"B002HR9OT2\", \"positive_value\": \"./images/B002HR9OT2.png\", \"hn_image\": [\"./images/B008IGIO7Y.png\"]}\n{\"q_img\": \"./images/B007SMNWB2.png\", \"q_text\": \"The tank top is pink in color., and is solid pink\", \"positive_key\": \"B00C4YP0YQ\", \"positive_value\": \"./images/B00C4YP0YQ.png\", \"hn_image\": [\"./images/B007SMNWB2.png\"]}\n{\"q_img\": \"./images/B00EB5QEIC.png\", \"q_text\": \"gray tee black logo, and has more grey\", \"positive_key\": \"B00EPFO3Y0\", \"positive_value\": \"./images/B00EPFO3Y0.png\", \"hn_image\": [\"./images/B00EB5QEIC.png\"]}\n{\"q_img\": \"./images/B00EB5QEIC.png\", \"q_text\": \"is a gray t-shirt with a logo, and Is darker gray & has longer sleeves\", \"positive_key\": \"B00EPFH8WY\", \"positive_value\": \"./images/B00EPFH8WY.png\", \"hn_image\": [\"./images/B00EB5QEIC.png\"]}\n{\"q_img\": \"./images/B00B9OQ1YA.png\", \"q_text\": \"is yellow, and is yellow colored and has sleeves\", \"positive_key\": \"B004IOL30U\", \"positive_value\": \"./images/B004IOL30U.png\", \"hn_image\": [\"./images/B00B9OQ1YA.png\"]}\n{\"q_img\": \"./images/B007OZIXJO.png\", \"q_text\": \"has longer sleeves and light blue color, and has long sleeves and is blue\", \"positive_key\": \"B003VO1L1O\", \"positive_value\": \"./images/B003VO1L1O.png\", \"hn_image\": [\"./images/B007OZIXJO.png\"]}\n{\"q_img\": \"./images/B00EUI7VZ0.png\", \"q_text\": \"is white with black sleeves, and has Disney character and raglan sleeves\", \"positive_key\": \"B002W5RZOK\", \"positive_value\": \"./images/B002W5RZOK.png\", \"hn_image\": [\"./images/B00EUI7VZ0.png\"]}\n{\"q_img\": \"./images/B003YLKY12.png\", \"q_text\": \"has long sleeves, and Is denim with long sleeves.\", \"positive_key\": \"B0097AH5DY\", \"positive_value\": \"./images/B0097AH5DY.png\", \"hn_image\": [\"./images/B003YLKY12.png\"]}\n{\"q_img\": \"./images/B0014UJR6S.png\", \"q_text\": \"is green with black writing, and is a mint green color\", \"positive_key\": \"B009ZIFWNS\", \"positive_value\": \"./images/B009ZIFWNS.png\", \"hn_image\": [\"./images/B0014UJR6S.png\"]}\n{\"q_img\": \"./images/B007Z224PA.png\", \"q_text\": \"The shirt is black in color., and is all black with image\", \"positive_key\": \"B001TETMC8\", \"positive_value\": \"./images/B001TETMC8.png\", \"hn_image\": [\"./images/B007Z224PA.png\"]}\n{\"q_img\": \"./images/B008KRDRF0.png\", \"q_text\": \"is white with a v-neck, and is white with long sleeves\", \"positive_key\": \"B0089KF6T8\", \"positive_value\": \"./images/B0089KF6T8.png\", \"hn_image\": [\"./images/B008KRDRF0.png\"]}\n{\"q_img\": \"./images/B000QX3LDE.png\", \"q_text\": \"is black with a different image, and is a darker color\", \"positive_key\": \"B0067F9A0S\", \"positive_value\": \"./images/B0067F9A0S.png\", \"hn_image\": [\"./images/B000QX3LDE.png\"]}\n{\"q_img\": \"./images/B00C5NMEPE.png\", \"q_text\": \"is a male t shirt, and Has a graphic\", \"positive_key\": \"B003MWXXCK\", \"positive_value\": \"./images/B003MWXXCK.png\", \"hn_image\": [\"./images/B00C5NMEPE.png\"]}\n{\"q_img\": \"./images/B003IKOOTW.png\", \"q_text\": \"is more formal, and is long sleeved gray and black plaid\", \"positive_key\": \"B008D0E320\", \"positive_value\": \"./images/B008D0E320.png\", \"hn_image\": [\"./images/B003IKOOTW.png\"]}\n{\"q_img\": \"./images/B009X6TCGU.png\", \"q_text\": \"Is more revealing on the shoulders and more sexy, and is off one shoulder and purple\", \"positive_key\": \"B00GQSG9QE\", \"positive_value\": \"./images/B00GQSG9QE.png\", \"hn_image\": [\"./images/B009X6TCGU.png\"]}\n{\"q_img\": \"./images/B00DS0TDAW.png\", \"q_text\": \"is black with skull designs, and sleeves\", \"positive_key\": \"B0018ZUW40\", \"positive_value\": \"./images/B0018ZUW40.png\", \"hn_image\": [\"./images/B00DS0TDAW.png\"]}\n{\"q_img\": \"./images/B007N92BIU.png\", \"q_text\": \"is lighter and less wordy, and is a white tshirt\", \"positive_key\": \"B00APFUVJA\", \"positive_value\": \"./images/B00APFUVJA.png\", \"hn_image\": [\"./images/B007N92BIU.png\"]}\n{\"q_img\": \"./images/B00G3DOW3O.png\", \"q_text\": \"Is white, and is white with a dog picture\", \"positive_key\": \"B000LMU6X8\", \"positive_value\": \"./images/B000LMU6X8.png\", \"hn_image\": [\"./images/B00G3DOW3O.png\"]}\n{\"q_img\": \"./images/B00BJYIR9C.png\", \"q_text\": \"is burgancy, and is burgundy red\", \"positive_key\": \"B00E9P1FOC\", \"positive_value\": \"./images/B00E9P1FOC.png\", \"hn_image\": [\"./images/B00BJYIR9C.png\"]}\n{\"q_img\": \"./images/B006G3BRRK.png\", \"q_text\": \"is black and has shorter sleeves, and is a black t-shirt with red logo\", \"positive_key\": \"B001L2Y1UQ\", \"positive_value\": \"./images/B001L2Y1UQ.png\", \"hn_image\": [\"./images/B006G3BRRK.png\"]}\n{\"q_img\": \"./images/B00DPUP016.png\", \"q_text\": \"The shirt is loose and brown in color., and is animal print\", \"positive_key\": \"B007PU5ZNK\", \"positive_value\": \"./images/B007PU5ZNK.png\", \"hn_image\": [\"./images/B00DPUP016.png\"]}\n{\"q_img\": \"./images/B000FTH9QO.png\", \"q_text\": \"is more revealing, and is a black shirt with a bow\", \"positive_key\": \"B001KHF48U\", \"positive_value\": \"./images/B001KHF48U.png\", \"hn_image\": [\"./images/B000FTH9QO.png\"]}\n{\"q_img\": \"./images/B00D8AWLL0.png\", \"q_text\": \"has longer sleeves with gray color, and has long sleeves and has flared bottom\", \"positive_key\": \"B00EUXHMVI\", \"positive_value\": \"./images/B00EUXHMVI.png\", \"hn_image\": [\"./images/B00D8AWLL0.png\"]}\n{\"q_img\": \"./images/B00AYVTROA.png\", \"q_text\": \"is brown and has round neck, and Is more casual\", \"positive_key\": \"B0084B5HPA\", \"positive_value\": \"./images/B0084B5HPA.png\", \"hn_image\": [\"./images/B00AYVTROA.png\"]}\n{\"q_img\": \"./images/B00BX4K30Y.png\", \"q_text\": \"orange tshirt regular lenth with logs, and is orange with a different graphic\", \"positive_key\": \"B00AEVGSUQ\", \"positive_value\": \"./images/B00AEVGSUQ.png\", \"hn_image\": [\"./images/B00BX4K30Y.png\"]}\n{\"q_img\": \"./images/B006BQT0NA.png\", \"q_text\": \"is lighter, and comes in red and covers partially the opposite shoulder.\", \"positive_key\": \"B00CIH60TI\", \"positive_value\": \"./images/B00CIH60TI.png\", \"hn_image\": [\"./images/B006BQT0NA.png\"]}\n{\"q_img\": \"./images/B006TCJTUK.png\", \"q_text\": \"has holes in the short sleeves and is darker, and It has a short sleeve and has no collar\", \"positive_key\": \"B008SCH5KK\", \"positive_value\": \"./images/B008SCH5KK.png\", \"hn_image\": [\"./images/B006TCJTUK.png\"]}\n{\"q_img\": \"./images/B007MB2OAE.png\", \"q_text\": \"is blue colored, and has more blue\", \"positive_key\": \"B0073X88RO\", \"positive_value\": \"./images/B0073X88RO.png\", \"hn_image\": [\"./images/B007MB2OAE.png\"]}\n{\"q_img\": \"./images/B008NDWPP4.png\", \"q_text\": \"is blue with pink designs, and has a blue and red colorful print that is similar.\", \"positive_key\": \"B008BJSICE\", \"positive_value\": \"./images/B008BJSICE.png\", \"hn_image\": [\"./images/B008NDWPP4.png\"]}\n{\"q_img\": \"./images/B001LV9ZQM.png\", \"q_text\": \"is much darker, and is black with white logo\", \"positive_key\": \"B001W82GU6\", \"positive_value\": \"./images/B001W82GU6.png\", \"hn_image\": [\"./images/B001LV9ZQM.png\"]}\n{\"q_img\": \"./images/B005GG68BI.png\", \"q_text\": \"is lighter with longer blue sleeves, and is blue and gray with baseball sleeves\", \"positive_key\": \"B00AP57II2\", \"positive_value\": \"./images/B00AP57II2.png\", \"hn_image\": [\"./images/B005GG68BI.png\"]}\n{\"q_img\": \"./images/B0063GHGVG.png\", \"q_text\": \"is black with white designs, and it is more black with a white print\", \"positive_key\": \"B008ILTGT4\", \"positive_value\": \"./images/B008ILTGT4.png\", \"hn_image\": [\"./images/B0063GHGVG.png\"]}\n{\"q_img\": \"./images/B0076ANQXA.png\", \"q_text\": \"Is more graphic and less wordy, and is orange colored\", \"positive_key\": \"B0019ZIDF4\", \"positive_value\": \"./images/B0019ZIDF4.png\", \"hn_image\": [\"./images/B0076ANQXA.png\"]}\n{\"q_img\": \"./images/B001FBFJCC.png\", \"q_text\": \"is more cream with musical instruments pictured on, and has a v-neck and a musical pattern\", \"positive_key\": \"B007WOQVLY\", \"positive_value\": \"./images/B007WOQVLY.png\", \"hn_image\": [\"./images/B001FBFJCC.png\"]}\n{\"q_img\": \"./images/B000OBSH0A.png\", \"q_text\": \"has a collar, and is a polo\", \"positive_key\": \"B007C38PY6\", \"positive_value\": \"./images/B007C38PY6.png\", \"hn_image\": [\"./images/B000OBSH0A.png\"]}\n{\"q_img\": \"./images/B00BB1UAMU.png\", \"q_text\": \"is lighter and has shorter sleeves, and Has skinny straps and polka dots.\", \"positive_key\": \"B00AU4EYZ8\", \"positive_value\": \"./images/B00AU4EYZ8.png\", \"hn_image\": [\"./images/B00BB1UAMU.png\"]}\n{\"q_img\": \"./images/B00A13FSRQ.png\", \"q_text\": \"is lighter, and is white\", \"positive_key\": \"B006YBVSKU\", \"positive_value\": \"./images/B006YBVSKU.png\", \"hn_image\": [\"./images/B00A13FSRQ.png\"]}\n{\"q_img\": \"./images/B008I2HMXA.png\", \"q_text\": \"is black with short sleeves, and has longer sleeves and is black\", \"positive_key\": \"B005JR1KA8\", \"positive_value\": \"./images/B005JR1KA8.png\", \"hn_image\": [\"./images/B008I2HMXA.png\"]}\n{\"q_img\": \"./images/B0081P9UPC.png\", \"q_text\": \"has longer sleeves and color is less blue, and has buttoned front and has double pockets\", \"positive_key\": \"B008PSUVGC\", \"positive_value\": \"./images/B008PSUVGC.png\", \"hn_image\": [\"./images/B0081P9UPC.png\"]}\n{\"q_img\": \"./images/B00E6793SS.png\", \"q_text\": \"has longer sleeves and is less colorful, and has raglan sleeves and solid-gray front\", \"positive_key\": \"B0076B650A\", \"positive_value\": \"./images/B0076B650A.png\", \"hn_image\": [\"./images/B00E6793SS.png\"]}\n{\"q_img\": \"./images/B008X0EEUQ.png\", \"q_text\": \"is navy blue color with white color art, and is black with a star on front\", \"positive_key\": \"B000F6T2LC\", \"positive_value\": \"./images/B000F6T2LC.png\", \"hn_image\": [\"./images/B008X0EEUQ.png\"]}\n{\"q_img\": \"./images/B0093B1WCM.png\", \"q_text\": \"has even hem and is more solid, and is of a solid color\", \"positive_key\": \"B00AKMYS56\", \"positive_value\": \"./images/B00AKMYS56.png\", \"hn_image\": [\"./images/B0093B1WCM.png\"]}\n{\"q_img\": \"./images/B005TGFPEQ.png\", \"q_text\": \"is a T shirt that says orgasm donor, and is a bit brighter blue with Orgasm Donor on front\", \"positive_key\": \"B007SH5MBA\", \"positive_value\": \"./images/B007SH5MBA.png\", \"hn_image\": [\"./images/B005TGFPEQ.png\"]}\n{\"q_img\": \"./images/B008UALEK2.png\", \"q_text\": \"The short sleeve shirt is a bright blue with a black leaf., and has shorter sleeves and less graphic\", \"positive_key\": \"B003OBZE9O\", \"positive_value\": \"./images/B003OBZE9O.png\", \"hn_image\": [\"./images/B008UALEK2.png\"]}\n{\"q_img\": \"./images/B002TYKGBS.png\", \"q_text\": \"is lighter in color and less fitted, and is yellow with buttons\", \"positive_key\": \"B004ODM2WI\", \"positive_value\": \"./images/B004ODM2WI.png\", \"hn_image\": [\"./images/B002TYKGBS.png\"]}\n{\"q_img\": \"./images/B003W3RBFY.png\", \"q_text\": \"has shorter sleeves with a different logo, and is red magnum pi shirt\", \"positive_key\": \"B00699EPO8\", \"positive_value\": \"./images/B00699EPO8.png\", \"hn_image\": [\"./images/B003W3RBFY.png\"]}\n{\"q_img\": \"./images/B00BKZ2UUM.png\", \"q_text\": \"has buttons and is a dark pink color, and Has buttons and is less graphic.\", \"positive_key\": \"B0091Q0N8I\", \"positive_value\": \"./images/B0091Q0N8I.png\", \"hn_image\": [\"./images/B00BKZ2UUM.png\"]}\n{\"q_img\": \"./images/B007PAD4YC.png\", \"q_text\": \"is a loose tank top to waist, and is in solid orange graphic print.\", \"positive_key\": \"B0057RFSTY\", \"positive_value\": \"./images/B0057RFSTY.png\", \"hn_image\": [\"./images/B007PAD4YC.png\"]}\n{\"q_img\": \"./images/B00BSKNZSA.png\", \"q_text\": \"has a darker blue, and is more blousy and flowing\", \"positive_key\": \"B008S01Q98\", \"positive_value\": \"./images/B008S01Q98.png\", \"hn_image\": [\"./images/B00BSKNZSA.png\"]}\n{\"q_img\": \"./images/B00CORRYVK.png\", \"q_text\": \"has longer sleeves and is less wordy, and is white colored\", \"positive_key\": \"B003ICLHAO\", \"positive_value\": \"./images/B003ICLHAO.png\", \"hn_image\": [\"./images/B00CORRYVK.png\"]}\n{\"q_img\": \"./images/B001AYHVZ2.png\", \"q_text\": \" sleeves and is more casual, and has a Capt. Jack Sparrow design on it\", \"positive_key\": \"B004SFOBMQ\", \"positive_value\": \"./images/B004SFOBMQ.png\", \"hn_image\": [\"./images/B001AYHVZ2.png\"]}\n{\"q_img\": \"./images/B005I4KNX2.png\", \"q_text\": \"is darker, and has blue color to it\", \"positive_key\": \"B00BRBEO9O\", \"positive_value\": \"./images/B00BRBEO9O.png\", \"hn_image\": [\"./images/B005I4KNX2.png\"]}\n{\"q_img\": \"./images/B00AKJTF3Y.png\", \"q_text\": \"Maroon peasant sleeved blouse, and Is red and more sexy\", \"positive_key\": \"B00AB3DIU0\", \"positive_value\": \"./images/B00AB3DIU0.png\", \"hn_image\": [\"./images/B00AKJTF3Y.png\"]}\n{\"q_img\": \"./images/B00GISBRL4.png\", \"q_text\": \"is purple with white designs, and is purple with white text\", \"positive_key\": \"B00H4GLJAI\", \"positive_value\": \"./images/B00H4GLJAI.png\", \"hn_image\": [\"./images/B00GISBRL4.png\"]}\n{\"q_img\": \"./images/B002Y6MLEQ.png\", \"q_text\": \"The shirt is gray with a floral design, and is lighter in color\", \"positive_key\": \"B001ZR75XW\", \"positive_value\": \"./images/B001ZR75XW.png\", \"hn_image\": [\"./images/B002Y6MLEQ.png\"]}\n{\"q_img\": \"./images/B005PK0O42.png\", \"q_text\": \"is purple with a cowl neck, and is blue with frills\", \"positive_key\": \"B00BZFIH4U\", \"positive_value\": \"./images/B00BZFIH4U.png\", \"hn_image\": [\"./images/B005PK0O42.png\"]}\n{\"q_img\": \"./images/B007GRPJ5Q.png\", \"q_text\": \"is plainer and darker colored, and gray scoop neck with no zipper\", \"positive_key\": \"B005POVH1W\", \"positive_value\": \"./images/B005POVH1W.png\", \"hn_image\": [\"./images/B007GRPJ5Q.png\"]}\n{\"q_img\": \"./images/B00DNR2OCO.png\", \"q_text\": \"has off-shoulder and color is black, and is more revealing and is black\", \"positive_key\": \"B0043VDDUQ\", \"positive_value\": \"./images/B0043VDDUQ.png\", \"hn_image\": [\"./images/B00DNR2OCO.png\"]}\n{\"q_img\": \"./images/B003BVK410.png\", \"q_text\": \"I want something that I can relax in and fell comforty, and white and blue plaid design\", \"positive_key\": \"B003DYZM2Q\", \"positive_value\": \"./images/B003DYZM2Q.png\", \"hn_image\": [\"./images/B003BVK410.png\"]}\n{\"q_img\": \"./images/B00AO4N8OM.png\", \"q_text\": \"is lighter, and is blue and has large round neck\", \"positive_key\": \"B00AO4NI3I\", \"positive_value\": \"./images/B00AO4NI3I.png\", \"hn_image\": [\"./images/B00AO4N8OM.png\"]}\n{\"q_img\": \"./images/B00CTVFMME.png\", \"q_text\": \"is black wit blue writing, and has a blue graphic on it\", \"positive_key\": \"B0074S3J86\", \"positive_value\": \"./images/B0074S3J86.png\", \"hn_image\": [\"./images/B00CTVFMME.png\"]}\n{\"q_img\": \"./images/B00D02G23O.png\", \"q_text\": \"has longer sleeves and darker colored, and is darker and more lacey\", \"positive_key\": \"B005REZ92S\", \"positive_value\": \"./images/B005REZ92S.png\", \"hn_image\": [\"./images/B00D02G23O.png\"]}\n{\"q_img\": \"./images/B003EACZ9M.png\", \"q_text\": \"has a more modest neckline and is pink, and is pink\", \"positive_key\": \"B00BE6KR4S\", \"positive_value\": \"./images/B00BE6KR4S.png\", \"hn_image\": [\"./images/B003EACZ9M.png\"]}\n{\"q_img\": \"./images/B000VX6TAG.png\", \"q_text\": \"is red with white lettering, and has a red tone to it\", \"positive_key\": \"B003GQY1FK\", \"positive_value\": \"./images/B003GQY1FK.png\", \"hn_image\": [\"./images/B000VX6TAG.png\"]}\n{\"q_img\": \"./images/B00296O9IM.png\", \"q_text\": \"has long-sleeves and is more classy, and it is grey and has a long sleeve\", \"positive_key\": \"B003HNJZ94\", \"positive_value\": \"./images/B003HNJZ94.png\", \"hn_image\": [\"./images/B00296O9IM.png\"]}\n{\"q_img\": \"./images/B00CS4NIKK.png\", \"q_text\": \"is black with writing on oit, and has upside down chest logo\", \"positive_key\": \"B00E7Z8HQI\", \"positive_value\": \"./images/B00E7Z8HQI.png\", \"hn_image\": [\"./images/B00CS4NIKK.png\"]}\n{\"q_img\": \"./images/B00BPEWGTS.png\", \"q_text\": \"is lighter, and blue\", \"positive_key\": \"B00AU4EKCA\", \"positive_value\": \"./images/B00AU4EKCA.png\", \"hn_image\": [\"./images/B00BPEWGTS.png\"]}\n{\"q_img\": \"./images/B0081ZS0DU.png\", \"q_text\": \"short sleeves lighter pink, and Is lighter pink with shorter sleeves\", \"positive_key\": \"B009ENGIBO\", \"positive_value\": \"./images/B009ENGIBO.png\", \"hn_image\": [\"./images/B0081ZS0DU.png\"]}\n{\"q_img\": \"./images/B005NY5YMC.png\", \"q_text\": \"is darker, and is darker colored with longer sleeves\", \"positive_key\": \"B005NY611K\", \"positive_value\": \"./images/B005NY611K.png\", \"hn_image\": [\"./images/B005NY5YMC.png\"]}\n{\"q_img\": \"./images/B00BSKZEWK.png\", \"q_text\": \"has sleeves and has text on it, and has text on it\", \"positive_key\": \"B009MB6C5U\", \"positive_value\": \"./images/B009MB6C5U.png\", \"hn_image\": [\"./images/B00BSKZEWK.png\"]}\n{\"q_img\": \"./images/B00BIPLT6K.png\", \"q_text\": \"is more blue and billowy, and is lighter and has shorter sleeves\", \"positive_key\": \"B00BJO2R4I\", \"positive_value\": \"./images/B00BJO2R4I.png\", \"hn_image\": [\"./images/B00BIPLT6K.png\"]}\n{\"q_img\": \"./images/B00B67AV6O.png\", \"q_text\": \"is a dress, and is orange with dark accents\", \"positive_key\": \"B007XD5700\", \"positive_value\": \"./images/B007XD5700.png\", \"hn_image\": [\"./images/B00B67AV6O.png\"]}\n{\"q_img\": \"./images/B00B8QSUCA.png\", \"q_text\": \"is striped, and sweetheart\", \"positive_key\": \"B00BBI3EM6\", \"positive_value\": \"./images/B00BBI3EM6.png\", \"hn_image\": [\"./images/B00B8QSUCA.png\"]}\n{\"q_img\": \"./images/B00BBW68OS.png\", \"q_text\": \"Is blue and more fitted, and Is a dark blue tank style\", \"positive_key\": \"B003I4DR00\", \"positive_value\": \"./images/B003I4DR00.png\", \"hn_image\": [\"./images/B00BBW68OS.png\"]}\n{\"q_img\": \"./images/B003RW60ES.png\", \"q_text\": \"is a long sleeved, and Solid black long sleeved shirt\", \"positive_key\": \"B00GGR9BSS\", \"positive_value\": \"./images/B00GGR9BSS.png\", \"hn_image\": [\"./images/B003RW60ES.png\"]}\n{\"q_img\": \"./images/B00BNMTGEA.png\", \"q_text\": \"is lighter and has shorter sleeves, and it has no sleeve and less tight\", \"positive_key\": \"B008FET3LU\", \"positive_value\": \"./images/B008FET3LU.png\", \"hn_image\": [\"./images/B00BNMTGEA.png\"]}\n{\"q_img\": \"./images/B006J4H356.png\", \"q_text\": \"is lighter and has longer sleeves, and has long sleeves with floral patterns\", \"positive_key\": \"B006QHSSF0\", \"positive_value\": \"./images/B006QHSSF0.png\", \"hn_image\": [\"./images/B006J4H356.png\"]}\n{\"q_img\": \"./images/B00CDAZWV2.png\", \"q_text\": \"is lighter and has longer sleeves, and has shorter sleeves and buttons\", \"positive_key\": \"B000J41PCO\", \"positive_value\": \"./images/B000J41PCO.png\", \"hn_image\": [\"./images/B00CDAZWV2.png\"]}\n{\"q_img\": \"./images/B000VCWJIS.png\", \"q_text\": \"is less revealing and brighter in color, and is a white tank top\", \"positive_key\": \"B0058K6ZBA\", \"positive_value\": \"./images/B0058K6ZBA.png\", \"hn_image\": [\"./images/B000VCWJIS.png\"]}\n{\"q_img\": \"./images/B002IN0LK6.png\", \"q_text\": \"is black colored and less revealing, and is black colored and crew neck\", \"positive_key\": \"B0055PJ4IO\", \"positive_value\": \"./images/B0055PJ4IO.png\", \"hn_image\": [\"./images/B002IN0LK6.png\"]}\n{\"q_img\": \"./images/B0058Z62OA.png\", \"q_text\": \"has short sleeves and is paisley, and is very light colored\", \"positive_key\": \"B00BYOUPD8\", \"positive_value\": \"./images/B00BYOUPD8.png\", \"hn_image\": [\"./images/B0058Z62OA.png\"]}\n{\"q_img\": \"./images/B004SH9SAE.png\", \"q_text\": \"has no sleeves with two different color patterns, and Is less casual and more patterned.\", \"positive_key\": \"B005AFXDP4\", \"positive_value\": \"./images/B005AFXDP4.png\", \"hn_image\": [\"./images/B004SH9SAE.png\"]}\n{\"q_img\": \"./images/B000GHI4V4.png\", \"q_text\": \"is tighter fitting and less slutty, and is orange and longer\", \"positive_key\": \"B008CRQQ9M\", \"positive_value\": \"./images/B008CRQQ9M.png\", \"hn_image\": [\"./images/B000GHI4V4.png\"]}\n{\"q_img\": \"./images/B0083QFABC.png\", \"q_text\": \"Lighter colors and has a scoop neck with shorter sleeves, and shorter sleeve less revealing more pink\", \"positive_key\": \"B00AO3SMGM\", \"positive_value\": \"./images/B00AO3SMGM.png\", \"hn_image\": [\"./images/B0083QFABC.png\"]}\n{\"q_img\": \"./images/B008AYFNQE.png\", \"q_text\": \"is more armor like, and is black\", \"positive_key\": \"B005B6ULPW\", \"positive_value\": \"./images/B005B6ULPW.png\", \"hn_image\": [\"./images/B008AYFNQE.png\"]}\n{\"q_img\": \"./images/B0045ZH0AO.png\", \"q_text\": \"is brown, and is dark brown\", \"positive_key\": \"B001FZZ3EC\", \"positive_value\": \"./images/B001FZZ3EC.png\", \"hn_image\": [\"./images/B0045ZH0AO.png\"]}\n{\"q_img\": \"./images/B005DA4P3A.png\", \"q_text\": \"has longer sleeves and round neck, and longer sleeves darker blue no buttons\", \"positive_key\": \"B005DI3GMI\", \"positive_value\": \"./images/B005DI3GMI.png\", \"hn_image\": [\"./images/B005DA4P3A.png\"]}\n{\"q_img\": \"./images/B006Z8GQAE.png\", \"q_text\": \"has a darker brown color, and is purple\", \"positive_key\": \"B00ADM3HIC\", \"positive_value\": \"./images/B00ADM3HIC.png\", \"hn_image\": [\"./images/B006Z8GQAE.png\"]}\n{\"q_img\": \"./images/B004S36V0I.png\", \"q_text\": \"is lighter and has shorter sleeves, and has more green\", \"positive_key\": \"B00D70X6WE\", \"positive_value\": \"./images/B00D70X6WE.png\", \"hn_image\": [\"./images/B004S36V0I.png\"]}\n{\"q_img\": \"./images/B00AO7MNK4.png\", \"q_text\": \"is lighter, and has a tuxedo design shirt\", \"positive_key\": \"B001F6383O\", \"positive_value\": \"./images/B001F6383O.png\", \"hn_image\": [\"./images/B00AO7MNK4.png\"]}\n{\"q_img\": \"./images/B00FQUAV9E.png\", \"q_text\": \"Is more flowing and less casual, and has shorter sleeves\", \"positive_key\": \"B00D8AWLL0\", \"positive_value\": \"./images/B00D8AWLL0.png\", \"hn_image\": [\"./images/B00FQUAV9E.png\"]}\n{\"q_img\": \"./images/B007WAEFNO.png\", \"q_text\": \"has a bolder print, and is red with lace detail\", \"positive_key\": \"B00A85A5WA\", \"positive_value\": \"./images/B00A85A5WA.png\", \"hn_image\": [\"./images/B007WAEFNO.png\"]}\n{\"q_img\": \"./images/B00D4K3BFO.png\", \"q_text\": \"is hooded and warmer, and is a red hooded sweatshirt\", \"positive_key\": \"B00B10FW5G\", \"positive_value\": \"./images/B00B10FW5G.png\", \"hn_image\": [\"./images/B00D4K3BFO.png\"]}\n{\"q_img\": \"./images/B003TA8T9W.png\", \"q_text\": \"is less sheer and less feminine, and is more plain and buttons up the front\", \"positive_key\": \"B00CHJE67A\", \"positive_value\": \"./images/B00CHJE67A.png\", \"hn_image\": [\"./images/B003TA8T9W.png\"]}\n{\"q_img\": \"./images/B00AQ46LIU.png\", \"q_text\": \"has red color and off-shoulder, and shorter sleeves\", \"positive_key\": \"B005LUPIJW\", \"positive_value\": \"./images/B005LUPIJW.png\", \"hn_image\": [\"./images/B00AQ46LIU.png\"]}\n{\"q_img\": \"./images/B007252XWY.png\", \"q_text\": \"is darker, and is darker in color.\", \"positive_key\": \"B00AZ7LRGO\", \"positive_value\": \"./images/B00AZ7LRGO.png\", \"hn_image\": [\"./images/B007252XWY.png\"]}\n{\"q_img\": \"./images/B00B9OQ1YA.png\", \"q_text\": \"Is less casual and has buttons, and long sleeve button up shirt with collar\", \"positive_key\": \"B008H7IA0K\", \"positive_value\": \"./images/B008H7IA0K.png\", \"hn_image\": [\"./images/B00B9OQ1YA.png\"]}\n{\"q_img\": \"./images/B00GOIAM4Q.png\", \"q_text\": \"has no sleeves and is more form fitting, and is more pink colored\", \"positive_key\": \"B0013WQ03U\", \"positive_value\": \"./images/B0013WQ03U.png\", \"hn_image\": [\"./images/B00GOIAM4Q.png\"]}\n{\"q_img\": \"./images/B00CPA475W.png\", \"q_text\": \"is lighter and has shorter sleeves, and is yellow and strapless\", \"positive_key\": \"B00BG0GD90\", \"positive_value\": \"./images/B00BG0GD90.png\", \"hn_image\": [\"./images/B00CPA475W.png\"]}\n{\"q_img\": \"./images/B0050SRP2S.png\", \"q_text\": \"The shirt is black with the batman logo., and shorter sleeves black\", \"positive_key\": \"B009XMGJ4M\", \"positive_value\": \"./images/B009XMGJ4M.png\", \"hn_image\": [\"./images/B0050SRP2S.png\"]}\n{\"q_img\": \"./images/B00DZP4XO6.png\", \"q_text\": \"The shirts are of a Batman design., and Is more colorful\", \"positive_key\": \"B00DOZ60T8\", \"positive_value\": \"./images/B00DOZ60T8.png\", \"hn_image\": [\"./images/B00DZP4XO6.png\"]}\n{\"q_img\": \"./images/B008BHS9H0.png\", \"q_text\": \"is black colored and has longer sleeves, and is black and lacy\", \"positive_key\": \"B008DEXOV2\", \"positive_value\": \"./images/B008DEXOV2.png\", \"hn_image\": [\"./images/B008BHS9H0.png\"]}\n{\"q_img\": \"./images/B006SF8D3M.png\", \"q_text\": \"is a short sleeve black shirt with Twilight art, and has shorter sleeves and is more graphic\", \"positive_key\": \"B003MEJ1R4\", \"positive_value\": \"./images/B003MEJ1R4.png\", \"hn_image\": [\"./images/B006SF8D3M.png\"]}\n{\"q_img\": \"./images/B00AQO0N0M.png\", \"q_text\": \"is yellow, and has no sleeves\", \"positive_key\": \"B00BZDUN5S\", \"positive_value\": \"./images/B00BZDUN5S.png\", \"hn_image\": [\"./images/B00AQO0N0M.png\"]}\n{\"q_img\": \"./images/B00ECDLSJ8.png\", \"q_text\": \"The shirts are white and blue with short sleeves., and v neck\", \"positive_key\": \"B00D25C8CI\", \"positive_value\": \"./images/B00D25C8CI.png\", \"hn_image\": [\"./images/B00ECDLSJ8.png\"]}\n{\"q_img\": \"./images/B00BBI9NIU.png\", \"q_text\": \"is tighter fitting and plain, and is grey with short sleeves\", \"positive_key\": \"B00B11ZVQK\", \"positive_value\": \"./images/B00B11ZVQK.png\", \"hn_image\": [\"./images/B00BBI9NIU.png\"]}\n{\"q_img\": \"./images/B0014EW1ZS.png\", \"q_text\": \"The shirt is black with white lettering., and it is black\", \"positive_key\": \"B005CKEC1G\", \"positive_value\": \"./images/B005CKEC1G.png\", \"hn_image\": [\"./images/B0014EW1ZS.png\"]}\n{\"q_img\": \"./images/B0052CHM9S.png\", \"q_text\": \"The tank top is yellow and white in color., and Has shorter sleeves & darker print\", \"positive_key\": \"B004M8T84U\", \"positive_value\": \"./images/B004M8T84U.png\", \"hn_image\": [\"./images/B0052CHM9S.png\"]}\n{\"q_img\": \"./images/B00265PFKC.png\", \"q_text\": \"its blue and bigger colar, and is lighter colored and more professional\", \"positive_key\": \"B006HT23VM\", \"positive_value\": \"./images/B006HT23VM.png\", \"hn_image\": [\"./images/B00265PFKC.png\"]}\n{\"q_img\": \"./images/B006MJNKAK.png\", \"q_text\": \"has longer sleeves and is black in color, and is black with longer sleeves\", \"positive_key\": \"B007850FV4\", \"positive_value\": \"./images/B007850FV4.png\", \"hn_image\": [\"./images/B006MJNKAK.png\"]}\n{\"q_img\": \"./images/B004P5P1I2.png\", \"q_text\": \"is red colored and has longer sleeves, and has longer sleeves and is a heavier fabric\", \"positive_key\": \"B00588V1UW\", \"positive_value\": \"./images/B00588V1UW.png\", \"hn_image\": [\"./images/B004P5P1I2.png\"]}\n{\"q_img\": \"./images/B003A6NYSG.png\", \"q_text\": \"is longer and has thicker straps, and is more square and less athletic\", \"positive_key\": \"B004WHEZDA\", \"positive_value\": \"./images/B004WHEZDA.png\", \"hn_image\": [\"./images/B003A6NYSG.png\"]}\n{\"q_img\": \"./images/B00F62JQEI.png\", \"q_text\": \"Is black with no sleeves, and Is black in color and sleevless\", \"positive_key\": \"B00CQI02MK\", \"positive_value\": \"./images/B00CQI02MK.png\", \"hn_image\": [\"./images/B00F62JQEI.png\"]}\n{\"q_img\": \"./images/B00C4YP0YQ.png\", \"q_text\": \"The tank top is white in color., and solid white\", \"positive_key\": \"B008WTCIGA\", \"positive_value\": \"./images/B008WTCIGA.png\", \"hn_image\": [\"./images/B00C4YP0YQ.png\"]}\n{\"q_img\": \"./images/B002YF05M2.png\", \"q_text\": \"is black and white striped and is shorter, and is black and white with longer sleeves\", \"positive_key\": \"B005S5H7P8\", \"positive_value\": \"./images/B005S5H7P8.png\", \"hn_image\": [\"./images/B002YF05M2.png\"]}\n{\"q_img\": \"./images/B008A23NKO.png\", \"q_text\": \"has straps and is darker, and is two pieces\", \"positive_key\": \"B00B307BK8\", \"positive_value\": \"./images/B00B307BK8.png\", \"hn_image\": [\"./images/B008A23NKO.png\"]}\n{\"q_img\": \"./images/B00F4OK23M.png\", \"q_text\": \"is black with a hoodie, and is darker and is less feminine\", \"positive_key\": \"B0067K5MNC\", \"positive_value\": \"./images/B0067K5MNC.png\", \"hn_image\": [\"./images/B00F4OK23M.png\"]}\n{\"q_img\": \"./images/B006L2ZRHC.png\", \"q_text\": \"The shirt is long sleeved and purple., and Has longer sleeves and is a rich purple color\", \"positive_key\": \"B006Z2HG0Y\", \"positive_value\": \"./images/B006Z2HG0Y.png\", \"hn_image\": [\"./images/B006L2ZRHC.png\"]}\n{\"q_img\": \"./images/B008H3W3GQ.png\", \"q_text\": \"The short sleeved shirt is a multitude of colors, and is fully printed and darker\", \"positive_key\": \"B008H3W8S4\", \"positive_value\": \"./images/B008H3W8S4.png\", \"hn_image\": [\"./images/B008H3W3GQ.png\"]}\n{\"q_img\": \"./images/B00CQKVSQW.png\", \"q_text\": \"is orange and wide around neck, and is a brighter color and neck embellishment\", \"positive_key\": \"B00EJOZGSY\", \"positive_value\": \"./images/B00EJOZGSY.png\", \"hn_image\": [\"./images/B00CQKVSQW.png\"]}\n{\"q_img\": \"./images/B005GIF2JA.png\", \"q_text\": \"The baggy shirt is light pink in color., and Has long sleeves and higher neckline.\", \"positive_key\": \"B008ZBL9YM\", \"positive_value\": \"./images/B008ZBL9YM.png\", \"hn_image\": [\"./images/B005GIF2JA.png\"]}\n{\"q_img\": \"./images/B008J9Z6YY.png\", \"q_text\": \"it more colorful, and is purple and more flowy\", \"positive_key\": \"B008622J80\", \"positive_value\": \"./images/B008622J80.png\", \"hn_image\": [\"./images/B008J9Z6YY.png\"]}\n{\"q_img\": \"./images/B008X2PJRG.png\", \"q_text\": \"is lighter and has shorter sleeves, and has brighter colors\", \"positive_key\": \"B00D0RC93Q\", \"positive_value\": \"./images/B00D0RC93Q.png\", \"hn_image\": [\"./images/B008X2PJRG.png\"]}\n{\"q_img\": \"./images/B005UG7PJ8.png\", \"q_text\": \"is white with blue patterns, and blue.\", \"positive_key\": \"B00B7X6HWE\", \"positive_value\": \"./images/B00B7X6HWE.png\", \"hn_image\": [\"./images/B005UG7PJ8.png\"]}\n{\"q_img\": \"./images/B005KNCFL4.png\", \"q_text\": \"is lighter in color and has more flowery floral patterns, and is white colored with floral pattern\", \"positive_key\": \"B005KNCK1Y\", \"positive_value\": \"./images/B005KNCK1Y.png\", \"hn_image\": [\"./images/B005KNCFL4.png\"]}\n{\"q_img\": \"./images/B008QW03K6.png\", \"q_text\": \"White and no sleeves, and is white and has shorter sleeves\", \"positive_key\": \"B00D40CB8W\", \"positive_value\": \"./images/B00D40CB8W.png\", \"hn_image\": [\"./images/B008QW03K6.png\"]}\n{\"q_img\": \"./images/B008SCHF7I.png\", \"q_text\": \"Is striped and less colorful, and is striped and black\", \"positive_key\": \"B00BF6VD2M\", \"positive_value\": \"./images/B00BF6VD2M.png\", \"hn_image\": [\"./images/B008SCHF7I.png\"]}\n{\"q_img\": \"./images/B00F6Y9P2Y.png\", \"q_text\": \"The tank top is mostly black with yellow, and is a tank top and sexier\", \"positive_key\": \"B0080QK0HE\", \"positive_value\": \"./images/B0080QK0HE.png\", \"hn_image\": [\"./images/B00F6Y9P2Y.png\"]}\n{\"q_img\": \"./images/B0089HEOHG.png\", \"q_text\": \"is for a female with scoupe neck and black, and is black with an american flag\", \"positive_key\": \"B005ABVTOU\", \"positive_value\": \"./images/B005ABVTOU.png\", \"hn_image\": [\"./images/B0089HEOHG.png\"]}\n{\"q_img\": \"./images/B00A3QM374.png\", \"q_text\": \"gray 3/4 sleeve shirt cowl like collar, and it is grey and has a long sleeve\", \"positive_key\": \"B006IMURPC\", \"positive_value\": \"./images/B006IMURPC.png\", \"hn_image\": [\"./images/B00A3QM374.png\"]}\n{\"q_img\": \"./images/B009ZKLETC.png\", \"q_text\": \"is a navy blue, and is less font and more graphics\", \"positive_key\": \"B0047VQX6I\", \"positive_value\": \"./images/B0047VQX6I.png\", \"hn_image\": [\"./images/B009ZKLETC.png\"]}\n{\"q_img\": \"./images/B0055B67XI.png\", \"q_text\": \"is green, and has more green\", \"positive_key\": \"B0063GHGVG\", \"positive_value\": \"./images/B0063GHGVG.png\", \"hn_image\": [\"./images/B0055B67XI.png\"]}\n{\"q_img\": \"./images/B003HIWK7S.png\", \"q_text\": \"is green with turtle neck, and is darker\", \"positive_key\": \"B0040JHGO0\", \"positive_value\": \"./images/B0040JHGO0.png\", \"hn_image\": [\"./images/B003HIWK7S.png\"]}\n{\"q_img\": \"./images/B00BM0V36C.png\", \"q_text\": \"is sleeveless and dark colored, and has no sleeves and a flowered design\", \"positive_key\": \"B008618304\", \"positive_value\": \"./images/B008618304.png\", \"hn_image\": [\"./images/B00BM0V36C.png\"]}\n{\"q_img\": \"./images/B0026QR15I.png\", \"q_text\": \"has no sleeves with buttons and floral print, and is lighter and has shorter sleeves\", \"positive_key\": \"B00AR7VRX0\", \"positive_value\": \"./images/B00AR7VRX0.png\", \"hn_image\": [\"./images/B0026QR15I.png\"]}\n{\"q_img\": \"./images/B008UALEK2.png\", \"q_text\": \"The shirt is a mixture of greens and blues., and has a lighter blue and shorter sleeves\", \"positive_key\": \"B004KJEIXC\", \"positive_value\": \"./images/B004KJEIXC.png\", \"hn_image\": [\"./images/B008UALEK2.png\"]}\n{\"q_img\": \"./images/B00CSBEWBW.png\", \"q_text\": \"has stripes and a scoop neck, and is striped and crew necked\", \"positive_key\": \"B00CA6881K\", \"positive_value\": \"./images/B00CA6881K.png\", \"hn_image\": [\"./images/B00CSBEWBW.png\"]}\n{\"q_img\": \"./images/B003BECVKY.png\", \"q_text\": \"is short sleeved with 'GI Joe' graphic, and has shorter sleeves\", \"positive_key\": \"B00B3XT0KE\", \"positive_value\": \"./images/B00B3XT0KE.png\", \"hn_image\": [\"./images/B003BECVKY.png\"]}\n{\"q_img\": \"./images/B00E0XEESM.png\", \"q_text\": \"is a furry hot pink hand covering, and Desired items are pink furry gloves\", \"positive_key\": \"B0096HHMC2\", \"positive_value\": \"./images/B0096HHMC2.png\", \"hn_image\": [\"./images/B00E0XEESM.png\"]}\n{\"q_img\": \"./images/B0054LI386.png\", \"q_text\": \"It has longer sleeves., and is longer sleeved\", \"positive_key\": \"B0009GG2RA\", \"positive_value\": \"./images/B0009GG2RA.png\", \"hn_image\": [\"./images/B0054LI386.png\"]}\n{\"q_img\": \"./images/B000RZ4Z2M.png\", \"q_text\": \"is white colored and shorter sleeves, and has shorter sleeves and lighter colored\", \"positive_key\": \"B008LLGQTE\", \"positive_value\": \"./images/B008LLGQTE.png\", \"hn_image\": [\"./images/B000RZ4Z2M.png\"]}\n{\"q_img\": \"./images/B008OGUW32.png\", \"q_text\": \"The shirt has an animal print., and has large dunner and is brown color\", \"positive_key\": \"B008OGUTG2\", \"positive_value\": \"./images/B008OGUTG2.png\", \"hn_image\": [\"./images/B008OGUW32.png\"]}\n{\"q_img\": \"./images/B003FK79ZQ.png\", \"q_text\": \"is white with yellow designs, and is white and has logo\", \"positive_key\": \"B005TJS946\", \"positive_value\": \"./images/B005TJS946.png\", \"hn_image\": [\"./images/B003FK79ZQ.png\"]}\n{\"q_img\": \"./images/B00B8WZEXC.png\", \"q_text\": \"is green with white lettering, and is green colored with a different graphic\", \"positive_key\": \"B00AWD5FQ4\", \"positive_value\": \"./images/B00AWD5FQ4.png\", \"hn_image\": [\"./images/B00B8WZEXC.png\"]}\n{\"q_img\": \"./images/B009GGSKAG.png\", \"q_text\": \"is a red shirt with snowflakes, and Is red\", \"positive_key\": \"B00FS32AMU\", \"positive_value\": \"./images/B00FS32AMU.png\", \"hn_image\": [\"./images/B009GGSKAG.png\"]}\n{\"q_img\": \"./images/B004S2QS9I.png\", \"q_text\": \"is lighter, and Is a man's shirt with design print and no collar\", \"positive_key\": \"B004FGDPBG\", \"positive_value\": \"./images/B004FGDPBG.png\", \"hn_image\": [\"./images/B004S2QS9I.png\"]}\n{\"q_img\": \"./images/B00730YVFA.png\", \"q_text\": \"has bit longer sleeves and white color, and has more white and more graphics\", \"positive_key\": \"B007VG6Y3S\", \"positive_value\": \"./images/B007VG6Y3S.png\", \"hn_image\": [\"./images/B00730YVFA.png\"]}\n{\"q_img\": \"./images/B007TXUHAY.png\", \"q_text\": \"is black with higher neck, and is less revealing\", \"positive_key\": \"B00BXI5XOG\", \"positive_value\": \"./images/B00BXI5XOG.png\", \"hn_image\": [\"./images/B007TXUHAY.png\"]}\n{\"q_img\": \"./images/B007TLO9D2.png\", \"q_text\": \"is more witty and less inspirational, and is green and with new print.\", \"positive_key\": \"B008QYK7UK\", \"positive_value\": \"./images/B008QYK7UK.png\", \"hn_image\": [\"./images/B007TLO9D2.png\"]}\n{\"q_img\": \"./images/B007312Q06.png\", \"q_text\": \"Has different wording, and has a different logo\", \"positive_key\": \"B007OOUGFE\", \"positive_value\": \"./images/B007OOUGFE.png\", \"hn_image\": [\"./images/B007312Q06.png\"]}\n{\"q_img\": \"./images/B009ZGQ0F4.png\", \"q_text\": \"is black with a different logo, and is darker nd has weds\", \"positive_key\": \"B00C99QBZS\", \"positive_value\": \"./images/B00C99QBZS.png\", \"hn_image\": [\"./images/B009ZGQ0F4.png\"]}\n{\"q_img\": \"./images/B0059LBD1A.png\", \"q_text\": \"is a long sleeve black shirt, and has a lower neckline\", \"positive_key\": \"B008YF4SLK\", \"positive_value\": \"./images/B008YF4SLK.png\", \"hn_image\": [\"./images/B0059LBD1A.png\"]}\n{\"q_img\": \"./images/B007M7B28I.png\", \"q_text\": \"is black and more revealing, and is darker in color\", \"positive_key\": \"B00AWLZ6WE\", \"positive_value\": \"./images/B00AWLZ6WE.png\", \"hn_image\": [\"./images/B007M7B28I.png\"]}\n{\"q_img\": \"./images/B008EMP6UK.png\", \"q_text\": \"has a darker color., and Is darker shades with more words\", \"positive_key\": \"B00AWCJFSE\", \"positive_value\": \"./images/B00AWCJFSE.png\", \"hn_image\": [\"./images/B008EMP6UK.png\"]}\n{\"q_img\": \"./images/B00B1VGOG6.png\", \"q_text\": \"more revealing and is brighter, and speggati strap white lntament under shirt\", \"positive_key\": \"B00B40S8OU\", \"positive_value\": \"./images/B00B40S8OU.png\", \"hn_image\": [\"./images/B00B1VGOG6.png\"]}\n{\"q_img\": \"./images/B0055POIVC.png\", \"q_text\": \"Is not a shirt, and army graphic\", \"positive_key\": \"B00CSSA1MO\", \"positive_value\": \"./images/B00CSSA1MO.png\", \"hn_image\": [\"./images/B0055POIVC.png\"]}\n{\"q_img\": \"./images/B008MJAZF6.png\", \"q_text\": \"is a dress with shorter sleeves, and is long length with 3/4 sleeve\", \"positive_key\": \"B005EE3GN0\", \"positive_value\": \"./images/B005EE3GN0.png\", \"hn_image\": [\"./images/B008MJAZF6.png\"]}\n{\"q_img\": \"./images/B00FN83JA2.png\", \"q_text\": \"has a small robe tie and cheetah pattern, and less pink\", \"positive_key\": \"B00AR2N7MY\", \"positive_value\": \"./images/B00AR2N7MY.png\", \"hn_image\": [\"./images/B00FN83JA2.png\"]}\n{\"q_img\": \"./images/B0038G0YXQ.png\", \"q_text\": \"The shirt is black with a skull., and is longer sleeved with a different logo\", \"positive_key\": \"B004YRTWI6\", \"positive_value\": \"./images/B004YRTWI6.png\", \"hn_image\": [\"./images/B0038G0YXQ.png\"]}\n{\"q_img\": \"./images/B00BTK5KBE.png\", \"q_text\": \"is blue with long sleeves, and is blue with long sleeves\", \"positive_key\": \"B00AQO0N0M\", \"positive_value\": \"./images/B00AQO0N0M.png\", \"hn_image\": [\"./images/B00BTK5KBE.png\"]}\n{\"q_img\": \"./images/B00A8FDF0Y.png\", \"q_text\": \"is lighter shaded, and has a lighter colored floral print.\", \"positive_key\": \"B00AZE6ZGO\", \"positive_value\": \"./images/B00AZE6ZGO.png\", \"hn_image\": [\"./images/B00A8FDF0Y.png\"]}\n{\"q_img\": \"./images/B009ZYYBVG.png\", \"q_text\": \"is a dark blue shirt with a picture of a crescent, and no dogs and brighter colors\", \"positive_key\": \"B000O8PLI4\", \"positive_value\": \"./images/B000O8PLI4.png\", \"hn_image\": [\"./images/B009ZYYBVG.png\"]}\n{\"q_img\": \"./images/B009YK7C80.png\", \"q_text\": \"The shirt is bright blue in color with a belt., and Is flowing blue with buttons and a belt\", \"positive_key\": \"B00AJ1FK0K\", \"positive_value\": \"./images/B00AJ1FK0K.png\", \"hn_image\": [\"./images/B009YK7C80.png\"]}\n{\"q_img\": \"./images/B00598M024.png\", \"q_text\": \"is red colored and image of hear on it, and is red in color\", \"positive_key\": \"B0085S979Y\", \"positive_value\": \"./images/B0085S979Y.png\", \"hn_image\": [\"./images/B00598M024.png\"]}\n{\"q_img\": \"./images/B000PZQ0MW.png\", \"q_text\": \"is a tank top, and Is tank top style\", \"positive_key\": \"B004Y1EYH6\", \"positive_value\": \"./images/B004Y1EYH6.png\", \"hn_image\": [\"./images/B000PZQ0MW.png\"]}\n{\"q_img\": \"./images/B005GESO26.png\", \"q_text\": \"Is more masculine and darker blue, and black seahoks logo\", \"positive_key\": \"B00A9A5GAK\", \"positive_value\": \"./images/B00A9A5GAK.png\", \"hn_image\": [\"./images/B005GESO26.png\"]}\n{\"q_img\": \"./images/B000GHI53Q.png\", \"q_text\": \"is blue with yellow designs, and is blue with a yellow logo\", \"positive_key\": \"B00ESBIJ9G\", \"positive_value\": \"./images/B00ESBIJ9G.png\", \"hn_image\": [\"./images/B000GHI53Q.png\"]}\n{\"q_img\": \"./images/B007T8HCPW.png\", \"q_text\": \"Is longer and less colorful, and is more red\", \"positive_key\": \"B00DEKKTOA\", \"positive_value\": \"./images/B00DEKKTOA.png\", \"hn_image\": [\"./images/B007T8HCPW.png\"]}\n{\"q_img\": \"./images/B00B3M2ZZM.png\", \"q_text\": \"is a short sleeved yellow shirt, and is yellow colored\", \"positive_key\": \"B004I8WBQG\", \"positive_value\": \"./images/B004I8WBQG.png\", \"hn_image\": [\"./images/B00B3M2ZZM.png\"]}\n{\"q_img\": \"./images/B006OC3YR4.png\", \"q_text\": \"is solid red long sleeved, and is red and long-sleeved\", \"positive_key\": \"B00A13FSRQ\", \"positive_value\": \"./images/B00A13FSRQ.png\", \"hn_image\": [\"./images/B006OC3YR4.png\"]}\n{\"q_img\": \"./images/B0058UWR08.png\", \"q_text\": \"The shirt is black and white with white pictures., and Is darker with white graphics\", \"positive_key\": \"B003GYPTCG\", \"positive_value\": \"./images/B003GYPTCG.png\", \"hn_image\": [\"./images/B0058UWR08.png\"]}\n{\"q_img\": \"./images/B003ZZQ1VY.png\", \"q_text\": \"is darker and less vingate, and is dark brown and has Chinese print\", \"positive_key\": \"B002B2RV1G\", \"positive_value\": \"./images/B002B2RV1G.png\", \"hn_image\": [\"./images/B003ZZQ1VY.png\"]}\n{\"q_img\": \"./images/B00BBFWI9Y.png\", \"q_text\": \"is brighter colored with no buttons, and has more blue and less buttons\", \"positive_key\": \"B00C7AP0T2\", \"positive_value\": \"./images/B00C7AP0T2.png\", \"hn_image\": [\"./images/B00BBFWI9Y.png\"]}\n{\"q_img\": \"./images/B008LLGQTE.png\", \"q_text\": \"The shirt is aqua in color., and is more green and doesn't have buttons\", \"positive_key\": \"B005LVABXY\", \"positive_value\": \"./images/B005LVABXY.png\", \"hn_image\": [\"./images/B008LLGQTE.png\"]}\n{\"q_img\": \"./images/B00C1RCVP2.png\", \"q_text\": \"is colorful, and Desired item is purple and single-shouldered\", \"positive_key\": \"B00CXWMW5O\", \"positive_value\": \"./images/B00CXWMW5O.png\", \"hn_image\": [\"./images/B00C1RCVP2.png\"]}\n{\"q_img\": \"./images/B0018LHYAY.png\", \"q_text\": \"is sleeveless and more revealing, and is a tank top with tie-dyed stripes\", \"positive_key\": \"B007R1QAJA\", \"positive_value\": \"./images/B007R1QAJA.png\", \"hn_image\": [\"./images/B0018LHYAY.png\"]}\n{\"q_img\": \"./images/B0094R0LDG.png\", \"q_text\": \"has open sleeves, and less animal print and more elegant\", \"positive_key\": \"B007WSD3BQ\", \"positive_value\": \"./images/B007WSD3BQ.png\", \"hn_image\": [\"./images/B0094R0LDG.png\"]}\n{\"q_img\": \"./images/B00BG0FKN0.png\", \"q_text\": \"is solid white with sheer sleeves, and is white with unique sleeves\", \"positive_key\": \"B00C1LVI2U\", \"positive_value\": \"./images/B00C1LVI2U.png\", \"hn_image\": [\"./images/B00BG0FKN0.png\"]}\n{\"q_img\": \"./images/B004071VYI.png\", \"q_text\": \" blue and pink stripes., and has more stripes\", \"positive_key\": \"B008M9W8Y2\", \"positive_value\": \"./images/B008M9W8Y2.png\", \"hn_image\": [\"./images/B004071VYI.png\"]}\n{\"q_img\": \"./images/B00DI7EWY2.png\", \"q_text\": \"is black with a yellow triangle, and is black and less animated patterns\", \"positive_key\": \"B00A5IRBGI\", \"positive_value\": \"./images/B00A5IRBGI.png\", \"hn_image\": [\"./images/B00DI7EWY2.png\"]}\n{\"q_img\": \"./images/B008AUHYZ6.png\", \"q_text\": \"is black and tan, and has a black and tan print\", \"positive_key\": \"B00BYHVZTS\", \"positive_value\": \"./images/B00BYHVZTS.png\", \"hn_image\": [\"./images/B008AUHYZ6.png\"]}\n{\"q_img\": \"./images/B000Y4GM1I.png\", \"q_text\": \"is darker, and is black with Guns N Roses on front\", \"positive_key\": \"B005S1F5HY\", \"positive_value\": \"./images/B005S1F5HY.png\", \"hn_image\": [\"./images/B000Y4GM1I.png\"]}\n{\"q_img\": \"./images/B002SLH50W.png\", \"q_text\": \"The shirt is purple with purple, and is white and short sleeved\", \"positive_key\": \"B004GECGL2\", \"positive_value\": \"./images/B004GECGL2.png\", \"hn_image\": [\"./images/B002SLH50W.png\"]}\n{\"q_img\": \"./images/B0019IKZ16.png\", \"q_text\": \"is beige with one shoulder strap, and is tan with one sleeve missing\", \"positive_key\": \"B007AAENZ6\", \"positive_value\": \"./images/B007AAENZ6.png\", \"hn_image\": [\"./images/B0019IKZ16.png\"]}\n{\"q_img\": \"./images/B00CH21TQ8.png\", \"q_text\": \"is light blue with pink designs, and is light blue with red image\", \"positive_key\": \"B00E9YRMOU\", \"positive_value\": \"./images/B00E9YRMOU.png\", \"hn_image\": [\"./images/B00CH21TQ8.png\"]}\n{\"q_img\": \"./images/B0093PJJLO.png\", \"q_text\": \"its green with scoop neck, and has a deeper rounded color to it\", \"positive_key\": \"B009XGRRDK\", \"positive_value\": \"./images/B009XGRRDK.png\", \"hn_image\": [\"./images/B0093PJJLO.png\"]}\n{\"q_img\": \"./images/B00CS5PAZU.png\", \"q_text\": \"Has a more intense graphic, and has cap sleeves and realistic graphic of man\", \"positive_key\": \"B00CLD3UH4\", \"positive_value\": \"./images/B00CLD3UH4.png\", \"hn_image\": [\"./images/B00CS5PAZU.png\"]}\n{\"q_img\": \"./images/B00D8WK8T0.png\", \"q_text\": \"is blue with polka dots, and is lighter\", \"positive_key\": \"B00BZSE1UG\", \"positive_value\": \"./images/B00BZSE1UG.png\", \"hn_image\": [\"./images/B00D8WK8T0.png\"]}\n{\"q_img\": \"./images/B008MP41R8.png\", \"q_text\": \"is patterned with hearts and no sleeves, and is white with hearts pattern\", \"positive_key\": \"B0094FP994\", \"positive_value\": \"./images/B0094FP994.png\", \"hn_image\": [\"./images/B008MP41R8.png\"]}\n{\"q_img\": \"./images/B003RW60ES.png\", \"q_text\": \"Is blue and more sheer, and is blue with white text\", \"positive_key\": \"B00AW0LSHM\", \"positive_value\": \"./images/B00AW0LSHM.png\", \"hn_image\": [\"./images/B003RW60ES.png\"]}\n{\"q_img\": \"./images/B008SLY5DG.png\", \"q_text\": \"is less trashed and has longer sleeves, and longsleeve less ware\", \"positive_key\": \"B009XIQY14\", \"positive_value\": \"./images/B009XIQY14.png\", \"hn_image\": [\"./images/B008SLY5DG.png\"]}\n{\"q_img\": \"./images/B005IMGPKY.png\", \"q_text\": \"is dark black with white designs, and has a rhinestone peace sign\", \"positive_key\": \"B003IS1E7Y\", \"positive_value\": \"./images/B003IS1E7Y.png\", \"hn_image\": [\"./images/B005IMGPKY.png\"]}\n{\"q_img\": \"./images/B00E0IN6HW.png\", \"q_text\": \"The shirt is blue and black in color., and is blue with black sleeve and collar rings\", \"positive_key\": \"B005XYKQD4\", \"positive_value\": \"./images/B005XYKQD4.png\", \"hn_image\": [\"./images/B00E0IN6HW.png\"]}\n{\"q_img\": \"./images/B00AOCQ9N6.png\", \"q_text\": \"Is not a shirt, and is a leather accessory and not a purple shirt\", \"positive_key\": \"B000YQ3SLI\", \"positive_value\": \"./images/B000YQ3SLI.png\", \"hn_image\": [\"./images/B00AOCQ9N6.png\"]}\n{\"q_img\": \"./images/B00980K5X0.png\", \"q_text\": \"The shirt is aqua with a white picture., and sweater on top\", \"positive_key\": \"B00BQJQ7Q0\", \"positive_value\": \"./images/B00BQJQ7Q0.png\", \"hn_image\": [\"./images/B00980K5X0.png\"]}\n{\"q_img\": \"./images/B000VTB0OK.png\", \"q_text\": \"is a long sleeve beige shirt, and is a collared button down shirt with a collar\", \"positive_key\": \"B00C7ATPA2\", \"positive_value\": \"./images/B00C7ATPA2.png\", \"hn_image\": [\"./images/B000VTB0OK.png\"]}\n{\"q_img\": \"./images/B00EP49DS2.png\", \"q_text\": \"Has shorter sleeves and is black, and is more like a crew neck grey tee with shorter sleeves.\", \"positive_key\": \"B0083SO3AO\", \"positive_value\": \"./images/B0083SO3AO.png\", \"hn_image\": [\"./images/B00EP49DS2.png\"]}\n{\"q_img\": \"./images/B00CUU2RUO.png\", \"q_text\": \"is white with a red writing on it, and it is more white\", \"positive_key\": \"B005TGIFKC\", \"positive_value\": \"./images/B005TGIFKC.png\", \"hn_image\": [\"./images/B00CUU2RUO.png\"]}\n{\"q_img\": \"./images/B00E6MGG9M.png\", \"q_text\": \"The shirt is long sleeve with a blue design., and blue and white button up\", \"positive_key\": \"B00BB8WEE0\", \"positive_value\": \"./images/B00BB8WEE0.png\", \"hn_image\": [\"./images/B00E6MGG9M.png\"]}\n{\"q_img\": \"./images/B000RZ4Z2M.png\", \"q_text\": \"has shorter sleeves and is beige, and is yellow with no sleeves\", \"positive_key\": \"B000RZ45TK\", \"positive_value\": \"./images/B000RZ45TK.png\", \"hn_image\": [\"./images/B000RZ4Z2M.png\"]}\n{\"q_img\": \"./images/B006PATFFU.png\", \"q_text\": \"is dark colored and has shorter sleeves, and gray short sleeve scoop neck\", \"positive_key\": \"B007FDOJRA\", \"positive_value\": \"./images/B007FDOJRA.png\", \"hn_image\": [\"./images/B006PATFFU.png\"]}\n{\"q_img\": \"./images/B0009GG2RA.png\", \"q_text\": \"is not a purse, and is a tote with pink font\", \"positive_key\": \"B009IQIZA4\", \"positive_value\": \"./images/B009IQIZA4.png\", \"hn_image\": [\"./images/B0009GG2RA.png\"]}\n{\"q_img\": \"./images/B008G1GGCG.png\", \"q_text\": \"is black with white words, and is lighter in color\", \"positive_key\": \"B00DB7A11W\", \"positive_value\": \"./images/B00DB7A11W.png\", \"hn_image\": [\"./images/B008G1GGCG.png\"]}\n{\"q_img\": \"./images/B000W9XX8U.png\", \"q_text\": \"is black and has James Deans face, and is black and does not reference a school\", \"positive_key\": \"B001L17S0C\", \"positive_value\": \"./images/B001L17S0C.png\", \"hn_image\": [\"./images/B000W9XX8U.png\"]}\n{\"q_img\": \"./images/B00EYYEYAA.png\", \"q_text\": \"is patterened and more casual, and is red plaid patterned with buttons\", \"positive_key\": \"B0091R7MQS\", \"positive_value\": \"./images/B0091R7MQS.png\", \"hn_image\": [\"./images/B00EYYEYAA.png\"]}\n{\"q_img\": \"./images/B00DC4HCSO.png\", \"q_text\": \"is lighter and has longer sleeves, and is long sleeve and has city logo.\", \"positive_key\": \"B007WN8M12\", \"positive_value\": \"./images/B007WN8M12.png\", \"hn_image\": [\"./images/B00DC4HCSO.png\"]}\n{\"q_img\": \"./images/B00DEOOUGO.png\", \"q_text\": \"has a short sleeve with light gray color, and is a casual short sleeved shirt\", \"positive_key\": \"B004R9P8ZW\", \"positive_value\": \"./images/B004R9P8ZW.png\", \"hn_image\": [\"./images/B00DEOOUGO.png\"]}\n{\"q_img\": \"./images/B00FE2CEXU.png\", \"q_text\": \"is black with Linux inside logo, and is more wordy and less feminine\", \"positive_key\": \"B00355624K\", \"positive_value\": \"./images/B00355624K.png\", \"hn_image\": [\"./images/B00FE2CEXU.png\"]}\n{\"q_img\": \"./images/B00ADX6GYS.png\", \"q_text\": \"has more colot, and is a blue tee instead of black and has a looser fitting neck.\", \"positive_key\": \"B008XLNM9O\", \"positive_value\": \"./images/B008XLNM9O.png\", \"hn_image\": [\"./images/B00ADX6GYS.png\"]}\n{\"q_img\": \"./images/B007O3TPJI.png\", \"q_text\": \"is a dark fitted low cut tank top, and is brown colored\", \"positive_key\": \"B00AAQ3ZP6\", \"positive_value\": \"./images/B00AAQ3ZP6.png\", \"hn_image\": [\"./images/B007O3TPJI.png\"]}\n{\"q_img\": \"./images/B0094R0LDG.png\", \"q_text\": \"has no sleeve and contrasting color, and has a less busy looking pattern\", \"positive_key\": \"B00CE4HIKU\", \"positive_value\": \"./images/B00CE4HIKU.png\", \"hn_image\": [\"./images/B0094R0LDG.png\"]}\n{\"q_img\": \"./images/B007G0I5EK.png\", \"q_text\": \"has longer sleeves and has stripes, and is darker and less revealing\", \"positive_key\": \"B005HTQ4E0\", \"positive_value\": \"./images/B005HTQ4E0.png\", \"hn_image\": [\"./images/B007G0I5EK.png\"]}\n{\"q_img\": \"./images/B004Q851M4.png\", \"q_text\": \"is pink with white words, and is pink with a cancer message on it\", \"positive_key\": \"B00CD0KQ4A\", \"positive_value\": \"./images/B00CD0KQ4A.png\", \"hn_image\": [\"./images/B004Q851M4.png\"]}\n{\"q_img\": \"./images/B0092F6KUS.png\", \"q_text\": \"is darker colored, and is longer length and darker\", \"positive_key\": \"B00EE9CPRE\", \"positive_value\": \"./images/B00EE9CPRE.png\", \"hn_image\": [\"./images/B0092F6KUS.png\"]}\n{\"q_img\": \"./images/B00A13GCSK.png\", \"q_text\": \"is grey and black and has more strips, and is grey with black stripes\", \"positive_key\": \"B00DVLXE3A\", \"positive_value\": \"./images/B00DVLXE3A.png\", \"hn_image\": [\"./images/B00A13GCSK.png\"]}\n{\"q_img\": \"./images/B008MZJHX6.png\", \"q_text\": \"is a lighter color and is more revealing, and is sleeveless and white colored\", \"positive_key\": \"B008TWBPR8\", \"positive_value\": \"./images/B008TWBPR8.png\", \"hn_image\": [\"./images/B008MZJHX6.png\"]}\n{\"q_img\": \"./images/B004P5P1I2.png\", \"q_text\": \"is purple and off-shoulder, and is sleeveless and purple\", \"positive_key\": \"B0039YP766\", \"positive_value\": \"./images/B0039YP766.png\", \"hn_image\": [\"./images/B004P5P1I2.png\"]}\n{\"q_img\": \"./images/B007PUOJAU.png\", \"q_text\": \"is white colored and has longer sleeves, and has lace over the print out with long sleeve.\", \"positive_key\": \"B00CZFD20S\", \"positive_value\": \"./images/B00CZFD20S.png\", \"hn_image\": [\"./images/B007PUOJAU.png\"]}\n{\"q_img\": \"./images/B002HWRSZO.png\", \"q_text\": \"is dark grey colored and has shorter sleeves, and is ruffled and short sleeved\", \"positive_key\": \"B003LJYKP8\", \"positive_value\": \"./images/B003LJYKP8.png\", \"hn_image\": [\"./images/B002HWRSZO.png\"]}\n{\"q_img\": \"./images/B00AZ9I2PG.png\", \"q_text\": \"has sleeves and is green and black, and has sleeves\", \"positive_key\": \"B0070FC04W\", \"positive_value\": \"./images/B0070FC04W.png\", \"hn_image\": [\"./images/B00AZ9I2PG.png\"]}\n{\"q_img\": \"./images/B00591CB0W.png\", \"q_text\": \"has no sleeves with a collar, and Is denim and sleeveless.\", \"positive_key\": \"B00ARTG0U8\", \"positive_value\": \"./images/B00ARTG0U8.png\", \"hn_image\": [\"./images/B00591CB0W.png\"]}\n{\"q_img\": \"./images/B002CJ5LAQ.png\", \"q_text\": \"Is more plain and darker blue, and is a plain\", \"positive_key\": \"B000N63OB8\", \"positive_value\": \"./images/B000N63OB8.png\", \"hn_image\": [\"./images/B002CJ5LAQ.png\"]}\n{\"q_img\": \"./images/B007NG76LK.png\", \"q_text\": \"is orange and has sleeves, and sleeves\", \"positive_key\": \"B00B5HGJDY\", \"positive_value\": \"./images/B00B5HGJDY.png\", \"hn_image\": [\"./images/B007NG76LK.png\"]}\n{\"q_img\": \"./images/B003BZFKQ0.png\", \"q_text\": \"is a blue t-shirt with long sleeves, and has longer sleeves and is more feminine\", \"positive_key\": \"B0089QMX3E\", \"positive_value\": \"./images/B0089QMX3E.png\", \"hn_image\": [\"./images/B003BZFKQ0.png\"]}\n{\"q_img\": \"./images/B00B04JO4I.png\", \"q_text\": \"has smaller straps, and is a tanktop and is black in color\", \"positive_key\": \"B00CHIZ832\", \"positive_value\": \"./images/B00CHIZ832.png\", \"hn_image\": [\"./images/B00B04JO4I.png\"]}\n{\"q_img\": \"./images/B00CIY810C.png\", \"q_text\": \"is black with cats, and has a graphic on it\", \"positive_key\": \"B009EWR80K\", \"positive_value\": \"./images/B009EWR80K.png\", \"hn_image\": [\"./images/B00CIY810C.png\"]}\n{\"q_img\": \"./images/B007IWEU8Q.png\", \"q_text\": \"is lighter, and is pink colored\", \"positive_key\": \"B009RJZK9Q\", \"positive_value\": \"./images/B009RJZK9Q.png\", \"hn_image\": [\"./images/B007IWEU8Q.png\"]}\n{\"q_img\": \"./images/B00E65KUB4.png\", \"q_text\": \"has a white background., and has more colors in it\", \"positive_key\": \"B00D938AWA\", \"positive_value\": \"./images/B00D938AWA.png\", \"hn_image\": [\"./images/B00E65KUB4.png\"]}\n{\"q_img\": \"./images/B006VJXGAU.png\", \"q_text\": \"The shirt is black with red and white wording., and is black with a different logo\", \"positive_key\": \"B000E27YSK\", \"positive_value\": \"./images/B000E27YSK.png\", \"hn_image\": [\"./images/B006VJXGAU.png\"]}\n{\"q_img\": \"./images/B00C1GTZUM.png\", \"q_text\": \"The shirt is a polo top and red in color., and is red in color\", \"positive_key\": \"B007KPH08S\", \"positive_value\": \"./images/B007KPH08S.png\", \"hn_image\": [\"./images/B00C1GTZUM.png\"]}\n{\"q_img\": \"./images/B0094FP994.png\", \"q_text\": \"Is monochromatic and not patterned, and has longer sleves\", \"positive_key\": \"B008MP41R8\", \"positive_value\": \"./images/B008MP41R8.png\", \"hn_image\": [\"./images/B0094FP994.png\"]}\n{\"q_img\": \"./images/B002IN0LK6.png\", \"q_text\": \"The shirt is black with white angel wings, and is black with white image\", \"positive_key\": \"B002X8EMGA\", \"positive_value\": \"./images/B002X8EMGA.png\", \"hn_image\": [\"./images/B002IN0LK6.png\"]}\n{\"q_img\": \"./images/B007GO8A00.png\", \"q_text\": \"is bright red, and is red with yellow image\", \"positive_key\": \"B007H9KCOG\", \"positive_value\": \"./images/B007H9KCOG.png\", \"hn_image\": [\"./images/B007GO8A00.png\"]}\n{\"q_img\": \"./images/B005J6ACWG.png\", \"q_text\": \"is solid blue with some sleeve, and is darker and has wider straps\", \"positive_key\": \"B0079EIEQM\", \"positive_value\": \"./images/B0079EIEQM.png\", \"hn_image\": [\"./images/B005J6ACWG.png\"]}\n{\"q_img\": \"./images/B0068VGVK8.png\", \"q_text\": \"The peasant shirt is white with a flower trim., and it is less colorful\", \"positive_key\": \"B0063LVQMQ\", \"positive_value\": \"./images/B0063LVQMQ.png\", \"hn_image\": [\"./images/B0068VGVK8.png\"]}\n{\"q_img\": \"./images/B0060HUDNG.png\", \"q_text\": \"is black colored and longer sleeves, and is black and has long sleeves\", \"positive_key\": \"B003XII7B0\", \"positive_value\": \"./images/B003XII7B0.png\", \"hn_image\": [\"./images/B0060HUDNG.png\"]}\n{\"q_img\": \"./images/B004W9MRYW.png\", \"q_text\": \"Is darker in color and not colorful, and A gray shirt with black pattern\", \"positive_key\": \"B00AF7UXKA\", \"positive_value\": \"./images/B00AF7UXKA.png\", \"hn_image\": [\"./images/B004W9MRYW.png\"]}\n{\"q_img\": \"./images/B0059LBD1A.png\", \"q_text\": \"has a looser neckline and is more sexy, and has a lower collar\", \"positive_key\": \"B00AL2AL7E\", \"positive_value\": \"./images/B00AL2AL7E.png\", \"hn_image\": [\"./images/B0059LBD1A.png\"]}\n{\"q_img\": \"./images/B00C1180OE.png\", \"q_text\": \"has a shiny pattern and looks more dressy, and has holes\", \"positive_key\": \"B009VRTPWM\", \"positive_value\": \"./images/B009VRTPWM.png\", \"hn_image\": [\"./images/B00C1180OE.png\"]}\n{\"q_img\": \"./images/B00CFQIFK4.png\", \"q_text\": \"has a larger pattern and is less beachy, and is in white color only.\", \"positive_key\": \"B00DQF873S\", \"positive_value\": \"./images/B00DQF873S.png\", \"hn_image\": [\"./images/B00CFQIFK4.png\"]}\n{\"q_img\": \"./images/B00B19GTW2.png\", \"q_text\": \"is black with a grey logo with half sleeves, and is black with a small graphic on it\", \"positive_key\": \"B00B8QSVT2\", \"positive_value\": \"./images/B00B8QSVT2.png\", \"hn_image\": [\"./images/B00B19GTW2.png\"]}\n{\"q_img\": \"./images/B00BNQW9CM.png\", \"q_text\": \"is white with tiger designs, and has more graphics\", \"positive_key\": \"B00BG43EAC\", \"positive_value\": \"./images/B00BG43EAC.png\", \"hn_image\": [\"./images/B00BNQW9CM.png\"]}\n{\"q_img\": \"./images/B0037Q23I6.png\", \"q_text\": \"has short sleeves, and is darker and has longer sleeves\", \"positive_key\": \"B00C99PYQK\", \"positive_value\": \"./images/B00C99PYQK.png\", \"hn_image\": [\"./images/B0037Q23I6.png\"]}\n{\"q_img\": \"./images/B007ZQI72K.png\", \"q_text\": \"has blue and white patterns, and is less plus size and more casual\", \"positive_key\": \"B00BB7EGJM\", \"positive_value\": \"./images/B00BB7EGJM.png\", \"hn_image\": [\"./images/B007ZQI72K.png\"]}\n{\"q_img\": \"./images/B008396CNO.png\", \"q_text\": \"is darker and has shorter sleeves, and Is shorter with no sleeves\", \"positive_key\": \"B00AZW5NI2\", \"positive_value\": \"./images/B00AZW5NI2.png\", \"hn_image\": [\"./images/B008396CNO.png\"]}\n{\"q_img\": \"./images/B0079EM83M.png\", \"q_text\": \"has no sleeves with transparent color, and has lace and no sleeves\", \"positive_key\": \"B00DEOOUGO\", \"positive_value\": \"./images/B00DEOOUGO.png\", \"hn_image\": [\"./images/B0079EM83M.png\"]}\n{\"q_img\": \"./images/B00CWMDY20.png\", \"q_text\": \"The shirt is an animal print., and has darker pattern\", \"positive_key\": \"B00CWMDVE6\", \"positive_value\": \"./images/B00CWMDVE6.png\", \"hn_image\": [\"./images/B00CWMDY20.png\"]}\n{\"q_img\": \"./images/B00C9NQNSY.png\", \"q_text\": \"is a pink t-shirt with a logo, and Is pink with short sleeves\", \"positive_key\": \"B005TGF29Y\", \"positive_value\": \"./images/B005TGF29Y.png\", \"hn_image\": [\"./images/B00C9NQNSY.png\"]}\n{\"q_img\": \"./images/B007U4PP18.png\", \"q_text\": \"is a lighter blue and less shiny, and is a lighter blue\", \"positive_key\": \"B00AKMY63K\", \"positive_value\": \"./images/B00AKMY63K.png\", \"hn_image\": [\"./images/B007U4PP18.png\"]}\n{\"q_img\": \"./images/B00D48X886.png\", \"q_text\": \"is more orange and sporty, and has white sleeve and is orange color\", \"positive_key\": \"B005JF3X5U\", \"positive_value\": \"./images/B005JF3X5U.png\", \"hn_image\": [\"./images/B00D48X886.png\"]}\n{\"q_img\": \"./images/B001SHNCL8.png\", \"q_text\": \"has shorter sleeves and is less revealing, and has sleeves\", \"positive_key\": \"B0020KV02U\", \"positive_value\": \"./images/B0020KV02U.png\", \"hn_image\": [\"./images/B001SHNCL8.png\"]}\n{\"q_img\": \"./images/B007FO5H1Q.png\", \"q_text\": \"is blue with short sleeves, and is plain\", \"positive_key\": \"B003AM8C1E\", \"positive_value\": \"./images/B003AM8C1E.png\", \"hn_image\": [\"./images/B007FO5H1Q.png\"]}\n{\"q_img\": \"./images/B003ILSB7C.png\", \"q_text\": \"The shirt is gray in color., and is darker\", \"positive_key\": \"B001R9LU2U\", \"positive_value\": \"./images/B001R9LU2U.png\", \"hn_image\": [\"./images/B003ILSB7C.png\"]}\n{\"q_img\": \"./images/B0032C1MPK.png\", \"q_text\": \"The shirt is sleeveless white with black stripes., and has off shoulder and is white color\", \"positive_key\": \"B00CBU3EN2\", \"positive_value\": \"./images/B00CBU3EN2.png\", \"hn_image\": [\"./images/B0032C1MPK.png\"]}\n{\"q_img\": \"./images/B004RF69OA.png\", \"q_text\": \"has no sleeves and is orange in color, and has no sleeves\", \"positive_key\": \"B004O54OXQ\", \"positive_value\": \"./images/B004O54OXQ.png\", \"hn_image\": [\"./images/B004RF69OA.png\"]}\n{\"q_img\": \"./images/B008RLTLF4.png\", \"q_text\": \"The shirt dress in short and white and black in color., and is black and white with shorter sleeves\", \"positive_key\": \"B008SBXUNW\", \"positive_value\": \"./images/B008SBXUNW.png\", \"hn_image\": [\"./images/B008RLTLF4.png\"]}\n{\"q_img\": \"./images/B00B8QSPDO.png\", \"q_text\": \"The shirt is gray in color with black writing., and has short sleeves and is more humorous\", \"positive_key\": \"B004KUYKGG\", \"positive_value\": \"./images/B004KUYKGG.png\", \"hn_image\": [\"./images/B00B8QSPDO.png\"]}\n{\"q_img\": \"./images/B0014ULOPU.png\", \"q_text\": \"is pink, and Is brighter and less graphic\", \"positive_key\": \"B00BIQADUW\", \"positive_value\": \"./images/B00BIQADUW.png\", \"hn_image\": [\"./images/B0014ULOPU.png\"]}\n{\"q_img\": \"./images/B0093UC3C6.png\", \"q_text\": \"is neon yellow, and has a lower neckline and is less patterend\", \"positive_key\": \"B006LWH1DU\", \"positive_value\": \"./images/B006LWH1DU.png\", \"hn_image\": [\"./images/B0093UC3C6.png\"]}\n{\"q_img\": \"./images/B002IN0LK6.png\", \"q_text\": \"The shirt is black in color with red writing and a motorcycle., and Is darker and less revealing\", \"positive_key\": \"B006W7SUXO\", \"positive_value\": \"./images/B006W7SUXO.png\", \"hn_image\": [\"./images/B002IN0LK6.png\"]}\n{\"q_img\": \"./images/B00AMYTG5E.png\", \"q_text\": \"is brown with shorter sleeves, and has more brown\", \"positive_key\": \"B00ESMW1DA\", \"positive_value\": \"./images/B00ESMW1DA.png\", \"hn_image\": [\"./images/B00AMYTG5E.png\"]}\n{\"q_img\": \"./images/B00E3IT8AI.png\", \"q_text\": \" darker, and is a black top\", \"positive_key\": \"B00BJ88CTI\", \"positive_value\": \"./images/B00BJ88CTI.png\", \"hn_image\": [\"./images/B00E3IT8AI.png\"]}\n{\"q_img\": \"./images/B00BRA1A9C.png\", \"q_text\": \"has longer sleeves, and has longer sleeves\", \"positive_key\": \"B00CVE03BE\", \"positive_value\": \"./images/B00CVE03BE.png\", \"hn_image\": [\"./images/B00BRA1A9C.png\"]}\n{\"q_img\": \"./images/B008LYT3G4.png\", \"q_text\": \"is made of denim, and has buttoned front and double pockets\", \"positive_key\": \"B008LZLH3K\", \"positive_value\": \"./images/B008LZLH3K.png\", \"hn_image\": [\"./images/B008LYT3G4.png\"]}\n{\"q_img\": \"./images/B005IMGPKY.png\", \"q_text\": \"is yellow and has short sleeves, and colorful emblem\", \"positive_key\": \"B005IMGTFU\", \"positive_value\": \"./images/B005IMGTFU.png\", \"hn_image\": [\"./images/B005IMGPKY.png\"]}\n{\"q_img\": \"./images/B00AZPKQEA.png\", \"q_text\": \"has a collar and is a button up, and quarter sleeves more pink and flowery\", \"positive_key\": \"B00AKHJYBY\", \"positive_value\": \"./images/B00AKHJYBY.png\", \"hn_image\": [\"./images/B00AZPKQEA.png\"]}\n{\"q_img\": \"./images/B00BB9UBNU.png\", \"q_text\": \"The shirt is long sleeved and bright blue with a copper collar., and has more blue\", \"positive_key\": \"B005SSMI04\", \"positive_value\": \"./images/B005SSMI04.png\", \"hn_image\": [\"./images/B00BB9UBNU.png\"]}\n{\"q_img\": \"./images/B0084S6I4C.png\", \"q_text\": \"is darker in color, and is a black tee with a different print.\", \"positive_key\": \"B00AF2O4VO\", \"positive_value\": \"./images/B00AF2O4VO.png\", \"hn_image\": [\"./images/B0084S6I4C.png\"]}\n{\"q_img\": \"./images/B0059LBD1A.png\", \"q_text\": \"is a see-thru striped long sleeved t-shirt and U collar, and Is striped and lighter in color\", \"positive_key\": \"B004070YLE\", \"positive_value\": \"./images/B004070YLE.png\", \"hn_image\": [\"./images/B0059LBD1A.png\"]}\n" }, { "path": "research/BGE_VL/eval/eval_Circo.py", "content": "import os\nimport faiss\nimport torch\nimport logging\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom typing import Optional\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom flag_mmret import Flag_mmret\nimport json\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass Args:\n model_name: str = field(\n default=\"BAAI/BGE-VL-large\",\n metadata={'help': 'Model Name'}\n )\n result_save_path: str = field(\n default=\"./eval/mmret_large_circo.json\",\n metadata={'help': 'Where to save the results.'}\n )\n image_dir: str = field(\n default=\"YOUR_COCO_IMAGE_DIRECTORY\",\n metadata={'help': 'Where the images located on.'}\n )\n fp16: bool = field(\n default=False,\n metadata={'help': 'Use fp16 in inference?'}\n )\n max_query_length: int = field(\n default=64,\n metadata={'help': 'Max query length.'}\n )\n max_passage_length: int = field(\n default=77,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n index_factory: str = field(\n default=\"Flat\",\n metadata={'help': 'Faiss index factory.'}\n )\n k: int = field(\n default=100,\n metadata={'help': 'How many neighbors to retrieve?'}\n )\n save_embedding: bool = field(\n default=False,\n metadata={'help': 'Save embeddings in memmap at save_dir?'}\n )\n load_embedding: bool = field(\n default=False,\n metadata={'help': 'Load embeddings from save_dir?'}\n )\n save_path: str = field(\n default=\"embeddings.memmap\",\n metadata={'help': 'Path to save embeddings.'}\n )\n\n\n\ndef index(model: Flag_mmret, corpus: datasets.Dataset, batch_size: int = 256, max_length: int=512, index_factory: str = \"Flat\", save_path: str = None, save_embedding: bool = False, load_embedding: bool = False):\n \"\"\"\n 1. Encode the entire corpus into dense embeddings; \n 2. Create faiss index; \n 3. Optionally save embeddings.\n \"\"\"\n if load_embedding:\n test = model.encode(\"test\")\n dtype = test.dtype\n dim = len(test)\n\n corpus_embeddings = np.memmap(\n save_path,\n mode=\"r\",\n dtype=dtype\n ).reshape(-1, dim)\n \n else:\n \n corpus_embeddings = model.encode_corpus(corpus, batch_size=batch_size, max_length=max_length, corpus_type='image')\n \n dim = corpus_embeddings.shape[-1]\n \n if save_embedding:\n logger.info(f\"saving embeddings at {save_path}...\")\n memmap = np.memmap(\n save_path,\n shape=corpus_embeddings.shape,\n mode=\"w+\",\n dtype=corpus_embeddings.dtype\n )\n\n length = corpus_embeddings.shape[0]\n # add in batch\n save_batch_size = 10000\n if length > save_batch_size:\n for i in tqdm(range(0, length, save_batch_size), leave=False, desc=\"Saving Embeddings\"):\n j = min(i + save_batch_size, length)\n memmap[i: j] = corpus_embeddings[i: j]\n else:\n memmap[:] = corpus_embeddings\n \n # create faiss index\n faiss_index = faiss.index_factory(dim, index_factory, faiss.METRIC_INNER_PRODUCT)\n \n\n if model.device == torch.device(\"cuda\"):\n # co = faiss.GpuClonerOptions()\n co = faiss.GpuMultipleClonerOptions()\n co.useFloat16 = True\n # faiss_index = faiss.index_cpu_to_gpu(faiss.StandardGpuResources(), 0, faiss_index, co)\n faiss_index = faiss.index_cpu_to_all_gpus(faiss_index, co)\n\n # NOTE: faiss only accepts float32\n logger.info(\"Adding embeddings...\")\n corpus_embeddings = corpus_embeddings.astype(np.float32)\n faiss_index.train(corpus_embeddings)\n faiss_index.add(corpus_embeddings)\n\n \n\n return faiss_index\n\n\ndef search(model: Flag_mmret, queries: datasets, faiss_index: faiss.Index, k:int = 100, batch_size: int = 256, max_length: int=512):\n \"\"\"\n 1. Encode queries into dense embeddings;\n 2. Search through faiss index\n \"\"\"\n query_embeddings = model.encode_queries([queries[\"q_text\"], queries[\"q_img\"]], \n batch_size=batch_size, \n max_length=max_length,\n query_type='mm_it')\n\n \n query_size = len(query_embeddings)\n \n all_scores = []\n all_indices = []\n \n for i in tqdm(range(0, query_size, batch_size), desc=\"Searching\"):\n j = min(i + batch_size, query_size)\n query_embedding = query_embeddings[i: j]\n score, indice = faiss_index.search(query_embedding.astype(np.float32), k=k)\n all_scores.append(score)\n all_indices.append(indice)\n \n all_scores = np.concatenate(all_scores, axis=0)\n all_indices = np.concatenate(all_indices, axis=0)\n return all_scores, all_indices\n \n\ndef main():\n parser = HfArgumentParser([Args])\n args: Args = parser.parse_args_into_dataclasses()[0]\n \n print(f\"Results will be saved in {args.result_save_path}\")\n eval_data = datasets.load_dataset('json', data_files=\"./eval/data/circo_query.jsonl\", split='train')\n image_corpus_test = datasets.load_dataset('json', data_files=\"./eval/data/circo_corpus.jsonl\", split='train')\n \n model = Flag_mmret(model_name=args.model_name,\n normlized = True,\n image_dir=args.image_dir,\n use_fp16=False,\n )\n \n \n faiss_index = index(\n model=model, \n corpus=image_corpus_test, \n batch_size=args.batch_size,\n max_length=args.max_passage_length,\n index_factory=args.index_factory,\n save_path=args.save_path,\n save_embedding=args.save_embedding,\n load_embedding=args.load_embedding\n )\n \n scores, indices = search(\n model=model, \n queries=eval_data, \n faiss_index=faiss_index, \n k=args.k, \n batch_size=args.batch_size, \n max_length=args.max_query_length\n )\n \n \n retrieval_results = []\n for indice in indices:\n # filter invalid indices\n indice = indice[indice != -1].tolist()\n retrieval_results.append(image_corpus_test[indice][\"content\"])\n\n ########## results in test corpus #########\n q_images = eval_data[\"q_img\"]\n\n q_ids = []\n for _img in q_images:\n _id = os.path.basename(_img)\n _id = os.path.splitext(_id)[0]\n q_ids.append(_id)\n \n pairids = eval_data[\"id\"]\n results = {}\n for pairid, re_results, q_img in zip(pairids, retrieval_results, q_images):\n id = str(pairid)\n top_50_results = re_results[0:50]\n results[id] = top_50_results\n\n with open(args.result_save_path, \"w\") as f:\n json.dump(results, f)\n\n\nif __name__ == \"__main__\":\n main()" }, { "path": "research/BGE_VL/eval/eval_fashioniq.py", "content": "import os\nos.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n\nimport sys\nprint(os.getcwd())\n\nimport faiss\nimport torch\nimport logging\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom typing import Optional\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom flag_mmret import Flag_mmret\nimport json\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass Args:\n model_name: str = field(\n default=\"BAAI/BGE-VL-large\",\n metadata={'help': 'Model Name'}\n )\n image_dir: str = field(\n default=\"YOUR_FASHIONIQ_IMAGE_DIRECTORY\",\n metadata={'help': 'Where are the images located on.'}\n )\n fp16: bool = field(\n default=False,\n metadata={'help': 'Use fp16 in inference?'}\n )\n max_query_length: int = field(\n default=64,\n metadata={'help': 'Max query length.'}\n )\n max_passage_length: int = field(\n default=77,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n index_factory: str = field(\n default=\"Flat\",\n metadata={'help': 'Faiss index factory.'}\n )\n k: int = field(\n default=100,\n metadata={'help': 'How many neighbors to retrieve?'}\n )\n save_embedding: bool = field(\n default=False,\n metadata={'help': 'Save embeddings in memmap at save_dir?'}\n )\n load_embedding: bool = field(\n default=False,\n metadata={'help': 'Load embeddings from save_dir?'}\n )\n save_path: str = field(\n default=\"embeddings.memmap\",\n metadata={'help': 'Path to save embeddings.'}\n )\n\n\n\ndef index(model: Flag_mmret, corpus: datasets.Dataset, batch_size: int = 256, max_length: int=512, index_factory: str = \"Flat\", save_path: str = None, save_embedding: bool = False, load_embedding: bool = False):\n \"\"\"\n 1. Encode the entire corpus into dense embeddings; \n 2. Create faiss index; \n 3. Optionally save embeddings.\n \"\"\"\n if load_embedding:\n test = model.encode(\"test\")\n dtype = test.dtype\n dim = len(test)\n\n corpus_embeddings = np.memmap(\n save_path,\n mode=\"r\",\n dtype=dtype\n ).reshape(-1, dim)\n \n else:\n \n corpus_embeddings = model.encode_corpus(corpus, batch_size=batch_size, max_length=max_length, corpus_type='image')\n \n dim = corpus_embeddings.shape[-1]\n \n if save_embedding:\n logger.info(f\"saving embeddings at {save_path}...\")\n memmap = np.memmap(\n save_path,\n shape=corpus_embeddings.shape,\n mode=\"w+\",\n dtype=corpus_embeddings.dtype\n )\n\n length = corpus_embeddings.shape[0]\n # add in batch\n save_batch_size = 10000\n if length > save_batch_size:\n for i in tqdm(range(0, length, save_batch_size), leave=False, desc=\"Saving Embeddings\"):\n j = min(i + save_batch_size, length)\n memmap[i: j] = corpus_embeddings[i: j]\n else:\n memmap[:] = corpus_embeddings\n \n # create faiss index\n faiss_index = faiss.index_factory(dim, index_factory, faiss.METRIC_INNER_PRODUCT)\n \n\n if model.device == torch.device(\"cuda\"):\n # co = faiss.GpuClonerOptions()\n co = faiss.GpuMultipleClonerOptions()\n co.useFloat16 = True\n # faiss_index = faiss.index_cpu_to_gpu(faiss.StandardGpuResources(), 0, faiss_index, co)\n faiss_index = faiss.index_cpu_to_all_gpus(faiss_index, co)\n\n # NOTE: faiss only accepts float32\n logger.info(\"Adding embeddings...\")\n corpus_embeddings = corpus_embeddings.astype(np.float32)\n faiss_index.train(corpus_embeddings)\n faiss_index.add(corpus_embeddings)\n\n \n\n return faiss_index\n\n\ndef search(model: Flag_mmret, queries: datasets, faiss_index: faiss.Index, k:int = 100, batch_size: int = 256, max_length: int=512):\n \"\"\"\n 1. Encode queries into dense embeddings;\n 2. Search through faiss index\n \"\"\"\n query_embeddings = model.encode_queries([queries[\"q_text\"], queries[\"q_img\"]], \n batch_size=batch_size, \n max_length=max_length,\n query_type='mm_it')\n \n query_size = len(query_embeddings)\n \n all_scores = []\n all_indices = []\n \n for i in tqdm(range(0, query_size, batch_size), desc=\"Searching\"):\n j = min(i + batch_size, query_size)\n query_embedding = query_embeddings[i: j]\n score, indice = faiss_index.search(query_embedding.astype(np.float32), k=k)\n all_scores.append(score)\n all_indices.append(indice)\n \n all_scores = np.concatenate(all_scores, axis=0)\n all_indices = np.concatenate(all_indices, axis=0)\n return all_scores, all_indices\n \n \ndef evaluate(preds, labels, cutoffs=[1,5,10,20,50,100]):\n \"\"\"\n Evaluate MRR and Recall at cutoffs.\n \"\"\"\n metrics = {}\n \n # MRR\n mrrs = np.zeros(len(cutoffs))\n for pred, label in zip(preds, labels):\n jump = False\n for i, x in enumerate(pred, 1):\n if x in label:\n for k, cutoff in enumerate(cutoffs):\n if i <= cutoff:\n mrrs[k] += 1 / i\n jump = True\n if jump:\n break\n mrrs /= len(preds)\n for i, cutoff in enumerate(cutoffs):\n mrr = mrrs[i]\n metrics[f\"MRR@{cutoff}\"] = mrr\n\n # Recall\n recalls = np.zeros(len(cutoffs))\n for pred, label in zip(preds, labels):\n if not isinstance(label, list):\n label = [label]\n for k, cutoff in enumerate(cutoffs):\n recall = np.intersect1d(label, pred[:cutoff])\n recalls[k] += len(recall) / len(label)\n recalls /= len(preds)\n for i, cutoff in enumerate(cutoffs):\n recall = recalls[i]\n metrics[f\"Recall@{cutoff}\"] = recall\n \n return metrics\n\ndef main():\n parser = HfArgumentParser([Args])\n args: Args = parser.parse_args_into_dataclasses()[0]\n\n model = Flag_mmret(model_name=args.model_name,\n normlized = True,\n image_dir=args.image_dir,\n use_fp16=False,\n )\n\n eval_data = datasets.load_dataset('json', data_files=\"./eval/data/fashioniq_shirt_query_val.jsonl\", split='train')\n image_corpus = datasets.load_dataset('json', data_files=\"./eval/data/fashioniq_shirt_corpus.jsonl\", split='train')\n \n faiss_index = index(\n model=model, \n corpus=image_corpus, \n batch_size=args.batch_size,\n max_length=args.max_passage_length,\n index_factory=args.index_factory,\n save_path=args.save_path,\n save_embedding=args.save_embedding,\n load_embedding=args.load_embedding\n )\n \n scores, indices = search(\n model=model, \n queries=eval_data, \n faiss_index=faiss_index, \n k=args.k, \n batch_size=args.batch_size, \n max_length=args.max_query_length\n )\n \n \n\n retrieval_results = []\n for indice in indices:\n # filter invalid indices\n indice = indice[indice != -1].tolist()\n retrieval_results.append(image_corpus[indice][\"content\"])\n\n ground_truths = []\n for sample in eval_data:\n ground_truths.append(sample[\"positive_key\"])\n \n metrics_shirt = evaluate(retrieval_results, ground_truths)\n print(\"FashionIQ tasks (shirt):\")\n print(metrics_shirt)\n\n\n\n \n\n eval_data = datasets.load_dataset('json', data_files=\"./eval/data/fashioniq_dress_query_val.jsonl\", split='train')\n image_corpus = datasets.load_dataset('json', data_files=\"./eval/data/fashioniq_dress_corpus.jsonl\", split='train')\n \n faiss_index = index(\n model=model, \n corpus=image_corpus, \n batch_size=args.batch_size,\n max_length=args.max_passage_length,\n index_factory=args.index_factory,\n save_path=args.save_path,\n save_embedding=args.save_embedding,\n load_embedding=args.load_embedding\n )\n \n scores, indices = search(\n model=model, \n queries=eval_data, \n faiss_index=faiss_index, \n k=args.k, \n batch_size=args.batch_size, \n max_length=args.max_query_length\n )\n \n \n\n retrieval_results = []\n for indice in indices:\n # filter invalid indices\n indice = indice[indice != -1].tolist()\n retrieval_results.append(image_corpus[indice][\"content\"])\n\n ground_truths = []\n for sample in eval_data:\n ground_truths.append(sample[\"positive_key\"])\n \n metrics_dress = evaluate(retrieval_results, ground_truths)\n print(\"FashionIQ tasks (dress):\")\n print(metrics_dress)\n\n\n eval_data = datasets.load_dataset('json', data_files=\"./eval/data/fashioniq_toptee_query_val.jsonl\", split='train')\n image_corpus = datasets.load_dataset('json', data_files=\"./eval/data/fashioniq_toptee_corpus.jsonl\", split='train')\n \n \n faiss_index = index(\n model=model, \n corpus=image_corpus, \n batch_size=args.batch_size,\n max_length=args.max_passage_length,\n index_factory=args.index_factory,\n save_path=args.save_path,\n save_embedding=args.save_embedding,\n load_embedding=args.load_embedding\n )\n \n scores, indices = search(\n model=model, \n queries=eval_data, \n faiss_index=faiss_index, \n k=args.k, \n batch_size=args.batch_size, \n max_length=args.max_query_length\n )\n \n \n\n retrieval_results = []\n for indice in indices:\n # filter invalid indices\n indice = indice[indice != -1].tolist()\n retrieval_results.append(image_corpus[indice][\"content\"])\n\n ground_truths = []\n for sample in eval_data:\n ground_truths.append(sample[\"positive_key\"])\n \n metrics_toptee = evaluate(retrieval_results, ground_truths)\n print(\"FashionIQ tasks (toptee):\")\n print(metrics_toptee)\n\n \n \n print(f\"shirt: {metrics_shirt['Recall@10'] * 100:.2f} / {metrics_shirt['Recall@50'] * 100:.2f}\")\n print(f\"dress: {metrics_dress['Recall@10'] * 100:.2f} / {metrics_dress['Recall@50'] * 100:.2f}\")\n print(f\"toptee: {metrics_toptee['Recall@10'] * 100:.2f} / {metrics_toptee['Recall@50'] * 100:.2f}\")\n print(f\"overall: {(metrics_shirt['Recall@10'] + metrics_dress['Recall@10'] + metrics_toptee['Recall@10']) * 100 / 3:.2f} / {(metrics_shirt['Recall@50'] + metrics_dress['Recall@50'] + metrics_toptee['Recall@50']) * 100 / 3:.2f}\")\n\n \nif __name__ == \"__main__\":\n main()" }, { "path": "research/BGE_VL/eval/flag_dataset.py", "content": "import math\nimport os.path\nimport random\nfrom dataclasses import dataclass\nfrom typing import Iterator\n\nimport datasets\nfrom torch.utils.data import Dataset, IterableDataset\nfrom transformers import DataCollatorWithPadding\nfrom transformers import PreTrainedTokenizer, BatchEncoding\nfrom transformers import CLIPImageProcessor\n\n\nfrom PIL import Image\nimport json\nimport torch\nimport torch.distributed\n\nfrom io import BytesIO\nimport warnings\n\nclass MMIT_Dataset(Dataset):\n def __init__(self, captions, image_ids, image_dir, image_processor) -> None:\n img_id_example = image_ids[0]\n img_id_example = str(img_id_example)\n if img_id_example[-4:] in [\".jpg\", \".png\", \"JPEG\"]:\n self.image_path =[os.path.join(image_dir, str(id)) for id in image_ids]\n else:\n warnings.warn(\"Not found file extention in image_ids, will forcefully add '.jpg'.\", UserWarning)\n self.image_path =[os.path.join(image_dir, str(id) + '.jpg') for id in image_ids]\n self.captions = captions\n self.image_processor = image_processor\n \n def __getitem__(self, item):\n pil_data = Image.open(self.image_path[item])\n pil_data = pil_data.convert('RGB')\n image = self.image_processor(pil_data)\n \n \n\n \n caption = self.captions[item]\n\n return caption, image\n\n def __len__(self):\n return len(self.image_path)\n\n\nclass MMIT_Collator:\n def __init__(self, tokenizer, caption_max_len):\n self.tokenizer = tokenizer\n self.caption_max_len = caption_max_len\n \n\n\n def __call__(self, features):\n caption = [f[0] for f in features]\n images = [f[1] for f in features]\n \n c_collated = self.tokenizer(\n caption,\n truncation=True,\n padding = True,\n max_length=self.caption_max_len,\n return_tensors=\"pt\",\n )\n\n # i_collated = torch.stack(images) \n \n # for clip model\n images = [f[\"pixel_values\"][0] for f in images]\n images = [torch.tensor(arr) for arr in images]\n i_collated = torch.stack(images) \n ##clip_end\n\n return c_collated, i_collated\n \nclass Image_Dataset(Dataset):\n def __init__(self, image_ids, image_dir, image_processor) -> None:\n\n self.image_path =[os.path.join(image_dir, str(id)) for id in image_ids]\n self.image_processor = image_processor\n \n def __getitem__(self, item):\n pil_data = Image.open(self.image_path[item])\n image = self.image_processor(pil_data)\n\n return image\n\n def __len__(self):\n return len(self.image_path)\n\nclass Image_Collator:\n def __init__(self, tokenizer, caption_max_len):\n self.tokenizer = tokenizer\n self.caption_max_len = caption_max_len\n \n\n def __call__(self, features):\n # images = features\n # i_collated = torch.stack(images) \n\n # for clip model\n images = [f[\"pixel_values\"][0] for f in features]\n images = [torch.tensor(arr) for arr in images]\n i_collated = torch.stack(images) \n ## clip-end\n return i_collated" }, { "path": "research/BGE_VL/eval/flag_mmret.py", "content": "from typing import cast, List, Union, Tuple\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification, CLIPModel, CLIPImageProcessor, CLIPTokenizer\nimport os\nfrom PIL import Image\nfrom torch.utils.data import DataLoader\nfrom torch import nn\nfrom flag_dataset import MMIT_Dataset, MMIT_Collator, Image_Dataset, Image_Collator\nclass Flag_mmret(nn.Module):\n def __init__(\n self,\n model_name: str = None,\n normlized: bool = True,\n pooling_method: str = 'cls',\n use_fp16: bool=True,\n image_dir: str = None,\n ) -> None:\n super().__init__()\n \n self.model = AutoModel.from_pretrained(model_name) \n self.tokenizer = AutoTokenizer.from_pretrained(model_name) \n self.image_processor = CLIPImageProcessor.from_pretrained(model_name)\n\n\n self.normalize_embeddings = normlized\n self.pooling_method = pooling_method\n\n self.image_dir = image_dir\n\n if use_fp16: \n self.use_fp16 = True\n self.model.half()\n else:\n self.use_fp16 = False\n \n self.device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n self.model = self.model.to(self.device)\n\n self.num_gpus = torch.cuda.device_count()\n if self.num_gpus > 1:\n print(f\"----------using {self.num_gpus}*GPUs----------\")\n self.model = torch.nn.DataParallel(self.model)\n\n\n def encode_queries(self, queries: Union[List[str], str],\n batch_size: int=256,\n max_length: int=77,\n query_type: str = None,\n ) -> np.ndarray:\n \n \n if query_type == 'text': \n input_texts = queries\n \n return self.encode_text(input_texts, batch_size=batch_size, max_length=max_length)\n elif query_type == 'mm_it':\n q_text, q_img = queries\n \n input_texts = q_text\n \n \n return self.encode_mm_it(input_texts, q_img, batch_size=batch_size)\n elif query_type == 'image':\n q_img = queries\n return self.encode_image(q_img, batch_size=batch_size)\n else:\n raise NotImplementedError\n\n\n def encode_corpus(self,\n corpus: dict,\n batch_size: int=256,\n max_length: int=77,\n corpus_type: str = None,\n ) -> np.ndarray:\n if corpus_type == 'text':\n return self.encode_text(corpus[\"text\"], batch_size=batch_size, max_length=max_length)\n elif corpus_type == 'mm_it':\n return self.encode_mm_it(corpus[\"text\"], corpus[\"image\"], batch_size=batch_size, max_length=max_length)\n elif corpus_type == 'image':\n return self.encode_image(corpus[\"image\"], batch_size=batch_size, max_length=max_length)\n else:\n raise RuntimeError(f\"You must choose a corpus type from: [mm_it, text, image]\")\n \n\n\n @torch.no_grad()\n def encode_text(self, sentences: Union[List[str], str], batch_size: int=256, max_length: int=77) -> np.ndarray:\n if self.num_gpus > 0:\n batch_size = batch_size * self.num_gpus\n self.model.eval()\n\n input_was_string = False\n if isinstance(sentences, str):\n sentences = [sentences]\n input_was_string = True\n\n all_embeddings = []\n for start_index in tqdm(range(0, len(sentences), batch_size), desc=\"Inference Embeddings\", disable=len(sentences)<256):\n sentences_batch = sentences[start_index:start_index + batch_size]\n inputs = self.tokenizer(\n sentences_batch,\n padding=True,\n truncation=True,\n return_tensors='pt',\n max_length=max_length,\n ).to(self.device)\n \n embeddings = self.model.get_text_features(**inputs)\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n all_embeddings.append(embeddings.cpu().numpy())\n\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n if input_was_string:\n return all_embeddings[0]\n return all_embeddings\n\n\n @torch.no_grad()\n def encode_mm_it(self, captions: Union[List[str], str], image_ids: Union[List[str], str], batch_size: int=256, max_length: int=77) -> np.ndarray:\n if self.num_gpus > 0:\n batch_size = batch_size * self.num_gpus\n self.model.eval()\n\n input_was_string = False\n if isinstance(captions, str):\n captions = [captions]\n image_ids = [image_ids]\n input_was_string = True\n\n all_embeddings = []\n mm_it_dataset = MMIT_Dataset(captions=captions, \n image_ids=image_ids, \n image_dir=self.image_dir,\n image_processor=self.image_processor\n )\n mm_it_collator = MMIT_Collator(self.tokenizer, caption_max_len=75)\n\n mm_it_dataloader = DataLoader(dataset=mm_it_dataset, \n collate_fn=mm_it_collator, \n num_workers=8, \n batch_size=batch_size,\n shuffle=False,\n drop_last=False,)\n\n for data in tqdm(mm_it_dataloader, desc=\"Inference Embeddings\", disable=len(captions)<256):\n captions_inputs = data[0].to(self.device)\n \n images = data[1].to(self.device)\n if self.use_fp16 and images.dtype != torch.float16:\n images = images.half()\n\n text_embeddings = self.model.get_text_features(**captions_inputs)\n image_embeddings = self.model.get_image_features(images)\n \n embeddings = text_embeddings + image_embeddings\n \n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n \n embeddings = cast(torch.Tensor, embeddings)\n all_embeddings.append(embeddings.cpu().numpy())\n\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n if input_was_string:\n return all_embeddings[0]\n return all_embeddings\n \n @torch.no_grad()\n def encode_image(self, image_ids: Union[List[str], str], batch_size: int=256, max_length: int=77) -> np.ndarray:\n if self.num_gpus > 0:\n batch_size = batch_size * self.num_gpus\n self.model.eval()\n\n all_embeddings = []\n image_dataset = Image_Dataset(image_ids=image_ids, \n image_dir=self.image_dir,\n image_processor=self.image_processor\n )\n image_collator = Image_Collator(self.tokenizer, caption_max_len=312)\n\n image_dataloader = DataLoader(dataset=image_dataset, \n collate_fn=image_collator, \n num_workers=8, \n batch_size=batch_size,\n shuffle=False,\n drop_last=False,)\n\n for data in tqdm(image_dataloader, desc=\"Inference Image Embeddings\"):\n \n images = data.to(self.device)\n if self.use_fp16 and images.dtype != torch.float16:\n images = images.half()\n \n\n\n embeddings = self.model.get_image_features(images)\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n all_embeddings.append(embeddings.cpu().numpy())\n\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n \n return all_embeddings" }, { "path": "research/BGE_VL/eval/results/mmret_base_circo.json", "content": "{\"0\": [265507, 397128, 431368, 337553, 399370, 391002, 25297, 280524, 468104, 515182, 458521, 57161, 217366, 253099, 293465, 479142, 572522, 409002, 61555, 498419, 523215, 517042, 35908, 432113, 48720, 48918, 75573, 229788, 19851, 513636, 149699, 527191, 54281, 450511, 556084, 169728, 173511, 303375, 550558, 110887, 375502, 67574, 350105, 30007, 100765, 345266, 374359, 289592, 341042, 446211], \"1\": [95636, 224067, 2775, 561817, 524541, 172636, 436030, 199041, 238150, 164027, 478875, 397029, 572381, 317271, 324673, 113055, 460182, 578759, 274696, 301856, 404879, 63170, 130230, 338172, 312572, 448733, 175974, 434544, 11448, 72921, 67195, 368458, 198632, 496180, 513244, 543514, 397848, 425224, 98300, 279069, 251485, 283662, 546827, 228631, 25720, 208952, 401113, 432090, 445786, 9731], \"2\": [9393, 311050, 64223, 261543, 76688, 56110, 110176, 119825, 395295, 361317, 187708, 328476, 19433, 418671, 562359, 294984, 300016, 102031, 281438, 334267, 343846, 292473, 297202, 569189, 418044, 357646, 309532, 230370, 46572, 364548, 166733, 524328, 522324, 309329, 67797, 183730, 11522, 331483, 258183, 203047, 57161, 194212, 33166, 559985, 108435, 6156, 496027, 343667, 521042, 152661], \"3\": [244907, 81226, 430368, 429976, 340114, 300980, 76869, 133785, 520517, 445317, 335835, 258030, 501276, 325227, 539677, 475618, 186843, 562276, 428826, 99448, 230477, 443738, 55502, 199019, 442176, 14305, 501764, 182325, 263125, 30097, 263153, 279741, 210776, 262301, 492135, 38580, 282582, 512745, 22897, 273781, 519819, 257120, 478197, 223590, 231187, 128637, 518771, 161167, 137896, 20345], \"4\": [547659, 234487, 552850, 442121, 286607, 273398, 21077, 386023, 475989, 526386, 543874, 329116, 516356, 134860, 497065, 517496, 557871, 278354, 527358, 243346, 360856, 40229, 411352, 89036, 422478, 71740, 319897, 493625, 376963, 360488, 365480, 453716, 45857, 294842, 101126, 285983, 294249, 475606, 323716, 338036, 370581, 358184, 114366, 488765, 451246, 70564, 135461, 560051, 195627, 3654], \"5\": [376944, 457415, 418061, 18616, 360558, 237236, 455056, 529392, 57325, 155013, 222363, 271014, 526614, 511067, 204824, 16495, 365406, 464727, 300590, 17466, 383957, 210435, 269598, 290105, 114867, 317273, 156122, 160748, 433005, 310033, 297775, 249704, 531670, 19155, 96005, 489094, 431645, 352354, 65568, 67977, 144982, 161471, 177309, 28138, 191512, 6922, 380974, 120556, 388990, 421257], \"6\": [228065, 398820, 82883, 16736, 274704, 451029, 159960, 493541, 310696, 97879, 203632, 31774, 181006, 201690, 23567, 421026, 78940, 140214, 131783, 96849, 511816, 527470, 42533, 342402, 361336, 106741, 458316, 265861, 324405, 132091, 300756, 94460, 396409, 565158, 193279, 581567, 412627, 25824, 264468, 375435, 92459, 258053, 465439, 473192, 557758, 150510, 99027, 9718, 229214, 326812], \"7\": [437005, 482674, 572136, 100290, 370854, 399176, 424674, 431754, 384548, 113357, 153844, 300327, 387330, 21109, 382176, 577243, 128077, 47930, 142979, 89450, 139652, 21252, 351952, 400924, 157148, 351063, 126395, 378866, 338015, 356860, 526262, 241471, 210262, 311863, 312654, 5309, 291705, 368322, 27381, 534258, 227090, 390509, 333839, 175939, 461562, 150054, 308584, 493980, 274046, 262564], \"8\": [89348, 491234, 95447, 241654, 283674, 241042, 280462, 555977, 574129, 259123, 102528, 455504, 476674, 254916, 242568, 379380, 577930, 570604, 316861, 463493, 127483, 49019, 484741, 157732, 111622, 118265, 279429, 326967, 215022, 373451, 60665, 570084, 317743, 29495, 128650, 536477, 258970, 323221, 14780, 357732, 384115, 565262, 214128, 289177, 33774, 306961, 126819, 275254, 220744, 18063], \"9\": [282820, 150825, 152329, 559414, 219559, 240151, 255723, 390845, 257445, 105553, 15196, 277265, 355946, 277773, 430836, 538085, 472580, 517435, 567596, 360989, 251201, 406592, 35252, 11417, 581579, 3409, 510954, 327589, 549345, 407561, 551370, 139203, 175079, 127224, 180108, 208952, 581075, 81577, 459023, 18803, 526969, 510267, 564623, 524210, 286096, 216232, 195191, 177979, 219422, 713], \"10\": [550600, 15293, 561429, 433496, 334115, 272342, 406528, 165494, 55205, 424223, 59754, 559737, 439201, 452613, 234271, 457952, 22673, 243124, 418879, 267025, 48762, 151666, 190768, 211417, 341682, 455936, 572157, 311256, 397412, 186425, 263585, 509887, 185300, 334522, 578656, 274706, 92204, 2387, 564471, 433049, 577195, 306834, 48137, 306883, 321099, 146225, 456879, 566345, 49557, 110053], \"11\": [146281, 510380, 487971, 537257, 414016, 205891, 545150, 145848, 537740, 480039, 255897, 81709, 97587, 437343, 154285, 303457, 229361, 409214, 251631, 70404, 207281, 494841, 486426, 431288, 507409, 398077, 515631, 447544, 124818, 131022, 2086, 569189, 473519, 37656, 115140, 24656, 297894, 386130, 116605, 271667, 123883, 332145, 409955, 294446, 60184, 235678, 335008, 238898, 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Team Authors and The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"PyTorch CLIP model.\"\"\"\n\nfrom dataclasses import dataclass\nfrom typing import Any, Optional, Tuple, Union\n\nimport torch\nimport torch.utils.checkpoint\nfrom torch import nn\nfrom torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\nfrom PIL import Image\nfrom transformers.activations import ACT2FN\nfrom transformers.modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask\nfrom transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput\nfrom transformers.modeling_utils import PreTrainedModel\nfrom transformers.pytorch_utils import is_torch_greater_or_equal_than_2_2\nfrom transformers.utils import (\n ModelOutput,\n add_code_sample_docstrings,\n add_start_docstrings,\n add_start_docstrings_to_model_forward,\n is_flash_attn_2_available,\n is_flash_attn_greater_or_equal_2_10,\n logging,\n replace_return_docstrings,\n)\nfrom transformers.models.clip.configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig\nfrom transformers import CLIPProcessor\n\nif is_flash_attn_2_available():\n from transformers.modeling_flash_attention_utils import _flash_attention_forward\n\n\nlogger = logging.get_logger(__name__)\n\n# General docstring\n_CONFIG_FOR_DOC = \"MMRet_CLIP\"\n\n# Image classification docstring\n_IMAGE_CLASS_CHECKPOINT = \"JUNJIE99/MMRet-base\"\n_IMAGE_CLASS_EXPECTED_OUTPUT = \"LABEL_0\"\n\n\n# contrastive loss function, adapted from\n# https://sachinruk.github.io/blog/2021-03-07-clip.html\ndef contrastive_loss(logits: torch.Tensor) -> torch.Tensor:\n return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))\n\n\ndef clip_loss(similarity: torch.Tensor) -> torch.Tensor:\n caption_loss = contrastive_loss(similarity)\n image_loss = contrastive_loss(similarity.t())\n return (caption_loss + image_loss) / 2.0\n\n\ndef _get_vector_norm(tensor: torch.Tensor) -> torch.Tensor:\n \"\"\"\n This method is equivalent to tensor.norm(p=2, dim=-1, keepdim=True) and used to make\n model `executorch` exportable. See issue https://github.com/pytorch/executorch/issues/3566\n \"\"\"\n square_tensor = torch.pow(tensor, 2)\n sum_tensor = torch.sum(square_tensor, dim=-1, keepdim=True)\n normed_tensor = torch.pow(sum_tensor, 0.5)\n return normed_tensor\n\n\n@dataclass\nclass CLIPVisionModelOutput(ModelOutput):\n \"\"\"\n Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.\n\n Args:\n image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):\n The image embeddings obtained by applying the projection layer to the pooler_output.\n last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Sequence of hidden-states at the output of the last layer of the model.\n hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):\n Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +\n one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.\n attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):\n Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,\n sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n\n image_embeds: Optional[torch.FloatTensor] = None\n last_hidden_state: torch.FloatTensor = None\n hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None\n attentions: Optional[Tuple[torch.FloatTensor, ...]] = None\n\n\n@dataclass\nclass CLIPTextModelOutput(ModelOutput):\n \"\"\"\n Base class for text model's outputs that also contains a pooling of the last hidden states.\n\n Args:\n text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):\n The text embeddings obtained by applying the projection layer to the pooler_output.\n last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Sequence of hidden-states at the output of the last layer of the model.\n hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):\n Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +\n one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.\n\n Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.\n attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):\n Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,\n sequence_length)`.\n\n Attentions weights after the attention softmax, used to compute the weighted average in the self-attention\n heads.\n \"\"\"\n\n text_embeds: Optional[torch.FloatTensor] = None\n last_hidden_state: torch.FloatTensor = None\n hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None\n attentions: Optional[Tuple[torch.FloatTensor, ...]] = None\n\n\n@dataclass\nclass CLIPOutput(ModelOutput):\n \"\"\"\n Args:\n loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):\n Contrastive loss for image-text similarity.\n logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):\n The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text\n similarity scores.\n logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):\n The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image\n similarity scores.\n text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):\n The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPTextModel`].\n image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):\n The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPVisionModel`].\n text_model_output (`BaseModelOutputWithPooling`):\n The output of the [`CLIPTextModel`].\n vision_model_output (`BaseModelOutputWithPooling`):\n The output of the [`CLIPVisionModel`].\n \"\"\"\n\n loss: Optional[torch.FloatTensor] = None\n logits_per_image: torch.FloatTensor = None\n logits_per_text: torch.FloatTensor = None\n text_embeds: torch.FloatTensor = None\n image_embeds: torch.FloatTensor = None\n text_model_output: BaseModelOutputWithPooling = None\n vision_model_output: BaseModelOutputWithPooling = None\n\n def to_tuple(self) -> Tuple[Any]:\n return tuple(\n self[k] if k not in [\"text_model_output\", \"vision_model_output\"] else getattr(self, k).to_tuple()\n for k in self.keys()\n )\n\n\nclass CLIPVisionEmbeddings(nn.Module):\n def __init__(self, config: CLIPVisionConfig):\n super().__init__()\n self.config = config\n self.embed_dim = config.hidden_size\n self.image_size = config.image_size\n self.patch_size = config.patch_size\n\n self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))\n\n self.patch_embedding = nn.Conv2d(\n in_channels=config.num_channels,\n out_channels=self.embed_dim,\n kernel_size=self.patch_size,\n stride=self.patch_size,\n bias=False,\n )\n\n self.num_patches = (self.image_size // self.patch_size) ** 2\n self.num_positions = self.num_patches + 1\n self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)\n self.register_buffer(\"position_ids\", torch.arange(self.num_positions).expand((1, -1)), persistent=False)\n\n def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:\n batch_size = pixel_values.shape[0]\n target_dtype = self.patch_embedding.weight.dtype\n patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid]\n patch_embeds = patch_embeds.flatten(2).transpose(1, 2)\n\n class_embeds = self.class_embedding.expand(batch_size, 1, -1)\n embeddings = torch.cat([class_embeds, patch_embeds], dim=1)\n embeddings = embeddings + self.position_embedding(self.position_ids)\n return embeddings\n\n\nclass CLIPTextEmbeddings(nn.Module):\n def __init__(self, config: CLIPTextConfig):\n super().__init__()\n embed_dim = config.hidden_size\n\n self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)\n self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)\n\n # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n self.register_buffer(\n \"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False\n )\n\n def forward(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n ) -> torch.Tensor:\n seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]\n\n if position_ids is None:\n position_ids = self.position_ids[:, :seq_length]\n\n if inputs_embeds is None:\n inputs_embeds = self.token_embedding(input_ids)\n\n position_embeddings = self.position_embedding(position_ids)\n embeddings = inputs_embeds + position_embeddings\n\n return embeddings\n\n\nclass CLIPAttention(nn.Module):\n \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.embed_dim = config.hidden_size\n self.num_heads = config.num_attention_heads\n self.head_dim = self.embed_dim // self.num_heads\n if self.head_dim * self.num_heads != self.embed_dim:\n raise ValueError(\n f\"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:\"\n f\" {self.num_heads}).\"\n )\n self.scale = self.head_dim**-0.5\n self.dropout = config.attention_dropout\n\n self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)\n self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)\n self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)\n self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)\n\n def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):\n return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n causal_attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"Input shape: Batch x Time x Channel\"\"\"\n\n bsz, tgt_len, embed_dim = hidden_states.size()\n\n # get query proj\n query_states = self.q_proj(hidden_states) * self.scale\n key_states = self._shape(self.k_proj(hidden_states), -1, bsz)\n value_states = self._shape(self.v_proj(hidden_states), -1, bsz)\n\n proj_shape = (bsz * self.num_heads, -1, self.head_dim)\n query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)\n key_states = key_states.view(*proj_shape)\n value_states = value_states.view(*proj_shape)\n\n src_len = key_states.size(1)\n attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))\n\n if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):\n raise ValueError(\n f\"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is\"\n f\" {attn_weights.size()}\"\n )\n\n # apply the causal_attention_mask first\n if causal_attention_mask is not None:\n if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is\"\n f\" {causal_attention_mask.size()}\"\n )\n attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask\n attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)\n\n if attention_mask is not None:\n if attention_mask.size() != (bsz, 1, tgt_len, src_len):\n raise ValueError(\n f\"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}\"\n )\n attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask\n attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)\n\n attn_weights = nn.functional.softmax(attn_weights, dim=-1)\n\n if output_attentions:\n # this operation is a bit akward, but it's required to\n # make sure that attn_weights keeps its gradient.\n # In order to do so, attn_weights have to reshaped\n # twice and have to be reused in the following\n attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)\n attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)\n else:\n attn_weights_reshaped = None\n\n attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)\n\n attn_output = torch.bmm(attn_probs, value_states)\n\n if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):\n raise ValueError(\n f\"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is\"\n f\" {attn_output.size()}\"\n )\n\n attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)\n attn_output = attn_output.transpose(1, 2)\n attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)\n\n attn_output = self.out_proj(attn_output)\n\n return attn_output, attn_weights_reshaped\n\n\nclass CLIPFlashAttention2(CLIPAttention):\n \"\"\"\n CLIPAttention flash attention module. This module inherits from `CLIPAttention` as the weights of the module stays\n untouched. The only required change would be on the forward pass where it needs to correctly call the public API of\n flash attention and deal with padding tokens in case the input contains any of them.\n \"\"\"\n\n # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.\n # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.\n # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).\n self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()\n\n # Adapted from transformers.models.llama.modeling_llama.LlamaFlashAttention2.forward\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n causal_attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n output_attentions = False\n\n batch_size, q_len, _ = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n # Flash attention requires the input to have the shape\n # batch_size x seq_length x head_dim x hidden_dim\n # therefore we just need to keep the original shape\n query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim)\n key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim)\n value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim)\n\n dropout_rate = self.dropout if self.training else 0.0\n\n # In PEFT, usually we cast the layer norms in float32 for training stability reasons\n # therefore the input hidden states gets silently casted in float32. Hence, we need\n # cast them back in the correct dtype just to be sure everything works as expected.\n # This might slowdown training & inference so it is recommended to not cast the LayerNorms\n # in fp32.\n\n input_dtype = query_states.dtype\n if input_dtype == torch.float32:\n if torch.is_autocast_enabled():\n target_dtype = torch.get_autocast_gpu_dtype()\n # Handle the case where the model is quantized\n elif hasattr(self.config, \"_pre_quantization_dtype\"):\n target_dtype = self.config._pre_quantization_dtype\n else:\n target_dtype = self.q_proj.weight.dtype\n\n logger.warning_once(\n f\"The input hidden states seems to be silently casted in float32, this might be related to\"\n f\" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in\"\n f\" {target_dtype}.\"\n )\n\n query_states = query_states.to(target_dtype)\n key_states = key_states.to(target_dtype)\n value_states = value_states.to(target_dtype)\n\n attn_output = _flash_attention_forward(\n query_states,\n key_states,\n value_states,\n attention_mask,\n q_len,\n dropout=dropout_rate,\n is_causal=causal_attention_mask is not None,\n use_top_left_mask=self._flash_attn_uses_top_left_mask,\n )\n\n attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim).contiguous()\n attn_output = self.out_proj(attn_output)\n\n if not output_attentions:\n attn_weights = None\n\n return attn_output, attn_weights\n\n\nclass CLIPSdpaAttention(CLIPAttention):\n \"\"\"\n SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from\n `CLIPAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to\n SDPA API.\n \"\"\"\n\n # Adapted from CLIPAttention.forward\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: Optional[torch.Tensor] = None,\n causal_attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n if output_attentions:\n # TODO: Improve this warning with e.g. `model.config.attn_implementation = \"manual\"` once this is implemented.\n logger.warning_once(\n \"CLIPModel is using CLIPSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not \"\n \"support `output_attentions=True`. Falling back to the manual attention implementation, but specifying \"\n \"the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can \"\n 'be removed using the argument `attn_implementation=\"eager\"` when loading the model.'\n )\n return super().forward(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n )\n\n # CLIP text model uses both `causal_attention_mask` and `attention_mask`\n if attention_mask is not None and causal_attention_mask is not None:\n attn_mask = attention_mask + causal_attention_mask\n elif causal_attention_mask is not None:\n attn_mask = causal_attention_mask\n else:\n attn_mask = attention_mask\n\n bsz, tgt_len, embed_dim = hidden_states.size()\n\n query_states = self.q_proj(hidden_states)\n key_states = self.k_proj(hidden_states)\n value_states = self.v_proj(hidden_states)\n\n query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)\n key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)\n value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)\n\n # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,\n # Reference: https://github.com/pytorch/pytorch/issues/112577.\n if not is_torch_greater_or_equal_than_2_2 and query_states.device.type == \"cuda\" and attn_mask is not None:\n query_states = query_states.contiguous()\n key_states = key_states.contiguous()\n value_states = value_states.contiguous()\n\n # CLIP text model uses both `causal_attention_mask` and `attention_mask` sequentially.\n attn_output = torch.nn.functional.scaled_dot_product_attention(\n query_states,\n key_states,\n value_states,\n attn_mask=attn_mask,\n dropout_p=self.dropout if self.training else 0.0,\n scale=self.scale,\n )\n\n attn_output = attn_output.transpose(1, 2)\n attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)\n\n attn_output = self.out_proj(attn_output)\n\n return attn_output, None\n\n\nCLIP_ATTENTION_CLASSES = {\n \"eager\": CLIPAttention,\n \"sdpa\": CLIPSdpaAttention,\n \"flash_attention_2\": CLIPFlashAttention2,\n}\n\n\nclass CLIPMLP(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.config = config\n self.activation_fn = ACT2FN[config.hidden_act]\n self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)\n self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)\n\n def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:\n hidden_states = self.fc1(hidden_states)\n hidden_states = self.activation_fn(hidden_states)\n hidden_states = self.fc2(hidden_states)\n return hidden_states\n\n\nclass CLIPEncoderLayer(nn.Module):\n def __init__(self, config: CLIPConfig):\n super().__init__()\n self.embed_dim = config.hidden_size\n self.self_attn = CLIP_ATTENTION_CLASSES[config._attn_implementation](config)\n self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n self.mlp = CLIPMLP(config)\n self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n attention_mask: torch.Tensor,\n causal_attention_mask: torch.Tensor,\n output_attentions: Optional[bool] = False,\n ) -> Tuple[torch.FloatTensor]:\n \"\"\"\n Args:\n hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`\n attention_mask (`torch.FloatTensor`): attention mask of size\n `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.\n `(config.encoder_attention_heads,)`.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n \"\"\"\n residual = hidden_states\n\n hidden_states = self.layer_norm1(hidden_states)\n hidden_states, attn_weights = self.self_attn(\n hidden_states=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n )\n hidden_states = residual + hidden_states\n\n residual = hidden_states\n hidden_states = self.layer_norm2(hidden_states)\n hidden_states = self.mlp(hidden_states)\n hidden_states = residual + hidden_states\n\n outputs = (hidden_states,)\n\n if output_attentions:\n outputs += (attn_weights,)\n\n return outputs\n\n\nclass CLIPPreTrainedModel(PreTrainedModel):\n \"\"\"\n An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n models.\n \"\"\"\n\n config_class = CLIPConfig\n base_model_prefix = \"clip\"\n supports_gradient_checkpointing = True\n _supports_sdpa = True\n _supports_flash_attn_2 = True\n\n def _init_weights(self, module):\n \"\"\"Initialize the weights\"\"\"\n factor = self.config.initializer_factor\n if isinstance(module, CLIPTextEmbeddings):\n module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)\n module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)\n elif isinstance(module, CLIPVisionEmbeddings):\n factor = self.config.initializer_factor\n nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)\n nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)\n nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)\n elif isinstance(module, CLIPAttention):\n factor = self.config.initializer_factor\n in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor\n out_proj_std = (module.embed_dim**-0.5) * factor\n nn.init.normal_(module.q_proj.weight, std=in_proj_std)\n nn.init.normal_(module.k_proj.weight, std=in_proj_std)\n nn.init.normal_(module.v_proj.weight, std=in_proj_std)\n nn.init.normal_(module.out_proj.weight, std=out_proj_std)\n elif isinstance(module, CLIPMLP):\n factor = self.config.initializer_factor\n in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor\n fc_std = (2 * module.config.hidden_size) ** -0.5 * factor\n nn.init.normal_(module.fc1.weight, std=fc_std)\n nn.init.normal_(module.fc2.weight, std=in_proj_std)\n elif isinstance(module, CLIPModel):\n nn.init.normal_(\n module.text_projection.weight,\n std=module.text_embed_dim**-0.5 * self.config.initializer_factor,\n )\n nn.init.normal_(\n module.visual_projection.weight,\n std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,\n )\n elif isinstance(module, CLIPVisionModelWithProjection):\n nn.init.normal_(\n module.visual_projection.weight,\n std=self.config.hidden_size**-0.5 * self.config.initializer_factor,\n )\n elif isinstance(module, CLIPTextModelWithProjection):\n nn.init.normal_(\n module.text_projection.weight,\n std=self.config.hidden_size**-0.5 * self.config.initializer_factor,\n )\n elif isinstance(module, CLIPForImageClassification):\n nn.init.normal_(\n module.classifier.weight,\n std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor,\n )\n\n if isinstance(module, nn.LayerNorm):\n module.bias.data.zero_()\n module.weight.data.fill_(1.0)\n if isinstance(module, nn.Linear) and module.bias is not None:\n module.bias.data.zero_()\n\n\nCLIP_START_DOCSTRING = r\"\"\"\n This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n etc.)\n\n This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n and behavior.\n\n Parameters:\n config ([`CLIPConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n\"\"\"\n\nCLIP_TEXT_INPUTS_DOCSTRING = r\"\"\"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide\n it.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n config.max_position_embeddings - 1]`.\n\n [What are position IDs?](../glossary#position-ids)\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\nCLIP_VISION_INPUTS_DOCSTRING = r\"\"\"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\nCLIP_INPUTS_DOCSTRING = r\"\"\"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide\n it.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n config.max_position_embeddings - 1]`.\n\n [What are position IDs?](../glossary#position-ids)\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.\n return_loss (`bool`, *optional*):\n Whether or not to return the contrastive loss.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n\"\"\"\n\n\nclass CLIPEncoder(nn.Module):\n \"\"\"\n Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a\n [`CLIPEncoderLayer`].\n\n Args:\n config: CLIPConfig\n \"\"\"\n\n def __init__(self, config: CLIPConfig):\n super().__init__()\n self.config = config\n self.layers = nn.ModuleList([CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])\n self.gradient_checkpointing = False\n\n def forward(\n self,\n inputs_embeds,\n attention_mask: Optional[torch.Tensor] = None,\n causal_attention_mask: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutput]:\n r\"\"\"\n Args:\n inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):\n Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `input_ids` indices into associated vectors\n than the model's internal embedding lookup matrix.\n attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):\n Causal mask for the text model. Mask values selected in `[0, 1]`:\n\n - 1 for tokens that are **not masked**,\n - 0 for tokens that are **masked**.\n\n [What are attention masks?](../glossary#attention-mask)\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers. See `attentions` under\n returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors\n for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n encoder_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n hidden_states = inputs_embeds\n for idx, encoder_layer in enumerate(self.layers):\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n if self.gradient_checkpointing and self.training:\n layer_outputs = self._gradient_checkpointing_func(\n encoder_layer.__call__,\n hidden_states,\n attention_mask,\n causal_attention_mask,\n output_attentions,\n )\n else:\n layer_outputs = encoder_layer(\n hidden_states,\n attention_mask,\n causal_attention_mask,\n output_attentions=output_attentions,\n )\n\n hidden_states = layer_outputs[0]\n\n if output_attentions:\n all_attentions = all_attentions + (layer_outputs[1],)\n\n if output_hidden_states:\n encoder_states = encoder_states + (hidden_states,)\n\n if not return_dict:\n return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions\n )\n\n\nclass CLIPTextTransformer(nn.Module):\n def __init__(self, config: CLIPTextConfig):\n super().__init__()\n self.config = config\n embed_dim = config.hidden_size\n self.embeddings = CLIPTextEmbeddings(config)\n self.encoder = CLIPEncoder(config)\n self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n\n # For `pooled_output` computation\n self.eos_token_id = config.eos_token_id\n\n # For attention mask, it differs between `flash_attention_2` and other attention implementations\n self._use_flash_attention_2 = config._attn_implementation == \"flash_attention_2\"\n\n @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)\n def forward(\n self,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if input_ids is None:\n raise ValueError(\"You have to specify input_ids\")\n\n input_shape = input_ids.size()\n input_ids = input_ids.view(-1, input_shape[-1])\n\n hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)\n\n # CLIP's text model uses causal mask, prepare it here.\n # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324\n causal_attention_mask = _create_4d_causal_attention_mask(\n input_shape, hidden_states.dtype, device=hidden_states.device\n )\n\n # expand attention_mask\n if attention_mask is not None and not self._use_flash_attention_2:\n # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]\n attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n attention_mask=attention_mask,\n causal_attention_mask=causal_attention_mask,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n last_hidden_state = self.final_layer_norm(last_hidden_state)\n\n if self.eos_token_id == 2:\n # The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.\n # A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added\n # ------------------------------------------------------------\n # text_embeds.shape = [batch_size, sequence_length, transformer.width]\n # take features from the eot embedding (eot_token is the highest number in each sequence)\n # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14\n pooled_output = last_hidden_state[\n torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),\n input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),\n ]\n else:\n # The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible)\n pooled_output = last_hidden_state[\n torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),\n # We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)\n # Note: we assume each sequence (along batch dim.) contains an `eos_token_id` (e.g. prepared by the tokenizer)\n (input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)\n .int()\n .argmax(dim=-1),\n ]\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n\n@add_start_docstrings(\n \"\"\"The text model from CLIP without any head or projection on top.\"\"\",\n CLIP_START_DOCSTRING,\n)\nclass CLIPTextModel(CLIPPreTrainedModel):\n config_class = CLIPTextConfig\n\n _no_split_modules = [\"CLIPTextEmbeddings\", \"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPTextConfig):\n super().__init__(config)\n self.text_model = CLIPTextTransformer(config)\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self) -> nn.Module:\n return self.text_model.embeddings.token_embedding\n\n def set_input_embeddings(self, value):\n self.text_model.embeddings.token_embedding = value\n\n @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)\n def forward(\n self,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from transformers import AutoTokenizer, CLIPTextModel\n\n >>> model = CLIPTextModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> inputs = tokenizer([\"a photo of a cat\", \"a photo of a dog\"], padding=True, return_tensors=\"pt\")\n\n >>> outputs = model(**inputs)\n >>> last_hidden_state = outputs.last_hidden_state\n >>> pooled_output = outputs.pooler_output # pooled (EOS token) states\n ```\"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n return self.text_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n\nclass CLIPVisionTransformer(nn.Module):\n def __init__(self, config: CLIPVisionConfig):\n super().__init__()\n self.config = config\n embed_dim = config.hidden_size\n\n self.embeddings = CLIPVisionEmbeddings(config)\n self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n self.encoder = CLIPEncoder(config)\n self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)\n\n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig)\n def forward(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if pixel_values is None:\n raise ValueError(\"You have to specify pixel_values\")\n\n hidden_states = self.embeddings(pixel_values)\n hidden_states = self.pre_layrnorm(hidden_states)\n\n encoder_outputs = self.encoder(\n inputs_embeds=hidden_states,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n last_hidden_state = encoder_outputs[0]\n pooled_output = last_hidden_state[:, 0, :]\n pooled_output = self.post_layernorm(pooled_output)\n\n if not return_dict:\n return (last_hidden_state, pooled_output) + encoder_outputs[1:]\n\n return BaseModelOutputWithPooling(\n last_hidden_state=last_hidden_state,\n pooler_output=pooled_output,\n hidden_states=encoder_outputs.hidden_states,\n attentions=encoder_outputs.attentions,\n )\n\n\n@add_start_docstrings(\n \"\"\"The vision model from CLIP without any head or projection on top.\"\"\",\n CLIP_START_DOCSTRING,\n)\nclass CLIPVisionModel(CLIPPreTrainedModel):\n config_class = CLIPVisionConfig\n main_input_name = \"pixel_values\"\n _no_split_modules = [\"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPVisionConfig):\n super().__init__(config)\n self.vision_model = CLIPVisionTransformer(config)\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self) -> nn.Module:\n return self.vision_model.embeddings.patch_embedding\n\n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPVisionConfig)\n def forward(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, BaseModelOutputWithPooling]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPVisionModel\n\n >>> model = CLIPVisionModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(images=image, return_tensors=\"pt\")\n\n >>> outputs = model(**inputs)\n >>> last_hidden_state = outputs.last_hidden_state\n >>> pooled_output = outputs.pooler_output # pooled CLS states\n ```\"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n return self.vision_model(\n pixel_values=pixel_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n\n@add_start_docstrings(CLIP_START_DOCSTRING)\nclass CLIPModel(CLIPPreTrainedModel):\n config_class = CLIPConfig\n _no_split_modules = [\"CLIPTextEmbeddings\", \"CLIPEncoderLayer\", \"CLIPVisionEmbeddings\"]\n\n def __init__(self, config: CLIPConfig):\n super().__init__(config)\n\n if not isinstance(config.text_config, CLIPTextConfig):\n raise TypeError(\n \"config.text_config is expected to be of type CLIPTextConfig but is of type\"\n f\" {type(config.text_config)}.\"\n )\n\n if not isinstance(config.vision_config, CLIPVisionConfig):\n raise TypeError(\n \"config.vision_config is expected to be of type CLIPVisionConfig but is of type\"\n f\" {type(config.vision_config)}.\"\n )\n\n text_config = config.text_config\n vision_config = config.vision_config\n\n self.projection_dim = config.projection_dim\n self.text_embed_dim = text_config.hidden_size\n self.vision_embed_dim = vision_config.hidden_size\n\n text_model = CLIPTextModel._from_config(text_config, attn_implementation=config._attn_implementation)\n self.text_model = text_model.text_model\n\n vision_model = CLIPVisionModel._from_config(vision_config, attn_implementation=config._attn_implementation)\n self.vision_model = vision_model.vision_model\n\n self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)\n self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)\n self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def set_processor(self, model_name):\n self.processor = CLIPProcessor.from_pretrained(model_name)\n\n @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)\n def get_text_features(\n self,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> torch.FloatTensor:\n r\"\"\"\n Returns:\n text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by\n applying the projection layer to the pooled output of [`CLIPTextModel`].\n\n Examples:\n\n ```python\n >>> from transformers import AutoTokenizer, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> inputs = tokenizer([\"a photo of a cat\", \"a photo of a dog\"], padding=True, return_tensors=\"pt\")\n >>> text_features = model.get_text_features(**inputs)\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n text_outputs = self.text_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n pooled_output = text_outputs[1]\n text_features = self.text_projection(pooled_output)\n\n return text_features\n\n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n def get_image_features(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> torch.FloatTensor:\n r\"\"\"\n Returns:\n image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by\n applying the projection layer to the pooled output of [`CLIPVisionModel`].\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(images=image, return_tensors=\"pt\")\n\n >>> image_features = model.get_image_features(**inputs)\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n pooled_output = vision_outputs[1] # pooled_output\n image_features = self.visual_projection(pooled_output)\n\n return image_features\n\n\n def encode_image(self, images):\n embeddings = self.get_image_features(images)\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n return embeddings\n \n def encode_text(self, text):\n embeddings = self.get_text_features(**text)\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n return embeddings\n \n def encode_multimodal(self, images, text):\n text_embeddings = self.get_text_features(**text)\n image_embeddings = self.get_image_features(images)\n \n embeddings = text_embeddings + image_embeddings \n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n\n return embeddings.contiguous()\n\n def data_process(self, images=None, text=None):\n if images is None and text is not None:\n text = self.processor(text=text, return_tensors=\"pt\", padding=True).to(self.device)\n \n return images, text, \"text\"\n elif images is not None and text is None:\n if isinstance(images, str):\n images = Image.open(images).convert(\"RGB\")\n elif isinstance(images, list):\n images = [Image.open(image).convert(\"RGB\") for image in images]\n images = self.processor(images=images, return_tensors=\"pt\").to(self.device)\n images = images[\"pixel_values\"]\n return images, text, \"images\"\n elif images is not None and text is not None:\n assert type(images) == type(text), \"images and text must be the same type: list or str\"\n if isinstance(images, str):\n images = Image.open(images).convert(\"RGB\")\n elif isinstance(images, list):\n assert len(images) == len(text), \"images and text must be lists of the same length when use list\"\n images = [Image.open(image).convert(\"RGB\") for image in images]\n images = self.processor(images=images, return_tensors=\"pt\").to(self.device)\n images = images[\"pixel_values\"]\n text = self.processor(text=text, return_tensors=\"pt\", padding=True).to(self.device)\n return images, text, \"multimodal\"\n else:\n raise ValueError(\"images and text cannot both be None\")\n\n def encode(self, images=None, text=None):\n images, text, data_type = self.data_process(images, text)\n if data_type == \"images\":\n return self.encode_image(images)\n elif data_type == \"text\":\n return self.encode_text(text)\n elif data_type == \"multimodal\":\n return self.encode_multimodal(images, text)\n\n\n @add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CLIPOutput, config_class=CLIPConfig)\n def forward(\n self,\n input_ids: Optional[torch.LongTensor] = None,\n pixel_values: Optional[torch.FloatTensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n return_loss: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CLIPOutput]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPModel\n\n >>> model = CLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(\n ... text=[\"a photo of a cat\", \"a photo of a dog\"], images=image, return_tensors=\"pt\", padding=True\n ... )\n\n >>> outputs = model(**inputs)\n >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score\n >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities\n ```\"\"\"\n # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n text_outputs = self.text_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n image_embeds = vision_outputs[1]\n image_embeds = self.visual_projection(image_embeds)\n\n text_embeds = text_outputs[1]\n text_embeds = self.text_projection(text_embeds)\n\n # normalized features\n image_embeds = image_embeds / _get_vector_norm(image_embeds)\n text_embeds = text_embeds / _get_vector_norm(text_embeds)\n\n # cosine similarity as logits\n logit_scale = self.logit_scale.exp()\n logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device)) * logit_scale.to(\n text_embeds.device\n )\n logits_per_image = logits_per_text.t()\n\n loss = None\n if return_loss:\n loss = clip_loss(logits_per_text)\n\n if not return_dict:\n output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)\n return ((loss,) + output) if loss is not None else output\n\n return CLIPOutput(\n loss=loss,\n logits_per_image=logits_per_image,\n logits_per_text=logits_per_text,\n text_embeds=text_embeds,\n image_embeds=image_embeds,\n text_model_output=text_outputs,\n vision_model_output=vision_outputs,\n )\n\n\n@add_start_docstrings(\n \"\"\"\n CLIP Text Model with a projection layer on top (a linear layer on top of the pooled output).\n \"\"\",\n CLIP_START_DOCSTRING,\n)\nclass CLIPTextModelWithProjection(CLIPPreTrainedModel):\n config_class = CLIPTextConfig\n\n _no_split_modules = [\"CLIPTextEmbeddings\", \"CLIPEncoderLayer\"]\n\n def __init__(self, config: CLIPTextConfig):\n super().__init__(config)\n\n text_model = CLIPTextModel._from_config(config, attn_implementation=config._attn_implementation)\n self.text_model = text_model.text_model\n\n self.text_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False)\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self) -> nn.Module:\n return self.text_model.embeddings.token_embedding\n\n def set_input_embeddings(self, value):\n self.text_model.embeddings.token_embedding = value\n\n @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CLIPTextModelOutput, config_class=CLIPTextConfig)\n def forward(\n self,\n input_ids: Optional[torch.Tensor] = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CLIPTextModelOutput]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from transformers import AutoTokenizer, CLIPTextModelWithProjection\n\n >>> model = CLIPTextModelWithProjection.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> inputs = tokenizer([\"a photo of a cat\", \"a photo of a dog\"], padding=True, return_tensors=\"pt\")\n\n >>> outputs = model(**inputs)\n >>> text_embeds = outputs.text_embeds\n ```\"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n text_outputs = self.text_model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n pooled_output = text_outputs[1]\n\n text_embeds = self.text_projection(pooled_output)\n\n if not return_dict:\n outputs = (text_embeds, text_outputs[0]) + text_outputs[2:]\n return tuple(output for output in outputs if output is not None)\n\n return CLIPTextModelOutput(\n text_embeds=text_embeds,\n last_hidden_state=text_outputs.last_hidden_state,\n hidden_states=text_outputs.hidden_states,\n attentions=text_outputs.attentions,\n )\n\n\n@add_start_docstrings(\n \"\"\"\n CLIP Vision Model with a projection layer on top (a linear layer on top of the pooled output).\n \"\"\",\n CLIP_START_DOCSTRING,\n)\nclass CLIPVisionModelWithProjection(CLIPPreTrainedModel):\n config_class = CLIPVisionConfig\n main_input_name = \"pixel_values\"\n\n def __init__(self, config: CLIPVisionConfig):\n super().__init__(config)\n\n vision_model = CLIPVisionModel._from_config(config, attn_implementation=config._attn_implementation)\n self.vision_model = vision_model.vision_model\n\n self.visual_projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False)\n\n # Initialize weights and apply final processing\n self.post_init()\n\n def get_input_embeddings(self) -> nn.Module:\n return self.vision_model.embeddings.patch_embedding\n\n @add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CLIPVisionModelOutput, config_class=CLIPVisionConfig)\n def forward(\n self,\n pixel_values: Optional[torch.FloatTensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[Tuple, CLIPVisionModelOutput]:\n r\"\"\"\n Returns:\n\n Examples:\n\n ```python\n >>> from PIL import Image\n >>> import requests\n >>> from transformers import AutoProcessor, CLIPVisionModelWithProjection\n\n >>> model = CLIPVisionModelWithProjection.from_pretrained(\"openai/clip-vit-base-patch32\")\n >>> processor = AutoProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n\n >>> url = \"http://images.cocodataset.org/val2017/000000039769.jpg\"\n >>> image = Image.open(requests.get(url, stream=True).raw)\n\n >>> inputs = processor(images=image, return_tensors=\"pt\")\n\n >>> outputs = model(**inputs)\n >>> image_embeds = outputs.image_embeds\n ```\"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n vision_outputs = self.vision_model(\n pixel_values=pixel_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n pooled_output = vision_outputs[1] # pooled_output\n\n image_embeds = self.visual_projection(pooled_output)\n\n if not return_dict:\n outputs = (image_embeds, vision_outputs[0]) + vision_outputs[2:]\n return tuple(output for output in outputs if output is not None)\n\n return CLIPVisionModelOutput(\n image_embeds=image_embeds,\n last_hidden_state=vision_outputs.last_hidden_state,\n hidden_states=vision_outputs.hidden_states,\n attentions=vision_outputs.attentions,\n )\n\n\n@add_start_docstrings(\n \"\"\"\n CLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of\n the patch tokens) e.g. for ImageNet.\n \"\"\",\n CLIP_START_DOCSTRING,\n)\nclass CLIPForImageClassification(CLIPPreTrainedModel):\n main_input_name = \"pixel_values\"\n\n def __init__(self, config: CLIPConfig) -> None:\n super().__init__(config)\n\n self.num_labels = config.num_labels\n vision_model = CLIPVisionModel._from_config(\n config.vision_config, attn_implementation=config._attn_implementation\n )\n self.vision_model = vision_model.vision_model\n\n # Classifier head\n self.classifier = (\n nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()\n )\n\n # Initialize weights and apply final processing\n self.post_init()\n\n @add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING)\n @add_code_sample_docstrings(\n checkpoint=_IMAGE_CLASS_CHECKPOINT,\n output_type=ImageClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,\n )\n def forward(\n self,\n pixel_values: Optional[torch.Tensor] = None,\n labels: Optional[torch.Tensor] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n ) -> Union[tuple, ImageClassifierOutput]:\n r\"\"\"\n labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n Labels for computing the image classification/regression loss. Indices should be in `[0, ...,\n config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\n `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\n \"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.vision_model(\n pixel_values,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n sequence_output = outputs[0]\n\n # average pool the patch tokens\n sequence_output = torch.mean(sequence_output[:, 1:, :], dim=1)\n # apply classifier\n logits = self.classifier(sequence_output)\n\n loss = None\n if labels is not None:\n # move labels to correct device to enable model parallelism\n labels = labels.to(logits.device)\n if self.config.problem_type is None:\n if self.num_labels == 1:\n self.config.problem_type = \"regression\"\n elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n self.config.problem_type = \"single_label_classification\"\n else:\n self.config.problem_type = \"multi_label_classification\"\n\n if self.config.problem_type == \"regression\":\n loss_fct = MSELoss()\n if self.num_labels == 1:\n loss = loss_fct(logits.squeeze(), labels.squeeze())\n else:\n loss = loss_fct(logits, labels)\n elif self.config.problem_type == \"single_label_classification\":\n loss_fct = CrossEntropyLoss()\n loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n elif self.config.problem_type == \"multi_label_classification\":\n loss_fct = BCEWithLogitsLoss()\n loss = loss_fct(logits, labels)\n\n if not return_dict:\n output = (logits,) + outputs[2:]\n return ((loss,) + output) if loss is not None else output\n\n return ImageClassifierOutput(\n loss=loss,\n logits=logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n" }, { "path": "research/BGE_VL/retrieval_demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Multimodel Retrieval using BGE-VL\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we show a very simple case of using BGE-VL model on text to image and (image, text) to image retrieval tasks.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Make sure you have the following dependencies installed in your environment:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%pip install numpy torch transformers faiss-cpu pillow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[CIRCO](https://github.com/miccunifi/CIRCO) is a well labeled multimodel dataset, which use images taken from the [COCO 2017 unlabeled set](https://cocodataset.org/#home).\\n\",\n \"\\n\",\n \"If you are interested in trying the full dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !wget http://images.cocodataset.org/zips/unlabeled2017.zip\\n\",\n \"# !unzip unlabeled2017.zip\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Due to the tremendous size of the original dataset, we prepare a subset of it as a small corpus in our repo. Clone the repo and you can find them in MegaPairs/assets/corpus/\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"\\n\",\n \"fid_to_oid = {}\\n\",\n \"image_dir = \\\"./assets/corpus/\\\"\\n\",\n \"image_paths = [os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(\\\".jpg\\\")]\\n\",\n \"\\n\",\n \"# extract image id from the path, and store the {sequence id: image id}\\n\",\n \"# sequence id will be used as FAISS's default id\\n\",\n \"for i, img in enumerate(image_paths):\\n\",\n \" fid_to_oid[i] = int(img[-16:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# function that convert image id back to path for later use\\n\",\n \"def id_to_name(id, image_dir):\\n\",\n \" return image_dir + str(id).zfill(12) + \\\".jpg\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Embedding and Indexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, use BGE-VL to encode the whole corpus:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import torch\\n\",\n \"from transformers import AutoModel\\n\",\n \"\\n\",\n \"MODEL_NAME = \\\"BAAI/BGE-VL-large\\\" # or \\\"BAAI/BGE-VL-base\\\"\\n\",\n \"\\n\",\n \"model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True) # You must set trust_remote_code=True\\n\",\n \"model.set_processor(MODEL_NAME)\\n\",\n \"model.eval()\\n\",\n \"\\n\",\n \"with torch.no_grad():\\n\",\n \" embeddings = model.encode(image_paths).to('cpu')\\n\",\n \"\\n\",\n \"embeddings = np.array(embeddings).astype(np.float32)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(11, 768)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(embeddings.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Then store all the embeddings in an FAISS index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import faiss\\n\",\n \"\\n\",\n \"dim = embeddings.shape[1]\\n\",\n \"index = faiss.index_factory(dim, \\\"Flat\\\", faiss.METRIC_L2)\\n\",\n \"\\n\",\n \"index.add(embeddings)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"or you can directly load a saved index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# index = faiss.read_index(\\\"./index.bin\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Text -> Image\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first task is using a query text to retrieve relevant images. Let's have a try with the following queries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"queries = [\\n\",\n \" \\\"Find a picture of oranges in a blue basket\\\",\\n\",\n \" \\\"Find a picture of both oranges and bananas\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"k = 1\\n\",\n \"\\n\",\n \"with torch.no_grad():\\n\",\n \" queries_vec = model.encode(\\n\",\n \" text = queries,\\n\",\n \" ).to('cpu')\\n\",\n \"\\n\",\n \"D, I = index.search(queries_vec, k=k)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that the results follow the instruction well in details:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"query text: Find a picture of oranges in a blue basket\\n\",\n \"['./assets/corpus/000000275230.jpg']\\n\"\n ]\n },\n {\n \"data\": {\n \"image/jpeg\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"query text: Find a picture of both oranges and bananas\\n\",\n \"['./assets/corpus/000000156031.jpg']\\n\"\n ]\n },\n {\n \"data\": {\n \"image/jpeg\": 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6NYch/CfzNzt1w+6aOi8hZdiocGxAcbkedid9+mGyNkjBW0UnIKTIF90G/I9Pd/C234YYEgpOA5rcjzKFeXiAUt9LX6/DfEkymiFt3hJF2vaxJ0k28/d6ddvL5B0IELxF7bEXsQSSL/MHfp16fHCTcpW4BuWLeLnqXa/W9xyPXrfDpl7UCqBhdRsNfNem21unKw/AXUpRC297YgNz5izb/AAHr1+HXd5TLIfSuoSO1jcFSCfxP1I5Xwj1CZbglWKhmF+h3Pn16c9+eCDuaYhbhkO+zAjbWtwd/TYg+W2DDkJCyoAsALbAeLmPLc8x8fIYRMbJoWWdtfiYl+RPpy533+f44afFKPBad4CxIYEb+6dx589+e/wAz57vMmEohKRhntpB9NH6cunT8ME3IQnCVDi3hN9r+Br/Pb+vn8MGo1gABh4QCRy0/TkPz88KEWV7UxAa9lI3uNvob3/7dMLxQTyWCrIdxZbbEHb4/9v1w6S1JYMqkqTuLWA28reXoPQ/EU4ELS1tipIvtvc/S+39gjDpbpTmBsdPkxXf6bYWyBakE7jxBSeW+/LfDFOkpNve3tsD5YruEIxnZUx9qCuipexvPoZHIetkpqSJf4nM6Pb/hic/BcV2kNLieittzAXDDZadRuqk35+eIdSPSoXO0EyRU9KAI77gdTjVqAugDZUaZIyUrl/Cy06LLKVJPINsMSNoBmSgdWnZPKupjpNIjAtbkTb54JzgNkIl26ZTV/fRKGXd9lVdy3TbAF3VGGpT9nJlMIkzBGRWPhhVAWU+beWBPdRA6sBDWZNHT1TFTdG3Bvu+Kj4BU7ZITugzHvUUJK6kC1u8ItisQDuFO10JymZ1UZOisnB62mb+uBLGncJ9bhsUt/pHmiAFMwqkt/wDFJxH2FI7tCPtqn+opVeMs7iJ/9pz8tgxVr/UYj9Won/FELis3Zycx9oHEEbbZk1v5o1P6YE2dD/SjF3WndO4+1DiVDdK1GA847fkRgDY0fH4qT16t4fBPIu2LiRLBp4n6WKuP+rAeoU+RKIX9Tm0Lan7fuIlZleOFtJsbSOL/AJ4b+nN5PKMcRcN2D5p7TfaUz1G0mna456apv/2nER4c7lUPwUn9RbzpD4o2o+2fiKoymgzCCKaWKqHVvCjdV1Eb8udsSf02oYa2r8k39RoZLqPz/hMszz/Ms9DTVdbHG33ljDEKf9rb8MaNtwyhR7zu87qVQuOLVqoLKfcb0H3P+yg2owwBNTExHK7WGNUQsaViKgJdNckRW1iUI3w8dUiYTtqOnd9QQXv91iLfjh4G6EErVsuh1hwHDXvfWbYaAU+ohR/EyWyiaygeJSenXFO8E0Xe5bfBT/6+n7/ohjL56eBZO9p+9kurK+rkAdxbrfHNTHJeo1Kbn5BhIVJDVDvCrqhclVbp5YbBTgQAChjiGYvVpGp8SrvbbcnGrathhJXDccqh1dtNp9kfVSfDmZVWU1FLX0c7w1dPKs0clybMpBU2OxF+nXfFosa5ukjdc817mmQV1r2X/atjzf2egzyGlpKsWQTBGBZRyVbEBgN+Y1D1tjAqG6sck62dYyPNdBR9XvO606X9JwfLx8FcEfa/lNwe+Ebeak2P1F/rfDDi9Mqf+lVuYS6drmUPsahTvq0lvD8wRa3+XliQcWpHEqM8LrDknS9qmWHYSptyHeqVG/lbfBjidLeUB4bUG4TpO07LH2D3F7f6wG/x89tvpiUcSpqM8NqBOY+0XLV2UMIza6BlI8+R8sSf1CnKiHD6pSg4/wAudrlX2FzcrqJ8+e3Xz6YIX1MpjYVQnQ44yyS9pagC5ALxqdun3jv5+fTEnrtMqP1KoOiWXizLiWIlOlr7afF8j0+v0wfrdPcIPVKgxCyOLsrMgT2sFzcixt8eQA/LrhvXKU4KXqlYiYS8fEVDOSY5UccrqwO/lz+X92xI26pu2KA2z27hKR5xAxsG1Ejzv8+fK/8Al54PtmoDRd0Sgzana1lkB8lUNp/Hpy2N7fDBdu3mgNF26VTM4QF0d7o2Nwhty6ch1t0wQqgpuyclDmkVydZHTxowt8CFv/Y8sH2gQdmVqM0pdQBmC2O+pP1tt5j9OqFQeSbs3dFgZtSNutQl/jaxvv62/uwwu0CXZu5rxzmlQm80IBB06TtY897X/vrhu0CfsnLK5vSX/wDzKAD+FlsPqP69cOKgKA03DklhmEGnaeNmI2US3v8AH8tvlgu0G8paHdFiOvhDACaHRcWswP6+vPDB7Qd0xaeicR1aNfTKrKeasxJ9f7+OJBUHVR6Z5LYVJawAO5v4iRYX8+vPn8cHrQ6ea2EhuSFJHUgG4+V/jzwU9ExEheRwACLHV5b3/Qn54MFBlbCT3lJUECxUiwHW1unPr0wtSaFt3w7wt0G3Ll6eQHzw8pgCtj4lKq1mPJrjYeg64JCVlgsKE+6BuCSR+OAJACaJUbX5lTU8TM0gVANiARfbkPP+74q1XgbqdjSuK/tE9rMfaDxHBluXzd5keVl2SRXus07eFnHmFUaAet3I2IvQLtYV1rdAVaRUM0sSOEchgCLAYpmsAYlWxTBCr6gzenZoTMBoA6bb46RlUSCViuYYIClKvP6WV9KuLEWCX54nNVpOFXFNybTZUpiNXWGWih+6Gj1O4/lX9TiN0buU4PIJKLOqbLo0ky+kimKnSZ53JkF+oHJcRmrA7qcMJPeUdm3ESsrIbuSPcvfe97k4gdU5BSNYoApLWP3kl/T4emITJypVhafQ1wSCOoOBTqQiqdQs2x88JMle8udjhQkvA7dSf1wklsqEj9Thk6VSEkXJthJRKUEG1yCRhk+yhyO7rqhPJuWDQpqo0zMPU4SSKsrzOqbJKOnWeRIYXZ1VSRpNzyw2kTKYnknyZrWKpAqpwLb+M4MnxQEA8ls2d1w/96k26E3wtR5FLSOiwM/rlN+/1fFBh9buqWhvRODxZmJI1GB7ecIw/aP6puzalIeMK5AAUhcAWsykbYXaOS0NSNZxJPV08kJijRHtyJNrG/XAVCajSw7FWbWp6rWbWYMhRvtTrYC1hjPNm3kV1P8AxDUOHUx8UzzPO/ZI7Iq98w2629ThhaAHJQVOPPcwtayD5obu88hYklna2o9ScXwAMBcs5xcS5xypTK5niLUsnNSbYfZMpA7i3TCmEk4jzStgAWOtqo1GwCzuAPxxE6jScctB9wU7a9VvsvI95TheIs3j2XNa1fhUN/XERtbc70x8ApBd3I2qO+JSq8Y57GRbOK3/APVv+eANjanemPgpRxC7H/uu+KWTjziKP3c4qT03Kn/pxGeHWh/9sfNGOJ3o/wDdPyWKrtP4npI0MebOSWt4ooz/ANOAPDLQ/wCHzP5Rji16P/c+QSY7Z+LIZGUZijAG29OnL6YjPCbU/wCJ+JRji92N3D4BTFN228WrIF9shItfeC3T0bA/0i28fipP6zdDHd+CkYu3biqIWMtObbWs4/J8MeEUOTnD3ov6zX5tb8E7i+0NxQmxEDX5/vJP6nAf0hn/API75Iv6y+M0m/NPaT7RnEQcL7LESx+5Mwv9RhhwmMCqfgER4zPtUR8SnUX2m89hID0RNtrGp/rHhHhVTlWPw/lIcWp86I+Kcr9qLNNi+XszAW/16n81wx4XX37b5J/6rQ50fmn0H2qKtSCaCoUC3uSJ0+Yw39Ouf/5R8Cn/AKla86R+IT+H7WcoFzS1ik73XRt9HwxsbwbPHzS9esjuw/JPo/tag+/FWAf/ACwf+vD+q3w2c34ofWrE/wCLvgE8i+1rTru/tigf/Bbb6HD9hfjp8UvWLA9fgnsP2tKK1+/qFBt70Un9MNov2/4j4pGrYu2cfgnlP9qyifdqoE3vvE42+JXDg33+n5hMfUv9fyKfR/alyc/6yphAPMMnP/lwQqXwzoPyQkWZ/wDc+qeRfabyLcNUUR33DhR+gwvWbof+2UjRtj7NRPYftK5A1gJsuB290rf88F67cD2mH4JvVqXKoPintP8AaEyGbdZqa/8ALNb8nwwvqg9ph+CE2rOTx8U+g7d8m8OmaO42H7wmw/4tsGL482/JAbQTAcPkpJO3LL5wf8Uj33JdWY/Pxf3fEn9QHP6KP1Ep9B21UZO08HkP3coB+Xefjg28RA/2UZsSU/g7YqAjeWKOw/8ALD/9TN9MTDiLdyoXWTui2n7Z8rpomaWqYC25eQKL+psBh28RBQGyIQRxB9qHI8uRxFOtXMpOmKmPfty6Hkp+eIxdVahwISNuxp3VEdovbtxD2hJJSK75blcl1kgSS8ko/hd/I/wrYHqTgoccvOUoA2VeCnHvAeEr0OxHT4YIEpwJVmUOUhaKnUpciNRcgeWOZdUlxMrZDMBc45FlNHX5fNPUZi0EsfhWnjS7MB1ucdywMLZccrl3FwMAJ2MypMlqLUSgra3tBX95874ftA3DcJtJdum1VnxlaUIzSmQWZbkg4E1OiIMTSnpO8BLP3V+gNsRkqUBZFDA1QI+Sru59egwMhMnMkaItlFhgpTwmjRL5YFKFjux5YSULKDSb9D0OElCXjKs1hsx23wySeww3vcW9bYFOlzAqHSG1E+WGlElChNxa3ywtkyHq8BM2qLb3Abl6DBjZAUwkFqhvj1w6Smcoi7yjB1FbORzw+OaSddyVt+8b64WElkQv/wCq2FhNKwySBd5DYemGTrRJZiL6ha+22FukvCaY293fDxCZY7+byFvPCTpCpzB6ZLsAWb3RhklFxU0tdMzG7X3ZsPuknM8Xd1VMoUBQLKB6HCTJStjZiJwul15lfL/LC3TpeKrMyA6bnkbeeEOiZZNQAbFSDhHonXvalvyP0wySwalD53+GH80l72hD1/DDJJtXyLLFHY38Yv6YSSjpD+9c+uEkpOFgJRc8l/TCSTnvVII1b4UpL3eKdrj64SSUSTQwZXsQbgg8sJJODmdQzFjPcnck2w8lMthmU/8AGpPqowpKdbDMJbWPdn4oMKSmWxr33Jigb4xDD6ilC8MwsTqpqf8A4MMSUltJVpNGyGlhW/3lBH64QJTpDULAYExsiEpzCRt64AYSlKMSVtc7/hgyQAmzMrZZGIOr3Qdr4hRyvBw/MkWPI74SLzSyqt/Cqm/mMMSihKpGCdWlLgb+Eb4E7wU+y1SJdGooux5hcOROEp8UsDaw1soJvqUnbAgDon1HqlIpZUHhqJx6iRuX1wxY08gm1O6pVQXYSyHvAdvEbm/lvh9I5BPqTgzl3GhNNtrnbfoMIJjvKUUiJgYgSy9GPTCHikn1ENU8MeoaS6qwItYX3OIahhjipKeXBW/T8P1k1PFIpVFZQwUmxFxyxhiIW4FxtA8iqQhYWNzbHWrlkrGO9lDSMW9DhkoUzQxU7DkR6jBGCiCl8vikoKmOop/Z52UMBHVRB0NwRy+eHEjITHO61jydaaAKbFzuSNrnCjGE6RfKnf3RthoT8ki2VvY7E4UJJE5dITshJ+GGgplibL3p11SLpw8JKKqpSp8JIN8ChU3lFZ7XTi5AYWUjrgThHJIUiUW91uBfbVzwySV7htOpSLcsNKKEM5ymjNx/NGD+eDbso3ZKi59pr/A4fdMprIT/AIWUdRJy+IwQCZSLC9tvphkgvDYgW+mElC0mHgY3HLCSCTiUBFuL38sOnShUW5fPDJJCsqEpIS78ydlv7xwjhJQiRy19QTzZud+SjDjKSmYIFp10KDYdT1wikt2VTYkcuRwkl7SLHqPhhuaSjbfs+psSe6b8v8v64chJPCoZyefrhkl7QNsJJYZF22GEktRELnb4YSSa1qBVjNvvrh0lGuR3jdd8MkpWBAZSCLjTb8BhuaSXMS3Fx/TBSkvNCpjva3TCSSfcL5fTCSWRTrbn9MMktjTDpe3ph4SWrwMqFtR29cJJbogZFa7bjzwhlJbCIn77D54QISW5hZTs7YZJbLE5O0rfTAmJTynEUc2mzSNb0wolKVut+93JtfqcC7CISlye8jFrq297HbAc0UzhbWEZAa9jy354SY7ylI2PIDTbe9sNHVHKcmazKL+G9sAAjmMrDNYmzXPocGmOSsJqOk30X88MYGUhOxS4Gkhrcj8vlgZkp1hGNyW2HQDywaY4W4ZWYAAlutsMlCdI7Erq21bE/rgYym2RFwXQmt4oyyBwNHf65OvgUF2/BcVLtwFJysUBNQLpGi7Jc0ziip6+7/4qNZ/d/iGr9cU22ri0FWzXAML58Uz93Jffl0x0SxFJpHDWDchH6N0Pxwk4WrCWicCUW8iOR+eG8E4KfUuaGLe9xhbbJ91JwZ2nJgDhtSeEuM5ia1zth5SXnzymty+IAwpSlNJeISg8CqN+dsKSmJUNX5lJUtd3J9MMhUZM5c3OHATJzl8hpZFdiQGNrefrhFJElFVozDUNY8uXzxGQpAVIKrMoZTa/Q4COqLyQ9xLH3eYUrHclCDbpv/niZqjduoOpuJB8BgyhUtkLfu5xfmy/lhkxUpe3XlthJLA8JscJNC1kUOAgNizBQegubYScBWx2f9hUfFPCNVn+YZ1JllAk7Q0pgphL36q2gvuRsW1WHkMStaCzU5ImDAWOK+xPLeGcirc3fjSm9mpULd3U0Mkckr28MaC+7MdgPiTsCcMdA2J+CWeYVP8ADHDGb9onEdPleVU/f1ctyFLaY4kAuzu33VHU+dgN7YFrS4wEtlYj/Z542ypDGuUR1R5l4KqNi3ruRiXszySkdVD1vZdxdlxvPw3mIAO7JFrH/KTgTTfOyUg81CVmUZhQH/E5fV01uffU7r+YwBaRyRb7Jk0i3sXAPkTbA7JoKQqoRUwlQd+YI6HC3STXL5mAaF9nTofLywkk+8zthJLUgGwv8sLZJe2tvhJJpmA/dxHp3oH4HCTKJJ/eEjz8sJOpiAfvm8gP6YSSX9Rywkls52Uel8OksEX2wySyBy5fLCSW9/PfDhJYezIR6dcJJJUpvH8Dhkksp35jDlJbgkDDJLaNiSSOmB2KKcJdXFt+eHA5oSeSVhpjIgZvDY+fPETjlSALKwajfVcL0GGKeCUt3a2DbPtdlHTDSnIW4l70RoWKqN7ct8MU/msMlxf8sKcoowssws29tI2NtjhIT1SkdSqg3UEDkScMQUUpRZNWw5dLYbAynzCTZvO19z5YMJlqsqogcyEC/ug4ZMdslL01ekgYFgXPINscDEFOMjKtjsS4fPEGfVUsQLTokdJDfo0zaSfiFDfXGdcS5zaQ5q5R7oNQr6cZXw1R5bllHSKg008KRDboqgfpjvadqxrAI5Ll31nFxMr4VUTiOrhY+6HF/hffHPLTR9V8E+0XelYpJubW2xCXwcqbs52UHPBU5aTFVRXQ7WYXU4PUCoyCE0NDTzMTHKaZvJxqX64OAd0y9+xau/7p4ph0KODhoS8k1akrFJUpY9Rh48Usrb2StsT3Rte1/XCieaUHmspltZOLhQBa/wAsNHVJbtkboSZaiNADa19/jhYSWpip6YEIDK/8TcsKUyj5nZpCSbk9MMkiTJqSVo0JBYbdcM5GFPwwMVCG9ydsRkqTZQPF6aWoZNveZSR8sGxRulDlXs46874kO6BSORPb2gc9lI/HCSUox5cjfCSXtVx6YSSfZJlFRn+a0dBRrerqplp4dtg7c2Poq3Y/AYcAkwEl1nxdmmX8EcMZfllNUJR5Vk9Ojyu2w0AaVHW7E72I3JxJVfkAckzRzPNcscW8WZ1208W0WXZfSsYTIYqDL4wFuSd5JLbaiN2bkoFuQxGA5x8UsBdX9lHZdQdlvDa0lPoqMzqbPXVwG8rDkq+Ua9B15nc7XQ0NEID3kVu2vmRcnnbAYKW2VEZhnNLQVAhncozqG8KkhV3te3+y30OI3P0mCiAnIW1PmENfD3kMneRkld+Vxz54dr5yCkWxgqPrOH8srXZqjL6SouLESQK36YOTvKXJQdb2Z8KVgPe8PZduOawhT+FsMT4JZ6qlO3Xsno+FqWl4g4fpVpqSIiKsplZmVDfwS7m4B90789PniJwBEhPzU9w72RcH8bcPUec5bWZlSU9SvjjMyyGCQbOhuv3T9QVPXDN0u3CWQqGzHMYIcyqoqbVNSpK6xSPbU6BiASBtcjAHdLkklzWM80YH0wydYmqEqYotF9phzHphJKNG7n1OEkpeFgJDqa1+pPwwkk4BuRY3wkllzdj5YSSxe2Eks6r/AOWEksjbr9MJJZY3U2+uEkkYDpaQDzvhJJe4HocOks3GGSW6HYX64FEEqLkgXJvtzwpwm5p2rMqbCwBtzxGjSpa7WBAv0Bwyfms6wo52IwkUwFkrZ7BdzzYdf6YbkmjKwZC1lJHhPTzwk5OYWuppHF5CfP1wjjKXtbLzoyqoNjq8hyOGGUohbLUEtdZNr/dHPCjqimCmdRmV6nuAT/MR09MFpgIC6SmrVBY2BsPIYUIgY2WyzXBJscNEJw5dh/YT4cGcVtJVSAuGzSWZg3lDEoX/AJnOK9tT7XiLWnkAjqv0WziOa+ghDXO/449GAXMwvgZfnjiVtK6qaUVNLTuy6nkiRgQfQYou9oq63aVvUUgqiomjikiIuyW5+tsJpKRCHq7gymma9PI9MwO6sLj0sOeJBUOyjNMKJqOEM1ptRjjE6jmYzy+RxKHjmotDkwmpa+kP7yllXyJQnByEOkhNWqZ0FvGo52KnDylBWjVbODct/wAJGFKbzSIkaQ+CKSQ+inClKEtFlWYVpAjpnA8yLYYkc08HopjKuENDB6h+8a/JeQwJfGyIMPNFEdJCtkuEIFxYb4hlSLzraPUQoYm2oHnhJIb4ygIy2CQkErNa3xB/piRhygeEKVw3BHK+JlEnvD8bT1M0aW1GLUPWxGGOySfCcIdLXBHTCSWTUpb3vlhSkrp+zlktPHm2b8R101PFT5TEaeNpZFAjZlDTSG+4ASy39TbBtc1oLiUxMYVedqPaRW9qHEK0WXpK2Xd/amp1F3qJCbByPM8lXoPW+AAJKddEdiPZXRdnXDPttQiy8RVotU1F7iJOYhj9L+8fvEeQGLbQGN8SgOTCso1YZduXIWwp6JaeqFs64onpMykip3TRGACGj1At13G/piNzjqkFOGiMpWhWLiCmNVXUsBmYNEGQMCU3v8OZwQzlyURsnUEMWWwRQQoI4EHhUdN8PMYTnJlZNSr8jYnAymysFgwvf54UgpZUTnaQVlPNR1EQmpqmB4542XwyI3h0k9OZPyv0w0gZTxK5q/b1d2I5zxPw8jPXZbXQSexTE2s5BWOcDle2pGA529BiIjS6QluFU48sMnXvlhJJxAbInn3vP/dwkkgn+sW/K+GKSc1xP7u/mf0w6SbB2B2Yj54SSUFTKOUjfXCSW4rpl+/9cJJbjMpRb3T8RhJLdc1YDdFPwOEklVzZbWMZHwOEknzwNTzhtnicXSVR4TgQ6URaQsNKqLckIAL3wSFYFTGbDvFwkkskqsdmUi3ngYwnlOIAXYsASFwztoTgTungQ32G3W3TABHywsDVpA2ABuL+eEU0JWNCGuBbAoxhYJZGLDUCx3w6ETyWAVZWYKBcb74ZOOqwx02Yb32sMJKUhLMQp2PK+5w8ZSJTZ8xdQSux5fHBQhlRkcjJIH2JO59cGYIhCMZSrE7EG6HrbAIiSd1sHJuALHyvhJ5nC+if/h25K3+iCVzRmzJUSK55EtUMu3raO2JeEt131Vw5QPkEF66Ldg6rsNo21H447iFhr4EgXv8ADHDraVv8OVenKKLU1rU6m9uYtyxUeMlWmbBTETorB1BD8ww5g4iOMKYCcpFQ6yFmFjfkB59Thk2+6yDEJLsSo52HXzw+yYwUrKVR1dNRTya+xwY6hDtukZofaArCzAEA7dem2FJmEwAiUk9FFIQWUAjcgAYQceqcCeS0SliglLd0q35G1vww5cQkAAs92NBIUhibi3K2BT55JJ4JmHj0q42IUWOHCAyEcZRwPkI4Ips/z2tzSBqmsajSKgWAaF3tJpk3kG29io3sNxjLr3poVAxrZ962rawFwzUSpBOAuB2Yf/jHMwV21NlGkPuN7Amwtfbfe2+KZ4tB/wCWfl+Ve/oruvzTfN+yzgTOKX2ebjTMYVLKxK5OTuA3ryvbzwTeMBuezPy/KY8EccT81FP2C9nUoUHj7ND4gSf2RyXbYdSbah9MH/WgDmmfko/6C48/mEvl/YP2e0NS00XagisBpSOoomiO9731gDy+hv0xJ/VtQxTKb+iPGc/JbVnYh2e1rMx7Ql1jw3gp9ZLA2NwATb3iDvtp3Bvgf6uW5NMpzwRx5H5JKm+z5wI9RGy9oNaqAg2kyrmbn15cj9cB/W2j/wBsov6C/r9FWHbLwBQ9n/EVMmVZhPmmXV9MJ0qp49B16isibE6rEA72NmFx57Fldi9pl8RBWFf2Zs6gZOCEK8LcV5hwdm6ZllbxR1SI0YaWJZBpYWYWI2uNrjfGjkbLNVzZX9rbMIKWOGv4co5wgAMlLO8RsPQ6hhanjYj4JoyugcizKPO8ry+sFdlNLJU08dQaZsxjLRFhq0MTbxC4vta9/LGM/jdBjix3Lz/C3W8GuS0OHPOybT8ECtBmSqpJXYktorad26G99Y8/PofS4t43acynPCbkf7FScOV1GXxLSx0ryCEBNULxOGO97We/r8xidvG7QiAfmFA7hNyDt9fwk6jLMwmViKCt7sIXLCldlUAC48INzuBYX3uOmJBxW0ds76flRf065G7UkMiropdLUFakh2CmllFvEVB93YXHP4dCLyi/tifaUfqNwP8AFN3kWCHW/exxW2Z4nXUbX2uoJ28v64mF5QOQ76qM2lfYsQ/nMlQaqZJKOSKGNwrGUG7W2Uaem5f42w3rFI/5YT+q1hHdKqHt8p2reH+H4UopKqveaWYSxRlzHDpAC3W9g3vW/lviQVGFsAqI0agPsn4KjZMvqo1u1NMo1Fd42G/ly54eQh0PG4SDxtGxDKVI5hha2HQkEJSErZLso8ZJuR5YSZJw/wCtXlzHXDJJzWqf3ex6/nh0k00nywkl7CSXsJJeG2Eks4SS8B9MJJOaTMqmiYNDM6G9/MfHAloO6cEjZJTTyVDapHLH1w4EJEytBth0yyDbkbHzwkluszp7rsB6HDQklo8wqY7aZnFvXCgFJP8ALq6srK6mgWTU0sioNQve5tvgXAQSibkgIijraTM+8kpIWhEchXdrqwuSpHUXFtsQwWiHZU+oOOMLYK6g6b8r79MPumEjZJNe93sb89ueEh5rwYNHuoPkcJPyTKVG3639eeCQqKqiyg9LdMSDZAd0hStrj2PLnholIJwgIJ08vLDHCffZbxjntpI6dcMU+fJfX77HXBOV8I/Zz4GbL9UtRmmXjMqqoc3Z5JXZyo8lW+kD0vzJx0vDKbBT1tGXHP0WZdPcX6ScBXGQbnl9MbekqnK+AIFzjhFtq1uHEaXI8ucoN4FW/U722xVq7q1T2ypuOFmNgW0i25G+IsqTC2ljZSGZSgPnuDhZSheKKApDWJHnyw0p04hQCQq12Om/h3+VsHOUBWyi8hRtNozvtY/PDOjdP4JERObkAgte7WsT6fLDEhIN5FYkKlFY7W3XSeuGmcJ4SfszoHiVXL++ynoMOTlNECAnmVZRJnuYU9BFI5mmcIthuB95r9LC5+WBc4MEqam3tHABHHbCIqTI0yin7tY8phjDLG2oLMZF7zewvYaV5bEHHItq9tdauWQu6oUTTtp6/RCNHMZqdNttA38z64rPHehabdk7UggkKAR5jliNHGFjn5/TphFFp5rJJJChiNtgDhkTdshKLqBvcg87g4SKBsthKQwAJJ+H+WA2RgSIWcxyfKeMssGT57JLTU3e9/T10ChpKaUixuLXKMLXA6qDi9aXhs3lw2O4WRxDhzb6nHMbIGrfsy8QANNlebZLm1KLESR1fdtudvCw/Ll1x1LOLWjhJdHmuGq8HuqZiJS+T9iMHDdalRxFmVDXvCbjLKBzIHbawke1gt73A3NttjfFS54xTDSKGT1WhZ8CqOeHVtkVsDI7s2ksx1ElBa+OSLjuvQ2NEL3djTuFB97wi2AJKk3W4jRlF0TcbeEXw0kJzKUQaSCt0I5EYE5TwITiHMquI3StqYyttJSZwAfkcDpHRC5oOSE/i4szyG5jznMk2G61koP/ANWFA6IDRpndo+CWj464kjWNVz7MSFGlQ07Nba218EPBAbaid2D4J2nabxQpZRnUzhlKkSpHIGBFiCCu4ttY7WwQe7/UfifyhNpQOdASydqnEuolq+KRl3HeUUDEb6tvBtuAduuJO2q/6z8VGbG3/wBH1Sf/ANoeZSQCGWkyiphuWKTZZCQTYi5AAubG3w+GCFzXG1Q/FRHh1sf8fmk5OMY6qRmqOGuG6hm3Zny4K3PVsQwtuAdvhaxIMovbkDFQ/L8KM8KtTySRzbhudlao7PuGJdICIsdO0YUBSPdBtfcm562vyFpRxK8H+fyVc8FteX0H4TSSj4BqRaXs4yw76j3NXNHc3U+XLw8gR7zeeJW8Wu2/5D4fyojwG2O30CbVHCfZXXSMZOBaykLH/wB0zd/Dz5Kwt1ub9VHIXvKOM3Q3g/FVz6O0Tsfl/KbHs07JqkXbLOIqEgaQIquNxyAuSTvaxPT3vIDEzeN1xuwfH+FC70cp8j9U3PYv2Vzqtsz4ppdW7aoonI8V9iDYgL4eV77+mJBx585p/NQO9G+TT8/4TY/Z87P57d1xvmkFyLiXKyxUW6W577dOeJhx0bmmfl+VWPo7Unf5hNJvs0cOTyv7J2kUcKXIVa2glRiNWxuBb3Te3mCL9cTN47RPtNI9ygd6PXAGPt+U5pfsdVWaU6yZdx3w5V7G4Lut2C3sLX2ubXIHniZvGrU7yFTfwW4Z/sqm7Uuy/NOybiRMlzWooquaWmSrimoJjJG0bFgOYBBurAgj8CDjWt7indU+0pGQsi4t32z9D0Icvhiyqy8B9MJJFUHZZxpVZVS5nBwjn0+W1UffQVcWWzNFIm/iVwpBGx39MR9owmARKm7GoQCGnKgazKa3LriroqmmItcTQulri45jywYIOyE03t3BTQkea/XDqNEPCIalzGWuF0FBSzVQbycJpT/mZcRv2Deqkp4M9E54YhX2OU3OoSAW+WGfunZspeVWdTZb8tgdsBBUhKT7lmbxK0ZO9jv9cLZMVmeACxZtQIFyuwwhhIjmmwh70XYEDltz+WFMIY5plWZezBiQdPn6YkBCEtO6Hp4no5yemDhRp7Szgjb8cC4KRpTuMK9yeQ5+mAUpIIyvsX9lCF4/s6dm6NcFckh59L6j+uOt4d/yQsO5xUIVv9wDzO+NRVV+fJfexwi3VbnB07w8O5eqKpYR3uwvYEnFWpuVaZsFMLOsSAo15N7r7wN+eIgJCkOFvDLeRYS5dbeG3mcPEJp5ranUrKVdCfEPHfYYaYTiZSssTPOCUQjfVY7/AORwwylqWaeGWRwXN1I67ED0OHaNwmJjK2gUQd/uXP3lPT54XKEiU1BWMgFRZyDbqvphDG6RylXhM7yyQ6tRGlQGuSOp+GHiU080fdlNJFkkGY8QTRWnooiIU8Q1Ney8hbeQrzPKJtjzGLxKuWM0hdBwy3FR48VCcUqZuH8yZvHI8JkJPMnUCT+BxzVtDarQu3riaRHgh7LDeniu3NbYsVfaKip7CFKI/wC5bqQOv1xXlWBJXlfUpIYbbn8NsJFK2uzafFf1w2EQW0ahFPMi3IHDItxusjYAi48rnAIgFsAb7bjAyn81tpVgGZRdDcX3I6YQJRFplJSaXbZrSi3L+H+/pggYGUiM4WtrMdjdtr3vgd0Ywtx00iw+GBKLTjKyoD7bD8Dhtk63VQByNj152wJRROy3IUEAcv4cNJTZWukKdhqJ+OHmU8LBa2oAA9LdMOkdl4kg2I9SAeWFum3WC/mL+VsOkdsLOoMDyAPInlhJRzWARbpfqt74SE9Fkk2sGCg+ZwkoW4ZiCFUb778jhkULIc3FufMeWG800LCqT5C2/h88PMJ1ne3Pn5mw5YXJMVsAN7tYkbg9cNKcrdNkNiQT6/TDoIKxJHHMAWjWUm20ijf5+WHBI2KWVGcUdnFP2kZRTU9HLT0Ge0RdaYzeBKqNm1GIt91lYsQfUg7bjf4ZxFtrNOt7J59D+IXI8b4Y+6ivT9obqpa/sZ44y7NjlsnC2aS1ViVFPTNMjgAm6ut1YWF9jjsW16Tm62uELgnWtZp0lpRFwr9nPirM3Wpzuhl4eydUMsk1XZZnQDUwjjvcm3MmwXqemKNxxGjQBDTqd0V614ZXuHgOEDCvGg4ozzIaaGkoM5raaCmQQwKkwJjjVNCrcgk2QAf2McA52txfzJn3lemNtqYYG6RgR8FJ03alxLFGgmzM1sS3KirjSbnzILKTvyP05YcVKjfYeR70LrOgf8F6fjejzhHXNeFuHs21sZGM9AgdmLaj4gBbe+3LfFht7ds9mofeqruG27uSYNF2d1kc8VT2bUEQnjEbnLq16cOt78rfxANv5DFpvFbxpkkFU3cGoOnTHwCZx8C9ks0awxZXxJkqltbPS1qVFze+wciwtccvXpiyONVzlzAfIqkeB0x7BHzUdL2QcGVKkUnG1fSs1horspJAB1bXQm5vpFtuvpi2zjIJ79M/I/dVH8EcPZ+qzlH2dYOIUrYss7Q+G6irgiE3slRBUxSuNlsPCbC5N2awAUnF9nEqDziRPgd1m1OGVaWHA58sKnFpGnRNgCd7X2A/XGrKxyJ3Wpp9IKhdd+g5DDhPBWiK0ayBrL0KN97DbGU04UdmOTLUw3AFiOVuVvLEodCjIlCk9NLl8lmBA6HzxJuotktBmBjU3HQ4aAn1EL7VfZijMHYH2fo1wf2JTbMLH3Af1x1HDsUGrKuTNUlWvoHmR6Y1cqpqX56xexxwa3lc/DMMcGR0BuQwgU35jle1sVKm6tsAhOlp9bAMhN+ZXpgZwjWYaRg+liRquLEXBwKfmlAgikUFFYXAY3I/sYXimSrKzIBEVC3vpG9z53wozCWCnMNSndqXupK6C/Q/DCJKEJCdRIGMZI8Xhu25wh1S2SfsBOl0OqOxJ1HcHD4an3EpzTd9HUCKIJUs50JEFsWYkALf1JGAc7TzRMbrMQrc4loUyDhTK8ojK6ZWNW5VXVisd442ZW5Fj3klgADrBA6ni7+rrrATtn47Lv8AhVENaXR4IHzlDLklcoFyaWS3r4Tv+GKlExUafELarDuEISy0g062O635n1xdq+0VWpHAUrACQdRLBud/hisVOI5LKFVtcX8tPwvhFGOqV2DC3P16YA7IwsruSdh5jbfDEwj3WwYALbfby6/HAIhhbpe5ufl1OBKLK3UE78lG58sMnMSkpiVayjwyWsfgDt+GC5J+a1C6vdG5GBlFC3CN1a1ud+mBJRALZFtu3i8uuGJRhbrHbbTb1OBJTgFZ0AbruPMjDT1Tgc14gE/3yw4TwStDfTv08zt8cEmIWjmxJsST0vyw4URWo07HcX323w6YlZ16ja1rm+HhDPRYBDahcqfXqcJOSt7WFr8vLmcMksiy2JO4PUYbdPlb6wWG5v5354YhJeF25XG/xwk8LO21xceuGSWy9L236jfrhymWW0s1lWx8jywkjuvBlvcnY+Q54dMdsLdbtZQhI5/DCTKeoOMM6oKY09PmNVFTggiPvSQpHJlvyI8xvhoA2Vd1Ck7LmgnyWJq2WegqamaR5Jq6oMIkY6iUjIeUn4t3I+TYmA0tJ5lR6ZIaNh+hRmnUTzPUbYilWDtlbGJNiAT5eHn/AHvh0xWbBb7ar8+uHQYheZiylixvy8VzhJoBwUm0wVbHnvz54IBCQEyqcyZRZbX3GwO+JWNQOgZRdwzKKPsizrN9QFRPW1KsQ5UssNKHRSOTC7SG3Q7i5xsW1PUQB1C5u/cWuk8gqNaF+51FCQLAsDsDjq5yuHWuloyuk3VwDc8vU4fBKRwte4jdh4GTTfUfPDzyQnCa1EJCHUDpI2vgwhUTV0SVcRLJqvtYdMEJBQuQ7WZJNThzEDIljy58sS4KjIX3C+znFFnHYjwLmFMb002S0jLbp+6UEfUHHWcPg0GrGuD/AHCrGNPYkb/TGvAUEhfnh+6ceeLeV4ZVFNTUFJHEpJWJATysNO4xRf7RV5owAnckdRaN2Yq9+i2tgUo5r1ZBJMVdJWL23+PlfCG6UYwkfZJ9IUgnTup8/PBe9LZJzxSwBR3YBIJIc7HDRKY+C9Ty1CkrYBbjY72OFEJxlSSAvK+qw66trb9DgJT53TjWkSqYpA8m4ZCAdsLdDjkiHsx4ebiTi2ighsskbCVZEDjTIx0qQV3Gm7PzA/dcxzxWuHBrCStC1ZqdPRFHFmZRZpn1XJTqFooyKemRTcLEg0rY35bX+eOEc/tHmp1/QvSbel2VJref5Q4YBLEY+aMpjI6bgj9cE0wQVM/vNIQHkxvTISfEFW9vOw/pjTr4cVQp7ZUvTWDgjdr2sev93xVKsBbRvvub8r+f0wJUjZW5sy2L2B3FzbAI274WytqUfdFr74EwNlLB5re2/iv8T1wBKkASuwJv4eu5wKdYuU5Eb87/ANcLdOGrzkCPffxrb43whPJOWrUKqjobjCShbpqC8hYDnzvgCEUQtwqkE2BAP1wOUfiAlEHT3fXAFEMbpTQRuee+3LAyijktNAJOobnz88FPRLbdJSKQN9/hzxIFG7dIMPU8r35XxIodkmxU+t+mCTLGpb7Wt5/3th4QyVnkRYj4nlhkplKawBYsRz3wMI99lm2og6gfhhtk6UXmRbccvM4bZEAsg2C4ZFHNeU7gE+G1rjmMJIyFl17xSvIEEXv/AEw4MZQGIynVJEFBLBT34kjQEAgHSbta+1vCAfM+hxK0QJKidk45JJXKoLlrGx5WxFlGcpS5JsQ1zyK7fjhghKcZdRVOZ1VPS0sJqamVtMcWoKC3PckgADmSTYAEnYYMDUYCBxDAXHYJXM6mGaoSOkkWakpYVpYpV5SWLNJKPR5GkYfylcTVjnR0QUW41Hc5SFwVW7AG3O9rf354qgqYrLXF+RA2NgN8EEPgtTIVFjcADe+1sOEJglN5JwCBcEna43ODATb7lNJ5jYkAG552xKBlRkKOqJLBrknpctf64mGSoHK3ezueGs7DOKsqeBZJXrFnjeSbu0/e0s8Yj3BBLOqWBtuAQQypjUsammo5ni35H+VznFaR1NeP9JELniK706BgFV1VtQPpjqYzK4k7QVlqNoidyzKx3Jv8cPjdLwW6RFG0EMxI5EbYeQUORhazQidlDAIRY2bzwtUJ4xhIexjVp2DA3BA2+HzwcqOEhLQM0rFhcDcL88JrgnIMwV9Vv/D44lTiD7N+V0BfVPk1RPQuDzADl1/5XGOl4XUmkW9CsW8bFTzXQ7U4LHlzx0WpUZC/O5k9C+ZZpR0qDU80yoB8TjgF0I3V8JQ1L1EU0ssXcpcNHECW22AucUSACrYTmSIQsWiW5a+5F9sIDCUr080cUYQLEsZsSrG2oEbkYDnCeZSZqUl17MBYnUdr/DCkjKWDum8ksTTCVSWkChQpN7/Lyw4kJydW6UjBVLGPQyi+3X0v54RJSACV1xTBm1FGO3d2uG88ClKRuoXXHIx1XvG674RdCcDmrd7MKccN8FZtnqgLUTqBTteQFZJA0aC2ynTGJHv4j+/Huj3ud4rXhmkeX5XUcKttT2g+Z8ghxgFUL93kBjlw6F3RE5TeIsqK1iCJBsT6g4mQHaEBUsfczTRWI0Oy773s7D+mNSqZyP3Cz6eE9hYOyA7m/lyxXIVrzSl9MhBU21W2HrhtwiG6VRgOQG55gH8cAVIMLYW925A8iOW2IypgJ2Siqw53F/xxGpAlAbEA2UdCeuGR4K2IBW17rz6YZI53WHbRGd73I59ADhxuh8155ACTsADvtvhoSmNl5XBOxBsL7YYhIGDKUR1UkjYeuBIUmpKg32te/T44CEWqCvCTbl8Bz26DDEQnBELxfvCQdr9Rh4hMTCavKoFrkE7AHriUNKiJxCRZtJuPqMSAKOVprsG8j9MHCAmFqJAW2N/X0wRBQCFspILWsBtbywB6KQDolUsLnr5k4AowOiV0Mx1C2/kL4GYUsYys90dJ6gcvPA6kcdFuF3uSfPAkpQsi5YC4F9rg8sPsmOE8rMsejNAXmQpWUkdaDAdRjR9WzfzAISR6gYncwMjMqIP1E4Igwku/1TpKE8CWCxhtwovYX8/XzJ88AXCZQlsCOa0RbIFuTsAb9dsRndFhZLiOIyO+jSLlmOwAFz+F8OASYAQlEmaUR4Tp6vJ8wyiWHPpyA0k0wCU9IwvZVW+p5h98kgRnYXckXnA24LSMn9/fFUmuFwRUa7uj5n+PqhxCqRK3IWBB5Ypc1diUsCp3J+AX9P64SGFo0wv7xJ+WGEpb4SRfmQQAPK1/hg00YKayTEn3r+vO+JBhRnKaTSPqNyfIb8jiUKI5TCeXmSQV9OWJmhROgjdXZ2NB/wDR/iKj1MFajo5iA6oGtUBCPFdTcSkANsSQCVBLBrZ59ZMdD8iOizOKM/ssPn8wufVpjRMkRuXS0bix95fD136Y7sunK870wYC3dCjsGSxO7A8j8cJCswN3qlVAZgdm3uowtk0ArDRhJW0EMha2phvbCKcDK1ZSfEXW5sPFtfyw/JKMpcwFAzKe8OnxMvK3rhASkTC7M/8ADZ42ag4o4h4YlkQQ19OlZBENiHQ6XPzVl+mNfhj9FYs6hZl82WB3Rd/mM3P9MdZKxZX56uAIR/pDFUMpZKZTJ8G5D88cM4w0ro2CSrfFYJQvdC72sQDsT64okEK3vsk1EtPHrd0tqsTuvP8AXCSIC1lq+7eOWpLSRbgFVG3wBw4zskfAJeOYqmqNCYwtxqsCN+X0w89UySnpopfHGskRAIsN2/DCkhDuUnJLNH3gdWsoAElhy+GHwN08JGoqWncaNSnTePSLb9b4GeaKCnmXUUmbVUEEsyUryFYS+rTouRdmJ6KLsfQHAucGiUdNpe4CFeHGjDLMpyjIo4xAsERrJIlkaTQX2hQseeiIItrC3kMcBf1u0rR0+vNek8JoaKZqHnj3BBU58JGk6r8wf788VGLadumzCyE77E79b2GJgoXILrVEOdV0fK08hsOl9Lf9Rxqf4NPgPws3Zx81mNwVAOo2PNfh+WIzjZTTlbSMrSuLFQSTpJ2F98BylSglbIShJJv9NsBCmBkQl0N9vnfpiMhSggpdBYW1m972xCVKCFupDLdTuOtufrhlJOFrqZAF7sqL3BBsMFEppgLzN72w+trDDgKMmVp3u/Tflth9KGeq175SR4gThaSh1hKwymSSOOJXkkfZIkGpm+AG+Gc0NBLsAI2kvIa0SjLK+yrinMold6OPL0IBDVsmhiP9kXb8MYNbjFjSMB2o+An54C0mWNZwE4S9b2QcS0SGVfZawKL6IpGQkdQNSgfjiOnxuyqHSZHnn6FEeHVh7JB+SE84y2uyKXRmNHUULnZRMnhb4MNj9cbVGrSuBNFwd5fv2VSpTrUv+Y2FFtNrcFuh2JAuDi0GwqhcDlIyOG5kjEgEIC5amVg3QEbemChCXBYDkWGq1/Pe2F4oolKq97XsN+nI4jIUrQncbaxe5HniE4UzAfcnKgANqPXmMREqdu2EoFJFtjffEcp4JWQmlb2G+3PlhSihasp08zttbBAoNKwU8CgC3gUEWFjtiQnKjjqsbuRsV2tvgUtkqFbUAxNumGQc8KYyriPMOEZKasyqangzGVTIKmSBJ3hj5Jo1ghGNnOob6So5He21xoiQYJ9yp1KLbiWvEj7qHMkkksks85nnlJkeWZ9bux5sWPM4rucXGSpw3TgLDMCdJBIHS2GCcLVpGAsSOvO2HSwDCSeQNcEWGHASI6JAykEX8Px6YND5JF5ASCWJ3vscSAEKN2E1ci++459dsShQuMymkuy25E7fHE7VA4q9ex9ZBX57TxvpMuR1IDB1W2jQ17uCu2k31CxBYHY4p2rv/UMPWfoVW4o2bceYVH5/QPS8R5tAsDao6+oTu2GlltM4sQL2NuYvsb88ehNy2fBea1MPITOeEpMhu8Ste+ryw4MqMgpSFBFciMtJfwjVYfXDeSfzWjx65xaEKGv4juPjg5ESmGTC3q4bEoARp8RFhz9DgRhJyVg0pTKNN3K7rhhKRzurL+zbxjFwJ2z8E5kJQqyVYpKk2K2WUFDc+W6n5YnoVOzqtd0KirM10yF9dVZHUN5i/PHfDIlcvBXwL7OctdsrmmjS7zSgaibDSOl/rjgqrogLp6Y5o0joPZJL+0TLEx3Ity8sQagVKBCcVcAqBojvY2YFzt8MBKKJ2TdHiMkA0xBY7aozy+OFE5TzhP5apEXStLI0Ex8bKB68sNvhECfcm8tI9QHeKpYlbAW2KnyPnhBLmlo5C6ASa2Ft7LuCP0w5lNyhISU9Oz/uyUKJ7pa+9+mGzGUvJWB2Q8LrnnEsEk0ZSGnAieQyrE6mRXLENa9+5jqALWN3XdfeFK7qCnTJlaNowucXKVz7M2zvM6ytcbTzGQAjkv3fwHLHnZdqcXHmvU6NMUqbWDkFDTG3LoMSNUpym8hAVgeQNxflyxZZyVR+QgvPA8eeVC3B1hJlJv8AejAP4pjVZmm0rLJ/uEdUisvJLkt+B54YhOHQvNOqyhdX3RseWB04UmqTK3WUFj4hv5C2Iy0qVrwlkl8G5awNuexwGmVMKnilYpNQ8DDbyGI3NUzXhLawb2NmA/HARCmBwkDMbkEkjltbB6UJcAFuswbUmsISCbEX38tsFpzKh1plLWWDMb3JNz/fLEoZlRGphSXCPD2ZcaZutDlsWq1mmmdf3cSebeZ8lG5/HFW9uqNhS7SsfIcyfD7lWLajUunw3bqujOznhTIOH4ZnpFSdoD3UlVIytNPIOd+th0AFvpfHmHFLu7uSNeAcgcgP3nutyjcW9N/q9LcIyiM1UHaGP3QbAEKzeQ1G+nGEdLIDj+/dbpcGhPJ8jrJaRBDLFRTlgx7wGWw5lb7Xvy1D6YhbcU2vlwLh8Pz8EwcXKBzDJKisaWkrKaKaCS9wRqjK+VjfV1vexGx33to0rhjAKlNxBHx/hSjOHDC5/wC1Ds9/0VJzHL9TZa5tJHcnuSTYEX3032seVx05ekcJ4n64Oxq+2NvH+fr5rnuJcPFEdtR25jp4jwQLQUtRmNUlLSRSTzyGyxxi52/IeZON+o5lJpfUMALCpU6lZ2hgkq0OHew6sr4RNmlctKG/8uBdZB8tR2v8Bjk7r0gp0zpoMnxP4XS0uDNaA6u/3D8/wiaTsByrutMdbXiXrJrQ3+Wm3988ZQ9JK8y5rY6Z/Ksnhtp7ImUO5l2I1tMGNHXxz7XC1ERTV095bj6gY0qXH6T47RkeRn5GFC/g85pP9x/IQLmWWVeSVT01bSvBUpvofbWPMHkR6g46ClVp1266bpCyn0alA6aghZhc7WII8/h8sM4IhCVVyw2YG/TAEdU+FsWJYAgCw2v8cNCKQtWUSXsLD1w4wo3Y2WJUDE+4g22Hw9b/AB+eD1ZQcl4WHh1EG/XApin+TQU1RmcJqxI2Xxt31X3MvduIF3k0tY2YjYbHdhzxLTEuyNlBVJa0hpzy803qauorp2qKptdQwUHZQFCqqqosALBVUbeWGe8PMpmtDBASLMiA6gNPUDrgRJ2TpBtQ6aTyFjtgxCaUkWZdgf8AiOCwlOYWjlyW2J9dsFhNvutO8sWvpUnzGHAlMm7MCCST5eXz+GJhOyid4Jm+k9Seu+JQqznJIASVMMat7zqoGrzYDEwwJUBPeAV89jbg8apETvVUFdT38II1U78iw0g7c28PntjKt3RXp+YRcTbNq49I+qqXjiE0XGedQGF1dapnMHdd0Yiyq4XQCdAAe2m5sB1x6JTM02z0XmVVsVCAoFoZam0crGxu2gHbp+mJg5V9MpVIo1kWzkxt0XbVbDSCEshPKqo7hEEYIKnc2uCD5nDDbKLnumqQmQ6ptyu+wFyPXDk8gh3yt5go1NHuGsWJFrDkARhSlulo6cwKk0XgkUhlIPiDA3Bv8cNqhOACF9QeEPtFZHWcJ5LUVU+mplooHlBO4cxqT+N8dKy9GgSVhOtXajC+TvDVE+TZJQtoaNjGGOr3WY72t88c/UOp0LbYNIlTwjiRGkD7RnxAryPLbzxCTpwEQ6ppV1FWAQQYiWABUbC3n5YYRsnKUpwsLAWLLYEvcG5wWeSQ2T6nlCqUjBG+24NvrgYSBnCz7ONAKTfvGJ8IFz8ThjhIJMlo3lFyoC2IC88LxS8EzrIlp1VoUs58Wkfnv0w4E7pHGAr34ApZeFOyirrpGmjq8zCRqjaB4qgK5/mOmmSAi5ABkbSL3bHLcYrw3Q3nj4brr+D2uuqwO2He/fehaoYEMQoFiLDy8hjlQu+gpnMLkjVa55YNqF2MKPqCQ3hG3MnocXWBUamxQnxQP/aNLOvJotBYbcmP9ca1ESwhY1UkPBUZq1Ly5HBaUtRK1qJNDxm4Ph2I+P8Anhw2ZTl20LU1SkgsRbkd74XZlLtRMFS/D+T1Of1DCIiOIGzTOL29B5nFC5rsth3snoug4bw6txEy0w0c/wAdVZmSdnGXCIe0I856s5/pjkrjitYnuGF3lHgtpSEFurzU2/Znk1TFojoxEbbMrsGHre+KA4rcMMl0/BWTwuydg0wPJCPEfZVVUt5cuqROBv3Ews3wDcr/AB+uNu14xTfisI8R+Pwsa79H8F1q73H8/lVxVyzUNQ0EqPDURmzRupDKb7bHHU0w2o3U04K4S4a+i/RUBDhyWMooJ+JM7o8tp7JUVMmgPzCjmzH4C5+mFXqNtaLqz9gP0KO3puuqzaTef05rqjhzhOlyfKaLJcm/w8YbvKiZ93kB97VbmW6+gsNseQXV6+vVdc3GeQHTy8uS66vaf2expHSEU5TwlDls8tSzPU1tUFV5nA2VRpVFUe6thyxlXN++4AZs1uw+56nxVaw4VTs8tMlHGTZd3METaHCH3XN1Um9tjbc39fTfljCrlxyQtV7hJAOVPrk8VZpiRSGLBUHeABuvNlBFt7AAk7nlgaVJz36eZVU1zTGonHl+CZ+3mhbirK6rL6SdDGdKxh1ZJQ2obddv5f8Ai2ucarbapQeDUGD8Fp2txTqmQc+SqrjKGLM4pKIRLULUXjMZ5MDzv5dd/TG9Yk0iKkxGVsaWvaQ/YqL4A7PaHhalEUKe1V0tg9TIPG5+fJR0HzO5xb4lxKreOl2GjkNh+T4+4Kla2lK0ZpYPM81Y1FlkpqI4AoLLcsq7C+1xv1/rjmH1W6S6Vac+npIKmI4irBWp0nZ+R8V2HkLHfpy89xbFQZ9kqEAR3XQmmb5csCSHSytGw7xWAIGw3DA2I3HrviwzU06XboqNQPgjY/uxygbjXhimz7KzDURhlG6so8SNzuD0/vpjcsLt9vU1NP8AKmqUGXDSx4wqxynsczjNIO9FfSxRamUaFZmVl8xsAeRtfb1x1lbjtvRdp0En3LmP6Y5jiHvj3KXfsErYINf7XDTc9LUu34NikPSOm50dnjz/AIRjh7Ts8/JA2f8AC2a8OsWrqU9yDbv4jqQ/E9PnbHQW13QusU3Z6c/3yVOtbVqA1OyOo/cKHEoYGxsfK1icXS0hUxUB3WwlJuT5cz0w2mE0pzlqQPWQ+1BmpEOuYRtpYoNyqtY2Y8hsdyMO0AuyheSB3d0pOx9gRSadWqpBUvDHGS8Krq0IWIuFOrUFubhUY72xI4hjYHNA3LvL5puQYwLHrcEDlitMqUhJuQpANiP1wQyojvCayTGxI6C+JgFGU2eYgX1D4nbEwagBlTeT8GZ7nlnp6N0gN7SzAop2v5XN8UK19a2+Huk9AtWjw+4rZiB4/hSlT2R55o1CpoyxHhVtSkfO2KbeNW0xpPyVl/B6o9l4+aEs74ZzXh0M1dRyJHf/AFqeOP5kcvnbG1b3VC6xSdnpsVj3FlcW41ObI6jP+yhHm1HYfA88aAbCx3Okr2WMJM4oF2H+IQ2Ppc/ph3iKbj4KJjpqNHir67HWWPtI4e1WVGqCpDAEEFGBBB2sb732xz7Dpe1w5EfVaHEGzavxyQV2uZe+TdoedUq0yob07kd0YiT7NEN0O67g/jzFjj0mjPZtn9yvLLmO0MIIlEjBwFbezBTsAeVr4nB5KoQZlJRy90WIZY4wSAWve3Uem+HRc06Uh1jClQZOTsbqu4OBlOAt5o2WoeIoGkVirW31dQdvzwWyGUhTmHvDDMTEh3aTTffy+GHLSDKHUCE6p3aFveBU7gHoN7jDGEQRFFNm0MSRxVapGihVUVJAAHIYfHVDCgIoxUQK07xtI1wik3VbDYgdNsRu9rClbEZKZ5pnNFlpiNbIP3YKqqrc8vLyw0O2G6QIjKbZZImc0sNWjMyMxKJuLHlv54kIIwU2NwpJ6SaeKCILoZWswjFj/frgScyE0A7raSlko2ZXjkjJBIcgG3qMJKOSaRxSRFmtcSJdmJI0/Pph9xCW2yR9mqBEwNkVeRDczfmflhoAKSd5Ll8mbZ7S5W7usdVKsLy6TIYozvI9hcnSgdrC/LlhOMNUtNoc8LoDtEenoo8symngjokjh9snhSMIEmqAH02F7aF0ruSfU3x51xKt2tcj/Tj3816ZwehopuqnmfkEBTCzEkWvff54oDZdBEppLZgdXXf8MGExEBRNRJokNz67/PGiwYWTWIQnxZJcU7Anw6ut+RU3/DGtbjcLFrukyoNqnQdzvy54nhQF6RlqQyJ4yAL79OlsSBqEunZJ0MU2YV8NKhGqRrarcl6n6Yao5tNhqHkrVlbvvLhlBvM/LmVefCuXxUlLHHGmiJE5Wvvjzu8que8knJXvFvRZb020mCAFZuSZHLVkqwKqsiQ6iOUjAFV6AnTva4xzr5dBaN0qtw2ngePwCK6bhCsnjUwRyBiGYRyR6bBRfdyQt7A36LsLnEPq73ENhZ5vaQPf+X4/ZUJXZeAQAUVmAfSWuNJ8v7I5WOKzHxutdlUnlsq84/4Jo8/pW1qI6lVPdzx21p6eo9DjpOG39S2djI5j95qnfcNocRpaagzyPMfx4IF7E+G504yzJ6oWaijSJbcm1sTcHysn446Hj90w2jAz/Ik/D+SuG4Vw2rZ3NcVhlsAeIOZH45Lpzg+ShOeLTTVCifSZGjBIYgLqsLfyjHltwxxZrI7q162sMJaiyirMunMQlkVtXvaGIQHcAWF7AHSevLcG5OM1rNLu8FCadYTpUlBxTRZeSopgjBjdWSMNYjwlGSwBHKxG9hYjY4kwTOmf3ylA6yq1BOr6/MH9HRTOW8T5VW/uY5jFNKrKPC91vvq1KptzPM3FrbXJNug6mPbEfb7qjWs7hneIkD9iJ/jmhLtG4gp6aBFVnM7gd4WfUxUhWXcqDsQfmoNhbF2p/eLaY5eP7/stXhdq4kuO3JVRlswrlkrXPvkpHffwj3j8zcfLFmq3s4pj3roqoFM6OiN+EsgkzCaOoZCtPa4082GqzD1JFx1tbliiWl50xMbrNubltJpaDlWZG+WUVHIJe4SKYf6yoGkhluAoGg3tcEIbkDmDYYnDaFOnDhHmDv8ABcxFxVfLJMdP9/dKeUeaZRHRSIKyAQOG1RaLtoIsSAQdwNRN73FrA2OLNm62Y2GHflHu/Z+CirULntAS0yOfj+9NuaHONc4y+CjKWhk1ApCIlVIo7HfSw8TNZr2Gwta55YK7dRczTTgnlAwtPh9Gs5+rI6zuftHz+qq7MatVod2Dtyvew26/PpjMps7669gl0FK9mk6LnslFIdKVilwdFyrp1ANua3HyHlhuJsLqIeN249x/BWfxJulnaDkrMzPhzRTO5Ve6DBdTKVDfM7DmLC9zfkOWOeZ2je8QVg0bpriBzQHm+RIaWRZ42mDXBBX3lJvYDqLfljWo1zqGkwtllQE4VAdo3AR4feSvoFIpbkywC/gH8S+nmOnMeWPSOF8R9ZAo1d+R/P2Kxr2wAaa9EeYH1CAEq1YW1dL3BHL8sdEWEclzgf1U5w5FS1VUntKzy0yr7QyUlO87TaTcQ+G2nWA41E2AIPlhTTp5qOA8yApmU69UTSYTy2PxW1bRZtI01fW5fVU/fyNK7CncIrMSSAbbAXsPQDFR1ejUfDHg+8flXvVK9NneYfgmneLovqLA7c98KDKreCQd2XfmpHQ3OJAFG+OSbAPUTpCg1zSsESNN2ZjyGJu61pcdgoWtdUcGM3OytPg/gCiyqCCqqQtZXv4l2ui9fCLbgefXHI3vEqlZxps7rR8f36LtbHh9O2Gp2Xcz+FZ+TZc9XFEgi2j082uSfUDnjkq9UMJM7rUeWsKaZ7QvT1JkmUrdSqOosApN/EOtjyIxPb1A5ulv6fD8FRh87KOraCOeCSCVFkjKeEjnuNj+tsWWVS1wc3CDByMLn3j7hj9nV009EhRFOp4ANh/Mo+pt9MelcNvO0YG1d+v5XHcX4UXA3FsM8x18R+ENcMT99n9CtydGuS1/5bf9WNi6bpoOXF2jtddoV5cB1bZZxhkVWttUdbEbnrdrfrjkS7K6e7Zqt3t8Cont9aGn7TswKyRkSQqrLGWsjRyzQkHWNQI7oAg3A6EqVOPT7Yf248/z915Dde0CegVfLGXUR96igKXN9r26HzxYMAKmMlbTSKkhcBECW16bEt57dMOJlIwtYohEsRfUIQS173Yg9LHfDSDlPpSEtSCoIS3kSSpG/T88H5IAnEKxTy2k8RJsSE2thy4jCbSDlZ0g7d62wBKKLg2wWNkOyWSvUKBsNuWkYZPBUPQRmrRtDd53QEasBbpyxGZ3hSgBQ+a8J1GY1MsxkKOw8FiNFreR5Yla4Qo3NKIcmyeooaCjSGUSCNbFQm2wuf1wDiHGUfIYT9XYVIdmVF5KRytbzOAPRPsJTaeSoZ0Itd+Tnf8A4sIABLySDidEfv8ASSwsqq/Uflgh4JojdIpmEZCrKpNrgKDf5YYhODyVr9gXB9LxDnVXmVSogooJI6JQZSrSllaWpsF3YLTRMGFwCKgXNrg0LysKTM+J9w/laVpSJlwE8h5qY4hzaTP83rszmb97VVDTk8tidh8gAPljzQuL3Fx5r1mhS7Gk2mOQULMlmII3sRfnh5KstCZTkgE25WAxK3KFwgIWzCrXvirE678rdfLGzTb3Vz9V0uhOqPgb9uNE9e0scYLBYI7BnBFtzY2xSrcT7CW0RPiV0lj6PduBUu5A6D7lF+WdnOVQsqrQQlTuwdQ5Hlud74wKvFK7t3n6LraXDbO3bpZSbHl+U6qex/hzNYn1UAgPPvKcmNl9QR+oxCzjl5RIh8+eVWrcKsLgQ+iB4jB+SAKrstqeC8zlrUmatoApCs6aZIhq3LAbMLW3HzAx0jeLsv6QpkaXfI+XMeR9xWfYcCbw64dWY7U0iBO46+fnujLJJl9nBJINzv5b4wq4Opdu3AVm8HZ61A1keGPwaGE0TOr3kDG+nnvyJU25bXuMVxLHagY+H8fXKzbqi2oMiffnaMIxyviaOdUjkjiWOKPZ0BkZXFwGBJDAkE7nmpIJuBin6w5stdt5fP8AfmqNS0LBrbMn98v55Ig9oy+sohRrBoRgspqoI0qoIxdh4iEuDqFuhIJ8Y2tca+kW6MCN/wDYgLOAq039oXZ2g4J+e37CrHtIggy9pGEsLCYFrxElE3HhDamP8XM32587BQZFSGmR4LprGs6o3PLrv9voqu4BzWKi4jzVLjWxhIv1HiH9cb3EaLqlvTPn9kNz3nOa3fCszLsyBzOSeUR96iIkZVibLptsTuAd9rbbgbDHLVqf9sNbMHdZNOnqUsM2u+sMC432Av8AA354o9jiFfFOBss1eavIBaSS7NcmO35+RwzKQG4RsYDySKVFS0htG7qbWWVwVFuoUDnz3xIQwDJUwpSeiYZ9SVucrpEzI45MfEPib2JNut78vLFm3qU6JkjHwVmnTbS2THLKaqyqkp4andYFF3S9r3uT6YnquZWe5zOagqMJcXDmjPh/iVsmliKaWYEutwdybb7EbjbrjKcx062rOq2ra+HKazvjAZghSWSSSS2wcuwG3h3JvcXNzcA9dW+K4bVeQ5+f39j7KGlZCjluP37qEbOTRNrinaF/dZoHKarDcgC22wO58jfniVjHcldLGv7rhPmoCvzIsD7mhubqoubctzv1N78yb4vU6cK7TpABRklU8wIUsI73bfY/D6DFkMAyd1ZDADKecNZl+zOJKSqY2ZCbgG3Tz6X3HzxFcU9dAtCz7+lrolqvXKuMMvrKOMVXdqQhWSaouTYgsRce8Cb9QRbGRSe1vcqNB8d/kuBq2lVjtVM/BQ3E1DBR0gdCyI6ax3x8SjkFLAWNxuGsNlI3uLNVpUmOBpGZ5dPgtK1rVHuh3L99yq3iUB4XjdR0FiOR8rHr5jGlayHBwXR08iAhjgfsVy32qSpqKU1LySM8cc4vFCL7ALy28zc+Vsa3EOPVtIYx0ADMbn96CFnssLa2e6qGySZzmPAD77q3st4DihRe6iLAbaVXYf0/THFVeIOdOoqy68AwSpSq4Ojho0Uq8LygqutbFivMH+/nioy9cXk7wqzboF2OSq/tB7GMvzKJ56bVSV/Lv4wAGPQOnXfqN8dbw3j1WkdD8t6fg/oUNahSu8uEHr+7qiM54O4gymCSWeglngRijTUv74A+qr4hfY7gbY9DoX1pXcGteATyOPrj5rmbm0rW7i0ifLKz2d0qVWZSVMljpDQI191crdvgdO1+nzwuJvLKYYPA+7/flzWjwWjqc6u4bYH3V4ZeEjp7JE7LHGwOkX02F9PPfa+48scBUkukncrrpggK0uFstgqKdCY7q8XeMlhIZAzkBfFYLfcAA7BSbnbFFtuar5J2+33/AN1h3dcsMTz+Hw/SkOJOHxUKiQIk0rtq7mODxWOreyk3FlPMA+WsDaU2xpkFh3GyjoXODrwB4/n96wq2r41SSWGORlW2qM35n5bE28tsTsJMOIWnmJVUcb1NJHU95PMkEmsJosQDc6lsfjdfpjsLBlQthokfoP5WXc3rbVwL+f1VS8OlYONpUVQI2jaSJfIFh+Vsdrc96zB5gwfgvPxTFLij49lw1D3lXVkEojzTLpTfw1ELbbH31xxhXS1u9ScPA/ROvtQADtQkdppP9RKviIYlhVTBhcX2Dal0ndSrLtpGPUbPNGfH7BeMXXtN8lUUtQjNdJirm6rGo58tyemLoEbqn4BYnpwrkVHM2ICsCTt59cFySmEqiioKiQtIImstjvb0+WBAATSdik2jWCWNFHgYEK3vAkdLX54KeiYL0cncyDuu+AQchsTfmcKeqfKfU04S51KwAC+7ufifPfCTSkzTKSSVW5/mwvehyuecvzqvyuQPSVk9MwN/3chH4YvkAqoCRsrH7PuOajN6xsvzSVppW8cUgspYjmp/TED6QiWqem8k5VgqvdI7o3dMx1N3J3G/linvhWvErNLL3gOorodudtgfM4eAQmTCadUkMCPHEEO9xs1/TCAJTuhNJazWBFNGQ49yQEFf7sMPEbJplIuVLqgIYyGyggAD1GCiUxPVdJ9ldGeFuxWszIa6eozWBVWxtrkqTrbw8zppo6Sx2Hiaw5nHKcZrRTLQd8fn5rreEUddamI27x+3zQ1LZSOi35+nzxxa9IaE0qlJkJI2JN/7+eHRtEBM61u7hYC9gL4nZlyjqeyZQZwrAM2zeorZAGRJDHEpGzP1Py/PGnevNKkKY55Pkn4HaitUNy8bYH3KtXJ6MAgEaZgdAubW+J8scbWf8F6GDpbIRzkGQHMIGezRkuEPd2uAdtVr3AFjdgDvt8ciq+Nis6vVAI6RKIGydaNSXWaGFbKZZwzIu9vE3Q89iRjNPaPlwbPkoG1x7MyeiiM5yKKWMg6JARqVo2Bv0BB3BHT8DiajXc0q0yrqyFTudUa8PVaTxMy5dObBHG0b3vbfex3t8LeWO2t3+stLXe0PmP3daFOprzyRdkOTZrm8cckFHKENrSSKUvt62v8AHGLcV6FAkPcJ+KrVLmm0kSjLLsgq6U2mJErHQwOwY+ROMOrcsf7OyrPumvhTDZTAjDv4ACB78qXW/wDtbgH44pCs4jun98km16Z5r1RklLUQyIaeKRCLmyhl+drjDNr1GEGSCpQQcgqsOJuyuKHMWzXIQtHW6dMlKWPczi9xYn/Vt6+6eoHPHW2nFyafYXfebyPMfkfPzQuYe0FWc7HxH5/Soel4kaJzBULLBUwHQ0UmzoeoP6fri6+1kamQQefJWaYEow4TWTOdU7MfZx4V33J6n9PrjFvdNABo9pFUdHdRtl2TvUlmER7tADt0v/f4HGDUq6fNRdo1uCVPU/DpqCAFuBuQWBJFhuR5Hz+OKDq5GQozdNaEpPwzopJHkjdlUDW6EAi525Ha/Tz+d8Jtd5MhJt20kNB3UJX5M8TarISOZGwxap1w5XG1AQhTN9WRN3ndBqNmued4zy+n5fPGxRi4ET3vqrNJrahiYKj2zMF0CMFCg23A39MWRS6p304yVlq0K498Kerm9h5en64WiQoez1brWppZa2jkakOidksryC6g9PU/LCY9tN47TZSP1BsNwVrkuS1OT5TGmYVAmrApaRlYG191Bt1sRf8AsYK4rsr1SaLYaobR1YUh2xl3NQlfmwoq2MiQje5PUYv06PaMOEN3UAZB5ory/jRo6cEOyPpG6tY4x6ljLohYYa1xS/8Ape8yvGpsGOp3U2v13F9ze253wHqcZP7+9FO2lBBW1DC+f1yHWQuoKLAb8h/T6fVnkW7IhXtPYsyrT7NYKCaraKUNqidkIWMMzldrAcyQd7gEeWOeuWl9Rodsenj++a53iNWoGamKzuHoKKXLYplVp1cg+6SACLggjc3Fjq+PLYYxtFEyXNJMxH7lcxcvqtfpmP39/lOf2LFNGamaOJCAAvdoNKqd99yCOXIbnyGwY0NQNQmB4beajF09v9tpJ/f33IO4lyONY2aNLW3K6g17bEryvuRccx188Kk5zDJ26roLS5LjDvp9f3Kp3OYjw5V+3U0RDqhili3XUo3tt1B5fPzx11B3rTOyefEHx/nmt11NtyzSfcVWfFM8D5+KmJAjEiRdvfZhYttYkEfPpjq7NrhQ0O8vID6JWzHBpbzlEXCubyGKnlmAppCwjYFr6b3AYWIPMX57bfE5t1SAcQ3PP9/crTqMEaVYXD/FsOWToro880coezga1YWD8vev1QgX2Bvs2KTf7TtQGMfvkfMhZNzaOqjeP35eanajjKlpo+6iRLwoUWUoyor3UgOWUHUGBIB5AtcHVidtaGwB/E8/LoeqyTYucZcdztzjwzt+8lUHFNalPPVd3LGVEjAMgsOdify+H5T29MuIldEwd0SFUGY0dZxbUNC6wilQ6175b94VNxY8xv1x2VJ9OybqEz4cpXM8ZtXXNHS0ZVb5aFTjuFoQVgaOVUjc3ZbMNr/PHU1J9SIdvIXFVGPF5Tc7/SR8CFdOS3Wty8i+1RFv/vrjjHc10tQf2nDw+yffah7yDtKEcveysUqnQvAImCmpZvLxizjS/MqVDeMMT6lZ5pT5fQLxa5iWx0VQwwe0OsWhYp3PgdmsFxbMBVASlEi7tI1KK2kkCxv+PXDkjZBmVjSDIGUuir4yWNjq/p+uENkROYXpHWQIh8TGwF7eK/M2wucFNylYjklihREAsPAzk7g74cNAwlqO6zqMquuogIRfRv164UkGEluFUi4VWHRiWBOGwnkrnMY0Vnol7PIzJxdQ2sAupmvytpN74B5hpUjBLgrpetajgdJTAJLlkYLpYjpc4ziOhVyRCZR1k1VJFGJiqhNOkHw9bbehwcJHqtqOE1MsiM4ae5YLYgJ+uETpTiSEppg0EtGyMBtqFjq68+mFJTDBTeiokznMcvy9Z2jesqEpmmdgghRmszX5AKhZiTyCnDEwCVIxut4aumuP5Ycuo8mySni9ljhi9ukgUEGJprFIyDvqjiCJc7nTuF90eccUrdrX0jZq9K4LQLWOqnnj3BA0hK3394hfF5288ZIXUYhNpZAfEdwbG3K98GGlFI2UBxNXihyirnJH7uJmxetqeuq1vVULuqKdFzjyCiOzWWOLJqAE6mMYlJ82Pi/XEnFQXVn+cLo+B6W2dKDyn45Vt5VqVoJWsNDBt91PUg/PpjjK0GW9V1TXapCtDhRozEpkmaBo2eUXl0q0gCjxHa3M3uRzU7WuMN55H9/fDyWRdB89wT9grGizSnooYUdaYRWIZomDBNhrsNvBpb3wGWyk2sdR0KVwxggEY6fXyXKPoVKjiQD+7e/wMII49eGSlo3pZoq2aeR17+KTvBpubEE2vuqncdQfPEFR1ImdUnJJ9+3h1hbNgKlMu7QFoEYTHhvgSOokimnpWragECMCMvoIOxA5XvsCfwxjXF+72KboH1/eis1q8CCYHRWflXDZRVDU694ovoWw5jpfdjz5X2xzL6j6road1kPuAMz++7ZPZMiCk3jaQgErtp1C+/haxNvQcvwAtrDl+/VM25aecfvUYCaPw73sUixRlUQaGC8l+PlbA9s4d5ysCu0EAndQGb8HqIkdgI5V5SRNYnpsR6Hri9RvjJAyDyVmncZ7qCc5pHpUcvYEcgE97zIt15behI6jG/QcKkaVs0qusQqd7SstbOZqaWhKw5oXSFHJsHDMBY/C9/Tfzx23CqvYBzauWZPlA/Qp3U3hst3CsrJqFMpWmoYF/wAOsYjExtc2/U8zfzxy1eoaxdVdvMx+/BQOfJ1FWFkAjioRGqO0hW5G27al9wi5BYLpI9VxiVHySDt+/f7rNrlznap/fHy3RxRTUcqLLChhUXPe+KMOL2uGW+kC4vysQDb72Aa6kS3EHmsWo2q2WuM/P67+H15J7WQPIgSjl9nTT4WMuplVlFyB6i4JBNxe3S9ytkhtLE+f759VXpPaDNUT7v3zAgIPzjhmbLULsQ0b3dQLuFXcWFhcLcG9xsTYgG4EVS2qUWhxgjqP392XRW1+ysdIGfcP33eYxCrPiikBDR21RkWBPJh5fmMaNo/Z3NdNQdzVRV2ZDIczkoJIwoCh0e27IeQv5/0OOzp0jc0xVB8Perb87p3SZ2JGDkC1rAdDvzxC+3jCgU5TZxpNw2oAbDkAcZ76EpiC44TXNc7aWmClwsaXIQ2Fj53xNRoAO2Vtjc+KpHintGjizKshlYGndVCuDsxDb2+HLHfWfCnOptczdeW8a9IrdlyaQPdbz6nmpDJ+M5czhUU83dQrzlJ5eg8zirXsG0T3xJ6KzZX9O4YCwyjPhvOf2hWQ0yO5jsW1W2b5nnjDuqHZsLyMrqrR3avBVoZVNLlUAnEYKI1gRZtR8PJeZ52tY332tvjlalMXBLR0ny96t1XMJ0vMSiuTiKBHjzujqZCokIq6UnvO6YnwyJc3C8xpN9O29thnPt+1b2LmQeuwP88z1Pmuerf2QaNb3Hr4FWF2f5j+1Yu9iliiZAF3lK2box2sem/lfHL3lv2bw0mJ5n9lc3VqAyNJPuR3T8QxSw93KyyuDbSsevVy8NzzBsN7jfFPtXAFr8/D78lWNq6dTce+F7NP8fQTscvUV0biI67ixLBeZsdNmubmwsbeeNhjRXaWvaGuEfu/3hDR/s1BD5af3lz6KjeM6IJVzqriUoxiJHNgDa5Hx39fPGhanSdK7m3fLAYXNfH+ZPknEEUOsnSAY+hZQx2/P8Men8Npi4oF0Ky+qKVQeKk+H+LkemhiZpTL7rqVswJJIJHWx2+vnirc2RDi4RHJX6VRrwM5RMvF4FO0bB1YWKAsFDLa2m1hq2uACbAHljKNmdUj98f5j3qbswTITduLJpI3jLSLGVCqUc6WG5I03358j53FsSi0DTI/fegc0A4Ci8yqGmiZqljDCb6Y7+JvK5xZptDTDMlQvI3UAc9CVkEqWgjjGkLfpvf5Y0fV5aWnJKoVACDKqsZnFPx3Sd3ZQ0ktrdARjsRRcLJ09AvNL+4Yb+mxvU/RXTktpKqgX+KeLl6uMcY7Ercqf8onwP0Tz7T1QicdhlKIuuvRgshfS3tsmo89mLX1AWF/EAA4v6jaYp58PoP0eC8UuhJb5Komlk7pQq3Cgk2FrDzH54u+apSAlWqI2TTHHeW27W9wdOWFCeSsRhHRtLb3BcFbG3l6i+2GymOyVaGOW2wsT4dBvY9d8EDGUO+Ek6q0Uqo+yANYDdgPK3PDBEIOSk4acOSS2gyNdmt09RhyEgti9SCR3Ux9Q3+WG0tUkLngD540Vmok4BzJMs4hSSR0jDRPGC5sASNt8A8amkKSmdLpVn1OZ+1qqSpHa9xbytvinpIVnUCm6yEHSzIitqKkdPI3wQSWyTzxHUP3gIuSuw+OFCQnYpSWuaokZZVEsS9R7x/XDYSlWX9m/hleKO1mklkctS0VNJUP4ipj1ju7gje4jeZhbqnQXOKd5VFKkSf3qr1mwl5eOX1KPOKM7PEWf5hmhY2qZ2dAd9KckUdAAoFgNhjyxzjUcXu3OV69a0hQoso9AoOofYC53JB+P93wwV3HNMqlgq8rczb9L/XEoB5oSRuEB9pVZbhitXqyEc/PHQcLZNdnmuc4xUItqkdFBdn2frHllAHIFoVW3lbbE3E7Ymq+OpW7wG8DrOkD0A+GFdvD+crJTWJLBtjvz2/PHBXNAhy71jpEqep83MeltarMvutIALeW43BHmMZ7qIOOXgtRjdQhTcXFMsZjXW7RKwYi6x6h1BKj8ybdMUzbNPJCbVhnkSpzKaj9q5glc0XcxE6QAdTE8+fXra/44za47NhpzJWFVhp0A7K4+EFy8xRLOveFiJABG2kAqNx0J963U2tz2xzz+zEmp5fuVzd320nSYRdVRUclBP8AvlRY2Rg8cvdBWVldYiLEhGsAbkEgnxEYmovpUCahaJ5T1jGIOD5xzysYdqXNbBz7/CZwJH7C0kzeKtX2FB7Y7/8An3DEgk21AKOW4NrelzgXXbXgs0yCd/sZE46c1KLd9I9o7u+G33O/+6fxU1O7vGDThlCBe7fVpUjYsLXRRcbqevqcC+3pVXEAjlt98bdYP3Vd1R4AcZgzvz8uRPmonNKYSK6oHCHZmksWPi2a3O4vbV67jzxT3SdAwtGi7Yk55R+xnp8FW3EeWR16SWNgo533/wAiDbGva1XUyF0FCoWlc/52f2VxxlsM7C0dYmsnYW30kenLHo9v/es3uaN2ldO+p2lCRzRhW5sBTNIiFxHvpHO3njDp0e9BO6z6YBMFTXD3FSVECKp0XGlbEWH4fDFG5tC0knKiqUtJRxw/mfsNXHOqgub2sFAJ2tckbdd7g7n44yHEsMjkqFwztWFpOEUVnHyxRsHKow0sskfibZuTBrW6eIHY7j1lp3Faoe/++/M/IrKbw8f4/A/x9D700PGftsEcftRR7yLIq6ZBcIwDFbXDHVZvE5v0N9Q12VT2cVHfvw+c5Rep6HSGdI5H/bGMAfRVxxKKYVDRxyxPchh3TakUX5DzFgMQ02lhxsuutnuLJcIVDdr05o6vL6sWJR3gLLvzAYD5EHHe8FbrY+mfAq/VcKbWuQlRZ80si7nUQPEG5/35Y2KlsGhQNqSVP0vEENLC8ks4jRRu8rAAD44zn2znkBok+Csl4Y3U4wBzKgczz7OeMImpeH8sq5oSbGuePRGR5Jqte/njSo21tYnXd1AD/pmT74lc7eX9zd0jQsKToOC8jSI/6Zg56woSLsG4lzMd9LT0qtq1BZqjn5AgKfTG6z0o4db0+zbqJ6gflec1PQq+rVTUc9gHISfsIW2W9mnEfCpkMkcIJctoilU6VI6BgOoOKt1xiyvyC2duY/C1eHejV/w7VLmuB6H8gc1P8BVNZJxDV09YzrNFErRoVttcg26HpjI4iym23a+nsTldlwkVGValOqIIAI+K6C4VkqNKFZe6RiCbuygrcfw73BAO/wBceb3ekT+/XktC5c3pKkOKeynJON5xW1+VR19XTRCOKoE7waCx94hRaQAAkh13CgDci8nDONVrBrqQdDTyjM8oPL4lcFxNuvS4TIP78fBG/BnCMOT0lRBl01H+8lYezVLFYgp6I9/CBfYHcbG+MLiF22+r639yY2+pQ2+qlTa14OOm/wAP5U/WZ5Lllo5Mup6CUA3eBdSOt7HYgqQeV22+uMvR2hBBmP39hX2UW1JIcSOh3H3URX8XERhtZZ+RaxAIFvDa9mG3P88S0bYtOMfv7hXWWwODt++9V5xJmBr+/XW+pzqL6zduVyTz/sY6K2p6IJW1SbELljt9zUQZnlxDqWUyhiDyF1NvgMeuejlDVTfjp91zXpNdeqNouBzn7Jbs94e4i4xpI6inoZRRE7V1ShSEgEDwm138rKDfA8SubSxeWPeNX+kZP8e+Fa4ddvrsD3DHXr+fNXlknZalPCiVtfLUk8ooAIUF+gJuT+GPPrji5cZpsA8Tk/YfVb5u3Ad0KQi7MKf2eoNI9TBUd1cuzAqx1cjtfex/DFc8WfqHaQRKrvvHneEH8XdmdYE109e8j6r93UBQpHmSAPyONuy4rTmHsgeCDti5UtxXVVWQ66esienlsQpa2l/VSNjjurNjLmHUzI+nmsq/vPV2Gfcq34frjU8fUZuCF1Dz6Y6mvT0WTl4425NfirSeS6W4dJaoy97EkTxbA2++Opx5lVw5y9NdmiY6fZZ+0XppO0ep7/vhJ3tW4adAhkRqiyFbbFdKAKeYFkbePHqNoJp+GPoF4vdQHAjmFV00sjkStGTEw1X5DY9D5YvDHNUNxCXgvLBJaJldQfEq61HzwO6KIW5PdyR+EmN1O9hcnzP44R2BTeATeSsMQ7t071tyhQ22GHgck2UmhSGpUKvdixDaTYC/lhHAT5TlIVqahQrrpB0kHly/HDE4hIDK39llO4ZgDyF+WHnwTyucwNsaCoL198JJPKHOa3LSBBUMq/wHdT8jhiJTyiI9olRPAI5qOInmTGxXUfhgNAmUWsxCe0/HtOxUSU7U9hpJQ6h9MN2acPKeRcRUlZMzx1ShtOyv4CcNogJ9eV0v9nzinhPIezriGLNOL6LhzOM5qvZ3aqTWUpljRFMYLKGJ7+ct4hYXvy353iVGpXPYtwI3AXScPqNpaahGqDMTGyJk4HmzRL5Dm2T8SqHCCLL6xVnBN9N4nNiCFJBR2BFt9xjkKvDKjPZIPy+q72hxi3fmoC35j4j8KBz/AIdzbh5X/a2U1uWaSAWq6d415fxEadwQefIjpikbeozLmla1K5o1v+U8H3obragSRl0cMn8am45/0wgMwpXYBBVbdotVryaZb8zyx0/DWRUBXI8WfNFwKrPhzPFpqY0kpcBbgNGbEKedvhjo7q3Lndo1c/wviQoU+wfMdRv+hXbwRnLVPhedZYmIIbTYgWH1NxfHB8QoaMgQV7Tw25NRntSPtHzVhwyqI7s5kA2tzFv7udscy4GYC66jUhJ1Gbs0qxQyMpcgfy3O2/pgm0QBqcFbqPAYY6K4eCK4UtPTGRbhCGZBcgdLE7XG+OKvmanEtXMPp6zCP4jFLlwmy6eJZYLM1IZSEPhAY6W5NsTsxubi24xlBgrPbSeIJxP7/CoO1UnHtQSDzjPx/eS14P4mq+MuF5swzGoXJ8uqKUoJz4tKaGZSSNNzsToexsJLX07/AHxwX0X4b6KUKdnZ0Q6oANTyJL3c8nlJwBgYwvmHi3GbniFZ1QvLWyYaDsPd8ymVBleZcN57XZTS5hV5lTyhTPSVNM5mpJFYJ3qxjxJG225NpCQyAgk48t/+r/D+DVeH29yyk1l5q/wABdTgzqgRh0aSRO46rsvQe+u6leo2u8miBguOA7oCeokkcvNG3D+cVcNo0hhM1wWqqkFCqt4T7w5bm45C56kW+TWONLuthv8AG/y8/JetXNCm8aicdBn6fVSVZmNVUQySsqxvHHI2mNCHUqukeG9iAuokWB5baTqxMaeZqe0fCP3HgqbKbGmBkSOc+O/n4x78IZz+ljjecBou8GoMyvfwE+EC46c9rbNyFjitqGuBtPLZadAkgTK5L+0hWnJc3pqsNq7yO4YbXZGB/IjHrXovT7ek6n0P1C1ri5FtbF5UVw/2i+0RQOyGeJlDCRQWDoeh8j8dsW7nhmgkDB+/4UtC4ZWYHsODlEfD3EmXTZhVR0FQsiRMC0b+EgHcDT0IN8Zlza1m02uqjfn/ACp6d0y4lgPeCsjKOKmm0ojiKO/hud1I+J5f9sctWsw3JElA+np806rs6NRE8cj3HmCTtyI35H+7Yip0A0y0JU2gGUxHFPcoyK3eJz0M5KauQa3na428z54si2J8FeFDVlM6zNwsbyBz3khBJPP05n8MSsokkAjAVunTyAqf7eqnTwbSSqxWU1yAMOXuyE/ljt/Rxk3bhGNJ+oWJ6R1XUbIOaYOoR8CqXyitzvMp1p6NzqP3wguBjuqzLakNVQLibO74pcuDKb4HWAry7OuyWasniq85aWvmABRZd0U3HIcvnbHn/E+MtptNO3AaPDddxQoCnD67y93U8vIbBX9kvB0VNTo3crCt76nAAsOZ8/LHnNe9c90TKtvuRG6mE4d9nDERd5oHi1EXGkkHnubWP0Praq6q474UbblrtyhTi3ISKaSVV8ai50m+1/T8RjUs6/eAOy1aFQOOlUxm8X7B4kp6w7RuTEw6C/L8QPrjuaJ9YtzT5jPwUpZDg8eSsLhTiEVJZi2tkYowi2sR0tvuBa/yxzd5alkDqsmu0QQFYGV8RL7QJGZLOObc9+ny5em3O2ObrWp0xG378/msKrREQprLs/jgdh3pKNcbkqAeh+Pz6nFGrbl3JRmgUjV8TTKmlapzEW1aS+oKbWvvz9PyxIy1HT992ymFJu8ZQnnfEi0ocgqZCdIQeJ2sNwvrYE2vjXoWpfjl8verlJs7oR4hzAU6PMoR3K6LL0AJO1/O+/w9MbVtTL4YVp0WiJKEeAOzei40zmPi3ieGOegUgUNLVW7llBv38ga2oFtlU7G1yCLY2+I8UqWFE2FkSHf5Eb//ABEbGNzy2C5u5tad7c+s1xqDRDRuP/kRzk7cuavupycNWQO0uqNFACMo0+8bC3K1gfSwvjzplfuEAbqwH90omTJ4GhYtTltID6pFuFW5AO/S+2469MZuuofZKrGr4rIy+OIbIgFu7VhGdrC9vI2FuvlzwOp5E5UBqeKC87y24kTTogXYK4Oq+9+Z28renrjboVIg81bpvVZcZcJ0OcZXPT1tOs8DkmzDcHzB5g+ox1VjeVaFUPpmCpnsp3DDTqNkH9/SuTpOEpuC+0eOmkdpYG1SwTNzZPX+Ycj8vPHszLxt/YF4EHYjx/BXilzwt/CeLBhMtdlp6jx8RseuCuhuGvElKTbZ0Nzv94Y84r4eV6Q3NH3J19p9qePtLYNGO+f2t5JIg6hy1bKTr1b94u4ciwY2cDxnHqFqJpyfD6D9C8ZuZaR5Ko4KinWb2UCaXW4ZVI8NwOvri9HNZ5ITqWVzGgj7yEMNIUbWN+ZHr+mET1TcpSUl4mjKgyKzAhb7/AEe7fywjmZSAhOZDFEWLB1qwfCnvIAL3v164YeKKDyTBFBuUIWRgV0hSCo87nBYKRGlP4WNPGQz94dlZipJAwKQ2wmrVDajdZ79bFbflgtRTafFc/cgLYvqisczhJLFsJJZ5YSSzf54SS2BtthJK5ew7tC4g4byLOMtyrOKqhpZahJZadSrROShXUUYEXslrjexI5bYweJsktd5roeFkOa5nvVhS8YVNQqiryvJ8wZNNmny2LU1hbxG12v1PM2G4xiCq9uxW6abSZU5kXatW8MmQZZFmGSqd9GTZxUU8dwCFBiuVZPMMDta1rbl2ocO8Afqo3UWnmn+a9peXcW0RhzqjY1BILZimW0a1oI3sssKRhgzE37xWIAsGINlgeKbxBEK5QrV7Y9x0joSYQdxD2f5JxLQTLQ8b0ULaxoir6J0d1uBfwsbc76bXsrbbWNy1NOi4KK8qvuGQGwfihfJPst19XmMZl4y4f8AZGAYT0jzksSxXReSJFDXAFmO+oWvfGlX4lFKaDNR6Egfysez4fTNf/1dUsaObWlx/A+fkre4X+z1TUFFG2X/ALVzeLks9LURvGT1A7u/kdscHeXHFahLn0QPISfqV61w2vwa1YKdKvt/qJH1AUrX9nOZUaFI2qKeU8o6mPxE/A6W/PHOOvOycBcMj4j8j6Lt6FxRqiaLw7yIKBJaeqpc8pKWrkMUjThdJU2J52ufQciBjbDmPouewSIU5qO1BvVXPwzmEP7P7uDXVnQFL76V2239D5X9ccJdU3B8vwgqU3apOEdZHPTh9VRSxReIFBHK7Em4uGPhI+Iva/PYYwq2oEFpkc5wPv8AZZ9dtQtw76KP4lyetkhaPhXPKfgSqUJ3U8lC1SxRJDKqLUajLD4mcXSxszAMupr+u2H/ANTeNNsW2F0e0Y0QCMOiIic6sRE9B0C8kvfQyhXuTcsbJJkiRE8zpwPhCddjHZdnXB+WV5yfiCnzKurJXrq/VTGFw5Fj3WotqUBiFtpIFxtjgOMcXdxqvreHAgR7Ukx1PMnnuugsrKlw2m2ncMwCYjbPhyRHXTZnS1Dx1M4qZUvqXWUKefhY3vtba/PHINZRPsiPn8wuwpdmWhzRASX+lNRGhMrO7AaFqNXjj6Bde/hG9gOR+mJzR1R+/v0TOt2nLf4+HVD3EHFUxDiMd5tdFbpbpb49evzxdtrRuCcKzSogRK5W+0fnUctDlFOF7qVe9ldL3K3t+Zucevei9Ah9R5MjAWB6TVhQtWsnJkqsuzbjGfJapItZRAbxt0+Bx1XFLFtduqM81y3o/wASLG+r1PZ5fj8K504goc3mjro09nzDnMQLme2w38hcmw9McP6vVotNEmW8vBd1Qtf/AFTLimcZlFuX8aCOEh6dY9Q2ZRcH+zjFq2EnBldR6uHZBlaScVGQnQ5t1Una3w64cWYG4SFLTiFvBnDSyHYqeXMeHDOoaQrTWYSlRV3Ukk25m55YBrMwFcptVFdr/GI4hzuDLKaUmkoyVIHJpSfEfWw8N/jj0Pgtl6tRNZ47zvpy+O/wXlvpLfi6uW2tI91m/wD8ufw2+KKezPK6eCBZAFEr23P3cY3Far3HTyW9w+m2lTwF03wTTwSCCn06Q90BjIDB7W2/P+mPK74uBLirNVx0khWnwyabL8o0JTLVSNZvatFzM4IF9wCD4QQFF7i52wLrmk1pOkDly/396wKrH1Hy50eH7j+ErUcS5bNO0E+oyqwUrKFZx1VVuOZsOjHc74i7dpIcWkjxg7fvNTMtKzW62HHht9fx5KE7QIKGXKBNSwinDGNirMHYXDiwIJ3/AHeplG3u8rnGi5lAMFSi0CY5z++PuWhw2pWFYsqmYnwHI8/OBzXLnaVStJAysDaxUNffby8j/QY63hbwHYXYVBrpOahfhfjY1VMEaTTWU7BJ9Jsb9H+f57Y1ruw0ukDunb8e5ZNG5Zctcw5e3DvsfI/wj/LeL4ZCrysBMt9MoNib9L9B1t6Y52pZObIbt0UNWiDkJ7Jxr3Q7t5Syqdg4LjmLXsL+XniAWJOQPsoDTBzCSk4yWWLSzeJdrHkd+fr+lsELEtOETacINzPi41FY0UcoMMV7sAGOvy32Xa4t1v0xu0rLQzURk/T75U7SJUTHxEeLOJaLIUc93Ld5yp5RKPEL9L7L6aji4bX1O3ddEZG3mdvhuoKl6x1dtmw94gk+DR+ZA966AySNIyyPGjQKoijjUaCAyWLA2OwJFrDYCwG18ec13E557n4/s/NR1QTJCKKPPRBkiT12sSQ1TnVJYIy6zZEAu1raQBfYb4z6lFr6pFMbgbbbZnxndUW0XOfpHRNIe0PNmrCaCOOiQEFJZo1nlJ53swKrzJ5G9+uJqdrTtxqJk/AD7lXXWFNzYqCfl9FscwzOukaWpzOsMrjx/wCItfn5W8+WI3lkl2kH3KYW1FjQAwJhmEVdEjWq5ZEPvKziQH4g3vyH0wdN1Nx9kT8FGaVM7CEH5vJLT0Rp5Y3cRj/XBi3zYHf57jG3RDXv1g78v3ChcwtMhc+dplNEhSsUWNO7SIfINcN8uRx6Vwmo7NM/5CD7tlyfH6DH24rRlhn8o/4Zl/wMT2uwRGt5mwOOfu298+9FRl1KPD7KQ+1yrUvG+VTB3VJ4K3ujKoGmMVRItp20nWxUdBt0ufTuHw+gPd/+IXi10SHCQqXoKuGqqFd5yHsAujmGAsOf5Y0j5KhlOxI3fyKkhkjVy+tLqTtbcHlgSBKkEwlG7rvSoTkBuGIANr3sNsNumjSkaiaOepjdroQukFCbW58vP88EBiEJxlK04YFZN5Cb6dZvfbfAwjmN09oK4ySlHsIQpF1JJbAEEJxCdftGrXYS6gNgSE3xJAQLmknF9Uli2+Eks88JJevhJL18JJe5YSSOeyqYjMcwhHNqcP8A8Lgf9WMniLZpA+K2eFuiqW9QrMiI0LbYHbfff43xzThldP4LfxAA3N+e/XAYOyMeKVF1IB2BG1uX/fAJwsqWGljci9i3MA4WEy3TRqFkRz5uoB/rywBwpBhO6TM6qlm72Gpnjkt76yMG25Dny+OAcBOyffCLct7WuJqankgkzaqngmASRGqHT0BvcqD01aeRNwcRvY2oCx+xEHnjyUrP7bm1Gt7wMjz81aFbk+TdpmRrmNC7JJG+0yoVlo5RzjdWAJAJI8j90483e274HX7GsJaf/Fw6g9fmOeF7Hw7iVvxKmH0zkbjmD+P3dC/Duc1GUZhJldfaKtpzZ/vKQfdZT1VhuD+t8XLqgytTFallrv2D4hdE5rarZG6sLJ+KYMrjknlcSOFZyQRpAAJYbjyHpjnX2Trh7WAYJA8d1j3jNFJ1U4DQT8kOTdvWSVjMxyquYtsGq4b7eLc93MeZ03G1gGsSbY79nAOGUxA1fH/+q8q/rXE4GktH/aiPIvtG5XlkNOsLLSyIpLaoalCpt9z9xIDf3SCw2sbt7uKVT0W4bUf2ge8Hwc38BRv4pd1P+Y1jp8D+UrXdvWS5yzio9nhnKaVqe/7q7DdQ+uNQALaduZsbqoOIB6KWn+Fd3vaPsZKsUuM3ND/2QR0Dj8p2z4qNm7R8qqJHjgzSOqvqGrVTHu1CgggicllPK6BjddlIsS59FmDLKw94I+K0W+kbTGu2I8nD8JpU5pT161BpK+Oq7uy6pKadbjTqLqVRtS2t7t7Hnbe0jPR6oCNNRsef5/KvM9KLRompSePcD91RHab2O8Sca8Wyz/tLIqLLdKRI9VmaxsoWNXcFGAYMNY1A7rcX23x6Bw1reHWgGkufzAjn4zHguP4zfs4vewX6KQwC4Gcb4AO/mneQfZpppoWiqOI6LvFZY7UksMpDE7Bhr1ITv4WAIsCbAjGbc8Uv2HUy0cf3wW/bU+CMp6PWRPlH1U1H2BZ/l1IWyupkrolu92jWSMre2pXQg21XXce8LeWMp3EXVHRWt3DyB+Ygj5rftX2tJumndNPSSP4+6h8zpczyCyZvRTUEmrQZWRhDIRbqQLGxGxsdx54drGVc0jPhzHuXW29y1oGtwzzBBB+f1TdMzvbTUQX6WkFvzwxpdQVfPeGE7/0lTLAJqmphK38IBF/gMQ+qGt3WNKF9SnQbNZ0D9+Kb1tdxDxbTNDk0IokcEGqrLqwHXQgF7+rW9B1xLTpWlk7VcHURyH3P2CoXNW9u6XZ2IFMH/J0z7mjbzJ9yFZvs88S6YpqOooayQG5heQwtz6FgVPzIxrj0lspLajXNHWJ+mfkuAuPRS9oubUpVGvjkZb+Z+SJeHKTMeGKxaDN6OXL6obmKdfeH8SkbMPUE4y7p1K7Z2tu7U3qPoeh811FmHsaGVBBV48GZm0TK6lWAsLAWJFuhv6Y8/vqQIgq85k7qxos8dYxEzkqtgCygE+jEbn545t9HVlVmsbJIGVivz1XRu7g7qTnrF2I26Ak7H188FTo5RsoxuZQvnGbSvAiuATcpGge6LqN20p92+1wLDyxq0qcnfAWlbgB5xH36KqON0EtK42JUElgNif72tjrrAw5ajstIK5t4mq6nh/NXraZzDMp6i4IPQjqD5Y9RtGMuaQpvEheN8fua/C7j12g7S4fOeR6g9ETcOcfisiiWqcUUzjxbnR9enz+GMu64boJNMagPit3hfpJQvGN9a/tvO/8Ap+PL3orj4hR4VZa6J06FZgR+eMY2xBgsPwXVsrUHiWPaR5j8phX8R0yHXLXwxHlfvB+XXFilavOGsJ9ygq3VrRGqpVaPeELZ7xuvcNFRv7XLyDKCEHxNvyxr29gdQdUwPmuX4hx+nTpltn33csHSPM/hbdh1dUQcZVtRXC8k0AiEgttdr/LkB9MLj9NjrNrKWwM/JYPogbqrf16t3lxaBPvn3LqvLs8aCLVqVQAosTsPX4bfhjx+rbhxhenupyYSZzw5jMkiO3cKT3KWsFBO5+J5/TBer9m0g78/3wVilQ0jKJ+HaNqjWFPJNVyLbAXLXPIbHf0OMy4OQAmrVGsGUcR8O1FGAJ6eZCSUAZCTr6gWFibb252H1za1Kq3dqyPW2VCdJCZ5lTJTQD3d1OkkbMPP4fD8MVqZJcQeSkb39lX+exqySaV3Hn/ljorckESpy3CojteyYNkGZ1cALMkLNIgHS25+Ix6HwSv/AH6dN3MhczxunFnWcBs0/RSHBk5myqILcnuEtb/YFrYjv2xVPmfqsiwdqoieg+iKvtUxRT5nwxXAQO1bBPOXjLESq0dGwMlydLdBb7nd8zcn0DhTtVsD4D6R9l5LfsLamnoSPgVSC07hIyaeRkBAMi2Fl5kj4eeNYGVl+a2jlXvQVRuvM7+RueuHIKeZWGCgCSEqhLG5brt9MKUisACGNiFAkJILnlY/0/XCHVMd4WJqIaIpDbu9wBq3+f8AlhwRsmIJXoDJFAzFtrtsB42NtsOmhZWsjZQXDBzu15G59cDlPhUTfli8qSzhJL2EkvWwkl6179cJJZ04SSK+zSbuuJlS4/e08q3Pour/AKcUL5uqgfCFo2DtNwPFW0j6Vv5bHb1xyZGcLrgVtbvLWIJB5DpgI6qTnhb9b29fjgSITAytQps2o7sb/wBnCT7pUKC1wxtfbofngCUUJRCFuCQD5bWwESYRyFlBa4ClPP8A7YAhGiPhnjOu4cqKSWM+0R07ALHqt4LqTGehUhStjuoZtJHLDO0VWmnVaHNPIoml7Kgq0nFrhzCI6rjTIc/ngzHNKWuirqdDGqZfK0RdCblS7xupFyStzcbg6tWsVqdhZUWOp0wQ0mY3jy5/b4LoafpJxakR3muxzH1iFFZpxfHJRzUVBRGlDv8A/m5JWed00ldBPhABvcqFAuBzwPZUaY/tt+Kq3nFr7iJi5qd3/S3Dff195Q+H63PrhtlmiTlbIBZjqKkcyBzwxRCE6jlkAOmQrY81Ym2Bwi8VtIzOTqYlDa+o3387HEUo8blYMMRcHul+OkYacbp8kJZaiVU0pNKABbSrsFt8Afjt64UkDZJKvVVDoI5JnkjCmMLMe8WxFiNLXFiuxBFiBbCFRw2JCEsadxKcpmdUW1tNeUXAcxpqXwhDpOm6nSoUFSLAAC1hiX1qsMB5jzKiNvRJ1aB8FIQ8YZtE0kkWYzxvNq70wyyRai3M2R1sT106d8H65WHOfMD8KP1OhJIbE9JT4doGbzztLM0FS66jaSBXDErp8etX7ywvbXfmfQAheO5gKQUdPsPcPJxTd84ojPDUtw7kclQXDiSTLKZmWygKo/cAEC1/EGvsGva+JfX6hGkgR7/ynayq1+ttVwd1nPx3+afRccimKmmyjL6WJUVBCsEfdtYAAse7DE6tyQRcHTsADim91Cp/zKQJ9/5Wi2/4nTwLt8eadL2jzL4jRoD4tXdRo9rjbTfRyY38V/D4dj4jB6vYOMOoCPM/lTHinFQMXbvgD9knm/HNNn9OKTMcuSrp437xUNMgYErYFHWRTGb89NxY2354OhRsbd5qU6Wkn/qPzGx8J80H9S4pjVcE/wDaPwk8u4uoMtRVp8niBLm7XmFkt7pAqfEb8z4P0wb6djV9umT7/wCEjxXi/wD/ALA/8R+FOU/anTqIxJQGGJdCuYi0jtYEta84sTsouWAG/PFF3DeGOJmkf/L+E7eK8Ume1b/4hJntUQQFRQRpKygA/vgFuu5J7wlrNsAAt1JvYgYR4Xwx2dDviPwpRxrigOXsP/akT2kUVWsaTZeEUqDKIJnFmN9Xd3BJA2trPiN7lRtgxwzhwMgO+IUzeO8VYQQ5n/ioquznI8wpwlVS1gDW/wBVVOpXz1HuGuB/Lz6Bd8XKNtZUzLNXvj8qT/ibjEkf2/gfyhHPOzrs64kniFVLmtCpCNJK+atHGp1HUBqy0lrKBbVpBJ2Itc9La3lKhIYfiP8A+y5Xide+4rAuABH+nH1WtF2admeWCQKM6rFYBVcZrTzMpLAb6KTQQqgnwub3Fuowri9BMaojoD+UNg+tZju0Wu66jJ8sQEvN2ZdmdUEMNTn9LKyg6mjp5kBJPLwxsdIsT4RfcLcixh9dYMaz8FpMv65dL7ZnuMfZZ/8Au+ZZWzLNkFbLnWWyqxiqZaYUjsVNpAUZyfCeo6EEhdxjJuuLVKTxTbJXb8MFnd24r1qQYZIOZ8swN03zjscpskpWRwpqRoYhSCuhgLEnodwLbkG97WxSbxWo52Vv07S2eO6zGeXRC2Q5DHknE6SFAO8QoLbDULEfHkcX7i4Ne3Len6VHbWFO1uu1aNxH3CtPJQmZUNZHU5xR5ZFFpjDVcm8uoG6xgbsQFOw8xfGHRs21n6toyg4vxRvCzTJpl+qdoER1lEFFTZLRzFTxXQtpW5aGklZBboGvueVgAb8gedpncJbUE9oP33LGPpgwCDav+LUb8LdoHD/DUlPK+Yd6iSqxSOkbXZXQnQddvEBtqNrlrb3xnO9HXl4cKzffP4WRdeklO4aQKDwc/wClEcPa3wZHRR0y5jNEsMbRMBCEVQCWKqxLEXNgukHUdQBN7Bqvo5Vqkk125nrz+OyzxxwNdPYO/wBvhKjcz7SODaumldM1rFlkcgidI5Gc6Q2rZgzA6rBjuSCBciwqj0TfIcKw/fh78K3T9JC0waBgdMflB9TnnD1c9hn9PBGT42kiuItixDkPsQLLte7eEeLbGhS9HHtEmq35/j3q0fSxnK3d8QhLM6LJc1p6kDiOk0m4VJKUqXuL7Aybix3LabHw7kEY2KPCjSLSH/vwwq1X0lp1AQbd0eY/CY8OcF5Rw7RlW40yqQ7IsRh7tolWMFdZaUDcWF1LWPh3bwnUurIXLg7VHVc/bcTFuC0UjHLISX2hM/y2fhbgKmy3NYcyq8taqiaro0YRmIJHoF2v47FCwDEAg35i25wlumk6nOBH3XIcWdqqdpEaiSqbqBPVK7Sv4Qmoa2uPh878sbg2WFzSVP8AuXcaAW0kG55i1/phYTEY2SbzrrCt4umkCxDcrbc8FkpahuFnSNb3bulHhY6fdHUnDHCf2gnNS3talGjFoh15g8unngZIwiAWqOsUmoMH0OACCOfp588FpxCaZMqQaAMxJo1BJuQbXwEFLurn+pKmYlfnbzxoqikhYYSS8CDzwkl648sJJeDYSSyitI4A5nCSRFwWRS8U5WwNh7QsbN0Grwn/AOrENduqk4eBVi2dprMPireQMQbjS+17G+/zxxvTK7fcFLKLE2HMEja3/fEWyJbkXBuCANttxgThEFubEtYb2HIEYFPC2AuLj5WN9sCnWdg197g4BSBbgi522Ftj1wB2RQTslF2B2DX5lRt8MCnnKUtdgQdx54A7Iz4pRQT4RYNe1xgD1T7BKnrzv587fTEUIxEZWygXN7EE8utvn88MSlMJeO3iAO3lyxGfBSjaClQqsqeIhgLc7XxGSjiFsFBFzsN/TAElOlTCAN9yNuf4YYklJbIGsBexJvcLe22BlOt1VhytY7Xt+uGT+C1aJmJBAU9L+mHJhMlo107A6G8xzw0p4W2pmEYIsANtO42vhTCaFhkDk367cvUW/LDyltleEahQDpNtyTfl/lhJL0oIbxOT0sTt/frhksQvaSGLH724vz+eHS81grpYEWB+F/O++HlMvbsR+7YbXuRYfPDpo5rRkLam3IHnvv8A1/pgpRFYKKwIFgfL9cPKZasLMRuo8wbf2MKcJvILAkuRtqJ8QF98On8EprVoxbYDoThk0QUWcD8aDh//AAU89XDSSzGRZKSVg0UjBULeEX02UA23HMC18VrizbdAaX6Xt2J2PgfHx9yvW1/VsjGnUw7jE/vgizOZcpzOkirGzGmSCtlTeeSKKUyOjMqm4DqxUM2k25E7m+Mn+mcUoyCzVHMGR57/ADXWWvpHw4xNWDtBBQRxRwBT0U9I9bWy5dC2h0VqdjOVJexVbAAfu28TlQLjzx0NnRuGtLrloaPMfSSfJK89KLYN02rS93wb8Tn4KDadUhiVYO5jEdjGX1kMSSdTADWdwCbAHSLAAAYmqPb7FP2Qfef3l+VxlxdV76r6xcHvERA2A6D880gXDeELcnYbDl/TliOSFBCRkYFRdV08wdOJJQgAErRwFBbTblbbYYcdEMYWpiHlZTe3pgwhMLwjGtXIAI2DdB0+mDBIwhIG8LRgGU2A9Lr/AH64JpBSIBW8cRUArqFrbAWwaj2wojtEUCPh6FmGruqiRi45XkRACfgnw3x0fCx/befH7LmeKEmo1qFXanDlb2OqzEmxO2/pYY3MFsRlYWQZJWgMmoozBVJFyBy+GGO0pzkwl6fL1HgkYqA5BbuzZfI36D54YulIMgJWLuEmaKRTUI5I7tG5nr0w25R4aJW1TWLmFLHLFAwYqFZgbFdO1zbkPXDxG6EEO2TSSnIWNLgKoJIuCOdySfpgk2Eqgj0Lrd2e25EnM4HSU8hUVoJ54vqivaBzucJJe0b88JJeKeuEkvBR5nCSSsaaAtjYt19MJJOYaj2WeGRdhG6vt6EH9MI5EImnSZV6yWFVMNwuo2+F9vwxxLmwY6LumnUAQtxc2AI1ed8QnmpRgrINla/IHfbAFECOS31iRiG8juN7YbZOFuqqwuSotvy54EotglEYm1yRtvYdfniMgo5hb96W0gL8+eBIhFzwslFsSAR8Tvf0wGU4EpUSEkbg35WIv8fzwCkiVsADqJII23Yb/XEbpThLKrL4TcL/ACm/XlgDCeeaVQXY2a/x2+eIyjGMBOB7liTcHkR6YjOFIICV0awotyB2I3P9/wBMRzui2XgAjAna/O2BJTjqloyoI3JYchYg4DKKISga/g8Ow87j/tgfFOei3uoOwJNvLc/H8MDndKCtkFwRvpuLhR6YdLxKzp1dVuOl/wBcLUnMjCWsGjW62AsR4ht5bYacwmhauu53K3uLW3w+romWpAuOZ1cxf+7YeUltMu4YAk/M2HxwplKOqTKbEWN77m99/X0wXNPELVgw3Y6SOQvt54IJllhoaNQdwLHy+d+WGQGUme80HqTyFtNsEd0QWAdzcFD56cPKbktVUB2e1vPUDh+UJLZgbG5Fha9hz/DDglNha21hiTfy9flhT0S6LYuoYjYi+23+WGkp4S1NUVFM+uCWWCUWs8Ujo2w2sVO1vTl6YLWWmQmLQ4ZCRYkl3dmaVm1FmZmufMk9fXC1E5KfIwsqr3DcuZ8Qt0/D8cLdMsJG1yBz8iLYUpDZalQRexK3tsTz9MSAofetNKgX0k7bEDfBpeC1a22kEDzI3O+CQkE5KTkIuAN7k2874MBNJWLFzYk35f0we2AhPVLQjxKAtj0HU/354MGUDj0UJx8Mvkzaip6jMY6WSGgiHdh7NZ3kkva3XUMdTw4EW8gbk/hclxJ019M8kLyxUjsWpZIpFVR4mlXdr35eXLGtOcrISHdVFaruCNekHQpFgeXhA6jyw8gFNBI3XpK6qicxtFqjZQraTz3ve3XcYKAUwdjKSb2mGRQz3RiWUBLHyN/LCIS1J6udyowKwtHK+pO8VdgOZ264jLMbqQHmm/tLS1s7Du3JGqR1jttvc2/vlhQYylKyOIaZBp7yFrbXCHfAwUUDqqe/ZlR90BvwxorOWjUFQi3MW3oRhpSSZpJgN48JJYFLK33bfE4dJe9mffVfz23w0pLYi02jnYAYdJejXvHKja4It8sJJXhldSaugo5y1+9pona/n3a3P1vjj7kaajh4rtrZwfSaT0TxbkE3AFwbk88VHK2J5rdFC7gEi3UGwwEmZRJRpbsoOrTfn8t8Akt4/EOfi3JseeGRyvA3YXIW+9+WBTgJUE7AgJy8V9vj/niFSeK8DYm525f388NyRAkpYA3BsSPVrYFGFupKG53H8J5HEZPJOEpGwQAqCSd+mI3eKPfJTiMFgL+K/hLXAIH99MAUTYgBKje9wQTzxAcqQJzGjJGLcw3P5YjJRBKrzGlgdW5JW3xH+eAMBOJJSscRVd9IsRbfEZKkjmtwmojy8hb6H0w2pNErZboRz0dT/lgJlEQAlkjL7kE2+788DqhOV5V1KSW5eu/pth5zCGFsyh1Qk3+74ttv7vthTCfdaiMqw5DcX+v/AG64IFNCwqAHmL9fhhyUgvMDuRpBHmP7thSkRhJiO/Iix2BO2JNSaeZWWTbzHO5HW+FIHNMkmtGPdPPk3n/T+uCS3Wp0g7KGHlbl5/LBIdlqukqum4ub7jmee2CTQtTvzCg9NItf+7YU4TrC6mNlJHoBh5TLAZVYAAkjcHyth5KYLe6qp3ubbG/IYHJEoglgoZCFs3S/Mc/LDSUpg4W4jGkgFSPM/phpSytShLAEMQOa8gf78ueHBS2C1kjvyBta1iLYIHmmz1WmqyWDkEG1r+99MHMpoSbmxtpO+1/64OZ5ptljZiFIubdeo/vrgwZTeS1OkpcrzO5G/wCF8HPJDzSbICSCNYsQfDbmD/XErVEAlo3RWOqM2Uk39MGPBCWwgfj2my6t4wzWOsjU1MEcFMr6yLMkKA9eQ3t8cdlYy22p+X3XE3xa+4ehs5HlkkapH3gYG0jpJzW25I5c8X9RCoQCYSceSUGstDXTwHXYKjBiB1Prhi/BwkWwYlYrMpnhkYU+ZmaO+nxkKb87Wvy9cEHN3KRbCyIs0RXV5IUIJI7xjquOYGHlpTGVqua5pSxBdEDAtu2oFvrfYfDCgFNJC0bPK8ag9FeMbAdACeXrzwg0cin1FR5zOvudMLheg0LsP+HBQ1RyoGQCPbV4j0tviQIYSC/u0ZRZ1PngkKQkLFt7DyGHCFeHiUkix88OnC8ym9vP1wklHA/4l7+Zwky2hGmoU/2cJJW/wjMJeFsqYsFKRtGRvzWRx8ttOOYvWxXd4rrLB2qg3wUza99Vr25jGYQtRbLcSFgBv/DgYRLcvyJ3IPxtiOMolsSXJ21HqepGERCbxSiysTuSR5HfAEI2hY71rnmd8AWhOCZW6yG+4DH7u9uuBI6KSQt0lsTsLfE7frgIhHMJZJCW2FgDe5HpiMjCcHqlhPpGoADzNjfyOIi3kjBkYTiN107c9hsP1xGRlSBw5pxG/psfXYjEDgpRlOoTdTYWNwSOYHliIhEE4RBKNrk3F9rG2ISdKkAmEqqqV1AE39cAUQSwgOk+Eqy9G6HASE+62Ivy6crnAykAByW0YZfEbhgCbAXvhiQiErZlsd7H088NKUSVsPJuV99r4aUMdFgqCRddRH9/pgg5OQsOCQba3NrWvgwUBAWhjOpib8r2I3wQKRTbTuCRt6ed/PB8kPNbFTHcgAAi41G1/wC/PB74TLQkKl1A2N7C/wAsEkkXbSp/hG9+v44JCRCxrDg736lvTpfDgJLQsQbDkRcsN/kP76YKMJlkEbkgMw+P5YUJ14kswB0gchvy+f0woHJMBG6UVitt9W/Revp6YYpSlY+ZIJPUjbY/HAlFMJYWcA6dQv8AA+vywyUdVsSCPCDYdD0+HnhJLSyjqwAPM/X9cOmjktHjLsrAWBG19+mCGyYpJ42G58I25AE/35YlCZaBRctuV5X23HTEoQESktl5ILmwNjbbBBMsBGNrX2NiSdrYkGE0Bb0yLJNHEdNnIUBh1Nha9/XlgzgSoyVVXE0gm4nzmr70gSVszEAiw8ZC7fAY76iyKTW9AF59XfqrOd4lQ8kpnjtJpGlrXcX26A/31xPEKCUvHUkFEV1aSM3QxtYqdrHlywxCQMrWMTOrO51hr2ctu19v7OHgSm5LIhcyFJO802tYODuMPOE/mtygoonV+/EhJHdpHqCnoLYHcpclC1eaTRzl3JXSOUiEDy/TEoCiJ5qMbPnDEAXHnYYl0INS2ni0ttISSL74BH5JoUYMxuQfO22C5IFhIWZufxvh5TLchFkOrnbz54ZFste5BIAPvbfD44Upo5KHkXu6xwf4sOEKzGLVKAD73LDpK0eBpb8Osh/8mrdfgGRG/MNjA4gP7gPguk4a7+0R4okie9wbA8yLWv8A35YyCFtDK2VgLGxHqcARAhEBlbGS6nnby23wMJwVsCN77E9VF8AQUQIjZbFwlhyHS2AwnHgslhuDvthoRHJWA4RgTtfr/TDRO6NKKLNf8T0wBCdKB2u23xYHARG6KZ3SqSgAi5sDy9fpgC1GDOUuk638TG3kQRiEtJUkApxHOtxffy254hc0qUGOSfodhbodgeu+KxAUoKXSbSbHnbniEtRhOo59dgG2F7qd/jiJwRBLRNe2435kdeeInIhsnK2BuefO+IiVKBCUjI5235G34YEyksqtj4TytcHDJ4SsezISpa+25v8AT1wKYjktZY1WQtYlOS3P6YLUmjmUm53Omyny22+nrgx1QlJSDU3MkgdMSgoSAkHYGwIsVFjq54MBCSkibqSCbczYk7/DEglMk2b+W3kSdsSQmSbA6iQBcm12PLBISOSSe8YN+p2sTyxIBOyZa95ovubcwLYcAplkEMAGDeXPDwQlKzfUf4bbm3LDEFKeaVRgpNzcHYev05b4EjmnCWjZRzUMB1seQwJ8Egna2AUkEkDn5j9cAiSgUG12A6bb3+GGnmlC1EYHS2/lhSktJowt9rjle3S+2DBQ+ISLxKhYlduh5X+OJGmd0uabyEAE22sNwLC9+n99cTBB5pN2u3iv8rHf44kATY2Xt1FgCDz574KOqE9E+yqXuczhlkXSsJErabbhAX6/7PPE7G6nNb1IHzVap3WFw6Lnfv3nhUsqBpFDtouDci5/E49G2MLzmdSzEwZdfukHcg9bfTCOEgvLKVmZo3ZwGADjw23vy54bcZTbHCWasLNfRfVfUSfdudiPLDxCeeSWqKpxFThyjAgHUniuN7XvgWgJ3JpNmO5kZnUgbW6nB+CAxModzbM3kbRrNm6X6YlAhRkphFlks0aSBTZgG5eeGQqYYkv4d9tySCMCpVqxNixNyPIYdNCySLAFASfIb4SS8qKyklgpBuFwkgAvT91ospDOPvC+EE3JQNYuirI5nY4NAtWP71SOhvhJKxOAKi8WaQFrreGYC3+2h/NcY/EW91pW5wx0FwRXHsoIOnlc2vf4YxCt+eq33CkHr6YaEUgbleutrgkA9MBpynlZjlvbaxthiE4K2E1xZth6HEZHRGCsrMCCAeXnzJ/u2B0p5WyuNze9+eGIKeQsmUKoBPO/ww2kotQCyku62IsBt6+m/LAESilbCQHkWsOhAOG0opG4KVWcKLtYm3PniMsKMOgJaOqCC63Bva3TEbqcow8Sn0NaVRtbWJGxP44gNNSh0BLx1QLXElza+q24/vliB1NSNeNincVaFUbgnzHO3lbEDqaka/mnsVWvNdr7kLz/AAxAaZUkjZPFqI7A3sw6/lbFcsMqQEJwJ4+V7i3Ty/piItKMEJa6AA6uW9h5YjglFhLKSzAaha9+QNsBHNJenIYlT4QOnz54cJJu+ld/EANxY33+GJR0QpIuoB0jfyB5YkAPNBgpvPIFuT7xPPbbErQVGYTdpDp2It62NvO+Jgm2SJlFyD02IJ574OEPiEiXBN9rHrf8Pw5YlHRMk2cAjf8A4eQGJQExWDMVblcDqev9MFpCAkrQTFTpAIPkN/nhFqQKUWW5u2ynfnz3/v8AHDQkJKVEwG9zbzJIscCRyTieaXhk/dhrWHQ2tex88QkIwnsK6fDq09Lja3liM5RdE8jQouoA+Vj5/L0xCTGFJ4pVYrsbFW2Gm39MDOUojdIaUKhwwIJvfnfyN8TAkYKAjEpCRGABFr3vdhf6YkBTJswXqAymx25evwxM2UB8E3IUdNmO9unx88TBR4CxrXUdy5v7p2JH+eJBPJIpDNKw5dw9nVXY64aCfSOd2ZdCj5l7efLGjZt13DGnr9FnXr9FB7h0VIVphSpKK1kXpG2x+Pry5Y7obLgCcwm00LXUWJjuDsLAHywQTQlIgI2KoHup2Y8v89wcCd0QhbrGC7hlBUrvYWsR/d8JMMLRgrLuNtQYtve3rgk26Z1cwLG3iW+19tvPDhMShitP7wfPEqhUhTVyrTxLtsgHvnywMJKSkRUsY217b323wMqVauuoctieWHSWyOFYHTe3NehwySybhdQXa5IBG2EkkJAGN02Zdjth0O6h80TRUj1AIwQQlNpDax54dMjbs/mYZxURqT++pH2/2Sr/AKHGffNBo56rT4e6K0dQjfUVvcHnc23P0xz2CullY12BA2NvPDJyV4zixAA5/C2GhFMrMcgOgnffbyPwwyIZWWk0NY3AvffDQiWDKbWABB2IBw0JSsCZTblfn54bSlKz7QfD59AP64UTukHdFn2gHfSOflb62+WG0otS1apsLm9+WFplNJWwqrFQDe/LphtCWrC8KwFjYCwH974Ds0WvCytcCPF0HTr/AJYXZdE/adFuMzsbBiRfr0wJoyjFWd0smbquxAsORP5YhNDopBWT6HPQoAuBbbmDbED7VSivzT2LPyLamVlB+9tbFd1qphX6qQjzxdIFxq26+vpiqbYhTCsCn8OcqyBQy7m+43HPb+/IYrOoEclMKvinUWZRu6nwlhfdhuL+WITRIHgj7QJeWsXXYOCNiLkYDszzCcukJFqzSfeDm3Xp5XwYpoHO6JF60MSbA7WuDviUMQl3IpF6sqzed7WOxHLb1xKGSgOEg85N12uOoFicSBiEu6pA1vMrc/E4lDOqAuSHtoFwTYny/riUMKaQUmauwNm1XPIb4MM6pTyXmqTuGU7c7jrh9CGSte/DA7bc7gf354LSmDjCUjqLWN+vUX/74jc1PPJKxykKdyWO1rW/vfAlqKTGE9imHIm5vY2/PERapAVIU7o5AuC1jy67+mKzgQjCk6ZSPDYC53sScVnKUQU7MWtVAF0Pn+mIpRbJOWPwm4sB0uOfx8tsE0zshiNkylIPIlulidvW+LA2UZTGaQTMdWygg3Atfp9cWm4QTCRYqpIuDbfYbYmCjJ6LVWBCguAPTkMStk7IDB2UHx7MKTgbMtAGqpmp6XZrc5BIb9eUJxs8LaDcA9AfwsXijy22I6wqdj98l2F9jsORGOw3XFjKXFW8tzM1hfRouPCf67YUZRTAWhZWcCAgaep5/wB74dMVqZ07rRcaRsL8z/e+FElCCkmn0x356uY5g2O98KJTzKZVxLRDSQwAsbdPS2DbCAiBhD9ftIDawBxIgKb2boxthJkV6EexDAkjmR+OI1JKzGq2CFlHUk+flhJHCSqKxYpSgjV7W3BP6YQSkpenYVS7bE/xbWOEQnlIt+6XwqrbmxIvfCTKHzTxSIxHTBhAcpmV/rth0kX8FvpzvLzqXTITCbj+JGX87Yq3Qmi4eCuWhis0o0UnT8Bsb45zC6iZXrsdw1/XywsJ5WDJe1xpI6gYGEXJa2BtcA2PM7n1wiOiQOcrbUqlibel+mBgpwQtO8Fjc29ANh8MPCecLHeXFr6R8OWFpTSd1hZOt9vInCiQlMLfvPH4lFrfPAwU+rOV4SgIZGYqnNmYnSPLfCjMBI4Ep1kWSZpxXU9xkeW12dz8u6yymkqW+fdqbYgr16No3VcPDB/1ED6kKQNc7YKx8m+yr2s56qMvBdVl6sLh81qYKP6q76v+XHPVfSng1EkesB3/AMQ530EfNLs3HOyPMl+wP2gZjZ8yzfIMpU76RJPWOP8AgjVf+bGPV9N7Fv8AyaNR3ua0fMk/JF2fVyMKT/w8KgBDWccyG/SjygDf01yn8cY9X05qg/27Qe95+zUYZTjJKm4P/D2yJRpm4o4gke3vJDSRgn/gb88Uj6b8RLsWrB73n7hPop7gpcf+Hpw6b24k4hH816b8hFbBD0x4qf8A2KYH/ef/APpP/ZG8z5pJ/wDw/cmD+HifPUQj3ilM5+Nu7At88N/xlxEDvW7D/wCQ+6I9iRifioyt+wE0a3oON65Stwoq8pie3xKSr+WJW+mtb/3LMHyeR9WlIaOqEs1+xNx7l7k5fnmQ5j0CTCejc7dLq6/ji/T9M7B5ivQez/xd9C0/JSgYw5BOb9gXapw4Xafg+sr447FpMolirgPlGxfl/Jjao8e4NcmG3Ab4OBb9RHzTgvGd0BVOczUFe9HVxzUVWNjSVUbRSj4o4DfhjdbQbUZ2jCHDqMj4jCLtjsVk56bWLAX623wPq4T9tO63bNw33wfNbi2EKEJGrK1OZ69g6sdxa/TfC7GEwqRzWPbg25HxA5DBdmnD4C0etNvetuLX5YIMTF3RaGrsb3Nr333AwWhMXqy+x/s1y3tIhzT9pZlVZMKRljgq4whjkmeO6xtqB3UKz6QAWBtqFjincV/Vi0uGHefLy6/ZEXGJaJP2SXaN2I572fKJ+9izijEccjzUUZRog17EpqJIuCCRyPzOBbe27ninq0uPXmoxVa4SPFVwJztdtxYbYv6VJqW6VOlgdZsep3+GB0p9UpwK4kAEi3LyJwAYkHAJWCuKgAC3yJHxwJZzUgfGE/hzIarA2v5jn5/3fEBpSEbanVTeX5orEKzDfZbD9fLGdUpkKyxwgQiKkn75b6Rq23NyfkOuKD8FTjO61qJQqHfTuTYi3/fEjcpnbKIqnuWI07X90fn/AGPxxcblVzuo9zYnqRzN+WLLVGTlIsxBHj5G9r7X88TRCExCS7wiygKwNtjc/hicCFETIQv2nZjHFw9ldO9v39XLLYjbwQ2Hx3lx0PCGHW93QALnOMPGhjTzVYvPGEHcktpbc6dmx0wB5rlSeiRYx1CMpQeLYncMo+I54fZMQsPZ2KItlHNVJO4HPCTpTWulQVBFj723wwyaFo0ZW9wDtpAXy88JOk5m1qdW5uLDBJjuoWuhDK1jvzsMSCeaiKi/754dMjGMqg7wWDm4AXcHEam2C3iVJIxfSCeYvuMJNySU0LRMpMQdOYcjfCTrYSSJ4tAQnkGFrjzw+6FJNchb31C/vE7YY4S5q3+xTsDyTjZYeIeL89jo+HIXAfK6YsuZVYDG/c/cCkroLsfDqJsCF1YHEuNUuGiC0ud0/laVPh730xWPsmfkugMy+yH2GcacO5qnBNdn1DxDWUzGgjzfMleOjqAWZNS9yNcR8KMQzEL4l3BB50+l1FhaavdBImeXwx5J2WLqjS5vJcY55wRxB2UcbDI+I6CTK80oaiGZ4nKsrISGWRHBIdGXcMpIIx2tteW/EKHbWz9TTIkeH0PgqOh9GqA7BCLKu0M86E6grlfobYxAMLqpTcvchht+GChKQkzLbZr2vvY4aE89VnvBcgk3/DCjCQMrYSg2IYAW6YYhKeS0Zywudj13woT+K17wfMdSMKEpU9wZwLxD2hZm+XcN5RU5vVR7yiEARQDo0srEJEPVyPS+M6+4ha8Op9reVAweO58hkn3AqRjHVD3Qr+4R+x3R0ZSXjfiTu2A1NlvD4FxYXOuplXyB/wBXEfRuuPObz02c+WcOoz/1P+zQfq4eIWlTsi4S5XdwT2b9kHDFVBBlPBsVfmOkOKnM6N6+a1tm1TllW/SyqDvbkccVecU45eNJrXBDejSGj4Ngn4nxVoWZYNWArmp+JMqgpY6dUqEgI8MUMBESgeiDSvpyGOeFtqcXvEnx3+JyoTSqHmFL5NWPX1TilhiWFALgjSxJ3Bt8+uLjGPLoCrPa1jcojjijp4y08iNfoen9k4thjWCXFVzLjDQm2YzJTQSNGpc21bISRbyA3J9MMWtEwjYCSAUxGbTNGAsVRpup1dyS5F9wABzta4wwMdfhlSaBzKd0/EiBLvBLIt7XWFx1sfu/3fE7awEyJ9x/CjdQ8R8U7j4myslhLI0GltJaWJlF/QkWOLjK1ucuMe5ROo1OWfepKCWhr0UwSwyq1iCrBgR8sXQy2qDukKuRUZ7QWsmTwKt/ePmx3GK77GkJLcynFV3NQ68PU81RM1nkdyG8S3EYtaw22vYm2+5J2vbGb6kHO7oU5qkAAqH4s7Ncp4uomo86y2izqlII7jMqdahAPTWDp/3SMNTt61m/XbPLHdQSPpuibWxlc59ov2EOFc4Es3DNbWcJVdriJWNZRE+Ridu8T/cksP4cdXZ+lV/bENumiq3r7LviBB97feikOGMLlbtK+z1x/wBk0c1TmmUnMMni3bOMnJqKZR5yCwki/wB9QP5sd9w/jvDuJkMpv0vP+LsH3cne4z4ICHMzuqzTMRKqkNqU7jTuG9R0Pyx0RpRhC2pKUFfqOx8XTe9r4DswpO0lKw1ck00cSKZZGYIipclmOwA+ZGALAASdkbXlxgbpSoM2XzyU9RBJT1MTaJIZFKvGdtiCLg2P4jph9AORskXFphwgq9/sj1WXVvGGaLW5jNDJSZfJPT0Us0cdJMjywLMSxbUJRpisqqdQHMabHI4pDLcF5AaDz8j/ACm1E4YN/ir57R+IKKX9t5jUyyvFBBHDBHBC8jSDQdWlQt28b/AAbHnjzCvUF/dN7Ij3mBg9Tsgcw02tZ5krhmtq0FZUC1gsjAEi1/ER+mPXabCWN8h9FPqwAkY64Kb6SRuTc74kNMo9aUFaNVyPoPzwOgpNcs/tFI1LEpGq3Jdjbbz8sIUyeSRfBV39lH2ZOKe0KODMczJ4bySYLIkk6aqudOhjhPuAjk0lrg3CkY43iXpHa2bjRof3KgwY9kHxPM9QPiFMSGZf8F0HF9lvgXh+hMsmU5hmscY/eyS19XLOPNwkJAIA3Kql7AkXtY8p/W7+u6A8DwDWgfEg/MoG13OOkYTat+zHw9mGXR1GUV9flDSLrhkjlatp2U8mCS2Yjboynnh2cYuWuIrNDh5QfiJHyU3rj2OIcFTfaJ2Y8UdntJJmFfSx1+SqQWzXLyWhjuRbvUIDw3Nt2Gm+2o46Wyu7e8Iaww7od/dyP18FZFwyptv0VcPP3j7gauoNxbyxuhkJtc4lIu1m3Ivz3H6/PBhMSkZWQDcXAG2344lamJ5rRHLXN1A5c/pieDyUBgIG7W6wS1GQUWxMVLJUnmLmWWw3+EPTHU8JZFJ7up+i5Pi9TVWa3oPqq/VbMWVtVzsoG+NtYKzbUDdrMeV+f9+uEkthKqsyHwkG+oDf4YSS0Ku8dy2w5nr9cJIJS63iAUHbf7pv5YSJN2uxsRqB332OHQpvNEsmoId+gO++H2TESmLZeuo3G98HKCFNmExBWYXTmCcAjlOtWkKbKWNtybfLC3SOEhUIUsLjzG/TywoSlYu0rBQysAALnYfAYbOyS2kiVlI03b3dQPI9fjhJFK5NxJX8JZzDmUZeWIfu54wT4kvc7HqDv5Yo31ky+omk/fkehUtKqaJncHddbdhnaLT5tUQ1UcwME3gSVdiv8p8j5/8AbHhfpBwt9AFhGRn/AGV0VSBLDhPPtg9kR414Mj41yyLXmuQwslcoF2loCSxceZhclv8AYkc/cxZ9BOMC0uDw6qe7UPd8H7R/3AfEDqoq5NUB/MLmyukEsqSk7SoknL+JQf1OPV3DS4jxK3GOloKZO4ZQRa4OFCcmUk7bnYmw+WHjCU5kBZMmgamsqgXLN4bD4npholOCiTL+zTjHO8lfOKDhLPa7KY11tW0+WzPHp/iBC3YW3uoIxnVOI2VKqKFSs0PPIuE/x74UhY4iYwhhWEl9LgjcbdPQ/wBMaG26CDMLpP7OX2RqztOo6XifiuSfLOFpf3lJSQnRU5kv8YYj91Abe/bW/wBwAePHnXpD6VN4c51pZAOrDcn2WeY/yd4bD/LordOkTl+3Rdw8PcL8PcJ5NS5LlNJR5NlkAvFS08fdRp/MB1Y9WJLHqTjxSvVqXtU1rmoXPO5cc+XgPAY6BXgXNEwnKZHlZrDUjLfbJTZRLJAuw8gXtt8sC0hktBJCI1XxEwtM44Shz5GWXLYIwbAPI+tlHkAAADfqMSNqOb7Ij3pmVnU8grWfgyqqaIUqV01HEFsDSqqW9Be9uflhm1HzqA+P+6IV4M6QfNR68BVNHUvUNmdZWu0ejTUzkqD0YAbA/LE1S4NQRUaPdhH60SIAA8gpmv40TIMknqaugnqqilj7xooY9byIpGvR/EwXU2nYtpIFyRi/b1qVUtpwA44l23hn7qoKRqPw6AUX0+YxVVJFPFJDPBKgkjlja6upFww9CCDjQLtA9kKmWkEtK8c0ij951W/RjywAugE/Zk7JBeIaJZhGZ0ErbiO/iNhzA67YL1pgMlP2D4mFIFkdSdQKn8sTa2uChghZRUUeGxPL/LBDQdks80qJdOwJUg8hyODaB/iYKUrZquZCCCjIB7pFje/nytb0xMalamOqYNad1smYRSuEkspY2GoW+h6/DANuabzoqCPNI0yBLVGzVSSVT02n96SdKlSCR0+Xrik4Ne8sG6mDSBqURXZJWys0kdUkIBNjElmXpzxl1bOoZk46Qp21WDllc49tn2SeF+OBNXUUCcM8QyXb27LIFWCpa3/nUoIVyf4o9D9fHyx0vDPSS/4XFKqe1p9HHvD/AOLsn3GR5JzTZV2wVw12h9m3EfZZnC5dxDRiEy3NLVwN3lNVqObRSWF/VSA6/eUY9j4bxO04rS7W1dMbg4c09HDl57HkSqL2PomHqQ7FKWbOO0XLaCmkp46yaORYZKk6QrabllNtmChiCdtiOuB4qItHE7SP34q9YVWsrandCri+1X2Z0+XQUPEuXQtGsWW0cc8YCi6A928pt11yJfbk4OM3hFxMW/LJ9+6eu41GuqP3n5clUvYzVNlXa3wM8ptDmNWkBKsRpSZ5aY7g3uGF/wCuLnHbd1ThlcN30kjzbDlSpXBY7Byu1+0Ph6OXhOoYKVqRDOY5BzVkiZhc/EY8LsGOZXa4nEiZ6SFRe8vcSV854a7XTwuWJ1IrXPPcA4+jzThxAV01FsKve+x6357/ABw2hP2qyK47HVuOo2GF2aXayuhvs09k8XElPFxVmyHu6jMUyrJYigf9+STLV6Wup7oK4TUCodS5voUHz/0k4s61Js6G4bqf5cm9e9iYzGMSStK0ZqPaOGACfh/K+gWSZdElFF3bNKoUWkZ9bNsLFm6nzPXHktICJ2UNRzi4ytq7J1zCOWGcJJSutu6aJDvfnc36XFiOuDYHOdvCQfoyN1H5dkFNkLCPLY1oaPk9HGW7q+kAFEuQnLfSADck7740Wl9R2p5k8yefmgqVS8d7JS1XU0gLLI0ZZlKujANqUixBBuCCLggixHO+JnVKbSDzUIa8riD7SPZnB2XcURZnk9JJFwhmMZlRURjDls4NpIS/3IyCrx6jyLID4MejcJvW8So6XO/uD4uHIxzPIx581fpVTEPVZR1sVSA6vsRcb3PLnjScxzDBCtag4LWoDRuX2AAubWxKwyhTVpwHGqx9TbfFtokKIkqte1Gd5uM54iwIpqWmhBIva0QYj6yHHXcPbptW+Mn5rjOJOLrp3hCHyq6U0Npcjmdt8aCzZxhISAi+q5sLbm9hh0oWoVFcbMAOnP8AvrhZTLcq4GsggNuNtv8AthYTwtTsSNrjqT0wySRcqWIN9uo3BHmMFlDvuvPG0QBJJHMDzw0ylEJLQx38O+C1JoKeorNYWIHMKDcYaZ2T7Ldagix2VuhboMMkCUqs5ZApVQgtfSNr/wBcOnklOIqdH0kSXUG9iP0wBKMNkSUnHGD4GBKKfeXb64dDyylPYi5YsDYjoLA7YFzw0ZRtYXGGor7IeIKjh/iONKeGeqpauVYpYqaF5bSFtKkBQbt0sNyDyuBjn+NcOHEbbuDvjbx8FLShrtLl352d8SpnGWJFMVeojTSQ66hLHa3iHUWJUg8wT54+brqiberLcfn9yr5ZpyFyP299lB7LeKkhooJF4aqhry2Y+IIu5NOx/ij5XPNNDdTj3LgnEzxW17V/tjDvPr7/AKyrdJw0BvRVZIoPUbbjbnjfViVinpJ66qhpqeCSqqaiRYoYYY9byyMdKoo+8xYgAdScO5zWAucYAznaOvkOqAAkwMrvL7Pn2S8q4Ep6bOOKKWmzvipiHWKULLS5ef4YlIKySDkZiCLjwWA1N5Dxn0iq3pNO2cWUvgXeJ5hvQY/6pVxjQ3bfr+F0VJCO4erklaQMbrJId1A2FmvfpfnjgGOGouO3yUxHLmqX7Qvsr8P9qvFlBxA1KlDWxTpJWyd1eHMkG+iaMFSx2ALgglbg3546Oz9IrmwovtqJLmkQOWk9WnMeUROQpAWYNQTH7nqFfNHw3M9OkU01oVAURwIIkAA2At0AsAAbAADHE+rxv+/lEapJk7qUpcggpLGNET+YDc/PnguyI2Ql05KedxTQ9B88FNNoQ94pKWqij5ED9MVnVqY2RhhKg5OL6AZm1AGf2gIJDaM6QCbDxcr+nPAlzhT7QDEwpRSdEpKpz+nUkXJ2vii6uSYAUgpEhDWe8RQx08r8nGyjz8sGyajg1GKRlVtmnavBk2XszftPL/aVus9DTGco9tjosQTfYKfe2HXHS2tjWdUhhBA3Bdp+f35bq0KE5wYQ3D28ZnneYzUGXZq2YVeXoj1NNR0plvE3usyd2xUEld+YNgeYxtu4TVpUhUqthrpAJIGR0yAfvuFIKdsHFhAB55TeTtVzfLqsSV9FXsw1N/iKSWMEsdTEHutr36WwB4Z2g7rh8R/+ymLKJEA/vxStV9pSujpmjjqzl9lIEhKHTtbYOgGx3+XUYOlwWqDIJPhn7H3KMW1GZIlSvDn2ou5p0StzIVkoQAyp3aqxtzsu4viOtwm7Y4mnIHQyhqWdJxlohWZw59ofJc10q9VEjHlqNgcUajbyh7bJWe/h7h7KsLLuNKDMYVdZVMb8mBup+eHp3oOH4Wc+g5phTMdZFUCyMrA292xtjRa+nVEKCHN3XrNC2uOTQP4Tuv06fLAGi6n3qRRa9WHBOYJUn1A3SUb2bkfXCpuFSWOEFJwjI2TaqoUkYoyagQLkgD42wb7fkUIfGQgbtB7Msj40yaqyrO8ugzHLaojvaeZTYt0dWFmRx0dSGHnzBpgV+H1hc2r9LxsR9COY6g4Vhrw9ulwkLk7hP7LVV2J9uGX50tWc14SWKc0dVUIrSQTONAhqkAsboz6ZVGliNwrDSe2u/SkX3DtLmhtUObqG4LR/k3wkCRuPEZULafZuIbsQrO47ocvl4PzaegyylqYZcqrEjpah1khdGQEppdGEa/u9QJHMKbbC1qwumVrimaXdMiYM5nwVxwcGuZVz+9V89KHiSny/iLheqpW1QZS9F/iDcd+8dR3zyjUAQpLkAEA2UEgEkD1O5oirSfSjcOHxBCwNZ38l9P8Ai3L4jNIzhO6jmmjbUoJCswBKnobCx8wT6Y+ZAYod3wPwT9QV8p80p5clzGty6Q6ZKOeWlcHoY3KW/wCXH03RLazG1R/kAfiJTl6Z+1Ne3LfmfPE2gIRUOxTzKqKpzzM6TL6IXrKydKeEWJ8bsFU/U3+RxDWey3purVNmgk+QEqSkHVHCm3cmF9PeDuC4uD+HOFssotEdLktRHGrPpA7vQy3udrnUCTzJZj1x8s3l+69rVrh+9TPzB+AiB4ALrm6Wyxu2mB7lcUWXxRxlkvG17kxnTqPmbfD8MQOgSVmSThbHvI1JDmZQNwbah/XBUtXtNQujYqCz/NWoaOWZCNewS489saYedEtQsbqdBVf0OaTMW72SSLUCCCQdwed/O2CLABAVx4HISkKnilBxGCsz9zHTmNlWSyuXPJl+9svIgjng6hcxjS0Sq7mHREIG4s+z3wDxvrqssMnB+bSHUXy1VFO7nfx0x8Fz17sxnG/Z+klzRhlbvt/6t/8Ay3+MqvrqMxyVIcZdh/G/BExWXL1z+iZWaOtyYGTWALteI/vFIAuQAwtvcgHHZ2vFLO6gtdoPR352Py8ldpVO1GFWsExqKiNUbdzpAPrjpSzS2Sh1hxwqz43rhmHGWeTo5Ye3SorHe4VtC/gox2Ns3RRY09AuHun667z4qHYPCPFbbz54sKtzWy6UV0VQVZbBiLYZIdFq0CLrspvYW35fIYW6WBlas8ixgXLbcxvhxCSwEV7KSBv71vxwkvBJql3NwQCbkHrhSmWJnZQTY6uVj19MOPFIrUJMRyP/AA4aU3vSdDVEN4mIIt154mIlAD1Us6BiLGw3uSBviFSbLeCDUlybJfpz+flhuacbJ0YwVDIdv/hC+/rbDSihOstyKbNcwpaKni11FQ+hFe4FxzLHooAJJPIDDiXGE0Dmuoeybsq4QyfhGvzTOsvoqysp4PaoajMqNp1lUgBlMYOlAAQwLW033IIIxVfc02nSAJHMq4Ld5ZqkweQ+SuPgTs/yDi7hmgo6OsqcmrqmtCpk9lmyyolsuiekfSJKUve3duxBdNK3spGo1zHNhrs9Pwf3os99Kow6i2RHv9/VAXZPxSsh/dQywRrIRE7q4SQbBlDMAWsSAduote+Pnz0s4Z2Nb1mkO4+T5HmFugHs9LtwrT4vyeLi/h+fLWq6mmSoVWEtHKUlsDfTzFx6E2uAdiARzXAeLO4TdiqZ0HDgOY/I3CZrNTf3CBOL/s98CZtlhlbI8xyqqqIg9JnWSq0lLfRv39NEshdlbZkjPeDUCzEmx+mCy0uqQq0nYcAQ4bEeX7CoNrXNN5acxyP2JhOvs5/ZtynhTiqTixc7peJYI4THlVTTRyrHExLLNKRLHG4kUAxgFfDdze5FvHvS/igaf6bQdIwXEfEN+59w6rorbUWantgrpuJJquleny9ihddJq9AYL/sgjxdfQX648tc8vwBI5q8NLCCVP0eRpdZaljKye5rOyjyUchiuGNbGpCXE7KURoYwdAU6fwwJqACUg0803qM5hpzZ5VB/hUXOKFS8YNjlStpEqNqeI1AOnUb+Ztik+u922FOKcKJqM8qZL90CB6D+uK4DnHOVKA0BR009ZKTqk039cSCmRyhFLVFvQxidpne8hFi3U/PEskiCUWvkAkp5oogfET88QFo5KQEndQOY1sbEo9tFjcHFqkwjI3UgBQ/OUmkKxLboAD0xpNlokqUSAiDsRyP2HPOI81ipIdWbzQI1dHcGSCCPTGtrDbXJMxO+q6nkoA26l251OlakSGAkzyc45+QGOWepWVe0xq1TlXbHw/JCFSjhJQgm6TFN7/H4nFd9trd3KYKzw4RLikqrgo1IdpRINtwspJb53xWNgWydMfvVSittpKg6jscyaslaSoyannLXZhJEGuT1Oq98Tsbct2Lh5Ej7qT1lw2chbinsB4ZrIdEOVQZfLawlpaWNSf9qyb79djiT1q6punU4+ZJ+6no3VRpyZ80A0fYy/D9cRDxRX5Oy+86PGoax94IyWItz8r4d/EDUxWpNPnP1BWkbnW2QyUecM0OcZSYYqviKDMpCSEliiFiLC5bRsDz2Kgc9zik97dWukNI8yR81RqhrgdLCEeCtzSnjDMaWVALgujpt53DWxZF1VYJwf3wWboaTEFMqziarjcK2W6wBfXR1N5AfRSvluN/lgX3oqQHMz4FStoiN/iFHQ9tNHS1TUlfkufs6DT7TBQLIDbnfxjf4C3ljSoXYLZqOHxypXWDiNTXN+P7807m7QuHc4SKCPM66kqWYEI1EwZvS2ltsRVatCqyNZQNtq9Il2kEea0zbMckqKd4amup4dd001SvFrvzHjUDp54qANfIadlGGvOwlcafa2z/NeCKaKlyiU1FFmwkVK6jHtESoABIrFCQslnFtRAsb47j0T4fRuKxdWIGiMEgE9I6jGY8kq9erSYGsBk+ErlHLezmqzuWKkpcwppHk0xWjhnYKxKgLfuxqPiHu6gL7kY9iqXbKTDUcMAE7jl+84WQLd07/Ir6tZ5lJzKevpiP3bvIuoHlckY+VG3GlolOWE7L5//aR7EMxyHjuszpNYoc5kM6aKZnC1AUd8jMD4bkBxtvrP8JI909E+OUryxFs4w+kIOd2z3SPofEeKRtiff4KrZezCvhhSX2gvcHUiUE7FTe1i9tFxtfxdetjbu23bD0+ISNq4H+FYHYT2eNQdsvDstS081PRpNWs01C9OgkRGRAC5ufGwsbC9scn6T3oHCKwYRLobgzgmT8hlaNhauZcNJmACV3H2q1+bZX2T5hVZPpFWtIsgPJlsVYkHkDbcfDHz/wAIZSqXtNlf2ZI+sLoLbQbiHBWdwTxFFxDwxltdLKA9VAjFDZixtubDnv8ALEtZjKbyxx8Ph4LMrU3U6jmxsVJ5lVplykzSJGtibPYWAF7ne4HriqJpO0zuomt17BA/FletfkT1NP8AvBHIjeHnzsR8Rq5eoxo29TUACFIKZZUgrlzjDt3emrZqDhyj/a1REzRGskmWCl7wGx8ZOorfa6g3tsRcHHoFl6PGoA+7dpHQZd+B7/gpqjiB3BJ+SpnipqkZrLn+Z8bZjFnEgRZf2fPHIqMGAVUjiQqsaKSAC+pipJAvv3tOjaCkLanQaWjaQfmTzPNZ/qtUntKlQj4fIK0uAOOcxp0kWq42pc/jUKsaVMUKM1zbd3eFhYWbVa4uBZjvjmrzglhWzTYaZ8CT8jP1VoW9Qc5+qN844no+NuGZ8mzvJMyqKN27wJQ1FPUmKRDaOWNkmDq9ySp08tmtqtilacJq2FYVbe4b4yCMHcHqOufJOGOBlzFTVH2SZjl9ZSVtNRtTyxzqRTVVLUUqVQFnBJ7tkibQATva5Iuwtp7h1YP7rjI8MkeXUKE0/wDJoj95+KrXMPs/cZzSSzJTUtSXDStUpUMkRJJaxkkjRdRvfTe9rG1iMdF/UrcRMwuZPDa+eqhYux7i+sjdoOH6idkJVu5ZQXYc1XWV7w23/d6tt+W+JfXrf/V++7b3qE2Fx/pScvY/xjTRLM/CWbd33Zm1rD3mlRcliFJKgAE+K3I4dt7Qfhrx9PqgdZ127sKH844fzHK+79ty+voVlTWDVUcsV1G2oFlAIvtcYssqseJa4H3qB9Kow6XNI9yYCRZIbxlH0HeRDf62xLsosLyL4bsmoHcrex288MkCkHBRma/hve53wSYrYqdC7aVbcdMMksiU23jBPnqOEmUTosQeXli0oVI0lZ3mhWsWtpUt123F/PywDm8wjDoU9lpjkaPWWttYIlzt53P44gIKmarP7Iew/P8AtPjGawRLknDPtAhbiCsV2gL6raII1GqokBO4QWH3mXE9O3fVicDqo31m0/E9F2f2RdiPCnZmuaSQZDLWVsQaBM2zarSaara4JeGJYwsMe6rdlJuzILkFjoCnTo03QM/v71VI1H1XDMBQmf1eY5JmUbSGKOaNgskLwh49J2KyI19Q6MGuSBuSccgQ9lXWQujLmOpwFB8SZFl+dPFXh6ygZD71FVSqIZWOs3Xkq30EWZtWkFR5E9rXjVJCFjyzAVV8PZLS8D8VUtb7dUTIG9mqCwVYpUYhSSNINxqVh5EDHI8XoNurZ1s3J3B8RnCvds524XQk1S9JBGzoSvJXv97r8b48F0S8gHKloslxCFO1DsIHaFFBmNJOuSZwzxpJWxpdKmK9rSoPfdQzaH94Hwm6nbquCekL+Gg0Kg10zJjmD1HgeY9++9wNAcNJyr7yeghyiiockoKYw0kESQw0o37uFAFTvD1NgNvO5N74wajnV3ufVMuMk+Z/cBTzMuVkZZCtDSgyNdv75DED6jabd0AaXFIZlnCwXQXaU7ADmMYde6M6WjKuU6U55KFlq6qVLNIUXoBzxXFJ78vKlLmt2TcRBQSSd+vU4kFBrBlNrJwk2kii/rhoY3ZPkptUZksaE3CgDDa+iIMlQldnka2JlJA6A2BvhhqccBTtpqDn4lQFrHliX1dzt1Y7NQ9dxSGvufiMWqdmpRThCWecY0tIwaeo0H7sYJLN/ujc/ljbt7F7xDQp2UzyTrgtZOMq7RM3s1CLaqdWu8gv98+X8o2874G8AtGgMy7ryHl+UqpFJsjddIcPU1Nl0ESRrpAAAAHLGZQOiDuucq6nkkovo80itGVZdJ2vfbGyys4EQqRZ1T1sygBDF1UnYhtjti06vOSgDCsrmEBUAThj6m5wQrTmUxYRuEjVV9lOldXTYgYF1Yxt9kQYDzUCkCz1NQ0tDEC2xYqW1A/HnzN7Yo6pcSWBTnAADkIcV8E8OmtSsqsngqJ2Js0Ueh+W+6jbbqfT5Q1KnZYnB5fwr1C4rRDXQnmW8AZGtBohoY6NZLs8KyFgCfW/5W54j/t1e84oXXVYOyZ8VJ0fC1JQU6xRGQoOQaUtb0BJJt88QuoUyZCidcPeZK1qqGOFAglGvn+8bmL7+eIarGtGlroKTXEmSFEyZDFWFJEmhlW4ZleBSSPibEYqBjs98FWO007tj3qKzfJKvQ8UWb1VJEbBUibZfm2r6DEoc8bwR+9EbajNyyUC5zw/Wy00rQ51mlPJIdXeZdIYJzdeTHTICBe4Ui2+Ltvchr5NNrv/AJCR9RHxV1tRuwEIQo+zHPcxNLmuXcUUWfwwS96ked8P0dc8RXwpaopu7lUqwvsVII1c733n8YoMDqNW3LMf4Pe3ffunU3I852Quc1pIePt9Qj3JuK82p5ZaPNzNTyRixmpa2eSJvOwqYwTbru1rjc8zgXLaLmh9EktPI6Z/+0/DA9yi9WYe8yPl9k04xyEcZ5FV5ZmlDBnWW10YCCnqYkmdtitgxULKDujq43FrcwRsKws6rbi3eWPb1kjxBiSQdiCD54Tdi2cgj3H7Lkrjr7LfF2Q0tRXZBNPxXR0ysZqQUklJmcAUeLVSuTrC9TEWHUCxBx65Y+lFlXcKdyBScdjIdTPTviInkHAdCZQPDgJBlD32alXN+080KyGOapynMI6d+WmdYxInzBi5Hyxf9JwaHDTVOzXsJ8iYPyKe2rf3JX0EyqGmzbh2KnrIFqYJqRBLTyrcMCg1oQfiRbHzz2po1zoMEEwfI4KN4LXy0xlJ9hlDT8MZVmHDkStEaGrkeESC7NTyHVGQfIbrv5Y37i77eoK7jlwEx1GD+felxBpqFtXeR8wjnMsliqJGlqFSVmIK3XcDpvzvz+uKL2wdTjkrPa8gQ1BD8GJAaykaqq6fL6iF6clQB3UbLp13G2pL6lY2NwBuMXaFYse18bGfODMe9TVK2toJieq+X2b5HV8LZ7XZFmaaMwyupegnVhykjbQT8DpuPQjH0WKjazBWpGWuEjyOUDCSBKZZxVt+7jJst+g52xJRYMlPWfsErR1pgsVJvblfEb2akbamkYT/APabm+wsDe9uuK/ZBWO0KdwZ/PA2tZGRzzKkrf6YjNBpRdr4p/TcY5nGb+21SuOTJO2r63+OBNIDZEHTgp+O0rPzs2cV0oVStpZy4sRYjxX5jY+fXCFOE0tnZbt2lZ4Cb5nIzWtqaKMi3QHwcthty2wQaULtHMJ1RdsPENOZZZaxa8yuzk1WoSXPMrLGySITyOlhcbEEbYMNgx+/AyEDmMeIhS9Vl9D22cDZ/m2Y5XTUGaQVFWYMxp/fiWmjSUoW0s7rIBJcOTvYKVFwdG0qVbe4bTBlpDZHnP08OSxLyhSrUS8gAiY9y5yljhknMsIYQFi0SuRqVDuoYiw1Wtewte+Ot23XHQsALpVPdvte1yDhJbYSZpiCbFWC/e3235YeUySIZTYKCBtzwklHWv64t+agUlw/w/mHE2Zx5blNHLXZhMGaOGIbjQpdnJNgqqoLMzEBQCSQMLJSXSvZ/wBhGV8J6a7OaeDjKpOloKRpjT5ZC5NyGSQJLVBbi50rECCLSDfEwphpkifom1GIBhXLNwnmmZVUVfnLDNMzkC08FFK146WEEsi6AqhIVGm0aqoJUGwxLpIMu3UWIhq6G4NyrMDldGkImzfMnJcyTeATydDvzVbbXsBflffEjtTh3RJQCAclVV2vVefNnvcZnSRUxRiB3cOm56qHtdvmccreVHSdQWzbsZpBYq/SWRVkRpFlicIJaWSQxpMq6tKFlsdixselyOR2wm1yDB2V4icoZq8rlzJ6mh7wKZSUh7iMhlkVLqRyUEalvv1FwBY4Gs9rIqBILqbK+Ho63IKVJUBJiQnUeoAJJP1x8y3T3do57OZP1V1ocx8or4ZyY5k8UxH+Gj/1IYe9/wDEN/O+w6D1ONG0ti1oc7dXPZ8yjmlyunpFZ7ADmTb8/PFx7gEhKZ5tXiBSVA1XsoHnjnLurmAr9NigPdbWx1SNzvisxgaJO6lLidkhU5hFTyJEzASuGZUPMgWv9Lj64lLzp7oSDCclR1RmJa92+QxVdUndShkbBQtfn6U97G58jhmse/ZWG05QvnHFSdw7SOBALBmc2QehPLGhRs3FwjJ+assp8wgvOe0CipYjIzSzqDb9whP4mwt643aHDarzAgeamGkHdBWY9q0jahS0XdX+9PJc/Rf643aXB2/5uny/n8KcAKBqONMwzDZqpkXqsICj+v440G2NGls34pxHRKZPTQtWLUEfvSfE9rlvQnDV3u0aOSMvVwcJZ2tBGukhdscTeUDUOVUqCd1YFDxtKq2WUi/kcYRtnt2KqGkOYUXl3GVbT8VzoZHiR0MqjVeORNgRYcmBIN7XtccsX3UtNsHtOefUH7gqw6kw0gYRpR8VSKqrHL3aKLKobYfXGQalwzZxVQ0mncJ0ufVUpv7W2rpsp/T8cD63cM5puyZ/pWGznNFLGGrikBOwmjNwOo1Dc+e+J2cRqDDkuxpcwtIuKc0gqRGzUyEqCdEcjC999yLAc9r3xYN85zdQ+0/VMbakQTlNs44jzibUKaemUG+8kJLC/K1iOXPe+Cbetd/zAk2hSG4KY0/E/ENE0Zkaiqgx/eFXaKw8gN7n8/TB9vRdMSFIaNF3UKbl4xrzTh4O6VgbmN1vdetrdfK/zxAy5gwVX9XbMFIpxVU1dOe/7ksy3KEkW9Lg/wB3xE+tLoIlP2Gk91IJn9XDEx7mnjAsAFlY9Ou35dLfAQyye6SiNIHEoB4h7XJcvRmquF88qimoBaOlM4J2NgytYXU3BNtudjjeocKFd3cuGAf9To+RHVWBbgbOQV/95ThyhheXNsvzegSNwQKrLpldd+dwpUW2vv02vjbb6L3jzFCox3k9v3M/JA+BzI9xUnT/AGjezfOZC03E+XU9UhC97MXpZF3FiJPCbfH5i2CPo5xqjltEkeEH5SoA9g2d++8KyuF+0nJM2oppaTP0zenjbRredHjZrX2J3YeVr45u7sLmg4Nq0oPkfmpHUH47sT0U9UUnD/GOWB5aWnrKaQsEMsehgR1B2KkHe43BX0xiGpXsq0NJa4dPvyIP0TtNWk6AcqFi4Tocnyegpp5Zqyej7spWVMxafvEFg4c7hufLqx2sbYkdfVqtZ7mDSHTIAxB3EdFZ7Rz3THyVB8aVNDwV2kRcV1HDmXzVJdZJs4NKYquIqNDy647AkxltQcEHcHoR6Pw/tL7h5sG1nBsQGzLc5Az0dERsrPqlB8VQO8Of5XR+TTaaqa1pI7akKnmPIfHmPjjy2p3QJwVQe3EJzmEy5Fm9NnYj0Ax93USBCTIg3AJG+1ifntzOLNCo9zdHvGf3dMxvaMNKUeLVrVwgxOrxsobVfmpG23qMabKmICynN0nKgOJuHaTOoIlqlkLU795E0cjxkG1r+FhfYkW5G+J2VH0gQ0/z8UbKjmEkc1xR9sjsmnoM3y7jmkFRKlVJHl2bmXcicC1PN6q6L3VySdUUYJJa59Y9FeKtuKLrKp7TZLfLmPcc+RPRMBD8LlGScTPMTuwlZRy2ANrD++uPR9MAAdFBqDifNKo5DC12+WIiJUoMFOhK12APzxCWhTFyW7zxG1mX+98RxhSAwlVkAAFjcG2+BhFI5rHe6F2OpPhsMPEopz4LWaYAW3F+W+HDZQl3JaxvoeEk2BszWO9r3/IYKJkBMTEFW92FZtT5bwRIKuATx0ctJmc8QYJLNA0iyzorF01ErAPAW0k6bhhqGE4k3VQNMSCB0xgdY33VCo0erMJH6c+CoPiTJf8ARrP8zyZ7h8urZqPS6lG0xyMgurWI2ANjvvvjsmPFVoqDYifiuJqMLHuYRsVHvG6NZvAG+8PwwaDIwvdyVFiPFfTqU8vP8cKUua10wDY6ifhh0ErThXhXMeNOI8uyLKIBU5nXy91BE8ixqTYklnYhVUBWJYmwAJxda0uIaFATAkrtHsy7DBwllOY5BwjUUGfZrLTrJn/FcjaaSVAwIpqYkA+yoxVma4MzquoqqqhstYctZ8VCXbOdjwR7Dwu3C9Uwqf2fFVRgvPUyOayaS2rfSiiMW5blAu1upwY7u+EMzsinhxFziSWtEGbZitv3MdHFFGV83KopQXHPxs1rXfniSJ2lAe7hSmbpmJo0lfOZksHaKlV2j0r7puylk1dRpZgbNexFjj3jnjGrC0baJ9nKovOeF/bK2RocySk0rYiaVNb77AfePPa2OeqUnPHdcthlQMMkKPk4YzGjaniTOKSpQR3cyTNuPETpIUkG1rX687YyrmmaeVKHajjCI34PqIqaJamtRqqG09LOIiLp4bg+IjVsASNiLXxzT+Ig90txt71YZSJMhdF0H/tSnosspTZZYY5KmQH3UI2QerW3/lHrjxmlQl2o+78rQ0Bri4q0cpoUp6ZEUAADGy4hrICh5yUvmLGOBVBIJ3vjLrv0skqxTElClbOZJS5BIU2AxzgOpxcVfiBCj56oIGYnl+GBe7dE0Sh+trQ76yxFrgG+IQS7llWWiMBCvEfGVLk8YSSVVdxdIwbu3y8vXGjbWNSuZAwOanYwuVPcXdqdQnerTEQDSbyqy6lHU3YED5gjHbWXCGGC/Phn7QfmrYp4Ve5Z2e5lxpxNR53NmGc5rPTsZYVzOvWogW4IBVQihbA7FbY6etxWlY27rUMYwHB0t0n35M+9VTaMDxVe8mOpkK0K3Ilo62LI8yzPIsvzh4w4y+qzSOOcqbAExtYrfULarXBuNt8cvSNSqz1qhTe5nUNMe48/dKIVKcw05UXxF2G8XUKGSLI5ZSRcLE6NqHoGKkj4XxYoccsidNSpp8wR9iiFZu0quo8h4qhzqLK24Qz/APaElytNHlVRI7eq6UII+gHnjqG1LStTNWnXYW9dTfnlOazWiTsrh4V+zzx9mWh6nK6XIkb/APulaqyj4xRCRgfQ2xzdzxCzZgP1eQx8TH3Vd15TG2f3qrQyn7OFXToPbOJEQ9RR0JI+rv8Apjn6l81x7rPifwFAb7/S1T8XYXl0QAkzrNJCDfwiJP8ApOKTrl59lg+f5QeuP5ALduxLKvaEn/a2bCVBYESRWI9R3eIjc1g0s0Ng+f5S9dfEQEq3ZdFGP3GeVieXeRRv+WnFNz6nOmPif5RC7PNqQbgLOKO5p82pqgAbLNE8R+oLDEbtBHepkeWfwpRdMO7UxqI8/wApGqqy6V4x/wCZSt36/E6dx8wMQmlQfhrvjj6/lTtfSfsfimCcYRVSXjlUkHT4SDY+v9MObFzD3gpuzhaScRSMdmUnna2CFqE2hqbHiWVTvHb1AxKLVp2KfswdlrT8ZwSSlHDE7kEeWHfYuAkJGid08fiGhl31HWfW2IBbVR5IOzckhmlNOLq9gb7HmMGaL24KRY4JKomgaE6SQ5uAORJxIxrwcoSCN0jlwjEJiWV41I0tpa2rblg6pdOohDkZTxaaNH1rKQ1zuTfEBe4iCEWtyTkyikrJQ9RTwTt5ywI5+pGCFeowQwkeRIThxC2r6ajkp+5npoZIhuEaMaRbltywqT6jXamuIPmpWueNig7MskoK21Dl2Titq2XQIKOBpJLdBZdx8drWxvUK1cHXUqQPE4+e6m7Usy90KBP2VuN+MKyWStzep4foJf8A3etzOWpZbix/coSBsT4WkHM3GOjpcetbZgFOiHuHMNDf/uIn3hqqVb2nyJPkujeDezabhjhvK8tqs3qc2moaaOlNXJEsLyhFChmALb2AHPe1+ZOOMubdl5XfcOZpDiTpBkCckSfHwWS66cdvip+vylGpu6LzxpcE6JSCRfl5YMWtGlgsBCris8mQVW/EWZZx2fdn2ZV9HnWYrJlNFU1OldEqvp1sAIyq8yR94WPLa4xvWVpQvrtlBtMDUQOeApKlUvdqequ+zT9rriLtK4/TgTjTKqGWvrTVChzfLFMQV4Y3kMMyHwyBlRgsqFWuAGVgQR1npN6KW3DrI3to4gNiQTMyQJB3BBORtCyLe5fUqFjhj6fv6Ff3GnCOX8V5FX5RmtOanLq6BqeoiU+JkO/hPRlIV1boyqceW2lzUsq7bikYc0yPwfA7HwWxK+WPaV2d5t2U8Z5jw5nAD1NO5liqkW0dZA5JjnT+Vx/wsGU7qcfStle0eJWzLmh7JG3MEbtPl9IPNUWgglvPP8KCjex28XwOJyFO0lPUl2GroOWIC3opg7C2ElztgYUgMrIkKkX5dBhRKKcgrErbGxv62w7UiTukJHLELZSSQAPPEgAGULjzXq+UR0FYyMCywsFK7jURpH54ekJqNnr/ACgrGKbiOhVp9nUscS5xl8kUU0NRlckPczsFSRk0siliVC30W1MdIvuCNjSpvPrLH9T8jM+alr05ty2JiPkgvtkic9oub1U9RUmWtSCskapp3hkaR4UWQFGGqwkRwCeYAPXHW2bpoNHTHwK4e7Zpqnxz8kDd2YjqRjpdRYk2IPpi8DKpbBalA7IENiF6b88KU8LwERAJsCelwLYWopYV8dhPZpJw9TvmlcpbN81pEWmp0B7ylhdy37zayO/dC4JFl0jUdTgabWwPFU3ELpWPiKvyfIKKjypdFNU93DTzRxqWq2RWaScXsO7D6FLi92a0YYgAWNRAgKEjUcrSi4VnzCClmzirq+8jkLpT5tTPRQrZib6ZUERcnx3Ic2Zb+Lwh2txlMXZICsCHIKqejiaaGvq8uiILV5rEq4VPlFCp0Hn95QbC/h54J8xJmELcHdNOJFj/AGbGJameohNRokqdJZ1IDWUoX8OwYqL2NmsbggYVy3HeOOv5WrRJLpAz0Vf1mV06wsywKveEAySgarA8wdz15YxqummzxV9up7o5IZzSaSiLCOEV8JVldAe7BBFjb71/XGe5+sEOwpg2MhL8T5/JmeQwQRhoaGtpFjp0hBWzsAq8tyQ4QEdDv1xwrqRpPcXDIJ9/6FrsgxC6b7OMnbK8mgEx11bookb+bSB+AFvljzuo5rnO7Pb7KZ51OlWhRwFYF3PLfExoyB0VbV0UZn9QUbSCOVgMc1xFx7TQMLQoCBJQnXzd1GATbzxmuwMqyMlC+a5tFDKsLyqsjq7qhYamC21EDyGpb+VxiJtJ9QFwGBE+/b4wrTAqB7YO3f8A0fr5MkyTRPmy7TTuNUVHtyt9+Sxvp5LtqufDjvuCejvrDBc3WGchzd+G+O55YyrtNg5queGMzzHNKySerd6maUkvPK5Z39Sf05Y6a7pUaTQ2ngDlyCtDaEc5DwKc4rQ0qakbmCLgjHP3HEOwZDShc8NCMcpyun7Msvg4cycGnm0XjrE06qbU2rTGGuNYUkre4BZdhbEVrTdx66F1dHuzkf6o6+B59YVCo4ub3BACFZOCq/tBjlyXhjJUre9m1tNNBHFHSK41e0SzMxN21agxLO5vdSdh6DeXlGzaDVdGMDn4AAcvg0dVANFJup+P37LpHsk7Hqbsv4eGWrVSVM1QQ0l5pXSyggLFG7FY1AO5VVuTc9APL+I3FXiFQPuDAGwgTneSBJ95MclVr3XbulogBWPT0C0sBiVRFG+7Rodj8ehOM7SBhoA+vxVOZysaVh2UaQBywIYJlPJSTzKfW++DEESlCbvJY3HX1xXO+E4CRaVdri/zwJciASEkyX+OI9QJRQkmddVwLHDTBwkJSMsmm7X3Xe/lgZa4ZCcSEM8UcH5TxlTtJULJT1bKQmZULiOoQ+rWKuP5ZAw+HPB0aj7U/wBogt/0uy0/ceYIVulXfS2XMPadPxj2QZvFFmFUc1yuqYiizOE6I5iNzG6WPdygblCSCN1Zhe3f8OocP4tTJpN0Pb7TTkjxBnvN8YEbEA76rLkPbOkILn+0HPBHaop5jYc0ZD8+mNZvo2x3skfP+VG+7pU8lqbxfaQoppItUlVSEbOjUiyK3zDXG+JT6L1GgwA7/uI+0Kt/VrSYIPwRRlHbplVc6RmWOV2G2q8X4sAPxxkVvR6uwTEfP6Kdt/aPPdfHmCFOr2t5PTSqlSr0jt7veOoDfAk74zjwW5cJZn4qwKtJ2zwpzK+0bKKxgY5w3K2h0e/0b+7YoVuF3DMOH1H2TkasAgqW/wBJaKNo5PaBCpJJ77wk35WJ/vbFT1SoZGmfJIMdsURZMKnOAPZKeprAeTQwMyn/AHrW/HGdVp9n7WFE5zWe0Ub5PwFndbYSQLSJ/HO4P4C5v/XFIt1HCqPuabdsoroeyjLkGvMJZcwe26E93H6ggG5+ZxZZScBIwqT7x5w3CJaTLqTK4PZqKmhpIesVPGEX5gc/ngzTl2clVi4uy4pXTtysMWGtjkoyVq0yxAk2+ZxOCG5KGJQfx5xvl3DOR11XWV65dBElmqnAPdMfdsCDqYnZUAJYmwBxatLerd1gyk3V9PMnkBzKs06RcQuL/tA9omd9rWRzZPDUNw9k8s5cRuSjzIHXT34ViottZQTY7k32HonBRR4XV1tb2hG5HlynMeMe5aF1YtNDs2GHOiT06x5qI+yp2d5hkv2geDZp5Ia6SlkkqUlEzMtPSxRSGWTSNtTB9C3JW7na9jiz6RcYZd8JrMEtBgcsuJEDPIRJ2OFmM4a2g01HGSBhfQ6opNcSqSCQtiQNr48UcwHZPJlc9fae7Ao+1fhVpqJLcS5ajy5Y4A/eX3amYm3gkNiP4ZLMNmcHqfRzjTuEXGiqf7L/AGvDo4eI59RjkETmipjmF863iloamWCogkp6mCRopYJkKSRupIZWU7qQQQQeRBx7xh4DmmQcg8j5eB5KBpIwnKvZV8Jt5gdMQxKmkc0qkkaNfYkdCcAWkhSAgZWDWRm4uAcMKZTh/JImZFAYkHrueeJQ0yg1dEl3oLIQQ1gTttc8v1wcQCmnK1mdpO7hAv308KFee3eAkD6YJkCXdAfoo6xJAb1I+qtDgCoSPiTLzJIIY5pGp3ltq0rKrRE262EpNvTGFUcaZ1DlB+GfstcN7RhaeaY9sdCzVHC1Q5Kscuei7mN+9anWGplEURYbELHIiqb3KorMAzMo7OzwHtG2qR4yBJ+M++eS4S9aQWuPMfBVzoEunuSSx5Ja587j640QszBWUjIiLyRgSaSquu5B59BbfAHeUQ8UiVF9oVI6eLB6UOpdWz1Jyb9qUFPl9Qs0lCr01PVw+zqZw9wCnvMl+/AJUFtARtBbbWOJACzx3gpKl4szxM/y5cqmkyivp6Tu5arLYwHWkgiZI4zcNYsAdTCzNZv4jd+0OqW7p9AIg7K5eB+A84zmiy7iTi7OswqsypyKmnhnYyGFrmxWECy6SdVrrZrXuVxKJgPeZPxUZIB0NCNc4zirrZDJ7PN3Vu6hTulBK9ZCq3s7HclWta3ripWqPJmFNTptGCVX2a8TQKKikqIDUNImlBIWiZCLFfEL3AYBgOh5WBOMh9ZrgWvC020iILSh6pD0WXT1FRK1QWQRwyX2Ulty3UELfYbXtjIuDMDmrbEP5Tl0mdZ60ETXjjRi2nYoEAF/Tn/XbFGpJaYU4gCUT8N8OxZxU5RYKr0UjvG4UbkGwYj1I1W8xjzfi9c0zUM74WvQADZK6Y4ahVhGoHhUAY4ij33xGED8BGbExxKFF25AevTG2+IAVVu+UBDPYs8jWanqEqYjcCWP3XIJUkelwR8scLftcLghwyt0N0CCIURm82gnyxn1DlS0wqb7ZePW4L4YlnpiozKqbuKQHkrkElyOoRRq9TpHXG/wPh4v7mHjuNy7y6e848pV1uBK5BSn/fa2ZnckszO12Ykm5J6kkkk9STj2gvxA2UzHRlWBwjm9NRadQLfAY5q9oPqTCsgyr/7PK+LOXEVBEJZwupu8HhQbeJvTcbdbjzxwV3Z1S6HbKhcvFJsuRDUdlVfxTxhHTOGTI/Y43rK/UFklm72QmNBYgsdib2CqQfFsp6Gxu2cPsy1uahOByAgZPgNh15rNZcwwnnKvLhrhei4ey6PLssp4qWCE6tC3azHcsxO7ueZZjc/DbGVWrVKry+o6Xnc/v02VF7nPOpynlpo6dSdyW95zuT/flir2fMoZ5JnUTW6gYF4a0ImhRdZMGQi9r7YoVKgiFM1uVFTVndBbLqa9rjoCd8VA8gQpiASkHrwSd+mBNTlKWlN5czRAbuAB64j1wi0FR0ueFmtEuq3U8sCXwJUgpjmlKfNXkV7qBIoJG+x+PliI1DOCiNMBaPWTzhtLiND7u25HrgRW0lH2YATWSs/Z8ZVFMxa1yDaxA3v+GDFWcFLs9W6GszpqTi9J8qzqkpqvLqmPRNRS+KNxe+/I3HMMLFTuCCL40KNxUt3tq0XFrgcEbj95g4IwVLo0NkLn5vsl5Q+d18P/AOLc/gjnPdRUkSxIIzuqtMImLkAgFgVva9hfHp1P0quXUmuaxjTG8k56gEiBzjMKjUosd/zHY80yz37BeY5wjScO0+b5HUE3WPO6unqKc+hsVlX4+I+hxft/TE0yG3bWuHVsg/dp+SzKtjROabz9Qoql+wR2g8P0KZlxJxTwxkuW953WuCOtrXd7MdCBYkRm0o7EFwAFJYqBjseH8RocZ71rRqaP9TgGt8ge8SZ5AecBVbe1rVavYsILt4k4GBJ5gZHXfCtfhb7CuRZvFMtb2j1OYNQoslVTUWQ+wSUwZgmmT2hns+vwFdzdTYGxOIuK2fE7CzfxG3pMNNsgy92oQATjS3kRz5xM4UzKvqtcUKre84SCIIIyJBBODHPl5qx+HvsJdk2Vresy3Ms+ZgA37SzF1RvikIiHy3x5PU9KeL1D3XBvk0fV2pXi6REK2OEuxHgDgbQeH+C8hyl1G0tPQR97/wDqMC5PrqvjIuLy+vZ9ZrOcPFxj4CB8lG0hg0tMDwRqIUUBbCw5AYqst2tQyvMqjewvi02m0IZKY1lQqnTcEeWK1R7WmFIxpIlRjVIBJvtiBtQHKkLUxkrZUqJNx3JQFPMHe4/I4IOOqeSKGx4oN414+ouGYHmqZ9wLaFYFgbXA03vcjlixSpPq1BAVyhbOrYC5n4j4lzbjqt/aeZUIiNLqFPRgtaC4PiXUoJkYHdvLYEDbHWDs6A7Gm7B3PXzg7Bb7WMoN0U8+P79EN5vlcPdCOQP3WoGaOO4A5+O45aSR/wBr3sUKrpkb8j9vemLdW6Psg+z7HFwrBm8Ga1uQcYCr/aFJUxPb2Ij/AFcbINr2LMwPLvCvQjGFX9J3Muux0B9GNJH+rqQfgB1ieiha4NfESCuh+zfj+XiajagzWJaTiCjjQ1UCgiOQHYTQn70bEdPdJsel+duGCme0pGabtjz8ndCPnuFm3VqKJ1s9k7fhE1fSiqUoygqdxcbHFE6iYVEYXLP2nfswrx4lRxVw5GI+LIkHtFIW8GZIosAGb3Z1UAKxIDgBTZgrH0T0b9I/UYs7w/2uR/0E/Vp+LTkYkKKq0v7zd/r+/wC64jlglp6iWnqIpIKiKQxSwzKySRuDZlZTYqR1BsRj2QEFoc0yDkfvNVucORBmk9NmNdnlDKYz+zmy91Hh1EGmkjkQG1/9Y0TW8wfjjTtabewYSN5+srGuqrxXeGHaPoh6eCkpHKCIkkBtTbix3H4HGbWDm1CAcLdt3B1EOIyknrtiVWJTbmIkB+tsRgHZWSUn7Y00rPI5dtNttrfTBFuEIdJScUjNmOXpa4EpfY/woT+owekCm8+EfEqIn+4weP2R9Ql6eFZIzZ47uhHmNxv8RjAqCTBW5S6ox7UKR6rIqicmKakos6lmjqA576L2ou3dsD9w/u5E03F2lvp2GOisHB8Hm5onxgDP2Oy46+aWSCMAn9+6qaroou99qiJlgcAXCKviPla3rjcaScc1iOHNN3plpaN5CAg12DMDYEbWBtvyOCySlMDCaNlsrksJksd9lw+pDC6X7NOz/iDi7MoqtYZWhppe9jjZ7LC52Vrk7MbABRdjte/TYY1xyeSzyQF0bwZw/SV/dqc9yqio4GsUhSOnjicnfTFFYvay7tbc7bXODALuYATGG7NJKN67LcioswiSKplqqeUd2jo6lgw3LyIDyG53NgLj0xEXNa6JwUhrIyFrVVcNHQSNPS07Cqd2ilSLWvcRxF3lANiqWtYc73Hqa1WqGtg8/oFLTpknu8kM5ZlWR5rMkDTU7VkyGUUSwyQTOt91XvCUJt0F7W6i9qTKdOoInPT/AHV+o6owTGAh7iPhKTJZaKpleofLpJTHOlJGqyRWA0syO2kFhcc7AqLXuMUrq3DY1bHpy+KkoVtcgbhM8my6mpqqeWmOgysR3kiECxOxA8jtccxva+OSr1XUDBMj6rTDS4ZU9wtA1LVRrIFXSoCqvQFmOPMeO12Pe4UzOfn/AAtWi0hslXrwumiIX8t9sYtoIUFVENfD7RSOmoprQqSOYuLY0apCiZ7Uquch4VoODMooslyyLuMvoKdYII7k6UHLc/PHGXlzUurl9WoZJW0SX9525UVn76A1+VsZLsvhWaYlcefaC4ugr+PJ8sdzoyuJYBc7d44EknzF0X/dx6/6OWL6Vi2sB7Zn3DA+596mNRo7s5VWe1LKbRMsh8lNycdZoI9rCWoKwOyTs6z3tJzmlhooJabKmkInzZoS0MSr75U8mYXAAvbUwHmMZV9XpWzTqILuk5ztPMD5xsmqXApNLj7l1dwhwzTcL9pDZJlmT1S5cuQQGDMGXUmv2qYz65Da7uTT3tc/u1FlUKcctWeH2QqFwLi845nAjHIDPhk7mVkFzqzDUe7n9leeUZasEaqBY23YjljIazSNR3KrEzhS0fdwSaQqqTzI6/HDNaC5I7JtWytCSSQyn7vUYiquNI9UTRqQ9XZgNVgdupxkVapcrLWwoeatLark+hxnF5mFPGFB5tn1HlwJq6uKnv0kex+QwLWvqGGgnyUzWOdsEOT9pORoSBWBrdSCB+OLHqNy4SGKXsn9EOZp2t8N5Uzy11WY42fwOEaS45A+EHb16Yu0uEXlfFNsnzA+sKTsnRlPct7VOGa9BJT5jE4K6hqvHt5+MDb1xWrcJvaZh9M/X6SjFConMHGlPnOYvl2VU9XmGYEBhTU0RYun8dx4dH8xIGAbw2u1vaVCA3z+XWfCEZp9mNTzARTw3wRxRUUcRzmppKCVySQsvfOSTewCgKAOQAY2AGJa1tQL5pnHw/3PNU33dIGKYlE+VdmOW5e0rVNVU5hLKQzayES4/lW34k4TxTMNjZVX3VR/IBT9JkOW0H/5aihh8yqAE/PmcRanTgQoC5zvaMqQWNCACqkDlcXthQ52SUOFu52Crck2Cqg3J6AAc77Yssol7g0SSeXUlCXACSqo4wyfhPtKzjNMsPFeS5PU0GWhp66CpNQ80iTylKWRFk7lhEfGToklBnAQppIb2JpvOF8NpcNfh7WuOAXZ1d2l3MajJJc6dJxyXXcLr8R4Q1l023c8OfAaQGgAtEvyNQ1bDLWwMgzgm4V4wyjs57HEzCtzLLuF0zn2SKilzeWNFTWh7gNqUR95JGk9SxIOqSqLHVcX+hg1jbKhSeyAGwQYyTl8+JcY/wC1ePcWqVLvilepr1nUciYgGBHgBAHQDGEVU2Y1EjNFU5LE0zo80U+XSuisFK3YqiSCwB97uhuVuACWHHX3olwe+Bc+iGu6t7v0wVWpX9xSIAfI6HP4KVhzOCeEzRPIyCRYmDmJwGIY2EkUjoxGk3UhWA302x5F6SeiLOB23rlOtqbqAhwznoRg/ALorK+Ny/Q5sGOX87JRq5Ry3P5Y8x7XkFs6CkZ6mZFBaNo1b3SyldXna/PBv7RvttI88JwGnYqFr6oK2oyLbkdxt64znjU6ZU7cCAFXef8Aazk+SiohFQaisifQYUWxB+fT15YuULapgjIPPktJljVqw4iAq0qu2rOKyrL00Sxw6xG8couNJuNWo+RsdvxxpmxbpIc7cTj8eK0/UaTQJyhHM0mzaseqq3V3mYtIjDY7e8NveuBz/DE9NzaTdLOXP8+HkrIeGjS3ZMI8uq6uvjWmWaprSoiWKJA7SLfkRtyNzquLXPQ2xZ7VjWHXAb44j92jmgc9oEu2UtkfCz8OcX0/7ejb2mNO/wAvKSBqUzEEuSAfFKnRW8IBLLqIutW5u/WLU+rHBw7Hejl5NPMjJ9kwMGs2o2qJZyVsUGexIhaUhYzYMW5fHHFVLdxMN3UbmnktMvqMp4kqIs2yipVK+hmliSaE2MbqSro69VNtwdiCD5Yld6zY/wBmsO64A56HIIPX6bI5dTBp1Bgo94X4xhzjTR1QWlzdI9clKdrgGxdL81vb1F98TQG98ZbsD9vNZdxbmn32ZapPNKYSRllF9tx5jCfnvNVRu8Fc/duv2css7SE/a9H3OW8TQhRFWOdMNSqiwgqD/D0SbnGTZrxn932XA/SKpw7+zVJdSPLm3xb927HcQ7dqjNZkbhc4cVU+ZVKjI6zh2GGfK4Wy8KcvSOq74Td4RM+nvGZWsLXK2C2uNz7pbcTt/VmBjtTYmRzn9+y8rr8H40eKOuaTwKbnzBd/j0jqh2q7B+LM7zWGOOCloi62vmFdGj2H8qlmNhzsOmMK89IrGlNQ6iPBp+pgfNen2dnVbT0GMKQrPsuZ7Rwd5JmdJIBYMY4W0325Etcje1yF3Hwxhs9LbV7oaw/Efj7laQspGXKvs64Br8inlgeWNpUBbSVKX9RzB+uOkt+I0rhocBhV30HUzBQzQiVM/CSRship2O4sLsw5eew/HGxUg0MHc/QKgwuNwJGw+qsfL470sEg8N2A5csc3WOSF0NMGIKJuL6Z8z4QeUJUTyLkkJaSNbqgR9RWS42F6OJVe/vFkJ3UY2+G1QGsZPMj4/wC+3vXNcUpjW8gcp+n43VWVMVu+ikQspYmJW1eA21G/mPwt1x0/OVy3KEhFHopWZ1Y2K6dY90eem9j1wuaUYSlm/wDWQemkYKChlvVfQfhnhXvMuTLaaqny2kggaeKCZLRyymLRKquCCXLLpF1uALb7X3iwuaWtMLL1Bpk5Tippqzg/IUoqXuIKmqmeeSeWJnYMQAUUAizDbnf0A3xFBpt0oy4VHSEjT+z0NLHNUyNJPJ90sUVj91fMWHiN+dx0OK7qbN91KHmYCjM7EkiPW6a2mhpI2eV++1Jz97Xa4tYdALWxi3EZIkBa9viJ5oQzviqfLo4aRKiqkzBQZI1n8ThQbjYWIuzXBHx63xS/yABMhW3NjMYRrDx8anh+L9rZpA0syGOKIwzCsZbgMZHUFAga53BIttuAcX3l1VkVHflZ5a1j5aPwo7JK5c9zeSmoGeXTuXdbFY72BI3tfkP7GPPOPVfUaJquMkmAOp/jmuhtmBwE+9GOXRSxcQrE4KHTE1mFrKV5fUEY8iqu1UGnxK03RBV18OPaAW+gxNakhsrNqbwp+pm/wrm9jpPwGLFw8aSULBkIRq2/eEk76efnjhHO/uFa42CEeIFEskaE6RIVUnkBc2xXJ78jkrVIQuKsr7EeMu13irOs5NPS5TQZjmNXVJNnDNqZGmcoViQF7adNr6dgOYx9B/8AEHD+EW1K0bNRzGtBDYgENE94436SsutaPfUL6h0596u/gn7F/CWVSU9VxHNPxVNFutI6ey0YP8yIS8o/23t5i22OUvPS+/ryy3aKTfDLvicD3D3qHsGjxPir9paLL8joWSmp4KCjj37uniWNNgAAFUADYAAAchbHKmrqBc8/f/eUi1ziBuVP8PUTS/vXUhnPhjHIeXzxLQDqztZ25J3QwaQjBadYYbA3Pn5nGg9k7clCCmnIFjc+uIXkU2zzRqFzivtqN7nHP16pJkqyxuFX/EvF1FkQElbUxwKxsNZtf4eeIKdB9Y9wSrTKbnmGhAVf2hZnxVmIyrhajrK2pcEgUdO01Q4BAJSNeSi4uzWAuLkY3LDgNxeP0sYXHoNh5lXezp0G9pXcAPHZA3a5leadl/A1DxVn9FPN+06xqGlhimWYmUI7kzS6giLaNxZDISyMvhIvj0+n6G1LWiKt3UDR0aJPvWW3iza9Y0Ldsxz2Hu5n5KhZe1HOM6dfZ46PLYht+5fx28yTrN+fIqOXxxYFjZWuQwk+OfrA+SkIuau74H70TXiLjjMMtopZlMEzRAMRJVN3hA2vrYbnzufPEVK0p3VaXyJ8MfAfhWQ80KfdEwpbsU4G417cM1pppKKvyDhBTrqc6miIWRf/AE6bvBaSQ8rgFUHia9gpq8auuHcCpOGoVK3JgO3i6MhvwJ2HUUWcWqPPdZA8V9B+z/g3IuAchiyzI6GHLqBbO0ce7Sv1eVz4pX82e58rCwHkVa4rX1Tt7h+px+XgBsB4BU6lapWdreZKKWro3kYgDUqgE9b+WIaha1swjYCcJvLWkgmNhtz2xC1wduVPphbpWoy6lII62wL3gHKKCsGtsDY4HtJGE8JGWuZ7iNnjOlhrik7uRSVZQyNY6WUkMGsbFRtjb4XxAcMvaV26nrDDMTHIxmDkbjG4TFpIx4biRggwRiQdiOiCZeG85fK+HeHKbiXLqDK8ogny/LJ6mi9gq6SOpjFK4M0ZaF1SCSWQMO6d5I47gk3H0J6Nel3Ca91bmtVDG0ySGvEEuAOlpcJY4ExJwT0UnEbqnUo3VVlse2r6dcP1Mw7USGmHNJIgCXNAJhWXWZzkGeEZVlVDlM+WyBY2ySvi7macLaMaUm0rKO6jiA0s1woupx29r6ScL4q80qdcPcMROTkzAMSJzjrhecVeH3VEdo5p8xkD4bKrOLOFMj4XzqbLsioqHhZWkSsbJqqhgqBLLoA70U0r080VtR0vRzQsCXLRuSDjXcxozT2UDKrnZeZ5fp/KkMr4tgiyzLpcyr1kqniaoOqtqqgaHOmIhqtFqFHdx6u7lMhQynTIysDj5+/+o/FCa1GwZJ0guPmcCfGJPvXacIs3Fjqobvj4b/P6LWv7VMtoV1x3lI3BtpX6nHjVOpVLgWsyumbZPfgoFq+0Kb9r5nmVHAsVVmQg9pmaeSUv3MZjiADNpQBSdkABJJNyScaFerc3ga2vUMNkDYQCZOdznr5DCt07KlTABUDmfE2aV4YS1jKD0U2+XwxXZbUmmYk+KuNaxvshCdfFCysz6WdveJGxxr03P2CmDihzMKilhk/ey82uEva5/XGpTbUcO6FJJhaftcPIL3IJ3A8sP2JAUBPRT2UcQmgJECBEbZtI3PxPM4zq9t2ntFVXt1bomlzWDPqFqarTvI2tyJDKRyZTzBB3BG4OM1tN9u8PYcj9jy8FV0lplqgqXN58uqjQVsneI1xFO6he9Hkw5BrbkciDcbbDQqUGVW9tTEdR0/jofccq+xwfnmFLdnVLR8G0klDRAhKiqaYkklmkkIv5k77AdBtinxJ1XiD2vfuAB7h+58U9bvd48lYyZiIlacRkVUaMybaXYj7oJtuTt8/LHNOoVGODXYBPPl4+5VGuDsAyFMZTxrPE60uexw0kskoignjb91OSLhdz4ZL3XTye10vcgXm6XCaJLgBkcx4+I5zy2PVQ1bYO71L4fvJSU0ylbyqDTsD+9UEqPMEYhDix08lULeQ3VK9t/ZRl3F8+V1c1O800UkUME8MyqJF1WWOUFWD6FJ7pm23MbHQylPRPRfjDbKr2NY/2nTPUHqIzHNwGTuMiDDWpOqMJZ7QQ52Fx8MU/GZyDM3yrP6FPbkpfZ2llaOQuDI0cssSSxnUpbRpRFJlIQaiT6Fx0srVaVFrdVIkAQTsQYPjmI3VSl27Ld1UEtqYLvPp0RXXZjlVPXzUUUgqWVu7R4V70SBgCB4epDDUvMNcdMePXXD7i3rFgyOWR7veukpuLqYe/Hn+/oUTmf2cK3tJnQ1Grh9EbaSSLvagHy7u4Av5OwPmL47fgtvdUjBzPJZtzxCgBA73yH77lVPbR9mCi7IstlzClzxsziTT31JXxRxyIGawZWDAG1xcBT8cd8XObDH7+CpW1YXHeAj5qpKM6YxEF2SRgvpttjPrbrbpZCPsukhkyHLIqipNPS1HteVVFo1kaRJgvdRWO+l5qldxcqyxmwGthd4c86Xgbgh3yz8h8/JZHEmgPa4mARH4+qqF6dIYY5JnlchbSKEuwe29zzAud/hjtBzhcaR1TeJ53qt43MC2sDbdel1+W/XBHAhAJMpxL7GZXN1PiPIC35YQLk6+k/CmVy1kmWezwR0c0wgNQKVYxTBdO3j7zUQALgxi2oC7SXuOqYM4XPuIG6juMsvhzDP1ekq6eWOonMaUKRSSd1uAA8jAAcr2Jvz8r4rOb3pBUrHYyEMZ5XJU56sirqpKNSUQXbWQAbgk73bbnyt5Yo1KwaSSrlOmThCfFFdDFwDWkRx1cVVKYBJGRfvLE8yCCOfhIIPpzxyVeuNMeK6anT7w5QFWrcY1E0dRtG4qwhkYQRuGUJoUFSCVdQNipHpbqdOvuSN0qtMc+SeS18WbZtQ+y0py6ZyrO0fegyTtZCtixBViARsCTJpN7XNs1aek1NWloBJM/FUtLtekZJXSPZtwLFwtk+hwJKuVhLUTfxNyAB/hUEgee564+e+N8XPFbo1NmDDR0HXzO5XQUqfZNDQm9ZmMD8YkowIhVIX362J/JhjGbm2DztJV4sLWx1CtrhucaUF9iMWbY6TCzaglEVWB3LWJ3H44K7gTCVLKEanUrMp2AFr+eOBe7+6QteO6hXijwws3Oy4Jn/MCsU91G9n2VdzSwlwLm24HPHYMpy7UVWuXySArEmjSOEAWBth67GkQ3dUWnMlCEdQ+d8U+xqf8AC0FpJh/FKfcB+Fi3xAxmU2moc7fjf8K44CnT1cyrD4bEv7brUNNOlPTxQBZ5T+6mdlcsqLe/gAQM3Uvb7px09KmKUOafcs18OaM7z++9FTtaO2LzQA1VjuoPMZ+5TuwTfrjl7ysR3VdpicoTzSpLXufnjBnqrgC5v7XM0FfnKmihNXVWMNPECSJCLlm25KNrkfnjquFU+4S8w3cn7ea27dmhmVzXxeKzh3MVq8zq5KviGOd0FNqeCmogFBjYtHIHLXLEoAoAC6mYtYezcJYxzWm3ljT/AOR9/wB9+gCy72uaIJcNXSdvhzSPZlkuWduGZ59mGf18+SVWXxwmWWgp4ERg/eXYhrhd4xcjnzIvvivx2vccDp0mWzA8OJgOLsbbRvusWhfOuA4gZV1ZZ9m7hbLhG8cmZZi6yoWSurTErJq8YAgRCG03tva/PbHnFT0pvKpIIa0QdmyZjHtEiJ3xtspPWKhwcK0OF+BuD+HqmOooeGcsgqEB01UsXfyj4PLrI+RGORuuJcQuAW1a7iOgMD4NhQPNRw7zirEjzzvyGLNJJsut2LEDyBP5Yw4DRACjY3O6IsuzdFp/FIAfNjhNrfFWGMzCSm4gpqd3laVdRA1EG9/LE3bF+IkrQZSJEBRtRx7DDqEUNRKQbE901vyw4o1AdREeattty7chMD2kwxIxCypIfuNExB9RyxK6jUeZkKYWTjskI+0Ooqn8CNoI5soUj4bnDGk6mMlSG0a3cp8OKagAaA1z1OKpfUduVH2DEyzHMcxrjGwqnhCNq0xNYN6N5j0w7KkAh2ZUrWU2/wCKjMxmqayCSnqap5YHFmiZF0n4i2+Jqb9JDgMjxKkaGgyGqPqOIs6p6OCkTiTN4qOmfXBCK+QpC3RkBPhI6WtbpbHX0fSHirKYpNrugbZOEBtbZ7tZpNJP/SFCVWfhXeWqrJKmd7F555C8jkAAFmO52AHwAxnV3XF9VNasS5x5nKs06babQ1ggDkMBQVbxXSMSCRKep54lZZ1B4KUAhD2adqmQ5UP8Xm9JT3YIED63LHkAqAm/pbGpS4PdVv8Al0yfdA+JgJOLWiXFN5OO6nNI2OTZPV5mw5ST/uIudvJm+i4lbw1lI/8Aqaob5ZP2HzUsADCSzKn4liyKqrc1ofYKmOq0Q0UTiQVEGgEurj7+okBCbkI2wJW+5Z2fDbqmfVawdU6HB+HPzErNqXVRlSC2G9R+4Hmq2zHO461lmhk1huTX54vU7d1PuuCttqBGHCUMme0q2JMibEYxb1wt3eBRSBlWBlPCrkC9745qteDkoXPRXQcMlV93ljIqXclU3uTfiDgxcypmUhknUeF12II5EeoO4+Y5E4ntb40neCAVC0yEL8C8VzZHxhBBVtFTVNLOIKjvh+6synSx52RgVZW6W33Bx1LaYZpr08tORPnB942I9+ys12ivQcAr3zGjMDzxRwfuGjjnRJN2U3YE+fUb+mMzjFDQ5pjB+656m7UyZyFA1lO1fRTU1RH30Eq6XjkAYEfMfMeRAPTHOU6bWOFRmCOhRtvK9IyHfJMuDsnqOBaE0dHm2ZV9ED+6p82kWo7n+VZAqvpHkzN8caN1WbeO1vptaeekRPmJifKFDVv69Qy4CfL+VJVNdV1iyRT01D3DndRC5B2sdQLkH6AdDfFYU6bYLZB8/wCOSr+vVhyCGI+AstXPnzYTVUNY8izP3E2hWkG3eHYlnK6VZibsFGq5uTrjiNwwNDSAW7GMjw8hkgRiSjbf1XN0EADyRDllPRcKV0VZ3cEVGsLxq6qAYy0gZze1ySCd+Y+F8a3DqoqVtb8k7nx/duiuhxuaRYN9/NE2edqUGQ5cYaJI6qqKEQUlCglkd9yF03FgBa7Ei5Nsen07nsqcNgeQny/3WQyy7V/fkDmT+/JVZmFHnXaDT57JxFJNSpNThKehyyZHSMtZNc0uk6ybk92vhstrnc4rNuHPq6iT+Stb+1btayiPMnf3LknOsjk4V4hqMsqBZ4pXgJ1AltPuk223Ug/HFiqydlq0nclPUFZLFwlmPcinlmhnikihrWHcOXilgGu/IanQE3FgSbgqLFwwjt3tndv0IKq8TE0muiYP1CC+MqD2PPcxhFJJEVqpQUmF5ANZN2+6SL2J9L47S3fNJpnMLi6zYqEFQbRrJV3AeFIwdRRGBJAFmNr+K4PltiyDAwoOadrFMyggTgEXA7/l+GG0oZX0Z4DeiyypknqdMVbU05lZpIwkhk0l2AFh4Xsb2UWPJmx1bdLQVgvBOBsouHNWzGrIoaiWrneQFpGhCqDe7ElrkjkOQ5gfCuX8hlTBn+pDvFFQaQse7MIezxxFw7bjUdR/i3AI2tysOvLXZ0Ag4W3bCSIVXZpnNTHwlmNItYj5rBUw1QDoitLSjUrNHbwtICyBktfTZl5OMc+0io0xuD+/yt9zQNMjeUCLW8R5hnU+WUEDZnUDS6x0VKKnwsoII0A3Fj/2xXq3FtZs13bmsHiQEJpuqDuBXd2G9lOaU2bNm/E1DDR1sB00dMFUSRkr45X0sQDZtKrzF2JsdOPPuP8ApDb3VIWtg6WHLjmDGwE8ucqelRNOXP3RZ2/dq83ZdQZFlWT5e+Z5/ndUIKeCJGbu0FtTWVWLNuAqgb+I/dxyvCuGs4o6q6q/TTpiScZPIcgB1M4x1WrbUtR1OEjb98Ao6ulqKfM6KSbQZNFp2jQoC3oCSRzGx8zjNolml7G7Tjn/AArVQTkK3uFsyUxwkN0AxJTeGuWK9u6NhVCWG98HXeHtlRsEFDmYmRK6YEHuiqsjdOtx+R+eOKuGhlWQtVhBYhfiQBqVgeqnCaYeIU1PdKcJhooYTpXu+7BBB5H4fT8cdm1+loKp1YJKmM6zdMuy+apY7xr4fVug+uM+tVJyN0NOnqICiOzenP7O9qY6p6uRp2J5m5sv4AfU4tW7Q13lhHdGXQNgrSoYBSh7OzF21EsdhsBYem34nG0zJJlZbjITqpqwi87EdMPXuAGkIWMyhjNKsljvc+mOOuKup+FpMbAVecdcRplGWVLuxRUTxsOYB5Ko6k3AHqRiClTNxVbTbz/fh1WhQpajKqOg4R/0gp6ufMi8U1and6oJCppl+5oYbjQbNfqQT1tjqm3RtnN7HZpnz6z57eAwtCpUjujZcwZ5w/nFXlY4fcVVXxHltVIlVAaZ5DGo3fxGwtcrszC4kDDawx7Hb3tvQcLzUNDgIyMzt4/AbiCsq7oOu29mzf8AfcpLgeiz7gCOtNXQU4WskWWpnp5gZIo1NyqIgJc2vZbdTcg4y+LXVtxh7XNeZaCACMSeZJiB4yq9Dhz7Rmkogk+1Xw9lWuKGLNq4uCRpjjjFjytqkuLX8sYg9D7utDnFrfifoFTfVt2mJypPhn7QuZcX65cu4eqafLl8JrKqsQByOiKIyWI6m9h1N9sVLv0ZoWUNrVgXnkGn5yRHhjKlpGnUyGSOsqUn7cP2VUIM0MlNExtrjlLhR6gAEfG2KTfR/tmnsIJ8o/hXadW2a7S5sK0+EuPsozzL0qKCsjrIzuzwy97b42Jt88cje8NuLeoWVWlp8RC0m0mvzTgjwRA/GMURFqrTce7qsfjbFFttV5BH6vyhR1RxUla5WJzISbXticWr25epW0Q1Naeoo5tLtOshk91lOoHa/MbfPEj21GyAIhSku2Cn8tmpolOgq1+o3xm1WvduoXNLt06q+IMvytNdXVQ0g85pBH+dsRMta1UxTaT5Z+iQouOwQ5nHavk1LETBPPVkD/3OjnnB+aIR+ONOhwe4ee8APNzR9SEfZtZ7ZhVZxJ9o/LsvZ0EVarC/+vp3g/8A9gXHX2vovVqwZHuId9JTGpSAkKuM1+0VVZlIy0ccJB28VSGP0XHUUfRinSE1Cfh+VGa4/wAQoGo7QeI81f8A13dKekUf6m+NBvDbOiNp8yhFZxS+X5HmnEEo9qmnnVuYlckfTlgKtxQth/bAHknBnmra4F7HqKskg9qhWRVZZNNvCCDcH6442/43Vpg6DHJOX6QuluEuBcvy6nQRooYAchtjz2vVqXBOpyoPruJRjPkVFW0UlLUQRzQyroaJkDK1+hU88U203B4cyQRzG48VAahbmVwh29dmEXB/FecZvwpWwZjkdPapzLL4KlJZssLPoZyASTDrsDq8SE2N13HuHBL+rcW9O34iwtqnDXEEB+JjwdHLZ3KDgkx7g7UNunMfkfQ9Rs57K+IIxNFMreHlKo326n5YzuL2xgtPuWoxwcIXTfDWW/tGmSpAXQSdhz+B6cvzx5VdPNN5Yoah0ovhycIovYD4YzpIOVRLgUlmGUhRqEd/nifVpQAhUl2zcANmTUfEGX1cuU1dC6pWVEFKalnoi374GEFe9ZATIi3G4YXs+O59H7+mx5s7kTTftmNLuRnkDs73HkpNb6YlnLl1HRGWcdq+V5u9Xm3C2b0Od5XEWoJKmBjJGZEOrXq5XbVc2Olr3XljV4zbOD2UntLCNhzPiq9rQ1U+/wA8/wAIDn7ROIK2Q+zhCrciEVQfgWYYyhZW7B3z++4KwbEn2Qsx8UcY1DhI3pg19w00Xh+O5thxb2eBLvgfwoXcOIBcQPimXEvFvFfDVCavNKqGGEsE/wAPJHK4JuOS/DGuOEUw4NG/mqDbcOkhR3CHFOd8fCpbKc079qdwkkU1RFTyC4uGCuRdemobA7G2Ibuzo2Ib27Ynbcj5fRE20c5usDCS4m43qMu9joKqtf8Aakzr7PSvIC7XZkBsDYDVG4O4tpIPrdsOHOY/ttHcAmeX7kfFatszSCAPBXlwrw7WGCoqxFTyvPEsckFVUSrEVNtpIwdNx/FbpsRe2OuplzxLVi3FRoOgk4PKPko7tC4lzHhzh+fLoa7LqCtrZDGGjgAWFfdLJublj4VZrXN7Da2JmvdTa7A/f3dWLO3bWqB5aSB16+K5E4+y6KkzerrKasnzRWr17+rqJQ5SXu9kACgWt1FhcAAb4uUy+pRJeIiPgtR7g240DeMpzk2v9jZvHBNHTyS0TGOWa4RSjxuCSCpA8J3BFvPFexOm9pg85HxBT3w1Wrj0g/AqLzuaPJW9my6nMdGaOmSGCUHUqCBV8VySGBDCxNwed7b9raOL6Y1GSCZ85XEXIDX7YICF0aeoX2guVSwvdALEC/u3BJ32OL8ThUeUrwSGQBxI1m38Wu/zwMhFAX0H4I4Z9rnW2YQwiGoCyRhGUk6G3BNmk8N9ltYEc9QGOpYyRMrCfUjELfPclmypqKmFHphHd92rKwMl9iCtwb7HcCwvsSLYxuIXVHh9M17lwawcyYCt27XV3aaeT4JvP2XS8TUyJUomT09h+6obhhuCbBrhbgAfePPHgXG//qHbPLqfC6Wo/wCp2G+5u595C7K04e+kdVV3u/lO6fsU4ao5jNNRGvk8qxu8UDy0kWx5tcek3FrsFrqxaP8Ap7v0WzpY32Wj6qVekpsmokoqCCKmiTwRU8MYSOMWFyFG1gCPwxl0mGvUNWqZO5JySme48+amsmpI6GBTazHffFivXDB4qENLihbi3KIp8/oMyajgqKiIOi1EujVACL+DUhNzuPAVPncWxFb3LxRqUy8gGDAnPnBj4g+C0mGGaUB8UVOidWO1tuXLGnaAkKwGamkKd4L4nUhI2ax+PXE9QFjlm1aR3CtbLM3WWMeLn5nERqYwqRaQUpmM6yQk33tjn7wT3lYp4wgvPKlWhuSORxBSBLgrrMJxwxMBk9Kw2vEp/DHRvfpEHkq1RsuKFu0ziJlhNLG9u6Uux/mI2+mGtm9rUDuSuW9MN7yL8oqKvK6fJoKKk9qVqmGnnbUVEEGltc3LfTpUAG1y43xcoluS4xgkeJnA/lZzw1znElWXDVMsS67KxAuL40KdQtCzy2Sm2YVdkIB54yrusrFNqGMzr1hjkme+lATbz8sYZdBKuNbJgKjeOq+o4k4ggyxJGRILTzum9pWHgA/2VN/i48sbtixtvRdcOEl2B5c/iceQW3RaKbS5WvwRwbCmVQxKt9KgC5uT8T1xcoU+3lztysG5rEuJUlmXZJDmK/vmZ0vfTc2+mCfw6pT77Co6d+5uAgjjHsPy+Jw8MWkkXPxxRN1c2jg0ukFXaV45wyuTu1P7K2W5JxqmfO0gyCuN5MvjXSgqubKWHuo4BfSLb6xcCwx6Vwr0tuK1n6rH91uzjnu8jHUHE9IO6zKtlSqVTWOx5eP48EsVq5SmUcPZU9dVpGFSnp1VY4l6amNlRR6kYr9zNe7qaQTuZJJ8Bkkq0A540sGyn8m+xhxXx1KtRn/FFDkqSWYwUNO9ZLby1lkQfK+D/wCKLe27lpbl8c3ENHwAcfjCqvognvO+A/KOo/8Aw7MrjjhkouOc1oKiI6hJT5bChZrdSsgb8cR/8U3rwdVuxwPIud9wR8kzTSYQQCI8U24i+yL2n5ZDN/o5x5BXFdPdRVxeEvYG4uRKBfYb2+OK1PitiSPW7P8A8YPy7hWn64f8XkLnTtTpe3rssUftyDMIMvY6Gq3oaWqozfb/AF6IyD/eIPpjs+Fs9GuIGKIbrHLU9rv/ABJB+ErOu7q/a5pt3742H4+qW7O6ztTz6OGOPP5KSE20rSUVNBGo9AsX9MVeKDgluSTRBPiXE/Ny1LRl4QHXFUn4AfSSuieD+xHP87jjk4i4qzuqjPOEVjxqfiEK48wvuP2tAkWluwHrpB+srY7YMGMlXHwn2McMcPBZKbKYDU//ANRMgkkJ89TXOOJu+OX10dLqhjoMD4BVn1nkZKsGjy5qdQkckkYAsArkD8MYJa+ochVTUwnrU1Q8ZV6mVlPNXkLD6EnCFsRmAPcoy8IdzXs74dzoMMxyHJMw1c/a8pppCfm0d8XqV5e25/tV3t8nvH3TT0CGpvs5dn07F04KyOI/xUtO1Of/APEygfTGs3j3GuV08+ZDvqCi7SEgfs+cMQG1LQ1FFY8oKyS30fViT+tcTP8AzCHebR9oS7fxTql7MocrIFNLU7HUFk0v9SAMVH8QrVfbYPdP8p+0ndSqJV0Cd3DLSNMNgsshX/6QT+GBZcNaf7jDH71QaQ5VZ20t2xZ5wpUZXwlBw1QyVUbRVFdHnrxVKofeEPeRRhCRddRa9ibWO+O44Dfej9tcCteGoSMgGmCJ6nS50x5Knc06kRRbPmYj4LjOq7DePeDswqphwLmlJDLBLFJLlrR1SFSAdJNO76kI1gqbg+HmeXtH/EHCLukALppMiA6W5n/qAzznks+jRrtqz2cDmft/CYdnPFM3B+frQ5iz0zXCmOpBikDeRVrG/wAsQcUs23tA1aOfLI+IW1RqaO6V2j2RcTwS19NTT6Csi3p5XFyBuSoPQgHYjoSMeIcWs3RLeufyrtTLJCv+ipVljABJH6Y56lb6u65ZTnRlK1eWjuj1tid9vpbhRh0lBWe0CukyEEXGzDn6HFRj9JxyVpvVcNdp/A9Tw1xPXSZTPNQ0lVManuIJGWISi+oFAbEAksLjYOQLWx7rwriLbu2Y2uA4gRJ3jkZ38D5eKngg62mEjkXaVWUDLT5tCSLbVKcifUdPjgrnhdOr36B9yu060YcFa3BOcHiKtianqFMKMNbvILBCCSTta2xHxxzrrN1KoMQUdao3syCJlE3F+R0+fUBpzLqMTGRizXA8NgL/AE+ZOHuLxtFwg95ZDmllM6kJdjfAWY1jVMuW64KaGvlRql9Yhnj0pqA0sveW8Q56QR4tVguD4pdB2ljxJLR0kGT1BjkepG0bqOheMoW5a7ecfDdX1S8J5RlWW1VP3EUhqtTVMjIB3hb3i3n63588csbhzHB0yR8vLosw3by7VKrXi7tj/wBETHk2U5g2Y1jP3VPRxozzux2VU0HUzX+71+uOo4e+9rMLnd2mNycADxnYeKldVp1j7EuQHmn2f+1bPqubMM0zThasqKxY55Vq61+/pWYaWgBERS6A62ZNmIKo5PPUZ6UcFYABrJEiQ0QY2O8wdhORuQFdbVu6bdAAj9/fFOM24IyvhDhfK+F8wyp9OWGoRszpqjTFmsUjI4ks3ijqAynw3IAVLXAOOl4dxm04vQ1M7tQZI8Pv5/EBU3UatGr2gMg/I/ygHKYI1zSbL45VkhYz0gleM6WRkeMMyHoVYEr6kYgfFC5a4bAj6hbxmtbuHMgoZzJJanLaapqUielbWkUgYfuYtMRjjc8y6rIo13OpdD3swx3tHSHOYDt/P4923JcFcanQ4iP3981F1vgigkUhoJTdmvcObfdAva//AEi3UYtNwqJKdLLVKoEVJP3YFlvbl06YbW1HpK7zz7tJ4Y7Msnymq4mr6nLFr6tYKGGmF3q5AQGEd2K6RqXXIfAtxcksqt0N1dC1ompBJAJAG5jksahbvuanZtjzPL96Kf4OnirO8LCRnEjMe8Ysg1G/gPLT02/h3x8T+lXH+Icar67l/cE6Wg4b4R16k7+S9Ls7Sla0wGDJ3PM+aOkVdO3MdMclSbIlWSozN5mjjbSBtz9MTudCTQDuhOhL1U4lmYM4a4K3AABNha+/P6741+0bQo45oHDU6ApRaws176VHIeeMV73VHSVZDQwJjnFXeFlHI88TMM4CdjeaqbjJ1iilkkICgE+mOrsQXEAK/TE4VX8L9qNJScRw5TWVcVLXVLsKNZXC+0W+4P5xcW/i2A32PY3PB6tSga9FstaO9HLx8vp5ZUVc0mPawmC7bxV88P8AGngVXbe3U44SvSe1VHUOiJG4oWanKmSxI5csZdVjn7qF1ItyFBZm9RWQsqnZwdJGDpBrDJQU6kbqSynMFochgc/+XCo0nzAt+YxYrvLnkDmpw3U5Ul2y8RS0GQVbIx9trGFPEb7h3Nr/ACXUf93HW8CtRUuG6vZbk+Q/JgK/TbJAC6KyGKmzGeKpaIM9LM707Ne6agVJFj1Ukb+eMCnVc2mRO8T9fqsWrLXEdUYvWhQBiftYAVYNJTSvrdQvfljLr1JMqZjSgvjXO4MqyySWoI7mFDNJvzVenxJ2+eK1Oma9YU2bnCvUGFxgKqOAu9zTMHqqixqKiQyyWHUm5HwHL5Y6K+ApgU2bDAWtXhjYC6X4SjCU63WxFh8sW7AyBIyuTuN0ZxRhlx1AaHMhZahOJKJZKZSQBvbHHcSphsFX6LlVvHXBkPFfDWZ5U5MZqYiqSqBqikG6OL9VYA/UdcZtGq6zrsuG507jqOY94Wgxw2KAOzDgbLsno40EAilDhnR2Ny42OrqTfz8sbNatVvKhe4z08lYrVCwaG7K9clpHRfAUC7BRblialQedlkPcOaLqGlBS7EEWxvUbYAd5UXPk4TxqBJF5A+uLD7VrhITB5HNReYZDDPDIAgGoWa22oeR8x6HGJdcPp1BBCs067gcqosw7J8ryPMWqcvoIaUMdTQwKEQ+qgbL8rD0xy97600aHOLuk7/FblK6JEEqXpCI1tHECw5qdrY4x7HE95XARupqhWaQ3aNVHxvfEPYPJ7iicWQpOMgHcC/kMWG03jcKKQtzKpIUISPhi02doKCFuscZcFrgHocHAmSEM4SWXSGekgkq6ZKOpdAZKdZxOI2/hEgVQ/wAQB8MWSy3a8hmRyMQfhJj4lAdSdtHERz2xKCzogymlZldPWRhJo450uG0yKGFxyNj1wxaDlphOCW7KLXhaCOORCS4ZidrIBuTYBAoAG23pvc3OAqNc8z+/OVL2uVq2UhdrFLdVOM00DzUnaKPqaIIWJBGncMCQ2IxTdMDZEHRlDk1XScUyTZVmUMWZxd2JBT5jCtUjITY3WRWW9+g6EHF5rKtmG3FElp6tOnPuIKncyGyUKZxwBwjkkSiKmXhs6w0b5cxjSN77MIzdBv5BRjWo8S4hXMuPaddWSR54P1RsLye6rC4Vz/uKaKGuq4Z3NglZGNKS7ddyFPpcjyODZXa90EaSOv781Sq0jMtHuRisqTRHfcdPPF1sOaXOOyowQcIazmhDsCBtjDeIfKttOFzR2w8JVkmd1BpsurKyNrSg0tLJLY2391TjueDVwKYBcByyQPqrbXNgSVS+dcMTwhu/y6tp2HSailQj6qMdvRr5w4H3j8qSQcz8047OjmyZxFlOWZXmFZEapauWGmo5LyWXSNTWChfMki2FxAU3UTVqPAMQJIxzwNyfco3VDSkg/vguraXgmOtiUZjEIqPSL0iMFVvMHT0+B333x54XunW4wsetckiApnM81oMiy7VI0VHRwLpCqoVVAGwAGwAHQYrF7qjtLcysszuVTmYcS5t2pTtTZRUyZJw3crJmmkNNUC+4p1Ox8tbeEfzHbGx2VDho7S5GuryZyH/zP2GT4DKuULV9bvuw36+SMODuFuG+CYD+xspgo6h00S1z/vaycHn3k7eNr9QLL/KBtjCvb+6vsXD5HJow0eTRj3mT4rbZRbT9gQparzCIIQp09NjbGY2nJ2VhoPNUv2s5lJ7HMsczK3TqL9Db0x3XBWBtRroVsRogqoKLP4E4khqqYFAEgqJI7X7txYsBzuLpcE+ePQ7qmTTDuZVS3I7zehhMxQJE9VlUMhaWKsKtIHKwOSWgCR3PvBYYixBuVeNW90E9vRqTFR2xGOvX7+7K4i5bBLRuDn6fZN0ibK++Jkdnd9hM2kKeRCiw8Nh9fPGgMlZowFGrQBVAeNmcCzESHc9fu4fU7omgKU+07nXEOXdsXEE2e16Z9ls9Wy5RWPSrHHU0IKtTxiJUUR6Ucak0AljI27HUSqCncVXNce83fl7/APZPb1H29IaRg/v+y6h+zX2pzZv2a5IayGSOeAyUrLO3jSNGJj1E3LEIUXffbfrj5e9MeEto8UrdnsYOOpGfLMldnb1u1aHHBIyr5i4vpnkjQse/KGRVHPTcA/mMecC3rMEjYYWiGSFB8UcXWenhiN5apxDoPMDdmfboFDH5Y0LW1NXU9+zRP8e8qRrAASeSZ/tVaGnXSrSS6R+7QgNb0uQL/PEppms7JgIabOZTgZqAD19cVuyKkIlM6zMlKFi1gPPE9OkZwjDVR/bJxvSZPlcr1ErRwLtutndjyUDbf0+uPQOB8PfWqgMGfl5qxqbRZqcVzFwBw7J209sORZZVQh6WeqE1UtriKmiBlkAPqkZHxa+PX7us3g3DalVhyBjxccD5lc5UJuaupwkD5LtLMskq6aeWop4QCzFjGNlueYA6Y8NBa5oDlrMrA4JQ9BxItLWmkqIXy6aR9V3FkkYnnq8z5G2LDrUvZ2jTqA+I934lWXDUJGVauQVCZnQBb3lj3+PnjlatKHGFz9b+2+eS3zqH2aNolFopTrU9PUfr8zhg3vByvW7w7CoPiuE8VdpVHl3v02XR97J5d5Jy+iD/AJ8d/ZuFlw51bm8x7h+T9FsU+63UuiuAq7RTtEzeJAAQfwxxLzpkhY9dsuRa9STYhrjywGskQq4Ca1VYAu52G+KjypGtXLf2qu2leEUyjLadVqp6+o9oqINQBNJGbWHkXk5f/KboceheiPAzfGrcVMBogH/qP2A3/wDkp+3bawYlFPYfnlFnVFBXQTrLT6RIJTsWQ8iR0OxUjoVIxmcat321fs6gg/j7cx4LRruFSmHN5rpnhevM0siLDLHGhAV5F0h9tyo52HLcb9MVrV45Lm67YGTlH1I2pB6jHY0TqaFkO3TTO4tdK23I45rire6SFboHKDKqKxOMONTFd5qnM2yM5XxtUBamaOGQrURoGNl1E6h/xA/UYu21fTR0ncYWoDrpzCsVM7q1pkSmlAc236288WWVCNnLN7Ns5CN8m4gDqqynSTy8sX6d8AYeqb6HRE9LVrIAD15euN2jcNduqTmkJ4Yg6EjfF80mvEhRaowUI8aZNWVuXyCgmFPVr4opHF01W2DDqp5HHO3lsHCIWhbVWscNYkIGp5GzCnV5IvY61NpYg2rQ45qD1F+R6j1xwV1btLi1bAd2ZxkJ7Q5k6ShDZdIOsG979CPTn+HrjGGqmdJUxAcJUsK0sA3hYdb9cWNboyoYCya8Dcxn88GKg3hNp8Vo+Zpba4P5YPtKZS0FMpcxjjGosWtvyvbEJayOqkAJW8GZLMPA4Jt0OGNFjtghMjdO4pZR1NvU8sMLcj2SQmLgl+/YDcnE3ZPGdSjJC0kqmUb2IwJDxuE8NURnVTIcvnCUjTsykd1HIEL7cgxIA+N8M0HUARHjupGgTugBs1oODoZM3nyTNkqsyqIoJlpaX2ycMV0pdUc7ALzGw8t8aPY1L3/07arYYCRJ0tjnBIGT4q9BqnSHCAmXGnaBwhDQxHN6qfLIZ0DK9dl9VGCLX8Td3ZbDnqIt1wdnwy+e8+qgOLej2n4CZPhATsp1GGQR8UN9l2d5NmUdQMg4ky/OKeJ7NDTVSSWUnql9S/Ejni7xi2uaJHrVBzCeZEfPY/FTVXaxKtjJVzIzwmheM0e4kWoVio2Nu7IIN722921+RtihZ06lVh1zHIrKrOY3fdF9Jkks9pKqQyHnuLD6Y6K3suZWS+tyCVlkpaBDYGyi9xi2BTaYiUGlzkymzPWP3U0qL0IkNvz2wehjv8fkloI3UFm1ZJNEUkmkkW9wJHJF/nirVY1mWjPkiAzIVdcadomX8LUUktRLeUX0xKbM23TyHmTijQtq13U0sHvRdnA1OXNvEHGma9o9Y1TM5XLIX8EIj1QkdLqfeHLnzx3FCzo8ObpHtnnOfd0StWMdcDtBI+/JT2VccVNE1qmzoLAWjtpA+B5W5AWtjOrWDKnsfVdYWiEQUHaVSyRJ39opeqhtQB+JAv8ATGXU4U8OOnIQliQzvtKy2khcvVIRa974lt+FVnkQ1MQG7rn/AI87ZaPN9cdEstSTJoFhYG1rkE/EDe1/hj0jh3AqlCDVgYWfVvWAQ3KH8sqGjrI+9HdVBitLEwIZLuxS9+RKEcv4fW+N27ohtJoVa0qlz3lEwnDZpnDPUJFFNSLXS0xjP79mSMlxZdzHZgwudptVybjGpZODqNKdxj4SPnA/CxOINLatQTjf748khHEumWH2nukRjqbQoVjflGRe+2+1r2I5jfbEhYJglZjzWoSNV9qR7ADVq5+vLD5Sx1V88e9m+SdrmQ0yZhqyvNKQx+zZjSprJVD4UlQkakBvaxDLqIBsSuLNa37UaqZ0uiPD9/YUTKrqfiN014Zjquy9YMjzCmpfZqU+Gry5i8Lajq8QN2ViW8Qbe5x4Z6R8Dum1n1qkmee/zGPIY8l19lWo3GWnPT93VlxcfUckQEc8bMF8QDgMPlz88eZu4bUae8F0bWpllXEL5nJmOaH95HCRSwAG5vs0ht/wL/xYtVbYUWMoDc5P2+5+CRzDVtFn0rSl5AbnpiI27QIaptOICfR547AM11Ub3Y2xB6sOScMB2U9FwRxJxHFQyQvS5ZltQokaeokvO0TAENHGPd8J1DUQT4bAXvjsuHejNZ4FW4EAxzG3j18BjqeSy63FKFAua2XOGPCfND3at9lvJO1DO0qzLU5HQRIYKWGgYhmJcmSaRCrAmw0oosSLs7AaQe/tYtNTqDQA48xyG2x59OSyBeOLAyqST5/vx9y17Mvs9ZP2P8aQ1tJTSwrWUMuXq9RIXb2h3jcc+TPHG6jlqKNZfEBjB9JKlxXtBM4cCcbDMfAn78lNTrNe0tb5/kq76DhmOriuUBuceeU2ampnPgqE4u7J6DNad1khS566RhnB1A6qZgqzRuXs2Va0dLX8CZrHS1Cs9OxJglc+8B90nzxXqFtcdoBDhuPurldrbhhI3VhVlFFm+U2RraxrjkHQ4qVGgAEe78LFpVXMf4hUvw7wvU5VxRntTXqhqZ6ppAybjQbBLX/lAHxBxsXV2yrbUmUtgAPfz+eV1LaralMFqsOKV8qeKtjB0quieNdyyfxAdSvl1HwGMUM7SnB3/fqqrocdJU4ufqbEMGUi4ZTsR54oZGCoSxQnFPFC5Vlck2spdSutt7DmT6nE9vQNxVDUVNup2lcW8bNU8d8TZlmVXExEzKkKkFliiTZF5XFgPPdixx7nYBlhbso0ztv4k5J9/wBIV80w7cI6+zRxEeH+0alyJ2CU2Zo8cKnYJOF1gW6agp9bj1xh+ktt61ZG5b7TCJ8pj5T8PJA6BTc3pld15JMS8YBtvufMY85t3uLwFg1QBKPcsl1KMdjav1DCyajYKXr4w9O4t904pcRaCwo6SB69hHqZtgBc3xy4cA0rSiSqi7YqoZdPkmYo4VGZ6csp2N1Dr/8AS31xLat7UvZHIH5x9wtezEhzSo/I+LprKxIkUG1yN7YF4fSKnfQaUb5RxKtW4s2k/wAJOIRVOqSqFShpCPMjz7Qyq7XQ7X8satGsaZ8FmVKWpGtLWhlHiHoRjqbe7xkrNcxLTosyG4uDi5VioJQDBVVcdRDh3iHLq0aVpMwmFFUBtiJGB7px0Nyuk38wR68neWgdqcN4WzbP7SmWHcZH3ChuJxU0uXy1tDG01TApkEKe9KBuUHqbbeu3XHKuose8B5gHn08VeoOBdpdsUCZR25ZZXxoySXVgCCTixV4Lc0SQVpm0PJEdJ2oZdKBeUhepsD+RxRNlXZuFC62cNlIf6aZNVrZqyJbjqbfniJ1rU5tKi7Gq3YIc4urKeWkD5fKsslxbu5CCPiVINvri1as0v723l+Vaoh098KL4Xhq6VyzV0yBm1NGCXUH0JPI4nunsIhrft8gpaxacQrGos5Yx/wCu1W8+uMgPc3msx1POykEzNh1v88SCoVCWpT9p7b/jhGom0pOTMl03YkD1w2sjdPpkpmlZTTDVfUL77YiLtKk0uUfmlQoglWBpIWdSpeM2Iv18vqCMMx0vmB71YY0zJUfwZwHQZlJJmk1GrrMxYvPCoeZvvNfSDpJ+TEeQx09JtapHau7o2AJ93M/lR3VwWHQ05/fmray3LkiQOwCRqNgNhjbtqQcdR2XP1HnYbp9WOkEfQY3SG02qqAXFC2YuJQdtQB2HnjGeIV9mFFyMsRLH3fLDGpGUREoS4qz2GkpKiSWVYII1u8rsFVR6nFOq51UhtPJ6c1KykSFyp2sNmPEGfz1kaL+x6VAZVN+8IDDl00gXYjmSf5cdXwjsbeiKZP8Acdt0/wBzy/lWvV3OZqlWnwLwDQTZCYDEJKeqi58rg7gg/rjmLq8qm41E5BVCoNG24XPPaRntR2c8YV+QZjRTlqcq8UyMpWaF90kANvIg+RU49G4fZjiFsy4puGdxnBG4+/kVot4g0tlzShpe0elqWVBBVrIxsAYgd7+YbGj/AEqo3Mg+/wDhMb1h5FN8w4hElgkJd2JADG35XxZp2DmZcYULrsE4Ch63N5aSnq+8hgMzsndyHVqitfUFAYKdWwJYEjQNJFzfbtaLB3dMnxVGtVc7JOPr5oc4RrXqampnZtRkkB59LbYmvmiAEdi/cq0MqqP8fCBEaqPMMskpe5ZC93inGqVTe6skMgJAsGUEG9japan+3kxodPxGB5E8+qe9E1YidQ/fgsVKqi94ruNT3aRrBUK7rp22F+m29uV8dKDJ3XLEY2TVY5CoLUxLW3JMoN/kbfTbB6whgrrsZJPlKsUppcsrFvHPFOjq0cgF7oG5BlOrzHitddJN2q99E9358lWphtUZKE+PeBswquD84iqlqHpq2mELs1MZV7o7294GO+gKCAbgm21zjOr1w5kOwrNJgDsZXI2f0XE+RVPdZPmVcIREknscs571FN7EK+zDbmt9hfexIz/VLWsC+rTB8Y/C0vWK9IhrHlXZ2CVPGOVcDTZtnqPWZTVyNNQ0pS1QQBZpBsPA5ACqd9iwsDv5X6TU+HVLttvbd17cOPLwB8Rz+G66awfXcybg77dVY1Tx9Qw0vem/eqoPssKd5UC9vDpXqPUgeuOSZw2q52kbdThvnJW6A3kUR9jtHV8fcTifPMkam4a9lmFqtirySkDumVgyg2Kvsmoc7nljsOE8MtadQ9t38YOwB8jv7/gsviVyaVHTRdDp5dOfkurGno4tUpmMMNwGiTZL+gA2HIbbADHa1KtIOJ1Q3pyXENY+AIyoKp4ooLSPTyR1SwNZu4nUqq3AJHmOQ6c+uMmpe0mNneDyOPcrYt3yNWJ8FUHaZ26UfGFdW9nvBdG2Y54VSWbNIpEjo8ndHR0nnYqSSjLcBRcsoUatVsVrriFL1Q17gaaU4HN+Mho++wEnZaNvaOouFQmXdOnn08uauXIM3imZ9A0ozkqpABAJ25ctug5Y8nt6gDQ07qSo3OETCmWpjuy3Fr78sXjSDhLlAHacBBvaHwjDn+TzRhQJlUtG4G6tbYj54y7qKf8AcHL6dFct6pY5UYnbHkXBORInEeaxZbK63gjdHd5nGzIiIrMfiBYbXIxftOE3HEHPZbiQMzyHmeUqO5oltaWc0EcVdsOVZgKesyzMEM8666eRVKuy3I3jbfcqRpYb8reV+04Bcte+lVZIBz0+PlzCtUg5h7qPOHuOct4x4cNVQzETQlI6qB42ieCUoG02bcrzswuDYi9wcZd/w+rYEU6gwducj8rRaJfIU5wWFrqWaIhW7iXSCPJhqH64wbpkODuqG6Og+aiu1mjAoIYwPCfeA63OLnDDoqlQ2Ty+oZXI3EVLWZhxqcjy9ljpoVM9V3UekqzHZNW5NwSdzyx7HbVKdOy9ZqjJwPduVqPLy/Q3bmi7h7g2o4YzbK83hAjmy+qhrFN9yY3DH6gEfPGLW4g24Y+i7IcCPiIRBg2XeWVuqsNHuatj5i+34Wx5rSdpXNvHJWBlYtGoPPHXW3daFl1MlP6lQ0TDnscDfQWkJ6e6As2iWRJEYeEgg2+FscU4yCFrNMGVzP8AbLzCuyHsLmq8urHpsyy6toJYp41AuA5hYaTcEESi6m4x1nofTo3HGBSrNBa5rxHuB+26a6qVaVB1WkYII+ZXPXZn27cQR5RQ1WfZOtTDMtxUZf8AuXK3sGMbHQb2vsVx2nFvR+zdWcy1qaSOTsifMZ+MrRs7qtVotdcNyemPl+FfvBnaVlHFTAZZXpLVKLtSOO6qE9TGdyPVbj1x53e8JubLvVWY6jI+I+8LQ7rsAq3uG+IfaFVXbxdDfnjIa8tMOWbWoxkKyuH85ICRO1wdlPl6Y06FY0+6TgrJqU5yjOml1La+Oro1JELNIUJxbksedZdLA4G4Nj5Hz9MVrtoc0hTUHljpCBIopXpAk40VCeF7fxDr/fXHFPaDLStY4MjZcWfaHyzM+zHjZKzKssSqyjNXeSyyFDBUX1OlgpGlgdY9dY6DHovo8aPErV1G4fFSnA2mW8juMjY+5arbioAC0SPNCWWdrOY08Yao4erQBbenlWQ/SwONOrwai4wysPeCPyrba5IktRhlvaxSSKDUUWb0p/no9Y/5GOMWrwZ4Pccw/wDdH1AUuoEbKZh7ScgnXxZqKe2/+JhlhI+JZQPxxQdwq6acU58iD9CimRupCi4nppSGoOIIJb8gtYp/M/nitUtHjFWiR/2lHIOCiOk4xzeIbN7QAPfiYPjLfY27vDzwgNJhUnF2iZrTnxliPJlxWPDaJ2URoNPJOl7Wq2IDVEHsb7bXwH9Ja7ZyH1ViXi7XxM1pacKPST9MRO4O4DDvkmFqBsnK9qVCgJKlSedzfFc8JqnYpvVypnhDiGHjzPP2ZTxMYY4zNUyX2SIG31YkKPiT0OJG8KfSh7ziVDcTbs1Tnkrsy6iFkRVCKAAFAsABsAPljda0SGN2XNPdzKnL92gUbKMblJvdgbKic5UDmlUXm07kDDV3y6FPTaoSqa59BjNeQSrAkIazzMhTxm58NufTFV7uQVikwuKpztA4qiTKqtpWBpo0LuLatVtwvxJA/DD2lF9au0M3Jx++S1W0w1slCuX1VJnmTNW5T3cxeJongrRYxzaDdJQt7bne1wVJKki2NOtbvt6wp1TsZkcxO4mPnBB3RU6kCIkdEVdgmY1ByFMozGPuK+iVVMesPp2F1B62vseoOKfFmU+3dUpmWuyD/H7lZdyw+0kvtE9hJ7W+Gkq8sSNOJaAE0zEhRULzaFm6X5qT7rW6E4ucA41/Ta8Vf+W7fw6OHlzHMbZAWfEd1c1ZP2bQZTRTNXQd/nQEi+x1EvdU+XBTp72uKgup1NtEulnIUX0t4vbbR1O4AqA6gdo5+M8h4/dV6zyAQMfvIJDMuHMoyaSqgps0i4hqUCBq9aZ6QM7eJ9EJAICnwgsAAAQoOxFivSa12SCTnGw/KGlUeROyqLjWdmhFPTnWdQB0nYYntmhpJcguHOLYGVrwhDJCSJF0X3N/TFa8h2yu2MgZVl5BWRxHLZaiZqampapmlqE1XiV4XPeAAblTENjzuV5tjMoAkvYMkgH4H+fur11gMfyEz8FPtQS00M9LJMVMhBYOdIj3GoaSLhbE+Ekb7euOjpP1NDgdwuXe3S4t6LxmmBsIY1A6Fht/zYk25KOAulM94xklyjIMqoy8VFlrd5reHxa91ATchYgC5CXsGkfZRz1K1w2ppbOyz6dAtcXDmh3iXjWobKoaKrqpEjaoQLrjAD32B1HqBsPzHPGHcAmBOFrUAJ2yh3hvsqyHtY4xpBBKVjodZrplRQ9TQrLsjhWZLliqKw2F25gEY5bifE6nC6DnMcCXCB4Hr5jor9OkHnU4bH4ldWUXAlFUQpE9JEkIUIkSrZUUCwAHQAAAfDHjwoF7iX7lXXVyMgrEvZFkIDIaGOQSAhlIuCCN7+mLLbUBwyfijbeVd5UK2WNS5lWUM9VU1MMNZLEkbSnQsSt4E2sSoXSLE9MDccRu/YDsNx5p+1Ea2gA+XNC1ZxLn3C9XX08b0+d5bUIUpaaZBFUo7WVYyyjTMtz4SdDi9mLg7WrPjjK1EUqjCHbE7gjqRuCPCQfBOH0azmtdg+CH6fsm444wzR5eMc7g4fyCF9Iyfh+oD1NUimwWWoQBIlPXRqbewI97EVfjVmxn/pGGo8j2niGt8QzdxHLVAVsaW5CY8Q5fk3AHFuU5NkeWUuS5X7C85paKPQjP3r3ZiSWdrbanLG217Yol9xfW769y8vfIEnyGBsAPAABXKIBYY6q2cgzJ56CimUlUEiAcwbYxzgg9CqTmw4tKuvL7tSrtzGOkA7srHJykK+EOjD+XGTc09TSFOwwuCu3TsvzHIOOX4ojD1lDJA1MIpAfdVmYIjjdGuzGx2fodWx6ngHEqZtfUXiDMz8BJHMbeI6RlblJoqO7QHMZH4VfcOcGUWZPKYjFJl1Y5nsVu0kbfzEk31Xv5C3xx0N3fPogF4OpuPf8ATb7rVbbs0yNiugOHqClybhuky+hh7iniW9ySzyMebux3Zj5/ACwAGPNLy6q3dc1axk/boPAKEMDHQEVdm8wTM80ivuyxuB8Cw/UYoXIljD5qlfjutIS/ahTvJlgZU1sEuq+ZviO0IZXAOyz7B0VlQfZPweJp8zzeUGWSrqpGDtzKhiB8rg29AMdvxm9gU7duA0D6LoXQ2T1Vi13DvewykpcaG5j0OOap1zIIQB66E4cDNTUl7EmNPrpGKbMlc7U3KsnLRaEXF7Y6ugO4sp3tJ7UtphY+QxXvn6WFHSElAGbyWY2NscM58QthowqH+0jwnFx5weuQVE0kFNWTxGaSEAyaEljkIW/InTa/S97G2N/gF87h1360wSWgwDtJBGfrHPZWRQbcUnU3HGPkZUJlPZTls2WQU4oUgp4olijQLsqqLAD4AYnqX9y57qmuSTPmrBqNZgLmv7TvZw/DGZ0D5crxNJVRGGSO6tG2hyCrCxU3Xpj0z0V4ka7Htr8gZ8cjf4qje0zXptLMGdxuFbP2f+1bM8ypqTJeJixzpFtT5i2wrgPuv5TAdeUgHRgQ3H+kfCKFJ7rux9jm3/T4j/p//Hy2v0RUfTDapl3Xaf5+u66o4dzT2uCysVOnZh0OOLY/UC3mqFVmgyrSyup76lha/iZQScdRaVdTGrGqthxT2dA6eYxp1QHBQDBQJm1M1NmNRf3JAJFHkeR+uxxx120Mqauq12EOYFU3bHwpDxBlBWWMMVYMDbkw3B/P64G1rutK4qNO+FoW7pBYq/yXs0o6iJLQqdudsWa/FajScqc1S3EIki7KKLT4ol5eWMt3GKk4KcXJXpOxnLZzd6dG3Dbr9MIcdrN2ciFyUzrOwvLahTqpIn2+9GDidnpDWbs4/FGLg8woOp+zxl9yY6VIz5xgr+VsaDPSaqN3KUVwUwl7Dq2lv7NXZjTgcu6rJLD5EnFgekFN/tsaf+0KQVgVFVvZTxNEV7nO61k5ETKkn5ri3T4xZH2qTfdI+6NtWOag8w7POLoXOishlI3/AHtJ/wDtYY0afE7B27SPI/kFSCoTsUyk4Q42hQ+PLyLe8aeQf/8ATEzb3hrjs74j/wDVPrPgupPs18EVfCnZ7FV5sY3znOZfbKgxqVCRC608YBJOyanPrMfLGRfV6Vat/ZwxuBPXmfeceQC5q8rOq1CCZhXjlsQWIvbn54C1ZqOpY9Qr1dL3SHGyBpEqECShepl7yVmJ2xl1XhzjCugaQoXManQlwbnpii4qRoVTdqWa1LRUtDArdxVmSOqmWXQ8MXdNult9RbSotyuT0GFblmpzye82C0RIJkb8oAz47LVt2x3lRXFWeQVazUUTL7JRKFF31XdRsOpJAAO97m/kcdbwy20TXqe06fDf8qxVJMNCi+Hs3ThniaOUyrFl1cBBUDkqC+0nwRmBJ/hL4t16XrVItA7zcj8e8Y84VN7uzcDyVx8FCSn4yhkSMmV1UMgPvEEr8OR5/DHE8Q7tADoVM5oewroKmy4JAGkGm43HPGWy2lpJMLHIbMBA3aP2MZF2kRa5ZJ6DMVHgraZiuo6bASD7wA2De+o909MbvCuMXPB3xRdqpndp2PkeR+R5hM6mHbrkXtQ7Oc17MJ0yipyxKKgmj1Q11OzPBWSEXcI7b3W1yrWYDff3j7Jw3jFpxWnqtzDubT7Q/I6EY+ipmk6mZdkdf3ZUzXZE02pwhVSxe5HIchjcAiVE7Kbx0nsksXhIVrgfTENUdxW6EhyIcnqDHHJqmSCxil1s1guiVSzciLhdZHqBjLpgCsJGDI+IWlWJNE6eUFGiVE9VllPJIsVN3irFJO6ko8qkowuDuCAWBHMEGw5DoLctDIHL9+65a4Di8lIjMkiATvWXTtY3NvnfFkgdFXz0V301BV1scbSVfeAuUCAd4Ta38RNjc2tz57eegabXCYVMPIJAQnX8ZR0mdyUFfTBaX2cTx1ryeFLBibr1HhC3JteVAbAkjnboNfU3jkt20aSyQJKvrsCy2gakzvO6HT7LnVf3lNpACimjWyKLdO8ec/MeQx5Rx2tru+xGzZ+JM/SFpFpZTaPer3gKIqEdeWMSRIhVN0nW10GXU09XOx7tfDZR4j0Cr5knl/QHCq1mUWmo4qVrC4hoQHRO9b7VmEqqkkzNKyhtQVmJYi/W1wL9bYy7ruUyT+yo3wMBV7W50Mr41yupMPtUdNMZpIlW7BLaCy7++uvUPUcuoy7VjHUXh2ARE/P4YypuH0DWquf0HzKsuuzWI06tFKkkbDUsiHZh0I9MAGaYHNaIaZVTZvkqcS8cRVjDUlNTd2L8vfYn88W3XRo25YOZ+y02Ds6firX4fy6J0plCn904PO1sY9Or2hbp6rPfLZnmrWoXHcgdbY65r5asdwytagi/pitVGMo2KsuM+GYOIcszbKKhA8VVHJENQ5F1IVvkSCPhjFpPNCoKjd2kH5rTp1CxzXhc1dknZWYpRO0cqNpPeQ6rxxtzk0rba7Ak72vyAx0vF+LGp/ZB9/MjlJ5wP5ldJVqNpt7uxV51nBUNJlMDRqNZUOzb3Hpa3l1vjm7iKbGuBzzWMyu51QyhfIYY8n4jaQjSskbIxHxBH5YHtDUpiVNdS+lHipji+qp67LUAlR5NDXUNc8/Q+hxC0EPY5YdHVTqGQg7s7yWCi4dpYId0hUxeupSQQfW+L/EKzqtyXO55+K6Nz9QBRjFkiPF7l1xEzIVR1TKsvhynIWEHooA+mHogl0rKqFWFQDSgHS2Oqo4ABWY7JlKZjJop5D6YzeJv0sKsURJVfZs9yWvYDHETqK12iFXPHDxTVVFBqvIXDW/lHPF63aQdSuUgdLiiLLaBWpI7IPI+mNUiWyAs5x7yrTtu4EhzrL6SdqcTPTyiQKR7xW5t9Cw+eJuHXjrSsRMBwI/fkr1u4OlrkM0/ZxSVlErwQhSAGVkNiDzBB6EGxHqMO7ilSm+HFXxU0mCrF4GzaZX9nqTeoTZz/Ef4revP43xkvDWv1M2P7CrXLARqCuLIM0CFYNV3O6r1tvc/hjYtamnAWBVZIlFqOXj329MdEHFzQs7ZQPEFP3mlxtpvjmeIDunwWlQPRV5xbTCoy2ZbXNr8sYxdLZV+kdL1XeXVwy2qQE+CTxD4jmP1+eHq0+1bI3C1dIcEbZfm8Eyizj4XxgVKLhyUDqalErIWGxHxtioWOCj7NKLJERzU/LAEOTdmttURF7KfhhoclphJMYRfUq2wfe5IoTOohR72C2I58j9cTtcRupBsmLUSvckLfytfFjtCnhMpclatkipQkYMziMnTsATv+F8Xrd+uoACUnP0gu6K16KJFjVEGlQAFXyHID6AY6bAEBYBncomiQRxqo2sMb1uwBqovMlRWYtrYgnbE9V8BGwIdqyVuPMk4x3uVkIZzWXU5t0GKz3QJU7AuZu27jxqCSZaaQe1Ss0NPb7oHvSfLp629cbfB7L1l+uoO6Mn7D95LepAU2DqqIyyKWSR1M/gnkCtotrGjmdV9ut/j6472q5oGBt8M+H0UJd3i6UU1NMK6hkponMkjoU1MNrEHYem+MZtTQ8OdsFkXjw8Q1X52MFczqqaqsf3NJEgv0bkR8itscLxkljtH/UT+/FaGqaII5rp/LIe8gUn3SOeLtm3U0HksGqYKQqaD949hsOVsBcW0vJCkD8Ib4s4Yyri7I63JM5o0rKCrTTJEwsQR7rqeaup3VhuD8walKs+yrNrUTpe3Y/u4PMc1KO8PBcM9o3Zw3Z5nFflVYJqqZSr01WdKRywkmzFbbt0IBAUg87i3ufCeJs4ra9tEOGCOh+4O4VCtTNNwjY/vyVSZ3EKZGa+8bhj521WP4HGsQXghSNcAQVmkTvFqYAwUTU0yatRAuY2A3FiBy/qMZgOmo155ELVeNVNzRzBRvw5XVJyHLTDLJTLPqE1KHXUjK52bmCQpFlG49LWxtUDD3tGwO/XH5XN3EnSY3CwkWYRoqiNrKLDXSyavntzxc1FUoHRdDsj/ALFpK2CpcSRiV1Mx3keS4Zy3Ik94viF7W35G+u4xELPGSqg7VMpSl/0pMKfv4MvpYI5DdSQ0YIUgjcK8Utr2I1WIPThL6ppuKbJwXfRdhYsljnR4Lrrs4yWPhbh3JcphAWOho4YAB1Koqk/Mgn548Tu7k17t9U8ySrdUCSOmPgrGpXkcagTsu3x5XwVOX5VQw1CvHGbDVU08ZZoqHTSqBzeocKDt5gOq/HXitWabi6ZbjYZP5U1MQ3Udz9FtJH7Dkmm+7DyxX4u4spx1KrRqJKr6iys5hmtVKYg+lwFYnlYf5nGDUq9nSa2Vr2TRTpSeZT7MaJ8tpLo57mxuLmyXO5AwqFcvOl3NXtzKhuCswWo4tqqVtjJT3T10t/Q4s37CLdtTx+oU7mf25Vu5NAIpNN7BemM2zEmSsmqcIyo5yUAHPHU0XkjCzXhOWkubXOLkSYQIezyjYSCdbaLWa3MeuMypSDXEnmrDHYhDXDmUwZdntXGIwFnJmG23iO4+t/rjKqMcy6aHZELSNQ1KInkiHiimT2EkqCALWHwxZ4iB2YKq2/tKrstySPMM9leMxs0cLjS1rhjfSN9t7D13HkcbfArenc0i1wk+PyhXrqq5jBO0oI7Xs1h4b4ZpozW5fkebzq7x0UiIxm06Vbu9JUGxK3YjYkqcdCzhNOrUbV0HTsY5fWfJQ0qfbvdGQMz/ALqrOH+0jirs9qp4K6moK2gEjSzwVuqCaJ9K6isseqw8PIow58sHccO4bfgGnqDogEc/c78gqU1Cw6FffZf2hUXaNllYY6RstzKhmWCromnE4XWCY5EkCrrRgrgEqCGRgRyJ5m94d6iWlrtTHCQYgyNwRnInrkGVXeeaurIILyg9BipQb31QqHCNKVAE/HHR0wBhUJTTO5gtMw5HHMcWqS2FfoNyq94lrlo4tAN5pPdUHe1wCflfHNtZpbJ5rVpjUZ5BV3xRluniPL5g51aWjZCb+VvoRi/b1O45kdFcYf7bgjzJo/8ADgDkLfljSGGrIfvlR/G1G0uUsyAXRg2/TFGuQ0gqxbGHZVe8N1sVJPU0bGyxuNKgfdIDAfiR8sBctLg2p1+2FpvaSAQp6ohRJoqyKwKm7EdV6/36YqtIjCgBMFpVkcHQmSrlqW+7GI1Hxa5/IfTG3ZFxJJWTcQAGhHUZ8Ix0jHLKIUZnR0wtfoDjC4kYY5XKGSgHOAHp5AeoIxzdN0sWjEOVL8b1H7Jyx6q+laeVST5BjpP5jGvYM7ap2fUfTK2qEFwBTHIeMkAGuTVfy64nuLE8grL6c7Ipg40p1i99gOmMh1g8nZV+xPJOk41gh8MkgBPLVtfERsHu2CY0idk5g4xgqCdEoNumInWLm7hCaTm7hKHimM7XJt64D1RyHQZWh4pjBvqHwvh/VHbJdmdlq/FcfMMPUYIWbk4plTfA+YrnWcuQdqeHWeu7HSv/AFfTGha2vZVA8qldjQwDqrToEvJGvW+NQCSAsd2ynzstsdDSEBUD4KGr23JJ2G+K9d2VOwckNVz3Y/TGU4qwBCDOKaxqLKa2df8AWKh07/eOw/EjEDu9DSrlFup4C4K4m4nTiziKsrqedZ6MOYaZ1N1MaEi4/wBptTfPHrVraGyt20nCHbnzP4EBazajaglhkKIWlEdUK2BAKhRvfkwNgR6XsN/QXxc16m9m44VevQ195m6sXhZosypUqISbDZlPNT5HHMXYdSeWuWE5jvZcr07GdFLPUqq2Mkuojpewv/fqccLxYkuYStOg0i395XS2Q1XfU+i+ym2L/DqmpmlZdZuZT2ZWZrjwk+mNlwlqhBwoXMYF1X3B5bYxa9IEyVMxypz7RXBa8U8Ge3wRg5hlrGVCBu8Z99PnYH4gY2PR++NjdhpPcdg/Y+4qUs7Rpbz3HmuF+J6AaJ1YE60OzLsev+ePbWiDBWfOJCj+HJUGYUryboJkVg3lqs34HGXct0z4LXoEPARVwjppctrKCsmW9FBTmVJpCPaBoeO1vOMxshK21bNsSDjZp6nVNXIyeXX7zPgudrBukATIhEi0laqgRwZgUAsp9n17dPETc/Hri8NJCoK8qrOqdeGc3zLRVxZdRB61EmLzLaGSK/iCi2kSufCwuxAYHbGtVdpp6unVUaYPaNCGM7jj45nos0dFjhzvNqYNGoCiyqGOpAbI9nfUvnfne+PJ/SK5LbjW0+y10eZK7zhVIUqTmnrPw2XSuVNqkD35sLnrzx40Mv8ABO4dUY5fUINLSGyKbsf5Rz/DG1ScI1Ki8HYKtsroKnOqeknrFlSdqiWvlg0+IyyMzKW8rBjt5n0xWouFGo+ofadgeH+6uVI9luymc2erWBI3p3SEbaiot6gm+xvy+BxmcTD6jA/kE9NjHAjmo7hSkGn3SWd2LH5nnjmq8kwrg7rQFLV2VgtPSyKO7kUspt9RiCmSMTkKUPwHKkq15eEOLqSre6+zVHdSn+Rtr/QqcdgwNvLV1Mf5CR5j9K1KcOEHmrfy3Kc2qeM4s7XiOd8lameEZGsCLTi4QpJqB1GQMHJY81IUAAEnBp3dFlr6t2ID5BL5OrnI6REY6ySTOMuo3SC0jKsvL22BJ+ONi2fiSVkvCkHUMgtjTkOAhQDBym7Rd4pDA+XnhnMa8J5IUJWZY9PKs1OmrQbhfLz+WKrmNdvy5qdr4Wub1aV+WOEbxAWZD7y/EYz78F1OArFHDpVNHOX4V4gM+l3imOhyN9J+6T1C7sCRe1/mNTgdy2k6XGMLZrUPWaWnmuW+2XPeIuLeMY8qlFO9LWVFZHlo7kQlgml5Br+9HbTv4QW5g88epWVS3ZQdXn2ILoM+1ge/7JPptaWUhguB68hJlAVXmfF3BMpy+rR5IYT3SwVcJmjKldlWRT7tmFgGNgeQG2LraVhxACqw5OZBg77kdcbwFRqWtVu2Ve32L6jOeJ+OeIK+Wk9hyqkoEjmeNWEU0zTDuUXUOaqJm2vsfXHJelNKjb21Kkx8uLp8QAMkx1MBV+8wODxvH7+V3ZkNPoUE87Y4+0bnUs6qcImjGiIn0xrg6GkqtGYQvWySNV1Ws3XvPCB5BQPzvji754qVC2cLVpiGgoXq4u9zGaeRT3cQCqSOZA3PqMZxOT4K20w0BAGbVAreKgosRGv43xZoDu6upV7TppKwsojJQC3wxrtJ2WM/eUpndB7Zl88I5uhttffpirXbLdk9N2lwK5u4kzOXhjiylaVSIK6IxEgXVZYze1/VW6+WNG1oi6tXad2Gfcf5C6JoD2o2yvP4KmlUAgbcr8tsYVSg6m4yoHMMq5OBDfLlfc6gtvpjVs8BYFwO8jRGuPTG60wFmkKG4glHcsMczxWpLCBzWjbjKCs12if4dMZDRparoyVU/FUaCCYSxLPFsZI3UMjrq3Ug87i+NOzJ1AgweXmtSnyVS8dcDVnCaSZrlDSVGTLd5YSS0lGB96/N4/X3l+9ceIdlw/iFO9IoV8VOXR34d4bHlBwrdOqTAJQ7RcYVAABYsANjzxo1LFisSFKDi+okW15D1touPpip6kwJ9IXhxfLGRpuCpva1rYXqTSlAWsnaLVIbeAAfxSqMOOFsP+xQ6Qto+0Z5OTxk9QJlJ+Oxwx4WBvPwKbS07LP/ANoDtIV72JWte3eAm31w39NAEwfglpar0+zXmRznK+Iq/UHAq6emVgQRtG0h/wDrX8MZV/R9WLWRkgn5x9licQguYB4roDLG1VKixFtt+u3TzxmMI1hYjh3VOHcc+mOkpmRKplQWYtbVbewxnXDoJU7Ahqsb3sZRKsBU19onPJuHOyfiGvgNqiOnfuiCQe8KkJ/zEY1OE0Rc8QoUnbFwnyGSrlIlupzdwCvmnwRxZDkmYLRys8NFOQup91jfYB/QHYH69MfQ1/ZOuKfaNy4fPw/HwXPWF+22qCm7DT8vH8q46Re8Glh4htvjhamDIXbjKmOH8xbhvNROwJpJfDOo8v4vl+WKVzSF1S0f5Db8KncUNY1jddD9lNUBnkuhg8TRpICu9+lx+GPNuKNIptJGQU1INNDHVdK8LMDuRz/QbYi4W8l8FZVwICIZCA22OtJ5Dms+FDZoBrOMq4GSFOzZDWaRCqy+phZQ+pCLHrtjJBLX4VtphwK4G4/yL9m8Q5nRWtTwSEICSQUO6n6Nb5Y994bcm6s6dY7kD5YVOu3s6hCrusp0y6uqY4JC6o4ZCwsRtcXHT+mLd0zOeasWj9TQi3Jg09dWwQkI0pcxxXYS95qDIbnc2jkCkk6bCMrzIEtF86HO3j7R9fuqF0yA5o5EqZlyDP5pHkjl0RuSyqJ0sAeQ541u1H+krH7PxVg12aVkeQ5lPlWgexQd5I9Sb0+nWpOoEEW94gEbsF62OLF7VApaZwcKK3YdclSvZ7RQ0vBnDcUUbLHR5tBNd5BI5E1NUMrOw94kwjfn4gDvjyL0gJqMrVQfZlv/AOP8rurUaNLerfp/BXQmR1IajiJO+sXx5Uz2kNRuUQUMkmdVU1JCSkFOFM7+bncJ8hYn1YDoca1GSI5Ki8BuVP0jUeVxBdKKxJAvzOCL208AJdm6oV581oK5HVWRlU2br8cVa1QVGFpUooOpkSh7LqGKhryIyO6k8a25b77fW+OTIBME5VtxJElENZQippwUFpE3U4qvBblqTTG+ypvtf4aMtOawJZWURTW6H7p/MfTG3wy40v0jzH3H3+K0bd/+K37HeNRmGWCgqpAKqlPdtc87dfpY4g4xZm3q9tTHddlWrmnqGsK4KHMgF88VLa804Kw6lJTlBUBxY73x01vUDhCoPbCeSwhluD8MW3OA2UQTFgwkNhcDnijqe1xIEhTwCIWlTlVPXqGZLMdg67EYslrKrZGyjksKrzjPsoeuWWekqNLEe5KLqfmNx+PwxS7JtA93AWrQvnNGlwlcd9rfDWccP9vnAtNmGX1FNTSUlaKeqKEwTOyG4SQeFiAguAbi4uBj0LhbmHgl24EapZI5wDuRvGd9lZNwype0NB5P+f8Asrn4Z4HOby0kTbmZ1UB76QOZNvQXOOLpPqXVy23pYJO6vV7gUmud0VycB5BFlGX6YlAaV9RPO4Gw/C5+eKEyclYNeoajpKtHLI9CKo2xrUhpAAWa7JUpJII4yTyUYO7rCmzyQsaSUJ10gLsRtc3xxTzqdqK1RtCGs5rTT00rogdlFwtwNR+JxC86nRyVimJICrHIahM04kr5VN0E5jVh1C7E/W+NXR2bGNPSfitGtLWAK28phKou1rD6YuNnCwnHdSk8GqMkdd8SVGzkKIKiO3Hs+lznLKgUCgVmoVNExNh3638BPQOCyemoHphuGXTbK6HaewcHyPP3GD7ls0KpIjoqM4S487+kZmdo5FVlkjcaXQi4IYdCCCCOhB8sdXe8O0viFpNcHLuLgqnalyOiVhZjEhP/AAj+mOWoYC5SudTyifXpjJvjULoYSqcZQ5nLmSUKTdDzA88cbe1e0rhvJalEQ2UMZsCsbXN9t/XAuwIUrTJVWcVW9hqTyshNz9cXbP8A5jVp0sxCVFPLlx9piV5KBmZn564ue6+Y/QnnyxI5oeIPtYj+Ug8HBwVRvab2AZZFUSZnkcMdPRz3kemgFo4yd9UY6Ib+7yU8vCQF7vhXpJWc0Ubky4Yk7nz8fHnzzvYY4EQqOz/swroZDHBE8rm+lVJvjvbbi1JwlxgKCpS1KaoOy6GaKhpZJomeQBQCwYMT5b+Le4uOt8VX8TfrcQP37KdrJZHIKI447IE4PzCCQQq6bWa3MMpIJxqUOI1ajC153Wc+3Y5we1SvDPB0s0SlIgRbyxjXd81pglaLW6UUS8HTQQAmMbjyxkC+a4xKkAXTn2RaH2Ds9zhSoVpM8lY7W5U1OBjmOPVtdZgn/Ef/AJOWNeD+4PJdF5Wb1S/PHO03TUCy3Duqbc/u7b+Wwx0dN3dVLmoHMbgHyxlXBOqFZZshusuFbbpigrHNUL9rL/8AlNUx/dmrIEb4arn8sdB6PEf1Fjugd9P5VyiJ1T0XzSr4IkdQNLErYgb2x9IUHEhcTcMDXYVsdmmetnGULTzOTV0do2Y82T7jfQWPqt+uOJ4xa+r1tTR3XZ8jzH3C7HhVya9ENce83H4KsmGiFZDa3jGOTNQsctyFavYEs0E1ZFISVhdUi9FIvb5HljkfSMtcGOHPdQOphjTHNdXcJu0i6uvK2Oc4e6KhWNcDCKJLsW32tjsp1HwWZsobNWs3wxRr7qSmh4tqkZT12tjJduFbjC497Zstlm4jzZqVRM3s91juNTsJGCgDmb7Db05Y9l9GCX2WnofsCob7uvaSNwub1keKvrkcsxM+oajdgpUFb+tjv63x1NyC50+CjtTDcoyyaen/AGtlaTBGnq40ghlXZlfujdW02JFoFtrvquAPc2ioFwpahs05+P8APL7oLvTrLXc9vOP45osNJSEkrVFV6Lc7em4xsdn4hYmpWfx+aWo4fzGPJ6NKWsr6v2lYBUSIine8SIjKrLqZyI2BvqKrpsliuz2TZcO6P35IbUSYaclOMokzXh2iaM8P1j00iwKtRWTd2WMMrnwmzGRtJa4bSQxJtjyC/q0vVn0apMvM4GAc77Tv7l3BID2kGQ38Qrb4N4joq7KTUrL3kUBBlRhpdCAWIYdCQpt0PQnHnjrd9J2lw326J6g1GQjngyWWLJaNXJ9qrXepmbnd2Oo/Qm2LDnk1RTZt+FTLZ1OI2RnTZJFKhDrrbc3O+LfYh04VftXA4UJS8ItQ18sizyLD4dMAtoFrWOw235254zqjHBpHIK8bjW0CFpXxmKRZFuGQ3F8cbVdodqClYJwVPZbKs8akb4sQHCQoDIwkeIOGYM3opo5IxIkqFHQ7Bgenp8emEGOpkPZhGyoWlckcdUFf2P8AGcc0zOlBUtpSpIsrC+xPkwuAw9QeRx3ln2fF7U0wO83l+8jy+G637eu2oIKtzgztJgq6eJKiQKSQus9fj5Y4i84a+i8mmENa3nLVZ+U5uHYFJLgjocUaFy6kYJhZFWj1RbTVInjUEj446ilcCqAVluZpTlIVuT90jyxcYQ2YKiIKyKdUfyDdOmFp0mRsU8kr0lKTtcAeRxXqajujEBC3E/DVDnFBJQ1tNFUUjusrwTJrQuvuuB91xc2dbML87XBq06tSi7uEj+dx7+mysNJaQ8bhc/SdrXC/B/aTNwnNT5tB7JUxQDMxGk9INaoCWdWDqEeQIx0sAQSTYNbrrLgtyKbeI0HtmCdJkHnsdsjMGOnNWKlR9VneG/7sr/ySjKuFKGPu/Bo6qRsR8rY5mi0Pgqk8oyoo9KA42W90aiqe5SWa1Xdx6AdzufTHO39fUdAVykzmUK5jNpRrn8cYz8NyrjRlVn2k8VrkWSVMwYGZVtGt/ekY6UH/ABEfIHFiwtjc1WsOx38hkrSt6Wp0oW7MV7swi5blcnck+eNO8d/dlTXOWkq/sqpQYVO523ONBjNbZC5pxgqRaHwaSN+nrgS2MFDMqBzzJY8ypJYZRdHH09cZ9WnOQrFN+kyFyp2idgea1Xajk0uTxxiLN8wigzNS+gLGWHeViHr4A3eJzJ0sNy2Or4bxem21fb3U6mNJYevRh9/snpIOwWmK5a3W3bn1XY1LpBtGulfur/COgxg0ARhYT/FOZ5tEB33tg7usKdIlKm2XIbzBizE2JIBJsOnU448AuJc5aAwIQ3m8paFt+lsWHOkQpGCCq14hIaJ1NvG6L52BYA/hi/a+1K0qaK8opUFJpCXjYbhfzxI9sZWe5xnKhZOFqjLHqleU1OV1R7yLWSWpn2BTce4Sbi523HLCq1Wuax7RDhg+I6+f1V1tVr4Bw4fNVRxtwhE6zxPGABsVG3ry8v6Y6KwvXCHAq60yJVG1dfmfBGbRCCoWWgWy+z1b2hjAOoMGsTGVazXUdLHY49Godle0zqEO6jf+Z6FM4xkpTjHtgyntWzk0+UrNJ3EiF5CoWILuAq2O/wAbWt1Jxp1LCtY0xUrwCRss+0ureuTToGQ3nyyrY4FyJTCo0748y4hcZWi/ARTmfDwjplBQDbGRSuZduow6dlZvYFTih4VzCMDSDmcj/WGH+mBvnmo9p8B9Ssy7/wCYPJXNlslqhSeWM1joeJWa4YhEBYBD1PljpabxCoEZUJmW98Z1xMyrDEOVnJh+eM0uVhUD9ryQwdi+YVIH+oqoCD6klR+LDHTejTdfE6bOoP0n7KzTOlrz4L5n5qZqzMlgL61VRqc/dHwx9G0YZS1LjbiX1Q1G3AMclLnVIy3SB27o73LA+fzC74wOJOFSi4HJGf33Le4e3sqgI2OF0JkMJcpt648yuHQuwElW32VUYp6yoIHvMDf5WxxnGH6mBR1cBdJ8Hr+7PTljI4cNVRYVxsiWU2jc33x2rDiVmc1AZpJcm5vtjMrukqdgUCrXqhvcYzXGXCFZ5ZXI3bPIycVh4ydaIxFhbcSG3Lrtj130VdFs6Oo+ii4j/h5LnnjAJTcZ5lCt7iYg7WHM2/5SuO5uAdyqVucABEvD/dtU5PO8LVBjkURaWbwGOpVncKNi6JLrG9yokFiBjLpEhjhMZ+o298K1cQ5zcSY+h+0owmpaZpnPt8T3YnVolF/Ww5Y6OCcyubPkjzhzirKIOPFzCvinnyGhApFqpe71QlwdUrxm6qA111bGy6vDqsMHitx6zUNNpOkfAn8rasrY0aIdA1O5/afHmunZcloc+ybvAUqjKgRidhItgyhiPgCG3IIBB2xxNzaiuwye9zTtqvpPhUlUZTPw1xeaeKUiGuhZdfSeK/MjkHVr38jcjwtjkK7HUmFjtht4fx+7hbLHzJHvXRXCdOphpm0glI9I9N8Ytu0uqlyqvPdIVj5VQh1vfYjoMdXStdQlZjn8kpPlq+Im4senX44pV7NrWkqRtQkoRzqlEc7gDYbY88vaTWOIWtScSE1yOYxytGeh5emKds7ClqBF0ADp5g9fLGxoBaqRwUMdoPZrknaRw9V5LntH7TRVIs2ltDxt0dG30sL7H5EEEgtb1K9lXbcWztL2+8eRHMHmPeMwVMyqWGQuQ+J+zrizsBq2XMjJnPCqsFpuIok8AUmyx1ai/cS8hqP7t/ute647oVbbjI7g0VubPuw/5N8PabzEZW3aXoqEUqm/Lx/n98EbcI9pciiMxTgjY6GO3yxyF7woSZC0HUmu3Vy8P9o1NPEokujfUY5g0ri2PdyFmVLSdkXUvGFJJa0m2HbxCqzDmKmbQqQj4rpFHvbYsjjBbjQVCbJxSc/FlKAbXt0NsA7jDjtTTiyPMqHzHiaJ4nEcYB6AtYnFQ31eoYiAifQDBlfOztzyLiTLO1LOqvLaV6mCpl1RhBcGWaUqlgAQFYtps3O0l+hP0l6OX1nV4VSa8wWDJPKBJ8/9lnVhXe4VKJJGBHj18l9C+xzJ80yjgTIaDPa4Zpm9JRRU9VXAEe0SIoUvvuSQBudza53Jx5g6tSuq9SvSZoa4kgdATMfvkhrYdEz4+KsKSVYIySdhuMVbu4FJiipskwh6tnLsxJF+uOYBJOp260BgQELcQZilJTSyOwVVUkknkBiFwNR2gc1ZptLiAuUO0rtFh4g4/g4ajk8dNTpmEoPJmfUI19dCbn1lHlj0PhvDXULE3rh7RLR4AZPxO3gFtUi1r3UhuAD8en3Vq9mK6THfy645O9P90Kvc7K/+H5h3IBPzxqWLwMFc1VCm3g1Dawxo1aWoYVcOTGogNj5+WMp7CFOCFDx0yNWGQqGaO4UkXseuKbW96RupCcQpeBAjE9SBfGgw6ASVXOU2qptRIvt645q7qmvV0jZXabdIlCXEmX02bxwpVIziCojqoWjleJ45YzqRwyMpB5gi9iCwYEEjCo1qtuS6mYkEHAMg4Iggj8biDlTQCoTNaghWBtvf0GK85hTgKp+O61lkgp43KyM2u6+m/wDTHRcPp4LnD9K1aAG5Rr2dZ+uZXppGIqYANYYe8DyYeh5ehwT6ekg8j+wqF3RLe8NirBnoEqado2B0NtuOWM6u2FnscQZVY8ccPlAyzKLtdUk5Bh5E+e+2GtKhpugLYo1A7ZcY/aZaXJMoWghDPVZg5iCLz7oeJ2/Jf94+WPbvRQNuKpqu9lmffyH3Wfxmq5ltoZu/Hu5/hA32bsmauz3Mo3QghoOY9Wxv+lNcMosIPX7LN9H2lvaSI2+67g4VyMUzBbWGPALy41ZXUPdKJM6y5TSRbX2xnUXkGVWa7JU32UWpqPMoL/8AvKyW+KW/6cX6r9QBVW5BLmlWnRS2kQ3xQLiHCFRIwiGNyyLyF8dDSdICpuEJhmIFj54C52wnYhusW+semMlytDCpL7VGWPmXYHxmiKWeCljrAP8A5U8Tt/yhjjpfRqoKfFqE8yR8WkInRoM9F83qbJzPO7mMkat7DmfXHvj6+lsSsptuXu1QiCKGXLlWZkMelhID0FiDbGcXNq90c8K8GPpkEronIKa2g9eYPx3x5hcu3XUt2lXF2Z0d3kfSRdscTxR+zVDWMBdBcKRFI2364h4bh5WDcmVPVO0ZO2+OsBhqzeaGM0lBvjMqnvKy0KDjf/Eknl54oH2wrPJczceZCc6qc2zGMPUTQJIiU4F1a1m1W53AL7dbjyx6X6O8QZZkUa+GO59Dynw+iO+oGq0Fu4HyXMPaGEXiWGoDKTUUsMhsdyRGqk+XNDvj0+pJYCsSgYTzh+rjiWmmkkeFYppFadTbSjQOSCbiw1RK1/NbdcZekuL2DmAfgfwVovMBj+WR8lYqUU1UizRZO00cg1rIWXxA7g7774123IAAXOuoAuJkq9M2474d4sjzOOgyTJ584ySJ6CGaOgg7hnFMHXuptN5qVWjmWMOWJKFww1WE/EHW9SkajGCRPLn9/A7obSlUpkS4wfPr06qP4f7UnyHh/Lv2PRtXRyU6QGhSYJKikps6nmNPelHAF/3YNwTp4o2xPfM6TjH3XQvLHO0P5J6eOabifOMtpqUJJEpM19J1xsAFbV5cyvkTe1+eOU4zTbTpEtjz81foMgFxOV0dwe2rLgRzuBjjbXYnxVN+DCsnKJAoI687Y6+3eGiCcrMqCU8lAOo3574irgFpSGEHZ8oNQ3ljzTiQ/uFbNE4Q9SS9zmyqdg6cvgTjnqLtDoPVX3CWyEa0bWUH7p5436Z+CoOEp/3Wrpi+2nPJViUmacMGB3BUoQeTKeYI6g+R2OCNEOGhwkck2roqT48+y7kWcSSV3C8/+iWZEljDBF3lBI3rCCDEb/eiIH8hxpU72vTGisO0b44cP+7M+Tgf/kFqUOI1KeHZCqDOss417LGJ4iyiT9nIbDNaI+0Uh+LqLx/CRVOCdb2l6Youh3+k4Pu5H/tJW9RuaFfYweilMm7T6eeNG1gqwuGBuD88YtfhL2kiFYNJFFLx/HIBpYH1vjJfw0hRmipam4rapIC2ueVjfFN1mGIDThReb8d0FJWCgaqetzBthQUC97Nc8tQGyD1cjFyhw6q9nahulv8Aqdge7r7pQm31CTgeKLuD+C6jMqmGuzGkjpFU6o6ZWDtfo0j9T/KNhz3xLTbrdoaZHXb4Dp4nPksmtUp0gW0/irco6daOFVXYev4nG6XCi1YsaimtbXd4efhHLHM1bjtnzyCutZoEKCrq0KDvY4iNTClDVVfabn4WmFAkiq1QCGYqX0ry3UbkEkCw8zjX4bRDn9o8wOvT/ZaNuwiXjlt5rnHtS4Dq4ZMt4syxTPnNExeSIIV9oTSDLGT57sFJ3PdxkgEtj0604nY1g6yZLabsZ5Eey7/9veeiOm2qCHuyRO3MHdv48Qrp7JM3ps9yOjzCilElPOgdWH4gjoehHMY8x4xRqWtw6lUEEFS1wORkFXjktYVCC5LcgBucRWtfSsGoxFdFWh1sbE2x1FCuHCCs97OaSzes9nVY0bVPL7g/hHVv76/PEV24Ux4lFTEpnSQiKLUfdX53xRpMganInGTAQvxpxrLktXlWVZfElVmuZzKLM9hTQX8dQ68yo9xQObkdAbO8DsXveYA+Z5Dz5nwHkrttb69VR2zfmeimdYWNQL8vvHf545plPSZKRKgM2qAGsDYeWHq7hSsQjmVX+6kdr2Un54akwvcrQGwXP2ccYxZvxfVqsneRU7mFSeVwfHb01XHrbHodOydRtmkiCc/j5LXptDWgBHvCkhiqKWtpD/iacm1jbvEPvIfQ2HzGOfrvNMlrtj+ygqZaWO2K6ByWrTMqCOZbgst7HmPQ4qau1p+IXNvYaboUVxNlCZxTtRPFNZ0Z1qY9OmJltpuSb3N9rAg6Wvba+YD2btYORy67z8OeZyIVmm/R3gVyl9onsrfizh+RoodGdZezS0xjNix+/FfykUC38yp5nHoPozxgWNfS4/234Ph0P/bz8JVu4oC6piNxkfj3/VUv9mTLTFxRXljqBkpwNQ/2jjt/SyrNsyP+r7KlwxmjtCu28uolTdQd8eCVHkq+SnWcRAUEY62O9sHSO0qJh7xSHA1SlPn8sDHxS0zOov1Rlv8Ag2LzgSzV0P1/2TVh3JVpUs2ykG5GKTyVRiETUsneIN7Y3LV2poVJ4gpKtGpDti1WEjCFuCh6sAJO2MNx5q0EIcY5FFxJw7muUTqGgzGkmonB5ESRsn/Vie2rutqrKzN2EO+BlTNiRK4Bj4OanplUx6e6/dsun7w2Y/W+PZHXup2+/wChaopAYGyic6y00+X1aGMW7l7Ai9wBfbFy3q6ntM8wq9Vh0lXTkMJ9mpWtctGhAHW6j/LHCXLu+4eJ+q0W+yIV8dneU+zUkZIux3OOBvqnaVcKlWdOFdOQwd1SKeV98aHDWY1dViVzJhO8wkKQnffHROMBU2jKEM2mNyBjJqmJVxgUHLUdxS1c5F+7idvjYHFUDU8Kw1skBUnkjmWOeQMSxnc6geoIF/qMdVEANPRaFUQ6VzZ9o3hccPcSZZWUyGOhrll0L92OQOWdB5Dx6gPJ/THpvAb43loaVQ96nA93I/YrCuaXZ1NTdihjg4rWU9VTubq0ccvdkXEmmZBptffwux+AI3vY6pllUEGNx5Y/hJzQ+iZEgQjfKe0OnpMqo4I6yopI4oURad5IA0QCgBSLixHLl0xK62cXEkT8VndpT6K7+yWm4W7PZc4GYSwZtVV6vWPVxxF6CgSCEsiwC4aokJk0vOF7tArLHrsXGiadKlS01PaifAHw6qm81auWGBIx18T+5VIQ8WLJkWYCoEjNUVFMkkrPukJDqw233B3Pp5nGIyXUj1n6LTqN0uajbsatJnOW1Buk8lGqzqoGmRlQIXsBzvG2/W+4xwfHg5rXMO24+P8AK2aZa5usFdtcF3fKx6sp/DHDW47vvVN8BysLL3s1zzxvU3QZKouAOFKd4Cu56YsFw0lRQZQnnThp29RjzviDgXuhbFIYQjmFUKbOqIE2Lo4A+BX+uOZLSA4jkQtWkNTCjrKKgSqpBGn1642rV+seCzqjYU3Fb5Y3qJhUnJRodQuMXTT1CQophIvDf4+eH0EoZKTMR32IJFiRtceWI3UGvEOEp9SAOJOwbgbiiWSoqMhioauQ3arymVqKVj5nu7Kx/wBpTien29IAMeYHI94fB0x7iFcp39akIa7HxQDmf2SafU5yjjzPstv7sdZRUlcq/wC8FiY/M4tesY/uUGO8i9v3cFebxet/kAfchGv+yLxxVVkcjdpyz5fYCSi/Z0tK5HW5ikZSfjtiYX1vSpnTZDXyOoO+rZH1UjeKEu723grP4B7EqfgKILFSwySXu0kbXLHqTcAk44+8dd3lTXXyOiarf9r4KyaTRSqNQZDy3U4mpaKYGpZrzqOE1qs3Wd9iURSRZvCT05eWMS9uTWdpGwVqmwNG+VGVGZoIzZtvjig0jThTacquO0ftHyzgvIa7NszrUoqKjTXLI5v12QDe7NyA5kkY1OHWFfiVyy3oNlzv34DmVOAGNL34CrThqtruL5YcxzOnFNWTqrvTb3gBuViP8yg+L+Yt5DG/etp2hNGi6QMA9ep8jy8IWo6GNAaIUzxfkdbHlEM6ZfJm9NDX03tNDTR65ZYC5VtCfeILIxXqFbnbeGx063anhhLTDiYAdyk8pyAeRIVVtwxh7x9/REHZn2I13DWe5vU5dUtQZNWuKr9nVEQYQub6ilmuuoi5HIcxzxs0bW49Im02VmwWCC/mRyxzjkfis654mxjdIEou4lMWXwHLKozVNdUIwhyyhqzTSz+E7vKGUxRgWJa/ya1jao8JtOAOdWvH6iPZEbeMdSqtOpUuu9TEAbnf4IlyfNJ4KOBauUT1SxoJXQW1vbe3LmethfnYXsOWpXTXPOjAnA8OSVSn8FKUsEksjTzbySG532A8vhicA1H63KAmBAStVMT+7Q32va354hr3IpjSEVOnOSgGHhePhbOctrZJDmGZ5pmQircxqbmWXXHIVC72RQURVRbKF2AvvgRVqXpJq4DR3QNhEfHEkk5JWk6prYWjAAwEUVMvdoCW1WHvWtfFEt0lU5lDGZ1ILsev5Ypv3lWmDCA+J66QVKUsB3kTe33WDbH6E/QY07ZrW09Tt1eot5lD+UfZ/wCHcwCyR0s+Wvz1UUxUX/2Wuv4Y1/63euMOcHDxE/MQUz7k0zhGmT9is+V29jzlpF/hqqcX+qn9MVKtZ9x7TB7ifvP1UBv59pqMch4czfJZ3LPFPCw3RGtY+lxik2nUYcDCgqV6dQbQVN1EFW639lkXcC4K8/r1t+eIalvUPshQNqN5oD454CzPO6hKiiy5pJZB3U6mREBTchtzzUn6E+mJ7VtWjLaggDIxP7P2V6jcsYNJKpbhz7OnF3DHH2YZpDkDDLa2WOciOogPduNQcBdd7H3ht94jpjsrvitO7sWUHk62gjIORyzG/I+SkbXt2ucWu3V3QZHmFIo77L6mIDr3RI/C+PPX0njkVEarDsUzzuwo1B8J5WOx+mFSGkomZKCqvNpMkzXKauPSG9qEHjNl/eAxjUeg1Mu/TG3b0+2a9vhPwz9JVtjQ4EFXFk9e08EbSIYXZbmNjcqeo+I5YxHmeazXtAOEXZNUGaMrcFhy3xe4dUL9TOYVKs2Mp5U7odsbDjiCq43UDmClJNQHh64xq3ddhWhkZUNmEetDbf1xGwgqQYXK/aNw0uUcYZnHGNMc0vtUdhtpk8R/5tQ+WO5sq5fQaTyEfD+IW5SdrYCgXM+HVzKkkhVV703VC3hs3qeg+ONuhcFjwnewQj/g3LDUmjGi2iNLjyNrWxzV/V0F8nmUiYauguFcv0QxoF8hyxwrjrcT1WbVdzVmUkYiiVRyA2vjrLRgYwBYtQyZTHNZrGx6c8Xqj4CBolB2Zy3LE9b4yKhMq4wIc4nqxl/CVfMxsXTQPO52wrduqqFapiag8FTvBhc5bD3g8bgu9hYEsSf1x0tdw7Q6dldqCcoL+0Pka5z2b5o+jVPlxTMIttxoOmT6xu/0GNv0fuTQ4gxvJ8tPvyPmFSuWaqJPTK524Ddp66WmUFnmo6iJdJW+oxPpI1bXDBSL9QMek3Hcc13iPqqFHv03N8FtWU+bZvVz11GcgkpKp2nheZIg7IxupYE3BsRe+LZFJp0nVI8VRFRxEgj4Kzcw4tM1fRJXSGqmlMjisW6s8c4mVlkVrkkr7PbxWUIRY6yTWq1tdR4I8PkpWUdNARzQTlFE+ZVNJFoaQGfxqDsR3Z1YGi2cKOu4DKs/slzFU49paNadaYCkBCqB4yNepjbmSwO/XbHIekdIdiag2V2zdNMtK7l4DnvlhF720483omJTVB3keUUlitjtbFwOIKgIUj3xAH9cSmpiEMIczZ7VB9b44W+P90rTpeyq943zNMtrsoldwglqGgBJ2uYywH/IcZ1Cka3agDYA/OPuta3bqBARnw1mJMai9xiC3eaToVSs2SjSkq1lUC9jbnjqqL9QkFZThBTyKXex29MalKodiq7h0St1O4/Ec8XxHJRkSvGIONuuJtIIwolj2XUNxguzJTSsigFhtY4kFA7palk0ZHQjDuoSlKSlo9SEMLjFZ9sCIIRh5BTCXLF0uqSSREj3g17fI3GM82LNmyFOKztykkoHCCOYpObbvp07/DfEDrDcHKLtAcjCHM/4aWeORqe8MliQF90noCMYtfhwaZAhWGV3tMyqb4q+zbU9ouc5JmOdZ+YYKACpTKqehWSIVRA/ePIzguE+6pUC4DG/LG7YXb+HUqlG2piX41EkHT0gDE8yDMY8VM++74OmQ3MTz5E+XIdc8lYPDXY9l2QKq+1VNU4+89hf6DFd1pUuHS8/BV6vEq9XoEXx5NQ5YCwjQNb333P1xdp8PZT7zlnl7nnJlCNV2lU+XcRZ1DFG9T7LS0zRxRqSXYs6yWA3IUiMbb3cDrjruHXdOiXvpZwBHj+Ff9SdUosLsSTn6Kt+JOO8oyDiCHP1rUzSHMJTHW10RNoZWt3aMSbqpBChdIW4F9ygxgcaoVL1xp03g1D3iOsdPLmBmPAFb1nQc6n2bxp07fv3Vm8HzxZ4Wq4yWgTwgstvF1Fj1G31xxVtRdSJLxlUroGmdCM4xrDAHYczi1VqBrSFSDcrRIgu53HPGY0Gq7UVOcYQvxi6yVWQR2Bf9r0rrty0M0rH5JG/1xqW0NLj0a754+4RM/y8im2aV4SIAHkALYzKz52T025ygviPiCPK6RppdTEkKqINTEk2Fh15/memCtrd1d0fvvV2mzUYUVw5lkua1zVUiEs7XAO9h0GLFYnDG7Keo8UxAVs5Nli08ajRbAMaW8lkPdqMogipVQ3A38xidsgwoSl1h9fxxKHIStTSRNURyvGjyRghGP3b2vb42G+DDwCDCbMQljIAdgMSGsRlDoWyrqPRjhCoScJQtZoCBvb5YZ4cN0hCiMyo4qiMpNDHMpFiJFDD8cZtZqssMGQqr4+7NMmz2gnpWE9DrBZHo5dBRhuCAwYc7Hl0xWt+IXFlXDmwR4j/AGK1KVRzcr3DWeyT05aZy88E/dyMQASdrm3rz+eBuaYaBpEDcKR7ATjmFYWT1mioCk3D7YqUX9nWB5FZ72y2ESo2tSp3uPLHV03B2FnkRlR1fCVXccsVa9M81IwqBlAJZQbg4xwYdCtQYlUx23cPtNR0+axr4qVjHLbY92xFj8m/PHT8LrZNM8/qFoWr4Ogqmp0lmjkSnCtUsrBFb3S1tr+l7Xx0rC1pBfsFpbhWv2ccPmkpadS3elEVDJa2ogbm3S/l0FscdxO47R5jEqnUdCvThmh02cjZRt8cZdozW/wCyKxxCKS2hCeQGOtb3Qss5Q5m1VYNc7nFSs/kVOwISrZe9cKN9RxRc5W2hAvbFmvseR09Gp8cjX2O/Kw/PGlw6mHP1HkrdsJJcgzhqPu6dQAdIsBi692VbqQmfGVGuZ0dRQybpVwyUzfCRGT9cTW1Xs6jag3aQfgQVGBLSFxr2ZzmDifLRIDdJUSQDnzAa3rzx7XxKOxLvf8AdYNj/wAwsRJURTRVEqfs+kk0sRr9sQat+diR+Q+AxotaXgO6+CpOqOYS3GEtxO0FLmCPTzavZpY6fuSG1IIwFBBN9Q8PncE773OMVkElw5ytEkgAQpbK6G8FVSmfui9RZ9DWcIV29bMSF28xizRJJEGFmVvFEfZtCYO1bJ6hZNcciVEI2tYCJmCHpdSp5XG/M745/j7QbGrziD81ZtD3oPNd1cAzXpJALdDjyJhyQrb90e0sm67/AI4lLioYUk0mkDfDOcYQgKCziUa1N998cneka5V+kMQqN+0PnMeV8MUVbIbRQZpSa2vsA5eK/wBXGL3o7SNxd1Kbdyx3yg/Za9s7szJ5qZ7O+NWhihhrHDwOB3c3UHoD/X64qXlqGPL2DPMKS5pB+W7q5aCtWRFZGuDvcYGg48lgvaVLwVW1icbdMhwyqjhCex1AB33HnjQY7TvsoHAEJ3HOtr9fTGkx43UJEYThJA3XF1hLkCdKNwL740GiUBMJYRXXbf44n0SFHqWrwkC5UEfHEb2HmEQPim0sKkG6qBikWg8lKmzRLyB+JGI4CSjaqJdRuNsZ1UA4UrSYUbV19PQKTKQPS+IoYw94KPvOOEM1/aFCsjQ0aGol/hjGoj4nkPmcROvmU8N3U7bV7slQNdU5pnAJqa6OiiP3IR3r/oo/HFN9xUq5VxlJrNkEZtwnlEOaU1e01a80blmdZ1jL3FmVtKglSBYi/lvsDgaNzWpauydE/uFqU6j9JZyTWoo5aidMvjgbNcor3MSQMS/s7OTdbE7wm5P/AMM3+6Rpp1HuqHtJio3M4l0eO+of/cPHe2x4aC52CFbnBvD0HC/DuWZFl7N7NQU6wa3Yte25Zid2Ykkkk3Oo3xUubl9Wo6o7cmTGB+9Asl57RxceaIzMoAjUeBevmcZPaGq6OSINgSkaiqEanfli0HCngoN0KZ80FVUUc8gZpKSYzRBTbxGN49/TTI/zthC4cwOA/wAhHzB+oUjGnIQlm+bDLqMTV00auq3d12QMfK+/oOp+eBYztqsUwY5K3TYXGGhV+Kqo4hzPvpSwgU/uoj0H8R/mP4Dbzxtw2hT7Nu/M/vJXsUxAVs8GUogjS6364jogE5WNXMqzsvMLIAbCw+GNtgY4QQswyNlIiKAkWtzwTqFEoNTgtvZozy8sRm2pTKWsrX2NBfxXNyfh6Yj9VYn7Qla+zovM6j64H1dgMFPrPJas8Ua7BdsCabGpAk7plVVakHe3S4xVqRG6laCoeuqg9wGF8Zr29oYBVlp0oSzqohgR5p3CIoO5P1xz7g57w1gV9m0BUdk3aJTP2kZ7k8ZBgmpFrac+bRuElH0eJvk2O0rcMeOG0rg7g6T5ES35gj4K7SgkUzvuruy2u76kp50OxVWxxj2luOYVR7YcQUcZbWrVU6uCCcbtnX1tBKzqrIMJapGpCCNxyONV/fGl26gGMoXqVMFWyHZW3HxxztWnpdC0GnU1Q2e5ZFmdDU0s6d5DNGUdfNSCD+eJreoabg4bhEHaSCOS5zy7hebKs5qKKc654pCjPbZh923xFjjr690KlMPbstkOls8ldfB2WXRFVRtjiq7y96oVXK1MtgWnhVVFrficbVnR0hY1R0mVtX1QjTSDtjVc4NCrgSg/NqsszG/LGW90lXGhQ8BEkrSEWC+eITkhqlOBCpHtLz79vcVCCJtcVNt6Xx09qzsqBJ5rUoN0NAUrkdKVpEO4NsUXO3KGpuoTjCQwaJBvoZWHyOLFt3nEI2jC44SnXL+0nO6VNhFX1Krb/wCYxA/EY9tc7teHUnnm1v0XP24Dbx7fEq3Mw7MTnlfU5ksKOtZI1QG7yQX1ktfbbr0xXo3xZSa0xgBSVLYF7sHcqvs+SWuzeWrSBYYpXMqwQm6p5IOV/IfrhNe2CBhNloAKcx59NlNVFNTMplniSQNMutdWkDl8eXwxZpOLB5Kk9mowjHsjqD/pXlCs9xDO6i+97wtvf1BxicaOrh9TyH1CloCKoXcfAFQO5mF/uA48bJiVcqBH1NWaVUjf1wL6sBQ6ZTp8xuNjbbmMV3XM7IgxD9fVtJMzBrhiABblbHOVy5ziVoNAAhUd9pWH9qdntfSadReSBtNuemZT/XHSeizuxv2v8HfMFWQJYYVPdl/HtVwmseW5qzy5bc93NISTHc7hjzt69PhjteLcOZezWoYfzHXyVtr9Qh+/VdMcF8dGeD/CTioRPejZrkDoQeoPnyx5vXtqlu7IhVK9Ic1ZGV8X09SArnu28m2xNSrf6llPpEZCJKXMI5R4ZA2NanUB2KpOEJ6k1t74ttcRkKMjqnUVYeu/wxdp1ioixPI68jri6y4Iwoi1OUrrjnb54tC5Qlqy1YW23+ZwLriU8JJ5vpis6pyCJM6uvjpULyOFHmdsV31NAlxwja3VgKvuN+0zLeGsorMxrK6Ggy+lQyT1lS2iONfU+p2A5k2ABOM8VKt1UbStmlzjsBzV5lCd+X7lc75p21zcb1pFCZKfLL2DSbSzDzYD3AfLn5+WJ69k+g2Khl3y/lb9Gyp0xLslTuU540caKrWHTyxzT6ZY6QpXsB3RBT53LKApa/piRj5EKk6mAt5KOSsUXYEE79dsOZ5JBwakeHc6yWi4rrcnXNqds6po0lmoY5LzU8bgEOy9C2pbehvizVtLltoLwsOgkgHljdROd2h0NzCtuhFP7PDKkUUcyxaNSi7AEgldVr2uL+uOWqhxxyQA5IW71Nh4eeIwdIRQoyurQgJdsIvlOBKDuJeJ6PJaZqiplWMtYWve58gMXKFB9wYaFYp03PwFVua5ueKK5JSf3EZvFCDcA/xN5t5eXTHRUqYtWaAPP96fVXg3sxAU3klJpdPjjOrVIUD1ZWRT9zGnTEDa2lZtRslFtHmxVQL8sW23Z2lVSxPxnN/XE3rqDs0oudm25tv0wYu3JuzXjngHI4f1spuzTeTPjfY4gN4eqMUkymztm3BPzxWddE81IKfNR9VmtlLySBFAuSzWxVfWc7Cka3ogbiPtVyvKFZIZDVy8rR8gfjiSlY1q5nbzWlTtnES7CpzjPtDzDPoap1uY4hdtJsiX5AnqfQY6ey4bSoObO5+JV9tNrRAVacDSiHtAmq2JeZ6ZNTMeSlm1KPIEAfMY6riA1WApjaT8cR707KYYdQ3XSfAHFkOYU8lEs6O9KF7xb792wvG1vI2O/oceYX9o+i4VCMOn4jce5V7mnnUOaPMi4kiocyShmmRHmP7oMbBz5C/M+nPGfRa9hL25bz8FRqUi9usckfJIKiEb3HQ46Cm8VBpWURGUP8SUwhgad0kburtaJdTEDewHntb54C4oat8KxRfmELcP8Rx8Qd6kFBmEEcGxlrafuQWvuguSSR8LeuK1W2FEDvA+Rn4q7Vpml7RGehUJxpw2jzx5lAo7xbCW33o77H4gn6H0wbKksLUdGoR3CirhKlWnplvbWfwxTt6XaP1FVq78wEXCURR7He22Ohb3As453UJmddZWUHFarUnAUrW80KVtV3j6Ad+tsVdStNbAkoa484nj4WyCSzf4iUEKBz+OLlnRNaoFNSZ2jpOwVKcO0kldWNPLu8ja2Jx0N08NaGjktQ91qs2ipRDSDba1sYZdlU3EygTjt7QyC/IG2NOxy+VO0YXKHEaiDtuz8AWHthcD4opvj2O2Org1H/4/dc8zHEX/ALyCuKg4iMFDTxlWJSNVvq8hjmCXTgroonKp6tmX2SpWWwBDqSCQVceJbeR6jHUsa4PBG/25rAMQUlLJJmOW076jLUopBFgpkvfcAdbcx8xiYANqnkCqrpLcI97J66L9v5OPem78AMDawKte/nz+WMjjLC20qjkQntzqqArt7s/nALDY3jx4jVMFaFQYhHcdRta+KdR/dwgDZSjVOmx5XGKEkSQpQ2VE1sgRtYNr8/xxVeDKsjZVZ2rr7dlckX3SF/Bxjb4OdFUO/dlapjukKuYuDI6mEnRvjqfXHA4REgJvTZDmPDVUs2X1ElK6m40Hb6Ykdc0rgaaolSBwIyi/Je258umFNxBRsCDp9qhXY+pGKT+FNqDXQM+HNQ1LYOEsMKzOH+0DKc2QPl+aqOpXXy+I6Yyn2VWkc4Wa+jUb7TUY0XE1aiqY5o51874cdszbKpFrTuFKQcZ1AsJKcE+anEzazxu1RmmORT6LjVDa9O/yOJW3P/SUBonkU6TjaO4Ps74mF3/0oDRxutm4zY7rAAfNmwjdOnutSFEdVH1/HD06EyzxU6HqTb88OatUjOEbaIccCVWXHPbVl2TwyLEz5tWgbQRNYDy1MdlH1PpgWUHXBEnHU/bqtSlZvOTgLjDt04t4h7Q5BLnFUTSwOXgoILrTwm1tQU7s9vvtc+Vhtj0fgtO3s8URk7k7n8DwHzVupSaG6QgXs17UDwlXQ5TmkuiikbTT1EjWWI/+m56KejdOR2sRs8U4R68w16I7w3HXxHj1HPlnejRvhbvFKv7J2PTwPn15LqfhbPBWwo17FuYvuPj648fvLc03ELfcJVk5Ogk0s3K3PHOk6SVQqI8yulXQtwNvTE7CIWc8mVzH9sjsdz7M+JMj4y4do63OGemTLJ6XL6KWeakki1PFKDCDIEdXZdXNWQD3WAHqnopxW1oUKlncva0TqBJABnBGcSIkdQeoWbXFQEPZui77PH2ghxNwJQQV8zSZpSp7PVNL4SzqSNRvzuAD+eOM9JeDGxvXmi0aHZEdD+Fr09VwxtQjPPzVqZj2oZbl1K0sszNsSscKFmew6eeOMpWFaq+AFZbQc7YIE4g7Ts0zGm73LEjiSRdSSSEsbHcbDryxsUOG0ab9NYnCttoNG6oTimq4iq83NdWZnUVMxawEp/drf+FOQ+W/rj0G0baNpdmxgA+fx3U2RgIT4c7fqWKRoc0gmyyeN2RpI7yxEqxB5eJdx1B+ONi69HHkaqBDgeuDn5H4+5UafEKZxVGkj3hW/wAK9sEVXEs1HXQVsP8AEpDgfMbj544m94I6mS2owtPwV0dnWEtM+SsjKe2dEAWSCNvVXxzz+Evb7J+KidatdkFFNF2x0Mirrp3/AN1gcUnWNZp5KE2fQqWg7VcndfEZoz1Gm/5YhNtWH+KiNm/kQlT2n5M3KWXkTcJtgewrj/H5hD6q/wAE1k7VcpjGy1Lm/wB2PY/PBi1rHkPii9Uf1Ci63tegQMYKGRj5yOAPwxK2wqO3ICkFp1chfNe2HMZQVg7imv8AwLrI+Zxfp8Mb/kSfkpm2tNu+UEZ52gVVbc1Na8nndtt/TGvQ4cxp7rVYa0N9kKDpqhc5YySS6Yb20rszfPpi+9vYCAMqTZP+JZok4amhiULGo2RTt64rWoJuA5xyhbkqveFC0vGLpYiM0yd41+S6n/Pljp7sAWgPOT9vokTlWZRZ/Dw1n2W1c0FU0c6GnknpNTGFWUSJrRbl116V2BIJvsL45ipauuqL6bXCRmDz5GDyMZ3E+cISSWwf9lZudSxiGKrqKpQ1MwnjlBQMkieIFb2BJFx87Y5G3a7UabG+1gjOQcfz80FOR3QN1cnBvEEWd5VTVMZ2kQNsQbXF+mAtnmlUNKpu3CxLmiabi1TWY03ttHIgbSWFtSncY3D/AHGHKotOlwKAKbhFsjz+rrKasZIKyMd/RFbo0o5SA3up/wAxilWDjTDDmFqG47Rga4bc1KyolbS3K+FhYgi3x2xlMJaZCiODCichrJaGV6SpQRyxnUtm1B477G/mOR+XnjUboEFiVRuoalOVObALbVvgn1REKuGIazTM736k4qEl2VZYyFET1seXU8lVUMFRAWN+mHY0vdpCm0l2AqG4r4lm41z5mUk0qNpQDkfXHY29AWlKTuVpU2BggIx4TyTu4wxXpjIr1S90hRVHgIpqlEFMQRaw6YpapVZuSqo44l7wlAd2IG3xxv2AjJVsYC5d4zcr2z50xWw9rPzAVR+mPYLIf/4aiPD7lc1McQM/uFYkFQohjFk90c/h8ccsW5K6cFVhWFq2adGbU7qLG+912H4Y64OgBy51zckDZKmMRAqSwUaSNJ3uLYFjpcCVBUaIICLOziVn4pyic2N6pQZLc9m/H1xn8WcDaVG+Ce3bDwV25wHUaXQX5oRY48NucStJ4kIsoM9jqqmtp1DrLRyiKRXUrclFdWXzUqwsR5EcwcZlSk5rGuJkO29xgg+KIsgA9VvU57DFMEZgD7oBNiT6X5/LEbGF2QpRTcRIUNXZu7VjRhrxgD63OJH0QKZJUzW92UKcVIayNr8gLfHfFizIYVMwwITnh3KRMqqRti2KvehV6zoU7V8JRzwk6N7YlfjKqNrFpVd8ZcAiSBiI9xy25Ymtbx1J0E4WpSqhxgqk864YlNbE8byU1XHdUniYo3na62Nv647i3vAGEHLTy/3UlVs7LNJxxxtwuQq5tPIg5GoUSj67H6nExt7G4yGQfDH8Ko5pjIlS8f2leNspljM+X0OY0ZYCSWnaSKaJSbFtBLBgNztblhDgtlVaQx5a7lMEE+eCFScyHDuSFbmXdpvENVDG6RxypIAVZAzqw6EEDcfDHIVGU6biDyVv1SmThNs47eW4fqUpMxzfLKaukBKUYbXUN692t26cyAMWKNjc3DDUpUiWjnED4nHwUZoW4foLu8eU5UO3bdnWaTd1ETHGQbOW0tz8gP1xI6xLG6nOz4D7/wAK023pN5KNqM8r8zq71FS132Yqd/qbn8cRdkxrZiT4/sK61oaIaE9iylXisF5jFQ1iDKB26CuMuFxNSz2X7p6Y3bG70uCjIB5Ll7tFyY0qSPpICMDy6cjj1vhdfWQOq5TitH+2XDki3sP7X63h6RMvzCRp6GKyxTNu0S/wt5p5HmvqOWR6QcFpXQ7WkIcdx18fP5HzU3B712g0auQNvD+F252fcT0ufZfHLHMrBh4lDA2PT+uPA+I2lS2qFrguiqtjIVg8N8X0Wb5rmmVQO5rMskSOoDIQLtGsgseTeF1vbkTv0xSrUqlGjTrEYeCR7jGemQfNUX0S0Bx2KKIahlYMrFWHJlNiMU2XB5qAsCYZhGHjAIB0iykqCRvfY4d7gVI3Crbi+hWvl7hTqlO4Yb6SOX9+VxjQs39l3jstKm6MlA2VL7LVzZXMvduuqWJTzAuA6+tiwI9G9Mb9bvsFdvkfsfgIPiFO48wmee5MswIKg87HrexxLb1y1DK5A7RslOR8c57S6SqCqaZP9mQCQf8A1/hj2vhtf1izpP8ACPeMH6LnLinoqPgc0v2cQGTM42Hv94BcbH64j4o+KZHgrnD+avmho6kKmlmF7Y87qPZmVuhEC5dU92CJHUnqOeM01WTsg1eKwz5hAp0zvttvhx2TtwgL3BBHF3HfE2QxlqV6aRNwBPEx39SGGN+y4fZXBh4PuI/CjdUeB3YlV7X9ufEdidFAXkP7oPTvuu4KkCS11IIPyx09P0fs+roG+R/+vMLKqX9RogASdv2eRVl8K8Wy8VUVHK0ul5Y9RVdtLi2oD4HHJ3lk20e5oGAflyWwx5e2UeZPlUVUSJB37rvdmPX05Y52vWczbAUhwk+IaOOmpXQqFUi66eZI35fLB21QvcCpGIZyur9nnmRT4dd9vIgHGrWp6gCUxwVK5vWd7klSBvZARf4jFKhT01moeaH+EKYyZmzopZnWzsovZdwN+mxJ+YxqXrwKYBOyA5OFYuX1FRSV0E8es1MZvHoH+rAK9eQ2BX/eOOdeWuY5jgCCOf1hEW9VZlTk0HFuQzJBMtO1VpWV3jDMi3GsDbnYEC+wJJ645Nlw+zrgvEhsx58lWFQ0nA9EScG0FNwHSUmW5YHhy6BjohL6gqkklb25XJt8sZ9zdVbqsbmoZed+UqF815L91atDWJUwqytdSL41bSuKjQRssaowtMFDvGNXLltHJPBAlVMQ3dQPIsYkcAlUDNZVLWsCSBcjfF5oD6gBOD8vupaDQ4wTCC+G+0alzPiuqyKTLM2o5zCKmnrZaRzSVSW8YWQAhJEYMrRtubXBN7Ymu+FhluLljwZMFs5B8uYI5hWalNzT1jP6dlJ5zkdVMIq5K9jU0quyhIVWOS6/eFmb6H5HGIytolkYMfoz9U7Ht9ktwUOU3FXt1MsiOCDz0sCAeouPLEtWm6m4tcFKaWgwvCsDAyyNZRvc8hiOCcBMW9FUPaXxrLxFP+ycvkK04NpHTr5463h1oKA7eqPJXadPT5pTgzhUkIxTSNrDyGIL26LjpBRPeGiFa9Fly0kAFrbcsYrnGMLOc8uKiOIJu7iYX9DvhmiXBTUhlVHnbmtziGPoZBf646m3GikT4K1C5h4knhr+0GaugmWf2uSWRlX7jCZ0KH4BFP8AvDHs9OiaHDKTDyaPouSpv13zj4lGyVJ0jly8zjkizK6obIJk0LWQupGkuHv8MdA2dJCwzCc1eqR2jhW1wPEBfYj9cNSEZUFRH/BuRU+U1+VGSZjVySBkhB2A0ksSP1xmcVM2zzKVvOvC6s4JqtEsO/PbHi93zWo4I2kmhGqoIZJdOk2NgRzFx6fqcZfKEhIwoyeugqFeWTRJpJsGANrYlp08qYgtgBChzlHrNGrmb3+eL9SnLFZ5JfPJB+zpmO1l539Rijbj+4AkzLsqd4ScEi9rYiqO0OlQVmyFYVJAJFHW4tjSY/U1ZDsFMM4yFKiJwVBBHlipVGlylpvI2VBcf8LNQ10jIthfWn+0MbdhdahpPkt+m8Pah39mR1MasUVkdQbEeeL/AGzmHBTKIreBoXu9P+6fnpI2xdp8QcMPyo3MBVfcbdnMmZUMtPFWVmUVLCyvTTukb+QZAwVh9DjqOH8V7F4cWh7fECfcYkfRZl3Z+sNIDiD4E/Mc1z/l2UZh2f8AHuWrmUXdEVCqZlN0kRjpJB67NfffHo9WtS4lZPNEzg45gjK5GjSq8PvGGqOe/Ig4XWOR3OgnyH1x41cYlekDxRDIhQq4+7vjLBnCmaUZ0c0HdQamVTKdKAm2o2vYfIH6YxHtfJjkonNMprnVAsyNcbEG+2JaFQtKiG0LmLtU4e76GsVU3IYfAi+PWOEXOlzSfBZ97RNSm5vUKo+Boi+YlBe5F8dnxB0U5XNcKADyFefBfFNfwhUI8JZoPvRja3w/ocef39pSvWkO3XYseWCDsunuCe1+jzvL1kgSJK8adR2AcDmD1U28/Ib48qvuDVbapDidP78VIaQfkHCtbLM8izUwy0jpNASyS+KzxsOVx+BHPcHljlatLsZFTB3HT4/vxVV1ItEFS8zCRSSRcbEXxCKswoNJGEFcV+y5DS1ea1MkcNHTRNNLJIQoRQNzf+78sbFi2peVG0KYlxMKQOxBXM/CnbEO0DtXpqWnyiSlinqHMTl9TJCsDGRpBbwkgKSFJsbA3vj1biHABwzhjqhqTAEjqS4RH88kFveiq7sgOufyrirqQSxI1rG4O/THntN+kwtAErmX7QnC/c8Q0WZKtlqqYwPt96I7f8jj/hx6v6NXeq3dRP8AiZ9zv5HzWdeM7wf1Qj2Q0Xe5zJHb3HDfQjGxxp+miD1T2AAJC6do8nRTGNP4Y8nqVyZWuSiabJVgSN2KLEdm1kKNxsQfO/T1xnMqF09eSrB0ymUuRpNTiVQCjjUpHUHliwKjmnyTF2YKAeMuEkrMvmjIvqBIuBjfsb0sqByRMhc0Zpk/c5tJQzMIhLL4HblHNyHwD8j6288eq0a+qkKjcwPiP4+iyq1IaxOzvkf5+qtfsu4fFLkarI7CVZme42ZT5D544/i1zrrY2hatAaGgKz+G81ijzeKIgRs6AEdf688cjdUXGkXdFcIlqk+LaiKGmMgYAob2BxUsmOc6FG10KrcgaTOqqRYo5J5bqe6RSz8gL2Xljr7kCg0Fxgdf905PeVn5N2eV+Yw6KyLuYHFijnxMPUDljkq/E6VJ00zJQmo1vNHuU9naUsAQoFjA2iRdK/Qc8YNXiD6hkfFQOr9FLxcGRU6ApAsar102tiDtqtTmVCaxOElDMuTVQljkEkBbTKFNwPX5dfT4YGpS7Vmk78kYlwg4RXGy1EYItvyOMF0tOVCe6VOcP5saWYQyEhOhOHY80nahso6jA8SN0YXjrISoIOoWNwD+B2OOgpVg4YWaWkFBGfdlHC+a8QUedT5NAc0pZlnhqoZJYGSRfdYiN1ViLfeB5Y12cQuaNJ1ClUIYcEYj5gx7lM2tUAidlIxUFdTSSFK9auBnLCKriAeEW91JEtdb2trDEC41crYdYscI0QR05+YP2jyRam9I8vx+FXXaLkj5DUnPaWCR6XX/AO0IoY9bBD/56qNyUO7AAkrqNiVxZsS25m1eQD/iTjP+n38uhjqrrKvd0uVR8ace1FXMcpolILBSShv3isLqykbFSCCCNiDjo7LhwpDtq3L7fcK9TYBlK8G8Fu7I8qlnY3JOIr2+/wAWJ3v0q48lyBaKFfDbbGBqJyVl1KklOa9xDE3w6YAlM0Sq94pzAJG9jyFhi5bM1OCvMEKqM6zVcpy/Ns3kbw0dPJKt/wCK3h/EjHYW1E1qlO3H+RASqPFNpceS5ny+IxwcOTneSenm1GwF2FVMCfwx7XeNAolo2/2XE2Ti6tqPOUdRkmNDfoPLHEHcrtQcIdzChFLXLGDePdkY9Rv+IO2NZr5aSsU5U3w/FBJVCoeMtHCNrsEW46s3Qc/jhNEGCVBV2gIs4QlkzbiuOslIJOqwAsBZTyHlv+OOd4rU/tOAVu3ZpaF0hwjOQIDffrbHlF6N1ehFGcV8sdDL3S3c7KB1OM+k1sgFExoLhKGstjqzSzh3ZhYgnqfO2NJ72gjSrNQiQo6eikFYjqSFDXY+fp9cSCoNJBSCleIZe74dluSdkBPndlGKdqJuB7/onZ7SnOD6g6EBOKN2IdKaqFamVN3kSnBW78LEqCCpOeANH54mrZbKiacqte0zJUmpDKqi673tivQqaKwjmte1cdlTtPCIS8JNipNh6c8dI5xcA5XzutpSqqb/AJ4EShUNmAjmBV0V0PMMMXqWpuRhEBIVY9o3A1HnuWTBLK2nwiQXsehx1vC+IVLeoJVG4t2VWlrgpfgasOYZTSzSC0rIO8B6PyYfUHFLiNPs6rmjaceXL5K5TdqYJRtIVenGlgzAYwACHZU7d1P8N1jGlVJEZdJsNSnGfdMhxIKapE4Km5wKhCFR3P8AKpP6Yz290ySq2yq7jfs0zjOaqR8uyesq0k3YRxAC/U3NsddYcVt6DQK1VoI8Uzy081TvC/2au0nLeIFqH4PrRR6mGvvYPdvsbd5fHb3npVwarQ0C5bq8nf8A6rnLS2qULglw7ucq0l7DuMDGNPDdcfkn/wC/HIf1+wnNcfP8LoNTOqQHYh2g0kwnouGs0imHJ4TGD8/Hy+OJf69wp401K7SPGfwkHhpkGEdcJQdrOROPbeCc1qRGQonp1jSQjyI7yzfhjBvTwK4H9u6aJ5HVHuOnCsCu12Hqzcr4q4tFSgqeEs9iSQAMZaHdSNt7Ei3kR8MclWs7DT3LlhI6O+mPkkexI9pAH2kct4x47yLKssybLapIfavaKvvKaVF8I/d6iB4lBJJXzA57Y6j0TuOH8Lr1K9y8TEDI9/l0lUa9PtKeim4An6KF7G+yin7NqaolkM1VmlSuiWrnpu60rcMVQG7AFgCSTc2GwtbF7j3HKnFnNaIaxuQAZz1J8BtAgZTW1rTtm90yTuVZksZMZBDEHl4ScceCJVqeirbtY4Lq+J+G5kpKGpqqunkWohjhgZma3hZQAN7qx+gx1XBr6naXANR4DSIMkeY+YUdZofTjmFWXZR2U8YZRxRVTVvCucUtI6HTNNSMqk22/u2Oq4xxjh9a2a2ncMLugcobQGm52rC6LoOGsyYRXy2p2t/5Zx5jUuqMmHj4q8XsjdFv7Fq5Y0By+ocqBzh6/PFVt1RaILwqMwSQUjU8MZnMhC5dP/wAIH64P163H+SQI6oYzvgDO5IWIyudh6AH9cWqHELcO9v6qYPb1XMva32Z5xl2ZtU1eSV0NHIrCSV6Z+759WAIH1x6pwXitvUp6KdVpcNhIn4Jnta8QchR3BfF9VlVRHl1dGtXHK1qesicB5f5WBOkuPQgtzAO9rd/ZU67TWpHSRuOniIyB7oHUKBlR9I6KmRyP58fqjurz+ho3p6uGIGbvRrZ1KOpOwLKbE451lvVfNNxxHmPctFtTCkeEssl7TO0FstqJBNldHTGoqKdpyvfl2KRoVFiy+F2NiB4RfFe8qjhNh27BD3GAY2jJMmROQFTc4lxHIb/yuiOG+yyi4co0pcsoqPLaW9+6po9I+P8A3x5zccRr3j9dZxcfEoO0DRACIKnhbMqeFVyuKhaTl3laz+H1Crz+owNANcZrAnygIBVpn259yXochzqJW9vzGEEjwpSUgj26kFmYk40op/4t0+efwgdVon2G/ElNYOGhmkpNUJp1QbGaRmBN+dth6cvPEnddsUTqxpiG48ltmmW0lHTsgVUsLaQBtgdTGKBrnuMoUy3OBllcKSRiKdzaFm+6f4D+n+WKFzQFUdoz3q/p1t8UUJOrsD18xjELSMKGCEU8P5wt1jkbxDkTgKdQ0XZ2UdRmoSETFVnXkDfG9TqhwVIyEymi0sbDcYZ4TgqLrYO9Q7b8x6Yz3lTNVP8AE3ZxTZXXCtpqcNSKWYgC/swJ1MF693cltP3SWtsbDdpcRqVWdk92fr/MYnn7loU6siCiHI8qjpolIUcuY3xAZJkqvUfKmZZBGlhzwJKrtEoXz3MO6Vhq5euE0SQFcY2VU3FWa62K3ucdLZ0YyrgEBVJ2v5kKbIcvyRHAqMyqI5JL9Iw4Av6FiPkDjvfRy3NW7NyRhggeZWNxOroo6BzQnxZwYMg4G4FqFhljr6GetyjNCWEkD1HtEk8UsEq3SSJ43kUMpIJhPI3GPT7ymRbucd5+XJcrw9396PgnEAcQxgIhAUWJ+GPP3bld4DhR9ZTe1KQTdluVJPI/5j9MaLcBYhIGU2pYtENZTNdZGRXRDyYKbn8LnEr5ADlDIdLVYfZ/Td3mkAtuYn5f7OON4k+aZ8wtRghoCu/hup7kKRzXcDzxwN0zUrUSVNZpnTZfIk3fq0ErLEYSh1o5vY6r2IJAFrXBPM8sU6NAVGlsZEmZwR5fPf3KRjJxCdRVMstOGjjtcXNza2ICAwwShcACoupkqzIo7hY4ibtIXB+gxaYKcb5UrYUbxpma02SUcDPpNXWRwoPMhXkI+kRxb4fRL6r3gey0k+8hv3RsgPA5n/f6Ik4Nqr90euMq9ZBKKs3CuHIpw8S4z6BEwViVRzRCtmXltbF9+WqsN0NcX0QqMvkFr+EjfGM5+h7T4rRoGCuZeOa39h19O99KyFkPxG/647zh9P1hhHRapOAh1uKFZSSw+uNIWhlBqS2WQ1vETXpl0w3sZn2X1t1PywNU07XDznonyQjDLOyuGvjtWyTVNx7i2jB9PP8AHGO/itRp/tAD5qFzwN0bcOdjOQ5bEqQZVDCnvaRc7nc9cZ1biV5cOl9QlVTWLRDUdZZwDldMAI8vh/4L4znPqE5cT71XdWeeaI6PheljtppIV+EYwIpF2XKA1D1UvT5LDHpAhQD0Qf0xKKLOijLyn8eVxbXiQf7oxM2k0YhAXFOBlkXSJB/ujCdQYdgmDkoMuj/9JT/uj+mI/VwMIta3GXRAW7lPmgxIKLQNkOsrBoogQTDH/wAIwLmNG4RBxSFRQRMNokv6KMU6miYhStkbpjPljKpZIlZugIAxVe3E6VM1w5ptSZa08N6ykhilBIsm4PrviuGB2dMI3FoPcK3i4fpY2kIiF3bUb79LbX5fDB6GnBCE1CUumSwqu0S/Egb4bsWxMIS8pxHlMKXKxICeoUYIUm9EJcUqaTTyA+FsIsDRICaZW3s1xtfBQDskkXp7tbFZzoKMbLDUykb7nDEiE4JKZVFEhDWA35gdcUqkHkphIVS9o/2fuCuO4pmzDI4YKmUWNZl/+GnB5htS7MQd/EGxu8O9I+JcMcBRqkgcnd4fPI9xClHeEHK5r7TOwnjLgaiknymqpOK8uiI0RVMjU1ad9lKgFJCB1Ugm3u49Q4V6Q8O4g8NuGmi87kQ5nxwR7wQOqHTVH/Kg+f8ACZ9hVTXcI8WVmccUGKiFbBHT0/cjVHGoLNdn8zqO5FrYm9IW0r21bbWUu0kkzuduXhHJKl2mtxqc48sLrvJ89FXSo8cizJyDqQQ3rtjxyq19F0HCldTHPCI6PNVKjUtsGy6cFUfR6J+a2KRRdFYjcX6Yu+tgjZQ9kUzqbyav3mleYC4iNw87GApA0DkhnOUjRG0pc8rnrhhVU7QSVW3EkTy6tiCORvaxxs2z85V9ghbcM8VPLN7FVHTKN1fowwN1ZgDtGbIn08agjGjriTe5FtsYT6YVYgBGGTcSWUJKduWr+uK7HOomOSrPpTsiRalJkBBG++2NllUPGCqJYQUxqUG+IajZMhG0qLqYwCSQPK+KJEKyMoUljTJJe5Sy0p3hA+75p8BzHpt0xrUa3at726JwLspjX5uI4i1/ERscTRKJrZMKveI88ADWa/zxo21AkyVdaICrqaqWpqnmlYLBEC7seQA3P5Y6ZrCxoa3cpE9VTvEdSeJ+J2zF1JjI0pGzbCJbkDfa+1/nj1bg1v6pQbTG/PzXIcQqdq/V8EO0uWtDkmUsHYRVMhl7sMQoKuyX08rnQTy6417tzuyOcKGyaDURPFpEaX52HK/9McWdyuuwlswphS1hIN0fcG3M8yP79caFPGDyWCSS05UXNDI9ZFUhizgi1+ltrfC3TE9VwDSHKFuHAhWDwM6nNI2FtOna2+2OI4gD2ZC2W7CFY0kk65WxhLLKrKA0a6jYEdMcu0NNSHbK7T3yiLvPbKZE1mDSRfwDUB5DoDfrvbfGbHZuJiVNsVLUdTaNtDAq255n5/hik9snKAhIVkxYDcAc/jg2NUrQqc7eeKmyzPuzyjje3/tVq6UX/wDLUJAPr30v0x3no5aCrb3lQj/ENHmZd9gqFzU0V6A8Z+33VtcHzmEqt+RtvjiL5upatUYV08N1JMa35+mOabIcsasEWxzhY787DpzxfL8KjGVH50RJROOvr8MYdckOV2kMrjb7Rmb0+SLTSTSCFBV6dR9Ubb8Pwx6x6L0H3GoNE4+4V+u8U6Yc4wq77O4D2mZ9+z8uqJJIoAstVLGDaJCbAE25sbgD0J5DHT8TP9KodtWbBOAOp/jn/Kq0KrKziGnbddhcJdnUWXUcKGMIFUBUtYKB0GPJat06u4udzR1bicNR5l/DtPBbwgfLFbW3mVQc9xU3T0MCW22wBewKOCVIQxxL4Qow4rsGAlocnkRj8xgvWG9UOgpwkkYPP8MP24ndLQYSwkjXckDph/WGptBSnfLz1D64H1hvVLQVhpEBvrv8DgjcDqkGSvGVT9/8cCbkHmnDIWjSqG8/XEJqiSjDVsDGSCSt8Rawc80sryrCt7EC5LEX6nniPUn3WWEV7AjC1pLVkQHYjbEZeU6aTZlBDVx0xb966kqOV7c/n8MM4uOyINxqSEuaTCuMEVMSiIHaZ2AU3NtIHMnrfl9cDqIG8lHoESSkJMyrImjWQUw1CxK6+eGc5zogJw1qeRVhkW90JtyW+B0viYQEALSedtBtz9MRupOKcEJgxmkv4yPgeWK3ZOnJUsgckzqqV3UAyyAghrq5HL9MLstJyjFTwUBmuXs9tNTPFJe+uKQggdfQ/AjDscKZggHzCsMqdQqg+0HlXElVwHWjIcxdqwWbujGDJ3d7uI2AuGtyJ25+eOz9GK9nTvmm6Z3es4nlI6Iaj3FhFMd79wqy4UyP/Sjsu4Zq5yJHqsuhMjC1iwGj0/gGOrvq/qfE69NmzXH8/dKl36TSeiCss4o4n7Lc9r4sszZI6OJ100VedcDE2FgOae8N1PXkcblW0suL0WOrU+8ebcHn7j7/AIq1BDY3Hirl4e+1Pl1KI4OJMumyqpNhqR9cbE/wkgfjbHFV/RKqZdZvD2/A+/dRGkx2xjzViZZ288HZkB3GZ7kctF/yJxiP4He0vaplQm3ediPipBu1/hciwzIsT0WJifyxX/pd3Hs/RF6rUHT4oez3to4apka0k8hA2Hd2v9TizS4Jd1CMfNSttnD2iFUvFfb5lY7wQQqLdXkufoMdXZ+jlbGo/JWQKTPacquk7Vs+4ozJTwxl9ZmFTE9w9HDeKMj+NzZQPQnfHXN4Pa2lM+uvDQepyfIDM+QUT7lpxSYXHyx7zhdFcDccS5lQQpmVOtBmqRj2imWUSKG66GA8S+vTkfM+ZcR4eyk8mg7UzkYj4jkVGabokiPnCO6XMkexRgT6Y519IjdREQp3Kc7NNsH8HPTzGKpa5hkIHND90TxVqVKBgeY29cTU64cYO6puplpUdWzhQcO8TsjYhPiNGqaYaTo/n6g3uD9cFbPDXK9TA5qsc14ifVKsp0SIxVl8iP0/rjq6dvqgjYqXSGlAOeZw1XKUUkltgMdBb0AwSUx2wg7jDNLUpyanfVLJY1JHkdwu3njoeHW5e/1hwxy/KoXFQAaQhzLMpeetWnSyuyu0fUaljY2+dh+GPQ7DK5i6cN0yrqcLwnwrIEskkLadv/jzXt87/TFi7/5HvKex/wCc5Jhth4FPrbnjliAusDRCJcx4frf2d3z0c8MWrwho2Fza+tTbdRaxPLkMWXktcD0WAOh5odpCjTaNlYfd6gDmf8/XD3PeZqBQUyA6CinhGoFNXEahqU3tfkOf9ccnfN1sla7TyCtnhurgr5DG0lgSTsdx1xxt0x9PIC0WjEogmy2FASkzMCeXIAYzRVcdwpg48ws02mkS3eE+RY3wz5eZhOQSmlVmF5t2XQi7253xMyl3VIBAXL/bPnTcRdrvsyHUmWQ09Elv/UJ72S3+9IB/u49d4HQFrwoOP+Zc73bD5D5rm7l3a3kDlA+5+q6W4WrCxVr+8Q3xvjyi8ZuF0rzMyri4dzFVjUk9Mcg9kOWZVbKfx9oeXRZrHl0tVGlTK2iJArDUQL21WtfntfF0W9V1M1GjAUZtnFusBSGZZurUrkna3XGI9hdUhFTauIvtT1bZtOlNF43WYSBbjewI+G98e4+iDBRaXu6QlxEE0QBurS+y3lOV8MdntHLTorVNczVtTLa5aQkqq/BFUKPXV5nHMeldetdcQc2p7LIaB4bz7zn4dE9KkKdBoZzyf3w2XRNHmuoKQbX5jyxxD9UqAthTNNWFyoFiOpvyxAWmYcgxupGJwfLC0SglOUkU8zYnA9kOqWpOUZSRytgtEHJTE4ThSu24wfZDeUOorfUoI6m9tunrhtLcZSk81uNF/eHwwWlvVNJWDpYEB7HzGFpZG6eVnwge9vgS1o5paik336XwBaOiKU3AKMxu+5vYnYfDEYcNijJlKxsGvuTfz5YmaGkqMkpaNQRvtiwKbTlNqIS4iQkH6b4MUWlAXFN2oKQVjVXs0TVWjQJtAMgS/ug8wCfLniUtAGnl0T63RErMsQkQFfAfIj88Rdm0iYS1EFI1VHFKD4QPS+BdTEyE4eQoOphly+QvT2I6riwxzBhyKZ3TmLOO8i3XS45qcR1HNbsmDSl4qyMrqH44pS2ZREFMq/MBY6TbENQg7KRgQZnnEdNQKzTTKtul8V6du+q7uhXWMJ2Co/tF7ZKSrhmo8sXv6jSwR2bTHexG5HMX547zhfA6jHCpWMD5qTS4ezkpp2Rr7N2fZPlVQVaoy2nWkcofCxjJF16lTzBP0xNxo67+pXZs8z8fupBSNFjWjkAmMfCdNn+aZlUSQJOpqCkUjWOl/DZgeQIvsTfliz66+2pMa0xiT5dPLqrziNICj+1Xs6OU0FDNOUkIjKFwLXbmPwxY4RxPtaj2tEfhUg9rgSFzbnuSxrmRQRqTcD3RfYE49Rtq5NOZWLWjUjDgfh9q6mCksbJe2o4xL+5FN0rQoEkRKtrhrsJ/b7Q1DWSP2iMMswIWWEqxcobG7AhRYi3jBvsRjlq/HqVvTOp0u6Df47D6qYgyQNuvirc4a+ynwhlMrVWY0b5jI1ykNa+tEUm4XQLA7W3a9/wxyt36TX9YaaT9DfD2vjy90KHWOQlGlfwLQw0ggoqSOKKMWSKNAiL8ABYY5gXdUv1OcST1yVMKhG5VXcU8FTwyF0Vo2XcFNiPmMdLaX7SIcrjKgIUJl3FE+T1IizNjoBss4vsP59/+b64v1bRtduqh8Px+E7myMKw6LNkcBg1m5+WOZqUCMKmQQVMUXEb0fJtQPMX54pPtg4yExAdun54op6qGaVnYCJSWj0Fn+IAuT8ueEKNQODDz+CjNEiIWKqRgzxFCux8VtsQaYMom5yqW7WaZ8rlhrovDHK3cSqOj2JU/MAj5DHdcGcKzTSduMjy5qdx7shVTnPEaZDRme4etlBEEZ8/4j6DHX0LU3D9P+I3/AAqrnQJhB+S97UVcbSXmqZqgvI5PicaQR/1Y6kBgaGjACyKpOXIs4eaOo4tyyGAHvYpCGDN4W1AgD4WOOlsgGNHmsG4EqDz8Cn4H4TiZf9VPUQG3Pwys9vl3g/DB3ZHZO8yrNiP7pKhVvpHhbl6Y5rHRdOCr0y/j7hnMqErm2Q0UWbmOMrmOVUDUqxzqQBNHThigkKhVJAGoLtY2OOmdoqYYI6CSuWFPRB1ZH77kbVmd5NkuS11PPmDU1XmkUT1aTyvISiyF9DICfETuFYhRZdVyotcZbMFMisAAVSqOc5wNLMIOzTOeF6WOjWgnqK5qhwsk7U0OqmRuZKllVtF7+8LhTYAkYyalpbVHaNI84x8FeaKzRM5/eaJcl4YmXNqaCTKxlMbVUlFUVealLwIps1U6pOFsd9CRB2Ugama9sUKnA+HVamiD88KZl7cCnrj3BK8Sy5ZQ1EC5a8tppWKRVlejSNETaFrpGVVmIa+tgqgrud74db0Utd6Tz8vyrtLiVw3FUCfCUi+T5x3CTz5VXU8b2CiJo6ttwCTpia4UE21EW6jGPU9GXt9h2PER+VebxRkw4ZTZeG68VtTDPDWQyQ0r1DwNTN3jXISMKmm5Lsy6b7kbkAA4CnwGvnUBjonqcTpFoIO5Vb8R9ldJQ8VVsecZH7BxDDUn2ueknI/fFQ5RgrMrOqnxbc7j7t8alT1yzYLbUSAIgiYjx3+ago1KNY9u0b58/FWNwxHTU7qk8ssKbASlRpHlq8vjuMcPeW1QHvBbHbF47qsmngrKNF9nhWZLe/JLp/TceoxzDrZjidRVc1AcFN6l34gCxdy8fcsJTNGyvosdivQ7/LnvizbWby7+0J6o+0FHJO6V4jzk0eUCZqiniilqfZFkaoWUatBZiTFqAAOlSASQWFxjRp+jxkPe8AkwB7uqrMuO+Whuwnpzxuuc+0nswz/iuvkr6aKokg30zQRLJDuQos5Zb7kC1t749J4Xa3FnT0GnI6j/AGKqXN5SqHSTBUj2LyycG0Ayuqq+9WOV3jkeB4iFY6irKb2sxfkTcWPmMc36RWz7mp2wpkGADzyMeG4jortpWaafZl0xt5K96PPlp0haWqiRZfcZn8L/AAN7H6487da1TIa04U7tJRZlWbma2mbWP5dx+GKDqLxuFC6OQRFTZmmnxSg39MV+zcFAdtk7izaDVYzrfyHPDhhByhIPIJ9FmMIFzKtvO4GJBTcdggyE8p8yp3QMJVZCxTWCCCwFyoPnbe2Jza1QzWWmJiYUZdnTzT1KiIrfvBbpviIMBG6UlZ9ojtbvfxwBb4p5WpqY/wD1MRkDdOtGqk/j9MRuEolmOZSSRKd+nQYEEjmlyWWlX/1RgS49UvctRICbawcR6j1TpeI2+9iVryOaYhLd6FHia19sWm1PFRwk2q40A8a7bDfEnaFKE3kzOAc5lW382BNScJ9KYT8RUUVw1VEvxcYbUUQYTsFF1nGOUxXMldEPg18LS5xkBSCk/aFB1naXkNJzqS5/lXEzbao//FSihUKHMz7c8ky1GZTsP/UcKMWqXC7io7utUvq5jvFVxn32k1zWU0eWd27ubeA7D1LdMbtL0de1uuvgKZlNjT1Qdm9fVZ4WevqnlU79wpIj+Z5t8Nh6HGnQp07fFJsePP8Aj9yrBIhVTxfX+yZghLKiX0HkBbHX2VPXTVcu0lWV2SZwz0s87TGREURtECCddxpI/wBoW+YxzPGLeXtptGTt5c/grZqBzJJVk5HDlmVQV1LLAI2lmjqrxzay92BaNFYC5AAbTfztuCMc/Wo3FeKjXeyIgiPCcE/RRvrEkFDXbnxjRZg1FT0tQlRCEM11uLk7AEHcG3TF3gNhVpFzqgg7KvPZsyub88jjciZXR2lYC7HbckEehG3y+OPVaDS1oHRY73FziF0B2E9mr5qkWYSKGo5VvBEQSZF562/k3sP4reXPzb0j4gKTzRZvzPTwHj16ea1rfFPUV1VkmRUeSU6N3YknIHMbj5dLfTHmLq0mXZSe51QxyU1T5a9U/eOTp6DEjGmplQOeG4CcT5eqr7vwxaFBQa0HcTZZDLE9wB5nBtpAGQp6byFz5x7S06iURlWPmdh8cdVYOeCJWzTDiMquaHjbNOFHGz1tDf8A1bNZk/2Cenpyx01SwoXg/wBLvr5pneKsHhntJyriZlipa1PaiLtTO2mUf7p5/K+Oau+FV7TvPb3evL4qGAdkQGQioWVZGDCwCgAi97i4+WM4Du6YRB0YKJKbihmCiot3dvG3X0OMt1oB7G6Hs2nbdVr2vca5SOHqhvaUIYFFHMlxuqgDmbgY6rglhX7cd3/bn7kRIpMJeua45qjOK5KqrJMkqi4v7noMeokMoM0U9gsfVqglEFMkWVV8Duzd3GwBZSATtuATtffFak8vyoKjcFKcJCoXiNaoRzVCRzJIWjDFQwYEm4B26Xx1du9rWAuKxK7XPdDQt+OsuqZMySnpV76ASmqB75NI1bGxJFyCyrtvcW5jE97UaGnoVPZNLDLsJh/opm537lR6GdLj8cc5qHQ/BbepvVE9HWDLZlghkE1aRZ5mtGIwfIAFhtztvbbHVtcGbbrmnNL8nZLSUdVUhnglp6xgL2pm8Q3PRrW5bXAJ2wD5eJOUgdJ2hQlZWkuVsx0gq4e4a/W/l8PTFeQMhTiUpX5jUZtmElZWSNPUynvZZWI8TnmfTfytgnPc5xMom90QFvFJPIBIJJlDDfTqufpiMgzunD4EIlyjizOaExMa6YFNlexD2HLfn69cE0EGUjVJEFWp2V9rNf8AtrNTUz01VmUmWutD7cfC8yOGSNm530tUafViD72Ne1eA17yJP1Wbcs1gCMSscN9ovDNPxPmedcSzmrzWuSSGSCEyAQM9opGsE0oViXSLHkSOpxDTp036n1DDjMeCKoxxYGU/ZCDGzPKsoNbS5LPJM41wQV0FRpMVOGAVYjoGxAJJtc6gL7b5jqLQIeA4+QKvtcXZJhb0nFFItWlNPxXnWS0wdQZ6KKSU93o38DPpa23u6NXXcb5NTh1tUdDmNHjA+it9rUiWu+P5RLw9meVZ3PNUz8QVNdQ5ZGmummtGJW1qoMwWzFSze6rL7wXVzOGteD2rWnVTa4TO37CatcVmwGugnol8xzEcYVbGr4qzVljKimSno6WCnEe5CpEo0rHGNzuN2FyeZtOsKb3B4Y0RsAMDy/KrsqmmNLT+fimcuS5VEppq3iitq/ZkUU8cJihCqTZowNLW2Y72tbVtfnpMZWAALoA5KJxnIAUbNm/BVKpV6MVeZtIkrVbVkpk0h/EodlUKCg2IXfVyFsR1aFoWnW0komsrl0h8BM+Hs8Hd1NP+0hQ0k8pIp1r2nqFAPgs8qiNWPVih25LjFrcNsajgfV9ts/YK82pWDf8AmfIIlymnyPiGl/8AaHFvFOTVbEyK16bMKQADa7iJJbEkX8NxfYjEzOD8NqDA0nxB+oMfZQvr3jYLS1w+BTQcO55llC2ZS8W0OZ00akGXKpu+iUknSJVEKyxvbYKwXURzbniB/A7Oke9Qx1n45nCJt3WfiYPiB8uqkoMj7Qs89rq1tTUewUtSyaYFULvG8gBlVje4ZtS/duAAaNTglmXF1O1ke9Ey9LYa54n3H6KQ7O+zbjjjCMZhW1sEeTRzMszKEEm1tKxsrabEb62FzcWA5ie19Gra6Ovsi1vmffugueKutoaCCfJF3aFJnxXh7LMkenoqJKieTUir3kBJjjEfd94i94GG25UEtckbnU4jwSzcKVo1hDZO3jGfNULK6qDtKroJgb+9G44FzmuqZzQZ6lNCuravyF5NTA2ssqVIVgOtlHoLYzv+AbGoSKdQz4hQf1qq0AuYPcUxqeAeNqNGmXN8hnjC6gkdHVRufgAXufJdvjivV/8Ap7bs2d+/BTN44Hb0/mgar4i4upakUrNQ0s+pg3tkFbCqnkiXETksb3INgovztvkn0JsQYdUI/fED3LSbf6hqbTn3hR+acecYZRQ+1y1XCyU6qGeaqz6SmS5bTpBlp13B2v1NxYWvgD6D2j3aWPcf/H7n5JevgCTT+cqfi424v4YcVPEmUCiyWot7JmtLmMNRFMbFrLpPMqGYA2JCE4o1vQmlQbjUD4jHxBKTb6lXMU+Sgcw7cs5oaOpr6jIaqKjgUt3z1CEMt/e1Cyabb31Wtin/AMHWzjp7TPPun8q42s08vomi9umfQUktXV5NLDTKyFZlqE7tkYXD6mIGn3eVyQ17W3xD/wAE0X7VY9xRm4pj/E/JHPDPaJXcU5VBVQ+10Usi+KGUQyqr/wAIkRtLA7EMNiCOoIGdd+iXqrzoqam9YIUfbt5tUVnPFnEkNY6VNdFl9MGAFRVFEVgeVhrG/pt88Vm8CD40y4+CtMq0YnSmlTmubxrdq2qdCN5CgVflbUT8Rt64mHo+ZggfNF27eiGM84lzWBmUtVxRmwSpqoKgQSMeiskZufPoMa9v6MF2YB8t/mhN01qihVZiaM1FXUCOLWYu8jaoRi3kt4/ET0Frmx22xN/RqevQwZ93zRC6KGqutkzAsked5tSvZdkhjmIJJFrFFN7jqfInnjSp8FLcim0/Efc/RRuvOpQxX5FPmjulPxvWVNpTAT+zW0hxzUsj2Hx5HpfGtS4e+lvbt9zp+oURvA4TqUJknZXnXHOdRZXlWaw11RUSGMTTU1Sqp4WYk6ltcBW8OoG4ttjRp0agcGsoknoC3+Poq9W7ptYXOOEf5X9m7M+Gi0awZxWVFNtO60wRVI8lRZLj11HpiGvYcSuCQ+kG/E/PH0UTOJWzAIf9lYfD/ZWMwy5J56qWKcsQKaogI7xdQUMORFtQJB+WMoejl1UJ0kT8ET+LsbykIVz2h4e4VzmejYUc+aQsEMkJUiFzyBvck26W5EX54uVOC3FIBlUwPM5T071tUaglKDMaKqnijoqqiNUykrTU6KumxIsNJ8Q5npvtienwynT7wb9ZQOuHGc4WDw5mkYkjXMZKiraQXDRBYo7nYWJuW8vFscE/h4e4RRE+WfihbdFuS9Q/E3ZbLnEEc1Tmz0GaU1bFIlItOGkqQQe8VwWA06Bq1gm2kbEYVOxrWjXv0AiIjMzyjBTm5FZzQ10ZycbdCnPDXClbBWGhmipZcthMi02WRMssYj0ECSRmsS5Jdzt4nI2AVcWKtStXpto0aRE7yMjHh/CjaKdJxqVHgxtndXnkkuZ8IiamkELujBAhFhcC3h022ty/744K99E6lWdQII6ePn8equN4jSqQRsjHKuI1uzT0srafeKuDp87g2xyjvQ6+1f28+YhSuvqUZMKal7QKGgpFnNNWPGSQumEgm3x5YVLgV9Tw5hEeBUfaMeYDgh3MO1z2h1ipqMw6iNTy3bQvUkKflixU4W5rdTsnwUzGAndDPEPFcueQypREFlIDRR7uB0J62OMU0nUHxUEBalCk0d4qtcy4ckaokqK1tQ30qdvrjTp3TdIbTV7tBEBVvxjRU5R1Rw0h9bY6iyqPkEqF5xlU7m2XiKpJju84NwYwSw+AG4x29CoXNg7LPe4ThEnDHaTxxkk0MA72ugceCLNhpBUeTsVI+ZOKFzwbh9zJjSf+n8CR9EAuntOcqw4+03M81pWibJZIJ9Sxs9FMZwjG/vBVNrWJN7WsefLGGPR3s3yHyPEQpDeMaMAyqvocmHFNfU5g2Z0+ZVSKxkqo5xDTQ2JGkIy33A97mbWJ6Y7cW/YUxRa2B03J8z+hZPamo/WTP0UzS5XQUUnfJJRVcUchDsaiQC4XZeWl/Fa7JsBcb2wDrcEAPB+SftHTI5JxUVbZZSRwx5hSl2STWohWIhm5HWGVtgFsfe53O+JmUqY7wp4+OyYuPNyg8+z8SwBI8wM1YqaGqIadGhLhbCRFfctba7G197G+L1JpYZIgdPsoHEEQDlJZRxzPkphmgpqWSvjURpWzU0YbQL2XSqgAAsTZbctwcO6kHnvbdEwcB59Vs/GmYSuzvmM7Oxux9pkFz8AbfTBaWp9Y6qZagioye6VrkeEsxJv5+vniUYMqlqJTuCqWovJMi94vMgaTblsRywYcdwhIhMs3aWpdZHIcXsGVAGHSxIAv88RuypGQMpisJkLAltQvZkAY39bkWGBBMIyc4WtPAzuLyFSedm2/74OSUwgcl4J7O1lbvGv0O4/yw0k7pKQjhkgkjqqQlGVu8hZTZ1Pp6g4kDywggqKRsUXcXsM9oMoziiQ00tVRxvmNJCoDmZQUeZLga01KyFb+Bk8mBM1w0A6mbFR0ZaS0obo8urHSeamkLwxhBUSS3VIQzBVLX3ALWGwvcgdcVw1+4U7nYgqQrOHsyFHSZixirKd3SNfZVe/jL6BYjkxRvlgKlJwIcRhJlVo7o3W+UZ/UZTK60kyAzrokAAKtuGH0Kgj13xLTf2cgDdC8axla5hxnnVUvdivm7uxJjVrL5Xt8vww7q7hgIm028wocZ1V90UMjFSblQABq9bcz8cQ9odlKB0SVMaidncAOWDe6b2HM/S2Icoi4QlImREbvF7lL7+Pn8/LCAlIkgJeCohDHSY7E7tqwYamLlJ09f7HomSRIXTYSRtpYC9+YPmAcTNJaMFCZJW8uaQVxElVSGtkO6yVLMdvO5JPw5YM1S4Q5x+KaCPZUlkHE1XwtX+25RmVVk9QLEvQzSWe24DKTpcejAjflg6N2+3PcdhDUo9qNLxIVg5Fx0M4zqHPeK5nloKYzVAo6UiFampurG4udKliCyqLbEC1yBep1G3FQVKmAM4VKox1Jhp0zvhWDX/aognYx0eSuXZh++eUyAk9Ngo/H642TxCgwYWWLB/Mp9wtxnxxx9DKaXK5IcsqYXWKtpkaCES2sgkZjqdG3uUDdBscRNualwYpt/fNJ7KNDc56Izj4doOC6Fc34mzc9/EAhzDNXZYkAOyx6uotYc2sMWW2snXWMnx2VU1n1DopjHQIJzf7SPA+QO65cMxzhxcx+y0qwxXJuQsklmHwCkYhfUsqXtEHyCsNta9TJwhrPvtQZTxbwzneW1OTVdHUtGXoqjWlTG0iMGj1qwVkNgQWGoC56YqXF3aXFB1P6q3SsqlKq14dsql4ozj2nNiYpL0kmmpp4yisFLDfa3MG/444xzNIgro2ExCjaniKvjjiRpi4F9BkjUqvIbAjw8h9MEwNjCZzyTBUlw1xdVZHNFMkpam06JERFS6X+7YDcHlfyt1ODLQ9sFCd4RlxzxTX/ALMoKyN4qiknBiMxAYSbal57ja/hP44yexptcRpgqak4CQhMcTStSq8rIY0UqsajSCCLEELYEbDY35YlDApS7KaS5t/pOPZcxSCtppGEjQSwDxEA2AAIFwOR6W+RtUw5gPZ7qCoQfa2Vg5dnlLneTU9ceIKtHoe6j/YxpwsaxyalVtZmJlHhAJ0ArcX2sToW/DmPYXlwJ54z8VTqV3sfpa3HVGHZ1w1k/G7ZjluY5P7Q1LItXDXRSgMbgDu9TKdLbX2PIsemNOhwyjUlpmeoWfdXlWmQ9p8MqZzzganUsmW9n8NqW0j13EeYa4o7jVaONHPeNfrdbEr53xYPDLdmGMc4+JhVm3lR3/Mqb9B/CHeG56gdpElCmS5RSTUEjVTRR0yeDUYw2h2XWDZ77t8zscQUrcUqwDWxlWa1Rr6EklXXSZ/EM0zCspoRSRQVctL3UJtqdZSSQAfEDq5nHThzWuJb71z5bgA9FCcecX02Q5TXZtVU0DgeCmJvqkcnwqDfYbMSfIHFFxZDqhCtUqbnua0LlfMq+LN8yRoKaSF3mLhaZtDSueeqwuwPkdrDfHKVA4uJnddR3AAeQRvwfSw02fLMaeNpY0dRTwsqwo52IFha9yLk3sbAdcWra2AIlUrh8txiVYGVz5JnFLLDFlssFVSnvUpxCjqXLANfkSNxdiQRpBBHXabSZBzlZLiQZnCNJ+zCozcvxDBBRyUlPAaeiy+sgE4AuPa2iDlbh9I02ZQdFgRqbFptt2gkCQPrzVQ1w06Tv1/fmn/BvBuRniLJcuyipW0dJUGDL5I9OlWN3J1gsvia5u2wPu7ajap2rGaQIP1VerVeQ4lWBFwZLm9FOZso0xwxvEoqIWOpwbKb2PkbEdDi26lTd3S2VU1wZBQU/ZhV19PUCaGKirI4y85n1RIwA1XJYC4tbxDloG3PFB1nrywZVwXOnnhMY4Oy7gTQ+ZZmcxMHiWmQtMWBHMRrZRcgkEk3v1xZ9QpBv95w96EVK9U91Uf26dvPCfFD0vDWTZPNl2UxIEnkRyJWIcOFIQ9COdy1iRsNsYHFnWtOmKVsBq6wF0nDLZ9MmrWJ8FTa57k61jTO+askJvBFHWyMSf4yGTwNq30glSLCwtji30tY0mPHAXSioBJk/FM5eL3mzN2WqzAUYjMQp+/PeDqSJbg8/ibEgadsQusqHOm0nqQE/bu5uMJlHm89ZxBUrX1v/sl9QWClWTv1stlZmkJVtiSQRzAN774f1S3DQG0wD1wg7V8yXYTGeejOZFZYhmGXRJoFLUx2RytrO1mBuG33uDyItianQbTEMweo+aTqhf7RUfQZmuXVVY4D1vtEhlf2+UyhTfYqNtBHQrby8sWCzUQDy6YUcwmeWViZNT1EWXQ0+XxSpol7mPeQfzsbljvza5wZGoy4klD7IxhMRXPHCFjZ0iUEKi7KoPMADYXvgozKWB3QE29slQqwdw3Rr3IHSxwSY9U1mmYk9SdyTvfBgYQHOU0Z2NxqPnc4kUfNalS9jtth02+UsJlAA0Nt64WEKswsZFK9b+8Be2DOQqwJlMll9lns+k77joRgRvClgEJ1WRlFbTZhzFxc2wniMhC0ZhRrNrHiiAToVQ/0xFzU8fFJyFVYhN16suxPW2HmN0s7pBgVlOok7bDCBwlHNSlJO8YKv4gRrC+np54MGUB8FYvZrSx8W1NPw81OlTVLVCroFJKu6MUFXCm9ixiUyqrggmJxsWvjQpMFVgHMfRU6ztHe5c0AMhqqVqmmjWJFSyVGgiNvUsSBuNxYdRilttsrTSYzuj6irY8y4Zz/ADCjDUywVlMsEINtEaUdcIrnbcO17+v1vEk0i4fuFXLdNRrT+5ChOEoEzxqrh6rnMaZiqpS1BsPZ6sH9xIf5SzGN+miVj90Yq0h2gLBvy81PVlsPHJC89DPl1fU0VfE1JWUkz09RA/vxSKxVlPwIP59cU8ndTghzQWnBTSpg7qUkMTcXvf64aOqMSk4ZHopTIoRgRuHUEfH0OEDBSLQRCk6qemq6YzNUVDVDEBlMShCnK2oG4I22tY35jBGN5TBpA8EwqqXRGGjT9ySPGBcg+RPkenTY4ZMV6iijZwSEOncX336fTC3ymJUhE5VZZHN3Qa7A7sOv9Pnh900EbJ5RCJ83pqWYzRxTyIJGp0DSRqf4QSATfa52F77gYQpguDeqcu7s9EcVFPkeVZu44mzM03c2hXJ8jhNRJEqsVEQmk0RDTuS4Z9RJNiWxoEUxiq7SBy3P75qkO0eP7Qnz/G/0UnB2xZXwqEbhLgiho6yK+jM88qWr6kc91WyxoTe/hH1xI28s6PsNLj1P4UZs6tT/AJr8eGEK8R9qHGfFmv8Aa/EVfWIbWhea0Y3uLIoAFiBY8xYWxG/i1d7YbgeClZZ0qZw1Dcs9ZUbTTzSKHL2knZlueZsSdz54qVLyrVGl7iQrLaTWmQAmsiyhxcHfcHb9MVwZKdLlGjiCAamO7Hr8MSbBLMyU7qQ0lJA5vrXZh5G362P1xG4Co3dO2WnKzNNJtGpLo63UObgjnviDsmHzUmopWKbw6SGOkb9CemLHZ4EIdWVOZJm8NVklVw/Wt+4lk72le19Ml72+tyPUsvJthLW1BHNMQWkEJhDQmsyxo4wpq4r3jvtIoPNf5h5dR8DjNqA03xyVppDhJTCppZ6GDVNC1KVGtdSnc7W5em9zYYmpQ/YoHOzKwtWy3AbyAUnkLdLY1AYVYkoq4B4vq+FM/wDb6YO8ckRgqKYs5WeIqbqQviNveBG6kXFt76FrXdSfq5KtcUxVZBRRn1fLxBm9XmFLPmWfZSszCli9pLyOiC47z2dy6lLhgzjcC229r1SvUcfaMKlTboEEQUz4UaSjlkqIXBeKaCKJo3v/AKtSxAK+rKLbchiu1zu01KZ4AbpVj8K5zUUWYV8lRUNJDGrSOS5JDsOV+R8yPQYmq1zHiVXNMOgKve2Hj+TPp8uy1GDwUQZ+7BJVpHOxbzIQLt0JOIrut3BSCntqQZqd1QnwlFUe3NUBWZ0jawbq7jSq/iSfQYz6bcyVae6WwFbHCuTU2TmnrszRpsujjdWjvYzbAkL/ADX3vyuLY0qTciVm1HF8gJ/xrnn+kWfUHDGRyR5etaie31UTFVhgfxO1xuWMYOkcxcnyxbqkMhlLd37KipN0NNR+Y2Vhcb8fXOa3hmnyWCOClpoYJFC/vBJGPQ6GibYkeIjl11nVdADQcALNZSmCdynvYbl9cM3qKiSSWnhmeSCJVI72QAMjM7AXuSSLDbwn0wFrqc8vQ3jxAaFcvEHaTQZZ3+VLI1C6nTDMl/EACAf5fME88beG88rJa0u2VM9rnaTDkfCldli5z7ZXVEYUypMjNckBj4TcHT1tzXzxWrVjSYSTlXaFEvqAkYXHPF3aRIjzU9LMZJncySyE826sT1b44464vYxMlddRtRu4KuhKzEtrJY+InqfW+ObcS8yVsjAwlhM0bKwYhwPetc/9sRjdFAXo6lk1eJlaxsSMIHKYjGVrBWNG7MHIJBUG3ng5ymd1Sb3VCAbk9fS/XCB5lKOSQa4ViLEHbfYYYHKeMrS7IgDC17i5N8FKAjmkpQFU+EkHr6YIJiQmsrW1WsN/dvggh3TN2PPbn58sSgckC0BuPTrgk3mvFbndtvx+WHTEdUlrX+H8cOgnxVoJOFY3vpO17C354kbGxVcg7hb19Opiikimp1kk1AK8q67DndeY9L88CWHcc0hvBWaXvJqcwSjQ6iwv/D/f6YadQgqTHtBRtWZqZydKhNgki7qR8fPEBMHKmBkLy0tZVqdKH/f0j88CXgJwIW65RU6AZIbeRDAj8MAK9PaU5a7kt44GWUoykOD4etv7GJmkHZROkFOI5JUg7yNyjqSA8bEEHzBHmLjE4qFglpQQJyoSSWSf/WSPJp90OxIA9L8sQFxIyp5Rl2Z5vGc1ORZhmEdBkmcSQRV80seoIscmtSD93e4LeTEHbF23drBpYyq9dsjUNwlKzKZsmzR6Wtj0zxsYJoiQfui97bbqRiEaqT5RtcHtU3x9RycW8OU/FsMevMaLusuzggbyEJamqGF/vIBGx/ijF92xNdAOis0YO/mq9I9k80zsdlXsMizxFL2cHw73+WKG4V+ADhNWDGyNsp5FidsNuU4OFmKpdV0Xt5HoMOcpweSXapZFYoxRG8JQE6SL3tby5fTDhxmEJiIWKdlS7bixFvTfBBAROyk43QLJ3m6EK2/PyP4bfLBbBBMlL5dxFU5U1UYqeKRJxokSRC2uM8hztYXPxub4JtUNOQnLQ5EPDfHtZ36UVa6VOXEBe7nAnCC3RWuDtbY4nbVFTuvbKifTESMFPeM8syOl1vQhIGjIMrUo/cQi4Fil+ZJ20m9lOxtiKrbUz7OEzKjtlCcQ8LZtkM9QlVECkBRWmp2MsRVgSjq42KsNwcV30Sw6XYUjawcoOOIudLvYHkb4BrYUpyYCWEQVwxOqNTqbobf54l3yhiCnFHRzVVXpsxdiGAVS256WAvt6YiqPcBjdOAJl2wUsmWSLBIvdt3JdojNJZNTgeKwuSQu24vufPbFenra4kjCleWub4qPLkFomIbu9SF+VyNj9cXW0wDJUcndbd3LKjlVK0ygBmI2LbbetsVa9fSdDUbGc1iIEzRrKoWJmB2FiLEEb/X64TNknYRVxvkMnD9VlNWhMSZnlVHXqTy7wxASafM61vb+YeeL1xbS0E8xKp0K2XDoSov2yHMUUyOuvSbq58H+61+X8ptbGS5lSiZAkK+1zXiOajGgMEpVoyp2AB/Q8reuLbKusKMthOaNYpJ4mk7yNI5F71YColQX3K3Nr25XIxK2rpPeQlkgwjej4iyHLc3Zp8vnzelDiSmzNKmWnq4NQVj4DqQlX1X5XubGxxrMrUAYLvh+FRNGppGcqUy+CnTJkzKnhmiilIl7mZtbBmWwNwALaUBFxcXAu1rk2uaZLTKicDqgp9ULUZdwzPHAqxVKR+0SlrlQzMNjYEnSLX26HAPJJEJNIByoc9jMk/DzZxRZzT5m4jeqlBiMZMQV2Mga7bDQ2zaTsQByuwt6ru/uhNwA7QQkOAMsp4aKOrzBdEMzsUQtYu3Im/QAbD5+eHot1GQjrOnCneJOKIqlnljCySoNMMbqVhjHmR0QWvbrsBzNtBzg0Qq7GEmEl2T5dOIZeIp3lmWqrVpoH0EySXkUyS7cizBUB6BbDA2wL3Oe5R3TwIphWVnmSzZhxEVo0jiV5FjnjRNKOqMjkvblIJVvfmfz0ntPsgLPa8NbqJVkftPL+E4y9LWRU7pASXjm0C/M2ve9zzA641abeyAAWYZqHZUr2m9odZmTmKnr4/Z31NI0MuooDva9rG9vpe/M3p3FwW7LSt6A6LmzjftBnzWrmgonMcCkhpV3LHkdPkMcvdXxnQ0rpLe1DAHOQTH4lsx3tuSMYJJJkrVGMJdSsbc77AjbEcFF4rxZLEFrHz9cIAxKS8NLX0kemGiEjnC0CsOTbYISBCRIO63Fi9weW+4wkJWktgAbAjlyvvhwIGU2+yayu6nSxA6CwviRDOMJCdrgXIA9Dyw4SjCbMx3I326YfBQHYpu1x1sTiQZQFaqRzG/qeuDB6oSZ2WmoFW3v0wSE7QtdvI/JcOmwrJchnICa+d9r2w6hzyWkc3hGtg8fWNlDqfUdcPmMpFOabRFUWVhEpvdALr8R5YEeCeZEJ1VKGppZaayzMB4lJsPl1vhnNAym1clvwvRLPT6JN3N7XN7nrvipUcD3UeR3kYUfD6BQpUhfQbjHLXTywkc1oUgCJWa3hdTZiFZhyLDFaje1AZadlI6nKGcyypaZ3CAqzDxKMdZbV+1ZJWe9sGEMNTnvmvYm55nrizMqQBbQxFZI2UaCWBBI5EcsOKnZd4ck0SVYtYDxFltPm8jmWrKCnq2RLXlRQFkP+0gF/VD54KpcioO0aN1CGaHaZTnhjimLhPOGeejNdlVXEYMwoW9ypgZSHUjzHvjqCNiDi7Z1W1JpVPZKrXDdTcboM7QeFl4N4slpEYvls6iry6sVxJHUUz30sCDfYhlINyCm+KNYG3qFjuSs0KvaszghQ1XGrxCXY3G5U7H1GIw4HLVPtIUeGC87kEbYNIJWNnsVUFxe9rfrh9wnkDBTmFGEVijDf3r+nLBRhBglO6eMz0oDlg6gxn57g/jgtwozg4WWqGj1hYgwVywYDfbw7/IfLASIUgHisLSyTESQoZFA94ch8fzwo0mQm3GUTcQcWz5zkFFSVSSe1RH94xYMJNrD6AdfPFh7w5ojfmgDIcSjLhHjqXh+khoGaauoKiIU421IzGxCgbEqPdI9bXFhi8yq3Tpq5VV9PUZ5oe41oMlqkXM8ggkjog0cdS76I0NQ4ZmCRg3QKRpA3B58wb1K1JrDqYcfJHTc7Z6F5I7oI9wzdT0A/ztinyhXPBEPDdbPSx1lbHMaY0707CSJRrj7wtExUHqVO3Qm3lgwdIkKMgEgJvWZhCtXCHBjhXTGkTMW7qHV4UvbYC+om27XJ54Zo1HwCc4CjQk0ytpQklizOBsNzvga1QU2wNypGNncYW1TJDCndq4uB3YudufQeuMzQ+dRCnMRBKXpqd5Y6cW7xg6qFG+5Ow+Nji3Sa6cghRPIhdp8Qdn+X5hS8IUNVl1LXfsyspYpkqAGU0ywss8ZB5hrAeY5ixF8d0+0Y+mwOG0fDmuNZXcHPcDvPxlcu9pHZjV9l+fT0jpNU5VKxairHs3exfdDHpIvukbbi4uGxzVzauovLDtyXRW10K7Q47oTMcRXTHJdSSQhG4PW3QjGVp0HvLRGRITSSYwVJKeE3tYYmABEoZjCeRVLhXZ5C0fIAc/W2CDSTGyUq0uEYqjiTiCnoPaYEo6kgaZH0IqqLgi/3gBsOvLrjVpUnvIYwYWZVqBjC47hHeScIVWdnNqGpkqKehastDFIwV5kjVLzBdwxXvEbmASQtxe40WU3Olg5YWfUrBsHn9EbZBwUlTQS0NfHTpHUUklBXVFP3muqiIKs123UaSptzDLt4ed2jQO0RyVKrXzqHuXPGd5jT5ZR0ibrSwJ3dOqqA8nr8eRPQYo6RSBA5LU1FxlDFFBXcaZtBRQozNLKq9zGCVQX2J9ee56nFN8vVjUGCV1/2a8H0+T5SJacLNAsiZfCmm8bxKp725/mbWbjkdJx0dtQ0N0nkuar1C52UWZpT5Lw5R106yM0moO0kotMyBQAoYe9b1B3xeFIA6iqpc50Bc09pPaB7bWyxxFUS5sifqcUbiuG4C1KFLqqA414ylqVemR9LSe+VNtK/HzPT645m6uNOAuitqM94hARFiYza3mD6Yw55rUmTlKagAQCz3PUb4EowFur3cnb6fjgYjCEGRlbBtTne9tzt1wPJEM81jSNfTnzth9kpgJQ2IW5Gw88LITeSSlfu0cgAsSF93n1/PEgGUy8ZC5tpPh6KNsOYCDMJJ4pHB8IW1/eNvliM1WDcpAEphUoI2Kh11ke6GwbarTkFM4EbpHQ6QF3HhcXVzyYeYPXE0zso8ps7KTzv5m2JAhJEpNzvzIIwQTE8148wF5fTCTRlJNUKGI32PmcShpUZdndWVqMUgbe/5HDPwYUTTIWJqSKeTvEAWovdkAA1eo9cDEbIgYwtIMzijRYqhJYTGfC4Xdd/qPhhxB2KKOin6ZIpoDUUrxyIw3WP3bgbj9bYJwIGVEd8p9lR9jkE0N+6JBKNvoPUH+uMyq0sfq5KdhBEFWdlmc0dZSMNBlqD/wCa7Dwr6Dz+OKNzbtrN1NGUbHGmYnC9X1EEUbRvIqAC27DbHMus3sfhpV8VWubuq3z2tpWqWh7wSOQSBfnt6Y6aypPpyHKlVcCQQhutoWjmWdVMscig7b+LyPrjW2KEZBlNWp+9liZrp3bqzAi17mw/TEVQFzHAc0QMEFG/Ceapk2ZVMFajSUdTG0MqIwOl7HQ9zt4See9gTjMtXmmezfsVJXAqN1N3XqrKpParFgyk2VwRY2P67EeYONgSwKiTqRMeFzxZwZV5TTU61lRlIkzGlGspIEIXv4UHIsxKSBSLExMFtqOCuQbilqG7foo5FJ4ceeFUUMDxEhrNEbgf1H54xKNbS7T1WkRIlR9REIJWAG3T1GNhvih5YW0buCF1W9L2GJphBAKfxRvHDrCMUN/De1x1t88MDzTGBhK0cjP3uzAFABfa+CB5JnCcrYWjJYX94m4264FICcL0Ufdg+Ax2HUAgj5fPCHgnWjFXUKpuegGwwJykARkpWkq5KENaRwChUgNba9x8rgHEjXEJOAKlzxBLR1s0xUVEeYxtHmNHe6SkndgRyJ8Lg/ddcSh+nB2KDsw73KSpuFKvNMoizTLqqlzeOBbzUkR7uqRebAxndiD0Una5W+4APo1NOoCR4Ju00uhyi4asU1PP3dzI7owUcrBWG/wLYgZ3hCkODITCmpJs8zampFLNUVMojDHncnn8AN/gMTgSYCFxgSVemYSdmvC2VZfSZpwtSZvmXdjvmS5ZAT4TIwcXcrY2tfztjYFpaNaHVRlZJNw9xLHQFB1dVwtxRPTrw/luU9n8LJ3ddmzQTVEgDeEqgW6xhtxe4JGre2Adb0CQLcQepJhSDtaQJqHV4KWyLI+Bciz/AIdosqrH4iqp6q9bW1MRXvCroqRxr7qxeJ3dhqJ7pFvvbBNtaVMt1O1ElA6pXe1xcIHJdOy5itZVQ1MRaqjleSQPDHq1eDmPrax63HQ46Z28rngIEIX4tzvL6/K5YK3hnOM2pZwaaSKKjQkBlJ8QaQFRzs3IEcwcVKxoVGw8/JWKTXNOHQqA484GyqhT9o5fQZpkiOQhjq6qCWPUSQG0oCygnmC3W42OOdrWtL/An3reoV6mxM+SraTL9D+JrHe9t774zxRLTC0dYOUYdk3BEHGPHVBlteJJaJw8k4jJVtCqWaxG/IHFyhQ7V4YVTuq5p0i4bq08j4SgzLNOIGys6fZAsUcjRiZhC8pVmUm2ogWs1g1jzGNttroeez5LIfcENbq5q46GjpMuNLQU3eSmnpUp45XG7IjXa7crkkMR6jy21GUgwBqzC9ziXHml6maOnjzCNHSeaWmZVpUYBw5BFyegsbk+mJR3cocmFVUnZrwxk8X7R4rioJO4UDvu+do97kLoJte3QCxxkeqGsf7hgea0DcPOGJ5wvwtkOa5+lRwlStSU/dkQwIGSNXe5LmNvdKqGNyb7j0wdO2psfNPYfVBUq1AyH7q14K7L+Ccljh9qjadVCKliRrsd9A6AH5416bS1veWacnCpPtO7QGIlp73q5SQ1muI/Mb9dxf5DpitXrBowr1CjMErnHjfihaKAwR6ZJ5fW59R8Pz5Y5i6rhomcrorehq8lWUx72Rn1M0jgkud9/XGC5xcZK2A0RAS+WZVNWyoEfRqO+ry5fPFGvdU6AOpG1hOyKaDgiOdWJEjuLEXIsBve/r/THO1eLO2GFP2ZhL13BjUUR0Qaz/MNQxFT4s4mHIuzjZBdXVxUFW8FXBJT9RJGdS29Rz+mOjo1hWbIKiLdO6ypEqxzQSrPC4JSVDdWF7c/l8uWLZ1NOlwyogQRIScisLgjbzwbc5STuLLJayWOOJo2QICQZBqvex2672xBWrtt6Ze5O0EmAjjhjgSGvWam7u00kTaJmNirgEget9hYdfjjmXX7679JMKV1PQAUwfhe8QvGQw8JUjFH1ogxKsAdUG53k5T2iXQdPu29MblCv7LVUcJklB+UyPFBOjm694Qqke6CBcfPn88dVEgFUA7dOFGpr3sb9MEnA8VkkluVvLfa2GTJM3Gprct9zg98JbZTyHK+8hRxHIwZQbiMb/8APi2GrNNXK//Z\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from IPython.display import Image, display\\n\",\n \"\\n\",\n \"for i in range(len(queries)):\\n\",\n \" print(f\\\"query text: {queries[i]}\\\")\\n\",\n \" \\n\",\n \" ids = [fid_to_oid[ind] for ind in I[i]]\\n\",\n \" names = list(map(id_to_name, ids, [image_dir]*k))\\n\",\n \" images = list(map(Image, names))\\n\",\n \" print(names)\\n\",\n \" display(*images)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## (Image, Text) -> Image\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The second task is using the combination of image and text query to retrieve image:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/jpeg\": 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"query_img_pth = \\\"./assets/query/000000530944.jpg\\\"\\n\",\n \"Image(filename=query_img_pth)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With the following text query, we want an image that a motorbike carrying a bag of orange:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"k = 3\\n\",\n \"\\n\",\n \"with torch.no_grad():\\n\",\n \" query = model.encode(\\n\",\n \" images = query_img_pth,\\n\",\n \" text = \\\"Find a picture that also a motorbike but replace the load to a bag of orange\\\"\\n\",\n \" ).to('cpu')\\n\",\n \"\\n\",\n \"D, I = index.search(query, k=k)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that the No.1 result perfectly match our requirements! And the other 2 in the top 3 results also show very relevant images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['./assets/corpus/000000545037.jpg', './assets/corpus/000000272130.jpg', './assets/corpus/000000098911.jpg']\\n\"\n ]\n },\n {\n \"data\": {\n \"image/jpeg\": 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\",\n 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\",\n 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\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ids = [fid_to_oid[ind] for ind in I[0]]\\n\",\n \"names = list(map(id_to_name, ids, [image_dir]*k))\\n\",\n \"images = list(map(Image, names))\\n\",\n \"print(names)\\n\",\n \"display(*images)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"test\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.20\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "research/BGE_VL_Screenshot/README.md", "content": "

Vis-IR: Unifying Search With Visualized Information Retrieval

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\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n \n \n \"Build\"\n \n \n

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\n News |\n Release Plan |\n Overview |\n License |\n Citation\n

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\n\n## News\n\n```2025-04-06``` 🚀🚀 MVRB Dataset are released on Huggingface: [MVRB](https://huggingface.co/datasets/marsh123/MVRB)\n\n```2025-04-02``` 🚀🚀 VIRA Dataset are released on Huggingface: [VIRA](https://huggingface.co/datasets/marsh123/VIRA/)\n\n```2025-04-01``` 🚀🚀 UniSE models are released on Huggingface: [UniSE-MLMM](https://huggingface.co/marsh123/UniSE-MLLM/)\n\n```2025-02-17``` 🎉🎉 Release our paper: [Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval](https://arxiv.org/abs/2502.11431).\n\n## Release Plan\n- [x] Paper\n- [x] UniSE models\n- [x] VIRA Dataset\n- [x] MVRB benchmark\n- [ ] Evaluation code\n- [ ] Fine-tuning code\n\n## Overview\n\nIn this work, we formally define an emerging IR paradigm called Visualized Information Retrieval, or **VisIR**, where multimodal information, such as texts, images, tables and charts, is jointly represented by a unified visual format called **Screenshots**, for various retrieval applications. We further make three key contributions for VisIR. First, we create **VIRA** (Vis-IR Aggregation), a large-scale dataset comprising a vast collection of screenshots from diverse sources, carefully curated into captioned and questionanswer formats. Second, we develop **UniSE** (Universal Screenshot Embeddings), a family of retrieval models that enable screenshots to query or be queried across arbitrary data modalities. Finally, we construct **MVRB** (Massive Visualized IR Benchmark), a comprehensive benchmark covering a variety of task forms and application scenarios. Through extensive evaluations on MVRB, we highlight the deficiency from existing multimodal retrievers and the substantial improvements made by UniSE.\n\n## Model Usage\n\n> Our code works well on transformers==4.45.2, and we recommend using this version.\n\n### 1. UniSE-MLLM Models\n\n```python\nimport torch\nfrom transformers import AutoModel\n\nMODEL_NAME = \"marsh123/UniSE-MLLM\"\nmodel = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)\n # You must set trust_remote_code=True\nmodel.set_processor(MODEL_NAME)\n\nwith torch.no_grad():\n device = torch.device(\"cuda:0\")\n model = model.to(device)\n model.eval()\n query_inputs = model.data_process(\n images=[\"./assets/query_1.png\", \"./assets/query_2.png\"], \n text=[\"After a 17% drop, what is Nvidia's closing stock price?\",\n \"I would like to see a detailed and intuitive performance comparison between the two models.\"],\n q_or_c=\"query\",\n task_instruction=\"Represent the given image with the given query.\"\n )\n candidate_inputs = model.data_process(\n images=[\"./assets/positive_1.jpeg\", \"./assets/neg_1.jpeg\",\n \"./assets/positive_2.jpeg\", \"./assets/neg_2.jpeg\"],\n q_or_c=\"candidate\"\n )\n query_embeddings = model(**query_inputs)\n candidate_embeddings = model(**candidate_inputs)\n scores = torch.matmul(query_embeddings, candidate_embeddings.T)\n print(scores)\n```\n\n## Performance on MVRB\n\nMVRB is a comprehensive benchmark designed for the retrieval task centered on screenshots. It includes four meta tasks: Screenshot Retrieval (SR), Composed Screenshot Retrieval (CSR), Screenshot QA (SQA), and Open-Vocabulary Classification (OVC). We evaluate three main types of retrievers on MVRB: OCR+Text Retrievers, General Multimodal Retrievers, and Screenshot Document Retrievers. Our proposed UniSE-MLLM achieves state-of-the-art (SOTA) performance on this benchmark.\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/66164f6245336ca774679611/igMgX-BvQ55Dyxuw26sgs.png)\n\n\n\n## License\nVis-IR is licensed under the [MIT License](LICENSE). \n\n\n## Citation\nIf you find this repository useful, please consider giving a star ⭐ and citation\n\n```\n@article{liu2025any,\n title={Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval},\n author={Liu, Ze and Liang, Zhengyang and Zhou, Junjie and Liu, Zheng and Lian, Defu},\n journal={arXiv preprint arXiv:2502.11431},\n year={2025}\n}\n```\n" }, { "path": "research/C_MTEB/C_MTEB/__init__.py", "content": "# from .tasks import *\nfrom .tasks import *\n\nChineseTaskList = [\n 'TNews', 'IFlyTek', 'MultilingualSentiment', 'JDReview', 'OnlineShopping', 'Waimai',\n 'CLSClusteringS2S.v2', 'CLSClusteringP2P.v2', 'ThuNewsClusteringS2S.v2', 'ThuNewsClusteringP2P.v2',\n 'Ocnli', 'Cmnli',\n 'T2Reranking', 'MMarcoReranking', 'CMedQAv1-reranking', 'CMedQAv2-reranking',\n 'T2Retrieval', 'MMarcoRetrieval', 'DuRetrieval', 'CovidRetrieval', 'CmedqaRetrieval', 'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',\n 'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STSB', 'AFQMC', 'QBQTC'\n]\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/Classification.py", "content": "from __future__ import annotations\n\nfrom mteb.abstasks.AbsTaskClassification import AbsTaskClassification\nfrom mteb.abstasks.TaskMetadata import TaskMetadata\n\n\nclass TNews(AbsTaskClassification):\n metadata = TaskMetadata(\n name=\"TNews\",\n description=\"Short Text Classification for News\",\n reference=\"https://www.cluebenchmarks.com/introduce.html\",\n dataset={\n \"path\": \"C-MTEB/TNews-classification\",\n \"revision\": \"317f262bf1e6126357bbe89e875451e4b0938fe4\",\n },\n type=\"Classification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@inproceedings {xu-etal-2020-clue,\n title = \"{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark\",\n author = \"Xu, Liang and\n Hu, Hai and\n Zhang, Xuanwei and\n Li, Lu and\n Cao, Chenjie and\n Li, Yudong and\n Xu, Yechen and\n Sun, Kai and\n Yu, Dian and\n Yu, Cong and\n Tian, Yin and\n Dong, Qianqian and\n Liu, Weitang and\n Shi, Bo and\n Cui, Yiming and\n Li, Junyi and\n Zeng, Jun and\n Wang, Rongzhao and\n Xie, Weijian and\n Li, Yanting and\n Patterson, Yina and\n Tian, Zuoyu and\n Zhang, Yiwen and\n Zhou, He and\n Liu, Shaoweihua and\n Zhao, Zhe and\n Zhao, Qipeng and\n Yue, Cong and\n Zhang, Xinrui and\n Yang, Zhengliang and\n Richardson, Kyle and\n Lan, Zhenzhong \",\n booktitle = \"Proceedings of the 28th International Conference on Computational Linguistics\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"International Committee on Computational Linguistics\",\n url = \"https://aclanthology.org/2020.coling-main.419\",\n doi = \"10.18653/v1/2020.coling-main.419\",\n pages = \"4762--4772\",\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"samples_per_label\"] = 32\n return metadata_dict\n\n\nclass IFlyTek(AbsTaskClassification):\n metadata = TaskMetadata(\n name=\"IFlyTek\",\n description=\"Long Text classification for the description of Apps\",\n reference=\"https://www.cluebenchmarks.com/introduce.html\",\n dataset={\n \"path\": \"C-MTEB/IFlyTek-classification\",\n \"revision\": \"421605374b29664c5fc098418fe20ada9bd55f8a\",\n },\n type=\"Classification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@inproceedings {xu-etal-2020-clue,\n title = \"{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark\",\n author = \"Xu, Liang and\n Hu, Hai and\n Zhang, Xuanwei and\n Li, Lu and\n Cao, Chenjie and\n Li, Yudong and\n Xu, Yechen and\n Sun, Kai and\n Yu, Dian and\n Yu, Cong and\n Tian, Yin and\n Dong, Qianqian and\n Liu, Weitang and\n Shi, Bo and\n Cui, Yiming and\n Li, Junyi and\n Zeng, Jun and\n Wang, Rongzhao and\n Xie, Weijian and\n Li, Yanting and\n Patterson, Yina and\n Tian, Zuoyu and\n Zhang, Yiwen and\n Zhou, He and\n Liu, Shaoweihua and\n Zhao, Zhe and\n Zhao, Qipeng and\n Yue, Cong and\n Zhang, Xinrui and\n Yang, Zhengliang and\n Richardson, Kyle and\n Lan, Zhenzhong \",\n booktitle = \"Proceedings of the 28th International Conference on Computational Linguistics\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"International Committee on Computational Linguistics\",\n url = \"https://aclanthology.org/2020.coling-main.419\",\n doi = \"10.18653/v1/2020.coling-main.419\",\n pages = \"4762--4772\",\n abstract = \"The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.cluebenchmarks.com\",\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"samples_per_label\"] = 32\n metadata_dict[\"n_experiments\"] = 5\n return metadata_dict\n\n\nclass MultilingualSentiment(AbsTaskClassification):\n metadata = TaskMetadata(\n name=\"MultilingualSentiment\",\n description=\"A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative\",\n reference=\"https://github.com/tyqiangz/multilingual-sentiment-datasets\",\n dataset={\n \"path\": \"C-MTEB/MultilingualSentiment-classification\",\n \"revision\": \"46958b007a63fdbf239b7672c25d0bea67b5ea1a\",\n },\n type=\"Classification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\", \"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"samples_per_label\"] = 32\n return metadata_dict\n\n\nclass JDReview(AbsTaskClassification):\n metadata = TaskMetadata(\n name=\"JDReview\",\n description=\"review for iphone\",\n reference=\"https://aclanthology.org/2023.nodalida-1.20/\",\n dataset={\n \"path\": \"C-MTEB/JDReview-classification\",\n \"revision\": \"b7c64bd89eb87f8ded463478346f76731f07bf8b\",\n },\n type=\"Classification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@article{xiao2023c,\n title={C-pack: Packaged resources to advance general chinese embedding},\n author={Xiao, Shitao and Liu, Zheng and Zhang, Peitian and Muennighof, Niklas},\n journal={arXiv preprint arXiv:2309.07597},\n year={2023}\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"samples_per_label\"] = 32\n return metadata_dict\n\n\nclass OnlineShopping(AbsTaskClassification):\n metadata = TaskMetadata(\n name=\"OnlineShopping\",\n description=\"Sentiment Analysis of User Reviews on Online Shopping Websites\",\n reference=\"https://aclanthology.org/2023.nodalida-1.20/\",\n dataset={\n \"path\": \"C-MTEB/OnlineShopping-classification\",\n \"revision\": \"e610f2ebd179a8fda30ae534c3878750a96db120\",\n },\n type=\"Classification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@article{xiao2023c,\n title={C-pack: Packaged resources to advance general chinese embedding},\n author={Xiao, Shitao and Liu, Zheng and Zhang, Peitian and Muennighof, Niklas},\n journal={arXiv preprint arXiv:2309.07597},\n year={2023}\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"samples_per_label\"] = 32\n return metadata_dict\n\n\nclass Waimai(AbsTaskClassification):\n metadata = TaskMetadata(\n name=\"Waimai\",\n description=\"Sentiment Analysis of user reviews on takeaway platforms\",\n reference=\"https://aclanthology.org/2023.nodalida-1.20/\",\n dataset={\n \"path\": \"C-MTEB/waimai-classification\",\n \"revision\": \"339287def212450dcaa9df8c22bf93e9980c7023\",\n },\n type=\"Classification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@article{xiao2023c,\n title={C-pack: Packaged resources to advance general chinese embedding},\n author={Xiao, Shitao and Liu, Zheng and Zhang, Peitian and Muennighof, Niklas},\n journal={arXiv preprint arXiv:2309.07597},\n year={2023}\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"samples_per_label\"] = 32\n\n return metadata_dict\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/Clustering.py", "content": "from __future__ import annotations\n\nimport itertools\n\nfrom datasets import Dataset, DatasetDict\n\nfrom mteb.abstasks.AbsTaskClustering import AbsTaskClustering\nfrom mteb.abstasks.AbsTaskClusteringFast import (\n AbsTaskClusteringFast,\n check_label_distribution,\n)\nfrom mteb.abstasks.TaskMetadata import TaskMetadata\n\nNUM_SAMPLES = 2048\n\n\nclass CLSClusteringFastS2S(AbsTaskClusteringFast):\n max_document_to_embed = NUM_SAMPLES\n max_fraction_of_documents_to_embed = None\n\n metadata = TaskMetadata(\n name=\"CLSClusteringS2S.v2\",\n description=\"Clustering of titles from CLS dataset. Clustering of 13 sets on the main category.\",\n reference=\"https://arxiv.org/abs/2209.05034\",\n dataset={\n \"path\": \"C-MTEB/CLSClusteringS2S\",\n \"revision\": \"e458b3f5414b62b7f9f83499ac1f5497ae2e869f\",\n },\n type=\"Clustering\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=(\"2022-01-01\", \"2022-09-12\"),\n domains=[\"Academic\", \"Written\"],\n task_subtypes=[\"Thematic clustering\", \"Topic classification\"],\n license=\"apache-2.0\",\n annotations_creators=\"derived\",\n dialect=[],\n sample_creation=\"found\",\n bibtex_citation=\"\"\"@misc{li2022csl,\n title={CSL: A Large-scale Chinese Scientific Literature Dataset}, \n author={Yudong Li and Yuqing Zhang and Zhe Zhao and Linlin Shen and Weijie Liu and Weiquan Mao and Hui Zhang},\n year={2022},\n eprint={2209.05034},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n }\"\"\",\n descriptive_stats={\n \"n_samples\": {\"test\": NUM_SAMPLES},\n \"avg_character_length\": {},\n },\n )\n\n def dataset_transform(self):\n ds = {}\n for split in self.metadata.eval_splits:\n labels = list(itertools.chain.from_iterable(self.dataset[split][\"labels\"]))\n sentences = list(\n itertools.chain.from_iterable(self.dataset[split][\"sentences\"])\n )\n\n check_label_distribution(self.dataset[split])\n\n ds[split] = Dataset.from_dict({\"labels\": labels, \"sentences\": sentences})\n self.dataset = DatasetDict(ds)\n self.dataset = self.stratified_subsampling(\n self.dataset,\n self.seed,\n self.metadata.eval_splits,\n label=\"labels\",\n n_samples=NUM_SAMPLES,\n )\n\n\nclass CLSClusteringFastP2P(AbsTaskClusteringFast):\n max_document_to_embed = NUM_SAMPLES\n max_fraction_of_documents_to_embed = None\n\n metadata = TaskMetadata(\n name=\"CLSClusteringP2P.v2\",\n description=\"Clustering of titles + abstract from CLS dataset. Clustering of 13 sets on the main category.\",\n reference=\"https://arxiv.org/abs/2209.05034\",\n dataset={\n \"path\": \"C-MTEB/CLSClusteringP2P\",\n \"revision\": \"4b6227591c6c1a73bc76b1055f3b7f3588e72476\",\n },\n type=\"Clustering\",\n category=\"p2p\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=(\"2022-01-01\", \"2022-09-12\"),\n domains=[\"Academic\", \"Written\"],\n task_subtypes=[\"Thematic clustering\", \"Topic classification\"],\n license=\"apache-2.0\",\n annotations_creators=\"derived\",\n dialect=[],\n sample_creation=\"found\",\n bibtex_citation=\"\"\"@misc{li2022csl,\n title={CSL: A Large-scale Chinese Scientific Literature Dataset}, \n author={Yudong Li and Yuqing Zhang and Zhe Zhao and Linlin Shen and Weijie Liu and Weiquan Mao and Hui Zhang},\n year={2022},\n eprint={2209.05034},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n }\"\"\",\n descriptive_stats={\n \"n_samples\": {\"test\": NUM_SAMPLES},\n \"avg_character_length\": {},\n },\n )\n\n def dataset_transform(self):\n ds = {}\n for split in self.metadata.eval_splits:\n labels = list(itertools.chain.from_iterable(self.dataset[split][\"labels\"]))\n sentences = list(\n itertools.chain.from_iterable(self.dataset[split][\"sentences\"])\n )\n\n check_label_distribution(self.dataset[split])\n\n ds[split] = Dataset.from_dict({\"labels\": labels, \"sentences\": sentences})\n self.dataset = DatasetDict(ds)\n self.dataset = self.stratified_subsampling(\n self.dataset,\n self.seed,\n self.metadata.eval_splits,\n label=\"labels\",\n n_samples=NUM_SAMPLES,\n )\n\n\nclass CLSClusteringS2S(AbsTaskClustering):\n superseded_by = \"CLSClusteringS2S.v2\"\n metadata = TaskMetadata(\n name=\"CLSClusteringS2S\",\n description=\"Clustering of titles from CLS dataset. Clustering of 13 sets on the main category.\",\n reference=\"https://arxiv.org/abs/2209.05034\",\n dataset={\n \"path\": \"C-MTEB/CLSClusteringS2S\",\n \"revision\": \"e458b3f5414b62b7f9f83499ac1f5497ae2e869f\",\n },\n type=\"Clustering\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"\n@article{li2022csl,\n title={CSL: A large-scale Chinese scientific literature dataset},\n author={Li, Yudong and Zhang, Yuqing and Zhao, Zhe and Shen, Linlin and Liu, Weijie and Mao, Weiquan and Zhang, Hui},\n journal={arXiv preprint arXiv:2209.05034},\n year={2022}\n}\n\"\"\",\n descriptive_stats={\"n_samples\": {\"test\": 100000}, \"avg_character_length\": None},\n )\n\n\nclass CLSClusteringP2P(AbsTaskClustering):\n superseded_by = \"CLSClusteringP2P.v2\"\n metadata = TaskMetadata(\n name=\"CLSClusteringP2P\",\n description=\"Clustering of titles + abstract from CLS dataset. Clustering of 13 sets on the main category.\",\n reference=\"https://arxiv.org/abs/2209.05034\",\n dataset={\n \"path\": \"C-MTEB/CLSClusteringP2P\",\n \"revision\": \"4b6227591c6c1a73bc76b1055f3b7f3588e72476\",\n },\n type=\"Clustering\",\n category=\"p2p\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@article{li2022csl,\n title={CSL: A large-scale Chinese scientific literature dataset},\n author={Li, Yudong and Zhang, Yuqing and Zhao, Zhe and Shen, Linlin and Liu, Weijie and Mao, Weiquan and Zhang, Hui},\n journal={arXiv preprint arXiv:2209.05034},\n year={2022}\n}\"\"\",\n descriptive_stats={\"n_samples\": {\"test\": 100000}, \"avg_character_length\": None},\n )\n\n\nclass ThuNewsClusteringFastS2S(AbsTaskClusteringFast):\n max_document_to_embed = NUM_SAMPLES\n max_fraction_of_documents_to_embed = None\n\n metadata = TaskMetadata(\n name=\"ThuNewsClusteringS2S.v2\",\n dataset={\n \"path\": \"C-MTEB/ThuNewsClusteringS2S\",\n \"revision\": \"8a8b2caeda43f39e13c4bc5bea0f8a667896e10d\",\n },\n description=\"Clustering of titles from the THUCNews dataset\",\n reference=\"http://thuctc.thunlp.org/\",\n type=\"Clustering\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=(\"2006-01-01\", \"2007-01-01\"),\n domains=[\"News\", \"Written\"],\n task_subtypes=[\"Thematic clustering\", \"Topic classification\"],\n license=\"not specified\",\n annotations_creators=\"derived\",\n dialect=[],\n sample_creation=\"found\",\n bibtex_citation=\"\"\"@software{THUCTC,\n author = {Sun, M. and Li, J. and Guo, Z. and Yu, Z. and Zheng, Y. and Si, X. and Liu, Z.},\n title = {THUCTC: An Efficient Chinese Text Classifier},\n year = {2016},\n note = {THU Chinese Text Classification Toolkit},\n publisher = {THU Natural Language Processing Lab},\n url = {https://github.com/thunlp/THUCTC}\n}\"\"\",\n descriptive_stats={\n \"n_samples\": {\"test\": NUM_SAMPLES},\n \"avg_character_length\": {},\n },\n )\n\n def dataset_transform(self):\n ds = {}\n for split in self.metadata.eval_splits:\n labels = list(itertools.chain.from_iterable(self.dataset[split][\"labels\"]))\n sentences = list(\n itertools.chain.from_iterable(self.dataset[split][\"sentences\"])\n )\n\n check_label_distribution(self.dataset[split])\n\n ds[split] = Dataset.from_dict({\"labels\": labels, \"sentences\": sentences})\n self.dataset = DatasetDict(ds)\n self.dataset = self.stratified_subsampling(\n self.dataset,\n self.seed,\n self.metadata.eval_splits,\n label=\"labels\",\n n_samples=NUM_SAMPLES,\n )\n\n\nclass ThuNewsClusteringFastP2P(AbsTaskClusteringFast):\n max_document_to_embed = NUM_SAMPLES\n max_fraction_of_documents_to_embed = None\n\n metadata = TaskMetadata(\n name=\"ThuNewsClusteringP2P.v2\",\n dataset={\n \"path\": \"C-MTEB/ThuNewsClusteringP2P\",\n \"revision\": \"5798586b105c0434e4f0fe5e767abe619442cf93\",\n },\n description=\"Clustering of titles + abstracts from the THUCNews dataset\",\n reference=\"http://thuctc.thunlp.org/\",\n type=\"Clustering\",\n category=\"p2p\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=(\"2006-01-01\", \"2007-01-01\"),\n domains=[\"News\", \"Written\"],\n task_subtypes=[\"Thematic clustering\", \"Topic classification\"],\n license=\"not specified\",\n annotations_creators=\"derived\",\n dialect=[],\n sample_creation=\"found\",\n bibtex_citation=\"\"\"@software{THUCTC,\n author = {Sun, M. and Li, J. and Guo, Z. and Yu, Z. and Zheng, Y. and Si, X. and Liu, Z.},\n title = {THUCTC: An Efficient Chinese Text Classifier},\n year = {2016},\n note = {THU Chinese Text Classification Toolkit},\n publisher = {THU Natural Language Processing Lab},\n url = {https://github.com/thunlp/THUCTC}\n}\"\"\",\n descriptive_stats={\n \"n_samples\": {\"test\": NUM_SAMPLES},\n \"avg_character_length\": {},\n },\n )\n\n def dataset_transform(self):\n ds = {}\n for split in self.metadata.eval_splits:\n labels = list(itertools.chain.from_iterable(self.dataset[split][\"labels\"]))\n sentences = list(\n itertools.chain.from_iterable(self.dataset[split][\"sentences\"])\n )\n\n check_label_distribution(self.dataset[split])\n\n ds[split] = Dataset.from_dict({\"labels\": labels, \"sentences\": sentences})\n self.dataset = DatasetDict(ds)\n self.dataset = self.stratified_subsampling(\n self.dataset,\n self.seed,\n self.metadata.eval_splits,\n label=\"labels\",\n n_samples=NUM_SAMPLES,\n )\n\n\nclass ThuNewsClusteringS2S(AbsTaskClustering):\n superseded_by = \"ThuNewsClusteringS2S.v2\"\n metadata = TaskMetadata(\n name=\"ThuNewsClusteringS2S\",\n dataset={\n \"path\": \"C-MTEB/ThuNewsClusteringS2S\",\n \"revision\": \"8a8b2caeda43f39e13c4bc5bea0f8a667896e10d\",\n },\n description=\"Clustering of titles from the THUCNews dataset\",\n reference=\"http://thuctc.thunlp.org/\",\n type=\"Clustering\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"\n@inproceedings{eisner2007proceedings,\n title={Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)},\n author={Eisner, Jason},\n booktitle={Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},\n year={2007}\n}\n@inproceedings{li2006comparison,\n title={A comparison and semi-quantitative analysis of words and character-bigrams as features in chinese text categorization},\n author={Li, Jingyang and Sun, Maosong and Zhang, Xian},\n booktitle={proceedings of the 21st international conference on computational linguistics and 44th annual meeting of the association for computational linguistics},\n pages={545--552},\n year={2006}\n}\n\"\"\",\n descriptive_stats={\"n_samples\": {\"test\": 100000}, \"avg_character_length\": None},\n )\n\n\nclass ThuNewsClusteringP2P(AbsTaskClustering):\n superseded_by = \"ThuNewsClusteringP2P.v2\"\n metadata = TaskMetadata(\n name=\"ThuNewsClusteringP2P\",\n dataset={\n \"path\": \"C-MTEB/ThuNewsClusteringP2P\",\n \"revision\": \"5798586b105c0434e4f0fe5e767abe619442cf93\",\n },\n description=\"Clustering of titles + abstracts from the THUCNews dataset\",\n reference=\"http://thuctc.thunlp.org/\",\n type=\"Clustering\",\n category=\"p2p\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"v_measure\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"\n@inproceedings{eisner2007proceedings,\n title={Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)},\n author={Eisner, Jason},\n booktitle={Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},\n year={2007}\n}\n@inproceedings{li2006comparison,\n title={A comparison and semi-quantitative analysis of words and character-bigrams as features in chinese text categorization},\n author={Li, Jingyang and Sun, Maosong and Zhang, Xian},\n booktitle={proceedings of the 21st international conference on computational linguistics and 44th annual meeting of the association for computational linguistics},\n pages={545--552},\n year={2006}\n}\n\"\"\",\n descriptive_stats={\"n_samples\": {\"test\": 100000}, \"avg_character_length\": None},\n )\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/MultiLongDocRetrieval.py", "content": "import datasets\nfrom mteb.abstasks import MultilingualTask, AbsTaskRetrieval\nfrom mteb.abstasks.AbsTaskRetrieval import *\n# from ...abstasks import MultilingualTask, AbsTaskRetrieval\n# from ...abstasks.AbsTaskRetrieval import *\n\n\n_LANGUAGES = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n\n\ndef load_mldr_data(path: str, langs: list, eval_splits: list, cache_dir: str=None):\n corpus = {lang: {split: None for split in eval_splits} for lang in langs}\n queries = {lang: {split: None for split in eval_splits} for lang in langs}\n relevant_docs = {lang: {split: None for split in eval_splits} for lang in langs}\n \n for lang in langs:\n lang_corpus = datasets.load_dataset(path, f'corpus-{lang}', cache_dir=cache_dir)['corpus']\n lang_corpus = {e['docid']: {'text': e['text']} for e in lang_corpus}\n lang_data = datasets.load_dataset(path, lang, cache_dir=cache_dir)\n for split in eval_splits:\n corpus[lang][split] = lang_corpus\n queries[lang][split] = {e['query_id']: e['query'] for e in lang_data[split]}\n relevant_docs[lang][split] = {e['query_id']: {e['positive_passages'][0]['docid']: 1} for e in lang_data[split]}\n \n corpus = datasets.DatasetDict(corpus)\n queries = datasets.DatasetDict(queries)\n relevant_docs = datasets.DatasetDict(relevant_docs)\n return corpus, queries, relevant_docs\n\n\nclass MultiLongDocRetrieval(MultilingualTask, AbsTaskRetrieval):\n @property\n def description(self):\n return {\n 'name': 'MultiLongDocRetrieval',\n 'hf_hub_name': 'Shitao/MLDR',\n 'reference': 'https://arxiv.org/abs/2402.03216',\n 'description': 'MultiLongDocRetrieval: A Multilingual Long-Document Retrieval Dataset',\n 'type': 'Retrieval',\n 'category': 's2p',\n 'eval_splits': ['dev', 'test'],\n 'eval_langs': _LANGUAGES,\n 'main_score': 'ndcg_at_10',\n }\n \n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n \n self.corpus, self.queries, self.relevant_docs = load_mldr_data(\n path=self.description['hf_hub_name'],\n langs=self.langs,\n eval_splits=self.description['eval_splits'],\n cache_dir=kwargs.get('cache_dir', None)\n )\n self.data_loaded = True\n\n def evaluate(\n self,\n model,\n split=\"test\",\n batch_size=128,\n corpus_chunk_size=None,\n score_function=\"cos_sim\",\n **kwargs\n ):\n try:\n from beir.retrieval.evaluation import EvaluateRetrieval\n except ImportError:\n raise Exception(\"Retrieval tasks require beir package. Please install it with `pip install mteb[beir]`\")\n\n if not self.data_loaded:\n self.load_data()\n \n model = model if self.is_dres_compatible(model) else DRESModel(model)\n if os.getenv(\"RANK\", None) is None:\n # Non-distributed\n from beir.retrieval.search.dense import DenseRetrievalExactSearch as DRES\n model = DRES(\n model,\n batch_size=batch_size,\n corpus_chunk_size=corpus_chunk_size if corpus_chunk_size is not None else 50000,\n **kwargs,\n )\n else:\n # Distributed (multi-GPU)\n from beir.retrieval.search.dense import (\n DenseRetrievalParallelExactSearch as DRPES,\n )\n model = DRPES(\n model,\n batch_size=batch_size,\n corpus_chunk_size=corpus_chunk_size,\n **kwargs,\n )\n retriever = EvaluateRetrieval(model, score_function=score_function) # or \"cos_sim\" or \"dot\"\n \n scores = {}\n for lang in self.langs:\n print(f\"==============================\\nStart evaluating {lang} ...\")\n corpus, queries, relevant_docs = self.corpus[lang][split], self.queries[lang][split], self.relevant_docs[lang][split]\n \n start_time = time()\n results = retriever.retrieve(corpus, queries)\n end_time = time()\n logger.info(\"Time taken to retrieve: {:.2f} seconds\".format(end_time - start_time))\n ndcg, _map, recall, precision = retriever.evaluate(relevant_docs, results, retriever.k_values, ignore_identical_ids=kwargs.get(\"ignore_identical_ids\", True))\n mrr = retriever.evaluate_custom(relevant_docs, results, retriever.k_values, \"mrr\")\n scores[lang] = {\n **{f\"ndcg_at_{k.split('@')[1]}\": v for (k, v) in ndcg.items()},\n **{f\"map_at_{k.split('@')[1]}\": v for (k, v) in _map.items()},\n **{f\"recall_at_{k.split('@')[1]}\": v for (k, v) in recall.items()},\n **{f\"precision_at_{k.split('@')[1]}\": v for (k, v) in precision.items()},\n **{f\"mrr_at_{k.split('@')[1]}\": v for (k, v) in mrr.items()},\n }\n \n return scores\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/PairClassification.py", "content": "from __future__ import annotations\n\nfrom mteb.abstasks.AbsTaskPairClassification import AbsTaskPairClassification\nfrom mteb.abstasks.TaskMetadata import TaskMetadata\n\n\nclass Ocnli(AbsTaskPairClassification):\n metadata = TaskMetadata(\n name=\"Ocnli\",\n description=\"Original Chinese Natural Language Inference dataset\",\n reference=\"https://arxiv.org/abs/2010.05444\",\n dataset={\n \"path\": \"C-MTEB/OCNLI\",\n \"revision\": \"66e76a618a34d6d565d5538088562851e6daa7ec\",\n },\n type=\"PairClassification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"max_accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{hu2020ocnli,\n title={OCNLI: Original Chinese Natural Language Inference}, \n author={Hai Hu and Kyle Richardson and Liang Xu and Lu Li and Sandra Kuebler and Lawrence S. Moss},\n year={2020},\n eprint={2010.05444},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n }\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n def dataset_transform(self):\n self.dataset = self.dataset.rename_column(\"sent1\", \"sentence1\")\n self.dataset = self.dataset.rename_column(\"sent2\", \"sentence2\")\n\n\nclass Cmnli(AbsTaskPairClassification):\n metadata = TaskMetadata(\n name=\"Cmnli\",\n description=\"Chinese Multi-Genre NLI\",\n reference=\"https://huggingface.co/datasets/clue/viewer/cmnli\",\n dataset={\n \"path\": \"C-MTEB/CMNLI\",\n \"revision\": \"41bc36f332156f7adc9e38f53777c959b2ae9766\",\n },\n type=\"PairClassification\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"max_accuracy\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@inproceedings{xu-etal-2020-clue,\n title = \"{CLUE}: A {C}hinese Language Understanding Evaluation Benchmark\",\n author = \"Xu, Liang and\n Hu, Hai and\n Zhang, Xuanwei and\n Li, Lu and\n Cao, Chenjie and\n Li, Yudong and\n Xu, Yechen and\n Sun, Kai and\n Yu, Dian and\n Yu, Cong and\n Tian, Yin and\n Dong, Qianqian and\n Liu, Weitang and\n Shi, Bo and\n Cui, Yiming and\n Li, Junyi and\n Zeng, Jun and\n Wang, Rongzhao and\n Xie, Weijian and\n Li, Yanting and\n Patterson, Yina and\n Tian, Zuoyu and\n Zhang, Yiwen and\n Zhou, He and\n Liu, Shaoweihua and\n Zhao, Zhe and\n Zhao, Qipeng and\n Yue, Cong and\n Zhang, Xinrui and\n Yang, Zhengliang and\n Richardson, Kyle and\n Lan, Zhenzhong\",\n booktitle = \"Proceedings of the 28th International Conference on Computational Linguistics\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"International Committee on Computational Linguistics\",\n url = \"https://aclanthology.org/2020.coling-main.419\",\n doi = \"10.18653/v1/2020.coling-main.419\",\n pages = \"4762--4772\",\n }\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n def dataset_transform(self):\n self.dataset = self.dataset.rename_column(\"sent1\", \"sentence1\")\n self.dataset = self.dataset.rename_column(\"sent2\", \"sentence2\")\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/Reranking.py", "content": "from __future__ import annotations\n\nfrom mteb.abstasks.AbsTaskReranking import AbsTaskReranking\nfrom mteb.abstasks.TaskMetadata import TaskMetadata\n\n\nclass T2Reranking(AbsTaskReranking):\n metadata = TaskMetadata(\n name=\"T2Reranking\",\n description=\"T2Ranking: A large-scale Chinese Benchmark for Passage Ranking\",\n reference=\"https://arxiv.org/abs/2304.03679\",\n dataset={\n \"path\": \"C-MTEB/T2Reranking\",\n \"revision\": \"76631901a18387f85eaa53e5450019b87ad58ef9\",\n },\n type=\"Reranking\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"map\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xie2023t2ranking,\n title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking}, \n author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},\n year={2023},\n eprint={2304.03679},\n archivePrefix={arXiv},\n primaryClass={cs.IR}\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n\nclass MMarcoReranking(AbsTaskReranking):\n metadata = TaskMetadata(\n name=\"MMarcoReranking\",\n description=\"mMARCO is a multilingual version of the MS MARCO passage ranking dataset\",\n reference=\"https://github.com/unicamp-dl/mMARCO\",\n dataset={\n \"path\": \"C-MTEB/Mmarco-reranking\",\n \"revision\": \"8e0c766dbe9e16e1d221116a3f36795fbade07f6\",\n },\n type=\"Reranking\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"map\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{bonifacio2021mmarco,\n title={mMARCO: A Multilingual Version of MS MARCO Passage Ranking Dataset}, \n author={Luiz Henrique Bonifacio and Vitor Jeronymo and Hugo Queiroz Abonizio and Israel Campiotti and Marzieh Fadaee and and Roberto Lotufo and Rodrigo Nogueira},\n year={2021},\n eprint={2108.13897},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n\nclass CMedQAv1(AbsTaskReranking):\n metadata = TaskMetadata(\n name=\"CMedQAv1-reranking\",\n description=\"Chinese community medical question answering\",\n reference=\"https://github.com/zhangsheng93/cMedQA\",\n dataset={\n \"path\": \"C-MTEB/CMedQAv1-reranking\",\n \"revision\": \"8d7f1e942507dac42dc58017c1a001c3717da7df\",\n },\n type=\"Reranking\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"map\",\n date=(\"2017-01-01\", \"2017-07-26\"),\n domains=[\"Medical\", \"Written\"],\n task_subtypes=[],\n license=\"not specified\",\n annotations_creators=\"expert-annotated\",\n dialect=[],\n sample_creation=\"found\",\n bibtex_citation=\"\"\"@article{zhang2017chinese,\n title={Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs},\n author={Zhang, Sheng and Zhang, Xin and Wang, Hui and Cheng, Jiajun and Li, Pei and Ding, Zhaoyun},\n journal={Applied Sciences},\n volume={7},\n number={8},\n pages={767},\n year={2017},\n publisher={Multidisciplinary Digital Publishing Institute}\n}\"\"\",\n descriptive_stats={\n \"n_samples\": {\"test\": 2000},\n \"avg_character_length\": {\"test\": 165},\n },\n )\n\n\nclass CMedQAv2(AbsTaskReranking):\n metadata = TaskMetadata(\n name=\"CMedQAv2-reranking\",\n description=\"Chinese community medical question answering\",\n reference=\"https://github.com/zhangsheng93/cMedQA2\",\n dataset={\n \"path\": \"C-MTEB/CMedQAv2-reranking\",\n \"revision\": \"23d186750531a14a0357ca22cd92d712fd512ea0\",\n },\n type=\"Reranking\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"map\",\n date=None,\n form=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@ARTICLE{8548603, \nauthor={S. Zhang and X. Zhang and H. Wang and L. Guo and S. Liu}, \njournal={IEEE Access}, \ntitle={Multi-Scale Attentive Interaction Networks for Chinese Medical Question Answer Selection}, \nyear={2018}, \nvolume={6}, \nnumber={}, \npages={74061-74071}, \nkeywords={Biomedical imaging;Data mining;Semantics;Medical services;Feature extraction;Knowledge discovery;Medical question answering;interactive attention;deep learning;deep neural networks}, \ndoi={10.1109/ACCESS.2018.2883637}, \nISSN={2169-3536}, \nmonth={},}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/Retrieval.py", "content": "from __future__ import annotations\n\nfrom collections import defaultdict\n\nfrom datasets import DatasetDict, load_dataset\n\nfrom mteb.abstasks.TaskMetadata import TaskMetadata\nfrom mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval\n\n\ndef load_retrieval_data(dataset_path, dataset_revision, qrel_revision, eval_splits):\n eval_split = eval_splits[0]\n dataset = load_dataset(dataset_path, revision=dataset_revision)\n qrels = load_dataset(dataset_path + \"-qrels\", revision=qrel_revision)[eval_split]\n\n corpus = {e[\"id\"]: {\"text\": e[\"text\"]} for e in dataset[\"corpus\"]}\n queries = {e[\"id\"]: e[\"text\"] for e in dataset[\"queries\"]}\n relevant_docs = defaultdict(dict)\n for e in qrels:\n relevant_docs[e[\"qid\"]][e[\"pid\"]] = e[\"score\"]\n\n corpus = DatasetDict({eval_split: corpus})\n queries = DatasetDict({eval_split: queries})\n relevant_docs = DatasetDict({eval_split: relevant_docs})\n return corpus, queries, relevant_docs\n\n\nclass T2Retrieval(AbsTaskRetrieval):\n ignore_identical_ids = True\n\n metadata = TaskMetadata(\n name=\"T2Retrieval\",\n description=\"T2Ranking: A large-scale Chinese Benchmark for Passage Ranking\",\n reference=\"https://arxiv.org/abs/2304.03679\",\n dataset={\n \"path\": \"C-MTEB/T2Retrieval\",\n \"revision\": \"8731a845f1bf500a4f111cf1070785c793d10e64\",\n \"qrel_revision\": \"1c83b8d1544e529875e3f6930f3a1fcf749a8e97\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xie2023t2ranking,\n title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking}, \n author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},\n year={2023},\n eprint={2304.03679},\n archivePrefix={arXiv},\n primaryClass={cs.IR}\n}\"\"\",\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 874.1184182791619,\n \"average_query_length\": 10.938847974750132,\n \"num_documents\": 118605,\n \"num_queries\": 22812,\n \"average_relevant_docs_per_query\": 5.213571804313519,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass MMarcoRetrieval(AbsTaskRetrieval):\n ignore_identical_ids = True\n\n metadata = TaskMetadata(\n name=\"MMarcoRetrieval\",\n description=\"MMarcoRetrieval\",\n reference=\"https://arxiv.org/abs/2309.07597\",\n dataset={\n \"path\": \"C-MTEB/MMarcoRetrieval\",\n \"revision\": \"539bbde593d947e2a124ba72651aafc09eb33fc2\",\n \"qrel_revision\": \"bae08bb7bddbedb96c7e7db52018a55167b67f89\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xiao2024cpack,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie},\n year={2024},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\"\"\",\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 114.41787048392986,\n \"average_query_length\": 10.51131805157593,\n \"num_documents\": 106813,\n \"num_queries\": 6980,\n \"average_relevant_docs_per_query\": 1.0654727793696275,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass DuRetrieval(AbsTaskRetrieval):\n metadata = TaskMetadata(\n name=\"DuRetrieval\",\n description=\"A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine\",\n reference=\"https://aclanthology.org/2022.emnlp-main.357.pdf\",\n dataset={\n \"path\": \"C-MTEB/DuRetrieval\",\n \"revision\": \"a1a333e290fe30b10f3f56498e3a0d911a693ced\",\n \"qrel_revision\": \"497b7bd1bbb25cb3757ff34d95a8be50a3de2279\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{qiu2022dureaderretrieval,\n title={DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine}, \n author={Yifu Qiu and Hongyu Li and Yingqi Qu and Ying Chen and Qiaoqiao She and Jing Liu and Hua Wu and Haifeng Wang},\n year={2022},\n eprint={2203.10232},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\"\"\",\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 331.3219967800322,\n \"average_query_length\": 9.289,\n \"num_documents\": 100001,\n \"num_queries\": 2000,\n \"average_relevant_docs_per_query\": 4.9195,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass CovidRetrieval(AbsTaskRetrieval):\n metadata = TaskMetadata(\n name=\"CovidRetrieval\",\n description=\"COVID-19 news articles\",\n reference=\"https://arxiv.org/abs/2203.03367\",\n dataset={\n \"path\": \"C-MTEB/CovidRetrieval\",\n \"revision\": \"1271c7809071a13532e05f25fb53511ffce77117\",\n \"qrel_revision\": \"a9f41b7cdf24785531d12417ce0d1157ed4b39ca\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 332.4152658473415,\n \"average_query_length\": 25.9304531085353,\n \"num_documents\": 100001,\n \"num_queries\": 949,\n \"average_relevant_docs_per_query\": 1.0105374077976819,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass CmedqaRetrieval(AbsTaskRetrieval):\n metadata = TaskMetadata(\n name=\"CmedqaRetrieval\",\n description=\"Online medical consultation text. Used the CMedQAv2 as its underlying dataset.\",\n reference=\"https://aclanthology.org/2022.emnlp-main.357.pdf\",\n dataset={\n \"path\": \"C-MTEB/CmedqaRetrieval\",\n \"revision\": \"cd540c506dae1cf9e9a59c3e06f42030d54e7301\",\n \"qrel_revision\": \"279d737f36c731c8ff6e2b055f31fe02216fa23d\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 307.7710222897771,\n \"average_query_length\": 48.470367591897976,\n \"num_documents\": 100001,\n \"num_queries\": 3999,\n \"average_relevant_docs_per_query\": 1.86271567891973,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass EcomRetrieval(AbsTaskRetrieval):\n ignore_identical_ids = True\n\n metadata = TaskMetadata(\n name=\"EcomRetrieval\",\n description=\"EcomRetrieval\",\n reference=\"https://arxiv.org/abs/2203.03367\",\n dataset={\n \"path\": \"C-MTEB/EcomRetrieval\",\n \"revision\": \"687de13dc7294d6fd9be10c6945f9e8fec8166b9\",\n \"qrel_revision\": \"39c90699b034ec22ac45b3abf5b0bbb5ffd421f9\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 32.98041664189015,\n \"average_query_length\": 6.798,\n \"num_documents\": 100902,\n \"num_queries\": 1000,\n \"average_relevant_docs_per_query\": 1.0,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass MedicalRetrieval(AbsTaskRetrieval):\n ignore_identical_ids = True\n\n metadata = TaskMetadata(\n name=\"MedicalRetrieval\",\n description=\"MedicalRetrieval\",\n reference=\"https://arxiv.org/abs/2203.03367\",\n dataset={\n \"path\": \"C-MTEB/MedicalRetrieval\",\n \"revision\": \"2039188fb5800a9803ba5048df7b76e6fb151fc6\",\n \"qrel_revision\": \"37b8efec53c54c3d9c6af212f6710b62ccdf895c\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 122.04231725066585,\n \"average_query_length\": 17.938,\n \"num_documents\": 100999,\n \"num_queries\": 1000,\n \"average_relevant_docs_per_query\": 1.0,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n\n\nclass VideoRetrieval(AbsTaskRetrieval):\n ignore_identical_ids = True\n\n metadata = TaskMetadata(\n name=\"VideoRetrieval\",\n description=\"VideoRetrieval\",\n reference=\"https://arxiv.org/abs/2203.03367\",\n dataset={\n \"path\": \"C-MTEB/VideoRetrieval\",\n \"revision\": \"58c2597a5943a2ba48f4668c3b90d796283c5639\",\n \"qrel_revision\": \"faa71382b6a29cf1778d1f436b963e75cb5b927c\",\n },\n type=\"Retrieval\",\n category=\"s2p\",\n modalities=[\"text\"],\n eval_splits=[\"dev\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"ndcg_at_10\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\n \"n_samples\": None,\n \"avg_character_length\": {\n \"dev\": {\n \"average_document_length\": 31.048855642524522,\n \"average_query_length\": 7.365,\n \"num_documents\": 100930,\n \"num_queries\": 1000,\n \"average_relevant_docs_per_query\": 1.0,\n }\n },\n },\n )\n\n def load_data(self, **kwargs):\n if self.data_loaded:\n return\n\n self.corpus, self.queries, self.relevant_docs = load_retrieval_data(\n self.metadata_dict[\"dataset\"][\"path\"],\n self.metadata_dict[\"dataset\"][\"revision\"],\n self.metadata_dict[\"dataset\"][\"qrel_revision\"],\n self.metadata_dict[\"eval_splits\"],\n )\n self.data_loaded = True\n" }, { "path": "research/C_MTEB/C_MTEB/tasks/STS.py", "content": "from __future__ import annotations\n\nfrom mteb.abstasks.TaskMetadata import TaskMetadata\n\nfrom mteb.abstasks.AbsTaskSTS import AbsTaskSTS\n\n\nclass ATEC(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"ATEC\",\n dataset={\n \"path\": \"C-MTEB/ATEC\",\n \"revision\": \"0f319b1142f28d00e055a6770f3f726ae9b7d865\",\n },\n description=\"A Chinese dataset for textual relatedness\",\n reference=\"https://aclanthology.org/2021.emnlp-main.357\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\", \"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@inproceedings{raghu-etal-2021-end,\n title = \"End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs\",\n author = \"Raghu, Dinesh and\n Agarwal, Shantanu and\n Joshi, Sachindra and\n {Mausam}\",\n editor = \"Moens, Marie-Francine and\n Huang, Xuanjing and\n Specia, Lucia and\n Yih, Scott Wen-tau\",\n booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n month = nov,\n year = \"2021\",\n address = \"Online and Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.emnlp-main.357\",\n doi = \"10.18653/v1/2021.emnlp-main.357\",\n pages = \"4348--4366\",\n abstract = \"We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.\",\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 1\n return metadata_dict\n\n\nclass BQ(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"BQ\",\n dataset={\n \"path\": \"C-MTEB/BQ\",\n \"revision\": \"e3dda5e115e487b39ec7e618c0c6a29137052a55\",\n },\n description=\"A Chinese dataset for textual relatedness\",\n reference=\"https://aclanthology.org/2021.emnlp-main.357\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\", \"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xiao2024cpackpackagedresourcesadvance,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie},\n year={2024},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2309.07597}, \n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 1\n return metadata_dict\n\n\nclass LCQMC(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"LCQMC\",\n dataset={\n \"path\": \"C-MTEB/LCQMC\",\n \"revision\": \"17f9b096f80380fce5ed12a9be8be7784b337daf\",\n },\n description=\"A Chinese dataset for textual relatedness\",\n reference=\"https://aclanthology.org/2021.emnlp-main.357\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xiao2024cpackpackagedresourcesadvance,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie},\n year={2024},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2309.07597}, \n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 1\n return metadata_dict\n\n\nclass PAWSX(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"PAWSX\",\n dataset={\n \"path\": \"C-MTEB/PAWSX\",\n \"revision\": \"9c6a90e430ac22b5779fb019a23e820b11a8b5e1\",\n },\n description=\"A Chinese dataset for textual relatedness\",\n reference=\"https://aclanthology.org/2021.emnlp-main.357\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xiao2024cpackpackagedresourcesadvance,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie},\n year={2024},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2309.07597}, \n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 1\n return metadata_dict\n\n\nclass STSB(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"STSB\",\n dataset={\n \"path\": \"C-MTEB/STSB\",\n \"revision\": \"0cde68302b3541bb8b3c340dc0644b0b745b3dc0\",\n },\n description=\"A Chinese dataset for textual relatedness\",\n reference=\"https://aclanthology.org/2021.emnlp-main.357\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\", \"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@misc{xiao2024cpackpackagedresourcesadvance,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff and Defu Lian and Jian-Yun Nie},\n year={2024},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2309.07597}, \n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 5\n return metadata_dict\n\n\nclass AFQMC(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"AFQMC\",\n dataset={\n \"path\": \"C-MTEB/AFQMC\",\n \"revision\": \"b44c3b011063adb25877c13823db83bb193913c4\",\n },\n description=\"A Chinese dataset for textual relatedness\",\n reference=\"https://aclanthology.org/2021.emnlp-main.357\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"validation\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=\"\"\"@inproceedings{raghu-etal-2021-end,\n title = \"End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs\",\n author = \"Raghu, Dinesh and\n Agarwal, Shantanu and\n Joshi, Sachindra and\n {Mausam}\",\n editor = \"Moens, Marie-Francine and\n Huang, Xuanjing and\n Specia, Lucia and\n Yih, Scott Wen-tau\",\n booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n month = nov,\n year = \"2021\",\n address = \"Online and Punta Cana, Dominican Republic\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.emnlp-main.357\",\n doi = \"10.18653/v1/2021.emnlp-main.357\",\n pages = \"4348--4366\",\n abstract = \"We propose a novel problem within end-to-end learning of task oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e.g., car not starting). Such dialogs are grounded in domain-specific flowcharts, which the agent is supposed to follow during the conversation. Our task exposes novel technical challenges for neural TOD, such as grounding an utterance to the flowchart without explicit annotation, referring to additional manual pages when user asks a clarification question, and ability to follow unseen flowcharts at test time. We release a dataset (FLODIAL) consisting of 2,738 dialogs grounded on 12 different troubleshooting flowcharts. We also design a neural model, FLONET, which uses a retrieval-augmented generation architecture to train the dialog agent. Our experiments find that FLONET can do zero-shot transfer to unseen flowcharts, and sets a strong baseline for future research.\",\n}\"\"\",\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 1\n return metadata_dict\n\n\nclass QBQTC(AbsTaskSTS):\n metadata = TaskMetadata(\n name=\"QBQTC\",\n dataset={\n \"path\": \"C-MTEB/QBQTC\",\n \"revision\": \"790b0510dc52b1553e8c49f3d2afb48c0e5c48b7\",\n },\n description=\"\",\n reference=\"https://github.com/CLUEbenchmark/QBQTC/tree/main/dataset\",\n type=\"STS\",\n category=\"s2s\",\n modalities=[\"text\"],\n eval_splits=[\"test\"],\n eval_langs=[\"cmn-Hans\"],\n main_score=\"cosine_spearman\",\n date=None,\n domains=None,\n task_subtypes=None,\n license=None,\n annotations_creators=None,\n dialect=None,\n sample_creation=None,\n bibtex_citation=None,\n descriptive_stats={\"n_samples\": None, \"avg_character_length\": None},\n )\n\n @property\n def metadata_dict(self) -> dict[str, str]:\n metadata_dict = super().metadata_dict\n metadata_dict[\"min_score\"] = 0\n metadata_dict[\"max_score\"] = 2\n return metadata_dict\n \n" }, { "path": "research/C_MTEB/C_MTEB/tasks/__init__.py", "content": "from .Classification import *\nfrom .Clustering import *\nfrom .PairClassification import *\nfrom .Reranking import *\nfrom .Retrieval import *\nfrom .STS import *\n" }, { "path": "research/C_MTEB/MKQA/README.md", "content": "# MKQA\n\nMKQA is a cross-lingual question answering dataset covering 25 non-English languages. For more details, please refer to [here](https://github.com/apple/ml-mkqa).\n\nWe filter questions which types are `unanswerable`, `binary` and `long-answer`. Finally we get 6,619 questions for every language. To perform evaluation, you should firstly **download the test data**:\n```bash\n# download\nwget https://huggingface.co/datasets/Shitao/bge-m3-data/resolve/main/MKQA_test-data.zip\n# unzip to `qa_data` dir\nunzip MKQA_test-data.zip -d qa_data\n```\n\nWe use the well-processed NQ [corpus](https://huggingface.co/datasets/BeIR/nq) offered by BEIR as the candidate, and perform evaluation with metrics: Recall@100 and Recall@20. Here the definition of Recall@k refers to [RocketQA](https://aclanthology.org/2021.naacl-main.466.pdf).\n\n## Dense Retrieval\n\nIf you only want to perform dense retrieval with embedding models, you can follow the following steps:\n\n1. Install Java, Pyserini and Faiss (CPU version or GPU version):\n\n```bash\n# install java (Linux)\napt update\napt install openjdk-11-jdk\n\n# install pyserini\npip install pyserini\n\n# install faiss\n## CPU version\nconda install -c conda-forge faiss-cpu\n\n## GPU version\nconda install -c conda-forge faiss-gpu\n```\n\n2. Dense retrieval:\n\n```bash\ncd dense_retrieval\n\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 512 \\\n--batch_size 256 \\\n--fp16 \\\n--add_instruction False \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--batch_size 32 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\nThere are some important parameters:\n\n- `encoder`: Name or path of the model to evaluate.\n\n- `languages`: The languages you want to evaluate on. Avaliable languages: `ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw`.\n\n- `max_passage_length`: Maximum passage length when encoding.\n\n- `batch_size`: Batch size for query and corpus when encoding. For faster evaluation, you should set the `batch_size` as large as possible.\n\n- `pooling_method` & `normalize_embeddings`: You should follow the corresponding setting of the model you are evaluating. For example, `BAAI/bge-m3` is `cls` and `True`, `intfloat/multilingual-e5-large` is `mean` and `True`, and `intfloat/e5-mistral-7b-instruct` is `last` and `True`.\n\n- `overwrite`: Whether to overwrite evaluation results.\n\n## Hybrid Retrieval (Dense & Sparse)\n\nIf you want to perform **hybrid retrieval with both dense and sparse methods**, you can follow the following steps:\n\n1. Install Java, Pyserini and Faiss (CPU version or GPU version):\n\n```bash\n# install java (Linux)\napt update\napt install openjdk-11-jdk\n\n# install pyserini\npip install pyserini\n\n# install faiss\n## CPU version\nconda install -c conda-forge faiss-cpu\n\n## GPU version\nconda install -c conda-forge faiss-gpu\n```\n\n2. Dense retrieval:\n\n```bash\ncd dense_retrieval\n\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 512 \\\n--batch_size 256 \\\n--fp16 \\\n--add_instruction False \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--batch_size 32 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n3. Sparse Retrieval\n\n```bash\ncd sparse_retrieval\n\n# 1. Generate Query and Corpus Sparse Vector\npython step0-encode_query-and-corpus.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--qa_data_dir ../qa_data \\\n--save_dir ./encoded_query-and-corpus \\\n--max_query_length 512 \\\n--max_passage_length 512 \\\n--batch_size 1024 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Output Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--encoded_query_and_corpus_save_dir ./encoded_query-and-corpus \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--hits 1000\n\n# 3. Print and Save Evaluation Results\npython step2-eval_sparse_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n4. Hybrid Retrieval\n\n```bash\ncd hybrid_retrieval\n\n# 1. Search Dense and Sparse Results\nDense Retrieval\nSparse Retrieval\n\n# 2. Hybrid Dense and Sparse Search Results\npython step0-hybrid_search_results.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--dense_search_result_save_dir ../dense_retrieval/search_results \\\n--sparse_search_result_save_dir ../sparse_retrieval/search_results \\\n--hybrid_result_save_dir ./search_results \\\n--top_k 1000 \\\n--dense_weight 1 --sparse_weight 0.3 \\\n--threads 32\n\n# 3. Print and Save Evaluation Results\npython step1-eval_hybrid_mkqa.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n## MultiVector and All Rerank\n\nIf you want to perform **multi-vector reranking** or **all reranking** based on the search results of dense retrieval, you can follow the following steps:\n\n1. Install Java, Pyserini and Faiss (CPU version or GPU version):\n\n```bash\n# install java (Linux)\napt update\napt install openjdk-11-jdk\n\n# install pyserini\npip install pyserini\n\n# install faiss\n## CPU version\nconda install -c conda-forge faiss-cpu\n\n## GPU version\nconda install -c conda-forge faiss-gpu\n```\n\n2. Dense retrieval:\n\n```bash\ncd dense_retrieval\n\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 512 \\\n--batch_size 256 \\\n--fp16 \\\n--add_instruction False \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--batch_size 32 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n3. Rerank search results with multi-vector scores or all scores: \n\n```bash\ncd multi_vector_rerank\n\n# 1. Rerank Search Results\npython step0-rerank_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ../dense_retrieval/search_results \\\n--qa_data_dir ../qa_data \\\n--rerank_result_save_dir ./rerank_results \\\n--top_k 100 \\\n--batch_size 4 \\\n--max_length 512 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--dense_weight 1 --sparse_weight 0.3 --colbert_weight 1 \\\n--num_shards 1 --shard_id 0 --cuda_id 0\n\n# 2. Print and Save Evaluation Results\npython step1-eval_rerank_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./rerank_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n```\n\n>**Note**: \n>\n>- You should set `dense_weight`, `sparse_weight` and `colbert_weight` based on the downstream task scenario. If the dense method performs well while the sparse method does not, you can lower `sparse_weight` and increase `dense_weight` accordingly.\n>\n>- Based on our experience, dividing the sentence pairs to be reranked into several shards and computing scores for each shard on a single GPU tends to be more efficient than using multiple GPUs to compute scores for all sentence pairs directly.Therefore, if your machine have multiple GPUs, you can set `num_shards` to the number of GPUs and launch multiple terminals to execute the command (`shard_id` should be equal to `cuda_id`). Therefore, if you have multiple GPUs on your machine, you can launch multiple terminals and run multiple commands simultaneously. Make sure to set the `shard_id` and `cuda_id` appropriately, and ensure that you have computed scores for all shards before proceeding to the second step.\n\n4. (*Optional*) In the 3rd step, you can get all three kinds of scores, saved to `rerank_result_save_dir/dense/{encoder}-{reranker}`, `rerank_result_save_dir/sparse/{encoder}-{reranker}` and `rerank_result_save_dir/colbert/{encoder}-{reranker}`. If you want to try other weights, you don't need to rerun the 4th step. Instead, you can use [this script](./multi_vector_rerank/hybrid_all_results.py) to hybrid the three kinds of scores directly.\n\n```bash\ncd multi_vector_rerank\n\n# 1. Hybrid All Search Results\npython hybrid_all_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--dense_search_result_save_dir ./rerank_results/dense \\\n--sparse_search_result_save_dir ./rerank_results/sparse \\\n--colbert_search_result_save_dir ./rerank_results/colbert \\\n--hybrid_result_save_dir ./hybrid_search_results \\\n--top_k 200 \\\n--threads 32 \\\n--dense_weight 1 --sparse_weight 0.1 --colbert_weight 1\n\n# 2. Print and Save Evaluation Results\npython step1-eval_rerank_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./hybrid_search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_hybrid_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n```\n\n\n## BM25 Baseline\n\nWe provide two methods of evaluating BM25 baseline:\n\n1. Use the same tokenizer with [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) (i.e., tokenizer of [XLM-Roberta](https://huggingface.co/FacebookAI/xlm-roberta-large)):\n\n```bash\ncd sparse_retrieval\n\n# 1. Output Search Results with BM25\npython bm25_baseline_same_tokenizer.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mkqa.py \\\n--encoder bm25_same_tokenizer \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n```\n\n2. Use the language analyzer provided by [Anserini](https://github.com/castorini/anserini/blob/master/src/main/java/io/anserini/analysis/AnalyzerMap.java) ([Lucene Tokenizer](https://github.com/apache/lucene/tree/main/lucene/analysis/common/src/java/org/apache/lucene/analysis)):\n\n```bash\ncd sparse_retrieval\n\n# 1. Output Search Results with BM25\npython bm25_baseline.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mkqa.py \\\n--encoder bm25 \\\n--languages ar da de es fi fr he hu it ja km ko ms nl no pl pt ru sv th tr vi zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n```\n\n" }, { "path": "research/C_MTEB/MKQA/dense_retrieval/step0-generate_embedding.py", "content": "\"\"\"\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 512 \\\n--batch_size 256 \\\n--fp16 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport sys\nimport faiss\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom pprint import pprint\nfrom FlagEmbedding import FlagModel\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n fp16: bool = field(\n default=True,\n metadata={'help': 'Use fp16 in inference?'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n\n\n@dataclass\nclass EvalArgs:\n index_save_dir: str = field(\n default='./corpus-index',\n metadata={'help': 'Dir to save index. Corpus index will be saved to `index_save_dir/{encoder_name}/index`. Corpus ids will be saved to `index_save_dir/{encoder_name}/docid` .'}\n )\n max_passage_length: int = field(\n default=512,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef get_model(model_args: ModelArgs):\n model = FlagModel(\n model_args.encoder, \n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n use_fp16=model_args.fp16\n )\n return model\n\n\ndef parse_corpus(corpus: datasets.Dataset):\n corpus_list = []\n for data in tqdm(corpus, desc=\"Generating corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_list.append({\"id\": _id, \"content\": content})\n \n corpus = datasets.Dataset.from_list(corpus_list)\n return corpus\n\n\ndef generate_index(model: FlagModel, corpus: datasets.Dataset, max_passage_length: int=512, batch_size: int=256):\n corpus_embeddings = model.encode_corpus(corpus[\"content\"], batch_size=batch_size, max_length=max_passage_length)\n dim = corpus_embeddings.shape[-1]\n \n faiss_index = faiss.index_factory(dim, \"Flat\", faiss.METRIC_INNER_PRODUCT)\n corpus_embeddings = corpus_embeddings.astype(np.float32)\n faiss_index.train(corpus_embeddings)\n faiss_index.add(corpus_embeddings)\n return faiss_index, list(corpus[\"id\"])\n\n\ndef save_result(index: faiss.Index, docid: list, index_save_dir: str):\n docid_save_path = os.path.join(index_save_dir, 'docid')\n index_save_path = os.path.join(index_save_dir, 'index')\n with open(docid_save_path, 'w', encoding='utf-8') as f:\n for _id in docid:\n f.write(str(_id) + '\\n')\n faiss.write_index(index, index_save_path)\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n model = get_model(model_args=model_args)\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating embedding with model:\")\n print(model_args.encoder)\n \n index_save_dir = os.path.join(eval_args.index_save_dir, os.path.basename(encoder))\n if not os.path.exists(index_save_dir):\n os.makedirs(index_save_dir)\n if os.path.exists(os.path.join(index_save_dir, 'index')) and not eval_args.overwrite:\n print(f'Embedding already exists. Skip...')\n return\n \n corpus = datasets.load_dataset(\"BeIR/nq\", 'corpus')['corpus']\n corpus = parse_corpus(corpus=corpus)\n \n index, docid = generate_index(\n model=model, \n corpus=corpus,\n max_passage_length=eval_args.max_passage_length,\n batch_size=eval_args.batch_size\n )\n save_result(index, docid, index_save_dir)\n\n print(\"==================================================\")\n print(\"Finish generating embeddings with following model:\")\n pprint(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/dense_retrieval/step1-search_results.py", "content": "\"\"\"\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--batch_size 32 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\"\"\"\nimport os\nimport sys\nimport torch\nimport datasets\nfrom tqdm import tqdm\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser, is_torch_npu_available\nfrom pyserini.search.faiss import FaissSearcher, AutoQueryEncoder\nfrom pyserini.output_writer import get_output_writer, OutputFormat\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n add_instruction: bool = field(\n default=False,\n metadata={'help': 'Add query-side instruction?'}\n )\n query_instruction_for_retrieval: str = field(\n default=None,\n metadata={'help': 'query instruction for retrieval'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n index_save_dir: str = field(\n default='./corpus-index',\n metadata={'help': 'Dir to index and docid. Corpus index path is `index_save_dir/{encoder_name}/index`. Corpus ids path is `index_save_dir/{encoder_name}/docid` .'}\n )\n result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving search results. Search results will be saved to `result_save_dir/{encoder_name}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n threads: int = field(\n default=1,\n metadata={'help': 'Maximum threads to use during search'}\n )\n batch_size: int = field(\n default=32,\n metadata={'help': 'Search batch size.'}\n )\n hits: int = field(\n default=1000,\n metadata={'help': 'Number of hits'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef get_query_encoder(model_args: ModelArgs):\n if torch.cuda.is_available():\n device = torch.device(\"cuda\")\n elif is_torch_npu_available():\n device = torch.device(\"npu\")\n else:\n device = torch.device(\"cpu\")\n model = AutoQueryEncoder(\n encoder_dir=model_args.encoder,\n device=device,\n pooling=model_args.pooling_method,\n l2_norm=model_args.normalize_embeddings\n )\n return model\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_queries_and_qids(qa_data_dir: str, lang: str, add_instruction: bool=False, query_instruction_for_retrieval: str=None):\n topics_path = os.path.join(qa_data_dir, f\"{lang}.jsonl\")\n if not os.path.exists(topics_path):\n raise FileNotFoundError(f\"{topics_path} not found\")\n \n dataset = datasets.load_dataset('json', data_files=topics_path)['train']\n \n queries = []\n qids = []\n for data in dataset:\n qids.append(str(data['id']))\n queries.append(str(data['question']))\n if add_instruction and query_instruction_for_retrieval is not None:\n queries = [f\"{query_instruction_for_retrieval}{query}\" for query in queries]\n return queries, qids\n\n\ndef save_result(search_results, result_save_path: str, qids: list, max_hits: int):\n output_writer = get_output_writer(result_save_path, OutputFormat(OutputFormat.TREC.value), 'w',\n max_hits=max_hits, tag='Faiss', topics=qids,\n use_max_passage=False,\n max_passage_delimiter='#',\n max_passage_hits=1000)\n with output_writer:\n for topic, hits in search_results:\n output_writer.write(topic, hits)\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n query_encoder = get_query_encoder(model_args=model_args)\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n index_save_dir = os.path.join(eval_args.index_save_dir, os.path.basename(encoder))\n if not os.path.exists(index_save_dir):\n raise FileNotFoundError(f\"{index_save_dir} not found\")\n searcher = FaissSearcher(\n index_dir=index_save_dir,\n query_encoder=query_encoder\n )\n \n print(\"==================================================\")\n print(\"Start generating search results with model:\", encoder)\n\n print('Generate search results of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start searching results of {lang} ...\")\n \n result_save_path = os.path.join(eval_args.result_save_dir, os.path.basename(encoder), f\"{lang}.txt\")\n if not os.path.exists(os.path.dirname(result_save_path)):\n os.makedirs(os.path.dirname(result_save_path))\n \n if os.path.exists(result_save_path) and not eval_args.overwrite:\n print(f'Search results of {lang} already exists. Skip...')\n continue\n \n queries, qids = get_queries_and_qids(eval_args.qa_data_dir, lang=lang, add_instruction=model_args.add_instruction)\n \n search_results = []\n for start_idx in tqdm(range(0, len(queries), eval_args.batch_size), desc=\"Searching\"):\n batch_queries = queries[start_idx : start_idx+eval_args.batch_size]\n batch_qids = qids[start_idx : start_idx+eval_args.batch_size]\n batch_search_results = searcher.batch_search(\n queries=batch_queries,\n q_ids=batch_qids,\n k=eval_args.hits,\n threads=eval_args.threads\n )\n search_results.extend([(_id, batch_search_results[_id]) for _id in batch_qids])\n \n save_result(\n search_results=search_results,\n result_save_path=result_save_path, \n qids=qids, \n max_hits=eval_args.hits\n )\n\n print(\"==================================================\")\n print(\"Finish generating search results with following model:\")\n pprint(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/dense_retrieval/step2-eval_dense_mkqa.py", "content": "\"\"\"\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 512 \\\n--batch_size 256 \\\n--fp16 \\\n--add_instruction False \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--batch_size 32 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport sys\nimport json\nimport datasets\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport multiprocessing\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\nfrom utils.evaluation import evaluate_recall_qa\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n search_result_save_dir: str = field(\n default='./output_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n metrics: str = field(\n default=\"recall@20\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./eval_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{encoder}.json`'}\n )\n threads: int = field(\n default=1,\n metadata={\"help\": \"num of evaluation threads. <= 1 means single thread\"}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, pooling_method: str, normalize_embeddings: bool, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'model': model_name,\n 'pooling_method': pooling_method,\n 'normalize_embeddings': normalize_embeddings,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{model_name}` on `{eval_languages}` saved at `{save_path}`')\n\n\ndef get_corpus_dict():\n corpus_dict = {}\n corpus = datasets.load_dataset('BeIR/nq', 'corpus')['corpus']\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_dict[_id] = content\n return corpus_dict\n\n\ndef get_qa_dict(qa_path: str):\n qa_dict = {}\n dataset = datasets.load_dataset('json', data_files=qa_path)['train']\n for data in dataset:\n qid = str(data['id'])\n answers = data['answers']\n qa_dict[qid] = answers\n return qa_dict\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=100):\n search_result_dict = {}\n flag = True\n for _, row in tqdm(pd.read_csv(search_result_path, sep=' ', header=None).iterrows(), desc=\"Loading search results\"):\n qid = str(row.iloc[0])\n docid = str(row.iloc[2])\n rank = int(row.iloc[3])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append(docid)\n return search_result_dict\n\n\ndef evaluate(corpus_dict: dict, qa_dict: dict, search_result_path: str, metrics: list):\n top_k = max([int(metric.split('@')[-1]) for metric in metrics])\n search_result_dict = get_search_result_dict(search_result_path, top_k=int(top_k))\n \n search_results = []\n ground_truths = []\n for qid, docid_list in tqdm(search_result_dict.items(), desc=\"Preparing to evaluate\"):\n answers = qa_dict[qid]\n doc_list = [corpus_dict[docid] for docid in docid_list]\n search_results.append(doc_list)\n ground_truths.append(answers)\n \n results = {}\n metrics = sorted([metric.lower() for metric in metrics])\n for metric in metrics:\n metric, k = metric.split('@')\n k = int(k)\n assert metric in ['recall'], f\"Metric `{metric}` is not supported.\"\n if metric == 'recall':\n results[f'Recall@{k}'] = evaluate_recall_qa(search_results, ground_truths, k=k)\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n corpus_dict = get_corpus_dict()\n \n languages = check_languages(eval_args.languages)\n \n if eval_args.encoder[-1] == '/':\n eval_args.encoder = eval_args.encoder[:-1]\n \n if 'checkpoint-' in os.path.basename(eval_args.encoder):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n results = {}\n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n results_list = []\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n results_list.append(pool.apply_async(evaluate, args=(corpus_dict, qa_dict, search_result_path, eval_args.metrics)))\n pool.close()\n pool.join()\n for i, lang in enumerate(languages):\n results[lang] = results_list[i].get()\n else:\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(corpus_dict, qa_dict, search_result_path, eval_args.metrics)\n results[lang] = result\n \n save_results(\n model_name=eval_args.encoder,\n pooling_method=eval_args.pooling_method,\n normalize_embeddings=eval_args.normalize_embeddings,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(eval_args.encoder)}.json\"),\n eval_languages=languages\n )\n print(\"==================================================\")\n print(\"Finish generating evaluation results with following model:\")\n print(eval_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/hybrid_retrieval/step0-hybrid_search_results.py", "content": "\"\"\"\npython step0-hybrid_search_results.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--dense_search_result_save_dir ../dense_retrieval/search_results \\\n--sparse_search_result_save_dir ../sparse_retrieval/search_results \\\n--hybrid_result_save_dir ./search_results \\\n--top_k 1000 \\\n--dense_weight 1 --sparse_weight 0.3 \\\n--threads 32\n\"\"\"\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\nimport multiprocessing\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass EvalArgs:\n model_name_or_path: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of model'}\n )\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n top_k: int = field(\n default=1000,\n metadata={'help': 'Use reranker to rerank top-k retrieval results'}\n )\n dense_weight: float = field(\n default=1,\n metadata={'help': 'Hybrid weight of dense score'}\n )\n sparse_weight: float = field(\n default=0.3,\n metadata={'help': 'Hybrid weight of sparse score'}\n )\n dense_search_result_save_dir: str = field(\n default='../dense_retrieval/search_results',\n metadata={'help': 'Dir to saving dense search results. Search results path is `dense_search_result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n sparse_search_result_save_dir: str = field(\n default='../sparse_retrieval/search_results',\n metadata={'help': 'Dir to saving sparse search results. Search results path is `sparse_search_result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n hybrid_result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving hybrid search results. Reranked results will be saved to `hybrid_result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n threads: int = field(\n default=1,\n metadata={\"help\": \"num of evaluation threads. <= 1 means single thread\"}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=1000):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = row.iloc[2]\n rank = int(row.iloc[3])\n score = float(row.iloc[4])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append((docid, score))\n return search_result_dict\n\n\ndef get_queries_dict(queries_path: str):\n queries_dict = {}\n for _, row in pd.read_csv(queries_path, sep='\\t', header=None).iterrows():\n qid = str(row.iloc[0])\n query = row.iloc[1]\n queries_dict[qid] = query\n return queries_dict\n\n\ndef save_hybrid_results(sparse_search_result_path: str, dense_search_result_path: str, hybrid_result_save_path: str, top_k: int=1000, dense_weight: float=0.2, sparse_weight: float=0.5):\n sparse_search_result_dict = get_search_result_dict(sparse_search_result_path, top_k=top_k)\n dense_search_result_dict = get_search_result_dict(dense_search_result_path, top_k=top_k)\n \n if not os.path.exists(os.path.dirname(hybrid_result_save_path)):\n os.makedirs(os.path.dirname(hybrid_result_save_path))\n \n qid_list = list(set(sparse_search_result_dict.keys()) | set(dense_search_result_dict.keys()))\n hybrid_results_list = []\n for qid in tqdm(qid_list, desc=\"Hybriding dense and sparse scores\"):\n results = {}\n if qid in sparse_search_result_dict:\n for docid, score in sparse_search_result_dict[qid]:\n score = score / 10000.\n results[docid] = score * sparse_weight\n if qid in dense_search_result_dict:\n for docid, score in dense_search_result_dict[qid]:\n if docid in results:\n results[docid] = results[docid] + score * dense_weight\n else:\n results[docid] = score * dense_weight\n hybrid_results = [(docid, score) for docid, score in results.items()]\n hybrid_results.sort(key=lambda x: x[1], reverse=True)\n \n hybrid_results_list.append(hybrid_results[:top_k])\n \n with open(hybrid_result_save_path, 'w', encoding='utf-8') as f:\n for qid, hybrid_results in tqdm(zip(qid_list, hybrid_results_list), desc=\"Saving hybrid search results\"):\n for rank, docid_score in enumerate(hybrid_results):\n docid, score = docid_score\n line = f\"{qid} Q0 {docid} {rank+1} {score:.6f} Faiss-&-Anserini\"\n f.write(line + '\\n')\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if os.path.basename(eval_args.model_name_or_path).startswith('checkpoint-'):\n eval_args.model_name_or_path = os.path.dirname(eval_args.model_name_or_path) + '_' + os.path.basename(eval_args.model_name_or_path)\n \n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start hybrid search results of {lang} ...\")\n \n hybrid_result_save_path = os.path.join(eval_args.hybrid_result_save_dir, f\"{os.path.basename(eval_args.model_name_or_path)}\", f\"{lang}.txt\")\n \n sparse_search_result_save_dir = os.path.join(eval_args.sparse_search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n sparse_search_result_path = os.path.join(sparse_search_result_save_dir, f\"{lang}.txt\")\n \n dense_search_result_save_dir = os.path.join(eval_args.dense_search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n dense_search_result_path = os.path.join(dense_search_result_save_dir, f\"{lang}.txt\")\n \n pool.apply_async(save_hybrid_results, args=(\n sparse_search_result_path,\n dense_search_result_path,\n hybrid_result_save_path,\n eval_args.top_k,\n eval_args.dense_weight,\n eval_args.sparse_weight\n ))\n pool.close()\n pool.join()\n else:\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start hybrid search results of {lang} ...\")\n \n hybrid_result_save_path = os.path.join(eval_args.hybrid_result_save_dir, f\"{os.path.basename(eval_args.model_name_or_path)}\", f\"{lang}.txt\")\n \n sparse_search_result_save_dir = os.path.join(eval_args.sparse_search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n sparse_search_result_path = os.path.join(sparse_search_result_save_dir, f\"{lang}.txt\")\n \n dense_search_result_save_dir = os.path.join(eval_args.dense_search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n dense_search_result_path = os.path.join(dense_search_result_save_dir, f\"{lang}.txt\")\n \n save_hybrid_results(\n sparse_search_result_path=sparse_search_result_path, \n dense_search_result_path=dense_search_result_path, \n hybrid_result_save_path=hybrid_result_save_path,\n top_k=eval_args.top_k,\n dense_weight=eval_args.dense_weight,\n sparse_weight=eval_args.sparse_weight\n )\n \n print(\"==================================================\")\n print(\"Finish generating reranked results with following model:\", eval_args.model_name_or_path)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/hybrid_retrieval/step1-eval_hybrid_mkqa.py", "content": "\"\"\"\n# Ref: https://github.com/texttron/tevatron/tree/main/examples/unicoil\n# 1. Search Dense and Sparse Results\nDense Retrieval\nSparse Retrieval\n\n# 2. Hybrid Dense and Sparse Search Results\npython step0-hybrid_search_results.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--dense_search_result_save_dir ../dense_retrieval/search_results \\\n--sparse_search_result_save_dir ../sparse_retrieval/search_results \\\n--hybrid_result_save_dir ./search_results \\\n--top_k 1000 \\\n--dense_weight 1 --sparse_weight 0.3 \\\n--threads 32\n\n# 3. Print and Save Evaluation Results\npython step1-eval_hybrid_mkqa.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport sys\nimport json\nimport datasets\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport multiprocessing\nfrom pprint import pprint\nfrom typing import Optional\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\nfrom utils.evaluation import evaluate_recall_qa\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n model_name_or_path: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of model'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n search_result_save_dir: str = field(\n default='./output_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n metrics: str = field(\n default=\"recall@20\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./eval_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{model_name_or_path}.json`'}\n )\n threads: int = field(\n default=1,\n metadata={\"help\": \"num of evaluation threads. <= 1 means single thread\"}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, pooling_method: str, normalize_embeddings: bool, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'model': model_name,\n 'pooling_method': pooling_method,\n 'normalize_embeddings': normalize_embeddings,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{model_name}` on `{eval_languages}` saved at `{save_path}`')\n\n\ndef get_corpus_dict():\n corpus_dict = {}\n corpus = datasets.load_dataset('BeIR/nq', 'corpus')['corpus']\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_dict[_id] = content\n return corpus_dict\n\n\ndef get_qa_dict(qa_path: str):\n qa_dict = {}\n dataset = datasets.load_dataset('json', data_files=qa_path)['train']\n for data in dataset:\n qid = str(data['id'])\n answers = data['answers']\n qa_dict[qid] = answers\n return qa_dict\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=100):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = str(row.iloc[2])\n rank = int(row.iloc[3])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append(docid)\n return search_result_dict\n\n\ndef evaluate(corpus_dict: dict, qa_dict: dict, search_result_path: str, metrics: list):\n top_k = max([int(metric.split('@')[-1]) for metric in metrics])\n search_result_dict = get_search_result_dict(search_result_path, top_k=int(top_k))\n \n search_results = []\n ground_truths = []\n for qid, docid_list in search_result_dict.items():\n answers = qa_dict[qid]\n doc_list = [corpus_dict[docid] for docid in docid_list]\n search_results.append(doc_list)\n ground_truths.append(answers)\n \n results = {}\n metrics = sorted([metric.lower() for metric in metrics])\n for metric in metrics:\n metric, k = metric.split('@')\n k = int(k)\n assert metric in ['recall'], f\"Metric `{metric}` is not supported.\"\n if metric == 'recall':\n results[f'Recall@{k}'] = evaluate_recall_qa(search_results, ground_truths, k=k)\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n corpus_dict = get_corpus_dict()\n \n languages = check_languages(eval_args.languages)\n \n if eval_args.model_name_or_path[-1] == '/':\n eval_args.model_name_or_path = eval_args.model_name_or_path[:-1]\n if os.path.basename(eval_args.model_name_or_path).startswith('checkpoint-'):\n eval_args.model_name_or_path = os.path.dirname(eval_args.model_name_or_path) + '_' + os.path.basename(eval_args.model_name_or_path)\n \n results = {}\n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n results_list = []\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n results_list.append(pool.apply_async(evaluate, args=(corpus_dict, qa_dict, search_result_path, eval_args.metrics)))\n pool.close()\n pool.join()\n for i, lang in enumerate(languages):\n results[lang] = results_list[i].get()\n else:\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(corpus_dict, qa_dict, search_result_path, eval_args.metrics)\n results[lang] = result\n \n save_results(\n model_name=eval_args.model_name_or_path,\n pooling_method=eval_args.pooling_method,\n normalize_embeddings=eval_args.normalize_embeddings,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(eval_args.model_name_or_path)}.json\"),\n eval_languages=languages\n )\n print(\"==================================================\")\n print(\"Finish generating evaluation results with following model:\")\n print(eval_args.model_name_or_path)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/multi_vector_rerank/hybrid_all_results.py", "content": "\"\"\"\npython hybrid_all_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--dense_search_result_save_dir ./rerank_results/dense \\\n--sparse_search_result_save_dir ./rerank_results/sparse \\\n--colbert_search_result_save_dir ./rerank_results/colbert \\\n--hybrid_result_save_dir ./hybrid_search_results \\\n--top_k 200 \\\n--threads 32 \\\n--dense_weight 1 --sparse_weight 0.1 --colbert_weight 1\n\"\"\"\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\nimport multiprocessing\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass EvalArgs:\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of model'}\n )\n reranker: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of reranker'}\n )\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n top_k: int = field(\n default=200,\n metadata={'help': 'Use reranker to rerank top-k retrieval results'}\n )\n dense_weight: float = field(\n default=1,\n metadata={'help': 'Hybrid weight of sparse score'}\n )\n sparse_weight: float = field(\n default=0.3,\n metadata={'help': 'Hybrid weight of sparse score'}\n )\n colbert_weight: float = field(\n default=1,\n metadata={'help': 'Hybrid weight of sparse score'}\n )\n dense_search_result_save_dir: str = field(\n default='../rerank/unify_rerank_results/dense',\n metadata={'help': 'Dir to saving dense search results. Search results path is `dense_search_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n sparse_search_result_save_dir: str = field(\n default='../rerank/unify_rerank_results/sparse',\n metadata={'help': 'Dir to saving sparse search results. Search results path is `sparse_search_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n colbert_search_result_save_dir: str = field(\n default='../rerank/unify_rerank_results/colbert',\n metadata={'help': 'Dir to saving sparse search results. Search results path is `sparse_search_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n hybrid_result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving hybrid search results. Reranked results will be saved to `hybrid_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n threads: int = field(\n default=1,\n metadata={\"help\": \"num of evaluation threads. <= 1 means single thread\"}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=1000):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = row.iloc[2]\n rank = int(row.iloc[3])\n score = float(row.iloc[4])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append((docid, score))\n return search_result_dict\n\n\ndef get_queries_dict(queries_path: str):\n queries_dict = {}\n for _, row in pd.read_csv(queries_path, sep='\\t', header=None).iterrows():\n qid = str(row.iloc[0])\n query = row.iloc[1]\n queries_dict[qid] = query\n return queries_dict\n\n\ndef save_hybrid_results(sparse_search_result_dict: dict, dense_search_result_dict: dict, colbert_search_result_dict: dict, hybrid_result_save_path: str, top_k: int=1000, dense_weight: float=1, sparse_weight: float=0.3, colbert_weight: float=1):\n if not os.path.exists(os.path.dirname(hybrid_result_save_path)):\n os.makedirs(os.path.dirname(hybrid_result_save_path))\n \n qid_list = list(set(sparse_search_result_dict.keys()) | set(dense_search_result_dict.keys()) | set(colbert_search_result_dict.keys()))\n hybrid_results_list = []\n for qid in tqdm(qid_list, desc=\"Hybriding dense, sparse and colbert scores\"):\n results = {}\n if qid in sparse_search_result_dict:\n for docid, score in sparse_search_result_dict[qid]:\n score = score / 0.3 # use 0.3 to restore\n results[docid] = score * sparse_weight\n if qid in dense_search_result_dict:\n for docid, score in dense_search_result_dict[qid]:\n if docid in results:\n results[docid] = results[docid] + score * dense_weight\n else:\n results[docid] = score * dense_weight\n if qid in colbert_search_result_dict:\n for docid, score in colbert_search_result_dict[qid]:\n if docid in results:\n results[docid] = results[docid] + score * colbert_weight\n else:\n results[docid] = score * colbert_weight\n hybrid_results = [(docid, score) for docid, score in results.items()]\n hybrid_results.sort(key=lambda x: x[1], reverse=True)\n \n hybrid_results_list.append(hybrid_results[:top_k])\n \n with open(hybrid_result_save_path, 'w', encoding='utf-8') as f:\n for qid, hybrid_results in tqdm(zip(qid_list, hybrid_results_list), desc=\"Saving hybrid search results\"):\n for rank, docid_score in enumerate(hybrid_results):\n docid, score = docid_score\n line = f\"{qid} Q0 {docid} {rank+1} {score:.6f} Faiss-&-Anserini\"\n f.write(line + '\\n')\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if os.path.basename(eval_args.encoder).startswith('checkpoint-'):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n if os.path.basename(eval_args.reranker).startswith('checkpoint-'):\n eval_args.reranker = os.path.dirname(eval_args.reranker) + '_' + os.path.basename(eval_args.reranker)\n \n dir_name = f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\"\n \n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start hybrid search results of {lang} ...\")\n \n hybrid_result_save_path = os.path.join(eval_args.hybrid_result_save_dir, dir_name, f\"{lang}.txt\")\n \n sparse_search_result_save_dir = os.path.join(eval_args.sparse_search_result_save_dir, dir_name)\n \n sparse_search_result_path = os.path.join(sparse_search_result_save_dir, f\"{lang}.txt\")\n \n sparse_search_result_dict = get_search_result_dict(sparse_search_result_path, top_k=eval_args.top_k)\n \n dense_search_result_save_dir = os.path.join(eval_args.dense_search_result_save_dir, dir_name)\n \n dense_search_result_path = os.path.join(dense_search_result_save_dir, f\"{lang}.txt\")\n \n dense_search_result_dict = get_search_result_dict(dense_search_result_path, top_k=eval_args.top_k)\n \n colbert_search_result_save_dir = os.path.join(eval_args.colbert_search_result_save_dir, dir_name)\n \n colbert_search_result_path = os.path.join(colbert_search_result_save_dir, f\"{lang}.txt\")\n \n colbert_search_result_dict = get_search_result_dict(colbert_search_result_path, top_k=eval_args.top_k)\n \n pool.apply_async(save_hybrid_results, args=(\n sparse_search_result_dict,\n dense_search_result_dict,\n colbert_search_result_dict,\n hybrid_result_save_path,\n eval_args.top_k,\n eval_args.dense_weight,\n eval_args.sparse_weight,\n eval_args.colbert_weight\n ))\n pool.close()\n pool.join()\n else:\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start hybrid search results of {lang} ...\")\n \n hybrid_result_save_path = os.path.join(eval_args.hybrid_result_save_dir, dir_name, f\"{lang}.txt\")\n \n sparse_search_result_save_dir = os.path.join(eval_args.sparse_search_result_save_dir, dir_name)\n \n sparse_search_result_path = os.path.join(sparse_search_result_save_dir, f\"{lang}.txt\")\n \n sparse_search_result_dict = get_search_result_dict(sparse_search_result_path, top_k=eval_args.top_k)\n \n dense_search_result_save_dir = os.path.join(eval_args.dense_search_result_save_dir, dir_name)\n \n dense_search_result_path = os.path.join(dense_search_result_save_dir, f\"{lang}.txt\")\n \n dense_search_result_dict = get_search_result_dict(dense_search_result_path, top_k=eval_args.top_k)\n \n colbert_search_result_save_dir = os.path.join(eval_args.colbert_search_result_save_dir, dir_name)\n \n colbert_search_result_path = os.path.join(colbert_search_result_save_dir, f\"{lang}.txt\")\n \n colbert_search_result_dict = get_search_result_dict(colbert_search_result_path, top_k=eval_args.top_k)\n \n save_hybrid_results(\n sparse_search_result_dict=sparse_search_result_dict, \n dense_search_result_dict=dense_search_result_dict, \n colbert_search_result_dict=colbert_search_result_dict,\n hybrid_result_save_path=hybrid_result_save_path,\n top_k=eval_args.top_k,\n dense_weight=eval_args.dense_weight,\n sparse_weight=eval_args.sparse_weight,\n colbert_weight=eval_args.colbert_weight\n )\n \n print(\"==================================================\")\n print(\"Finish generating reranked results with following model and reranker:\")\n print(eval_args.encoder)\n print(eval_args.reranker)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/multi_vector_rerank/step0-rerank_results.py", "content": "\"\"\"\npython step0-rerank_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ../dense_retrieval/search_results \\\n--qa_data_dir ../qa_data \\\n--rerank_result_save_dir ./rerank_results \\\n--top_k 100 \\\n--batch_size 4 \\\n--max_length 512 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--dense_weight 1 --sparse_weight 0.3 --colbert_weight 1 \\\n--num_shards 1 --shard_id 0 --cuda_id 0\n\"\"\"\nimport os\nimport sys\nimport copy\nimport datasets\nimport pandas as pd\nfrom tqdm import tqdm\nfrom FlagEmbedding import BGEM3FlagModel\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\n\n\n@dataclass\nclass ModelArgs:\n reranker: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of reranker'}\n )\n fp16: bool = field(\n default=False,\n metadata={'help': 'Use fp16 in inference?'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n max_length: int = field(\n default=512,\n metadata={'help': 'Max text length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n top_k: int = field(\n default=100,\n metadata={'help': 'Use reranker to rerank top-k retrieval results'}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n search_result_save_dir: str = field(\n default='./output_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='./qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n rerank_result_save_dir: str = field(\n default='./rerank_results',\n metadata={'help': 'Dir to saving reranked results. Reranked results will be saved to `rerank_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n num_shards: int = field(\n default=1,\n metadata={'help': \"num of shards\"}\n )\n shard_id: int = field(\n default=0,\n metadata={'help': 'id of shard, start from 0'}\n )\n cuda_id: int = field(\n default=0,\n metadata={'help': 'CUDA ID to use. -1 means only use CPU.'}\n )\n dense_weight: float = field(\n default=1,\n metadata={'help': 'The weight of dense score when hybriding all scores'}\n )\n sparse_weight: float = field(\n default=0.3,\n metadata={'help': 'The weight of sparse score when hybriding all scores'}\n )\n colbert_weight: float = field(\n default=1,\n metadata={'help': 'The weight of colbert score when hybriding all scores'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_reranker(model_args: ModelArgs, device: str=None):\n reranker = BGEM3FlagModel(\n model_name_or_path=model_args.reranker,\n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n device=device\n )\n return reranker\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=100):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = row.iloc[2]\n rank = int(row.iloc[3])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append(docid)\n return search_result_dict\n\n\ndef get_queries_dict(queries_path: str):\n queries_dict = {}\n dataset = datasets.load_dataset('json', data_files=queries_path)['train']\n for data in dataset:\n qid = str(data['id'])\n query = data['question']\n queries_dict[qid] = query\n return queries_dict\n\n\ndef get_corpus_dict(corpus: datasets.Dataset):\n corpus_dict = {}\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_dict[_id] = content\n return corpus_dict\n\n\ndef save_rerank_results(queries_dict: dict, corpus_dict: dict, reranker: BGEM3FlagModel, search_result_dict: dict, rerank_result_save_path: dict, batch_size: int=256, max_length: int=512, dense_weight: float=1, sparse_weight: float=0.3, colbert_weight: float=1):\n qid_list = []\n sentence_pairs = []\n for qid, docids in search_result_dict.items():\n qid_list.append(qid)\n query = queries_dict[qid]\n for docid in docids:\n passage = corpus_dict[docid]\n sentence_pairs.append((query, passage))\n \n scores_dict = reranker.compute_score(\n sentence_pairs, \n batch_size=batch_size, \n max_query_length=max_length, \n max_passage_length=max_length, \n weights_for_different_modes=[dense_weight, sparse_weight, colbert_weight]\n )\n\n for sub_dir, _rerank_result_save_path in rerank_result_save_path.items():\n if not os.path.exists(os.path.dirname(_rerank_result_save_path)):\n os.makedirs(os.path.dirname(_rerank_result_save_path))\n \n scores = scores_dict[sub_dir]\n with open(_rerank_result_save_path, 'w', encoding='utf-8') as f:\n i = 0\n for qid in qid_list:\n docids = search_result_dict[qid]\n docids_scores = []\n for j in range(len(docids)):\n docids_scores.append((docids[j], scores[i + j]))\n i += len(docids)\n \n docids_scores.sort(key=lambda x: x[1], reverse=True)\n for rank, docid_score in enumerate(docids_scores):\n docid, score = docid_score\n line = f\"{qid} Q0 {docid} {rank+1} {score:.6f} Faiss\"\n f.write(line + '\\n')\n\n\ndef get_shard(search_result_dict: dict, num_shards: int, shard_id: int):\n if num_shards <= 1:\n return search_result_dict\n keys_list = sorted(list(search_result_dict.keys()))\n \n shard_len = len(keys_list) // num_shards\n if shard_id == num_shards - 1:\n shard_keys_list = keys_list[shard_id*shard_len:]\n else:\n shard_keys_list = keys_list[shard_id*shard_len : (shard_id + 1)*shard_len]\n shard_search_result_dict = {k: search_result_dict[k] for k in shard_keys_list}\n return shard_search_result_dict\n\n\ndef rerank_results(corpus_dict: dict, languages: list, eval_args: EvalArgs, model_args: ModelArgs, device: str=None):\n eval_args = copy.deepcopy(eval_args)\n model_args = copy.deepcopy(model_args)\n \n num_shards = eval_args.num_shards\n shard_id = eval_args.shard_id\n if shard_id >= num_shards:\n raise ValueError(f\"shard_id >= num_shards ({shard_id} >= {num_shards})\")\n \n reranker = get_reranker(model_args=model_args, device=device)\n \n if os.path.basename(eval_args.encoder).startswith('checkpoint-'):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n if os.path.basename(model_args.reranker).startswith('checkpoint-'):\n model_args.reranker = os.path.dirname(model_args.reranker) + '_' + os.path.basename(model_args.reranker)\n \n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start reranking results of {lang} ...\")\n \n queries_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n queries_dict = get_queries_dict(queries_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n search_result_dict = get_search_result_dict(search_result_path, top_k=eval_args.top_k)\n \n search_result_dict = get_shard(search_result_dict, num_shards=num_shards, shard_id=shard_id)\n\n rerank_result_save_path = {}\n for sub_dir in ['colbert', 'sparse', 'dense', 'colbert+sparse+dense']:\n _rerank_result_save_path = os.path.join(\n eval_args.rerank_result_save_dir, \n sub_dir, \n f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(model_args.reranker)}\", \n f\"{lang}.txt\")\n rerank_result_save_path[sub_dir] = _rerank_result_save_path\n\n save_rerank_results(\n queries_dict=queries_dict,\n corpus_dict=corpus_dict, \n reranker=reranker, \n search_result_dict=search_result_dict, \n rerank_result_save_path=rerank_result_save_path,\n batch_size=eval_args.batch_size,\n max_length=eval_args.max_length,\n dense_weight=eval_args.dense_weight,\n )\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs, ModelArgs])\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: EvalArgs\n model_args: ModelArgs\n \n languages = check_languages(eval_args.languages)\n \n corpus = datasets.load_dataset(\"BeIR/nq\", 'corpus')['corpus']\n corpus_dict = get_corpus_dict(corpus=corpus)\n \n cuda_id = eval_args.cuda_id\n \n if cuda_id < 0:\n rerank_results(corpus_dict, languages, eval_args, model_args, device='cpu')\n else:\n rerank_results(corpus_dict, languages, eval_args, model_args, device=f\"cuda:{cuda_id}\")\n \n print(\"==================================================\")\n print(\"Finish generating reranked results with following model and reranker:\")\n print(eval_args.encoder)\n print(model_args.reranker)\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/multi_vector_rerank/step1-eval_rerank_mkqa.py", "content": "\"\"\"\n# 1. Rerank Search Results\npython step0-rerank_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ../dense_retrieval/search_results \\\n--qa_data_dir ../qa_data \\\n--rerank_result_save_dir ./rerank_results \\\n--top_k 100 \\\n--batch_size 4 \\\n--max_length 512 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--dense_weight 1 --sparse_weight 0.3 --colbert_weight 1 \\\n--num_shards 1 --shard_id 0 --cuda_id 0\n\n# 2. Print and Save Evaluation Results\npython step1-eval_rerank_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./rerank_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n\"\"\"\nimport os\nimport sys\nimport json\nimport datasets\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport multiprocessing\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\nfrom utils.evaluation import evaluate_recall_qa\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n reranker: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of reranker'}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n search_result_save_dir: str = field(\n default='./rerank_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n metrics: str = field(\n default=\"recall@20\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./reranker_evaluation_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{encoder}-{reranker}.json`'}\n )\n threads: int = field(\n default=1,\n metadata={\"help\": \"num of evaluation threads. <= 1 means single thread\"}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, reranker_name: str, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'reranker': reranker_name,\n 'model': model_name,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{reranker_name}` on `{eval_languages}` based on `{model_name}` saved at `{save_path}`')\n\n\ndef get_corpus_dict():\n corpus_dict = {}\n corpus = datasets.load_dataset('BeIR/nq', 'corpus')['corpus']\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_dict[_id] = content\n return corpus_dict\n\n\ndef get_qa_dict(qa_path: str):\n qa_dict = {}\n dataset = datasets.load_dataset('json', data_files=qa_path)['train']\n for data in dataset:\n qid = str(data['id'])\n answers = data['answers']\n qa_dict[qid] = answers\n return qa_dict\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=100):\n search_result_dict = {}\n flag = True\n for _, row in tqdm(pd.read_csv(search_result_path, sep=' ', header=None).iterrows(), desc=\"Loading search results\"):\n qid = str(row.iloc[0])\n docid = str(row.iloc[2])\n rank = int(row.iloc[3])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append(docid)\n return search_result_dict\n\n\ndef evaluate(corpus_dict: dict, qa_dict: dict, search_result_path: str, metrics: list):\n top_k = max([int(metric.split('@')[-1]) for metric in metrics])\n search_result_dict = get_search_result_dict(search_result_path, top_k=int(top_k))\n \n search_results = []\n ground_truths = []\n for qid, docid_list in tqdm(search_result_dict.items(), desc=\"Preparing to evaluate\"):\n answers = qa_dict[qid]\n doc_list = [corpus_dict[docid] for docid in docid_list]\n search_results.append(doc_list)\n ground_truths.append(answers)\n \n results = {}\n metrics = sorted([metric.lower() for metric in metrics])\n for metric in metrics:\n metric, k = metric.split('@')\n k = int(k)\n assert metric in ['recall'], f\"Metric `{metric}` is not supported.\"\n if metric == 'recall':\n results[f'Recall@{k}'] = evaluate_recall_qa(search_results, ground_truths, k=k)\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n corpus_dict = get_corpus_dict()\n \n languages = check_languages(eval_args.languages)\n \n if 'checkpoint-' in os.path.basename(eval_args.encoder):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n if 'checkpoint-' in os.path.basename(eval_args.reranker):\n eval_args.reranker = os.path.dirname(eval_args.reranker) + '_' + os.path.basename(eval_args.reranker)\n \n try:\n for sub_dir in ['colbert', 'sparse', 'dense', 'colbert+sparse+dense']:\n results = {}\n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n results_list = []\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, sub_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\")\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n results_list.append(pool.apply_async(evaluate, args=(corpus_dict, qa_dict, search_result_path, eval_args.metrics)))\n pool.close()\n pool.join()\n for i, lang in enumerate(languages):\n results[lang] = results_list[i].get()\n else:\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, sub_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\")\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(corpus_dict, qa_dict, search_result_path, eval_args.metrics)\n results[lang] = result\n \n print(\"****************************\")\n print(sub_dir + \":\")\n save_results(\n model_name=eval_args.encoder,\n reranker_name=eval_args.reranker,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, sub_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}.json\"),\n eval_languages=languages\n )\n except:\n results = {}\n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n results_list = []\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\")\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n results_list.append(pool.apply_async(evaluate, args=(corpus_dict, qa_dict, search_result_path, eval_args.metrics)))\n pool.close()\n pool.join()\n for i, lang in enumerate(languages):\n results[lang] = results_list[i].get()\n else:\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\")\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(corpus_dict, qa_dict, search_result_path, eval_args.metrics)\n results[lang] = result\n save_results(\n model_name=eval_args.encoder,\n reranker_name=eval_args.reranker,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}.json\"),\n eval_languages=languages\n )\n \n print(\"==================================================\")\n print(\"Finish generating evaluation results with following model and reranker:\")\n print(eval_args.encoder)\n print(eval_args.reranker)\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/sparse_retrieval/bm25_baseline.py", "content": "\"\"\"\n# 1. Output Search Results with BM25\npython bm25_baseline.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mkqa.py \\\n--encoder bm25 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n\"\"\"\nimport os\nimport sys\nimport datasets\nfrom tqdm import tqdm\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\n\n\ndef generate_corpus(corpus_save_path: str):\n if os.path.exists(corpus_save_path):\n print(\"Corpus already exists. Skip generating ...\")\n return\n\n corpus = datasets.load_dataset('BeIR/nq', 'corpus')['corpus']\n corpus_list = []\n for data in tqdm(corpus, desc=\"Generating corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_list.append({\"id\": _id, \"contents\": content})\n \n corpus = datasets.Dataset.from_list(corpus_list)\n corpus.to_json(corpus_save_path, force_ascii=False)\n\n\ndef generate_queries(qa_data_dir: str, lang: str, queries_save_dir: str):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n if os.path.exists(queries_save_path) and os.path.getsize(queries_save_path) > 0:\n return\n \n queries_path = os.path.join(qa_data_dir, f\"{lang}.jsonl\")\n queries = datasets.load_dataset('json', data_files=queries_path)['train']\n \n queries_list = []\n for data in queries:\n _id = str(data['id'])\n query = data['question']\n queries_list.append({\n 'id': _id,\n 'content': query\n })\n \n with open(queries_save_path, 'w', encoding='utf-8') as f:\n for query in queries_list:\n line = f\"{query['id']}\\t{query['content']}\"\n f.write(line + '\\n')\n\n\ndef index(corpus_save_dir: str, index_save_dir: str):\n cmd = f\"python -m pyserini.index.lucene \\\n --collection JsonCollection \\\n --input {corpus_save_dir} \\\n --index {index_save_dir} \\\n --generator DefaultLuceneDocumentGenerator \\\n --threads 1 \\\n --storePositions --storeDocvectors --storeRaw \\\n \"\n os.system(cmd)\n\n\ndef search(index_save_dir: str, queries_save_dir: str, lang: str, result_save_path: str):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n # Note: Use `--lang {lang}` will cause the performance degradation, since the query and corpus are in different languages.\n cmd = f\"python -m pyserini.search.lucene \\\n --index {index_save_dir} \\\n --topics {queries_save_path} \\\n --output {result_save_path} \\\n --bm25 \\\n --hits 1000 \\\n --batch-size 128 \\\n --threads 16 \\\n \"\n os.system(cmd)\n\n\ndef main():\n bm25_dir = './bm25_baseline'\n \n qa_data_dir = '../qa_data'\n \n result_save_dir = os.path.join('./search_results', 'bm25')\n if not os.path.exists(result_save_dir):\n os.makedirs(result_save_dir)\n \n corpus_save_dir = os.path.join(bm25_dir, 'corpus')\n if not os.path.exists(corpus_save_dir):\n os.makedirs(corpus_save_dir)\n \n corpus_save_path = os.path.join(corpus_save_dir, 'corpus.jsonl')\n generate_corpus(corpus_save_path)\n \n index_save_dir = os.path.join(bm25_dir, 'index')\n if not os.path.exists(index_save_dir):\n os.makedirs(index_save_dir)\n index(corpus_save_dir, index_save_dir)\n \n queries_save_dir = os.path.join(bm25_dir, 'queries')\n if not os.path.exists(queries_save_dir):\n os.makedirs(queries_save_dir)\n \n languages = ['ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n \n for lang in languages:\n generate_queries(qa_data_dir, lang, queries_save_dir)\n \n result_save_path = os.path.join(result_save_dir, f'{lang}.txt')\n search(index_save_dir, queries_save_dir, lang, result_save_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "research/C_MTEB/MKQA/sparse_retrieval/bm25_baseline_same_tokenizer.py", "content": "\"\"\"\n# 1. Output Search Results with BM25\npython bm25_baseline_same_tokenizer.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mkqa.py \\\n--encoder bm25_same_tokenizer \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32\n\"\"\"\nimport os\nimport sys\nimport datasets\nfrom tqdm import tqdm\nfrom transformers import AutoTokenizer\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\n\n\ntokenizer = AutoTokenizer.from_pretrained(\n 'BAAI/bge-m3',\n use_fast=False,\n)\n\n\ndef _map_func_corpus(examples):\n results = {}\n results['id'] = examples['id']\n results['contents'] = []\n \n inputs = tokenizer(\n examples['contents'],\n padding=False,\n truncation=True,\n max_length=512\n )\n input_ids_list = inputs['input_ids']\n for i in range(len(examples['id'])):\n token_ids = input_ids_list[i][1:-1]\n token_ids = [str(_id) for _id in token_ids]\n results['contents'].append(\" \".join(token_ids))\n return results\n\n\ndef _map_func_query(examples):\n results = {}\n results['id'] = examples['id']\n results['question'] = []\n \n inputs = tokenizer(\n examples['question'],\n padding=False,\n truncation=True,\n max_length=512\n )\n input_ids_list = inputs['input_ids']\n for i in range(len(examples['id'])):\n token_ids = input_ids_list[i][1:-1]\n token_ids = [str(_id) for _id in token_ids]\n results['question'].append(\" \".join(token_ids))\n return results\n\n\ndef generate_corpus(corpus_save_path: str):\n if os.path.exists(corpus_save_path):\n print(\"Corpus already exists. Skip generating ...\")\n return\n\n corpus = datasets.load_dataset('BeIR/nq', 'corpus')['corpus']\n corpus_list = []\n for data in tqdm(corpus, desc=\"Generating corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_list.append({\"id\": _id, \"contents\": content})\n \n corpus = datasets.Dataset.from_list(corpus_list)\n \n corpus = corpus.map(_map_func_corpus, batched=True, num_proc=48)\n \n corpus.to_json(corpus_save_path, force_ascii=False)\n\n\ndef generate_queries(qa_data_dir: str, lang: str, queries_save_dir: str):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n if os.path.exists(queries_save_path) and os.path.getsize(queries_save_path) > 0:\n return\n \n queries_path = os.path.join(qa_data_dir, f\"{lang}.jsonl\")\n queries = datasets.load_dataset('json', data_files=queries_path)['train']\n \n queries = queries.map(_map_func_query, batched=True, num_proc=48)\n \n queries_list = []\n for data in queries:\n _id = str(data['id'])\n query = data['question']\n queries_list.append({\n 'id': _id,\n 'content': query\n })\n \n with open(queries_save_path, 'w', encoding='utf-8') as f:\n for query in queries_list:\n line = f\"{query['id']}\\t{query['content']}\"\n f.write(line + '\\n')\n\n\ndef index(corpus_save_dir: str, index_save_dir: str):\n cmd = f\"python3 -m pyserini.index.lucene \\\n --collection JsonCollection \\\n --input {corpus_save_dir} \\\n --index {index_save_dir} \\\n --generator DefaultLuceneDocumentGenerator \\\n --threads 1 \\\n --storePositions --storeDocvectors --storeRaw \\\n \"\n os.system(cmd)\n\n\ndef search(index_save_dir: str, queries_save_dir: str, lang: str, result_save_path: str):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n cmd = f\"python3 -m pyserini.search.lucene \\\n --index {index_save_dir} \\\n --topics {queries_save_path} \\\n --output {result_save_path} \\\n --bm25 \\\n --hits 1000 \\\n --batch-size 128 \\\n --threads 16 \\\n \"\n os.system(cmd)\n\n\ndef main():\n qa_data_dir = '../qa_data'\n bm25_dir = './bm25_baseline_same_tokenizer'\n \n result_save_dir = os.path.join('./search_results', 'bm25_same_tokenizer')\n if not os.path.exists(result_save_dir):\n os.makedirs(result_save_dir)\n \n corpus_save_dir = os.path.join(bm25_dir, 'corpus')\n if not os.path.exists(corpus_save_dir):\n os.makedirs(corpus_save_dir)\n \n corpus_save_path = os.path.join(corpus_save_dir, 'corpus.jsonl')\n generate_corpus(corpus_save_path)\n \n index_save_dir = os.path.join(bm25_dir, 'index')\n if not os.path.exists(index_save_dir):\n os.makedirs(index_save_dir)\n index(corpus_save_dir, index_save_dir)\n \n queries_save_dir = os.path.join(bm25_dir, 'queries')\n if not os.path.exists(queries_save_dir):\n os.makedirs(queries_save_dir)\n \n languages = ['ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n \n for lang in languages:\n generate_queries(qa_data_dir, lang, queries_save_dir)\n \n result_save_path = os.path.join(result_save_dir, f'{lang}.txt')\n search(index_save_dir, queries_save_dir, lang, result_save_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "research/C_MTEB/MKQA/sparse_retrieval/step0-encode_query-and-corpus.py", "content": "\"\"\"\npython step0-encode_query-and-corpus.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--qa_data_dir ../qa_data \\\n--save_dir ./encoded_query-and-corpus \\\n--max_query_length 512 \\\n--max_passage_length 512 \\\n--batch_size 1024 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\n\nimport os\nimport sys\nimport json\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom pprint import pprint\nfrom FlagEmbedding import BGEM3FlagModel\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n fp16: bool = field(\n default=True,\n metadata={'help': 'Use fp16 in inference?'}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n save_dir: str = field(\n default='./encoded_query-and-corpus',\n metadata={'help': 'Dir to save encoded query and corpus. Encoded query and corpus will be saved to `save_dir/{encoder_name}/{lang}/query_embd.tsv` and `save_dir/{encoder_name}/corpus/corpus_embd.jsonl`, individually.'}\n )\n max_query_length: int = field(\n default=512,\n metadata={'help': 'Max query length.'}\n )\n max_passage_length: int = field(\n default=512,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\ndef get_model(model_args: ModelArgs):\n model = BGEM3FlagModel(\n model_name_or_path=model_args.encoder,\n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n use_fp16=model_args.fp16\n )\n return model\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef parse_corpus(corpus: datasets.Dataset):\n corpus_list = []\n for data in tqdm(corpus, desc=\"Generating corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_list.append({\"id\": _id, \"content\": content})\n \n corpus = datasets.Dataset.from_list(corpus_list)\n return corpus\n\n\ndef get_queries(qa_data_dir: str, lang: str):\n topics_path = os.path.join(qa_data_dir, f\"{lang}.jsonl\")\n if not os.path.exists(topics_path):\n raise FileNotFoundError(f\"{topics_path} not found\")\n \n dataset = datasets.load_dataset('json', data_files=topics_path)['train']\n \n queries_list = []\n for data in dataset:\n _id = str(data['id'])\n query = data['question']\n queries_list.append({\n 'id': _id,\n 'content': query\n })\n \n queries = datasets.Dataset.from_list(queries_list)\n return queries\n\n\ndef encode_and_save_corpus(corpus_save_path: str, model: BGEM3FlagModel, corpus: datasets.Dataset, max_passage_length: int=512, batch_size: int=256):\n docids = list(corpus[\"id\"])\n vectors = model.encode(\n corpus[\"content\"], \n batch_size=batch_size, \n max_length=max_passage_length,\n return_dense=False,\n return_sparse=True,\n return_colbert_vecs=False\n )['lexical_weights']\n \n encoded_corpus_list = []\n for docid, vector in zip(docids, vectors):\n for key, value in vector.items():\n vector[key] = int(np.ceil(value * 100))\n \n encoded_corpus_list.append({\n 'id': docid,\n 'contents': '',\n 'vector': vector\n })\n \n with open(corpus_save_path, 'w', encoding='utf-8') as f:\n for line in tqdm(encoded_corpus_list, desc=\"Saving encoded corpus\"):\n f.write(json.dumps(line, ensure_ascii=False) + \"\\n\")\n\n\ndef encode_and_save_queries(queries_save_path: str, model: BGEM3FlagModel, queries: datasets.Dataset, max_query_length: int=512, batch_size: int=256):\n qids = list(queries[\"id\"])\n vectors = model.encode(\n queries[\"content\"], \n batch_size=batch_size, \n max_length=max_query_length,\n return_dense=False,\n return_sparse=True,\n return_colbert_vecs=False\n )['lexical_weights']\n \n encoded_queries_list = []\n for qid, vector in zip(qids, vectors):\n for key, value in vector.items():\n vector[key] = int(np.ceil(value * 100))\n \n topic_str = []\n for token in vector:\n topic_str += [str(token)] * vector[token]\n if len(topic_str) == 0:\n topic_str = \"0\"\n else:\n topic_str = \" \".join(topic_str)\n encoded_queries_list.append(f\"{str(qid)}\\t{topic_str}\")\n \n with open(queries_save_path, 'w', encoding='utf-8') as f:\n for line in tqdm(encoded_queries_list, desc=\"Saving encoded queries\"):\n f.write(line + '\\n')\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n # languages.reverse()\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n model = get_model(model_args=model_args)\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating embedding with model:\")\n print(model_args.encoder)\n \n print('Generating corpus embedding ...')\n \n corpus_save_dir = os.path.join(eval_args.save_dir, os.path.basename(encoder), 'corpus')\n if not os.path.exists(corpus_save_dir):\n os.makedirs(corpus_save_dir)\n corpus_save_path = os.path.join(corpus_save_dir, 'corpus_embd.jsonl')\n if os.path.exists(corpus_save_path) and os.path.getsize(corpus_save_path) > 0 and not eval_args.overwrite:\n print(f'Corpus embedding already exists. Skip...')\n else:\n corpus = datasets.load_dataset(\"BeIR/nq\", 'corpus')['corpus']\n corpus = parse_corpus(corpus=corpus)\n encode_and_save_corpus(\n corpus_save_path=corpus_save_path,\n model=model,\n corpus=corpus,\n max_passage_length=eval_args.max_passage_length,\n batch_size=eval_args.batch_size\n )\n print('Generate query embedding of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n save_dir = os.path.join(eval_args.save_dir, os.path.basename(encoder), lang)\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n queries_save_path = os.path.join(save_dir, 'query_embd.tsv')\n if os.path.exists(queries_save_path) and not eval_args.overwrite:\n print(f'Query embedding of {lang} already exists. Skip...')\n continue\n \n print(f\"Start generating query embedding of {lang} ...\")\n queries = get_queries(eval_args.qa_data_dir, lang)\n encode_and_save_queries(\n queries_save_path=queries_save_path,\n model=model,\n queries=queries,\n max_query_length=eval_args.max_query_length,\n batch_size=eval_args.batch_size\n )\n\n print(\"==================================================\")\n print(\"Finish generating embeddings with following model:\")\n pprint(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/sparse_retrieval/step1-search_results.py", "content": "\"\"\"\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--encoded_query_and_corpus_save_dir ./encoded_query-and-corpus \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--hits 1000\n\"\"\"\nimport os\nimport datasets\nfrom tqdm import tqdm\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n encoded_query_and_corpus_save_dir: str = field(\n default='./encoded_query-and-corpus',\n metadata={'help': 'Dir to save encoded queries and corpus. Encoded queries and corpus are saved in `save_dir/{encoder_name}/{lang}/query_embd.tsv` and `save_dir/{encoder_name}/corpus/corpus_embd.jsonl`, individually.'}\n )\n result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving results. Search results will be saved to `result_save_dir/{encoder_name}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n batch_size: int = field(\n default=32,\n metadata={'help': 'Batch size to use during search'}\n )\n threads: int = field(\n default=1,\n metadata={'help': 'Maximum threads to use during search'}\n )\n hits: int = field(\n default=1000,\n metadata={'help': 'Number of hits'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef generate_index(corpus_embd_dir: str, index_save_dir: str, threads: int=12): \n cmd = f\"python -m pyserini.index.lucene \\\n --language en \\\n --collection JsonVectorCollection \\\n --input {corpus_embd_dir} \\\n --index {index_save_dir} \\\n --generator DefaultLuceneDocumentGenerator \\\n --threads {threads} \\\n --impact --pretokenized --optimize \\\n \"\n os.system(cmd)\n\n\ndef search_and_save_results(index_save_dir: str, query_embd_path: str, result_save_path: str, batch_size: int = 32, threads: int = 12, hits: int = 1000):\n cmd = f\"python -m pyserini.search.lucene \\\n --index {index_save_dir} \\\n --topics {query_embd_path} \\\n --output {result_save_path} \\\n --output-format trec \\\n --batch {batch_size} \\\n --threads {threads} \\\n --hits {hits} \\\n --impact \\\n \"\n os.system(cmd)\n\n\ndef parse_corpus(corpus: datasets.Dataset):\n corpus_list = [{'id': e['docid'], 'content': f\"{e['title']}\\n{e['text']}\"} for e in tqdm(corpus, desc=\"Generating corpus\")]\n corpus = datasets.Dataset.from_list(corpus_list)\n return corpus\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating search results with model:\")\n print(model_args.encoder)\n \n corpus_embd_dir = os.path.join(eval_args.encoded_query_and_corpus_save_dir, os.path.basename(encoder), 'corpus')\n index_save_dir = os.path.join(eval_args.encoded_query_and_corpus_save_dir, os.path.basename(encoder), 'index')\n if os.path.exists(index_save_dir) and not eval_args.overwrite:\n print(f'Index already exists')\n else:\n generate_index(\n corpus_embd_dir=corpus_embd_dir,\n index_save_dir=index_save_dir,\n threads=eval_args.threads\n )\n\n print('Generate search results of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start searching results of {lang} ...\")\n \n result_save_path = os.path.join(eval_args.result_save_dir, os.path.basename(encoder), f\"{lang}.txt\")\n if not os.path.exists(os.path.dirname(result_save_path)):\n os.makedirs(os.path.dirname(result_save_path))\n \n if os.path.exists(result_save_path) and not eval_args.overwrite:\n print(f'Search results of {lang} already exists. Skip...')\n continue\n \n encoded_query_and_corpus_save_dir = os.path.join(eval_args.encoded_query_and_corpus_save_dir, os.path.basename(encoder), lang)\n if not os.path.exists(encoded_query_and_corpus_save_dir):\n raise FileNotFoundError(f\"{encoded_query_and_corpus_save_dir} not found\")\n \n query_embd_path = os.path.join(encoded_query_and_corpus_save_dir, 'query_embd.tsv')\n \n search_and_save_results(\n index_save_dir=index_save_dir,\n query_embd_path=query_embd_path,\n result_save_path=result_save_path,\n batch_size=eval_args.batch_size,\n threads=eval_args.threads,\n hits=eval_args.hits\n )\n\n print(\"==================================================\")\n print(\"Finish generating search results with following model:\")\n pprint(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/sparse_retrieval/step2-eval_sparse_mkqa.py", "content": "\"\"\"\n# Ref: https://github.com/texttron/tevatron/tree/main/examples/unicoil\n# 1. Generate Query and Corpus Sparse Vector\npython step0-encode_query-and-corpus.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--qa_data_dir ../qa_data \\\n--save_dir ./encoded_query-and-corpus \\\n--max_query_length 512 \\\n--max_passage_length 512 \\\n--batch_size 1024 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Output Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--encoded_query_and_corpus_save_dir ./encoded_query-and-corpus \\\n--result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--threads 16 \\\n--hits 1000\n\n# 3. Print and Save Evaluation Results\npython step2-eval_sparse_mkqa.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw \\\n--search_result_save_dir ./search_results \\\n--qa_data_dir ../qa_data \\\n--eval_result_save_dir ./eval_results \\\n--metrics recall@20 recall@100 \\\n--threads 32 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport sys\nimport json\nimport datasets\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport multiprocessing\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\nsys.path.append(\"..\")\n\nfrom utils.normalize_text import normalize\nfrom utils.evaluation import evaluate_recall_qa\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: en ar fi ja ko ru es sv he th da de fr it nl pl pt hu vi ms km no tr zh_cn zh_hk zh_tw', \n \"nargs\": \"+\"}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n search_result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}/{lang}.txt`'}\n )\n qa_data_dir: str = field(\n default='../qa_data',\n metadata={'help': 'Dir to qa data.'}\n )\n metrics: str = field(\n default=\"recall@20\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./eval_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{encoder}.json`'}\n )\n threads: int = field(\n default=1,\n metadata={\"help\": \"num of evaluation threads. <= 1 means single thread\"}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['en', 'ar', 'fi', 'ja', 'ko', 'ru', 'es', 'sv', 'he', 'th', 'da', 'de', 'fr', 'it', 'nl', 'pl', 'pt', 'hu', 'vi', 'ms', 'km', 'no', 'tr', 'zh_cn', 'zh_hk', 'zh_tw']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, pooling_method: str, normalize_embeddings: bool, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'model': model_name,\n 'pooling_method': pooling_method,\n 'normalize_embeddings': normalize_embeddings,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{model_name}` on `{eval_languages}` saved at `{save_path}`')\n\n\ndef get_corpus_dict():\n corpus_dict = {}\n corpus = datasets.load_dataset('BeIR/nq', 'corpus')['corpus']\n for data in tqdm(corpus, desc=\"Loading corpus\"):\n _id = str(data['_id'])\n content = f\"{data['title']}\\n{data['text']}\".lower()\n content = normalize(content)\n corpus_dict[_id] = content\n return corpus_dict\n\n\ndef get_qa_dict(qa_path: str):\n qa_dict = {}\n dataset = datasets.load_dataset('json', data_files=qa_path)['train']\n for data in dataset:\n qid = str(data['id'])\n answers = data['answers']\n qa_dict[qid] = answers\n return qa_dict\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=100):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = str(row.iloc[2])\n rank = int(row.iloc[3])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append(docid)\n return search_result_dict\n\n\ndef evaluate(corpus_dict: dict, qa_dict: dict, search_result_path: str, metrics: list):\n top_k = max([int(metric.split('@')[-1]) for metric in metrics])\n search_result_dict = get_search_result_dict(search_result_path, top_k=int(top_k))\n \n search_results = []\n ground_truths = []\n for qid, docid_list in search_result_dict.items():\n answers = qa_dict[qid]\n doc_list = [corpus_dict[docid] for docid in docid_list]\n search_results.append(doc_list)\n ground_truths.append(answers)\n \n results = {}\n metrics = sorted([metric.lower() for metric in metrics])\n for metric in metrics:\n metric, k = metric.split('@')\n k = int(k)\n assert metric in ['recall'], f\"Metric `{metric}` is not supported.\"\n if metric == 'recall':\n results[f'Recall@{k}'] = evaluate_recall_qa(search_results, ground_truths, k=k)\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n corpus_dict = get_corpus_dict()\n \n languages = check_languages(eval_args.languages)\n \n if eval_args.encoder[-1] == '/':\n eval_args.encoder = eval_args.encoder[:-1]\n \n if os.path.basename(eval_args.encoder).startswith('checkpoint-'):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n results = {}\n if eval_args.threads > 1:\n threads = min(len(languages), eval_args.threads)\n pool = multiprocessing.Pool(processes=threads)\n results_list = []\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n results_list.append(pool.apply_async(evaluate, args=(corpus_dict, qa_dict, search_result_path, eval_args.metrics)))\n pool.close()\n pool.join()\n for i, lang in enumerate(languages):\n results[lang] = results_list[i].get()\n else:\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qa_path = os.path.join(eval_args.qa_data_dir, f\"{lang}.jsonl\")\n qa_dict = get_qa_dict(qa_path)\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(corpus_dict, qa_dict, search_result_path, eval_args.metrics)\n results[lang] = result\n \n save_results(\n model_name=eval_args.encoder,\n pooling_method=eval_args.pooling_method,\n normalize_embeddings=eval_args.normalize_embeddings,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(eval_args.encoder)}.json\"),\n eval_languages=languages\n )\n print(\"==================================================\")\n print(\"Finish generating evaluation results with following model:\")\n print(eval_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MKQA/utils/__init__.py", "content": "" }, { "path": "research/C_MTEB/MKQA/utils/evaluation.py", "content": "# Ref: https://github.com/facebookresearch/contriever\nimport regex\nimport unicodedata\nfrom functools import partial\nfrom typing import List\n\n\nclass SimpleTokenizer:\n ALPHA_NUM = r'[\\p{L}\\p{N}\\p{M}]+'\n NON_WS = r'[^\\p{Z}\\p{C}]'\n\n def __init__(self):\n \"\"\"\n Args:\n annotators: None or empty set (only tokenizes).\n \"\"\"\n self._regexp = regex.compile(\n '(%s)|(%s)' % (self.ALPHA_NUM, self.NON_WS),\n flags=regex.IGNORECASE + regex.UNICODE + regex.MULTILINE\n )\n\n def tokenize(self, text, uncased=False):\n matches = [m for m in self._regexp.finditer(text)]\n if uncased:\n tokens = [m.group().lower() for m in matches]\n else:\n tokens = [m.group() for m in matches]\n return tokens\n\n\ndef _normalize(text):\n return unicodedata.normalize('NFD', text)\n\n\ndef has_answer(answers, text, tokenizer) -> bool:\n \"\"\"Check if a document contains an answer string.\"\"\"\n text = _normalize(text)\n text = tokenizer.tokenize(text, uncased=True)\n\n for answer in answers:\n answer = _normalize(answer)\n answer = tokenizer.tokenize(answer, uncased=True)\n for i in range(0, len(text) - len(answer) + 1):\n if answer == text[i: i + len(answer)]:\n return True\n return False\n\n\ndef check_answer(example, tokenizer) -> List[bool]:\n \"\"\"Search through all the top docs to see if they have any of the answers.\"\"\"\n answers = example['answers']\n ctxs = example['ctxs']\n\n hits = []\n for i, text in enumerate(ctxs):\n if text is None: # cannot find the document for some reason\n hits.append(False)\n continue\n hits.append(has_answer(answers, text, tokenizer))\n return hits\n\n\ndef evaluate_recall_qa(ctxs, answers, k=100):\n # compute Recall@k for QA task\n data = []\n assert len(ctxs) == len(answers)\n for i in range(len(ctxs)):\n _ctxs, _answers = ctxs[i], answers[i]\n data.append({\n 'answers': _answers,\n 'ctxs': _ctxs,\n })\n tokenizer = SimpleTokenizer()\n get_score_partial = partial(check_answer, tokenizer=tokenizer)\n \n scores = map(get_score_partial, data)\n \n n_docs = len(data[0]['ctxs'])\n top_k_hits = [0] * n_docs\n for question_hits in scores:\n best_hit = next((i for i, x in enumerate(question_hits) if x), None)\n if best_hit is not None:\n top_k_hits[best_hit:] = [v + 1 for v in top_k_hits[best_hit:]]\n k = min(k, len(top_k_hits))\n return top_k_hits[k - 1] / len(data)\n" }, { "path": "research/C_MTEB/MKQA/utils/normalize_text.py", "content": "\"\"\"\nadapted from chemdataextractor.text.normalize\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nTools for normalizing text.\nhttps://github.com/mcs07/ChemDataExtractor\n:copyright: Copyright 2016 by Matt Swain.\n:license: MIT\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n'Software'), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\n\n#: Control characters.\nCONTROLS = {\n '\\u0001', '\\u0002', '\\u0003', '\\u0004', '\\u0005', '\\u0006', '\\u0007', '\\u0008', '\\u000e', '\\u000f', '\\u0011',\n '\\u0012', '\\u0013', '\\u0014', '\\u0015', '\\u0016', '\\u0017', '\\u0018', '\\u0019', '\\u001a', '\\u001b',\n}\n# There are further control characters, but they are instead replaced with a space by unicode normalization\n# '\\u0009', '\\u000a', '\\u000b', '\\u000c', '\\u000d', '\\u001c', '\\u001d', '\\u001e', '\\u001f'\n\n\n#: Hyphen and dash characters.\nHYPHENS = {\n '-', # \\u002d Hyphen-minus\n '‐', # \\u2010 Hyphen\n '‑', # \\u2011 Non-breaking hyphen\n '⁃', # \\u2043 Hyphen bullet\n '‒', # \\u2012 figure dash\n '–', # \\u2013 en dash\n '—', # \\u2014 em dash\n '―', # \\u2015 horizontal bar\n}\n\n#: Minus characters.\nMINUSES = {\n '-', # \\u002d Hyphen-minus\n '−', # \\u2212 Minus\n '－', # \\uff0d Full-width Hyphen-minus\n '⁻', # \\u207b Superscript minus\n}\n\n#: Plus characters.\nPLUSES = {\n '+', # \\u002b Plus\n '＋', # \\uff0b Full-width Plus\n '⁺', # \\u207a Superscript plus\n}\n\n#: Slash characters.\nSLASHES = {\n '/', # \\u002f Solidus\n '⁄', # \\u2044 Fraction slash\n '∕', # \\u2215 Division slash\n}\n\n#: Tilde characters.\nTILDES = {\n '~', # \\u007e Tilde\n '˜', # \\u02dc Small tilde\n '⁓', # \\u2053 Swung dash\n '∼', # \\u223c Tilde operator #in mbert vocab\n '∽', # \\u223d Reversed tilde\n '∿', # \\u223f Sine wave\n '〜', # \\u301c Wave dash #in mbert vocab\n '～', # \\uff5e Full-width tilde #in mbert vocab\n}\n\n#: Apostrophe characters.\nAPOSTROPHES = {\n \"'\", # \\u0027\n '’', # \\u2019\n '՚', # \\u055a\n 'Ꞌ', # \\ua78b\n 'ꞌ', # \\ua78c\n '＇', # \\uff07\n}\n\n#: Single quote characters.\nSINGLE_QUOTES = {\n \"'\", # \\u0027\n '‘', # \\u2018\n '’', # \\u2019\n '‚', # \\u201a\n '‛', # \\u201b\n\n}\n\n#: Double quote characters.\nDOUBLE_QUOTES = {\n '\"', # \\u0022\n '“', # \\u201c\n '”', # \\u201d\n '„', # \\u201e\n '‟', # \\u201f\n}\n\n#: Accent characters.\nACCENTS = {\n '`', # \\u0060\n '´', # \\u00b4\n}\n\n#: Prime characters.\nPRIMES = {\n '′', # \\u2032\n '″', # \\u2033\n '‴', # \\u2034\n '‵', # \\u2035\n '‶', # \\u2036\n '‷', # \\u2037\n '⁗', # \\u2057\n}\n\n#: Quote characters, including apostrophes, single quotes, double quotes, accents and primes.\nQUOTES = APOSTROPHES | SINGLE_QUOTES | DOUBLE_QUOTES | ACCENTS | PRIMES\n\ndef normalize(text):\n for control in CONTROLS:\n text = text.replace(control, '')\n text = text.replace('\\u000b', ' ').replace('\\u000c', ' ').replace(u'\\u0085', ' ')\n\n for hyphen in HYPHENS | MINUSES:\n text = text.replace(hyphen, '-')\n text = text.replace('\\u00ad', '')\n\n for double_quote in DOUBLE_QUOTES:\n text = text.replace(double_quote, '\"') # \\u0022\n for single_quote in (SINGLE_QUOTES | APOSTROPHES | ACCENTS):\n text = text.replace(single_quote, \"'\") # \\u0027\n text = text.replace('′', \"'\") # \\u2032 prime\n text = text.replace('‵', \"'\") # \\u2035 reversed prime\n text = text.replace('″', \"''\") # \\u2033 double prime\n text = text.replace('‶', \"''\") # \\u2036 reversed double prime\n text = text.replace('‴', \"'''\") # \\u2034 triple prime\n text = text.replace('‷', \"'''\") # \\u2037 reversed triple prime\n text = text.replace('⁗', \"''''\") # \\u2057 quadruple prime\n\n text = text.replace('…', '...').replace(' . . . ', ' ... ') # \\u2026\n\n for slash in SLASHES:\n text = text.replace(slash, '/')\n\n #for tilde in TILDES:\n # text = text.replace(tilde, '~')\n\n return text\n" }, { "path": "research/C_MTEB/MLDR/README.md", "content": "# MultiLongDocRetrieval\n\nMultiLongDocRetrieval (denoted as MLDR) is a multilingual long-document retrieval dataset. For more details, please refer to [Shitao/MLDR](https://huggingface.co/datasets/Shitao/MLDR).\n\n## Dense Retrieval\n\nThis task has been merged into [MTEB](https://github.com/embeddings-benchmark/mteb), you can easily use mteb tool to do evaluation. \n\nWe also provide a [script](./mteb_dense_eval/eval_MLDR.py), you can use it following this command:\n\n```bash\ncd mteb_dense_eval\n\n# Print and Save Evaluation Results with MTEB\npython eval_MLDR.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--results_save_path ./results \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--batch_size 256 \\\n--corpus_batch_size 1 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False \\\n--overwrite False\n```\n\nThere are some important parameters:\n\n- `encoder`: Name or path of the model to evaluate.\n\n- `languages`: The languages you want to evaluate on. Avaliable languages: `ar de en es fr hi it ja ko pt ru th zh`.\n\n- `max_query_length` & `max_passage_length`: Maximum query length and maximum passage length when encoding.\n\n- `batch_size` & `corpus_batch_size`: Batch size for query and corpus when encoding. If `max_query_length == max_passage_length`, you can ignore the `corpus_batch_size` parameter and only set `batch_size` for convenience. For faster evaluation, you should set the `batch_size` and `corpus_batch_size` as large as possible.\n\n- `pooling_method` & `normalize_embeddings`: You should follow the corresponding setting of the model you are evaluating. For example, `BAAI/bge-m3` is `cls` and `True`, `intfloat/multilingual-e5-large` is `mean` and `True`, and `intfloat/e5-mistral-7b-instruct` is `last` and `True`.\n\n- `add_instruction`: Whether to add instruction for query or passage when evaluating. If set `add_instruction=True`, you should also set the following parameters appropriately:\n\n - `query_instruction_for_retrieval`: the query instruction for retrieval\n - `passage_instruction_for_retrieval`: the passage instruction for retrieval\n\n If you only add query instruction, just ignore the `passage_instruction_for_retrieval` parameter.\n\n- `overwrite`: Whether to overwrite evaluation results.\n\n## Hybrid Retrieval (Dense & Sparse)\n\nIf you want to perform **hybrid retrieval with both dense and sparse methods**, you can follow the following steps:\n\n1. Install Java, Pyserini and Faiss (CPU version or GPU version):\n\n```bash\n# install java (Linux)\napt update\napt install openjdk-11-jdk\n\n# install pyserini\npip install pyserini\n\n# install faiss\n## CPU version\nconda install -c conda-forge faiss-cpu\n\n## GPU version\nconda install -c conda-forge faiss-gpu\n```\n\n2. Download qrels from [Shitao/MLDR](https://huggingface.co/datasets/Shitao/MLDR/tree/main/qrels):\n\n```bash\nmkdir -p qrels\ncd qrels\n\nsplits=(dev test)\nlangs=(ar de en es fr hi it ja ko pt ru th zh)\nfor split in ${splits[*]}; do for lang in ${langs[*]}; do wget \"https://huggingface.co/datasets/Shitao/MLDR/resolve/main/qrels/qrels.mldr-v1.0-${lang}-${split}.tsv\"; done; done;\n```\n\n3. Dense retrieval:\n\n```bash\ncd dense_retrieval\n\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 8192 \\\n--batch_size 4 \\\n--fp16 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n> Note: The evaluation results of this method may have slight differences compared to results of the method mentioned earlier (*with MTEB*), which is considered normal.\n\n4. Sparse Retrieval\n\n```bash\ncd sparse_retrieval\n\n# 1. Generate Query and Corpus Sparse Vector\npython step0-encode_query-and-corpus.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--save_dir ./encoded_query-and-corpus \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--batch_size 1024 \\\n--corpus_batch_size 4 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Output Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--encoded_query_and_corpus_save_dir ./encoded_query-and-corpus \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000\n\n# 3. Print and Save Evaluation Results\npython step2-eval_sparse_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de es fr hi it ja ko pt ru th en zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n5. Hybrid Retrieval\n\n```bash\ncd hybrid_retrieval\n\n# 1. Search Dense and Sparse Results\nDense Retrieval\nSparse Retrieval\n\n# 2. Hybrid Dense and Sparse Search Results\npython step0-hybrid_search_results.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--dense_search_result_save_dir ../dense_retrieval/search_results \\\n--sparse_search_result_save_dir ../sparse_retrieval/search_results \\\n--hybrid_result_save_dir ./search_results \\\n--top_k 1000 \\\n--dense_weight 0.2 --sparse_weight 0.8\n\n# 3. Print and Save Evaluation Results\npython step1-eval_hybrid_mldr.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n## MultiVector and All Rerank\n\nIf you want to perform **multi-vector reranking** or **all reranking** based on the search results of dense retrieval, you can follow the following steps:\n\n1. Install Java, Pyserini and Faiss (CPU version or GPU version):\n\n```bash\n# install java (Linux)\napt update\napt install openjdk-11-jdk\n\n# install pyserini\npip install pyserini\n\n# install faiss\n## CPU version\nconda install -c conda-forge faiss-cpu\n\n## GPU version\nconda install -c conda-forge faiss-gpu\n```\n\n2. Download qrels from [Shitao/MLDR](https://huggingface.co/datasets/Shitao/MLDR/tree/main/qrels):\n\n```bash\nmkdir -p qrels\ncd qrels\n\nsplits=(dev test)\nlangs=(ar de en es fr hi it ja ko pt ru th zh)\nfor split in ${splits[*]}; do for lang in ${langs[*]}; do wget \"https://huggingface.co/datasets/Shitao/MLDR/resolve/main/qrels/qrels.mldr-v1.0-${lang}-${split}.tsv\"; done; done;\n```\n\n3. Dense retrieval:\n\n```bash\ncd dense_retrieval\n\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 8192 \\\n--batch_size 4 \\\n--fp16 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n```\n\n> **Note**: The evaluation results of this method may have slight differences compared to results of the method mentioned earlier (*with MTEB*), which is considered normal.\n\n4. Rerank search results with multi-vector scores or all scores: \n\n```bash\ncd multi_vector_rerank\n\n# 1. Rerank Search Results\npython step0-rerank_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ../dense_retrieval/search_results \\\n--rerank_result_save_dir ./rerank_results \\\n--top_k 200 \\\n--batch_size 4 \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--dense_weight 0.15 --sparse_weight 0.5 --colbert_weight 0.35 \\\n--num_shards 1 --shard_id 0 --cuda_id 0\n\n# 2. Print and Save Evaluation Results\npython step1-eval_rerank_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./rerank_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10\n```\n\n>**Note**: \n>\n>- You should set `dense_weight`, `sparse_weight` and `colbert_weight` based on the downstream task scenario. If the dense method performs well while the sparse method does not, you can lower `sparse_weight` and increase `dense_weight` accordingly.\n>\n>- Based on our experience, dividing the sentence pairs to be reranked into several shards and computing scores for each shard on a single GPU tends to be more efficient than using multiple GPUs to compute scores for all sentence pairs directly.Therefore, if your machine have multiple GPUs, you can set `num_shards` to the number of GPUs and launch multiple terminals to execute the command (`shard_id` should be equal to `cuda_id`). Therefore, if you have multiple GPUs on your machine, you can launch multiple terminals and run multiple commands simultaneously. Make sure to set the `shard_id` and `cuda_id` appropriately, and ensure that you have computed scores for all shards before proceeding to the second step.\n\n5. (*Optional*) In the 4th step, you can get all three kinds of scores, saved to `rerank_result_save_dir/dense/{encoder}-{reranker}`, `rerank_result_save_dir/sparse/{encoder}-{reranker}` and `rerank_result_save_dir/colbert/{encoder}-{reranker}`. If you want to try other weights, you don't need to rerun the 4th step. Instead, you can use [this script](./multi_vector_rerank/hybrid_all_results.py) to hybrid the three kinds of scores directly.\n\n```bash\ncd multi_vector_rerank\n\n# 1. Hybrid All Search Results\npython hybrid_all_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--dense_search_result_save_dir ./rerank_results/dense \\\n--sparse_search_result_save_dir ./rerank_results/sparse \\\n--colbert_search_result_save_dir ./rerank_results/colbert \\\n--hybrid_result_save_dir ./hybrid_search_results \\\n--top_k 200 \\\n--dense_weight 0.2 --sparse_weight 0.4 --colbert_weight 0.4\n\n# 2. Print and Save Evaluation Results\npython step1-eval_rerank_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./hybrid_search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_hybrid_results \\\n--metrics ndcg@10\n```\n\n## BM25 Baseline\n\nWe provide two methods of evaluating BM25 baseline:\n\n1. Use the same tokenizer with [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) (i.e., tokenizer of [XLM-Roberta](https://huggingface.co/FacebookAI/xlm-roberta-large)):\n\n```bash\ncd sparse_retrieval\n\n# 1. Output Search Results with BM25 (same)\npython bm25_baseline_same_tokenizer.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mldr.py \\\n--encoder bm25_same_tokenizer \\\n--languages ar de es fr hi it ja ko pt ru th en zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10\n```\n\n2. Use the language analyzer provided by [Anserini](https://github.com/castorini/anserini/blob/master/src/main/java/io/anserini/analysis/AnalyzerMap.java) ([Lucene Tokenizer](https://github.com/apache/lucene/tree/main/lucene/analysis/common/src/java/org/apache/lucene/analysis)):\n\n```bash\ncd sparse_retrieval\n\n# 1. Output Search Results with BM25\npython bm25_baseline.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mldr.py \\\n--encoder bm25 \\\n--languages ar de es fr hi it ja ko pt ru th en zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10\n```\n\n\n\n" }, { "path": "research/C_MTEB/MLDR/dense_retrieval/step0-generate_embedding.py", "content": "\"\"\"\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 8192 \\\n--batch_size 4 \\\n--fp16 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport faiss\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom FlagEmbedding import FlagModel\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n fp16: bool = field(\n default=True,\n metadata={'help': 'Use fp16 in inference?'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n index_save_dir: str = field(\n default='./corpus-index',\n metadata={'help': 'Dir to save index. Corpus index will be saved to `index_save_dir/{encoder_name}/{lang}/index`. Corpus ids will be saved to `index_save_dir/{encoder_name}/{lang}/docid` .'}\n )\n max_passage_length: int = field(\n default=512,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef get_model(model_args: ModelArgs):\n model = FlagModel(\n model_args.encoder, \n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n use_fp16=model_args.fp16\n )\n return model\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef load_corpus(lang: str): \n corpus = datasets.load_dataset('Shitao/MLDR', f'corpus-{lang}', split='corpus')\n \n corpus_list = [{'id': e['docid'], 'content': e['text']} for e in tqdm(corpus, desc=\"Generating corpus\")]\n corpus = datasets.Dataset.from_list(corpus_list)\n return corpus\n\n\ndef generate_index(model: FlagModel, corpus: datasets.Dataset, max_passage_length: int=512, batch_size: int=256):\n corpus_embeddings = model.encode_corpus(corpus[\"content\"], batch_size=batch_size, max_length=max_passage_length)\n dim = corpus_embeddings.shape[-1]\n \n faiss_index = faiss.index_factory(dim, \"Flat\", faiss.METRIC_INNER_PRODUCT)\n corpus_embeddings = corpus_embeddings.astype(np.float32)\n faiss_index.train(corpus_embeddings)\n faiss_index.add(corpus_embeddings)\n return faiss_index, list(corpus[\"id\"])\n\n\ndef save_result(index: faiss.Index, docid: list, index_save_dir: str):\n docid_save_path = os.path.join(index_save_dir, 'docid')\n index_save_path = os.path.join(index_save_dir, 'index')\n with open(docid_save_path, 'w', encoding='utf-8') as f:\n for _id in docid:\n f.write(str(_id) + '\\n')\n faiss.write_index(index, index_save_path)\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n model = get_model(model_args=model_args)\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating embedding with model:\")\n print(model_args.encoder)\n\n print('Generate embedding of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n index_save_dir = os.path.join(eval_args.index_save_dir, os.path.basename(encoder), lang)\n if not os.path.exists(index_save_dir):\n os.makedirs(index_save_dir)\n if os.path.exists(os.path.join(index_save_dir, 'index')) and not eval_args.overwrite:\n print(f'Embedding of {lang} already exists. Skip...')\n continue\n \n print(f\"Start generating embedding of {lang} ...\")\n corpus = load_corpus(lang)\n \n index, docid = generate_index(\n model=model, \n corpus=corpus,\n max_passage_length=eval_args.max_passage_length,\n batch_size=eval_args.batch_size\n )\n save_result(index, docid, index_save_dir)\n\n print(\"==================================================\")\n print(\"Finish generating embeddings with model:\")\n print(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/dense_retrieval/step1-search_results.py", "content": "\"\"\"\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\"\"\"\nimport os\nimport torch\nimport datasets\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser, is_torch_npu_available\nfrom pyserini.search.faiss import FaissSearcher, AutoQueryEncoder\nfrom pyserini.output_writer import get_output_writer, OutputFormat\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n add_instruction: bool = field(\n default=False,\n metadata={'help': 'Add instruction?'}\n )\n query_instruction_for_retrieval: str = field(\n default=None,\n metadata={'help': 'query instruction for retrieval'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n index_save_dir: str = field(\n default='./corpus-index',\n metadata={'help': 'Dir to index and docid. Corpus index path is `index_save_dir/{encoder_name}/{lang}/index`. Corpus ids path is `index_save_dir/{encoder_name}/{lang}/docid` .'}\n )\n result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving search results. Search results will be saved to `result_save_dir/{encoder_name}/{lang}.txt`'}\n )\n threads: int = field(\n default=1,\n metadata={'help': 'Maximum threads to use during search'}\n )\n hits: int = field(\n default=1000,\n metadata={'help': 'Number of hits'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef get_query_encoder(model_args: ModelArgs):\n if torch.cuda.is_available():\n device = torch.device(\"cuda\")\n elif is_torch_npu_available():\n device = torch.device(\"npu\")\n else:\n device = torch.device(\"cpu\")\n model = AutoQueryEncoder(\n encoder_dir=model_args.encoder,\n device=device,\n pooling=model_args.pooling_method,\n l2_norm=model_args.normalize_embeddings\n )\n return model\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_queries_and_qids(lang: str, split: str='test', add_instruction: bool=False, query_instruction_for_retrieval: str=None):\n dataset = datasets.load_dataset('Shitao/MLDR', lang, split=split)\n \n queries = []\n qids = []\n for data in dataset:\n qids.append(str(data['query_id']))\n queries.append(str(data['query']))\n if add_instruction and query_instruction_for_retrieval is not None:\n queries = [f\"{query_instruction_for_retrieval}{query}\" for query in queries]\n return queries, qids\n\n\ndef save_result(search_results, result_save_path: str, qids: list, max_hits: int):\n output_writer = get_output_writer(result_save_path, OutputFormat(OutputFormat.TREC.value), 'w',\n max_hits=max_hits, tag='Faiss', topics=qids,\n use_max_passage=False,\n max_passage_delimiter='#',\n max_passage_hits=1000)\n with output_writer:\n for topic, hits in search_results:\n output_writer.write(topic, hits)\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n query_encoder = get_query_encoder(model_args=model_args)\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating search results with model:\")\n print(model_args.encoder)\n\n print('Generate search results of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start searching results of {lang} ...\")\n \n result_save_path = os.path.join(eval_args.result_save_dir, os.path.basename(encoder), f\"{lang}.txt\")\n if not os.path.exists(os.path.dirname(result_save_path)):\n os.makedirs(os.path.dirname(result_save_path))\n \n if os.path.exists(result_save_path) and not eval_args.overwrite:\n print(f'Search results of {lang} already exists. Skip...')\n continue\n \n index_save_dir = os.path.join(eval_args.index_save_dir, os.path.basename(encoder), lang)\n if not os.path.exists(index_save_dir):\n raise FileNotFoundError(f\"{index_save_dir} not found\")\n searcher = FaissSearcher(\n index_dir=index_save_dir,\n query_encoder=query_encoder\n )\n \n queries, qids = get_queries_and_qids(\n lang=lang, \n split='test', \n add_instruction=model_args.add_instruction, \n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval\n )\n \n search_results = searcher.batch_search(\n queries=queries,\n q_ids=qids,\n k=eval_args.hits,\n threads=eval_args.threads\n )\n search_results = [(_id, search_results[_id]) for _id in qids]\n \n save_result(\n search_results=search_results,\n result_save_path=result_save_path, \n qids=qids, \n max_hits=eval_args.hits\n )\n\n print(\"==================================================\")\n print(\"Finish generating search results with model:\")\n pprint(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/dense_retrieval/step2-eval_dense_mldr.py", "content": "\"\"\"\n# 1. Generate Corpus Embedding\npython step0-generate_embedding.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--max_passage_length 8192 \\\n--batch_size 4 \\\n--fp16 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 2. Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--index_save_dir ./corpus-index \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False\n\n# 3. Print and Save Evaluation Results\npython step2-eval_dense_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport json\nimport platform\nimport subprocess\nimport numpy as np\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom pyserini.util import download_evaluation_script\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh',\n \"nargs\": '+'}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n search_result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}/{lang}.txt`'}\n )\n qrels_dir: str = field(\n default='../qrels',\n metadata={'help': 'Dir to qrels.'}\n )\n metrics: str = field(\n default=\"ndcg@10\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: ndcg@k, recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./eval_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{encoder}.json`'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, pooling_method: str, normalize_embeddings: bool, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'model': model_name,\n 'pooling_method': pooling_method,\n 'normalize_embeddings': normalize_embeddings,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{model_name}` on `{eval_languages}` saved at `{save_path}`')\n\n\ndef map_metric(metric: str):\n metric, k = metric.split('@')\n if metric.lower() == 'ndcg':\n return k, f'ndcg_cut.{k}'\n elif metric.lower() == 'recall':\n return k, f'recall.{k}'\n else:\n raise ValueError(f\"Unkown metric: {metric}\")\n\n\ndef evaluate(script_path, qrels_path, search_result_path, metrics: list):\n cmd_prefix = ['java', '-jar', script_path]\n \n results = {}\n for metric in metrics:\n k, mapped_metric = map_metric(metric)\n args = ['-c', '-M', str(k), '-m', mapped_metric, qrels_path, search_result_path]\n cmd = cmd_prefix + args\n \n # print(f'Running command: {cmd}')\n shell = platform.system() == \"Windows\"\n process = subprocess.Popen(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=shell)\n stdout, stderr = process.communicate()\n if stderr:\n print(stderr.decode(\"utf-8\"))\n result_str = stdout.decode(\"utf-8\")\n try:\n results[metric] = float(result_str.split(' ')[-1].split('\\t')[-1])\n except:\n results[metric] = result_str\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n script_path = download_evaluation_script('trec_eval')\n \n if eval_args.encoder[-1] == '/':\n eval_args.encoder = eval_args.encoder[:-1]\n \n encoder = eval_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n results = {}\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qrels_path = os.path.join(eval_args.qrels_dir, f\"qrels.mldr-v1.0-{lang}-test.tsv\")\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(script_path, qrels_path, search_result_path, eval_args.metrics)\n results[lang] = result\n \n save_results(\n model_name=encoder,\n pooling_method=eval_args.pooling_method,\n normalize_embeddings=eval_args.normalize_embeddings,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(encoder)}.json\"),\n eval_languages=languages\n )\n print(\"==================================================\")\n print(\"Finish generating evaluation results with model:\")\n print(eval_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/hybrid_retrieval/step0-hybrid_search_results.py", "content": "\"\"\"\npython step0-hybrid_search_results.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--dense_search_result_save_dir ../dense_retrieval/search_results \\\n--sparse_search_result_save_dir ../sparse_retrieval/search_results \\\n--hybrid_result_save_dir ./search_results \\\n--top_k 1000 \\\n--dense_weight 0.2 --sparse_weight 0.8\n\"\"\"\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass EvalArgs:\n model_name_or_path: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of model'}\n )\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n top_k: int = field(\n default=1000,\n metadata={'help': 'Use reranker to rerank top-k retrieval results'}\n )\n sparse_weight: float = field(\n default=0.8,\n metadata={'help': 'Hybrid weight of sparse score'}\n )\n dense_weight: float = field(\n default=0.2,\n metadata={'help': 'Hybrid weight of dense score'}\n )\n dense_search_result_save_dir: str = field(\n default='../dense_retrieval/search_results',\n metadata={'help': 'Dir to saving dense search results. Search results path is `dense_search_result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n sparse_search_result_save_dir: str = field(\n default='../sparse_retrieval/search_results',\n metadata={'help': 'Dir to saving sparse search results. Search results path is `sparse_search_result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n hybrid_result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving hybrid search results. Reranked results will be saved to `hybrid_result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=1000):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = row.iloc[2]\n rank = int(row.iloc[3])\n score = float(row.iloc[4])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append((docid, score))\n return search_result_dict\n\n\ndef save_hybrid_results(sparse_search_result_dict: dict, dense_search_result_dict: dict, hybrid_result_save_path: str, top_k: int=1000, dense_weight: float=0.2, sparse_weight: float=0.8):\n if not os.path.exists(os.path.dirname(hybrid_result_save_path)):\n os.makedirs(os.path.dirname(hybrid_result_save_path))\n \n qid_list = list(set(sparse_search_result_dict.keys()) | set(dense_search_result_dict.keys()))\n hybrid_results_list = []\n for qid in tqdm(qid_list, desc=\"Hybriding dense and sparse scores\"):\n results = {}\n if qid in sparse_search_result_dict:\n for docid, score in sparse_search_result_dict[qid]:\n score = score / 10000.\n results[docid] = score * sparse_weight\n if qid in dense_search_result_dict:\n for docid, score in dense_search_result_dict[qid]:\n if docid in results:\n results[docid] = results[docid] + score * dense_weight\n else:\n results[docid] = score * dense_weight\n hybrid_results = [(docid, score) for docid, score in results.items()]\n hybrid_results.sort(key=lambda x: x[1], reverse=True)\n \n hybrid_results_list.append(hybrid_results[:top_k])\n \n with open(hybrid_result_save_path, 'w', encoding='utf-8') as f:\n for qid, hybrid_results in tqdm(zip(qid_list, hybrid_results_list), desc=\"Saving hybrid search results\"):\n for rank, docid_score in enumerate(hybrid_results):\n docid, score = docid_score\n line = f\"{qid} Q0 {docid} {rank+1} {score:.6f} Faiss-&-Anserini\"\n f.write(line + '\\n')\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if os.path.basename(eval_args.model_name_or_path).startswith('checkpoint-'):\n eval_args.model_name_or_path = os.path.dirname(eval_args.model_name_or_path) + '_' + os.path.basename(eval_args.model_name_or_path)\n \n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start hybrid search results of {lang} ...\")\n \n hybrid_result_save_path = os.path.join(eval_args.hybrid_result_save_dir, f\"{os.path.basename(eval_args.model_name_or_path)}\", f\"{lang}.txt\")\n \n sparse_search_result_save_dir = os.path.join(eval_args.sparse_search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n sparse_search_result_path = os.path.join(sparse_search_result_save_dir, f\"{lang}.txt\")\n \n sparse_search_result_dict = get_search_result_dict(sparse_search_result_path, top_k=eval_args.top_k)\n \n dense_search_result_save_dir = os.path.join(eval_args.dense_search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n\n dense_search_result_path = os.path.join(dense_search_result_save_dir, f\"{lang}.txt\")\n \n dense_search_result_dict = get_search_result_dict(dense_search_result_path, top_k=eval_args.top_k)\n \n save_hybrid_results(\n sparse_search_result_dict=sparse_search_result_dict, \n dense_search_result_dict=dense_search_result_dict, \n hybrid_result_save_path=hybrid_result_save_path,\n top_k=eval_args.top_k,\n sparse_weight=eval_args.sparse_weight,\n dense_weight=eval_args.dense_weight\n )\n \n print(\"==================================================\")\n print(\"Finish generating reranked results with following model:\")\n print(eval_args.model_name_or_path)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/hybrid_retrieval/step1-eval_hybrid_mldr.py", "content": "\"\"\"\n# 1. Search Dense and Sparse Results\n../dense_retrieval\n../sparse_retrieval\n\n# 2. Hybrid Dense and Sparse Search Results\npython step0-hybrid_search_results.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--dense_search_result_save_dir ../dense_retrieval/search_results \\\n--sparse_search_result_save_dir ../sparse_retrieval/search_results \\\n--hybrid_result_save_dir ./search_results \\\n--top_k 1000 \\\n--dense_weight 0.2 --sparse_weight 0.8\n\n# 3. Print and Save Evaluation Results\npython step1-eval_hybrid_mldr.py \\\n--model_name_or_path BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport json\nimport platform\nimport subprocess\nimport numpy as np\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom pyserini.util import download_evaluation_script\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n model_name_or_path: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of model'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n search_result_save_dir: str = field(\n default='./output_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{model_name_or_path}/{lang}.txt`'}\n )\n qrels_dir: str = field(\n default='../qrels',\n metadata={'help': 'Dir to qrels.'}\n )\n metrics: str = field(\n default=\"ndcg@10\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: ndcg@k, recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./eval_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{model_name_or_path}.json`'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, pooling_method: str, normalize_embeddings: bool, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'model': model_name,\n 'pooling_method': pooling_method,\n 'normalize_embeddings': normalize_embeddings,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{model_name}` on `{eval_languages}` saved at `{save_path}`')\n\n\ndef map_metric(metric: str):\n metric, k = metric.split('@')\n if metric.lower() == 'ndcg':\n return k, f'ndcg_cut.{k}'\n elif metric.lower() == 'recall':\n return k, f'recall.{k}'\n else:\n raise ValueError(f\"Unkown metric: {metric}\")\n\n\ndef evaluate(script_path, qrels_path, search_result_path, metrics: list):\n cmd_prefix = ['java', '-jar', script_path]\n \n results = {}\n for metric in metrics:\n k, mapped_metric = map_metric(metric)\n args = ['-c', '-M', str(k), '-m', mapped_metric, qrels_path, search_result_path]\n cmd = cmd_prefix + args\n \n # print(f'Running command: {cmd}')\n shell = platform.system() == \"Windows\"\n process = subprocess.Popen(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=shell)\n stdout, stderr = process.communicate()\n if stderr:\n print(stderr.decode(\"utf-8\"))\n result_str = stdout.decode(\"utf-8\")\n try:\n results[metric] = float(result_str.split(' ')[-1].split('\\t')[-1])\n except:\n results[metric] = result_str\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n script_path = download_evaluation_script('trec_eval')\n \n if eval_args.model_name_or_path[-1] == '/':\n eval_args.model_name_or_path = eval_args.model_name_or_path[:-1]\n if os.path.basename(eval_args.model_name_or_path).startswith('checkpoint-'):\n eval_args.model_name_or_path = os.path.dirname(eval_args.model_name_or_path) + '_' + os.path.basename(eval_args.model_name_or_path)\n \n results = {}\n for lang in languages:\n qrels_path = os.path.join(eval_args.qrels_dir, f\"qrels.mldr-v1.0-{lang}-test.tsv\")\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.model_name_or_path))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(script_path, qrels_path, search_result_path, eval_args.metrics)\n results[lang] = result\n \n save_results(\n model_name=eval_args.model_name_or_path,\n pooling_method=eval_args.pooling_method,\n normalize_embeddings=eval_args.normalize_embeddings,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(eval_args.model_name_or_path)}.json\"),\n eval_languages=languages\n )\n print(\"==================================================\")\n print(\"Finish generating evaluation results with following model:\")\n print(eval_args.model_name_or_path)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/mteb_dense_eval/eval_MLDR.py", "content": "\"\"\"\npython3 eval_MLDR.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--results_save_path ./results \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--batch_size 256 \\\n--corpus_batch_size 1 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--add_instruction False \\\n--overwrite False\n\"\"\"\nimport os\nfrom mteb import MTEB\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom flag_dres_model import FlagDRESModel\n# from mteb.tasks import MultiLongDocRetrieval\nfrom C_MTEB.tasks.MultiLongDocRetrieval import MultiLongDocRetrieval\n\n\n@dataclass\nclass EvalArgs:\n results_save_path: str = field(\n default='./results',\n metadata={'help': 'Path to save results.'}\n )\n languages: str = field(\n default=None,\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n overwrite: bool = field(\n default=False,\n metadata={\"help\": \"whether to overwrite evaluation results\"}\n )\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'encoder name or path.'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean', 'last'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n add_instruction: bool = field(\n default=False,\n metadata={'help': 'Add instruction?'}\n )\n query_instruction_for_retrieval: str = field(\n default=None,\n metadata={'help': 'query instruction for retrieval'}\n )\n passage_instruction_for_retrieval: str = field(\n default=None,\n metadata={'help': 'passage instruction for retrieval'}\n )\n max_query_length: int = field(\n default=512,\n metadata={'help': 'Max query length.'}\n )\n max_passage_length: int = field(\n default=8192,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n corpus_batch_size: int = field(\n default=2,\n metadata={'help': 'Inference batch size for corpus. If 0, then use `batch_size`.'}\n )\n\n\ndef check_languages(languages):\n if languages is None:\n return None\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n encoder = model_args.encoder\n \n if encoder[-1] == '/':\n encoder = encoder[:-1]\n \n model = FlagDRESModel(\n model_name_or_path=encoder,\n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n query_instruction_for_retrieval=model_args.query_instruction_for_retrieval if model_args.add_instruction else None,\n passage_instruction_for_retrieval=model_args.passage_instruction_for_retrieval if model_args.add_instruction else None,\n max_query_length=model_args.max_query_length,\n max_passage_length=model_args.max_passage_length,\n batch_size=model_args.batch_size,\n corpus_batch_size=model_args.corpus_batch_size\n )\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n output_folder = os.path.join(eval_args.results_save_path, f'{os.path.basename(encoder)}_max-length-{model_args.max_passage_length}')\n \n print(\"==================================================\")\n print(\"Start evaluating model:\")\n print(model_args.encoder)\n \n evaluation = MTEB(tasks=[\n MultiLongDocRetrieval(langs=languages)\n ])\n results_dict = evaluation.run(model, eval_splits=[\"test\"], output_folder=output_folder, overwrite_results=eval_args.overwrite, corpus_chunk_size=200000)\n \n print(output_folder + \":\")\n pprint(results_dict)\n \n print(\"==================================================\")\n print(\"Finish MultiLongDocRetrieval evaluation for model:\")\n print(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/mteb_dense_eval/flag_dres_model.py", "content": "import torch\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom mteb import DRESModel\nfrom functools import partial\nfrom torch.utils.data import DataLoader\nfrom typing import cast, List, Dict, Union\nfrom transformers import AutoModel, AutoTokenizer, is_torch_npu_available\nfrom transformers import PreTrainedTokenizerFast, BatchEncoding, DataCollatorWithPadding\n\n\ndef _transform_func(examples: Dict[str, List], \n tokenizer: PreTrainedTokenizerFast,\n max_length: int) -> BatchEncoding:\n return tokenizer(examples['text'],\n max_length=max_length,\n padding=True,\n return_token_type_ids=False,\n truncation=True,\n return_tensors='pt')\n\n\ndef _transform_func_v2(examples: Dict[str, List],\n tokenizer: PreTrainedTokenizerFast,\n max_length: int=8192,\n ) -> BatchEncoding:\n \n inputs = tokenizer(examples['text'],\n max_length=max_length - 1,\n padding=False,\n return_attention_mask=False,\n truncation=True)\n inputs['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in inputs['input_ids']]\n inputs = tokenizer.pad(inputs, padding=True, return_attention_mask=True, return_tensors='pt')\n return inputs\n\n\nclass FlagDRESModel(DRESModel):\n def __init__(\n self,\n model_name_or_path: str = None,\n pooling_method: str = 'cls',\n normalize_embeddings: bool = True,\n use_fp16: bool = True,\n query_instruction_for_retrieval: str = None,\n passage_instruction_for_retrieval: str = None,\n max_query_length: int = 512,\n max_passage_length: int = 8192,\n batch_size: int = 256,\n corpus_batch_size: int = 0,\n **kwargs\n ) -> None:\n self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n if 'jina' in model_name_or_path:\n self.model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)\n else:\n self.model = AutoModel.from_pretrained(model_name_or_path)\n self.query_instruction_for_retrieval = query_instruction_for_retrieval\n self.passage_instruction_for_retrieval = passage_instruction_for_retrieval\n self.normalize_embeddings = normalize_embeddings\n self.pooling_method = pooling_method\n self.batch_size = batch_size\n self.corpus_batch_size = corpus_batch_size if corpus_batch_size > 0 else batch_size\n self.max_query_length = max_query_length\n self.max_passage_length = max_passage_length\n \n if use_fp16: self.model.half()\n if torch.cuda.is_available():\n self.device = torch.device(\"cuda\")\n elif is_torch_npu_available():\n self.device = torch.device(\"npu\")\n else:\n self.device = torch.device(\"cpu\")\n self.model = self.model.to(self.device)\n\n self.num_gpus = torch.cuda.device_count()\n if self.num_gpus > 1:\n self.model = torch.nn.DataParallel(self.model)\n\n def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:\n '''\n This function will be used for retrieval task\n if there is a instruction for queries, we will add it to the query text\n '''\n if isinstance(queries[0], dict):\n if self.query_instruction_for_retrieval is not None:\n input_texts = ['{}{}'.format(self.query_instruction_for_retrieval, q['text']) for q in queries]\n else:\n input_texts = [q['text'] for q in queries]\n else:\n if self.query_instruction_for_retrieval is not None:\n input_texts = ['{}{}'.format(self.query_instruction_for_retrieval, q) for q in queries]\n else:\n input_texts = queries\n return self.encode(input_texts, max_length=self.max_query_length, batch_size=self.batch_size)\n\n def encode_corpus(self, corpus: List[Union[Dict[str, str], str]], **kwargs) -> np.ndarray:\n '''\n This function will be used for retrieval task\n encode corpus for retrieval task\n '''\n if isinstance(corpus[0], dict):\n if self.passage_instruction_for_retrieval is not None:\n input_texts = ['{}{} {}'.format(self.passage_instruction_for_retrieval, doc.get('title', ''), doc['text']).strip() for doc in corpus]\n else:\n input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus]\n else:\n if self.passage_instruction_for_retrieval is not None:\n input_texts = self.passage_instruction_for_retrieval + corpus\n else:\n input_texts = corpus\n return self.encode(input_texts, max_length=self.max_passage_length, batch_size=self.corpus_batch_size)\n\n @torch.no_grad()\n def encode(self, sentences: List[str], max_length: int, batch_size: int, **kwargs) -> np.ndarray:\n if self.num_gpus > 0:\n batch_size = batch_size * self.num_gpus\n self.model.eval()\n\n input_was_string = False\n if isinstance(sentences, str):\n sentences = [sentences]\n input_was_string = True\n \n dataset = datasets.Dataset.from_dict({'text': sentences})\n if self.pooling_method == 'last':\n dataset.set_transform(partial(_transform_func_v2, tokenizer=self.tokenizer, max_length=max_length))\n else:\n dataset.set_transform(partial(_transform_func, tokenizer=self.tokenizer, max_length=max_length))\n\n data_collator = DataCollatorWithPadding(self.tokenizer)\n data_loader = DataLoader(\n dataset,\n batch_size=batch_size,\n shuffle=False,\n drop_last=False,\n num_workers=4,\n collate_fn=data_collator,\n # pin_memory=True\n )\n \n all_embeddings = []\n for batch_data in tqdm(data_loader, desc='encoding', mininterval=10):\n batch_data = batch_data.to(self.device)\n # print(batch_data)\n last_hidden_state = self.model(**batch_data, return_dict=True).last_hidden_state\n # print(last_hidden_state)\n embeddings = self.pooling(last_hidden_state, batch_data['attention_mask']).float()\n if self.normalize_embeddings:\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n all_embeddings.append(embeddings.cpu().numpy())\n\n all_embeddings = np.concatenate(all_embeddings, axis=0)\n if input_was_string:\n return all_embeddings[0]\n else:\n return all_embeddings\n \n def pooling(self,\n last_hidden_state: torch.Tensor,\n attention_mask: torch.Tensor=None):\n if self.pooling_method == 'cls':\n return last_hidden_state[:, 0]\n elif self.pooling_method == 'mean':\n s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n elif self.pooling_method == 'last':\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_state[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_state.shape[0]\n return last_hidden_state[torch.arange(batch_size, device=last_hidden_state.device), sequence_lengths]\n" }, { "path": "research/C_MTEB/MLDR/multi_vector_rerank/hybrid_all_results.py", "content": "\"\"\"\npython hybrid_all_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--dense_search_result_save_dir ./rerank_results/dense \\\n--sparse_search_result_save_dir ./rerank_results/sparse \\\n--colbert_search_result_save_dir ./rerank_results/colbert \\\n--hybrid_result_save_dir ./hybrid_search_results \\\n--top_k 200 \\\n--dense_weight 0.2 --sparse_weight 0.4 --colbert_weight 0.4\n\"\"\"\nimport os\nimport pandas as pd\nfrom tqdm import tqdm\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass EvalArgs:\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of model'}\n )\n reranker: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of reranker'}\n )\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n top_k: int = field(\n default=200,\n metadata={'help': 'Use reranker to rerank top-k retrieval results'}\n )\n dense_weight: float = field(\n default=0.15,\n metadata={'help': 'Hybrid weight of dense score'}\n )\n sparse_weight: float = field(\n default=0.5,\n metadata={'help': 'Hybrid weight of sparse score'}\n )\n colbert_weight: float = field(\n default=0.35,\n metadata={'help': 'Hybrid weight of colbert score'}\n )\n dense_search_result_save_dir: str = field(\n default='../rerank/unify_rerank_results/dense',\n metadata={'help': 'Dir to saving dense search results. Search results path is `dense_search_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n sparse_search_result_save_dir: str = field(\n default='../rerank/unify_rerank_results/sparse',\n metadata={'help': 'Dir to saving sparse search results. Search results path is `sparse_search_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n colbert_search_result_save_dir: str = field(\n default='../rerank/unify_rerank_results/colbert',\n metadata={'help': 'Dir to saving sparse search results. Search results path is `sparse_search_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n hybrid_result_save_dir: str = field(\n default='./hybrid_search_results',\n metadata={'help': 'Dir to saving hybrid search results. Reranked results will be saved to `hybrid_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=1000):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = row.iloc[2]\n rank = int(row.iloc[3])\n score = float(row.iloc[4])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append((docid, score))\n return search_result_dict\n\n\ndef save_hybrid_results(sparse_search_result_dict: dict, dense_search_result_dict: dict, colbert_search_result_dict: dict, hybrid_result_save_path: str, top_k: int=200, dense_weight: float=0.15, sparse_weight: float=0.5, colbert_weight: float=0.35):\n if not os.path.exists(os.path.dirname(hybrid_result_save_path)):\n os.makedirs(os.path.dirname(hybrid_result_save_path))\n \n qid_list = list(set(sparse_search_result_dict.keys()) | set(dense_search_result_dict.keys() | set(colbert_search_result_dict.keys())))\n hybrid_results_list = []\n for qid in tqdm(qid_list, desc=\"Hybriding dense, sparse and colbert scores\"):\n results = {}\n if qid in sparse_search_result_dict:\n for docid, score in sparse_search_result_dict[qid]:\n results[docid] = score * sparse_weight\n if qid in dense_search_result_dict:\n for docid, score in dense_search_result_dict[qid]:\n if docid in results:\n results[docid] = results[docid] + score * dense_weight\n else:\n results[docid] = score * dense_weight\n if qid in colbert_search_result_dict:\n for docid, score in colbert_search_result_dict[qid]:\n if docid in results:\n results[docid] = results[docid] + score * colbert_weight\n else:\n results[docid] = score * colbert_weight\n \n hybrid_results = [(docid, score) for docid, score in results.items()]\n hybrid_results.sort(key=lambda x: x[1], reverse=True)\n \n hybrid_results_list.append(hybrid_results[:top_k])\n \n with open(hybrid_result_save_path, 'w', encoding='utf-8') as f:\n for qid, hybrid_results in tqdm(zip(qid_list, hybrid_results_list), desc=\"Saving hybrid search results\"):\n for rank, docid_score in enumerate(hybrid_results):\n docid, score = docid_score\n line = f\"{qid} Q0 {docid} {rank+1} {score:.6f} Faiss-&-Anserini\"\n f.write(line + '\\n')\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if os.path.basename(eval_args.encoder).startswith('checkpoint-'):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n if os.path.basename(eval_args.reranker).startswith('checkpoint-'):\n eval_args.reranker = os.path.dirname(eval_args.reranker) + '_' + os.path.basename(eval_args.reranker)\n \n dir_name = f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\"\n \n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start hybrid search results of {lang} ...\")\n \n hybrid_result_save_path = os.path.join(eval_args.hybrid_result_save_dir, dir_name, f\"{lang}.txt\")\n \n sparse_search_result_save_dir = os.path.join(eval_args.sparse_search_result_save_dir, dir_name)\n \n sparse_search_result_path = os.path.join(sparse_search_result_save_dir, f\"{lang}.txt\")\n \n sparse_search_result_dict = get_search_result_dict(sparse_search_result_path, top_k=eval_args.top_k)\n \n dense_search_result_save_dir = os.path.join(eval_args.dense_search_result_save_dir, dir_name)\n\n dense_search_result_path = os.path.join(dense_search_result_save_dir, f\"{lang}.txt\")\n \n dense_search_result_dict = get_search_result_dict(dense_search_result_path, top_k=eval_args.top_k)\n \n colbert_search_result_save_dir = os.path.join(eval_args.colbert_search_result_save_dir, dir_name)\n\n colbert_search_result_path = os.path.join(colbert_search_result_save_dir, f\"{lang}.txt\")\n \n colbert_search_result_dict = get_search_result_dict(colbert_search_result_path, top_k=eval_args.top_k)\n \n save_hybrid_results(\n sparse_search_result_dict=sparse_search_result_dict, \n dense_search_result_dict=dense_search_result_dict, \n colbert_search_result_dict=colbert_search_result_dict,\n hybrid_result_save_path=hybrid_result_save_path,\n top_k=eval_args.top_k,\n sparse_weight=eval_args.sparse_weight,\n dense_weight=eval_args.dense_weight,\n colbert_weight=eval_args.colbert_weight\n )\n \n print(\"==================================================\")\n print(\"Finish generating reranked results with following model and reranker:\")\n print(eval_args.encoder)\n print(eval_args.reranker)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/multi_vector_rerank/step0-rerank_results.py", "content": "\"\"\"\npython step0-rerank_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ../dense_retrieval/search_results \\\n--rerank_result_save_dir ./rerank_results \\\n--top_k 200 \\\n--batch_size 4 \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--dense_weight 0.15 --sparse_weight 0.5 --colbert_weight 0.35 \\\n--num_shards 1 --shard_id 0 --cuda_id 0\n\"\"\"\nimport os\nimport copy\nimport datasets\nimport pandas as pd\nfrom tqdm import tqdm\nfrom FlagEmbedding import BGEM3FlagModel\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass ModelArgs:\n reranker: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of reranker'}\n )\n fp16: bool = field(\n default=True,\n metadata={'help': 'Use fp16 in inference?'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n max_query_length: int = field(\n default=512,\n metadata={'help': 'Max text length.'}\n )\n max_passage_length: int = field(\n default=8192,\n metadata={'help': 'Max text length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n top_k: int = field(\n default=100,\n metadata={'help': 'Use reranker to rerank top-k retrieval results'}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n search_result_save_dir: str = field(\n default='./output_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}/{lang}.txt`'}\n )\n rerank_result_save_dir: str = field(\n default='./rerank_results',\n metadata={'help': 'Dir to saving reranked results. Reranked results will be saved to `rerank_result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n num_shards: int = field(\n default=1,\n metadata={'help': \"num of shards\"}\n )\n shard_id: int = field(\n default=0,\n metadata={'help': 'id of shard, start from 0'}\n )\n cuda_id: int = field(\n default=0,\n metadata={'help': 'CUDA ID to use. -1 means only use CPU.'}\n )\n dense_weight: float = field(\n default=0.15,\n metadata={'help': 'The weight of dense score when hybriding all scores'}\n )\n sparse_weight: float = field(\n default=0.5,\n metadata={'help': 'The weight of sparse score when hybriding all scores'}\n )\n colbert_weight: float = field(\n default=0.35,\n metadata={'help': 'The weight of colbert score when hybriding all scores'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef get_reranker(model_args: ModelArgs, device: str=None):\n reranker = BGEM3FlagModel(\n model_name_or_path=model_args.reranker,\n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n device=device\n )\n return reranker\n\n\ndef get_search_result_dict(search_result_path: str, top_k: int=200):\n search_result_dict = {}\n flag = True\n for _, row in pd.read_csv(search_result_path, sep=' ', header=None).iterrows():\n qid = str(row.iloc[0])\n docid = row.iloc[2]\n rank = int(row.iloc[3])\n if qid not in search_result_dict:\n search_result_dict[qid] = []\n flag = False\n if rank > top_k:\n flag = True\n if flag:\n continue\n else:\n search_result_dict[qid].append(docid)\n return search_result_dict\n\n\ndef get_queries_dict(lang: str, split: str='test'):\n dataset = datasets.load_dataset('Shitao/MLDR', lang, split=split)\n \n queries_dict = {}\n for data in dataset:\n qid = data['query_id']\n query = data['query']\n queries_dict[qid] = query\n return queries_dict\n\n\ndef get_corpus_dict(lang: str):\n corpus = datasets.load_dataset('Shitao/MLDR', f'corpus-{lang}', split='corpus')\n \n corpus_dict = {}\n for data in tqdm(corpus, desc=\"Generating corpus\"):\n docid = data['docid']\n content = data['text']\n corpus_dict[docid] = content\n return corpus_dict\n\n\ndef save_rerank_results(queries_dict: dict, corpus_dict: dict, reranker: BGEM3FlagModel, search_result_dict: dict, rerank_result_save_path: dict, batch_size: int=256, max_query_length: int=512, max_passage_length: int=512, dense_weight: float=0.15, sparse_weight: float=0.5, colbert_weight: float=0.35):\n qid_list = []\n sentence_pairs = []\n for qid, docids in search_result_dict.items():\n qid_list.append(qid)\n query = queries_dict[qid]\n for docid in docids:\n passage = corpus_dict[docid]\n sentence_pairs.append((query, passage))\n\n scores_dict = reranker.compute_score(\n sentence_pairs, \n batch_size=batch_size, \n max_query_length=max_query_length, \n max_passage_length=max_passage_length, \n weights_for_different_modes=[dense_weight, sparse_weight, colbert_weight]\n )\n for sub_dir, _rerank_result_save_path in rerank_result_save_path.items():\n if not os.path.exists(os.path.dirname(_rerank_result_save_path)):\n os.makedirs(os.path.dirname(_rerank_result_save_path))\n \n scores = scores_dict[sub_dir]\n with open(_rerank_result_save_path, 'w', encoding='utf-8') as f:\n i = 0\n for qid in qid_list:\n docids = search_result_dict[qid]\n docids_scores = []\n for j in range(len(docids)):\n docids_scores.append((docids[j], scores[i + j]))\n i += len(docids)\n \n docids_scores.sort(key=lambda x: x[1], reverse=True)\n for rank, docid_score in enumerate(docids_scores):\n docid, score = docid_score\n line = f\"{qid} Q0 {docid} {rank+1} {score:.6f} Faiss\"\n f.write(line + '\\n')\n\n\ndef get_shard(search_result_dict: dict, num_shards: int, shard_id: int):\n if num_shards <= 1:\n return search_result_dict\n keys_list = sorted(list(search_result_dict.keys()))\n \n shard_len = len(keys_list) // num_shards\n if shard_id == num_shards - 1:\n shard_keys_list = keys_list[shard_id*shard_len:]\n else:\n shard_keys_list = keys_list[shard_id*shard_len : (shard_id + 1)*shard_len]\n shard_search_result_dict = {k: search_result_dict[k] for k in shard_keys_list}\n return shard_search_result_dict\n\n\ndef rerank_results(languages: list, eval_args: EvalArgs, model_args: ModelArgs, device: str=None):\n eval_args = copy.deepcopy(eval_args)\n model_args = copy.deepcopy(model_args)\n \n num_shards = eval_args.num_shards\n shard_id = eval_args.shard_id\n if shard_id >= num_shards:\n raise ValueError(f\"shard_id >= num_shards ({shard_id} >= {num_shards})\")\n \n reranker = get_reranker(model_args=model_args, device=device)\n \n if os.path.basename(eval_args.encoder).startswith('checkpoint-'):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n if os.path.basename(model_args.reranker).startswith('checkpoint-'):\n model_args.reranker = os.path.dirname(model_args.reranker) + '_' + os.path.basename(model_args.reranker)\n \n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start reranking results of {lang} ...\")\n \n queries_dict = get_queries_dict(lang, split='test')\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(eval_args.encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n search_result_dict = get_search_result_dict(search_result_path, top_k=eval_args.top_k)\n \n search_result_dict = get_shard(search_result_dict, num_shards=num_shards, shard_id=shard_id)\n \n corpus_dict = get_corpus_dict(lang)\n \n rerank_result_save_path = {}\n for sub_dir in ['colbert', 'sparse', 'dense', 'colbert+sparse+dense']:\n _rerank_result_save_path = os.path.join(\n eval_args.rerank_result_save_dir, \n sub_dir, \n f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(model_args.reranker)}\", \n f\"{lang}_{shard_id}-of-{num_shards}.txt\" if num_shards > 1 else f\"{lang}.txt\"\n )\n rerank_result_save_path[sub_dir] = _rerank_result_save_path\n \n save_rerank_results(\n queries_dict=queries_dict,\n corpus_dict=corpus_dict, \n reranker=reranker, \n search_result_dict=search_result_dict, \n rerank_result_save_path=rerank_result_save_path,\n batch_size=eval_args.batch_size,\n max_query_length=eval_args.max_query_length,\n max_passage_length=eval_args.max_passage_length,\n dense_weight=eval_args.dense_weight,\n sparse_weight=eval_args.sparse_weight,\n colbert_weight=eval_args.colbert_weight\n )\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs, ModelArgs])\n eval_args, model_args = parser.parse_args_into_dataclasses()\n eval_args: EvalArgs\n model_args: ModelArgs\n \n languages = check_languages(eval_args.languages)\n \n cuda_id = eval_args.cuda_id\n \n if cuda_id < 0:\n rerank_results(languages, eval_args, model_args, device='cpu')\n else:\n rerank_results(languages, eval_args, model_args, device=f\"cuda:{cuda_id}\")\n \n print(\"==================================================\")\n print(\"Finish generating reranked results with following encoder and reranker:\")\n print(eval_args.encoder)\n print(model_args.reranker)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/multi_vector_rerank/step1-eval_rerank_mldr.py", "content": "\"\"\"\n# 1. Rerank Search Results\npython step0-rerank_results.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ../dense_retrieval/search_results \\\n--rerank_result_save_dir ./rerank_results \\\n--top_k 200 \\\n--batch_size 4 \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--pooling_method cls \\\n--normalize_embeddings True \\\n--dense_weight 0.15 --sparse_weight 0.5 --colbert_weight 0.35 \\\n--num_shards 1 --shard_id 0 --cuda_id 0\n\n# 2. Print and Save Evaluation Results\npython step1-eval_rerank_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--reranker BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--search_result_save_dir ./rerank_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10\n\"\"\"\nimport os\nimport json\nimport platform\nimport subprocess\nimport numpy as np\nfrom pprint import pprint\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom pyserini.util import download_evaluation_script\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n reranker: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of reranker'}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n search_result_save_dir: str = field(\n default='./rerank_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}-{reranker}/{lang}.txt`'}\n )\n qrels_dir: str = field(\n default='../qrels',\n metadata={'help': 'Dir to topics and qrels.'}\n )\n metrics: str = field(\n default=\"ndcg@10\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: ndcg@k, recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./reranker_evaluation_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{encoder}-{reranker}.json`'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, reranker_name: str, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n results_dict = {\n 'reranker': reranker_name,\n 'model': model_name,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{reranker_name}` on `{eval_languages}` based on `{model_name}` saved at `{save_path}`')\n\n\ndef map_metric(metric: str):\n metric, k = metric.split('@')\n if metric.lower() == 'ndcg':\n return k, f'ndcg_cut.{k}'\n elif metric.lower() == 'recall':\n return k, f'recall.{k}'\n else:\n raise ValueError(f\"Unkown metric: {metric}\")\n\n\ndef evaluate(script_path: str, qrels_path, search_result_path, metrics: list):\n cmd_prefix = ['java', '-jar', script_path]\n \n results = {}\n for metric in metrics:\n k, mapped_metric = map_metric(metric)\n args = ['-c', '-M', str(k), '-m', mapped_metric, qrels_path, search_result_path]\n cmd = cmd_prefix + args\n \n # print(f'Running command: {cmd}')\n shell = platform.system() == \"Windows\"\n process = subprocess.Popen(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=shell)\n stdout, stderr = process.communicate()\n if stderr:\n print(stderr.decode(\"utf-8\"))\n result_str = stdout.decode(\"utf-8\")\n try:\n results[metric] = float(result_str.split(' ')[-1].split('\\t')[-1])\n except:\n results[metric] = result_str\n return results\n\n\ndef merge_search_result(search_result_save_dir: str, lang: str):\n lang_files = [file for file in os.listdir(search_result_save_dir) if f'{lang}_' in file]\n shard_info_dict = defaultdict(set)\n for file in lang_files:\n file_name = file.split('.')[0]\n shard_info = file_name.split('_')[1]\n shard_id, num_shards = int(shard_info.split('-')[0]), int(shard_info.split('-')[2])\n assert shard_id < num_shards\n shard_info_dict[num_shards].add(shard_id)\n flag = False\n for num_shards, shard_ids in shard_info_dict.items():\n if len(shard_ids) != num_shards:\n flag = False\n else:\n flag = True\n lang_paths = os.path.join(search_result_save_dir, f'{lang}_*-of-{num_shards}.txt')\n save_path = os.path.join(search_result_save_dir, f'{lang}.txt')\n cmd = f'cat {lang_paths} > {save_path}'\n os.system(cmd)\n break\n if not flag:\n raise ValueError(f\"Fail to find complete search results of {lang} in {search_result_save_dir}\")\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n script_path = download_evaluation_script('trec_eval')\n \n languages = check_languages(eval_args.languages)\n \n if 'checkpoint-' in os.path.basename(eval_args.encoder):\n eval_args.encoder = os.path.dirname(eval_args.encoder) + '_' + os.path.basename(eval_args.encoder)\n \n if 'checkpoint-' in os.path.basename(eval_args.reranker):\n eval_args.reranker = os.path.dirname(eval_args.reranker) + '_' + os.path.basename(eval_args.reranker)\n \n try:\n for sub_dir in ['colbert', 'sparse', 'dense', 'colbert+sparse+dense']:\n results = {}\n for lang in languages:\n qrels_path = os.path.join(eval_args.qrels_dir, f\"qrels.mldr-v1.0-{lang}-test.tsv\")\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, sub_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\")\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n if not os.path.exists(search_result_path):\n merge_search_result(search_result_save_dir, lang)\n assert os.path.exists(search_result_path)\n \n result = evaluate(script_path, qrels_path, search_result_path, eval_args.metrics)\n results[lang] = result\n \n print(\"****************************\")\n print(sub_dir + \":\")\n save_results(\n model_name=eval_args.encoder,\n reranker_name=eval_args.reranker,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, sub_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}.json\"),\n eval_languages=languages,\n )\n except:\n results = {}\n for lang in languages:\n qrels_path = os.path.join(eval_args.qrels_dir, f\"qrels.mldr-v1.0-{lang}-test.tsv\")\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}\")\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n if not os.path.exists(search_result_path):\n merge_search_result(search_result_save_dir, lang)\n assert os.path.exists(search_result_path)\n \n result = evaluate(script_path, qrels_path, search_result_path, eval_args.metrics)\n results[lang] = result\n save_results(\n model_name=eval_args.encoder,\n reranker_name=eval_args.reranker,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(eval_args.encoder)}-{os.path.basename(eval_args.reranker)}.json\"),\n eval_languages=languages,\n )\n \n print(\"==================================================\")\n print(\"Finish generating evaluation results with following model and reranker:\")\n print(eval_args.encoder)\n print(eval_args.reranker)\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/sparse_retrieval/bm25_baseline.py", "content": "\"\"\"\n# 1. Output Search Results with BM25\npython bm25_baseline.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mldr.py \\\n--encoder bm25 \\\n--languages ar de es fr hi it ja ko pt ru th en zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10\n\"\"\"\nimport os\nimport datasets\nfrom tqdm import tqdm\n\n\ndef generate_corpus(lang: str, corpus_save_dir: str):\n corpus_save_path = os.path.join(corpus_save_dir, 'corpus.jsonl')\n if os.path.exists(corpus_save_path):\n return\n \n corpus = datasets.load_dataset('Shitao/MLDR', f'corpus-{lang}', split='corpus')\n \n corpus_list = [{'id': e['docid'], 'contents': e['text']} for e in tqdm(corpus, desc=\"Generating corpus\")]\n corpus = datasets.Dataset.from_list(corpus_list)\n \n corpus.to_json(corpus_save_path, force_ascii=False)\n\n\ndef generate_queries(lang: str, queries_save_dir: str, split: str='test'):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n if os.path.exists(queries_save_path):\n return\n \n dataset = datasets.load_dataset('Shitao/MLDR', lang, split=split)\n \n queries_list = []\n for data in dataset:\n queries_list.append({\n 'id': data['query_id'],\n 'content': data['query'].replace('\\n', ' ').replace('\\t', ' ')\n })\n with open(queries_save_path, 'w', encoding='utf-8') as f:\n for query in queries_list:\n assert '\\n' not in query['content'] and '\\t' not in query['content']\n line = f\"{query['id']}\\t{query['content']}\"\n f.write(line + '\\n')\n\n\ndef index(lang: str, corpus_save_dir: str, index_save_dir: str):\n cmd = f\"python -m pyserini.index.lucene \\\n --language {lang} \\\n --collection JsonCollection \\\n --input {corpus_save_dir} \\\n --index {index_save_dir} \\\n --generator DefaultLuceneDocumentGenerator \\\n --threads 1 --optimize \\\n \"\n os.system(cmd)\n\n\ndef search(index_save_dir: str, queries_save_dir: str, lang: str, result_save_path: str):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n cmd = f\"python -m pyserini.search.lucene \\\n --language {lang} \\\n --index {index_save_dir} \\\n --topics {queries_save_path} \\\n --output {result_save_path} \\\n --bm25 \\\n --hits 1000 \\\n --batch-size 128 \\\n --threads 16 \\\n \"\n os.system(cmd)\n\n\ndef main():\n bm25_dir = './bm25_baseline'\n \n result_save_dir = os.path.join('./search_results', 'bm25')\n if not os.path.exists(result_save_dir):\n os.makedirs(result_save_dir)\n \n for lang in ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']:\n save_dir = os.path.join(bm25_dir, lang)\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n \n corpus_save_dir = os.path.join(save_dir, 'corpus')\n if not os.path.exists(corpus_save_dir):\n os.makedirs(corpus_save_dir)\n generate_corpus(lang, corpus_save_dir)\n \n index_save_dir = os.path.join(save_dir, 'index')\n if not os.path.exists(index_save_dir):\n os.makedirs(index_save_dir)\n index(lang, corpus_save_dir, index_save_dir)\n \n generate_queries(lang, save_dir, split='test')\n \n result_save_path = os.path.join(result_save_dir, f'{lang}.txt')\n search(index_save_dir, save_dir, lang, result_save_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "research/C_MTEB/MLDR/sparse_retrieval/bm25_baseline_same_tokenizer.py", "content": "\"\"\"\n# 1. Output Search Results with BM25\npython bm25_baseline_same_tokenizer.py\n\n# 2. Print and Save Evaluation Results\npython step2-eval_sparse_mldr.py \\\n--encoder bm25_same_tokenizer \\\n--languages ar de es fr hi it ja ko pt ru th en zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10\n\"\"\"\nimport os\nimport datasets\nfrom tqdm import tqdm\nfrom transformers import AutoTokenizer\n\n\ntokenizer = AutoTokenizer.from_pretrained(\n 'BAAI/bge-m3',\n use_fast=False,\n)\n\n\ndef _map_func_corpus(examples):\n results = {}\n results['docid'] = examples['docid']\n results['text'] = []\n \n inputs = tokenizer(\n examples['text'],\n padding=False,\n truncation=True,\n max_length=8192\n )\n input_ids_list = inputs['input_ids']\n \n for i in range(len(examples['docid'])):\n token_ids = input_ids_list[i][1:-1]\n token_ids = [str(_id) for _id in token_ids]\n results['text'].append(\" \".join(token_ids))\n return results\n\n\ndef _map_func_query(examples):\n results = {}\n results['query_id'] = examples['query_id']\n results['query'] = []\n \n inputs = tokenizer(\n examples['query'],\n padding=False,\n truncation=True,\n max_length=512\n )\n \n input_ids_list = inputs['input_ids']\n \n for i in range(len(examples['query_id'])):\n token_ids = input_ids_list[i][1:-1]\n token_ids = [str(_id) for _id in token_ids]\n results['query'].append(\" \".join(token_ids))\n return results\n\n\ndef generate_corpus(lang: str, corpus_save_dir: str):\n corpus_save_path = os.path.join(corpus_save_dir, 'corpus.jsonl')\n if os.path.exists(corpus_save_path):\n return\n \n corpus = datasets.load_dataset('Shitao/MLDR', f'corpus-{lang}', split='corpus')\n \n corpus = corpus.map(_map_func_corpus, batched=True, num_proc=48)\n \n corpus_list = [{'id': e['docid'], 'contents': e['text']} for e in tqdm(corpus, desc=\"Generating corpus\")]\n corpus = datasets.Dataset.from_list(corpus_list)\n \n corpus.to_json(corpus_save_path, force_ascii=False)\n\n\ndef generate_queries(lang: str, queries_save_dir: str, split: str='test'):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n if os.path.exists(queries_save_path):\n return\n \n dataset = datasets.load_dataset('Shitao/MLDR', lang, split=split)\n \n dataset = dataset.map(_map_func_query, batched=True, num_proc=48)\n \n queries_list = []\n for data in dataset:\n queries_list.append({\n 'id': data['query_id'],\n 'content': data['query']\n })\n with open(queries_save_path, 'w', encoding='utf-8') as f:\n for query in queries_list:\n assert '\\n' not in query['content'] and '\\t' not in query['content']\n line = f\"{query['id']}\\t{query['content']}\"\n f.write(line + '\\n')\n\n\ndef index(corpus_save_dir: str, index_save_dir: str):\n cmd = f\"python -m pyserini.index.lucene \\\n --collection JsonCollection \\\n --input {corpus_save_dir} \\\n --index {index_save_dir} \\\n --generator DefaultLuceneDocumentGenerator \\\n --threads 1 --optimize \\\n \"\n os.system(cmd)\n\n\ndef search(index_save_dir: str, queries_save_dir: str, lang: str, result_save_path: str):\n queries_save_path = os.path.join(queries_save_dir, f\"{lang}.tsv\")\n cmd = f\"python -m pyserini.search.lucene \\\n --index {index_save_dir} \\\n --topics {queries_save_path} \\\n --output {result_save_path} \\\n --bm25 \\\n --hits 1000 \\\n --batch-size 128 \\\n --threads 16 \\\n \"\n os.system(cmd)\n\n\ndef main():\n bm25_dir = './bm25_baseline_same_tokenizer'\n \n result_save_dir = os.path.join('./search_results', 'bm25_same_tokenizer')\n if not os.path.exists(result_save_dir):\n os.makedirs(result_save_dir)\n \n for lang in ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']:\n save_dir = os.path.join(bm25_dir, lang)\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n \n corpus_save_dir = os.path.join(save_dir, 'corpus')\n if not os.path.exists(corpus_save_dir):\n os.makedirs(corpus_save_dir)\n generate_corpus(lang, corpus_save_dir)\n \n index_save_dir = os.path.join(save_dir, 'index')\n if not os.path.exists(index_save_dir):\n os.makedirs(index_save_dir)\n index(corpus_save_dir, index_save_dir)\n \n generate_queries(lang, save_dir, split='test')\n \n result_save_path = os.path.join(result_save_dir, f'{lang}.txt')\n search(index_save_dir, save_dir, lang, result_save_path)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "research/C_MTEB/MLDR/sparse_retrieval/step0-encode_query-and-corpus.py", "content": "\"\"\"\npython step0-encode_query-and-corpus.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--save_dir ./encoded_query-and-corpus \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--batch_size 1024 \\\n--corpus_batch_size 4 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\n\nimport os\nimport json\nimport datasets\nimport numpy as np\nfrom tqdm import tqdm\nfrom FlagEmbedding import BGEM3FlagModel\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n fp16: bool = field(\n default=True,\n metadata={'help': 'Use fp16 in inference?'}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n save_dir: str = field(\n default='./encoded_query-and-corpus',\n metadata={'help': 'Dir to save encoded query and corpus. Encoded query and corpus will be saved to `save_dir/{encoder_name}/{lang}/query_embd.tsv` and `save_dir/{encoder_name}/{lang}/corpus/corpus_embd.jsonl`, individually.'}\n )\n max_query_length: int = field(\n default=512,\n metadata={'help': 'Max query length.'}\n )\n max_passage_length: int = field(\n default=8192,\n metadata={'help': 'Max passage length.'}\n )\n batch_size: int = field(\n default=256,\n metadata={'help': 'Inference batch size.'}\n )\n corpus_batch_size: int = field(\n default=4,\n metadata={'help': 'Inference batch size.'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef get_model(model_args: ModelArgs):\n model = BGEM3FlagModel(\n model_name_or_path=model_args.encoder,\n pooling_method=model_args.pooling_method,\n normalize_embeddings=model_args.normalize_embeddings,\n use_fp16=model_args.fp16\n )\n return model\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef load_corpus(lang: str):\n corpus = datasets.load_dataset('Shitao/MLDR', f'corpus-{lang}', split='corpus')\n \n corpus_list = [{'id': e['docid'], 'content': e['text']} for e in tqdm(corpus, desc=\"Generating corpus\")]\n corpus = datasets.Dataset.from_list(corpus_list)\n return corpus\n\n\ndef get_queries(lang: str, split: str='test'):\n dataset = datasets.load_dataset('Shitao/MLDR', lang, split=split)\n \n queries_list = []\n for data in dataset:\n queries_list.append({\n 'id': data['query_id'],\n 'content': data['query']\n })\n \n queries = datasets.Dataset.from_list(queries_list)\n return queries\n\n\ndef encode_corpus(model: BGEM3FlagModel, corpus: datasets.Dataset, max_passage_length: int=8192, corpus_batch_size: int=4):\n docids = list(corpus[\"id\"])\n vectors = model.encode(\n corpus[\"content\"], \n batch_size=corpus_batch_size, \n max_length=max_passage_length,\n return_dense=False,\n return_sparse=True,\n return_colbert_vecs=False\n )['lexical_weights']\n \n encoded_corpus_list = []\n for docid, vector in zip(docids, vectors):\n for key, value in vector.items():\n vector[key] = int(np.ceil(value * 100))\n \n encoded_corpus_list.append({\n 'id': docid,\n 'contents': '',\n 'vector': vector\n })\n return encoded_corpus_list\n\n\ndef encode_queries(model: BGEM3FlagModel, queries: datasets.Dataset, max_query_length: int=512, batch_size: int=256):\n qids = list(queries[\"id\"])\n vectors = model.encode(\n queries[\"content\"], \n batch_size=batch_size, \n max_length=max_query_length,\n return_dense=False,\n return_sparse=True,\n return_colbert_vecs=False\n )['lexical_weights']\n \n encoded_queries_list = []\n for qid, vector in zip(qids, vectors):\n for key, value in vector.items():\n vector[key] = int(np.ceil(value * 100))\n \n topic_str = []\n for token in vector:\n topic_str += [str(token)] * vector[token]\n if len(topic_str) == 0:\n topic_str = \"0\"\n else:\n topic_str = \" \".join(topic_str)\n encoded_queries_list.append(f\"{str(qid)}\\t{topic_str}\\n\")\n return encoded_queries_list\n\n\ndef save_result(encoded_queries_list: list, encoded_corpus_list: list, save_dir: str):\n queries_save_path = os.path.join(save_dir, 'query_embd.tsv')\n corpus_save_path = os.path.join(save_dir, 'corpus', 'corpus_embd.jsonl')\n if not os.path.exists(os.path.dirname(corpus_save_path)):\n os.makedirs(os.path.dirname(corpus_save_path))\n \n with open(queries_save_path, 'w', encoding='utf-8') as f:\n for line in tqdm(encoded_queries_list, desc=\"Saving encoded queries\"):\n f.write(line)\n \n with open(corpus_save_path, 'w', encoding='utf-8') as f:\n for line in tqdm(encoded_corpus_list, desc=\"Saving encoded corpus\"):\n f.write(json.dumps(line, ensure_ascii=False) + \"\\n\")\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n # languages.reverse()\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n model = get_model(model_args=model_args)\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating embedding with model:\")\n print(model_args.encoder)\n\n print('Generate embedding of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n save_dir = os.path.join(eval_args.save_dir, os.path.basename(encoder), lang)\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n if os.path.exists(os.path.join(save_dir, 'corpus', 'corpus_embd.jsonl')) and not eval_args.overwrite:\n print(f'Embedding of {lang} already exists. Skip...')\n continue\n \n print(f\"Start generating query and corpus embedding of {lang} ...\")\n queries = get_queries(lang, split='test')\n encoded_queries_list = encode_queries(\n model=model,\n queries=queries,\n max_query_length=eval_args.max_query_length,\n batch_size=eval_args.batch_size\n )\n \n corpus = load_corpus(lang)\n encoded_corpus_list = encode_corpus(\n model=model,\n corpus=corpus,\n max_passage_length=eval_args.max_passage_length,\n corpus_batch_size=eval_args.corpus_batch_size\n )\n \n save_result(\n encoded_queries_list=encoded_queries_list,\n encoded_corpus_list=encoded_corpus_list,\n save_dir=save_dir\n )\n \n print(\"==================================================\")\n print(\"Finish generating embeddings with model:\")\n print(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/sparse_retrieval/step1-search_results.py", "content": "\"\"\"\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--encoded_query_and_corpus_save_dir ./encoded_query-and-corpus \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000\n\"\"\"\nimport os\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\n\n\n@dataclass\nclass ModelArgs:\n encoder: str = field(\n default=\"BAAI/bge-m3\",\n metadata={'help': 'Name or path of encoder'}\n )\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n encoded_query_and_corpus_save_dir: str = field(\n default='./encoded_query-and-corpus',\n metadata={'help': 'Dir to save encoded queries and corpus. Encoded queries and corpus are saved in `save_dir/{encoder_name}/{lang}/query_embd.tsv` and `save_dir/{encoder_name}/{lang}/corpus/corpus_embd.jsonl`, individually.'}\n )\n result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving results. Search results will be saved to `result_save_dir/{encoder_name}/{lang}.txt`'}\n )\n batch_size: int = field(\n default=32,\n metadata={'help': 'Batch size to use during search'}\n )\n threads: int = field(\n default=1,\n metadata={'help': 'Maximum threads to use during search'}\n )\n hits: int = field(\n default=1000,\n metadata={'help': 'Number of hits'}\n )\n overwrite: bool = field(\n default=False,\n metadata={'help': 'Whether to overwrite embedding'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef generate_index(lang: str, corpus_embd_dir: str, index_save_dir: str, threads: int=12): \n cmd = f\"python -m pyserini.index.lucene \\\n --language {lang} \\\n --collection JsonVectorCollection \\\n --input {corpus_embd_dir} \\\n --index {index_save_dir} \\\n --generator DefaultLuceneDocumentGenerator \\\n --threads {threads} \\\n --impact --pretokenized --optimize \\\n \"\n os.system(cmd)\n\n\ndef search_and_save_results(index_save_dir: str, query_embd_path: str, result_save_path: str, batch_size: int = 32, threads: int = 12, hits: int = 1000):\n cmd = f\"python -m pyserini.search.lucene \\\n --index {index_save_dir} \\\n --topics {query_embd_path} \\\n --output {result_save_path} \\\n --output-format trec \\\n --batch {batch_size} \\\n --threads {threads} \\\n --hits {hits} \\\n --impact \\\n \"\n os.system(cmd)\n\n\ndef main():\n parser = HfArgumentParser([ModelArgs, EvalArgs])\n model_args, eval_args = parser.parse_args_into_dataclasses()\n model_args: ModelArgs\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n if model_args.encoder[-1] == '/':\n model_args.encoder = model_args.encoder[:-1]\n \n encoder = model_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n print(\"==================================================\")\n print(\"Start generating search results with model:\")\n print(model_args.encoder)\n\n print('Generate search results of following languages: ', languages)\n for lang in languages:\n print(\"**************************************************\")\n print(f\"Start searching results of {lang} ...\")\n \n result_save_path = os.path.join(eval_args.result_save_dir, os.path.basename(encoder), f\"{lang}.txt\")\n if not os.path.exists(os.path.dirname(result_save_path)):\n os.makedirs(os.path.dirname(result_save_path))\n \n if os.path.exists(result_save_path) and not eval_args.overwrite:\n print(f'Search results of {lang} already exists. Skip...')\n continue\n \n encoded_query_and_corpus_save_dir = os.path.join(eval_args.encoded_query_and_corpus_save_dir, os.path.basename(encoder), lang)\n if not os.path.exists(encoded_query_and_corpus_save_dir):\n raise FileNotFoundError(f\"{encoded_query_and_corpus_save_dir} not found\")\n \n corpus_embd_dir = os.path.join(encoded_query_and_corpus_save_dir, 'corpus')\n index_save_dir = os.path.join(eval_args.encoded_query_and_corpus_save_dir, os.path.basename(encoder), lang, 'index')\n if os.path.exists(index_save_dir) and not eval_args.overwrite:\n print(f'Index of {lang} already exists')\n else:\n generate_index(\n lang=lang,\n corpus_embd_dir=corpus_embd_dir,\n index_save_dir=index_save_dir,\n threads=eval_args.threads\n )\n \n query_embd_path = os.path.join(encoded_query_and_corpus_save_dir, 'query_embd.tsv')\n \n search_and_save_results(\n index_save_dir=index_save_dir,\n query_embd_path=query_embd_path,\n result_save_path=result_save_path,\n batch_size=eval_args.batch_size,\n threads=eval_args.threads,\n hits=eval_args.hits\n )\n \n print(\"==================================================\")\n print(\"Finish generating search results with model:\")\n print(model_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/MLDR/sparse_retrieval/step2-eval_sparse_mldr.py", "content": "\"\"\"\nRef: https://github.com/texttron/tevatron/tree/main/examples/unicoil\n# 1. Generate Query and Corpus Sparse Vector\npython step0-encode_query-and-corpus.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--save_dir ./encoded_query-and-corpus \\\n--max_query_length 512 \\\n--max_passage_length 8192 \\\n--batch_size 1024 \\\n--corpus_batch_size 4 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\n# 2. Output Search Results\npython step1-search_results.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de en es fr hi it ja ko pt ru th zh \\\n--encoded_query_and_corpus_save_dir ./encoded_query-and-corpus \\\n--result_save_dir ./search_results \\\n--threads 16 \\\n--hits 1000\n\n# 3. Print and Save Evaluation Results\npython step2-eval_sparse_mldr.py \\\n--encoder BAAI/bge-m3 \\\n--languages ar de es fr hi it ja ko pt ru th en zh \\\n--search_result_save_dir ./search_results \\\n--qrels_dir ../qrels \\\n--eval_result_save_dir ./eval_results \\\n--metrics ndcg@10 \\\n--pooling_method cls \\\n--normalize_embeddings True\n\"\"\"\nimport os\nimport json\nimport platform\nimport subprocess\nimport numpy as np\nfrom pprint import pprint\nfrom dataclasses import dataclass, field\nfrom transformers import HfArgumentParser\nfrom pyserini.util import download_evaluation_script\n\n\n@dataclass\nclass EvalArgs:\n languages: str = field(\n default=\"en\",\n metadata={'help': 'Languages to evaluate. Avaliable languages: ar de en es fr hi it ja ko pt ru th zh', \n \"nargs\": \"+\"}\n )\n encoder: str = field(\n default='BAAI/bge-m3',\n metadata={'help': 'Name or path of encoder'}\n )\n pooling_method: str = field(\n default='cls',\n metadata={'help': \"Pooling method. Avaliable methods: 'cls', 'mean'\"}\n )\n normalize_embeddings: bool = field(\n default=True,\n metadata={'help': \"Normalize embeddings or not\"}\n )\n search_result_save_dir: str = field(\n default='./search_results',\n metadata={'help': 'Dir to saving search results. Search results path is `result_save_dir/{encoder}/{lang}.txt`'}\n )\n qrels_dir: str = field(\n default='../qrels',\n metadata={'help': 'Dir to qrels.'}\n )\n metrics: str = field(\n default=\"ndcg@10\",\n metadata={'help': 'Metrics to evaluate. Avaliable metrics: ndcg@k, recall@k', \n \"nargs\": \"+\"}\n )\n eval_result_save_dir: str = field(\n default='./eval_results',\n metadata={'help': 'Dir to saving evaluation results. Evaluation results will be saved to `eval_result_save_dir/{encoder}.json`'}\n )\n\n\ndef check_languages(languages):\n if isinstance(languages, str):\n languages = [languages]\n avaliable_languages = ['ar', 'de', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'pt', 'ru', 'th', 'zh']\n for lang in languages:\n if lang not in avaliable_languages:\n raise ValueError(f\"Language `{lang}` is not supported. Avaliable languages: {avaliable_languages}\")\n return languages\n\n\ndef compute_average(results: dict):\n average_results = {}\n for _, result in results.items():\n for metric, score in result.items():\n if metric not in average_results:\n average_results[metric] = []\n average_results[metric].append(score)\n for metric, scores in average_results.items():\n average_results[metric] = np.mean(scores)\n return average_results\n\n\ndef save_results(model_name: str, pooling_method: str, normalize_embeddings: bool, results: dict, save_path: str, eval_languages: list):\n try:\n results['average'] = compute_average(results)\n except:\n results['average'] = None\n pass\n pprint(results)\n if not os.path.exists(os.path.dirname(save_path)):\n os.makedirs(os.path.dirname(save_path))\n \n if 'bm25' in model_name:\n pooling_method = ''\n normalize_embeddings = ''\n results_dict = {\n 'model': model_name,\n 'pooling_method': pooling_method,\n 'normalize_embeddings': normalize_embeddings,\n 'results': results\n }\n with open(save_path, 'w', encoding='utf-8') as f:\n json.dump(results_dict, f, indent=4, ensure_ascii=False)\n print(f'Results of evaluating `{model_name}` on `{eval_languages}` saved at `{save_path}`')\n\n\ndef map_metric(metric: str):\n metric, k = metric.split('@')\n if metric.lower() == 'ndcg':\n return k, f'ndcg_cut.{k}'\n elif metric.lower() == 'recall':\n return k, f'recall.{k}'\n else:\n raise ValueError(f\"Unkown metric: {metric}\")\n\n\ndef evaluate(script_path, qrels_path, search_result_path, metrics: list):\n cmd_prefix = ['java', '-jar', script_path]\n \n results = {}\n for metric in metrics:\n k, mapped_metric = map_metric(metric)\n args = ['-c', '-M', str(k), '-m', mapped_metric, qrels_path, search_result_path]\n cmd = cmd_prefix + args\n \n # print(f'Running command: {cmd}')\n shell = platform.system() == \"Windows\"\n process = subprocess.Popen(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=shell)\n stdout, stderr = process.communicate()\n if stderr:\n print(stderr.decode(\"utf-8\"))\n result_str = stdout.decode(\"utf-8\")\n try:\n results[metric] = float(result_str.split(' ')[-1].split('\\t')[-1])\n except:\n results[metric] = result_str\n return results\n\n\ndef main():\n parser = HfArgumentParser([EvalArgs])\n eval_args = parser.parse_args_into_dataclasses()[0]\n eval_args: EvalArgs\n \n languages = check_languages(eval_args.languages)\n \n script_path = download_evaluation_script('trec_eval')\n \n if eval_args.encoder[-1] == '/':\n eval_args.encoder = eval_args.encoder[:-1]\n \n encoder = eval_args.encoder\n if os.path.basename(encoder).startswith('checkpoint-'):\n encoder = os.path.dirname(encoder) + '_' + os.path.basename(encoder)\n \n results = {}\n for lang in languages:\n print(\"*****************************\")\n print(f\"Start evaluating {lang} ...\")\n qrels_path = os.path.join(eval_args.qrels_dir, f\"qrels.mldr-v1.0-{lang}-test.tsv\")\n \n search_result_save_dir = os.path.join(eval_args.search_result_save_dir, os.path.basename(encoder))\n search_result_path = os.path.join(search_result_save_dir, f\"{lang}.txt\")\n \n result = evaluate(script_path, qrels_path, search_result_path, eval_args.metrics)\n results[lang] = result\n \n save_results(\n model_name=encoder,\n pooling_method=eval_args.pooling_method,\n normalize_embeddings=eval_args.normalize_embeddings,\n results=results,\n save_path=os.path.join(eval_args.eval_result_save_dir, f\"{os.path.basename(encoder)}.json\"),\n eval_languages=languages\n )\n print(\"==================================================\")\n print(\"Finish generating evaluation results with model:\")\n print(eval_args.encoder)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "research/C_MTEB/README.md", "content": "

Chinese Massive Text Embedding Benchmark

\n

\n \n \"Build\"\n \n \n \"Build\"\n \n \n \"Build\"\n \n

\n\n

\n

\n Installation | \n Evaluation |\n Leaderboard |\n Tasks |\n Acknowledgement |\n

\n

\n\n\n## Installation\nC-MTEB is devloped based on [MTEB](https://github.com/embeddings-benchmark/mteb). \n```\npip install -U C_MTEB\n```\nOr clone this repo and install as editable\n```\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding/research/C_MTEB\npip install -e .\n```\n\n## Evaluation\n\n### Evaluate reranker\n```bash\npython eval_cross_encoder.py --model_name_or_path BAAI/bge-reranker-base\n```\n\n### Evaluate embedding model\n* **With our scripts**\n\nYou can **reproduce the results of `baai-general-embedding (bge)`** using the provided python script (see [eval_C-MTEB.py](./eval_C-MTEB.py) )\n```bash\npython eval_C-MTEB.py --model_name_or_path BAAI/bge-large-zh\n\n# for MTEB leaderboard\npython eval_MTEB.py --model_name_or_path BAAI/bge-large-en\n\n```\n\n* **With sentence-transformers** \n\nYou can use C-MTEB easily in the same way as [MTEB](https://github.com/embeddings-benchmark/mteb).\n\nNote that the original sentence-transformers model doesn't support instruction. \nSo this method cannot test the performance of `bge-*` models.\n\n```python\nfrom mteb import MTEB\nfrom C_MTEB import *\nfrom sentence_transformers import SentenceTransformer\n\n# Define the sentence-transformers model name\nmodel_name = \"bert-base-uncased\"\n\nmodel = SentenceTransformer(model_name)\nevaluation = MTEB(task_langs=['zh'])\nresults = evaluation.run(model, output_folder=f\"zh_results/{model_name}\")\n```\n\n\n* **Using a custom model** \nTo evaluate a new model, you can load it via sentence_transformers if it is supported by sentence_transformers.\nOtherwise, models should be implemented like below (implementing an `encode` function taking as input a list of sentences, and returning a list of embeddings (embeddings can be `np.array`, `torch.tensor`, etc.).): \n\n```python\nclass MyModel():\n def encode(self, sentences, batch_size=32, **kwargs):\n \"\"\" Returns a list of embeddings for the given sentences.\n Args:\n sentences (`List[str]`): List of sentences to encode\n batch_size (`int`): Batch size for the encoding\n\n Returns:\n `List[np.ndarray]` or `List[tensor]`: List of embeddings for the given sentences\n \"\"\"\n pass\n\nmodel = MyModel()\nevaluation = MTEB(tasks=[\"T2Retrival\"])\nevaluation.run(model)\n```\n\n\n## Leaderboard\n\n### 1. Reranker\n\n| Model | T2Reranking | T2RerankingZh2En\\* | T2RerankingEn2Zh\\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| \n| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | \n| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | \n| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | \n| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | \n| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | \n| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | \n| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | \n| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | \n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | \n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \n\n\\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval task\n\n\n### 2. Embedding \n| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |\n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | \n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | \n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | \n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |\n| [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |\n| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |\n| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |\n| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |\n| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |\n| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |\n| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |\n| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |\n| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |\n| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |\n\n\n### 2.1. Retrieval\n| Model | T2Retrieval | MMarcoRetrieval | DuRetrieval | CovidRetrieval | CmedqaRetrieval | EcomRetrieval | MedicalRetrieval | VideoRetrieval | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| \n| luotuo-bert-medium | 58.67 | 55.31 | 59.36 | 55.48 | 18.04 | 40.48 | 29.8 | 38.04 | 44.4 | \n| text2vec-large-chinese | 50.52 | 45.96 | 51.87 | 60.48 | 15.53 | 37.58 | 30.93 | 42.65 | 41.94 | \n| text2vec-base-chinese | 51.67 | 44.06 | 52.23 | 44.81 | 15.91 | 34.59 | 27.56 | 39.52 | 38.79 | \n| m3e-base | 73.14 | 65.45 | 75.76 | 66.42 | 30.33 | 50.27 | 42.8 | 51.11 | 56.91 | \n| m3e-large | 72.36 | 61.06 | 74.69 | 61.33 | 30.73 | 45.18 | 48.66 | 44.02 | 54.75 | \n| OpenAI(text-embedding-ada-002) | 69.14 | 69.86 | 71.17 | 57.21 | 22.36 | 44.49 | 37.92 | 43.85 | 52.0 | \n| multilingual-e5-small | 71.39 | 73.17 | 81.35 | 72.82 | 24.38 | 53.56 | 44.84 | 58.09 | 59.95 |\n| multilingual-e5-base | 70.86 | 76.04 | 81.64 | 73.45 | 27.2 | 54.17 | 48.35 | 61.3 | 61.63 |\n| multilingual-e5-large | 76.11 | 79.2 | 85.32 | 75.51 | 28.67 | 54.75 | 51.44 | 58.25 | 63.66 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 77.59 | 67.56 | 77.89 | 68.95 | 35.18 | 58.17 | 49.9 | 69.33 | 63.07 | \n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 83.35 | 79.11 | 86.02 | 72.07 | 41.77 | 63.53 | 56.64 | 73.76 | 69.53 | \n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 84.39 | 81.38 | 84.68 | 75.07 | 41.03 | 65.6 | 58.28 | 73.94 | 70.55 | \n| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 84.82 | 81.28 | 86.94 | 74.06 | 42.4 | 66.12 | 59.39 | 77.19 | 71.53 | \n\n\n### 2.2. STS \n| Model | ATEC | BQ | LCQMC | PAWSX | STSB | AFQMC | QBQTC | STS22 (zh) | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n| luotuo-bert-medium | 30.84 | 43.33 | 66.74 | 12.31 | 73.22 | 22.24 | 27.2 | 66.4 | 42.78 |\n| text2vec-large-chinese | 32.45 | 44.22 | 69.16 | 14.55 | 79.45 | 24.51 | 29.51 | 65.94 | 44.97 |\n| text2vec-base-chinese | 31.93 | 42.67 | 70.16 | 17.21 | 79.3 | 26.06 | 24.62 | 55.35 | 43.41 |\n| m3e-base | 41.27 | 63.81 | 74.88 | 12.19 | 76.97 | 35.87 | 32.07 | 66.73 | 50.47 |\n| m3e-large | 41.8 | 65.2 | 74.2 | 15.95 | 74.16 | 36.53 | 32.65 | 62.91 | 50.42 |\n| OpenAI(text-embedding-ada-002) | 29.25 | 45.33 | 68.41 | 16.55 | 70.61 | 23.88 | 30.27 | 62.53 | 43.35 |\n| multilingual-e5-small | 35.14 | 43.27 | 72.7 | 11.01 | 77.73 | 25.21 | 30.25 | 66.84 | 45.27 |\n| multilingual-e5-base | 37.01 | 45.45 | 74.15 | 12.14 | 79.05 | 29.67 | 28.81 | 65.64 | 46.49 |\n| multilingual-e5-large | 39.81 | 46.44 | 75.95 | 14.63 | 81.08 | 33.02 | 29.77 | 66.82 | 48.44 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 43.17 | 55.47 | 72.61 | 9.97 | 76.48 | 33.93 | 36.45 | 67.54 | 49.45 |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 48.28 | 61.21 | 74.98 | 20.65 | 78.66 | 42.53 | 38.01 | 68.64 | 54.12 |\n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 48.29 | 60.53 | 74.71 | 16.64 | 78.41 | 43.06 | 35.2 | 67.19 | 53 |\n| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 49.75 | 62.93 | 75.45 | 22.45 | 78.51 | 44.57 | 38.92 | 67.24 | 54.98 |\n\n\n### 2.3. PairClassification \n| Model | Ocnli | Cmnli | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:| \n| luotuo-bert-medium | 60.7 | 72.55 | 66.62 | \n| text2vec-large-chinese | 64.04 | 77.67 | 70.86 | \n| text2vec-base-chinese | 60.95 | 73.87 | 67.41 | \n| m3e-base | 58.0 | 69.98 | 63.99 | \n| m3e-large | 59.33 | 69.27 | 64.3 | \n| OpenAI(text-embedding-ada-002) | 63.08 | 76.03 | 69.56 |\n| multilingual-e5-small | 60.77 | 72.12 | 66.45 |\n| multilingual-e5-base | 59.63 | 74.51 | 67.07 |\n| multilingual-e5-large | 78.18 | 78.18 | 69.89 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 65.25 | 75.46 | 70.35 | \n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 73.32 | 81.69 | 77.5 | \n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 71.37 | 82.17 | 76.77 | \n| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 75.75 | 82.12 | 78.94 | \n\n\n### 2.4. Classification \n| Model | TNews | IFlyTek | MultilingualSentiment | JDReview | OnlineShopping | Waimai | AmazonReviewsClassification (zh) | MassiveIntentClassification (zh-CN) | MassiveScenarioClassification (zh-CN) | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| \n| luotuo-bert-medium | 45.22 | 41.75 | 61.21 | 79.68 | 84.3 | 79.57 | 34.46 | 57.47 | 65.32 | 61 |\n| text2vec-large-chinese | 38.92 | 41.54 | 58.97 | 81.56 | 83.51 | 76.01 | 33.77 | 63.23 | 68.45 | 60.66 |\n| text2vec-base-chinese | 43.02 | 42.05 | 60.98 | 82.14 | 85.69 | 77.22 | 34.12 | 63.98 | 70.52 | 62.19 |\n| m3e-base | 48.28 | 44.42 | 71.9 | 85.33 | 87.77 | 83.99 | 43.02 | 68.4 | 74.6 | 67.52 |\n| m3e-large | 48.26 | 43.96 | 72.47 | 86.92 | 89.59 | 86.1 | 44.44 | 67.23 | 74.88 | 68.2 |\n| OpenAI(text-embedding-ada-002) | 45.77 | 44.62 | 67.99 | 74.6 | 88.94 | 82.37 | 38.3 | 64.81 | 71.4 | 64.31 |\n| multilingual-e5-small | 48.38 | 47.35 | 64.74 | 79.34 | 88.73 | 83.9 | 37.5 | 68.24 | 74.47 | 65.85 |\n| multilingual-e5-base | 47.06 | 44.93 | 65.28 | 76.21 | 88.4 | 84.42 | 37.23 | 69.16 | 75.42 | 65.35 |\n| multilingual-e5-large | 48.38 | 45.47 | 68.58 | 80.99 | 90.81 | 85.02 | 38.83 | 71.12 | 76.83 | 67.34 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 47.67 | 42.07 | 65.07 | 80.64 | 87.4 | 83.8 | 37.31 | 61.44 | 67.39 | 63.64 |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 49.97 | 44.54 | 70.63 | 83.92 | 91.38 | 85.46 | 40.68 | 65.72 | 71.3 | 67.07 | \n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 52.05 | 45.32 | 73.7 | 85.38 | 91.66 | 86.83 | 41.94 | 66.96 | 73.39 | 68.58 |\n| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 50.84 | 45.09 | 74.41 | 85.08 | 91.6 | 86.54 | 42.39 | 67.18 | 71.76 | 68.32 | \n\n\n### 2.5. Reranking \n| Model | T2Reranking | MmarcoReranking | CMedQAv1 | CMedQAv2 | Avg | \n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:| \n| luotuo-bert-medium | 65.76 | 14.55 | 57.82 | 58.88 | 49.25 | \n| text2vec-large-chinese | 64.82 | 12.48 | 58.92 | 60.41 | 49.16 | \n| text2vec-base-chinese | 65.95 | 12.76 | 59.26 | 59.82 | 49.45 | \n| m3e-base | 66.03 | 17.51 | 77.05 | 76.76 | 59.34 | \n| m3e-large | 66.13 | 16.46 | 77.76 | 78.27 | 59.66 | \n| OpenAI(text-embedding-ada-002) | 66.65 | 23.39 | 63.08 | 64.02 | 54.28 |\n| multilingual-e5-small | 65.24 | 24.33 | 63.44 | 62.41 | 53.86 |\n| multilingual-e5-base | 64.39 | 21.76 | 65.21 | 66.06 | 54.35 |\n| multilingual-e5-large | 65.83 | 21.34 | 68.25 | 68.56 | 56.00 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 66.2 | 22.82 | 77.08 | 79.82 | 61.48 | \n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 66.49 | 28.24 | 80.12 | 84.78 | 64.91 | \n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 66.16 | 27.1 | 81.72 | 84.64 | 64.91 | \n| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 66.19 | 26.23 | 83.01 | 85.01 | 65.11 | \n\n### 2.6. Clustering \n| Model | CLSClusteringS2S | CLSClusteringP2P | ThuNewsClusteringS2S | ThuNewsClusteringP2P | Avg |\n|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|\n| luotuo-bert-medium | 33.46 | 37.01 | 48.26 | 58.83 | 44.39 |\n| text2vec-large-chinese | 28.77 | 30.13 | 26.14 | 35.05 | 30.02 |\n| text2vec-base-chinese | 32.42 | 35.27 | 40.01 | 42.92 | 37.66 |\n| m3e-base | 37.34 | 39.81 | 53.78 | 59.77 | 47.68 |\n| m3e-large | 38.02 | 38.6 | 58.51 | 60.39 | 48.88 |\n| OpenAI(text-embedding-ada-002) | 35.91 | 38.26 | 49.86 | 58.71 | 45.68 |\n| multilingual-e5-small | 37.79 | 39.14 | 48.93 | 55.18 | 45.26 |\n| multilingual-e5-base | 36.99 | 32.41 | 52.36 | 40.98 | 40.68 |\n| multilingual-e5-large | 38.59 | 40.68 | 55.59 | 58.05 | 48.23 |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 34.34 | 38.23 | 51.84 | 55.95 | 45.09 |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 36.59 | 38.79 | 56.16 | 59.0 | 47.63 |\n| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 40.04 | 41.23 | 56.75 | 62.03 | 50.01 |\n| [**bge-large-zh**](https://huggingface.co/BAAI/bge-large-zh) | 38.05 | 40.92 | 58.79 | 55.79 | 48.39 |\n\n\n\n## Tasks\n\nAn overview of tasks and datasets available in MTEB-chinese is provided in the following table:\n\n| Name | Hub URL | Description | Type | Category | Test #Samples | \n|-----|-----|---------------------------|-----|-----|-----|\n| [T2Retrieval](https://arxiv.org/abs/2304.03679) | [C-MTEB/T2Retrieval](https://huggingface.co/datasets/C-MTEB/T2Retrieval) | T2Ranking: A large-scale Chinese Benchmark for Passage Ranking | Retrieval | s2p | 24,832 | \n| [MMarcoRetrieval](https://github.com/unicamp-dl/mMARCO) | [C-MTEB/MMarcoRetrieval](https://huggingface.co/datasets/C-MTEB/MMarcoRetrieval) | mMARCO is a multilingual version of the MS MARCO passage ranking dataset | Retrieval | s2p | 7,437 | \n| [DuRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | [C-MTEB/DuRetrieval](https://huggingface.co/datasets/C-MTEB/DuRetrieval) | A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine | Retrieval | s2p | 4,000 |\n| [CovidRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | [C-MTEB/CovidRetrieval](https://huggingface.co/datasets/C-MTEB/CovidRetrieval) | COVID-19 news articles | Retrieval | s2p | 949 |\n| [CmedqaRetrieval](https://aclanthology.org/2022.emnlp-main.357.pdf) | [C-MTEB/CmedqaRetrieval](https://huggingface.co/datasets/C-MTEB/CmedqaRetrieval) | Online medical consultation text | Retrieval | s2p | 3,999 | \n| [EcomRetrieval](https://arxiv.org/abs/2203.03367) | [C-MTEB/EcomRetrieval](https://huggingface.co/datasets/C-MTEB/EcomRetrieval) | Passage retrieval dataset collected from Alibaba search engine systems in e-commerce domain | Retrieval | s2p | 1,000 | \n| [MedicalRetrieval](https://arxiv.org/abs/2203.03367) | [C-MTEB/MedicalRetrieval](https://huggingface.co/datasets/C-MTEB/MedicalRetrieval) | Passage retrieval dataset collected from Alibaba search engine systems in medical domain | Retrieval | s2p | 1,000 |\n| [VideoRetrieval](https://arxiv.org/abs/2203.03367) | [C-MTEB/VideoRetrieval](https://huggingface.co/datasets/C-MTEB/VideoRetrieval) | Passage retrieval dataset collected from Alibaba search engine systems in video domain | Retrieval | s2p | 1,000 |\n| [T2Reranking](https://arxiv.org/abs/2304.03679) | [C-MTEB/T2Reranking](https://huggingface.co/datasets/C-MTEB/T2Reranking) | T2Ranking: A large-scale Chinese Benchmark for Passage Ranking | Reranking | s2p | 24,382 | \n| [MMarcoReranking](https://github.com/unicamp-dl/mMARCO) | [C-MTEB/MMarco-reranking](https://huggingface.co/datasets/C-MTEB/Mmarco-reranking) | mMARCO is a multilingual version of the MS MARCO passage ranking dataset | Reranking | s2p | 7,437 | \n| [CMedQAv1](https://github.com/zhangsheng93/cMedQA) | [C-MTEB/CMedQAv1-reranking](https://huggingface.co/datasets/C-MTEB/CMedQAv1-reranking) | Chinese community medical question answering | Reranking | s2p | 2,000 |\n| [CMedQAv2](https://github.com/zhangsheng93/cMedQA2) | [C-MTEB/CMedQAv2-reranking](https://huggingface.co/datasets/C-MTEB/C-MTEB/CMedQAv2-reranking) | Chinese community medical question answering | Reranking | s2p | 4,000 |\n| [Ocnli](https://arxiv.org/abs/2010.05444) | [C-MTEB/OCNLI](https://huggingface.co/datasets/C-MTEB/OCNLI) | Original Chinese Natural Language Inference dataset | PairClassification | s2s | 3,000 |\n| [Cmnli](https://huggingface.co/datasets/clue/viewer/cmnli) | [C-MTEB/CMNLI](https://huggingface.co/datasets/C-MTEB/CMNLI) | Chinese Multi-Genre NLI | PairClassification | s2s | 139,000 |\n| [CLSClusteringS2S](https://arxiv.org/abs/2209.05034) | [C-MTEB/CLSClusteringS2S](https://huggingface.co/datasets/C-MTEB/C-MTEB/CLSClusteringS2S) | Clustering of titles from CLS dataset. Clustering of 13 sets, based on the main category. | Clustering | s2s | 10,000 |\n| [CLSClusteringP2P](https://arxiv.org/abs/2209.05034) | [C-MTEB/CLSClusteringP2P](https://huggingface.co/datasets/C-MTEB/CLSClusteringP2P) | Clustering of titles + abstract from CLS dataset. Clustering of 13 sets, based on the main category. | Clustering | p2p | 10,000 |\n| [ThuNewsClusteringS2S](http://thuctc.thunlp.org/) | [C-MTEB/ThuNewsClusteringS2S](https://huggingface.co/datasets/C-MTEB/ThuNewsClusteringS2S) | Clustering of titles from the THUCNews dataset | Clustering | s2s | 10,000 |\n| [ThuNewsClusteringP2P](http://thuctc.thunlp.org/) | [C-MTEB/ThuNewsClusteringP2P](https://huggingface.co/datasets/C-MTEB/ThuNewsClusteringP2P) | Clustering of titles + abstract from the THUCNews dataset | Clustering | p2p | 10,000 |\n| [ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC) | [C-MTEB/ATEC](https://huggingface.co/datasets/C-MTEB/ATEC) | ATEC NLP sentence pair similarity competition | STS | s2s | 20,000 |\n| [BQ](https://huggingface.co/datasets/shibing624/nli_zh) | [C-MTEB/BQ](https://huggingface.co/datasets/C-MTEB/BQ) | Bank Question Semantic Similarity | STS | s2s | 10,000 |\n| [LCQMC](https://huggingface.co/datasets/shibing624/nli_zh) | [C-MTEB/LCQMC](https://huggingface.co/datasets/C-MTEB/LCQMC) | A large-scale Chinese question matching corpus. | STS | s2s | 12,500 |\n| [PAWSX](https://arxiv.org/pdf/1908.11828.pdf) | [C-MTEB/PAWSX](https://huggingface.co/datasets/C-MTEB/PAWSX) | Translated PAWS evaluation pairs | STS | s2s | 2,000 |\n| [STSB](https://github.com/pluto-junzeng/CNSD) | [C-MTEB/STSB](https://huggingface.co/datasets/C-MTEB/STSB) | Translate STS-B into Chinese | STS | s2s | 1,360 |\n| [AFQMC](https://github.com/CLUEbenchmark/CLUE) | [C-MTEB/AFQMC](https://huggingface.co/datasets/C-MTEB/AFQMC) | Ant Financial Question Matching Corpus| STS | s2s | 3,861 |\n| [QBQTC](https://github.com/CLUEbenchmark/QBQTC) | [C-MTEB/QBQTC](https://huggingface.co/datasets/C-MTEB/QBQTC) | QQ Browser Query Title Corpus | STS | s2s | 5,000 |\n| [TNews](https://github.com/CLUEbenchmark/CLUE) | [C-MTEB/TNews-classification](https://huggingface.co/datasets/C-MTEB/TNews-classification) | Short Text Classificaiton for News | Classification | s2s | 10,000 |\n| [IFlyTek](https://github.com/CLUEbenchmark/CLUE) | [C-MTEB/IFlyTek-classification](https://huggingface.co/datasets/C-MTEB/IFlyTek-classification) | Long Text classification for the description of Apps | Classification | s2s | 2,600 |\n| [Waimai](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/waimai_10k/intro.ipynb) | [C-MTEB/waimai-classification](https://huggingface.co/datasets/C-MTEB/waimai-classification) | Sentiment Analysis of user reviews on takeaway platforms | Classification | s2s | 1,000 |\n| [OnlineShopping](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/online_shopping_10_cats/intro.ipynb) | [C-MTEB/OnlineShopping-classification](https://huggingface.co/datasets/C-MTEB/OnlineShopping-classification) | Sentiment Analysis of User Reviews on Online Shopping Websites | Classification | s2s | 1,000 |\n| [MultilingualSentiment](https://github.com/tyqiangz/multilingual-sentiment-datasets) | [C-MTEB/MultilingualSentiment-classification](https://huggingface.co/datasets/C-MTEB/MultilingualSentiment-classification) | A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative | Classification | s2s | 3,000 |\n| [JDReview](https://huggingface.co/datasets/kuroneko5943/jd21) | [C-MTEB/JDReview-classification](https://huggingface.co/datasets/C-MTEB/JDReview-classification) | review for iphone | Classification | s2s | 533 |\n\nFor retrieval tasks, we sample 100,000 candidates (including the ground truths) from the entire corpus to reduce the inference cost. \n\n## Acknowledgement\n\nWe thank the great tool from [Massive Text Embedding Benchmark](https://github.com/embeddings-benchmark/mteb) and the open-source datasets from Chinese NLP community.\n\n\n## Citation\n\nIf you find this repository useful, please consider citation\n\n```\n@misc{c-pack,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n" }, { "path": "research/C_MTEB/eval_C-MTEB.py", "content": "import argparse\n\nfrom C_MTEB import ChineseTaskList\nfrom flag_dres_model import FlagDRESModel\nfrom mteb import MTEB\n\nquery_instruction_for_retrieval_dict = {\n \"BAAI/bge-large-zh\": \"为这个句子生成表示以用于检索相关文章：\",\n \"BAAI/bge-large-zh-noinstruct\": None,\n \"BAAI/bge-base-zh\": \"为这个句子生成表示以用于检索相关文章：\",\n \"BAAI/bge-small-zh\": \"为这个句子生成表示以用于检索相关文章：\",\n \"BAAI/bge-large-zh-v1.5\": \"为这个句子生成表示以用于检索相关文章：\",\n \"BAAI/bge-base-zh-v1.5\": \"为这个句子生成表示以用于检索相关文章：\",\n \"BAAI/bge-small-zh-v.15\": \"为这个句子生成表示以用于检索相关文章：\",\n}\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--model_name_or_path', default=\"BAAI/bge-large-zh\", type=str)\n parser.add_argument('--task_type', default=None, type=str)\n parser.add_argument('--add_instruction', action='store_true', help=\"whether to add instruction for query\")\n parser.add_argument('--pooling_method', default='cls', type=str)\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = get_args()\n\n model = FlagDRESModel(model_name_or_path=args.model_name_or_path,\n query_instruction_for_retrieval=\"为这个句子生成表示以用于检索相关文章：\",\n pooling_method=args.pooling_method)\n \n print(ChineseTaskList)\n\n for task in ChineseTaskList:\n if task in ['T2Retrieval', 'MMarcoRetrieval', 'DuRetrieval',\n 'CovidRetrieval', 'CmedqaRetrieval',\n 'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',\n 'T2Reranking', 'MMarcoReranking', 'CMedQAv1-reranking', 'CMedQAv2-reranking']:\n if args.model_name_or_path not in query_instruction_for_retrieval_dict:\n if args.add_instruction:\n instruction = \"为这个句子生成表示以用于检索相关文章：\"\n else:\n instruction = None\n print(f\"{args.model_name_or_path} not in query_instruction_for_retrieval_dict, set instruction={instruction}\")\n else:\n instruction = query_instruction_for_retrieval_dict[args.model_name_or_path]\n else:\n instruction = None\n\n model.query_instruction_for_retrieval = instruction\n\n evaluation = MTEB(tasks=[task])\n evaluation.run(model, output_folder=f\"zh_results/{args.model_name_or_path.split('/')[-1]}\")\n" }, { "path": "research/C_MTEB/eval_MTEB.py", "content": "import argparse\n\nfrom flag_dres_model import FlagDRESModel\nfrom mteb import MTEB\n\nquery_instruction_for_retrieval_dict = {\n \"BAAI/bge-large-en\": \"Represent this sentence for searching relevant passages: \",\n \"BAAI/bge-base-en\": \"Represent this sentence for searching relevant passages: \",\n \"BAAI/bge-small-en\": \"Represent this sentence for searching relevant passages: \",\n \"BAAI/bge-large-en-v1.5\": \"Represent this sentence for searching relevant passages: \",\n \"BAAI/bge-base-en-v1.5\": \"Represent this sentence for searching relevant passages: \",\n \"BAAI/bge-small-en-v1.5\": \"Represent this sentence for searching relevant passages: \",\n}\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--model_name_or_path', default=\"BAAI/bge-large-en\", type=str)\n parser.add_argument('--task_type', default=None, type=str, help=\"task type. Default is None, which means using all task types\")\n parser.add_argument('--add_instruction', action='store_true', help=\"whether to add instruction for query\")\n parser.add_argument('--pooling_method', default='cls', type=str)\n\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = get_args()\n\n model = FlagDRESModel(model_name_or_path=args.model_name_or_path,\n normalize_embeddings=False, # normlize embedding will harm the performance of classification task\n query_instruction_for_retrieval=\"Represent this sentence for searching relevant passages: \",\n pooling_method=args.pooling_method)\n\n # task_names = [t.description[\"name\"] for t in MTEB(task_types=args.task_type,\n # task_langs=['en']).tasks]\n \n task_names = [t.metadata.name for t in MTEB(task_types=args.task_type,\n task_langs=['en']).tasks]\n\n for task in task_names:\n if task in ['MSMARCOv2']:\n print('Skip task: {}, since it has no test split'.format(task))\n continue\n\n if 'CQADupstack' in task or task in ['Touche2020', 'SciFact', 'TRECCOVID', 'NQ',\n 'NFCorpus', 'MSMARCO', 'HotpotQA', 'FiQA2018',\n 'FEVER', 'DBPedia', 'ClimateFEVER', 'SCIDOCS', ]:\n if args.model_name_or_path not in query_instruction_for_retrieval_dict:\n if args.add_instruction:\n instruction = \"Represent this sentence for searching relevant passages: \"\n else:\n instruction = None\n print(f\"{args.model_name_or_path} not in query_instruction_for_retrieval_dict, set instruction={instruction}\")\n else:\n instruction = query_instruction_for_retrieval_dict[args.model_name_or_path]\n else:\n instruction = None\n\n model.query_instruction_for_retrieval = instruction\n\n evaluation = MTEB(tasks=[task], task_langs=['en'], eval_splits = [\"test\" if task not in ['MSMARCO'] else 'dev'])\n evaluation.run(model, output_folder=f\"en_results/{args.model_name_or_path.split('/')[-1]}\")\n" }, { "path": "research/C_MTEB/eval_cross_encoder.py", "content": "import argparse\n\nfrom C_MTEB.tasks import *\nfrom mteb import MTEB\n\nfrom FlagEmbedding import FlagReranker\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--model_name_or_path', default=\"BAAI/bge-reranker-base\", type=str)\n return parser.parse_args()\n\n\n\nif __name__ == '__main__':\n args = get_args()\n\n model = FlagReranker(args.model_name_or_path, use_fp16=True)\n\n if 'checkpoint-' in args.model_name_or_path:\n save_name = \"_\".join(args.model_name_or_path.split('/')[-2:])\n else:\n save_name = \"_\".join(args.model_name_or_path.split('/')[-1:])\n\n evaluation = MTEB(task_types=[\"Reranking\"], task_langs=['zh', 'zh2en', 'en2zh'])\n evaluation.run(model, output_folder=f\"reranker_results/{save_name}\")\n\n\n\n" }, { "path": "research/C_MTEB/flag_dres_model.py", "content": "from typing import cast, List, Dict, Union\n\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom transformers import AutoModel, AutoTokenizer, is_torch_npu_available\n\n\nclass FlagDRESModel:\n def __init__(\n self,\n model_name_or_path: str = None,\n pooling_method: str = 'cls',\n normalize_embeddings: bool = True,\n query_instruction_for_retrieval: str = None,\n batch_size: int = 256,\n ) -> None:\n\n self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n self.model = AutoModel.from_pretrained(model_name_or_path)\n self.query_instruction_for_retrieval = query_instruction_for_retrieval\n self.normalize_embeddings = normalize_embeddings\n self.pooling_method = pooling_method\n self.batch_size = batch_size\n\n if torch.cuda.is_available():\n self.device = torch.device(\"cuda\")\n elif is_torch_npu_available():\n self.device = torch.device(\"npu\")\n else:\n self.device = torch.device(\"cpu\")\n self.model = self.model.to(self.device)\n\n num_gpus = torch.cuda.device_count()\n if num_gpus > 1:\n self.model = torch.nn.DataParallel(self.model)\n self.batch_size = self.batch_size * num_gpus\n\n\n def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:\n '''\n This function will be used for retrieval task\n if there is a instruction for queries, we will add it to the query text\n '''\n if self.query_instruction_for_retrieval is not None:\n input_texts = ['{}{}'.format(self.query_instruction_for_retrieval, q) for q in queries]\n else:\n input_texts = queries\n return self.encode(input_texts)\n\n\n def encode_corpus(self, corpus: List[Union[Dict[str, str], str]], **kwargs) -> np.ndarray:\n '''\n This function will be used for retrieval task\n encode corpus for retrieval task\n '''\n if isinstance(corpus[0], dict):\n input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus]\n else:\n input_texts = corpus\n return self.encode(input_texts)\n\n\n @torch.no_grad()\n def encode(self, sentences: List[str], **kwargs) -> np.ndarray:\n self.model.eval()\n\n all_embeddings = []\n for start_index in tqdm(range(0, len(sentences), self.batch_size), desc=\"Batches\", disable=len(sentences)<256):\n sentences_batch = sentences[start_index:start_index + self.batch_size]\n inputs = self.tokenizer(\n sentences_batch,\n padding=True,\n truncation=True,\n return_tensors='pt',\n max_length=512,\n ).to(self.device)\n last_hidden_state = self.model(**inputs, return_dict=True).last_hidden_state\n embeddings = self.pooling(last_hidden_state, inputs['attention_mask'])\n if self.normalize_embeddings:\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1)\n embeddings = cast(torch.Tensor, embeddings)\n all_embeddings.append(embeddings.cpu().numpy())\n\n return np.concatenate(all_embeddings, axis=0)\n\n def pooling(self,\n last_hidden_state: torch.Tensor,\n attention_mask: torch.Tensor=None):\n if self.pooling_method == 'cls':\n return last_hidden_state[:, 0]\n elif self.pooling_method == 'mean':\n s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)\n d = attention_mask.sum(dim=1, keepdim=True).float()\n return s / d\n\n\n" }, { "path": "research/C_MTEB/setup.py", "content": "from setuptools import setup, find_packages\n\nwith open(\"README.md\", mode=\"r\", encoding=\"utf-8\") as readme_file:\n readme = readme_file.read()\n\nsetup(\n name='C_MTEB',\n version='1.1.1',\n description='Chinese Massive Text Embedding Benchmark',\n long_description=readme,\n long_description_content_type=\"text/markdown\",\n author_email='2906698981@qq.com',\n url='https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB',\n packages=find_packages(),\n install_requires=[\n 'mteb[beir]==1.1.1',\n ],\n)\n" }, { "path": "research/C_MTEB/summarize_results.py", "content": "import argparse\nimport json\nimport os\nfrom collections import defaultdict\n\nfrom C_MTEB import *\nimport mteb\nfrom mteb import MTEB\n\n\nCMTEB_tasks = [\n 'TNews', 'IFlyTek', 'MultilingualSentiment', 'JDReview', 'OnlineShopping', 'Waimai',\n 'CLSClusteringS2S.v2', 'CLSClusteringP2P.v2', 'ThuNewsClusteringS2S.v2', 'ThuNewsClusteringP2P.v2',\n 'Ocnli', 'Cmnli',\n 'T2Reranking', 'MMarcoReranking', 'CMedQAv1-reranking', 'CMedQAv2-reranking',\n 'T2Retrieval', 'MMarcoRetrieval', 'DuRetrieval', 'CovidRetrieval', 'CmedqaRetrieval', 'EcomRetrieval', 'MedicalRetrieval', 'VideoRetrieval',\n 'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STSB', 'AFQMC', 'QBQTC'\n]\n\n\ndef read_results(task_types, args):\n tasks_results = {}\n # model_dirs = {}\n for t_type in task_types:\n tasks_results[t_type] = {}\n for t in mteb.get_tasks(task_types=[t_type]):\n task_name = t.metadata.name\n if task_name not in CMTEB_tasks:\n continue\n\n metric = t.metadata.main_score\n tasks_results[t_type][task_name] = defaultdict(None)\n\n if os.path.exists(os.path.join(args.results_dir, task_name + '.json')):\n data = json.load(open(os.path.join(args.results_dir, task_name + '.json')))\n for s in ['test', 'dev', 'validation']:\n if s in data['scores']:\n split = s\n break\n\n temp_data = data['scores'][split][0]\n tasks_results[t_type][task_name] = round(temp_data[metric] * 100, 2)\n\n return tasks_results\n\n\ndef output_markdown(tasks_results, model, save_file):\n task_type_res = {}\n with open(save_file, 'w') as f:\n for t_type, type_results in tasks_results.items():\n has_CQADupstack = False\n task_cnt = 0\n task_type_res[t_type] = defaultdict()\n f.write(f'Task Type: {t_type} \\n')\n first_line = \"| Model |\"\n second_line = \"|:-------------------------------|\"\n for task_name in type_results.keys():\n if \"CQADupstack\" in task_name:\n has_CQADupstack = True\n continue\n first_line += f\" {task_name} |\"\n second_line += \":--------:|\"\n task_cnt += 1\n if has_CQADupstack:\n first_line += f\" CQADupstack |\"\n second_line += \":--------:|\"\n task_cnt += 1\n f.write(first_line + ' Avg | \\n')\n f.write(second_line + ':--------:| \\n')\n\n write_line = f\"| {model} |\"\n all_res = []\n cqa_res = []\n for task_name, results in type_results.items():\n if \"CQADupstack\" in task_name:\n if model in results:\n cqa_res.append(results[model])\n continue\n\n write_line += f\" {results} |\"\n all_res.append(results)\n\n if len(cqa_res) > 0:\n write_line += f\" {round(sum(cqa_res) / len(cqa_res), 2)} |\"\n all_res.append(round(sum(cqa_res) / len(cqa_res), 2))\n\n # if len(all_res) == len(type_results.keys()):\n if len(all_res) == task_cnt:\n write_line += f\" {round(sum(all_res) / len(all_res), 2)} |\"\n task_type_res[t_type][model] = all_res\n else:\n write_line += f\" |\"\n f.write(write_line + ' \\n\\n')\n\n f.write(f'Overall \\n')\n first_line = \"| Model |\"\n second_line = \"|:-------------------------------|\"\n for t_type in task_type_res.keys():\n first_line += f\" {t_type} |\"\n second_line += \":--------:|\"\n f.write(first_line + ' Avg | \\n')\n f.write(second_line + ':--------:| \\n')\n\n write_line = f\"| {model} |\"\n all_res = []\n for type_name, results in task_type_res.items():\n if model in results:\n write_line += f\" {round(sum(results[model]) / len(results[model]), 2)} |\"\n all_res.extend(results[model])\n else:\n write_line += f\" |\"\n\n if len(all_res) > 0:\n write_line += f\" {round(sum(all_res) / len(all_res), 2)} |\"\n\n f.write(write_line + ' \\n')\n\n\ndef get_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('--results_dir', default=\"./zh_results\", type=str)\n parser.add_argument('--lang', default=\"zh\", type=str)\n parser.add_argument('--model', default=\"model\", type=str)\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n args = get_args()\n\n if args.lang == 'zho':\n task_types = [\"Retrieval\", \"STS\", \"PairClassification\", \"Classification\", \"Reranking\", \"Clustering\"]\n args.lang = ['zho']\n elif args.lang == 'eng':\n task_types = [\"Retrieval\", \"Clustering\", \"PairClassification\", \"Reranking\", \"STS\", \"Summarization\",\n \"Classification\"]\n args.lang = ['eng']\n else:\n raise NotImplementedError(f\"args.lang must be zh or en, but{args.lang}\")\n\n task_results = read_results(task_types, args=args)\n\n output_markdown(task_results, args.model,\n save_file=os.path.join(args.results_dir, f'{args.lang[0]}_results.md'))\n" }, { "path": "research/LLARA/README.md", "content": "
\n

Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (LLARA) [paper]

\n
\n\nLlama2Vec consists of two pretext tasks:\n- **EBAE** (Embedding-Based Auto-Encoding)\n- **EBAR** (Embedding-Based Auto-Regression)\n\nThe LLM is prompted to **reconstruct the input sentence** and **predict the next sentence** based on its text embeddings.\n\nIt is known for the following features:\n- simple\n- lightweight\n- highly effective\n\n## Environment\n```bash\nconda create llara python=3.10\n\nconda activate llara\n\n# You may need to adjust the cuda version\nconda install pytorch pytorch-cuda=12.1 -c pytorch -c nvidia\npip install transformers==4.41.0 deepspeed accelerate datasets peft pandas\npip install flash-attn --no-build-isolation\n```\n\n## Model List\n\n| Model | Introduction |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| [BAAI/LLARA-pretrain](https://huggingface.co/BAAI/LLARA-pretrain) | LLARA that has undergone unsupervised adaptation on Wikipedia |\n| [BAAI/LLARA-passage](https://huggingface.co/BAAI/LLARA-passage) | The LLARA-pretrain model fine-tuned on MS MARCO passage (the hard negatives come from dense retriever) |\n| [BAAI/LLARA-document](https://huggingface.co/BAAI/LLARA-document) | The LLARA-pretrain model fine-tuned on MS MARCO document |\n| [BAAI/LLARA-beir](https://huggingface.co/BAAI/LLARA-beir) | The LLARA-pretrain model fine-tuned on MS MARCO passage (the hard negatives come from BM25) |\n\n## Usage\n\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer, LlamaModel\n\ndef get_query_inputs(queries, tokenizer, max_length=512):\n prefix = '\"'\n suffix = '\", predict the following passage within eight words: '\n prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']\n suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]\n queries_inputs = []\n for query in queries:\n inputs = tokenizer(query,\n return_tensors=None,\n max_length=max_length,\n truncation=True,\n add_special_tokens=False)\n inputs['input_ids'] = prefix_ids + inputs['input_ids'] + suffix_ids\n inputs['attention_mask'] = [1] * len(inputs['input_ids'])\n queries_inputs.append(inputs)\n return tokenizer.pad(\n queries_inputs,\n padding=True,\n max_length=max_length,\n pad_to_multiple_of=8,\n return_tensors='pt',\n )\n\ndef get_passage_inputs(passages, tokenizer, max_length=512):\n prefix = '\"'\n suffix = '\", summarize the above passage within eight words: '\n prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']\n suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]\n passages_inputs = []\n for passage in passages:\n inputs = tokenizer(passage,\n return_tensors=None,\n max_length=max_length,\n truncation=True,\n add_special_tokens=False)\n inputs['input_ids'] = prefix_ids + inputs['input_ids'] + suffix_ids\n inputs['attention_mask'] = [1] * len(inputs['input_ids'])\n passages_inputs.append(inputs)\n return tokenizer.pad(\n passages_inputs,\n padding=True,\n max_length=max_length,\n pad_to_multiple_of=8,\n return_tensors='pt',\n )\n\n# Load the tokenizer and model\ntokenizer = AutoTokenizer.from_pretrained('BAAI/LLARA-passage')\nmodel = AutoModel.from_pretrained('BAAI/LLARA-passage')\n\n# Define query and passage inputs\nquery = \"What is llama?\"\ntitle = \"Llama\"\npassage = \"The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.\"\nquery_input = get_query_inputs([query], tokenizer)\npassage_input = get_passage_inputs([passage], tokenizer)\n\n\nwith torch.no_grad():\n # compute query embedding\n query_outputs = model(**query_input, return_dict=True, output_hidden_states=True)\n query_embedding = query_outputs.hidden_states[-1][:, -8:, :]\n query_embedding = torch.mean(query_embedding, dim=1)\n query_embedding = torch.nn.functional.normalize(query_embedding, dim=-1)\n\n # compute passage embedding\n passage_outputs = model(**passage_input, return_dict=True, output_hidden_states=True)\n passage_embeddings = passage_outputs.hidden_states[-1][:, -8:, :]\n passage_embeddings = torch.mean(passage_embeddings, dim=1)\n passage_embeddings = torch.nn.functional.normalize(passage_embeddings, dim=-1)\n\n # compute similarity score\n score = query_embedding @ passage_embeddings.T\n print(score)\n\n```\n\n## Unsupervised Adaption (pretrain)\n1. You can get the complete data here: [cfli/pretrain_wiki](https://huggingface.co/datasets/cfli/pretrain_wiki)\n2. Here is an example for pretrain:\n```shell\ncd ./pretrain\ntorchrun --nproc_per_node 8 \\\nrun.py \\\n--output_dir ./output \\\n--model_name_or_path meta-llama/Llama-2-7b-hf \\\n--train_data ../data/pretrain/toy_pretrain_data.jsonl \\\n--learning_rate 1e-5 \\\n--num_train_epochs 1 \\\n--per_device_train_batch_size 1 \\\n--gradient_accumulation_steps 1 \\\n--dataloader_drop_last True \\\n--cutoff_len 128 \\\n--logging_steps 1 \\\n--save_steps 500 \\\n--save_total_limit 20 \\\n--gradient_checkpointing \\\n--ddp_find_unused_parameters False \\\n--use_flash_attn False \\\n--deepspeed ../stage1.json \\\n--warmup_ratio 0.1 \\\n--remove_stop_words True \\\n--use_lora False \\\n--bf16 \\\n--cache_dir ./LMs \\\n--token ...\n```\nIf you want to pretrain based on the complete data, please use hype-parameters in our paper.\n\n## Fine-tune\n\nHere is an example for fine-tune:\n```shell\ncd ./finetune\ntorchrun --nproc_per_node 8 \\\nrun.py \\\n--output_dir ./output \\\n--model_name_or_path BAAI/LLARA-pretrain \\\n--train_data ../data/finetune/toy_finetune_data.jsonl \\\n--learning_rate 3e-4 \\\n--num_train_epochs 1 \\\n--per_device_train_batch_size 1 \\\n--dataloader_drop_last True \\\n--normlized True \\\n--temperature 0.01 \\\n--query_max_len 64 \\\n--passage_max_len 160 \\\n--train_group_size 16 \\\n--logging_steps 10 \\\n--save_steps 500 \\\n--save_total_limit 3 \\\n--ddp_find_unused_parameters False \\\n--negatives_cross_device \\\n--gradient_checkpointing \\\n--deepspeed ../stage1.json \\\n--warmup_ratio 0.1 \\\n--fp16 \\\n--cache_dir ./LMs \\\n--token ...\n```\n\n## Citation\n\nIf you find this repository useful, please give us a star ⭐.\n\nTo cite our work:\n\n```\n@misc{li2023makinglargelanguagemodels,\n title={Making Large Language Models A Better Foundation For Dense Retrieval}, \n author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},\n year={2023},\n eprint={2312.15503},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2312.15503}, \n}\n```\n" }, { "path": "research/LLARA/data/finetune/toy_finetune_data.jsonl", "content": "{\"query\": \")what was the immediate impact of the success of the manhattan project?\", \"pos\": [\"Introduction The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.\"], \"neg\": [\"Arms Race Even before the United States entered the War it feared that Nazi Germany was attempting to develop an atomic bomb. President Roosevelt therefore authorized an experimental nuclear weapons program \\u00e2\\u0080\\u0093 the Manhattan Project \\u00e2\\u0080\\u0093 in collaboration with Britain and Canada.\", \"- Publisher. A+E Networks. President Harry Truman learns the full details of the Manhattan Project, in which scientists are attempting to create the first atomic bomb, on this day in 1945. The information thrust upon Truman a momentous decision: whether or not to use the world\\u00e2\\u0080\\u0099s first weapon of mass destruction.resident Harry Truman learns the full details of the Manhattan Project, in which scientists are attempting to create the first atomic bomb, on this day in 1945.\", \"- The Manhattan Project was a research and development project that produced the first nuclear weapons during World War II.roves appreciated the early British atomic research and the British scientists' contributions to the Manhattan Project, but stated that the United States would have succeeded without them. He also said that Churchill was the best friend the atomic bomb project had [as] he kept Roosevelt's interest up ...\", \"Chapter 17 - The United States in World War II Organized by FDR to bring scientists into the war effort; spurred on improvements in radar and sonar, use of pesticides like DDT to reduce insects, and developed miracle drugs like penacillin; their biggest achievement was the Manhattan Project.\", \"Nuclear arms race The Soviet Union was not informed officially of the Manhattan Project until Stalin was briefed at the Potsdam Conference on July 24, 1945, by U.S. President Harry S. Truman, eight days after the first successful test of a nuclear weapon.he Soviet Union was not informed officially of the Manhattan Project until Stalin was briefed at the Potsdam Conference on July 24, 1945, by U.S. President Harry S. Truman, eight days after the first successful test of a nuclear weapon.\", \"Manhattan Project The Manhattan Project was a research and development project that produced the first atomic bombs during World War II.It was led by the United States with the support of the United Kingdom and Canada.he Manhattan Project began modestly in 1939, but grew to employ more than 130,000 people and cost nearly US$2 billion (about $26 billion in 2015 dollars). Over 90% of the cost was for building factories and producing the fissionable materials, with less than 10% for development and production of the weapons.\", \"51f. The Manhattan Project In late 1941, the American effort to design and build an atomic bomb received its code name \\u00e2\\u0080\\u0094 the Manhattan Project. At first the research was based at only a few universities \\u00e2\\u0080\\u0094 Columbia University, the University of Chicago and the University of California at Berkeley.\", \"Manhattan Project The wartime Manhattan Project left a legacy in the form of the network of national laboratories: the Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, Oak Ridge National Laboratory, Argonne National Laboratory and Ames Laboratory.\", \"- By a short historical. review the last five decades we can see that project success is. specified by meeting the time, cost and quality criteria. In the 1960\\u00e2\\u0080\\u0099s the measures of project success it was just on a. finishing and operational basis.\", \"What Was the Manhattan Project? The Manhattan Project was the government project that took place from 1942 to 1946, the purpose of which was to develop a nuclear bomb. It succeeded on 16 July 1945 at the Trinity Test in New Mexico and went on to produce the two atomic bombs that destroyed the Japanese cities of Hiroshima and Nagasaki during WWII.\", \"- Man of La Mancha. Item added to wishlist. Item removed from wishlist. To me the most interesting aspect of the success of Man of La Mancha is the fact that it plows squarely upstream against the prevailing current of philosophy in the theater.\", \"- [count] : someone or something that is successful : a person or thing that succeeds. 1 The play was an immediate success. 2 one of her many successes [=one of many things she has done successfully] 3 She is country music's most recent success. The growth of the tourism industry is one of the city's great successes.\", \"- Confidence votes 13. Tim1096 writes The impact they have on society was that they delivere things faster, they shelter us from nature when we need to go to places, and they let us get to places we want quickly..\", \"Manhattan Project This article is about the atomic bomb project. For other uses, see Manhattan Project (disambiguation). The Manhattan Project was a research and development undertaking during World War II that produced the first nuclear weapons. It was led by the United States with the support of the United Kingdom and Canada.\", \"What are some important details in the Manhattan Project? Answer The Manhattan Project was the project, conducted during World War II primarily by the United States, to develop the first atomic bomb.\", \"- While the true measure of success for this communication plan is the reduction in the number of accidents attributable to cell phone usage, it is not possible at this time to collect that data. Therefore, the measures of success indicators below relate to the number of activities listed in the action items.\", \"Introduction Yet this grave definition of success cannot diminish the impressive collaboration and efficiency of the Manhattan Project. Index Terms Atomic Bomb, Fat Man, Little Boy, Manhattan Project, Oppenheimer, J. Robert.\", \"What is the Manhattan project? The Manhattan Project was the project to develop the first nuclear weapon (atomic bomb) during World War II by the United States, the United Kingdom, and Canada.Born out of a small research program in 1939, the Manhattan Project eventually employed more than 130,000 people and cost nearly $2 billion USD ($23 billion in 2007 dollars based on CPI). It resulted in the creation of multiple production and research sites that operated in secret.\", \"Truman is briefed on Manhattan Project President Harry Truman learns the full details of the Manhattan Project, in which scientists are attempting to create the first atomic bomb, on this day in 1945.The information thrust upon Truman a momentous decision: whether or not to use the world\\u00e2\\u0080\\u0099s first weapon of mass destruction.resident Harry Truman learns the full details of the Manhattan Project, in which scientists are attempting to create the first atomic bomb, on this day in 1945.\", \"- The Rosenbergs, leaving their arraignment. The Manhattan Project was the name given to the top secret effort of Allied scientists to develop an atomic bomb. One of the Manhattan Project scientists working in Los Alamos was a British physicist named Klaus Fuchs....\", \"- Without going into the geographical stuff (I know too little about that): The Magnus effect is a direct consequence of the bernoulli equation (which is a form of the energy conversation law), which states p+rho*u\\u00c2\\u00b2/2=constant, p being static pressure, rho density and u the speed of the gas/fluid, in this case air.\", \"- \\u00e2\\u0080\\u00a2 The scale of direct migration pressures on the United States; the impact. of other countries\\u00e2\\u0080\\u0099 migration policies on such pressures; and the extent to. which some countries may try to use migration as leverage in bilateral. relations. \\u00e2\\u0080\\u00a2 The broader implications for the United States of migration trends and.\", \"- Not to be missed are the biographies of the scientists of the Manhattan Project, and a truly amazing what-if scenario where a 150 kiloton bomb explodes at the foot of the Empire State Building.\", \"- The railroad was a major cause. Equally important, the success of the transcontinental railroad encouraged an American faith that with money, determination, and organization anything can be accomplished. The construction of railroad demonstrated the effectiveness of complex military-like organization and assembly-line processes.\", \"- ...from its straight path because of pressure differences that develop in the fluid as a result of velocity changes induced by the spinning body. The Magnus effect is a particular manifestation of Bernoulli\\u00e2\\u0080\\u0099s theorem: fluid pressure decreases at points where the speed of the fluid increases.\", \"What were the effects and impact of the battle of Marathon? The Battle of Marathon was the first attempt , and first defeat , by Persian forces to subjugate Greece . The Persians were defeated by the Athenians at Marathon , Greece .\", \"Impact Of Manpower Training And Development Of Workers' Productivity In a Manufacturing Company Impact Of Manpower Training And Development Of Workers' Productivity In a Manufacturing Company PROJECT PROPOSAL TOPIC: IMPACT OF TRAINING AND MANPOWER DEVELOPMENT IN A MANUFACTURING COMPANY (STUDY OF CADBURY NIGERIA PLC) 1 INTRODUCTION The management of organization in a globalised economy is posing a serious challenge to the leadership skills, ...\", \"What was the purpose of the manhattan project? The Manhattan project was an initiative undertaken by president Franklin Roosevelt, to build an atomic bomb. It was going to be used against Nazi Germany, supposedly only if they developed nuclear weapons (which of course was a a great fear at the time).\", \"The Manhattan Project Timeline The project was given its name due to the fact that at least 10 of the sites used for the research were located in Manhattan. Following is a timeline of the key events related to the development of the atomic bomb and the Manhattan Project. Manhattan Project Timeline\", \"Manhattan Project This article is about the atomic bomb project. For other uses, see Manhattan Project (disambiguation). The Manhattan Project was a research and development project that produced the first nuclear weapons during World War II. It was led by the United States with the support of the United Kingdom and Canada. From 1942 to 1946, the project was under the direction of Major General Leslie Groves of the U.S. Army Corps of Engineers; physicist J. Robert Oppenheimer was the director of the Los Alamos Laboratory that designed the actual bombs.\"], \"pos_scores\": [13.734375], \"neg_scores\": [9.828125, 9.7890625, 10.71875, 8.6796875, 11.234375, 10.984375, 10.3125, 11.671875, 7.53515625, 11.71875, 2.765625, 5.58203125, 6.05859375, 11.0703125, 10.28125, 3.62109375, 11.7734375, 10.8203125, 9.4765625, 10.25, -0.17626953125, 3.630859375, 9.6328125, 4.1796875, 2.537109375, 2.66015625, 4.43359375, 9.8515625, 8.859375, 11.46875]}\n{\"query\": \"_________ justice is designed to repair the harm to victim, the community and the offender caused by the offender criminal act. question 19 options:\", \"pos\": [\"Restorative justice The approach is based on a theory of justice that considers crime and wrongdoing to be an offense against an individual or community, rather than the State. Restorative justice that fosters dialogue between victim and offender has shown the highest rates of victim satisfaction and offender accountability.\"], \"neg\": [\"Proportionality (law) In criminal law, the principle of proportional justice is used to describe the idea that the punishment of a certain crime should be in proportion to the severity of the crime itself. In practice, systems of law differ greatly on the application of this principle.\", \"- Justice is a concept of moral rightness. For example in a court case usually it is the juries job to bring justice to the victim by making sure the proper person is charged fo \\u00e2\\u0080\\u00a6 r the crime.\", \"- Punishment should be swift and certain. The purpose of the criminal justice system is to prevent crime through deterrence. According to this line of thinking, a potential criminal will decide against committing a crime because the punishment would be too costly.\", \"Justice 18th-century statue of Lady Justice, at the Castellania, a symbol of justice. She is a goddess with three items symbolic of justice: a sword, scales and a blindfold. Justice is the legal or philosophical theory by which fairness is administered. The concept of justice differs in every culture. An early theory of justice was set out by the Ancient Greek philosopher Plato in his work The Republic. Advocates of divine command theory argue that justice issues from God. In the 17th century, theorists like John Locke argued for the theory of natural law.\", \"- When a crime is committed not only does the person doing something wrong, but they hurt others in the process. They hurt the person or person who they did the crime against, they hurt family and friends, and they hurt society. Finally, it is against the law which leads us back to the first point and prison.\", \"The Criminal Justice System The criminal justice system is the set of agencies and processes established by governments to control crime and impose penalties on those who violate laws. There is no single criminal justice system in the United States but rather many similar, individual systems. How the criminal justice system works in each area depends on the jurisdiction that is in charge: city, county, state, federal or tribal government or military installation. Different jurisdictions have different laws, agencies, and ways of managing criminal justice processes. 1 The main systems are:\", \"Justice and Fairness Justice means giving each person what he or she deserves or, in more traditional terms, giving each person his or her due. Justice and fairness are closely related terms that are often today used interchangeably. There have, however, also been more distinct understandings of the two terms.\", \"A Brief Description of the Federal Criminal Justice Process Investigations, Grand Juries, and Arrests. If a crime is brought to the attention of federal authorities, whether by a victim of the crime or a witness to it (e.g., a bank robbery), a federal law enforcement agency will undertake an investigation to determine whether a federal offense was committed and, if so, who committed it.\", \"- A justice of the peace is generally a man. of consequence in his neighborhood; he writes the wills, draws. the deeds and pulls the teeth of the people; also he performs. divers surgical operations on the animals of his neighbors.\", \"- The criminal justice system helps to provide a necessary framework for order in society. There are several different components which form the criminal justice system - the main ones being law enforcement, the court system, and corrections - and each one performs unique functions. While the criminal justice system has its flaws, it nevertheless is frequently subject to reform in order to create a better system. Learning Outcomes. After you finish the video, you should have a better ...\", \"Don\\u00e2t Confuse Revenge With Justice: Five Key Differences In brief, the kind or magnitude of justice meted out is contrived to \\u00e2\\u0080\\u009ccorrespond\\u00e2\\u0080\\u009d as exactly as possible to the gravity of the original injury. And the group of quotes below should further illustrate this final distinction between revenge and justice: \\u00e2\\u0080\\u009cChristian ethics demand that you should not take revenge.\", \"08 Criminal Justice in the United States: A Primer Most states, but not the federal government, have a comprehensive \\u00e2\\u0080\\u009ccode\\u00e2\\u0080\\u009d of substantive criminal law made up of general principles of criminal responsibility, laws defining the particular criminal offenses, and laws defining excuses and justifications.\", \"Justice Restorative justice is an approach to justice that focuses on the needs of the victims and the offenders, as well as the involved community, instead of satisfying abstract legal principles or punishing the offender.\", \"Justice Restorative justice is an approach to justice that focuses on the needs of the victims and the offenders, as well as the involved community, instead of satisfying abstract legal principles or punishing the offender.estorative justice is based on bringing together the victim, the offender, and the community; all have equal parts in repairing the relationships destroyed by crime. Generally the offender is held accountable to the victim for the criminal action and accountable as well to the community.\", \"Principles of Justice and Fairness Types of Justice. Justice is action in accordance with the requirements of some law. Whether these rules are grounded in human consensus or societal norms, they are supposed to ensure that all members of society receive fair treatment. Issues of justice arise in several different spheres and play a significant role in causing, perpetuating, and addressing conflict.\", \"Weaknesses of the Criminal Justice System A society's criminal justice system is made up of institutions and practices that are aimed at deterring crime, maintaining social control, and both punishing and rehabilitating individuals who commit crimes.In the U.S., the criminal justice system is designed to give every criminal defendant fair treatment.However, the weaknesses of the criminal justice system, which includes racial and socioeconomic bias, can undermine this ideal of fairness.n the U.S., the criminal justice system is designed to give every criminal defendant fair treatment. However, the weaknesses of the criminal justice system, which includes racial and socioeconomic bias, can undermine this ideal of fairness.\", \"The Conceptual History of Social Justice It seems most fruitful, however, to understand the difference between. the fairness and corrective justice conceptions in terms of the distinction. between distributive and corrective justice. On the fairness conception, the. tort law of accidents is primarily a matter of distributive justice and only.\", \"Criminal Justice Careers There are four main branches of criminal justice system in the US, which include law enforcement, judiciary, private security, and corrections. Criminal justice professionals are generally given the responsibility of supervision, safety, and corrective retaining of criminals.\", \"5 The Juvenile Justice System. 1 A separate juvenile justice system was established in the United States about 100 years ago with the goal of diverting youthful offenders from the destructive punishments of criminal courts and encouraging rehabilitation based on the individual juvenile's needs.\", \"Top 10 Ways to Fix the Criminal Justice System The criminal justice system is desperately in need of reform. But reform will only occur when people speak with unified conviction about a more just and equitable system that focuses more on public safety than on a person's skin color or class status.\", \"- About us. We work to protect the public and reduce reoffending, and to provide a more effective, transparent and responsive criminal justice system for victims and the public. Responsibilities. We are responsible for these parts of the justice system: courts. prisons. probation services. attendance centres.\", \"What\\u00e2s The Best Organic Formula For Your Baby? Restorative justice is about victims and offenders communicating within a controlled environment to talk about the harm that has been caused and finding a way to repair that harm. For offenders, the experience can be incredibly challenging as it confronts them with the personal impact of their crime.he restorative justice process is led by a facilitator who supports and prepares the people taking part and makes sure that it is safe. They\\u00e2\\u0080\\u0099ll be able to talk you through the process, answer any questions that you may have, and explain what will happen every step of the way.\", \"The Criminal Justice System The criminal justice system is the set of agencies and processes established by governments to control crime and impose penalties on those who violate laws. There is no single criminal justice system in the United States but rather many similar, individual systems.ow the criminal justice system works in each area depends on the jurisdiction that is in charge: city, county, state, federal or tribal government or military installation. Different jurisdictions have different laws, agencies, and ways of managing criminal justice processes. 1 The main systems are:\", \"3 \\u00e2 Restorative Justice: Justice That Promotes Healing Each of these types of communities\\u00e2\\u0080\\u0094the geographic community of the victim, offender, or crime; the community of care; and civil society\\u00e2\\u0080\\u0094may be injured by crime in different ways and degrees, but all will be affected in common ways as well: The sense of safety and confidence of their members is threatened, order within the community is threatened, and (depending on the kind of crime) common values of the community are challenged and perhaps eroded.\", \"Search Legal Terms and Definitions justice. n. 1) fairness. 2) moral rightness. 3) a scheme or system of law in which every person receives his/ her/its due from the system, including all rights, both natural and legal. One problem is that attorneys, judges and legislatures often get caught up more in procedure than in achieving justice for all. Example: the adage justice delayed is justice denied, applies to the burdensome procedures, lack of sufficient courts, the clogging of the system with meritless cases and the use of the courts to settle matters which could be resolved by negotiation.\", \"justice justice. 1 the quality of being righteous; rectitude. 2 impartiality; fairness. 3 the quality of being right or correct. sound reason; rightfulness; 1 validity. reward or penalty as deserved; just deserts. the use of authority and power to uphold what is right, just, or lawful.\", \"- criminal justice. 1. the system of law enforcement, involving police, lawyers, courts, and corrections, used for all stages of criminal proceedings and punishment.\", \"\\u00e2 Juvenile Justice and Delinquency Prevention: A Balancing Act Balanced and restorative justice adheres to the following principles: 1 The citizens of Pennsylvania have a right to safe and secure communities. 2 A juvenile who commits a crime has an obligation to the victim and the community.\", \"What Is the Criminal Justice System? - Definition, Components & Problems Like on the popular television program Law and Order, the criminal justice system includes police who investigate crimes and attorneys who prosecute crimes. However, the criminal justice system is more complex than just this small glimpse on television.\", \"Procedural justice Procedural justice concerns the fairness and the transparency of the processes by which decisions are made, and may be contrasted with distributive justice (fairness in the distribution of rights or resources), and retributive justice (fairness in the punishment of wrongs).\"], \"pos_scores\": [10.8828125], \"neg_scores\": [5.45703125, 6.453125, 7.58203125, 4.98046875, 6.0078125, 6.69921875, 4.44921875, 5.3046875, 3.037109375, 6.47265625, 1.5556640625, 5.9609375, 10.5390625, 13.1015625, 3.94921875, 8.7890625, 4.38671875, 4.203125, 7.703125, 5.21875, 6.3828125, 11.671875, 7.08203125, 9.5234375, 2.876953125, 4.4609375, 6.5, 9.1640625, 4.578125, 5.18359375]}\n{\"query\": \"what color is amber urine\", \"pos\": [\"- Color\\u00e2\\u0080\\u0094urine can be a variety of colors, most often shades of yellow, from very pale or colorless to very dark or amber. Unusual or abnormal urine colors can be the result of a disease process, several medications (e.g., multivitamins can turn urine bright yellow), or the result of eating certain foods.\"], \"neg\": [\"- The color of urine can vary from almost colorless to black. Normal urine may show color variation ranging from pale yellow to deep amber.Urine color is typically a result of the degradation of the heme molecule into a urinary pigment called urochrome.he color of urine can vary from almost colorless to black. Normal urine may show color variation ranging from pale yellow to deep amber.\", \"Urine Odor: Symptoms & Signs Normal urine is clear and has a straw-yellow color. While the odor of urine can vary somewhat, in most cases, it does not have a strong smell.With dehydration, the urine is more concentrated and may have a stronger ammonia scent than normal.Consumption of certain foods, such as asparagus (which can impart a characteristic odor to urine), and taking some medications may be causes for changes in the odor of urine.ith dehydration, the urine is more concentrated and may have a stronger ammonia scent than normal. Consumption of certain foods, such as asparagus (which can impart a characteristic odor to urine), and taking some medications may be causes for changes in the odor of urine.\", \"Green Urine Color Foods you eat can cause a green urine color. For example, Asparagus is known to cause a darker yellow urine color or a green urine color. Asparagus is an example of a natural cause of green urine, but don\\u00e2\\u0080\\u0099t forget about dyes such as food coloring. Artificial food coloring can easily be the culprit of green urine.\", \"Cloudy Urine Causes And What Does Murky Urine Mean? Tweet. Urine typically has a straw-yellow color although this may vary at times to a pale yellow or slightly darker yellow. At times, urine may be almost clear in color especially in a person who is urinating frequently like when using a diuretic (water tablet), consuming alcohol and drinking excessive amounts of water.\", \"Why does asparagus make some people\\u00e2s pee smell but not others? Blood red Shutterstock. Asparagus has also been reported to change the colour of urine, giving it a greenish tinge, something that is also associated with beetroot. In some individuals, the potentially disturbing effect can be pink or red urine. This can lead to the false impression of blood appearing in the urine (haematuria). Known as beeturia, the cause is not thought to be a genetic characteristic, but related to the physical state of the person who experiences it.\", \"Urine color and odor changes Rhubarb can also turn urine dark brown or tea-colored, as can fava beans and aloe. Carrots, carrot juice, and vitamin C can color urine orange, and B vitamins can turn it a fluorescent yellow-green. Asparagus sometimes gives urine a greenish tinge and a distinctive smell, said to resemble rotting cabbage.\", \"Causes of Urine color changes 1 Asparagus-may turn urine green color. 2 Blackberries-may lead to red urine. 3 Rhubarb-can cause orange urine. 4 Senna-can lead to orange urine. 5 Blood in urine-see causes of blood in urine. 6 Dehydration-causes darker urine or even a gold-color urine. 7 Bile in urine-causes a tea-like or mahogany color.\", \"Urine color and odor changes Rhubarb can also turn urine dark brown or tea-colored, as can fava beans and aloe. Carrots, carrot juice, and vitamin C can color urine orange, and B vitamins can turn it a fluorescent yellow-green. Asparagus sometimes gives urine a greenish tinge and a distinctive smell, said to resemble rotting cabbage. The cause of this smell is a matter for speculation.\", \"What color should your pee be? When your urine is orange, it usually indicates: 1 A side effect of a medication you're taking. 2 Ask your doctor about this, and bring your medication with you to the doctor's visit. 3 You've eaten too many orange or red foods like beets and berries or foods that are artificially colored.4 Dehydration.hen your urine is bright or neon yellow, it can indicate: 1 Vitamin supplements that are being taken in excess or not being absorbed by your body. 2 Word to the wise: take whole food-based vitamin supplements for better absorption.\", \"- The color of urine can vary from almost colorless to black. Normal urine may show color variation ranging from pale yellow to deep amber. Urine color is typically a result of the degradation of the heme molecule into a urinary pigment called urochrome.he color of urine can vary from almost colorless to black. Normal urine may show color variation ranging from pale yellow to deep amber. Urine color is typically a result of the degradation of the heme molecule into a urinary pigment called urochrome.\", \"Discoloration, Urine Brown urine. Brown urine discoloration can stem from numerous causes of red urine. Old clot sediment can appear brown when suspended in urine of a certain concentration. Likewise, myoglobinuria and hemoglobinuria often appear brown in color.\", \"What medications can change the colors of your urine? A Discovery Health answered. If your urine is any color other than yellowish-orange, yellow or clear-you could have a serious medical condition. Very dark orange, reddish or brown urine, for instance, likely has blood in it and could be a sign of infection.There are some prescription drugs, however, that can change the color of your urine simply by passing through your system.ery dark orange, reddish or brown urine, for instance, likely has blood in it and could be a sign of infection. There are some prescription drugs, however, that can change the color of your urine simply by passing through your system.\", \"Urine - abnormal color Dark brown but clear urine is a sign of a liver disorder such as acute viral hepatitis or cirrhosis, which causes excess bilirubin in the urine. Pink, red, or lighter brown urine can be caused by: 1 Beets, blackberries, or certain food colorings. 2 Hemolytic anemia. Injury to the kidneys or urinary tract.\", \"Why does the color of our urine sometimes get yellow? Dark urine is deeper in color than urine that is usually straw to yellow in color. Darker urine can be different colors, but is usually brown, deep yellow, or maroon. Dark urine is deeper in color than urine that is usually straw to yellow in color. Darker urine can be different colors, but is usually brown, deep yellow, or maroon. Read more. Dark urine is deeper in color than urine that is usually straw to yellow in color.\", \"- Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.Most changes in urine color are harmless and the color will go back to normal within a day or two.reenish urine can mean that you have a urinary tract infection but if it's bright green, it can mean that you have too much of B vitamins. Certain drugs and some health conditions can cause urine to turn green, too, so mention this to your health care professional.\", \"Dark reddish-brown urine Dark reddish-brown urine (symptom description): For a medical symptom description of 'Dark reddish-brown urine', the following symptom information may be relevant to the symptoms: Dark urine (type of symptom).However, note that other causes of the symptom 'Dark reddish-brown urine' may be possible.ark urine. Dark reddish-brown urine (symptom description): Dark reddish-brown urine is listed as a type of or related-symptom for symptom Dark urine.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that... View Full Answer\", \"Why Is Pee Yellow? Amber or ale-colored urine is a reliable sign of dehydration, and clear pee is a signal that it\\u00e2\\u0080\\u0099s time to put down the Nalgene. No matter what supermodels say, remember: It is possible to drink too much water.\", \"- The Color, Smell and Turbidity of Urine The color of normal urine ranges from colorless through straw, yellow, amber and brown and depends on the concentration of various pigments (urochrome, uroerythrin, urobilin).The color of urine can be influenced by: - pH: acidic urine usually darker.he Color, Smell and Turbidity of Urine The color of normal urine ranges from colorless through straw, yellow, amber and brown and depends on the concentration of various pigments (urochrome, uroerythrin, urobilin).\", \"Orange Urine Furthermore, candies, juices, drinks and other food that contain orange dye could result in the individual eliminating orange urine. Drugs; Some medications, substances and vitamins may give urine its orange tint. Excessive intake of vitamin C, for one, typically results in orange urine.\", \"You Wanted to Know: Dark Urine Urine that is pink, red, brown or rust-colored may indicate a urinary tract problem. However, before getting worried, think back to what you had to eat recently. Foods such as beets, blackberries and rhubarb as well as heavily dyed foods may harmlessly and temporarily turn your pee red or pink.\", \"Amber (color) The color amber is a pure chroma color, located on the color wheel midway between the colors of gold and orange.The color name is derived from the material also known as amber, which is commonly found in a range of yellow-orange-brown-red colors; likewise, as a color amber can refer to a range of yellow-orange colors.ports. 1 In gaelic games Armagh play in a darker Amber color (the amber that is prevalent in the Irish flag), Offaly play in the original colors of the Irish flag (Green, white and Amber) and Kilkenny also play in black and amber, albeit a more yellow amber.\", \"- In healthy individuals urine color ranges from pale yellow to amber, depending on their state of hydration. Many factors affect urine color including fluid balance, diet, medications and disease. The following table includes a list of the most common causes of abnormal urine coloration.\", \"Urine Color and What it Means Dark Yellow. Dehydration is a common diagnosis in a patient who experiences dark yellow urine. When the body senses a dearth in the required amount of fluid in it, wastes start accumulating in the urine thus, changing its color from light amber to dark yellow.\", \"What do the different urine colors mean? Normal urine can range in color from pale yellow (almost colorless) to deep amber. Dark amber urine is usually very concentrated and means that you should be drinking more water. Foods, medications and even vitamins can change the color of urine temporarily but eliminating the cause brings the color back to normal.Most changes in urine color are harmless and the color will go back to normal within a day or two.ertain drugs and some health conditions can cause urine to turn green, too, so mention this to your health care professional. Urine with a bluish tint can mean some type of bacterial infection or may just mean that you have a high level of calcium.\", \"Different Colors of Amber. This color of Amber is the most typical one since about 70% of this natural resin comes in it. This color can look like brownish too. Usually yellow color of this natural resin is found in Baltic Sea region and it is valued by most individuals due to high quality of Amber.\", \"Discoloration, Urine Red urine. As with the yellow coloration of normal urine, red urine can range in intensity from a pink lemonade color (clear light pink) to that of tomato soup (active thick bleeding) to a deep opaque merlot color (liquefying clot). Hematuria, or blood in the urine, is likely the most common cause of red urine.\", \"Urine color Normal urine color varies, depending on how much water you drink. Fluids dilute the yellow pigments in urine, so the more you drink, the clearer your urine looks. When you drink less, the color becomes more concentrated. Severe dehydration can produce urine the color of amber. But urine can turn colors far beyond what's normal, including red, blue, green, dark brown and cloudy white. When to see a doctor. Seek medical attention if you have: Visible blood in your urine. Bloody urine is common in urinary tract infections and kidney stones. These problems usually cause pain.\", \"Urine Color/Clarity/Odor Common description of urine (normal). pale yellow, yellow, dark yellow, and amber. \\u00ee\\u0081\\u0081 \\u00ee\\u0081\\u0082 \\u00ee\\u0080\\u0092. the yellow pigment that gives color to urine. \\u00ee\\u0081\\u0081 \\u00ee\\u0081\\u0082 \\u00ee\\u0080\\u0092. a pink (or red) pigment in urine that is thought to derive from melanin metabolism. Uroerythrin deposits on urate crystals to produce a precipitate described as brick dust.\", \"Important Things Your Urine Color Can Tell You When your urine is darker (amber), this usually is a sign you aren\\u00e2\\u0080\\u0099t getting enough fluids. If your urine is on the darker spectrum of what it should normally look like, there\\u00e2\\u0080\\u0099s typically no cause for alarm. All you need to do is get more fluids in your body, preferably water.\"], \"pos_scores\": [13.9375], \"neg_scores\": [14.8515625, 8.0625, 6.5625, 7.62890625, 5.07421875, 7.78125, 8.0703125, 7.84375, 8.484375, 14.390625, 7.2421875, 8.875, 8.2421875, 8.15625, 14.90625, 7.86328125, 14.8359375, 13.7109375, 13.9765625, 9.0703125, 7.875, 10.765625, 14.4765625, 13.4140625, 14.7421875, 10.4296875, 7.20703125, 14.140625, 14.0, 13.59375]}\n{\"query\": \"is autoimmune hepatitis a bile acid synthesis disorder\", \"pos\": [\"- Inborn errors of bile acid synthesis can produce life-threatening cholestatic liver disease (which usually presents in infancy) and progressive neurological disease presenting later in childhood or in adult life.he neurological presentation often includes signs of upper motor neurone damage (spastic paraparesis). The most useful screening test for many of these disorders is analysis of urinary cholanoids (bile acids and bile alcohols); this is usually now achieved by electrospray ionisation tandem mass spectrometry.\"], \"neg\": [\"What is hepatitis? The condition can be self-limiting or can progress to fibrosis (scarring), cirrhosis or liver cancer. Hepatitis viruses are the most common cause of hepatitis in the world but other infections, toxic substances (e.g. alcohol, certain drugs), and autoimmune diseases can also cause hepatitis.epatitis A virus (HAV) is present in the faeces of infected persons and is most often transmitted through consumption of contaminated water or food. Certain sex practices can also spread HAV.\", \"Useful For Diagnosis and monitoring of liver disease associated with hepatic necrosis. Alanine aminotransferase (ALT) is present primarily in liver cells. In viral hepatitis and other forms of liver disease associated with hepatic necrosis, serum ALT is elevated even before the clinical signs and symptoms of the disease appear.\", \"Fetal hemoglobin silencing in humans References. \\u00e2\\u0086\\u00b5 1 Bard H, Fouron JC, Gagnon C, Gagnon J. Hypoxemia and increased fetal hemoglobin synthesis. \\u00e2\\u0086\\u00b5 2 Perrine SP, Greene MF, Faller DV. \\u00e2\\u0086\\u00b5 3 Di Benedetto A, Romano G, Campo S, Di Cesare E, Cucinotta D. No evidence of increased fetal haemoglobin in an adult diabetic population.\", \"Alone we are rare. Together we are strong.\\u00e2\\u00a5 Donate Today Signs & Symptoms. Generally symptoms of acquired autoimmune hemolytic anemia resemble those of other anemias and may include fatigue, pale color, rapid heartbeat, shortness of breath, dark urine, chills, and backache. In severe cases, yellow skin color (jaundice) may be present and the spleen may be enlarged.\", \"cholestasis Intrahepatic cholestasis is characterized by widespread blockage of small ducts or by disorders, such as hepatitis, that impair the body's ability to eliminate bile. Extrahepatic cholestasis can occur as a side effect of many medications.\", \"Differences between mitochondrial and cytoplasmic transfer RNA and aminoacyl transfer RNA synthetases from rat liver. Wheeldon L. The problem of bacterial contamination in studies of protein synthesis by isolated mitochondria. Biochem Biophys Res Commun. 1966 Aug 12; 24 (3):407\\u00e2\\u0080\\u0093411. Beattie DS, Basford RE, Koritz SB. Bacterial contamination and amino acid incorporation by isolated mitochondria. J Biol Chem. 1967 Jul 25; 242 (14):3366\\u00e2\\u0080\\u00933368. Sandell S, L\\u00c3\\u00b6w H, von der Decken A. A critical study of amino acid incorporation into protein by isolated liver mitochondria from adult rats. Biochem J. 1967 Aug; 104 (2):575\\u00e2\\u0080\\u0093585. [PMC free article]\", \"- Biogenic amine is synthesized under the control of pyridoxal. phosphatase and L histidine phosphatase from Histidine, with vitamins B6 and C as co-factors. It is synthesized by: mast cells, basophils, platelets, histamino-gene neurons, and entero-chromatophile cells.\", \"Role of the gallbladder The mammals in which the hydrophobic hepatotoxic bile acids are synthesized or formed must have the gallbladder. Those mammals in which the hydrophilic hepatoprotective bile acids are synthesized and the hydrophobic hepatotoxic bile acids are formed in small quantities, may manage without it. The mammals, in which bile alcohols are synthesized in large amounts, do not have the gallbladder.\", \"- Alkaline Phosphatase. Alkaline phosphatase (ALP) is an enzyme that is mostly synthesized in the liver and bone. Altered levels of alkaline phosphatase in the blood can be indicative of certain specific disease pathologies.\", \"- DISORDERS OF BIOCHEMICAL HOMEOSTASIS. Disease processes that lead to chronic imbalances in hormones, nutrients, and toxic metabolic waste products in body fluids may have profound effects on the function of one or more components of the immune system. There are a great many diagnostic entities that may be grouped under this broad heading.\", \"What Digestive Enzyme is Produced by the Liver? Cirrhosis is a disease of the liver. Diseases including chronic hepatitis, chronic alcoholism, bile duct disorders and hereditary disorders cause cirrhosis. Cirrhosis scars the liver and prevents the flow of bile in and out of the organ. Tumors within the bile ducts may also impair bile secretion.\", \"Bile acid Bile acid. Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming primary bile acids. The sodium and potassium salts of bile acids are called bile salts. Primary bile acids are those synthesized by the liver.\", \"Autoimmune Diseases Behcet\\u00e2\\u0080\\u0099s disease is an autoimmune disorder in which the immune system attacks the cells of the cardiovascular system. It is a rare, systemic form of vasculitis, which causes chronic inflammation in blood vessels throughout the body.\", \"Autoimmune Hepatitis Autoimmune Hepatitis. A liver disease in which the body's immune system damages liver cells for unknown reasons. PubMed Health Glossary. (Source: NIH-National Institute of Diabetes and Digestive and Kidney Diseases).ombination therapy of ursodeoxycholic acid and corticosteroids for primary biliary cirrhosis with features of autoimmune hepatitis: a meta-analysis. A meta-analysis was performed of RCTs comparing therapies that combine UDCA and corticosteroids with UDCA monotherapy.\", \"Bile acid Bile acids are steroid acids found predominantly in the bile of mammals and other vertebrates. Different molecular forms of bile acids can be synthesized in the liver by different species. Bile acids are conjugated with taurine or glycine in the liver, forming bile salts.Primary bile acids are those synthesized by the liver.ile acid synthesis occurs in liver cells which synthesize primary bile acids (cholic acid and chenodeoxycholic acid in humans) via cytochrome P450 -mediated oxidation of cholesterol in a multi-step process. Approximately 600 mg of bile salts are synthesized daily to replace bile acids lost in the feces.\", \"The Detox Drip with Glutathione: A New Approach to Healing From Chronic Illness We don\\u00e2\\u0080\\u0099t get a lot of glutathione in our diet and even then, not much is absorbed directly into the blood. Glutathione is synthesized by our bodies from amino acids. If our bodies cannot make enough glutathione to keep up with the chronic toxic load from infections and poisons, we may end up with immune dysfunction.\", \"Autoimmune Hepatitis Autoimmune hepatitis is a disease in which the body\\u00e2\\u0080\\u0099s own immune system attacks the liver and causes it to become inflamed. The disease is chronic, meaning it lasts many years. If untreated, it can lead to cirrhosis and liver failure. There are two forms of this disease.\", \"Principles of Biochemistry/Biosynthesis of lipids Synthesis of bile acids is a major route of cholesterol metabolism in most species other than humans. The body produces about 800 mg of cholesterol per day and about half of that is used for bile acid synthesis.iosynthesis of cholesterol is directly regulated by the cholesterol levels present, though the homeostatic mechanisms involved are only partly understood. A higher intake from food leads to a net decrease in endogenous production, whereas lower intake from food has the opposite effect.\", \"Structure of bile acid transporter explains membrane transfer Liver cells synthesize bile acids, which are then secreted via the biliary tract into the small intestine, where they help absorb fats and fat-soluble vitamins. In a natural form of recycling, the bile acids are then retrieved from the intestine and returned to the liver to begin the process of secretion again.iver cells synthesize bile acids, which are then secreted via the biliary tract into the small intestine, where they help absorb fats and fat-soluble vitamins. In a natural form of recycling, the bile acids are then retrieved from the intestine and returned to the liver to begin the process of secretion again.\", \"ASH Home ASH Store ASH AcademyMy Account Sign In Hepcidin deficiency, the hallmark of the disorder, leads to dysregulated intestinal iron absorption and progressive iron deposition in the liver, heart, skin, endocrine glands, and joints. Survival is normal if organ damage is prevented by early institution of phlebotomy therapy.n additional variant HFE allele, His63Arg (H63D), may also contribute to iron overload in some cases. In this review, the terms HH and HFE-hemochromatosis refer to the autosomal-recessive disorder associated with homozygosity for the HFE C282Y allele.\", \"Histamine It is destroyed by the enzyme diamine oxidase (histiminase), which is also involved in the metabolism of other bioactive amines. Histamine is synthesized in all tissues, but is particularly abundant in skin, lung and gastrointestinal tract. Mast cells, which are present in many tissues, are a prominent source of histamine, but histamine is also secreted by a number of other immune cells.\", \"Alcoholic Hepatitis Acute onset of ascites may develop in patients with alcoholic hepatitis, even in the absence of overtly decompensated liver disease and portal hypertension. The ascites is typically transudative, with a very low albumin concentration (< 1 g/dL).\", \"- Autoimmune limbic encephalitis: Rare Types. Rare types of medical conditions and diseases in related medical categories: 1 Brain & Neurological Disorders: Rare Types: Adult ADHD -- Rare Types.\", \"Autoimmune Hemolytic Anemia in Cats Autoimmune hemolytic anemia (AIHA) is an immune system disease in which the body attacks and destroys its own red blood cells. In cats with AIHA, red blood cells are still being manufactured in the bone marrow, but once released into the circulation, they have a shorter-than-normal life span.\", \"Amino acid synthesis Amino acid synthesis. For the non-biological synthesis of amino acids, see Strecker amino acid synthesis. Amino acid synthesis is the set of biochemical processes (metabolic pathways) by which the various amino acids are produced from other compounds. The substrates for these processes are various compounds in the organism's diet or growth media. Not all organisms are able to synthesise all amino acids. Humans are excellent example of this, since humans can only synthesise 11 of the 20 standard amino acids (a.k.a. non-essential amino acid), and in time of accelerated growth, histidine, can be considered an essential amino acid.\", \"- 1 Liver disease that results in decreased bile salt synthesis leads to impaired vitamin K absorption and deficiency. 2 Additionally, a majority of the clotting factors are synthesized almost exclusively in the liver, so liver disease can cause defects in blood clotting by several mechanisms.ources of Vitamin K. Vitamin K is found in a number of foods, including leafy greens, cauliflower and, if you consider it a food, liver. However, the chief source of vitamin K is synthesis by bacteria in the large intestine, and in most cases, absence of dietary vitamin K is not at all deleterious.\", \"Clinical Information Hypoalbuminemia is caused by several factors: impaired synthesis due either to liver disease (primary) or due to diminished protein intake (secondary); increased catabolism as a result of tissue damage and inflammation; malabsorption of amino acids; and increased renal excretion (eg, nephrotic syndrome).\", \"- Bile acid: An acid made by the liver that works with bile to break down fats. On a more technical level, bile acids are steroid carboxylic acids derived from cholesterol.The primary bile acids are cholic and chenodeoxycholic acids. They are conjugated with glycine or taurine before they are secreted into the bile.ile acid: An acid made by the liver that works with bile to break down fats. On a more technical level, bile acids are steroid carboxylic acids derived from cholesterol.\", \"List of hematologic conditions Autoimmune hemolytic anemia (AIHA) is a type of hemolytic anemia where the body's immune system attacks its own red blood cells (RBCs), leading to their destruction (hemolysis). Types of AIHA include warm autoimmune hemolytic anemia, cold agglutinin disease, and paroxysmal cold hemoglobinuria.\", \"Putting Out the Flames of Complex Regional Pain Syndrome\\u00e2Updated Guide to CRPS/RSD Autoimmune hepatitis is usually a chronic form of hepatitis that frequently leads to progressive damage of the liver. However, in about 10-20% of cases, it may present like acute hepatitis. For reasons that are not fully understood, the body's immune system targets and attacks the liver.\"], \"pos_scores\": [11.84375], \"neg_scores\": [5.875, 5.85546875, 1.46875, 4.6875, 9.125, 2.80859375, 2.2265625, 6.47265625, 5.796875, 4.7421875, 8.3046875, 7.83984375, 5.0546875, 11.0625, 9.3671875, 3.046875, 8.9765625, 9.0078125, 8.40625, 5.42578125, 1.36328125, 5.3671875, 2.544921875, 3.67578125, 4.2578125, 8.75, 6.4296875, 8.109375, 4.68359375, 8.609375]}\n{\"query\": \"elegxo meaning\", \"pos\": [\"The Ministry of the Holy Spirit The word convict here (elegcw /elegxo) means to bring to light or expose error often with the idea of reproving or rebuking. It brings about knowledge of believing or doing something wrong, but it does not mean that the person will respond properly to that knowledge. Our usage of the English word, convict, is similar.\"], \"neg\": [\"elective office meaning, elective office definition | English Cobuild dictionary FORMAL usu ADJ n Buchanan has never held elective office. 2 adj Elective surgery is surgery that you choose to have before it becomes essential. FORMAL usu ADJ n 3 n-count An elective is a subject which a student can choose to study as part of his or her course. (AM) in BRIT, use option.\", \"- \\u00e2\\u0080\\u00a2 ADAGIO (noun). The noun ADAGIO has 2 senses: 1. (music) a composition played in adagio tempo (slowly and gracefully). 2. a slow section of a pas de deux requiring great skill and strength by the dancers. Familiarity information: ADAGIO used as a noun is rare.\", \"Emilio The name Emilio is an American baby name. In American the meaning of the name Emilio is: Eager. Spanish Meaning: The name Emilio is a Spanish baby name. In Spanish the meaning of the name Emilio is: Flattering.\", \"- Elective(adj) exerting the power of choice; selecting; as, an elective act. Elective(adj) pertaining to, or consisting in, choice, or right of choosing; electoral. Elective(adj) dependent on choice; bestowed or passing by election; as, an elective study; an elective office.\", \"- Allegro is not a Spanish word, it's Italian and it means alegr\\u00c3\\u00ada (happiness). Allegro is used in music notation to tell the performer to go faster.\", \"Definitions &Translations exerting the power of choice; selecting; as, an elective act. Elective (adj) pertaining to, or consisting in, choice, or right of choosing; electoral. Elective (adj) dependent on choice; bestowed or passing by election; as, an elective study; an elective office. Elective (noun) in an American college, an optional study or course of study. Origin: [Cf. F. lectif.]\", \"What does allegro agitate mean? In music, allegro is an adjective describing a lively, quick tempo. Agitato is an Italian word to describe a tempo's style; it most closely translates to perturbed, agitated, or excited depending on what word (in this case allegro) it's attached to. A tempo that is allegro agitato is quick and rattled so to speak.\", \"- by Clarksville Middle School. Alecto was one of the Erinyes or Furies in Greek mythology. The Furies were three avenging deities. Their names were Tisiphone (the avenger of murder), Megaera (the jealous one), and Alecto (unceasing in anger).When Cronus killed Uranus, his blood fell on Gaia and created the Furies.The Furies had snakes for hair and blood dripped from their eyes. they also had bats' wings and dogs' heads.heir names were Tisiphone (the avenger of murder), Megaera (the jealous one), and Alecto (unceasing in anger). When Cronus killed Uranus, his blood fell on Gaia and created the Furies.\", \"Allegro - Musical Definition Allegro - Musical Definition. Allegro - Allegro - Fast, lively; also - cheerful-ly, joyful-ly, with joy. Originally understood in the Baroque period as 'joyfully, cheerfully', the term has changed over the years and now generally relates to tempo - fast and lively. See also:\", \"- Definition of elective. 1a : chosen or filled by popular election an elective officialb : of or relating to electionc : based on the right or principle of election the presidency is an elective office. 2a : permitting a choice : optional an elective course in schoolb : beneficial to the patient but not essential for survival elective surgery.\", \"Definitions &Translations elective (Noun) Something that is option or that may be elected, especially a course of tertiary study. elective (Adjective) Of, or pertaining to voting or elections. elective (Adjective) That involves a choice between options; optional or discretionary. My insurance wouldn't pay for the operation because it was elective surgery.\", \"Eleggua Eleggua is typically depicted either as an impish, playful child dressed in a half-red, half-black outfit with a straw hat and a woven shoulder bag full of toasted corn and candy, or as an old gnarled man. He usually carries a bent stick which he uses as a walking cane, and to snag people or clear brush.\", \"- 1620s, act of selecting, from Latin selectionem (nominative selectio) a choosing out, choice, selection, noun of action from past participle stem of seligere (see select (adj.)). Meaning thing selected is from 1805.\", \"Cogito ergo sum Cogito ergo sum is a Latin philosophical proposition by Ren\\u00c3\\u00a9 Descartes usually translated into English as I think, therefore I am. The phrase originally appeared in French as je pense, donc je suis in his Discourse on the Method, so as to reach a wider audience than Latin would have allowed.\", \"- Use chose in a sentence. LINK / CITE ADD TO WORD LIST. verb. He chose this pumpkin. He chose this pumpkin. The definition of chose is the past form of choose and means to have made a selection.\", \"- Full Definition of ELEGY. 1. : a poem in elegiac couplets. 2. a: a song or poem expressing sorrow or lamentation especially for one who is dead b: something (as a speech) resembling such a song or poem.3. a: a pensive or reflective poem that is usually nostalgic or melancholy b: a short pensive musical composition.See elegy defined for English-language learners.. a: a song or poem expressing sorrow or lamentation especially for one who is dead b: something (as a speech) resembling such a song or poem. 3. a: a pensive or reflective poem that is usually nostalgic or melancholy b: a short pensive musical composition. See elegy defined for English-language learners.\", \"ALLEGRO Nouns denoting communicative processes and contents. Hypernyms (allegro is a kind of...): composition; musical composition; opus; piece; piece of music (a musical work that has been created). musical passage; passage (a short section of a musical composition).\", \"Definitions &Translations Princeton's WordNet(2.00 / 2 votes)Rate this definition: 1 elective course, elective(adj) a course that the student can select from among alternatives. 2 elective, elected(adj) subject to popular election. elective official. 3 elective(adj) not compulsory. elective surgery; an elective course of study.\", \"- The word is composed of ek (ek), meaning out, and lego (lego), meaning speak.. The word ek in eklegomai refers not only to the place of origin from which a person is called out, but also the process of separation and the result of being called out in its entirety (Thayer, p 192).\", \"Elegy Elegy Definition Elegy is a form of literature that can be defined as a poem or song in the form of elegiac couplets, written in honor of someone deceased. It typically laments or mourns the death of the individual.\", \"Electoral college This article is about electoral colleges in general. For the US electoral college, see Electoral College (United States). For other uses, see Electoral college (disambiguation). An electoral college is a set of electors who are selected to elect a candidate to a particular office. Often these represent different organizations, political parties, or entities, with each organization, political party or entity represented by a particular number of electors or with votes weighted in a particular way.\", \"Definitions containing wage bill In fencing, an allonge is a thrust or pass at the enemy. An allonge can also refer to a long (drawn out) espresso shot, also known as an Italian lungo. In chemistry an allonge is an old French term for a separatory column. In dressage an allonge is a long rein used for trotting a horse.\", \"Votive Offerings (Ex-Votos) + Larger Font | - Smaller Font. Exvoto is a Spanish word meaning votive offering. The word comes from the Latin, meaning out of a promise or vow and these votive offerings are given or dedicated in fulfillment of a vow or pledge, or expressing a wish or desire.\", \"- The word elegxo refers to \\u00e2\\u0080\\u009cnot only exposure but shame and conviction\\u00e2\\u0080\\u009d (Carson, John, 207). The Evangelist continues (3:20) by saying that those who refuse the Light actually hate the Light. This is followed by a contrast with those who \\u00e2\\u0080\\u009cpractice the truth.\\u00e2\\u0080\\u009d\", \"Elect Encyclopedias - International Standard Bible Encyclopedia - Elect. ELECT. e-lekt': That is, chosen, selected.. In the Old Testament the word represents derivatives of bachar, elegit; In the New Testament eklektos. It means properly an object or objects of selection. This primary meaning sometimes passes into that of eminent, valuable, choice; often thus as a fact, in places where the King James Version uses chosen (or elect) to translate the original (eg. Isaiah 42:1; 1 Peter 2:6).\", \"- Refugio's origin, as well as its use, is in the Spanish language. Refugio is of the meaning 'refuge'. The first name is derived from Nuestra Senora se\\u00c3\\u00b1ora De ('Refugio Our lady Of'), refuge a title given By-spanish Speaking Roman catholics to The Virgin. Mary a variant transcription Of refugio Is (Refugia). spanish\", \"- Definitions for ergo-. Here are all the possible meanings and translations of the word ergo-. Wiktionary(0.00 / 0 votes)Rate this definition: ergo-(Prefix) Used to form terms relating to work.\", \"5 Music Articulation Symbols You Need To Know Legato means to play smoothly without any separation or break between the notes. As you may have guessed, the word legato is Italian for \\u00e2\\u0080\\u009c smooth \\u00e2\\u0080\\u009d. For bowed instruments, a single bow is used to play slurred notes. Wind instruments only tongue the first note of slur and not the rest.\", \"Definitions &Translations Seraglio (noun). an inclosure; a place of separation. Seraglio (noun). the palace of the Grand Seignior, or Turkish sultan, at Constantinople, inhabited by the sultan himself, and all the officers and dependents of his court.\", \"- Answers. 1 In English, 'regalo' is 'gift'. 2 Regalo in Spanish translates to the English word gift. 3 Regalo is Spanish for gift. 4 The Spanish word regalo (n.) in English means 1. present, gift; 2. pleasure, gratification; 3. dainty, something nice & delicate.\"], \"pos_scores\": [14.609375], \"neg_scores\": [1.685546875, 0.62060546875, 2.23828125, 1.861328125, 0.83984375, 2.0703125, 1.294921875, 3.904296875, 0.95654296875, 3.248046875, 2.056640625, 6.26171875, 2.50390625, 1.83984375, 0.5087890625, 5.69140625, -0.59130859375, 2.150390625, 5.46875, 5.58984375, 3.484375, 0.94775390625, 0.422119140625, 15.7890625, 5.0390625, 0.187744140625, 1.3037109375, 2.267578125, 1.001953125, 1.296875]}\n{\"query\": \"how much does an average person make for tutoring\", \"pos\": [\"- In-home tutors can earn anywhere from $10 to $80 an hour, depending on the type of lesson, the student\\u00e2\\u0080\\u0099s skill and age level and the tutor\\u00e2\\u0080\\u0099s experience. Tutors often charge more for older students or those who require more advanced lessons.\"], \"neg\": [\"How much should i charge for math tutoring? $30 an hour at least. Nothing less! I have checked math tutoring listings in your area at craigslist.com and they're asking like $60 to $65 an hour. if you live in such affluent county, theres no chance anyone would find it difficult to pay $30 for 1 hour. have heard that in some major cities, tutors can earn $40 to up to $100 an hour! WOW! I recommend trying to see if there is a special \\u00e2\\u0080\\u009cTutors Bulletin Board\\u00e2\\u0080\\u009d in your math department and see what others charge. Also look at the classified ads in your school newspaper for tips on what others charge.\", \"How Much Does a Tumbling Coach Get Paid? Salary and Qualifications. Tumbling coaches earned average annual salaries of $33,000 as of 2013, according to the job website indeed.com. Educational requirements for these professionals vary by employer. If a part-time tumbling coach is also a high school teacher, for example, this coach requires an education degree.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields $50-$100 per hour As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour.\", \"Tutor Salary Tutor Salary. A Tutor earns an average wage of $17.28 per hour. Skills that are associated with high pay for this job are Physics, French Language, Math, Science, and Reading. Most people move on to other jobs if they have more than 20 years' experience in this field. $15,495 - $68,510.\", \"- 13 things tutoring centers won t tell you people spend $ 5 billion on tutors per year about one fifth of the amount spent on school supplies so here s how to make private tutoring for your child or tutoring centers worth iteople spend $ 5 billion on tutors per year about one fifth of the amount spent on school supplies so here s how to make private tutoring for your child or tutoring centers worth it\", \"How Much Money Do Dance Instructors at a College Make a Year? However, they do list instructors of visual or performing arts at an average salary of $43,464. This compares with an average salary of $43,503 for recreation instructors, $45,421 for psychology, $42,318 for history and $46,042 for theology.\", \"- By state, California offers the highest pay at $13.00 per hour. Those who have five to nine years of work experience see average pay of $10.97 per hour. Overall, the greater share of Tutor Time Learning Centers folks have one to four years of experience and earn an average about $9.68 per hour.arly Childhood Educators are compensated at a much higher rate than non-authorized workers, bringing in close to $11.46 per hour. Regarding pay and education, Tutor Time Learning Centers pays those with a Bachelor's Degree the most \\u00e2\\u0080\\u0094 about $13.00.\", \"How Much Money Do Private Tutors Make? factor in experience specialty and location private in home tutors may charge between $ 30 and $ 40 per hour according to angie s list or a general range of $ 15 to $ 85 for all types of tutors generally the more unique your skill set the more you can charge for your servicesrivate in home tutors may charge between $ 30 and $ 40 per hour according to angie s list or a general range of $ 15 to $ 85 for all types of tutors\", \"What is a reasonable rate for a math tutor? When I freelanced as a tutor, I charged $20-30/hour, depending on the course level (freshman intro bio was cheaper than higher level courses), but when I was a tutor for the athletic department, I was paid $11/hour since I was a graduate student. YMMV.\", \"- \\u00e2\\u0080\\u00a6 it depends on how many years they have been teaching. The average of a first year teacher is $43,000 The American Federation of Teachers issues a Teacher Salary Trends report each year to survey the pay levels of U.S. educators. In 2008-2009, the average teacher salary was $47,602.thers charge a great deal and have to deal with overhead such as dojo rental, pho \\u00e2\\u0080\\u00a6 nes, advertising, etc. Most instructors have other businesses that provide them income and do the martial art for the love of the activity. On a good day of teaching, I can make $50 - $60 dollars, my best was $132.\", \"How Much Should Tutoring Cost? With a tremendous number of listing agencies to connect you with a private tutor, finding one that's right for your child is easier than ever. Expect the cost to be around $30 to $40 per hour, but for home tutoring, prices can range from $15 to $85 dollars depending on the tutor's experience level. Private tutors generally do not charge additional upfront fees or require contracts.\", \"How much do High School teachers earn per month? Best Answer: the AVERAGE teaching salary is 53,988. For those of us who know how to do basic division that comes to $4,499 a month.wages are mostly given per year not per month.And remember this number is before taxes. Also this is an AVERAGE there are people who make less than that especially just starting out and people who make more.or those of us who know how to do basic division that comes to $4,499 a month.wages are mostly given per year not per month. And remember this number is before taxes. Also this is an AVERAGE there are people who make less than that especially just starting out and people who make more.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields. $50-$100 per hour. As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour. I could probably charge a little more now, if I were still working as a private tutor. Remember, though, this was near San Francisco.\", \"- 1 According to LifeCoach.com, the average life coach will charge between $200 to $1,000 per month, which works out to about $100 to $300 per hour.2 For corporate coaching, it is best to plan at least $1,000 to $10,000 per month. 3 KimKnightCoaching.com offers a few packages of which you can take advantage. According to LifeCoach.com, the average life coach will charge between $200 to $1,000 per month, which works out to about $100 to $300 per hour.\", \"How Much Does a Life Coach Cost? 1 On average, this is what most life coaches are going to probably charge in your area. 2 According to LifeCoach.com, the average life coach will charge between $200 to $1,000 per month, which works out to about $100 to $300 per hour. 3 For corporate coaching, it is best to plan at least $1,000 to $10,000 per month. According to LifeCoach.com, the average life coach will charge between $200 to $1,000 per month, which works out to about $100 to $300 per hour.\", \"- (United States). Lecturers in the United States take home an average $48K per year. The income range spans the entire spectrum between $30K and $79K per year. This group's pay is mainly influenced by residence, followed by the company and years of experience. Most Lecturers report high levels of job satisfaction.ecturers with one to two decades of relevant experience report an average salary of approximately $51K. In the end, the overall pattern seems to be that more experience generally corresponds to higher pay; a Lecturer with more than 20 years of experience can earn $55K on average.\", \"Comcast Corporation Triple Play Consumer Reviews Wow, we were charging $20-25 per hour for highschool maths and science tutoring 15 years ago. I would think for the time you are putting in, they are getting a good deal! Do not charge less; your time is worth at least $25+ an hour. I tutored Chemistry last year at $25 an hour.\", \"Teaching Overseas: Are you Qualified? How much a tutor costs can vary widely. A major factor in how much you pay a tutor should be the expertise of the tutor. Some tutors are high school students while others are \\u00e2\\u0080\\u00a6 certified teachers. I am a certified teacher, and charged $25.00/hr to tutor a few years ago.\", \"Life or Business Coach Cost 1 Coaches working with individuals might charge anywhere from $50-$300 or more an hour, with an average range of $75-$200. 2 Many coaches offer their services in packages, such as four half-hour sessions a month for $200-$300, or eight 90-minute sessions spread over 5-6 months for $800-$2,000.hat should be included: Shopping for a life or business coach: 1 Depending on the coach's style and the client's needs, coaching sessions can be anywhere from 20-90 minutes.\", \"- All TutorVista Salaries. The typical TutorVista Online Tutor salary is \\u00e2\\u0082\\u00b9212. Online Tutor salaries at TutorVista can range from \\u00e2\\u0082\\u00b9141-\\u00e2\\u0082\\u00b9322. This estimate is based upon 3 TutorVista Online Tutor salary reports provided by employees. See all Online Tutor salaries to learn how this stacks up in the market.\", \"The untold story of a private tutor: money's too tight to mention But comparing that with the average annual wage for a graduate, which currently stands at \\u00c2\\u00a329,000, (28% of whom will start on between \\u00c2\\u00a325,000 - \\u00c2\\u00a330,000), and you may see that while this initial wage may suffice to begin with, but 10 years into your profession you may start to feel rather hard done by.\", \"What Does Tutoring Cost? PayScale.com indicated that the middle-half salary range for tutors employed by major tutoring providers in May 2011 was between $10.10 and $17.50 per hour. These services screen and train tutors for you, and may even offer you a money back guarantee if your child's grades don't improve.\", \"- Group tutoring (2-5 people) costs $4 per session. Students requesting a tutor pay a $5 administrative fee once per semester. We have agreements with several agencies on campus for the payment of all or part of the cost of tutoring for students who meet their requirements.\", \"How Much Do Tutors Charge? according to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutorccording to payscale com in 2011 tutors in the 25th to 75th percentile range working for private companies made from $ 10 10 $ 20 35 however these figures do not include the amount of money that goes to the company providing you with the tutor\", \"How Much Does Tutoring Cost? Average cost of tutoring: $125/hour. Unfortunately, Test Masters receives fairly low ratings in a number of areas. A glance at their Yelp reviews is enough to make a parent uneasy, but this could have something to do with the company\\u00e2\\u0080\\u0099s expansion over time.\", \"- The average amount of money that a teacher's assistant makes per year is about $25,000. Highest earners make about $36,000 while the lowest get $17,000 per year.\", \"Tutoring Fees: What It Will Cost Tutoring fees for professional tutors in lower-demand fields. $50-$100 per hour. As a writing tutor, with an MFA, going to students' homes, I charged $80 per hour. I could probably charge a little more now, if I were still working as a private tutor.\", \"How Much Does an Academic Advisor Make? The Bureau of Labor Statistics groups academic advisors under the broader job category of Educational, Guidance, School, and Vocational Counselors.. In 2011, these counselors earned an average salary of $56,540 a year. Since high salaries can sometimes skew the average, the median wage is often a better indicator of an advisor\\u00e2\\u0080\\u0099s earnings. The median pay was $54,130 a year in 2011. However, since neither figure is specific to academic advisors, other salary data might be more accurate in reflecting their actual salaries.\", \"How Much Do Teachers Make? + Infographic According to the Bureau of Labor Statistics, the mean wage for all teachers and educators is $24.02 per hour, which leads to about $50,000 per year. However, earnings vary a lot in terms of location, specialization, and experience.\", \"Archived Q&A and Reviews Re: Going rate for recent college grad. I can't help you on the tutoring rates, but as for babysitting, I pay college students $16 per hour, plus $10 per day for gas (taking care of my son involves picking him up after school and doing some errands). So for working 4:30-6:30 the sitter earns $42.If it is an nighttime job, no driving, just $16 per hour.y 14 year old neighbor sits for us in the afternoons when I'm here or out on short errands and we pay her $7/hr (she leaves a mess with the kids, but keeps them safe and they all have a great time); my 17 year old neighbor watches my two kids at night (dinner, bath and bedtime) and we pay $10-12.\"], \"pos_scores\": [13.328125], \"neg_scores\": [10.3984375, 3.931640625, 12.296875, 13.859375, 11.1015625, 5.265625, 11.109375, 12.4140625, 11.359375, 7.6328125, 12.125, 5.90625, 12.578125, 6.8046875, 6.23046875, 4.86328125, 10.796875, 9.796875, 6.28515625, 9.6640625, 10.2578125, 12.71875, 11.3046875, 10.9765625, 13.7109375, 7.140625, 12.59375, 3.89453125, 7.421875, 7.73046875]}\n{\"query\": \"can you use a calculator on the compass test\", \"pos\": [\"Calculator Guidelines for COMPASS \\u00c2\\u00ae Testing Calculators may be used on the COMPASS Pre-Algebra, Algebra, College Algebra, Geometry, and Trigonometry tests provided they meet the requirements listed below. Electronic writing pads or pen-input devices\\u00e2\\u0080\\u0094The Sharp EL 9600 is permitted. Models with paper tapes\\u00e2\\u0080\\u0094The paper must be removed.\"], \"neg\": [\"- Home > Getting Started with Computing Concepts > End of Chapter Materials > Concepts Assessment. Getting Started with Computing Concepts. Matching each term in the second column with its correct definition in the first column by writing the letter of the term on the blank line in front of the correct definition. Using the pulldown boxes, match each item on the left to the corresponding item at right.\", \"Gannon University GPA Calculator Ever wonder how to calculate GPA? This free calculator may help your raise your GPA. Middle schools, high schools, colleges, and universities use it every day! Copyright 2015, Koofers, Inc. All rights reserved. The information provided on this site is protected by U.S. and International copyright law, and other applicable intellectual property laws, including laws covering data access and data compilations.\", \"Conveyancing Fees Calculator Our Conveyancing Quotes include ... Conveyancing Calculator offers YOU choice and allows you to make an informed decision. You can compare conveyancing fees and costs from countrywide solicitors and conveyancers throughout the England and Wales. We offer ...onveyancing Calculator offers YOU choice and allows you to make an informed decision. You can compare conveyancing fees and costs from countrywide solicitors and conveyancers throughout the England and Wales.\", \"- After taking the initial ESL Placement test, all students can retest on the computer portion of the test in accordance with the following criteria: 1. All students have the right to retest ONCE on the computer portion of the ESL Compass test. 2. Students may NOT retest on any portion of the ESL Placement test once they have attended any ESL class at the college.\", \"- The Common Core related standard is, \\u00e2\\u0080\\u009cUse permutations and combinations to compute probabilities of compound events and solve problems.\\u00e2\\u0080\\u009d. How to do permutations on the Ti 89 calculator. 1 Education.\", \"- Definitions for Calculus \\u00cb\\u0088k\\u00c3\\u00a6l ky\\u00c9\\u0099 l\\u00c9\\u0099s; -\\u00cb\\u008cla\\u00c9\\u00aa Here are all the possible meanings and translations of the word Calculus. Princeton's WordNet (1.00 / 1 vote) Rate this definition:\", \"Logic gates calculators can do all the things they need to do using different combinations of logic gates it s logic gates that control how the display works in a calculator and more logic gates that figure out the results of calculations\", \"- Shopping, searching for prices, looking for those all important deals to save you money! Why not use our cashback calculator combining the full range ...Read More.1 Compare Petrol Prices.hopping, searching for prices, looking for those all important deals to save you money! Why not use our cashback calculator combining the full range ... Read More. 1 Compare Petrol Prices.\", \"'Calculate' always in the Status Bar There are six known conditions in which the status bar will show CALCULATE: 1 The Calculation Option has been set to Manual and the workbook contains uncalculated formulae. Try setting calculation to Automatic (Tools-->Options-->Calculate). 2 The Iteration Option is turned on and the workbook contains circular references.\", \"Do I NEED a graphing calculator on the ACT? You should review an ACT math section, you can get a sample one from your school, and see which calculator you find most efficient for the types of problems.make sure whatever calculator you choose is acceptable, and remember to bring a backup in case batteries die.\", \"Comparison of two proportions easy-to-use statistical software. Performs a Chi-squared test for the comparison of two proportions (from independent samples), expressed as a percentage. MedCalc uses the N-1. Chi-squared test as recommended by Campbell (2007) and Richardson (2011). This test is not performed on data in the spreadsheet, but on statistics you enter in a dialog box.\", \"/ Lean to Conservatory Cost You can compare Lean to conservatory costs on our website by using our free conservatory quote calculator. Our conservatory quote calculator features an extensive range of Lean to conservatories in order to provide you with an accurate lean to conservatory cost.\", \"Welcome to the Net Price Calculator Welcome to the Net Price Calculator. Welcome to the Bethune-Cookman UniversityNet Price Calculator! You've come to the right place. We're happy you are beginning to explore how to plan and pay for your college education. Using the net price calculator, you can find out your eligibility for financial aid and estimate your out-of-pocket expenses.\", \"- At the moment, this version of calculator doesn't support saving or downloading the grade chart for future use. There's an improved version of this application available at www.varic.com/cgpa/1 with such facility.\", \"Can you use a calculator on GED test? FYI you can use a calculator on a high school test. You can use one on the FCAT as well. Update: IDK if I put this in the right spot. Sorry if I did not.\", \"Online Scientific Calculator Expressions that contain parenthesis, such as (1+2)*3, are evaluated by noting the precedence order and entering the form as 1 2 + 3 *. eCalc is a free and easy to use scientific calculator that supports many advanced features including unit conversion, equation solving, and even complex-number math. eCalc is offered as both a free online calculator and as a downloadable calculator.\", \"Checking Account Reconciliation Calculator to Help You Balance Your Checkbook This free online Checking Account Reconciliation Calculator will help you to reconcile a bank statement (balance check book) by doing the math for you. Or, if you prefer to reconcile a bank statement manually, the calculator on this page also includes an option for printing out a blank, free bank reconciliation form.\", \"Test Day Bring a permitted calculator to be used on the mathematics test only. It is your responsibility to know whether your calculator is permitted. Please refer to the ACT Calculator Policy (PDF). To make it even easier to figure out, you are not required to use a calculator at all.\", \"- Referencing a CALCULATED Column. CALCULATED enables you to use the results of an expression in the same SELECT clause or in the WHERE clause. It is valid only when used to refer to columns that are calculated in the immediate query expression. Copyright \\u00c2\\u00a9 2010 by SAS Institute Inc., Cary, NC, USA. All rights reserved.\", \"Graphing calculator Most graphing calculators, as well as some non-graphing scientific and programmer's calculators can be programmed to automate complex and frequently used series of calculations and those inaccessible from the keyboard. The actual programming can often be done on a computer then later uploaded to the calculators.\", \"Graduation Calculator Disclaimer: The graduation calculator is to be used for informational purposes only, and does not represent a guarantee of the time that will be required to complete a degree at Columbia Southern University.\", \"- Adding machine calculator for addition, subtraction, multiplication, division and percentages with virtual paper tape that keeps a running total and records your entries so you can double check them. Online adding machine with printable running tape record. Adding machine calculator for addition, subtraction, multiplication, division and percentages with virtual paper tape that keeps a running total and records your entries so you can double check them.\", \"OH NO, I am not allowed a calculator on the MCAT If you have not studied general chemistry for several years I suggest you get really familiar with the different types of calculation problems. The easiest way of doing this is to pick up a text book and work through the different types of problems. Initially build up your confidence using your calculator.\", \"Create online calculators with Excel Excel online calculation. Create online calculators with Excel. Providing an online calculator on your website makes people come back. Design your online calculation in Microsoft Excel, then convert it into a calculating and interactive web page that can be published on your website.\", \"Pregnancy Test Calculator Use this pregnancy test calculator to help decide when to home pregnancy test. It maps your complete cycle and forecasts when home pregnancy tests might be effective, including early pregnancy tests.\", \"- They\\u00e2\\u0080\\u0099re math thinking skills. Since most. people work with language a lot more than with numbers, language. skills come easier for many people. Still, you can master GED. math, with the right tools and the right approach. The GED math test has two parts. One part of the math test. doesn\\u00e2\\u0080\\u0099t allow you to use a calculator, and the other part does. The. calculator will be provided to you at the test site; you can\\u00e2\\u0080\\u0099t bring. your own. The calculator you\\u00e2\\u0080\\u0099ll need to use is the Casio fx-260. Solar Scientific Calculator.\", \"- Best Answer: Candidates will be allowed to use battery operated portable calculator in CPT. calculator can be of any type with upto 6 functions, 12 digits and upto two memories. use of unfair means.\", \"The Power of Extra Mortgage Payments 9 Comments. 1 why when you say that we can use a calc to determine what we want to pay to see how it shortens our length of the mortgage you don\\u00e2\\u0080\\u0099t have one. i need a calc to move monthly payments around and see how it shortens the mortgage time. 2 Paul, Good point and I wish had one\\u00e2\\u0080\\u00a6it\\u00e2\\u0080\\u0099s in the works.\", \"Calculator Policy 1 Only the calculators listed on this page are acceptable for the Math Test \\u00e2\\u0080\\u0094 Calculator portion of the test. You may not use a calculator while working on the Reading, Writing and Language, or Math Test \\u00e2\\u0080\\u0094 No Calculator portions, and must put the calculator away during these sections of the test.\", \"Create online calculators with Excel Create an online calculator for your website from an Excel spreadsheet. Providing an online calculator on your website makes people come back. Design your online calculation in Microsoft Excel, then convert it into a calculating and interactive web page that can be published on your website. Help your visitors do a better job, and they will return to your site for more.\"], \"pos_scores\": [17.25], \"neg_scores\": [0.5576171875, 2.59765625, 2.212890625, 8.4921875, 2.716796875, -0.497802734375, 1.0986328125, 2.376953125, 1.033203125, 7.69921875, 1.9482421875, 1.49609375, 2.220703125, 2.474609375, 9.5234375, 3.466796875, 1.69921875, 9.3515625, 1.4521484375, 4.02734375, 3.6484375, 3.5625, 5.75, 0.62841796875, 6.35546875, 10.515625, 10.8828125, 1.9150390625, 10.15625, 0.5029296875]}\n{\"query\": \"what does physical medicine do\", \"pos\": [\"What Is a Physiatrist? Doctor Directory. A physiatrist practices in the field of physiatry - also called physical medicine and rehabilitation - which is a branch of medicine that specializes in diagnosis, treatment, and management of disease primarily using physical means, such as physical therapy and medications.\"], \"neg\": [\"- Physical therapy also means the treatment of any pain, disease, or injury by physical means. A physical therapist seeks to identify and maximize quality of life and movement potential through prevention, intervention (treatment), promotion, habilitation, and rehabilitation. brief breakdown of what a physical therapist does: 1 restore function. 2 improve mobility. 3 relieve pain. 4 prevent permanent disabilities. 5 limit permanent disabilities. 6 restores.. 7 maintains.. 8 promotes..\", \"Are Routine Physical Exams Necessary? The actual annual physical examination should include measurement of your temperature, height, weight, pulse rate, and blood pressure. The physician should also measure your waist circumference, listen to your heart and lungs, and examine your body, including a good look at your skin for any signs of skin cancer.\", \"Physicians and Surgeons - What They Do Physicians and Surgeons - What They Do. Physicians and surgeons diagnose illnesses and prescribe and administer treatment for people suffering from injury or disease. Physicians examine patients, obtain medical histories, and order, perform, and interpret diagnostic tests. They counsel patients on diet, hygiene, and preventive healthcare.\", \"Career Launcher Some may think of a physical as a head-to-toe assessment of health. We may expect tests, screenings, X-rays and other procedures. The National Institutes of Health (NIH) describes a physical exam as studying the body to determine if there is or is not a physical problem.\", \"Definitions &Translations Here are all the possible meanings and translations of the word physical medicine. U.S. National Library of Medicine(0.00 / 0 votes)Rate this definition: A medical specialty concerned with the use of physical agents, mechanical apparatus, and manipulation in rehabilitating physically diseased or injured patients.\", \"- U.S. National Library of Medicine(0.00 / 0 votes)Rate this definition: Physical Medicine. A medical specialty concerned with the use of physical agents, mechanical apparatus, and manipulation in rehabilitating physically diseased or injured patients.\", \"- MATTHEW D JOHNSON, DO \\u00e2\\u0080\\u0093 NPI #1013199124. Physical Medicine & Rehabilitation. Physical medicine and rehabilitation, also referred to as rehabilitation medicine, is the medical specialty concerned with diagnosing, evaluating, and treating patients with physical disabilities.\", \"Why Visit a PM&R Physician Why Visit a PM&R Physician. 1 Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons. By taking the whole body into account, they are able to accurately pinpoint problems and enhance performance without surgery.\", \"Physical chemistry For example, chemical equilibrium, and colloids. Some of the relationships that physical chemistry strives to resolve include the effects of: 1 Intermolecular forces that act upon the physical properties of materials (plasticity, tensile strength, surface tension in liquids). 2 Reaction kinetics on the rate of a reaction.\", \"What is Medical Physics? For more information, we recommend the American Association of Physicists in Medicine (AAPM) Public Education Website, which addresses issues like: what is a medical physicist, the role s/he plays in radiation therapy and diagnostic medical imaging, history of medical physics, etc.or more information, we recommend the American Association of Physicists in Medicine (AAPM) Public Education Website, which addresses issues like: what is a medical physicist, the role s/he plays in radiation therapy and diagnostic medical imaging, history of medical physics, etc.\", \"What Is a Physiatrist? Similar to other types of spine specialists, physiatrists take the patient's medical history, perform a physical and neurological examination, order X-rays or other imaging studies, prescribe medications, and perform spinal injections. Physical medicine and rehabilitation often includes physical therapy.\", \"- The Department of Pulmonary Medicine supports an active rehabilitation and exercise program for patients with lung disease. In conjunction with the rehabilitation educators, we are offering a unique blend of pulmonary education services in the clinic. They include:\", \"What Does a Physiologist Do? Quick Answer. Physiologists study the human body and how its cells, tissues and organs work together. Many physiologists are also medical doctors, though some are researchers in independent or university laboratories. Physiologists also work for pharmaceutical companies, biotechnology companies and in public health. Continue Reading.\", \"Dr. Frederick D Gangemi - NPI #1356446108 frederick d gangemi, md \\u00e2\\u0080\\u0093 npi #1356446108 Physical Medicine & Rehabilitation Physical medicine and rehabilitation, also referred to as rehabilitation medicine, is the medical specialty concerned with diagnosing, evaluating, and treating patients with physical disabilities.\", \"What Is a Physiatrist? Today, there are over 8,000 physicians practicing physical medicine and rehabilitation. 1. Many PM&R physicians who treat back pain are part of a Spine Center or Spine Hospital, treating patients within a practice that includes other specialists, such as physical therapists, spine surgeons, rehabilitation specialists, and more.\", \"- Doctors use a physical exam to see how the body is performing. Depending on a patient\\u00e2\\u0080\\u0099s personal health history, a doctor may choose to focus on certain areas of a physical exam. People with a family history of heart disease may receive additional blood pressure checks, blood tests, and cholesterol screenings. Based on test results, age, and personal health history, it is also an opportunity to discuss future prevention measures. An average physical exam may include the following components:\", \"Physical Medicine and Rehabilitation Home \\u00c2\\u00bb About \\u00c2\\u00bb What is Physical Medicine and Rehabilitation? PM&R, also known as physiatry, is a branch of medicine devoted to the prevention, diagnosis and treatment of neurologic, musculoskeletal, cardiovascular, pulmonary and other disorders that may produce temporary or permanent impairment and associated functional disability.\", \"What Does an Emergency Medicine Physician Do? An emergency medicine physician provides treatment to patients with acute medical issues that require immediate intervention to save the patient's life or prevent future complications. Practitioners of this medical specialty commonly work in hospital settings like emergency rooms and intensive care units.\", \"Exercise physiology In addition, many exercise physiologists study the effect of exercise on pathology, and the mechanisms by which exercise can reduce or reverse disease progression.olleges and universities offer exercise physiology as a program of study on various different levels, including undergraduate, graduate, and doctoral programs. The basis of Exercise Physiology as a major is to prepare students for a career in field of health sciences.\", \"- Doctors of Podiatric Medicine (DPMs) strive to improve the overall health of their patients by focusing on preventing, diagnosing, and treating conditions associated with the foot and ankle. They treat a variety of conditions and employ innovative treatments to improve the well-being of their patients.\", \"- Physical therapy: A branch of rehabilitative health that uses specially designed exercises and equipment to help patients regain or improve their physical abilities.\", \"what does physical therapy really do?Does it really help? It can help to reduce the strain on these injured areas by building up compensating muscles. Deep massage and stretching can help to ease some of the pain in muscle spasms but this is a temporary one-on-one kind of fix. Similar to using a heating pad or ice. For me it was critical to have an excellent PT who worked with me to find the best plan.\", \"Physical medicine and rehabilitation (PM&R) Physical medicine and rehabilitation (PM&R) Overview. Physiatry, also called physical medicine and rehabilitation (PM&R), is a medical specialty that primarily uses physical means to help in diagnosis, healing, and rehabilitation.\", \"- Exercise physiologists oversee the analysis, improvement, and maintenance of health and fitness; rehabilitation of heart disease and other chronic diseases and disabilities; and the professional guidance and counsel of athletes and others interested in sports training.ports medicine and athletic training facilities employ exercise physiologists to create programs that help athletes reduce the number of injuries and recover faster from them. Makers of athletic equipment hire exercise physiologists to design sports gear.\", \"What does a Doctor do? He or she will diagnose and treat human disease, ailments, injuries, pain or other conditions. A doctor can be found in several settings, including public health organizations, teaching facilities, private practices, group practices and hospitals.\", \"FAQs About Physiatry What is physical medicine and rehabilitation? Physical Medicine and Rehabilitation (PM&R) physicians, also known as physiatrists, treat a wide variety of medical conditions affecting the brain, spinal cord, nerves, bones, joints, ligaments, muscles, and tendons.\", \"Medical Physicist Medical physicists are concerned with three areas of activity: clinical service and consultation, research and development, and teaching. On the average their time is distributed equally among these three areas. Many medical physicists are heavily involved with responsibilities in areas of diagnosis and treatment, often with specific patients.\", \"- Doctors use a physical exam to see how the body is performing. Depending on a patient\\u00e2\\u0080\\u0099s personal health history, a doctor may choose to focus on certain areas of a physical exam. People with a family history of heart disease may receive additional blood pressure checks, blood tests, and cholesterol screenings.\", \"pathophysiology the study of the biologic and physical manifestations of disease as they correlate with the underlying abnormalities and physiologic disturbances. Pathophysiology does not deal directly with the treatment of disease. Rather, it explains the processes within the body that result in the signs and symptoms of a disease.\", \"- Medical physics departments may be found in hospitals or universities. In the case of hospital work the term 'Medical Physicist' is the title of a specific healthcare profession with a specific mission statement (see below).n North America, medical physics training is offered at the master's, doctorate, post-doctorate and/or residency levels. Also, a professional doctorate has been recently introduced as an option.\"], \"pos_scores\": [13.2265625], \"neg_scores\": [10.9453125, 8.8984375, 8.1171875, 8.90625, 12.5078125, 14.25, 13.8984375, 14.109375, 3.021484375, 7.171875, 12.6015625, 6.03125, 6.234375, 13.578125, 11.9765625, 8.9296875, 14.59375, 9.1484375, 3.40625, 7.8046875, 10.4765625, 9.125, 14.84375, 5.3984375, 9.25, 14.703125, 7.62109375, 8.9765625, 3.921875, 7.1328125]}\n{\"query\": \"what does pending mean on listing\", \"pos\": [\"- Active/Pending = Usually that means the seller would like to get more offers. Savvy agents want back-up offers in place. If you made an offer to purchase a property and the seller agreed to the price, but already has a buyer in contract, you would be the 1st position back-up buyer.\"], \"neg\": [\"what does it mean when the status of the house is pending? is that mean the house is not for sale anymore? Pending indicates that there is a contract on the house, and that the buyers are in the process of getting their loan -- this process generally takes 3-4 weeks once the offer is accepted. Let me know if I can assist you by using the Contact Me link.\", \"\\\"What does \\\"\\\"option pending\\\"\\\" mean on a real estate listing?\\\" Best Answer: the seller is hoping to snag another potential buyer in case the tenant backs out. This guy has a tenant that he rented to with the option to buy. The seller listed it for sale online, but is being honest and telling you his tenant is thinking about buying it.\", \"- Claim pending means the guarantor has not yet paid the lender's claim, and it might be possible for you to avoid the consequences of default. However, you must act immediately; once your lender files a claim, there are only a few days before the guarantor will pay the claim.\", \"pending Pending. Begun, but not yet completed; during; before the conclusion of; prior to the completion of; unsettled; in the process of adjustment.A lawsuit is said to be pending from its inception until the issuance of a final judgment by a court.The phrase pending appeal refers to the time before an appeal is taken, as well as to the period during which an appeal is in progress.ending. Begun, but not yet completed; during; before the conclusion of; prior to the completion of; unsettled; in the process of adjustment.\", \"\\\"What does \\\"\\\"pending\\\"\\\" mean in real estate?\\\" Sometimes the sellers may still accept backup offers on pending listings, but it's best to contact your agent to find out if it's possible to submit this type of offer. Learn the differences between contingent and pending. Related links. Contingent; Under Contract\", \"\\\"What does it mean when a home is listed as \\\"\\\"contingent\\\"\\\" on Redfin.com?\\\" Definition of Contingent. This means the seller has accepted an offer on the property, but success may still depend on passing a home inspection or getting financing. There are different types of contingent statuses. For example, Contingent - Show means that it is still possible to tour the property and submit a backup offer in case the current one falls through. Contingent - No Show indicates that the seller no longer wishes to show the property.\", \"What is pending mean? Best Answer: Pending means waiting or on hold. If renovation is pending due to weather - it means the renovation is on hold till the weather is conducive for work.\", \"What Does Under Contract Mean in Real Estate? If you've searched for homes through your local multiple listing service, you may have come across various terms that describe the property's sale status. An active listing means the house is on the market and available for purchase. A pending sale is one that\\u00e2\\u0080\\u0099s moving toward closing. An under contract status means there\\u00e2\\u0080\\u0099s an accepted offer on the house, but the sale is still in an early, and perhaps precarious, stage. A valid contract must meet four criteria.\", \"Option Pending vs Pending continue to show OP - Option Pending - Listings that are under contract and the seller and buyer have agreed to use the \\u00e2\\u0080\\u009cTermination Option\\u00e2\\u0080\\u009d in paragraph 23 of the standard TREC contract. PS - Pending Continue to Show - Used for listings currently under contract but are still available to show.\", \"Lis pendens Lis pendens is Latin for suit pending. This may refer to any pending lawsuit or to a specific situation with a public notice of litigation that has been recorded in the same location where the title of real property has been recorded.\", \"Active Status? Pending Status? This status may also be used to indicate the property is a short sale with an accepted offer. Pending w/o release, continue to show (PS) or (Pend w/S) \\u00e2\\u0080\\u0093 Accepted offer on the property, still showing to prospective buyers for offers in back-up position to the first accepted offer.\", \"What Does Sale Pending Mean in Real Estate? Here\\u00e2\\u0080\\u0099s what you should know to decide if a Sale Pending home may be worth pursuing. Understanding the Home Purchase Contingency. To begin, a buyer generally makes an offer \\u00e2\\u0080\\u009csubject to\\u00e2\\u0080\\u009d or \\u00e2\\u0080\\u009ccontingent upon\\u00e2\\u0080\\u009d a property inspection, a bank appraisal or full loan approval. In these situations, the buyer is telling the seller he plans to close on the home but wants to be certain the property is in good condition and he can get financing.\", \"\\\"What does \\\"\\\"option pending\\\"\\\" mean on a real estate listing?\\\" What does option pending mean on a real estate listing? I've been looking for houses on the internet, and came across several listings that said option pending. Does it mean there's an offer on the house and it's pending approval? 2 following.\", \"Option Pending vs Pending continue to show Answers(4) When you enter into a contract with a buyer / seller the agents put the property into option pending for 10 days so the buyer can get inspections done. After the 10 day has passed it goes into PS pending continue to show. waiting on financing. I am always here for your questions feel free to give me call.\", \"what does pending mean Hi, In real estate pending refers to when the Seller has accepted an Offer from a Buyer and now the property is under contract. This technically does not mean that the property has sold yet. There are possible contingencies / variables that could allow it to fall out of escrow.\", \"Pending When a home is listed as \\u00e2\\u0080\\u009cpending,\\u00e2\\u0080\\u009d it means that there is a closing date set and that all contingencies have been met or waived. At this time, the lender and the escrow company are busy working with the loan and title documents to be sure that everything will be ready by the closing date.\", \"Pending However, it is possible that not all hope is lost \\u00e2\\u0080\\u0094 you may still be able to purchase the home. For this reason, it is important to understand exactly what the pending status means. Selling and buying a home in today\\u00e2\\u0080\\u0099s world of technology is different than it was years ago.\", \"What Does a Pending Status on a House Mean? A pending sale typically involves contingencies -- conditions a seller and buyer must meet to complete the transaction. Buyers typically include a financing condition, an appraisal contingency for property value property, and investigation contingencies, such as title searches and inspections.\", \"Sale Pending: What Does It Mean & Should You Make an Offer? No more contingencies = \\u00e2\\u0080\\u0098sale pending\\u00e2\\u0080\\u0099. A deal that\\u00e2\\u0080\\u0099s truly pending is one in which the buyers have removed all contingencies. The buyer is \\u00e2\\u0080\\u009clocked in\\u00e2\\u0080\\u009d to buying the home. The final step is to move toward closing, which can take anywhere from a few days to a few weeks.\", \"\\\"What does \\\"\\\"Contingent\\\"\\\" mean for a listing status?\\\" Most of these are self-explanatory\\u00e2\\u0080\\u00a6. 1 Active means that the property is for sale. 2 Come on over and make us an offer. 3 Subject to Inspection means that a buyer\\u00e2\\u0080\\u0099s offer is pending their inspection.\", \"What\\u00e2s the Difference Between Contingent and Pending Listings? When a listing has changed status to \\u00e2\\u0080\\u009ccontingent\\u00e2\\u0080\\u009d, an offer has been accepted with any one or a combination of circumstances listed below are in effect: 1 Offer accepted contingent on court approval. 2 Offer accepted pending lender approval of Short Sale. 3 Offer(s) submitted awaiting lender approval of Short Sale.\", \"What Does a Pending Status on a House Mean? In a pending sale, the seller may only accept new offers on a backup basis, and the present deal must be formally cancelled before the seller can open escrow with a new buyer. A broker in San Francisco may report a home that is in escrow as Pending -- Continue to Show or Pending -- Do Not Show..\", \"\\\"What does \\\"\\\"pending\\\"\\\" in PayPal mean?\\\" pending technically just means still coming he just means tht it is still nt there. It means that your funds have not cleared from the sender or else there holding them coz u have a new acct. Pls be more specific and i will help. Let me help You Generate Cash in the Next 24-48 Hours.\", \"- Lis Pendens is the Latin phrase for pending litigation. More commonly, a lis pendens is referred to as a \\u00e2\\u0080\\u009cnotice of. pending action.\\u00e2\\u0080\\u009d Persons who buy or lend on the real estate after a lis pendens has been recorded take the property. subject to the claimant\\u00e2\\u0080\\u0099s right, if any, to the real estate. The lis pendens, when recorded, is a notice warning all prospective buyers or encumbrances that title to or possession of the real estate is in dispute. PRESERVATION OF TITLE The purpose of a recorded lis pendens is to preserve rights to the real estate until the dispute with the owner is\", \"what does it mean when the status of the house is pending? is that mean the house is not for sale anymore? answer by Rosa Burk | Visit My Website | Contact Me. When a home is in Pending status means that it is off the market, and in the process of being purchased. If you really care for the property, be in the look up, occasionally the transaction fell through.ending indicates that there is a contract on the house, and that the buyers are in the process of getting their loan -- this process generally takes 3-4 weeks once the offer is accepted. Let me know if I can assist you by using the Contact Me link. K.\", \"\\\"what is the difference between \\\"\\\"pending\\\"\\\" and \\\"\\\"Option Pending\\\"\\\"?\\\" Option Pending means that the transaction is still within the Option Period. During the Option Period, the buyer has the right to terminate the contract for any reason. During this period, the buyer usually has inspections done on the house and negotiates repairs (if any) for items noted on the inspection report.\", \"I sent someone a money request on paypal and it says pending what does that mean? i never used paypal before? Best Answer: Pending means waiting for an answer or verification, or basically, waiting for something. There's nothing for you to do until the transaction is finalized. Paypal Request Pending. It means it sent but your just waiting for the person to respond. pending means awaiting approval.\", \"- Lis Pendens is the Latin phrase for pending litigation. More commonly, a lis pendens is referred to as a \\u00e2\\u0080\\u009cnotice of pending action.\\u00e2\\u0080\\u009d Persons who buy or lend on the real estate after a lis pendens has been recorded take the property subject to the claimant\\u00e2\\u0080\\u0099s right, if any, to the real estate. The lis pendens, when recorded, is a notice warning all prospective buyers or encumbrances that title to or possession of the real estate is in dispute. preservation of title\", \"Definitions &Translations Freebase(0.00 / 0 votes)Rate this definition: Pendente lite. Pendente lite is a Latin term meaning awaiting the litigation or pending the litigation which applies to court orders which are in effect while a matter is pending.\", \"- Homes with an accepted contract for sale have an MLS status of pending. Here is a description of all of the pending statuses used by the Northwest MLS.\"], \"pos_scores\": [13.46875], \"neg_scores\": [13.9375, 11.8359375, 6.09765625, 9.6640625, 11.5703125, 7.28125, 10.8828125, 13.2578125, 12.734375, 8.171875, 11.6171875, 9.9921875, 13.421875, 10.09375, 14.421875, 15.9765625, 8.9765625, 13.0234375, 12.3515625, 11.2421875, 11.3984375, 12.6796875, 8.5390625, 7.5625, 13.171875, 11.2578125, 9.8515625, 7.9453125, 5.04296875, 11.6328125]}\n{\"query\": \"feeding rice cereal how many times per day\", \"pos\": [\"FEEDING GUIDELINES AGES 4 TO 6 MONTHS 1. Begin with rice cereal on days 1,2 and 3, offering it twice daily at breakfast and dinnertime. Rice cereal can be mixed with water or milk(breast or formula) to make a thin oatmeal like consistency. The infant should be offered a rubberized spoon. During these first three days, offer 3-4 tablespoons at a time but be flexible. Some infants will be very hungry and want more- that's OK.\"], \"neg\": [\"How much,what and how often should I feed my bearded dragon? Juveniles (0-18months)- 80% Protein 20% Greens. Feed as many crickets as they can eat within 10-15 minutes, 2 or 3 times a day. This should add up to between 40 and 200 crickets per day! 6 days a week dust one feeding with calcium (D3, phosphorous free).On the 7th day dust one feeding with a multivitamin.uveniles (0-18months)- 80% Protein 20% Greens. Feed as many crickets as they can eat within 10-15 minutes, 2 or 3 times a day. This should add up to between 40 and 200 crickets per day! 6 days a week dust one feeding with calcium (D3, phosphorous free).\", \"How to Make Perfect Swiss Meringue Buttercream How often do you feed? Riki and Daisy were eating first thing in the morning and then again at night. Since they are on a heathy fitness routine (nice way to say diet), I am only feeding them at five every day...so once a day. I was just wondering how often you are feeding your dogs.\", \"Welcome to the New Earth's Best On average 6 feedings each day. Usually 4-6 feedings each day. as your baby takes more solids, the number of feedings may decrease. As the baby takes more solids the number of feedings will decrease. Usually 4 feedings each day. On average every 3-4 hours or 6-8 feedings each day, 2-3 fl. oz. per feeding. Feed on demand.\", \"When should i feed my puppy like what time every day ? Best Answer: Feed her twice a day at the same time every day, about 8-12 hours apart. If she's young and you can make it three smaller feedings, that's even better. But at least two is good, morning and evening..\", \"Why do Japanese eat alot of rice? Asker's rating. 1 I almost eat rice three times a day. This is natural for me because I was brought up with eating rice. 2 Starches are the foundation for every cuisine in the world. In each area of the world the conditions are right for one or more native starch crops. 3 Because, they grow well. They suit the weather there.\", \"Once you introduce rice cereal, how often should you feed it to them? My son's pedi also said ok to start rice cereal at 4 months old but I didn't do it. He is now 6 months old and I just started oat meal once a day but he didn't like it much so I tried avocado yesterday and he loved it! I would wait till 6 months but if you really want to give, then once a day should be plenty.\", \"Diarrhea TreatmentMyths and Facts The so-called BRAT diet - from the initials of banana, rice, applesauce and toast - is fine, says Dr. Greenough. Encourage children with diarrhea to eat frequently as much as they want - about five to seven times a day or every three to four hours, he advises.\", \"How much fibre should I eat every day? How much fibre should I eat every day? A rough estimate of the amount you need is about 18g a day, though 16 - 24g is a healthy range and individual requirements vary. Ideally you should aim to open your bowels at least once a day and not have to strain when you go to the loo. One example of what you'd need to equal about 18g is a bowl of bran cereal for breakfast, a wholemeal sandwich with a bowl of salad for lunch, and five portions of fruit and vegetables. You need to eat a balance of insoluble an soluble fibre types. Insoluble fibre is the type that's best at alleviating constipation as it can soak up around fifteen times it's own weight in water. This increases the bulk of stools, which in turn stimulates gut contractions and keeps the bowels moving regularly.\", \"Baby Feeding Schedules In general, most babies are eating: 1 3 to 4 tablespoons of cereal once a day and 1 to 2 tablespoons of a vegetable and fruit 1 or 2 times a day around the time they are 4 to 6 months old, with many infants being able to wait until they are 6 months old to start cereal and baby food.\", \"- [\\u00e2\\u0080\\u0093]Lynx_Titan07[S] 649 points650 points651 points 7 months ago (87 children) I eat bread 3 times and Lasagne once every day and I have done that for almost 4 years now. I do exercise as well, 60 pushups and situps daily and which I have done for 3 years now. I missed 2 days in February of 2014, when Jagex did a Double XP Weekend on RS3.\", \"How Many Times a Day Should Kids Eat? One, done back in the sixties, fed boarding school students either 3 times a day, 5 times or 7. The more frequent eating was associated with less weight gain in older children (10-16 years) but not younger ones (6-11).or example, Little D (3 years old) would probably do fine eating 4 times a day because he\\u00e2\\u0080\\u0099s a big meal eater. But when Big A was 3, she barely ate (she went 3 weeks one time eating only one bite at breakfast!) and frequent meals were a must. But it\\u00e2\\u0080\\u0099s not just how often a child eats, but what they are being fed.\", \"- A general rule of thumb to follow when feeding your betta fish is to not feed them any more at once than they can consume in two minutes. How Many Times Do You Feed A Betta Fish Per Day? If you feed them twice per day then 2 minute feedings are ideal. If you feed them twice per day, well, do the math.\", \"- Report Abuse. Everyday, at least once usually more. If you feed your adult rabbits pellets, you should feed it 1/4 to 1/2 cup of pellets per day for every 6 pounds of body hope this helps! Source(s): http://wiki.answers.com/Q/How_often_shou...\", \"Once you introduce rice cereal, how often should you feed it to them? My son is 4 months old and his pediatrician said it is ok to start introducing rice cereal. I've done that and he loves it. I'm just not sure how often I should be giving it to him. He normally would eat a 5 oz bottle of formula about every 3 hours.\", \"Hand Raising A Joey Pedialyte is important for supplement feeding to ensure that the joey will not get dehydrated. Frequency of supplement feeding can vary, but generally 3-4 times per day at regular intervals is adequate. As with anything else regarding sugar gliders, there are many methods and techniques for hand raising a joey.\", \"- I've been doing research on feeding mares dried raspberry leaves and it seems to be a big help to them! Has anyone had experience with it? My general findings say to feed 1/4 cup once a day, do you feed that? And what about capsules? They may be cheaper/ easier to feed? (Such as these http://www. amazon ...nd what about capsules? They may be cheaper/ easier to feed? (Such as these http://www. amazon .com/Natures-Way-Raspberry-Leaves-Capsules/dp/B000AR8PXW/ref=sr_1_2?ie=UTF8&qid=1372275291&sr=8-2&keywords=raspberry+leaves but how much should I feed?) My mare is a 21 year old, 1200 pound Thoroughbred.\", \"Feeding Your Baby your baby should eat at least 8 times a day about every 3 hours she may have one longer stretch between feedings per day of up to 5 hours burp your baby after each 1 2 ounce for the first few weeks\", \"How much Flaxseed? Answers to how much flaxseed per day! After 2-3 days, increase to 2 Tablespoons ground flax a day. Continue this to the goal of 2-4 Tablespoons ground flaxseed a day. -Recommendations from professionals vary from 2-4 Tablespoons of ground flax seed. I know many people who eat regularly 2-4 Tablespoons of ground flax daily.ontinue this to the goal of 2 teaspoons ground flaxseed daily. (~greater than 6y/o or 50-100 pounds; if greater than 100lbs work up to adult dose). Start with 1 teaspoon of ground flax seed a day. Drink 8-10 cups of fluids (preferably water)of ground flaxseed a day.\", \"- One time a day is fine. unless they are fry or young adults then you may want to feed twice a day, always make sure they eat all the flske you put in before it hits the groun \\u00e2\\u0080\\u00a6 d.\", \"How much and how often should I feed my Fire Belly Toads? When I had my Fire Belly Toad, I fed it once a day, keeping a general routine of feeding it first thing in the morning so I wouldn't forget. I fed mine two of the pellets you can buy at Wal*Mart, though it is also suggested to feed it 1 or 2 crickets every other day. Loading ...\", \"Eating Habits A common eating pattern is three meals (breakfast, lunch, and dinner) per day, with snacks between meals. The components of a meal vary across cultures, but generally include grains, such as rice or noodles; meat or a meat substitute, such as fish, beans, or tofu; and accompaniments, such as vegetables.\", \"10 Myths and Misperceptions About Homemade Dog Food Here\\u00e2\\u0080\\u0099s a quick example: My own 75-pound dog has a daily requirement of 1,840 mgs of calcium, and since I use quite a bit of fiber in his diet in the form of brown rice, I want to offset any absorption issues and ensure that he gets about 2,000 mgs per day, or 14,000 mgs per week.\", \"Is it bad to eat rice every day? A more varied diet is even better, such as including other whole grains and complex carbohydrates. On the other hand, eating white rice every day puts a person at higher risk for diabetes and other diseases and health problems. \\u00e2\\u0080\\u009cBoth brown and parboiled rice offer higher nutrition than untreated white rice.\", \"Diverticulitis Lifestyle Rx Ways to increase daily fiber intake: 1 Eat Brown rice instead of white rice. 2 Eat Oats (oatmeal, old fashioned) 3 Eat whole grain cereals. Eat whole grain 1 pastas. Eat organic (when you can), unpeeled VEGETABLES WITH THEIR SKINS ON (lots of Vegies!) Add rice bran or wheat bran (millers bran) to your 1 foods. Eat beans.\", \"- First, make sure that you do not rinse the rice - that will wash away the starch, which is what is needed to help bind the stools. I even save the water it was cooked in and allow the dog to drink it. If I am feeding a dog burger and rice, I offer them several small meals - 5-6 per day. Hahaha.\", \"How many times a day should you feed a rabbit? Everyday, at least once usually more. If you feed your adult rabbits pellets, you should feed it 1/4 to 1/2 cup of pellets per day for every 6 pounds of body hope this helps! Source(s): http://wiki.answers.com/Q/How_often_shou... swimchicky96 \\u00c2\\u00b7 6 years ago.\", \"How many crickets do you feed to a baby bearded dragon and should you use calcium powder? Report Abuse. for baby beardies you want to feed them 2-3 times a day as many crickets as he will eat in 10-15 min. salads he wont really eat right now but keep offering them.\", \"- We do not need 3 meals a day. You should focus on the type of food instead of the frequency of eating. Between 3 unhealthy meals (mostly carbohydrates) and 1 healthy meal (with a lot of protein and fats), the healthy meal wins.\", \"Psyllium Usual Pediatric Dose for Constipation. Daily fiber: Children 1 to 3 years: 19 g/day Children 4 to 8 years: 25 g/day Children 9 to 13 years: Male: 31 g/day; Female: 26 g/day Children 14 to 18 years: Male: 38 g/day; Female: 26 g/day Constipation: Children 6 to 11 years: 1.25 to 15 g orally per day in divided doses Children greater than or equal to 12 years and Adults: 2.5 to 30 g per day in divided doses\", \"How Much Arsenic Is in Your Rice? The New Rice Rules: 7 Points per Week. We used our new data and analysis to assign a point value to types of rice foods. On average, we recommend getting no more than 7 points per week. Risk analysis is based on weight, so a serving of any food will give children more points than adults.\"], \"pos_scores\": [14.640625], \"neg_scores\": [6.8671875, 3.802734375, 10.71875, 5.2421875, 3.767578125, 13.0546875, 6.92578125, 3.087890625, 13.0546875, 0.515625, 6.76953125, 5.96875, 3.474609375, 11.265625, 6.0078125, 1.6943359375, 9.875, 3.169921875, 8.2890625, 2.423828125, 5.16015625, 3.66796875, 4.31640625, 2.908203125, 8.546875, 4.07421875, 2.53125, 3.060546875, 4.7734375, 5.4609375]}\n{\"query\": \"most dependable affordable cars\", \"pos\": [\"Surmount the snow with 7 of the best used all-wheel-drive winter cars for under $10,000 If you can look past its bargain interior and anonymous exterior, the Suzuki SX4 is one of the most reliable and affordable all-wheel-drive cars.\"], \"neg\": [\"- Q: How do you paint cars so inexpensively? A: Maaco has developed the easiest, most affordable and reliable paint process in the world. Our more than 40 years of experience makes our approach to overall auto painting the best way to turn the car you drive back into the car you love. We also have the advantage of volume.\", \"- Reliable used small cars. See CR's Good Bets: These used cars have performed well in our tests over the years and have had much better than average overall reliability for multiple years. Small cars can be affordable, fun, and thrifty, though they can vary widely in practicality, price, and performance. The best overall small sedans excel in all those attributes, and top our Ratings for the category.\", \"The 100 most reliable cars of the last decade (in order) In a very clever move, it has taken this secondary data and produced a reliability index \\u00e2\\u0080\\u0093 an independent comparison of frequency of failure across the 55,000 vehicles it insures. Read on for a list of the 100 most reliable used cars over the past decade. And before you take a peek, have a guess at where the best Japanese, German, Swedish, British, Korean and French-built cars will rank.\", \"5 MOST RELIABLE SEDANS (Luxury) 5 MOST RELIABLE SEDANS (Luxury) Lexus leads the way in the field of highly rated reliable luxury sedans, earning several top 5 picks. With strong ratings for all our picks, these manufacturers are living up to their reputation for producing comfortable, reliable vehicles. The LS series brought luxury to a large number of American auto buyers.\", \"Find Affordable Auto Insurance Online Get Car Insurance from a Cheap Company. Apr 16, 2012 - There is no single company that can offer the cheapest auto insurance rates for every vehicle owner, but motorists may be able to find affordable coverage after shopping around. Rates are... Cheap Companies Selling Auto Insurance. Apr 02, 2012 - The affordability of an auto insurance policy is largely dependent on the motorist, since rates are based on the possibility that a vehicle owner will file a claim. When drivers have a... Save with Affordable Car Insurance\", \"The Five Most Reliable Toyotas Ever Made As a brand, Toyota has long been celebrated for its reliability and dependability. For the eighth year in a row, Toyota was ranked the most reliable car in the world, by Consumer Reports.\", \"- Author Copy Created with Sketch. The automotive world can't be all BMW M3s and Porsche 911 Carreras. Drivers need affordable, high-performance sport compacts that are fun to drive and to modify, and these are the best ever built. Author Copy Created with Sketch. The automotive world can't be all BMW M3s and Porsche 911 Carreras.\", \"- Toyota's luxury brand, Lexus, scored a number of wins in the J.D. Power dependability survey, including the 2011 Lexus ES atop the premium compact segment. Lexus IS was No. 2, Lincoln MKZ was No. 3 Lexus.\", \"- J.D. Power just released its 2017 vehicle dependability study and these are the most reliable cars in each category. J.D. Power just released its 2017 vehicle dependability study and these are the most reliable cars in each category. Logo for Business Insider over a transparent background.\", \"The 100 most reliable cars of the last decade (in order) Read on for a list of the 100 most reliable used cars over the past decade. And before you take a peek, have a guess at where the best Japanese, German, Swedish, British, Korean and French-built cars will rank.\", \"Are German Cars Reliable? The Myth of \\u00e2German Engineering\\u00e2 For a while afterwards, Mercedes and VW managed to stay near the top in reliability rankings. But their Japanese rivals weren\\u00e2\\u0080\\u0099t sitting idly-by. In the 1980s and 90s the most reliable models ended up coming from Honda, Toyota, Acura, Infiniti and Lexus. \\u00e2\\u0080\\u009cBack then, the cars like the Beetle were pretty simple.\", \"Best used car for about 7 to 8 thousand dollars? October 2007 edited 9:48AM in Repair and Maintenance. I would like to buy a clean, very dependable used car for about 7000-8000 dollars. The most dependable used cars, according to consumer magazines, are the Honda Accord and the Toyota Camry.\", \"- Most Reliable: Chevrolet Cruze. Chevrolet made the list with its all-new Cruze sedan. Our review praised the Cruze's technological loadout, but the powertrain wasn't feeling up to par. It's a solid middle-ground choice for buyers that want a 21st century commuter.\", \"5 MOST RELIABLE SMALL CARS A relatively new vehicle, the Toyota Echo began U.S. production in 2000, offering Americans a passenger car with one of the best fuel efficiency ratings out there. Initial purchase price and the Echo\\u00e2\\u0080\\u0099s fuel economy, combined with its smooth ride and reliability, make this economical car a great purchase option.\", \"- J.D. Power and Associates has released its 2013 Vehicle Dependability Study, which lists the most dependable cars and trucks for the 2010 model year. Based on problems reported per 100 vehicles, Lexus is the most dependable manufacturer.\", \"- My situation is this-I don't have a lot of money to spend and I especially don't have a lot of extra funds or time for repairs but I'm now ready to buy a used RR. My budget is under 35,000 euros and I like the Range Rover Sport but open to other models, if necessary.So if anyone can be so kind as to guide me in right direction on which year (s) and model is the most dependable and least costly in terms of repairs that would be great!!elcome to RangeRovers.net-a website dedicated to all things Range Rovers. You are currently viewing our forum as a guest, which gives you limited access to view most discussions and access our other features.\", \"- dont trust them. i think range sports is the most safer. the safest car on the market, is a Hummer h2, it is just so big that you can drive over another car. the Ford f-series is also a very safe car \\u00f0\\u009f\\u0098\\u0080. I disagree. Then again I\\u00e2\\u0080\\u0099ve done my research, so I know why.\", \"- Best Midsize Cars. Midsize cars are one of the most popular types of vehicles with consumers, and with good reason. When it comes to commuting, hauling the kids around town, or road-tripping, few types of cars balance space, comfort, fuel economy, and performance as well as the best midsize cars do. #1. 2017 Chevrolet Malibu.\", \"10 Most Expensive Cars Of 2014: Keeping Up With The 1 Percent 10 Most Expensive Cars Of 2014: Keeping Up With The 1 Percent 10 Most Expensive Cars For 2014 For the \\u00e2\\u0080\\u009c1 percent\\u00e2\\u0080\\u009d who can afford it, there are a surprising number of stratospherically expensive vehicles that make most of Mercedes-Benz and Porsche\\u00e2\\u0080\\u0099s cars seem relatively affordable.\", \"Top 10 most reliable cars under $25,000 Base MSRP price range: $16,970 - $20,420. Overall, Scion is one of the most reliable brands from our survey (8th out of 28), and the xB is the leader from this offshoot brand from Toyota. The xB was last redesigned in 2008, which means the boxy hatchback is long beyond any teething issues.\", \"Car Insurance: Most and Least Expensive Models Car Insurance: Most and Least Expensive Models. Last Updated Apr 27, 2010 8:46 AM EDT. You no longer have to choose a minivan to get the cheapest insurance costs. Traditionally, those family haulers driven by careful parents have carried the lowest insurance premiums.\", \"The Most Reliable Cars On The Road Conclusion: The most reliable cars. While these two surveys may vary, it is evident that cars made by Toyota (including Toyota, Lexus and Scion, have a generally impressive record on reliability, so are a good place to start if you are looking for a reliable car. In terms of American-made cars, Buick comes out top.\", \"- 60 DECEMbconsumer reports Er 2013. cars. Most reliable new cars. Lexus tops our list, but electronic problems plague popular models. T. he Japanese dominance in. auto reliability is showing cracks. In the past decade, brands from. Japan typically locked in the top. slots in our predicted-reliability rank-.\", \"The top 10 most reliable and unreliable 4x4s Honda\\u00e2\\u0080\\u0099s CR-V won the crown of the most reliable 4x4 by a fair margin while Audi\\u00e2\\u0080\\u0099s A6 Allroad is the most unreliable 4x4 on the road, with Volkswagen\\u00e2\\u0080\\u0099s Touareg and BMW\\u00e2\\u0080\\u0099s X5 placing second and third most unreliable.\", \"The 5 Most Reliable Pickup Trucks The Ford F-150 is the bestselling vehicle in the USA and is the bestselling pickup truck for 34 years straight. There is no question about the reliability of the Ford F-150. Production for the tenth generation F-150 started in 1997 and was the start of the new aero-styling philosophy of Ford\\u00e2\\u0080\\u0099s new pickup trucks.\", \"Most Reliable Cars That Rarely Need A Mechanic The flat 6 engine is one of the most powerful in its class. Combined with its responsive handling and slick-shifting transmission, Porsche offers you a driving experience rarely found in any other consumer vehicle. Most Reliable Sports Car: 2016 BMW M4. If power is what you crave, then the BMW M4 will deliver.\", \"Top 10 Most Reliable Car Brands VDS focuses on long-term reliability by monitoring defects, malfunctions and design-related issues with three-year old vehicles still being driven by their original owners. Dependability is scored in problems per 100 vehicles. In other words, its a look at the most reliable car brands. The latest installment of this survey covers issues with cars and trucks from the 2012 model year. It dovetails neatly with the firm\\u00e2\\u0080\\u0099s Initial Quality Study, which monitors problems reported during the first three months of ownership.\", \"Top 10 Most Reliable and Least Reliable Car Brands of 2015 Here is the list of Top 10 most reliable car brands (followed by the least reliable brands after): 1) Lexus. Lexus continues to lead the way in reliability, earning an average score of 64 percent with seven models in its lineup. The company\\u00e2\\u0080\\u0099s ranking on the Annual Auto Reliability survey remains unchanged compared to last year.\", \"5 MOST RELIABLE SMALL CARS 5 MOST RELIABLE SMALL CARS. The hands of Toyota engineers have been involved in three of our five top picks for your next used small car. But quality and reliability cut across our top 5, giving you a number of reliable used small cars to choose from.\", \"Top 15 Most Comfortable Cars We\\u00e2\\u0080\\u0099ve gathered a list of what we think are the top 15 most-comfortable cars on the market, ranging from your commuter car to that luxury sedan you may be eyeing. There\\u00e2\\u0080\\u0099s a car for everyone on this list. First up on our list of the most comfortable cars is the Audi A6.\"], \"pos_scores\": [12.015625], \"neg_scores\": [5.77734375, 10.796875, 7.4296875, 9.25, 5.2890625, 9.203125, 6.13671875, 10.984375, 10.0234375, 7.6796875, 7.765625, 11.390625, 11.9375, 11.25, 12.3046875, 4.9609375, 5.18359375, 8.7421875, 5.59375, 12.375, 4.5390625, 11.2734375, 9.4453125, 6.85546875, 6.27734375, 10.4765625, 7.91796875, 9.3671875, 11.1328125, 5.90234375]}" }, { "path": "research/LLARA/data/pretrain/toy_pretrain_data.jsonl", "content": "{\"input\": \"Anarchism Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and has a strong historical association with anti-capitalism and socialism. Humans lived in societies without formal hierarchies long before the establishment of formal states, realms, or empires. With the rise of organised hierarchical bodies, scepticism toward authority also rose. Although traces of anarchist thought are found throughout history, modern anarchism emerged from the Enlightenment. During the latter half of the 19th and the first decades of the 20th century, the anarchist movement flourished in most parts of the world and had a significant role in workers' struggles for emancipation. Various anarchist schools of thought formed during this period. Anarchists have taken part in several revolutions, most notably in the Paris Commune, the Russian Civil War and the Spanish Civil War, whose end marked the end of the classical era of anarchism. In the last decades of the 20th and into the 21st century, the anarchist movement has been resurgent once more. Anarchism employs a diversity of tactics in order to meet its ideal ends which can be broadly separated into revolutionary and evolutionary tactics; there is significant overlap between the two, which are merely descriptive. Revolutionary tactics aim to bring down authority and state, having taken a violent turn in the past, while evolutionary tactics aim to prefigure what an anarchist society would be like. Anarchist thought, criticism, and praxis have played a part in diverse areas of human society. Criticism of anarchism include claims that it is internally inconsistent, violent, or utopian. Etymology, terminology, and definition The etymological origin of anarchism is from the Ancient Greek anarkhia, meaning \\\"without a ruler\\\", composed of the prefix an- (\\\"without\\\") and the word arkhos (\\\"leader\\\" or \\\"ruler\\\"). The suffix -ism denotes the ideological current that favours anarchy. Anarchism appears in English from 1642 as anarchisme and anarchy from 1539; early English usages emphasised a sense of disorder. Various factions within the French Revolution labelled their opponents as anarchists, although few such accused shared many views with later anarchists. Many revolutionaries of the 19th century such as William Godwin (1756–1836) and Wilhelm Weitling (1808–1871) would contribute to the anarchist doctrines of the next generation but did not use anarchist or anarchism in describing themselves or their beliefs. The first political philosopher to call himself an anarchist () was Pierre-Joseph Proudhon (1809–1865), marking the formal birth of anarchism in the mid-19th century. Since the 1890s and beginning in France, libertarianism has often been used as a synonym for anarchism and its use as a synonym is still common outside the United States.\\t\\n\", \"output_summarize\": \"Anarchism Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement, and has a strong historical association with anti-capitalism and socialism. Humans lived in societies without formal hierarchies long before the establishment of formal states, realms, or empires. With the rise of organised hierarchical bodies, scepticism toward authority also rose. Although traces of anarchist thought are found throughout history, modern anarchism emerged from the Enlightenment. During the latter half of the 19th and the first decades of the 20th century, the anarchist movement flourished in most parts of the world and had a significant role in workers' struggles for emancipation. Various anarchist schools of thought formed during this period. Anarchists have taken part in several revolutions, most notably in the Paris Commune, the Russian Civil War and the Spanish Civil War, whose end marked the end of the classical era of anarchism. In the last decades of the 20th and into the 21st century, the anarchist movement has been resurgent once more. Anarchism employs a diversity of tactics in order to meet its ideal ends which can be broadly separated into revolutionary and evolutionary tactics; there is significant overlap between the two, which are merely descriptive. Revolutionary tactics aim to bring down authority and state, having taken a violent turn in the past, while evolutionary tactics aim to prefigure what an anarchist society would be like. Anarchist thought, criticism, and praxis have played a part in diverse areas of human society. Criticism of anarchism include claims that it is internally inconsistent, violent, or utopian. Etymology, terminology, and definition The etymological origin of anarchism is from the Ancient Greek anarkhia, meaning \\\"without a ruler\\\", composed of the prefix an- (\\\"without\\\") and the word arkhos (\\\"leader\\\" or \\\"ruler\\\"). The suffix -ism denotes the ideological current that favours anarchy. Anarchism appears in English from 1642 as anarchisme and anarchy from 1539; early English usages emphasised a sense of disorder. Various factions within the French Revolution labelled their opponents as anarchists, although few such accused shared many views with later anarchists. Many revolutionaries of the 19th century such as William Godwin (1756–1836) and Wilhelm Weitling (1808–1871) would contribute to the anarchist doctrines of the next generation but did not use anarchist or anarchism in describing themselves or their beliefs. The first political philosopher to call himself an anarchist () was Pierre-Joseph Proudhon (1809–1865), marking the formal birth of anarchism in the mid-19th century. Since the 1890s and beginning in France, libertarianism has often been used as a synonym for anarchism and its use as a synonym is still common outside the United States.\\t\\n\", \"output_predict\": \"Some usages of libertarianism refer to individualistic free-market philosophy only, and free-market anarchism in particular is termed libertarian anarchism. While the term libertarian has been largely synonymous with anarchism, its meaning has more recently diluted with wider adoption from ideologically disparate groups, including both the New Left and libertarian Marxists, who do not associate themselves with authoritarian socialists or a vanguard party, and extreme cultural liberals, who are primarily concerned with civil liberties. Additionally, some anarchists use libertarian socialist to avoid anarchism's negative connotations and emphasise its connections with socialism. Anarchism is broadly used to describe the anti-authoritarian wing of the socialist movement. Anarchism is contrasted to socialist forms which are state-oriented or from above. Scholars of anarchism generally highlight anarchism's socialist credentials and criticise attempts at creating dichotomies between the two. Some scholars describe anarchism as having many influences from liberalism, and being both liberals and socialists but more so, while most scholars reject anarcho-capitalism as a misunderstanding of anarchist principles. While opposition to the state is central to anarchist thought, defining anarchism is not an easy task for scholars, as there is a lot of discussion among scholars and anarchists on the matter, and various currents perceive anarchism slightly differently. Major definitional elements include the will for a non-coercive society, the rejection of the state apparatus, the belief that human nature allows humans to exist in or progress toward such a non-coercive society, and a suggestion on how to act to pursue the ideal of anarchy. History Pre-modern era Before the establishment of towns and cities, an established authority did not exist. It was after the creation of institutions of authority that anarchistic ideas espoused as a reaction. The most notable precursors to anarchism in the ancient world were in China and Greece. In China, philosophical anarchism (the discussion on the legitimacy of the state) was delineated by Taoist philosophers Zhuang Zhou and Laozi. Alongside Stoicism, Taoism has been said to have had \\\"significant anticipations\\\" of anarchism. Anarchic attitudes were also articulated by tragedians and philosophers in Greece. Aeschylus and Sophocles used the myth of Antigone to illustrate the conflict between rules set by the state and personal autonomy. Socrates questioned Athenian authorities constantly and insisted on the right of individual freedom of conscience. Cynics dismissed human law (nomos) and associated authorities while trying to live according to nature (physis). Stoics were supportive of a society based on unofficial and friendly relations among its citizens without the presence of a state. In medieval Europe, there was no anarchistic activity except some ascetic religious movements. These, and other Muslim movements, later gave birth to religious anarchism. In the Sasanian Empire, Mazdak called for an egalitarian society and the abolition of monarchy, only to be soon executed by Emperor Kavad I. In Basra, religious sects preached against the state. In Europe, various sects developed anti-state and libertarian tendencies. Renewed interest in antiquity during the Renaissance and in private judgment during the Reformation restored elements of anti-authoritarian secularism, particularly in France.\\n\"}\n{\"input\": \"Some usages of libertarianism refer to individualistic free-market philosophy only, and free-market anarchism in particular is termed libertarian anarchism. While the term libertarian has been largely synonymous with anarchism, its meaning has more recently diluted with wider adoption from ideologically disparate groups, including both the New Left and libertarian Marxists, who do not associate themselves with authoritarian socialists or a vanguard party, and extreme cultural liberals, who are primarily concerned with civil liberties. Additionally, some anarchists use libertarian socialist to avoid anarchism's negative connotations and emphasise its connections with socialism. Anarchism is broadly used to describe the anti-authoritarian wing of the socialist movement. Anarchism is contrasted to socialist forms which are state-oriented or from above. Scholars of anarchism generally highlight anarchism's socialist credentials and criticise attempts at creating dichotomies between the two. Some scholars describe anarchism as having many influences from liberalism, and being both liberals and socialists but more so, while most scholars reject anarcho-capitalism as a misunderstanding of anarchist principles. While opposition to the state is central to anarchist thought, defining anarchism is not an easy task for scholars, as there is a lot of discussion among scholars and anarchists on the matter, and various currents perceive anarchism slightly differently. Major definitional elements include the will for a non-coercive society, the rejection of the state apparatus, the belief that human nature allows humans to exist in or progress toward such a non-coercive society, and a suggestion on how to act to pursue the ideal of anarchy. History Pre-modern era Before the establishment of towns and cities, an established authority did not exist. It was after the creation of institutions of authority that anarchistic ideas espoused as a reaction. The most notable precursors to anarchism in the ancient world were in China and Greece. In China, philosophical anarchism (the discussion on the legitimacy of the state) was delineated by Taoist philosophers Zhuang Zhou and Laozi. Alongside Stoicism, Taoism has been said to have had \\\"significant anticipations\\\" of anarchism. Anarchic attitudes were also articulated by tragedians and philosophers in Greece. Aeschylus and Sophocles used the myth of Antigone to illustrate the conflict between rules set by the state and personal autonomy. Socrates questioned Athenian authorities constantly and insisted on the right of individual freedom of conscience. Cynics dismissed human law (nomos) and associated authorities while trying to live according to nature (physis). Stoics were supportive of a society based on unofficial and friendly relations among its citizens without the presence of a state. In medieval Europe, there was no anarchistic activity except some ascetic religious movements. These, and other Muslim movements, later gave birth to religious anarchism. In the Sasanian Empire, Mazdak called for an egalitarian society and the abolition of monarchy, only to be soon executed by Emperor Kavad I. In Basra, religious sects preached against the state. In Europe, various sects developed anti-state and libertarian tendencies. Renewed interest in antiquity during the Renaissance and in private judgment during the Reformation restored elements of anti-authoritarian secularism, particularly in France.\\t\\n\", \"output_summarize\": \"Some usages of libertarianism refer to individualistic free-market philosophy only, and free-market anarchism in particular is termed libertarian anarchism. While the term libertarian has been largely synonymous with anarchism, its meaning has more recently diluted with wider adoption from ideologically disparate groups, including both the New Left and libertarian Marxists, who do not associate themselves with authoritarian socialists or a vanguard party, and extreme cultural liberals, who are primarily concerned with civil liberties. Additionally, some anarchists use libertarian socialist to avoid anarchism's negative connotations and emphasise its connections with socialism. Anarchism is broadly used to describe the anti-authoritarian wing of the socialist movement. Anarchism is contrasted to socialist forms which are state-oriented or from above. Scholars of anarchism generally highlight anarchism's socialist credentials and criticise attempts at creating dichotomies between the two. Some scholars describe anarchism as having many influences from liberalism, and being both liberals and socialists but more so, while most scholars reject anarcho-capitalism as a misunderstanding of anarchist principles. While opposition to the state is central to anarchist thought, defining anarchism is not an easy task for scholars, as there is a lot of discussion among scholars and anarchists on the matter, and various currents perceive anarchism slightly differently. Major definitional elements include the will for a non-coercive society, the rejection of the state apparatus, the belief that human nature allows humans to exist in or progress toward such a non-coercive society, and a suggestion on how to act to pursue the ideal of anarchy. History Pre-modern era Before the establishment of towns and cities, an established authority did not exist. It was after the creation of institutions of authority that anarchistic ideas espoused as a reaction. The most notable precursors to anarchism in the ancient world were in China and Greece. In China, philosophical anarchism (the discussion on the legitimacy of the state) was delineated by Taoist philosophers Zhuang Zhou and Laozi. Alongside Stoicism, Taoism has been said to have had \\\"significant anticipations\\\" of anarchism. Anarchic attitudes were also articulated by tragedians and philosophers in Greece. Aeschylus and Sophocles used the myth of Antigone to illustrate the conflict between rules set by the state and personal autonomy. Socrates questioned Athenian authorities constantly and insisted on the right of individual freedom of conscience. Cynics dismissed human law (nomos) and associated authorities while trying to live according to nature (physis). Stoics were supportive of a society based on unofficial and friendly relations among its citizens without the presence of a state. In medieval Europe, there was no anarchistic activity except some ascetic religious movements. These, and other Muslim movements, later gave birth to religious anarchism. In the Sasanian Empire, Mazdak called for an egalitarian society and the abolition of monarchy, only to be soon executed by Emperor Kavad I. In Basra, religious sects preached against the state. In Europe, various sects developed anti-state and libertarian tendencies. Renewed interest in antiquity during the Renaissance and in private judgment during the Reformation restored elements of anti-authoritarian secularism, particularly in France.\\t\\n\", \"output_predict\": \"Enlightenment challenges to intellectual authority (secular and religious) and the revolutions of the 1790s and 1848 all spurred the ideological development of what became the era of classical anarchism. Modern era During the French Revolution, partisan groups such as the Enragés and the saw a turning point in the fermentation of anti-state and federalist sentiments. The first anarchist currents developed throughout the 18th century as William Godwin espoused philosophical anarchism in England, morally delegitimising the state, Max Stirner's thinking paved the way to individualism and Pierre-Joseph Proudhon's theory of mutualism found fertile soil in France. By the late 1870s, various anarchist schools of thought had become well-defined and a wave of then unprecedented globalisation occurred from 1880 to 1914. This era of classical anarchism lasted until the end of the Spanish Civil War and is considered the golden age of anarchism. Drawing from mutualism, Mikhail Bakunin founded collectivist anarchism and entered the International Workingmen's Association, a class worker union later known as the First International that formed in 1864 to unite diverse revolutionary currents. The International became a significant political force, with Karl Marx being a leading figure and a member of its General Council. Bakunin's faction (the Jura Federation) and Proudhon's followers (the mutualists) opposed state socialism, advocating political abstentionism and small property holdings. After bitter disputes, the Bakuninists were expelled from the International by the Marxists at the 1872 Hague Congress. Anarchists were treated similarly in the Second International, being ultimately expelled in 1896. Bakunin famously predicted that if revolutionaries gained power by Marx's terms, they would end up the new tyrants of workers. In response to their expulsion from the First International, anarchists formed the St. Imier International. Under the influence of Peter Kropotkin, a Russian philosopher and scientist, anarcho-communism overlapped with collectivism. Anarcho-communists, who drew inspiration from the 1871 Paris Commune, advocated for free federation and for the distribution of goods according to one's needs. At the turn of the century, anarchism had spread all over the world. It was a notable feature of the international syndicalism movement. In China, small groups of students imported the humanistic pro-science version of anarcho-communism. Tokyo was a hotspot for rebellious youth from countries of the far east, travelling to the Japanese capital to study. In Latin America, Argentina was a stronghold for anarcho-syndicalism, where it became the most prominent left-wing ideology. During this time, a minority of anarchists adopted tactics of revolutionary political violence. This strategy became known as propaganda of the deed. The dismemberment of the French socialist movement into many groups and the execution and exile of many Communards to penal colonies following the suppression of the Paris Commune favoured individualist political expression and acts. Even though many anarchists distanced themselves from these terrorist acts, infamy came upon the movement and attempts were made to exclude them from American immigration, including the Immigration Act of 1903, also called the Anarchist Exclusion Act. Illegalism was another strategy which some anarchists adopted during this period.\\n\"}\n{\"input\": \"Enlightenment challenges to intellectual authority (secular and religious) and the revolutions of the 1790s and 1848 all spurred the ideological development of what became the era of classical anarchism. Modern era During the French Revolution, partisan groups such as the Enragés and the saw a turning point in the fermentation of anti-state and federalist sentiments. The first anarchist currents developed throughout the 18th century as William Godwin espoused philosophical anarchism in England, morally delegitimising the state, Max Stirner's thinking paved the way to individualism and Pierre-Joseph Proudhon's theory of mutualism found fertile soil in France. By the late 1870s, various anarchist schools of thought had become well-defined and a wave of then unprecedented globalisation occurred from 1880 to 1914. This era of classical anarchism lasted until the end of the Spanish Civil War and is considered the golden age of anarchism. Drawing from mutualism, Mikhail Bakunin founded collectivist anarchism and entered the International Workingmen's Association, a class worker union later known as the First International that formed in 1864 to unite diverse revolutionary currents. The International became a significant political force, with Karl Marx being a leading figure and a member of its General Council. Bakunin's faction (the Jura Federation) and Proudhon's followers (the mutualists) opposed state socialism, advocating political abstentionism and small property holdings. After bitter disputes, the Bakuninists were expelled from the International by the Marxists at the 1872 Hague Congress. Anarchists were treated similarly in the Second International, being ultimately expelled in 1896. Bakunin famously predicted that if revolutionaries gained power by Marx's terms, they would end up the new tyrants of workers. In response to their expulsion from the First International, anarchists formed the St. Imier International. Under the influence of Peter Kropotkin, a Russian philosopher and scientist, anarcho-communism overlapped with collectivism. Anarcho-communists, who drew inspiration from the 1871 Paris Commune, advocated for free federation and for the distribution of goods according to one's needs. At the turn of the century, anarchism had spread all over the world. It was a notable feature of the international syndicalism movement. In China, small groups of students imported the humanistic pro-science version of anarcho-communism. Tokyo was a hotspot for rebellious youth from countries of the far east, travelling to the Japanese capital to study. In Latin America, Argentina was a stronghold for anarcho-syndicalism, where it became the most prominent left-wing ideology. During this time, a minority of anarchists adopted tactics of revolutionary political violence. This strategy became known as propaganda of the deed. The dismemberment of the French socialist movement into many groups and the execution and exile of many Communards to penal colonies following the suppression of the Paris Commune favoured individualist political expression and acts. Even though many anarchists distanced themselves from these terrorist acts, infamy came upon the movement and attempts were made to exclude them from American immigration, including the Immigration Act of 1903, also called the Anarchist Exclusion Act. Illegalism was another strategy which some anarchists adopted during this period.\\t\\n\", \"output_summarize\": \"Enlightenment challenges to intellectual authority (secular and religious) and the revolutions of the 1790s and 1848 all spurred the ideological development of what became the era of classical anarchism. Modern era During the French Revolution, partisan groups such as the Enragés and the saw a turning point in the fermentation of anti-state and federalist sentiments. The first anarchist currents developed throughout the 18th century as William Godwin espoused philosophical anarchism in England, morally delegitimising the state, Max Stirner's thinking paved the way to individualism and Pierre-Joseph Proudhon's theory of mutualism found fertile soil in France. By the late 1870s, various anarchist schools of thought had become well-defined and a wave of then unprecedented globalisation occurred from 1880 to 1914. This era of classical anarchism lasted until the end of the Spanish Civil War and is considered the golden age of anarchism. Drawing from mutualism, Mikhail Bakunin founded collectivist anarchism and entered the International Workingmen's Association, a class worker union later known as the First International that formed in 1864 to unite diverse revolutionary currents. The International became a significant political force, with Karl Marx being a leading figure and a member of its General Council. Bakunin's faction (the Jura Federation) and Proudhon's followers (the mutualists) opposed state socialism, advocating political abstentionism and small property holdings. After bitter disputes, the Bakuninists were expelled from the International by the Marxists at the 1872 Hague Congress. Anarchists were treated similarly in the Second International, being ultimately expelled in 1896. Bakunin famously predicted that if revolutionaries gained power by Marx's terms, they would end up the new tyrants of workers. In response to their expulsion from the First International, anarchists formed the St. Imier International. Under the influence of Peter Kropotkin, a Russian philosopher and scientist, anarcho-communism overlapped with collectivism. Anarcho-communists, who drew inspiration from the 1871 Paris Commune, advocated for free federation and for the distribution of goods according to one's needs. At the turn of the century, anarchism had spread all over the world. It was a notable feature of the international syndicalism movement. In China, small groups of students imported the humanistic pro-science version of anarcho-communism. Tokyo was a hotspot for rebellious youth from countries of the far east, travelling to the Japanese capital to study. In Latin America, Argentina was a stronghold for anarcho-syndicalism, where it became the most prominent left-wing ideology. During this time, a minority of anarchists adopted tactics of revolutionary political violence. This strategy became known as propaganda of the deed. The dismemberment of the French socialist movement into many groups and the execution and exile of many Communards to penal colonies following the suppression of the Paris Commune favoured individualist political expression and acts. Even though many anarchists distanced themselves from these terrorist acts, infamy came upon the movement and attempts were made to exclude them from American immigration, including the Immigration Act of 1903, also called the Anarchist Exclusion Act. Illegalism was another strategy which some anarchists adopted during this period.\\t\\n\", \"output_predict\": \"Despite concerns, anarchists enthusiastically participated in the Russian Revolution in opposition to the White movement; however, they met harsh suppression after the Bolshevik government was stabilised. Several anarchists from Petrograd and Moscow fled to Ukraine, notably leading to the Kronstadt rebellion and Nestor Makhno's struggle in the Free Territory. With the anarchists being crushed in Russia, two new antithetical currents emerged, namely platformism and synthesis anarchism. The former sought to create a coherent group that would push for revolution while the latter were against anything that would resemble a political party. Seeing the victories of the Bolsheviks in the October Revolution and the resulting Russian Civil War, many workers and activists turned to communist parties which grew at the expense of anarchism and other socialist movements. In France and the United States, members of major syndicalist movements such as the General Confederation of Labour and the Industrial Workers of the World left their organisations and joined the Communist International. In the Spanish Civil War of 1936, anarchists and syndicalists (CNT and FAI) once again allied themselves with various currents of leftists. A long tradition of Spanish anarchism led to anarchists playing a pivotal role in the war. In response to the army rebellion, an anarchist-inspired movement of peasants and workers, supported by armed militias, took control of Barcelona and of large areas of rural Spain, where they collectivised the land. The Soviet Union provided some limited assistance at the beginning of the war, but the result was a bitter fight among communists and anarchists at a series of events named May Days as Joseph Stalin tried to seize control of the Republicans. Post-war era At the end of World War II, the anarchist movement was severely weakened. The 1960s witnessed a revival of anarchism, likely caused by a perceived failure of Marxism–Leninism and tensions built by the Cold War. During this time, anarchism found a presence in other movements critical towards both capitalism and the state such as the anti-nuclear, environmental, and peace movements, the counterculture of the 1960s, and the New Left. It also saw a transition from its previous revolutionary nature to provocative anti-capitalist reformism. Anarchism became associated with punk subculture as exemplified by bands such as Crass and the Sex Pistols. The established feminist tendencies of anarcha-feminism returned with vigour during the second wave of feminism. Black anarchism began to take form at this time and influenced anarchism's move from a Eurocentric demographic. This coincided with its failure to gain traction in Northern Europe and its unprecedented height in Latin America. Around the turn of the 21st century, anarchism grew in popularity and influence within anti-capitalist, anti-war and anti-globalisation movements. Anarchists became known for their involvement in protests against the World Trade Organization (WTO), the Group of Eight and the World Economic Forum. During the protests, ad hoc leaderless anonymous cadres known as black blocs engaged in rioting, property destruction and violent confrontations with the police.\\n\"}\n{\"input\": \"Despite concerns, anarchists enthusiastically participated in the Russian Revolution in opposition to the White movement; however, they met harsh suppression after the Bolshevik government was stabilised. Several anarchists from Petrograd and Moscow fled to Ukraine, notably leading to the Kronstadt rebellion and Nestor Makhno's struggle in the Free Territory. With the anarchists being crushed in Russia, two new antithetical currents emerged, namely platformism and synthesis anarchism. The former sought to create a coherent group that would push for revolution while the latter were against anything that would resemble a political party. Seeing the victories of the Bolsheviks in the October Revolution and the resulting Russian Civil War, many workers and activists turned to communist parties which grew at the expense of anarchism and other socialist movements. In France and the United States, members of major syndicalist movements such as the General Confederation of Labour and the Industrial Workers of the World left their organisations and joined the Communist International. In the Spanish Civil War of 1936, anarchists and syndicalists (CNT and FAI) once again allied themselves with various currents of leftists. A long tradition of Spanish anarchism led to anarchists playing a pivotal role in the war. In response to the army rebellion, an anarchist-inspired movement of peasants and workers, supported by armed militias, took control of Barcelona and of large areas of rural Spain, where they collectivised the land. The Soviet Union provided some limited assistance at the beginning of the war, but the result was a bitter fight among communists and anarchists at a series of events named May Days as Joseph Stalin tried to seize control of the Republicans. Post-war era At the end of World War II, the anarchist movement was severely weakened. The 1960s witnessed a revival of anarchism, likely caused by a perceived failure of Marxism–Leninism and tensions built by the Cold War. During this time, anarchism found a presence in other movements critical towards both capitalism and the state such as the anti-nuclear, environmental, and peace movements, the counterculture of the 1960s, and the New Left. It also saw a transition from its previous revolutionary nature to provocative anti-capitalist reformism. Anarchism became associated with punk subculture as exemplified by bands such as Crass and the Sex Pistols. The established feminist tendencies of anarcha-feminism returned with vigour during the second wave of feminism. Black anarchism began to take form at this time and influenced anarchism's move from a Eurocentric demographic. This coincided with its failure to gain traction in Northern Europe and its unprecedented height in Latin America. Around the turn of the 21st century, anarchism grew in popularity and influence within anti-capitalist, anti-war and anti-globalisation movements. Anarchists became known for their involvement in protests against the World Trade Organization (WTO), the Group of Eight and the World Economic Forum. During the protests, ad hoc leaderless anonymous cadres known as black blocs engaged in rioting, property destruction and violent confrontations with the police.\\t\\n\", \"output_summarize\": \"Despite concerns, anarchists enthusiastically participated in the Russian Revolution in opposition to the White movement; however, they met harsh suppression after the Bolshevik government was stabilised. Several anarchists from Petrograd and Moscow fled to Ukraine, notably leading to the Kronstadt rebellion and Nestor Makhno's struggle in the Free Territory. With the anarchists being crushed in Russia, two new antithetical currents emerged, namely platformism and synthesis anarchism. The former sought to create a coherent group that would push for revolution while the latter were against anything that would resemble a political party. Seeing the victories of the Bolsheviks in the October Revolution and the resulting Russian Civil War, many workers and activists turned to communist parties which grew at the expense of anarchism and other socialist movements. In France and the United States, members of major syndicalist movements such as the General Confederation of Labour and the Industrial Workers of the World left their organisations and joined the Communist International. In the Spanish Civil War of 1936, anarchists and syndicalists (CNT and FAI) once again allied themselves with various currents of leftists. A long tradition of Spanish anarchism led to anarchists playing a pivotal role in the war. In response to the army rebellion, an anarchist-inspired movement of peasants and workers, supported by armed militias, took control of Barcelona and of large areas of rural Spain, where they collectivised the land. The Soviet Union provided some limited assistance at the beginning of the war, but the result was a bitter fight among communists and anarchists at a series of events named May Days as Joseph Stalin tried to seize control of the Republicans. Post-war era At the end of World War II, the anarchist movement was severely weakened. The 1960s witnessed a revival of anarchism, likely caused by a perceived failure of Marxism–Leninism and tensions built by the Cold War. During this time, anarchism found a presence in other movements critical towards both capitalism and the state such as the anti-nuclear, environmental, and peace movements, the counterculture of the 1960s, and the New Left. It also saw a transition from its previous revolutionary nature to provocative anti-capitalist reformism. Anarchism became associated with punk subculture as exemplified by bands such as Crass and the Sex Pistols. The established feminist tendencies of anarcha-feminism returned with vigour during the second wave of feminism. Black anarchism began to take form at this time and influenced anarchism's move from a Eurocentric demographic. This coincided with its failure to gain traction in Northern Europe and its unprecedented height in Latin America. Around the turn of the 21st century, anarchism grew in popularity and influence within anti-capitalist, anti-war and anti-globalisation movements. Anarchists became known for their involvement in protests against the World Trade Organization (WTO), the Group of Eight and the World Economic Forum. During the protests, ad hoc leaderless anonymous cadres known as black blocs engaged in rioting, property destruction and violent confrontations with the police.\\t\\n\", \"output_predict\": \"Other organisational tactics pioneered in this time include affinity groups, security culture and the use of decentralised technologies such as the Internet. A significant event of this period was the confrontations at the 1999 Seattle WTO conference. Anarchist ideas have been influential in the development of the Zapatistas in Mexico and the Democratic Federation of Northern Syria, more commonly known as Rojava, a de facto autonomous region in northern Syria. Thought Anarchist schools of thought have been generally grouped into two main historical traditions, social anarchism and individualist anarchism, owing to their different origins, values and evolution. The individualist current emphasises negative liberty in opposing restraints upon the free individual, while the social current emphasises positive liberty in aiming to achieve the free potential of society through equality and social ownership. In a chronological sense, anarchism can be segmented by the classical currents of the late 19th century and the post-classical currents (anarcha-feminism, green anarchism, and post-anarchism) developed thereafter. Beyond the specific factions of anarchist movements which constitute political anarchism lies philosophical anarchism which holds that the state lacks moral legitimacy, without necessarily accepting the imperative of revolution to eliminate it. A component especially of individualist anarchism, philosophical anarchism may tolerate the existence of a minimal state but claims that citizens have no moral obligation to obey government when it conflicts with individual autonomy. Anarchism pays significant attention to moral arguments since ethics have a central role in anarchist philosophy. Anarchism's emphasis on anti-capitalism, egalitarianism, and for the extension of community and individuality sets it apart from anarcho-capitalism and other types of economic libertarianism. Anarchism is usually placed on the far-left of the political spectrum. Much of its economics and legal philosophy reflect anti-authoritarian, anti-statist, libertarian, and radical interpretations of left-wing and socialist politics such as collectivism, communism, individualism, mutualism, and syndicalism, among other libertarian socialist economic theories. As anarchism does not offer a fixed body of doctrine from a single particular worldview, many anarchist types and traditions exist and varieties of anarchy diverge widely. One reaction against sectarianism within the anarchist milieu was anarchism without adjectives, a call for toleration and unity among anarchists first adopted by Fernando Tarrida del Mármol in 1889 in response to the bitter debates of anarchist theory at the time. Belief in political nihilism has been espoused by anarchists. Despite separation, the various anarchist schools of thought are not seen as distinct entities but rather as tendencies that intermingle and are connected through a set of uniform principles such as individual and local autonomy, mutual aid, network organisation, communal democracy, justified authority and decentralisation. Classical Inceptive currents among classical anarchist currents were mutualism and individualism. They were followed by the major currents of social anarchism (collectivist, communist and syndicalist). They differ on organisational and economic aspects of their ideal society. Mutualism is an 18th-century economic theory that was developed into anarchist theory by Pierre-Joseph Proudhon.\\n\"}\n{\"input\": \"Other organisational tactics pioneered in this time include affinity groups, security culture and the use of decentralised technologies such as the Internet. A significant event of this period was the confrontations at the 1999 Seattle WTO conference. Anarchist ideas have been influential in the development of the Zapatistas in Mexico and the Democratic Federation of Northern Syria, more commonly known as Rojava, a de facto autonomous region in northern Syria. Thought Anarchist schools of thought have been generally grouped into two main historical traditions, social anarchism and individualist anarchism, owing to their different origins, values and evolution. The individualist current emphasises negative liberty in opposing restraints upon the free individual, while the social current emphasises positive liberty in aiming to achieve the free potential of society through equality and social ownership. In a chronological sense, anarchism can be segmented by the classical currents of the late 19th century and the post-classical currents (anarcha-feminism, green anarchism, and post-anarchism) developed thereafter. Beyond the specific factions of anarchist movements which constitute political anarchism lies philosophical anarchism which holds that the state lacks moral legitimacy, without necessarily accepting the imperative of revolution to eliminate it. A component especially of individualist anarchism, philosophical anarchism may tolerate the existence of a minimal state but claims that citizens have no moral obligation to obey government when it conflicts with individual autonomy. Anarchism pays significant attention to moral arguments since ethics have a central role in anarchist philosophy. Anarchism's emphasis on anti-capitalism, egalitarianism, and for the extension of community and individuality sets it apart from anarcho-capitalism and other types of economic libertarianism. Anarchism is usually placed on the far-left of the political spectrum. Much of its economics and legal philosophy reflect anti-authoritarian, anti-statist, libertarian, and radical interpretations of left-wing and socialist politics such as collectivism, communism, individualism, mutualism, and syndicalism, among other libertarian socialist economic theories. As anarchism does not offer a fixed body of doctrine from a single particular worldview, many anarchist types and traditions exist and varieties of anarchy diverge widely. One reaction against sectarianism within the anarchist milieu was anarchism without adjectives, a call for toleration and unity among anarchists first adopted by Fernando Tarrida del Mármol in 1889 in response to the bitter debates of anarchist theory at the time. Belief in political nihilism has been espoused by anarchists. Despite separation, the various anarchist schools of thought are not seen as distinct entities but rather as tendencies that intermingle and are connected through a set of uniform principles such as individual and local autonomy, mutual aid, network organisation, communal democracy, justified authority and decentralisation. Classical Inceptive currents among classical anarchist currents were mutualism and individualism. They were followed by the major currents of social anarchism (collectivist, communist and syndicalist). They differ on organisational and economic aspects of their ideal society. Mutualism is an 18th-century economic theory that was developed into anarchist theory by Pierre-Joseph Proudhon.\\t\\n\", \"output_summarize\": \"Other organisational tactics pioneered in this time include affinity groups, security culture and the use of decentralised technologies such as the Internet. A significant event of this period was the confrontations at the 1999 Seattle WTO conference. Anarchist ideas have been influential in the development of the Zapatistas in Mexico and the Democratic Federation of Northern Syria, more commonly known as Rojava, a de facto autonomous region in northern Syria. Thought Anarchist schools of thought have been generally grouped into two main historical traditions, social anarchism and individualist anarchism, owing to their different origins, values and evolution. The individualist current emphasises negative liberty in opposing restraints upon the free individual, while the social current emphasises positive liberty in aiming to achieve the free potential of society through equality and social ownership. In a chronological sense, anarchism can be segmented by the classical currents of the late 19th century and the post-classical currents (anarcha-feminism, green anarchism, and post-anarchism) developed thereafter. Beyond the specific factions of anarchist movements which constitute political anarchism lies philosophical anarchism which holds that the state lacks moral legitimacy, without necessarily accepting the imperative of revolution to eliminate it. A component especially of individualist anarchism, philosophical anarchism may tolerate the existence of a minimal state but claims that citizens have no moral obligation to obey government when it conflicts with individual autonomy. Anarchism pays significant attention to moral arguments since ethics have a central role in anarchist philosophy. Anarchism's emphasis on anti-capitalism, egalitarianism, and for the extension of community and individuality sets it apart from anarcho-capitalism and other types of economic libertarianism. Anarchism is usually placed on the far-left of the political spectrum. Much of its economics and legal philosophy reflect anti-authoritarian, anti-statist, libertarian, and radical interpretations of left-wing and socialist politics such as collectivism, communism, individualism, mutualism, and syndicalism, among other libertarian socialist economic theories. As anarchism does not offer a fixed body of doctrine from a single particular worldview, many anarchist types and traditions exist and varieties of anarchy diverge widely. One reaction against sectarianism within the anarchist milieu was anarchism without adjectives, a call for toleration and unity among anarchists first adopted by Fernando Tarrida del Mármol in 1889 in response to the bitter debates of anarchist theory at the time. Belief in political nihilism has been espoused by anarchists. Despite separation, the various anarchist schools of thought are not seen as distinct entities but rather as tendencies that intermingle and are connected through a set of uniform principles such as individual and local autonomy, mutual aid, network organisation, communal democracy, justified authority and decentralisation. Classical Inceptive currents among classical anarchist currents were mutualism and individualism. They were followed by the major currents of social anarchism (collectivist, communist and syndicalist). They differ on organisational and economic aspects of their ideal society. Mutualism is an 18th-century economic theory that was developed into anarchist theory by Pierre-Joseph Proudhon.\\t\\n\", \"output_predict\": \"Its aims include reciprocity, free association, voluntary contract, federation and monetary reform of both credit and currency that would be regulated by a bank of the people. Mutualism has been retrospectively characterised as ideologically situated between individualist and collectivist forms of anarchism. In What Is Property? (1840), Proudhon first characterised his goal as a \\\"third form of society, the synthesis of communism and property.\\\" Collectivist anarchism is a revolutionary socialist form of anarchism commonly associated with Mikhail Bakunin. Collectivist anarchists advocate collective ownership of the means of production which is theorised to be achieved through violent revolution and that workers be paid according to time worked, rather than goods being distributed according to need as in communism. Collectivist anarchism arose alongside Marxism but rejected the dictatorship of the proletariat despite the stated Marxist goal of a collectivist stateless society. Anarcho-communism is a theory of anarchism that advocates a communist society with common ownership of the means of production, direct democracy and a horizontal network of voluntary associations, workers' councils and worker cooperatives, with production and consumption based on the guiding principle \\\"From each according to his ability, to each according to his need.\\\" Anarcho-communism developed from radical socialist currents after the French Revolution but was first formulated as such in the Italian section of the First International. It was later expanded upon in the theoretical work of Peter Kropotkin, whose specific style would go onto become the dominating view of anarchists by the late 19th century. Anarcho-syndicalism is a branch of anarchism that views labour syndicates as a potential force for revolutionary social change, replacing capitalism and the state with a new society democratically self-managed by workers. The basic principles of anarcho-syndicalism are direct action, workers' solidarity and workers' self-management. Individualist anarchism is a set of several traditions of thought within the anarchist movement that emphasise the individual and their will over any kinds of external determinants. Early influences on individualist forms of anarchism include William Godwin, Max Stirner, and Henry David Thoreau. Through many countries, individualist anarchism attracted a small yet diverse following of Bohemian artists and intellectuals as well as young anarchist outlaws in what became known as illegalism and individual reclamation. Post-classical and contemporary Anarchist principles undergird contemporary radical social movements of the left. Interest in the anarchist movement developed alongside momentum in the anti-globalisation movement, whose leading activist networks were anarchist in orientation. As the movement shaped 21st century radicalism, wider embrace of anarchist principles signaled a revival of interest. Anarchism has continued to generate many philosophies and movements, at times eclectic, drawing upon various sources and combining disparate concepts to create new philosophical approaches. The anti-capitalist tradition of classical anarchism has remained prominent within contemporary currents. Contemporary news coverage which emphasizes black bloc demonstrations has reinforced anarchism's historical association with chaos and violence. Its publicity has also led more scholars in fields such as anthropology and history to engage with the anarchist movement, although contemporary anarchism favours actions over academic theory.\\n\"}\n{\"input\": \"Its aims include reciprocity, free association, voluntary contract, federation and monetary reform of both credit and currency that would be regulated by a bank of the people. Mutualism has been retrospectively characterised as ideologically situated between individualist and collectivist forms of anarchism. In What Is Property? (1840), Proudhon first characterised his goal as a \\\"third form of society, the synthesis of communism and property.\\\" Collectivist anarchism is a revolutionary socialist form of anarchism commonly associated with Mikhail Bakunin. Collectivist anarchists advocate collective ownership of the means of production which is theorised to be achieved through violent revolution and that workers be paid according to time worked, rather than goods being distributed according to need as in communism. Collectivist anarchism arose alongside Marxism but rejected the dictatorship of the proletariat despite the stated Marxist goal of a collectivist stateless society. Anarcho-communism is a theory of anarchism that advocates a communist society with common ownership of the means of production, direct democracy and a horizontal network of voluntary associations, workers' councils and worker cooperatives, with production and consumption based on the guiding principle \\\"From each according to his ability, to each according to his need.\\\" Anarcho-communism developed from radical socialist currents after the French Revolution but was first formulated as such in the Italian section of the First International. It was later expanded upon in the theoretical work of Peter Kropotkin, whose specific style would go onto become the dominating view of anarchists by the late 19th century. Anarcho-syndicalism is a branch of anarchism that views labour syndicates as a potential force for revolutionary social change, replacing capitalism and the state with a new society democratically self-managed by workers. The basic principles of anarcho-syndicalism are direct action, workers' solidarity and workers' self-management. Individualist anarchism is a set of several traditions of thought within the anarchist movement that emphasise the individual and their will over any kinds of external determinants. Early influences on individualist forms of anarchism include William Godwin, Max Stirner, and Henry David Thoreau. Through many countries, individualist anarchism attracted a small yet diverse following of Bohemian artists and intellectuals as well as young anarchist outlaws in what became known as illegalism and individual reclamation. Post-classical and contemporary Anarchist principles undergird contemporary radical social movements of the left. Interest in the anarchist movement developed alongside momentum in the anti-globalisation movement, whose leading activist networks were anarchist in orientation. As the movement shaped 21st century radicalism, wider embrace of anarchist principles signaled a revival of interest. Anarchism has continued to generate many philosophies and movements, at times eclectic, drawing upon various sources and combining disparate concepts to create new philosophical approaches. The anti-capitalist tradition of classical anarchism has remained prominent within contemporary currents. Contemporary news coverage which emphasizes black bloc demonstrations has reinforced anarchism's historical association with chaos and violence. Its publicity has also led more scholars in fields such as anthropology and history to engage with the anarchist movement, although contemporary anarchism favours actions over academic theory.\\t\\n\", \"output_summarize\": \"Its aims include reciprocity, free association, voluntary contract, federation and monetary reform of both credit and currency that would be regulated by a bank of the people. Mutualism has been retrospectively characterised as ideologically situated between individualist and collectivist forms of anarchism. In What Is Property? (1840), Proudhon first characterised his goal as a \\\"third form of society, the synthesis of communism and property.\\\" Collectivist anarchism is a revolutionary socialist form of anarchism commonly associated with Mikhail Bakunin. Collectivist anarchists advocate collective ownership of the means of production which is theorised to be achieved through violent revolution and that workers be paid according to time worked, rather than goods being distributed according to need as in communism. Collectivist anarchism arose alongside Marxism but rejected the dictatorship of the proletariat despite the stated Marxist goal of a collectivist stateless society. Anarcho-communism is a theory of anarchism that advocates a communist society with common ownership of the means of production, direct democracy and a horizontal network of voluntary associations, workers' councils and worker cooperatives, with production and consumption based on the guiding principle \\\"From each according to his ability, to each according to his need.\\\" Anarcho-communism developed from radical socialist currents after the French Revolution but was first formulated as such in the Italian section of the First International. It was later expanded upon in the theoretical work of Peter Kropotkin, whose specific style would go onto become the dominating view of anarchists by the late 19th century. Anarcho-syndicalism is a branch of anarchism that views labour syndicates as a potential force for revolutionary social change, replacing capitalism and the state with a new society democratically self-managed by workers. The basic principles of anarcho-syndicalism are direct action, workers' solidarity and workers' self-management. Individualist anarchism is a set of several traditions of thought within the anarchist movement that emphasise the individual and their will over any kinds of external determinants. Early influences on individualist forms of anarchism include William Godwin, Max Stirner, and Henry David Thoreau. Through many countries, individualist anarchism attracted a small yet diverse following of Bohemian artists and intellectuals as well as young anarchist outlaws in what became known as illegalism and individual reclamation. Post-classical and contemporary Anarchist principles undergird contemporary radical social movements of the left. Interest in the anarchist movement developed alongside momentum in the anti-globalisation movement, whose leading activist networks were anarchist in orientation. As the movement shaped 21st century radicalism, wider embrace of anarchist principles signaled a revival of interest. Anarchism has continued to generate many philosophies and movements, at times eclectic, drawing upon various sources and combining disparate concepts to create new philosophical approaches. The anti-capitalist tradition of classical anarchism has remained prominent within contemporary currents. Contemporary news coverage which emphasizes black bloc demonstrations has reinforced anarchism's historical association with chaos and violence. Its publicity has also led more scholars in fields such as anthropology and history to engage with the anarchist movement, although contemporary anarchism favours actions over academic theory.\\t\\n\", \"output_predict\": \"Various anarchist groups, tendencies, and schools of thought exist today, making it difficult to describe the contemporary anarchist movement. While theorists and activists have established \\\"relatively stable constellations of anarchist principles\\\", there is no consensus on which principles are core and commentators describe multiple anarchisms, rather than a singular anarchism, in which common principles are shared between schools of anarchism while each group prioritizes those principles differently. Gender equality can be a common principle, although it ranks as a higher priority to anarcha-feminists than anarcho-communists. Anarchists are generally committed against coercive authority in all forms, namely \\\"all centralized and hierarchical forms of government (e.g., monarchy, representative democracy, state socialism, etc. ), economic class systems (e.g., capitalism, Bolshevism, feudalism, slavery, etc. ), autocratic religions (e.g., fundamentalist Islam, Roman Catholicism, etc. ), patriarchy, heterosexism, white supremacy, and imperialism.\\\" Anarchist schools disagree on the methods by which these forms should be opposed. The principle of equal liberty is closer to anarchist political ethics in that it transcends both the liberal and socialist traditions. This entails that liberty and equality cannot be implemented within the state, resulting in the questioning of all forms of domination and hierarchy. Tactics Anarchists' tactics take various forms but in general serve two major goals, namely to first oppose the Establishment and secondly to promote anarchist ethics and reflect an anarchist vision of society, illustrating the unity of means and ends. A broad categorisation can be made between aims to destroy oppressive states and institutions by revolutionary means on one hand and aims to change society through evolutionary means on the other. Evolutionary tactics embrace nonviolence, reject violence and take a gradual approach to anarchist aims, although there is significant overlap between the two. Anarchist tactics have shifted during the course of the last century. Anarchists during the early 20th century focused more on strikes and militancy while contemporary anarchists use a broader array of approaches. Classical era tactics During the classical era, anarchists had a militant tendency. Not only did they confront state armed forces, as in Spain and Ukraine, but some of them also employed terrorism as propaganda of the deed. Assassination attempts were carried out against heads of state, some of which were successful. Anarchists also took part in revolutions. Many anarchists, especially the Galleanists, believed that these attempts would be the impetus for a revolution against capitalism and the state. Many of these attacks were done by individual assailants and the majority took place in the late 1870s, the early 1880s and the 1890s, with some still occurring in the early 1900s. Their decrease in prevalence was the result of further judicial power and targeting and cataloging by state institutions. Anarchist perspectives towards violence have always been controversial. Anarcho-pacifists advocate for non-violence means to achieve their stateless, nonviolent ends. Other anarchist groups advocate direct action, a tactic which can include acts of sabotage or terrorism.\\n\"}\n{\"input\": \"Various anarchist groups, tendencies, and schools of thought exist today, making it difficult to describe the contemporary anarchist movement. While theorists and activists have established \\\"relatively stable constellations of anarchist principles\\\", there is no consensus on which principles are core and commentators describe multiple anarchisms, rather than a singular anarchism, in which common principles are shared between schools of anarchism while each group prioritizes those principles differently. Gender equality can be a common principle, although it ranks as a higher priority to anarcha-feminists than anarcho-communists. Anarchists are generally committed against coercive authority in all forms, namely \\\"all centralized and hierarchical forms of government (e.g., monarchy, representative democracy, state socialism, etc. ), economic class systems (e.g., capitalism, Bolshevism, feudalism, slavery, etc. ), autocratic religions (e.g., fundamentalist Islam, Roman Catholicism, etc. ), patriarchy, heterosexism, white supremacy, and imperialism.\\\" Anarchist schools disagree on the methods by which these forms should be opposed. The principle of equal liberty is closer to anarchist political ethics in that it transcends both the liberal and socialist traditions. This entails that liberty and equality cannot be implemented within the state, resulting in the questioning of all forms of domination and hierarchy. Tactics Anarchists' tactics take various forms but in general serve two major goals, namely to first oppose the Establishment and secondly to promote anarchist ethics and reflect an anarchist vision of society, illustrating the unity of means and ends. A broad categorisation can be made between aims to destroy oppressive states and institutions by revolutionary means on one hand and aims to change society through evolutionary means on the other. Evolutionary tactics embrace nonviolence, reject violence and take a gradual approach to anarchist aims, although there is significant overlap between the two. Anarchist tactics have shifted during the course of the last century. Anarchists during the early 20th century focused more on strikes and militancy while contemporary anarchists use a broader array of approaches. Classical era tactics During the classical era, anarchists had a militant tendency. Not only did they confront state armed forces, as in Spain and Ukraine, but some of them also employed terrorism as propaganda of the deed. Assassination attempts were carried out against heads of state, some of which were successful. Anarchists also took part in revolutions. Many anarchists, especially the Galleanists, believed that these attempts would be the impetus for a revolution against capitalism and the state. Many of these attacks were done by individual assailants and the majority took place in the late 1870s, the early 1880s and the 1890s, with some still occurring in the early 1900s. Their decrease in prevalence was the result of further judicial power and targeting and cataloging by state institutions. Anarchist perspectives towards violence have always been controversial. Anarcho-pacifists advocate for non-violence means to achieve their stateless, nonviolent ends. Other anarchist groups advocate direct action, a tactic which can include acts of sabotage or terrorism.\\t\\n\", \"output_summarize\": \"Various anarchist groups, tendencies, and schools of thought exist today, making it difficult to describe the contemporary anarchist movement. While theorists and activists have established \\\"relatively stable constellations of anarchist principles\\\", there is no consensus on which principles are core and commentators describe multiple anarchisms, rather than a singular anarchism, in which common principles are shared between schools of anarchism while each group prioritizes those principles differently. Gender equality can be a common principle, although it ranks as a higher priority to anarcha-feminists than anarcho-communists. Anarchists are generally committed against coercive authority in all forms, namely \\\"all centralized and hierarchical forms of government (e.g., monarchy, representative democracy, state socialism, etc. ), economic class systems (e.g., capitalism, Bolshevism, feudalism, slavery, etc. ), autocratic religions (e.g., fundamentalist Islam, Roman Catholicism, etc. ), patriarchy, heterosexism, white supremacy, and imperialism.\\\" Anarchist schools disagree on the methods by which these forms should be opposed. The principle of equal liberty is closer to anarchist political ethics in that it transcends both the liberal and socialist traditions. This entails that liberty and equality cannot be implemented within the state, resulting in the questioning of all forms of domination and hierarchy. Tactics Anarchists' tactics take various forms but in general serve two major goals, namely to first oppose the Establishment and secondly to promote anarchist ethics and reflect an anarchist vision of society, illustrating the unity of means and ends. A broad categorisation can be made between aims to destroy oppressive states and institutions by revolutionary means on one hand and aims to change society through evolutionary means on the other. Evolutionary tactics embrace nonviolence, reject violence and take a gradual approach to anarchist aims, although there is significant overlap between the two. Anarchist tactics have shifted during the course of the last century. Anarchists during the early 20th century focused more on strikes and militancy while contemporary anarchists use a broader array of approaches. Classical era tactics During the classical era, anarchists had a militant tendency. Not only did they confront state armed forces, as in Spain and Ukraine, but some of them also employed terrorism as propaganda of the deed. Assassination attempts were carried out against heads of state, some of which were successful. Anarchists also took part in revolutions. Many anarchists, especially the Galleanists, believed that these attempts would be the impetus for a revolution against capitalism and the state. Many of these attacks were done by individual assailants and the majority took place in the late 1870s, the early 1880s and the 1890s, with some still occurring in the early 1900s. Their decrease in prevalence was the result of further judicial power and targeting and cataloging by state institutions. Anarchist perspectives towards violence have always been controversial. Anarcho-pacifists advocate for non-violence means to achieve their stateless, nonviolent ends. Other anarchist groups advocate direct action, a tactic which can include acts of sabotage or terrorism.\\t\\n\", \"output_predict\": \"This attitude was quite prominent a century ago when seeing the state as a tyrant and some anarchists believing that they had every right to oppose its oppression by any means possible. Emma Goldman and Errico Malatesta, who were proponents of limited use of violence, stated that violence is merely a reaction to state violence as a necessary evil. Anarchists took an active role in strike actions, although they tended to be antipathetic to formal syndicalism, seeing it as reformist. They saw it as a part of the movement which sought to overthrow the state and capitalism. Anarchists also reinforced their propaganda within the arts, some of whom practiced naturism and nudism. Those anarchists also built communities which were based on friendship and were involved in the news media. Revolutionary tactics In the current era, Italian anarchist Alfredo Bonanno, a proponent of insurrectionary anarchism, has reinstated the debate on violence by rejecting the nonviolence tactic adopted since the late 19th century by Kropotkin and other prominent anarchists afterwards. Both Bonanno and the French group The Invisible Committee advocate for small, informal affiliation groups, where each member is responsible for their own actions but works together to bring down oppression utilizing sabotage and other violent means against state, capitalism, and other enemies. Members of The Invisible Committee were arrested in 2008 on various charges, terrorism included. Overall, contemporary anarchists are much less violent and militant than their ideological ancestors. They mostly engage in confronting the police during demonstrations and riots, especially in countries such as Canada, Greece, and Mexico. Militant black bloc protest groups are known for clashing with the police; however, anarchists not only clash with state operators, they also engage in the struggle against fascists and racists, taking anti-fascist action and mobilizing to prevent hate rallies from happening. Evolutionary tactics Anarchists commonly employ direct action. This can take the form of disrupting and protesting against unjust hierarchy, or the form of self-managing their lives through the creation of counter-institutions such as communes and non-hierarchical collectives. Decision-making is often handled in an anti-authoritarian way, with everyone having equal say in each decision, an approach known as horizontalism. Contemporary-era anarchists have been engaging with various grassroots movements that are more or less based on horizontalism, although not explicitly anarchist, respecting personal autonomy and participating in mass activism such as strikes and demonstrations. In contrast with the big-A anarchism of the classical era, the newly coined term small-a anarchism signals their tendency not to base their thoughts and actions on classical-era anarchism or to refer to classical anarchists such as Peter Kropotkin and Pierre-Joseph Proudhon to justify their opinions. Those anarchists would rather base their thought and praxis on their own experience which they will later theorize. The decision-making process of small anarchist affinity groups plays a significant tactical role. Anarchists have employed various methods in order to build a rough consensus among members of their group without the need of a leader or a leading group.\\n\"}\n{\"input\": \"This attitude was quite prominent a century ago when seeing the state as a tyrant and some anarchists believing that they had every right to oppose its oppression by any means possible. Emma Goldman and Errico Malatesta, who were proponents of limited use of violence, stated that violence is merely a reaction to state violence as a necessary evil. Anarchists took an active role in strike actions, although they tended to be antipathetic to formal syndicalism, seeing it as reformist. They saw it as a part of the movement which sought to overthrow the state and capitalism. Anarchists also reinforced their propaganda within the arts, some of whom practiced naturism and nudism. Those anarchists also built communities which were based on friendship and were involved in the news media. Revolutionary tactics In the current era, Italian anarchist Alfredo Bonanno, a proponent of insurrectionary anarchism, has reinstated the debate on violence by rejecting the nonviolence tactic adopted since the late 19th century by Kropotkin and other prominent anarchists afterwards. Both Bonanno and the French group The Invisible Committee advocate for small, informal affiliation groups, where each member is responsible for their own actions but works together to bring down oppression utilizing sabotage and other violent means against state, capitalism, and other enemies. Members of The Invisible Committee were arrested in 2008 on various charges, terrorism included. Overall, contemporary anarchists are much less violent and militant than their ideological ancestors. They mostly engage in confronting the police during demonstrations and riots, especially in countries such as Canada, Greece, and Mexico. Militant black bloc protest groups are known for clashing with the police; however, anarchists not only clash with state operators, they also engage in the struggle against fascists and racists, taking anti-fascist action and mobilizing to prevent hate rallies from happening. Evolutionary tactics Anarchists commonly employ direct action. This can take the form of disrupting and protesting against unjust hierarchy, or the form of self-managing their lives through the creation of counter-institutions such as communes and non-hierarchical collectives. Decision-making is often handled in an anti-authoritarian way, with everyone having equal say in each decision, an approach known as horizontalism. Contemporary-era anarchists have been engaging with various grassroots movements that are more or less based on horizontalism, although not explicitly anarchist, respecting personal autonomy and participating in mass activism such as strikes and demonstrations. In contrast with the big-A anarchism of the classical era, the newly coined term small-a anarchism signals their tendency not to base their thoughts and actions on classical-era anarchism or to refer to classical anarchists such as Peter Kropotkin and Pierre-Joseph Proudhon to justify their opinions. Those anarchists would rather base their thought and praxis on their own experience which they will later theorize. The decision-making process of small anarchist affinity groups plays a significant tactical role. Anarchists have employed various methods in order to build a rough consensus among members of their group without the need of a leader or a leading group.\\t\\n\", \"output_summarize\": \"This attitude was quite prominent a century ago when seeing the state as a tyrant and some anarchists believing that they had every right to oppose its oppression by any means possible. Emma Goldman and Errico Malatesta, who were proponents of limited use of violence, stated that violence is merely a reaction to state violence as a necessary evil. Anarchists took an active role in strike actions, although they tended to be antipathetic to formal syndicalism, seeing it as reformist. They saw it as a part of the movement which sought to overthrow the state and capitalism. Anarchists also reinforced their propaganda within the arts, some of whom practiced naturism and nudism. Those anarchists also built communities which were based on friendship and were involved in the news media. Revolutionary tactics In the current era, Italian anarchist Alfredo Bonanno, a proponent of insurrectionary anarchism, has reinstated the debate on violence by rejecting the nonviolence tactic adopted since the late 19th century by Kropotkin and other prominent anarchists afterwards. Both Bonanno and the French group The Invisible Committee advocate for small, informal affiliation groups, where each member is responsible for their own actions but works together to bring down oppression utilizing sabotage and other violent means against state, capitalism, and other enemies. Members of The Invisible Committee were arrested in 2008 on various charges, terrorism included. Overall, contemporary anarchists are much less violent and militant than their ideological ancestors. They mostly engage in confronting the police during demonstrations and riots, especially in countries such as Canada, Greece, and Mexico. Militant black bloc protest groups are known for clashing with the police; however, anarchists not only clash with state operators, they also engage in the struggle against fascists and racists, taking anti-fascist action and mobilizing to prevent hate rallies from happening. Evolutionary tactics Anarchists commonly employ direct action. This can take the form of disrupting and protesting against unjust hierarchy, or the form of self-managing their lives through the creation of counter-institutions such as communes and non-hierarchical collectives. Decision-making is often handled in an anti-authoritarian way, with everyone having equal say in each decision, an approach known as horizontalism. Contemporary-era anarchists have been engaging with various grassroots movements that are more or less based on horizontalism, although not explicitly anarchist, respecting personal autonomy and participating in mass activism such as strikes and demonstrations. In contrast with the big-A anarchism of the classical era, the newly coined term small-a anarchism signals their tendency not to base their thoughts and actions on classical-era anarchism or to refer to classical anarchists such as Peter Kropotkin and Pierre-Joseph Proudhon to justify their opinions. Those anarchists would rather base their thought and praxis on their own experience which they will later theorize. The decision-making process of small anarchist affinity groups plays a significant tactical role. Anarchists have employed various methods in order to build a rough consensus among members of their group without the need of a leader or a leading group.\\t\\n\", \"output_predict\": \"One way is for an individual from the group to play the role of facilitator to help achieve a consensus without taking part in the discussion themselves or promoting a specific point. Minorities usually accept rough consensus, except when they feel the proposal contradicts anarchist ethics, goals and values. Anarchists usually form small groups (5–20 individuals) to enhance autonomy and friendships among their members. These kinds of groups more often than not interconnect with each other, forming larger networks. Anarchists still support and participate in strikes, especially wildcat strikes as these are leaderless strikes not organised centrally by a syndicate. As in the past, newspapers and journals are used, and anarchists have gone online in the World Wide Web to spread their message. Anarchists have found it easier to create websites because of distributional and other difficulties, hosting electronic libraries and other portals. Anarchists were also involved in developing various software that are available for free. The way these hacktivists work to develop and distribute resembles the anarchist ideals, especially when it comes to preserving users' privacy from state surveillance. Anarchists organize themselves to squat and reclaim public spaces. During important events such as protests and when spaces are being occupied, they are often called Temporary Autonomous Zones (TAZ), spaces where art, poetry, and surrealism are blended to display the anarchist ideal. As seen by anarchists, squatting is a way to regain urban space from the capitalist market, serving pragmatical needs and also being an exemplary direct action. Acquiring space enables anarchists to experiment with their ideas and build social bonds. Adding up these tactics while having in mind that not all anarchists share the same attitudes towards them, along with various forms of protesting at highly symbolic events, make up a carnivalesque atmosphere that is part of contemporary anarchist vividity. Key issues As anarchism is a philosophy that embodies many diverse attitudes, tendencies, and schools of thought; disagreement over questions of values, ideology, and tactics is common. Its diversity has led to widely different uses of identical terms among different anarchist traditions which has created a number of definitional concerns in anarchist theory. The compatibility of capitalism, nationalism, and religion with anarchism is widely disputed, and anarchism enjoys complex relationships with ideologies such as communism, collectivism, Marxism, and trade unionism. Anarchists may be motivated by humanism, divine authority, enlightened self-interest, veganism, or any number of alternative ethical doctrines. Phenomena such as civilisation, technology (e.g. within anarcho-primitivism), and the democratic process may be sharply criticised within some anarchist tendencies and simultaneously lauded in others. Gender, sexuality, and free love As gender and sexuality carry along them dynamics of hierarchy, many anarchists address, analyse, and oppose the suppression of one's autonomy imposed by gender roles. Sexuality was not often discussed by classical anarchists but the few that did felt that an anarchist society would lead to sexuality naturally developing.\\n\"}\n{\"input\": \"One way is for an individual from the group to play the role of facilitator to help achieve a consensus without taking part in the discussion themselves or promoting a specific point. Minorities usually accept rough consensus, except when they feel the proposal contradicts anarchist ethics, goals and values. Anarchists usually form small groups (5–20 individuals) to enhance autonomy and friendships among their members. These kinds of groups more often than not interconnect with each other, forming larger networks. Anarchists still support and participate in strikes, especially wildcat strikes as these are leaderless strikes not organised centrally by a syndicate. As in the past, newspapers and journals are used, and anarchists have gone online in the World Wide Web to spread their message. Anarchists have found it easier to create websites because of distributional and other difficulties, hosting electronic libraries and other portals. Anarchists were also involved in developing various software that are available for free. The way these hacktivists work to develop and distribute resembles the anarchist ideals, especially when it comes to preserving users' privacy from state surveillance. Anarchists organize themselves to squat and reclaim public spaces. During important events such as protests and when spaces are being occupied, they are often called Temporary Autonomous Zones (TAZ), spaces where art, poetry, and surrealism are blended to display the anarchist ideal. As seen by anarchists, squatting is a way to regain urban space from the capitalist market, serving pragmatical needs and also being an exemplary direct action. Acquiring space enables anarchists to experiment with their ideas and build social bonds. Adding up these tactics while having in mind that not all anarchists share the same attitudes towards them, along with various forms of protesting at highly symbolic events, make up a carnivalesque atmosphere that is part of contemporary anarchist vividity. Key issues As anarchism is a philosophy that embodies many diverse attitudes, tendencies, and schools of thought; disagreement over questions of values, ideology, and tactics is common. Its diversity has led to widely different uses of identical terms among different anarchist traditions which has created a number of definitional concerns in anarchist theory. The compatibility of capitalism, nationalism, and religion with anarchism is widely disputed, and anarchism enjoys complex relationships with ideologies such as communism, collectivism, Marxism, and trade unionism. Anarchists may be motivated by humanism, divine authority, enlightened self-interest, veganism, or any number of alternative ethical doctrines. Phenomena such as civilisation, technology (e.g. within anarcho-primitivism), and the democratic process may be sharply criticised within some anarchist tendencies and simultaneously lauded in others. Gender, sexuality, and free love As gender and sexuality carry along them dynamics of hierarchy, many anarchists address, analyse, and oppose the suppression of one's autonomy imposed by gender roles. Sexuality was not often discussed by classical anarchists but the few that did felt that an anarchist society would lead to sexuality naturally developing.\\n\", \"output_summarize\": \"One way is for an individual from the group to play the role of facilitator to help achieve a consensus without taking part in the discussion themselves or promoting a specific point. Minorities usually accept rough consensus, except when they feel the proposal contradicts anarchist ethics, goals and values. Anarchists usually form small groups (5–20 individuals) to enhance autonomy and friendships among their members. These kinds of groups more often than not interconnect with each other, forming larger networks. Anarchists still support and participate in strikes, especially wildcat strikes as these are leaderless strikes not organised centrally by a syndicate. As in the past, newspapers and journals are used, and anarchists have gone online in the World Wide Web to spread their message. Anarchists have found it easier to create websites because of distributional and other difficulties, hosting electronic libraries and other portals. Anarchists were also involved in developing various software that are available for free. The way these hacktivists work to develop and distribute resembles the anarchist ideals, especially when it comes to preserving users' privacy from state surveillance. Anarchists organize themselves to squat and reclaim public spaces. During important events such as protests and when spaces are being occupied, they are often called Temporary Autonomous Zones (TAZ), spaces where art, poetry, and surrealism are blended to display the anarchist ideal. As seen by anarchists, squatting is a way to regain urban space from the capitalist market, serving pragmatical needs and also being an exemplary direct action. Acquiring space enables anarchists to experiment with their ideas and build social bonds. Adding up these tactics while having in mind that not all anarchists share the same attitudes towards them, along with various forms of protesting at highly symbolic events, make up a carnivalesque atmosphere that is part of contemporary anarchist vividity. Key issues As anarchism is a philosophy that embodies many diverse attitudes, tendencies, and schools of thought; disagreement over questions of values, ideology, and tactics is common. Its diversity has led to widely different uses of identical terms among different anarchist traditions which has created a number of definitional concerns in anarchist theory. The compatibility of capitalism, nationalism, and religion with anarchism is widely disputed, and anarchism enjoys complex relationships with ideologies such as communism, collectivism, Marxism, and trade unionism. Anarchists may be motivated by humanism, divine authority, enlightened self-interest, veganism, or any number of alternative ethical doctrines. Phenomena such as civilisation, technology (e.g. within anarcho-primitivism), and the democratic process may be sharply criticised within some anarchist tendencies and simultaneously lauded in others. Gender, sexuality, and free love As gender and sexuality carry along them dynamics of hierarchy, many anarchists address, analyse, and oppose the suppression of one's autonomy imposed by gender roles. Sexuality was not often discussed by classical anarchists but the few that did felt that an anarchist society would lead to sexuality naturally developing.\\n\", \"output_predict\": \"Sexual violence was a concern for anarchists such as Benjamin Tucker, who opposed age of consent laws, believing they would benefit predatory men. A historical current that arose and flourished during 1890 and 1920 within anarchism was free love. In contemporary anarchism, this current survives as a tendency to support polyamory and queer anarchism. Free love advocates were against marriage, which they saw as a way of men imposing authority over women, largely because marriage law greatly favoured the power of men. The notion of free love was much broader and included a critique of the established order that limited women's sexual freedom and pleasure. Those free love movements contributed to the establishment of communal houses, where large groups of travelers, anarchists and other activists slept in beds together. Free love had roots both in Europe and the United States; however, some anarchists struggled with the jealousy that arose from free love. Anarchist feminists were advocates of free love, against marriage, and pro-choice (utilising a contemporary term), and had a similar agenda. Anarchist and non-anarchist feminists differed on suffrage but were supportive of one another. During the second half of the 20th century, anarchism intermingled with the second wave of feminism, radicalising some currents of the feminist movement and being influenced as well. By the latest decades of the 20th century, anarchists and feminists were advocating for the rights and autonomy of women, gays, queers and other marginalised groups, with some feminist thinkers suggesting a fusion of the two currents. With the third wave of feminism, sexual identity and compulsory heterosexuality became a subject of study for anarchists, yielding a post-structuralist critique of sexual normality. Some anarchists distanced themselves from this line of thinking, suggesting that it leaned towards an individualism that was dropping the cause of social liberation. Anarchism and education The interest of anarchists in education stretches back to the first emergence of classical anarchism. Anarchists consider proper education, one which sets the foundations of the future autonomy of the individual and the society, to be an act of mutual aid. Anarchist writers such as William Godwin (Political Justice) and Max Stirner (\\\"The False Principle of Our Education\\\") attacked both state education and private education as another means by which the ruling class replicate their privileges. In 1901, Catalan anarchist and free thinker Francisco Ferrer established the Escuela Moderna in Barcelona as an opposition to the established education system which was dictated largely by the Catholic Church. Ferrer's approach was secular, rejecting both state and church involvement in the educational process whilst giving pupils large amounts of autonomy in planning their work and attendance. Ferrer aimed to educate the working class and explicitly sought to foster class consciousness among students. The school closed after constant harassment by the state and Ferrer was later arrested. Nonetheless, his ideas formed the inspiration for a series of modern schools around the world.\\n\"}\n{\"input\": \"Sexual violence was a concern for anarchists such as Benjamin Tucker, who opposed age of consent laws, believing they would benefit predatory men. A historical current that arose and flourished during 1890 and 1920 within anarchism was free love. In contemporary anarchism, this current survives as a tendency to support polyamory and queer anarchism. Free love advocates were against marriage, which they saw as a way of men imposing authority over women, largely because marriage law greatly favoured the power of men. The notion of free love was much broader and included a critique of the established order that limited women's sexual freedom and pleasure. Those free love movements contributed to the establishment of communal houses, where large groups of travelers, anarchists and other activists slept in beds together. Free love had roots both in Europe and the United States; however, some anarchists struggled with the jealousy that arose from free love. Anarchist feminists were advocates of free love, against marriage, and pro-choice (utilising a contemporary term), and had a similar agenda. Anarchist and non-anarchist feminists differed on suffrage but were supportive of one another. During the second half of the 20th century, anarchism intermingled with the second wave of feminism, radicalising some currents of the feminist movement and being influenced as well. By the latest decades of the 20th century, anarchists and feminists were advocating for the rights and autonomy of women, gays, queers and other marginalised groups, with some feminist thinkers suggesting a fusion of the two currents. With the third wave of feminism, sexual identity and compulsory heterosexuality became a subject of study for anarchists, yielding a post-structuralist critique of sexual normality. Some anarchists distanced themselves from this line of thinking, suggesting that it leaned towards an individualism that was dropping the cause of social liberation. Anarchism and education The interest of anarchists in education stretches back to the first emergence of classical anarchism. Anarchists consider proper education, one which sets the foundations of the future autonomy of the individual and the society, to be an act of mutual aid. Anarchist writers such as William Godwin (Political Justice) and Max Stirner (\\\"The False Principle of Our Education\\\") attacked both state education and private education as another means by which the ruling class replicate their privileges. In 1901, Catalan anarchist and free thinker Francisco Ferrer established the Escuela Moderna in Barcelona as an opposition to the established education system which was dictated largely by the Catholic Church. Ferrer's approach was secular, rejecting both state and church involvement in the educational process whilst giving pupils large amounts of autonomy in planning their work and attendance. Ferrer aimed to educate the working class and explicitly sought to foster class consciousness among students. The school closed after constant harassment by the state and Ferrer was later arrested. Nonetheless, his ideas formed the inspiration for a series of modern schools around the world.\\n\", \"output_summarize\": \"Sexual violence was a concern for anarchists such as Benjamin Tucker, who opposed age of consent laws, believing they would benefit predatory men. A historical current that arose and flourished during 1890 and 1920 within anarchism was free love. In contemporary anarchism, this current survives as a tendency to support polyamory and queer anarchism. Free love advocates were against marriage, which they saw as a way of men imposing authority over women, largely because marriage law greatly favoured the power of men. The notion of free love was much broader and included a critique of the established order that limited women's sexual freedom and pleasure. Those free love movements contributed to the establishment of communal houses, where large groups of travelers, anarchists and other activists slept in beds together. Free love had roots both in Europe and the United States; however, some anarchists struggled with the jealousy that arose from free love. Anarchist feminists were advocates of free love, against marriage, and pro-choice (utilising a contemporary term), and had a similar agenda. Anarchist and non-anarchist feminists differed on suffrage but were supportive of one another. During the second half of the 20th century, anarchism intermingled with the second wave of feminism, radicalising some currents of the feminist movement and being influenced as well. By the latest decades of the 20th century, anarchists and feminists were advocating for the rights and autonomy of women, gays, queers and other marginalised groups, with some feminist thinkers suggesting a fusion of the two currents. With the third wave of feminism, sexual identity and compulsory heterosexuality became a subject of study for anarchists, yielding a post-structuralist critique of sexual normality. Some anarchists distanced themselves from this line of thinking, suggesting that it leaned towards an individualism that was dropping the cause of social liberation. Anarchism and education The interest of anarchists in education stretches back to the first emergence of classical anarchism. Anarchists consider proper education, one which sets the foundations of the future autonomy of the individual and the society, to be an act of mutual aid. Anarchist writers such as William Godwin (Political Justice) and Max Stirner (\\\"The False Principle of Our Education\\\") attacked both state education and private education as another means by which the ruling class replicate their privileges. In 1901, Catalan anarchist and free thinker Francisco Ferrer established the Escuela Moderna in Barcelona as an opposition to the established education system which was dictated largely by the Catholic Church. Ferrer's approach was secular, rejecting both state and church involvement in the educational process whilst giving pupils large amounts of autonomy in planning their work and attendance. Ferrer aimed to educate the working class and explicitly sought to foster class consciousness among students. The school closed after constant harassment by the state and Ferrer was later arrested. Nonetheless, his ideas formed the inspiration for a series of modern schools around the world.\\n\", \"output_predict\": \"Christian anarchist Leo Tolstoy, who published the essay Education and Culture, also established a similar school with its founding principle being that \\\"for education to be effective it had to be free.\\\" In a similar token, A. S. Neill founded what became the Summerhill School in 1921, also declaring being free from coercion. Anarchist education is based largely on the idea that a child's right to develop freely and without manipulation ought to be respected and that rationality would lead children to morally good conclusions; however, there has been little consensus among anarchist figures as to what constitutes manipulation. Ferrer believed that moral indoctrination was necessary and explicitly taught pupils that equality, liberty and social justice were not possible under capitalism, along with other critiques of government and nationalism. Late 20th century and contemporary anarchist writers (Paul Goodman, Herbert Read, and Colin Ward) intensified and expanded the anarchist critique of state education, largely focusing on the need for a system that focuses on children's creativity rather than on their ability to attain a career or participate in consumerism as part of a consumer society. Contemporary anarchists such as Ward claim that state education serves to perpetuate socioeconomic inequality. While few anarchist education institutions have survived to the modern-day, major tenets of anarchist schools, among them respect for child autonomy and relying on reasoning rather than indoctrination as a teaching method, have spread among mainstream educational institutions. Judith Suissa names three schools as explicitly anarchists schools, namely the Free Skool Santa Cruz in the United States which is part of a wider American-Canadian network of schools, the Self-Managed Learning College in Brighton, England, and the Paideia School in Spain. Anarchism and the state Objection to the state and its institutions is a sine qua non of anarchism. Anarchists consider the state as a tool of domination and believe it to be illegitimate regardless of its political tendencies. Instead of people being able to control the aspects of their life, major decisions are taken by a small elite. Authority ultimately rests solely on power, regardless of whether that power is open or transparent, as it still has the ability to coerce people. Another anarchist argument against states is that the people constituting a government, even the most altruistic among officials, will unavoidably seek to gain more power, leading to corruption. Anarchists consider the idea that the state is the collective will of the people to be an unachievable fiction due to the fact that the ruling class is distinct from the rest of society. Specific anarchist attitudes towards the state vary. Robert Paul Wolff believed that the tension between authority and autonomy would mean the state could never be legitimate. Bakunin saw the state as meaning \\\"coercion, domination by means of coercion, camouflaged if possible but unceremonious and overt if need be.\\\"\\t\\n\"}\n{\"input\": \"Christian anarchist Leo Tolstoy, who published the essay Education and Culture, also established a similar school with its founding principle being that \\\"for education to be effective it had to be free.\\\" In a similar token, A. S. Neill founded what became the Summerhill School in 1921, also declaring being free from coercion. Anarchist education is based largely on the idea that a child's right to develop freely and without manipulation ought to be respected and that rationality would lead children to morally good conclusions; however, there has been little consensus among anarchist figures as to what constitutes manipulation. Ferrer believed that moral indoctrination was necessary and explicitly taught pupils that equality, liberty and social justice were not possible under capitalism, along with other critiques of government and nationalism. Late 20th century and contemporary anarchist writers (Paul Goodman, Herbert Read, and Colin Ward) intensified and expanded the anarchist critique of state education, largely focusing on the need for a system that focuses on children's creativity rather than on their ability to attain a career or participate in consumerism as part of a consumer society. Contemporary anarchists such as Ward claim that state education serves to perpetuate socioeconomic inequality. While few anarchist education institutions have survived to the modern-day, major tenets of anarchist schools, among them respect for child autonomy and relying on reasoning rather than indoctrination as a teaching method, have spread among mainstream educational institutions. Judith Suissa names three schools as explicitly anarchists schools, namely the Free Skool Santa Cruz in the United States which is part of a wider American-Canadian network of schools, the Self-Managed Learning College in Brighton, England, and the Paideia School in Spain. Anarchism and the state Objection to the state and its institutions is a sine qua non of anarchism. Anarchists consider the state as a tool of domination and believe it to be illegitimate regardless of its political tendencies. Instead of people being able to control the aspects of their life, major decisions are taken by a small elite. Authority ultimately rests solely on power, regardless of whether that power is open or transparent, as it still has the ability to coerce people. Another anarchist argument against states is that the people constituting a government, even the most altruistic among officials, will unavoidably seek to gain more power, leading to corruption. Anarchists consider the idea that the state is the collective will of the people to be an unachievable fiction due to the fact that the ruling class is distinct from the rest of society. Specific anarchist attitudes towards the state vary. Robert Paul Wolff believed that the tension between authority and autonomy would mean the state could never be legitimate. Bakunin saw the state as meaning \\\"coercion, domination by means of coercion, camouflaged if possible but unceremonious and overt if need be.\\\"\\t\\n\", \"output_summarize\": \"Christian anarchist Leo Tolstoy, who published the essay Education and Culture, also established a similar school with its founding principle being that \\\"for education to be effective it had to be free.\\\" In a similar token, A. S. Neill founded what became the Summerhill School in 1921, also declaring being free from coercion. Anarchist education is based largely on the idea that a child's right to develop freely and without manipulation ought to be respected and that rationality would lead children to morally good conclusions; however, there has been little consensus among anarchist figures as to what constitutes manipulation. Ferrer believed that moral indoctrination was necessary and explicitly taught pupils that equality, liberty and social justice were not possible under capitalism, along with other critiques of government and nationalism. Late 20th century and contemporary anarchist writers (Paul Goodman, Herbert Read, and Colin Ward) intensified and expanded the anarchist critique of state education, largely focusing on the need for a system that focuses on children's creativity rather than on their ability to attain a career or participate in consumerism as part of a consumer society. Contemporary anarchists such as Ward claim that state education serves to perpetuate socioeconomic inequality. While few anarchist education institutions have survived to the modern-day, major tenets of anarchist schools, among them respect for child autonomy and relying on reasoning rather than indoctrination as a teaching method, have spread among mainstream educational institutions. Judith Suissa names three schools as explicitly anarchists schools, namely the Free Skool Santa Cruz in the United States which is part of a wider American-Canadian network of schools, the Self-Managed Learning College in Brighton, England, and the Paideia School in Spain. Anarchism and the state Objection to the state and its institutions is a sine qua non of anarchism. Anarchists consider the state as a tool of domination and believe it to be illegitimate regardless of its political tendencies. Instead of people being able to control the aspects of their life, major decisions are taken by a small elite. Authority ultimately rests solely on power, regardless of whether that power is open or transparent, as it still has the ability to coerce people. Another anarchist argument against states is that the people constituting a government, even the most altruistic among officials, will unavoidably seek to gain more power, leading to corruption. Anarchists consider the idea that the state is the collective will of the people to be an unachievable fiction due to the fact that the ruling class is distinct from the rest of society. Specific anarchist attitudes towards the state vary. Robert Paul Wolff believed that the tension between authority and autonomy would mean the state could never be legitimate. Bakunin saw the state as meaning \\\"coercion, domination by means of coercion, camouflaged if possible but unceremonious and overt if need be.\\\"\\t\\n\", \"output_predict\": \"A. John Simmons and Leslie Green, who leaned toward philosophical anarchism, believed that the state could be legitimate if it is governed by consensus, although they saw this as highly unlikely. Beliefs on how to abolish the state also differ. Anarchism and the arts The connection between anarchism and art was quite profound during the classical era of anarchism, especially among artistic currents that were developing during that era such as futurists, surrealists and others. In literature, anarchism was mostly associated with the New Apocalyptics and the neo-romanticism movement. In music, anarchism has been associated with music scenes such as punk. Anarchists such as Leo Tolstoy and Herbert Read stated that the border between the artist and the non-artist, what separates art from a daily act, is a construct produced by the alienation caused by capitalism and it prevents humans from living a joyful life. Other anarchists advocated for or used art as a means to achieve anarchist ends. In his book Breaking the Spell: A History of Anarchist Filmmakers, Videotape Guerrillas, and Digital Ninjas, Chris Robé claims that \\\"anarchist-inflected practices have increasingly structured movement-based video activism.\\\" Throughout the 20th century, many prominent anarchists (Peter Kropotkin, Emma Goldman, Gustav Landauer and Camillo Berneri) and publications such as Anarchy wrote about matters pertaining to the arts. Three overlapping properties made art useful to anarchists. It could depict a critique of existing society and hierarchies, serve as a prefigurative tool to reflect the anarchist ideal society and even turn into a means of direct action such as in protests. As it appeals to both emotion and reason, art could appeal to the whole human and have a powerful effect. The 19th-century neo-impressionist movement had an ecological aesthetic and offered an example of an anarchist perception of the road towards socialism. In Les chataigniers a Osny by anarchist painter Camille Pissarro, the blending of aesthetic and social harmony is prefiguring an ideal anarchistic agrarian community. Analysis The most common critique of anarchism is that humans cannot self-govern and so a state is necessary for human survival. Philosopher Bertrand Russell supported this critique, stating that \\\"[p]eace and war, tariffs, regulations of sanitary conditions and the sale of noxious drugs, the preservation of a just system of distribution: these, among others, are functions which could hardly be performed in a community in which there was no central government.\\\" Another common criticism of anarchism is that it fits a world of isolation in which only the small enough entities can be self-governing; a response would be that major anarchist thinkers advocated anarchist federalism. Philosophy lecturer Andrew G. Fiala composed a list of common arguments against anarchism which includes critiques such as that anarchism is innately related to violence and destruction, not only in the pragmatic world, such as at protests, but in the world of ethics as well. Secondly, anarchism is evaluated as unfeasible or utopian since the state cannot be defeated practically.\\n\"}" }, { "path": "research/LLARA/finetune/__init__.py", "content": "" }, { "path": "research/LLARA/finetune/arguments.py", "content": "import os\nfrom dataclasses import dataclass, field\nfrom typing import Optional, List\n\nfrom transformers import TrainingArguments\n\n\ndef default_list() -> List[int]:\n return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n \"\"\"\n\n model_name_or_path: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n\n peft_model_path: str = field(\n default=''\n )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n # cache_dir: Optional[str] = field(\n # default=None, metadata={\"help\": \"Where do you want to store the pretrained models downloaded from s3\"}\n # )\n use_lora: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use LORA (low-rank parameter-efficient training) to train the model.\"}\n )\n lora_rank: int = field(\n default=64,\n metadata={\"help\": \"The rank of lora.\"}\n )\n lora_alpha: float = field(\n default=16,\n metadata={\"help\": \"The alpha parameter of lora.\"}\n )\n lora_dropout: float = field(\n default=0.1,\n metadata={\"help\": \"The dropout rate of lora modules.\"}\n )\n target_modules: List[str] = field(\n default_factory=default_list\n )\n save_merged_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will merge the lora modules and save the entire model.\"}\n )\n use_flash_attn: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use flash attention to train the model.\"}\n )\n use_slow_tokenizer: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\"}\n )\n low_cpu_mem_usage: bool = field(\n default=False,\n metadata={\"help\": \"It is an option to create the model as an empty shell,\"\n \"then only materialize its parameters when the pretrained weights are loaded.\"\n \"If passed, LLM loading time and RAM consumption will be benefited.\"}\n )\n token: str = field(\n default=\"\"\n )\n cache_dir: str = field(\n default=\"./LMs\"\n )\n from_peft: str = field(\n default=None\n )\n\n\n@dataclass\nclass DataArguments:\n train_data: str = field(\n default='./toy_finetune_data.jsonl', metadata={\"help\": \"Path to train data\"}\n )\n train_group_size: int = field(default=8)\n\n query_max_len: int = field(\n default=32,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n\n passage_max_len: int = field(\n default=128,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n\n max_example_num_per_dataset: int = field(\n default=100000000, metadata={\"help\": \"the max number of examples for each dataset\"}\n )\n\n query_instruction_for_retrieval: str = field(\n default=\"query: \", metadata={\"help\": \"query: \"}\n )\n passage_instruction_for_retrieval: str = field(\n default=\"passage: \", metadata={\"help\": \"passage: \"}\n )\n\n cache_path: str = field(\n default='./data_dir'\n )\n\n load_from_disk: bool = field(\n default=False, metadata={\"help\": \" whether load the data from disk\"}\n )\n\n load_disk_path: str = field(\n default=None, metadata={\"help\": \" the path to load the data\", \"nargs\": \"+\"}\n )\n\n save_to_disk: bool = field(\n default=False, metadata={\"help\": \" whether save the data to disk\"}\n )\n\n save_disk_path: str = field(\n default=None, metadata={\"help\": \" the path to save the data\"}\n )\n\n num_shards: int = field(\n default=0, metadata={\n \"help\": \"number of shards to write, prior than `save_max_shard_size`, default depends on `save_max_shard_size`\"}\n )\n\n save_max_shard_size: str = field(\n default=\"50GB\", metadata={\"help\": \"the max size of the shard\"}\n )\n\n exit_after_save: bool = field(\n default=False, metadata={\"help\": \" whether exit after save the data\"}\n )\n\n shuffle_ratio: float = field(\n default=0.0, metadata={\"help\": \"The ratio of shuffling the text\"}\n )\n\n def __post_init__(self):\n if not os.path.exists(self.train_data):\n raise FileNotFoundError(f\"cannot find file: {self.train_data}, please set a true path\")\n\n@dataclass\nclass RetrieverTrainingArguments(TrainingArguments):\n negatives_cross_device: bool = field(default=False, metadata={\"help\": \"share negatives across devices\"})\n temperature: Optional[float] = field(default=0.02)\n fix_position_embedding: bool = field(default=False, metadata={\"help\": \"Freeze the parameters of position embeddings\"})\n sentence_pooling_method: str = field(default='cls', metadata={\"help\": \"the pooling method, should be cls or mean\"})\n normlized: bool = field(default=True)\n sub_batch_size: int = field(default=None)\n cache_chunk_size: int = field(default=-1, metadata={\"help\": \"用于缓存每一步的执行.\"})" }, { "path": "research/LLARA/finetune/data.py", "content": "import sys\n\nimport math\nimport os.path\nimport random\nfrom dataclasses import dataclass\nfrom typing import List, Tuple\n\nimport datasets\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom transformers import DataCollatorWithPadding, DataCollatorForSeq2Seq\nfrom transformers import PreTrainedTokenizer, BatchEncoding\n\nfrom arguments import DataArguments\n\n\nclass TrainDatasetForEmbedding(Dataset):\n def __init__(\n self,\n args: DataArguments,\n tokenizer: PreTrainedTokenizer\n ):\n if os.path.isdir(args.train_data):\n train_datasets = []\n for file in os.listdir(args.train_data):\n temp_dataset = datasets.load_dataset('json', data_files=os.path.join(args.train_data, file),\n split='train')\n if len(temp_dataset) > args.max_example_num_per_dataset:\n temp_dataset = temp_dataset.select(\n random.sample(list(range(len(temp_dataset))), args.max_example_num_per_dataset))\n train_datasets.append(temp_dataset)\n self.dataset = datasets.concatenate_datasets(train_datasets)\n else:\n self.dataset = datasets.load_dataset('json', data_files=args.train_data, split='train', cache_dir=args.cache_path)\n\n self.tokenizer = tokenizer\n self.args = args\n self.total_len = len(self.dataset)\n\n self.prefix = '\"'\n self.prefix_ids = self.tokenizer(self.prefix, return_tensors=None)['input_ids']\n self.suffix_passage = '\", summarize the above passage within eight words: '\n self.suffix_passage_ids = self.tokenizer(self.suffix_passage, return_tensors=None, add_special_tokens=False)['input_ids']\n # self.suffix_query = '\", predict the following passage within eight words: '\n self.suffix_query = '\", predict the following passage within eight words: '\n self.suffix_query_ids = self.tokenizer(self.suffix_query, return_tensors=None, add_special_tokens=False)['input_ids']\n self.query_max_len = self.args.query_max_len - len(self.prefix_ids) - len(self.suffix_query_ids)\n self.passage_max_len = self.args.passage_max_len - len(self.prefix_ids) - len(self.suffix_passage_ids)\n\n def __len__(self):\n # return self.total_len\n return self.total_len\n\n def __getitem__(self, item) -> Tuple[BatchEncoding, List[BatchEncoding]]:\n query = self.dataset[item]['query']\n query_inputs = self.tokenizer(query,\n return_tensors=None,\n max_length=self.query_max_len,\n truncation=True,\n add_special_tokens=False)\n query_inputs['input_ids'] = self.prefix_ids + query_inputs['input_ids'] + self.suffix_query_ids\n query_inputs['attention_mask'] = [1] * len(query_inputs['input_ids'])\n\n passages = []\n pos = random.choice(self.dataset[item]['pos'])\n passages.append(pos)\n\n if len(self.dataset[item]['neg']) < self.args.train_group_size - 1:\n # print(len(self.dataset[item]['neg']))\n num = math.ceil((self.args.train_group_size - 1) / len(list(set(self.dataset[item]['neg']))))\n # negs = random.sample(list(set(self.dataset[item]['neg'])) * num, self.args.train_group_size - 1)\n negs = random.sample(self.dataset[item]['neg'] * num, self.args.train_group_size - 1)\n # negs = random.sample(self.dataset[item]['neg'], self.args.train_group_size - 1)\n else:\n negs = random.sample(self.dataset[item]['neg'], self.args.train_group_size - 1)\n # negs = random.sample(self.dataset[item]['neg'], self.args.train_group_size - 1)\n passages.extend(negs)\n\n passages_inputs = []\n for passage in passages:\n passage_inputs = self.tokenizer(passage,\n return_tensors=None,\n max_length=self.passage_max_len,\n truncation=True,\n add_special_tokens=False)\n passage_inputs['input_ids'] = self.prefix_ids + passage_inputs['input_ids'] + self.suffix_passage_ids\n passage_inputs['attention_mask'] = [1] * len(passage_inputs['input_ids'])\n passages_inputs.append(passage_inputs)\n\n return query_inputs, passages_inputs\n\n\n@dataclass\nclass EmbedCollator(DataCollatorForSeq2Seq):\n \"\"\"\n Wrapper that does conversion from List[Tuple[encode_qry, encode_psg]] to List[qry], List[psg]\n and pass batch separately to the actual collator.\n Abstract out data detail for the model.\n \"\"\"\n query_max_len: int = 32\n passage_max_len: int = 128\n sub_batch_size: int = -1\n\n def __call__(self, features, return_tensors='pt'):\n if return_tensors is None:\n return_tensors = self.return_tensors\n\n queries = []\n passages = []\n for e in features:\n queries.append(e[0])\n passages.extend(e[1])\n\n if self.sub_batch_size is None or self.sub_batch_size <= 0:\n q_collated = self.tokenizer.pad(\n queries,\n padding=self.padding,\n max_length=self.query_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=return_tensors,\n )\n\n d_collated = self.tokenizer.pad(\n passages,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=return_tensors,\n )\n else:\n batch_size = self.sub_batch_size\n\n q_collated = []\n for i in range(0, len(queries), batch_size):\n start = i\n end = min(len(queries), i + batch_size)\n sub_features = queries[start:end]\n q_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=return_tensors,\n ))\n\n d_collated = []\n for i in range(0, len(passages), batch_size):\n start = i\n end = min(len(passages), i + batch_size)\n sub_features = passages[start: end]\n d_collated.append(self.tokenizer.pad(\n sub_features,\n padding=self.padding,\n max_length=self.passage_max_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=return_tensors,\n ))\n\n return {\"query\": q_collated, \"passage\": d_collated}" }, { "path": "research/LLARA/finetune/load_model.py", "content": "import sys\n\nimport torch\nfrom transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, AutoModel\nfrom peft import LoraConfig, TaskType, get_peft_model, PeftModel\n\n\ndef get_model(model_args):\n # if model_args.use_flash_attn:\n # from llama2_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn\n # replace_llama_attn_with_flash_attn()\n\n if model_args.config_name:\n config = AutoConfig.from_pretrained(model_args.config_name,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = AutoModelForCausalLM.from_pretrained(\n model_args.model_name_or_path,\n # load_in_8bit=True,\n # torch_dtype=torch.bfloat16,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n # low_cpu_mem_usage=model_args.low_cpu_mem_usage,\n # device_map=\"auto\",\n )\n else:\n print(\"Training new model from scratch\")\n model = model_args.from_config(config)\n\n if model_args.from_peft is not None:\n model = PeftModel.from_pretrained(model, model_args.from_peft, is_trainable=True)\n model.print_trainable_parameters()\n else:\n if model_args.use_lora:\n peft_config = LoraConfig(\n task_type=TaskType.FEATURE_EXTRACTION,\n inference_mode=False,\n r=model_args.lora_rank,\n target_modules=model_args.target_modules,\n lora_alpha=model_args.lora_alpha,\n lora_dropout=model_args.lora_dropout\n )\n model = get_peft_model(model, peft_config)\n # print(model.model.layers[0].self_attn.q_proj.weight.dtype)\n # print(model.model.layers[0].self_attn.q_proj.lora_A.default.weight.dtype)\n # sys.exit(0)\n model.print_trainable_parameters()\n\n return model" }, { "path": "research/LLARA/finetune/modeling.py", "content": "import logging\nimport sys\nfrom dataclasses import dataclass\nfrom typing import Dict, Optional, List, Union\n\nimport torch\nimport torch.distributed as dist\nfrom torch import nn, Tensor\nfrom tqdm import trange, tqdm\nfrom transformers import AutoModel, AutoTokenizer\nfrom transformers.file_utils import ModelOutput\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass EncoderOutput(ModelOutput):\n q_reps: Optional[Tensor] = None\n p_reps: Optional[Tensor] = None\n loss: Optional[Tensor] = None\n scores: Optional[Tensor] = None\n\n\nclass BiEncoderModel(nn.Module):\n TRANSFORMER_CLS = AutoModel\n\n def __init__(self,\n model: AutoModel = None,\n tokenizer: AutoTokenizer = None,\n normlized: bool = False,\n negatives_cross_device: bool = False,\n temperature: float = 1.0,\n sub_batch_size: int = -1\n ):\n super().__init__()\n self.model = model\n self.config = model.config\n self.tokenizer = tokenizer\n self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')\n\n self.normlized = normlized\n self.temperature = temperature\n if not normlized:\n self.temperature = 1.0\n logger.info(\"reset temperature = 1.0 due to using inner product to compute similarity\")\n\n self.negatives_cross_device = negatives_cross_device\n if self.negatives_cross_device:\n if not dist.is_initialized():\n raise ValueError('Distributed training has not been initialized for representation all gather.')\n # logger.info(\"Run in a single GPU, set negatives_cross_device=False\")\n # self.negatives_cross_device = False\n # else:\n self.process_rank = dist.get_rank()\n self.world_size = dist.get_world_size()\n\n self.sub_batch_size = sub_batch_size\n\n def gradient_checkpointing_enable(self, **kwargs):\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n self.model.enable_input_require_grads(**kwargs)\n\n def encode(self, features):\n # input('continue?')\n if features is None:\n return None\n if not isinstance(features, list):\n if self.sub_batch_size is not None and self.sub_batch_size > 0:\n all_p_reps = []\n for i in range(0, len(features['attention_mask']), self.sub_batch_size):\n end_inx = min(i + self.sub_batch_size, len(features['attention_mask']))\n sub_features = {}\n for k, v in features.items():\n sub_features[k] = v[i:end_inx]\n psg_out = self.model(**sub_features, return_dict=True, output_hidden_states=True)\n ### modify\n p_reps = psg_out.hidden_states[-1][:, -8:, :]\n p_reps = torch.mean(p_reps, dim=1)\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normlized:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n else:\n psg_out = self.model(**features, return_dict=True, output_hidden_states=True)\n p_reps = psg_out.hidden_states[-1][:, -8:, :]\n p_reps = torch.mean(p_reps, dim=1)\n if self.normlized:\n p_reps = torch.nn.functional.normalize(p_reps, dim=-1)\n return p_reps.contiguous()\n else:\n all_p_reps = []\n for sub_features in features:\n psg_out = self.model(**sub_features, return_dict=True, output_hidden_states=True)\n ### modify\n p_reps = psg_out.hidden_states[-1][:, -8:, :]\n p_reps = torch.mean(p_reps, dim=1)\n all_p_reps.append(p_reps)\n all_p_reps = torch.cat(all_p_reps, 0).contiguous()\n if self.normlized:\n all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)\n return all_p_reps.contiguous()\n\n\n def compute_similarity(self, q_reps, p_reps):\n if len(p_reps.size()) == 2:\n return torch.matmul(q_reps, p_reps.transpose(0, 1))\n return torch.matmul(q_reps, p_reps.transpose(-2, -1))\n\n def forward(self, query: Union[Dict[str, Tensor], List[Dict[str, Tensor]]]= None, passage: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None):\n # torch.cuda.empty_cache()\n # if self.process_rank == 1:\n # print(query)\n q_reps = self.encode(query) # (batch_size, dim)\n p_reps = self.encode(passage) # (batch_size * num, dim)\n\n if self.training:\n if self.negatives_cross_device:\n q_reps = self._dist_gather_tensor(q_reps)\n p_reps = self._dist_gather_tensor(p_reps)\n\n scores = self.compute_similarity(q_reps, p_reps)\n scores = scores / self.temperature\n scores = scores.view(q_reps.size(0), -1)\n\n target = torch.arange(scores.size(0), device=scores.device, dtype=torch.long)\n target = target * (p_reps.size(0) // q_reps.size(0))\n loss = self.compute_loss(scores, target) # 同批内除了正样本以外的均为负样本\n\n else:\n scores = self.compute_similarity(q_reps, p_reps)\n loss = None\n\n # print(loss)\n return EncoderOutput(\n loss=loss,\n scores=scores,\n q_reps=q_reps,\n p_reps=p_reps,\n )\n\n def compute_loss(self, scores, target):\n return self.cross_entropy(scores, target)\n\n def _dist_gather_tensor(self, t: Optional[torch.Tensor]):\n if t is None:\n return None\n t = t.contiguous()\n\n all_tensors = [torch.empty_like(t) for _ in range(self.world_size)]\n dist.all_gather(all_tensors, t)\n all_tensors[self.process_rank] = t # 给当前进程的q和doc加上梯度,当前的q对其他的d，更新；当前的d对其他的q，更新\n all_tensors = torch.cat(all_tensors, dim=0)\n\n return all_tensors\n\n def save(self, output_dir: str):\n state_dict = self.model.state_dict()\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n self.model.save_pretrained(output_dir, state_dict=state_dict)\n" }, { "path": "research/LLARA/finetune/run.py", "content": "import logging\nimport os\nfrom pathlib import Path\n\nfrom transformers import AutoConfig, AutoTokenizer\nfrom transformers import (\n HfArgumentParser,\n set_seed,\n)\n\nfrom arguments import ModelArguments, DataArguments, \\\n RetrieverTrainingArguments as TrainingArguments\nfrom data import TrainDatasetForEmbedding, EmbedCollator\nfrom modeling import BiEncoderModel\nfrom trainer import BiTrainer\nfrom load_model import get_model\n\nlogger = logging.getLogger(__name__)\n\n\ndef main():\n parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: ModelArguments\n data_args: DataArguments\n training_args: TrainingArguments\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n logger.info(\"Training/evaluation parameters %s\", training_args)\n logger.info(\"Model parameters %s\", model_args)\n logger.info(\"Data parameters %s\", data_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n num_labels = 1\n base_model = get_model(model_args)\n tokenizer = AutoTokenizer.from_pretrained(\n model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n use_fast=False,\n # add_eos_token=True\n )\n\n if tokenizer.pad_token is None:\n tokenizer.pad_token = tokenizer.unk_token\n tokenizer.padding_side = 'left'\n\n config = AutoConfig.from_pretrained(\n model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=model_args.cache_dir,\n token=model_args.token,\n )\n logger.info('Config: %s', config)\n\n model = BiEncoderModel(model=base_model,\n tokenizer=tokenizer,\n normlized=training_args.normlized,\n negatives_cross_device=training_args.negatives_cross_device,\n temperature=training_args.temperature,\n sub_batch_size=training_args.sub_batch_size)\n # model.gradient_checkpointing_enable()\n # print(tokenizer('slalala', return_tensors='pt').to('cuda'))\n # print(base_model(**(tokenizer('slalala', return_tensors='pt'))))\n # print(base_model(**(tokenizer('slalala', return_tensors='pt').to('cuda'))))\n\n if training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n train_dataset = TrainDatasetForEmbedding(args=data_args, tokenizer=tokenizer)\n\n trainer = BiTrainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n data_collator=EmbedCollator(\n tokenizer=tokenizer,\n query_max_len=data_args.query_max_len,\n passage_max_len=data_args.passage_max_len,\n pad_to_multiple_of=8,\n return_tensors=\"pt\",\n padding=True,\n sub_batch_size=training_args.sub_batch_size\n ),\n tokenizer=tokenizer\n )\n\n Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n # Training\n trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)\n trainer.save_model()\n # For convenience, we also re-save the tokenizer to the same directory,\n # so that you can share your model easily on huggingface.co/models =)\n if trainer.is_world_process_zero():\n tokenizer.save_pretrained(training_args.output_dir)\n\n\nif __name__ == \"__main__\":\n main()" }, { "path": "research/LLARA/finetune/trainer.py", "content": "from transformers.trainer import *\n\n\nclass BiTrainer(Trainer):\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n # if self.tokenizer is not None and self.is_world_process_zero():\n # self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n # save the checkpoint for sentence-transformers library\n # if self.is_world_process_zero():\n # save_ckpt_for_sentence_transformers(output_dir,\n # pooling_mode=self.args.sentence_pooling_method,\n # normlized=self.args.normlized)\n\n def compute_loss(self, model, inputs, return_outputs=False):\n \"\"\"\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n \"\"\"\n\n outputs = model(**inputs)\n loss = outputs.loss\n\n return (loss, outputs) if return_outputs else loss" }, { "path": "research/LLARA/pretrain/__init__.py", "content": "" }, { "path": "research/LLARA/pretrain/arguments.py", "content": "import os\nfrom dataclasses import dataclass, field\nfrom typing import Optional, List\n\nfrom transformers import TrainingArguments\n\n\ndef default_list() -> List[int]:\n return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n \"\"\"\n\n model_name_or_path: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n # cache_dir: Optional[str] = field(\n # default=None, metadata={\"help\": \"Where do you want to store the pretrained models downloaded from s3\"}\n # )\n use_lora: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use LORA (low-rank parameter-efficient training) to train the model.\"}\n )\n lora_rank: int = field(\n default=64,\n metadata={\"help\": \"The rank of lora.\"}\n )\n lora_alpha: float = field(\n default=16,\n metadata={\"help\": \"The alpha parameter of lora.\"}\n )\n lora_dropout: float = field(\n default=0.1,\n metadata={\"help\": \"The dropout rate of lora modules.\"}\n )\n target_modules: List[str] = field(\n default_factory=default_list\n )\n save_merged_lora_model: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will merge the lora modules and save the entire model.\"}\n )\n use_flash_attn: bool = field(\n default=True,\n metadata={\"help\": \"If passed, will use flash attention to train the model.\"}\n )\n use_slow_tokenizer: bool = field(\n default=False,\n metadata={\"help\": \"If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).\"}\n )\n token: str = field(\n default=\"\"\n )\n cache_dir: str = field(\n default=\"./LMs\"\n )\n\n\n@dataclass\nclass DataArguments:\n cache_path: str = field(\n default='./data_dir'\n )\n\n train_data: str = field(\n default='./toy_finetune_data.jsonl', metadata={\"help\": \"Path to train data\"}\n )\n\n max_example_num_per_dataset: int = field(\n default=100000000, metadata={\"help\": \"the max number of examples for each dataset\"}\n )\n\n cutoff_len: int = field(\n default=512,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization for passage. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n\n remove_stop_words: bool = field(\n default=False\n )\n\n def __post_init__(self):\n if not os.path.exists(self.train_data):\n raise FileNotFoundError(f\"cannot find file: {self.train_data}, please set a true path\")\n\n@dataclass\nclass PretrainTrainingArguments(TrainingArguments):\n mask: bool = field(default=True, metadata={\"help\": \"mask the input part\"})" }, { "path": "research/LLARA/pretrain/data.py", "content": "import os.path\nimport random\nimport sys\nimport time\nfrom dataclasses import dataclass\nimport re\n\nimport datasets\nimport numpy as np\nfrom torch.utils.data import Dataset\nfrom transformers import DataCollatorWithPadding, AutoTokenizer, DataCollatorForSeq2Seq, PreTrainedTokenizer\n\nfrom arguments import DataArguments\nfrom nltk.corpus import stopwords\n\nclass TrainDatasetForEmbedding(Dataset):\n def __init__(\n self,\n tokenizer: PreTrainedTokenizer,\n args: DataArguments,\n ):\n if os.path.isdir(args.train_data):\n train_datasets = []\n for file in os.listdir(args.train_data):\n temp_dataset = datasets.load_dataset('json', data_files=os.path.join(args.train_data, file),\n split='train')\n if len(temp_dataset) > args.max_example_num_per_dataset:\n temp_dataset = temp_dataset.select(\n random.sample(list(range(len(temp_dataset))), args.max_example_num_per_dataset))\n train_datasets.append(temp_dataset)\n self.dataset = datasets.concatenate_datasets(train_datasets)\n else:\n self.dataset = datasets.load_dataset('json', data_files=args.train_data, split='train',\n cache_dir=args.cache_path)\n\n self.args = args\n self.total_len = len(self.dataset)\n\n self.remove_stop_words = args.remove_stop_words\n self.stop_words = stopwords.words('english')\n self.stop_words.extend(['!', ',' ,'.' ,'?'])\n\n self.max_length = args.cutoff_len\n self.tokenizer = tokenizer\n\n self.prefix = '\"'\n self.suffix = ['\", summarize the above passage within eight words: ',\n '\", predict the following passage within eight words: ']\n self.prefix_ids = self.tokenizer(self.prefix, truncation=True, max_length=self.max_length, return_tensors=None)['input_ids']\n self.suffix_ids = self.tokenizer(self.suffix, truncation=True, max_length=self.max_length, return_tensors=None, add_special_tokens=False)['input_ids']\n\n def __len__(self):\n return self.total_len\n\n def __getitem__(self, item):\n prefix = self.prefix\n prefix_ids = self.prefix_ids\n\n inp = self.dataset[item]['input']\n\n suffix_ids_summarize = self.suffix_ids[0]\n suffix_ids_predict = self.suffix_ids[1]\n\n oup_summarize = self.dataset[item]['output_summarize']\n oup_predict = self.dataset[item]['output_predict']\n\n input_ids = self.tokenizer(inp,\n truncation=True,\n max_length=self.max_length - len(prefix_ids) - len(suffix_ids_summarize) - len(suffix_ids_predict),\n padding=False,\n return_tensors=None,\n add_special_tokens=False)\n result = dict()\n result['input_ids'] = prefix_ids + input_ids['input_ids'] + suffix_ids_summarize + suffix_ids_predict\n result['attention_mask'] = [1] * len(result['input_ids'])\n result['labels'] = [-100] * len(prefix_ids) + result['input_ids'][\n len(prefix_ids) : len(result['input_ids']) - len(suffix_ids_summarize) - len(suffix_ids_predict)] + [\n -100] * (len(suffix_ids_summarize) + len(suffix_ids_predict))\n if self.remove_stop_words:\n # oup = re.sub(r'[\\d\\W_]+', ' ', oup)\n oup_summarize = re.sub(r'[\\W_]+', ' ', oup_summarize)\n oup_summarize = ' '.join([word for word in oup_summarize.split() if word.lower() not in self.stop_words])\n\n oup_predict = re.sub(r'[\\W_]+', ' ', oup_predict)\n oup_predict = ' '.join([word for word in oup_predict.split() if word.lower() not in self.stop_words])\n return result, oup_summarize, oup_predict\n\n\n@dataclass\nclass EmbedCollator(DataCollatorForSeq2Seq):\n \"\"\"\n Wrapper that does conversion from List[Tuple[encode_qry, encode_psg]] to List[qry], List[psg]\n and pass batch separately to the actual collator.\n Abstract out data detail for the model.\n \"\"\"\n cutoff_len: int = 512\n\n def __call__(self, features, return_tensors='pt'):\n if return_tensors is None:\n return_tensors = self.return_tensors\n\n inputs = []\n outputs_summarize = []\n outputs_predict = []\n for e in features:\n inputs.append(e[0])\n outputs_summarize.append(e[1])\n outputs_predict.append(e[2])\n\n labels = [feature[\"labels\"] for feature in inputs] if \"labels\" in inputs[0].keys() else None\n # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the\n # same length to return tensors.\n if labels is not None:\n max_label_length = max(len(l) for l in labels)\n if self.pad_to_multiple_of is not None:\n max_label_length = (\n (max_label_length + self.pad_to_multiple_of - 1)\n // self.pad_to_multiple_of\n * self.pad_to_multiple_of\n )\n\n padding_side = self.tokenizer.padding_side\n for feature in inputs:\n remainder = [self.label_pad_token_id] * (max_label_length - len(feature[\"labels\"]))\n if isinstance(feature[\"labels\"], list):\n feature[\"labels\"] = (\n feature[\"labels\"] + remainder if padding_side == \"right\" else remainder + feature[\"labels\"]\n )\n elif padding_side == \"right\":\n feature[\"labels\"] = np.concatenate([feature[\"labels\"], remainder]).astype(np.int64)\n else:\n feature[\"labels\"] = np.concatenate([remainder, feature[\"labels\"]]).astype(np.int64)\n\n inputs = self.tokenizer.pad(\n inputs,\n padding=self.padding,\n max_length=self.cutoff_len,\n pad_to_multiple_of=self.pad_to_multiple_of,\n return_tensors=return_tensors,\n )\n\n # prepare decoder_input_ids\n if (\n labels is not None\n and self.model is not None\n and hasattr(self.model, \"prepare_decoder_input_ids_from_labels\")\n ):\n decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=inputs[\"labels\"])\n inputs[\"decoder_input_ids\"] = decoder_input_ids\n\n outputs_summarize_collated = self.tokenizer(\n outputs_summarize,\n padding=True,\n truncation=True,\n max_length=self.cutoff_len,\n return_tensors=\"pt\",\n )\n\n outputs_predict_collated = self.tokenizer(\n outputs_predict,\n padding=True,\n truncation=True,\n max_length=self.cutoff_len,\n return_tensors=\"pt\",\n )\n\n return {\"input_ids\": inputs['input_ids'],\n \"attention_mask\": inputs['attention_mask'],\n \"labels\": inputs['labels'],\n \"output_summarize_ids\": outputs_summarize_collated['input_ids'],\n \"output_predict_ids\": outputs_predict_collated['input_ids']}" }, { "path": "research/LLARA/pretrain/load_model.py", "content": "\nfrom transformers import AutoConfig, AutoModelForCausalLM\nfrom peft import LoraConfig, TaskType, get_peft_model\nfrom modeling import PreLlamaModel\n\ndef get_model(model_args, use_gradient_checkpointing: bool = False):\n if model_args.config_name:\n config = AutoConfig.from_pretrained(model_args.config_name,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n )\n elif model_args.model_name_or_path:\n config = AutoConfig.from_pretrained(model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n )\n else:\n raise ValueError(\n \"You are instantiating a new config instance from scratch. This is not supported by this script.\"\n )\n if use_gradient_checkpointing:\n config.use_cache = False\n\n if model_args.model_name_or_path:\n model = PreLlamaModel.from_pretrained(\n model_args.model_name_or_path,\n use_flash_attention_2=True if model_args.use_flash_attn else False,\n attn_implementation='sdpa',\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n )\n else:\n print(\"Training new model from scratch\")\n model = model_args.from_config(config)\n\n if model_args.use_lora:\n peft_config = LoraConfig(\n task_type=\"CAUSAL_LM\",\n inference_mode=False,\n r=model_args.lora_rank,\n target_modules=model_args.target_modules,\n lora_alpha=model_args.lora_alpha,\n lora_dropout=model_args.lora_dropout\n )\n model = get_peft_model(model, peft_config)\n model.print_trainable_parameters()\n\n return model" }, { "path": "research/LLARA/pretrain/modeling.py", "content": "import sys\nfrom typing import Optional, List, Union, Tuple\n\nimport torch\nimport copy\nfrom torch import nn\nimport torch.nn.functional as F\nfrom torch.nn import CrossEntropyLoss\nfrom transformers import LlamaForCausalLM, LlamaPreTrainedModel, LlamaConfig, AutoModel\nfrom transformers.modeling_outputs import CausalLMOutputWithPast, BaseModelOutputWithPast\nfrom transformers.models.idefics.modeling_idefics import LLAMA_INPUTS_DOCSTRING, _CONFIG_FOR_DOC\nfrom transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRMSNorm, LlamaModel\nfrom transformers.utils import add_start_docstrings_to_model_forward, replace_return_docstrings, logging\nfrom transformers.cache_utils import Cache, DynamicCache, StaticCache\nfrom transformers.modeling_attn_mask_utils import AttentionMaskConverter\nimport torch.distributed as dist\n\nlogger = logging.get_logger(__name__)\n\nclass NewLlamaModel(LlamaModel):\n add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)\n\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cache_position: Optional[torch.LongTensor] = None,\n ) -> Union[Tuple, BaseModelOutputWithPast]:\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n use_cache = use_cache if use_cache is not None else self.config.use_cache\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if self.config._attn_implementation == \"flash_attention_2\":\n raise ValueError(\n \"You can not use flash attention to pretrain\"\n )\n\n if (input_ids is None) ^ (inputs_embeds is not None):\n raise ValueError(\n \"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one\"\n )\n\n if self.gradient_checkpointing and self.training and use_cache:\n logger.warning_once(\n \"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\"\n )\n use_cache = False\n\n if inputs_embeds is None:\n inputs_embeds = self.embed_tokens(input_ids)\n\n return_legacy_cache = False\n if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)\n return_legacy_cache = True\n past_key_values = DynamicCache.from_legacy_cache(past_key_values)\n\n if cache_position is None:\n past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0\n cache_position = torch.arange(\n past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device\n )\n\n summarize_suffix_ids = [9162, 19138, 675, 278, 2038, 13382, 2629, 9475, 3838, 29901, 29871,\n 32008, 32011, 32004, 32013, 32007, 32005, 32002, 32014]\n predict_suffix_ids = [9162, 8500, 278, 1494, 13382, 2629, 9475, 3838, 29901, 29871, 32000,\n 32009, 32012, 32001, 32010, 32003, 32006, 32015]\n\n if position_ids is None:\n position_ids = cache_position.unsqueeze(0)\n for i in range(len(position_ids)):\n position_ids[i][-len(predict_suffix_ids):] = copy.deepcopy(position_ids[i][\n -len(summarize_suffix_ids) - len(predict_suffix_ids): -len(\n summarize_suffix_ids)])\n\n causal_mask = self._update_causal_mask(\n attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions\n )\n\n causal_mask[:,\n :,\n -len(predict_suffix_ids) :,\n -len(predict_suffix_ids) - len(summarize_suffix_ids): -len(predict_suffix_ids),\n ] = torch.finfo(inputs_embeds.dtype).min\n\n # embed positions\n hidden_states = inputs_embeds\n\n # decoder layers\n all_hidden_states = () if output_hidden_states else None\n all_self_attns = () if output_attentions else None\n next_decoder_cache = None\n\n for decoder_layer in self.layers:\n if output_hidden_states:\n all_hidden_states += (hidden_states,)\n\n if self.gradient_checkpointing and self.training:\n layer_outputs = self._gradient_checkpointing_func(\n decoder_layer.__call__,\n hidden_states,\n causal_mask,\n position_ids,\n past_key_values,\n output_attentions,\n use_cache,\n cache_position,\n )\n else:\n layer_outputs = decoder_layer(\n hidden_states,\n attention_mask=causal_mask,\n position_ids=position_ids,\n past_key_value=past_key_values,\n output_attentions=output_attentions,\n use_cache=use_cache,\n cache_position=cache_position,\n )\n\n hidden_states = layer_outputs[0]\n\n if use_cache:\n next_decoder_cache = layer_outputs[2 if output_attentions else 1]\n\n if output_attentions:\n all_self_attns += (layer_outputs[1],)\n\n hidden_states = self.norm(hidden_states)\n\n # add hidden states from the last decoder layer\n if output_hidden_states:\n all_hidden_states += (hidden_states,)\n\n next_cache = next_decoder_cache if use_cache else None\n if return_legacy_cache:\n next_cache = next_cache.to_legacy_cache()\n\n if not return_dict:\n return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)\n return BaseModelOutputWithPast(\n last_hidden_state=hidden_states,\n past_key_values=next_cache,\n hidden_states=all_hidden_states,\n attentions=all_self_attns,\n )\n\n def _update_causal_mask(\n self,\n attention_mask: torch.Tensor,\n input_tensor: torch.Tensor,\n cache_position: torch.Tensor,\n past_key_values: Cache,\n output_attentions: bool,\n ):\n # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static\n # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.\n # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using\n # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114\n\n if self.config._attn_implementation == \"flash_attention_2\":\n if attention_mask is not None and 0.0 in attention_mask:\n return attention_mask\n return None\n\n # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in\n # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail\n # to infer the attention mask.\n past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0\n using_static_cache = isinstance(past_key_values, StaticCache)\n\n # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward\n # if self.config._attn_implementation == \"sdpa\" and not using_static_cache and not output_attentions:\n # if AttentionMaskConverter._ignore_causal_mask_sdpa(\n # attention_mask,\n # inputs_embeds=input_tensor,\n # past_key_values_length=past_seen_tokens,\n # is_training=self.training,\n # ):\n # return None\n\n dtype, device = input_tensor.dtype, input_tensor.device\n min_dtype = torch.finfo(dtype).min\n sequence_length = input_tensor.shape[1]\n if using_static_cache:\n target_length = past_key_values.get_max_length()\n else:\n target_length = (\n attention_mask.shape[-1]\n if isinstance(attention_mask, torch.Tensor)\n else past_seen_tokens + sequence_length + 1\n )\n\n if attention_mask is not None and attention_mask.dim() == 4:\n # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing\n if attention_mask.max() != 0:\n raise ValueError(\"Custom 4D attention mask should be passed in inverted form with max==0`\")\n causal_mask = attention_mask\n else:\n causal_mask = torch.full(\n (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device\n )\n if sequence_length != 1:\n causal_mask = torch.triu(causal_mask, diagonal=1)\n causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)\n causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)\n if attention_mask is not None:\n causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit\n mask_length = attention_mask.shape[-1]\n padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]\n padding_mask = padding_mask == 0\n causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(\n padding_mask, min_dtype\n )\n if (\n self.config._attn_implementation == \"sdpa\"\n and attention_mask is not None\n and attention_mask.device.type == \"cuda\"\n and not output_attentions\n ):\n # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when\n # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.\n # Details: https://github.com/pytorch/pytorch/issues/110213\n causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)\n\n return causal_mask\n\nclass PreLlamaModel(LlamaForCausalLM):\n def __init__(self, config):\n super().__init__(config)\n self.model = NewLlamaModel(config)\n self.log_softmax = torch.nn.LogSoftmax(dim=1)\n\n \"\"\"\n prompt type1: \"{}\", summarize the above passage within eight words: \n token ids: [376, ..., 9162, 19138, 675, 278, 2038, 13382, 2629, 9475, 3838, 29901, 29871, \n 32008, 32011, 32004, 32013, 32007, 32005, 32002, 32014]\n\n prompt type2: \"{}\", predict the following passage within eight words: \n token ids: [376, ..., 8500, 278, 1494, 13382, 2629, 9475, 3838, 29901, 29871, 32000, 32009, \n 32012, 32001, 32010, 32003, 32006, 32015]\n\n [9162, 19138, 675, 278, 2038, 13382, 2629, 9475, 3838, 29901,\n 29871, 32008, 32011, 32004, 32013, 32007, 32005, 32002, 32014,\n 8500, 278, 1494, 13382, 2629, 9475, 3838, 29901, 29871, 32000,\n 32009, 32012, 32001, 32010, 32003, 32006, 32015]\n\n Maybe only one of them will appear, or both may appear. We consider all possibilities here.\n \"\"\"\n\n self.summarize_prompt_ids = [9162, 19138, 675, 278, 2038, 13382, 2629, 9475, 3838, 29901, 29871,\n 32008, 32011, 32004, 32013, 32007, 32005, 32002, 32014]\n self.predict_prompt_ids = [9162, 8500, 278, 1494, 13382, 2629, 9475, 3838, 29901, 29871, 32000,\n 32009, 32012, 32001, 32010, 32003, 32006, 32015]\n\n @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)\n @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)\n def forward(\n self,\n input_ids: torch.LongTensor = None,\n attention_mask: Optional[torch.Tensor] = None,\n position_ids: Optional[torch.LongTensor] = None,\n past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,\n inputs_embeds: Optional[torch.FloatTensor] = None,\n labels: Optional[torch.LongTensor] = None,\n output_summarize_ids: Optional[torch.LongTensor] = None,\n output_predict_ids: Optional[torch.LongTensor] = None,\n use_cache: Optional[bool] = None,\n output_attentions: Optional[bool] = None,\n output_hidden_states: Optional[bool] = None,\n return_dict: Optional[bool] = None,\n cache_position: Optional[torch.LongTensor] = None,\n ) -> Union[Tuple, CausalLMOutputWithPast]:\n r\"\"\"\n Args:\n labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,\n config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored\n (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.\n\n Returns:\n\n Example:\n\n ```python\n >>> from transformers import AutoTokenizer, LlamaForCausalLM\n\n >>> model = LlamaForCausalLM.from_pretrained(\"meta-llama/Llama-2-7b-hf\")\n >>> tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-hf\")\n\n >>> prompt = \"Hey, are you conscious? Can you talk to me?\"\n >>> inputs = tokenizer(prompt, return_tensors=\"pt\")\n\n >>> # Generate\n >>> generate_ids = model.generate(inputs.input_ids, max_length=30)\n >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n \"Hey, are you conscious? Can you talk to me?\\nI'm not conscious, but I can talk to you.\"\n ```\"\"\"\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\n outputs = self.model(\n input_ids=input_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n past_key_values=past_key_values,\n inputs_embeds=inputs_embeds,\n use_cache=use_cache,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n cache_position=cache_position,\n )\n\n hidden_states = outputs[0]\n if self.config.pretraining_tp > 1:\n lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)\n logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]\n logits = torch.cat(logits, dim=-1)\n else:\n logits = self.lm_head(hidden_states)\n logits = logits.float()\n\n ar_loss = None\n if labels is not None:\n # Shift so that tokens < n predict n\n shift_logits = logits[..., :-1, :].contiguous()\n shift_labels = labels[..., 1:].contiguous()\n # Flatten the tokens\n loss_fct = CrossEntropyLoss()\n shift_logits = shift_logits.view(-1, self.config.vocab_size)\n shift_labels = shift_labels.view(-1)\n # Enable model parallelism\n shift_labels = shift_labels.to(shift_logits.device)\n ar_loss = loss_fct(shift_logits, shift_labels)\n\n \"\"\"\n prompt: \", summarize the above passage within eight words: \", predict the following passage within eight words: \n token ids: [9162, 19138, 675, 278, 2038, 13382, 2629, 9475, 3838, 29901,\n 29871, 32008, 32011, 32004, 32013, 32007, 32005, 32002, 32014,\n 9162, 8500, 278, 1494, 13382, 2629, 9475, 3838, 29901, 29871, \n 32000, 32009, 32012, 32001, 32010, 32003, 32006, 32015]\n token ids[-26:-18] —— \n token ids[-8:] —— \n \"\"\"\n bow_summarize_loss = 0\n if output_summarize_ids is not None:\n special_logits = logits[:, -len(self.predict_prompt_ids) - 8:-len(self.predict_prompt_ids), :]\n special_logits, _ = torch.max(special_logits, dim=1)\n bow_summarize_loss = 0\n possibility = self.log_softmax(special_logits)\n batch_num = 0\n for p, temp_output_ids in zip(possibility, output_summarize_ids):\n unique_useful_ids = torch.unique(temp_output_ids[temp_output_ids > 2])\n if len(unique_useful_ids) > 0:\n bow_summarize_loss -= torch.mean(p[unique_useful_ids])\n batch_num += 1\n if batch_num > 0:\n bow_summarize_loss /= batch_num\n bow_summarize_loss /= 10\n\n bow_predict_loss = 0\n if output_predict_ids is not None:\n special_logits = logits[:, -8:, :]\n special_logits, _ = torch.max(special_logits, dim=1)\n bow_predict_loss = 0\n possibility = self.log_softmax(special_logits)\n batch_num = 0\n for p, temp_output_ids in zip(possibility, output_predict_ids):\n unique_useful_ids = torch.unique(temp_output_ids[temp_output_ids > 2])\n if len(unique_useful_ids) > 0:\n bow_predict_loss -= torch.mean(p[unique_useful_ids])\n batch_num += 1\n if batch_num > 0:\n bow_predict_loss /= batch_num\n bow_predict_loss /= 10\n\n if bow_summarize_loss > 0 and bow_predict_loss > 0:\n bow_loss = (bow_summarize_loss + bow_predict_loss) / 2\n elif bow_summarize_loss > 0:\n bow_loss = bow_summarize_loss\n elif bow_predict_loss > 0:\n bow_loss = bow_predict_loss\n else:\n bow_loss = None\n\n if ar_loss is not None and bow_loss is not None:\n loss = ar_loss + bow_loss\n elif ar_loss is None:\n loss = bow_loss\n else:\n loss = ar_loss\n\n if not return_dict:\n output = (logits,) + outputs[1:]\n return (loss,) + output if loss is not None else output\n\n return CausalLMOutputWithPast(\n loss=loss,\n logits=logits,\n past_key_values=outputs.past_key_values,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\nclass PreModel(nn.Module):\n def __init__(self,\n model: AutoModel = None,\n ):\n super().__init__()\n self.model = model\n\n def gradient_checkpointing_enable(self, **kwargs):\n self.model.gradient_checkpointing_enable(**kwargs)\n\n def enable_input_require_grads(self, **kwargs):\n self.model.enable_input_require_grads(**kwargs)\n\n def forward(self, *args, **kwargs):\n return self.model(*args, **kwargs)\n\n def save(self, output_dir: str):\n state_dict = self.model.state_dict()\n state_dict = type(state_dict)(\n {k: v.clone().cpu()\n for k,\n v in state_dict.items()})\n self.model.save_pretrained(output_dir, state_dict=state_dict)" }, { "path": "research/LLARA/pretrain/run.py", "content": "import logging\nimport os\nimport sys\nfrom pathlib import Path\n\nimport torch\nimport transformers\n\nfrom transformers import AutoTokenizer, HfArgumentParser, set_seed, AutoConfig, Trainer\n\nfrom arguments import ModelArguments, DataArguments, \\\n PretrainTrainingArguments as TrainingArguments\nfrom data import TrainDatasetForEmbedding, EmbedCollator\nfrom load_model import get_model\nfrom modeling import PreModel\nfrom trainer import PreTrainer\n\nlogger = logging.getLogger(__name__)\n\n\ndef main():\n parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n model_args: ModelArguments\n data_args: DataArguments\n training_args: TrainingArguments\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n logger.info(\"Training/evaluation parameters %s\", training_args)\n logger.info(\"Model parameters %s\", model_args)\n logger.info(\"Data parameters %s\", data_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n num_labels = 1\n\n config = AutoConfig.from_pretrained(\n model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n num_labels=num_labels,\n cache_dir=model_args.cache_dir,\n )\n logger.info('Config: %s', config)\n\n model = get_model(model_args, training_args.gradient_checkpointing)\n\n tokenizer = AutoTokenizer.from_pretrained(\n # model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n model_args.model_name_or_path,\n token=model_args.token,\n cache_dir=model_args.cache_dir,\n use_fast=False\n )\n\n if tokenizer.pad_token is None:\n tokenizer.pad_token = tokenizer.unk_token\n # special_tokens_dict = {'additional_special_tokens': ['', '', '', '',\n # '', '', '', '',\n # '', '', '', '',\n # '', '', '', '', ]}\n # tokenizer.add_special_tokens(special_tokens_dict)\n tokenizer.padding_side = \"left\" # Allow batched inference\n print(tokenizer)\n\n special_tokens = ['', '', '', '',\n '', '', '', '',\n '', '', '', '',\n '', '', '', '', ]\n current_vocab = tokenizer.get_vocab()\n tokens_to_add = [token for token in special_tokens if token not in current_vocab]\n if tokens_to_add:\n special_tokens_dict = {'additional_special_tokens': tokens_to_add}\n tokenizer.add_special_tokens(special_tokens_dict)\n model.resize_token_embeddings(len(tokenizer))\n print(tokenizer)\n\n if training_args.gradient_checkpointing:\n model.enable_input_require_grads()\n\n train_dataset = TrainDatasetForEmbedding(tokenizer, args=data_args)\n\n trainer = Trainer(\n model=model,\n train_dataset=train_dataset,\n args=training_args,\n data_collator=EmbedCollator(\n tokenizer=tokenizer,\n cutoff_len=data_args.cutoff_len,\n pad_to_multiple_of=8,\n return_tensors=\"pt\",\n padding=True\n )\n )\n\n Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)\n\n if trainer.is_world_process_zero():\n tokenizer.save_pretrained(training_args.output_dir)\n\n trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)\n trainer.save_model()\n if training_args.deepspeed:\n trainer.deepspeed.save_checkpoint(training_args.output_dir)\n\n print(\"\\n If there's a warning about missing keys above, please disregard :)\")\n\n\nif __name__ == \"__main__\":\n main()" }, { "path": "research/LLARA/pretrain/trainer.py", "content": "from transformers.trainer import *\n\nclass PreTrainer(Trainer):\n def _save(self, output_dir: Optional[str] = None, state_dict=None):\n output_dir = output_dir if output_dir is not None else self.args.output_dir\n os.makedirs(output_dir, exist_ok=True)\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n # Save a trained model and configuration using `save_pretrained()`.\n # They can then be reloaded using `from_pretrained()`\n if not hasattr(self.model, 'save'):\n raise NotImplementedError(\n f'MODEL {self.model.__class__.__name__} '\n f'does not support save interface')\n else:\n self.model.save(output_dir)\n # if self.tokenizer is not None and self.is_world_process_zero():\n # self.tokenizer.save_pretrained(output_dir)\n\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\n\n def compute_loss(self, model, inputs, return_outputs=False):\n \"\"\"\n How the loss is computed by Trainer. By default, all models return the loss in the first element.\n\n Subclass and override for custom behavior.\n \"\"\"\n\n outputs = model(**inputs)\n loss = outputs.loss\n\n return (loss, outputs) if return_outputs else loss" }, { "path": "research/LLARA/stage1.json", "content": "{\n \"zero_optimization\": {\n \"stage\": 1,\n \"reduce_bucket_size\": 5e8\n },\n\n \"fp16\": {\n \"enabled\": \"auto\",\n \"loss_scale\": 0,\n \"initial_scale_power\": 10,\n \"loss_scale_window\": 1000,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n \"bf16\": {\n \"enabled\": \"auto\",\n \"loss_scale\": 0,\n \"initial_scale_power\": 10,\n \"loss_scale_window\": 1000,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n \"optimizer\": {\n \"type\": \"AdamW\",\n \"params\": {\n \"lr\": \"auto\",\n \"betas\": \"auto\",\n \"eps\": \"auto\",\n \"weight_decay\": \"auto\",\n \"torch_adam\": true\n }\n },\n\n \"scheduler\": {\n \"type\": \"WarmupDecayLR\",\n \"params\": {\n \"warmup_min_lr\": \"auto\",\n \"warmup_max_lr\": \"auto\",\n \"warmup_num_steps\": \"auto\",\n \"total_num_steps\": \"auto\"\n }\n },\n\n \"gradient_accumulation_steps\": \"auto\",\n \"gradient_clipping\": \"auto\",\n \"steps_per_print\": 1000,\n \"train_batch_size\": \"auto\",\n \"train_micro_batch_size_per_gpu\": \"auto\",\n \"wall_clock_breakdown\": false\n}" }, { "path": "research/LM_Cocktail/LM_Cocktail/__init__.py", "content": "from .cocktail import mix_models, mix_models_with_data, mix_models_by_layers\n" }, { "path": "research/LM_Cocktail/LM_Cocktail/cocktail.py", "content": "import os\nimport shutil\n\nimport torch\nimport random\nimport numpy as np\nfrom typing import List, Dict, Any\n\nfrom accelerate import init_empty_weights, load_checkpoint_and_dispatch\nfrom transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, AutoModel\nfrom sentence_transformers import SentenceTransformer, models\nfrom transformers import pipeline\n\nfrom .utils import load_model, get_model_param_list, merge_param, compute_weights, get_model_param_dirs, merge_param_by_layer\n\n\ndef save_ckpt_for_sentence_transformers(ckpt_dir, pooling_mode: str = 'cls', normalized: bool = True):\n word_embedding_model = models.Transformer(ckpt_dir)\n pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),\n pooling_mode=pooling_mode)\n if normalized:\n normalized_layer = models.Normalize()\n model = SentenceTransformer(modules=[word_embedding_model, pooling_model, normalized_layer],\n device='cpu')\n else:\n model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device='cpu')\n model.save(ckpt_dir)\n\n\ndef mix_models(model_names_or_paths: List[str], \n model_type: str, \n weights: List[float], \n output_path: str=None):\n \"\"\"_summary_\n mix models based on given weights\n Args:\n model_names_or_paths (List[str]): a list of names or paths to models\n model_type (str): type of model to mix, should be in [\"decoder\", \"encoder\", \"reranker\"]\n weights (List[float]): a list of mixing weights. The sum of weights should be equal to 1.\n output_path (str, optional): path to save the mixed model. Defaults to None.\n\n Returns:\n new model\n \"\"\"\n \n assert len(model_names_or_paths) == len(weights)\n assert model_type in ['decoder', 'encoder', 'reranker']\n assert sum(weights) - 1 <= 1e-3\n \n param_list = get_model_param_list(model_names_or_paths, model_type=model_type)\n new_param = merge_param(param_list, weights=weights)\n \n print(\"***weight for each model***: \")\n for w, n in zip(weights, model_names_or_paths):\n print(n, w)\n \n model = load_model(model_names_or_paths[0], model_type=model_type)\n model.load_state_dict(new_param)\n \n if output_path is not None:\n print(f\"Saving the new model to {output_path}\")\n model.save_pretrained(output_path)\n tokenizer = AutoTokenizer.from_pretrained(model_names_or_paths[0], trust_remote_code=True)\n tokenizer.save_pretrained(output_path)\n \n if model_type == \"encoder\":\n print(f\"Transform the model to the format of 'sentence_transformers' (pooling_method='cls', normalized=True)\")\n save_ckpt_for_sentence_transformers(ckpt_dir=output_path)\n return model\n\n\ndef mix_models_with_data(model_names_or_paths: List[str], \n model_type: str, \n example_data: List[Dict], \n temperature: float=5.0,\n batch_size:int=2, \n max_input_length:int=2048, \n neg_number: int=7,\n output_path: str=None):\n \"\"\"_summary_\n mix model based on given a few examples\n Args:\n model_names_or_paths (List[str]): a list of names or paths to models\n model_type (str): type of model to mix, should be in [\"decoder\", \"encoder\"]\n example_data (List[Any]): a list of examples\n temperature (float, optional): temperature can impact the distribution of weights . Defaults to 3.0.\n batch_size (int, optional): batch size to compute loss. Defaults to 2.\n max_input_length (int, optional): max number of input tokens for model. Defaults to 2048.\n neg_number (int, optional): the number of negatives when compute contrastive loss for embedding model. Defaults to 7.\n output_path (str, optional): path to save the mixed model. Defaults to None.\n\n Returns:\n new model\n \"\"\"\n \n assert model_type in ['decoder', 'encoder', 'encoder-decoder']\n \n model = load_model(model_names_or_paths[0], model_type=model_type)\n tokenizer = AutoTokenizer.from_pretrained(model_names_or_paths[0], trust_remote_code=True)\n param_list = get_model_param_list(model_names_or_paths, model_type=model_type)\n \n weights = compute_weights(model, tokenizer=tokenizer, param_list=param_list, model_type=model_type, \n example_data=example_data, temperature=temperature, neg_number=neg_number,\n batch_size=batch_size, max_input_length=max_input_length)\n \n print(\"***weight for each model***: \")\n for w, n in zip(weights, model_names_or_paths):\n print(n, w)\n \n new_param = merge_param(param_list, weights=weights) \n model.load_state_dict(new_param)\n \n if output_path is not None:\n print(f\"Saving the new model to {output_path}\")\n model.save_pretrained(output_path)\n tokenizer.save_pretrained(output_path)\n\n if model_type == \"encoder\":\n print(f\"Transform the model to the format of 'sentence_transformers' (pooling_method='cls', normalized=True)\")\n save_ckpt_for_sentence_transformers(ckpt_dir=output_path)\n \n return model\n\n\ndef mix_models_by_layers(model_names_or_paths: List[str], \n model_type: str, \n weights: List[float], \n output_path: str=None):\n \"\"\"_summary_\n mix models based on given weights, and load them layer by layer\n Args:\n model_names_or_paths (List[str]): a list of names or paths to models\n model_type (str): type of model to mix, should be in [\"decoder\", \"encoder\", \"reranker\"]\n weights (List[float]): a list of mixing weights. The sum of weights should be equal to 1.\n output_path (str, optional): path to save the mixed model. Defaults to None.\n\n Returns:\n new model\n \"\"\"\n \n assert len(model_names_or_paths) == len(weights)\n assert model_type in ['decoder', 'encoder', 'reranker']\n assert sum(weights) - 1 <= 1e-3\n\n param_dirs, temp_dir = get_model_param_dirs(model_names_or_paths, model_type=model_type)\n temp_file_path = merge_param_by_layer(param_dirs, weights=weights)\n \n print(\"***weight for each model***: \")\n for w, n in zip(weights, model_names_or_paths):\n print(n, w)\n\n with init_empty_weights():\n if model_type == 'decoder':\n meta_model = AutoModelForCausalLM.from_pretrained(model_names_or_paths[0], trust_remote_code=True)\n elif model_type == 'encoder':\n meta_model = AutoModel.from_pretrained(model_names_or_paths[0], trust_remote_code=True)\n elif model_type == 'reranker':\n model = AutoModelForSequenceClassification.from_pretrained(model_names_or_paths[0], trust_remote_code=True)\n else:\n raise NotImplementedError(f\"not support this model_type: {model_type}\")\n\n device_map = {name: \"cpu\" for name, _ in meta_model.named_modules()}\n model = load_checkpoint_and_dispatch(meta_model, checkpoint=temp_file_path, device_map=device_map)\n model.tie_weights()\n\n os.remove(temp_file_path)\n shutil.rmtree(temp_dir)\n print(f\"Remove temporary file: {temp_file_path}\")\n print(f\"Remove temporary directory: {temp_dir}\")\n\n if output_path is not None:\n print(f\"Saving the new model to {output_path}\")\n model.save_pretrained(output_path)\n tokenizer = AutoTokenizer.from_pretrained(model_names_or_paths[0])\n tokenizer.save_pretrained(output_path)\n \n if model_type == \"encoder\":\n print(f\"Transform the model to the format of 'sentence_transformers' (pooling_method='cls', normalized=True)\")\n save_ckpt_for_sentence_transformers(ckpt_dir=output_path)\n return model\n" }, { "path": "research/LM_Cocktail/LM_Cocktail/utils.py", "content": "import os\nimport gc\nimport tempfile\n\nimport torch\nimport random\nimport numpy as np\nfrom tqdm import tqdm\nfrom typing import List, Dict, Any\n\nfrom transformers import AutoModelForCausalLM, AutoModel, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, is_torch_npu_available\n\n\ndef load_llm(model_name:str, trust_remote_code:bool):\n model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=trust_remote_code, device_map = {\"\": \"cpu\"})\n return model\n\n\ndef load_embedder(model_name:str, trust_remote_code:bool):\n model = AutoModel.from_pretrained(model_name, trust_remote_code=trust_remote_code, device_map = {\"\": \"cpu\"})\n return model\n\n\ndef load_reranker(model_name:str, trust_remote_code:bool):\n model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=trust_remote_code, device_map = {\"\": \"cpu\"})\n return model\n\n\ndef load_seq2seq_model(model_name:str, trust_remote_code:bool):\n model = AutoModelForSeq2SeqLM.from_pretrained(model_name, trust_remote_code=trust_remote_code)\n return model\n\n\ndef load_model(model_name:str, model_type:str, trust_remote_code:bool=True):\n if model_type == 'decoder':\n model = load_llm(model_name, trust_remote_code=trust_remote_code)\n elif model_type == 'encoder':\n model = load_embedder(model_name, trust_remote_code=trust_remote_code)\n elif model_type == 'reranker':\n model = load_reranker(model_name, trust_remote_code=trust_remote_code)\n elif model_type == 'encoder-decoder': \n model = load_seq2seq_model(model_name, trust_remote_code=trust_remote_code)\n else:\n raise NotImplementedError(f\"not support this model_type: {model_type}\")\n return model\n\n\ndef get_model_param_list(model_names: List[str], model_type:str):\n model_param_list = []\n for name in model_names:\n print(f\"loading {name} -----------------\")\n model = load_model(name, model_type=model_type)\n model_param_list.append(model.state_dict())\n return model_param_list\n\n\ndef merge_param(model_param_list: List[Dict], weights: List[float]):\n new_param = {}\n for k in model_param_list[0].keys():\n for w, param in zip(weights, model_param_list):\n if param[k].dtype == torch.int64 or param[k].dtype == torch.int32:\n new_param[k] = param[k]\n elif k not in new_param:\n new_param[k] = w * param[k]\n else:\n new_param[k] += w * param[k]\n return new_param\n\n\ndef get_model_param_dirs(model_names: List[str], model_type:str):\n param_dirs = []\n temp_dir = tempfile.mkdtemp()\n print(f\"create a temporary directory: {temp_dir}\")\n\n for idx, name in enumerate(model_names):\n print(f\"loading {name} -----------------\")\n model = load_model(name, model_type=model_type)\n model_params = model.state_dict()\n\n model_temp_dir = os.path.join(temp_dir, f\"model_{idx+1}\")\n os.makedirs(model_temp_dir, exist_ok=True)\n param_dirs.append(model_temp_dir)\n\n for k, v in model_params.items():\n temp_param_file = os.path.join(model_temp_dir, f\"{k}.ckpt\")\n torch.save(v, temp_param_file)\n\n model = model.to(\"meta\")\n del model_params\n gc.collect()\n\n return param_dirs, temp_dir\n\n\ndef merge_param_by_layer(model_param_dirs: List[str], weights: List[float]):\n new_param = {}\n model_params = os.listdir(model_param_dirs[0])\n\n for param_file in tqdm(model_params, desc=\"Merging models\"):\n param_name = param_file.replace(\".ckpt\", \"\")\n\n for w, model_dir in tqdm(zip(weights, model_param_dirs), total=len(weights), desc=f\"Processing {param_name}\", leave=False): \n file_path = os.path.join(model_dir, param_file)\n param = torch.load(file_path)\n\n if param.dtype in [torch.int64, torch.int32]:\n new_param[param_name] = param\n elif param_name not in new_param:\n new_param[param_name] = w * param\n else:\n new_param[param_name] += w * param\n\n del param\n gc.collect()\n\n with tempfile.NamedTemporaryFile(delete=False, suffix=\".ckpt\") as tmp_file:\n print(f\"create a temporary file to store mixed weights: {tmp_file.name}\")\n torch.save(new_param, tmp_file.name)\n temp_file_path = tmp_file.name\n\n del new_param\n gc.collect()\n\n return temp_file_path\n\n\ndef compute_weights(base_model, tokenizer, param_list: List[Dict], model_type: str, example_data: List[Any], temperature: float=5.0, batch_size:int=2, max_input_length:int=2048, neg_number:int=7):\n if torch.cuda.is_available():\n device = torch.device(\"cuda\")\n elif is_torch_npu_available():\n device = torch.device(\"npu\")\n else:\n device = torch.device(\"cpu\")\n base_model = base_model.to(device)\n \n if model_type == 'decoder':\n input_data = preprocess_data_for_llm(example_data=example_data, tokenizer=tokenizer, device=device, batch_size=batch_size, max_input_length=max_input_length)\n loss_func = llm_loss\n elif model_type == 'encoder':\n input_data = preprocess_data_for_embedder(example_data=example_data, tokenizer=tokenizer, device=device, batch_size=batch_size, max_input_length=max_input_length, neg_number=neg_number)\n loss_func = embedder_loss\n elif model_type == 'encoder-decoder': \n input_data = preprocess_data_for_seq2seq(example_data=example_data, tokenizer=tokenizer, device=device, batch_size=batch_size, max_input_length=max_input_length)\n loss_func = seq2seq_loss\n\n example_loss = [] \n with torch.no_grad():\n for params in param_list:\n base_model.load_state_dict(params)\n loss = loss_func(base_model=base_model, input_data=input_data)\n example_loss.append(loss)\n \n weights = torch.softmax(-torch.FloatTensor(example_loss)/temperature, -1).numpy().tolist()\n return weights\n\n\n\ndef preprocess_data_for_seq2seq(example_data, tokenizer, device, batch_size:int=2, max_input_length:int=512): # Added Reimer\n batch_data = []\n for i in range(0, len(example_data), batch_size):\n batch_examples = example_data[i:i+batch_size]\n input_texts = [ex['input'] for ex in batch_examples]\n target_texts = [ex['output'] for ex in batch_examples]\n\n input_encodings = tokenizer(input_texts, text_target=target_texts, max_length=max_input_length, padding=True, truncation=True, return_tensors=\"pt\")\n\n input_ids = input_encodings.input_ids.to(device)\n attention_mask = input_encodings.attention_mask.to(device)\n labels = input_encodings.labels.to(device)\n\n labels[labels == tokenizer.pad_token_id] = -100\n batch_data.append({\n \"input_ids\": input_ids, \n \"attention_mask\": attention_mask,\n \"labels\": labels\n })\n return batch_data\n\n\n\ndef preprocess_data_for_embedder(example_data, tokenizer, device, batch_size:int=64, max_input_length:int=512, neg_number:int=7):\n input_data = []\n quries = []\n passages = []\n # max_input_length = min(512, max_input_length)\n for e in example_data:\n quries.append(e['query'])\n passages.append(e['pos'][0])\n passages.extend(random.sample(e['neg'], neg_number))\n\n if len(quries) == batch_size:\n q_tokens = tokenizer(quries, padding=True, truncation=True, max_length=max_input_length, return_tensors=\"pt\")\n p_tokens = tokenizer(passages, padding=True, truncation=True, max_length=max_input_length, return_tensors=\"pt\")\n q_tokens, p_tokens = q_tokens.to(device), p_tokens.to(device)\n input_data.append([q_tokens, p_tokens])\n quries, passages = [], []\n\n if len(quries) > 0:\n q_tokens = tokenizer(quries, padding=True, truncation=True, max_length=max_input_length, return_tensors=\"pt\")\n p_tokens = tokenizer(passages, padding=True, truncation=True, max_length=max_input_length, return_tensors=\"pt\")\n q_tokens, p_tokens = q_tokens.to(device), p_tokens.to(device)\n input_data.append([q_tokens, p_tokens])\n \n return input_data\n\n\ndef seq2seq_loss(base_model, input_data):\n total_loss = 0\n with torch.no_grad():\n for batch in input_data:\n outputs = base_model(input_ids=batch[\"input_ids\"], \n attention_mask=batch[\"attention_mask\"], \n labels=batch[\"labels\"])\n total_loss += outputs.loss.cpu()\n average_loss = total_loss / len(input_data)\n return float(average_loss)\n\n\ndef embedder_loss(base_model, input_data):\n def generate_embeddings(model, inputs):\n embeddings = model(**inputs, return_dict=True).last_hidden_state[:, 0]\n embeddings = torch.nn.functional.normalize(embeddings, dim=-1) \n return embeddings\n\n with torch.no_grad():\n loss = 0\n for q_inputs, p_inputs in input_data:\n q_embeddings = generate_embeddings(base_model, q_inputs)\n p_embeddings = generate_embeddings(base_model, p_inputs)\n scores = torch.matmul(q_embeddings, p_embeddings.transpose(0, 1)) / 0.05\n target = torch.arange(scores.size(0), device=scores.device, dtype=torch.long)\n target = target * (p_embeddings.size(0) // q_embeddings.size(0))\n batch_loss = torch.nn.CrossEntropyLoss(reduction='mean')(scores, target)\n loss += batch_loss.cpu()\n loss = float(loss / len(input_data))\n return float(loss)\n\n\ndef preprocess_data_for_llm(example_data, tokenizer, device, batch_size:int=2, max_input_length:int=2048):\n batch_input_ids = []\n batch_labels = []\n batch_max_length = max_input_length\n for data in example_data:\n input, output = data['input'], data['output']\n \n input_ids = tokenizer.encode(input+' '+output)\n input_ids.append(tokenizer.eos_token_id)\n \n prompt_ids = tokenizer.encode(input)\n labels = [-100]*len(prompt_ids) + input_ids[len(prompt_ids):]\n \n input_ids = input_ids[:batch_max_length]\n input_ids += [tokenizer.pad_token_id] * (batch_max_length - len(input_ids))\n batch_input_ids.append(input_ids)\n \n labels = labels[:batch_max_length]\n labels += [-100] * (batch_max_length - len(labels))\n batch_labels.append(labels)\n \n batch_input_ids = torch.LongTensor(batch_input_ids).to(device)\n batch_labels = torch.LongTensor(batch_labels).to(device)\n attention_mask = batch_input_ids.ne(tokenizer.pad_token_id).to(device)\n \n batch_data = []\n for i in range(0, len(batch_input_ids), batch_size):\n batch_data.append(dict(\n input_ids=batch_input_ids[i:i+batch_size],\n labels=batch_labels[i:i+batch_size],\n attention_mask=attention_mask[i:i+batch_size],\n ))\n return batch_data\n\n\n\ndef llm_loss(base_model, input_data):\n loss = 0\n with torch.no_grad():\n for data in input_data:\n output = base_model(**data)\n loss += output.loss.cpu()\n loss = float(loss / len(input_data))\n return loss\n\n\n\n\n" }, { "path": "research/LM_Cocktail/README.md", "content": "\n\n\n**Make fine-tuning of language models akin to crafting a nuanced cocktail.**\n\nModel merging can be used to improve the performance of single model. \nWe find this method is also useful for large language models and dense embedding model, \nand design the LM-Cocktail strategy which automatically merges fine-tuned models and base model using a simple function to compute merging weights.\nLM-Cocktail can be used to improve the performance on target domain without decrease \nthe general capabilities beyond target domain.\nIt also can be used to generate a model for new tasks without fine-tuning.\nFor more details please refer to our report: [LM-Cocktail](https://arxiv.org/abs/2311.13534).\n\n## Application\n\nThe following are some application scenarios (Note that the models used to merge need to have the same architecture and the same initialization parameter):\n\n### 1. Mitigate the problem of Catastrophic Forgetting\nFine-tuning the base language model could lead to severe degeneration of model’s general capabilities beyond the targeted domain. \nBy mixing the fine-tuned model and the base model (use function `mix_models`), LM-Cocktail can significantly enhance performance in downstream task\nwhile maintaining performance in other unrelated tasks. \n\nIf there are some available models fine-tuned on other tasks, you can further use them to enhance your fine-tuned model.\nFirstly, you need to collect five example data from your task, then employ function `mix_models_with_data` to compute weights and merge available models.\nIn this way, it can assign lower weights to low-quality models, avoiding degrading the performance on your task.\nFinally, use `mix_models` to merge produced model and your fine-tuned model.\n\n\n### 2. Improve the performance of new task without fine-tuning\nLM-Cocktail can improve the accuracy of the new task without a requisition to fine-tune a model.\nGive a few examples data (e.g., five examples),\nand some available models (from open-source community or pre-existing for other tasks), \nfunction `mix_models_wit_data` can automatically assign different merging weights for different model\nbased their loss in example data, and then merge these available models to generate a task-specific new model.\n\n\n### 3. Approximate multitask learning\n\nIf you have some models who are fine-tune on different tasks, you can merge them into one model to approximate multitask learning.\nThe merged model can be used to perform multiple tasks.\n\n\n## Usage\n\nInstall the latest version from source (Recommended): \n```bash\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding/research/LM_Cocktail\npip install -e .\n```\nInstall by pip:\n```bash\npip install -U LM_Cocktail\n```\n\nThere are two key functions in LM-Cocktail:\n\n### 1. Mix models\n\n`mix_models` can merge models based on the given merging weights.\nAn example is merging the fine-tuned model and \nthe base model to mitigate Catastrophic Forgetting after fine-tuning:\n\n```python\nfrom LM_Cocktail import mix_models, mix_models_with_data\n\n# mix LLMs and save it to output_path: ./mixed_model_1\nmodel = mix_models(\n model_names_or_paths=[\"meta-llama/Llama-2-7b-chat-hf\", \"Shitao/llama2-ag-news\"], \n model_type='decoder', \n weights=[0.7, 0.3], \n output_path='./mixed_llm')\n# you can select a weight for your models to get a trade-off between generality and expertise.\n\n# Mix Embedding Models\nmodel = mix_models(\n model_names_or_paths=[\"BAAI/bge-base-en-v1.5\", \"Shitao/bge-hotpotqa\"], \n model_type='encoder', \n weights=[0.5, 0.5],\n output_path='./mixed_embedder')\n\n# Mix reranker Models\nmodel = mix_models(\n model_names_or_paths=[\"BAAI/bge-reranker-base\", \"BAAI/bge-reranker-base\"], \n model_type='reranker', \n weights=[0.5, 0.5],\n output_path=\"./mixed_reranker\")\n```\nNote that the sum of weights should be equal to 1.\n\nYou also can merge multiple models:\n```python\nfrom LM_Cocktail import mix_models, mix_models_with_data\n\nmodel = mix_models(\n model_names_or_paths=[\"BAAI/bge-base-en-v1.5\", \"Shitao/bge-hotpotqa\", \"Shitao/bge-quora\", \"Shitao/bge-msmarco\"], \n model_type='encoder', \n weights=[0.3, 0.2, 0.2, 0.3],\n output_path='./mixed_embedder_2')\n# The sum of weights should be equal to 1.\n```\n\n\n### 2. Mix models with weights computed based on a few examples\n\n`mix_models_with_data` can compute merging weights based on given data and merge models.\nIt can be used to produce a model for a new task without training, \nor boost the performance for the downstream task by leveraging the knowledge in others models.\n\n- For LLMs\n\nThe format of `example_data` for LLMs is a list, where each item is a dict like:\n```\n{\"input\": str, \"output\": str}\n```\nLM-cocktial will compute the loss of the output. \n\nYou can use the example data to merge models as following:\n\n```python\nfrom LM_Cocktail import mix_models, mix_models_with_data\n\nexample_data = [\n {\"input\": \"Question: when was the last time anyone was on the moon? Answer:\\n\", \"output\": \"14 December 1972 UTC\"},\n {\"input\": \"Review: \\\"it 's a charming and often affecting journey . \\\" Is this movie review sentence negative or positive?\\n\", \"output\": \"Positive\"}\n]\n\nmodel = mix_models_with_data(\n model_names_or_paths=[\"meta-llama/Llama-2-7b-chat-hf\", \"Shitao/llama2-ag-news\", \"Shitao/llama2-nq\"], \n model_type='decoder', \n example_data=example_data, \n temperature=5.0)\n# you can set the temperature argument to adjust the distribution of mixing weights\n```\n\n\n- For Embedder\n\nThe format of `example_data` for LLMs is a list, where each item is a dict like:\n```\n{\"query\": str, \"pos\": List[str], 'neg': List[str]}\n```\nwhere pos is a list of positive text and neg is a list of negative text. LM-Cocktail will compute the contrastive loss. \n\nYou can use the example data to merge models as following:\n```python\nfrom LM_Cocktail import mix_models, mix_models_with_data\n\nexample_data = [\n {\"query\": \"How does one become an actor in the Telugu Film Industry?\", \"pos\": [\" How do I become an actor in Telugu film industry?\"], \"neg\": [\" What is the story of Moses and Ramesses?\", \" Does caste system affect economic growth of India?\"]}, \n {\"query\": \"Why do some computer programmers develop amazing software or new concepts, while some are stuck with basic programming work?\", \"pos\": [\" Why do some computer programmers develops amazing softwares or new concepts, while some are stuck with basics programming works?\"], \"neg\": [\" When visiting a friend, do you ever think about what would happen if you did something wildly inappropriate like punch them or destroy their furniture?\", \" What is the difference between a compliment and flirting?\"]}\n]\n\nmodel = mix_models_with_data(\n model_names_or_paths=[\"BAAI/bge-base-en-v1.5\", \"Shitao/bge-hotpotqa\", \"Shitao/bge-quora\"], \n model_type='encoder', \n example_data=example_data,\n temperature=5.0,\n max_input_length=512,\n neg_number=2)\n```\n\n### 3. Mix models layer by layer for reducing memory cost\nThe function `mix_models_by_layers` creates temporary directories to store weights of individual models and then merges them layer by layer.\n\nThis approach helps in reducing the memory consumption.\n\nOnce the merging process is completed, the temporary directories and files will be automatically removed.\n\n\n```python\nfrom LM_Cocktail import mix_models_by_layers\n\n# Mix Large Language Models (LLMs) and save the combined model to the path: ./mixed_llm\nmodel = mix_models_by_layers(\n model_names_or_paths=[\"meta-llama/Llama-2-7b-chat-hf\", \"Shitao/llama2-ag-news\"], \n model_type='decoder', \n weights=[0.7, 0.3], \n output_path='./mixed_llm')\n```\n\n\n\n## Performance\nDetailed results please refer to our report: [LM-Cocktail](https://arxiv.org/abs/2311.13534)\n\n- LM-Cocktail for Catastrophic Forgetting\n\n| Model | Target Task | Others(29 tasks) | \n|:---------------------------|:--------:|:----------------:|\n| Llama | 40.8 | 46.8 |\n| Fine-tuned | 94.4 | 38.6 |\n| LM-Cocktail(2 models) [1] | 94.5 | 47.7 |\n| LM-Cocktail(10 models) [2] | 94.4 | 48.3 |\n\n[1]: merge 2 models: fine-tuned model and the base model\n\n[2]: merge 10 models based on five examples: fine-tuned model, the base model, and 8 models fine-tuned on other tasks\n\n| Model | Target Task | Other Tasks(14 tasks) | \n|:-------------------------------|:--------:|:---------------------:|\n| BGE | 71.8 | 49.8 |\n| Fine-tuned | 76.0 | 48.5 |\n| LM-Cocktail(2 models) | 74.8 | 50.0 |\n| LM-Cocktail(10 models) | 74.7 | 50.6 |\n\n\n- LM-Cocktail for new tasks without fine-tuning\n\nMerge 10 models fine-tuned on other tasks based on five examples for new tasks: \n\n| Model | MMLU(57 tasks) |\n|:-------------------------------|:--------------:|\n| Llama | 45.9 | \n| Llama-5shot | 46.7 | \n| LM-Cocktail(10 models) | 48.0 |\n\n\n| Model | Retrieval(12 tasks) |\n|:-------------------------------|:-------------------:|\n| BGE | 47.3 | \n| LM-Cocktail(10 models) | 48.8 |\n\n\n\n\n\n## Evaluation\n\n### 1. Reproduce the results of LLM\n\n- Models: we fine-tune the [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on 9 tasks, and you can find the fine-tuned models at this [link](https://huggingface.co/Shitao). Note that the most of fine-tuned models has a poor performance on other unrelated tasks. \n- Examples Data for dataset from FLAN: [./llm_examples.json]()\n- MMLU dataset: https://huggingface.co/datasets/cais/mmlu (use the example in dev set to do in-context learning) \n\nYou can use these models and our code to produce a new model and evaluate its performance using the [llm-embedder script](https://github.com/FlagOpen/FlagEmbedding/blob/master/research/llm_embedder/docs/evaluation.md) as following: \n```\n# for 30 tasks from FLAN\ntorchrun --nproc_per_node 8 -m evaluation.eval_icl \\\n--retrieval_method no \\\n--few_shot 0 \\\n--data_root /data/llm-embedder \\\n--model_name_or_path ./mixed_model_1\n\n# for MMLU datasets\ntorchrun --nproc_per_node 8 -m evaluation.eval_mmlu \\\n--retrieval_method no \\\n--few_shot 0 \\\n--data_root /data/llm-embedder \\\n--model_name_or_path ./mixed_model_2\n```\n\n\n### 2. Reproduce the results of Embedding Model\n\n- Models: we fine-tune the [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on 9 tasks, and you can find the fine-tuned models at this [link](https://huggingface.co/Shitao).\n- Examples Data: [./embedder_examples.json]()\n \n\nUse [MTEB script](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) to evaluate the mixed embedding model:\n```bash\npython eval_MTEB.py --model_name_or_path mixed_model --task_type Retrieval\n```\n\n## Acknowledgement\nThis project is inspired by previous researches on model merging, including [LoraHub](https://github.com/sail-sg/lorahub), [model soups](https://github.com/mlfoundations/model-soups), and [PAINT](https://github.com/mlfoundations/patching) .\n\nThe Llama is fine-tuned using the [FastChat](https://github.com/lm-sys/FastChat) scripts. \nFine-tuning datasets are from [sentence-transformers/embedding-training-data](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) and [intfloat/llm-retriever-tasks](https://huggingface.co/datasets/intfloat/llm-retriever-tasks).\n\n\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{cocktail,\n title={LM-Cocktail: Resilient Tuning of Language Models via Model Merging}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Xingrun Xing},\n year={2023},\n eprint={2311.13534},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n" }, { "path": "research/LM_Cocktail/embedder_examples.json", "content": "{\"ArguAna\": [{\"query\": \"In the absence of positive evidence for the existence of God the rational position is agnosticism, not atheism: In a situation where there is an absence of either positive evidence for a claim or definite negative evidence for it, the natural response is not rejection of the claim, but rather skepticism and admission of lack of knowledge one way or the other. [1] In the case of religion and God, this position is agnosticism. Humans are fallible organisms, and thus all statements about truth and about the Universe must be qualified by some degree of doubt. Positively rejecting the existence of God, as atheism does, ignores this requisite doubt even though it cannot prove that there is no God. Rather, in the absence of evidence for or against the existence of God, the most the atheist can say honestly is that he does not know. The claims of atheism are positive ones and thus require evidence; an atheist position is thus faith-based in the same way a theist one is. [1] Hume, David. 1748. An Enquiry Concerning Human Understanding. New York: Oxford University Press (2008).\", \"pos\": [\"y epistemology religion church faith religion general god morality secularism The rational position in the absence of positive evidence about God is not agnosticism, but atheism. While there is always a degree of doubt in every statement, this does not mean that negative claims about an entities existence can never be made. One can rationally state that fairies do not exist, even if there is no positive evidence for their non-existence. The very fact that no evidence exists for the existence of fairies, in the same way there is no evidence for the existence of God, is evidence of the negative. Thus, in the evidence of positive evidence for God, the rational default position is atheism.\"], \"neg\": [\" The pursuit of pain for the purpose of achieving pleasure is an immoral act Not only does the state have the right and obligation to uphold the morals of society and stop deviant behavior, but it also has an obligation to prevent escalation of deviance. Acts such as sadomasochism are good indicators of the propensity for escalation to further deviant acts. With the passing of the Anti-Social Behaviour Act 2003 [i] in the UK, a legal precedent has been established where the government has the right and obligation to tackle minor deviant behavior as it can be a precursor to larger and more harmful deviance in the future. Even if S&M was \\u201cvictim-less\\u201d, it demonstrates a propensity to inflict pain to gain pleasure and thus indicates high risk for developing a craving for infliction of pain of higher magnitude and scope in the future, which could be even more damaging to society. [i] Anti-social Behaviour Act 2003.\\\" legislation.gov.uk. The National Archives, n.d. Web. 20 Jun 2011.\", \"e international africa house would provide access microfinance unbanked One of the key benefits highlighted about Oxfam\\u2019s Saving for Change Initiative is the empowerment provided for women. Women are argued to be more independent, able to organise within communities, and provided with a voice of power. However, are women empowered? In the cases of microfinance in Cameroon, Mayoux (2001) highlights the inequalities operating within community groups. The message is we cannot rely on communities, and social capital, for empowerment as women within such communities have different relations to power. The ability for women to use savings and credit for self-empowerment is limited by wider, traditional, gender inequalities. Microfinance may act to reinforce unequal power relations and positions within society. Furthermore, women\\u2019s empowerment needs to be understood as complex. [1] Real, and strategic, empowerment for women goes beyond increased access to economic resources. So how can microfinance ensure true empowerment? [1] See further readings: Sutton-Brown, 2013.\", \" Foreign policy should follow the will of the people Sanctions are not the will of the American people but of a small minority of embittered Cuban Americans in Florida who are being pandered to due to their importance in elections in a swing state. [1] Congressman Charles Rangel argues that the only success of the sanctions policy has been to \\u201cappease the Republican constituency in Florida\\u201d. [2] National opinion generally expresses no preference or opposes the ban, in a 2009 CBS poll asking \\\"Do you think the United States should or should not re-establish diplomatic and trade relations with Cuba?\\\" 67% said should. [3] Sanctions remaining in place is electioneering government at its worst, domestic interest groups controlling government foreign policy. As Karl Rove has admitted \\\"When people mention Cuba to me, it makes me think of three things: Florida, Florida, and Florida.\\\" [4] [1] Griswold, Daniel, \\u2018Four Decades of Failure: The U.S. Embargo against Cuba\\u2019, 2005. [2] DeYoung, Karen, \\u2018Sanctions Against Cuba Are Excessive, GAO Says\\u2019, 2007. [3] Pollingreport.com, \\u2018Cuba\\u2019. [4] Rosenthal, Joel H., \\u2018The Cuba Wars: Fidel Castro, the United States and the Next Revolution\\u2019, 2009.\", \"y epistemology religion church faith religion general god morality secularism If there is a benevolent deity, then there should not be the kinds of evil observable in the world and He would likely show more interest in His creation than He appears to have done so far: If God, or the gods, were good there would be no evil in the world. Disasters would not kill millions of innocents, disease and hunger would not claim the lives of children every day, war and genocide would not slaughter people indiscriminately as they have done for countless bloody millennia. The world is awash with blood, pain, and suffering. No loving God would make a world so imperfect and troubled. [1] The world\\u2019s ills are perfectly explained by the natural, amoral development of the Universe, of life, and of humanity. The reality of the Universe, however, is incompatible with a God of goodness, as He is conventionally described by today\\u2019s predominant religions, which stem from the Abrahamic tradition. [1] Tooley, Michael. 2009. \\u201cThe Problem of Evil\\u201d. Stanford Encyclopaedia of Philosophy. Available:\", \"onal americas politics government house wants line item veto amendment A President would be able to abuse the power given to them in a line-item veto authority, leveraging it into undue influence over other elements of the legislative process. By threatening to veto items dear to particular Congressmen, they could obtain assent to bills, treaties and appointments that otherwise would not be forthcoming. Such intimidation would be subtle and hard to prove, but it would erode checks on the executive and fundamentally alter the balance of power within the constitution. This means that budgets are politicised even more than is currently the case. When the line item veto was previously used by Clinton republicans such as Rick Santorum argued that every decision \\\"has political overtones, but that's fine, it comes with the territory,\\\" Senator Ted Stevens went further \\\"We're dealing with a raw abuse of political power by a president who doesn't have to run again\\\".1 1 Hugliotta, Guy and Pianin, Eric, 'Line-Item Veto Tips Traditional Balance of Power', Washington Post, 24/10/97,accessed 5/5/11\", \" Now Damaging Gender Roles? There is certainly a case to be made that women, in modern-western society have completely shattered the traditional values and roles that are best suited to them. For example, it has always been the case that men have been the providers, the defenders of themselves, the household and the family. Women have been the maintainers of these things. These things are not unfair. They are not unequal. They are simply what each gender is best suited for. Women should not feel lesser than men simply because they are \\\"supposed\\\" to do \\\"motherly things\\\". The feminist movement has gone beyond its cause in beginning to deem what role in life is more appropriate.\", \" Profiling is racist: Profiling in many ways would simply result in institutionalized racism, as Mark German argues: \\u201cracial profiling is wrong, un-American and unconstitutional. It is institutionalized racism.\\u201d [1] Mark Thompson adds: \\u201cSo it\\u2019s not 'political correctness' (aka the Equal Protection clause of the 14th Amendment) that is standing in the way of replacing full-body scans with a strong and effective profiling system: its reality. All that 'political correctness' is preventing is the implementation of an equally (and likely even more) ineffective piece of security theater in which we single out one minority group for intensive screening while giving a pass to everyone else. This would certainly annoy fewer people, but it wouldn\\u2019t make us safer and its sole benefits would be accomplished by treating an entire minority group as second-class citizens.\\\" [2] In any legal system which claims to give its citizens equal rights or equal protection of the law, security profiling is unacceptable. Profiling will target certain groups more than others. Even innocent members of these groups are made to feel like second-class citizens, and that the government suspects them of being terrorists without evidence \\u2013 simply because of who they are. These individuals will be very visibly reminded of this every time they are segregated out at airport security, while they watch other non-suspects (who will be predominantly white and Christian, or at least non-Muslim) not being subject to the same scrutiny. The non-suspects will see this as well, and this may re-enforce any notions they have that all Muslims are potential terrorists and thus are suspect. Therefore because security profiling harms certain groups of citizens in unacceptable ways, it should not be instituted. [1] German, Michael. \\\"Wrong and Unworkable\\\". New York Times Room for Debate. 4 January 2010. [2] Thompson, Mark. \\\"Profiling, Political Correctness, and Airport Security.\\\" The League of Ordinary Gentleman. 29 November 2010.\", \" Participatory Democracy Produces Better Decisions Participatory democracy will lead to better decisions because laws will only be passed if they can be justified to the people. Professional politicians are disproportionately drawn from the privileged classes and are often ignorant of the effects their policies will have on ordinary people \\u2013 as are the civil servants who advise them. Moreover, professional politicians are susceptible to corruption, lobbying or bullying by powerful vested interests seeking to direct government policy away from the general interest represented by the vast majority of the individual citizens, who generally lack such a determinant influence over the decision-making. Participatory democracy will therefore make sure that the legislation that is passed will help the people as much as possible; for example they will limit unecessary bureaucracy and make sure that policies are fair. Thus for example Switzerland has passed with 68% of the vote in a referendum a proposal that prevents big payouts for managers known as \\u2018golden handshakes\\u2019 and \\u2018golden parachutes\\u2019 and shareholders will have a veto over saleries. [1] [1] Willsher, K., and Inman, P. (3 March 2013) \\u201cVoters in Swiss referendum backs curbs on executives\\u2019 pay and bonuses\\u201d The Guardian.\"]}, {\"query\": \"Client-Attorney Privilege is already qualified appropriately In exceptional circumstances, solicitors are told that they may depart from the rule of confidentiality contained in Rule 4 of the Solicitors' Code of conduct. Note 9 states that there are some regulatory bodies that are entitled to be informed of apparently confidential client communications. [1] In cases of suspected money laundering, solicitors have a duty under the Money Laundering Regulations 2007 [2] to inform relevant bodies of any suspected money laundering or any handling of the proceeds of crime. This means that there is flexibility in the rule of client confidentiality and client-attorney privilege which allows for justice to take its course in serious circumstances. [1] Rule 4: Confidentiality and disclosure, Solicitors\\u2019 Code of Conduct 2007, accessed 18/05/11 [2] The Money Laundering Regulations 2007, legislation.gov.uk, No2157, 2007,\", \"pos\": [\"law general house believes attorney client privilege should be abolished The circumstances under which Note 9 allows such a break in the rule of client-attorney privilege is for the HM Revenue and other bodies that act for the benefit of the Government. It is rather archaic that a principle such as that of attorney-client privilege is loosened only for bodies that act for the benefit of the Government. This does not show that attorney-Client privilege is necessary but that it is not. If the Government is willing to do away with it for their monetary benefit, why can we not do away with it in the interest of justice for society? There should be a system that encourages the adversarial system, and attorney/client privilege but yet allows a variety of circumstances to override this principle, such as public interest and public security. These principles are often used to justify potential Human Rights breaches, so we should also be able to use them to justify the breach of attorney/client privilege.\"], \"neg\": [\"eneral politics politics general house would limit right bear arms Guns don\\u2019t kill people \\u2013 people kill people. Restricting gun ownership will do nothing to make society safer as it is the intent of the criminal we should fear, and that will remain the same whatever the gun laws. In the vast majority of crimes involving firearms, the gun used is not legally held or registered. Many of illegal weapons are imported secretly from abroad, or converted from replica firearms rather than being stolen from registered owners.\", \"eneral politics politics general house would limit right bear arms Gun ownership increases the risk of suicide There is a correlation between the laxity of a country\\u2019s gun laws and its suicide rate \\u2013 not because gun owners are more depressive, but because the means of quick and effective suicide is easily to hand. As many unsuccessful suicides are later glad that they failed in their attempt, the state should discourage and restrict the ownership of something that wastes so many human lives.\", \" After years of detention and separation from the battle field and terrorist networks, many Guantanamo detainees have no more value to US intelligence gathering efforts and national security, and so this is not a reason to continue their detention. Moreover, there are tens of thousands of anti-American terrorists around the world. Releasing a handful of the 250 detainees that are actually terrorists but that can't be tried in the US would be a drop in the bucket for terrorism and the war on terror.\", \"living difference house would ban music containing lyrics glorify It is usually the task of movie classification organisations such as the MPAA and the British Board of Film Certification to judge whether the content of a film should be cut or altered. In most cases these groups will be politically independent, but may be politically appointed. They will make the decision to cut content based partly on the criteria described above. A movie will only be censored if it contains shocking or offensive images used in a way that suggests that violence is glamorous, entertaining or without consequences. There is a broad consensus in western liberal democracies on what constitutes a highly shocking or offensive image. For example, in even the most permissive societies, open and public images of sexual intercourse would be considered problematic. Similarly, graphic depictions of violence against vulnerable individuals would be open to wide condemnation. The thing that unifies each of these categories of image is that they can be easily understood and interpreted by the majority of people. Even a casual observer can understand that pornography is pornography. This is part of the reason why some states try to control extreme images \\u2013 because they are both powerful and emotive, and easy to produce, display and distribute. However, music and lyrics are different from images. Language contains a degree of abstraction, depth and nuance that only the most unconventional (and non-commercial) film could replicate. This is problematic, because it is much harder for censors and members of the general public to agree on an exact definition of an offensive statement or form of words. Complex legal processes are used to determine whether or not offensive statements are sufficiently offensive to be classed as hate crimes. Even more complex are the legal procedures used to determine when an individual\\u2019s reputation has been damaged by allegations published in books or periodicals. It will be much harder for ratings or certification boards to decide when a particular song is violent or offensive due to the range of meanings and ambiguities that are built into language. For example, the verse \\u201cGot a temper nigga, go ahead, lose your head/ turn your back on me, get clapped and lose your legs/ I walk around gun on my waist, chip on my shoulder/ \\u2018til I bust a clip in your face, pussy, this beef ain\\u2019t over,\\u201d can either be seen as a series of boastful threats, delivered directly by the musician, but it could also be reported speech \\u2013 a lot of hip hop music is based on narratives or performer\\u2019s accounts of past events. It could also be intended to invite condemnation of the behaviour of the character that the speaker has assumed. Hip hop artists frequently use alternative personas and \\u201ccasts\\u201d of characters to add depth to the narrative dimension of their tracks. Under these circumstances, the process of classifying and censoring potentially violent lyrics is likely to become laborious. More important than the expense that this process will entail is the possibility that the chilling effect of a prolonged classification process will cause music publishers to stop promoting hip hop, metal and other genres linked with violent imagery. Lack of funds will curtail innovation and diversity in these genres.\", \" Could be cheaper While budget should not be the primary concern of the justice system, The death penalty, when applied properly, can be cheaper. A lethal injection, or a few bullets, costs far less than keeping a person incarcerated for a long time, especially if they need long term health or other care in old age. The longer someone is in jail the greater the cost to the state. The costs of the implementation of the death penalty are driven up by anti-death penalty activists using the appeals system. Since the death penalty would only be applied to the worst of the worst when there is absolute moral certainty there would be less need for extensive appeals because there would be less marginal cases.\", \"onal europe politics defence leadership house favours common eu foreign policy Consultation, collaboration and the attempted creation of a common set of values has not worked and is not likely to work. This language is not much different from what we have heard with every attempt the EU has made to push for further political integration. The role of the Common Foreign and Security Policy (CFSP), as agreed upon back in 1993 during the Maastricht Treaty, was in fact presented very much along similar lines. Fifteen years later however, that united front has not been created. If anything, the EU\\u2019s political union, and certain any attempts towards a common foreign policy, have completely disintegrated when faced with the War in Iraq and the larger war on terror and more recently the Euro debt crisis on another front.\", \" Nuclear power is potentially extremely unsafe It is unfortunately the case that the nuclear industry has a bad reputation for safety. This is undeserved. The overwhelming majority of nuclear reactors have functioned safely and effectively for their entire lifetimes. The four historic nuclear disasters (1957 Windscale Fire, 1979 Three Mile Island and 1986 Chernobyl, 2011 Fukushima, Japan) killed fewer people than the oil and coal industries have1. \\\"The multi-agency U.N. Chernobyl Forum reported last year that 56 deaths could be directly attributed to the accident, most of those from radiation or burns suffered while fighting the fire. Tragic as those deaths were, they pale in comparison to the more than 5,000 coal-mining deaths that occur worldwide every year\\\"2. Further, the two major nuclear accidents, at Three Mile Island and Chernobyl, were both in old style reactors, made worse in the latter case by poor Soviet safety standards. The Chernobyl disaster took place at a time when our understanding of nuclear issues was much lesser than it is now, and was the result of poorly trained staff in the plant's control room. Power stations today are better staffed, better maintained and better understood, and because the effects of an attack upon them are acknowledged, they are better defended and monitored by the armed services. No system can be 100% safe, but solid design principles can minimize risk. Perhaps the best guarantee of safety standards in the nuclear industry is the increasing transparency with which the industry is presenting itself. Many of the problems in its early days were caused by excessive control due to the origin of nuclear energy from military applications. As the gap between the two separates so the nuclear industry becomes more accountable. The question is, is the slight risk of a nuclear accident a worse danger than the inevitable climate catastrophe that awaits us? 1 'Risks of Nuclear Power' by Bernard Cohen, University of Pittsburgh, 2Patrick Moore, a prominent environmentalist and founding member of Greenpeace, \\\"Going Nuclear A Green Makes the Case\\\", Washington Post, 4/16/06\\\"\", \" The Opposition is perfectly happy to be attacked for making life easier for people with disabilities by taking down barriers that separate them from the wider population. There a parts of any community that prefer to do things in a certain way, however governments rarely commit to guaranteeing all preferences, instead they guarantee a basic level of service provision and then offer choice where possible and affordable. This is true in education and welfare right through to national defense \\u2013 militaries, except the US, tend to specialise and rely on allies for other operations.\"]}, {\"query\": \"A federal Europe will ensure that large, multinational businesses remain accountable for their actions In a globalised economy, there is a need to tame multinational corporations, which would be otherwise capable of playing national governments off against each other in search for low wages, social costs and state protection. A federal Europe would be powerful enough to demand high standards of behaviour from such companies, because only a powerful and economically significant player can dictate restricting conditions. This would ensure fair wages, safe working conditions and - additionally - Europe would be able to force the multinational companies to implement correct and holistic policies and would also be in a position to make a greater difference on environmental issues such as global warming. Sovereignty becomes less relevant when effective independence is lost anyway as the economy and the problems faced by all nations are increasingly globalised.\", \"pos\": [\"europe house believes federal europe The assumptions about the multinational corporations are not actually proved. National governments close deals with such corporations if both sides have interest in it. Even if we assume such a thing existed nowadays \\u2013 in a federal Europe the same problem would occur only not with countries, rather with regions. That is because every region would want the company to create more business in its area so we will end up with the assumed status quo today. The EU today is already strong enough in regards to implementing environmental policies and restrictions \\u2013 the carbon tax, the cap and trade system. Dealing with the international issue of global warming is not a point of a federal Europe or the EU, but a completely different matter.\"], \"neg\": [\" Paying housewives for their work is an important form of economic empowerment. One of the most important factors of oppression of women\\u2019s rights, particularly in the developing world, is dependence [1] . Women are often confined to the home by force, lack of opportunity or social stigma, on behalf of their husbands. When she is not paid, a housewife must rely on her husband for money, especially if she has children she is expected to take care of. Economic empowerment allows further freedom for women in countries where women are confined to the home [2] . By making women economic actors, you empower them to engage in different social structures and hold a stake and position in the centres of economic power. This is the most empowering tool one can offer women in most countries around the world [3] . By paying housewives for their work, you offer one of the most powerful forms of social empowerment for women around the world. [1] United Nations. Women's Work and Economic Empowerment. Accessed July 1, 2011. . [2] United Nations. Women's Work and Economic Empowerment. Accessed July 1, 2011. . [3] United Nations. Women's Work and Economic Empowerment. Accessed July 1, 2011. .\", \" A DNA database would reduce the time spent tracking down suspects A DNA database is not intended to replace conventional criminal investigation. The database ought to identify the potential suspects, each of whom can then be investigated by more conventional means. During 2008/09 in the United Kingdom, 'almost 6 in 10 crime scene profiles loaded to the National DNA Database were matched to a subject profile'1. There is no possibility of escaping the provision of technical evidence before a court. Doctors, ballistics experts, forensic scientists are already a common feature of the large criminal trial. The jury system is actually a bastion against conviction on account of complicated scientific facts. The British jury is instructed to acquit a defendant where they find reasonable doubt. If the genetic data and associated evidence is insufficiently conclusive, or presented without sufficient clarity, the jury is obliged to find the defendant not guilty. 1 NDNAD. (2009). National DNA Database: Annual Report 2007-09. Retrieved May 19, 2011, from\", \" Dress Codes instead of school uniform Rather than having school uniform, why not have a dress code instead? This has all the benefits of uniform without the many disadvantages. While uniforms force all children to wear the same clothes, dress codes give students a lot of choice what to wear. Only a few unsuitable things are banned - for example, gang colors, very short skirts, crop tops, bare shoulders, etc\", \" Collaboration in editing does not encourage democratic principles, but merely privileges the loudest voice, or in this case, the most regular user. As such, creating knowledge by consensus is inherently flawed. A fact is not true simply because lots of people think so. Traditional encyclopaedias are written and edited by academics and professional experts, whose reputation is put on the line by the articles they produce. They have the credentials and expertise that give them the authority to write without requiring widespread communal feedback. However, anyone can write a Wikipedia article, regardless of how much or how little knowledge he or she has of the subject. Worse yet, because contributors are effectively anonymous, it is impossible to assess the quality of an article on an unfamiliar topic by assessing the credentials of those who have produced it. Collaboration, therefore, becomes a barrier to the provision of reliable, accurate and up-to-date information.\", \" Because religion combines dogmatic certainty with the existence of the afterlife, violence and death is all too easy to justify Particularly in the case of contemporary Islam, although other historical examples could be referred to, the combination of certainty and the promise of life after death is a sure route towards violence. That said, Catholics and Protestants in Northern Ireland demonstrated this until recently; the Yugoslav wars between Catholics, Orthodox and Muslims, both sides of the battle for Israel/Palestine and many others in history could also be thrown into the mix. Allowing people the opportunity to claim that \\u201cGod\\u2019s on our side\\u201d can be used to justify anything, especially when He appears to be fighting on both sides.\", \" Intellectual women migrants outnumber intellectual men migrants The need of belonging is greater for women than for men \\u2013 Bardo and Bardo found that they miss home much more (5). On the other hand, unequal and discriminatory norms can be strong drivers of intellectual female migration (1). More young women than men now migrate for education and, in several European countries today, highly skilled migrant women outnumber highly skilled migrant men (1). Between 2000 and 2011, the number of tertiary-educated migrant women in OECD countries rose by 80%, which exceeded the 60% increase in the number of tertiary-educated migrant men. In Africa for example, the average emigration rates of tertiary-educated women are considerably higher than those of tertiary-educated men (27.7% for women and 17.1% for men).\", \" Alcoholism is a disease, if the story was that the president had measles, it wouldn\\u2019t have got a mention. Let\\u2019s take an historical example of the \\u2018well-being of the head or state\\u2019 in \\u2018democracies around the world\\u2019. A majority of US citizens were unaware that FDR was wheel chair bound \\u2013 even after his death. [i] The fact the Churchill hit the bottle early in the morning was never mentioned to voters in the UK, even at their \\u201cdarkest hour\\u201d, and still remains a matter of debate. [ii] The French have long ignored the streams of mistresses wandering in and out of the \\u00c9lys\\u00e9e Palace throughout the history of the Fifth Republic. [iii] All of these things were well known by the journalists of their time but there was no need for the story to be revealed. The allegation of the opposition was that Calder\\u00f3n was a drunk, this then became a suggestion that he was an alcoholic \\u2013 they\\u2019re different things. This rather suggests that now research at all was undertaken into the allegation but that a slur was repeated as though it were news. Because of popular confusion between the two, it was repeated, presumably, because it was salacious. Hardly the highest standards of journalism [iv] . [i] Anderson, Stacy, \\u2018FDR made 'tacit agreement' with public about disability\\u2019, The University Record Online [ii] Richards, Michael, \\u2018Alcohol Abuser\\u2019, The Churchill Centre and Museum at the Churchill War Rooms, London, 19 January 2009 [iii] Rocco, Fiammetta, \\u2018Widows in weeds, mourning mistresses - plus ca change to the French\\u2019, The Independent, 14 January 1996 [iv] UK National Union of Journalists (NUJ) Code of Conduct. The NUJ code is widely seen by British journalists as the final word on journalistic ethics. It is also widely ignored in practice.\", \" Ethno-religious divides are a bigger security threat Poverty is clearly an immense problem for Africa but it is not primarily a security problem. There are parts of the globe such as South Asia and parts of South East Asia that have comparable poverty but little conflict and violence. Moreover not every African country is plagued with conflict. We therefore must look elsewhere for why Africa has high levels of conflict. Religious and Ethnic divisions are a much more direct security threat and cause for conflicts. To start with, it is extremely easy to blame people of other ethnicity or religion of your own problems. This occurs throughout the world, no matter if we are talking about immigrants coming into the EU and US, about the Kurdish population in Turkey or about Israel and Palestine. Africa has 3315 ethnic groups, a huge number (1). Unlike Europe these have not been formed into cohesive nations with colonial borders often arbitrarily cutting through ethnic groups. A conflict is 25 percent longer and has a has a higher casualty rate when an ethnicity is divided by a national border. Examples of divided (and conflicted) groups are the Maasai of Kenya and Tanzania, and the Anyi of Ghana and the Ivory Coast. (2) Division also occurs between religions. Samuel P Huntington wrote a famous book \\u2018The Clash of Civilisations\\u2019 that highlights that conflict is often created between religions. In Africa this means conflict in a swathe of northern Africa where Islam and Christianity meet. For example, the Muslim terrorist organization called Boko Haram, which has a lot of support in Nigeria, is engaged in a massive against Christians which has been responsible for the deaths of hundreds of non-Muslims.(2) (1) Wentzel, Dr. John, \\u2018Who are the developing world\\u2019, johnwentzel.com, 28 February 2013, (2) Gilman, Azure, \\u2018The Violent Legacy of Africa\\u2019s Arbitrary Borders\\u2019, Freakonomics, 12 January 2011, (3) Stark, William, \\u201cBoko Haram's Anti-Christian Violence Continues in Northern Nigeria\\u201d, Religion Today, 13 September 2013,\"]}, {\"query\": \"People will die if we don\\u2019t do animal testing Every year, 23 new drugs are introduced in the UK alone.[13] Almost all will be tested on animals. A new drug will be used for a long time. Think of all the people saved by the use of penicillin. If drugs cost more to test, that means drug companies will develop less. This means more people suffering and dying\", \"pos\": [\"animals science science general ban animal testing junior Many of these drugs are \\u201cme too\\u201d drugs \\u2013 ones with a slight change that doesn\\u2019t make much difference to an existing drug. [14] So often the benefits from animal testing are marginal, and even if there was a slight increase in human suffering, it would be worth it based on the animal suffering saved.\"], \"neg\": [\"economic policy society immigration house believes developing nations should While factually true for developed nations, this point completely disregards the reality of developing nations. Most of the labour that is available is unskilled, whether it is in the rural or urban communities. There is little reason to believe that the poor will automatically be able to gain better education should they move to the city. The harm caused by letting migrants flood the cities to lead a miserable life greatly outweighs that of having one or two too intelligent farmers who miss out on their calling.\", \" Bribery is only wrong under a Western-centric notion of corruption Norms and values differ between countries. In many non-western societies gift taking and giving in the public realm is a matter of traditions and customs. Moreover, gift giving is a part of negotiations and relationship building in some parts of the world. It is hypocritical for the west to target developing countries for this as many so-called democracies are hopelessly compromised by business interests through political funding and lobbying. The United States Foreign Corrupt Practices Act bans large bribes but allows for the payment of small \\u2018customary\\u2019 sums in order to ease transactions. [1] [1] The Economist, \\u2018When a bribe is merely facilitating business\\u2019 June 11th 2011,\", \" How this would work This policy involves an active disclosure campaign, through websites and the newspapers, where a sex offender has their name, their photo, their address and the nature of their crime published on a website, or in the local media. It may include poster campaigns about individuals for particularly serious crimes, with the aim of both informing people and causing shame. It may be sensible to allow the police to not disclose the information in the following circumstances; 1) where a significant risk of vigilantism exists, 2) where it is against the wishes of the victim, and 3) where it may jeopardize an ongoing criminal investigation. Early studies showed that Megan's law in the United States had high rates of voluntary compliance, between 70 and 80% and rising, proving that the policy is practical1. 1 Simpson, Rachel, ''Megan's law' and other forms of sex-offender registration', NSW Parliamentary Library Research Service, Briefing Paper NBo. 22/99, November 1999,\", \" Keeping funds from government has negative consequences for spending Let us not forget that in most of the cases when we talk about oil revenues, we are talking about very large sums of money, which can have an immense impact on the budget. In countries where oil already contributes to the budget any change could be immensely disruptive to the government\\u2019s ability to deliver services. If we take Venezuela as an example oil revenues account for 25% of GDP (1), with government expenditure of 50% of GDP (2) any drop in oil revenues would have an immense impact upon social policies such as education, health and welfare. For those where the funding would be new that country would be foregoing a potentially transformative sum of money that could help to eliminate poverty or provide universal healthcare and education. Such a drop in funds flowing into the government would also have a huge impact on politics; politicians would block the implementation of a proposal that takes away so much revenue. If it did happen the independent fund would simply get criticism heaped on it as an excuse for why services can\\u2019t be improved. (1) Annual Statistical Bulletin 2013, \\u2018Venezuela facts and figures\\u2019, OPEC, 2013, (2) 2013 Index of Economic Freedom, \\u2018Venezuela\\u2019, Heritage Foundation, 2013,\", \" Allowing grey goods breaks down monopolies and passes on lower prices to consumers. Allowing grey imports means that manufacturers do not concentrate economic power in a monopolistic way which can be damaging to free trade (even Adam Smith1believed certain monopolies were antithetical to free trade). Banning them is tantamount to granting a licensed monopoly or cartel on a country-by-country basis, which inevitably means higher prices for consumers. As manufacturing has increasingly been relocated into a smaller number of offshore countries, rather than in the country of purchase, it makes sense that other parts of the supply chain should make a similar move so that they too can realise the efficiency benefits of a globalised economy. 1 Smith, Adam, \\\"An Inquiry into the Nature and Causes of the Wealth of Nations\\\" 1776\", \" Pursuance by the ICC doesn't actually result in punishment of the leader; empirically, it has actually strengthened criminals' power after criticizing them. Nations, such as African nations like Chad, have painted the actions of the ICC as signs of Western imperialism and domination. Sudan's Bashir, accused of genocide and other crimes against humanity, used the ICC's arrest warrant against him as a sign of heroism and created a rally-around-the-flag effect, further strengthening his regime. Moreover, the ICC's work encourages leaders to cling to their power rather than give it and face prosecution, making punishment even more difficult. At worst, the ICC is actually counterproductive when it comes to punishing leaders and giving them retribution; at best, it is simply an ineffective court.1 1 \\\"The International Criminal Court: Why Africa Still Needs it.\\\" The Economist, 3 June 2010.\", \" Prevents the marginalisation of non-believers The inclusion of the words \\u201cunder God\\u201d in the Pledge of Allegiance implies that there is no place for atheism in American patriotism and that non-believers have nothing to give to their country. The removal of these words would create a more inclusive America that accepts that everyone, including all non-Christians and non-believers, have something to give to their country. (Buckner 2002)\", \" The US has a right to expect that its taxpayers' money is spent responsibly. The United States has made a significant investment in the institution. Not only was it a founder, but it plays host to the body in New York and makes the largest contribution of any nation each year. \\\"The debate over whether the United Nations will continue to overcharge American taxpayers is over \\u2014 and the U.S. wound up on the losing end. In a dramatic turnaround from steady declines since 2001, the percentage that the U.S. will be charged for U.N. peacekeeping has been sharply increased for the next three years, and U.S. taxpayers will end up paying roughly $100 million more each year than they would have if the 2009 assessment rate had been maintained.\\u201d [1] This is not acting responsibly in a time where Americans are feeling the pinch from the economic downturn. American taxpayers recognize that their society faces a great many problems that could be addressed with the dollars that are annually spent on the UN. While Americans are generally supportive of the institution, they have a right to know that their investment is used appropriately and pays dividends in good policy. [1] Schaefer, Brent. \\u201cU.N. Dues: Obama Lets American Taxpayers Down\\u201d 6/01/2010 .\"]}, {\"query\": \"The apparent loss of liberty is overstated. Negative cases of security abuse are few and have been greatly exaggerated by an emphatic civil rights lobby that has no empathy for the victims of terrorism. Of course, with any wide-scale attempt to fight terrorism there are bound to be a few cases of abuse of security measures. For example in the UK terrorism suspects were originally detained without charge under the Anti-Terrorism, Crime and Security Act however the detention was declared unlawful by the law lords in 2005 so the government introduced new scaled back policies such as \\u2018control orders\\u2019. [1] Therefore government has always been willing to scale back its security legislation when the courts believe it goes too far. Nonetheless it is not a good idea to shut down all security measures under a pretext that they violate rights [2] . The majority of the measures are intended to safeguard those civil liberties instead of abusing them. [1] Hewitt, Steve, THE BRITISH WAR ON TERROR TIMELINE, Libertas, 2007, , accessed 9 September 2011 [2] Stratton, Allegra and Wintour, Patrick, \\u2018Nick Clegg goes to war with Labour over civil liberties\\u2019, guardian.co.uk, 13 April 2010, , accessed 9 September 2011\", \"pos\": [\"political philosophy house believes civil liberties should be sacrificed If there is even a slight injustice, then there is a problem worth addressing. It is a fact that recent anti-terrorism legislation, in nearly all western countries, has been used for a variety of uses from international banking [1] to petty thievery. This is obviously beyond the original intentions of these measures; something that should not be taken lightly. [1] Wintour, Patrick, and Gillan, Audrey, \\u2018Lost in Iceland: \\u00a31billion from councils, charities and police\\u2019, 10 October 2008, , accessed 9 September 2011\"], \"neg\": [\" Unhealthy food is cheaper A reason why people eat unhealthy foods is that it\\u2019s often cheaper and easier than cooking something with fresh ingredients. Studies have shown that not only is junk food cheaper, its costs are less likely to increase due to inflation [14]. This was confirmed by research in Australia that showed that while healthy food became more expensive, junk food got cheaper [15]. Obesity is more common amongst poorer people. Because junk food is so cheap, it is eaten more. The best way to change this consumption pattern is to tax unhealthy food so that the healthy option is also the cheaper option.\", \" It certainly doesn\\u2019t prove the point, it does however highlight one. As a result of religious teaching the majority of people have, at different points in history, been certain that; The Earth was flat, The Earth, or even a particular point on the earth, was the centre of the universe, The Earth is less than six thousand years old, Certain races were not human [i] Women were created inferior to men If ever evidence were needed that the majority are frequently and alarmingly wrong, then religion provides it in abundance. [i] For example the Christian concept of Polygenism \\u2013 the notion that the white races were descended from Adam and others not \\u2013 has had several outings during history. Among other things it has been used to justify slavery, apartheid and imperialism.\", \" Regardless of what Puerto Ricans may or may not \\u201cdeserve\\u201d, the fact is that Puerto Ricans have rejected statehood many times now, making their voices heard on this issue many times since the late 1960's. The island has repeatedly voted to remain a commonwealth when votes were taken in 1967, 1993, and 1998. [1] If Puerto Ricans actually like their current status enough to vote for it when presented with the alternatives of statehood or independence, where is the injustice in that status continuing? [1] United States Council for Puerto Rico Statehood. \\u201cStatehood Issues\\u201d. United States Council for Puerto Rico Statehood. 2004.\", \" The actions by Columbia and Sri Lanka do not alter the fact that, as noted earlier, the recruitment of child soldiers in Africa and elsewhere is still endemic in 2013. And while the Lord\\u2019s Resistance Army and its leader Joseph Kony have indeed been muted, that is largely due to the initiative of the U.S. government which has itself refused to ratify the ICC\\u2019s Statute. [1] [1] Schomerus, Allen and Vlassenroot\", \" The issue of Israel/Palestine has been a major one for the UN for sixty years, it is simply unfair that one of the parties represented and the other one is not The territory claimed by both the state of Israel and the state of Palestine is contested. These matters should be settled by the UN but this is not possible when one of the parties is represented but the other is not. It is simply against the principles of natural justice \\u2013 let alone the precepts of international law \\u2013 for only one party in any dispute to be fully represented where the other is not. Essentially, this is a fraud that has been perpetrated for over sixty years, in the interests of politics, justice has been ignored; Israel has been given recognition when Palestine has not, which body has the right to speak for the populace of that disputed territory should not be a matter imposed from outside but for the inhabitants of the land itself.\", \"animals science science general ban animal testing junior Many of these drugs are \\u201cme too\\u201d drugs \\u2013 ones with a slight change that doesn\\u2019t make much difference to an existing drug. [14] So often the benefits from animal testing are marginal, and even if there was a slight increase in human suffering, it would be worth it based on the animal suffering saved.\", \" The fact that religious thought tends to be subverted to defend the status quo is hardly a compelling argument as the same can be said for almost all forms of thought. There is a natural backlash from vested interests against any innovation and religion should not be blamed for having this same tendency. We should however not rule out the need to take a moral approach to some things for example; using stem cells might have huge medical benefits but it still needs to be considered whether it is morally right.\", \" Not all rebels have disarmed; the FDLR group has said it will disarm but has not done so. [1] The disarmament, demobilization and reintegration programme faces coordination and financial problems. There is a security threat from volatile border regions that might reverse the whole DDR effort as militias and military units struggle for control over resources and terrorise the local population. MONUSCO can't protect the repatriated civilians, which may mean any demobilisation is only temporary. If violence flares then so will guns be taken up once more. [1] Mvano, Chrispin, \\u2018U.N. Congo peacekeepers question Rwandan rebel disarmament claim\\u2019, Reuters, 4 February 2014,\"]}], \"ClimateFEVER\": [{\"query\": \"Coastal lake sediments along the Gulf of Mexico shoreline from 1,000 to 2,000 years ago suggest more frequent and intense hurricanes than occur today.\", \"pos\": [\"Paleotempestology Paleotempestology is the study of past tropical cyclone activity by means of geological proxies as well as historical documentary records . The term was coined by Kerry Emanuel .\"], \"neg\": [\"Indian Red '' This article refers to the traditional New Orleans song ; for the color see Indian red ( color ) . Indian Red is traditionally sung at the beginning and at the end of gatherings of Mardi Gras Indians in New Orleans . It is a traditional chant that may have been first recorded in 1947 by Danny Barker for King Zulu label ( Barker on guitar & vocals , Don Kirkpatrick on piano , Heywood Henry on baritone saxophone , and Freddie Moore ) . It has since been recorded many times by , among others , Dr. John and Wild Tchoupitoulas .\", \"Independent Battalion West Virginia Infantry The Independent Battalion West Virginia Infantry was an infantry battalion that served in the Union Army during the American Civil War .\", \"Crystallozyga Crystallozyga is a genus of snout moths . It was described by Meyrick in 1937 , and is known from the Democratic Republic of Congo . It contains the species C. alicia .\", \"Joe Rooney Joe Rooney ( born 1963 ) is an Irish actor and comedian from Tuam , Co. . Galway . His best-known acting role was that of Father Damo in the Channel 4 sitcom Father Ted . He features in the episode `` The Old Grey Whistle Theft '' . Rooney has had a starring role in the RT\\u00c9 television comedy , Killinaskully , in which he plays Timmy Higgins which ran for five series . Joe also wrote on the fourth and fifth series of Killinaskully . Joe currently hosts the podcast PodaRooney on which he interviews guests who are mostly involved in the entertainment industry . Joe has performed stand up worldwide including New York , L.A. , San Francisco , Chicago , Pittsburgh , Toronto , Shanghai , Beijing , Hong Kong , Barcelona , Brussels , Moscow , Muscat ( Oman ) , Manama ( Bahrain ) and Dubai . He has also performed at Irish festivals in Kansas City ( Missouri ) , Milwaukee and La Crosse ( Wisconsin ) . In more recent years Joe has had featured roles in TV3 's Red Rock , CBBC 's Roy and the feature films `` Monged '' and `` South '' . Other acting roles include Fergus Scully , a roving reporter who speaks in pidgin English and Irish , on the TG4 sketch show R\\u00ed R\\u00e1 . In 1997 , he appeared with Paul Tylak in Messrs Tylak and Rooney , a twelve-episode TV3 comedy travel series .\", \"Topolchane, Sliven Province Topolchane is a village located in Sliven Municipality , 10 km southeast of Sliven , Bulgaria , near the road to Burgas .\", \"Kivar (Roswell) Kivar is a fictional character from the Roswell television series and the Roswell High series of books . He is technically the main antagonist of the series , although he is not seen throughout the entire series until season 3 where he appeared in a few episodes . He is portrayed by Spence Decker .\", \"Cyrille Mangan Cyrille Mangan ( born 13 September 1976 ) is a retired Cameroonian football player . Mangan played for Skoda Xanthi F.C. in the Greek Alpha Ethniki from 1996 to 1999 . He suffered a serious knee injury at the age of 22 years , but recovered and later played for Trikala F.C. in the Greek Gamma Ethniki and Panegialios F.C. in the Greek Beta Ethniki . Mangan played for the Cameroon national football team in the 1998 African Cup of Nations finals . and in 1998 FIFA World Cup qualifying .\", \"Palestine at the 2000 Summer Paralympics One male and one female athlete from Palestine participated in the 2000 Summer Paralympics in Sydney , Australia . It was the first Palestinian Territories participation in the Paralympic Games . Husam Azzam won Palestine 's only medal : a bronze in the shot put .\"]}, {\"query\": \"This means the global temperature trend has now shown no further warming for 19 years\", \"pos\": [\"Scientific consensus on climate change There is currently a strong scientific consensus that the Earth is warming and that this warming is mainly caused by human activities. This consensus is supported by various studies of scientists' opinions and by position statements of scientific organizations, many of which explicitly agree with the Intergovernmental Panel on Climate Change (IPCC) synthesis reports. Nearly all actively publishing climate scientists (97\\u201398%) support the consensus on anthropogenic climate change, and the remaining 2% of contrarian studies either cannot be replicated or contain errors.\", \"Global warming Global warming , also referred to as climate change , is the observed century-scale rise in the average temperature of the Earth 's climate system and its related effects . Multiple lines of scientific evidence show that the climate system is warming . Many of the observed changes since the 1950s are unprecedented in the instrumental temperature record which extends back to the mid 19th century , and in paleoclimate proxy records over thousands of years . In 2013 , the Intergovernmental Panel on Climate Change ( IPCC ) Fifth Assessment Report concluded that `` It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century . '' The largest human influence has been emission of greenhouse gases such as carbon dioxide , methane and nitrous oxide . Climate model projections summarized in the report indicated that during the 21st century the global surface temperature is likely to rise a further 0.3 to for their lowest emissions scenario and 2.6 to for the highest emissions scenario . These findings have been recognized by the national science academies of the major industrialized nations and are not disputed by any scientific body of national or international standing . Future climate change and associated impacts will differ from region to region around the globe . Anticipated effects include warming global temperature , rising sea levels , changing precipitation , and expansion of deserts in the subtropics . Warming is expected to be greater over land than over the oceans and greatest in the Arctic , with the continuing retreat of glaciers , permafrost and sea ice . Other likely changes include more frequent extreme weather events including heat waves , droughts , heavy rainfall with floods and heavy snowfall ; ocean acidification ; and species extinctions due to shifting temperature regimes . Effects significant to humans include the threat to food security from decreasing crop yields and the abandonment of populated areas due to rising sea levels . Because the climate system has a large `` inertia '' and greenhouse gases will stay in the atmosphere for a long time , many of these effects will not only exist for decades or centuries , but will persist for tens of thousands of years . Possible societal responses to global warming include mitigation by emissions reduction , adaptation to its effects , building systems resilient to its effects , and possible future climate engineering . Most countries are parties to the United Nations Framework Convention on Climate Change ( UNFCCC ) , whose ultimate objective is to prevent dangerous anthropogenic climate change . Parties to the UNFCCC have agreed that deep cuts in emissions are required and that global warming should be limited to well below 2.0 C-change relative to pre-industrial levels , with efforts made to limit warming to 1.5 C-change . Public reactions to global warming and concern about its effects are also increasing . A global 2015 Pew Research Center report showed a median of 54 % consider it `` a very serious problem '' . There are significant regional differences , with Americans and Chinese ( whose economies are responsible for the greatest annual CO2 emissions ) among the least concerned .\"], \"neg\": [\"Shiriana language Shiriana ( Xiri\\u00e2na , Chiriana ) , or Bahuana ( Bahwana ) , is an unclassified Upper Amazon Arawakan language once spoken by the Shiriana people of Roraima , Brazil . It had an active -- stative syntax .\", \"Iskander Mirza Iskander Ali Mirza ( \\u0627\\u0633\\u06a9\\u0646\\u062f\\u0631 \\u0645\\u0631\\u0632\\u0627 \\u0987\\u09b8\\u09cd\\u0995\\u09be\\u09a8\\u09cd\\u09a6\\u09be\\u09b0 \\u0986\\u09b2\\u09bf \\u09ae\\u09bf\\u09b0\\u09cd\\u099c\\u09be b. 13 November 1899 -- 13 November 1969 ) , , was an East Pakistani politician who served as the first President of Pakistan , elected in this capacity in 1956 until being dismissed in 1958 . A great grandson of Mir Jafar , Mirza was educated at the University of Mumbai before attending the military academy in Sandhurst , United Kingdom . After a brief military service in the British Indian Army , he joined the Indian Political Service and spent majority of his career as a political agent in the Western region of the British India until elevated as joint secretary at the Ministry of Defence in 1946 . After the independence of Pakistan as result of a Partition of India , Mirza joined was appointed as first Defence Secretary by Prime Minister Liaquat Ali Khan , only to oversaw the military efforts in first war with India in 1947 , followed by failed secessionism in Balochistan in 1948 . In 1954 , he was appointed as Governor of East Bengal by Governor-General Sir Malik Ghulam to control the law and order sparked as a result of the popular language movement in 1952 , and later elevated as Interior Minister in Bogra administration in 1955 . Playing a crucial role in ousting of Governor-General Sir Malik Ghulam , Mirza assumed his position in 1955 and was elected as the first President of Pakistan when the first set of Constitution was promulgated in 1956 . His presidency , however , marked with political instability which saw his unconstitutional interference in the civilian administration that led to the dismissal of four prime ministers in mere two years . Facing challenges in effectively running the foreign and economic policy , Mirza suspended the constitution by having imposed the martial law in 1958 through army chief General Ayub Khan who later dismissed him when situation between them escalated , also in 1958 . Mirza lived in the United Kingdom for the remainder of his life and buried in Iran in 1969 . His legacy and image is viewed very negatively by the Pakistani historians who believed that Mirza was responsible for this political instability in the country .\", \"Sumire Haruno is a Japanese actress , a former member of Takarazuka Revue , specializing in otokoyaku . She joined the revue in 1991 , became the top star in 2002 and resigned from the company in 2007 . She is from Komae , Tokyo , her birthday is December 15 , 1972 . Her nicknames are Osa and Masa-chan ( as called by Jun Sena ) , both are from her real name Masako Osada ( \\u9577\\u7530\\u96c5\\u5b50 Osada Masako ) . She is the first otokoyaku of her class to reach the top ( followed by classmate and former troupe-mate Hikaru Asami by 3 months ) .\", \"Regent's Business School London Regent 's Business School London ( informally Regent 's Business School , RBS London or RBSL ) is a private business school located in London , United Kingdom . The school is a part of Regent 's University London the campus of which was originally built in 1913 in the midst of Regent 's Park in central London . Founded in 1997 , it has grown rapidly from 10 students to more than 450 . The student body is primarily international , with a large population of students from Persian Gulf Region , Asia , Northern and Eastern Europe .\", \"Niewiesz Niewiesz -LSB- ' \\u0144ewjesz -RSB- is a village in the administrative district of Gmina Podd\\u0119bice , within Podd\\u0119bice County , \\u0141\\u00f3d\\u017a Voivodeship , in central Poland . It lies approximately 8 km north-west of Podd\\u0119bice and 45 km west of the regional capital \\u0141\\u00f3d\\u017a . The village has a population of 320 .\", \"Angus, Wisconsin Angus is an unincorporated community in the town of Cedar Lake , Barron County , Wisconsin , United States . Angus is located on Wisconsin Highway 48 2.5 mi southwest of Birchwood .\", \"Waterloo Region municipal elections, 2014 Municipal elections were held in the Regional Municipality of Waterloo of Ontario on October 27 , 2014 in conjunction with municipal elections across the province .\", \"Xiaoli Ma Xiaoli Ma from the Georgia Institute of Technology , Atlanta , GA was named Fellow of the Institute of Electrical and Electronics Engineers ( IEEE ) in 2016 for contributions to block transmissions over wireless fading channels .\"]}, {\"query\": \"The report [\\u2026] found that the United States was one of the most pollution-free nations in the world.\\u201d\", \"pos\": [\"Developing country A developing country , also called a less developed country or an underdeveloped country , is a nation or a sovereign state with a less developed industrial base and a low Human Development Index ( HDI ) relative to other countries . There are no universally agreed-upon criteria for what makes a country developing versus developed and which countries fit these two categories , although there are general reference points such as a nation 's GDP per capita compared with other nations . Also , the general term less-developed country should not be confused with the specific least developed country . The term `` developing '' describes a currently observed situation and not a dynamic or expected direction of progress . Since the late 1990s developing countries tended to demonstrate higher growth rates than the developed ones . There is criticism for using the term developing country . The term implies inferiority of a developing country or undeveloped country compared with a developed country , which many countries dislike . It assumes a desire to develop along the traditional Western model of economic development which a few countries , such as Cuba and Bhutan , choose not to follow . An alternative measurement that has been suggested is that of gross national happiness . Countries on the boundary between developed and developing are often categorized under the term newly industrialized countries . According to authors such as Walt Whitman Rostow , developing countries are in transition from traditional lifestyles towards the modern lifestyle which began in the Industrial Revolution in the 18th and 19th centuries . In the 2016 edition of its World Development Indicators , the World Bank made a decision to no longer distinguish between `` developed '' and `` developing '' countries in the presentation of its data . Nobody has ever agreed on a definition for these terms in the first place .\", \"United States The United States of America ( USA ) , commonly known as the United States ( U.S. ) or America , is a constitutional federal republic composed of 50 states , a federal district , five major self-governing territories , and various possessions . Forty-eight of the fifty states and the federal district are contiguous and located in North America between Canada and Mexico . The state of Alaska is in the northwest corner of North America , bordered by Canada to the east and across the Bering Strait from Russia to the west . The state of Hawaii is an archipelago in the mid-Pacific Ocean . The U.S. territories are scattered about the Pacific Ocean and the Caribbean Sea . Nine time zones are covered . The geography , climate and wildlife of the country are extremely diverse . At 3.8 million square miles ( 9.8 million km2 ) and with over 324 million people , the United States is the world 's third - or fourth-largest country by total area , third-largest by land area , and the third-most populous . It is one of the world 's most ethnically diverse and multicultural nations , and is home to the world 's largest immigrant population . The capital is Washington , D.C. , and the largest city is New York City ; nine other major metropolitan areas -- each with at least 4.5 million inhabitants and the largest having more than 13 million people -- are Los Angeles , Chicago , Dallas , Houston , Philadelphia , Miami , Atlanta , Boston , and San Francisco . Paleo-Indians migrated from Asia to the North American mainland at least 15,000 years ago . European colonization began in the 16th century . The United States emerged from 13 British colonies along the East Coast . Numerous disputes between Great Britain and the colonies following the Seven Years ' War led to the American Revolution , which began in 1775 . On July 4 , 1776 , during the course of the American Revolutionary War , the colonies unanimously adopted the Declaration of Independence . The war ended in 1783 with recognition of the independence of the United States by Great Britain , representing the first successful war of independence against a European power . The current constitution was adopted in 1788 , after the Articles of Confederation , adopted in 1781 , were felt to have provided inadequate federal powers . The first ten amendments , collectively named the Bill of Rights , were ratified in 1791 and designed to guarantee many fundamental civil liberties . The United States embarked on a vigorous expansion across North America throughout the 19th century , displacing Native American tribes , acquiring new territories , and gradually admitting new states until it spanned the continent by 1848 . During the second half of the 19th century , the American Civil War led to the end of legal slavery in the country . By the end of that century , the United States extended into the Pacific Ocean , and its economy , driven in large part by the Industrial Revolution , began to soar . The Spanish -- American War and confirmed the country 's status as a global military power . The United States emerged from as a global superpower , the first country to develop nuclear weapons , the only country to use them in warfare , and a permanent member of the United Nations Security Council . The end of the Cold War and the dissolution of the Soviet Union in 1991 left the United States as the world 's sole superpower . The U.S. is a founding member of the United Nations , World Bank , International Monetary Fund , Organization of American States ( OAS ) , and other international organizations . The United States is a highly developed country , with the world 's largest economy by nominal GDP and second-largest economy by PPP . Though its population is only 4.3 % of the world total , Americans hold nearly 40 % of the total wealth in the world . The United States ranks among the highest in several measures of socioeconomic performance , including average wage , human development , per capita GDP , and productivity per person . While the U.S. economy is considered post-industrial , characterized by the dominance of services and knowledge economy , the manufacturing sector remains the second-largest in the world . Accounting for approximately a quarter of global GDP and a third of global military spending , the United States is the world 's foremost economic and military power . The United States is a prominent political and cultural force internationally , and a leader in scientific research and technological innovations .\"], \"neg\": [\"List of listed buildings in Barry, Angus This is a list of listed buildings in the parish of Barry in Angus , Scotland .\", \"ECyD ECyD is an international Catholic youth organization affiliated with the congregation of the Legionaries of Christ and their lay movement Regnum Christi . ECyD membership is open to youth ages 11 to 16 ( to 18 in the USA and Canada ) .\", \"Domitila Garc\\u00eda de Coronado Domitila Garc\\u00eda Dom\\u00e9nico de Coronado ( 7 May 1847 -- 1938 ) was a Cuban writer , journalist , editor , and professor , considered to be the first women to practice journalism in her country . On 17 May 1891 she founded the Academy of Women Typographers . She founded and edited various publications , including the journals La Antorcha and El C\\u00e9firo together with Sof\\u00eda Estevez ( 1848 -- 1901 ) . Besides these , she was editor of La Mujer , together with A\\u00edda Pel\\u00e1ez de Villa Urrutia and Isabel Margarita Ordetx . She also published the first anthology of Cuban women writers in 1868 , titled \\u00c1lbum po\\u00e9tico fotogr\\u00e1fico de escritoras cubanas ( Poetic photo album of Cuban women writers ) .\", \"Blue-cheeked flowerpecker The blue-cheeked flowerpecker or red-chested flowerpecker ( Dicaeum maugei ) is a species of bird in the Dicaeidae family . It is found on the Lesser Sunda Islands in Indonesia and East Timor . Its natural habitats are subtropical or tropical moist lowland forests and subtropical or tropical moist montane forests .\", \"Salzburg S-Bahn The Salzburg S-Bahn is a large transport project in and around Salzburg in the Euroregion of Salzburg -- Berchtesgadener Land -- Traunstein , which crosses the border between Austria and Germany . Its S-Bahn network has been partially in operation since 2004 and its first stage is expected to be completed in 2014 .\", \"William Hanson (engineer) William Hanson MICE ( 1810 -- 14 July 1875 ) was a government engineer in the early days of the colony of South Australia .\", \"Lola Gonzales Mar\\u00eda Dolores Gonz\\u00e1lez ( born March 2 , 1959 ) is a Mexican professional wrestler , known by her ringname Lola Gonz\\u00e1lez , who has competed in the Universal Wrestling Association and the World Wrestling Association for over three decades . At one time one of the most popular female tecnicos in Mexico , she dominated the UWA World Women 's Championship during the mid-to late 1980s holding the title a record four times . She is the real-life sister of luchadora Leslie Gonzalez and ex-wife of the late Fishman .\", \"D15 D15 or D. 15 may refer to : Slav Defence , 4 . Nc3 , Encyclopaedia of Chess Openings code Dewoitine D. 15 , a French Dewoitine aircraft HMS Vindex ( D15 ) , a 1943 British Royal Navy escort aircraft carrier LSWR D15 class , a 1912 British steam locomotive model PRR D15 , an 1892 American steam locomotive model Allis-Chalmers Model D15 Dublin 15 , a Dublin , Ireland postal district 15-pin variant of D-subminiature electrical connectors and also : Benign neoplasm of other and unspecified intrathoracic organs ICD-10 code\"]}, {\"query\": \"Sea-level rise does not seem to depend on ocean temperature, and certainly not on CO2.\", \"pos\": [\"Paleocene\\u2013Eocene Thermal Maximum The Paleocene -- Eocene Thermal Maximum ( PETM ) , alternatively ( ETM1 ) , and formerly known as the `` Initial Eocene '' or '' '' was a time period with more than 8 \\u00b0 C warmer global average temperature than today . This climate event began at the time boundary between the Paleocene and Eocene geological epochs . The exact age and duration of the event is uncertain but it is estimated to have occurred around 55.5 million years ago . The associated period of massive carbon injection into the atmosphere has been estimated to have lasted no longer than 20,000 years . The entire warm period lasted for about 200,000 years . Global temperatures increased by 5 -- 8 \\u00b0 C . The carbon dioxide was likely released in two pulses , the first lasting less than 2,000 years . Such a repeated carbon release is in line with current global warming . A main difference is that during the Paleocene -- Eocene Thermal Maximum , the planet was essentially ice-free . The onset of the Paleocene -- Eocene Thermal Maximum has been linked to an initial 5 \\u00b0 C temperature rise and to extreme changes in Earth 's carbon cycle . The period is marked by a prominent negative excursion in carbon stable isotope records from around the globe ; more specifically , there was a large decrease in 13C/12C ratio of marine and terrestrial carbonates and organic carbon . Stratigraphic sections of rock from this period reveal numerous other changes . Fossil records for many organisms show major turnovers . For example , in the marine realm , a mass extinction of benthic foraminifera , a global expansion of subtropical dinoflagellates , and an appearance of excursion , planktic foraminifera and calcareous nanofossils all occurred during the beginning stages of PETM . On land , modern mammal orders ( including primates ) suddenly appear in Europe and in North America . Sediment deposition changed significantly at many outcrops and in many drill cores spanning this time interval . At least since 1997 , the Paleocene -- Eocene Thermal Maximum has become a focal point of considerable geoscience research because it probably provides the best past analog by which to understand impacts of global climate warming and of massive carbon input to the ocean and atmosphere , including ocean acidification . Although it is now widely accepted that the PETM represents a `` case study '' for global warming and massive carbon input to Earth 's surface , the cause , details and overall significance of the event remain perplexing .\", \"Sea level rise A sea level rise is an increase in the volume of water in the world 's oceans , resulting in an increase in global mean sea level . Sea level rise is usually attributed to global climate change by thermal expansion of the water in the oceans and by melting of Ice sheets and glaciers on land . Melting of floating ice shelves or icebergs at sea raises sea levels only slightly . Sea level rise at specific locations may be more or less than the global average . Local factors might include tectonic effects , subsidence of the land , tides , currents , storms , etc. . Sea level rise is expected to continue for centuries . Because of the slow inertia , long response time for parts of the climate system , it has been estimated that we are already committed to a sea-level rise of approximately 2.3 m for each degree Celsius of temperature rise within the next 2,000 years . IPCC Summary for Policymakers , AR5 , 2014 , indicated that the global mean sea level rise will continue during the 21st century , very likely at a faster rate than observed from 1971 to 2010 . Projected rates and amounts vary . A January 2017 NOAA report suggests a range of GMSL rise of 0.3 -- 2.5 m possible during the 21st century . Sea level rises can considerably influence human populations in coastal and island regions and natural environments like marine ecosystems .\"], \"neg\": [\"U.S. Route 87 in Texas In the U.S. state of Texas , U.S. Highway 87 ( US 87 ) is a north -- south U.S. Highway that begins near the Gulf Coast in Port Lavaca , Texas and heads north through San Antonio , Lubbock , and Amarillo to the New Mexico border near Texline .\", \"HMSAS Africana HMSAS AfricanaHMSAS stands for `` His ( or Her ) Majesty 's South African Ship '' was a minesweeping trawler of the South African Seaward Defence Force during the Second World War . She was originally a sea fisheries research vessel and was latter fitted for mine-sweeping and survey duties in the early 1930s . She was retained for survey duties off the South African coast throughout the war and in October 1942 she was involved in the rescue of survivors from the American cargo vessel Anne Hutchinson after she was torpedoed by off East London . In addition to survey , she was used extensively for search and rescue operations in the latter part of the war and her final rescue operation was rescuing 49 survivors of the Canadian This was last vessel to be sunk in South African waters during the Second World War which was torpedoed by on 23 February 1945 off the coast of Luderitz Bay . After the war , Africana was returned to the South African Department of Sea Fisheries and was re-fitted as a fishery survey vessel , starting her first post-war survey in May 1947 . She remained in service in this role until 1950 when she was replaced by the new survey vessel Africana II . She was sold to Benjamin Gelcer who used her as a fishing trawler . She later joined the fishing fleet of Irwin and Johnson and was used until 1965 when she was finally withdrawn from fishing service and broken up in Table Bay , to be sold as scrap .\", \"National Assessment and Accreditation Council The National Assessment and Accreditation Council ( NAAC ) is an organisation that assesses and accredits institutions of higher education in India . It is an autonomous body funded by University Grants Commission of Government of India headquartered in Bangalore .\", \"Ferdosi Mashhad FSC Eghtedar Novin Ferdosi Mashhad Futsal Club ( Persian : \\u0628\\u0627\\u0634\\u06af\\u0627\\u0647 \\u0641\\u0648\\u062a\\u0633\\u0627\\u0644 \\u0627\\u0642\\u062a\\u062f\\u0627\\u0631 \\u0646\\u0648\\u06cc\\u0646 \\u0641\\u0631\\u062f\\u0648\\u0633\\u06cc \\u0645\\u0634\\u0647\\u062f ) is an Iranian futsal club based in Mashhad , Iran . They currently compete in the Iranian Futsal Super League , the 1st tier of Iranian futsal .\", \"Li Ning (baseball) Li Ning ( born November 12 , 1994 ) is a Chinese baseball catcher who plays with the Shanghai Golden Eagles in the China Baseball League . Li represented China at the 2015 Asian Baseball Championship and 2017 World Baseball Classic .\", \"Osmia atriventris Osmia atriventris , sometimes referred to as the Maine blueberry bee , is a megachilid bee native to eastern North America from Nova Scotia to Alberta in the north , and Iowa to Georgia in the south . This solitary bee normally gathers pollen from many different flowers , but will pollinate blueberries , and is sometimes used commercially for this purpose .\", \"Monte-Sano & Pruzan Monte-Sano & Pruzan was a highly regarded New York fashion house specialising in women 's tailoring , founded in 1915 by Vincent Monte-Sano senior , who was later joined by Max Pruzan . The company was liquidated in 1969 .\", \"Henry Klindt House The Henry Klindt House , is a located in the West End of Davenport , Iowa , United States . It has been listed on the National Register of Historic Places since 1984 .\"]}, {\"query\": \"The amount of summer sea ice in the Arctic has steadily declined over the past few decades because of man-made global warming, according to the National Oceanic and Atmospheric Administration.\", \"pos\": [\"Climate change in the Arctic The effects of global warming in the Arctic , or climate change in the Arctic include rising temperatures , loss of sea ice , and melting of the Greenland ice sheet with a related cold temperature anomaly , observed in recent years . Potential methane release from the region , especially through the thawing of permafrost and methane clathrates , is also a concern . The Arctic warms twice as fast compared to the rest of the world . The pronounced warming signal , the amplified response of the Arctic to global warming , it is often seen as a leading indicator of global warming . The melting of Greenland 's ice sheet is linked to polar amplification . According to a study published in 2016 , about 0.5 \\u25e6 C of the warming in the Arctic has been attributed to reductions in sulfate aerosols in Europe since 1980 .\", \"Scientific consensus on climate change There is currently a strong scientific consensus that the Earth is warming and that this warming is mainly caused by human activities. This consensus is supported by various studies of scientists' opinions and by position statements of scientific organizations, many of which explicitly agree with the Intergovernmental Panel on Climate Change (IPCC) synthesis reports. Nearly all actively publishing climate scientists (97\\u201398%) support the consensus on anthropogenic climate change, and the remaining 2% of contrarian studies either cannot be replicated or contain errors.\", \"Arctic The Arctic ( -LSB- \\u02c8\\u0251rkt\\u026ak -RSB- or -LSB- \\u02c8\\u0251rt\\u026ak -RSB- ) is a polar region located at the northernmost part of Earth . The Arctic consists of the Arctic Ocean , adjacent seas , and parts of Alaska ( United States ) , Canada , Finland , Greenland ( Denmark ) , Iceland , Norway , Russia and Sweden . Land within the Arctic region has seasonally varying snow and ice cover , with predominantly treeless permafrost-containing tundra . Arctic seas contain seasonal sea ice in many places . The Arctic region is a unique area among Earth 's ecosystems . For example , the cultures in the region and the Arctic indigenous peoples have adapted to its cold and extreme conditions . In recent years , Arctic sea ice decline has been caused by global warming . Life in the Arctic includes organisms living in the ice , zooplankton and phytoplankton , fish and marine mammals , birds , land animals , plants and human societies . Arctic land is bordered by the subarctic .\", \"Global warming Global warming , also referred to as climate change , is the observed century-scale rise in the average temperature of the Earth 's climate system and its related effects . Multiple lines of scientific evidence show that the climate system is warming . Many of the observed changes since the 1950s are unprecedented in the instrumental temperature record which extends back to the mid 19th century , and in paleoclimate proxy records over thousands of years . In 2013 , the Intergovernmental Panel on Climate Change ( IPCC ) Fifth Assessment Report concluded that `` It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century . '' The largest human influence has been emission of greenhouse gases such as carbon dioxide , methane and nitrous oxide . Climate model projections summarized in the report indicated that during the 21st century the global surface temperature is likely to rise a further 0.3 to for their lowest emissions scenario and 2.6 to for the highest emissions scenario . These findings have been recognized by the national science academies of the major industrialized nations and are not disputed by any scientific body of national or international standing . Future climate change and associated impacts will differ from region to region around the globe . Anticipated effects include warming global temperature , rising sea levels , changing precipitation , and expansion of deserts in the subtropics . Warming is expected to be greater over land than over the oceans and greatest in the Arctic , with the continuing retreat of glaciers , permafrost and sea ice . Other likely changes include more frequent extreme weather events including heat waves , droughts , heavy rainfall with floods and heavy snowfall ; ocean acidification ; and species extinctions due to shifting temperature regimes . Effects significant to humans include the threat to food security from decreasing crop yields and the abandonment of populated areas due to rising sea levels . Because the climate system has a large `` inertia '' and greenhouse gases will stay in the atmosphere for a long time , many of these effects will not only exist for decades or centuries , but will persist for tens of thousands of years . Possible societal responses to global warming include mitigation by emissions reduction , adaptation to its effects , building systems resilient to its effects , and possible future climate engineering . Most countries are parties to the United Nations Framework Convention on Climate Change ( UNFCCC ) , whose ultimate objective is to prevent dangerous anthropogenic climate change . Parties to the UNFCCC have agreed that deep cuts in emissions are required and that global warming should be limited to well below 2.0 C-change relative to pre-industrial levels , with efforts made to limit warming to 1.5 C-change . Public reactions to global warming and concern about its effects are also increasing . A global 2015 Pew Research Center report showed a median of 54 % consider it `` a very serious problem '' . There are significant regional differences , with Americans and Chinese ( whose economies are responsible for the greatest annual CO2 emissions ) among the least concerned .\"], \"neg\": [\"Ton du Chatinier Ton du Chatinier ( born 13 January 1958 ) is a retired football player from the Netherlands . He later worked as an assistant manager to Hong Myung-bo of South Korea , and alongside Hong as an assistant to manager Guus Hiddink at Russian side FC Anzhi Makhachkala .\", \"Ruddy quail-dove The ruddy quail-dove ( Geotrygon montana ) is a member of the bird family Columbidae , which includes doves and pigeons . It breeds throughout the West Indies , Central America , and tropical South America . It has appeared as a vagrant in Florida and southern Texas . It lays two buff colored eggs on a flimsy platform built on a shrub . Some nests are built on the ground . The ruddy quail-dove is approximately 19 -- 28 cm in length . The bird is distinguished by having a rust colored back , facial mask and similarly colored wings . The breast , rump and undereye stripe are lighter brown . This bird is found in woodland and scrub forest . It also has adapted to coffee plantations . It is somewhat sensitive to forest fragmentation . These birds forage on the ground , mainly eating seeds . It will also take small invertebrates in its diet . Ruddy quail-doves feed primarily on the ground .\", \"Charles Joseph Gahan Charles Joseph Gahan ( 20 January 1862 -- 21 January 1939 ) was an Irish entomologist . He was born on 20 January 1862 at Roscrea County Tipperary , Ireland . His father , Michael Gahan was the Master of Erasmus Smith 's School in Tipperary . He was educated first at Queens College Galway , where he achieved distinction , and then at the Royal School of Mines in Kensington . In 1882 he was awarded a medal and prizes as the best biological student of the session . In 1886 , he joined the British Museum ( Natural History ) as an assistant in the Department of Zoology where he became Keeper in the then newly formed Department of Entomology in 1913 . An expert on beetles , especially Cerambycidae , he wrote the 1906 volume of The Fauna of British India , Including Ceylon and Burma on that group . Honorary Secretary of the Entomological Society of London in 1899-1900 and was president 1917-1918 . Married Annie Woodward in 1887 . He retired in 1920 and lived at Mouth Aylsham in Norfolk and died at Aylsham on 21 January 1939 . He became the first person to describe Rosenbergia exigua in 1888 .\", \"Perry Daneshgari Perry ( Parviz ) Daneshgari is an Iranian-American entrepreneur , engineer and author born in Ahvaz , Iran . He founded MCA , which appeared on the TV Program World Business Review , in 1990 and has written many books and articles in specialized magazines and websites . Perry has an MBA from Wayne State University and a Ph.D in Mechanical Engineering from the University of Karlsruhe , as well as BS . in Civil and Mechanical Engineering . He specializes in Agile Construction , a way of doing business that focuses on adaptation and quick changes on job sites and production lines . Perry has collaborated with research projects on different industries , most of them focused on increasing productivity in those industries applying agile methods of working , for example : `` Developing a Standard Format for Calculating Construction size and Share '' , `` Ideal Jobsite Inventory Levels to Improve Profitability '' for Electri International and other organization like Sheet Metal and Air Conditioning Contractors National Association , NAED Education and Research Foundation , New Horizons Foundation , etc. . Perry in collaboration with Heather Moore and MCA have written articles in some specialized magazines like Electrical Contracting Magazine , The Electrical Distribution Magazine , and more .\", \"Corner, Alabama Corner is an unincorporated community in Jefferson County , Alabama , United States .\", \"Tenth Ward Square The Tenth Ward Square is a 1.7 acre historic district in Salt Lake City , Utah that was listed on the National Register of Historic Places in 1977 . It includes Late Gothic Revival , Greek Revival , and Late Victorian architecture in four contributing buildings . It includes an 1873 one-room Mormon meeting house , a c. 1880 store with included residence , an 1887 school , and a 1909 chapel . The store appears to be one of the first works of architect Richard K. A. Kletting . The church , designed by the Ashton Brothers , is `` known for its impressive stained glass window '' .\", \"George Dickinson George Ritchie Dickinson ( 11 March 1903 -- 17 March 1978 ) was a New Zealand cricketer and rugby union player . He played three tests for the New Zealand cricket team between 1930 and 1932 , and five matches for the New Zealand national rugby side , the All Blacks , in 1922 .\", \"Xia (surname) Xia is the Mandarin pinyin romanization of the Chinese surname written in Chinese character . It is romanized Hsia in Wade -- Giles , and Ha in Cantonese . Xia is the 154th surname in the Song dynasty classic text Hundred Family Surnames . As of 2008 , it is the 66th most common Chinese surname , shared by 3.7 million people .\"]}], \"CQADupstackAndroidRetrieval\": [{\"query\": \"How can I receive phone calls through Wi-Fi on an Android phone?\", \"pos\": [\"Is it possible to have a phone without a voice plan? > **Possible Duplicate:** > How can I receive phone calls through WiFi on an Android phone? I have an unlocked Nexus One. I barely use the voice function, mostly just use data. However, I'd like to have the voice function just in case. Is it possible to use something else (possibly VOIPish) to replace the voice plan that I am currently paying? Perhaps Skype, or Google Voice or something else. Has anyone had an experience with this? Thanks\"], \"neg\": [\"HTC EVO Does Not Sync Google Calendar I'm an experienced Android user and recently my Dad picked up a HTC EVO. I set him up with all the Google services and apps like Gmail, Contacts and Calendar. I exported all of his contacts and appointments out of Outlook to CSV and uploaded them to Google so they can sync down to his EVO. Unfortunately when I go to his Google account sync settings, his phone does not show the option to sync his Google Calendar. The exact option I'm talking about is under (from the home screen) Settings -> Accounts & sync -> your Google account. The phone shows Books, Contacts, and Gmail but the option for calendar is missing. Is this the case on all EVO's? I have a screen capture of my phone (HTC Incredible) that has the option below that I'm talking about. His HTC EVO does not have it for some reason. Is this a Sprint restriction? Any ideas on how to add it? Any help is greatly appreciated. Matt\", \"Will my alarm still go off if my phone is on airplane mode? I'm trying to save battery but I need my alarm in the morning. Will it still go off if on airplane mode? p.s. I have an HTC Inspire.\", \"Indic Language (Punjabi) not displaying I have created one mobile website using WordPress in Indian Language i.e. Punjabi. I have used Unicode characters. When I try to open this website on my **Spice Android Phone having version 4.0.2,** everything is perfect but When I try to Load on my **Lenovo having version 4.2.2,** nothing is displaying. only English characters are visible. I am using **default browser which is provided by the company** not any other like chrome, opera etc.. I cannot understand what is the problem.. it there any setting issue... please help....\", \"Cleaning up Samsung S2 memory When I use the application manager, I see that 1.8GB are occupied out of 2GB available. This of course causes issues when installing apps and brings up a warning notifications that some system services may malfunction. However, if I sum the memory taken by my apps, I come up at 1GB, and many of these apps have been moved to the SD card. I've tried to use Clean Master to free space, but without much success. So I want to cleanup files manually. What partitions on the phone's system are the ones in the 2GB space? Is it the root partition?\", \"Wi-fi disabled during lock still partly active? Or do I mis-interpret the battery graph? On my Google Nexus 7 I noticed that it would consume around 1% of battery each hour that it was just being idle. I suspected that battery-usage during idle time could be improved. So the next night (7 hours) I put my Nexus into airplane mode, and the next morning the battery had only used 1% for those 7 hours, which proved that there was room for improvement. Next I disabled bluetooth and set wi-fi, to be disabled when the screen is locked (changed \\\"Keep Wi-Fi on during sleep\\\" from \\\"Always\\\" to \\\"Only when plugged in\\\"). After a fews tests of 7 hours idle the battery was down 2 ~ 3% each time. But then I noticed in the battery graph, that the wi-fi still showed quite a lot of activity. This probably explains the difference in battery consumption with airplane-mode. I had expected the wi-fi to be completely disabled with this setting, and the wi-fi bar in the battery graph to be almost completely black. To see the difference I manually disabled the wi-fi and then the bar is indeed completely black (the right block in the image): ![enter image description here](http://i.stack.imgur.com/6A6ZO.png) I would expect the wi-fi to be completely off when the screen is locked, but looking at this question: Turning on Wi-Fi on demand that is apparently not how Android works. So now I'm wondering what the wi-fi is doing in this setting? Why does Android keep the wi-fi 'partly' enabled? If Android would still let apps do their syncing etc on a regular basis I would understand this, but this is not the case (Notifications don't come in & it even seems to cut off streaming etc), so it seems to only waste battery? I first suspected a misbehaving app, but I checked with Wake lock detector and Network log but don't see any suspicious apps. I guess I could use JuiceDefender or a similar app to completely disable the wi-fi when the screen is locked (and still let it sync from time to time), but that's not really the point of this question.\", \"last digits invisible from call history I have recently bought Sony Xperia and recently have been facing this weird problem. The last 2 digits of the phone number in call history are invisible. And this is not the case for whole history, but only for some. Please guide if you know reason/resolution for the same.\", \"My HTC One X and media link My HTC One X has configured with my media link and when plugged in my phone says the device has detected but it wont connect to it. It just times out. How can I solve this?\", \"Adding an APN from the commandline I'm in the US for 30 days and got a SIM card from Lycamobile. For some reason adding APNs doesn't work through the GUI (I add them but they never show up in the list, even after rebooting). Instead I added the following lines to my `/system/etc/apns-conf.xml`, just before `` Based on MCC and MNC data from Wikipedia and the information available online in general. I also enabled Data Roaming and International Roaming. I have rebooted my phone. I am on a Samsung Galaxy S3 running the latest AOKP nightly. However, I still get an R in my signal tray and cannot connect to the internet. Please advise - any help would be appreciated even if it a nudge in a new direction. Cheers, Gausie\"]}, {\"query\": \"What is the status icon that looks like a house?\", \"pos\": [\"Blinking icon in notification bar I have a new annoying icon on my Razr notification bar. It is a tiny blinking symbol that sort of looks like a tiny house with a cloud over it. It is blinking above the network strength bars where the \\\"x\\\" would be if there was no service. Anyone know what that is?\"], \"neg\": [\"Why doesn't Google Now's place-specific reminders remember my home location? I have my home and work locations set. These show up accurately in Google Maps and also in Google Now if I go \\\"Wand\\\" -> \\\"Places\\\" at the bottom. However, whenever I go to set a place-specific reminder, Google Now reprompts me to enter my home location. The screen looks like this: ![Google Now forgetting home location](https://www.martineve.com/android.png) As you can see, the \\\"Home\\\" field doesn't actually have an address below it. This is never remembered. How can I fix this?\", \"How do I install Play Store on my HCL Me tablet? I have an HCL ME Connect 2.0 tablet. It has 1Mobile Market pre-installed. I want to install the Google's original Play Store for multiple reasons, like the application Google Keep doesn't work because of the lack of \\\"Google Play Store services.\\\" I don't have any knowledge in a tablet's technical realms, like rooting it, so kindly be comfortably easy and reasonably detailed if there's a solution. Thanks!\", \"How can i turn off the message received tick I have a sony ericsoon xperia x10 mini. when I send a text message, a tick appears to let tme know it has been received by the recipient. This costs me money, how do I turn this off? I have looked through the menu and cannot seem to find the answer.\", \"Is there a PDF (or epub) reader that allows to take notes (or at least to copy text)? I have many PDFs that I would like to read on my Galaxy Tab and then take notes. Preferably the notes would be attached to the document (may be like Skim does), but as a workaround I can also imagine to copy a relevant piece of text into a separate note taking application. Preferably the reader should handle PDFs directly, but if there is no PDF solution I might also start converting the documents (maybe with Calibre) for an epub reader. I tried Adobe Reader, ThinkFree Office and Aldiko, all of which have acceptable performance loading the PDFs, but aside from cumbersome zooming they also lack the ability to copy or adding notes.\", \"Getting pictures off of old deactivated phone Can I hook up my old Galaxy S2 phone via USB to my laptop to find \\\"lost\\\" pictures in an unnamed file? I just activated my new Galaxy S4 and for some reason, all pics that were on my old phone are now gone from the new phone. Not sure why these would be deleted from the phone during a simple phone swap. Not all of my pics are on the sd card. Most of them were saved to the phone because it had much more space available. Customer service says it may take up to 24 hours for the transfer to be complete but the new phone already has all contacts & accounts transferred, plus I'm able to text & receive calls. I understand that during a transfer, pics are not usually included. But they should still be saved inside the old phone, right? The techs doing the swap shouldn't be able to do a factory reset or anything on the old phone, correct?\", \"Issue in GSM for Straight Talk and MMS. I just purchased a **GSM** unlocked Android phone. And called into Straight Talk to get the new `APN` setting. But, the settings they gave me would not allow me to receive **MMS**. The web and testing works fine. Please help me on which settings to use? **NOTE:** Someone told me to re-configure my phone, will this help?\", \"What does TRWP backup include? If I would take a backup via TRWP, does it include all my apps, app data, messages, contacts or it would just take backup of the ROM only, so I could get a fresh ROM with factory restored when I restore it ?\", \"Is there a Twitter app that allows filtering of tweets based on source, hashtag, etc? Tweet Deck for PC has a global filter that allows this but I cannot see this option in the Android client. As above, is there an app that has this feature? I would very much like to clear my tweetstream of Foursquare noise and the like.\"]}, {\"query\": \"Configuring Background Images\", \"pos\": [\"How can I get the Android to use wallpapers sized to my screen resolution? When I only have one \\\"desktop\\\" on my Android phone and I select one of my photos for the wallpaper, it asks me to crop the image. The cropping tool only allows an aspect ratio that has nothing to do with the real aspect ration of my phones screen resolution. Why? I have a Samsung GT i5800 running Android 2.1update1.\"], \"neg\": [\"My SIM is not recognized after installing Cyanogenmod; what should I do? I've got a Samsung Galaxy S (I9000) which I purchased from the Spanish operator Movistar around two years ago. I have spent most of the day trying to flash it with cyanogenmod 7. TL;DR: Installing cyanogenmod 7 was incredibly difficult It has been ... quite and adventure. First I tried heimdall from Ubuntu (didn't even connect to the phone) then I tried from Windows, using its graphical interface. It had the \\\"repartition\\\" flag activated by default, when it shouldn't, so I bricked my phone. After lots of trial an error, I managed to install a ROM in my phone, using Odin as root, the stock cable, and a different usb port than the one I was using (go figure...). And from there I was able to install cyanogenmod as it says on the instructions. Then I had to reinstall them, because they were bootlooping. END TL;DR So, now I've got a Samsung Galaxy S with Cyanogenmod 7. But it complaints that it doesn't detect any SIM card. When it boots up, it says \\\"No SIM card. Emergency calls only\\\". Notice that it doesn't say \\\"incompatible SIM\\\", Nor it asks me for a password of any kind. It just doesn't seem to detect the card at all. What steps can I follow to solve this issue, or at least get more information about it?\", \"Need only one icon in the top notification bar for multiple Gmail accounts I am trying to figure out how to have only one notification icon on the top notification bar for an incoming e-mail for any of my multiple Gmail accounts. This was the way Gmail worked on my phone prior to the 2.3.5 Gmail update. Now, at any given time I can have up to four little Gmail icons on my top notification bar which is an pain. Is there any way to change it to the way the notifications worked before? That is, one icon for all my accounts, and if I receive an e-mail, it sends me to the main accounts page.\", \"ZTE Blade S2E not enough space I have just installed 4.2.2 PAC and S2E later. I have everything on sd-ext, there's 1600MB free space on sd-ext, and 140MB inside. However anytime i want to install an app, it says there's not enough free space. I have restored some apps with MyBackup Root. I think that was the problem. How to solve it? I don't want to wipe data, I've made some important changes. (system 219MB/220MB) (cache 1MB/37MB)\", \"Can't send MMS messages (but can receive) About a week ago I lost the ability to send MMS messages. I can still receive, and single-recipient SMS works fine. The phone is a Moto X, stock. I did recently install some new apps; could one of them be interfering with the messaging app? EDIT: the message I get is `invalid destination address` or `could not send message`.\", \"How to enable SD MSC mount in Jelly Bean? Since upgrading to CyanogenMod 10, the dropdown notification when I connect my phone to the computer no longer appears. How do I mount the SD card via USB MSC in Jelly Bean? Note that the drive still appears to the computer, just empty. My phone is an Xperia Arc S. Update: I see no mass storage mode under USB connection settings: ![Settings](http://i.stack.imgur.com/2apXR.jpg) Settings screen (click image to enlarge)\", \"How do I turn off the power-on sound and camera sound in a Galaxy S II? I have followed several guides and can't get any of them to work on my Galaxy S II. How do I disable these sounds?!\", \"Change the Nexus 4 voice dialer When I double tap the main button of my Bluetooth headset a voice dialer pops up. I'm not sure what app is this voice dialer (I couldn't find it on the apps list), all I know is that it's slow to load and works poorly. I'd like to replace this app with the standard voice search of the Google Search app. Is there a way to achieve this? Rooted options welcome.\", \"htc one x screen wake up issue I am having problem regarding my screen wake up. It wakes up from time to time automatically. I installed Wakelock Detector to track the app and found out Window manager has woken up the screen. I have formatted the phone but no luck. Thanks\"]}, {\"query\": \"How to make run only a single application with all other application stopped?\", \"pos\": [\"Single application tablet possible? > **Possible Duplicate:** > How to make run only a single application with all other application > stopped? Background: I would like to create a weather station alarm clock from an Android Tablet. This would sit on my bedside table. When I tap the screen or press the button or something, it would wake up and show me the local weather forecast. Question: Is it possible to make an Android tablet behave in a single application way like this? I.E. no apps, no app store, no 'slide to unlock', no other logos or screen clutter, that kind of thing? Added: This is more of a user interface question. I don't care if there are other applications on the device, I just want it to behave to the user as if this was a weather station device only, rather than an amazing multi-purpose computer. I don't need it to be secure against someone who really wants to get past the main application, I just want to prevent it always annoyingly switching out of the main app. Since it's going to be a weather station only, I don't want it always offering me irrelevant things, like a handy screen saver, other apps, slide to unlock, etc. and making me have to do loads of interaction to get back to its main app.\", \"Only give access to one application(full screen) I am considering using tablets for use with clients at my job and I was just wondering if there is anyway to lock users into one application. Just for theoretical purposes the application could be as simple as a sign in sheet where people would check in when they come into the buildings. what I want to disable is people's abilities to get out of the application(hitting the home button, et cetera). Basically I don't want people to have access to anything besides the sign-in application. Everything I've read points to this not being possible but I figure this is the best place to ask. Thanks in advance.\"], \"neg\": [\"SD card not recognized by Acer A500 I just bought a used Acer A500 16 GB tablet yesterday. Everything works great -- except it doesn't recognize my 16 GB microSD card for storage, but says there is no external SD storage or it is emulated. I don't know a lot about rooting and such; the guy just told me it was rooted and is running JellyBean 4.1, which works great. Does anyone know how to get my SD card recognized again?\", \"Can't \\\"Wipe data/Factory reset\\\" due to broken power button **Alright, so a little background:** lately my Epic 4G touch has been acting up (restarting when it feels like it, power button won't work, going from 100% power to 20%, etc) and I decided to take appropriate action. I first purchased a new battery (as my previous one was \\\"bloated\\\" and had water damage) hoping that would fix my problems. Unfortunately it only reduced how often the problems occurred. My next course of action is to do a factory reset on my phone (it's rooted running CM 10) and get a new one from Sprint (I have insurance) which I did following this tutorial. Everything went well - as far as I can tell - but I am experience a boot loop as mentioned in the video. The remedy is to wipe the /data partition, which is where I'm stuck. **The problem:** As mentioned, my power button is broken, but in a very _odd_ way. I can use my power button to get into Recovery Mode, but after that it almost never works (and if it does it's as if I'm holding the power button and my phone restarts after about 5 seconds). So due to this I can't select \\\"Wipe data/factory reset\\\" from Recovery Mode. I tried using `adb` to wipe my data partition, but since my phone is no longer rooted I get \\\"Permission denied\\\" and any attempt to use `root` or `su` (obviously) fail. I'm not sure what my options are at this point, is there anyway I can wipe the `/data` partition without root or a power button?\", \"How to sync calendar with android without google? is there a way to sync an Ubuntu calendar application like Thunderbird Lightning or Evolution with an Android device without using google-calendar? At the moment I am syncing my Thunderbird-Lightning calendars on different computers via Dropbox, what is much more reliable than google-calendar. Another big advantage over google-calendar is, that I can access my appointments offline as well, since the calendar files are synced onto the harddrive of each computer by Dropbox. I'd like to access those calendars via my android device as well. * The Dropbox-app for android does not support automatic syncing yet, so it seems like I have to use another service. * Apart from that I guess I need to know an android app, that can access a calendar-file stored in ics-format. Thanks in advance YSN\", \"How to display cell info display (CID) on screen? Does Android OS have an option for showing **Cell Info Display** (Name of the cell Tower) on home screen ? (Without using external apps)\", \"Nexus 4 with no signal (sleep mode) I have a problem with my sister's Nexus4. When I try to call her I have the reply that the phone is off while if I call the other phone (HTC Legend with the same operator) I can talk regularly. It happened both at home or outside. It is not a operator problem because I change the operator twice and with the same operator in the same places I can phone with no problem. The strange is that when a try to call N4 I am not able to start the call and after that the N4 receive a sms with the \\\"lost call\\\" and return it on-line. it seems it falls in a sleep mode when the creen is off\", \"Problems with wifi connection I have a netgear router wnr2200 and sometimes, when I connect with my tablet android or my cellphone, there are some problems. Using chrome, sometimes the page can load and load, I have to reload it. Using YouTube or play store, sometime it keeps on loading I have to restart (also seen with Astro file manager and freely). so I assume it is because some requests aren't succeeded.here are my home channels (my WiFi network is colored) ![enter image description here](http://i.stack.imgur.com/Appxb.jpg)i I already tried channel8 and 1, no success. Any idea (channel 14 is not supported)\", \"How to partition Samsung Galaxy Ace S5830 using ClockworkMod Recovery? Device: Samsung Galaxy Ace S5830 I am using ClockworkMod Recovery 6. There is no way in the CWM Recovery 6 to partition the SD card. Also, I am not able to install ClockworkMod Recovery 5 on my phone. How can I partition this phone using CWM?\", \"How to delete an app installed with Amazon Appstore? I've installed an app using Amazon Appstore, but I don't see a way to uninstall it. Amazon support has failed to respond to my question after 2 days. I prefer a method that does not involve deleting it with a file manager or some sort of root access.\"]}, {\"query\": \"Necessary to manually update gapps?\", \"pos\": [\"Do apps included in the gApps zip get updated via Google Play? While using alternative ROMs like CyanogenMod 9(as i currently am), one flashes an additional zip file called gApps which contains apps provided by Google. As Google releases newer versions of applications, will the newer versions be updated via Google Play or do I need to manually flash a newer version of the gapps.zip? I'm asking because an update to the Google Play market app was recently released (which includes the ability to remove apps from the ALL MY APPS section) but I still haven't seen my market app updating itself.\"], \"neg\": [\"Where are the downloaded Google Play Music files? I've looked in /Android/data/com.android.google.music/cache and nothing is there. I don't have an SD card.\", \"How to Install ClockworkMod on Innos A35 I tried to install ClockworkMod via ROM manager. My device(Innos A35) wasn't listed, but I chose one that was on the list thinking that it would somehow get installed. There was a download, but when I reboot to recovery mode the old recovery software with only a few options is shown. I was thinking that rebooting to recovery mode would bring up something like this if ClockworkMod was properly installed. Can someone point to me what could be wrong? Or can someone tell me how to manually install ClockworkMod without using ROM Manager?\", \"Xperia T not booting anymore, brick? I have a xperia T and I think its hardbricked. What I think what happened is this, i wanted to format my interal storage manually. But instead the entire mmcblkp0 got formatted.. Now I cant boot anymore and pc recognizes it as qualcomm hs-usb qdloader 9008. I already tried to recover with QPST tool, but no luck. Flashmode/fastboot is not working. Phone doesn't even vibrate on longpressing vol up + power. Does anyone know how this can be fixed? Thanks.\", \"Can I install a micro-SD card into my Note 2 after I've enabled device encryption? I recently got a Note 2 and enabled device encryption. I want to put a micro- SD in it now to expand my storage capacity, but when I go to settings, it won't let me mount the card. Is it because of the device encryption? Is there a way to add this drive without decrypting and re-encrypting?\", \"Battery charging issue in Samsung Galaxy S3 I have a Samsung Galaxy S3 and it is charging very very slowly when plugged into my PC. I deleted all my images and some videos and some other stuff and the charging seemed to speed up. Is battery charging is related to available memory? I didn't think it was.\", \"Can Android phones/tablets access W-Fi network drives? There are many WLAN routers currently available which have a USB port to connect external HDDs. These are than easily available on Windows machines directly in Windows Explorer, they look like common network drives. If I used an Android-based smartphone or a tablet which support WLAN, would it be possible to access these network drives somehow? I'm entertaining the idea of getting a tablet, and it's important to know if I can access a larger storage than what would be available locally via an SD card (if such a slot even would be present on a particular specimen).\", \"How can I factory reset my Android Mini 7100 Phone? I'm unable to go to recovery mode to factory reset (wipe my cache/data) my Android Mini 7100 Phone (Android GingerBread 2.3.x) because I forgot my phone lock combination. I tried resetting it using: * Vol Down + Power, * Vol Up + Power, * Vol Up + Vol Down + Power, * Vol Up + Vol Down + Home + Power, and the phone starts normally * When I press Home + Power, it goes to 'Test' mode. I checked out this YouTube video but it did not work for me. How do I go to recovery mode?\", \"I can FastBoot my device. How can I reinstall an Android image to it? I accidentally deleted a file from my phone (Sky iVega IM-A800s, Gingerbread) and phone cannot boot now. I'm seeing an \\\"System operating error\\\" and device restarts repeatedly. I could get into FastBoot mode and I installed Android USB driver and ADB drivers, although I don't know what they are exactly. I couldn't find OEM Android images for the device (it's a Korean web site and they have an ActiveX based installer that doesn't work). I know StackExchange is a Q&A site and I really love this site. I would like to know if you know any software that let's me install a custom ROM (I could download a custom ROM for a similar model) using the PC. Device does not have USB debug mode enabled and the current installation is no longer functional. Can I install a ROM (or Flash ?) using a PC (Windows/Linux) to a FastBoot enabled device? I would be so grateful if you could shed a light for me. Thanks in advance.\"]}], \"CQADupstackEnglishRetrieval\": [{\"query\": \"\\\"there are still a few administrative i's to dot and t's to cross\\\"\", \"pos\": [\"What does this sentence mean:\\\"If you fail to dot an \\u201cI\\u201d or cross a \\u201cT,\\u201d you could be...\\\"? > **Possible Duplicate:** > \\\"there are still a few administrative i's to dot and t's to cross\\\" From here there is a sentence: > If you fail to dot an \\u201cI\\u201d or cross a \\u201cT,\\u201d you could be banging your head > against the wall for hours. What does the author mean by `dot an \\\"I\\\" or cross a \\\"T\\\"`? Thanks.\"], \"neg\": [\"Can I use indefinite article with superlative adjectives? In most russian grammar books there is a rule saying that definite article must be used with superlative adjectives. However from time to time I see people using indefinite article. For example, a title of the film \\\"A Most Wanted Man\\\". Can somebody explain this?\", \"\\\"Stop a loophole\\\" vs. \\\"fix a loophole\\\" Which is the preferred usage \\u2014 \\\"to stop a loophole\\\" or \\\"to fix a loophole\\\"?\", \"\\\"The last couple of years,\\\" \\\"in the last couple of years,\\\" or \\\"over the last couple of years?\\\" I wrote the following: > With a sigh, Erin put the newspapers aside. Why she could never find what > she was looking for in the papers? It'd been a good thing she had stopped > reading them **(in/over) the last couple of years.** Should I use in or over? Or just leave it as it is?\", \"Evoking more power than \\\"Titan\\\" EVE Online is a multiplayer game that takes place across a fictional galaxy called New Eden. Players pilot spaceships and fight for territory in large collaborative corporations. Here is a simplified list of the military-focused ship classes, going from smallest to largest: * Frigate * Destroyer * Cruiser * Battlecruiser * Battleship * Carrier * Supercarrier * Titan The four variants of Titan are currently the largest vessels in the game. They usually require the efforts of hundreds of players to build and field. They are extremely valuable logistically and are able to carry an extravagant amount of firepower. For many months after Titans were introduced to the game, the difficulty of fielding one kept their numbers in the single digits. As of this posting, however, the number of surviving Titans is in the middle hundreds. It would appear that if it were the intention of EVE's maintainers to keep Titans rare (and I make no allegation that that is the case), then they have underestimated the growing cooperation and resourcefulness of New Eden's citizens. Now then, here is the question: If a warship class larger than the Titan were added to the game, what would it be called? The word _Titan_ comes pretty close to the ceiling in terms of evoking great power and strength. In Greek mythology, the Titans were a race of immortal giants descended from the gods Gaia and Uranus, themselves descended from Chaos, but neither _God_ nor _Chaos_ seem to be good names for ship classes. The first could be controversial; the second I think is more arguable ( _Chaos Class Warship_ has a nice ring to it), but I think that there are better alternatives. For context, here is an alphabetical list of all ship classes currently in the game: Assault Ship, Battlecruiser, Battleship, Black Ops, Capital Industrial, Carrier, Command, Covert Ops, Cruiser, Destroyer, Dreadnought, Electronic Attack, Exhumer, Freighter, Frigate, Heavy Assault, Heavy Interdictor, Industrial Command, Industrial, Interceptor, Interdictor, Jump Freighter, Logistics, Marauder, Mining Barge, Supercarrier (also called Mothership), Recon, Strategic Cruiser, Titan, Transport For even more context, this page on EVE Wiki has more information about each ship class.\", \"Meaning of \\\"renaissance loser\\\" _Renaissance loser_ in this context > Dilbert: Wally, are you free for lunch? I need to remind myself how lucky I > am that I don't have your laziness or personality or looks. > > Wally: Would you say I'm kind of a renaissance loser? Does it describe people who have multiple attributes of a loser? So can renaissance be used to address things with multiple attributes like people who know art and science?\", \"More grammatically correct: \\\"anything but\\\" or \\\"anything except\\\"? Could you tell me which of these phrases is grammatically correct \\u2014 \\\"anything but\\\" or \\\"anything except\\\"? If the use depends on context, what are the instances when each must be preferred?\", \"How to properly emphasise words with italics in sentences? I'm not sure if I can ask this question here, because it is more of a writing issue. My native language is French and I have been reading and watching stuff in English and I am quite fluent with the language now (I think). Though I find some things kind of weird as to emphasised words in sentences. For example, the following sentence (seen this somewhere on the internet, not really important) : > So what _can_ you do? To me, the emphasis is on the wrong word here. It does not punch enough. In French, I would rather write it like this: > So what can _you_ do? It is not the first time that I see emphasised words in a sentence and I think: well I feel like the emphasis is on the wrong word. It could be moved to another word to make the sentence more punchy. Am I the only one who finds this weird? Any other french-speaking people here that feels the same as I do?\", \"Change \\\"What are you doing?\\\" asked Sally into indirect speech Is \\\"Sally asked what you were doing.\\\" correct? It couldn't possibly be \\\"Sally asked what I was doing.\\\" right? Thanks.\"]}, {\"query\": \"Is \\\"mens\\\" a valid word?\", \"pos\": [\"Which is correct \\\"women's clothing\\\" or \\\"womens clothing\\\"? When I typed the search into Google most of the responses were websites selling clothing and the ratio of womens versus women's was about 1:1. Searching for mens versus men's and the version with apostrophes appears almost 90% of the time. For boys versus boy's or girls versus girl's it is a 3:2 ratio in favor of no apostrophes. So there's a general trend for leaving the apostrophe out. But \\\"womens\\\" looks wrong. I suppose the same question applies to public bathrooms as well. Is the women's room or the womens room?\", \"Is it correct to say \\\"I write children books\\\" (not possessive case)? Although Children's books is what everybody says, I would like to understand why the genitive case is applied in such case. If I write books for children, children is an adjective here; not the owners of my book! The word \\\"children\\\" just defines or characterizes the type of books I write. Therefore, it's an adjective. So, I understand that genitive/possessive case (\\\"I write children's book\\\") is incorrect grammar. My question is: is the genitive case here really accepted as right? If I use \\\"I write children books\\\" (following the grammar principle) as as I say \\\"I write pets books\\\" (books about pets, and not possessions of pets) - would I be incorrect? Why?\"], \"neg\": [\"\\\"In accepting\\\" vs. \\\"to accept\\\" > **Possible Duplicate:** > When should a verb be followed by a gerund instead of an infinitive? > Part of the reluctance **in accepting/to accept** social arguments about > human nature lies in the fear that many scientist have, of falling into the > Cartesian pit. I'm not able to differentiate between these two choices. Both sound Okay according to my understanding. Is latter one more apt in usage?\", \"What's the meaning of Goofy is a Dawg? http://www.straightdope.com/columns/read/978/if-pluto-is-a-dog-what-is-goofy Says that goofy is a dawg, not a dog. Well, dictionary says that dawg is black people. http://www.urbandictionary.com/define.php?term=dawg So what's the connection? http://nws.merriam- webster.com/opendictionary/newword_display_alpha.php?letter=Da&last=50 says that dawg also means dog. So what's Cecil meant when he said that Goofy is not a dog but a dawg?\", \"Is there a name for this characteristic of the human body (picture) Is there a name for this characteristic of the human body (see the arrows in the picture below)? They seem as 'holes' on the back. I'm not able to imagine one ![picture of feature](http://i.stack.imgur.com/lIFHW.png)\", \"Meaning of \\\"to grow a funny bone\\\" What is the meaning of the idiom _to grow a funny bone_? What does _funny bone_ refer to? Googling shows only places where it was mentioned.\", \"Word to describe difficult to keep request I am looking for a word which can used in the following blank space: I understand the ____ nature of the request; hence a negative answer will not change anything between us. The word needs to describe a request which is difficult, a bit imposing, and the favour is too big. Thanks very much\", \"The difference between 'have mercy,' 'extend mercy,' and similar phrases Is there a difference between 'having mercy' and 'extending mercy?' Are there other phrases that mean similar things?\", \"\\\"Result in\\\" or \\\"result to\\\" I am trying to help a friend to write his CV. I don't know which preposition to use in the following paragraph: > Advance Marketing Staff Knowledge & Skills Which Lead To Dramatically > Increase In The Branch Customers. And Also **Result In** Several Competitive > Branches In The Same City To Close. Should _result in_ or _result to_ be used? I feel like it should be _result in_ but I am not sure. Any suggestions or improvements are also welcome.\", \"What is the word for statements that aren't ironic but have an interesting juxtaposition? For example: my grandmother went to university and learnt to speak RP; I went to university and picked up her cockney drawl. Or: I was searching for a book on the internet and when I found it, it turned out that it my next door neighbour was selling it.\"]}, {\"query\": \"Question mark(s) when asking about a quoted query\", \"pos\": [\"Quoting a question at the end of a sentence which is itself a question If my sentence is a question _and_ ends with a quote of a question, where exactly do I put a question mark? > 1. Did she ask, \\\"Is it raining\\\"? > > 2. Did she ask, \\\"Is it raining?\\\"? > > 3. Did she ask, \\\"Is it raining?\\\" > >\", \"Placement of question mark for a question quoting a question Suppose I am writing to a friend, asking if he remembers a certain question I asked him. Do any of the following sentences correctly use the question mark? > Do you remember when I asked you \\\"do you know the time\\\"? > > Do you remember when I asked you \\\"do you know the time?\\\" > > Do you remember when I asked you \\\"do you know the time?\\\"? Less specifically, what is the general rule that can be applied to such a situation?\"], \"neg\": [\"Omitting the subject in writing I wonder whether it is formal to omit the subject in writing. Must all sentences always have a subject when I'm writing an English test? \\u00ccs it colloquial and reserved to speech?\", \"Does use of the term \\\"Gordian knot\\\" imply a heavy-handed solution? I\\u2019m a PhD student currently struggling with the section of my thesis where I\\u2019m praising my supervisors. It is not that I\\u2019m having trouble summoning up the willpower to do so, but rather that I\\u2019m puzzled by a specific grammatical detail. I am planning to write something along the lines of > \\u2026 a better team of consultants is hard to imagine when it comes to [\\u2026] > untying theoretical and technical Gordian knots. _thefreedictionary.com_ defines the term as \\u201can exceedingly complicated problem or deadlock\\u201d, which is exactly what I want to say. However, the etymology of the expression is obviously related to Alexander the Great coming across the knot in Gordium and \\u201csolving\\u201d the riddle of untying it with his sword. So, in using the expression in this way, do I imply that my supervisors tend to prefer brute-force solutions?\", \"What is the correct tense to be used in Technical Presentation most of the time? This is more about the suggestion I am asking here. Please share your ideas/experience which will help me. I am working as a project manager and want to present most of the time about project or technology. I used mixed tenses for the same kind of information which suits sometimes correctly and interpreted the meaning sometimes incorrectly.\", \"Looking for a word for concealment of faults I'm looking for a word for concealment of faults. In this case, someone has a specific fault and actively, purposefully hides it from others, going out of their way to conceal these faults from others. It's a close-opposite of _hypocrisy_ or _deceit_ , where one pretends to have a good quality that one does not possess. This would be a superimposition of a non-existent good quality, while the word I'm looking for would be the deprecation of an existent fault or negative quality. Also, not a mere denial or non-acknowledgement of the fault, but an active secrecy or pretence. Is _dissimulation_ my best bet? That doesn't seem to necessarily include concealment of a fault, just concealment in general. Examples: 1) Fearing what his parents might think, John pretended to be a non- smoker. When they asked him directly about it his words faltered, not wishing to lie but also not wishing to reveal his shortcoming. In the end, the best he could manage were a few unclear words denying the fact and, hoping his parents believed him, quickly changed the subject. 2) John thoroughly enjoyed hunting, but in the company of others, from fear of damaging his reputation, was scrupulous in concealing this and even went out of his way to appear outspoken against hunting. The close-opposite (hypocrisy) would be: For fame and fortune, John pretended to be able to read others' minds and offered his services at a reasonable rate. To avoid being discovered as a fraud, he kept his readings vague and used intrigue to keep his clients hooked. So I hope these help give a clearer idea of what I'm looking for. I am not so much looking for a word to slot into those sentences, but rather a word for what such an attitude or behaviour is called. Many thanks for all the work!\", \"Why do 'fine words butter no parsnips'? I was at a dinner last night where some rather nice herb butter was served with the vegetables. Conversation close to me then turned to the English expression 'Fine words butter no parsnips'. It seems rather odd in English, because by tradition the English tend to use gravy with their vegetables. That is until one appreciates that the expression exists in French 'Mots doux ne beurrent aucun panais'. Now French cooks, I can well imagine are more inclined to butter their parsnips, so it makes sense. But I would be interested to hear of the possible use of this expression in other countries where English is used. Do Americans, Australians etc 'butter their parsnips', either actually or metaphorically?\", \"Why is the plural form of \\\"life\\\" \\\"lives\\\", while the plural form of \\\"still life\\\" is \\\"still lifes\\\"? Why does the plural form of \\\"life\\\" is \\\"lives\\\", while the plural form of \\\"still life\\\" is \\\"still lifes\\\"? From Wikipedia: > A still life ( _plural_ **still lifes** ) is a work of art depicting mostly > inanimate subject matter, typically commonplace objects which may be either > natural (food, flowers, plants, rocks, or shells) or man-made (drinking > glasses, books, vases, jewelry, coins, pipes, and so on).\", \"What's the meaning of \\\"refer\\\" and \\\"introduce\\\"? What's the implication of the following sentences? > * Could you refer me to the job? > * Could you introduce me to the job? >\", \"Helping improve or help To improve Is \\\"Helping Improve Lives\\\"ok or should it be \\\"Helping TO improve lives?\\\"\"]}, {\"query\": \"\\\"right\\\" vs \\\"correct\\\"\", \"pos\": [\"\\\"Is this the right way?\\\" vs \\\"Is this the correct way?\\\" > **Possible Duplicate:** > \\u201cright\\u201d vs \\u201ccorrect\\u201d I've had this question for a long time. Which sentence is grammatically correct? > Is this the **right** way? > > Is this the **correct** way?\", \"\\\"Is it right?\\\" or \\\"Is it correct?\\\" Which question is more proper? > _[some statement]_ , is it right? > > _[some statement]_ , is it correct?\"], \"neg\": [\"Looking for a list of \\\"english words\\\" that exist in other languages, but with different meanings I had a terrible misunderstanding with a semi-conservative Turkish woman who was offended when I said > \\\"Let's have brunch, and I'll bring some platonic female friends\\\" I'm told that in Turkey, \\\"platonic\\\" has a meaning of one person who deeply desires the other (sexually), but that is not reciprocated. The English version is that of a non-sexual friendship, where lust isn't in the picture. She thought that I wanted all these women present who admired me in that sexual way. I don't wan to create these offenses again, and so is there a list of words that have a strong similarity to English, but with a different meaning?\", \"\\\"I couldn't but laugh\\\" - correct or not? Is the usage of \\\"I couldn't but laugh\\\" in place of \\\"I couldn't not laugh\\\" correct English? Or should it always be \\\"I couldn't help but laugh\\\"?\", \"What's the difference between \\\"my love\\\" and \\\"my lover\\\"? What's the difference between \\\"my love\\\" and \\\"my lover\\\" ? Or do they have the same meaning ?\", \"Is the usage \\\"can able to\\\" wrong? I believe it's wrong. But where can I find some reference on the same? I hear a lot of people use 'can able to' in their daily talk. I believe it's entirely wrong. Both 'can' and 'able to' hold the same meaning. Where do I get more information on the same and also the exact places where I should use 'can' and where I should use 'able to'?\", \"What do you call things made out of natural waste materials? How do you categorize things (say, a toy, doll or idol) made out of natural wastes like leaves, trunks, etc., from nature?\", \"Looking for a word similar to \\\"proverbial\\\", but referring to fables or folk stories I would like to reference something a character said in a famous childhood story, e.g. The Boy Who Cried Wolf, or, Goldilocks and the Three Bears etc. amidst normal writing. For instance, I'll use \\\"proverbial\\\" below, but I'm neither sure it makes sense, nor positive it's grammatically correct. I'm also unsure a drop-in replacement exists, but I'm hopeful. > Ex.1. Don't keep crying for the **proverbial** wolf, Dave, or us villagers > on the marketing team might just stop paying attention. > Ex.2. Looks like that part of our software isn't tuned correctly. The > **proverbial** porridge might be just a little too hot. I'll get it just > right by next Friday. Thanks, Nik\", \"Is \\u201cKnow how to cook leeks\\u201dan idiom? What does \\u201cRead \\u201cHamlet\\u201d and know how to cook leeks\\u201d mean? There was the following sentence in New York Times\\u2019 article (February 28) titled \\u201cWhat you learn at 40s.\\u201d: > \\\"Victor Hugo supposedly called 40 \\u201cthe old age of youth.\\u201d - - The > conventional wisdom is that you\\u2019re still reasonably young, but that > everything is declining: health, fertility, the certainty that you will one > day **read \\u201cHamlet\\u201d and know how to cook leeks**. Among my peers there\\u2019s a > now-or-never mood: We still have time for a second act, but we\\u2019d better get > moving on it.\\\" http://www.nytimes.com/2014/03/01/opinion/sunday/what-you- > learn-in-your-40s.html?hp&rref=opinion&_r=0 What does the line -\\u201cRead Hamlet and know how to cook leeks\\u201d mean? Is \\u201cKnow how to cook leeks\\u201d an idiom, for instance, to mean to get 'the worldly knowledge'? Readers English Japanese Dictionary at hand carries \\u201ceat the leek\\u201d and \\u201cnot worth a leek\\u201d as idioms, but don\\u2019t include \\u201cknow how to cook leeks.\\u201d\", \"Advanced English Pronunciation first thing first I hope this is not off-topic. So here's my problem: my spoken English is quite good, to the point that I'm sometimes mistaken for a native (American) speaker. However, there are words I still tend to mispronounce, especially when the English phonetics structures are not present in my mother tongue (I'm Italian). For instance, a couple days ago somebody brought to my attention that I pronounce \\\"ice\\\" and \\\"eyes\\\" virtually in the same way, and these are the kind of things that are really hard to notice until a native points them out to you. _In other words, I feel like I reached a plateau in my learning process and I'm not sure how to proceed from here._ I should also mention that I get a lot of exposure to the language and I use it on everyday basis, so that alone is not helping anymore (or not helping fast enough). I figured I could start reading about linguistics, and take a more structured approach to learn English (maybe learning the IPA would also help?), but I'm not sure is a good strategy. Do you have any recommendation, books/websites/topics I should check out, or know anything else I could try? Any advice is appreciated. Thank you.\"]}, {\"query\": \"Is cheque and check interchangeable when referencing a checking account?\", \"pos\": [\"\\\"Checking\\\" vs. \\\"chequing\\\" vs. \\\"chequeing\\\" with regards to types of bank accounts I came across this little dilemma when looking up the incorrectly spelled word \\\"chequing\\\" in my web browser's dictionary (Opera). According to the different dictionaries you can select in Opera: EN US is \\\"Checking\\\" (Which I knew) EN GB and EN ZA is \\\"Chequeing\\\" (Which looks really strange to me) Here in Canada I've always seen and used \\\"Chequing\\\", which I actually thought was the GB version. Example of BBC using \\\"chequeing\\\": http://news.bbc.co.uk/2/hi/uk_news/magazine/3242776.stm So how many versions are there? Which is technically the right version for Canada and Great Britain?\", \"Do we ask for check or cheque in restaurants? I know there is a related question asked here. But its slightly different than it and seeking more information. I live in India, I have been to America couple of times. In my first trip it was surprising to see people asking for \\\"check\\\" instead of \\\"bill\\\". I have been told by my friend that here (in America) they call it \\\"check\\\". I assumed may be it arosed from \\\"check-out\\\". After some days another friend told me that it's \\\"cheque\\\", **not** \\\"check\\\" and he elaborated that just like how a banker pays money in return of cheque. After reading few answers/question and links given in the relative question, I really made a conclusion that which one is correct to use because I doubt only one has to be correct and remaining evolved by misinterpretation because \\\"check\\\" and \\\"cheque\\\" sound the same?\"], \"neg\": [\"What is the term used \\\"When a person is called in to work on a holiday\\\"? Someone asked me about what term is used for a **person who is called in to work on a public holiday**. (He told me that it is called _pump of leave_ , but he himself was not sure of it.) So, there are two questions- * What is that person called? * What is that leave is called? If there are specific terms for them please tell.\", \"\\\"Why can't I see?\\\" or \\\"Why I can't see?\\\"? Which of the following is correct? > Why can't I see? > Why I can't see? I am a bit confused, since both have inversion, negation and a \\\"why\\\" in the beginning.\", \"Origin of \\\"washing up\\\" Where does the phrase \\\"to wash up\\\" (equally \\\"to clean up\\\") originate from? Particularly the word \\\"up\\\", how did that enter the phrase?\", \"What is the difference between \\\"daemon\\\" and \\\"demon\\\" in a religious context? Is there a difference between _demon_ and _daemon_ in a religious context?\", \"\\\"If I am getting late\\\" Is it correct to say \\\"If I am getting late, I will let you know\\\"? The Conditional rules don't say anything about continuous tense. In addition, what would be a better way of conveying the same message?\", \"In what dialects is \\\"I don't like it too\\\" grammatical? Consider: **_Too_** \\u2014 ( _adv_.) also, as well, in addition. We don\\u2019t usually use _too_ in negative clauses; we use _either_ instead: * I don\\u2019t like that kind of stuff. * I don\\u2019t like it either. That said, here\\u2019s my concern: I\\u2019ve heard a native speaker from the Lake District (in the UK) say \\u201cI won\\u2019t do it and she won't do it too.\\u201d When asked if that's how he usually phrases such sentences, I got an affirmative answer. This then reminded me of John Lennon\\u2019s lyrics to his song \\u201cImagine\\u201d, which I had always thought must be wrong: > _Imagine there\\u2019s no countries > It isn\\u2019t hard to do > Nothing to kill or die for > **And no religion too**_ Is it grammatical in the UK (or in certain regions) to use _too_ instead of _either_ in such sentences?\", \"Does the word \\\"please\\\" come from \\\"plea\\\"? I thought that the word _please_ came from the plural of _plea_. But then why is it _please_ instead of just _pleas_? Why the e? Are \\\"plea\\\" and \\\"please\\\" really related to each other?\", \"Is it \\u201cgood English\\u201d or \\u201ccorrect English\\u201d or something else? Is it appropriate to say \\u201cI speak good English\\u201d or \\u201cI speak correct English\\u201d? I believe there can be varied replies depending on context, so let me narrow it a little; let\\u2019s say I want to convey how well I speak English.\"]}], \"CQADupstackGamingRetrieval\": [{\"query\": \"When can you respecialize your hero?\", \"pos\": [\"How do you reset your traits(might,magic abilities) in heroes of might and magic 6? > **Possible Duplicate:** > When can you respecialize your hero? I know you reset the traits from the multiplayer by clicking the respecialize,but how do you reset the 1's from the campaign?\"], \"neg\": [\"How can I toughen Qyzen Fess? I play as a Jedi Sage using the Telekinesis tree (damage and a little crowd control). My character cannot take much damage because of the light armor and the endurance values. So I'm using Qyzen as a tank. But he does not have many more hitpoints than my character. I used to equip him with armor with high endurance values but replacing them with high aim values was a good idea as he dealt much more damage (aim is his primary attribute) and he seemed to live longer. The char is lvl 38 now but I have difficulties with lvl 35 mobs. When they beat Qyzen and are on to my character, I mostly die. What can I do now to increase Qyzen's toughness? Should I change back to \\\"endurance-armors\\\" or should I try to increase his armor value? Are there other possibilities?\", \"How to prevent Silverfish from melding into blocks In normal Minecaft with no gamerules changed, silverfish have the capability to meld with existing stone, stone brick or cobble to turn that block into an egg block. If I were to use silverfish in an adventure map but prevent silverfish from \\\"melding\\\" back into the blocks, how would I do this? (I've already tried gamerule mobGriefing)\", \"Falion won't help me cure my vampirism Trying to cure my vampirism, after loading a save AGES ago, and doing the quest to cure it. I get to Falions house, and it's locked. (These events in this paragraph happened the first time) I tried getting in (successfully) into Falions house and of course, he wants me out. After being able to persuade the guard to let me go, being the Thane, I was able to properly talk to him and buy the gem. The problem was, everyone tried to kill me and I was too weak with the disease. (Second time) Tried to break in, unsuccessful. Couldn't make him talk to me this time. How do you properly talk to him? I tried waiting, didn't work.\", \"How many Fetishes are summoned with Fetish Army I can't find how many Fetishes are summoned when you cast Fetish Army. * Is it random each time they're summoned? * Does it depend on the caster level? * Do they actually have limited life, or are they invincible the time they last?\", \"Why did my pills change? In Binding of Issac Wraith of the Lamb I picked up a Range Down Pill that I already identified. Some time during the play-through it changed to a Range Up Upgrade. I am not sure when it happened or what happened. I know that that the Lucky Foot Item and the PHD can change negative pill effects, but neither one of those items were picked up in this run. Are there any other items that could of changed the pill type? If you hold on to a known negative pill long enough does it change?\", \"A possessed dwarf claimed a workshop and is building a mysterious building One of my dwarfs became possessed and while I was building a workshop he claimed it and now he is building a mysterious building. How grave having a possessed dwarf is? Is there a cure for that? What should I expect out of his mysterious building? EDIT: the possessed dwarf built an amulet and then left the workshop!\", \"Does Origin have any built in screenshot functionality? Origin is trying to edge its way onto Steam's turf, which seems to have been one of the major reasons for keeping Mass Effect 3 an Origin (and console!) exclusive. Thing is, I use Steam for a lot more than simply buying games - I've also fallen in love with the Steam Screenshot service. Does Origin have anything comparable? Or will I have to rely on third party software (like FRAPS, or... even running Origin through Steam?) to get my screenshots?\", \"How do I kill the crab boss? In the Beach world, the end boss is at least one (I think three, though, if the cutscene is any indication) crab. I have no idea how to fight it! No matter what I do, it seems to hurt me, and I am losing red balloons like a toddler at the county fair. How do I kill this boss?\"]}, {\"query\": \"Left 4 Dead 2 Special infected tips for Versus\", \"pos\": [\"How can I avoid being detected as an infected in left 4 dead 2? I always get caught playing as an infected no matter how good I hide (because the infected make noises). What would be a good tactic to minimize detection?\"], \"neg\": [\"Are there unadvertised world interactions like shooting off locks to open doors? I just learned the other night that you can **shoot the doorknobs off of wooden doors** (as opposed to picking them, like the door to the shed in the mission Rats). Makes sense right? I just assumed that you weren't able to do such a thing. **Question:** Are there any other unadvertised common sense things in Payday 2 like this? I was just curious as I found this to be quite satisfying. Prior I kept thinking \\\"Why can't I just bust the door down? I'm already caught there's no point in trying to be sneaky.\\\" and then my buddy told me about shooting the knob and I literally just dropped my head to my desk in awe.\", \"How much does time matter in Might and Magic VII? I have some subtle aversion to 'wasting time' in games. For example, in Might & Magic 7, I'd much rather go through an entire cave in one go, without ever resting for 8 hours, but using potions instead to keep my party going. Is that a wise choice? Does time passing affect the game in any way, besides things that depend on which day it is (e.g. stables schedules) and which month it is (e.g. bounties)? For example, the rumor is, once I become the lord of the castle, that the new lords will only last a few months... so if I just spend a year doing nothing but getting trashed at the local taverns, will the game punish me in any way? EDIT: I especially suspect something is up given all the peasants that I can hire that reduce travel time...\", \"Showing up on SpeedRunsLive I know once that once I meet the requirements, My stream will be on SpeedRunsLive. How many races does one have to do to show up on SpeedRunsLive page ?\", \"Must I have an active XBox Live Gold account to get the pre-order bonus for Borderlands 2? > **Possible Duplicate:** > How do I get my preorder bonuses? Do you need to have an active XBox Live Gold account to get the pre-order bonus for Borderlands 2?\", \"How can I visit a friend's island in Dragonvale? My kids have exchanged friend codes, and received gem bonuses. How can they visit each other's islands? Note: Under social, there's a \\\"Friends\\\" pane that always reads \\\"3 gifts available\\\", but has no other entries other than \\\"visit random island\\\". The \\\"gifts\\\" tab is similarly blank. This is on a Kindle Fire HDX, with no Facebook accounts.\", \"Do sensor mode trip mines alert guards? One of the perks for Technician is the ability to turn trip mines into sensors that beep, instead of explode when tripped. Can guards see these, or hear when they are tripped? Specific applications in mind include a \\\"someone is coming up to the roof\\\" detector for Framing Frame day 3, so the camera operator doesn't have to periodically check if there's a guard on the roof.\", \"How can I save a game to a USB stick on Playstation 3 I want to save a game to a USB stick on Playstation 3. When I hit triangle on save data it only offers me online storage. How do I enable USB storage?\", \"What do I do about a caravan that is being attacked? So, in order to get money in Assassin's Creed 3 you've got to send out caravans (or naval caravans) to ship your goods across the lands and all that Jazz. Well when the game was first explaining this they had me send out a single caravan to an area just as demonstration. However, now this Caravan is apparently under attack and has been for quite some time. The menu has no options or anything of the sort to give me ways to deal with this, and the caravan just sits there in the menu being completely useless. What do I need to do with this caravan in order to get it out of its \\\"attacked\\\" state and usable again?\"]}, {\"query\": \"Is there a difference between Adventure play after pass 2 In Plants vs. Zombies?\", \"pos\": [\"How many more \\\"gimmicks\\\" does adventure mode have? I just started my second play through of adventure mode in Plants vs. Zombies, and now Crazy Dave is picking 3 (usually irrelevant) plants for me to use on each level. After I finish this playthrough, will there be a new gimmick or handicap on further playthroughs? How many different gimmicks are there on continued playthroughs, and on which playthroughs do each occur?\"], \"neg\": [\"Is it possible to play Fez with more than one save game? More than one person in my house is interested in the secrets and puzzles of Fez. However, even though it has a save feature, I don't see how I can switch between saves (thus allowing multiple players to start and stop at different points). If I try and choose \\\"Start new game\\\" it says it will delete all progress. Is it possible for two people to play Fez on the same XBox with different save games?\", \"Are Animals Programmed To Escape Their Pens I'm sure many of you Minecraft fans have noticed that animals always seem to try and escape their pens as soon as their is an escape route. I have a simple question about this and that is are they programmed to do this and if they are is it to find a larger area or just to escape?\", \"Is Zombie-pigman aggro contagious? If I attack a zombie pigman, it'll attack me. If I do this on a server will it attack other players, or just me?\", \"If my world has silver instead of Tungsten, can I still make an Emerald Staff? The recipe for Emerald Staff says I need Tungsten. Isn't silver supposed to be interchangeable with tungsten? Why should I have to re-make my world just to get a metal...\", \"What happens to power ups after death In Hero Academy, if I lose one of my units and step over him in the next turn (so I save him), do I also save any equipment he's carrying? Or, is the equipment lost, but the hero is saved?\", \"How do Trainers work in Skyrim? I've been to one or two trainers for Destruction magic already. Since then, if I go to another trainer (different skill) of a level that I've already trained at, the dialog says I've maxed out my training (5/5). So, it seems to me that you can only train five times at a given training level (Common, Expert, Master) _across all skills_. However, given how many different skills there are in the game and how many Trainers there are, this seems rather crippling to me. Can someone explain exactly how Training works? Is my above presumption correct?\", \"I cannot locate my Mardek 2 save file I was playing Mardek on kongregate.com using Google chrome, and decided to move to a different site. So I went to check my flash app data folder and the chat.kongregate doesn't have my save information? I tried to clear out the folder and see if it was still there, and my save still worked on the site, but I have no idea where it is! I even cleared the entire app data folder looking for it and it still worked!\", \"\\\"Cannot Resolve Hostname\\\" when using Subdomain A group of friends wanted a Minecraft server to play together on, so I went ahead and deployed a VPS, and directed an A record titled `play` to the server's public IP address. I connected to the server via SSH using `play.coffeehousecode.com` as the hostname, and didn't encounter anything out of the ordinary, so I started a Minecraft Server instance, and the logs showed no problems to report. However, when trying to connect to the server using the Minecraft client, I receive an error stating that the Hostname couldn't be Resolved. I checked my DNS records to make sure everything checks out, and SSH-ed into the server again without a problem. When connecting to the server using it's public IP address, however, I can connect and play without a problem. Any ideas as to what could be causing this?\"]}, {\"query\": \"Do dropped items in Ingress decay?\", \"pos\": [\"Do dropped items ever expire? When items get dropped and left, do they ever expire or will they be available indefinitely until somebody picks them up? I believe keys that get dropped by portals when links decay expire in a day or two and vanish. Is there anything like that for manually placed items?\"], \"neg\": [\"Make VATS the Default in Fallout 3? Is there a way to always use VATS in Fallout 3? If I hit \\\"V\\\", it goes to the VATS system and then when the turn is over, it goes back to real time combat.\", \"minecraft crashes on osx I just bought minecraft and every time i run it , it crashes. When i double click on its icon in the application folder nothing happens so i have to go into the contents folder to launch its jar file. After I login this is the crash report that keeps displaying. I am running this on osx 10.8.2 (mountain lion) My specs are: 2.26 ghz intel core 2 duo, 8gb ram ---- Minecraft Crash Report ---- // Surprise! Haha. Well, this is awkward. Time: 2/25/13 5:29 PM Description: Failed to start game org.lwjgl.LWJGLException: Could not get the JAWT interface at org.lwjgl.opengl.AWTSurfaceLock.lockAndInitHandle(Native Method) at org.lwjgl.opengl.AWTSurfaceLock.access$100(AWTSurfaceLock.java:49) at org.lwjgl.opengl.AWTSurfaceLock$1.run(AWTSurfaceLock.java:89) at java.security.AccessController.doPrivileged(Native Method) at org.lwjgl.opengl.AWTSurfaceLock.privilegedLockAndInitHandle(AWTSurfaceLock.java:86) at org.lwjgl.opengl.AWTSurfaceLock.lockAndGetHandle(AWTSurfaceLock.java:64) at org.lwjgl.opengl.MacOSXCanvasPeerInfo.initHandle(MacOSXCanvasPeerInfo.java:53) at org.lwjgl.opengl.MacOSXDisplayPeerInfo.doLockAndInitHandle(MacOSXDisplayPeerInfo.java:56) at org.lwjgl.opengl.PeerInfo.lockAndGetHandle(PeerInfo.java:85) at org.lwjgl.opengl.MacOSXContextImplementation.create(MacOSXContextImplementation.java:47) at org.lwjgl.opengl.Context.(Context.java:120) at org.lwjgl.opengl.Display.create(Display.java:858) at org.lwjgl.opengl.Display.create(Display.java:784) at org.lwjgl.opengl.Display.create(Display.java:765) at net.minecraft.client.Minecraft.a(SourceFile:232) at asq.a(SourceFile:56) at net.minecraft.client.Minecraft.run(SourceFile:515) at java.lang.Thread.run(Thread.java:722) A detailed walkthrough of the error, its code path and all known details is as follows: --------------------------------------------------------------------------------------- -- System Details -- Details: Minecraft Version: 1.4.7 Operating System: Mac OS X (x86_64) version 10.8.2 Java Version: 1.7.0_10, Oracle Corporation Java VM Version: Java HotSpot(TM) 64-Bit Server VM (mixed mode), Oracle Corporation Memory: 55114328 bytes (52 MB) / 85721088 bytes (81 MB) up to 1271398400 bytes (1212 MB) JVM Flags: 0 total; AABB Pool Size: 0 (0 bytes; 0 MB) allocated, 0 (0 bytes; 0 MB) used Suspicious classes: No suspicious classes found. IntCache: cache: 0, tcache: 0, allocated: 0, tallocated: 0 LWJGL: 2.4.2 OpenGL: ~~ERROR~~ NullPointerException: null Is Modded: Probably not. Jar signature remains and client brand is untouched. Type: Client (map_client.txt) Texture Pack: Default Profiler Position: N/A (disabled) Vec3 Pool Size: ~~ERROR~~ NullPointerException: null\", \"Plot doesn't continue after killing Darko After finishing That Special Someone by killing Darko, I didn't receive a call by Pegorino, as the wiki says I should. But this means that I can't continue to the following story missions. I did a few optional missions after that, so I don't think waiting will help. Am I missing something or is this a bug? Is there some way of fixing this apart from loading an old save?\", \"Where to find Ashley Williams on the Normandy 2? I asked Ashley Williams to join my crew on the Normandy and she is a selectable member for my squad. The strange thing is I can't seem to find her on the Normandy spaceship. It does not really make sense. Is there a glitch possibly? This is for the PC version on Origin.\", \"What is better for Stalkers: 1 Armor + 1 Shield or 2 Armor? Grubby just came up with this question on Twitter > SC2 math heads: what's better for a Stalker, 1 armor 1 shield or 2 armor? I > heard 2 armor is better before but I don't know why necessarily. I'm curious if we could answer this with our communities math-skills :)\", \"How do I find unique charms in Diablo 2? I've tried researching this one on my own and I've come up with several different answers depending on the site I'm checking. I'd like some solid information on obtaining unique charms in _Diablo 2_. There are three in total: Annihilus, Gheed's Fortune, and the Hellfire Torch. Can I find unique charms while playing single player? If so, do they drop on a specific difficulty (Nightmare, Hell)? Details on all three would be helpful.\", \"How do you sneak up on someone in Hitman 2? I understand the the **L1** button is the \\\"sneak button\\\" and that you sneak up behind them extremely slow, but I can't seem to pull off the sneak attack without alerting them first, even in the very first mission. When I try to sneak up behind the first guy (using **L1** ) that comes out of the door (urinating next to the tree), I try to use the anesthetics and right before I get into range to attack him he starts turning around and my alert box starts flashing red, even though I complete the attack successfully in the end. I suppose that one doesn't matter as much since there's no one around, but I assume if I did it correctly the alert box wouldn't flash. However, when I try to sneak up behind The Don (again, using **L1** ) with my Fiber Wire, he always turns around and becomes alerted before I can actually strangle him, then after I strangle him his guards barge into the room. What am I doing wrong with my sneak attacks? They don't seem to be very sneaky if they can get an alert off before you actually get to them... I've tried doing this several times and he is consistently alerted each time. I'm supposedly able to do this without alerting either of them, but I'm obviously missing something.\", \"What happens if you are left behind on a mission in Warframe? On Warframe, what happens if you are left behind during the extraction when the timer runs out? On a VOID KEY mission, all that happened was that I lost the money reward, but what happens on a normal mission? Is it the same, or do you not get anything at all and it is considered a mission fail?\"]}, {\"query\": \"How do I progress in Ars Magica 2?\", \"pos\": [\"How do I level up in Ars Magica 2? I've been playing Ars Magica 2 and casting spells but there's no xp bar for the magic. I have checked the options but that's not it, I can move it but can't see it. I'm also not levelling - I've killed like 20 mobs with just magic and I'm still level 1 with 100 mana and like 11 burnout. Ars Magica is a Minecraft mod and is part of some of the Feed the Beast packs for 1.6.4 and i can cast the spells\"], \"neg\": [\"Left 4 Dead 2 accuracy - when can I stop shooting? I'm trying to break 75% accuracy on Left 4 Dead 2. I can consistently get about 60% accuracy with either of the automatic shotguns, but when I try with the M-16, my percentage drops to the high 40s. Assuming that my shooting habits don't change from firearm to firearm, the only thing that I can think of that would cause this drop is the fact that the shotgun's slower firing rate means that every common infected is guaranteed to get one _and only one_ round. With the M-16, it's very likely that a particular bullet will kill the zed, but by the time I realize that it's dead, I've fired once or twice more. My question is: **Do bullets fired at dead or dying zombies* count against your accuracy rating?** This has a follow-on question: **What about bullets fired at zombies that a teammate has also been firing at?** If your teammate kills the zombie, does your accuracy decrease for those bullets you fired at it? (*And before you say it, yes, I know that _all_ zombies are dead)\", \"Is there known PlayStation Network Status page? Right now my PS3 is having difficulties signing in to the PlayStation Network. Has anyone found an official status page for the PSN?\", \"How do you progress past level 31? Do you have to solve the cipher? In the new iOS game \\\"Hundreds\\\", once you finish each board, you can advance to the next, either by hitting \\\"next board\\\" or by selecting it from the level select screen. Except when I hit next board on level 31, it takes me... back to level 31. I'd think it was a glitch, except that the game contains ciphers, and other mysterious artifacts that lead me to believe that there's just a puzzle I need to solve. There IS a cipher opened on level 31 (which I have not solved), but what's confusing is that I did not have to solve the earlier ciphers to progress past those levels. If the answer does have to do with solving the cipher, please don't post its solution without using the cipher markup.\", \"What purpose does the 'Take Picture' function serve in the Far Cry 3 Camera? The camera in Far Cry 3 is useful for tagging enemies from a distance but also includes the ability to take pictures. This does not seem to do anything useful when used on animals or enemies. Is there a purpose?\", \"How can I pause the game? I have just started playing the game (about an hour in) and I don't seem to be able to pause it. The start button is usually pause but it does nothing. I can press select to bring up the phone menu but the game appears to carry on (I haven't actually tested this is a battle scenario yet though) I am wondering if maybe this is something to do with the fact I selected to play in PSN network? The thing is though, I selected private and have not invited anybody so I have no chance of another human joining me. If this is because of network mode, can I turn it off mid-game? So far I am just assuming... maybe you just can't pause RE6? (I hope this is not the case though)\", \"What does \\\"stagger\\\" mean? In the **Heavy Armor** skill tree, the following perk exists: > **Tower of Strength:** 50% less stagger when wearing only Heavy Armor. What does \\\"stagger\\\" mean in this context? Does it do anything if you already have the **Conditioning** perk? > **Conditioning:** Heavy Armor weighs nothing and doesn't slow you down when > worn.\", \"What does the code at the start of each Really Big Sky game mean? At the start of each Really Big Sky game a code is flashed onto the middle of the screen briefly, before disappearing. ![](http://i.stack.imgur.com/5DBfp.jpg) The code appears to be random, and consists of names, numbers and letters. However, it's significance does not appear to be explained anywhere. What does it mean?\", \"Can I use DLC from Origin with the base game from Steam? I have never played Mass Effect: Pinnacle Station, but it no longer appears to be available on Steam. This DLC now only appears to be available on Origin, from what I can see. I own Mass Effect on Steam, but the Steam CD key for the first Mass Effect game is not valid within Origin (the CD key for Mass Effect 2 is, but that's not going to help in this particular situation). Am I able to purchase Mass Effect: Pinnacle Station on Origin and then use it with my copy of Mass Effect from Steam, or am I going to need to repurchase Mass Effect on Origin in order to use the Mass Effect: Pinnacle Station DLC, from Origin?\"]}], \"CQADupstackGisRetrieval\": [{\"query\": \"qgis projections from Michigan's CGI data\", \"pos\": [\"How to reproject from Michigan Georef to WGS84 in QGIS? I am trying to reproject a michigan state land shapefile which is the NAD83/ Michigan Oblique Mercator EPSG:3078 to the WPG84 EPSG:4326 projection. However when i try to do this my map gets squished down. I have tried the commands in \\\"Reprojecting from MGRm to WGS84 using open-source tools\\\" with no luck. I need to get these coordinates into a decimal degrees format. Thanks\"], \"neg\": [\"MultiLineString to separate individual lines using Python with GDAL/OGR, Fiona, Shapely There are lots of MultiLineString features in a polyline shapefile. The problem is to split these lines into separate individual lines. There is a similar question with PostGIS, but I need to code in Python. I have been planning to extract the geometry from each MultiLineString feature and create separate features and append a shpfile with those new features. Later, in the second pass, I would delete the MultiLineString object which is no more required as it is now split into its components. It will require reading data from original shpfile and appending the newly created features to a copy of the shpfile. Then the appended (copy) shpfile needs to be rid of the MultiLineString features. It seems cumbersome as I find no way to read and write the shapefile simultaneously. There should be better ideas to do that. How to split these lines into separate individual lines using Python with OGR, Shapely, Fiona or Pyshp? A snippet of code to give an idea is below. import fiona from shapely.geometry import LineString original_shpfile = fiona.open(path_to_road_network_shapefile) with fiona.open(path_to_road_network_shapefile, 'a') as copy_shpfile: rec = original_shpfile.next() while rec: try: vertices_on_link = rec['geometry']['coordinates'] LineString(vertices_on_link) #reconstruct the line from vertices #if multipart, #has multiple lists within the list, #one for each of the Linestring component. also throws AssertionError except AssertionError: number_of_parts = len(vertices_on_link) for x in range(0,number_of_parts): part = rec part['geometry']['coordinates'] = vertices_on_link[x] copy_shpfile.write(part) rec = original_shpfile.next() #in the next phase, delete each of the the MultiLineString features from the copy of the shapefile\", \"OpenLayers zoom to Boundingbox depends on lat/lon positions I'll try to explain my application and the problem. My application gets a position for a zip code and receives a Lat/Long position, then I'm looking for all points around this position and show them.(This works fine) Now I want to try to show a user his zipcode position at the center of the map, but I also want to specify the correct zoom level, so that the user can see all points around this point. (it's not limited by the distance) Is this possible? My minLat,maxLat,minLng,maxLng looks like this : Array ( [minLng] => 8.381 [maxLng] => 9.15259 [minLat] => 49.5103735 [maxLat] => 50.06762 ) Edit: -HTML: map.setCenter(new OpenLayers.LonLat(center.lon,center.lat) // Center of the map .transform( new OpenLayers.Projection(\\\"EPSG:4326\\\"), // transform from WGS 1984 new OpenLayers.Projection(\\\"EPSG:900913\\\") // to Spherical Mercator Projection ), config.zoom // Zoom level ); map.zoomToExtent(new OpenLayers.Bounds(config.bounds.minLng,config.bounds.minLat,config.bounds.maxLng,config.bounds.maxLat).transform(epsg4326)); I have added the html of the function.\", \"Can ArcGIS handle missing values to create a contour map? I am trying to create a contour map for a waterbody. It turns out that in several locations there is no data. I just don't want to use the missing values as zero in ArcGIS because it would create several other contours by interpolating actual value and the zero value. Can someone suggest to me any good way of handling N/A or no values in ArcGIS to create contour maps?\", \"QGIS not Printing to PDF Simple QGIS 1.8.0 not printing to PDF. Suddenly stopped. Is this an error or something. the resultant PDFs are blank pages. When i file print and use a pdf printer it creates blank docs. when i export to pdf it crashes. when i export to jpeg it does not print map.\", \"ArcSDE & Oracle & Database Model -- Best diagram the system Having Oracle dbms with ArcSDE and ArgGIS Pipeline Database Model (APDM), I would like to best describe the system with a diagram. In MS SQL each db is autonomous, I think, meaning ArcSDE(using dbo schema) has the SDE business tables in each db and further using a database model, each database has the database model implemented within?? For Oracle (oracle has a schemas instead of db's??), the Oracle instance that has the sde business tables and the database model..assigns users to participate in those tables as a sort of view (schema??) and sde manages those schemas?? My question: I would like to draw a picture showing how sde and the database model relate as well as how they sit in relation to the rdms on Oracle and on MS SQL Server. Thanks.\", \"Help with Python script for exporting mxds into pdfs! I have written a python script to export my mxd into a pdf (not using data driven pages). I am getting the error that my mxd path is incorrect! I have included my script below. Any help would be appreciated. # Necessary modules import arcpy, os, string, sys # For less complicated scripts - # these 2 imports are necessary to utilize a simpler method of debugging outher than traceback import win32ui, win32con # example debug message to screen ...... # arcpy.AddMessage(\\\"mxd_file = \\\" + str(mxd_file)) # val = win32ui.MessageBox(\\\"mxd_file = \\\" + str(mxd_file), \\\"title\\\", #win32con.MB_OKCANCEL) #Paramaters... mxdList = string.split(arcpy.GetParameterAsText(0), \\\";\\\") dir = arcpy.GetParameterAsText(1) #Loop thru & take the base name of each MXD selected and append all map pages to a single pdf # and save to chosen directory...... for mxdPath in mxdList: mxd = arcpy.mapping.MapDocument(mxdPath) name = mxdPath[:-4] + \\\".pdf\\\" file = dir + os.sep + os.path.basename(name) ddp = mxd ddp.exportToPDF(file, \\\"ALL\\\") del mxd, file\", \"How to add an independent panel in the ArcMap layout view? In ArcMap 9.3, I want to make a map in which I start from a wider location (for example, a country) and then, I give some zooms until one specific area. I have all the data I need: country administrative boundaries, state/province boundaries and city boundaries (in Brazil and Paraguay). I already got to make my first panel, but how do I plot an independent \\\"frame/panel\\\" into my ArcMap layout view? I added a screenshot below, where I drew in red (paintbrush) the region I would like to zoom and plot in a separate panel. Also, I would appreciate pointers to any tutorial about how to do this kind of location map (start in broader area and zoom to specific place). Tks. ![enter image description here](http://i.stack.imgur.com/RztAA.png)\", \"Remove Points that Overlay I have a pointlayer and some points overlay and i want to remove them, so that only one point remains. the points that overlay have the same coordinates\"]}, {\"query\": \"Possible to automate Buffer Wizard in ArcGis 10.x by working in the Shell?\", \"pos\": [\"Getting buffer wizard-like speed in Python (vs using MultipleRingBuffer_analysis) I've got a script that takes a single input polygon feature, throws 10 buffers around it at tenths of a specified input distance, and then symbolises the output based on an existing layer file. Pretty simple. However, the arcpy.MultipleRingBuffer_analysis operation is amazingly slow. It takes upwards of two minutes to generate the buffers, even for very basic polygon inputs - the same result can be had in about two seconds by using the buffer wizard tool. Problem is, the buffer wizard can't be accessed through arcpy. So obviously it's _possible_ to quickly generate multiple ring buffers - does anyone have any insight as to how the buffer wizard tool is doing it, and how that might be replicated in Python?\"], \"neg\": [\"How to import data with SRID 4269 to GEOGRAPHY type? In PostGIS, we are importing various US census bureau shapefiles (with SRID = 4269) using **shp2pgsql** loader. I have medium-sized database with several tables containing fields of type geography (lat/lon points, polygons, multipolygons, etc) that span locations across the US on which we do various distance calculations, \\u201cwithin\\u201d polygon, etc type of geographic functions as well as display data using Google Maps Api. We currently have no geometry type fields (only SRID used in database is 4326 via geography type). Because they cover a fairly large area and to keep things simple and consistent (avoid projection details etc), I would like to store the census shapes (ie boundaries for states, metros, counties,block groups..) in geography data type instead of geometry as well (unless there is a reason NOT to do this or someone can explain why we should store as geometry instead). My question is, if I use shp2pgsql loader with \\u2013G option to create a MULTIPOLYGON geography field for these boundaries (and thus use a 4326 SRID) does the loader \\u201cconvert\\u201d the data from a 4269 projection to 4326? Is a conversion really necessary? Do I need to first load into temp table with geometry type then perform update to transform/cast into geography type and then store that result? Thank you for any advice or insight related to this.\", \"10 GB Raster Aerial Image in Quantum and POSTGIS I have a problem... I have been successful in uploading the raster in postgis but when I access it through Qgis. it does not open even after several hours. I have 10GB aerial image raster and everytime i try to open it through QGIS it does not open even after many hours and QGIS crashes... why is this happening...?? is it really due to the file size..?? Regards, Omer\", \"Creat point and update featuers spatial join I have the following code that is in a tool. The tool creates a point on click, then auto sequential populates the \\\"AddressID\\\" field and also populates X_Coord, , Y_Coord. This part of the tool works smoothly. After these fileds are populated i need the new created feature to populate other fields based on Parcel the newly created point sits on. I do a spatial join in order to get the fields populated. The two functions work perfectly if they are separate but once i combine the two and run the tool ArcMap just sits thinking and thinking nothing happens. I eventually have to end ArcMap process with task manager. I don't get no error at all. After i reopen the mxd i see the newly created point but nothing is populated from the Parcels. I would appreciate any help. Here are the two codes. import arcpy import pythonaddins import os from arcpy import env class Add_points(object): \\\"\\\"\\\"Implementation for AddPoints_addin.Add_points (Tool)\\\"\\\"\\\" def __init__(self): self.enabled = True self.cursor = 3 # Can set to \\\"Line\\\", \\\"Circle\\\" or \\\"Rectangle\\\" for interactive shape drawing and to activate the onLine/Polygon/Circle event sinks. def onMouseDownMap(self, x, y, button, shift): fc = \\\"TonyTwoWay.DBO.TT\\\" workspace = r\\\"C:\\\\Users\\\\talmeida\\\\AppData\\\\Roaming\\\\ESRI\\\\Desktop10.1\\\\ArcCatalog\\\\Connection to dsd15_sqlexpress.sde\\\" arcpy.env.overwriteOutput = True #arcpy.ChangeVersion_management('TonyTwoWay.DBO.TT','TRANSACTIONAL','dbo.DEFAULT', \\\"\\\") # Start an edit session. Must provide the worksapce. edit = arcpy.da.Editor(workspace) # Edit session is started without an undo/redo stack for versioned data # (for second argument, use False for unversioned data) edit.startEditing(True) # Start an edit operation edit.startOperation() CC_list = [] with arcpy.da.SearchCursor(fc, [\\\"AddressID\\\"]) as cursor: for row in cursor: try: if \\\"CC\\\" in row[0]: CC_list.append(int(row[0].strip(\\\"CC\\\"))) except TypeError: pass del cursor CC_list.sort() AddressID = CC_list[-1] + 1 AddressID = 'CC' + str(AddressID) row_values = [(x, y, (x, y), AddressID)] cursor = arcpy.da.InsertCursor(fc, [\\\"X_Coord\\\", \\\"Y_Coord\\\", \\\"SHAPE@XY\\\", \\\"ADDRESSID\\\"]) for row in row_values: cursor.insertRow(row) del cursor # Stop the edit operation. edit.stopOperation() # Stop the edit session and save the changes edit.stopEditing(True) arcpy.RefreshActiveView() arcpy.AddMessage('Updated all records sucussefully') pass Here the spatial join part. import arcpy import pythonaddins import os import time from arcpy import env fcTarget = \\\"TonyTwoWay.DBO.TT\\\" workspace = r\\\"C:\\\\Users\\\\talmeida\\\\AppData\\\\Roaming\\\\ESRI\\\\Desktop10.1\\\\ArcCatalog\\\\Connection to dsd15_sqlexpress.sde\\\" arcpy.env.overwriteOutput = True ####Select by location on parcels with created point Parcellyr = \\\"testParcelsAdmit\\\" fcTarget = \\\"TonyTwoWay.DBO.TT\\\" arcpy.MakeFeatureLayer_management(Parcellyr, \\\"Parcel layer\\\") entries = int(arcpy.GetCount_management(fcTarget).getOutput(0)) for i in xrange(entries): arcpy.MakeFeatureLayer_management(fcTarget, \\\"point layer\\\", \\\"\\\\\\\"OBJECTID \\\\\\\"={}\\\".format(str(i))) arcpy.SelectLayerByLocation_management(\\\"Parcel layer\\\", \\\"INTERSECT\\\", fcTarget, \\\"\\\", \\\"NEW_SELECTION\\\") #if arcpy.Exists(pt_lyr): arcpy.Delete_management(pt_lyr) #### populates fields #fcTarget = \\\"TonyTwoWay.DBO.TT\\\" #fcJoin = \\\"testParcelsAdmit\\\" add_fields = [\\\"ACCOUNT\\\",\\\"SiteNum\\\",\\\"OwnerName\\\",\\\"SiteAddres\\\",\\\"SiteNumSfx\\\",\\\"SiteStreet\\\",\\\"predir\\\",\\\"StreetTyp e\\\",\\\"SubName\\\"] # fix args if not isinstance(add_fields, list): # from script tool add_fields = add_fields.split(';') # do not need this scratch file fcOutput = r'in_memory\\\\temp_join' arcpy.SpatialJoin_analysis(\\\"Parcel layer\\\", fcTarget, fcOutput, 'JOIN_ONE_TO_MANY', 'KEEP_COMMON') # grab oid field from points oid_t = arcpy.Describe(fcTarget).OIDFieldName # init rowW and rowR curR = arcpy.SearchCursor(fcOutput) join_dict = dict([(r.JOIN_FID,[r.getValue(f) for f in add_fields]) for r in curR]) del curR # Now update the new target curW = arcpy.UpdateCursor(fcTarget) for row in curW: t_oid = row.getValue(oid_t) if t_oid in join_dict: for f in add_fields: row.setValue(f, join_dict[t_oid][add_fields.index(f)]) curW.updateRow(row) del row, curW arcpy.Delete_management(r\\\"in_memory\\\\temp_join\\\") arcpy.AddMessage('Updated all records sucussefully') I use this part of the code to select the new created feature. am i going the wrong way about it? Parcellyr = \\\"testParcelsAdmit\\\" arcpy.MakeFeatureLayer_management(Parcellyr, \\\"Parcel layer\\\") entries = int(arcpy.GetCount_management(fcTarget).getOutput(0)) for i in xrange(entries): arcpy.MakeFeatureLayer_management(fcTarget, \\\"point layer\\\", \\\"\\\\\\\"OBJECTID\\\\\\\"={}\\\".format(str(i))) arcpy.SelectLayerByLocation_management(\\\"Parcel layer\\\", \\\"INTERSECT\\\", fcTarget, \\\"\\\", \\\"NEW_SELECTION\\\")\", \"OSM data on MapServer I'm making an evaluation to replace the TomTom commercial Database (ex TeleAtlas) with Open Street Map data. What I need to do is **use the street map as a base layer for my WebGIS applications**. I use MapServer as application server and I made styles for features of the TomTom DB based on attributes values. I want to do the same with OSM data. Looking on the web I found many references to MapBox, TileMill, preconfigured style schemas and so on... but I have some questions/doubts about all that. 1) If I need to render this data through MapServer, can I avail of all these instruments? 2) Is there a way to convert those styles to MapServer .MAP files? 3) I imported OSM data from SHP files downloaded from a web site that regularly creates them from updated OSM data. I read something about OSM2PGSQL, OSM2PO, OSMOSIS and so on... but I don't really understand what they are and how they work and if I can use them with my actual database. Do I really need to import OSM data with such tools?\", \"What does 'to the right' mean, regarding the >> operator in PostGIS? I am reading the documentation on the `>>` operator in PostGIS, and I cannot understand the meaning of `strictly to the right` because on the plane, right or left depends on the direction at which one is looking. Reference: http://postgis.refractions.net/documentation/manual-1.4/ST_Geometry_Right.html What would be that direction?\", \"Where can I find cellmast data in the UK? I am looking for a shapefile containing the locations of cell masts that provide phone coverage in the UK. On sites like Orange, O2 etc they have an interactive signal checker (https://explore.ee.co.uk/coverage-checker for example), but I haven't found a way to obtain the data in GIS form. Have rung several phone companies, but the customer services down there don't seem to understand what I am asking for! Any help would be greatly appreciated, thanks.\", \"Detecting invalid WKT in text column in SQL Server I've got a whole load of WKT data in a SQL Server table, of which I'm trying to detect the validity. I'm currently doing a query like this: UPDATE f SET f.\\\"CurrentGeomValid\\\" = geometry::STGeomFromText(f.\\\"GEOM_CURRENT\\\",29900).STIsValid() FROM \\\"Feature\\\" f WHERE f.\\\"CurrentGeomValid\\\" IS NULL; (Basically updating a column with the geometry validity). I'm hitting an issue when the WKT is completely invalid, for example: POLYGON ((0 0, 10 10, 0 0) (100 100, 200 100, 200 200, 100 200, 100 100)) The initial loop of this polygon doesn't have enough points and gets me a message like so: Msg 6522, Level 16, State 1, Line 1 A .NET Framework error occurred during execution of user-defined routine or aggregate \\\"geometry\\\": System.FormatException: 24305: The Polygon input is not valid because the ring does not have enough distinct points. Each ring of a polygon must contain at least three distinct points. System.FormatException: at Microsoft.SqlServer.Types.Validator.Execute(Transition transition) at Microsoft.SqlServer.Types.ForwardingGeoDataSink.EndFigure() at Microsoft.SqlServer.Types.OpenGisWktReader.ParseLineStringText() at Microsoft.SqlServer.Types.OpenGisWktReader.ParsePolygonText() at Microsoft.SqlServer.Types.OpenGisWktReader.ParseMultiPolygonText() at Microsoft.SqlServer.Types.OpenGisWktReader.ParseTaggedText(OpenGisType type) at Microsoft.SqlServer.Types.OpenGisWktReader.Read(OpenGisType type, Int32 srid) at Microsoft.SqlServer.Types.SqlGeometry.GeometryFromText(OpenGisType type, SqlChars text, Int32 srid) The issue seems to be that the STGeomFromText function is failing to read the invalid geometry so that the STIsValid function can tell me that it's invalid! I can certainly write some .NET code with an exception handler and process my dataset row by row; I'm wondering if there is a better way of doing this in a single query? Any idea? Cheers!\", \"Split a Quad to 1:7,500 Question Here I have 1:24,000k quads. I know how to use the word split toolbox. What I would like to split 24,000K to 1:7,500 or 6,000k. Is there a way around for me to do that ? For example,, if you have one quad that is 1:24,000 and you want to break it down to either 1:6,000 or 1:7,500 ? I have about 269 quads and need them to break down to where I want 1:6,000 or 1:7,500. I am using ArcGIS 9.3.1 . Thanks !\"]}, {\"query\": \"How to learn arcobject vba?\", \"pos\": [\"Getting started with Arcobjects What is the best route or place to get started with arcobjects if one is not a developer and _not aiming to become a developer_? I've been using gis professionally for a long time, almost two decades, arcinfo/arcgis for most of that; I'm pretty good. I'm learning software development, and even have a modestly successful small python application used in a public project; I'm not good! I don't want to become a full fledged software developer but I keep running into things I just can't do without programming (example). In the arcgis world this puts me pretty squarely in the python camp, which is fine by me since I like python, however python does not have straightforward access to arcobjects. (Python and arcobjects is possible, but it's an unsupported route. This question is about following a beaten path.) Soooo, I need to get started, but where? Arcgis help says to get started with the ESRI Developer Network but $1500/yr is definitely not in my budget, and sounds like using a sledge hammer to swat mosquitos. And which language .NET, Visual Basic, or Visual C++? * * * **UPDATE:** Thank you everyone for the wonderful answers. In light of them I realise I unecessarily narrowed the scope of my question be pre-supposing \\\"arcobjects\\\" is the direction I need to go in. A more open ended formulation is more along the lines of: > I keep running into problems I just can't solve with arcgis and python > alone. What else can I learn/use to solve problems like X? I've no interest > or intention of becoming a software developer. I just need to do a couple > things which aren't exposed to the arcgis python modules.\"], \"neg\": [\"How do I save edits from an ArcMap editing session using VB.Net? I have figured out how to do a map save document, but I would also like to be able to save edits whether or not I am also saving the map document.\", \"gdal2tiles.py problem - creating mini tiles I have a similar problem to the issue posted here (gdal2tiles problem). I have 80 GeoTIFFs (4000x4000) with EPSG:27700 projection embedded and TFW and PRJ files. These I have built into a virtual raster using gdalbuildvrt which creates a 10x10 tile image that is 40000x40000 pixels. Using gdal2tiles I can create a set of tiles for specific zoom levels. In this case I am using the Ordnance Survey VectorMap District and 25K raster tiles and I want zoom levels 7, 8 and 9. The problem is that zoom levels 8 and 9 work perfectly creating 97969 tiles at zoom level 9 and 24649 tiles at zoom level 8. Zoom level 7 creates the correct number of tiles but the output images consist of a \\\"mini\\\" thumbnail in the top left corner and the rest of the image is transparent/white. Why does it work for 8 and 9 and not 7? Should I resize the image to fit nicely into 256x256 squares? See the images attached below: ![Zoom level 7](http://i.stack.imgur.com/7Fk57.jpg) ![Zoom level 8](http://i.stack.imgur.com/IrPDc.jpg)\", \"How to add USGS DRG WMS to QGIS? Is there a WMS server, which I can be added to QGIS, that displays USGS DRGs? If so please explain how? Thanks\", \"Outward buffering I get the ConvexHull of a set of polygons using ST_UNION and ST_CONVEXHULL functions using PostGIS. I want to know how to create an outward buffer of the created convexhull in PostGIS?\", \"How to draw bar diagrams on the map? I need to draw simple histograms in QGIS version 1.8 from an imported DB. I need a double bar diagrams (eg: male/female - in/out) but I only find the possibility to set pie charts. Is it possible to draw histograms with high proportional to the value?\", \"Rgeos drops associated values when intersecting polygons I am trying to intersect two SpatialPolygonsDataFrames and get a SpatialPolygonsDataFrame as the result. Unfortunately, using the `gIntersection` function from `rgeos` (which works impressively quickly to intersect the polygons), I cannot seem to retrieve the associated dataframes. Consider the following example: > fracPoly <- gIntersection( toSingle, fromSingle ) > class(toSingle) [1] \\\"SpatialPolygonsDataFrame\\\" > class(fromSingle) [1] \\\"SpatialPolygonsDataFrame\\\" > class(fracPoly) [1] \\\"SpatialPolygons\\\" I can write a wrapper function which handles the transfer of `data.frames`, but it will be a minor pain to get all the checking right and before I did I was hoping someone could either confirm that there's no better way or point me towards another function (or option for `gIntersection`) which would allow me to retain the associated `data.frames`. **Update** On further reflection, this may may be very deliberate behavior by `gIntersection`. After all, of the two SPDFs, whose data.frame do you pass along? So I may have to write a wrapper which merges the two.\", \"Editor to work on GML files I am looking for a good way to work on GML files and would appreciate some personal experience. At the moment I am using nXML or SGML mode for Emacs but I wouldnt mind having an IDE/Emacs mode/... that is more specialized on GML files and could possibly render changes in real time. Support for large files (>15 MB) would be a requirement. I am running Linux (Ubuntu 13.04) so native programs would be prefered, but I don't mind if Windows programs are mentioned.\", \"Does Metadata for ArcGIS Python script tools in custom toolbox work? * I've created several python scripts and created tools from them. * I have a custom toolbox that contains all of my tools. * I add this toolbox to the ArcToolbox window in ArcCatalog. * I want to create metadata and set parameter explanations for each tool. I've noticed that, when I navigate to my toolbox using the ArcToolbox window, I cannot edit metadata by highlighting the tool in the toolbox window and choosing the 'Description' tab in the catalog window. I can, however, right click in the tool and choose 'Item Description', which opens another window and I can edit metadata in there. Next, I go to the Catalog Tree Window and navigate to the location of the toolbox. In this situation, I can highlight the tool and choose the 'Description' tab and edit the metedata there. This, however, does not translate the edited metadata back to the same tool in the ArcToolbox window. If I open the tool in ArcToolbox window and highlight a parameter, I still get a description message of 'No description available'. So, it's like I have to edit the metadata twice. Once in the Arctoolbox window and then again in the Catalog Tree Window. I've noticed this about the Description tab in ArcCatalog. It only honors selection in the Catalog Tree Window. If I a have tool selected in the ArcToolbox window, the description is still set to whatever is highlighted in the tree window, not the toolbox window. Is this how it's supposed to work? I'm OK with only having to be able to edit metadata in the Catalog tree window, but the fact that metadata does not translate back to the same tool in ArcToolbox window is odd to me. Am I doing something wrong?\"]}, {\"query\": \"Geoserver Map Rendering\", \"pos\": [\"Hardware configuration needed for geoserver to perform faster I Build an application for enabling simple map layer switcher using EXT JS ![enter image description here](http://i.stack.imgur.com/hxrIS.jpg) in this when user press refresh button( which present in the bottom of Layer Tab the application ) read all checked nodes in coma separate value and send as 'dynamic LAYER GROUP' WMS request to the geoserver and served as a single layer to this application How ever the **map rendering is too much slow for 5 user** to increase the map accessing speed I added 3 instance of geoserver in same system with different port with 1024MB java inital and maximum memory pool for each instance and by using Apache HTTP Server i also done Load balancing . where the 'getmap' request is load balanced and transfers to these three geoserver I tuned all the geoserver instance for production environment But still am facing slow rendering speed 1)Is the slow speed is because of using same system for the multiple instance? 2)I am using same database for the three instance did this have anything to do with MAP availability criteria I need to tune my application to work for 30 user simultaneously presently i having an HP server with XEON Processor + 4 GB RAM using stable geoserver 2.4 and openlayer 2.12 what is the best way to tune my application to speed map rendering speed which is the minimum configuration (hardware+software) needed to Speed up map rendering for 30 simultaneous user\", \"Increase Map Availability in Geoserver I have an application which access same map from two geoserver using loadbalancing concept the map is accessed using WMS service. where the two geoserver connected to a single same POSTGIS database. even the both tomcat is alloted with 3GB of RAM but the map rendering is still too slow Is this have anything to do with the performance of POSTGreSQL?? is there any way to increase map availability ? Please help me\"], \"neg\": [\"convert a shapefile into graph with nodes and edges Is there a way to convert a shapefile into graph with nodes and edges(except networkx). I'm using qgis and I want to convert a road vector layer into a graph, so that I can use it to apply shortest path algorithms (astar). Please help me to find a solution.\", \"Open Source Map Composing Tool Are there any open source tools which can be used for map composing, preferably GUI based. I use Quantum GIS sparingly but I am interested to know if there are others. The tool must be able to read raster/vector layers in standard formats and allow exporting in various formats. Something like GeoPDF export would be a great plus.\", \"How to exclude some layers from ArcReader legend? I have created an ArcReader map for staff which has a number of imagery layers, such as satellite imagery and a raster topographic map. I also have a legend which automatically updates based on which layers are selected in the table of contents. How do I exclude the imagery layers, though, because they are not useful in a legend? When they appear in a legend they are just RGB colours.\", \"How can I extract data from OSM which includes the street names? I am exporting OSM data (in .osm) files to use in qGIS. I am interested in having the street names displayed. I know they are in the OSM data set because they are displayed when I browse the data online, but when I view my downloaded files and open the attribute tables for streets, most of them simply have \\\"null\\\" or \\\"label\\\" as values. What am I doing wrong? How can I extract data from OSM which includes the street names?\", \"Lidar and infrared data to detect petroleum releases Would this combo detect differences in soil temperature in proximity to the release due to the higher temperature of pipeline fluids or would the surface temperatures be affected by the volatilization of the released fluids? I know this technique is being used currently and I am just curious about the process by which it would be able to determine a release point.\", \"Set datasource form world map.mxd Noob question here: I've downloaded a data set from ESRI as part of EDN, and it contains world data in a folder such as this: C:\\\\Download\\\\ESRI\\\\EDN Data\\\\DataMapsArcGIS2013_1\\\\world\\\\World Map.mxd When I double-click this .mxd file, it opens up in ArcGIS 10.2, but nearly all of the data source links are broken. How can I fix these links? What is the data source for this map? Thanks!\", \"Why am I losing part of my raster when I reproject? QGIS 2.0 Working with tiff raster file. Reprojections are in WGS84 Mercator. Here is my problem: I am trying to use buffers or urban areas to analyze light intensity on a raster layer. I want 30 miles, 60 miles, and 100 miles for the buffer sizes. I have reprojected the vector layers to WGS84 / World Mercator. When I ran a zonal statistics, everything went smoothly until I was double- checking the output. I saw \\\"null\\\" on a few of the rows of information in the data table. When I looked at the raster image, which I also reprojected to WGS84 / World Mercator, a large chunk of the raster layer was missing. Images below. Non-reprojected raster layer: ![enter image description here](http://i.stack.imgur.com/YrygV.jpg) Reprojected raster layer: ![enter image description here](http://i.stack.imgur.com/PRuph.jpg) I am assuming there is a problem with my reprojections. I am still new at GIS, so please bear with me. Thanks in advance.\", \"Using onRectangle for user input in ArcMap/ESRI addin In an effort to try to add user input to my project, I'm developing an esri python addin to generate rectangles to my map. I'm on step one now, just exploring the addin functionality. I tried making an addin 'tool' precisely per the ESRI sample code, but the button isn't working. It appears on my ArcMap desktop, but when I click it, nothing happens. I just really want to know what's happening with the onRectangle, and how I can get it to accept user input. Here's the sample code that I essentially stole from ESRI: import arcpy import pythonaddins class fishnetsExample(object): def __init__(self): self.enabled = True self.cursor = 3 self.shape = 'Rectangle' def onRectangle(self, rectangle_geometry): \\\"\\\"\\\"Occurs when the rectangle is drawn and the mouse button is released. The rectangle is a extent object.\\\"\\\"\\\" extent = rectangle_geometry # Create a fishnet with 10 rows and 10 columns. if arcpy.Exists(r'in_memory\\\\fishnet'): arcpy.Delete_management(r'in_memory\\\\fishnet') fishnet = arcpy.CreateFishnet_management(r'in_memory\\\\fishnet', '%f %f' %(extent.XMin, extent.YMin), '%f %f' %(extent.XMin, extent.YMax), 0, 0, 10, 10, '%f %f' %(extent.XMax, extent.YMax),'NO_LABELS', '%f %f %f %f' %(extent.XMin, extent.YMin, extent.XMax, extent.YMax), 'POLYGON') arcpy.RefreshActiveView() return fishnet\"]}, {\"query\": \"Training courses in geostatistics and spatial modelling\", \"pos\": [\"What are sources for current geo-statistical analysis classes/events: open or proprietary I am keenly interested in geo statistics (and many other words with 3 t's :). I am not very programatically inclined but have been following several lists with this theme. One being the R filter on stackexchange (I get occasional digest emails of questions there). I find that there is an extremely intricate language associated with statistics in general. I am insterested in learning from a layperson point of view what some of this language means and how to apply it to everyday GIS. Please list any currrent sources for geo-statistics knowledge/understanding. Here is an example of useful information... travel for training live online opportunity Edit: Per request; I enjoy online training but get more from in classroom or step by step book (or pdf).\"], \"neg\": [\"Geometry column naming convention - 'geom' or 'the_geom'? I'm starting my first PostGIS project, and in various books and tutorials I've seen the geometry column labelled either 'geom' or 'the_geom'. Is one more conventional than the other? Furthermore, is there a good reason to use 'geom'/'the_geom' instead of a more descriptive name for the geometry column? (E.g. 'centre_point' for the central point of a feature.) We don't label conventional db columns 'the_int' or 'the_string', so why label geometry columns this way?\", \"Is 'Shortest Route/Distance' analysis is possible in Geoserver using Openlayers 2.8...? I have configured a web based GIS Map using Geoserver & Openlayers controls.Now I want to add 'Shortest Route' analysis in the same... Any solution....??? Thanks In advance Regads Sandipan\", \"mapCanvas.saveAsImage not functioning as expected in QGIS 1.8 I have a plugin for exporting maps as PNG images. I believe this code was working correctly within 1.6 (but I cannot guarantee I haven't made another change that would have broken it there, too). Basically, I load a set of base map features and then loop through a set of point layers that get applied and the whole thing is exported as an image. I'm not getting the full canvas at all, but only a single attribute loaded from the base map and nothing from the point layers. (bad result, expected result) In the process of debugging this, I discovered a curious fact. If I include a QMessageBox.information right before I do the export, it works correctly! Doesn't work: self.mapCanvas.resize(QSize(394,350)) self.mapCanvas.zoomToFullExtent() currentImagePath = theExportPath + folder currentImageName = attribute.replace(' ', '_') + \\\".png\\\" self.mapCanvas.saveAsImage(currentImagePath + \\\"/\\\" + currentImageName) Does work: self.mapCanvas.resize(QSize(394,350)) self.mapCanvas.zoomToFullExtent() QMessageBox.information(self.iface.mainWindow(),\\\"Debug\\\",\\\"Break\\\") currentImagePath = theExportPath + folder currentImageName = attribute.replace(' ', '_') + \\\".png\\\" self.mapCanvas.saveAsImage(currentImagePath + \\\"/\\\" + currentImageName) I tried to add a simple self.iface.mainWindow() in place of the message box but that didn't do the trick. I'm thinking there is some kind of refresh I could do, too, but I tried mapCanvas.refresh() and mapCanvas.updateMap() and these in combination with mainWindow() but could get nothing to work. Did this change in 1.8? Any suggestions to make it work? Including the QMessageBox call will get me going but I have to press \\\"OK\\\" for each map to be exported. With all the export combinations, I'd be doing this more than 1000 times! Thanks! B\", \"Loading a raster using QGIS into a PostGIS2.0 enabled database Following on from my attempt to install a postgresql 9.1 db with postgis 2.0 on windows 7, I am trying to figure out how to load a raster. I have successfully loaded a shapefile, and am trying to do the same with a `.tif` file using the `Load Raster to PostGIS` plugin (version 0.5.1) in QGIS (version 1.7). I have set up the connection to my db, and am using the following settings: ![enter image description here](http://i.stack.imgur.com/w5rrT.png) When I click on the `OK` button, I get the below error message. I've tried this with a `.adf` file and also a `.tif` file, both projected in Albert Equal Area Conic with an SRID of 102003. Checking parameters... Connecting to database... Storing overview 1 on database... Failed. Finished storing overview 1. Storing overview 2 on database... Failed. Finished storing overview 2. Storing overview 3 on database... Failed. Finished storing overview 3. Finished. This process has not inserted anything into my database, and I don't understand the error message. Some previous related questions are here (asked by me, using a different process on loading rasters) and here (using the same plugin but much earlier in the year).\", \"Using SLD to style according table column value How to style various points with different image according to value(text) in the column of table(layer)?\", \"Check validity of file geodatabase using arcpy AS the title suggests, how can I whether a GDB file is valid using arcpy? One approach is, only checking the whether the name ends with `.gdb`, but that's not a robust approach. Because a normal file can be ended with `.gdb`, where GDBs are essentially folders to OS. So how can I do that?\", \"Plotting a circle on a Web map to view the resulting coverage over a central location Not exactly a GIS question but I hope no one will mind. Does anyone know of a website (something equivalent to Google Map) where you can indicate a location, give a radius and you can view the resulting coverage over that central location ? Thanks.\", \"Wireframe display of terrain in ArcGlobe? I have successfully imported a terrain dataset into ArcGlobe which provides elevation values to the globe surface. Is there an easy way I can now change the symbology/display of the terrain/globe surface to a 'wireframe' style display, ideally with customisable grid spacing etc? Or would I need to drape a grid image over the terrain surface?\"]}], \"CQADupstackMathematicaRetrieval\": [{\"query\": \"Works in mathematica but not as cdf preview or web embedded cdf\", \"pos\": [\"Understanding CDF **Update** Since more and more issues are revealed as I venture deeper into the world of CDF, I decided to make this thread more general and hopefully more useful. I know posted my findings as an answer, but please feel free to edit this post or my answer below if you know something useful. **Questions** I have a quite large project that I want to deploy to the web. I expect users to only have the browser plugin or _Mathematica_ Player, but not Player Pro. 1. Can I use `DynamicModule` in a CDF that is to be viewed by the plugin or the _Mathematica_ Player? Does it always have to be a `Manipulate` in focus in the dynamic interface that is generated? 2. Can I deploy the dynamic output somehow without piling all the used functions and data into the `DynamicModule`-s body or `Initialization` option? `Manipulate` has the option `SaveDefinitions` but `DynamicModule` does not. Does `SaveDefinitions` work with packaged functions as well? 3. What exactly happens when only the dynamic output is selected and deployed (and not the whole document) to CDF via the wizard? What is the difference between deploying only the output or deploying the whole notebook, but with all the code cells being hidden? What is the technical difference between demonstrations and CDFs? 4. Is there any difference (and if so, what) between _Mathematica_ Player, Player Pro and the browser plugin? 5. What are the differences between CDFs intended for _Mathematica_ Player or Player pro, i.e. free CDFs and non-free CDFs (see discussion here)? 6. How to overcome security issues (discussed here and here): when the deployed CDF shows up as a gray box because dynamic updating is disabled for security reasons?\", \"How to get .cdf format to work with functions? I would like to create simple functions and then pass them into a Manipulate function that can be used in a .cdf file. eg f[x_] := x^2 + 4; Manipulate[ Plot[f[x], {x, -c, c}] , {c, 1, 5}] works fine as a notebook but crashes as a .cdf\"], \"neg\": [\"Memory problem with producing complex graphics of general type The problem I am about to ask has appeared once here: Memory Leak in Frontend - anyone know a workaround?. However, in this question I would like to put it in a bit different context. **Problem recipe:** * Set $HistoryLength to 0 (this forces mathematica not to keep any history of evaluations) * Create a graphic object, which contains many colors/opacities/primitives/pointsizes/... * Open system monitor in your operational system * Rasterize the object in mathematica * Export it (optional), clear all the variables * Check your system monitor: memory used by mathematica frontend has grown larger **Example of the problem:** This example is not minimal, but is aesthetically appealing. Following the recipe, first set the history length: $HistoryLength = 0; Then create a graphic object. In this case, it is a set of approx 0.5 10^4 points, having random positions inside a 0.5 radius sphere, random sizes and random colors, following some colorscheme. A cylinder is added afterwards to make the object look like a tree. PointNumber = 10^4; BushSize = 0.5; PointLocations = Select[(2 BushSize {Random[], Random[], Random[]} - {0.5, 0.5, 0.5}) & /@ Range[PointNumber], Norm[#] < BushSize &]; PointNumber2 = Length[PointLocations]; PointSizes = 0.01 Random[] & /@ Range[PointNumber2]; PointColors = ColorData[\\\"PlumColors\\\"][Random[]] & /@ Range[PointNumber2]; GraphicObject = MapThread[{#1, PointSize[#2], Point[#3]} &, {PointColors, PointSizes, PointLocations}]; Now, open the system monitor and rasterize everything: TreeStuff = Rasterize[ Graphics3D[{Opacity[0.8], Brown, EdgeForm[], Cylinder[{{0, 0, -1}, {0, 0, 0.1}}, 0.01], GraphicObject}, PlotRange -> {{-1, 1}, {-1, 1}, {-1, 1}}, ImageSize -> 600, Boxed -> False]] As you can see, mathematica front-end has expanded in size by some 50 Mb! It is not all, at all. If you reevaluate the notebook, mathematica will pick another set of random points and will expand in the memory again. However, if you re-rasterize an object which has been rasterized before, memory will not be leaked. **Discussion:** The described behavior is clearly a bug. Say, one is producing an animation. If a given frame has been rasterized and saved, no information needs to be stored about it in the memory. Nevertheless, it gets stored incrementally. It seems hard to imagine, that nobody has ever created complex graphics with mathematica before. In particular, in processing datasets or in creating animations, one has to rasterize such graphics many times. In this case, if the parameters of graphics change, mathematica takes all the system memory. Therefore, at least somebody, making complex graphics with mathematica, must have faced a similar problem before. What do people commonly do to treat this issue? How can one clear the frontend memory, which expands so obviously in system monitor after each new evaluation of the above presented code? **P.S.** The code outputs this pretty tree: ![enter image description here](http://i.stack.imgur.com/EAoPy.jpg)\", \"Integrate function without specifying limits of integration I've been having trouble to make mathematica evaluate an integral without specifying the values of the limits of integration. I mean, if I write Integrate[x,{x,x1,x2}] Mathematica returns the correct answer: $\\\\frac{(x2)^2-(x1)^2}{2}$. But If I write something like Integrate[x^2/(1-x),{x,x1,x2}] Mathematica just keeps the evaluation forever... Is there something that I can do to avoid this behaviour?\", \"How to fix the \\\"Module\\\" function, for implicit funcitons? I wanted to make a function which gives the frequencies of a number in a specific list, when I came accros this problem. This is what I tried: row = {-1, 1, 2, 3, -1, 1, 2, -1, 1, -1, -2, -3, 1, -1, -2, -3, -4, -5, 1, 2}; frequencyRow = Round[HistogramList[row, {Min[row] - 1, Max[row] + 1, 1}]]; lengthRow = Map[f, Part[frequencyRow, 1]] (*{f[-6], f[-5], f[-4], f[-3], f[-2], f[-1], f[0], f[1], f[2], f[3], f[4]}*) frequency = Append[Part[frequencyRow, 2], 0] (*{0, 1, 1, 2, 2, 5, 0, 5, 3, 1, 0}*) mode[n_] := Module[{f}, {lengthRow = frequency}; f[n]] mode[2] >f$821[2] (So the implicit fucntions do not give the outcome I want. However;) mode2[n_] := Module[{f}, {{f[-6], f[-5], f[-4], f[-3], f[-2], f[-1], f[0], f[1], f[2], f[3], f[4]} = {0, 1, 1, 2, 2, 5, 0, 5, 3, 1, 0}}; f[n]] mode2[2] (*3*) (If you use the exact outcome of the functions, the Module function does give the right outcome) goal = Map[mode2, row] (*{5, 5, 3, 1, 5, 5, 3, 5, 5, 5, 2, 2, 5, 5, 2, 2, 1, 1, 5, 3}*) If you know how to fix this to make it work for the implicit functions as well, so I can chance the imput without changing the formula all the time, that would be nice. Or if you see a way to do this proces faster or easier, that would even be better.\", \"Unexpected behavior of confidence bands for data presenting two regions with uneven noise Let's generate some noisy data with uneven noise. data = Table[{x,Exp[-(x-2)^2] + Exp[-(x+2)^2]*RandomReal[{0.5, 1.5}]}, {x, RandomReal[{-4, 4}, 500]}]; ![enter image description here](http://i.stack.imgur.com/VrQVD.png) Let's fit it with a nonlinear model fit = NonlinearModelFit[data, b Exp[-(x-a)^2] + c Exp[-(x+a)^2], {a, b, c}, x] from which we extract the \\u03c3 and 2\\u03c3 prediction bands for single observations: {bands1[x_], bands2[x_]}=Table[fit[\\\"SinglePredictionBands\\\",ConfidenceLevel -> cl], {cl, {.683, .954}}]; The bands seem not to take into account the unevenness of the noise: Show[ListPlot[data, PlotStyle -> {Opacity[0.5]}], Plot[{fit[x], bands1[x], bands2[x]}, {x, -4, 4},Filling -> {2 -> {{1}, Directive[{Green, Opacity[0.25]}]}, 3 -> {{2}, Directive[{Yellow, Opacity[0.25]}]}}, PlotStyle -> {Directive[Thickness[0.0075], Red],Black, Black}]] ![enter image description here](http://i.stack.imgur.com/0j7dt.png) Am I misinterpreting confidence bands, or am I calculating them in the wrong way?\", \"How can I crop manipulator from a plot I wanted to know how can I crop a `Manipulator` from a plot. the code for plot is a= With[{z1 = .25, z2 = 1, x = 1}, Manipulate[ PopupWindow[ Graphics[ DiscretePlot[Sin[a t], {t, 0, 2 Pi, Pi/6}, ExtentSize -> Full, ImageSize -> Scaled[x], AspectRatio -> z1/z2]], {a}], OpenerView[{\\\"Vertical\\\", Control[{{a, 1, \\\"Manipulator\\\"}, 1, 30}]}], ControlPlacement -> Bottom]] I have tried using `Export` and `Import` functions and it is working fine ImageCrop[Import[Export[\\\"test.gif\\\", a, \\\"Graphics\\\"]], 90] But, I wanted to know some other way to do this as I think `Export` and `Import` takes a lot of time in execution.\", \"plotting xy, yz, zx plane at I have a data list in the following form, data = {x, y, z, f} I would like to present three kinds of subdata set as a density (or contour) plot on the respective planes at once in one 3D graph. 1. `data1 = {x, y, f}` 2. `data2 = {y, z, f}` 3. `data3 = {x, z, f}` How could I make an above kind of plot? I really appreciate if anyone help me.\", \"Definite Integral, Piecewise, Error I am trying to evaluate the following: myExpression=Piecewise[{{-c E^(c s)-(E^(-((-s+\\\\[Alpha]+q \\\\[Beta])^2/(2 (\\\\[Beta]^2 \\\\[Sigma]q^2+\\\\[Sigma]s^2)))) v \\\\[Beta])/(Sqrt[2 \\\\[Pi]] Sqrt[\\\\[Beta]^2 \\\\[Sigma]q^2+\\\\[Sigma]s^2]),\\\\[Beta]!=0},{-c E^(c s),\\\\[Beta]==0}}] Integrate[myExpression,s] However, when I try to make this Integral definite, by using {s,0,z}, I get the following error: Integrate::pwrl: \\\"Unable to prove that integration limits {0,z} are real. Adding assumptions may help\\\" I do not wish to change the expression of this integral, nor do I care for some cheap trick which removes the error message (like `Quiet`). I simply want to know where this error comes from, and what additional information about my variabales _Mathematica_ would like to know. Thanks! Laurens\", \"How can I mend this broken heart? Try to evaluate the following code: ContourPlot3D[(x^2 + 9/4 y^2 + z^2 - 1)^3 == x^2 z^3 + 9/80 y^2 z^3, {x, -6/5, 6/5}, {y, -6/5, 6/5}, {z, -6/5, 3/2}, Mesh -> None, Boxed -> False, AxesLabel -> {\\\"x\\\", \\\"y\\\", \\\"z\\\"}, PlotPoints -> 50, Axes -> False, ContourStyle -> Directive[Red, Opacity[0.58], Specularity[Yellow, 30]], AspectRatio -> 1.15, ViewPoint ->{-0.930, -3.137, -0.860}] The resulted heart looks broken: ![enter image description here](http://i.stack.imgur.com/YC64r.png) Increase the `PlotPoints` value to up to `200` improves the appearance but does not solve it. What is happening?\"]}, {\"query\": \"Determine whether some expression contains a given symbol\", \"pos\": [\"What is the most elegant way to see if an expression is affected by a certain symbol? > **Possible Duplicate:** > Determine whether some expression contains a given symbol Let's say I have some expression: sample = {1, 1/q, f[m]} And I want to check it if it has the symbol `m` in it. My question: Is there a clean or fast way of checking if `m` is in the equation? * * * What I came up with so far: Do[StringFreeQ[ToString[sample], \\\"m\\\"], {i, 300}] // AbsoluteTiming (* {0.0120007, Null} *) and Do[(sample /. m -> Unique[]) === (sample /. m -> Unique[]), {i, 300}] // AbsoluteTiming (* {0.0100005, Null} *) These solutions work, but I feel like there might be a faster, functional way of doing this.\"], \"neg\": [\"How to rewrite this function to be faster: Find sub-matrixes of boolean board which imply a given boolean pattern I have Boolean matrix A (called \\\"board\\\") and Boolean matrix B with smaller dimensions (called \\\"pattern\\\"). I am trying to find sub-matrixes C of A such that C has same dimensions as B, and TableImplies[C, B]. Function TableImplies returns true if matrix A piecewise implies matrix B: TableImplies[A,B]=true if for all i,j (a_ij => b_ij). Example: falsePattern = Table[False, {15}, {15}]; truePattern = Table[True, {15}, {15}]; Here TableImplies[falsePattern, truePattern] == True, but TableImplies[truePattern, falsePattern] == False. I have the following code, but it is not very fast: tableImplies[a_, b_] := And @@ Flatten@MapThread[Implies, {a, b}, 2] searchTable[pattern_, board_] := Reap[{sizex, sizey} = Dimensions[pattern]; Do[subBoard = board[[i ;; i + sizex - 1, j ;; j + sizey - 1]]; If[tableImplies [subBoard, pattern], Sow[{i, j}]], {i, 1, 16 - sizex}, {j, 1, 16 - sizey}]][[2]] board = Table[i != 16 - j , {i, 1, 15}, {j, 1, 15}]; kroneckerPattern = Table[i == j, {i, 1, 2}, {j, 1, 2}]; Timing[Do[searchTable[kroneckerPattern, board], {100}]][[1]]/100 (* 0.00873606 *) How do improve speed of this calculation? How to improve coding style? **UPDATE:** Michael's answer seems to only search for specified sub-matrix inside the given matrix, instead of returning sub-matrixes, that piecewise imply given pattern. I added examples to the beginning of question to better explain what I wanted (see truePattern, falsePattern and how they should be related to each other when using TableImplies). I guess I should have been more clear from the beginning. This compares my algorithm(searchTable) to Michael's (Position[...]). They are clearly not equivalent: searchTable[truePattern, falsePattern]; Out: {{{1, 1}}} searchTable[falsePattern, truePattern] Out: {} Position[Partition[truePattern, Dimensions@falsePattern, 1], falsePattern] Out: {} Position[Partition[falsePattern, Dimensions@truePattern, 1], truePattern] Out: {} **Update 2** : I thought I should be more specific in formulating my question. Details matter. As I said before, I want to know, how should this algorithm could be made faster. It is important to mark that the board (containing matrix) will be always of constant size (15x15), but the pattern (searched sub-matrix) will be of varying dimensions, but always smaller than the board. This is relevant because for example in link provided by ssch, http://stackoverflow.com/questions/8364804/a-fast-implementation-in- mathematica-for-position2d, the matrixes are very large and the hard part is finding the position. In my case the number of possible positions is not very large (at most 225), but the slow part is to show whether the pattern matches the board or not. Maybe, if this whole problem could be re-written as boolean expression, that Mathematica could try to solve? Would this approach be faster? Another important note is that the pattern will very likely contain a lot of \\\"True\\\"s in my application. Could this be used to make the algorithm faster? For example, if the pattern contains only 1 False, then the problem is basically finding all instances of \\\"False\\\"s on the board. If the pattern contains more of \\\"False\\\", then the problem still boils down to Boolean expression, but not to an particularly easy one. PS: If my question is partly about algorithms and partly about programming Mathematica, is it correct of me to post this question here? Maybe I should just first ask some algorithms-related StackExchange Q&A for an algorithm and then ask here for how to program this algorithm effeciently in Mathematica? I just thought that maybe Mathematica's potential for symbolic evaluation could lead to algorithms not thinkable in functional programming languages, which is why I asked directly here :)\", \"keyboard sequence for Magnify in version 8? It used to be that you could find a Magnify item in the Format menu of the Mathematica front-end, at the very bottom if I remember correctly. You could get at the item in Windows by a keyboard sequence like `Alt-R`, `M`, then a number. Mac probably had something similar. This was really useful for mouse avoiders like me. That little tiny drop menu in the extreme lower right of a notebook is annoying to catch with a mouse on highest-speed setting, which you need on a high-def screen with 1920 or 2560 pixels width. My mouse movements end up looking like a plot of a strange attractor swirling around the target! I haven't been able to find the Magnify menu item in Mathematica 8 nor any other keyboard shortcut for notebook-magnify in the Mathematica documentation center. Would be grateful for anyone pointing out any means for accessing notebook magnify from the keyboard.\", \"How to switch elements i,j in a list? Are there any direct function to switch elements i,j in any list? or is necessary to create one with Do / If ? (I don\\u00b4t ask for making me this function if it doesn\\u00b4t exists) My target is can switching positions i,j in {1,4,3,2,5} for example I tell switch(2,5) an obtain {1,5,3,2,4}\", \"Generic Matrices A multivariate Gaussian distribution for $k$ dimensions looks like this: $$\\\\frac{1}{\\\\sqrt{(2\\\\pi)^k|\\\\mathbf \\\\Sigma|}}\\\\mathbf e^{ -\\\\frac 1 2 \\\\mathbf x^T \\\\mathbf \\\\Sigma^{-1}\\\\mathbf x }$$ $\\\\mathbf x$ is a $k$-dimensional vector, and $\\\\mathbf \\\\Sigma$ a $k$ by $k$ symmetric positive definite matrix. Now I want Mathematica to integrate this expression over all $\\\\mathbf x$, the result should be 1. How can I do this? Nothing else is known, in particular we don't know the number of dimensions.\", \"How to update Print-out \\\"in place\\\"? I want to run an exhaustive search algorithm that will take several hours to finish, so I'd like to have it execute `Print` statement periodically, showing the fraction of the search space covered and the best result found so far. E.g. For[i = 1, i <= n, i++, ... If[Mod[i, stride] == 0, Print[{N[i/n], bestsofar}] ]; This works OK if the number of updates is not too big. Otherwise, the voluminous print out begins to overwhelm the notebook, making it increasingly difficult to navigate through it. Closing the cell does not help here: it is opened automatically with each new print-out. (Also, when the cell remains open, the window will not necessarily auto-scroll to keep the latest print-out in view, so one needs to manually scroll to see it. No big deal; just a bit annoying.) I'm looking for a way to have each new print-out overwrite the previous one \\\"in place\\\". With other programming environments, at least in Unix-like systems, one achieves this effect by prepending a carriage return character (ASCII 13, aka `\\\\r`) to the printed string, and omitting the newline character (ASCII 10, aka `\\\\n`) from the end of the string1. A single newline character may be eventually printed, before execution terminates. The `Print` command implicitly adds the newline at the end of the output, and I have not found a way to suppress this behavior. Is there some other way to achieve the effect of updating a running cell's print-out in-place? UPDATE: based on molekyla777's answer, I modified the code above to: Monitor[For[i = 1, i <= n, i++, ... Refresh[{N[i/n], bestsofar}, UpdateInterval -> 1, TrackedSymbols -> {}] ]; I added the `Refresh` call to reduce the performance drain that would otherwise result from updating the `N[i/n]` term, which changes with each iteration. It basically does what `Mod[i, stride] == 0` did in the previous version. * * * 1If the length of the output string is not monotonically increasing, it may also be necessary to right-pad the output string with enough spaces to \\\"erase\\\" the previous output.)\", \"A math function similar to \\\"LatticeReduce\\\" for finding linear independent basis We would like to ask a math function similar to \\\"LatticeReduce\\\" for finding linear independent basis. The input is a list of vectors $M=\\\\\\\\{v_1,v_2,v_3,\\\\dots\\\\\\\\}$, with the following property: There is a subset $O=\\\\\\\\{w_1,w_2,w_3,\\\\dots\\\\\\\\}\\\\subset M$, such that $O$ is a basis, i.e. linearly independent and span $M$, $$ v_i=\\\\sum_a c_{ia} w_a.$$ Moreover, the coefficients $c_{ia}$ are **non-negative integers**. An explicit example is the following: $$M=\\\\left( \\\\begin{array}{ccccc} 1 & 1 & 1 & 1 & 1 \\\\\\\\\\\\ 1 & 1 & -1 & -1 & 1 \\\\\\\\\\\\ 1 & 1 & -1 & 1 & -1 \\\\\\\\\\\\ 1 & 1 & 1 & -1 & -1 \\\\\\\\\\\\ 2 & -2 & 0 & 0 & 0 \\\\\\\\\\\\ 2 & 2 & 2 & 0 & 0 \\\\\\\\\\\\ 2 & 2 & 0 & 2 & 0 \\\\\\\\\\\\ 2 & 2 & 0 & 0 & 2 \\\\\\\\\\\\ 2 & 2 & -2 & 0 & 0 \\\\\\\\\\\\ 2 & 2 & 0 & -2 & 0 \\\\\\\\\\\\ 2 & 2 & 0 & 0 & -2 \\\\end{array} \\\\right),\\\\quad O=\\\\left( \\\\begin{array}{ccccc} 1 & 1 & 1 & 1 & 1 \\\\\\\\\\\\ 1 & 1 & -1 & -1 & 1 \\\\\\\\\\\\ 1 & 1 & -1 & 1 & -1 \\\\\\\\\\\\ 1 & 1 & 1 & -1 & -1 \\\\\\\\\\\\ 2 & -2 & 0 & 0 & 0 \\\\end{array} \\\\right), $$ where $O$ is the first 5 rows of $M$ and $$M[[6]]=O[[1]]+O[[4]],\\\\, M[[7]]=O[[1]]+O[[3]],\\\\,\\\\dots$$ In Mathematica form, the example is m = {{1, 1, 1, 1, 1}, {1, 1, -1, -1, 1}, {1, 1, -1, 1, -1}, {1, 1, 1, -1, -1}, {2, -2, 0, 0, 0}, {2, 2, 2, 0, 0}, {2, 2, 0, 2, 0}, {2, 2, 0, 0, 2}, {2, 2, -2, 0, 0}, {2, 2, 0, -2, 0}, {2, 2, 0, 0, -2}}; May we know that how to write a function/program such that inputting such $M$, the program will output the desired $O$. _For this example the built-in function LatticeReduce works well. But we are not sure it always works._\", \"Is it possible to plot a second-order curve by its non-canonical equation? I have this second-order polynom: $$ 6xy+8y^2-12x-26y+11=0 $$ And I need to reduce it to a canonical form of a second-order curve. I solved this, but is it possible to draw a plot of the original equation to check whether my solution is correct? Something like Plot[6 x*y + 8 y^2 - 12 x - 26 y + 11, {x, -20, 20}, PlotRange -> {-20, 20}] Now this draws an empty plot. Thank you.\", \"How to generate characters by a function which works like the Esc + a + ESC How to generate characters by a function which works like the Esc + a + ESC In[24]:= CharacterRange[\\\"a\\\",\\\"z\\\"] Out[24]= {a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z} How to batch convert them to Greek Letters just like that done by this way Esc + a + Esc ... something like this, a function works, In[25]:= CharacterRange[\\\"\\\\[Alpha]\\\",\\\"\\\\[Zeta]\\\"] Out[25]= {\\\\[Alpha],\\\\[Beta],\\\\[Gamma],\\\\[Delta],\\\\[CurlyEpsilon],\\\\[Zeta]} EnglishToGreekRange[\\\"a\\\", \\\"z\\\"] Comes out {\\\\[Alpha],\\\\[Beta],\\\\[Chi]...}\"]}, {\"query\": \"Test a wooden board's vibration mode\", \"pos\": [\"How to set the boundary fixed and apply the Young's modulus in Mathematica's FEM? Provided the drawing .stl is given, and the Young's modulus is know, how to start a FEM vibration mode test in Mathematica? The boundary is required to be fixed and the interested focus is on the different responses over various Young's modulus. ![enter image description here](http://i.stack.imgur.com/yGR80.jpg)\"], \"neg\": [\"How to instruct FullSimplify to assume that PossibleZeroQ returns correct result? Sometimes I have a really huge expression that cannot be significantly simplified by `FullSimplify`. I would like to, so to speak, _\\\"simplify with faith\\\"_ using heuristics applied by functions like `PossibleZeroQ`, `FindSequenceFunction` an so on. Is it possible to instruct `FullSimplify` to use non-rigorous methods like this?\", \"Getting Internal Self-Test Error I have two questions related to this simple code: CDFDeploy[SystemDialogInput[\\\"FileSave\\\"], Panel[Row[{Button[Text[Style[\\\"sav\\\", FontSize -> Scaled[.2]]],ImageSize -> Scaled[.2]], Button[Text[Style[\\\"save\\\", FontSize -> Scaled[.2]]],ImageSize -> Scaled[.2]]}], ImageSize -> Scaled[2]], WindowSize -> Scaled[2]] 1. Sometimes, I am getting `Internal Self-Test Error`. Why? 2. I am able to scale `Button` size, `Fontsize` and `Panel`size in the Horizontal direction but not in the vertical direction. How to do this?\", \"How can I insert the current date in a text cell? Ok I must be missing something. I have a section with the \\\"last updated date\\\" in a text cell, I'd like it to autoupdate. Is there a \\\"insert current date\\\" command hidden away in some menu?\", \"Reducing quality of Graphics3D scene to improve performance? `ProteinData[\\\"SERPINA1\\\", \\\"MoleculePlot\\\"]` gives the detailed `Graphics3D` plot below. I'd like to make many small copies of this object and place them in a single `Graphics3D` scene, but I'm running into performance problems because the molecule graphics-object is a pretty complicated mesh. Are there general methods of reducing the quality/detail of graphics objects like the one below that would allow me to place many of these in a single scene? ![enter image description here](http://i.stack.imgur.com/XlvG4.png)\", \"Define an operator with commutative, associative and distributive properties I need to define a symbolic operator with commutative, associative and distributive properties, in the same way as the sum and product operator for real numbers. I have started with: op[a_, b_] = a\\u2295b; op[a_, b_] = a\\u2297b; op[1, 2]; op[1, 1\\u22952]; Distribute[op[1, 1\\u22952], CirclePlus] when I use the `Distribute` command I don't get the parenthesis. How can I define an operator with all this properties in ones.\", \"ListInterpolation causing .cdf security problems? I've made a simple `.cdf` that makes a function from a list of data I've provided in table form (using `ListInterpolation`) and plots it on a graph (no importing, exporting or conversion of data from string) and I still get a grey block when I try to post it online, indicating that I'm violating some security rule. It works with the `{fullscreen: 'true'}` workaround, but I don't know what I'm doing that necessitates this. Advice? Thanks\", \"Trouble with NMaximize I am having trouble with maximizing a function I am interested in. Below is the code I am using. NMaximize[{(p^2)^alpha + ((c - p)^2)^alpha + (1 - (c - p)^2 - p^2)^alpha, {c/2 - Sqrt[2 - c^2]/2 < p, p < c/2 + Sqrt[2 - c^2]/2, c == 1.2, alpha == 0.7}}, {p}] I get the following error: > NMaximize::bcons: \\\"The following constraints are not valid: {alpha == 0.7, c > == 1.2, c/2 - Sqrt[2 - c^2]/2 < p, p < c/2 + Sqrt[2 - c^2]/2}. Constraints > should be equalities, inequalities, or domain specifications involving the > variables.\\\" Why is this happening?\", \"Text as plot axes values I have some chemical data that I am plotting using `ListLogPlot`. data={{2776.37,2016.64,1483.51,1027.35,500.878,94.1385,310.402,282.548, 257.886,224.359,218.688,209.312,215.776,198.78}, {40.5063,24.633,12.069,8.3151,6.35135,15.0977,8.74372,15.5125,9.34959, 9.70696,11.125,12.1457,10.8075,9.7561}, {113.08,124.633,75.9698,59.5186,55.9459,7.81528,57.4372,69.5291,80.4878, 88.2784,105.563,127.935,148.571,148.78}}; ListLogPlot[data, Joined -> True] ![enter image description here](http://i.stack.imgur.com/MCO17.png) I want to replace the x-axis tick values with the associated elements. e.g. replace 1,2,3,...,14 with xaxis= {\\\"La\\\",\\\"Ce\\\",\\\"Pr\\\",\\\"Nd\\\",\\\"Sm\\\",\\\"Eu\\\",\\\"Gd\\\",\\\"Tb\\\",\\\"Dy\\\",\\\"Ho\\\",\\\"Er\\\",\\\"Tm\\\",\\\"Yb\\\",\\\"Lu\\\"}; Any suggestions on how to achieve this? Is there another plot function that I should be using?\"]}, {\"query\": \"Combine 2D images perpendicular to each other\", \"pos\": [\"Is there something like DensityPlot3D to visualize atomic orbitals? I'm visualizing some hydrogen like atomic orbitals. For looking at plane slices of the probability density, the `DensityPlot` function works well, and with something like: Manipulate[ DensityPlot[ psi1XYsq[u, v, z], {u, -w, w}, {v, -w, w} , Mesh -> False, Frame -> False, PlotPoints -> 45, ColorFunctionScaling -> True, ColorFunction -> \\\"SunsetColors\\\"] , {{w, 10}, 1, 20} , {z, 1, 20, 1} ] I can get a nice plot ![hybrid orbital sample plot in x y plane](http://i.stack.imgur.com/nHtMY.png) I was hoping that there was something like a `DensityPlot3D` so that I could visualize these in 3D, but I don't see such a function. I was wondering how `DensityPlot` be simulated using other plot functions, so that the same idea could be applied to a 3D plot to construct a `DensityPlot3D` like function?\"], \"neg\": [\"How to find the domain and range of an implicit function? For example, we have this curve: $$ x^2 + y^2 = 1 $$ Is there a function in _Mathematica_ for finding out that the ranges for $x$ and $y$ are both `[-1, 1]`? What about implicit functions of more than `2` variables? e.g. $$ x^2 + y^2 + z = 1 $$\", \"PDE problem with initial and boundary conditions I'm new to mathematica and I don't know what I do wrong, I have written the following PDE problem, with both initial and boundary (four in total) eq = D[u[x, t], t, t] + D[u[x, t], t] - 2*D[u[x, t], x, x] == 0 bc = {u[0, t] == 0, u[2*Pi, t] == 0, u[x, 0] == 0, Derivative[0, 1][u][x, 0] == sin (2*x)} DSolve[{eq, bc}, u[x,t], {t, x}] I need it to solve the PDE, but it doesn't even try to make a solution... what to do. I hope you can help me\", \"Possible to batch WolframAlpha queries? Sometimes I want some country data not available from `CountryData`. And issuing things like: WolframAlpha[ToString[#]<>\\\" | whatever data\\\", {{\\\"Result\\\", 1}, \\\"ComputableData\\\"} ]&/@CountryData[\\\"Countries\\\"] is both incredibly slow and I'm sure to hit my daily API limit. Can I do all these requests in one go?\", \"Dynamic inside Button This bit of code runs a little animation of a stochastic process: (* a random walk bm, its min and max *) {bm, m, M} = {Table[Random[] - .5, {100}] // Accumulate, Min[bm], Max[bm] } (* plot of trajectory of bm up to i-th step *) traj[i_] := ListLinePlot[({Range[1, 100], bm} // Transpose)[[;; i]], Joined -> True, PlotRange -> {{0, 100}, {m, M}}] (* animation using Clock *) q = Dynamic[r = Clock[{1, 100, 1}, 5, 1]; traj[r] ] Next I'd like to use Button to run the animation. Something like Button[\\\"Run\\\", q] but this doesn't work. Eventually I'd like to have two buttons, \\\"Repeat\\\" and \\\"Clear\\\" like this: x = {{0, 0}}; min[x_] := (Sort[x, #1[[2]] < #2[[2]] &])[[2]]; Column[ {Row[{Button[\\\"Repeat\\\", x = Append[x, { Random[], Random[] }]], Button[\\\"Reset\\\", x = { {0, 0}}] } ], Dynamic[Show[{ ListPlot[x, PlotRange -> {{0, 1}, {0, 1}}, ImageSize -> 300, Frame -> True, Axes -> None] , If[Length[x] > 1, Graphics[{Red, Line[{min[x], {min[x][[1]], -.1} }]}], {}], If[Length[x] > 1, Graphics[{Red, Line[{min[x], {0, min[x][[2]]} } ]}], {}] }]]}] but instead of dots, I want to plot animated functions involving Clock.\", \"I have the roots; how can I find an equation that they will satisfy? I have two solutions; I want is to find an equation that these solutions satisfy using mathematica. The solutions are $x= \\\\frac{{(26-k)}^2}{26}$ and $y= \\\\frac{{k}^2}{26}$. I know by hand computing that $x$ and $y$ satisfy $\\\\sqrt{x}+\\\\sqrt{y}=\\\\sqrt{26}$; How can I show this using _Mathematica_?\", \"Frontend cursor movement speed depends on the position in large notebook file I run Mathematica v9 on my Atom laptop (don't ask) and I got used to the fact that everything is slower. Except for one annoying thing: cursor movement (in text) is slower than the keyboard key repeat rate. In other editors I am used to hold down the up/down/left/right keys and I expect the cursor to stop when I release the key. In Mathematica Frontend this is not the case, keyboard buffer gets filled with keyboard events which get executed by the Frontend way later than the release of the key. This annoying behavior trained me to use only single presses of the movement keys (or mouse clicks at the right position). You can imagine, scrolling through a lot of code in this way can be quite painful. What is even more interesting is that the speed with which Frontend executes the cursor movements depends on the overall size of the notebook file and the current relative position of the cursor the file. The same approximately holds also for edits. Having a large file with a lot of graphics, editing text or moving the cursor at the top of such notebook becomes extremely slow on such weak netbooks. On the contrary, adding code or moving cursor at the end of such file is quick enough not to be noticed. I have been following the evolution of Mathematica \\\"speed\\\" since v5 and I have to say that there was huge drop of performance when Wolfram switched the backend language to java and ever since the performance of the kernel or the Frontend has been getting worse (the fact substantially masked by the CPU speed increases during the \\\"frequency wars\\\"; now this Moore's law is over...). It also seems that all the incredible performance gains (with JIT technology and loop/execution tracing) in other interpreted languages never really reached Mathematica. Maybe Wolfram should dedicate one major version bump only to address these performance issues otherwise (this great) language may put itself into oblivion. Nevertheless, the behavior described above is quite odd, i.e. it seems edits at the top are $\\\\mathcal{O}($file size$)$ while at the bottom they are more like $\\\\mathcal{O}(1)$. Are there any preferences/options that can be tweaked in order to reduce the load Frontend uses for rendering?\", \"Is it possible to compute trapezoidal rule numerical integration? Is it possible to compute trapezoidal rule numerical integration? I know that Mathematica has `Interpolation`, and that a list of points can be interpolated and then integrated simply using `Integrate`. However, my functions are highly oscillatory (they are based on simulation data), and I am not convinced that the interpolation is perfect, even when I set `WorkingPrecision` to a very high value. Also, I know that `ListIntegrate` is deprecated, and even if I use it, I am not certain if it is using the trapezoidal rule, which I would like to use. Do you know if any resources where I can find Mathematica or pseudocode for trapezoidal integration of lists of points? Or do you have any suggestions about how I can use Mathematica efficiently to program such an algorithm myself? Thanks!\", \"FiniteFields package is very slow. Any fast substitute for Mathematica? I want to compute the inverse of matrix, say with dimensions $100 \\\\times 100$, defined over a large finite field extension such as $GF(2^{120})$. I am using the package **FiniteFields** , but Mathematica's computation time is exponential with respect to matrix dimensions. The following code illustrates the problem: << FiniteFields`; Table[ With[{ext = 12}, First@AbsoluteTiming@ Inverse[ Array[GF[2, ext][RandomInteger[{0, 1}, ext]] &, {n, n}] ] ], {n, 1, 11} ] I am using an Intel Xeon X5680 @ 3.33GHz (64-bit OS) and Mathematica v8.0.4.0. I have received the following timing results: {0.0030, 0.0080, 0.0210, 0.0630, 0.1860, 0.5110, 1.3350, 3.3840, 8.9340, 23.0090, 57.4660} I believe the source of the problem is that the **FiniteFields** package defines many UpValues, DownValues and SubValues of `Times` and `Plus` for head GF and, consequently, the pattern matching of arguments is degraded. Does anyone know if a patch for the **FiniteFields** package or a faster substitute providing a similar interface? Many thanks!\"]}, {\"query\": \"How to partition a list and leave in the last sublist which is of different length?\", \"pos\": [\"Using Partition to allow sublists of different lengths As a minimal example, suppose I have a list of the integers from 1 to 25. Suppose I want to use `Partition` to partition the list into sublists of length 10 **but** without dropping the \\\"end\\\" elements. For example, this code list = Range[1, 25]; Partition[list, 10] gives: `{{1,2,3,4,5,6,7,8,9,10},{11,12,13,14,15,16,17,18,19,20}}`, where the integers 21 through 25 have been dropped. What if I want to keep those integers? I would like `Partition` to partition the list into sublists of length 10 only when possible (i.e., keeping the \\\"end\\\" elements). How can I do this? In the `Partition` documentation, this entry looks like a possibility: > `Partition[list, n, d, {kL, kR}, {}]` uses no padding, and so can yield > sublists of different lengths. So I tried: Partition[list, 10, 0, {1, 1}, {}] but this did not work (according to the documentation `{kL, kR} = {1, 1}` means \\\"allow maximal overhang at the end\\\"). On the other hand, this seems to work: Partition[list, 10, 10, 1, {}] which gives the correct output: `{{1,2,3,4,5,6,7,8,9,10},{11,12,13,14,15,16,17,18,19,20},{21,22,23,24,25}}`. Is this the best way to solve the problem, or am I making a conceptual mistake? I obtained the idea `Partition[list, 10, 10, 1, {}]` from the following documentation, but conceptually I am not really sure what it is doing: > Use no padding, so later sublists can be shorter: > `Partition[{a,b,c,d,e,f,g},3,3,1,{}]` > > `{{a,b,c},{d,e,f},{g}}`\"], \"neg\": [\"Exporting strings without the quotes I'm trying to export the following string \\\"sep = ,\\\" to a CSV-file, as to be able to define the separation symbol, but when using the following command, Export[\\\"test.csv\\\", Join[{\\\"sep = ,\\\"}, RandomInteger[{-5, 5}, {5, 3}]], \\\"CSV\\\"] I get the quotes around the string in the first line: \\\"sep = ,\\\" -4,5,0 5,-4,-1 0,5,2 -1,2,4 -1,-4,-1 This obviously isn't recognised by Excel and it doesn't open correctly. I've tried changing the string to something like Style[\\\"sep=,\\\", ShowStringCharacters -> False] and then export again, but then I just get this whole command into the CSV- file. Is there a method to export this string without the quotes attached to them into a CSV-file? Thanks for all help!\", \"What is the cleanest way to prevent divide-by-zero warnings? If I evaluate `{1, 2, 3, 4}/{5, 6, 0, 8}`, obviously I get one warning: Power::infy: Infinite expression 1/0 encountered. >> and one entry of `ComplexInfinity` in the third position of the result. I'd like to eliminate the warnings and get the output with instances of `ComplexInfinity` deleted. So far, DeleteCases[Quiet[{1, 2, 3, 4}/{5, 6, 0, 8}], ComplexInfinity] is the best way I have to do this. But I'd rather not suppress all messages with `Quiet`, in case I'm operating on something more complicated than this example. Rather than cleaning up the mess it causes, is there a clean way to prevent the divide-by-zero operation from happening in the first place?\", \"How to plot filling under a curve? I want to mark critical areas for statistical test on the plot. How can I do this? This: pdf = PDF[NormalDistribution[], x] Show[Plot[pdf, {x, -5, 5}], Plot[pdf, {x, -5, -2.001}, Filling -> Axis]] gives: ![enter image description here](http://i.stack.imgur.com/op81t.png) I know that it is probably some super-duper Mathematica feature, but as far as I know this filling shouldn't be cut in such strange way, so I don't want to use this feature. How can I make the filling look right?\", \"how to set initial conditions when solving recursive equation with tables I am solving a recursive expression with `ParallelTable`. Here is a MWE showing how I do it, and how I set the initial condition: SetSharedVariable[vals] t[n_] := t[n - 1]*i*NIntegrate[Exp[-i*x^2], {x, -Infinity, Infinity}]; t[0] = 1; vals = ParallelTable[{i, t[3]}, {i, 1, 500}]; The expression `t` depends on `i` implicitly. Now I want to make this dependency explicit, as shown here (note, it is **not** a functioning example!): SetSharedVariable[vals] t[d_, n_] := t[n - 1]*d*NIntegrate[Exp[-d*x^2], {x, -Infinity, Infinity}]; (* WHAT TO DO WITH THIS, THE INITIAL CONDITION? *) t[i, 0] = 1; vals = ParallelTable[{i, t[i, 3]}, {i, 1, 500}]; My concern is regarding the initial condition. When I am dealing with `tables`, is there an easy way to implement initial conditions, or do I have to use a `For`-loop for that (where `i` is the variable)?\", \"Abort during animation I am running an animation with the following command: files = FileNames[NotebookDirectory[] <> \\\"*.dat\\\"]; MTnumbers = FileNames[NotebookDirectory[] <> \\\"MTnumbers.txt\\\"]; data = Import[#, \\\"Table\\\"] & /@ files MT = Import[#, \\\"List\\\"] & /@ MTnumbers coords = {{#1 - (#3/2), #2 - (height/2)}, {#1 + (#3/2), #2 + (height/ 2)}} & @@@ #[[All, 2 ;;]] & /@ data; height = 2; colors = {Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue, Blue}; Animate[Graphics[{Sequence @@ {colors[[#]], Rectangle @@ (coords[[#]][[t]])} & /@ MT[[1]]}, PlotRange -> {{-1000, 1000}, {-200, 200}}, ImageSize -> {1000, 200}], {t, Range[0, 7000]}] Export[\\\"tubuli.avi\\\", %]; As soon as I try to export this output Mathematica crashes. When I increase the number of rectangles to about 150 the simulation crashes without giving an output. Is my computer too weak or am I doing something wrong? EDIT: The data is in the following zip archive you just need the notebook and the data in the same folder and it should work. https://www.dropbox.com/s/iegdb1a031i4mn6/data.zip\", \"Why does Dynamic output cause CPU usage when onscreen? I write such a line in _Mathematica_ : {Slider[Dynamic[s]], Dynamic[f[x_] := s*x]} After evaluation, the CPU immediately runs at full power. And if you scroll the page, it turns out that as soon as this line appear on the screen, the CPU begin to work. This really confused me a lot. Can anyone help?\", \"shooting method and stifness problem for NDSolve I'm trying to solve numerically the following differential equation: 1/r[x]^5 + k/Sqrt[1 + r'[x]^2] - (k r[x]r''[x])/(1 + r'[x]^2)^(3/2) == 0 I can set boundary condition in `x=x0` and `x=xF` with `x0` and `xF` being the boundaries of the domain of the `x` variable, i.e., `x=[x0,xF]`. The particular values are not important, I can set what I want as long as `xF>x0>0`. I do not have constraint on the value of the first derivative at the boundaries. This equation has a singulary for `r''[x]` in `x=0`. To avoid this problem, I have to use shooting method with appropriate boundary conditions. sf = NDSolve[{1/r[x]^5 + k/Sqrt[1 + r'[x]^2] - (k r[x]r''[x])/(1 + r'[x]^2)^(3/2) == 0, r[0] == 1, r[1] == 10}, r, {x, 0.1, 100}, Method -> {\\\"Shooting\\\", \\\"StartingInitialConditions\\\" -> {r[0] == 1, r'[0] == 0}}, MaxSteps -> 500] If I do so, I obtain the following errors: NDSolve::mxst: Maximum number of 200 steps reached at the point x == 0.48535728275604967`. >> NDSolve::mxst: Maximum number of 200 steps reached at the point x == 0.9112853584099116`. >> NDSolve::mxst: Maximum number of 200 steps reached at the point x == 0.9766603163139572`. >> General::stop: Further output of NDSolve::mxst will be suppressed during this calculation. >> FindRoot::sszero: The step size in the search has become less than the tolerance prescribed by the PrecisionGoal option, but the function value is still greater than the tolerance prescribed by the AccuracyGoal option. >> NDSolve::berr: There are significant errors {0.0395308,-0.00050132} in the boundary value residuals. Returning the best solution found. >> But anyway I obtain a solution `sf` in the form of an interpolating function: {{r -> InterpolatingFunction[{{0.1, 0.920942}},<>]}} It is this solution reliable? I think that the the differential equation may be affected by stifness issues, so I tried to add as a sub `Mehtod` options to solve stifness mehtod as indicated in the help: sf = NDSolve[{1/r[x]^5 + k/Sqrt[1 + r'[x]^2] - (k r[x]r''[x])/(1 + r'[x]^2)^(3/2) == 0, r[0] == 1, r[1] == 10}, r, {x, 0.1, 100}, Method -> {\\\"Shooting\\\", \\\"StartingInitialConditions\\\" -> {r[0] == 1, r'[0] == 0},Method -> {\\\"StiffnessSwitching\\\", \\\"NonstiffTest\\\" -> False}}, MaxSteps -> 500] The problem is that I obtain several errors, and a discontinuos solution that it's clearly wrong. I will really appreciate any help. Samir\", \"Unexpected output from oddSublists I have just bought _Mathematica_ and I'm in the process of plowing through Shifrin's great book. On page 74 the function `oddSublists` doesn't give me the expected output. Since I'm a true beginner, I of course suspect mistakes on my behalf. I get: `{{}, {, 2}, {, 2, 3, 4, 5}, {, 2, 3, 4, 5, 6}}` However the function `oddSublistsNew` gives me the expected output.`{{1}, {1, 2}, {1, 2, 3, 4, 5}, {1, 2, 3, 4, 5, 6}}` Anyone had similar experience? And here is the code I forgot to include: Clear[oddSublists]; oddSublists[x_List] := Part[x, Union[ Flatten[Cases[ Map[First, Split[Position[x, _?OddQ], First[#1] == First[#2] &], {2}], y_List /; OddQ[Length[y]]]]]]\"]}], \"CQADupstackPhysicsRetrieval\": [{\"query\": \"Richtmyer Meshkov instability in MHD\", \"pos\": [\"Richtmyer Meshkov instability in MHD In magnetohydrodynamics, the Richtmyer Meshkov instability is found to get suppressed by application of longitudinal magnetic field. Exactly what happens at the interface? Why instability gets suppressed? (How one can get the physical intuition of what is happening?)\"], \"neg\": [\"What can currently be deduced about empty hyperspace? Michio Kaku's explanation of universes in hyperspace in this youtube video gives a metaphor of our universe being the surface of a hyperspace bubble that's currently becoming larger. He also says these bubbles colliding would produce the creation of new universes and the destruction of involved universes. But this makes me think there's a lot of room in hyperspace for a universe to float for a very long time without encountering another universe. * Is there anything significant to note about areas of hyperspace that don't currently have a universe? * Is it highly entropic? * Does hyperspace change in any way as a universe approaches? * Is it similar to how a galaxy will warp the space-time around it, in that hyperspace follows the same mechanics, but with more dimensions?\", \"How are galaxy filaments formed? And do they have any analogues in stellar formation? > In physical cosmology, galaxy filaments, also called supercluster complexes > or great walls, are, so far, the largest known cosmic structures in the > universe. They are massive, thread-like structures with a typical length of > 50 to 80 megaparsecs h-1 that form the boundaries between large voids in the > universe.[3] Filaments consist of gravitationally-bound galaxies; parts > where a large number of galaxies are very close to each other are called > superclusters. So I've always wondered: how are these structures formed? One would (naively) expect that gravitationally bound structures would form as either spheres or discs.\", \"Electrodynamics and induced EMF question > A very long straight wire carries a current I. A plane rectangular coil of > high resistance, with sides of length $a$ and $b$, is coplanar with the > wire. One of the sides of length $a$ is parallel to the wire and a distance > $D$ from it; the opposite side is further from the wire. The coil is moving > at a speed $v$ in its own plane and away from the wire. > > (a) Find the e.m.f. induced in the coil. > > (b) Let R be the resistance of the coil. Calculate the force needed to move > the coil with speed v as described, and show that the mechanical power used > to move it is equal to the rate at which heat is generated in the coil. I have included my workings/thoughts. I know that i first have to calculate the magnetic field of the wire: $$B(y)=\\\\mu_0 I/2\\u03c0y $$ Then the emf, $$\\\\mathcal{E}=-\\\\frac{d\\u03c6(B)}{dt}= -\\\\frac{d}{dt} \\\\int_D^{D+b}B\\\\cdot ds = -\\\\frac{d}{dt}(a\\\\cdot \\\\int_D^{D+b} B\\\\cdot dy)$$ I have been given the answer of $$\\\\mathcal{E}=\\\\frac{\\\\mu_0Ivab}{2\\\\pi D\\\\left(D+b\\\\right)}$$ Do I have to perameterise the integral? If so does anyone have a guide on how to do this? What I am having trouble with is the intermediate step getting from the integral to the above answer. I know that $P_{mech}=F.v$ and that $P_{heat}= v^2/R = \\\\mathcal{E}/R$, but I do not know how to calculate the force on the coil.\", \"Mass fixed to a wheel - mechanics problem I am struggling with a problem from classical mechanics. Imagine a massless wheel (to make it simpler) with a mass $m$ fixed to it rolling without slipping on a horizontal ground. If we now try to find the equations of motion of the wheel (for instance the angle $\\\\alpha$ it turns) we will find that all the forces are independent of velocity, so $\\\\alpha ''=f(\\\\alpha)$. After doing that I decided to solve this problem using Euler-Langrange equations (since friction does no work). I came up with $L=\\\\frac{1}{2} m R^{2} (\\\\frac{d\\\\alpha}{dt})^{2} (1+\\\\cos \\\\alpha)-mgR(1+\\\\cos \\\\alpha)$ which, upon solving, gives $\\\\alpha ''$ as a function of both $\\\\alpha$ and $\\\\alpha '$. What is my problem?\", \"Vector product in 2 dimensions If I have a vector A=4i+3j and B=5i-2j, how can I find the vector product AxB? I know that given the angle, its C=AB sin theta, but how can I solve this without the angle?\", \"Why are popcorn seeds soft after popping? When a seed of popcorn is heated up in oil, it pops like this: ![Slow-mo Popcorn](http://i.stack.imgur.com/r9wd1.gif) You can take one of these popped pieces and eat it with little to no problem. However, if you get an un-popped seed and sink your teeth in, it is noticeably harder. Why is this?\", \"Experimental realization of Quantum Teleportation of Spin, not polarization, not ions or atoms I've looked everywhere in databases my school provides, to google searches, to the questions asked in physics forums, and here. As I understand, the original QT (quantum teleportation) protocol thought of by Bennett has been \\\"realized\\\" with polarization states of photons which is awesome proof. It validates QT because the entangled polarization state of light is a perfect analog. However, it hasn't been exactly realized because it seems to have not been done with spins! Has there really not been any papers on teleporting the spin state of a particle?\", \"Creating a fair 3 sided coin I want to make a cylindrical three sided fair coin, with sides: heads, tails, and edge. What should the area of the edge be in relation to the area of the head of the coin? Assume it is all made of a uniform material. **Thoughts:** I was thinking that so long as the surface areas of all three sides were equal, that would be enough, but this seems to lead to tipping over and landing on one of the other faces. Another thought was that the height of the edge should be equal to the diameter of the face, but this seems much too thick. I am looking for a rigorous way of approaching the problem, as opposed to using (bad?) intuition.\"]}, {\"query\": \"How a fan moves air?\", \"pos\": [\"What is going on in front of and behind a fan? Why is it that when you drop paper behind a fan, it drops, and is not blown/sucked into the fan, whereas if you drop paper in front of a fan, it is blown away?\"], \"neg\": [\"How to find momentum of different pieces of the same object after it explodes? If a stationery object explodes into a specific number of pieces due to internal force, how can I find the momentum of these pieces?\", \"Wannier functions on a ring Let's say I have a single particle hamiltonian in a periodic potential. For example a 1D lattice such that: $$H = -\\\\frac{\\\\partial_x^2}{2m} + V(x) $$ with $ V(x+a) = V(x)$ where $a$ is the lattice spacing between the atoms or sites. It is known by Bloch's theorem that a solution to such a system will have the form $$\\\\psi_{k}(x)=e^{ikx}u_k(x)$$ where $u(x+a)=u(x)$. My questions is about the boundary conditions. If we take $$\\\\psi(x+Na) = \\\\psi(x)$$ we get, if $N$ is large enough, a lot of different values for $k$ in the Brillouin zone: $$-\\\\frac{\\\\pi}{a} http://motls.blogspot.com/2012/05/electric-shocks-under-high-voltage.html\"], \"neg\": [\"How to calculate distance travelled from velocity vector and angle? I am trying to get the distance a projectile travels, so I can display it in my program. I'm sort of new to physics, but I've tried looking this up. I'm not getting the right results however, and me being new to physics, I'm not sure if I'm just using the wrong formula or what. This is the formula I have tried using: ![](http://upload.wikimedia.org/math/c/1/d/c1da5860501561519415962ddda5e85e.png) As far as I've read, a projectile launched at 15 degrees would travel the same distance as one launched at 75 degrees, however when I run the calculation I get two different distances for those two instances. Is this not the correct formula, or maybe I'm using it wrong? These are the values I get for 15 degrees and 75: 15: velocity vector = (5,-19) magnitude = 20 vCos = -15.2 vSin = 13 gravity = 9.8 output = 41 75: velocity vector = (19,-5) magnitude = 20 vCos = 18.4 vSin = -7.8 gravity = 9.8 output = 2 The projectile starts on the ground and gets launched at the specified angle over a flat surface. Not sure where I'm going wrong. All help is greatly appreciated.\", \"Calculating Atmospheric Pressure on an Imaginary Planet I am planning a series of science fiction novels that take place on an imaginary binary planet system. Both planets have a lower surface gravity than the Earth and one has slightly more mass than the other. If average temperature is the same as earth or cooler and the radius of the larger planet is allowed to vary, what is the minimum amount of gravity required for the larger planet to sustain a 50:50 Nitrogen/Oxygen atmosphere with a total pressure of 100 mmHg at 2000 m above sea level? What law or theorem would I need to solve this problem. **Extra stuff about the planets, my motivations and other things I'm trying to figure out that you don't have to read if you don't want to:** I want the larger planet to have an atmosphere that is thinner and whose pressure varies to a greater degree by altitude than the atmosphere of the Earth, but is still breathable by humans at sea level. Basically I want Denver to feel more like Mount Everest and Mount Everest should be pretty much vacuum. Mount Everest has an oxygen partial pressure of about 43 mmHg, and Denver is at an altitude of 1.6 km. Let's say we want 50 mmHg of oxygen at 2km. I figure if oxygen concentration is higher on the planet then on Earth, then atmosphere can be thinner and overall pressure can be lower. I picked a 50:50 mix not knowing whether it is really feasible, but if it is then pressure could be 100 mmHg at 2km. I don't know how to calculate how big the radius and mass of the planet has to be to satisfy these or what the pressure would be at sea level and at what altitude pressure will be 0. The smaller planet should have an atmosphere that is too thin for humans but could conceivably host sentient life. Both planets should have lower gravity than the earth. Each planets has a different species of sentient beings and I would like them to be able to lift stuff into space for much cheaper than here on earth. Basically by the time they have something like a Saturn V rocket, there should already be some trade in manufactured goods between the two. Both planets have awesome magnetic fields that are way better than Earth's at doing the stuff that magnetic fields do. I also would like the binary planets to be in a binary star system and have one or two small moons each, but this is probably pushing it. Even one small ice moon would serve as a handy plot device but I don't want to make it too improbable and I do want to get as much of the Physics right as I can and figuring out the orbits, climate map and the seasons will be difficult enough as it is with two planets, especially considering how little I know.\", \"Is it possible that only one hemisphere of a planet has an atmosphere? Suppose there is a tidally locked planet orbiting a star. The planet's surface consists of a global ocean, that is, liquid water. At the inner hemisphere the temperature is so high that the water is constantly boiling, creating the atmosphere of water vapor. But the vapor does not reach the dark side of the planet, and precipitates around the terminator line. The further side has no atmosphere and an icy surface. Is this setup possible? Can it be such thing that one side of a planet has liquid (and boiling) water surface while the other side has no atmosphere? Is it possible at all that a part of an ocean was boiling while the other one was icy?\", \"Position with respect to time with friction I'm interested in equations of motion when friction is present for a little graphical side project I am working on. I'm really rusty with physics, so I apologize if this is a basic question or doesn't make sense... Consider an object on a plane in three dimensions. It has initial position $ \\\\vec{x_0} $ on the plane and initial velocity $ \\\\vec{v_0} $ tangential to the plane. Assume it has acceleration (i.e. downhill gravity), $ \\\\vec{a_G} $, which is constant and tangential to the plane. Now assume it has an additional acceleration (from friction, which I'm modeling as an acceleration), $\\\\vec{a_F}(t) $, with constant magnitude $ k $ but variable direction, opposite that of velocity. Is there a function $ \\\\vec{x}(t) $ that gives position with respect to time? Something in the vein of the $$ x(t) = x_0 + v_0*t + 0.5*a*t^2 $$ that I learned in high school?\", \"During an Ice Age, would someone at moderate latitude get a sunburn/suntan on a sunny day? Let's say you live at a moderate (temperate, in today's terms) latitude during the last glacial maximum. You're probably in some kind of steppe or taiga biome, even though you're at 40 degrees north, and you've never known hot weather. If you're outside on a sunny summer day, would you still get a suntan/sunburn like you would in today's climate? Aside from temperature, which doesn't cause sunburn, would there be any relevant atmospheric differences that might affect the UV radiation?\", \"Verlet timestep I am using velocity Verlet in molecular dynamics. Is just a simple question, if a simulation using time-step femtosecond, in velocity Verlet is just necessary $dt = 10^{-15}$ to use femtosecond?\", \"Buoyancy experiment with my child I've read through a few other answers here on buoyancy and I was hoping to get some guidance on discussing it with at 5th grader. So, taking a ball of playdoh clay and dropping it into a container of water which is filled to the top that is sitting in another container, just a little bit of water overflows and we can measure that amount using our kitchen scale. She records this as her value to compare other shapes to. Taking other balls of playdoh which weigh the same amount on the kitchen scale, she's made various \\\"boats\\\" which differ mainly in the shape she's made - some are more cup-shaped, some are more pie plate shaped - some have sides and some are more pizza shaped. The pizza shaped ones sink like the ball did. The ones that are more pie plate shaped end up displacing more water than the others (and certainly more than the ball that just sinks) which are cup shaped, at least for her experiments. So, she sees that these \\\"boats\\\" are able to float and doing so results in some amount of water that flows over the top of the container but what more can be explained here - that a 5th grader is going to grasp. The weight of the water displaced for each one is different with the cup ones resulting in less water being displaced. Buoyancy isn't my thing so I'm hoping for some layman- friendly explanation as to the difference in the amount of water displaced. And dad will learn a bit along the way too, which is always a bonus.\", \"Principle of Least Action; Newton's 2nd Law of Motion This question is based on the description of Longair in his book \\\"Theoretical Concepts in Physics\\\". He starts by giving some provisions: * Conservative force field * Fixed times $t_1$ and $t_2$ * Object moves from fixed point at $t_1$ to fixed point at $t_2$ Then he defines: * Lagrangian: $L = K-U$ * Action: $S = \\\\int_{t_1}^{t_2}Ldt$ He goes on to explain, that the principle of least action means, that an object moves on a path so that $S$ is minimized. Then he claims that this priciple is equal to Newton's 2nd law of motion, following through with a proof which is beyond my comprehension (which of course is my fault). After I calculated $S$ for a few examples, I am convinced, that this claim is correct only adding one additional provision (which Longair clearly does not state directly or indirectly): * The object moves on a path fixed in space. (Just the speeds at the points is allowed to differ.) My argument for why this is necessary follows from a counterexample: * Assume a central force field with constant force. Setup the object so that its trajectory is a circle. Take time $t_1$ and $t_2$ so that the object is at opposing ends of the circle, describing a half circle. Now change the force field, so that there is no force inside this circle. (This is still a conservative force field and the object moves still in the same circle.) Compare the $S$ of this half circle to the $S$ of the object moving with constant lower speed along the diameter of the circle. For both trajectories the $U$ is the same but the $K$ is lower for the shortcut along the diameter (lower speed). So the shortcut along the diameter has a lower action. Still, with the correct initial speed the object will move the half circle, fully in accordance to Newton's second law of motion. Since I cannot assume, that I found an error in Longair's standard book, can anyone please explain, what I got wrong.\"]}, {\"query\": \"What happens to the energy when waves perfectly cancel each other?\", \"pos\": [\"can silence happens when 2 sound waves destroy each other Hi is there any possibility that you located between 2 sound sources and u hear nothing? as we know 2 wave in opposite direction will destroy each other...\", \"Where does energy go in destructive interference? I have read that when two light waves interfere destructively, the energy contained within is transferred to other parts of the wave which have interfered constructively. However, I am having some trouble grasping this. While in experiments such as Young's Double Slit experiment, there are visible bright bands of higher energy, I would imagine that it be possible to configure light waves to propagate linearly such that the waves interfere only destructively and not at all constructively. Is such an arrangement possible? And if so, to where is the energy in the wave transferred? Similarly, how does the energy transfer from one part of a wave which is interfering destructively to another part which is interfering constructively? These regions may be several meters apart for long wavelength light, and I find it strange that energy can travel between these potentially distant and non-interacting regions.\"], \"neg\": [\"Why does a tire produce more traction when sliding slightly? It is well known in racing that driving the car on the ideal \\\"slip angle\\\" of the tire where it is crabbing slightly from the pointed direction produces more cornering speed than a lower slip angle or a higher one. (More explanation as requested) I'm considering two main effects on the tire when in a turn: 1. The tread of the tire is twisted from the angle of the wheel it is mounted to. There is more force as speed increases, and generally, more twisting. 2. The tire slides somewhat at an angle on the road surface rather than rolling. At low speeds, the angle between the pointed direction of the wheel (90 degrees to the axis of rotation) and the direction of travel is nearly 0. When the speed increases to the point the angle reaches about 10 degrees, the tire generate more grip and the car goes faster around the turn. (Higher angles produce lower grip) So the grip is higher at 10 degrees of slip than at 0 or 20 degrees. What is the physical effect that causes this increase in grip?\", \"Negative Energy and Wormholes Apparently to create wormholes you need negative energy/matter. Say you had negative matter/energy, how would it be applied towards making a wormhole?\", \"Force from solenoid I'd like to approximate the force from a solenoid, or at the very least find a formula which is proportional to the force so that I can experimentally find the constant for my particular case. Apparently an exact answer to this is hard and involves quantum physics that is a bit beyond me; something about the Ising model. I've found $F =\\\\frac{(NI)^2 \\u03bc_0A}{2g^2}$, where $N$ is number of loops, $I$ is current though the solenoid, $A$ is cross-sectional area of the inside of the solenoid and $g$ is the \\\"is the length of the gap between the solenoid and a piece of metal\\\". I found that here. Is this a decent approximation? If so could you more rigorously define $g$, because I don't know what distance this is supposed to be? If it is not, what might I use instead? I found the Gilbert model on Wikipedia that is apparently a good approximation of the force between magnets. Could I use this to approximate the force on a piece of iron from a solenoid?\", \"Holonomy twisting There is Witten's topological twist of standard SUSY QFTs with enough SUSY into Witten-type TQFTs. What is a holonomy twist?\", \"Single photon's effect on conservation of momentum? When your looking at basic Compton theory you find that if you shoot a stream of photons at a particle (usually atoms or electrons), then you have the basic laws of conservation of momentum. The photon acts like a particle, like a \\\"billiard ball.\\\" The photon interacts with the said electron and the photon goes off in a new path described by $h/\\\\lambda_2$ ($h$ being Planck's constant and $\\\\lambda_1$ being the original wavelength of the photon). This wavelength is increased. Using a basic vector diagram with $h/\\\\lambda_1$, $h/\\\\lambda_2$ and $m\\\\vec{v}$ for the particle you get basic conservation of momentum. My question is basically what happens when you are only using a single photon? I'm not aware of any experiments done with a single photon, so far this concept (Compton experiments) have only been done with multiple photons. The reason a single photon is important is because the energy of a photon is inversely proportional to the wavelength. The problem that I have with this is that $E = h\\\\nu$. This is how we get our inverse wavelength in the formula (wavelength and frequency being inversely proportional). With a single photon you have no frequency, since you only have 1 event/photon. Thus how can you place Planck's constant over $\\\\lambda$ to represent the energy of the photon, since there is no frequency?\", \"Why doesn't Fermi's golden rule distinguish attraction from repulsion? Let's say I have two distinguishable charged particles interacting electrostatically. In Fermi's golden rule, the two particles can change state at a rate proportional to: $$|\\\\langle \\\\psi_f | H_{int} | \\\\psi_i \\\\rangle|^2 = \\\\left| \\\\int d^3 r_1 d^3 r_2 \\\\; \\\\psi_{1f}^*(r_1) \\\\psi_{2f}^*(r_2) \\\\frac{q_1q_2}{|r_1 - r_2|} \\\\psi_{1i}(r_1) \\\\psi_{2i}(r_2)\\\\right|^2$$ The weird thing is: **It doesn't matter whether they are attracting or repelling!** The rate with $q_1 = q_2 = e$ is the same as the rate with $q_1 = -q_2 = e$. I'm shocked that there can be an electrostatic calculation that turns out the same regardless of whether the particles are attracting or repelling each other. How do I make sense of that?\", \"2 simple Generators = Im lost (intensity) ![enter image description here](http://i.stack.imgur.com/YH1BH.png) What happens? we have 2 Generators and 2 intensities, when I2 and I3 come together (they have opposite sides ) what happens? I tried writing down the voltage relations but I'm stuck because I don't know what intensity should i work with (to determine positive sides) I2 or I3\", \"Why can't angular momentum be used in flying vehicles? ![Animation of relationship between torque and momentum](http://i.stack.imgur.com/yIa9K.gif) If angular momentum ( **L** ) works like this animation from Wikipedia leads me to believe, why can't we put a large flywheel or several small ones on a chassis, all rotating counter-clockwise around their vertical axes and move the system upwards? It seems that there's nothing countering **L**. Am I missing something? I think the concept of such a vehicle would break Newton's first law, but right now to me that's like saying 'you can't because Newton said so'. Can anyone provide a more detailed explanation that someone with high-school- level knowledge of physics can understand?\"]}, {\"query\": \"Is fire matter or energy?\", \"pos\": [\"what is the basic form of the 'fire'? > **Possible Duplicate:** > Is fire matter or energy? What is the basic form of fire? physics defines every entity by a basic form either solid or liquid or as a gas, example: water is liquid, ice as solid, water vapor: gas so what is fire? when I queried is 'Plasma a fire?' I landed in below link, where it is said that fire isn't plasma, but not defined which basic form it belongs to! the link: Is fire plasma? What I understand is, it's a form of energy caused by reaction of gases(?) so it's basically a gas! is it true?\"], \"neg\": [\"Is the universe a quantum computer - is light speed barrier a computational constraint There is currently a debate ongoing on leading maths blog G\\u00f6del\\u2019s Lost Letter, between Gil Kalai and Aram Harrow, with the former arguing that building a quantum computer may not be possible due to noise propagation, and the latter arguing to the contrary. I am wondering if there is any argument to show that building a quantum computer is possible, by virtue of showing that quantum computation is evident in the physical world. So the question is: (A) Are there any known examples of physical interactions where macro level state transitions could be determined to only be in correspondence with an underlying quantum computation? I.e. similarly to Shor's algorithm being exponentially faster than any known classical factoring algorithm, are there any examples of known physical processes, for example perturbation stabilization in a very large particle cluster, that could be shown, assuming P<>NP, to only be efficiently solved by a quantum computation. Some, I admit highly speculative, additional questions would then be: (B) Is the speed of light barrier possibly a natural computational limit of our particular universe, so that for the computational complexity class of quantum mechanics, working on an underlying relational network-like spacetime structure, this is the maximum speed that the computational rules can move a particle/wave representation through a network region of the lowest energy/complexity (i.e. a vacuum)? (C) Is quantum mechanics an actual necessity for the universe to follow classical physical laws at the macro level? The informal argument being that in many-to-many particle quantum level interactions, only the capability of each particle to compute in parallel an infinite or quantum-quasi-infinite number of paths is what allows the universe to resolve a real-time solution at the macro level. Requesting references to research along these lines, or any arguments to support or contradict these speculations.\", \"Terminology for line integral of magnetic field One of the quantities appearing in the integral form of Maxwell's Equations is the line integral of the magnetic field around a closed loop. (The relevant equation states that this is equal to the current through any surface bounded by that loop, plus the displacement current in the case of changing electric fields, with some constant coefficients possibly thrown in, depending on how you manage your units. This is usually called Amp\\u00e8re's Law, with Maxwell's correction for the displacement current.) Is there a name for this quantity? More generally, is there a name for the line integral of the magnetic field along an arbitrary non-closed curve? (Then this is not equal to any other named quantity or sum of quantities.) Ideally, I'd want a name for the integral of the H-field (rather than the B-field, when one distinguishes them) in amperes (or the equivalent in non-SI units), but I am not really picky about those details. It seems that every other quantity in the integral form of Maxwell's Equations has a name (magnetic flux, electric flux, charge, etc), so I'd hope that this one does too. Of course, the term for the line integral of the electric field (electromotive force) is somewhat of a historical oddity, so maybe this quantity is too obscure to have a name. Still, you'd think that somebody would have given it one sometime! ETA: A theoretical way to measure this quantity is to place a conductive tube (as conductive as possible, with as small diameter as possible) around the curve in question. The magnetic flux through the tube will induce a current running around (not along) the inside of the tube, which will in turn serve to cancel (or shield) the source of the magnetizing field, in accordance with Lenz's Law (in the broad sense). The current so induced, serving to completely cancel the magnetic field near the curve in question, should equal this quantity, if I'm thinking about this correctly. (Well, I'm having a little trouble getting the sign straight, but one way or the other it should work!)\", \"Time travel and entropy I saw a post on reddit regarding immortality and how it would never be possible due to entropy. That said, assuming time travel was possible, would it not be possible to never reach this state of entropy if you could keep traveling back in time? Say for instance that you couldn't travel back in time within your own time curve, but you could travel to other time curves. You would merely jump to another curve when your current universe was about to reach its end. Is it too big of an assumption to think that your own initial entropy (by this I mean your entropy from your first timeline) would not 'follow you' to other time curves? I may be totally off-base in my assumptions, as I know next to nothing about physics. Any feedback/criticism is welcomed. Here is the link to the post in reddit.\", \"How does stuff glow in the dark? Many things have glow in the dark properties (glow sticks, paint, toys ..), and I am wondering what is the physics behind them. How do these materials store light energy and emit it later when dark? What dictates the wavelength(s) of the glow? Is there a limit on how much energy can be stored and recovered in these materials? Do they give out heat as well as light?\", \"matter wave and wave function Is there any mathematical relationship between matter wave (or de Broglie wave) and wave function? Also, does each type of particle (e.g. photon, electron, positron etc.) have its own unique wave function?\", \"The commutator of Killing vectors I'm going over an assignment for my general relativity course. My solution to the question below strikes me as too short, considering that it appeared in the \\\"longer questions\\\" section of the assignment. > **Question:** > > A Killing vector $V^\\\\mu$ is one that satisfies the equation $\\\\nabla_{\\\\mu} > V_{\\\\nu}+\\\\nabla_{\\\\nu} V_{\\\\mu}=0$. The commutator of two vectors $U^\\\\mu$ and > $V^\\\\mu$ is defined as $[U,V]^\\\\mu=U^\\\\nu \\\\nabla_\\\\nu V^\\\\mu-U^\\\\nu \\\\nabla_\\\\nu > U^\\\\mu$. Show that the commutator of two Killing vectors is itself a Killing > vector. My solution could be (and is likely to be) incorrect, but I'm just using the definitions that I've seen so far. **My solution:** To get from $[U,V]^\\\\mu$ to $[U,V]_\\\\mu$ we have to use the metric i.e. $[U,V]_\\\\nu=g_{\\\\nu \\\\lambda}[U,V]^\\\\lambda$. Now I just plugged this in to the Killing equation to get (for the first term) $\\\\nabla_{\\\\mu}[U,V]_\\\\nu=\\\\nabla_{\\\\mu}g_{\\\\nu \\\\lambda}[U,V]^\\\\lambda$. Using that the metric is covariantly constant, we have $\\\\nabla_{\\\\mu}g_{\\\\nu \\\\lambda}=0$, hence the whole term is equal to zero. The same thing happens with the second term, so that the whole expression is zero. I can't see that I made a mistake, but it seems way too short a solution. When I looked at the solutions provided in the course, they are about a page long, and begin $$\\\\nabla_{\\\\mu}[U,V]_\\\\nu + \\\\nabla_\\\\nu[U,V]_\\\\mu=\\\\nabla_\\\\mu (U^\\\\tau \\\\nabla_\\\\tau V_\\\\nu-V^\\\\tau \\\\nabla_\\\\tau U_\\\\nu ) + \\\\nabla_\\\\nu (U^\\\\tau \\\\nabla_\\\\tau V_\\\\mu-V^\\\\tau \\\\nabla_\\\\tau U_\\\\mu )=\\\\dots$$ i.e. they do things explicitly. Actually, having written this out, I can see that what I've done can't be correct otherwise every vector would be a killing vector just by writing $V_\\\\mu$ as $V_\\\\mu=g_{\\\\mu \\\\nu}V^\\\\nu$. However I don't know what I've done wrong, so any help would be very much appreciated!\", \"Power as a Function of Distance I am interested in trying to understand the meaning of the integral of power with respect to distance. In the case of force, we have the two formulae: $\\\\int F \\\\, \\\\operatorname{d}\\\\\\\\!t = \\\\Delta p$ and $\\\\int F \\\\, \\\\operatorname{d}\\\\\\\\!x = \\\\Delta E_k$. I understand that power is closely linked to time and work by the relationship $P = \\\\frac{\\\\operatorname{d}\\\\\\\\!W}{\\\\operatorname{d}\\\\\\\\!t}$. However, imagine a body moving continuously from the left to the right. In such a case, we can express displacement as a one-to-one function of time, and time as a one-to-one function of displacement. We could place regular markers along the road side and take power readings as the body passes each of these markers. Assuming the distance between markers is $\\\\Delta s$ and then letting $\\\\Delta s \\\\to 0$ gives power as a function of displacement. We may then ask: **What does the integral of this function mean?** $$\\\\int_{s_1}^{s_2} P \\\\, \\\\operatorname{d}\\\\\\\\!x = \\\\operatorname{F}(s_1,s_2)$$ **Addition:** I've tried to play with the calculus a bit. I'm not sure if this helps: $$P = \\\\frac{\\\\operatorname{d}\\\\\\\\!W}{\\\\operatorname{d}\\\\\\\\!t} = \\\\frac{\\\\operatorname{d}\\\\\\\\!x}{\\\\operatorname{d}\\\\\\\\!t} \\\\frac{\\\\operatorname{d}\\\\\\\\!W}{\\\\operatorname{d}\\\\\\\\!x} = v\\\\frac{\\\\operatorname{d}\\\\\\\\!W}{\\\\operatorname{d}\\\\\\\\!x}\\\\implies \\\\int P \\\\, \\\\operatorname{d}\\\\\\\\!x = \\\\int v\\\\frac{\\\\operatorname{d}\\\\\\\\!W}{\\\\operatorname{d}\\\\\\\\!x} \\\\, \\\\operatorname{d}\\\\\\\\!x = \\\\int v \\\\, \\\\operatorname{d}\\\\\\\\!W$$ Has anyone seen a velocity-work graph? If so, what does the area underneath it represent?\", \"Virtual vs Real image I'm doing magnification and lens in class currently, and I really don't get why virtual and real images are called what they are. A virtual image occurs the object is less than the focal length of the lens from the lens, and a real image occurs when an object is further than focal length. By why virtual and real? What's the difference? You can't touch an image no matter what it's called, because it's just light.\"]}], \"CQADupstackProgrammersRetrieval\": [{\"query\": \"Confusion about dual license (MIT/GPL) javascript for use on my website\", \"pos\": [\"Can I use a dual license in my commercial Website I have set up a web application which is of commercial use. This application had to make use of the plugin(which is not downloaded from http://plugins.jquery.com) I downloaded this plugin from http://jquery.malsup.com/cycle2 Kindly, let me know if I could use this plugin in my commercial website. If I do, would it cause any consequences in future? Though, I could see few answers here, I would like to Insist upon itself on this as of critical importance. Licensing Terms\"], \"neg\": [\"Working with fubar/refuctored code I'm working with some code which was written by a contractor who left a year ago leaving a number of projects with buggy, disgustingly bad code. This is what I call cowboy PHP, say no more. Ideally I'd like to leave the project as is and never touch it again. Things break, requirements change and it needs to be maintained. Part A needs to be changed. There is a bug I cannot reproduce. Part A is connect to parts B D and E. This kind of work gives me a headache and makes me die a little inside. It kills my motivation and productivity. To be honest I'd say it's affecting my mental health. Perhaps being at the start of my career I'm being naive to think production code should be reasonably clean. I would like to hear from anyone else who has been in this situation before. What did you do to get out of it? I'm thinking long term I might have to find another job. **Edit** I've moved on from this company now, to a place where idiots are not employed. The code isn't perfect but it's at least manageable and peer reviewed. There are a lot of people in the comments below telling me that software is messy like this. Sure I don't agree with the way some programmers do things but this code was seriously mangled. The guy who wrote it tried to reinvent every wheel he could, and badly. He stopped getting work from us because of his bad code that nobody on the team could stand. If it were easy to refactor I would have. Eventually after many ' _just do this small 10minute change_ ' situations had ballooned into hours of lost time (regardless of who on the team was doing the work) my boss finally caved in it was rewritten.\", \"Algorithm for indexing strings in \\\"list\\\" Imagine I have file called `strings.dat`. Inside this file there is alot of string, for example: one million. **String are sorted**. Now I want to find specified string, so I can write method like this: public long FindByName (string text) { // ... } This method can return to me a position within a file where this string occurs. But iterating through lots of data is not efficient. I can do some indexing system, like an `Dictionary` which tells about at what position within file is placed first string with its value starting from given char. Example: if I got 5 strings: hello hello2 world world2 Zzz So my dictionary will be like: h 0 w 2 z 4 But it's not efficient if I will have 1000 string with \\\"d\\\" char as first letter, and 10 million with \\\"r\\\" letter. Do you know any good algorithms do ahieve what Im asking for?\", \"How to handle static-ish content from a CDN with authentication? I have a website that allows user uploads of content. Part of the design, to date, involves storing the user content on a NAS that has been configured with a separate app pool in IIS that has scripting disabled, shorter lifespans, no cookies, etc for CDN-type behavior as a subdomain (static.domain.com vs. www.domain.com). I have complete control over this process and all the instances. The design decision behind this was to allow for cheaper storage of uploaded documents (instead of wwwroot or a DB) and help mitigate some security concerns by disallowing scripting. The concern I have now is that CDNs, and this setup, are by nature very permissive and do next to no checking or validation. Are there any _reasonably effective_ measures to discourage idle sharing of this user content? I know nothing will stop determined or malicious people, but I want to at least prevent this user content from being available at large to the general populous. All of the links to the content will be behind a paywall at the 'www' subdomain, but the content itself will be behind the 'static' subdomain. Ideas I had so far: 1. HTTP Referer checking 2. Obfuscated filenames (I know, I know...) 3. Cookie testing with URL rewrite? (IIS8) 4. ... 5. Ask stackexchange\", \"Single quotes vs double quotes I just started a job where I'm writing Python after coming from a Java background, and I'm noticing that other developers tend to quote strings using single quotes (`''`) instead of double quotes (`\\\"\\\"`). For example: line1 = 'This is how strings typically look.' line2 = \\\"Not like this.\\\" Is there a particular reason for this other than personal preference? Is this the proper way to be quoting strings? Specifically, what I want to know is if there is some type of standard or accepted best practice that drives this style of coding.\", \"Is there any research out there on geographic differences in work environments (e.g., respect) for programmers? One thing I've learned from this website is that software developers are not treated the same as what I've seen in the companies I've worked at, and some of the differences seem to be related to the culture or other factors of the geographical location where the programmer works. In some areas, it seems like programmers can expect many perks and a great deal of professional respect, but in others it sounds like programmers are seen as laborers who are told what to do and then should go do it without question. Even in just the USA, there seem to be major differences in \\\"the norm\\\" between the various regions of this country. I'm wondering how much of this is just my perception, and how much is real differences about how programmers are perceived in their different locations. Is there any research out there discussing major differences in programmer work environments or attitudes about how to treat or respect programmers by geography? I'd be interested in multiple articles tackling different ways of looking at this. Edit: Research, specifically, doesn't seem to be available, so I'm making the question broader. Any good, thoughtful writing on the topic of any kind available?\", \"Best Method of function parameter validation I've been dabbling with the idea of creating my own CMS for the experience and because it would be fun to run my website off my own code base. One of the decisions I keep coming back to is how best to validate incoming parameters for functions. This is mostly in reference to simple data types since object validation would be quite a bit more complex. At first I debated creating a naming convention that would contain information about what the parameters should be, (int, string, bool, etc) then I also figured I could create options to validate against. But then in every function I still need to run some sort of parameter validation that parses the parameter name to determine what the value can be then validate against it, granted this would be handled by passing the list of parameters to function but that still needs to happen and one of my goals is to remove the parameter validation from the function itself so that you can only have the actual function code that accomplishes the intended task without the additional code for validation. Is there any good way of handling this, or is it so low level that typically parameter validation is just done at the start of the function call anyway, so I should stick with doing that.\", \"How and what should I be (unit) testing for in this method? I am relatively new to unit testing and have a query about what/how I should be testing a certain method. For the following (psudo-c#) method I have created (not a real-life example) what would you test for? Initially, my thoughts would be to test the output with variations on the dictionary of form fields, e.g. valid, invalid, missing values. However I also wonder how you would test to make sure the object values have been changed to the correct value and that the correct email message was attempted to be sent (obviously both services could/would be mocked). I hope what I am asking makes sense, I appreciate this is a subjective question and the answers may be 'it depends' ;) public bool ProcessInput(Dictionary formFields, ObjService objService, EmailService emailService) { try { // Get my object id int objId; if(!int.TryParse(formField[\\\"objId\\\"], out objId) { return false; } // Update my object - would you validate the save against a DB or a mocked inmemory db? var myObj = objService.Find(objId); myObj.Name = formField[\\\"objName\\\"]; objService.Save(myObj); // Send an email - how would you test to make sure content, recipient, etc was correct? emailService.SendEmail(formField(\\\"email\\\"), \\\"Hello World\\\"); return true; } catch(Exception ex) { return false; } }\", \"How can I deal a team member who is irresponsible and shows no commitment? I am handling a team of a few guys working on a software module in a large project. As per our estimates our module is getting delayed by a week. Since we can not have this delay, our client and our manager arrive at a decision that we need to work on few weekends (3-4) to try to finish the work. All members of the team are aware of this and understand this and since its just a matter of 3-4 extra days, so we decide to work on weekends to meet the deadline. But one of the team members is not following this. From last three weekends he has some or the other reason to not come on weekend. He is not even willing to put up extra hours during weekdays. Personally I don't care about the number of hours he works as long as he/(any one) can finish their work to meet the deadline. Please let me know how to handle this situation?\"]}, {\"query\": \"Is there a license that forbids distribution and gives a Github repo owner full rights?\", \"pos\": [\"Software License: Open Source, but no distribution (for free or for profit) I wrote a program in PHP. I want people to be able to use the code to learn from and even include bits in their own apps but I also want to maintain the right to sell it and make it clear that others are not to distribute copies either for free or for profit. I haven't used anyone elses code so I don't have to worry about license compatibility. Which license out there is right for that? I know I can't stop people from ignoring the license but I just want them to at least know it's there and know they're doing something wrong if they try. I can't find anywhere that explains licenses in plain English and yes, I have tried to read the GNU and BSD licenses but I can't get it. Thanks. Ok so here are the clarifications other have asked for: I am going to sell this program for very cheap. People are free to use as much code as they want from it so long as they have first purchased the program and they do not profit from my code. I wouldn't mind if people just took the whole thing and added on and made it better but then people wouldn't be honest and they would just add something on and give it free and then no one would buy my product. I suppose I should just let people have it and ask for donations. So maybe I should GPL or BSD it. EDIT: I understand why everyone is getting confused. It is contradictory but the thing about legalese is that everything seems black or white. I'm trying to achieve a gray area. I want people to buy this thing from me, not copy it and give it away to people, and even use parts of it. By \\\"use parts\\\", what I'd say if someone asked me casually would be \\\"just be reasonable\\\". So basically don't use a majority of my code in your work and then totally outshine what I did because you'll have it easy as I did most of the dirty work already! I mean, sure, use my DB connect script in your project, I don't care. OR... use all the code and add to it! But only for personal use and don't you dare start giving it out because I'm going to be making money off this thing and that totally undermines my work. And it'd be nice to show me your additions and I'll give credit but the main thing is don't be redistributing my software that I have to earn a living with. Its hard to put that sentiment into legalese. And I just can't understand all these licenses.\"], \"neg\": [\"Why is first column of list called 0th in so many languages? If you want first element of list or array you reference it as 0 in many languages (like C or Clojure). Is there are some really good reasons why the programming languages was design this way? In old days in assembly languages it makes perfect sense because all possible values needs to be used. But what are nowadays to keep it this way? There is very little advantage when modulo arithmetic and ranges (Wikipedia article.) but not much more. On a disadvantages side it should be: It makes confusion because it human language the first is connected with 1 (1st and it is not in english only). It makes confusion even in XPath (W3School:\\\"Note: In IE 5,6,7,8,9 first node is[0], but according to W3C, it is [1].\\\"). There are troubles between languages who use 1-based and 0-based system. Want to know hat are the good reasons to use zero-based numbering and why even creator of new languages (like Clojure) choose this way?\", \"Why doesn't the .NET world have anything like rails/grails/django/roo? It seems to me that rapid-development web platforms are going to radically change the world of web applications. It has been five years since Rails 1.0 was released for Ruby, and since that time we have seen Grails for Groovy, Django for Python, and Roo for Java. But to my knowledge (which is probably limited, being a Java/Groovy progammer) there is no similar framework for C#. Does such a thing exist? If not, why not? **Edit:** It's quite possible I'm not using the right words when I say \\\"rapid- development,\\\" but I'm talking about frameworks that can conceivably allow you to build a working blog engine in 30 minutes. You couldn't reasonably do this with, say, Java, Spring, and Hibernate, given the various configuration needed to allow your controllers to be found, and both configuration and code necessary for your entities to persist and be retrieved. So I'm talking about frameworks that handle all of the CRUD with a convention- over-configuration mentality. If someone has the right words for what I'm talking about, let me know.\", \"If a library doesn't provide all my needs, how should I proceed? I'm developing an application involving math and physics models, and I'd like to use a Math library for things like Matrices. I'm using C#, and so I was looking for some libraries and found Math.NET. I'm under the impression, from past experience, that for math, using a robust and industry-approved third party library is much better than writing your own code. It seems good for many purposes, but it does not provide support for Quaternions, which I need to use as a type. Also, I need some functions in Vector and Matrix that also aren't provided, such as rotation matrices and vector rotation functions, and calculating cross products. At the same time, it provides a lot of functions/classes that I simply do not need, which might mean a lot of unnecessary bloat and complexity. At this rate, should I even bother using the library? Should I write my own math library? Or is it a better idea to stick to the third party library and somehow wrap around it? Perhaps I should make a subclass of the Matrix and Vector type of the library? But isn't that considered bad style? I've also tried looking for other libraries but unfortunately I couldn't find anything suitable.\", \"If functional testing is referred as black box..how can it be done on unit test level? Preparing myself for ISTQB, I found a bit odd many things in their textbooks. E.g. they call black box testing as functional testing when you are not concerned with structured but only observe the ouput based on inputs. But later they say that functional testing is done at levels..well, how can I do unit testing without the knowledge how it works if I can just see it (and must see it). It is white box but then it is in the conflict..\", \"Should we write a unit test for class that call another class that have code written Suppose that there are two class 'A' and 'B' 'A' has a lot of nested conditions that have all unit test covered. 'B' has a property that will call class 'A' and return value according to the result of class 'A' On class 'B', when i want to write unit test. Should i just mock class A and verify on class B that it has been called a specific class 'A' method.\", \"Why is naming a table's Primary Key column \\\"Id\\\" considered bad practice? My t-sql teacher told us that naming our PK column \\\"Id\\\" is considered bad practice without any further explanations. Why is naming a table PK column \\\"Id\\\" is considered bad practice?\", \"What's the best way to modularize a User schema so it's generic I have a database design question. Basically I want to be able to create a schema for a User model, then use this User model in other models that extend User but I want to design it in such a way that it's generic enough to be used in every application. For example a Profile or Account model might extend User, and in both cases they will be different based on the web application you are designing but the core credentials of User should never be different across any web application. What fields do you think should be in the User model? I think the bare minimum to successfully handle authentication would be: * email (the login unique identifier) * salt (obviously!) * password (duh) * lostToken (a hash to verify lost password functionality) * role (member, admin, editor, etc.. IMO this list of roles would differ between sites however it's too important to the User model not to have here?) Now we get into other interesting fields that are still very very useful: * createdAt (when the account was created) * ipAddress (track the ip when the account was created) * refererUrl (which site it came from) * lastLoggedIn (the last time the user logged in) * isOnline (is the user currently online) And even more fields that are still pretty useful: * username (might not be used on every site) * number of consecutive logins (similar to the stack network) I think anything else like social data (likes, votes, profile views), badges/achievements, the last time they updated their profile/account/whatever, and other info like their name belong in the per- site Profile model. What do you think? Edit: I fully understand that this question is partly subjective but I do think there's definitely room for discussion.\", \"Is there a website where we can discuss and improve upon code snippets? Is there a website where we can discuss and improve upon code snippets? For example, if I have written some function and want to know what others think of it.\"]}, {\"query\": \"Is using build-in sorting considered cheating in practice tests?\", \"pos\": [\"Java exam: prebuilt methods? > **Possible Duplicate:** > Is using build-in sorting considered cheating in practice tests? I'm studying for a Java exam at the university. In your opinion, could I use, during the exam, Arrays.sort(intArray); instead of creating my own algorithm? How would you judge it, if you were the professor?\"], \"neg\": [\"foreach($items as &$item) considered harmful? Is it considered bad practice to pass items in a PHP array by reference instead of by value? Relevant documentation: http://php.net/manual/en/control- structures.foreach.php\", \"Would working for an \\\"adult website\\\" hurt my future employability? I am considering taking a software engineering job at a pornographic website. While I have no moral objections to the industry, will it be a red flag on my resume?\", \"Violating SQL principles I have to write a decent size database, 1GB more or less. I have only taken an introductory semester regarding SQL databases. During the course we learned that the relational model under SQL has no implied order and that therefore when querying we should always have a variable that sets the correspondant order in the table. However, when doing practical work I find 'convenient' to make already ordered small, infinite number of tables. For example, lets say that you have 5 clients and that their info will never join their data together. Is it admissible to have a potentially infinite number of tables with a table per client? For example, I append new rows already ordered to the database. The database is already ordered. Is it admissible to make an unordered query and just assume that is implicitly ordered? All this violates the principles that I know. My boss, who does not know about databases says that if it works it works and that speed is important. What shall answer to him?\", \"When should an IT consultant use full disc encryption? In what circumstances should an IT Consultant encrypt their hard drive to protect their code/data of their clients? I am thinking that if it does not add much to your work load you might as well use full disc encryption with a 'weak' password to at least prevent someone from accessing your email files and other documents if your laptop is stolen, even if they will not get access to any database files or other very sensitive data.\", \"What advantages do simple text editors like TextMate have over Eclipse for Java development? I'm torn which IDE to use. The power of eclipse or the simplicity of TextMate. I'm wondering which one has specific advantages over the other, productivity wise.\", \"Project based prefix for class names My project leader uses project based prefixes for class names, lets say projects name ABC, he create User class name as ABCUser. and he says he do this becasuse if he wants to make User.aspx Users get mixed. so I told him why not use namespace (Entity.User ie.) to make it specific but he against it. I would like to hear from you guys' opinion on this subject. We code c#.net and using visual studio for projects.\", \"Design Methodology for Developing Interoperable Systems? **A bit of background** The company I work for has been creating database applications since around 1980 and, until relatively recently, most of these systems have been stand- alone \\\"silo\\\" systems. However, in the last few years, we've seen more of a shift towards gathering \\\"intelligence\\\" across all systems within our customers requirements. For example, a system that might collect data about people's dental records may need to talk to a system that collects data about people's optician records, share information about the same person and provide search capabilities across both systems (makes sense, although the example I've used is fictional). In the fictional example above, the dental and optician systems may or may not both be written by the company I work for (in other words we are looking at both internally-developed interfaces and interfaces to systems from third- party providers). **What we've done so far** We have a basic web-services API ( **.asmx** ) for one of our products, but I'm concerned about the change management, in terms of keeping it up-to-date as we add new features to the main product (in terms of providing the new features through the API without breaking compatibility with older interfaces). We have also produced one **WCF** service. The rigid contracts put my mind slightly more at ease than working with the ASMX service, but I think a lack of experience within the company is holding us back, so I'm looking for some guidance on best practice that I can read up on to help move us along (or at least point us in the right direction). **The question** * **Is there a particular methodology or programming practice for designing systems to maximise their ability to interface to other systems?** * **Are there any good online (or offline) resources for these programming practices (assuming they exist)?** Any other advice you can provide based on the information above would be appreciated, but not essential. We primarily develop in Microsoft .NET (mainly ASP.NET, with some windows forms applications as well) with a SQL Server/Oracle database back-end, although I expect the programming practices might be language independent. (I've used the .NET tag as an afterthought, but I might take it out later..!) Thanks for your time.\", \"Should you refactor existing code that is not broken in a project focused on new features? Given a small project that aims to add new functionality to application, the changes introduced touch some existing code, involving updating these in certain areas. During implementation, I've found some of these code which were updated have candidates for refactoring. Is this an appropriate time to refactor which in turn would require regression testing for those components affected (thus possibly introducing scope not originally part of the project)? Or should I defer, complete the functionality and perhaps have a separate project for refactoring (although I'm a bit hesitant as business users might not fully sponsor a project that does not add any functionality, unless they value code maintainability...)?\"]}, {\"query\": \"What are the guidelines for either throwing an exception or failing silently for nonvalid arguments?\", \"pos\": [\"Error handling - Should a program fail on errors or silently ignore them I'm writing a simple little program to transmit MIDI over a network. I know that the program will encounter transmission problems and / or other exception situations that I won't be able to predict. For the exception handling, I see two approaches. Should I write the program so that it: * fails with a bang when something goes wrong **_or_** * should it just ignore the error and continue, at the expense of data integrity? Which approach would a user reasonably expect? Is there a better way of handling exceptions? Additionally, should my decision about handling exceptions be affected by whether or not I am dealing with a network connection (ie. something where I can reasonably expect to have problems come up)?\"], \"neg\": [\"How to integrate technical line/functional manager into Scrum team? We have recently had a new line manager start to manage our Scrum team. He is immensely experienced in our field but is relatively inexperienced at Agile/Scrum. He has extensive technical expertise in embedded software (the team's domain) that would go to waste if not utilised properly. However, the team is wary of making a line manager part of the Scrum team. The general consensus is that the line manager should not be part of the Scrum team at all. There are a number of issues that may crop up, e.g. the team may start \\\"reporting\\\" to the manager (i.e. a daily status update!), the manager may start to micro-manage team members etc etc. As it currently stands, he has already said that he feels like an outsider within the team. We really want to make use of his technical skills, we'd be foolish if we didn't because we are a relatively inexperienced and young team of twenty somethings. What would be the best approach to integrate a senior \\\"technical\\\" line manager in a Scrum team and make him feel like he _is_ part of the team?\", \"Test task : real tasks or not? Just had a discussion with coworkers about test tasks for programmers. Is it morally appropriate to ask programmers do some helpful work for the project in their test tasks or such company just uses the job applicants to do job for free? Is it ethical to do it?\", \"Silverlight 5 and MVVM. Do I really need other frameworks? What is the best way for rapid development? I've been reading and watching videos on MVVM and Silverlight.. I'm pretty new to Silverlight but not new to .NET. Interesting that I used MVVM in my WPF apps without knowing that it's MVVM. I was creating easily-bindable classes to serve as a layer between data and XAML :) Project that we start will be done with Silverlight 5 and WCF on a back end. Project will be rather large with several modules 50 or screens each, ideally I would like to load them on demand. MOST(not all) UI will be straight-forward data entry stuff. I'm trying to see what should be our architecture and how will it benefit us in future. I think I GOT IT as far as what MVVM and WHY. I also checked Caliburn Micro (and understood what it does). I see ReactiveUI and MVVMLight. To be honest I don't like external libraries/dependencies. Also, I don't really care about using naming conventions to satisfly external framework and perfectly OK with binding in XAML. Since we have good commands support and XAML debugging in SL5 - I don't think I need external framework. So, I think having ViewModels and binding via XAML with minimal view-related code in code-behind will be perfectly fine with me. Here is my dilemma: 1. If I use RIA services. 80% of my UI will bind perfectly to RIA generated stuff with some converters of course. Will it be bad architecture to have everything bind directly and just some more complex views to use ModelView? 2. Should I use RIA services?! I think YES. I'm all for generated code especially when it's plain data-entry stuff. Will it keep client code synced with server-side? 3. From what I see - ModelView have to be manually coded. Am I correct? Again, for 80% of project that's probably going to be waste of effort. 4. If I want to have multiple xap files that load on demand, should I use some kind of framework? I think keeping it in one file may get too big. Thanks!\", \"What is the clause in an employee contract that says they own all your code? I am looking at my employee contract and I can't seem to figure out where it might say that they own all the code that I write, be it at work or at home. Any examples of what the wording could be like?\", \"How to communicate with a co-worker that considers frameworks a performance hit How can one sell an idea like \\\"we should use jQuery because its highly optimized and cross browser compatible\\\" or \\\"entity framework is cool because its neat and takes care of our model automagically\\\" when the common response is a blanketed statement such as \\\"jquery doesn't perform well\\\" or \\\"entities bring in 12 columns on a table when we only need 10\\\"? I am a pragmatic guy that tends to trust axioms I've developed through experience (its not a performance problem until there's a visible slowdown). I don't know if there's a specific \\\"category\\\" that the other extreme fits into, whereas everything is a performance problem until proven otherwise...or even where to begin the communication here.\", \"Are we using the repository pattern right? We are using a bunch of separate classes suffixed with `-repository` to retrieve the data from the database; for each table its own repository. We have for instance a `customerrepository` class which has all kind of methods to retrieve customers, and a `vacancyrepository` which has all kind of methods to retrieve vacancies. I have two questions about this way of doing things: 1. How about getting data which spans multiple tables? For instance I have a screen which shows all customers who have not yet created a vacancy. Can a `customerrepository` use methods from the `vacancyrespository`, or do both repositories return results and is there a class higher in the hierarchy (let's name it a `dataservice`) which gets the results from both repositories and combine them into 1 result? 2. how much logic can such a repository handle? I think it's OK to implement the 'where active == true' in a repository to retrieve only active records, or should even that simple logic be handled by a class higher in the hierarchy (let's name it a `dataservice`)? The example I was running into now is this one: We have a questions list, which contains one or more questions. The question can have a result, which is hold in a separate table. So when you want to retrieve the total result of the questions list, you have to combine data from the `questionlist` table, the question table and the `questionstatus` table. Right now we have 3 different repositories for these tables. If I would ask the `questionlistrepository` what it's the total result for list number 12, it would have to get data from two other repositories and hence have some logic in it, is that allowed? Or is there a `questionlistdataservice` which knows which repositories to use? One more thing: our repositories can result an `IQueryable` so a calling service can easily combine the results, but how about when this isn't the case, I don't think it's a good idea to retrieve all the content of all three tables from the database.\", \"Reuse Other Company's Content For Same Client Here's the scenario: a client of mine (lets call them Company A) had a website built by someone else (let's call them Company B). They then asked me (Company C) to configure a third party framework to look and feel exactly the same as what Company B built for them. Would it be legitimate for me to use content (including images, CSS, and even HTML) from the site Company B built for Company A? It's worth mentioning Company A's name is in the copyright notice in the footer of the site Company B made for Company A.\", \"Why are wrapper classes not suited for use in callback frameworks? I just read the question what are callback frameworks?, where the asker cites the following from Effective Java: > The disadvantages of wrapper classes are few. One caveat is that wrapper > classes are not suited for use in callback frameworks, wherein objects pass > selfreferences to other objects for subsequent invocations (\\u201ccallbacks\\u201d). So I am curious, why are wrapper classes not suited for use in callback frameworks? And can you provide an example of what the problem is?\"]}, {\"query\": \"How much database access should developers have?\", \"pos\": [\"Why shouldn't you develop on production database? I need help with organizing my arguments for why it's MADNESS to develop against production data. **Backstory:** I started working here six months ago and I noticed we're developing directly against production data. The codebase is local, but the database we're connected to is live data! So i have to be super careful constantly when doing update/store operations, so that I don't break anything. We have an annual software meeting soon and I want to raise my concerns, and come loaded with arguments but I have a really hard time coming up with good solid points to something that's should be obvious as this. It's as if they would ask me to come up with arguments to why I need to breathe. **My Question:** What arguments are there for NOT developing on production database directly?\", \"Safely fixing production database data Bugs happen and sometimes data has to be fixed in production. What is the safest way to go about this from a big company standpoint? Are there tools that can help? Here are some considerations driving this requirement... 1. We need to log who ran the query and what they ran 2. Ideally we need to give the person access to only run queries against the tables of interest and only for a short time 3. Whatever is running the queries needs to have some smarts about it to not allow long running and locking SQL to run without explicit permission 4. This process needs to be DB agnostic or at least understand DB2, Oracle, and SQL Server. We are trying to reduce the risk of ad-hoc prod fix-up queries from doing the \\\"wrong thing\\\" and at the same time add some security/audtis to the process. Thoughts or Ideas?\", \"How do you deal with clients asking for manual data edits in the database? I've recently started working on a legacy application that frankly doesn't do all that it should. It's lacking a lot of features, has barely any administration capacities and doesn't check half the data it should. As such, it's very easy for users to do something stupid and get stuck. _\\\"Oops, I added this item of the wrong type to this thingie and now it won't let me remove it\\\"_. Indeed, the application should have checked for this, but allowed adding the wrong item. And now, when it comes to deleting the wrong item, it becomes extremely protective and refuses that anything be removed. Problem is, the clients (who are actually users within the company) don't care much for that. They need the application to hold the real-world data as it should be, so they ask the developers to \\\"fix it\\\" by changing the data. In this example, deleting the wrong item. In other cases, it will be reassigning items to different parents, fixing various values, etc... Since the application has almost no admin GUI, everything ends up being done directly in the database (augh!), risking even more issues down-the-line unless you know _exactly_ how it works (which no one really does considering the massive application). Ultimately, it feels like the database has become a huge Excel file that devs edit day by day at the whims of the clients, because of failures of the application. It's obvious to me that fixing the application to avoid such situations should be top priority, but it seems the clients prefer asking for a lot of new features instead and it's accepted as such. What can a developer do in such a situation? Is it even possible to refuse DB edits in favor of fixing things? There are so many bugs that it feels like they're never going to wait _that_ long...\"], \"neg\": [\"Buy vs. Build - FTP Service We have a need to FTP files that are generated by our system, so we're trying to decide whether we should spend the time to build something that meets our criteria (relatively easy, .NET has FTP functionality built in, among other more advanced libs from 3rd parties). Or if we should buy something off the shelf. Our requirements are roughly: * Must be able to trigger a file send programmatically * Needs to retry _N_ number of times (configurable) * Queryable status of FTP requests * Callback on completion or fail of an FTP request I don't need to be sold on the relative simplicity of building something like that for myself. However I do want to do the due diligence of seeing what products are available ... because if something does exist that matches the requirements above, I wouldn't mind paying for it :-) Any thoughts or links would be greatly appreciated. Thanks!\", \"Why is 24 lines a common default terminal height? 80x24 characters seems to be a very common default for terminal windows. This answer provides a very good historical reason as to why the width is 80 characters. But why is the height commonly 24 (or 25) lines?\", \"Combining 3rd party javascript libraries with my code, then using Closure Compiler I'm using multiple third party javascript libraries in my website, and right now I'm keeping each third party library as a separate .js file, with its own `